Reviewerly/exHarmony huggingface dataset:
Game Theory Based Privacy Preserving Approach for Collaborative Deep Learning in IoT

The exponential growth of Internet of Things (IoT) has become a transcending force in creating innovative smart devices and connected domains including smart homes, healthcare, transportation and manufacturing. With billions of IoT devices, there is a huge amount of data continuously being generated, transmitted, and stored at various points in the IoT architecture. Deep learning is widely being used in IoT applications to extract useful insights from IoT data. However, IoT users have security and privacy concerns and prefer not to share their personal data with third party applications or stakeholders. In order to address user privacy concerns, Collaborative Deep Learning (CDL) has been largely employed in data-driven applications which enables multiple IoT devices to train their models locally on edge gateways. In this chapter, we first discuss different types of deep learning approaches and how these approaches can be employed in the IoT domain. We present a privacy-preserving collaborative deep learning approach for IoT devices which can achieve benefits from other devices in the system. This learning approach is analyzed from the behavioral perspective of mobile edge devices using a game-theoretic model. We analyze the Nash Equilibrium in N-player static game model. We further present a novel fair collaboration strategy among edge IoT devices using cluster based approach to solve the CDL game, which enforces mobile edge devices for cooperation. We also present implementation details and evaluation analysis in a real-world smart home deployment.
A Two Side Histogram Shifting Based Reversible Data Hiding Technique in Encrypted Images

Reversible data hiding means lossless recovery of hidden data as well as the original cover, but it lacks the security, to get over this problem, reversible data hiding in encrypted covers came into existence. Many reversible data hiding algorithms have been introduced by researchers but most of them have some flaws such as lack of complete reversibility, low embedding capacity, less security and others. This paper, introduces a simple iterative method which combines encryption and embedding schemes to increase the embedding capacity and security with the use of two side histogram shifting with reference to the peak point. The proposed work is divided into two methods: one uses side information while other does not. Results obtained after comparison of proposed work and existing works, proves the efficiency of scheme.
The relationship between organizational changes and job satisfaction in the Jordanian telecommunication industry

The aim of this paper is to investigate the relationship between organizational change and job satisfaction in telecommunication companies in Jordan. A convenience sample was selected from among employees working at three communication companies (3636 employees) in Jordan. The findings indicate a significant positive relationship between organizational change and job satisfaction. to increase employees’ job satisfaction, their level of job stress during organization change operations in telecommunication companies must be decreased.&#x0D;
Zero-resource Multi-dialectal Arabic Natural Language Understanding

A reasonable amount of annotated data is required for fine-tuning pre-trained language models (PLM) on downstream tasks.However, obtaining labeled examples for different language varieties can be costly.In this paper, we investigate the zero-shot performance on Dialectal Arabic (DA) when finetuning a PLM on modern standard Arabic (MSA) data only -identifying a significant performance drop when evaluating such models on DA.To remedy such performance drop, we propose self-training with unlabeled DA data and apply it in the context of named entity recognition (NER), part-of-speech (POS) tagging, and sarcasm detection (SRD) on several DA varieties.Our results demonstrate the effectiveness of self-training with unlabeled DA data: improving zero-shot MSA-to-DA transfer by as large as ~10% F1 (NER), 2% accuracy (POS tagging), and 4.5% F1 (SRD).We conduct an ablation experiment and show that the performance boost observed directly results from the unlabeled DA examples used for self-training.Our work opens up opportunities for leveraging the relatively abundant labeled MSA datasets to develop DA models for zero and low-resource dialects.We also report new state-of-the-art performance on all three tasks and open-source our fine-tuned models for the research community.
JPG, PNG and BMP image compression using discrete cosine transform

This paper proposes image compression using discrete cosine transform (DCT) for the format of joint photographic expert groups (JPEG) or JPG, portable network graphic (PNG) and bitmap (BMP). These three extensions are the most popular types used in current image processing storage. The purpose of image compression is to produce lower memory usage or to reduce memory file. This process removes redundant information of each pixel. The challenge for image compression process is to maintain the quality of images after the compression process. Hence, this article utilizes the DCT technique to sustain the image quality and at the same time reduces the image storage size. The effectiveness of the DCT technique has been reasonable over some real images and the implementation of the technique has been compared with different types of image extensions. Matlab software is an important platform for this project in order to write a program and perform the progress of project phase by phase to achieve the expected results. Based on the tested images, the DCT technique in image compression is capable to reduce the image storage memory in average about 50% of each image tested.
How Do Active Reading Strategies Affect Learning Outcomes in Web Search?

Prior work in education research has shown that various active reading strategies, notably highlighting and note-taking, benefit learning outcomes. Most of these findings are based on observational studies where learners learn from a single document. In a Search as Learning (SAL) context where learners have to iteratively scan and explore a large number of documents to address their learning objective, the effect of these active reading strategies is largely unexplored. To address this research gap, we carried out a crowd-sourced user study, and explored the effects of different highlighting and note-taking strategies on learning during a complex, learning-oriented search task. Out of five hypotheses derived from the education literature we could confirm three in the SAL context. Our findings have important design implications on aiding learning through search. Learners can benefit from search interfaces equipped with active reading tools—but some learning strategies employing these tools are more effective than others. (This research has been supported by DDS (Delft Data Science) and NWO projects SearchX (639.022.722) and Aspasia (015.013.027).)
Features of Social-Perceptual Properties of Mathematically Gifted Students

The attention of modern society to intellectual potential makes the problem of studying mathematically gifted youth at the stage of self-determination in higher education relevant. Practical problems related to the psychological features of social adaptation of mathematically gifted youth require solving. The main goal of the research is to study the social and perceptual abilities of mathematically gifted students. The study sample consisted of 76 natural science students aged 17-23 years (M=19.8, SD=3.2 (58% men). The research methods were: testing (test of analytical mathematical abilities, test of the structure of intelligence (TSI) of R. Amthauer), expert assessment, survey (questionnaire of V. A. Krutetsky, questionnaires aimed at diagnosing socio-perceptual abilities), statistical methods. Self-assessment of intelligence, composite assessment, and some components of social intelligence and some components of empathy are significantly different. The ability of mathematical generalization and practical mathematical thinking have a greater number of relationships with social and perceptual properties. Here we found relationships not only with empathy, but also the ability to recognize verbal expression and the General ability to understand and manage their own and other people’s emotions. The ability to operate images in two-dimensional space is related only to the level and components of emotional intelligence. According to the results of the study, the features of socio-perceptual properties of students with different levels of analytical mathematical abilities are described. The conclusions can be used in the development of a program of psychological support for this category of students.
Retaining Named Entities for Headline Generation

Text summarization is a way of generating a more succinct block of text which sustains noteworthy data and the general significance of the source text. This paper overcomes some of the concerns of three existing text summarization architectures (transformer, pointer generator, attention), specifically used for converting circumlocutory news articles into crisp headlines. The dataset was scraped from a popular news Web site, Inshorts, where a variety of news articles and headlines are readily available (Inshorts.com in Breaking news headlines: read all news updates in English—Inshorts, 2020 [1]). A major challenge in headline summarization is the named entities viz. names of people, places, products, etc., which are lost in this process. These named entities are important for better headline summarization. In this paper, an approach is proposed that preserves these named entities in the output headline resulting in a better summarization. The paper also presents an evaluation of these abstractive approaches based on ROGUE metrics.
Towards Expert Preference on Academic Article Recommendation Using Bibliometric Networks

Expert knowledge can be valuable for academic article recommendation, however, hiring domain experts for this purpose is rather expensive as it is extremely demanding for human to deal with a large volume of academic publications. Therefore, developing an article ranking method which can automatically provide recommendations that are close to expert decisions is needed. Many algorithms have been proposed to rank articles but pursuing quality article recommendations that approximate to expert decisions has hardly been considered. In this study, domain expert decisions on recommending quality articles are investigated. Specifically, we hire domain experts to mark articles and a comprehensive correlation analysis is then performed between the ranking results generated by the experts and state-of-the-art automatic ranking algorithms. In addition, we propose a computational model using heterogeneous bibliometric networks to approximate human expert decisions. The model takes into account paper citations, semantic and network-level similarities amongst papers, authorship, venues, publishing time, and the relationships amongst them to approximate human decision-making factors. Results demonstrate that the proposed model is able to effectively achieve human expert-alike decisions on recommending quality articles.
WITHDRAWN: Animal species image classification

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal ( https://www.elsevier.com/about/our-business/policies/article-withdrawal ). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Shuffler

Datasets in the computer vision academic research community are primarily static. Once a dataset is accepted as a benchmark for a computer vision task, researchers working on this task will not alter it in order to make their results reproducible. At the same time, when exploring new tasks and new applications, datasets tend to be an ever changing entity. A practitioner may combine existing public datasets, filter images or objects in them, change annotations or add new ones to fit a task at hand, visualize sample images, or perhaps output statistics in the form of text or plots. In fact, datasets change as practitioners experiment with data as much as with algorithms, trying to make the most out of machine learning models. Given that ML and deep learning call for large volumes of data to produce satisfactory results, it is no surprise that the resulting data and software management associated to dealing with live datasets can be quite complex. As far as we know, there is no flexible, publicly available instrument to facilitate manipulating image data and their annotations throughout a ML pipeline. In this work, we present Shuffler, an open source tool that makes it easy to manage large computer vision datasets. It stores annotations in a relational, human-readable database. Shuffler defines over 40 data handling operations with annotations that are commonly useful in supervised learning applied to computer vision and supports some of the most well-known computer vision datasets. Finally, it is easily extensible, making the addition of new operations and datasets a task that is fast and easy to accomplish.
Exploring Direct Convolution Performance on the Gemmini Accelerator

Convolutional Neural Network (CNN) algorithms are becoming a recurrent solution to solve Computer Vision related problems. These networks employ convolutions as main building block, which greatly impact their performance since convolution is a costly operation. Due to its importance in CNN algorithms, this work evaluates convolution performance in the Gemmini accelerator and compare it to a conventional lightlyand heavily-loaded desktop CPU in terms of execution time and energy consumption. We show that Gemmini can achieve lower execution time and energy consumption when compared to a CPU even for small convolutions, and this performance gap grows with convolution size. Furthermore, we analyze the minimum Gemmini required frequency to match the same CPU execution time, and show that Gemmini can achieve the same runtime while working in much lower frequencies.
Pedagogical Model of Formation of Engineering Competence of Students of Basic General Education

The article presents a pedagogical model for the formation of engineering competence of students of basic General education. The developed model includes the goal, tasks, methodological approaches (system, competence-activity, personality-oriented), components, stages, principles (structure, complexity, openness of the system, consciousness and creative activity, professional orientation of training, polysubject interaction, creativity, trust and self-realization, individuality), pedagogical conditions of the organization and the expected result of the process. The work used mainly theoretical research methods, with the selection and analysis of current scientific data on the problem under study, synthesis, generalization of the information obtained and summing up the results of the study. The scientific novelty of the research consists in presenting a model of practice-oriented nature, which can be considered as a tool for achieving a high level of engineering competence of students of basic General education. The model reveals the pedagogical conditions of an effective process, determines the levels of formation of engineering competence of students. The proposed model serves as a theoretical and practical basis for the successful organization of students ' education in the study of engineering, extracurricular engineering activities, and project work in the field of science and technology.
Value-Centered Model Moral Development of the Person

The health of students and their physical fitness are among the most important and urgent problems. Health depends to a large extent on the way of life, covering the entire complex of life processes and the entire area of human interaction with the environment. When studying the functional state of the body, the most important are the indicators of the cardiovascular and respiratory systems, the assessment of the level of physical development, fitness, functional capabilities and deviations in health. Motor activity increases the adaptation of the human body to changing environmental conditions and improves all adaptive mechanisms. Decreased physical activity contributes to an increase in the number of disorders in the body, which entails the risk of mental and physical discomfort in students.
The dialogue between Didaktik and curriculum studies within mainland China

This chapter reviews in some depth the historical trajectories by which first didactics and then curriculum studies were introduced and localised in mainland China. Like their colleagues in general didactics, researchers in subject didactics (including chemistry didactics, physics didactics, biology didactics, mathematics didactics, and Chinese language didactics) in mainland China since the 1980s have to an extent misread and misunderstood subject didactics and curriculum studies. In 1997, a group of curriculum researchers networked and went on to establish the National Association of Curriculum Studies, also within the Chinese Society of Education, claiming independent identity of curriculum studies as a sub-discipline of educational sciences in mainland China. The development of the new discipline of Curriculum and Didactics in mainland China is of course rooted in reforms of teaching, learning, and schooling. Another aspect importantly requiring study is the need to build a Chinese culture of education, with the new and blended discipline of Curriculum and Didactics as an integral part.
Research on Rheumatoid Arthritis Detection Support System

Rheumatoid arthritis is a disease that causes inflammation in Joints due to an abnormality in the immune system, and as the symptoms progress, it causes deformity in the joints and interferes with daily life. This assessment of the progression of rheumatoid arthritis is graded from grade 0 to grade 3 and is made by the doctor by visual inspection of the ultrasound images of the affected area. However, the visual inspection results depend on the doctors and are time consuming. This study aims to develop an automated diagnostic system for rheumatoid arthritis using imaging diagnosis to help the medical community.
Applying Ateb–Gabor Filters to Biometric Imaging Problems

This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.
An Image Detection Method Based on Parameter Optimization of Support Vector Machine

With the progress and development of multimedia image processing technology, and the rapid growth of image data, how to efficiently extract the interesting and valuable information from the huge image data, and effectively filter out the redundant data, these have become an urgent problem in the field of image processing and computer vision. In recent years, as one of the important branches of computer vision, image detection can assist and improve a series of visual processing tasks. It has been widely used in many fields, such as scene classification, visual tracking, object redirection, semantic segmentation and so on. Intelligent algorithms have strong non-linear mapping capability, data processing capacity and generalization ability. Support vector machine (SVM) by using the structural risk minimization principle constructs the optimal classification hyper-plane in the attribute space to make the classifier get the global optimum and has the expected risk meet a certain upper bound at a certain probability in the entire sample space. This paper combines SVM and artificial fish swarm algorithm (AFSA) for parameter optimization, builds AFSA-SVM classification model to achieve the intelligent identification of image features, and provides reliable technological means to accelerate sensing technology. The experiment result proves that AFSA-SVM has better classification accuracy and indicates that the algorithm of this paper can effectively realize the intelligent identification of image features.
Yazılım Sürdürülebilirlik Tahmininde Güvenlik Açığı Yazılım Metriklerinin Rolü

Software maintainability is among the basic quality features of software engineering.Vulnerability prediction is crucial to protect software maintainability from attacks for cybersecurity.Hence, managing vulnerability in an accurate way is an important phase for the efficient prediction of software maintenance.The existing technologies have achieved many good results in vulnerability detection, but no significant results have been obtained on how effective vulnerability metrics for software maintainability prediction is.As far as we know, this paper is the first study that applies the Deep Learning-based Symbiotic Immune Network Model to develop a software maintainability prediction model using vulnerability software metrics.This study proposes a novel methodology capable of discovering software maintainability metrics in open-source software programs efficiently and accurately.The current study also tries to identify vulnerability metrics frequently utilized in software maintainability.In this paper, five commonly employed open-source projects subjected to attacks, such as Mozilla, Linux Kernel, Xen Hypervisor, glibc, and httpd, are used.In the scope of this research, mentioned five open-source software projects were used as datasets, and they were analyzed with their effect on software maintainability prediction.The analysis of the software metrics was performed, and the descriptive statistics of the software metrics were presented.The current research obtained results of software metrics that accurately predicting software maintenance.Furthermore, the experimental findings confirm the effectiveness of the obtained vulnerability metrics for predicting software maintainability.Our experimental results claim that the proposed Deep Learning-based Symbiotic Immune Network Model enables the prediction of software maintainability to be substantially more effective.