Reviewerly/exHarmony huggingface dataset:
PlagAL: Plagiarism detection system for Albanian texts

Nowadays the amount of information that is on the web is considerable. Students who prepare projects, seminars or diploma theses can copy others ideas, results or papers from from the internet. The information they copy and use as their own without referencing is plagiarism. To prevent this, several plagiarism detections programs have been developed in recent years, but most Kosovo Universities do not have their plagiarism system. In this paper, we proposed system for identifying cross-language plagiarism and text plagiarism in Albanian and English languages. This paper is expected to increase the quality and responsibility in universities and educational institutions by monitoring student work through this system. The system is web based and developed with Python and Php programming languages. In this project we have used the Rabin Karp algorithm to identify plagiarism and some heuristics are used to determine the location of the copied text and identifies the language of the uploaded document to the system. Also, we used multiprocessing to make the system work faster. Our system has been evaluated on University “Ukshin Hot” dataset. The experiments results show that our system can be used for cross-language plagiarism and text plagiarism detection.
Comparison of the histogram of oriented gradient, GLCM, and shape feature extraction methods for breast cancer classification using SVM

Breast cancer originates from the ducts or lobules of the breast and is the second leading cause of death after cervical cancer. Therefore, early breast cancer screening is required, one of which is mammography. Mammography images can be automatically identified using Computer-Aided Diagnosis by leveraging machine learning classifications. This study analyzes the Support Vector Machine (SVM) in classifying breast cancer. It compares the performance of three features extraction methods used in SVM, namely Histogram of Oriented Gradient (HOG), GLCM, and shape feature extraction. The dataset consists of 320 mammogram image data from MIAS containing 203 normal images and 117 abnormal images. Each extraction method used three kernels, namely Linear, Gaussian, and Polynomial. The shape feature extraction-SVM using Linear kernel shows the best performance with an accuracy of 98.44 %, sensitivity of 100 %, and specificity of 97.50 %.
Cybersecurity for E-Banking and E-Commerce in Pakistan

Digital banking allows banks to make their new products more readily available, as they can package them with their essential software that has implications for cybersecurity. Internet presence also allows banks to extend their geographical reach without overbearing costs. In Pakistan, most internet offerings currently used by banks are more based on information than transactions; therefore, the issues for cybersecurity have gained high interest. The offerings on the websites by banks generally seem like electronic brochures. As they mainly offer company events, product information, along with a list of services rendered. Pakistan has various barriers to e-commerce, such as relatively few internet users, inadequate infrastructure (i.e., frequent power failures, fewer phone lines), and lack of cybersecurity for online transactions being hacked. The Government of Pakistan is rigorously working on these areas to overcome respective problems and progress in this sphere. This research focuses on the challenges for e-banking and e-commerce in Pakistan and issues relating to cybersecurity.
An Improved Brain Storm Optimization Algorithm Based on Maximum Likelihood Estimation

Brain Storm Optimization (BSO) is a relatively new swarm intelligence algorithm. It employs clustering, creating, and selecting operators, all connected and significantly impacting the optimization performance. However, with the increase of the problem dimension and the offset of the optimal value, the performance of the basic BSO algorithm becomes worse. This paper designs the double grouping operator, which employs k-means clustering in the horizontal and differential grouping in the vertical. A new creating operator is designed based on maximum likelihood estimation for the mean and variance of the Gaussian distribution. The numerical experiments are carried out to amplify and highlight the performance of the proposed algorithm artificially. Experimental results show that the proposed algorithm achieves satisfactory results on shifted and rotated benchmark functions.
Deep Collaborative Learning for Randomly Wired Neural Networks

A deep collaborative learning approach is introduced in which a chain of randomly wired neural networks is trained simultaneously to improve the overall generalization and form a strong ensemble model. The proposed method takes advantage of functional-preserving transfer learning and knowledge distillation to produce an ensemble model. Knowledge distillation is an effective learning scheme for improving the performance of small neural networks by using the knowledge learned by teacher networks. Most of the previous methods learn from one or more teachers but not in a collaborative way. In this paper, we created a chain of randomly wired neural networks based on a random graph algorithm and collaboratively trained the models using functional-preserving transfer learning, so that the small network in the chain could learn from the largest one simultaneously. The training method applies knowledge distillation between randomly wired models, where each model is considered as a teacher to the next model in the chain. The decision of multiple chains of models can be combined to produce a robust ensemble model. The proposed method is evaluated on CIFAR-10, CIFAR-100, and TinyImageNet. The experimental results show that the collaborative training significantly improved the generalization of each model, which allowed for obtaining a small model that can mimic the performance of a large model and produce a more robust ensemble approach.
Conceptual and cognitive field of prevention of deviant behavior in departmental universities

The actual problem of preventing departmental university graduates’ deviant behavior, whose professional activity is particularly important for society and the state, is analyzed in the aspect of cadets’ valuable and meaningful dominants formation in the process of moral education, aimed at their professional identity development and professional marginalisation prevention.&#x0D; The research problem relevance is determined by need to resolve conflicts continued in modern conditions of anomie, especially for professional communities of servicemen and law enforcement agencies. In addition, the importance of the research problem is explained by the fact that many aspects are not elaborated in national psychological and pedagogical research.&#x0D; In order to prevent deviant behavior in the educational process of departmental institutions, the author proposes the pedagogical technology of formation of valuable and meaningful dominants, which is implemented in cadets’ moral education and ensures their professional identity development and prevention of its antipode – professional marginalization.&#x0D; The article presents refined concepts of “professional identity”, “professional marginalization”. Moral education characteristic, implemented in line with cadets’ personal development in departmental institutions’ educational process is given. Pedagogical technology of education content and structure, methodological approaches to its development are described.&#x0D; On the basis of conducted research, directions for further development of studied problem are proposed: holistic concept substantiation of cadets’ moral education in line with the formation of valuable and meaningful dominants of departmental institutions graduates’ professional activity; identification and testing of pedagogical conditions that ensure prevention of deviant behavior; methodological support development for pedagogical technology implementation, taking into account the latest pedagogical science and practice achievements; selection of methods and tools for diagnosing the formation of valuable and meaningful dominants of cadets and assessing the dynamics of their professional identity formation at various stages of pedagogical technology of education implementation.
Extraction of 3D Pose in Video for Building Virtual Learning Avatars

From an image of a person, we can easily guess the 3D coordinates of the body parts. This is because we have acquired a 3D mental model from observing humans and interacting with them. This capacity easily achievable for humans is not systematic when it comes to computers. In this paper, we describe an approach that aims at estimating poses from video with the objective of reproducing the observed movements by a virtual avatar. We propose the fragmentation of submitted videos into series of RGB frames to process individually. We aim two main objectives in our work. First, we achieve the extraction of initial 2D joints coordinates using a method that predicts joint locations by part affinities (PAFs). Then we infer 3D joints coordinates based on a human full 3D mesh reconstruction approach supplemented by the previously estimated 2D coordinates. Secondly, we explore the reconstruction of a virtual avatar using the extracted 3D coordinates with the prospect to transfer human movements towards the animated avatar. This would allow to extract the behavioral dynamics of a human, allowing to detect some health problems, for instance in Alzheimer. Our approach consists of multiple subsequent stages that show better results in the estimation and extraction than similar solution due to this supplement of 2D coordinates. With the final extracted coordinates, we apply a transfer of the positions (per frame) to the skeleton of a virtual avatar in order to reproduce the movements extracted from the video.
A Literature Review of Virtual Reality Interpersonal Training for Salespeople

This literature review analyses research in virtual reality (VR) interpersonal training and applies the findings to the niche area of training salespeople. Good interpersonal skills are the primary skills required in the sales industry. VR can train interpersonal skills by immersing the user in virtual role-playing environments and simulating social interactions, which build interpersonal skills. During virtual role-play, trainees interact with virtual humans who are either agents or avatars. Agents are AI computer systems, while avatars are virtual humans possessed and controlled by the trainer. The research illustrated that VR could successfully train the interpersonal skills needed for job interviews and public speaking. This literature review also highlighted the lack of research in using VR for sales training. No studies were found that described the development of a VR system dedicated to training salespeople. The implications of these findings are discussed below. Based on this research, a VR sales training system is proposed to address this gap in the field.
A novel spam detection technique for detecting and classifying malicious profiles in online social networks

Online social networks (OSNs) are utilized by millions of people from the entire world to communicate with others through Facebook and Twitter. The removal of fake accounts will increase the efficiency of the protection in OSNs. The construction of the OSN model has the nodes and the links to identify the fake profiles on Twitter. This paper proposes a novel technique to detect spam profiles and the proposed classifier is to classify the profile images from the dataset. The malicious profile detection technique is used to identify the fake profiles with the concept of a Twitter crawler that implements the extraction of data from the profile. The feature set analysis has been implemented with the feature related analysis. The user behavior detection utilizes the adjacent matrix to measure the similarity values within the friend’s profiles. The multi-variant Support Vector Machine classifier is developed for efficient classification with the kernel function. The proposed technique is compared with the well-known techniques of ECRModel, ISMA and DeepLink that the detection rate is 2.5% higher than the related techniques, the computation time is 220 s lesser than the related techniques and the proposed technique has 3.1% higher accuracy.
Transformation of Users’ Technical Knowledge into Design-With an Example from Bra Assistive Device

When women have limited upper limb function, it is difficult for women to wear bras independently, and they need the help of assistive devices, but the number of related products is currently small. Some people discovered the “life tips” of wearing a bra with one hand and shared them on the Internet. This research refers to “life tips” as “users’ technical knowledge”. Its use is limited. However, there is currently a lack of design theory research to expand its versatility. Therefore, this research takes user technical knowledge as the main research object, analyzes and identifies user technical knowledge through typical examples, and establishes the process of transforming user technical knowledge into designer technical knowledge. Construct a design method from “usable” to “easy to use”, and verify it with the design of bra accessories. This method can help designers systematically transform users’ technical knowledge and improve the convenience of users’ lives.
Alexa in Phishingland: Empirical Assessment of Susceptibility to Phishing Pretexting in Voice Assistant Environments

This paper investigates what cues people use to spot a phishing email when the email is spoken back to them by the Alexa voice assistant, instead of read on a screen. We configured Alexa to read there emails to a sample of 52 participants and ask for their phishing evaluations. We also asked a control group of another 52 participants to evaluate these emails on a regular screen to compare the plausibility of phishing pretexting in voice assistant environments. The results suggest that Alexa can be used for pretexting users that lack phishing awareness to receive and act upon a relatively urgent email from an authoritative sender. Inspecting the sender (authority cue”) and relying on their personal experiences helped participants with higher phishing awareness to use Alexa towards a preliminary email screening to flag an email as potentially “phishing.”
Artificial Intelligence Against Climate Change

The industrial, transportation, and residential sectors draw the most energy in the United States. With most energy created by burning fossil fuels, a highly inefficient method of energy creation, global greenhouse gas levels are rising, raising the temperature of the earth, causing natural processes to become unbalanced. The health of the earth is declining. The rise of technology and persisting growth of computing devices known as the Internet of Things (IoT) and increasing automation of systems through Artificial Intelligence (AI) and Machine Learning (ML) is a factor of energy expenditure as more humans desire devices and more systems are built. The ethical implications of utilizing new technology should be evaluated before creating more. This paper explores modern computing systems in the sectors that draw the most energy, and, more specifically, the role AI and IoT play in them. Each sector may become more energy efficient, productive, and safer by introducing edge computing through IoT devices and coupling it with AI computing abilities that already automate most processes. Multiple studies show energy consumption and costs are lowered when edge computing is paired with the IoT and AI. There is less human involvement, more regularity in execution and performance, and more widespread use because of the accessibility. This creates safer, cheaper, energy-efficient systems that utilize existing technology. The ethical implications of these systems are much more positive than what already exists. Coupling the power of AI with the IoT will reduce energy expenditure in modern systems and create a more sustainable world.
Designing Usability and UX with UXUG-AP: An Observational Study and an Interview with Experts

Nowadays, there are several technologies (methods, techniques, among others) that help agile development teams to improve the Usability and User eXperience (UX) in their projects. Usability and UX are relevant aspects of software quality. In the literature, we find few technologies that help to improve the Usability and UX jointly at the beginning of agile process, mainly during the design phase, where to perform changes is easier and cheaper. Therefore, in our paper, we present the technique User Experience and Usability Guidelines for Agile Project (UXUG-AP), that aims to support the Usability and UX design. UXUG-AP is composed by categories, subcategories, and specific guidelines to help the prototyping process. We also present an observational study with agile professionals. The study was done to evaluate the ease of use, perceived usefulness and the intention to future use from UXUG-AP. The results show that is necessary requirements well defined in order to have a better use of the technique. In additional, we present an interview with experts in agile design which provide their opinions about UXUG-AP. The interview results show that some designers are used to evaluating their projects after development, instead of avoiding usability and UX issues at the beginning of process.
Innovation System of Film Art Creation Based on Virtual Reality Technology

In order to overcome the difficulties in the creation of virtual reality film art, this paper proposes a novel innovation system of film art creation based on virtual reality technology. The innovation system combines the advantages of virtual reality technology, improves the viewing experience, and meets the diverse needs of the audience. On this basis, the innovation system also expounds the innovation path of virtual reality film art creation from four aspects of preproduction, form of expression, aesthetic concept and viewing effect. The results show that the innovation system can break through the limitations of traditional film art creation and improve the innovation and immersion of virtual reality film art creation.
Chinese Short Text Classification Based on Dependency Syntax Information

Short text has the characteristics of sparse features and discrete semantics. In order to extract the features of short text better, we propose a short text classification algorithm based on dependency syntax information in this paper. In terms of text representation, we train word vector based on sentences dependency triples. By concatenating the dependency word vector and original word vector, text can be represented at both semantic and syntactic levels. In terms of classification model, we use the dependency syntax information of the short text to guide the state update process of the recurrent neural network. In addition, we run experiments based on Chinese news-title dataset. Experiment results show that the proposed algorithm improves the performance of short text classification remarkably.
Covert Communication via Modulating Soft Label of Neural Network

In this paper, we present a novel method to covert communication based on a host deep neural network (DNN) itself, which is totally different from many traditional works that embed secret data into a digital image since: 1) there has no direct image transmission between the data hider and the data receiver, 2) there has no modification to the image content, and 3) the presence of the covert communication can be concealed by the large number of ordinary queries. The main idea is embedding secret data into the soft output of the host DNN by modifying the soft label according to the secret data and further fine-tuning the host DNN with the modified soft label together with its input. When the fine-tuned DNN is put into use, the data receiver can fully retrieve the secret data from the soft output of the DNN by uploading the corresponding image to the DNN. Experimental results have shown that, the secret data can be successfully embedded and extracted. And, it does not impair the performance of the original task, which has demonstrated the superiority and applicability. Moreover, the Kullback-Leibler divergence between the soft output produced by the embedded DNN and that produced by the original DNN is quite low, which can ensure the security.
NiaAML2: An Improved AutoML Using Nature-Inspired Algorithms

Using machine learning methods in the real-world is far from being easy, especially because of the number of methods on the one hand, and setting the optimal values of their parameters on the other. Therefore, a lot of so-called AutoML methods have emerged nowadays that also enable automatic construction of classification pipelines to users, who are not experts in this domain. In this study, the NiaAML2 method is proposed that is capable of constructing the classification pipelines using nature-inspired algorithms in two phases: pipeline construction, and hyper-parameter optimization. This method improves the original NiaAML capable of this construction in one phase. The algorithm was applied to four UCI ML datasets, while the obtained results encouraged us to continue with the research.
Impact of energy efficiency on the morphology and behaviour of evolved robots

Most evolutionary robotics studies focus on evolving some targeted behavior without considering energy usage. In this paper, we extend our simulator with a battery model to take energy consumption into account in a system where robot morphologies and controllers evolve simultaneously. The results show that including the energy consumption in the fitness in a multi-objective fashion reduces the average size of robot bodies while reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set.
FutureMe: Negotiating Learning Goals with your Future Learning-Self Avatar

This paper discusses the challenges towards designing an educational avatar which visualizes the future learning-self of the student in order to promote their self-regulated learning skills. More specifically, the avatar follows a negotiation-based interaction with the student during the goal-setting process of self-regulated learning. The goal of the avatar is to help the student get insights of their possible future leaning-selves, given a set of short-term goals regarding their study materials and homework. Our approach utilizes a recurrent neural network as the underlying prediction model for the future learning outcomes, in the context of language learning.
Analysis of Encryption Algorithm for Data Security in Cloud Computing

Cloud computing offers various types of resources to its users. In recent years with the development of Cloud Computing, there has been an increase in the use of storage service from the cloud service providing organizations. However, there are still active issues regarding security, privacy, interoperability and reliability, which needs to be solved fast. Among these issues the issue regarding the security of cloud storage and how the same can be provided easily. This paper has helped in the study and proposal of a simple and secure as well as privacy-preserving design and architecture to cloud data sharing. The architecture has been based on the process of encryption and decryption algorithms for the protection of data stored in the cloud servers from unauthorized and illegal access.