Reviewerly/exHarmony huggingface dataset:
Credit Card Fraud Detection Using Machine Learning and Predictive Models: A Comparative Study

With the advent of digitalization, online payment transactions have risen to a great deal. But this has also led to an increase in many illegal activities. Credit Card Fraud is a threat to many individuals as well as small business setups. People from different walks of life, researchers, Engineers have developed numerous techniques for detection of same. This paper is an attempt to compare these techniques and evaluate the most efficient one. The algorithms used for this study are Support Vector Classifiers, Logistic Regression, Random Forests, K Nearest Neighbours, J48 decision trees, Artificial Neural Networks (ANN), We have also used boosting frameworks for decision trees like AdaBoost, lightGBM, CatBoost, Xgboost. European Dataset and German datasets are used for model building. The results are evaluated and compared on the basis of three parameters, the precision score, the recall score and the F1 score. This study helps to conclude the most efficient algorithm among the different algorithms used.
Sentiment Analysis as a New Source of Information

Sentiment analysis is one of the key methodologies of this thesis; it is used to measure the attitudes—positive, negative, or neutral—towards the topics under observation. This section focuses on the sentiment analysis applications that provided input for this thesis. While sentiment analysis is a well-researched topic, there are many technical issues to solve, and it continues to evolve at an accelerating pace.
Multi-tenancy Management and Zero Downtime Upgrades using Cray-HPE Shasta Supercomputers

As supercomputing systems gradually become an integral part of data driven workflows such as ML and AI or tightly-coupled pre- and post-processing pipelines, users need programmable access to shared resources to avoid moving large volume of data to dedicated systems or to public cloud providers. Public clouds or the private ones using technologies like OpenStack, multi-tenancy on shared hardware has been a commonplace over a decade, offering users programmable and privileged access to resources like compute, network and storage. Such access is unavailable to users on batch-scheduled, multi-Petascale supercomputing systems, which are designed for achieving close-to-metal performance for scientific applications at unprecedented scales. In this paper, we focus on multi-tenancy within hardware and software stacks of Cray-HPE EX Shasta supercomputing systems for creating high performance and cloud clusters for HPC and AI/ML workloads respectively. Using orchestration examples for zero downtime upgrades of virtual clusters, we demonstrate benefits of multi-tenant machines for achieving close-to-metal performance, as well as elasticity and customization of resources without interruption to operational services.
WSL Architecture

To get the most out of Windows Subsystem for Linux, it is useful to understand its underlying architecture and history. If you plan to hack on WSL or just deploy it in your enterprise, it is necessary to know. This chapter will cover the architecture of WSL 1 and WSL 2, how we got here, and then dive into some of the bleeding edge features on the WSL platform.
Additional Out-Group Search for JADE

JADE is a method to adaptively select parameters using probability distribution, and shows good searching accuracy and speed. However, the search outside the solution group is not considered as well as other methods. Though it is possible to forcibly increase the search outside the solution group by dividing the solution group or adding random search, the search speed lowers. In this study, referring to the Nelder-Mead method, the algorithm for improving the accuracy without reducing the search speed as much as possible by adding the solution group outside search to JADE is proposed. Concretely, when a fixed condition is satisfied, one point of the search point carries out the group outside search. In order to prevent the search speed from decreasing even in the case of a high dimension, the number of search points for out-of-group search is set not to increase even when the dimension increases. The effectiveness of the proposed method is confirmed by numerical experiments.
Richard Killmer

Professor of oboe at the Eastman School of Music, Richard Killmer was principal oboist of the St. Paul Chamber Orchestra for eleven years. In this interview, he talks about his early career and observations on the evolution of the oboe world. He discusses his teaching philosophy and shares specifics on breathing, intervallic placement, long tones, and articulation. He shares tips for technical practice and his ideas on vibrato and reeds. Killmer shares stories of his experience in helping to design the Ross gouge, and he talks about his inspirations.
Study of Education Strategy for Writing in Korean –Teaching Strategy of Fallacies of Writing in Korean and Akward Expressions Based on Case Study–

This thesis aims at study of education strategy for the students who learn Korean as their major at universities in China. It also suggests some effective strategies for solving the problems. In partucular, It deals with the study of teaching strategy of fallacies of writing in Korean and Akward expressions based on case study. Writing ability is not only process to communicate with people around the world but also is regarded as one of the essential virtues to be a competent person in our future society. Due to these reasons, the importance of writing capability has been considered as a crucial key for language learners. The current problems students of universities in China have been going through must be addressed seriously for improvement Korean language education. According to my experiences while I have been teaching Korean in China, I classified certain of mistakes students mainly make into categories, which is Words, Sentences and Expressions. As a result, I found that there are some similarities: 7 mistakes in the Akward expressions and 12 mistakes in the Words and Sentences. (Jilin International Studies University)
Upaya Peningkatan Perkembangan Bahasa melalui Metode Role Play bagi Anak Tunagrahita di SLBN Pembina Mataram

Mentally retarded children are children who have intelligence below the average that occurs during development and have obstacles in adaptive assessment. The language development of mentally retarded children is basically lower than that of children in general, most children cannot achieve perfect language skills, language development is very lagging compared to children in general, even at the same Mental Age. Talking activities are very important for mild type mentally retarded students, because the potential for mild-type mentally retarded students to speak can still be maximally optimized, by speaking, students can communicate formally with the teacher during activities teaching and learning is carried out in the classroom. The goal is to improve language skills in mentally retarded children. The method of implementing activities using Role Play method. The results before the intervention of 5 samples of speech ability in the moderate category of 3 people (83.3%), and in the poor category as many as 2 people (16.7%), after getting the intervention speaking ability in the good category 2 people (40%), enough 3 people (60%). So it can be concluded that there is an increase in language skills in children with mild mental retardation.
Spectral characteristics, petrography and opaque mineralogy of the Oligo-Miocene basalts at Wadi Abu Qada- Wadi Wata area, west-central Sinai, Egypt

The Oligo-Miocene basaltic exposures at Wadi Abu Qada – Wadi Wata area, west-central Sinai Peninsula occur as intra-continental fissure-fed eruptions in the form of km-scale sill attaining thickness of about 30 m and dyke of about 70 m thick, intruding/extruding the Early Cretaceous to Early Eocene sedimentary sequences. Stratigraphy of the study area was dealt with in the context of the geologic setting of these Oligo-Miocene basalts, which are genetically related to the Gulf of Suez rifting in the late Oligocene. These basaltic exposures were discriminated from their surrounding sedimentary rocks using ASTER, Landsat-8 and Sentinel-2 datasets. Spectral characteristics of these basalts were determined by remotely-sensed satellite data as well as by using a handled ASD mineral identifier. Advanced image processing techniques were applied and many images processing algorithms were used in image enhancement including Principal Component Analysis (PCA), Band ratios, Decorrelation Stretch (DS), Mixture Tuned Match Filter (MTMF), and ASTER mineral's indices. All these techniques in conjunction with petrographical investigations were applied in lithological discrimination of the studied Oligo-Miocene basalts and new geological mapping were accomplished and verified by field data. The considered Oligo-Miocene basaltic rocks are mainly olivine dolerite with minor olivine porphyritic basalts. The main mineralogical components are plagioclase, augite and olivine with subordinate Fe-Ti oxides (ferriilmente and titanomagnetite).
Safe and Effective Elliptic Curve Cryptography Algorithm against Power Analysis

Having high safety and effective computational property, the elliptic curve cryptosystem is very suitable for embedded mobile environment with resource constraints. Power attack is a powerful cipher attack method, it uses leaking information of cipher-chip in its operation process to attack chip cryptographic algorithms. In view of the situation that the power attack on the elliptic curve cryptosystem mainly concentrates on scalar multiplication operation an improved algorithm FWNAF based on RWNAF is proposed. This algorithm utilizes the fragments window technology further improves the utilization ratio of the storage resource and reduces the “jitter phenomenon” in system computing performance caused by the sharp change in system resources.
Use of fuzzy logic approach for software quality evaluation in Agile software development environment

Quality is not only required by customers but also by serving organizations, no matter if they are giving services for factory-made products, utility items, or software products. In terms of software products, quality estimation process is different and becomes little hard as the developer needs to work according to the customer requirements which are actually always updating in modern scenario. Some decades ago, when traditional software development life cycles like waterfall model and prototype model were mostly in use. Software quality evaluation was performed using function points and line of codes but since 2001 when Agile software development practices came into existence, software quality evaluation parameters have also been updated and various different techniques are being implemented for the purpose. This chapter focuses on the approach of using soft computing techniques for software quality evaluation and also gives information about major software quality evaluation parameters in Agile methods.
Moving Target Shooting Control Policy Based on Deep Reinforcement Learning

Robots are playing a more and more important role in people&#x2019;s production and life, recently. However, robot control in dynamic environment is still a difficulty. With the great breakthrough of deep reinforcement learning in the field of video games, this method is also extended to the field of robots. Due to the gap between the simulation environment and the real environment, the deep reinforcement learning algorithm trained in the simulation environment is difficult to be applied to the real environment. Aiming at the gimbal control with two degrees of freedom (DOF), a pipline combining system identification and deep reinforcement learning is proposed. On the one hand, the shooting accuracy of the gimbal to moving objects is improved through deep reinforcement learning algorithm. On the other hand, the gap between simulation and reality is reduced through system identification. The method is verified in the RoboMaster University AI Challenge (RMUA) shooting system. The results show that the shooting accuracy is better than the classical control method.
Privacy Preserving Data Mining Technique to Secure Distributed Client Data

Data is now acquired and stored in databases from a variety of sources. The collecting of data has little influence until the database owner does data analysis, such as by applying data mining techniques to the databases. Data mining techniques and algorithms are currently being developed, and they are providing substantial improvements to the information extraction process. With respect to data mining, privacy-preserving data categorization is an important study area. Data mining-based privacy attacks, which entail analyzing data over a lengthy period of time to retrieve useful information, are one of the cloud’s security issues. In this paper, we introduce concept of privacy preserving data mining. We investigate the privacy and security issues and propose a privacy preservation data mining approach. The Proposed Privacy Model for distributed client has been proposed using decision tree with resulting parameter accuracy, error rate, time complexity and space complexity. At last to validate the performance of the proposed approach, we compare by traditional PPARM approach is used with similar dataset for comparison. The experimental outcome shows that the effectiveness of the proposed algorithm of privacy preserving that secure multi-party computation can be practically, even for compound problems and large size inputs.
Impacts of Behavioral Biases on Active Learning Strategies

Cyber-Physical-Human Systems (CPHS) interconnect humans, physical plants and cyber infrastructure across space and time. Industrial processes, electromechanical systems operations and medical diagnosis are some examples where one can see the intersection of humans, physical and cyber components. Emergence of Artificial Intelligence (AI) based computational models, controllers and decision support engines have improved the efficiency and cost effectiveness of such systems and processes. These CPHS typically involve a collaborative decision environment, comprising of AI-based models and human experts. Active Learning (AL) is a category of AI algorithms which aims to learn an efficient decision model by combining domain expertise of the human expert and computational capabilities of the AI model. Given the indispensable role of humans and lack of understanding about human behavior in collaborative decision environments, modeling and prediction of behavioral biases is a critical need. This paper, for the first time, introduces different behavioral biases within an AL context and investigates their impacts on the performance of AL strategies. The modelling of behavioral biases is demonstrated using experiments conducted on a real-world pancreatic cancer dataset. It is observed that classification accuracy of the decision model reduces by at least 20% in case of all the behavioral biases.
User Experience Model using Motivation and Usability Factors in Serious Games for Rehabilitation: A Case Study (Preprint)

<sec> <title>BACKGROUND</title> Rehabilitation is a term used to describe a range of responses in disabilities that improve quality of life. Nowadays, one of the new treatments in rehabilitation is assistive technology by using serious games. This treatment can be a success with sophisticated technologies such as simulators, robotics, and virtual reality </sec> <sec> <title>OBJECTIVE</title> Meanwhile, motivation and usability are two important factors that focus on user experience. Previous studies suggested that attention, relevance, confidence, and satisfaction for motivation and ease of use, learnability, memorability, and satisfaction for usability are the important attributes for user experience in serious games. The first stage of identifying the user experience model is by exploring the motivation and usability factors that influence serious games for rehabilitation. There are eight relationships that come from eight different attributes in motivation and usability factors of user experience in serious games. </sec> <sec> <title>METHODS</title> This research was held at one rehabilitation center in Malaysia that uses serious games as the assistive technology tool for exercises and a total of forty-five respondents have answered the finalized questionnaires at the end of the study. PLS-SEM is the method chosen for this research. SmartPLS software is used to calculate and analyze the data and relationships through Composite Reliability, Cronbach’s Alpha, Average Variance Extraction (AVE) for convergent validity, and Fornell-Larcker criterion for discriminant validity. </sec> <sec> <title>RESULTS</title> As a result, the study shows there are only two negative relationships were formed based on the intersection of attributes from motivation and usability factors where ease of use that related to relevance and satisfaction in usability that connected to satisfaction in motivation </sec> <sec> <title>CONCLUSIONS</title> Finally, a model of new user experience through motivation and satisfaction factors is developed for serious games. </sec>
The application of European standards of excellence for teachers of physical education in Secondary Schools from the technical Supervisors Point of View in Baghdad

The research aimed to applicate the European standards of excellence in Secondary Schools from the technical Supervisors Point of View in Baghdad. The researcher used the descriptive method on (143) supervisor. The researcher designed a special scale for the Iraqi ministry of education with three fields; social services, students’ satisfaction and procedural operations. The data was collected and treated using SPSS to conclude that social services field require from physical educators to collect information to cope with institutional reality inside school. Students’ satisfaction field shows that physical educators should pay more attention to individual differences among students. Procedural operations field showed that educators should create an environment rich with love, cooperation. Finally the researcher recommended paying attention to students and involving them in problem solving and decision making to create a loving environment inside class.
Automatic Threat Detection Using Deep Neural Networks

Baggage inspection is a primary task for threat detection in any public facility area (such as Railway Station, Bus Terminal, and Airport). Most often these detections are done manually by security personnel that is prone to human error. In this paper, we have proposed a model of automatic threat detection mechanism that will produce a warning text message to the authority using X-ray images of baggage to prevent terrorist attacks. The proposed model is consisting of a Convolutional Neural Network with a Long Short-Term Memory (LSTM), which produces the real-time caption of the X-Ray Images. There will be an algorithm which will detect the keywords such as, Gun, Explosive, Knife, Blade, and Shuriken in the generated caption. Finally, a warning message can be generated to alert the security and authority to take precautionary measures. In this paper, we have used the GDX-ray dataset to train the model.
A quantum walk with both a continuous-time and a continuous-spacetime limit

Nowadays, quantum simulation schemes come in two flavours. Either they are continuous-time discrete-space models (a.k.a Hamiltonian-based), pertaining to non-relativistic quantum mechanics. Or they are discrete-spacetime models (a.k.a Quantum Walks or Quantum Cellular Automata-based) enjoying a relativistic continuous spacetime limit. We provide a first example of a quantum simulation scheme that unifies both approaches. The proposed scheme supports both a continuous-time discrete-space limit, leading to lattice fermions, and a continuous-spacetime limit, leading to the Dirac equation. The transition between the two can be thought of as a general relativistic change of coordinates, pushed to an extreme. As an emergent by-product of this procedure, we obtain a Hamiltonian for lattice-fermions in curved spacetime with synchronous coordinates.
Optoelectronically interconnected hardware-efficient deep learning using silicon photonic chips

Deep neural networks (DNNs) have shown their superiority in a variety of complicated machine learning tasks. However, large-scale DNNs are computation- and memory-intensive, and significant efforts have been made to improve the efficiency of DNNs through the use of better hardware accelerators as well as software training algorithms. The optical neural network (ONN) is a promising candidate as a next-generation neurocomputing platform due to its high parallelism, low latency, and low energy consumption. Here, we devise a hardware-efficient optical neural network architecture named optical subspace neural network (OSNN), which targets lower optical component usage, area cost, and energy consumption of previous ONN architectures with comparable task performance. Additionally, a hardware-aware training framework is provided to minimize the required control precision, lessen the chip area, and boost the noise robustness.
EXPLORING COGNITIVE PROCEDURES OF TEXTING

S. Kurbakova 1, A. Kurbakov2 1Moscow State Institute of International Relations (MGIMO University) (RUSSIAN FEDERATION) 2Moscow Aviation Institute (MAI University) (RUSSIAN FEDERATION)
Micro‐Reflection on Classroom Communication: A FAB FrameworkH. Z.Waring and S. C.Creider. Bristol, CT: Equinox Publishing, 2021

TESOL QuarterlyVolume 56, Issue 1 p. 437-439 Invited Book Review Micro-Reflection on Classroom Communication: A FAB Framework , Waring, H. Z. and Creider, S. C.. Bristol, CT: E quinox Publishing, 2021 Stephen Daniel Looney, Corresponding Author Stephen Daniel Looney sdl16@psu.edu orcid.org/0000-0001-9968-7276 Pennsylvania State University, University Park, Pennsylvania, United States *Correspondence author. Email: sdl16@psu.eduSearch for more papers by this author Stephen Daniel Looney, Corresponding Author Stephen Daniel Looney sdl16@psu.edu orcid.org/0000-0001-9968-7276 Pennsylvania State University, University Park, Pennsylvania, United States *Correspondence author. Email: sdl16@psu.eduSearch for more papers by this author First published: 22 March 2022 https://doi.org/10.1002/tesq.3084Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume56, Issue1March 2022Pages 437-439 RelatedInformation
An Intelligent Multi-turn Dialogue Classification Method Based on Resampling

Abstract Intelligent multi-turn dialogue classification uses the whole text as input to predict tags. As one of the most popular research topics in dialogue system, intelligent multi-turn dialogue classification has important research significance in academia and industry. In this paper, we focus on removing redundant information from dialogue text. Multi-turn dialogue classification is essentially a text classification problem. We selects Bert as the core model of multi-turn dialogue classification in view of its strong learning and redundancy removing ability. However, Bert limits the length of its input to 512 and the length of dialogues often exceeds 512. A long text segmentation method based on resampling is presented to solve the problem of input length limitation. The resampling mechanism can also further remove the redundant information in multi-turn dialogues. Experimental results show that the proposed method outperforms all the baselines and achieves an F 1 of 53.91%.