Keeping up with technological advances can be hard which is why we try go to the best and more notable technological conferences to learn and teach the best technologies available.
In April 9th-17th I had the pleasure to attend PyCon 2014 in Montréal. PyCon is a conference related to the usage of the programming language Python. Python is one of the tools we use at Push Interactions to develop web applications, automate workflows and transform data. One of the reasons we use Python is because it allows us to build elegant solutions that are easy to maintain.
The event was an amazing success. It was sold out with all types of technology enthusiasts!
Many of the large tech companies use python for similar reasons. The following pictures are some of the notable companies booths I got to chat with during the conference.
At PyCon I got to network with many companies. I learned about new patterns and technologies and also gave advice on patterns and technologies we use here at Push Interactions.


Copyright © 2015 Jose María Álvarez-Rodríguez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The present paper introduces a hybrid technique to measure the expertise of users by analyzing their profiles and activities in social networks. Currently, both job seekers and talent hunters are looking for new and innovative techniques to filter jobs and candidates where candidates are trying to improve and make their profiles more attractive. In this sense, the Skillrank approach is based on the conjunction of existing and well-known information and expertise retrieval techniques that perfectly fit the existing web and social media environment to deliver an intelligent component to integrate the user context in the analysis of skills confidence. A major outcome of this approach is that it actually takes advantage of existing data and information available on the web to perform both a ranked list of experts in a field and a confidence value for every professional skill. Thus, expertise and experts can be detected, verified, and ranked using a suited trust metric. An experiment to validate the Skillrank technique based on precision and recall metrics is also presented using two different datasets: (1) ad hoc created using real data from a professional social network and (2) real data extracted from the LinkedIn API.
In recent years, social network research has been carried out using data collected from online interactions and from explicit relationship links in online social network platforms like, for instance, Facebook and Linkedin . Among these tasks, expert and people search is one of the most challenging tasks that one can try in social networks [2, 3].
Expertise represents the skill of answering some questions or conducting some activities . Thus, the focus of expertise location is finding an answer, a solution, or a person with whom details of a problem can be discussed  or a task can be performed . In other words, expert finding addresses the task of identifying the right person with the appropriate skills and knowledge. Effective management of expertise can benefit both organizations and individuals by easing the access to knowledge, as well as sharing and applying knowledge .
In this light, expert finding involves two main aspects including expertise identification ("Who are the experts on Topic X?") and expertise selection ("What does Expert Y know?") . In the later topic, expert profiling turns the expert-finding task around and asks the following: What topic(s) does a person know about? . Topics such as expertise relevance and authority within a community have been pointed out as some of the factors to assess expert's competence [10, 11]. Given that complete and accurate expert profiles enable people and search engines to effectively and efficiently locate the most appropriate experts for an information need , this paper presents an expert profiling approach to analyze expert's skills confidence by means of hybrid soft computing techniques. One of the main advantages of Skillrank is the use of LinkedIn as a source of expertise. LinkedIn is likely the most notable example of business-oriented social networking site. The company was founded in December 2002 and launched six months late. LinkedIn reports by December 2014 more than 330 million users in 200 countries and territories. In this professional social networking site, users are allowed to track and publish their career paths, skills and past experiences, the size and tenure of the teams with whom they've worked, and the roles they played on each team . LinkedIn users self-report their expertise and ask members of their social network to provide positive references or recommendations for them . Although LinkedIn has been used in the literature for expertise search [14, 15], to the best of authors knowledge, there is not a study devoted to the application of self-disclosure and social network integrators to assess the quality and confidence of professional skills in this network.
On the other hand, a good number of techniques have been designed to exploit the information available in social networks and, in general, to address problems that contain an implicit graph. The well-known algorithm PageRank  by Google Inc. was developed to assign a measure of importance to each web page. This algorithm works by counting the number and quality of links to a page to determine a rough estimate of how important a website is. The underlying assumption is that more important websites are likely to receive more links from other websites. In the same way, the HITS (Hyperlink-Induced Topic Search) algorithm  also known as "hubs and authorities" is a kind of analysis technique that also rates web pages. It was designed by Kleinberg from the Department of Computer Science at Cornel and the idea behind hubs and authorities stemmed from a particular insight into the creation of web pages when the Internet was originally forming; that is, some web pages are known as hubs and serve as hubs that compile large directories of web pages. These directories are not actually authoritative in some topic but a good hub represents a page that points to many other pages and a good authority page is expected to be linked to many hubs. The main restriction of the HITS algorithm lies in its applicability since it only operates in a small subgraph. This subgraph is considered to be query dependent, whenever the search contains a different query phrase, the seed changes as well as the HITS algorithm ranks the seed nodes according to their authority and hub weights. The SPEAR (Spamming-resistant Expertise Analysis and Ranking) algorithm  is another tool for ranking users in social networks by their expertise and influence within a community. It is also a graph-based technique to measure the expertise of users by analyzing their activities and interaction. The main idea behind this technique lies on the ability of users to find new and high-quality information on the Internet. This algorithm is an extension of the aforementioned HITS algorithm including two main elements: (1) Mutual reinforcement of user expertise and document quality and (2) Discoverers versus followers. The combination of both elements has been demonstrated to reward quality over quantity of user activities and that is why it has been also applied to detect spam attacks . Although graph analysis techniques  have been widely used to study social networks (e.g., trend detection, opinion mining, sentiment analysis, information retrieval, etc.) and, in most of cases, the PageRank algorithm can be seen as a precursor of this kind of approach, there is still lack of techniques to deal with quality over quantity. In this sense, the SPEAR algorithm offers us a technique that can be applied to a rather wide area of domains such as assessment of skills quality. In the context of graph-based algorithms for expertise ranking, the ExpertRank  algorithm proposes a novel technique to evaluate the expertise of users based on both document-based relevance and one's authority in this or her knowledge community. Authors modified the PageRank algorithm to evaluate one's authority so that it reduces the effect of certain biasing communication behavior in online communities. As an important cornerstone and relevant to this work, they explored three different expert ranking strategies that combine document-based relevance and authority: linear combination, cascade ranking, and multiplication scaling. This evaluation has been done using a popular online knowledge community showing that the proposed algorithm achieves the best performance when both document-based relevance and authority are considered.
In this paper a reinterpretation and extension of the SPEAR algorithm, called Skillrank, is presented. Furthermore, the evaluation of the presented approach is carried out by comparing existing approaches for expertise ranking such as the HITS and SPEAR algorithms to the proposed technique when tests are executed on top of two datasets extracted from the LinkedIn API. To do so, a panel of experts has established a set of expected results and values that are compared to the real results provided by each algorithm with the aim of obtaining measures of precision and recall.
The remainder of this paper is organized as follows. Section 2 presents the related literature. The proposed approach for skill ranking is illustrated in Section 3. In Section 4, experiment evaluations are conducted to compare our approach with other methods. Section 5 presents main conclusions and future research directions.
Expert and credibility finding is not a new issue in literature. As a result of this, literature has vastly reported works on the topic and even produced relevant surveys on the topic for example [21, 22]. Methodologies of expert finding can be divided into three categories [4, 7]: Content-Based Approach, Network-Based Approach, and Hybrid Approach. On the other hand, other works , propose a different taxonomy of existing expert finding systems. These authors indicate that these systems are based on four kinds of expertise indicators: self-disclosed information, authored documents, social network analysis and hybrid techniques . An analysis performed by these authors reveal that hybrid techniques are not combining self-disclosure indicators with social network analysis or document-based indicators. In other words, authors underline that self-disclosure indicators can be seen as isolated indicators that need deeper analysis.
In , authors make in-depth review of benchmarking techniques and components that constitute a test collection with special emphasis on error analysis. They also give an overview of different test collections for expert profiling and expert finding. In , author reviews more recent examinations of the validity of a test collection approach and evaluation measures as well as he outlines trends in current research exploiting query logs and live labs to finally show that, despite its age, this long-standing evaluation method is still a highly valued tool for retrieval research. Furthermore, the Text REtrieval Conference (TREC) or the Yandex Personalized Web Search Challenge also falls in this area of methods for assessment ad-hoc datasets through train and test processes in different topics. For instance, in 2008, the main topic of the TREC conference was focused on expert finding where a dataset from the Tilburg University (http://ilk.uvt.nl/uvt-expert-collection/documentation/documentation.html) was used as input of a competition to find and rank experts. Usually these evaluation methodologies are in charge of asserting the results of an information retrieval process by comparing expected results to actual results and taking into account measures  of precision, recall, sensitivity, or stability. On the other hand, relevance assessment methods are usually created by a panel of experts and a good number of collections can be found in different domains such as web search, movie/tourism recommendation, medical diagnosis, and so forth. Finally statistical significance tests are used to estimate the average of system performance according to a set of queries that can be generalized. In this sense, the Wilcoxon test, Student's -test, and the Fisher pairwise comparison are common techniques to assess a value under certain degrees of freedom. All these techniques have been reviewed in  and they are relevant to this work due to the fact that a validation of the expected results must be done to assess if the Skillrank algorithm is properly working. To do so, the validation section introduces the evaluation method that is a combination of an ad hoc collection built by a panel expert with measures of precision and recall.
Regarding online skills evaluation, in  a technique is introduced to establish a credibility rank (also known as Skillrank) to online profiles based on user's confirmations and six requirements for online skills evaluation. According to these six requirements, a credibility model is defined and populated from on-line profiles. Afterwards, a pilot is implemented to show the functional architecture that supports the online evaluation of skills. Nevertheless, the real evaluation of skills and online profiles is still an open issue and, only the architecture is presented. Authors also comment some of the limitations of their approach: (1) skills are evaluated as they are and a scale will be necessary to establish an order of expertise and (2) spread of experience and skills are also under evaluation. On the other hand, they also raise some relevant questions in the evaluation of online profiles: What kind of information can be incorporated to enrich the skill evaluation model? and How half-time jobs, activities or tasks can also be included in the evaluation model? This work is very closely related to the approach presented in this paper. However, they have been focused in the definition of a skill model and a pilot architecture instead of comparing different algorithms working on the same datasets. Other recent works  can also be found applying gamification techniques to build online personal skills and boost the learning process. Thus, it is possible to ensure the acquisition of skills from the early stages of learning by analyzing the online behavior and interactions  between peers. Finally, other works are also paying attention to the evaluation of online profiles with different purposes such as digital inclusion. As an example, authors in  theorize how people's online social networking skills may condition their uses of various digital media for communication.
On the other hand, reputation management systems have emerged to understand the influence of individuals or groups in a certain group. Different metrics with a particular level of effectiveness  are applied to assess the reputation in a social network, mailing list or any other collaborative site. For instance the Stackoverflow system, a question and answer system , uses a simple formula to establish a level of "karma" for each individual depending on their participation in the system. The Research Gate site, a social network for science and research, also establishes a score depending on publications and contributions that you have added to the site. In this case, reputation is passed from researcher to researcher, allowing us to build and leverage our reputation based on anything we choose to contribute. Interactions or activities in this social network will determine our score by looking in our activities (how our peers receive them) but also at who these peers are. Higher scores will be reached as much as higher scores peers interact with our activities; it can be seen as an application of the Spreading Activation technique  that has been widely used in information/document retrieval and recommending systems .
The idea behind of all these reputation management systems lies in a set of internal metrics (they are usually private to avoid fraudulent profiles) that are collected in just one value to create a rank of users by tracking their activity: asking and responding questions, using online feedback of other users (How much a response is better than another?), and so forth. These systems are also relevant to professional social network sites such as LinkedIn, ResearchGate, or Xing in which users try to complete, at the most, their profiles adding own education, professional experience, rewards, publications, and so forth as well as feedback from their connections to improve and enrich their profiles. In this sense, talent hunters have got a new way of detecting specialists in a topic by performing advanced search through tools in these websites. Nevertheless the access to this valuable information is commonly restricted and only quantitative information can be found. In this context some works have emerged for topic extraction systems and online reputation management  which they use a set of evaluation metrics based on handmade metadata annotation to assess the quality of different factors. Thus, trust and provenance analysis is becoming a major challenge to avoid vandalism, fraud, and so forth in public profiles, more specifically in user and company profiles. Existing works for example  are then focused on applying techniques to characterize profiles in reputation management systems to demonstrate through algorithms such as Eigen Trust, TNA-SL, or distributed approaches  are secure enough to effectively manage trust in communities.
In the field of information retrieval, expertise retrieval , and expert finding  systems have been widely studied to provide a new approach to tackle the discovery of experts in some area . Currently, practices as such competitions  or challenges are common techniques to retrieve experts based on their performance in a certain topic. The main objective of expertise retrieval  lies in the application of existing information retrieval techniques as building blocks to design advanced algorithms that can serve to create content-based links between topics and people. Nevertheless, authors have also outlined both applications to other domains such as entity retrieval as well as some conjectures on what the future may hold for expertise retrieval research. For instance, last times new novel approaches  are emerging to include time and evolution of skills over time as variables in expertise retrieval processes.
As a closely related field to expertise retrieval, community detection  in social networks is a widely active research area to segment large communities by a certain criteria. In the specific case of expertise in some topic, these algorithms can be applied to narrow down the search of experts. These techniques can be roughly divided into two groups: (1) global and (2) local approaches. The first ones assume knowledge of the entire network while local ones only assume knowledge of subcommunities featured by some attributes such as location. Global detection algorithms were firstly proposed by Girvan and Newman by iteratively removing edges until the social graph is partitioned (each partition can be consider as a community). The key point of these techniques lies in the selection of the edge to be removed and, in general, some metrics such as betweeness centrality are calculated for each edge. Thus, a large social network is divided into high-dense connected communities that share some features and, therefore, can be considered sub-communities. The main drawback of global approaches lies in the necessity of knowing the full graph (it is usually expensive in terms of time and size). In order to decrease the complexity of handling a large graph, local approaches aim for detecting communities in a more scalable and applicable way starting with a set of seed nodes to detect implicit communities. For instance, Clauset's algorithm uses intracommunity and intercommunity measures to iteratively establish or remove the connection between two nodes. Thus from a starting node communities dynamically emerge. Although these approaches are completely correct to detect underlying communities the use or inferring of dynamic attributes of nodes (users in most of cases) is still an open issue that has been studied in some works such as  and it allows a better and more accurate community partition. As a possible application of these aforementioned techniques, the detection of violent communities, hostility, or rivalry is currently under study  since the internet is understood to be a social space conducive to increased hostility, greater disinhibition, and increased social freedom. Moreover, these authors see a link between virtual hostility and actual violence. In social networks research,  predicts user personality by mining social interactions including Aggression-Hostility traits and  modeled online social interactions incorporating the effects of hostile interactions.
Finally the relevance of expertise ranking in social networks and Internet has been presented in some works to understand and exploit enterprise know-how , find competence gaps and learning needs inside corporations , improve Scrum processes , improve human to human interactions , or tackling information asymmetries in electronic marketplaces  to name a few.
In conclusion, a list of methods and techniques for benchmarking has been introduced with the aim of comparing existing approaches to assess personal skills quality. Furthermore, existing works related to reputation management systems and, more specifically, trust and provenance analysis can be also applied to the Skillrank technique since methods to evaluate public profiles are an emerging topic due to the current use of the web. That is why the Skillrank technique seeks for providing an innovative method to assess user profiles from a qualitative point of view through an agnostic technique that can help both talent hunters and managers to exactly know where a capability or skill can be found in their connections or employees with a certain degree of trust and provenance.
As the previous section has introduced, the HITS algorithm  identifies good authorities and hubs for a certain topic by assigning two numbers to a page: an authority and a hub weight where weights are recursively defined. A higher authority weight occurs if the page is pointed to by pages with high hub weights. A higher hub weight occurs if the page points to many pages with high authority weights. More specifically in the context of web search, the HITS algorithm first collects a base document set for each query. After that it recursively calculates the hub and authority values for each document. In order to gather the base document set , first, a root set matching the query is fetched from the search engine. Once this root set is configured for each document , a set of documents that point to another set of documents that are pointed to by are added to the set as 's neighborhood. Then, for each document , let and be the authority and hub values, respectively, that are initialized to 1. While the values have not converged, the algorithm iteratively proceeds as follows.(1)For all which points to , (2)For all which is pointed to , (3)Normalize and values so that .
A good hub increases the authority weight of the pages it points to. A good authority increases the hub weight of the pages that point to it. The idea is then to apply the two operations above alternatively until equilibrium values for the hub and authority weights are reached. The author also demonstrated that the algorithm will likely converge but the bound on the number of iterations is unknown (in practice the algorithm converges quickly). New improved versions of this algorithm have emerged such as BHITS by giving a document a default authority weight of if the document is in a group of documents on a first host which link to a single document on a second host, and a default hub weight of if there are links from the document on a first host to a set of documents on a second host. Nevertheless and according to its authors, this new version of the algorithm generated bad results when a root link has few in-links but a large number of out-links that are not relevant to the query.
On the other hand, the SPEAR algorithm [18, 19] makes use of the HITS definition to introduce the concept of expert, someone with a high level of knowledge, technique, or skills in a particular domain. This implies that experts are reliable sources of relevant resources and information but with two main assumptions.(1)Mutual reinforcement of user expertise and document quality. The expertise of a user in a particular domain will depend on the quality of the documents he/she has found. In the same way, quality of documents will depend on the expertise of the user who has found them. This is an issue that has been studied in Psychology and it states that expertise involves the ability of selecting best and relevant information in a certain context. The SPEAR algorithm is based on this assumption and an expert should be someone who selects by quality instead of quantity.(2)Discoverers versus followers. The second assumption of the SPEAR algorithm lies in the definition of a discoverer (expert user that finds high-quality and relevant information) versus a follower (an user that annotates a document after a discoverer does).Under the aforementioned assumptions the SPEAR algorithm produces a ranking of users with regard to a set of one or more tags. It assumes that a topic of interest is represented by a tag . The algorithm works as follows [18, 19].(i)Firstly the set of tags is extracted from an underlying folksonomy in a certain social network. Each tag is represented by the tuple where is the user, is the time when the tag was assigned to the document , and if refers to an earlier time than .(ii)Then, the next vectors are defined:(a)a vector containing the expertise scores of users where is the number of unique users in ,(b)a vector containing the quality scores of documents where is the number of unique documents in .(iii)According to the first assumption, mutual reinforcement refers to the idea that the expertise score of a user depends on the quality scores of the documents to which he tags with , and the quality score of a document depends on the expertise score of the users who assign tag to it. Authors define an adjacency matrix of size where if user has annotated with the tag the document , and otherwise. Based on this matrix, the calculation of expertise and quality scores is an iterative process similar to that of the HITS algorithm: and .(iv)On the other hand, the second assumption is implemented by changing the definition of the aforementioned adjacency matrix. Instead of assigning either 0 or 1 (like the HITS algorithm) the following equation is used to populate the initial values of the matrix .(a).(b)Thus, the cell is equal to 1 plus the number of users who have assigned tag to document after user . Hence, if is the first to assign to , will be equal to the total number of users who have assigned to . If is the most recent user to assign to , will be equal to 1. The effect of such initialization is that matrix represents a sorted timeline of any users who tagged a given document .(v)The last step is to assign a proper credit score to users by applying a credit scoring function to each element . According to the authors three different functions could be applied to the matrix .(a)A linear credit score . This function was initially discarded by the authors because discoverers of a popular document would receive a comparatively higher expertise score although they might have not contributed in any other document thereafter.(b)An increasing function but with a decreasing first derivative to retain the ordering of the scores in . Authors demonstrated that this kind of function enables the possibility of keeping discoverers score higher than followers but differences between higher scores will be reduced to avoid the undesirable effect of assigning high expertise scores to users who were the first in tagging a few set of popular documents but without further contribution in high-quality documents thereafter. Finally, the authors selected the function as credit score for their experiments.
Commonly, the set of users are not disjoint sets, so a user can be a member of several subcommunities. Nevertheless, the set of features , dynamic features and resources could be shared among subcommunities but they could be also disjoint sets depending of the characteristics of the social network.
Following these definitions, we can describe a social network such as LinkedIn containing a subcommunity "MyLinkedIn" that can be also partitioned in several subcommunities such as "MyUniversity" or "MyWork". According to the theoretical model,(i) where(ii) is the set of all registered users.(iii), type = "professional social network", name = "Linkedin", is a set of key-value pairs.(iv) is also a set of dynamic key-value pairs in a certain moment time.(v)On the other hand we can also define this social network by the union of several disjoint sub-communities. Thus where .(vi)Finally, users will be members of some community so represents a LinkedIn user that creates the subcommunity "MyLinkedIn". This user and its subcommunity generate resources such as "posts," "connections," "endorsements," and so forth as an example = "endorsement", time = timestamp, tag = "skill"} describes the resource in the community "MyLinkedIn" that was created by the user using a "endorsement" in a certain moment time on the user .
Although communities, users, and resources can be described through different static attributes there is still a set of dynamic or behavioral features that must be inferred to make a better description of foreknown communities and to be able to create new intercommunity relationships. Since communities, user endorsements, and so forth are evolving characteristics, it is necessary to analyze emerging or implicit user's behaviors [41, 51]. In this sense, the aforementioned community detection algorithms follow a similar approach but studying the structure of the social graph instead of analyzing contents.
Here, we propose the adaptation of the SPEAR algorithm to support the quality assessment of endorsements generated by a subcommunity; more specifically the following contexts can be identified.(i)Community (Local context). Figure 1 shows that a user endorses another user with the skill "java" in the time . After that another user belonging to the same sub-community generated by user also uses the same endorsement but at time where . The assumption behind this behavior is that after seeing the new endorsement (made by ) also realizes that this endorsement is correct and adds again the same endorsement to the user . This situation implies that the first post (see discoverer in the SPEAR algorithm) has activated new annotations (see follower in the SPEAR algorithm) reinforcing both: (1) the skill "java" in user and (2) the initial annotation of the user . Similarly, if a user notices that a user has endorsed another user at time this can lead to an endorsement of user for the same skill by user at time where (Figure 2). Finally Figure 3 depicts the situation in which a user is activated by some activity but instead of applying the same tag she uses another tag to annotate knowledge of user (the one that started the interaction).(ii)Community (Global context). Figure 4 shows that an user endorses another user with the skill "java" in the time . After that another user , outside of the subcommunity (represented by a dashed circle) also uses the same endorsement but in time , where , to endorse user . The idea behind this behavior is that after seeing the new endorsement (made by ) also realizes that this endorsement can be applied to . This situation implies that the first post (see discoverer in the SPEAR algorithm) has activated new annotations (see follower in the SPEAR algorithm) reinforcing the skill of the user .
Figure 1: Correlated-endorsements to the same user in a subcommunity.
Figure 2: Correlated-endorsements to different users in a subcommunity.
Figure 3: Independent endorsements to the same user in a subcommunity.
Figure 4: Correlated-endorsements to different users in a community.
On the other hand, Figures 3 and 5 depict a situation in which an user assigns a skill to user in time but although other user is activated and assigns another skill, different from the one assigned by , there is not actually a correlation between them and both assignments can be interpreted as independent endorsements. The Skillrank technique covers the aforementioned scenarios to take advantage of the data delivered by tracking user activities.
Figure 5: Independent endorsements to the same user in a community.
Taking into account the inputs required by the SPEAR algorithm, the following vectors are redefined and the pseudocode of the algorithm is also presented in Listing 1.(i)The set of skills is extracted from the activities generated by a subcommunity in a certain social network. Each endorsement is also represented by the tuple where is the source user that endorses with the skill to a target user at time .(ii)Then, the next vectors are also redefined:(a)a vector containing the expertise scores of users where is the number of unique users in ,(b)a vector containing the quality scores of skills where is the number of unique skills in .
Listing 1: Skillrank pseudocode. Reinterpreting the SPEAR algorithm [18, 19].
According to these definitions the unique difference between the original version of the SPEAR algorithm and this new version seems to be the naming of elements ("document" by "skill"). Nevertheless, the Skillrank facilitates a two-step process to run the SPEAR algorithm in before with the aim of populating both vectors and with real values. Hence, a new interpretation of the adjacency matrix can be done. Instead of considering the adjacency matrix for the whole social network or folksonomy, each user will generate an adjacency matrix in which rows represent connections and columns skills respectively (see Table 1). The interpretation of this table is as follows: 0 represents that the user have not yet endorsed using the skill while another value such 2 in cell and 1 in cell represents that user used the skill before user .
Table 1: Example of generated adjacency matrix for a user .
On the other hand, an analysis of the temporal and spatial complexity of the algorithm can be carried out to show the computational complexity of the technique depicted in Error! Reference source not found. Firstly and regarding the spatial complexity, the algorithm makes use of a set of skills which contains all skill endorsements registered for a community , the aforementioned vectors and and a matrix of dimensions: , number of users in a community and , number of unique skills in a community . Secondly and regarding the temporal complexity, the standard SPEAR algorithm performs iterations containing a main operation (matrix multiplication) two times, an operation to transpose a matrix and two vector normalizations. Assuming that the adjacency matrix, , has dimension , the computation complexity of the algorithm can be expressed through the next expression using the Big-O notation: = .
To illustrate the performance in terms of precision and recall of the presented algorithms, HITS and SPEAR, with regards to the adaptation designed in Skillrank a case study using different datasets is provided. Here, the evaluation of performance is not a mere question since there is a lack of real datasets containing the required information of users, connections, skills and time. To mitigate this problem, a synthetic dataset, as the basis of the experiment, has been designed after collecting real data from the LinkedIn API (currently, this API provides access to valuable but incomplete information that must be fixed by the own users). Thus, simulated communities, users, skills, and endorsements are generated to study the behavior of the different approaches. To carry out both experiments the following steps have been carried out.(1)Select and prepare dataset. For every dataset to be evaluated a set of tuples in the form must be provided.(2)Create a dataset for unit testing purposes. To do so a panel of experts has established a category, using an official competence scale , for every user and skill.(3)Definition of precision and recall. In order to calculate both measures, next definitions are also required (given an user):(i)true positives (tp): "number of skills that were expected to reach a certain level of quality."(ii)false positives (fp): "number of skills that have reached a different level of quality."(iii)true negative (tn): "number of skills that were not expected to reach a certain level of quality."(iv)false negative (fn) "number of skills that have not reached a different level of quality." Once we have the aforementioned definitions, precision and recall can be defined and calculated as follows.(i)Precision is defined as "the number of user skills that have reached the proper level of quality established by the panel of experts": (ii)Recall is defined as "the number of user skills that have not reached the proper level of quality established by the panel of experts": (iii) score is then defined as (4)Inclusion of the frequency as a basic technique for each user and skill. Given a user and a skill the quality of the skill is calculated as follows: (5)Run the experiment for every dataset and technique.(6)Configure all techniques with default parameters (credit score function, etc.) as previous section has presented.(7)Extract measures of precision, recall, and by comparing expected results to real results.
The first step to run the experiments lies in the proper creation of a synthetic dataset inspired by real data extracted from the LinkedIn API. To do so a community must be modeled including the required input parameters for the target algorithms. Thus, a set of users, containing 10 different profiles has been designed including an average between 30 and 50 connections per user (these values have been inferred from the real data). On the other hand, a set of skills , see Table 2, must be also designed according to next features: (1) technical, professional, and management skills must be available for each user and (2) all skills must be, at least, in one profile but no all profiles contain all skills. Finally, a set of endorsements in the form are generated from each user being the user/connection that assigns the skill to the user in a certain time .
Table 2: Set of selected skills .
Once the input dataset is designed, it is necessary to create a dataset containing the expected results with the aim of performing automatic unit testing. To do so, a panel experts that has already participated easing the access to their profiles in LinkedIn, has also established for their real connections a level of expertise for each skill in . Thus, this dataset contains a set of tuples in the form , where is an user with a level of competence on the skill . The different levels of competence have been taken from  in which authors present "The Individual Competency Index (ICI)," see Table 3.
Table 3: The Individual Competency Index (ICI).
After the creation of the input and test datasets, the algorithms and unit tests can be executed to finally extract the measures of precision, recall, and and compare the different techniques. Last step involves the creation of a function to convert numerical values into a level of expertise. To do so, a percentile rank for every level of expertise is defined. The aforementioned steps have been also followed to perform the same experiment on the LinkedIn dataset. As a final remark it is relevant to discuss some research limitations that have emerged during the creation of both datasets.(i)The use of the LinkedIn API is restricted and it is not possible to access all information that is available through the public website. Thus, some relevant information with regards to the skills is missing such as who has endorsed someone. To overcome this issue our panel of experts and collaborators were asked to complete this information.(ii)Another issue in the use of the LinkedIn API lies in the lack of time for each endorsement. This is a critical point since algorithms are based in this assumption. To overcome this issue we have follow two strategies: (1) ask the panel of experts and collaborators to estimate a date in which the endorsements were created and (2) estimate the time of the endorsement by using the join data in the social network.(iii)The LinkedIn API also provides a level of proficiency for each skill. Nevertheless these features cannot be used since it is not available in all skills and it is based on a particular taxonomy.(iv)Finally, in order to access all required information, an URL to query the official LinkedIn REST API (https://developer.linkedin.com/apis) was designed as shown in Listing 2. Nevertheless and due to privacy setting of the API, every participant in the experiments was asked to execute this request through the APIgee service (https://apigee.com/console/linkedin) using their own OAuth credentials and to send us the request's output as XML. Through this request the user will be asked to grant access to their full profile. Then, all public personal information, a profile containing: first name, last name, headline, industry, location, number of connections, summary, specialties, positions, associations, honors, interests, publications, patents, languages, skills (id, proficiency, and years), certifications, education, courses, volunteer, three-current-positions, number of recommenders, and connections (and their full profile) will be gathered using the LinkedIn REST API.
Listing 2: URL to extract user data from the LinkedIn API.
After the execution of the different techniques, the averaged measures (for all skills) and for every user in dataset are presented in Table 4. Obviously, the first technique based on the number of times a user has been endorsed is not actually relevant in terms of quality as results show. On the other hand, the HITS algorithm provides better results in terms of precision but the drawbacks of this algorithm (not considering time as a relevant variable—see Section 3.1) implies a low-precision in some users with a behavior close to the frequency-based technique. As an improvement or more accurate version of the HITS algorithm, the SPEAR technique seems to get better results that are closer to the expert's opinion. Here, it is clear that the assumption of time as a key-variable to assess quality is a determinant to detect the level of expertise. Finally, the Skillrank technique that previously configures the level of expertise of every user before making endorsement seems to have a similar behavior to the SPEAR algorithm. Although in some cases there is a relevant gain, the truth is that values in both techniques are very similar and to actually assert that Skillrank is better than the simple version of the SPEAR technique more data should be used.
Table 4: Aggregated measures in dataset .
Following this discussion, Table 5 shows the results of the different techniques using real data. In general, a decrease of precision can be found in this table with regards to previous results. This can be explained due to the fact that this dataset is not customized and real behavior of users and skills is found implying, in general, worse results.
Table 5: Aggregated measures in dataset .
As final remark, a change in the parameters such as the set of users and skills could lead us to get better results. Nevertheless, this initial effort will be used as a baseline to compare further improvements. Regarding similar approaches that have been implemented, as the related work section has outlined, the presented approach is closely related to  since the same problem is being addressed. The main difference is that they have also outlined a functional architecture while we focus here in addressing some of the existing open issues: alignment of the skills quality to an existing competency index. On the other hand, we have tried to reuse the most existing techniques. That is why we have made use of the well-known techniques such as the HITS and SPEAR algorithms that have been demonstrated to detect experts under certain characteristics of a graph. The Skillrank technique gets inspiration of these techniques to adapt the underlying concepts and execution steps to the problem of quality assessment of skills available in online profiles.
The present paper has introduced different techniques to assess quality in graph-based structures. The well-known algorithms HITS and SPEAR have been also presented as inspiration for the Skillrank technique. This approach reinterprets the notions and underlying concepts of the SPEAR algorithm to apply them to the context of skills quality assessment in professional social networks. On the other hand two main experiments have been conducted using synthetic and real data to evaluate the behavior of the aforementioned techniques in terms of precision and recall. Both approaches—the SPEAR and Skillrank algorithms—have shown similar results in the test datasets implying that these techniques can be meaningfully applied to assess quality of skills. From another perspective the quality assessment of user profiles and more specifically user skills is an active research area that ranges from applying expertise retrieval techniques to expertise profiling, topic extraction, and so forth. In this sense there are still some open issues that must be tackled in order to provide automatic methods for user profiling, talent hunter or expert finding processes. Currently, a lot of professional social networks are emerging but the problem of creating groups of users by a certain topic is becoming a major challenge since it is necessary to improve trust and provenance of information or user's activities. The relevance of this work is that it can serve to manage enterprise know-how and to detect experts inside organizations. Their competitiveness can be increased by better human resources management processes that could take advantage of exploiting existing data generated by tracking user's activities. From a technical point of view Skillrank is a first substantial effort (including a good number of "artisan" tasks) and new capabilities such as including new data sources to assess skills quality, use of more advanced ordered weighted averaging (OWA) operators and adaptation of other datasets for experimenting purposes should be added in the future as well as new variables in the core of the algorithm. Finally, we also plan to release the information of our experiments using some existing standard such as nanopublications to ease the reuse and comparison with new techniques.
This wok has been partially supported by the European Commission (programme LifeLong Learning—action Leonardo da Vinci—Transfer of Innovation) through project "ECQA Certified Social Media Networker Skills" (2011-1-ES1_LEO05-35930), by the Spanish Ministry of Economy and Competitiveness through INNPACTO project Post-Via 2.0 (IPT-2011-0973-410000), by the German Federal Ministry of Education and Research trough project "OpSIT—Optimaler Einsatz von Smart-Items-Technologien in der Stationären Pflege" (16SV6048), and by the German Federal Ministry of Economics and Technology through project "PrevenTAB" (KF3144902DB3).
M. Taylor and D. Richards, "Finding and validating expertise," in Proceedings of the 19th European Conference on Information Systems (ECIS '11), Helsinki, Finland, 2011.
L. Page, S. Brin, R. Motwani, and T. Winograd, "The PageRank citation ranking: bringing order to the web," Stanford Digital Library Technologies Project, 1998.
A. G. West, A. J. Aviv, J. Chang et al., "QuanTM: a quantitative trust management system," in Proceedings of the 2nd European Workshop on System Security (EUROSEC '09), pp. 28–35, Nuremberg, Germany, March 2009.
S. Fortunato and A. Lancichinetti, "Community detection algorithms: a comparative analysis: invited presentation, extended abstract," in Proceedings of the 4th International ICST Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS '09), p. 27, 2009.
A. Hannak, P. Sapiezynski, A. M. Kakhki et al., "Measuring personalization of web search," in Proceedings of the 22nd international conference on World Wide Web (WWW '13), pp. 527–538, 2013.


(Washington, DC) – Congressman Doug LaMalfa (R-Richvale) issued the following statement after the House passed H.R. 1181, the Veterans 2nd Amendment Protection Act. Under current law, if a veteran or beneficiary is appointed a fiduciary from the Department of Veterans Affairs (VA), they are labeled as incompetent and automatically sent to the Federal Bureau of Investigation's National Criminal Background Check System (NICS). Once in the system, they are prohibited from legally purchasing or owning a firearm. This bill violates due process and grants the authority determine whether a beneficiary can be removed from the list in the hands of VA bureaucrats. H.R. 1181 prohibits the VA from automatically submitting names to the NICS without a magistrate or judicial authority ruling that the beneficiary is unfit to possess or purchase a firearm.


Why I love to work with the Microsoft products stack?
It's something I'd always loved to do, I mean the Microsoft universe is truly vast and I've always find new stuff to learn every time. But I've always missed something. I'm working in cross platform environments and I've got to say that Linux is perfect in a professional world thanks to the efficiency of the cmdline, so when PowerShell has arrived 9 years ago it's started to change the whole game, but not enough in my opinion. I don't know why people in Redmond hadn't in the flow of PowerShell at this time, but right now everything is different, and it's absolutely awesome how the future seems brilliant for them/us.
If you read comparisons with Linux, don't worry if isn't a problem with your glasses or something else, this is just because Microsoft is doing the good call filling the gap between the two platforms.
Below I'll speak about products I know and use the most. I'm infra oriented and I won't speak about Office 365 or Sharepoint!
Yes, you read it correctly, Microsoft has learnt from past errors, they are not redoing the wheel each time they encounter a rival. They now work with them, in order to make the product available on Microsoft systems AND invest in it to make it better for everyone. Who could have said that Microsoft could be doing that 3 years ago?
You want examples? Think about the .Net code available now (for a little part) as an open sourced product! Think also about the Chef solution, which works hands to hands with Microsoft to bring us a tool to manage our platforms.
In addition to that, and this is something really important to my eyes, Desired State Configuration modules and resources are now hosted on Github, it means for us, users, that we have the power to make the tool better or not, but everything is clear from the start and our opinions are taken in charge for the future.
As I said with Github and DSC Resources, PowerShell team is going into a good direction. Indeed, providing so many CMDlets past years and now they can use them to push PowerShell beyond our horizon.
And this horizon is called Configuration Management. It's something existing for many years on Linux and you can already configure systems with tools like Puppet, Chef, Ansible or Salt… But nothing really built or the Windows Stack, I mean WinRM isn't really user friendly associated with Kerberos using Python or Ruby libraries, and the best tool to interact with a Windows is and will be PowerShell.
THIS is maybe the best move of Jeffrey Snover teams. Who have never dreamed about a light Windows OS Server, booting so quick that you don't have time to drink your coffee? This reality is coming and this'll be a new start for you and your colleagues! You won't have a GUI anymore, you WILL have to learn PowerShell if it's not already the case.
This is where Azure is very important as well, because Nano Server will bring us better and smoother services without paying more money for our infrastructure.
I hope Microsoft will drop definitely the GUI one day, because if you want to be aggressive, you can't let old schooled sysadmins run the world!
As sure sysadmins won't run the world, devs want to for sure and docker is something you should take care of, and masterize.
Docker helps you to get rid of hypervisor stack, you'll be able to use every single remaining power of your physical servers. We'll have to start thinking about things like Micro-services and now more things like "One Server – One task".
I wanted to talk here about Azure, but I won't hehehe. For me, Azure is not a tool but an ecosystem and it deserves more than a blog post to list every single awesomeness present on it.
On the opposite, We (IT, Ops, Sysadmins), we'll have to change our way of work, we have to think everything as code, we have to control our versioning. Nothing exist anymore between Devs and Ops, we both drive in the same world, a world which move so fast that we can't spend our time waiting for a GUI to load or searching for a script to rollback an action.
Linux already have most of these tools and now that Microsoft brings us everything in order to simplify our work, let's use them, or, at least convince our chiefs that this is the road we need to take! We can change this mortifying Windows looks like clicker in a brilliant sysadmin who has embraced a DevOps way of life !


Mark Germain is the founder and CEO of Beacon and has over thirty years of experience giving financial guidance to high net-worth individuals and families. He began his professional career at the "Big Eight" accounting firm Touche Ross and Company (Deloitte and Touche), where he honed his financial forecasting skills as a Municipal Bond Specialist. Later, Mark worked as Chief Financial Officer and Head of Worldwide Field Operations at Apogee Networks, an international software company. Mark holds the Certified Financial Planner (CFP®) and Accredited Domestic Partnership Advisor (ADPA ®) designations and has an MBA in Finance from Northeastern University where he also taught accounting. He received his Bachelors in Economics and Accounting at Southern Connecticut State University where he was inducted into the Delta Mu Delta International Honor Society in Business in 2015 and honored as a Distinguished Alumnus in 2011.


text:
Get On board The Giving Bandwagon This Christmas For The Ultimate Festive Feels
By Kathryn Steane
It's no secret we love Christmas and all the glitz and glam that comes with it, from beautifully wrapped gifts beneath pretty trees, to swanky end-of-year parties with fancy food and drink. However we also know that, as the Grinch himself discovered, 'Christmas means a little bit more'. It's about spending time with loved ones, creating special memories and most important—helping out those less fortunate than ourselves. With that said, here are some simple ways you can give a little something back this festive season—who knows, maybe your heart will even grow a few sizes bigger, just as the Grinch's did...
Give To The Kmart Wishing Tree Appeal
In the 26 years since it started, the annual Kmart Wishing Tree Appeal—a partnership with long-running charity, The Salvation Army—has handed out more than half a million Christmas presents to Kiwis in need, earning itself the title of New Zealand's largest and longest-running gift appeal.
The premise for this one's simple—buy a present of your choice, and leave it under the Christmas tree instore. In a true show of Christmas spirit, the gift doesn't even have to be purchased from Kmart (though, let's be honest, it probably will be!); however the company does encourage practical presents that last beyond the festive season, as well as those for teenagers and older boys (the groups it struggles most to get gifts for).
This year, the retail giant is also accepting $1 donations at the checkout or online which go directly to The Salvation Army, making the spirit of giving even easier. Oh, and all you last-minute shoppers out there needn't worry—Kmart will be accepting these offers of help right up until Christmas Eve. Get giving.
Shop At Oxfam
The award for most creative way to give back this Christmas goes to Oxfam, with meaningful gift ideas that are completely outside the box (and as such, don't come in one). This well-known charity/development organisation offers a great range of alternative pressies through its Unwrapped range, whereby you choose a gift-card representing a particular issue, which is then given to your loved one while the physical present is sent to the person/s in need.
If you're in the mood for splashing cash, choose the Water For A School option (from $106), which helps bring clean water to an entire school in Papua New Guinea; or for something a little cheaper but with equally sweet sentiment, consider the Honey Bees (from $24), which helps families in countries like Vanuatu start up their own beekeeping business.
Our personal favourite, however, has to be the hilariously-named Pile of Poo (from $18)—send someone this card, bearing the words "No expense spared: I got you the best crap" and you'll be helping people in developing nations grow crops with the ultimate fertiliser.
However if that's not quite to your taste, consider buying a more traditional gift like the beautiful, ethically-made homewares and clothing, or delish FairTrade goodies such as chocolate and coffee that are available from Oxfam's online store. Naturally, money from your purchase will go toward supporting the charity and its work with these artisans.
Donate To The Auckland City Mission
The Auckland City Mission is a shining light for many people at this time of the year, helping to provide those in need with the means to enjoy a special Christmas. Assist this awesome organisation in spreading some holiday cheer by making a simple donation, which could be used to pay for anything from festive food packages through to toys and other gifts to put under the tree.
Your contribution may even help finance the Mission's annual Christmas Lunch, this year taking place at The Cloud. Around 1600 guests, including low-income families, isolated elderly people and the city's homeless are expected to descend on the venue for Auckland's biggest festive feast, which will be prepared in the nearby Spark Arena kitchens.
Until the end of December you can also help contribute to the Auckland City Mission's much-used food bank, by adding a $2 donation to your bill at one of the restaurants participating in the DINEAID initiative (think The French Café, Euro, Café Hanoi). Alternatively, purchase the DINEAID special from the menu, and $2 from the cost of the dish will also go to the cause. Food with feels—we like it.
Keep the good vibes going and take in the best Christmas lights Auckland has to offer.
Image credit: Ben White

redpajama_set_name: RedPajamaCommonCrawl


The Womens Drum Chorus was formed in Fall 2006 and is now named MDOFE (Multicultural Drumming Originates From Everywhere) directed by Wahru Cleveland.
We are still drumming occasionally, although my participation is limited these days.
photo credits for this slideshow can be found in the picasa photo album by clicking on the icon.


text:
Home Finance Blockchain Bitcoin
How much did I lose on my 2022 bitcoin investment? It's complicated
Last year on New Year's Day, I made an investment in bitcoin with the goal of holding it for a year and reporting back on the results. To say they were unexpected is an understatement.
Written by David Gewirtz, Senior Contributing Editor on Jan. 3, 2023
Reviewed by Alyson Windsor
This was supposed to be an easy article to write. In January of 2022, I spent a few bucks investing in bitcoin. My plan back then was to wait a year, and then write an article (this one, in fact) showing how much I gained (or lost) over the course of the year. It was to be stunt writing at its best.
Learn about the leading tech trends the world will lean into over the next 12 months and how they will affect your life and your job.
But that's not how its turning out. Oh, sure. I lost a huge percentage of my investment. But I can't just throw bitcoin (or even cryptocurrency in general) under the bus and claim that crypto is a bad investment. That's because a wide range of investments (especially tech) plummeted in 2022.
We're way beyond the scope of a "correction," where the market adjusts for an unexpected bout of exuberance. But we're not at the legendary (but not necessarily true) tulip craze either, where investors get their comeuppance for believing in some bonkersly stupid investment idea.
No, we're at the beginning of 2023 and much of the market has tanked. This isn't a tulip or crypto or craze sort of story. Instead, it's a story about the vagaries of investment and how you need to be careful where you put your hard-earned money.
My bitcoin journey
On January 1, 2022, I bought $50 worth of bitcoin through PayPal. That was an interesting process all on its own. It cost me a buck fifteen in fees to make the purchase, so all-in, I spent $51.15 to embark upon this grand experiment.
I bought Bitcoin from PayPal. Here's what happened
In just a week, my Bitcoin 'investment' plummeted by almost 14%
Let me be blunt: Don't buy crypto unless you're fully prepared to lose your shirt
My Bitcoin 'investment': After exactly six months, how much did I gain or lose?
Within minutes, I lost 25 cents. After one night, my losses amounted to a whole 32 cents. But, of course, that was just the beginning.
I next checked in seven days later and my investment had dropped again. This time, I was down a total of $7.08, for a 13.84% drop. I checked again at the end of January, and my holdings had dropped again. In three weeks, my losses doubled. I was down $14.38, for a total drop of 28.11%.
Then, I waited. I gave it a full six months. Exactly six months into my bitcoin investment, I checked into my vast holdings. As of July 1, my $50 was worth $20.30. My portfolio had lost 60.31% of its value.
Now, we're at a full year. I haven't looked at the value of my bitcoin since July. Let's check it together. I'm about to login to PayPal and take a look.
David Gewirtz/ZDNET
Well, that's interesting. My bitcoin investment lost 60.31% percent of its value in the first six months of my holding it. But it lost only another 5.63% in the second six months. My bitcoin investment is now worth only a paltry $17.42.
Considering how volatile it was during the first six months (and especially the first month of 2022), it's been remarkably stable these last six months.
The rest of the story
Now is the time when I expected to mock bitcoin. After all, losing 60.31% of your investment in six months is a terrible loss. I expected to discuss how it would be better to invest in something that wasn't totally fake, yada, yada, yada.
But life isn't that simple.
CES 2023: The Biggest Trends for Pros
Find all the exciting innovation from CES 2023 in this ZDNET special feature.
Okay, look. I'm about to start discussing stock values and investments, so a massive disclaimer is in order. I am not a stocks or investment expert. I'm not telling you any of this to give you investment advice. I'm just reporting on behavior based on publicly available data. So, before you go betting your money on anything I talk about, think again.
Frankly, beyond the fifty bucks I gambled away on bitcoin, I have no other investments. I'm not big on gambling. Since I don't have confidence in my ability to spot a sure thing, I've avoided investing in stocks and crypto altogether. End disclaimer.
Let's get back to the whole crypto investment theme. To do that, we need to take a pause and look at the news of the day.
When the markets closed for the final time in 2022 on Friday, investors were shaken by how much of a beating they'd taken overall. CNBC reported, "Stocks fall to end Wall Street's worst year since 2008."
If you look at the major indexes (the Dow, S&P, and NASDAQ), all were down for 2022. These are aggregated indexes of companies across industries and, as such, reflect business performance overall.
But these are index stocks, which means their value is representative of the movement of all their members. If some members do great, but most do poorly, then the entire index will trend down.
The biggest tech stocks did particularly badly in 2022. Apple lost 26% of its value, Amazon lost almost half, and Meta (Facebook) dropped almost 65%. IBM, on the other hand, did particularly well.
Stocks across other industries had more varied performance. Retailers like Home Depot and Walmart lost value, but health providers and oil companies continued to grow in value. This list is far from comprehensive, but it helps showcase that parts of the economy did rather well, even as investment performance in tech -- particularly the top tier big names -- didn't do as well.
So, the top-line conclusion is simple. Investing is something of a gamble. You could gain a lot. For example, if you'd invested $1,000 in Exxon Mobile at the beginning of the year, you'd have $1,802 today.
But if you'd invested that same $1,000 in Tesla last January (and yes, we all know the whole Twitter/Musk story is part of this), you'd have only $349 left.
But you can't blame all of tech stock's loss on Musk. A thousand dollars invested in Facebook a year ago would have been worth only $357 today.
This is why investing seems like gambling to me. Last January, I considered my bitcoin investment to be an experiment for the benefit of ZDNET readers. I thought there was a (very) slim chance I might make a few bucks, but I was pretty convinced I'd lose a few instead.
But had you asked me whether my bitcoin investment was more chancy than, say, an investment in Tesla, I would have told you "sure." After all, bitcoin is all magic rainbows and unicorns. It's imaginary currency and nothing is real. On the other hand, Tesla makes fine cars and has factories and technology and such.
The idea that a thousand dollar investment in Tesla stock on January 1, 2022 would have the same value on January 1, 2023 (within pennies) of a thousand dollars invested in Bitcoin would have seemed ludicrous.
Also: Elon Musk's Tesla has sold 75% of its Bitcoin: Here's why
As I said, you can't blame it all on Elon Musk, no matter how much he seems like a comic book villain. A thousand bucks invested in Facebook last January is worth just a few bucks more than a thousand dollars invested in bitcoin, and Musk had nothing directly to do with that collapse.
Timing matters, of course. Back in the day, I actually started my first company using the profits I made from an Apple stock investment. But had I made that investment in Apple in 2022, I wouldn't have had any profits to use to start much of anything.
Investment is about (hopefully) buying low and selling high. It's not about buying according to a calendar entry. So no sane investor would buy on January 1, 2022 and plan on selling on January 1, 2023. Instead, a good investor would expect to hold onto an investment until it's time to sell. That concept became such an issue of controversy among crypto enthusiasts, though, that a misspelling of the word "hold" launched a mocking meme for traditional investment strategy, HODL (hold on for dear life), in response.
So, what does it all mean?
I started this experiment at the beginning of 2022 expecting to show you that cryptocurrency was a much less reliable investment than more traditional investment vehicles. I'm ending 2022 showing you that it's all a crap shoot.
My experiment was, essentially, a data gathering expedition that didn't come home with reliable results. I can't tell you to avoid crypto. I can't tell you to invest in crypto. I can't tell you to avoid stocks. I can't tell you to invest in stocks.
What I can tell you is this: if you have no idea, don't risk your money. Unless you know a lot more about stocks and investment and crypto than I apparently do, hang onto your cash or invest it in whatever is considered safe these days. That's probably something like mutual funds, but at this point, I wouldn't feel comfortable making any kind of recommendation.
Do your research. Ask around. Listen to your gut. And definitely don't invest more than you can afford to lose.
That said, I have a good overall feeling about 2023. Have a great year, folks!
How are you feeling about cryptocurrency investments after 2022? What are your plans for 2023? Let us know in the comments below. And hey, if you had a great New Year's weekend, let us know about that, too.
You can follow my day-to-day project updates on social media. Be sure to follow me on Twitter at @DavidGewirtz, on Facebook at Facebook.com/DavidGewirtz, on Instagram at Instagram.com/DavidGewirtz, and on YouTube at YouTube.com/DavidGewirtzTV.
Demand for blockchain skills shot up 552% in 2022. Here's why
SK Hynix posts first loss in 10 years from memory chip price drop


This is an annual event for us and this year it will take the place of Saturday church one week prior to Easter weekend on Saturday, April 13th from 4-6pm. Please join us for fun for all ages and the whole community. We have some special surprises for you this year that you won't want to miss! It all happens right in our parking lot. We can't wait to see you here!
As August approaches, we believe kids should go back to school with everything they need to succeed. We do what we can to make that possible. Numerous volunteers give their time, talents, donations, and love to see hundreds of kids equipped with a backpack full of school supplies, a new backpack, new clothing, new shoes, and a fresh haircut. This is one of our favorite events of the year. For date and time info please click on the calendar link or view the current flyer. View the current year's flyer here. This is the supply list if you'd like to help out that way.
Boo Bash is an annual free community event we host for everyone! There is always trunk or treating, music, cafe, jumping castles, face painting, and a few surprises (Dunk the Pastors)! For more information click here. For date and time info, click on the calendar link.
Every year, Vision Church hosts Rudolph's Rockin' Eve. There's live music, games, kids' sing-a-long, prizes, snowball fights, and more. We always get a visit from the big jolly man himself...Santa Claus! So mark your calendars in December for this fun, family event. Don't forget to invite your friends because they're all welcome to join the party. Please click on the calendar link for date/time info.


When you are a kid you pretty much look at someone, throw them the ball and you are friends. Its a done deal. Another little person has entered your life who you can laugh with, play with and cry with. The process of forming relationships is simple and easy. As we grow up with these same people at our side, and become overthinking adults, we start to take the ease with which we communicate between each other for granted. Additional friends do come and go over the years, but having a core group who don't just laugh but actually get your jokes, know when to give you space or to keep calling when you don't answer, is something you really only truly appreciate when you don't have it.
Moving to a city across the other side of the world, where we didn't know one single person has really illustrated how different it is trying to start the friend list from scratch, in another country, in another language, as an adult. As a kid, I grew up, moving around every couple of years, going to different schools, living in different houses and making new friends. So the whole arriving-in-a-new-place-as-a-stranger thing didn't scare me. I went on exchange to France during university and have travelled for months (and years) at a time. But over the hill of 30, working and being married changes the dynamics of striking up new relationships. I mean creating the bonds beyond smiling at work, talking about the weather and being seen as a novelty addition from Australia.
As an adult, in everyday life the people you generally come into contact with are settled, established and adding people to their circle takes effort. They have routine, they have stability and are more than often not looking to disrupt their bubble. When you are on the outside, there is much more push to get in than pull. Perhaps it is because you don't have an abundant selection of good quality friends to choose from so the bar is not so high. But for the outsider, the new friends just need potential, and for the established one, there needs to be something amazing about this new person.
It is not that people aren't friendly. They definitely are. Mexicans in general are warm, inviting… and friendly. But that does not mean you are friends. The story of creating solid friendships during adult hood is tough. It takes time and effort and is almost like dating. You pretty much need to go online, whether signing up for meetup events, conversation exchange or facebook groups. You really want someone to like you, you hope they message you back and maybe invite you over for dinner or to a party. So the relationship goes from online to offline and becomes something real. And maybe you will start to talk about things beyond the fact that we have kangaroos in Australia, it takes a really long time to fly there and yes you do like that all Mexican food is spicy.
After almost a year of living here in Mexico City, we can probably count the true friends we have on one hand. Those are the people who will pick you up at 1am in the morning when you have a crisis, or who you share a bunk room with and hangout in your pyjamas drinking tea. It is funny that when you have no common history to hang onto, the people you connect with are those in similar circumstances. Upon moving here, we really wanted to make Mexican friends and not be part of the expat, English speaking circle. Although we have partially succeeded, all of these new friends are in a slight state of disruption themselves. They are not settled, established and stable. They have moved around, have a partner of a different nationality or are just establishing a life here in Mexico City also. They understand what it is like to arrive in a new place and build relationships with people from a different culture.
But this is not the norm. Compared to when we are kids and everyone is open to making new friends and meeting new people, as working adults in relationships we put up barriers to building new and real friendships. We have a happy established network that does not need disrupting. We want to be good friends to the ones we already have and this takes time and effort. There is no room for more. So when you arrive to established communities on the outside of the circle it is tough. Just like a first date, you have to be you, but perhaps your best version. Which does get tiring. So taking advice from kids is often the best way to go. Don't over think it. Throw someone a ball, smile and offer them some of your lunch (or take them for coffee).
Great post. A couple years ago, as our kids started to become a little less moment-to-moment reliant on us, my wife and I decided to start branching out and seeking some new adult friendships. It was a fascinating experience. I joined an adult rock climbing course. For the first few weeks, i found it a little awkward… as if adults don;t know how to open themselves to new relationships. After about month, though, I found it interesting how barriers came down and we all got really close.


text:
Q: Using `pkg-config` as command line argument under cygwin/msys bash I'm trying to use cygwin as a build environment under Windows. I have some dependencies on 3rd party packages, for example, GTK+.
Normally when I build under Linux, in my Makefile I can add a call to pkg-config as an argument to gcc, so it comes out like so:

gcc example.c `pkg-config --libs --cflags gtk+-2.0`

This works fine under Linux, but in cygwin I get:

:Invalid argument
make: *** [example] Error 1

Right now, I am just manually running pkg-config and pasting the output into the Makefile, which is truly terrible. Is there a good way to workaround or fix for this issue?
Make isn't the culprit. I can copy and paste the command line that make uses to call gcc, and that by itself will run gcc, which halts with ": Invalid argument".
I wrote a small test program to print out command line arguments:
for (i = 0; i < argc; i++)
    printf("'%s'\n", argv[i]);

Notice the single quotes.

$ pkg-config --libs gtk+-2.0
-Lc:/mingw/lib -lgtk-win32-2.0 -lgdk-win32-2.0 -latk-1.0 -lgdk_pixbuf-2.0 -lpang
owin32-1.0 -lgdi32 -lpangocairo-1.0 -lpango-1.0 -lcairo -lgobject-2.0 -lgmodule-
2.0 -lglib-2.0 -lintl

Running through the test program:

$ ./t `pkg-config --libs gtk+-2.0`
'C:\cygwin\home\smo\pvm\src\t.exe'
'-Lc:/mingw/lib'
'-lgtk-win32-2.0'
'-lgdk-win32-2.0'
'-latk-1.0'
'-lgdk_pixbuf-2.0'
'-lpangowin32-1.0'
'-lgdi32'
'-lpangocairo-1.0'
'-lpango-1.0'
'-lcairo'
'-lgobject-2.0'
'-lgmodule-2.0'
'-lglib-2.0'
'-lintl'
'

Notice the one single quote on the last line. It looks like argc is one greater than it should be, and argv[argc - 1] is null. Running the same test on Linux does not have this result.
That said, is there, say, some way I could have the Makefile store the result of pkg-config into a variable, and then use that variable, rather than using the back-tick operator?

A:
That said, is there, say, some way I could have the Makefile store the result of pkg-config into a variable, and then use that variable, rather than using the back-tick operator?

GTK_LIBS = $(shell pkg-config --libs gtk+-2.0)

A: Are you sure that you're using the make provided by Cygwin? Use
which make
make --version

to check - this should return "/usr/bin/make" and "GNU Make 3.8 [...]" or something similar.

A: Hmmm... have you tried
make -d

That will give you some (lots) of debugging output.

A: My guess would be that cygwin's gcc can't handle -Lc:/mingw/lib.  Try translating that to a cygwin path.
GTK_LIBS = $(patsubst -Lc:/%,-L/cygdrive/c/%,$(shell pkg-config --libs gtk+-2.0))

A: The single quote at the end of the "t" output may be an artifact of CRLF translation.  Is your pkg-config a cygwin app?  The $(shell) solution I posted earlier may help with this, as GNU make seems to be fairly tolerant of different line ending styles.

A: I had a similar issue and I found a fix here: http://www.demexp.org/dokuwiki/en:demexp_build_on_windows

Take care to put /usr/bin before /cygwin/c/GTK/bin in your PATH so that you use /usr/bin/pkg-config. This is required because GTK's pkg-config post-processes paths, often transforming them in their Windows absolute paths equivalents. As a consequence, tools under cygwin may not understand those paths.

redpajama_set_name: RedPajamaStackExchange


So why don't you get ahead of the game and sort fantastic, personalised mugs for your friends and family now with 30% OFF! This is Keith's exclusive offer specially to help you get your bespoke mugs as champion Christmas presents.
Write anything you want… someone's name, something funny, something sentimental or that special little phrase no one else understands 🙂 The recipient will remember your thoughtfulness and kindness every time that they have a cuppa.
Head over to the custom mug page here. Ends Tuesday 14th November midnight! LAST CHANCE to get a bespoke mug delivered before Christmas!
UK orders only this time!
Orders should take 5-6 weeks to arrive due to their personalised nature (reduced from 12 weeks normally).
Thirty percent discount is already applied to the custom mug on the website.
Discount only available on orders placed and delivered to the UK.
Discount is only valid for the standard and large custom mug on the Keith Brymer Jones website keithbrymerjones.com/product/custom-mug.
Discount will be live from Thursday 9th November until Tuesday 14th November midnight.
Custom mugs are individually made to order.
Non-custom items ordered within the same order will be delivered separately within the normal 3-5 working day period although delivery will only be charged once.
There are no more options available for customisation other than what is displayed on the custom mug page keithbrymerjones.com/product/custom-mug.
Custom mugs are unique so if damaged in transit they will take at least six weeks to replace. Keith Brymer Jones Team cannot be held responsible for this but will do their best in the situation.
Custom mugs will arrive in branded KBJ tissue paper and box.
Custom mugs are not exchangeable or refundable.
No cash alternative for this offer.
Discount does not apply to delivery prices.
Delivery before Christmas is probable but cannot be guaranteed due to the nature of personalisation.


text:
KODA MISONO WALLPAPER
Home / KODA MISONO WALLPAPER
This page was last edited on 19 December , at Cover Album 2 Two months later misono released the third with a single produced by onso9line titled " Mugen Kigen ". After being signed to the label avex trax , misono would debut in as the lead vocalist of a new band; day after tomorrow. This page was last edited on 12 December , at
Name: Akigrel
When she returned home she suddenly started to feel different, and her physical condition became worse. Their single " moon gate ", used as the theme song to the Namco game Tales of Symphoniabrought them their first 2 single.
Retrieved September 15, By using this site, you agree to the Terms of Use and Privacy Policy. The trouble started when she traveled to Bali Indonesia in February, which allegedly re-sparked a mysterious disease that was currently dormant in the singers body.
It's All Love!
Interestingly enough, this single was released on the same day as fellow labelmate and former Do As Infinity member Van Tomiko 's debut solo album. She stayed with the unit for three years until the group disbanded in Retrieved from " http: InMisono debuted as a solo artist with the release of her first single, "VS", which was used as theme song for Nintendo DS videogame Tales of the Tempestfollowing previous songs by day after tomorrow that were used in this videogame franchise.
CORDELIA KASUKAMI WALLPAPERS
Views Read Edit View history. Retrieved 31 January Out of the hundreds of comments all of the top ones were in agreement, saying that the only disease she suffered from was attention seeking.
Koda X misono Sister Collabo!
This post is tagged with gossipmeaning it contains information that is purely speculation. She claimed that as of January for the first time in her life the disease was cured completely.
She was born in KyotoJapan, and is the younger sister of singer Koda Kumi. Throughout her whole entry she never once mentioned the name of the disease, which was a dead giveaway for netizens that she was perhaps just looking for a bit of attention.
Misono – Biography – IMDb
The single was originally set to be released on March 4,but was pushed back to March The release of her second album was announced in earlybut it was unexpectedly pulled. The single was the last of the rock project singles miisono had moderate success, charting at 10 on the Oricon weekly charts.
Her chubby face was a reason for their diminishing popularity and the eventual disbandment of day after tomorrow. The single was certified Gold for shipment ofcopies and "It's all Love!
Retrieved from " https: Her second album say was released in Junebut failed to make an impact much like her debut album due to lack of promotion. Six months after the debut of day after tomorrowmisono began to gain weight. The single had been long-awaited by Japanese fans, due to both Koda sisters being musicians under the same label, Avexdespite the sisters having differing musical styles.
AKSHEY WALLPAPER
Cover Album Cover Album 2. The low sales have been thought to be from the lack of number of new songs on the album only 4 songs out of 12 were new and lack of promotion from avex trax. Compilation albums [ edit ] miwono Along with the sisters' collaboration song, there were two other songs on misonp single, each performed solo by each sister: Views Read Edit View history.
Views Read View source View history. The single charted at 1 on Oriconmaking it Kumi's third consecutive number-one single and misono's first number-one single.
Misono – Wikipedia
In total, the album reached 20 on the Oricon weekly charts and sold 12, copies. By this time they had achieved a total of two singles at 2 and two albums at 2 on the Oricon charts.
In late Marchmisono would return to the music scene as a solo artist with the single " VS ". Former singer misono seems to be on a mission to be the worlds biggest stunt queen. Retrieved 19 January
SCHNUFFEL DASHBOARD LIVE WALLPAPER
LATERALUS WALLPAPER
ALEXIS Y FIDO PERREOLOGIA WALLPAPER
ZEBRAHEAD PHOENIX WALLPAPER
THE SISTERS OF MERCY FLOODLAND FREE DOWNLOAD
MANAMI OKU WALLPAPERS
ONADIO LEONARDO WALLPAPER
HOW TO HIDE SEAMS IN PAINTABLE WALLPAPER
FOR LG KE770 WALLPAPER
RASTAFARA WALLPAPER


Jen Spyra, a writer for 'The Late Show With Stephen Colbert,' has found herself in hot water over a tweet she wrote about the death of former first lady Barbara Bush.
A writer for "The Late Show With Stephen Colbert" has found herself in hot water over a tweet she wrote about the death of former first lady Barbara Bush.
"RIP Barbara Bush, the only woman who was 92 for 30 years," Jen Spyra tweeted shortly after news broke of Bush's passing.
Spyra quickly garnered a negative reaction for the tweet from her followers.
"Wow! That is incredibly insensitive and rude," one user wrote.
"Quite sickening!" another one said.
"A family just lost a love one and you have jokes about said person…let alone a joke that's not funny. Then again, neither is Colbert!" a user tweeted.
FYI: Sometimes it's better not to _try_ to be funny. Sometimes saying nothing is better.
A rep for the "Late Show" did not immediately return Fox News' request for comment.
It's not the first time a CBS employee has raised eyebrows on social media.


From the initial seed of an idea, to listing on the stock industry and beyond, developing a business is in no way a simple journey from A to B. Businesses continually evolve, and as business requirements alter, so finance requirements adjust also. Quite beneficial lens, especially these days when a lot more and a lot more folks are turning to a little business choice as an alternative of seeking for a job. I am fortunate that a lot of it comes pretty naturally for me, but it is obviously usually very best to find out from other people's experiences and suggestions by reading books like these. In the UK, the degree is a higher doctorate, above the PhD, and is issued on the basis of a long record of study and publication.
There are many ways to improve your company or corporation, one of the best way is by using praise, recognition and awards. Awards, praise and recognition can makes people feel good and increase the feeling of self-esteem. Using these three things, every employee or worker could feel good and continue for being innovative. If someone feels good, they will be able to be more creative especially in problem solving in works. Employees who receive regular praise and awards are more productive, engaged and more likely to stay with their organization. Therefore, you need to provide some awards to your employee to ensure that your company are improving. Today, I will provide you several reason about benefits of using award as a corporate gift especially plaques.
First reason for choosing plaques is that you will be able to combine benefit of awarding trophies and certificate. Trophy are great for awards, however, trophy have limited space to convey the message about the award. On the other hands, certificate is great to convey the message about the award, but certificate is easily damaged and could be deteriorate over time. You can simply order cheap plaques with great appearance and durable.
The second reason for choosing plaques is the ease of personalize the award. You can easily personalize and do some custom engraving the plaque as unique as you can be specifically for particular occasion. A unique award could make the recipient feel truly valued and appreciated.
The third reason is the material that can be used into plaques. There are many materials that can be used to make plaque engraving, almost no limitation when it comes to selecting the material for a good plaque. You can made engraved plaques from glass, plastic, metal, wood depending on the purpose and the occasion. Using combination of material can give some special effects and enhance its appearance.
At Queen's, the price range method is made to support the university's vision as the research-intensive university with a transformative student finding out knowledge, although also making certain monetary sustainability. Budget models can be complicated, which can lead to a lack of transparency, or at least a lack of understanding. Nevertheless, taking into consideration that on March six, 2013 came in force Fiscal Discipline Law, it is needed to integrate smoothly all needs on fiscal discipline into medium term organizing technique.
Spending budget execution authority permits the state to reallocate current appropriations for fiscal emergencies that occur between legislative sessions. Projected money flow -your cash price range projects your future cash position on a month-by-month basis.
Some applications also incorporate a single or more system assistants The assistance of an additional individual on-website is suggested, but faculty directors ought to think about the impact of further employees on the system budget even though in the planning stages.
Now that you have calculated how much you spend, what your earnings is, and divided out into a budget plan that works for your particular scenario, it's time decide on how you are going to track every thing each and every month. This worksheet may be a great tool to use in preparation for a meeting with a price range counsellor.
Converting this into a cohesive method to handle your business' development doesn't have to be tough or time-consuming. Obtaining a budget that specifics your income and expenditure will help you to sustain manage of your finances and, if necessary, help to illustrate the difficulties you may be possessing with your creditors.
The significant conclusion (of Hawthorne Experiments) was that the workers respond to their work scenario as a complete and their attitudes and social relations constitute an critical part of the total scenario. The amount of function to be accomplished by a worker is not determined by his physical capacity but by the social norms. The functions of human relations school (noted above) are essential as they have been introduced in the management theory for the initial time.
The Modern Theory of Management: It consists of the following 3 streams of believed: (i) Quantitative Approach to Management (Operations Study) (ii) Systems Strategy to Management and (iii) Contingency Approach to Management. Social responsibility of business towards its personnel is crucial because they are the wheels of an organization.
Our Corporate Sustainability Suggestions preserve us focused on important concepts that support align our social responsibility efforts with our business goals, wants and operations. Philanthropy: Businesses also practice social responsibility by donating to national and regional charities.
The social program which intends to provide equal possibilities for the advancement of all has to make provisions for equal educational possibilities also. He introduced human relations approach to management and is rightly regarded as one particular of the pioneers of the Human Relations Theory of Management. Student social responsibility is the responsibility of every single student for their actions.
Georgia Law's negotiation competitors group, which boasts national championships, allows select second- and third-year students to vie against teams from other respected law schools across the nation to hone their negotiation, vital pondering and issue-solving abilities. The final rule also makes it possible for for up to 10 % of the minimum salary level for non-HCE employees to be met by non-discretionary bonuses, incentives, or commissions, if these payments are made on at least a quarterly basis. Business law fundamentals are vital to the good results of any lawyer, law firm, company, entrepreneur, and in negotiating and drafting the documents for any transaction.
In the 2015-16 academic year, Western Law will turn into the 1st law school in Canada to supply first-year J.D. students the choice of taking Corporate Law in the Winter Term of their very first year. A savvy businessperson will be typically familiar with business laws and know when to seek the advice of a licensed lawyer. While this is not my favored class topic, this book has ready me nicely for my class and helped me earn a greater grade.
As soon as they are published, articles are submitted to our Quality Assessment Method , which combines each human and algorithmic analyses to decide whether or not or not a piece will be Featured on the web site. This choice will permit interested students to start taking a lot more advanced courses quickly in the fall of second year, a single term sooner than students at any other Canadian law college.
You also have the option display all published articles on your profile by going to My Account Profile Edit Profile, and deciding on the radio button at the bottom of the web page that enables all articles to show on the Profile. Following reading all of these posts, I fear I have been offered some misinformation from the law workplace.
International law can refer to 3 things: public international law, private international law or conflict of laws and the law of supranational organisations. Our faculty teach and analysis business law in locations encompassing a wide range of financial, critical, public law and social justice perspectives.


Rev up for some fast spins around the reels with the Good To Go pokie from Microgaming. It's got images of racing cars, pit lane girls and chequered flags to go with the theme, while wild wins and a free spins round that can bring in some big payouts add to the appeal.
Good To Go comes with the standard 5 reel layout, with 9 paylines. The reels dominate the game, with no room for a background image, so we just get plain black behind them, although the payline indicators are nicely designed to look like starter lights and the logo below the reels looks good, with a red racing car added to make them stand out.
At the very bottom of the screen is a control panel that players use to set the amount staked on a spin, and to actually spin the reels. There's the option to bet just 0.01 on a single line for anyone who wants to just see how it all works, while the value of a coin can be adjusted up to a maximum of 1.00 and anything from 1 to 10 coins can be staked on a line for a maximum possible bet of 90.00 per spin.
Wins occur when 3 or more of the same symbol land across an activated line, provided that the row of them starts from the 1st reel on the left side.
As for the symbols themselves, we have the usual low value 10, J, Q, K and A icons, all made to look as if they are made from stainless steel, while the higher value ones are specific to the theme and include a racing car wheel, the flags, a racing helmet, the G2G sign and a front view of one of the cars.
In practice, the lower value card symbols tend to dominate the reels, with just the odd example of the higher value ones dotted around, which makes the game look a little plain, but wins should come fairly frequently with just the 5 symbols taking up much of the available spaces and they flash a little to add some visual appeal when part of a winning combination.
The pit lane girl is a wild symbol, so she helps out players by acting as all others except for the scatter to make up winning combinations, plus she's a very valuable young lady when making up a line on her own, with just 2 examples needed for a 2x the line bet prize and a full row of 5 being worth the game jackpot of 5000x.
The scatter is a rev counter, although technically it's not a scatter symbol at all, as it needs to run from left to right for a win, rather than being literally scattered around the reels as the name would suggest and is usually the case. But never, mind, as it will still pay out some decent prizes that are multiples of the total bet per spin, while 3 or more will start the main feature of this game.
It's a free spins round and although only 3 spins are awarded, whichever reels contained the scatter symbols that triggered the free spins will be held in place throughout them. Any additional scatters will also remain in place, leading to extra wins, and to top it all off, should players end up with 5 scatters in total at the end of the spins, the win is repeated 3 times over.
Wins below a certain amount can be gambled, with players just having to decide if a playing card will turn out to be red or black to double the amount just won. A correct guess means the new amount can either be collected, or gambled again, over and over until the limit is reached, while a wrong guess at any stage forfeits the winning bet and any gamble gains.
Is it in Pole Position, or is it The Pits?
While it's not perfect, Good To Go is a solid all-rounder from Microgaming. Although there are a few other pokies that cover a similar theme, it's surprisingly not very common, so racing fans are sure to enjoy the images of cars and pit lane girls. Others however, may feel that 3 free spins, even with extra scatters, isn't a great deal, but overall there's little wrong with the game.