Reviewerly/exHarmony huggingface dataset:
On a property of the ideals of the polynomial ring $R[x]$

Let RR be a commutative ring with unity 1≠01≠0. In this paper we introduce the definition of the first derivative property on the ideals of the polynomial ring R[x]R[x]. In particular, when RR is a finite local ring with principal maximal ideal m≠{0}m≠{0} of index of nilpotency ee, where 1&amp;lt;e≤|R/m|+11≤e≤|R/m|+1, we show that the null ideal consisting of polynomials inducing the zero function on RR satisfies this property. As an application, when RR is a finite local ring with null ideal satisfying this property, we prove that the stabilizer group of RR in the group of polynomial permutations on the ring R[x]/(x2)R[x]/(x2), is isomorphic to a certain factor group of the null ideal.
Venn Diagrams and Logical Connectives

It’s possible to work with sets by listing all of their elements—provided they are finite sets, of course—and then comparing sets by comparing the elements in these lists. But a visual representation provides much better support for human intuition.
SORGATE: Extracting Geometry and Texture from Images of Solids of Revolution

We describe SORGATE, a procedure for extracting geometry and texture from images of solids of revolution (SORs). It uses multivariate optimization to determine the parameters of the camera in order to build the viewing transform, as well as to reconstruct the geometry of the SOR using the silhouette. In addition to individual image analyses, it can use the data extracted from the same SOR viewed from different directions to produce a single, composite texture which can be combined with their blended geometries to produce a reconstructed 3D model. No prior knowledge other than the object’s rotational symmetry is required. Camera viewing parameters are derived directly from the image.SORGATE is useful when 3D modeling of SORs is needed yet direct measurement the physical objects is infeasible. As it does not require camera calibration, it is also fast, inexpensive, and practical. One use case might be for researchers and curators who wish to display and/or analyze the art on historical vases; metric reconstruction and proper texturing of the objects would allow this without requiring viewing in person.
Parallelization of Cocktail Sort with MPI and CUDA

Sorting is an essential operation that has a wide range of applications in the day-to-day life of programming. It is quicker and easier to recognize items in a sorted list compared to that in unsorted. The parallelization of an algorithm is appropriate when the problem is highly intense computationally or when it is crucial to get the results rapidly. In the past few years, a plethora of works has been proposed to contract the time complexity and space complexity. Parallelizing the sorting algorithms gives additional strength to the algorithm by enhancing the performance. In this paper, we derive an algorithm to parallelize one of the in-place sorting algorithms called cocktail sort. We have employed message passing interface (MPI) and Nvidia compute unified device architecture (CUDA) parallel computing platforms for parallelizing the cocktail sort. Comparison of time taken by the algorithm with MPI and CUDA platform is exhibited and a graph depicting same is plotted. Calculation of the speedup of the cocktail sort has been showcased. Also, proved that the time complexity of the parallelized cocktail sort has been reduced to O(n) from O(n2) when compared with the sequential algorithm. The results unveil that the parallelizing enhances the performance and efficiency of the cocktail sorting algorithm.
What Type of Data Are Images?

Images are an intriguing source of data for social scientists. Increasingly, the phrase "images as data" implies big data, digitized images, and quantitative analyses with elements of machine learning. This perspective, however, obscures the many other ways to conceive of what images are as data for social scientists. For example, big data, machine learning image researchers rely on labeled data to build classification algorithms. The process of labeling the data is essentially content analysis, a familiar tool to qualitative researchers. Thus, quantitative analyses are built on qualitative approaches and both are a potentially valid way to use images to better understand the world. This chapter considers how the complicated nature of meaning derived from images lends itself to a pluralistic research approach.
Covid-19 and Firms’ Stock Price Growth: The Role of Market Capitalization

This paper studies the role of capitalization on firms’ stock price growth in response to new cases of Covid-19 infections in the United States. Controlling for firm and time fixed effects, our panel model estimates show that the effect of new cases of Covid-19 infections on firms’ stock price growth is significantly increasing in capitalization: For each one standard deviation increase in capitalization, a one standard deviation increase in new cases of Covid-19 infections increases the weekly growth rate of firms’ stock prices by about 0.7 percentage points. Effects of capitalization on the impact that Covid-19 infections have on firms’ stock price growth are largest in the travel, tourism, and hospitality sector. Smaller but still positive effects of capitalization are present in the pharmaceutical products, high-tech, and banking and finance sectors.
Exploring Learning Resource Recommendation Approaches for Secondary Education

Recommender Systems are a well researched area and there are many approaches and algorithms solving different kind of problems. In TEL recommending appropriate learning resources is a common problem that cannot be generalized well due to different didactic strategies, educational needs and heterogeneous data sources. We therefore argue that a design science process is best suited to apply, combine and improve different established recommendation system approaches to TEL systems. In this paper, we report our architecture to support our design process and the lessons learned from our specific TEL use case. Finally, we conclude discussing the open problems, advantages of our architectural approach and directions for future research.
East African Cybersecurity

ICT has become an essential factor in Ethiopia and other East African countries to transform their economies. Meanwhile, the agriculture sector is still the primary driver of economic development. The difference between the past and present years is the injection of technology to further drive this business. For Ethiopia, this means using a database to support countrywide micro-financing to farmers in seeking capital for leasing land, purchasing needed materials, and hiring workers. The objectives of this chapter were to redesign and harden the database architecture of the researched micro-financing organization. The researchers performed an assessment of the organization's information systems. After identifying weaknesses, they followed controls identified in the NIST SP 800-53. This chapter describes the security faults within the database, showing the lack of baseline cybersecurity controls to ensure a hardened system. The authors proposed a security-centered database architecture after auditing the researched organization's IT infrastructure and information systems.
Automated Counterfactual Generation in Financial Model Risk Management

The increasing adoption of machine learning and AI models in finance and banking poses challenges for model testing and model risk management (MRM). Conventional testing has limitations in evaluating more complex models while the latest testing methodologies have not yet been widely productionized. We propose a model-agnostic testing algorithm based on counterfactual generation for machine learning models in finance. Our method provides actionable counterfactual samples that can guide future model analysis and improvement. To make our algorithm modeler-friendly, we design a cloud-native implementation that is easy to integrate with any new and existing models, and ready for production-level automation, scalability, and parallel processing.
On Trade Cloud Ecosystem Structure for Shared Learning

Cloud computing (CC) is a latest type of network automation stack; its advancement velocity is growing in a very increasing fashion. In this work, we proposed an implementation of cloud ecosystem for shared learning to explore and use infrastructure, platform and software as a service (IaaS), (PaaS) and (SaaS), which makes a prerequisite planning and design of the general environment of learning cloud for the needs of education domain. With this system, workload of a staff can be reduced with respect to laboratory setup for various laboratory subjects in each semester of an academic year. It allows to access cloud over Web for shared learning, so as to call the required application programs with respect to various subject groups together as and when required.
G-Nmp: Accelerating Graph Neural Networks with Dimm-Based Near-Memory Processing

Graph Neural Networks (GNNs) are of great value in numerous applications and promote the development of cognitive intelligence, due to the capability of modeling non-euclidean data structures. However, the inherent irregularity makes GNNs memory-bound, and the hybrid computing paradigm of GNNs poses significant challenges for efficient deployment on existing hardware architectures. Near-Memory Processing (NMP) is a promising solution for alleviating the memory wall problem. In this paper, we present G-NMP, a practical and efficient DIMM-based NMP solution for accelerating GNNs, which accelerates both sparse Aggregation and dense Combination computations on DIMM for the first time. We propose a novel G-NMP hardware architecture to exploit rank-level memory parallelism efficiently, and the G-ISA instructions to reduce host memory requests significantly. We conduct several data flow optimizations on the G-NMP to improve memory-compute overlap and to realize minimum memory accesses. Then we develop an adaptive data allocation strategy for diverse vector sizes to further exploit feature-level parallelism. We also propose a novel memory request scheduling method to achieve flexible and low-overhead DRAM ownership transition between host and G-NMP. Overall, G-NMP achieves consistent performance advantages across diverse GNN models and datasets, and offers up to 3.50× overall performance compared with the state-of-the-art work.
Spreading digital literacy via Minimal English. The concept of ‘class’ in Swift programming language

The article aims at providing explications of the concept of a class, as it is implemented in the Swift programming language offered by Apple. The explications are framed in Minimal English, which is based on the theory of Natural Semantic Metalanguage. Detailed analysis of the Swift concept of class leads to four distinct core explications of the programming construct in question and the related feature that Swift classes possess, namely the concept of property. The article’s primary purpose is to offer a more smooth experience with programming, especially with beginners in mind. Their initial exposure to programming might face several challenges due to the complicated digital jargon of the documentation. Minimal English is implemented to ease the learning curve and promote digital literacy as one of the most fundamental skills in today’s world.
Benchmarking Transformer-Based Transcription on Embedded GPUs for Space Applications

Speech transcription is a necessary tool for backend applications commonly found in voice assistants. Transcription is typically performed using cloud-based servers or custom hardware, but those resources are not always amenable to space environments due to size, weight, power, and cost constraints. Therefore, it is important to determine the performance of and optimal conditions for running transcription on hardware that is feasible for deployment in a space application. This research investigates and evaluates the performance of the wav2vec2 speech transcription engine, the current state-of-the-art model for this domain with and without optimizations. The target hardware, the NVIDIA Xavier NX Jetson embedded GPU, was chosen for its modern GPU architecture and small form factor. In addition to examining the input scaling behavior, we evaluate the hyperparameters of the clustered attention optimization, and average power and energy for inference relative to the operating power mode of the device. The clustered attention model outperformed the improved-clustered model for large input sizes, but the wav2vec2 model without clustering performed better for small input sizes. The clustered model energy per inference (13.90 J) was less than energy per inference of the improved-cluster model (15.03 J) and the vanilla softmax model (15.85 J). All models meet real-time speech processing requirements necessary to perform onboard inference entirely on a space system.
Visualization of Heliostat Field of Solar Thermal Tower Power Plant Using Virtual Reality (VR) Technologies

An important part of future global energy depends on the development of the solar industry. To date, we have noticed the shift from fossil fuels energy towards renewable energy. The past decade has shown significant progress in computer science, and CAD is increasingly used for design and development. Visualization of the data generated from the models in the CAD program plays an important role in the creation of state-of-the-art designs. An important limitation during the design phase is the visualization of three-dimensional geometry. This article attempts to illustrate the use of VR technologies in solar thermal power plant development. This article analyzes various strategies and methods for the visualization of CAD models in virtual reality. Android phone interfaces with a desktop computer, as well as head movement control strategies, are discussed. It is concluded that VR technologies can help with visualization, as well as in the development of the field of solar thermal power plants, having minimal design-related issues.
PlayChemy: Educational Application in Augmented Reality for Equating Redox Equations

Augmented reality combines the virtual world with the real world by superimposing various digital objects on top of real objects.This type of technology brings many benefits to education, including making it easier to accumulate new information or boosting memory capacity.We propose to develop an augmented reality-based game with the aim of facilitating learning and understanding of methods for determining oxidation numbers and equating redox equations.The paper presents the development steps that have been followed in the realization of this project, from the definition of the application context to the actual implementation.
Cluster Analysis of Influencing Factors of Regional Economic Growth Based on Random Walk Model

The development of circulation in China should vary from place to place. There is a curve relationship similar to the marginal productivity between the elasticity of the circulation industry and the regional economic aggregate. With the gradual deepening of China's reform and opening up, the financial market continues to develop, and the degree of trade integration continues to deepen. However, the development of China's regions is extremely weak. However, the problem of balance has become increasingly serious along with development. This article analyzes the influencing factors of regional economic growth in different regions of the country, and clusters the influencing factors based on the random walk model. The results show that the model is effective.
Face Recognition Efficiency Enhancements Using Tensorflow and WebAssembly: A Practical Approach

In this research paper we have studied the use of WebAssembly technology applied to the problem of facial recognition in real world Human-Computer Interaction (HCI) applications. Multiple parameters determining the system were tested using a large image data set. Experiments have shown how, with careful choice of system parameters, very high levels of accuracy can be achieved for large populations, even using only a few pre-stored images per person. In addition, a web application was developed to compare the efficiency of facial recognition using different backends of Tensorflow.js in the browser. The results show that WebAssembly technology is perfectly operational for use in this area and provides user experience improvements in terms of efficiency and stability. In the coming months, WebAssembly is expected to incorporate two relevant developments: Single Instruction Multiple Data (SIMD) and Multithreading (MT). Our experiments show that by enabling these features WebAssembly can provide a very suitable technical solution for in-browser face recognition.
Isn’t It Ironic, Don’t You Think?

Currently, dealing with figurative language, such as irony, is one of the challenging and interesting problems in natural language processing. Because irony is so widespread in user-created content (UCC) such as social media posts, its prevalence makes it technically challenging to determine sentiments or interpret opinions. Investigating irony content is an essential problem which can be extended and leveraged for other tasks such as sentiment and opinion mining. The present work proposes a Hierarchical Attention based Neural network for identifying ironic tweets. Our method exploits the structure and semantic features like POS tagging and sentiment flow shifts. We then present our results on the Task 3 of the Semantic Evaluation 2018 workshop named “Irony Detection in English Tweets” dataset. Furthermore, we perform a comprehensive analysis over the generated results, shedding insights on future research for the irony detection task.
Decentralized Non-Custodial Algorithmic Trading &amp;amp; Fund Management

The goal of this body of research work is to set up an operational, globally available, algorithmic trading-based fund in a streamlined and low-cost manner. The ongoing growth and development of blockchain technologies has led to the creation of specific smart-contracts which separate the aspects of asset ownership from the ability to trade the same asset. This advancement means that a manager can trade someone’s assets without ever having custodial ownership of them. A complete Python trading system was thus developed. This includes scalable live market data ingestion, an alpha generating cryptocurrency trading strategy, and a flexible execution layer. The data ingestion is engineered for minimal dependencies and points of failure. The trading strategy can trade multiple markets and time frames. Finally, the trade execution layer can support centralized or decentralized exchanges, a primary goal of our development.
Tool for Creating Dataset and Software Defect Predictions

One of the most difficult and time-consuming aspects of Machine Learning is gathering high-quality data that can be used to train the algorithm. Historic data is a gold mine for predicting the future with a high level of confidence and accuracy in a certain field. Data is typically available in its raw form, which is in most circumstances unsuitable for machine learning applications. The work presented here introduces a tool that will considerably assist in the generation of a high-quality dataset, starting with raw data collection and progressing on to data pre-processing and validation, and finally to prediction using the selected machine learning algorithm. So far, we have built capability in the tool to collect data dynamically from a GitHub repository. Data pre-processing, various defect predictions, and other machine learning settings are all in the pipeline. Once the data is ready to use, this tool will assist in selecting the appropriate Machine Learning algorithm and performs the desired prediction in the associated field. The entire data collection process, from data collection to prediction, will be orchestrated in a user-friendly Java Swing application.
Reduce Estimation Error Rate and Power in Database Center During Query Processing &amp; Query Optimization

Data centers are well-known for consuming a significant amount of energy. When it comes to server and data center op erations, energy management is becoming more important, with the goal of reducing all energy-related expenses, including capital and operating exp enditures as well as environmental implications. One of the most power-intensive applications in a data center is the database application. The primary goal of a conventional database is performance, which implies that queries must be completed as quickly as possible without any concern for energy consumption. This paper will look at the amount of energy used by a database server during query processing and optimization. As a result, this research work will develop a power and energy estimation model and compare it to an existing model. Result: Include experimental results demonstrating that our technique is more accurate than the current method.
Security Enhancement for CO-OFDM/OQAM System using Twice Chaotic Encryption Scheme

In this paper, we propose a twice chaotic encryption scheme to improve the security of CO-OFDM/OQAM system. Simulation results show that the proposed scheme enhance the physical-layer security within the acceptable performance penalty.
Exsumm-VIZ: Visual Interpretation of Attention Model In Text Summarizations

Deep learning has enhanced the state of the art in several domains, including text summarizing, with pointer generator summarization one of the dominant approaches in recent years. However, several obstacles remain for the summarizing model, such as over-and-under summarization and processing large phrases, requiring human evaluation. Furthermore, summarization raises issues of explainability, and user interaction, compelling the development of better analytics systems. To address these limitations, we introduce EXSUMM, a tool for visualizing analysis of attention weights, data, and evaluation metrics associated with text summarization. Through its linguistic and semantic filtering, the tools help users understand what their models are “focusing on.” such as abstractiveness and factual consistency. We validated our visual system's effectiveness through various use cases and user studies and discovered a favorite for EXSUMM over typical text-based summarization systems, particularly for lengthy texts.
Security Scheme and Conceptual Model of Internet Banking System

In this paper is given security and threat analyses of Uzbek and international internet bank systems. Also, main functions of internet banking are presented in example CLICK. The number of cybercrimes in Internet banking systems and security terms by percent are discussed. For protected transactions is proposed conceptual model and security scheme. At the end of the work is given some recommendations to secure client and seller information.
Modify Twofish Algorithm to Lightweight using Present Techniques for Data Protection

Cryptography is required to transmit data over network communication channels to increase security and lower costs, and Twofish’s algorithm is suffer from the F-Function. It has two G-Function which is one of the complex operations in the original Twofish algorithm. To solve this problem, this paper modifies F-Function structure using the techniques of the Present algorithm, which is used to make the original algorithm better in the memory consumption, faster and better security. In this paper, propose two versions of Lightweight Twofish Algorithm: The first version is called (LTFA1) which includes modified F-function by replaced g-Function, PHT, and h-function with (permutation layer and S-box) technique found in the Present lightweight algorithm. A Second version is called (LTFA2) is keeping PHT – function in F-function and replaced g and h-Function with (permutation layer and S-box) technique in the Present algorithm. Experiments and results of the proposed algorithm based on NIST testing, security and lightweight measures (execution time, memory usage, and throughput) are shown that the proposed algorithms are safer and faster compared to the original algorithm.