Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

We propose a solution for evaluation of mathematical expression.
However, instead of designing a single end-to-end model we propose a Lego bricks style architecture.
In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick.
More difficult or complex task can then be solved using a combination of these smaller network.
In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc).
These fundamental operations are then learned using simple feed forward neural networks.
We then shows that different operations can be designed simply by reusing these smaller networks.
As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product.
This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers.
Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.
The success of feed-forward Artificial Neural Network (ANN) lies in their ability to learn that allow an arbitrarily connected network to develop an internal structure appropriate for a particular task.
This learning is dependent on the data provided to the network during the training process.
It has been commonly observed that almost all ANNs lack generalization and their performance drastically degrades on unseen data.
This includes degradation of performance on data containing the seen categories but acquired under from a different setup (location, lighting, view point, size, ranges etc) .
Although there are techniques such as Domain Adaptation to address these generalization issues, however this behaviour indicates that the learning process in neural network is primarily based on memorization and they lack understanding of inherent rules.
Thus the decision making process in ANN is lacking quantitative reasoning, numerical extrapolation or systematic abstraction.
However when we observe other living species, numerical extrapolation and quantitative reasoning is their fundamental capability what makes them intelligent beings.
For e.g. if we observe the learning process among children, they can memorize single digit arithmetic operation and then extrapolate it to higher digits.
More specifically our ability to +, −, × and ÷ higher digit number is based on understanding how to reuse the examples that we have memorized for single digits.
This indicates that the key to generalization is in understanding to reuse what has been memorized.
Furthermore, complex operations are usually combination of several simple function.
Thus complex numerical extrapolation and quantitative reasoning among ANNs can be developed by identifying and learning the fundamental operations that can be reused to develop complex functions.
Inspired from the methodology of learning adopted by humans, in this work we first identify several fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, merging of two number based on their place value, learning to merge sign +/− etc).
These fundamental operations are then learned using simple feed forward neural networks.
We then reuse these smaller networks to develop larger and a more complex network to solve various problems like n-digit multiplication, n-digit division, cross product etc.
To the best of our knowledge this is the first work that proposed a generalized solution for these arith-metic operations.
Furthermore, unlike exiting methods ( Hornik et al. (1989) ; Siu & Roychowdhury (1992); Peddada (2015) ; Sharma (2013) ; Trask et al. (2018) ) ours is also the first solution that works for both positive as well as negative numbers.
In this paper we show that many complex tasks can be divided into smaller sub-tasks, furthermore many complex task share similar sub-tasks.
Thus instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation.
More difficult or complex task can then be solved using a combination of these smaller network.
In this work we first identify several fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, place value shifter etc).
These fundamental operations are then learned using simple feed forward neural networks.
We then reuse these smaller networks to develop larger and a more complex network to solve various problems like n-digit multiplication and n-digit division.
One of the limitation of the proposed work is the use of float operation in the tokenizer which limits the end-to-end training of complex networks.
However, since we are only using pre-trained smaller network representing fundamental operations, this does not creates any hinderance in our current work.
However, we aim to resolve this issue in future.
We have also designed a cross product network using the same strategy and we are currently testing its accuracy.
As a future work we aim to develop a point cloud segmentation algorithm by using a larger number of identical smaller network (i.e. cross product) that can compute a normal vector using 3 3D points as input.
We train many small networks each for a specific operation, these are then combined to perform complex operations