Reviewerly/exHarmony huggingface dataset:
Machine Comprehension Comparison using Latest Advancements in Deep Learning

Machine Comprehension or Question Answering (QA) is one of the most challenging natural language processing tasks due to the language’s dynamic nature and understanding the context of the question. In this paper, we propose a similarity attention layer with an aim to reduce human labor by automating tedious QA tasks using the attention mechanism in deep learning model; it uses attention scores and obtains good results even without pre-training. The QA using attention has immense scope in search engine optimization, page ranking, and chatbots. The traditional rule-based models and statistical methods underperform due to variations in the language. This dynamic nature of the language is well captured by the nonlinear learning of the neural networks. The conventional encoder-decoder architecture of neural networks for QA works well in the case of short sentences. However, the performance comes down for paragraphs and very long sentences as it is difficult for the network to memorize the super-long sentences. In contrast, the attention model helps the network focus on smaller attention areas in the complex input paragraph, part by part, until the entire text is processed. The results are very promising; our (single) model outperforms the existing ensemble method too.
Differential Test Prediction

This chapter covers Anne Clear’s model of differential test prediction. Differential prediction is the most widely understood and widely used approach to test bias. Under the differential prediction model, different average test scores for groups are not bias. Instead, a poorly fitting overall regression line is an indication of bias. If fitting regression lines individually to subgroups substantially improve our accuracy, then there is test bias. In such a situation, the test predicts differently for each subgroup. To complicate the matter, the 1991 Civil Rights Act requires that a single overall regression line be used when predicting. This causes increased measurement error beyond what we would have if we used the best fitting model. This can create systematic over- and underprediction for the subgroups and lead to unexpected results.
A Perceptual Loss For Screen Content Image Super-Resolution

The acceptable results of deep learning led to the use of the deep neural network on a wide range of models, including image super-resolution. The performance of the deep neural network is directly affected by its loss function. Most methods use intensity loss, such as MSE, which computes the difference between the predicted image and the ground truth. Because the structural information of a scene is more sensitive to the human visual system, it is desired that the loss function could measure the impact of the structural error. In addition, the use of screen content images has become widespread because of many applications such as desktop-sharing and remote computing. As a result, super-resolution of screen content images becomes a crucial technique to enhance the quality of low-resolution images. In the presented loss function, the structural error is weighted employing DCT components. The model is trained and tested using the screen content images, and the experimental subjective and objective results illustrate the effectiveness of the presented loss for screen content images.
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This paper presents a combined two-dimensional word-based serial-in/serial-out systolic processor for field multiplication and squaring over GF(2m) to improve hardware utilization and power consumption. The proposed processor is extracted by applying non-linear scheduling and projection functions to the algorithm dependency graph. The extracted processor features scalability that gives the designer more flexibility to control the processor size and the execution time. ASIC Implementation results of the proposed combined two-dimensional word-serial design and the best existing designs show that the proposed structure realizes a considerable saving in the area and consumed energy up to 93.7% and 98.2%, respectively. This makes it more suitable for restricted implementations of cryptographic primitives in resource-constrained consumer electronics devices such as hand-held devices, wearable and implantable medical devices, smart cards, wireless sensor nodes, restricted nodes in the Internet of Things (IoT), and radio frequency identification (RFID) devices.
A Study of the Internal Structure of N+N Compounds

This paper addresses the issue of how the language user can interpret two-constituent compounds. After reviewing the previous studies regarding classifications, this study finds out that although the studies suggest several sets of semantic relations or syntactic structures, the criteria of the classifications are not clear, and the previous studies usually depend on our encyclopedic information to get the appropriate interpretation of two noun compounds, especially attributive-appositive compounds. In order to distinguish similar-looking N+N compounds from each other, this study explores relevant notions, such as autonomacy/dependency in a cognitive framework regarding the relation between the two elements in compounds. The same examples in noun-noun compounds in the previous studies can be analyzed and examined from this perspective. Finally, this study goes back to Lees’ (1960) classification on the syntactic aspects and tries to elaborate upon his proposal by employing the findings in this study.
Natural deduction

Abstract Natural deduction is a philosophically as well as pedagogically important logical proof system. This chapter introduces Gerhard Gentzen’s original system of natural deduction for minimal, intuitionistic, and classical predicate logic. Natural deduction reflects the ways we reason under assumption in mathematics and ordinary life. Its rules display a pleasing symmetry, in that connectives and quantifiers are each governed by a pair of introduction and elimination rules. After providing several examples of how to find proofs in natural deduction, it is shown how deductions in such systems can be manipulated and measured according to various notions of complexity, such as size and height. The final section shows that the axiomatic system of classical logic presented in Chapter 2 and the system of natural deduction for classical logic introduced in this chapter are equivalent.
Posture Control of the Passenger Based on Caregiver’s Wrist Motion for a Step-Climbing Stroller

This letter proposes a novel manually actuated mechanism to control the position and posture of a passenger for preventing accidents due to stroller tipping while ascending or descending steps. This mechanism is designed as a four-bar linkage, which uses the rotational torque applied to the stroller’s handle by the user’s wrist motion while tilting to change the position and posture of the seat. Link parameters were determined to ensure sufficient posture transition while ascending or descending steps based on kinematic and dynamic analyses of the stroller. By using the rotational motion of the user’s wrist relative to the stroller body as an input, the position and posture of the seat could be controlled to maintain stability. We present the detailed design process of the mechanism using a simulation environment and the results of validation experiments conducted using a prototype.
Encrypted SQL Arithmetic Functions Processing for Secure Cloud Database

With the growing opportunities in cloud computing, different types of databases are used for storing and managing heterogeneous data in the cloud. However, several reported data breaches show that data security promises as per cloud agreement are not adequate to move critical data to cloud. Homomorphic Encryption (HE) emerges as a security solution in this regard. However, it is a non-trivial task to realize any algorithm in circuit-based representation to be implemented in homomorphic domain. Moreover, existing encrypted databases are with limited features and still not fully able to perform SQL query processing along with complex mathematical operations in encrypted domain without any need of intermediate decryption. In this work, we explore implementing the encrypted counterparts of a few complex transaction-SQL related mathematical functions like ABS(), CEILING(), FLOOR(), SIGN(), SQUARE(), POWER(), and SQRT(), which are heavily used in cloud database queries. We have evaluated these mathematical operators without any intermediate decryption, considering the support of underlying fully homomorphic encryption (FHE). Though the usage of underlying FHE scheme incurs some performance bottleneck, our proposed designs are flexible enough to be realized on any leveled homomorphic encryption (LHE) scheme for performance improvement. Experimental results show FHE encrypted SQL conditional SELECT operations with complex mathematical functions can be performed within 48 min on a single processor for a dataset of 768 rows with 9 columns, and each data size of 16-bit. Performance can be further improved by suitable implementation platform translation from CPU to GPU or with the restrictions of leveled fully homomorphic encryption.
Multimodal Traditional Art Image Association Mining Method Based on a Genetic Algorithm

Aiming at the problems of long time and low accuracy of traditional multimodal art image mining, a multimodal traditional art image association mining method based on a genetic algorithm is proposed. By obtaining the relationship between conditional entropy and joint entropy in the information entropy of traditional art images, the mutual information of traditional art images is normalized to normalize the traditional art images. The real array coding method is adopted, and the fitness function adopts the strategy of range contraction to make the attribute boundary approach in a more accurate direction. The selection, crossover and mutation operators are set to avoid the local convergence and premature problem of the genetic algorithm. The image association mining rules are extracted to realize image association mining. The experimental image was selected, and the experimental environment and experimental process were set. Experimental results: Compared with the two groups of methods selected in this experiment, the time required for mining image association is 3.5 s and 10.4 s, and the accuracy is 20 s higher.
Approaches Towards A Recommendation Engine For Life Insurance Products

Recommender engines are powerful tools in today’s world to overcome the problem of over choice. As the world is moving towards information overload, the life insurance industry is no more immune than any other domain. Three broad categories of life insurance plans are namely – Endowment, Term and ULIP. This paper discusses a variety of ML models that aim to classify the right fit product category for a new customer (extendable to existing customers) on a real-time life insurance company dataset. The dataset used for the modelling were of 2 kinds. The first kind contained features of customer demographics – age, location, education and occupation. The second dataset included these customer demographics as well as the bureau information of the respective customers which included multiple features describing their credit history. By the means of clustering, collaborative filtering approaches were tried on. Also, the problem was tackled using predictive modelling techniques such as Random Forest, Decision Trees and XGBoost.
Virtual Personal Trainer

The increased interest of users towards healthier lifestyles has motivated the development of a virtual personal trainer application using Android as platform. Despite the availability of numerous fitness apps and gyms, everyone needs proper training at their ease and wishes to monitor calories burnt. Thus, this paper proposes a novel idea of virtual personal trainer applications that recognizes user actions through videos. The video data is processed using convolutional neural network and bidirectional long short-term memory network. The motive of work is to recognize exercise accurately from video and calculate the number of calories expended. The proposed application provides not only detailed information about exercise but also ascertains the correct way of performing exercises as this is a major challenge that users face due to lack of knowledge. The idea is implemented on UCF-101 Action Recognition dataset, and experimental results show significant improvements as compared to baseline methods. This study would benefit users who are fitness enthusiasts and are more prone to gadgets.
Multiple Target Tracking on SE(2) Using Recursive-RANSAC

This paper introduces an algorithm for multiple target tracking where the tracks are evolving on the Special Euclidean group SE(2). Applications include tracking ground targets using cameras on-board a hovering multirotor UAV. Our approach extends the Recursive RANSAC (R-RANSAC) algorithm to nonlinear motion models. In particular, we show how tracks can be initialized in the presence of significant measurement clutter in a way that leads to computationally efficient implementation. Simulation and flight results show significant tracking improvement over using linear constant velocity models.
Analysis of the Structural Balance in the Salary of the Firm’s Personnel

In modern approaches to the formation of integrated reporting, special attention is paid to social aspects. The basis of social stability of business, first of all, is the level of wages of employees of the firm, as well as compliance with the fair structure of employees based on the level of remuneration. The growth of social tension, as a rule, occurs imperceptibly. The economic analysis of the dynamics of the wage fund due to its structural changes allows us to timely see the dangerous trends of stratification of employees of the company with different incomes. The structural factor is indirectly determined when the results of factor analysis are compared based on the generalized data of the company as a whole and the total results of the analysis in the context of individual groups of employees of the company. However, a direct determination of the effect of the structural factor is also possible. This slightly increases the complexity of the analysis. Social stability management requires appropriate tools for quantitative analysis of the wage fund. These tools are discussed in this article.
TCCFD: An Efficient Tree-based Framework for Credit Card Fraud Detection

Due to digitalization, credit card fraud has become one of the most prevalent global risks. The global economy loses billions of dollars annually due to credit card fraud. Financial institutions adopt a strategic approach to credit card fraud mitigation, relying on business rules to combat the occurrence of fraud and sophisticated analysis to detect fraud. However, Fraudsters are altering their patterns regularly with the help of new technologies. Thus, organizations continually seek to detect new fraud patterns. The detection of credit card fraud has improved tremendously through using artificial intelligence, particularly machine learning techniques. We introduce through this paper an efficient framework that adopts various tree-based machine learning models to detect fraudulent transactions. We apply the proposed framework to a highly asymmetric real-world dataset. The proposed framework integrating the extra trees classifier with the borderline synthetic minority oversampling technique surpasses others and previously developed models with 99.96 % accuracy and 96% area under the ROC curve. The proposed framework asserts its efficacy and reliability in classification while also addressing the class imbalance issue.
Multi-Round Recommendations for Stable Groups

Recommender systems have been used for suggesting the most suitable products and services for users in diverse scenarios. More recently, the need for making recommendations for groups of users has become increasingly relevant. In addition, there are applications in which recommendations are required in a consecutive sequence. Group recommendations present a challenge for recommender systems: how to balance the preferences of the individual members of a group. On the other hand, when making recommendations for a group for multiple rounds, a recommender has a possibility to dynamically try to balance the preference differences between the group members. This paper introduces two novel methods for multi-round group recommendation scenarios: the adjusted average aggregation method and the average-min-disagreement aggregation method. Both methods aim to provide a group with highly relevant results for the group, while remaining fair for all group members. We experimentally evaluate our approach for groups with different characteristics and show that our methods outperform baseline solutions in all scenarios.
Strings

The string data type is used to store string constants. They are delimited by double quotes.
Computer Network Communication Data Encryption System Based on Improved RSA Algorithm

To realize faster computer network communication data encryption, this paper designs a computer network communication data encryption system based on an improved RSA algorithm. The module design of the system includes an upper computer module, a lower computer module and a data encryption module. Try to design a simple, convenient and practical operation page, which mainly realizes the following functions: displaying device list, entering key, etc. The main functions of the host computer are shaking hands with USB, monitoring USB devices, etc. The lower computer module selects USB as the interface to communicate with the upper computer, uses C8051F340 single chip microcomputer as the core of hardware design, uses C language programming according to the functions that the single chip microcomputer needs to complete, and designs the single chip microcomputer program in keil u vision3 compiling environment. The data encryption module is designed based on the improved RSA algorithm, and computer network communication data encryption is realized through the combination of hardware and software. Finally, the encryption speed of the system is tested, and it is found that the system has fast encryption speed and good data encryption performance.
Spatiotemporal sampling with spatial spreading and rotation of units in time

When the sampled population belongs to a metric space, the selection of neighboring units will imply often similarities in the collected data due to their geographical proximity. In order to estimate parameters such as means or totals, it is therefore more efficient to select samples that are well distributed in space. Often, the interest lies not only in estimating a parameter at one point in time, but rather in estimating it at several points and studying its evolution. Because of the temporal autocorrelation of successive values from the same unit, a system of temporal rotation of the units in the samples must be provided. In other words, this type of problem forces us to consider two types of autocorrelation: spatial and temporal. In this article, we propose two new spatiotemporal sampling methods for equal or unequal inclusion probabilities. Systematic sampling is used to promote a rotation of the selection of the same unit over time, and thus address temporal spread. Both methods select samples that are well distributed in space at each sampling time. They differ by the fact that these samples are of random size for the first one, while for the second one, more complex, their sizes are controlled. Thus, the first method is called spatiotemporal sampling with random sample sizes (SPAR) and the second, spatiotemporal sampling with fixed sample sizes (SPAF). Simulations show that our methods outperform and generalize existing methods.
A Novel Face Detection Framework Based on Incremental Learning and Low Variance Directions

Face detection has gained an ever increasing importance since it has a direct implication in several security systems and devices. Actually, several challenges related to visual variations have been associated with face detection. They can be attributed to many factors, such as occlusion, imaging conditions, illumination, image orientation and pose. Such factors may lead to the performance degradation of the face detectors. Therefore, developing a classifier able to adjust automatically its parameters according to the visual variations has become a fundamental topic for face detection in dynamic settings. In this paper, we investigate the potency of incrementally projecting data in low variance directions, after extracting useful facial information. In fact, Several feature extraction methods have shown high performance in real world applications, and particularly Human Face Detection. Besides, in one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. On the other hand, incremental learning is known to be powerful, when dealing with dynamic data. Extensive tests on human faces, and comparative experiments need to be carried out to find the best combination for an accurate human face detection framework.
An Android App: Virtual Queuing System for Public Distribution System

Ration distribution and food supplies to the public is one of the most fundamental government services. It has to be in the right manner especially for under people under povertyline. Its miles must important to well distribute ration cards. The card is also used to recognize people of the town or city like a voter card additionally it is very important for individuals who do not have a voter card to have a minimum ration card to determine his identity. Waiting in line is a critical problem inside the context of services because of its huge effect on operating skills and the pleasure of clients of the system. The way queues are arranged and the way lengthy clients need to anticipate products or services have laid the bottom for figuring out the maximum most desirable solution in the queue which can provide swap to reduce operating fee incurred during optimization queues, in addition to reducing the time spent through the means of ready-to-serve customers. Like the cutting-edge move from platform established to oriented carriers, that It grow to be unimportant to very well draw close the way to successfully manipulate ready strains and in flip enhance at the patron pleasure in an organization. The goal of this project is to save the group in ration stores the usage of the android application. First, the details like ration card numbers should be given in the android application, which is then connected to the corresponding ration shop. Then, the product details such as product names with quantity will be displayed in the mobile application. This app can call To the respective servers to access the data that will surely help customers to book Their tokens are online without waiting in the queue. This project tested the shortcomings of conventional and cutting-edge approached to queuing control, studying the sorts and programs of diverse queue control things, and eventually coined a generic Technique and method for planning and expanding the digital queue control system.