text:
Session 2011-2012/
March 2012/
Annual Theft and Fraud Report 2010-11: Departmental Briefing
Session: 2011/2012
PDF version of this report (200.49 kb)
Committee for Finance and Personnel
The Chairperson: You are very welcome. For members' information, the secretariat has tabled a paper. There are some suggested areas for discussion, although if we do not cover them all, we may submit them to the witnesses for a written response. I welcome Fiona Hamill, the treasury officer of accounts; Alison Caldwell, the head of fraud and internal audit policy; and Colm Doran, the acting head of the finance branch of the corporate services group. I ask you to make some opening remarks, after which we will go into some discussion with the Committee.
Ms Fiona Hamill (Department of Finance and Personnel): Theft and fraud reports are compiled annually by the Department of Finance and Personnel (DFP) on the basis of information that is submitted by all Departments over the year. They include actual, attempted and suspected frauds that have been classified as involving public funds. We have produced the reports for a number of years, as far back as the early 1990s. The main aim of the production of the reports is to try to identify and highlight fraud, where it is coming from and the steps that can be taken by organisations. They provide a summary analysis of the fraud. As each fraud is identified, it is automatically under managing public money; there is a requirement that, at that point, it is reported to DFP and the Comptroller and Auditor General (C&AG). This report is a year-end summary of all the frauds that have been identified or suspected during the year.
The Chairperson: Thank you very much. The opening section of your report identifies the economic circumstances for the likely increase in fraud. Has that altered your approach with regard to prevention rather than detection? If economic circumstances continue to be difficult for the foreseeable future, it would seem to indicate that need is a more prevalent rationale than greed for fraud or theft. Does that alter your approach?
Ms Hamill: Judging by the cases to which we are being alerted, there seems to be an increase in the pilfering types of fraud. Prevention is always our first line, and that is the correct application of the controls. We look to Departments and organisations to make sure that they apply controls. The purpose of the report is to bring that issue to people's attention and to highlight it. We are using it to re-emphasise that that is where we are seeing cases at the moment. We cannot say that it is a result of the economic downturn, but it seems to be reflective of it. It is about reinforcing the fact that Departments need to be careful in the areas in which those kinds of pilfering crimes may occur.
The Chairperson: Another Committee has looked at issues related to social security. More was lost through staff error than through fraudulent claims. That assessment was made in the agency. Does DFP apply a similar exercise? As well as DFP's annual fraud and theft report across the Departments and the agencies, does it analyse what is lost through error across all those agencies and Departments? There is a very high-level return from some of the non-departmental public bodies (NDPBs).
Ms Alison Caldwell (Department of Finance and Personnel): No. Our exercise is done purely on cases of actual, attempted and suspected fraud during a year. That ranges from theft to grant fraud to income-related issues. DFP does not look at error rates in Departments, although there may be such exercises in individual Departments that are not in our remit.
The Chairperson: Do you agree that that might give you a false picture of what is lost from the public purse? I am not saying that the Social Security Agency's experience is replicated across the board, but while the focus is on misdemeanours, fraud and theft, more might be lost to the public purse through error and mistakes in Departments than through fraud and theft.
Ms Hamill: I understand the point. I suppose that that is where I see the role of the value-for-money studies that the C&AG undertakes. That office looks across the public sector to see where systems could be improved and tightened and to prevent those kinds of things.
The Chairperson: The C&AG is more specific and does not necessarily conduct overarching reports. This report is an overarching one on theft and fraud across the public sector. I am sure that the C&AG will look at individual areas about which errors and other issues are reported to it, without conducting an overarching review.
Ms Hamill: Chairman, I understand your point exactly, but I am not quite sure how it would be surveyed. Specific exercises would need to be undertaken. In social security, for instance, errors can be identified because claimants' error rates can be identified or corrections have to be made to awards to individuals. That would apply in areas in which money is moving out to members of the public. At a broader level, it is more difficult.
The Chairperson: Are the fairly high rates in NDPBs due to less control? Is it the case that there is less control in arm's-length bodies? Has a common cause been identified?
Ms Hamill: That is reflective of where the front line is. Public sector front-line services are delivered by agencies, NDPBs and health and social care boards; they are not delivered by core Departments. That is where the interaction is, and that is why the frauds appear at a higher level there.
Mr D Bradley: Good morning. I notice that you do not have any figures for prosecutions taken or convictions. Why is that?
Ms Caldwell: For compilation of the fraud report, we send out a commissioning letter at the end of March each year asking about cases that have been identified during the year. Quite often, action has not been taken at the time that we get the information because of the time it takes for cases to go to criminal prosecution. We gather some figures on that, but because of the timing, they are not particularly meaningful. Additionally, it is highly unlikely that prosecutions will be possible in a few of the cases. You will note from the report the number of thefts that occur and the number of cases in which the perpetrator is, unfortunately, unknown. So although we have some information to hand, we have not included it in this report because of the context in which it is set. That said, it is for organisations, along with the Police Service of Northern Ireland (PSNI), to decide whether a criminal prosecution can be taken, and it is up to them to liaise.
Mr D Bradley: I see that contractor fraud is highest in monetary value. What actions have been taken to come to terms with that and to ensure that it decreases in the future?
Mr Colm Doran (Department of Finance and Personnel): I can tell you a bit about that. There were no incidents of construction fraud in DFP as a Department in this report. However, the Central Procurement Directorate (CPD) issues organisations with a whole suite of guidance at procurement stage, based on the overriding principles of transparency, equality and non-discrimination. Those controls are set so that there is sufficient transparency around the tenderers that apply for specific procurement contracts. Also, there is now proportionate advertising of all contracts open to the public sector or the private sector to be applied for through the e-sourcing portal. If contracts are of a significant value, they are advertised through the Official Journal of the European Union (OJEU). All the contracts for which people submit tenders through e-sourcing are opened at the same time, so people cannot see tenders before others have been opened. That is a control in ensuring that there are no misdemeanours through contracts being tampered with. At post-award stage, project managers are assigned to each of the contracts, and there is a separation of duties between the placing of orders and their payment. There is a series of controls through the procurement guidance notes and the public control limits about procurement contracts, all of which are designed to combat construction fraud.
Mr D Bradley: It will be interesting to see what effect that has next year.
Mr McQuillan: My question is along the same lines as Dominic's point about contractor fraud. The report highlights two cases in paragraph 3.9; what value were the rest of the cases to make it up to 47%? Were they small or large cases?
Ms Caldwell: There were two very high-value cases of contractor fraud, neither of which were for the Department of Finance and Personnel. From memory, other cases that fell into that category were certainly smaller in value. At the time that the returns were submitted, some of them were unable to put a definite value on those particular ones. That was the case in 56 of the total number of cases that were reported to us. It is either very difficult to place an exact figure or, at the time of reporting, they had not yet got to that quantity.
Mr McQuillan: It is strange that you can get to 47% if you cannot put a value on them.
I will move on to whistle-blowing. The report states that 27 cases were reported. Had it not been for whistle-blowing, would those 27 cases have gone unnoticed? I refer to paragraph 3.22.
Ms Caldwell: Over the past five to six years, one part of our work has been to disseminate guidance on whistle-blowing to other Departments. There have been a number of cases in which fraud had come to light because of whistle-blowing. In 2008, we worked with Public Concern at Work, which is a registered charity that is seen as the expert on whistle-blowing legislation. With it, we developed a template that we issued to all Departments and agencies. It sets out that the public sector wants people to come forward when they have concerns or issues. The template sets out different routes at different levels at which concerns can be reported internally in the organisation and, indeed, externally, including to the Northern Ireland Audit Office (NIAO). It also refers staff to the fact that they can seek advice from Public Concern at Work, which will act in an advisory capacity.
There have always been cases in which people have brought forward concerns through what might be called whistle-blowing procedures. However, it is fair to say that since we issued that guidance and all Departments have put their own bespoke arrangements in place, we have noticed what is best described as a slight marked increase in the number of cases that people have said were brought forward because of formal whistle-blowing procedures. Whether people come forward through a formal whistle-blowing policy or just pick up the phone and ring somebody, the key issue is that Departments want to know about it.
Mr McQuillan: People now have the confidence to come forward because of the policy that was introduced by the Department.
Ms Caldwell: Yes.
Mr Cree: I am interested in the number of incidents versus their actual value. It is interesting to note that, in the tables in the report, although the theft of assets represents 45% of the number of frauds, it accounts for only 7% of the value of the frauds, whereas contractor fraud, to which other members referred, represents only 3% of the number of frauds that were reported but 47% of the value of fraud cases. How can we be assured that prevention and audit work is targeted to maximum effect, rather than just geared towards numbers or whatever?
Ms Caldwell: This year's report was quite unusual with regard to contractor fraud because there were two very high-value frauds, which have been mentioned. As regards the theft of assets, the reason why the value is quite small is because some of the items about which we are notified have quite low values. It takes quite a lot of them to build up.
The public sector's policy is that we do not want any fraud. However, it is also important that any checks and balances that are built in are cost-effective and proportionate and that while we are trying to diminish fraud overall, we are aware of the risk. A key purpose of the report is to identify risk areas and the sort of controls that can be built in to try to prevent the recurrence of such cases.
Mr Cree: Is there an awareness that numbers are not the only criteria? That is really the issue that I am getting at: where the biggest value and loss are likely to occur.
Ms Hamill: The way in which we look after and deal with fraud is through the fraud and internal policy unit, which Alison heads up. We have a fraud forum that involves all Departments and which is attended by representatives from all Departments — the heads of their fraud units. We work collectively to look at the issues and at emerging matters. The fraud forum also includes the PSNI. As a group, we are always looking collectively and trying to identify the key areas and ongoing risks and how they are moving. Alison also sits on one of the subgroups of the Organised Crime Task Force.
Mr Cree: So it is flexible and is targeted each year or several times in a year. Does the emphasis change?
Ms Hamill: It does. At the moment, one example is that there is a change of emphasis towards theft of —
Ms Caldwell: — fuel and metal. We have noticed a marked increase in the number of cases of oil and other fuel disappearing from tanks. Also scrap metal or metal being —
Mr Cree: Some of it is not even scrap but is taken out of people's plumbing.
Ms Caldwell: Absolutely. As that trend has emerged, we have been to the fraud forum and spoken with PSNI representatives. Through contacting the Organised Crime Task Force, I am aware that the Department of Justice, the Department of the Environment and the PSNI are looking at that area to see what action they can take collectively to help to prevent the metal thefts in particular.
In the report, meantime, we have replicated some information that the Northern Ireland Audit Office issued, because it had also noticed an increase in particular types of theft in recent years.
Mr Cree: Have any obvious steps been taken, such as getting in touch with scrap-metal dealers to see what they are up to?
Ms Caldwell: I believe that they have.
Mr McLaughlin: Down South, because that is where most of it is going.
The Chairperson: I was going to say, "Good luck with that."
Ms Caldwell: I understand that consideration is being given to tightening up the regulation of scrap-metal dealers down South. Obviously, there is the potential to shift focus as a result of any steps that are taken elsewhere.
Mr Cree: It has been going for only two years.
Mr McLaughlin: Cross-border body.
Mr P Maskey: Did the procurement fraud involve someone putting a falsified tender in or a member of the Civil Service?
Ms Caldwell: Off the top of my head, I cannot recollect the exact details. Again, I know that it was not a DFP case.
Mr P Maskey: I ask because some contractors have put in tenders that were way under the odds. They could not afford to do the job and came back to renegotiate. I know of Housing Executive examples in which contractors could not afford to do the work for a considerable time because they had to renegotiate. That meant that other people who had put in cost-effective tenders were being badly underpriced by contractors who then could not afford to do the work. Would that be deemed fraud?
Mr Doran: As I said, through the e-sourcing portal, all tenders for public works are now opened at the same time. The ability to tamper with, or open, a quote for a piece of work should not exist. The ability to undercut such a piece of work through the procurement guidelines, procurement guidance notes and the e-sourcing portal should be minimised. Therefore, the ability to undercut should not exist. All those controls are intended to minimise the potential for fraud, but I suppose it is sometimes difficult to eradicate fraud completely.
Mr P Maskey: Would it be deemed fraud if someone undercuts everyone else in a tendering exercise in a procurement process, and then renegotiates that price after being awarded the contract but failing to do the work?
Mr Doran: Controls are also in place to manage the quality of work and performance of contractors through procurement guidance notes. It would be for individual organisations to manage their contractors' performance — the quality of their work and the price at which they are supposed to deliver it. Again, it would be down to each organisation to manage individual contracts to minimise the risk of that happening.
Ms Hamill: I understand your concern, but I think that it probably would not ever be picked up or identified as a fraud, because you have to prove intent.
Mr P Maskey: That is fair enough. The reason I ask is that there is a small number of cases — five in total — which have no money, no costs awarded to them. Look at page 17. There are "cases where no value given", and no cost or estimated value. I suppose that you have to make sure that there is intent. Is it ever looked into to find out whether there was intent? Has someone deliberately tendered, won the contract, has not been able to afford to do the work and then renegotiated the deal after they had been awarded the contract?
Ms Hamill: Looking at it from the fraud side, I think that you would have to say no. You would have to speak to the Central Procurement Directorate to find out how it handles situations that may arise post-award and what thinking it puts into whether to renegotiate.
Ms Caldwell: The sorts of contract fraud that I have seen coming in over the past number of years are more likely to occur when work has been billed that has not actually been done, or when excessive rates have been charged. Obviously, the fraud is a bit more clear-cut in such cases. If the work is not done, it is not done.
Mr P Maskey: It might be worthwhile for the Department to undertake to look at that issue at some stage. I am concerned that it might be the case with a lot of contractors. It is not a level playing field if contractors who put in a tender are undercut, and then whoever won the contract cannot afford to do the work and renegotiates. I do not think that that is fair.
Mr Doran: As I say, the guidelines are clear. The playing field is level when the tenders are submitted, and none of them is opened until all of them are opened. I take your point: someone submits a tender intentionally low and cannot then deliver for that cost. That is a contractual issue, I would imagine, between the contracting Department and the contractor. It is a valid point.
Ms Hamill: We can certainly clarify CPD's guidance in those circumstances for the Committee.
Mr McLaughlin: Is there a lower threshold, in monetary value, at which you will record statistics?
Ms Caldwell: No. We record all cases of actual, attempted and suspected fraud that are reported to us, regardless of the value.
Mr McLaughlin: In theory, that could be something to the value of £5 or £10?
Mr McLaughlin: Is it a major task to indicate that type of petty fraud?
Ms Caldwell: To be honest, thefts of assets, by and large, are —
Mr McLaughlin: Petty?
Ms Caldwell: It depends on what sort of threshold you are talking about. The theft of some of the assets of which we are notified have limited values. Some of the income-related frauds can be £10 or £15 short in the petty cash. I stress that that may be actual or suspected fraud. It is sometimes very hard to distinguish between the two. It could just be an error, but there is also a reasonable suspicion that the money could have been taken by theft or pilfering. We require notification of all cases.
Mr Doran: The Department's anti-fraud policy and fraud response plan is clear: the Department has zero tolerance of fraud, which means that we expect all cases to be reported through the arrangements in our anti-fraud policy. In the report, the value of the loss to the Department was pretty low. It was less than £2,000, and most of that was as a result of theft of equipment. Nonetheless, the intention of the anti-fraud policy is to report all actual, suspected or attempted fraud.
Mr McLaughlin: I am interested in whether the reporting mechanism breaks that down so that we are talking about office consumables as opposed to equipment or assets, perhaps. I know that they are all assets, but I suppose it puts it into a particular category.
Paragraph 2.6 gives three figures for the "actual"; the "attempted but prevented"; and the remainder, which is almost £1 million, for the "suspected". How long do cases remain in that area? Does suspected mean not proven, or does it mean that it is a live investigation, in all these circumstances as they are reported? At what point do people have to move on?
Ms Caldwell: That is one of the limitations of the annual fraud report in that it is based on information at a point in time. At the point in time on which we sought that information, £965,000 was categorised as suspected. That could have been because investigations were ongoing, and they might come up with a more accurate figure in due course. Alternatively, it might be that fraud cannot be absolutely determined in the finish-up, or some of that money might be categorised later as being bona fide.
There are inherent difficulties with trying to put quantities on the value of fraud, which has been borne out in a number of studies across countries. We are aware of the limits of the report; it is a snapshot of only one point in time. Not all cases get a value at that time, or they might change subsequently. Also, some of those funds could be recovered down the line. Being honest, there is also the "unknown unknown", which is when people report something to us that they have picked up or suspected. There is no doubt that cases go undetected. In the absence of any other information, this is the best that we can have.
Mr McLaughlin: The report ends up with a fairly precise figure . Does that indicate that those frauds are under investigation?
Ms Caldwell: Again, it will depend on how far they are through the process at the time at which they are reported to us at the end of each financial year. Not all of them are finalised. At one stage, we considered basing the report on only those that had been finalised. However, given that the main purpose of the report is to flag up trends and, more importantly, actions that Departments should be taking to try to prevent frauds in the future, we felt that this was the most appropriate and timely mechanism.
Mr McLaughlin: I appreciate that. In that area, there are suspected frauds that continue to be investigated. I suspect that there are some that have been investigated but cannot be proven. Is there a point at which they come off the screen or go into a different subcategory of "suspected but not proven" or "no longer under investigation" or "written off by the Department", for instance?
Ms Caldwell: We do not keep a track of those, but the relevant Departments, agencies and NDPBs see those cases through to completion, whatever the outcome may be. We do not monitor the outcome of every case.
Mr McLaughlin: Do you see what I am getting at? At some stage, they have to be finalised. I absolutely accept that that will not be in any 12-month cycle, because cases could very easily run into subsequent years. However, I suppose that there would be a point at which the return for effort made and the resources for investigation would have to be looked at, and you would simply have to conclude that you are not going to take the case any further. How is that process recorded?
Ms Hamill: When cases are concluded in Departments, the loss will need to be written off. The cause of a loss, whether it is through fraud or being written off as a loss, will be an internal matter for the Department.
Mr McLaughlin: Even in circumstances in which there is a prosecution or whatever, you may not recover the money.
Ms Hamill: Losses should be recorded in the Department's annual accounts.
Mr Doran: Yes.
Mr McLaughlin: There is no column in the fraud report that details losses through fraud that did not result in the attribution of responsibility.
Ms Hamill: No. It is worth saying that the accuracy of the valuations of the actual losses, under the heading "suspected" in the table on page 13 that lists the number of frauds reported by category, has not been established. Some of those figures are estimates of the potential losses caused by the suspected fraud. They can increase dramatically or could decrease dramatically; it could prove to be a very low value. It is only an estimate.
When the Treasury produced a report such as this in the past — it does not do so any more — it did not include suspected fraud. It reported on the actual level of fraud across the sector rather than including matters that were under investigation. As Alison said, for us locally, if we use it as a vehicle to spot the trends, the suspected fraud figures are sometimes as valuable as the actual fraud figures.
Mr McLaughlin: First, it is clear that good work is being done here. The statistics that determine the patterns that may be established over a period might still be fairly young. Do we have a system — a red, amber and green (RAG) system or whatever — that gives us indicators that the anti-fraud measures in particular bodies, be they arm's-length bodies or departmental, are not sufficiently robust, or are there recurring patterns that indicate embedded problems? Do we then intervene?
Mr Doran: In our Department — I am speaking only about our own return — we would expect business areas to determine whether each attempted, suspected or actual fraud has been detected through existing controls or whether the controls need to be reviewed. If so, we would expect the business areas to report again in the returns about what they have done to tighten the controls. However, it has to be said that, of the 16 cases in DFP in the report, around half were detected by existing controls. Although business areas review those controls to ensure that they are still tight enough, it is, in a way, reassuring to a business area that existing controls are picking up on any attempted fraud. The majority of ours were attempted frauds and were caught on by the controls in our Department or by our external partners.
Ms Hamill: All Departments have fraud response plans. They should be part of their risk management processes, and it will be for Departments to decide whether their individual organisations have their own fraud response plans, or, for example, in the case of DFP, where it is part of the Department's. Land and Property Services, for example, which is a big element of the Department, is part of the core Department's fraud response plan rather than at arm's-length, because it is such a critical income area. The way in which the fraud response plan is managed internally in the risk management process of each organisation will be for individual Departments to decide.
Mr McLaughlin: Therefore, there is no overarching assessment or analysis that would point up the need for either an intervention or for people to be called in to discuss their fraud prevention processes.
Mr Doran: The Department has a fraud working group, which is almost a mirror image of what happens at Northern Ireland Civil Service (NICS) level. It is made up of representatives from each of the business areas across the Department as well as the Audit Office.
Mr McLaughlin: Is that the forum?
Ms Hamill: There is the fraud forum at NICS level, which Alison looks after. DFP and the Department for Regional Development (DRD), with all the roads, materials and assets, and so on, will have similar internal forums if it is appropriate. There is a scaling up.
Mr Doran: Our internal audit people undertake fraud awareness sessions for any business areas in which the risk of fraud is considered to be higher than others. Our internal audit team has done quite a lot of that over the past couple of years. We disseminate all the information through our fraud working group. The theft of fuel and materials, for instance, was recently discussed at our fraud working group at departmental level. Any central guidance or information that becomes available is disseminated across the Department and targeted to those areas in which the risk of fraud is considered to be higher.
The Chairperson: The Bribery Act 2010 has been in place for nine months. You said that there is specific guidance on that, but I was trying to get some sense of whether it applies to Departments and arm's-length bodies. Do you have any idea about penalties? What preparations are being made, and what are the mitigating factors? I presume that the fraud forum deals with it and its implications across all Departments.
Ms Caldwell: The Bribery Act is UK-wide legislation. Bribery has never been legal; it has always been illegal. That sounds as if I am stating the obvious, but there has been a lot of focus on the Bribery Act and the belief that it is something new, when, essentially, it is not. The new Act clarifies the legal position and makes it easier to prosecute. The fraud forum has considered it. Obviously, there are two forms of bribery: active and passive, whereby someone receives a bribe and gives a bribe. The section 7 offence created a bit more focus in Departments. It states that an organisation could be bribed if someone bribed on its behalf. A lot of thought went into where in the public sector it is likely that someone would bribe. We sought legal advice on whether a Department could be prosecuted for a section 7 offence. As a result of that advice, we issued a "Dear accounting officer" (DAO) letter to the Departments to advise that the Bribery Act had become effective. It gives some clarification on the section 7 offence and highlights the fact that bribery is a risk that should be considered as part of Departments' overall risk assessments. Obviously, some areas in the public sector will be more at risk from bribery than others. A number of Departments already had bribery on their risk assessments, depending on their business areas. The Ministry of Justice also provided some guidance on what an organisation might want to do to have a defence against the section 7 offence. That was included in our DAO.
The Chairperson: A new offence is created:
"Failure of commercial organisations to prevent bribery"
by persons associated with them. I appreciate the fact that bribery has always been a crime, and, therefore, there will be a risk assessment in the Departments that would be more susceptible. You imagine that there is a greater likelihood of bribery with planning or anything to do with major infrastructure. The Act has been in place for nine months. It has obviously changed the picture somewhat, and you are issuing further guidance based on the new Act that came in last July. How close are we to that? Has that brought about significant change in respect of your advice across the Departments?
Ms Caldwell: Since the annual fraud report has been issued, we have also issued guidance on the Bribery Act. It has not particularly changed what Departments have been doing because bribery should always have been on their radar. We have worked with colleagues in corporate HR, who will make sure that reference to the Bribery Act is included in the staff handbook. That will make it explicit, if it was not already, that bribery is not acceptable. Through discussions at the fraud forum, I know that other Departments, including DFP, have been more focused on bribery, although the risk is probably the same as it ever was.
The Chairperson: Thank you very much. We had some questions that the secretariat had worked on, and, if you do not mind, we will forward those to you in case any of them were not picked up in the discussion. Perhaps you will respond in writing.
Ms Hamill: We will, absolutely.
Members present
Murphy, Conor (Chairperson)
Bradley, Dominic (Deputy Chairperson)
Cochrane, Judith
Cree, Leslie MBE
Hilditch, David
Humphrey, William
Maskey, Paul
McLaughlin, Mitchel
McQuillan, Adrian
Department of Finance and Personnel:
Ms Alison Caldwell
Mr Colm Doran
Ms Fiona Hamill


If you run a shop, the front window is there to let in plenty of natural light and provide insulation. The problem is that it is also a magnet for thieves and other undesirables who simply want to break in. In these cases, a broken window should be repaired as soon as possible, but a broken window for any reason can place your business at risk.
Why Should You Have Your Window Repaired?
Perception: Even a small crack in your glass window can put customers off. Do you really want to send the wrong message to people about your business? If you walked past another shop and saw that the front window was smashed in, what would you think? Would it make you want to go in there or would you simply find another place? Lots of shop owners let small window cracks go because they think it won't affect them, but the truth is that customers may perceive your business in a negative way as a result.
Sending the wrong signals: When your shop doesn't look great, it can send the wrong signals. Broken glass windows can be an easy target for would-be thieves, and it's important to show them that you're on top of things. A cracked window can be a weak entry point for thieves, so it's important to get them fixed as soon as possible. In fact, people will even go out and look for businesses to break into that seem like easy targets. You don't want to send the message that you don't care about your business because you can't be bothered to fix your broken windows.
Higher power bills: Did you know that the windows in your shop aren't just for letting in light? They actually provide some insulation against the extreme heat and cold. When they're cracked or smashed, you're actually allowing heat and cold to enter and leave your shop. This can result in higher than normal utility bills and will affect your bottom line.
Whether your windows have been accidentally damaged or they have been deliberately smashed by trespassers, kids, or thieves, it's crucial to get them fixed by a professional glazier service. The way that your property looks says a lot about you and your attitude. Customers will notice this and will often make a decision on whether or not to go through your front door based on how your property looks. Broken or cracked windows that have obviously not been fixed will put up red flags in many people's minds.


It is surely no bad thing if the world has a possibility of diversification so that the above risks can be avoided. Of course something similar has long existed, namely gold. However, keeping gold is expensive and risky (it needs to be guarded or it risks being stolen, or interest is payable in the case of a futures contract). It cannot really be used to pay for goods and transferring it is also problematic. However, when the credit crisis broke out, there was no better tool for eliminating the above risks, so the price of gold rose between 2008 and 2011 to almost two and half times its pre-crisis price. If the Bitcoin had already been tried and tested at that point, then it could have played a similar role to gold. A lot of investors fled to gold, thereby driving up its price. From that point of view it's more helpful to think of Bitcoin as virtual gold than as virtual money.
Soaring inflation is another kind of economic stress situation. It's hard for us to image hyperinflation since it hasn't been experienced in the developed world for a long time, though the possibility that we'll witness it in our lifetime cannot be ruled out. In such circumstances economic players limit their production since there is no reliable means of payment, and costs and revenues cannot be planned. Compared to all other stand-ins for money, Bitcoin is the easiest to transfer and the cheapest to keep, so in such an environment Bitcoins could become the dominant means of payment. Traders would prefer to give their prices in Bitcoin, which can stay stable, instead of official money that is depreciating in value from day to day, perhaps even to the extent that it's not possible to keep up with the changes. That would prevent the total collapse of the economy. Once again, it cannot hurt for the world to have such a tool.
Nonetheless, Bitcoin comes with numerous risks. I'll write about those next time.


text:
Alteryx, Inc
Alteryx Named Best Place to Work in Orange County for Fourth Consecutive Year
Press Release from Alteryx, Inc
IRVINE, Calif., July 2, 2019 /PRNewswire/ -- Alteryx, Inc. (NYSE: AYX), revolutionizing business through data science and analytics, today announced it was named one of the 2019 Best Places to Work in Orange County for the fourth consecutive year. The company ranks among the top five companies honored with the award, which was created in 2009 by the Orange County Business Journaland the Best Companies Group to recognize companies amplifying the county's economy, workforce, businesses and communities.
Alteryx is committed to empowering customers, partners and associates to make an impact and fuel remarkable social and business outcomes with data science and analytics. The Alteryx for Good program enables the company's more than 1,000 associates around the world to make a difference every day on a local and global scale. The program, created in 2016, aims to influence social change by allowing associates the freedom to donate their time to the charity or cause of their choice with 20 paid volunteer hours, and provides universities, schools and qualifying nonprofits with free Alteryx licenses and support. In 2018, Alteryx associates dedicated more than 1770 volunteer hours with the program.
Alteryx is dedicated to building a workplace culture focused on innovation and inclusion, where associates show up each day as their whole and authentic selves, have both a seat and a voice at the table, and are rewarded for their achievements. In 2019, the company launched Alter.Us, a program that promotes inclusion with a goal of eliminating biases in the global workplace. The program encourages an environment of diverse perspectives, enhanced creativity, increased engagement and up-leveled innovation, crucial elements for improving personal, business and social outcomes. Alteryx also offers thorough onboarding and company/product orientation for new hires and continues to foster associate development via continued education, tuition reimbursement, leadership summits, career coaching and opportunity for global assignment. Evidenced by participation in stock purchase plans and other investment opportunities within the company, associates believe in the company mission to help every business in the world experience the thrill of problem solving with Alteryx.
"We are thrilled to be recognized as a best place to work in Orange County for the fourth year in a row," said Chad Bennett, senior vice president of human resources at Alteryx. "As a purpose-driven organization, we want to ensure every associate understands how their work is impacting the company's highest business imperatives while also supporting individual professional development. Alteryx associates have access to programs that allow them to contribute to the company, to the community and to the globe in a meaningful way. Our incredible growth this year has fueled new opportunities across the company and this award represents our commitment to our team and their contribution to Alteryx."
In determining the best places to work in Orange County, the Best Companies Group evaluated organizations from across the county in a two-part process that both reviews each employer's workplace policies, practices and demographics (accounting for 25 percent of the total score), and surveys employees to measure the associate experience (accounting for 75 percent). The combined scores determined the top organizations and the final rankings.
Alteryx is hiring! For additional information and to learn how you can join the Alteryx team (#TeamAYX), visit www.alteryx.com/careers.
About Alteryx
Revolutionizing business through data science and analytics, Alteryx offers an end-to-end analytics platform that empowers data analysts and scientists alike to break data barriers, deliver insights, and experience the thrill of getting to the answer faster. Organizations all over the world rely on Alteryx daily to deliver actionable insights. For more information visit www.alteryx.com.
Alteryx is a registered trademark of Alteryx, Inc. All other product and brand names may be trademarks or registered trademarks of their respective owners.
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When the Triumph TR7 was introduced in 1975, the ads called it "The Shape of Things to Come."
When the Triumph TR7 was introduced in 1975, the ads called it "The Shape of Things to Come." That was definitely the case as the wedge-shaped TR7 penned by Triumph designer Harris Mann was definitely a futuristic design, taking off from such shapes as those of the Lotus Esprit and Ferrari 308 GT4. Being this divergent from past Triumph design, though, TR7 was not what the average Triumph owner was hoping for, and the sports car was not very welcomed by the Triumph faithful.
The Pick of the Day is a 1978 Triumph TR7 coupe located in Windham, Maine, and is apparently a legitimate one-owner car with just 37,415 miles on its odometer and, according to the private seller, a completely original car that has been garaged since new.
In addition to their controversial new design, early TR7 cars were poorly assembled because of quality-control issues at the factory in Speke, England, which had numerous labor issues which trickled down to how the cars were built. The later roadsters and coupes, such as this one listed on ClassicCars.com, were built in Coventry, and the quality improved during the car's production run.
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As a result of this baggage, nice TR7s, and especially the coupes, have all but disappeared from the collector car market after suffering decades of disregard, misuse and lack of maintenance. That makes it very rare to find such a well-tended one-owner TR7 as this 1978 coupe.
This low-mileage example received Ziebart rust proofing when new, says the seller, who touts the extra-low mileage. The car's Carmine Red body with Beige cloth interior is a nice combination, and both paint and interior look to be in exceptional condition.
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The asking price of $5,500 or best offer seems like a gift, and I cannot think of any better value on a classic British car.


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text:
\section{Introduction}

Rip currents are the most significant safety risk to swimmers along the coastlines of oceans, seas, and large lakes. \cite{Brighton13,brewster2019estimations,castelle2016rip}. The majority of beach goers do not know how to identify rip currents, and there is no  robust and reliable location-independent method to identify  them. Globally
there are thousands of drownings each year due to rip currents \cite{Klein03, Lushine91}. A 20 year study by the US Lifesaving Association reports that 81.9\% of the 37,000 beach rescues each year are due to rip currents \cite{brewster2019estimations}. There has been no decline in the number of associated drowning fatalities, despite warning signs and educational material.

Rip currents are a well-studied ocean phenomenon \cite{bowen1969rip, RN118,RN170}. They are defined as strong and narrow channels of fast-moving water that flow towards the sea from beaches.
When waves break, they form a ``setup'' or an increase in mean water level.  This setup can vary along a shoreline depending on the amount or height of breaking waves.  Rip currents form as water tends to flow alongshore from regions of high setup (larger waves) to regions of lower setup
(smaller waves) where currents converge to form a seaward flowing rip.
The speed of seaward rips can be quite strong reaching 2 m/s, faster than an Olympic swimmer.
There are multiple factors that determine the location and strength of rips, such as bathymetry, wave height and direction, tide, and beach shape. Rip currents may either be transient or persistent in space and time.
Rips that are frequently found at the same location are usually indicative
of a fairly stable bathymetric feature such as a sand bar or reef, or a hard structure such as rocky outcrop, jetty or pier. These bathymetric features results in variations in wave breaking and setup leading to channelized rip current flow.
Transient or flash rips are independent of bathymetry and may move up or down the beach, and may appear or disappear.

Lifeguards are often trained to identify rip currents. \linebreak However the majority of drownings occur on beaches without trained personnel \cite{australia2009national,branche2001lifeguard}. Posted signs can provide a warning, but there is evidence that most people do not find existing signs helpful in actually identifying rip currents \cite{Brannstrom15}.

Experts at the National Oceanic and Atmospheric Administration (NOAA) use images and video to gather statistics about rip currents \cite{dusek2019webcat}. These data are supporting the validation of a rip current forecast model to alert people to potential hazards \cite{RN136}. The most commonly used method to visualize rip currents from video is time averaging, summarizing a video as a single image \cite{holman2007history}. In \cite{maryan2019machine} boosted cascade of simple Haar like features, a machine learning technique, was used to detect rip currents in time averaged images.  However these time averages when manually assessed can be misinterpreted. Furthermore, they are not readily available nor interpretable by the average beachgoers, and the process of averaging removes available information.

In recent years the coastal engineering community has successfully used deep neural networks to solve many problems. Classification problems such as classifying wave \linebreak breaking in infrared imagery \cite{buscombe_data-driven_2019}, beach scene and other landscape classification \cite{buscombe_landscape_2018}, automated plankton image classification \cite{luo_automated_2018} \emph{and} ocean front recognition \cite{lima_learning_2017} were formulated as deep learning problems using convolutional neural networks.  Furthermore, some regression problems such as optical wave gauging \cite{buscombe_optical_2020}, tracking remotely sensed waves \cite{stringari_novel_2019}, typhoon forecasting \cite{jiang_deep_2018} were also solved using deep neural networks. In addition, generative adversarial networks, a type of deep neural networks, were used to improve the quality of downscaling of ocean remote sensing data \cite{ducournau_deep_2016}.

Object detection with deep neural networks is well  \linebreak studied in the computer vision community. However most benchmarks and research focus on detecting physical \linebreak objects with boundaries between what is and is not \linebreak an object \cite{deng2009imagenet,everingham2010pascal,lin2014microsoft}. Rip currents are ephemeral ``objects'' which are not observable in every frame, and amorphous without clearly defined boundaries even when observable. It is not obvious whether existing methods are applicable. \linebreak Figure~\ref{teaserfig} provides a set of examples, illustrating the difficulty of the problem.

Our work is aimed at introducing this problem to the
coastal engineering community, and showing that object detection methods \emph{are} applicable. We gathered training data sets of rip currents and worked with experts at NOAA to ensure that test data were labeled correctly. We use Faster-RCNN~\cite{ren2015faster} with a custom temporal aggregation stage that allowed us to achieve detection accuracy that is higher than both humans and other methods of rip current detection previously reported in the literature.

\vspace{1mm}
\noindent
The contributions of this paper are:
\begin{itemize}
\itemsep0em
    \item Evidence that region based convolutional neural networks (CNN) approach for object detection is applicable to amorphous and ephemeral objects such as rip currents
    \item Analysis showing rip current detection accuracy above existing \linebreak published methods
   \item Data sets of rip current images and video for training and testing
\end{itemize}

The remainder of the paper is organized as follows. All the related work is summarized in Section \ref{s:related_work}. We discuss how the data was collected in Section \ref{s:data_sets}. Our method is discussed in \ref{s:methods}. Results are discussed in \ref{s:results}. Limitations and discussion are in Section \ref{s:limitations_and_discussion}. In Section \ref{s:conclusion} we conclude our paper. And in Appendix \ref{s:appendix} we provide the link to the supplementary materials.

\begin{figure*}
    \begin{center}
        \centering

           \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/no_rip_04.png}
           \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/no_rip_06.png}

            \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/no_rip_01.jpg}

    \vspace{0.5mm}

            \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/18463.jpg}
            \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/189.jpg}
            \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/9.jpg}
             \captionof{figure}{A collection of beach scenes, some of which contain dangerous rip currents. Unfortunately these ``objects'' do not have clear shape, and most people find them hard to identify. This paper describes a computer vision system with detection accuracy higher than both existing published methods and human observers.} \label{teaserfig}
    \end{center}
\end{figure*}

\section{\label{s:related_work} Related Work}

Rip currents are most commonly studied using in situ techniques or instrumentation. Fluorescein dye is commonly released into the ocean and the dispersion observed \cite{brander2014dye, Clark10,clark2014aerial,Pritchard60}.  Wave sensors, acoustic velocimeters, or current profilers can  be deployed at specific locations \cite{Elgar01,Inch14,Johnson04,liu2019lifeguarding}. Floating drifters with embedded GPS units have also been used to measure currents~\cite{Castelle14,castelle2016rip,schmidt2003gps}.
These methods are costly, time consuming, require technical expertise and are generally only applicable to highly localized instances in time and space. These limitations severely hinder the applicability of such approaches to both public warnings and model validation.

Time averaged images are a routine method for analyzing video in oceanic research, with 10 minutes being a common integration period~\cite{holland1997practical,holman2007history,lippmann1989quantification,pitman16}. This method is popular because averages often make identification of rip channels easier for the human eye.
While these images are usually intended for human interpretation, Maryan et. al. apply machine learning to recognize rip channels in time averaged images \cite{maryan2019machine}.  Nelko also used time averaged images and noted that prediction schemes developed at one beach location may not be directly applicable to another without some modifications~\cite{nelko14}. In contrast to these works,
the analysis in this paper suggests that object detection on individual frames outperforms time averaged images.

Dense optical flow \cite{Barron1994,Horn1981} has been used to detect rip currents in video \cite{eurovisshort.20161155}. This method is attractive since optical flow fields can be directly compared against ground truth flow fields obtained from in situ measurements \cite{derian2017wavelet}. Unfortunately these methods are sensitive to camera perturbation, and have difficulty in areas lacking textural information. The results in this paper suggests that object detection on individual frames outperforms previous optical flow based methods.

Certain kinds of rip currents are characterized by visible sediment plumes. These can be segmented based on changes in coloration. For example, Liu et al, use thresholding in HSV color space to detect rip currents \cite{liu2019lifeguarding}.
Unfortunately, not all rip currents contain sediment plumes, and thus this method is not applicable to our data sets.

Object detection in images is well studied in the computer vision literature
\cite{han2018advanced,liu2018deep,Papageorgiou98,Redmon16,Viola2004}. These methods have been extended to detect objects in videos \cite{Han16,Kang16,zhu2017flow}. This work has not previously been applied to rip currents both because it is not clear there is an ``object'' to detect, and because of a lack of publicly available data sets for training and testing.

This paper contributes labeled data sets for rip current detection, and evidence that object detection outperforms existing published methods on this application.

\begin{figure*}
        \centering
        \begin{turn}{90} \hspace{6.2mm}With Rips   \end{turn}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/rip/ex1.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/rip/ex2.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/rip/ex3.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/rip/ex4.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/rip/ex5.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/rip/ex6.jpg}

        \begin{turn}{90}\hspace{5mm}Without Rips\end{turn}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/no_rip/no_rip_1.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/no_rip/no_rip_2.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/no_rip/no_rip_3.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/no_rip/no_rip_4.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/no_rip/no_rip_5.jpg}
        \includegraphics[width=0.158\textwidth, height =1.1in]{images/train_data/no_rip/no_rip_6.jpg}
        \caption{Examples drawn from the 2440 images we collected and labeled to build a training data set. Ground truth bounding boxes are shown in red.}
        \label{no_rip_current_examples}
\end{figure*}

\section{\label{s:data_sets} Data sets}

\subsection{\label{s:training} Training Data Images}

Since rip currents are a new problem domain for computer vision, we did not find any existing public databases of rip current images.  Therefore, we assembled a training data set of rip current images and non-rip current images from scratch. Our primary source for the database was Google Earth$^{TM}$, which allowed us to extract high-resolution aerial images of rip currents and non-rip currents. In total, the database contains 1740 images of rip currents and 700 images of similar beach scenes without rip currents. The images range in size from $1086 \times 916$ to $234 \times 234$ . We annotated ground truth in the rip current images with axis-aligned bounding boxes.  Some examples of the training data set are shown in Figure~\ref{no_rip_current_examples}. Note that this data set contains unambiguous easy examples.
We used this static image data set for training models described in Section~\ref{s:methods}.

\subsection{Test Videos}

We also collected a data set of $23$ video clips consisting of $18042$ frames. There are a total of $9053$ frames with and $8989$ frames without rip currents. Image size varies from $1280 \times 720$ to $ 1080 \times 920 $ . Ground truth annotations were verified by a co-author who is also a rip current expert at NOAA. Figure~\ref{teaserfig} contains both positive and negative example frames, as well as a few frames from the training set that might be mistaken as containing a rip current. Note that these sequences contain more difficult cases. The rip currents are not visible in every frame, but a positive bounding box is applied whenever viewing the entire video segment indicates that a rip current is present.

The frames of this video data set were used for testing.
Note that the static images in the training set were taken from high elevation
while the videos used to test the model were taken from a lower perspective.
Even so, the trained model performed well on the test frames from the video collection.

In order to encourage progress in this domain, both data sets will be made available to the public. We are including a link to the data set in the supplementary materials in appendix \ref{s:appendix}.

\section{\label{s:methods} Method}

\subsection{Static Image Detector: Faster RCNN }

  Region-based convolutional neural networks have \linebreak  achieved  great success in object detection problems.  These object detection models usually consist of separate classification and localization networks with a shared feature extraction network. We use Faster RCNN which is also composed of two components. The first is the deep convolutional neural network that proposes regions. The second is the Fast RCNN detector \cite{girshick2015fast}.

  Faster RCNN follows the traditional object detection  \linebreak pipeline. It first generates region proposals, and then categorizes each proposal as either rip current or background. Secondly, the classified bounding boxes are further refined. Essentially, the model learns a mapping from the generated regions to the actual ground truth with a regression network. The model then uses this mapping ``function'' during testing to refine the generated region. These refined bounding boxes can be anywhere in a frame as features are translation invariant \cite{ren2015faster}. If there is more than one bounding box detected in a frame, we only keep track of the largest one and ignore any additional boxes.

  We trained the Faster RCNN model with the static image data set. Before training, each image was augmented by rotating $90^{\circ}$ degrees clockwise and counter clockwise, producing a training data set three times the size of the original training data set. All the training data was resized to $300 \times 300$ before training to save computation time.

\subsection{Frame Aggregation}

Static object detection models only consider the information in the frame currently being processed.
However, rip currents are natural ocean phenomena, with shape and texture change depending on many external factors such as weather, wind speed, wave field characteristics, water flow speed, floating debris, and dirt sediments. The exact boundaries of a rip current are not well defined. This is different than objects with well-defined edges such as pedestrians or vehicles. Applying detection algorithms to objects with amorphous boundaries such as rip currents produces bounding boxes with variable sizes and locations in adjacent video frames.  In Figure \ref{fig:multi_detections} we illustrate this variability by drawing all correctly detected bounding boxes from one video sequence onto a single frame.

This variability affects overall accuracy, and would not instill confidence in the results if these bounding boxes were presented to a user as a video overlay. Thus we investigate temporal smoothing and aggregation to improve the results.

\begin{figure}
\begin{center}
   \includegraphics[width=1.0\linewidth]{images/other/multiple_detections.png}
\end{center}
   \caption{Amorphous phenomena without clear boundaries, like rip currents, result in wide variation in detected bounding boxes. All detected bounding boxes from the frames of one video are superimposed onto a single image. Bounding boxes may differ significantly even in consecutive frames.}
\label{fig:multi_detections}
\end{figure}

\begin{figure}
\begin{center}
   \includegraphics[width=1.0\linewidth]{images/other/buffer.png}
\end{center}
  \caption{Frame aggregation for a window of length $N$. First row shows the input frame sequence. Second row shows the detections from Faster RCNN.  Third row shows the accumulation buffer. }
\label{fig:frame_aggregation}
\end{figure}

\begin{figure}
\begin{center}
  \includegraphics[width=1.0\linewidth]{images/other/285.jpg}
\end{center}
  \caption{A visualization of the resulting accumulation buffer values. Regions with a higher value are shown in more opaque red. The resulting bounding box around thresholded values is shown in solid dark red. The full video can be seen in the supplementary material in appendix  \ref{s:appendix}   }
\label{fig:buffer}
\end{figure}

\begin{figure}
\begin{center}
  \includegraphics[width=1.0\linewidth]{images/frame_area/Figure_combo_copy.png}
\end{center}
  \caption{Plots showing the differences in area and center of bounding boxes in consecutive frames. Without frame aggregation (in red) there are much higher differences in bounding box sizes and positions than there is after frame aggregation (in blue).}
\label{fig:diff_bbox_size}
\end{figure}

We find the overlapping regions of the detected bounding boxes by using an accumulation buffer with the same size as the input frame and initialized as a zero matrix. We consider a temporal window of $N$ frames to build the accumulation buffer. In the first $N-1$ frames, the accumulation buffer is incremented by 1 for each region within a detection bounding box.  Starting with frame $N$, the area covered by a detection bounding box is incremented by 1 and capped at a maximum of $N$.  Regions not covered by a detection bounding box are decremented by $1$, but retains a minimum of 0 in the accumulation buffer. In effect, the accumulation buffer keeps track of the bounding boxes using a sliding window of $N$ frames. The process of building the accumulation buffer is illustrated in Figure \ref{fig:frame_aggregation}, where areas with higher values are displayed as darker regions in the accumulation buffer.
For purposes of identifying a single bounding box over the collection of bounding boxes across $N$ frames, we consider only the parts of the accumulation buffer where the value is at least $T$, and draw the tightest possible axis-aligned bounding box around this region (see Figure~\ref{fig:buffer}). This is the aggregated detection.
In our implementation we use N=60 and T=50.

Before frame aggregation, large variations in bounding box size occur in almost all consecutive frames, shown in Figure \ref{fig:diff_bbox_size} top. After frame aggregation, the average size \linebreak change is much smaller, with most frames having zero \linebreak change in size from the prior frame.
The variation in position of the bounding boxes is similarly reduced by frame aggregation, as seen in Figure \ref{fig:diff_bbox_size} bottom.
This improved temporal coherence provides a smoother and more consistent portrayal of the rip current extent when shown as an overlay on the video.

\begin{figure*}
\begin{center}
    \centering
       \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/no_rip_04.png}
       \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/no_rip_06.png}

        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/no_rip_01.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser/18463.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/189.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/9.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/new_01.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/no_rip_no_detection_2.png}
         \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/new_2.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/new_4.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/no_rip_no_detection_3.jpg}
        \includegraphics[width=0.32\textwidth, height =1.1in]{images/teaser_results/36.jpg}

         \caption{Rip current detections on some of the frames from the test data set. Bounding boxes show the correctly detected rip currents. Frames without bounding boxes do not contain any rip currents.} \label{teaserfig_output}

\end{center}%
\end{figure*}

\newcommand{1.6cm}{1.6cm}
\begin{table*}
\small
 \centering

\begin{tabular}{P{3cm} | P{1.6cm} P{1.6cm} P{1.6cm} P{1.6cm} P{1.6cm} P{1.6cm}}.
    & \text{Human}        & \text{Philip \cite{eurovisshort.20161155}}    & \text{Maryan \cite{maryan2019machine} }     & \text{Maryan}   & \text{F-RCNN}       & \text{F-RCNN+FA}     \\
    & \text{}   & \text{}       & \text{ }  & \text{[modified]}     & \text{[ours]}       & \text{[ours]}     \\
    \hline
\text{$rip\_01.mp4$}  & 0.976 & 0.895  & 0.358 & 0.460 & 0.966 & 1.000 \\
\text{$rip\_02.mp4$}  & 0.700 & 0.098 & 0.698 & 0.135 & 0.776 & 0.860 \\
\text{$rip\_03.mp4$}  & 0.231 & 0.347 & 0.194 & 0.540 & 0.831 & 0.950 \\
\text{$rip\_04.mp4$}  & 0.757 & 0.800 & 0.487 & 0.780 & 0.939 & 0.970 \\
\text{$rip\_05.mp4$}  & 0.883 & 0.280 & 0.736 & 0.450 & 0.834 & 0.957 \\
\text{$rip\_06.mp4$}  & 0.881 & 0.000 & 0.167 & 0.470 & 0.753 & 0.890 \\
\text{$rip\_08.mp4$}  & 0.492 & 0.063 & 0.328 & 0.730 & 0.860 & 0.850 \\
\text{$rip\_11.mp4$}  & 0.824 & 0.000  & 0.563 & 0.940 & 0.930 & 0.951 \\
\text{$rip\_12.mp4$}  & 1.000 & 0.000 & 0.734 & 1.000 & 1.000 & 1.000 \\
\text{$rip\_15.mp4$}  & 0.967 & 0.137 & 0.315 & 0.390 & 0.760 & 0.870\\
\text{$rip\_16.mp4$}  & 0.614 & 0.073 & 0.468 & 0.640 & 0.820 & 0.920 \\
\text{$rip\_17.mp4$}  & 1.000 & 0.321 & 0.064 & 0.750 & 0.980 & 1.000 \\
\text{$rip\_18.mp4$}  & 0.563 & 0.218 & 0.250 & 0.240 & 0.790 & 0.890 \\
\text{$rip\_21.mp4$}  & 0.901 & 0.543 & 0.486 & 0.180 & 0.940 & 1.000 \\
\text{$rip\_22.mp4$}  & 0.583 & 0.000 & 0.479 & 0.395 & 0.880 & 0.974 \\
\text{$no\_rip\_01.mp4$}  & 0.986 & 0.169 & 0.000 & 0.972 & 0.813 & 1.000 \\
\text{$no\_rip\_02.mp4$}  & 1.000 & 0.789 & 0.000 & 0.985 & 0.807 & 1.000 \\
\text{$no\_rip\_03.mp4$}  & 0.919 & 0.000 & 0.000 & 0.981 & 0.984 & 1.000 \\
\text{$no\_rip\_04.mp4$}  & 0.952 & 0.000 & 0.000 & 0.974 & 0.835 & 1.000 \\
\text{$no\_rip\_05.mp4$}  & 0.903 & 0.000 & 0.000 & 0.986 & 0.833 & 1.000 \\
\text{$no\_rip\_06.mp4$}  & 1.000 & 0.246 & 0.000 & 0.986 & 0.875 & 1.000 \\
\text{$no\_rip\_07.mp4$}  & 0.983 & 0.525 & 0.000 & 0.982 & 0.875 & 1.000 \\
\text{$no\_rip\_11.mp4$}  & 0.988 & 0.198 & 0.000 & 0.964 & 0.924 & 1.000 \\
\hline
\text{$average\,\, accuracy$} & 0.760 & 0.307 & 0.210 & 0.729 & 0.884& \textbf{0.984}
\end{tabular}
\caption{Accuracy for each video in the test set. Column 3: Maryan\cite{maryan2019machine} is trained on their training data. Column 4: Maryan [modified] is trained on our training data. Our method has higher overall accuracy than humans or any of the prior methods tested. Frame aggregation does contribute to improvement in accuracy. }
\label{test_data_suite}
\end{table*}

\section{\label{s:results} Results}

We compared the accuracy of our method with human observers as well as two prior methods. We also compared our own method with and without temporal aggregation.

 \boldheading{Comparison metric. }All methods were tested using the video data set. Frames were labeled as correctly \linebreak classified if the detected bounding boxes have an Intersection over Union (IoU) score versus ground truth above 0.3. IoU is calculated as  $area\_of\_intersection/area\_of\_union$ of the ground truth and the detected bounding boxes. Accuracy of the video was computed as $correct\_labels/total\_frames$, and Table~\ref{test_data_suite} provides the results for all methods.

\boldheading{Humans.} The primary reason for an automated method of rip current detection is that most people are not good at identifying rip currents~\cite{Brannstrom15}.
Human annotators were asked to draw bounding boxes around places they believe to have rip currents. We sampled approximately every tenth frame from our video test set and randomized presentation order across all positive and negative examples. Human annotators were not carefully trained, instead they were provided three positive and three negative examples, roughly the amount of information which might fit on a sign at the beach. Annotators were acquired using Mechanical Turk, with basic screening for reliable workers, and paid \$0.10 per image.

Although human performance was relatively poor with only 76\% of frames labeled correctly, it was higher than we expected based on past studies~\cite{Brannstrom15}. We hypothesize that the clearly labeled sample images on a web page directly adjacent to the image they were asked to label was a positive contributing factor.

\boldheading{Time averaged images.} Maryan et al. \cite{maryan2019machine} perform detection on time averaged images using a boosted cascade of simple Haar like features \cite{Viola2004}. We used Maryan's time averaged data for training since our training data set consists of only static frames.  Testing was performed by first computing time averaged images on each video in our test data set. This method did not perform well. In order to determine if the cause was the images available for training or the model itself, we repeated the experiment with new data. We replaced the relatively small number of low resolution time-averaged images from \cite{maryan2019machine} with the static images from our training data set. Testing this time was against single frames in our test videos. This modification is called Maryan[modified] in the results table. When using our training data the model accuracy improved considerably, leading us to conclude that appropriate training data is critical to good results.  Furthermore, it suggests further investigation on whether using crisp images, rather than time averaged images, to train a model might produce more accurate results.

In our test images/videos the beach is always located at the bottom half of the image/video. However, the training data used by \cite{maryan2019machine} was cropped from images where the beach is located in the top half of the image. Therefore, we were concerned that maybe the difference of orientation between the training data and the test data contributed to the low accuracy of the model. However, when we retrained the model with vertically flipped training data we did not see any significant difference in accuracy on our test images/videos. We hypothesize that the reason for this is that the cropped training images contain insignificant amount of beach pixels.

\boldheading{Optical flow.} Philip et al. \cite{eurovisshort.20161155} compute optical flow on video sequences and make the simplifying assumption that
rip currents can be identified by regions with the second most predominant flow direction, after that
of the primary incoming wave direction, and that they flow in a single seaward direction.
This results in regions of actual rip currents, but also picks up swash regions
where water is washed up the beach and back out to sea with the passing of each wave.
This method was introduced with the primary intention of providing visualizations to users, rather than automated detection. To allow comparison, we modify the method to return a bounding box around the largest detected region, ignoring smaller regions which are less likely to be correct. This method performed poorly on our test videos. We noticed that in videos where there is not enough textural information on the rip current, the optical flow field generated was weak, leading to either missed detections or detection in other regions of the video with stronger texture.

\boldheading{Frame aggregation.} We implemented frame aggregation as a post process to Faster RCNN initially to temporally stabilize detections, driven by a need for user interpretable visualization of rip current location.

In order to understand whether temporal smoothing also increased accuracy we analyzed our implementation both with and without frame aggregation. We found that temporal aggregation leads to higher accuracy than using Faster RCNN alone. Example detection results are shown in Figure \ref{teaserfig_output}. Numerical comparison of humans,  prior methods, and our model are provided in Table~\ref{test_data_suite}. Faster RCNN with frame aggregation had the highest accuracy in nearly all cases, and the highest overall (last column of Table~\ref{test_data_suite}). For visual comparisons we have added all the results in the supplementary materials at appendix \ref{s:appendix}.

\section{\label{s:limitations_and_discussion} Discussion and Future Work}

As with all machine learning models, our implementation can fail when used with images that do not resemble the training data set. Our data sets included primarily rip currents characterized by a gap in  breaking waves, the most common visual indicator for bathymetry controlled rip currents. Thus we would expect to miss rip currents with other visual indicators like sediment plumes. We also expect to fail when presented with new imagery, and occasionally for no apparent reason at all, as seen in Figure \ref{fig:unexpected}.

We found it  difficult to compare our method with prior work and verify that our model performs well in all conditions previously researched, due to a lack of public data sets on which to verify our results. In order to ensure that future work has a baseline from which to compare, our data sets with thousands of labeled frames will be made public. Nevertheless our data sets are still limited. The accuracy numbers presented in this paper are correct on this limited data, but almost certainly overstate probable outcomes in real world deployment. We expect that future work will need to collect more examples including less common rip current visual presentation, a greater variety of scales, and a wider array of beach distractors.

Lastly, our work lacks a success metric which is meaningful to real users. Certainly IoU, true positive rate, mAP, and the like are common in computer vision research, but is it appropriate to measure accuracy on single frames? Most conceivable deployed systems, such as pointing a mobile phone at the beach, have access to video. If measuring accuracy on video, should we measure accuracy aggregated over 1 second or 1 minute? Is a tight bounding box measured by IoU needed, or just a general region? A metric useful to researchers and simultaneously meaningful to users is needed.

\begin{figure}
\begin{center}
   \includegraphics[width=0.49\linewidth, height=0.95in]{images/other/189.jpg}
    \includegraphics[width=0.49\linewidth, height=0.95in]{images/other/fail_case.jpg}
\end{center}
  \caption{Example failure cases. The false positives on the beach scene (right) are not easily explainable. The false positive on the left scene happens only on spurious frames, which is then corrected by frame aggregation. }
\label{fig:unexpected}
\end{figure}

\section{\label{s:conclusion}Conclusion}

We present a machine learning approach for identifying rip currents automatically. We use Faster RCNN and a custom temporal aggregation stage to make detections from still images or videos with higher measured accuracy than both humans and other methods of rip current detection previously reported in the literature.
Training data set and suite of test videos are made available for other researchers.

redpajama_set_name: RedPajamaArXiv


Douglas Feith, the undersecretary of defence for policy who helped to define the US policy of pre-emption that propelled the invasion of Iraq in 2003, has decided to resign, Pentagon officials say.
US Defence Secretary Donald Rumsfeld said on Wednesday that Feith had told him after the US presidential election in November that he planned to step down by the middle of the year to return to private life.
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"Feith was a theorist whose ideas were often impractical; among some uniformed officers in the building, he had a reputation for confusing abstract memoranda with results in the field"
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But Lawrence DiRita, the Pentagon's chief spokesman, said Frank's comments arose from the natural tensions between a commander and a civilian policymaker.
He said Feith enjoyed close relations with other military leaders but stood out for keeping the discussion on the "war on terror" above the purely military solutions.
thinking about other ways to think about this battle of ideas," he said.
Earlier in the day, Feith talked with journalists about the shifting priorities that will underpin a new strategy review that will shape the US military for years to come, saying it will give greater emphasis to terrorism and other unconventional threats rather than the usual conventional military threats.
He was asked how his thinking on pre-emption had changed in light of the failure to find weapons of mass destruction in Iraq.
"The issue of when the United States should act to deal with a problem is a really tough question, and I think gets really oversimplified in a lot of the discussion that focuses on the term pre-emption," he said.
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Feith had a wealth of experience in the private sector before joining the US Department of Defence and has expressed interest in continuing with his previous career in law.
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Before entering government, and as an attorney at the Washington firm of Feith and Zell, his website publicised his legal expertise in "establishing joint ventures with leading US aerospace manufacturers for manufacture and sale of missile systems, to the US Department of Defence and worldwide".


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In recent years, export activities of Vinh Phuc Pharmaceutical Joint Stock Company – Vinphaco have achieved important achievements, contributed to the development of the pharmaceutical industry in Vietnam. This is a positive signal, shows that the quality of the company's products has been improved, creaied confidence for customers in the country and foreign counties. With the development of the domestic pharmaceutical industry, Vinphaco has continuously developed and improved the management and improvement of product quality, the competitiveness, efficiency and brand building in the market. The outputs of produced drugs not only meet, maintain the market share in the country but also expand, export to foreign countries. These initial achievements are the foundation to promote the development of the company in particular and the pharmaceutical industry in general, to build the pharmaceutical brand of Vietnam. Vinphaco exports injecting and tablet drugs to Afghanistan market With the advantage of quality and price of products, over the years, Vinphaco has received orders from many countries in the world, such as China, Laos, Thailand, ... It is worth mentioning that the export items are not only injection drug - the strength of the company but also many other items, including tablets,.... In the coming time, boosting exports is still a priority in the company's development strategy; enhancing the introduction of Vinphaco's image and brand to international friends. The export route of Vietnamese pharmacy in general, and Vinphaco in particular are still many difficulties. However, in addition to these difficulties, the company has the potential, the advantage to develop this activity, it can be mentioned as follows: First: modern science and technology, it is constantly being renovated and upgraded. In 2004, Vinphaco was the first company in the North to build a factory producing injecting drugs and tablets that meet GMP-ASEAN standards. Continuous innovation in technology and material- technical infrastructure, in 2009, the company invested in upgrading and perfecting entire production system according to GMP – WHO. Second: The foundation and experience of a pharmaceutical enterprise that has been developing for nearly 60 years, step by step confirming the leading roles and advantages in the Vietnamese pharmaceutical industry. Third: Good quality products, high competitiveness. The company cooperates with hospitals, schools, professors, doctors, ... to research, successfully produce injecting drugs and high-grade tablets with the same treatment effect, the price is only equal to 50-60% of imported drugs. In addition, factors such as: quality of personnel, extensive distribution network, ...are also indispensable factors when mentioning VINPHACO. With these strengths, we can confidently believe that export activities of Vinphaco will be even stronger, and the principle of "for public health" will not be limited to the community of a country or territory, it will extend to the whole world.
On the evening of February 7, in Ho Chi Minh City, Vietnam High Quality Product Association awarded the title of Vietnam High Quality Products in 2018 to Vinh Phuc Pharmaceutical Joint Stock Company – Vinphaco. Number of participants in the survey increased 3 times compared to 2017 According to information from the Vietnam High Quality Product Association, the 22nd survey to vote Vietnam High Quality Products has been conducted in more than 3 months with 17,300 standard votes, 380 interviews with 13,000 households and 3,000 selling places in 12 provinces and cities which are the key economic centers of the country, (North, Central - Central Highlands, South East and South West). In addition, the survey conducted in 2018 also recorded the comments and reviews of consumers through the online channel, which is the basis for comparison with the results of direct investigation and attract more young people, who often communicate online. As well as data from the organizing committee, through the survey, the number of consumers in this year have increased 3 times compared to 2017. In the final step in the process of selection, appraisal, Vietnam High Quality Product Association has approached agencies as well as localities to update the enterprise's transparency information profile. As a result, the association found 640 enterprises that achieved the mark of Vietnam High Quality Products in 2018. Once again, Vinh Phuc Pharmaceutical Joint Stock Company – Vinphaco was honored at the award ceremony. This proves that Vinphaco's products are quality products, voted for by Vietnamese consumers, trusted by consumers.. Vinh Phuc Pharmaceutical Joint Stock Company – Vinphaco is awarded the certificate of Vietnam High Quality Products in 2018 Vinh Phuc Pharmaceutical Joint Stock Company – Vinphaco is known as the leading pharmaceutical manufacturer of injecting drugs with the leading capacity in the country, it was granted the certificate of recognizing the first tube-shaped injecting drug factory with GMP - ASEAN standard in the North. Along with that, the company is constantly innovating in technology, investing in depth to create quality products. With the same treatment effect, the price is only equal to 50-60% of medicines imported from Europe, injecting drug of Vinphaco is occupying the domestic market and sharing market share with imported drugs. With the efforts and achievements achieved in 59 years of construction and growth, this certificate is fully worthy of Vinphaco. Hopefully, Vinphaco will further develop, contribute to the care and protection of people's health.
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