Reviewerly/exHarmony huggingface dataset:
Phishing Detection with Machine Learning

Abstract: The goal of our project is to implement a machine learning solution to the problem of detect- ing phishing and malicious web links. The end result of our project will be a software product which uses a machine learning algorithm to detect malicious URLs. Phishing is the technique of extracting user credentials and sensitive data from users by masquerading as a genuine website. In phishing, the user is provided with a mirror website which is identical to the legitimate one but with malicious code to extract and send user credentials to phishers. Phishing attacks can lead to huge financial losses for customers of banking and financial services. The traditional approach to phishing detection has been to either to use a blacklist of known phishing links or heuristically evaluate the attributes in a suspected phishing page to detect the presence of malicious codes. The heuristic function relies on trial and error to define the threshold, which is used to classify malicious links from benign ones. The drawback to this approach is poor accuracy and low adapt- ability to new phishing links. We plan to use machine learning to overcome these drawbacks by implementing some classification algorithms and comparing the performance of these algorithms on our dataset. We will test algorithms such as Logistic Regression, SVM, Decision Trees and Neural Networks on a dataset of phishing links from UCI Machine Learning repository and pick the best model to develop a browser plugin, which can be published as a browser extension.
Principles of building and implementation of the system manager of postponed programs launch

The article examines how to plan the launch of various programs on a personal computer. The main problems with the work of large teams among IT specialists, and the general difficulties in delaying the launch of the necessary programs are considered. The analysis shows that now more and more IT companies are moving to remote mode, which is accompanied by irregular schedules and work of the development team in different time zones, so the development and maintenance of software for scheduling software tasks is appropriate and relevant. The article pays great attention to the factors that arise, both at a more general level in the world and within the IT companies themselves. The purpose of the article is to highlight the main aspects of the study and the stages of creating a special software solution that provides a solution to the problem of coordinated work of the team of program developers. In particular, it provides the ability to set the list of programs to run, choose your own conditions for running programs, display the status of tasks in real time. In addition, run programs in parallel in separate timelines and keep a complete report of completed tasks.
Principles of developing a web application for monitoring employee skills

Summarizing the definition of the concept of "skills" from the reviewed literary sources, we determine that: skills are a combination of knowledge and skills that a person has acquired during his life. To improve skills, you need to choose certain tools (trainings, seminars, webinars, online courses, refresher courses, reading special literature, etc.). Personal qualities and attitudes of young people are important in choosing tools and achieving results. As part of this study, the principles of building a web application for monitoring the skills of employees were designed and developed. The web resource has a convenient user interface, the ability to view an employee’s skill map, leave comments on a specific employee skill, allows users to create and sign up for existing training courses, generate certificates confirming mastery of a skill, and view statistics. Unlike other analogs of programs for monitoring skills, this system provides for the interaction of three types of users: manager, mentor, and employee. The manager’s options include: creating a technology ownership template, personnel management, skills management, certificate management, notification management; Mentor options include: skill management, training management, notification management; Employee options include the following: skill management, training management, notification management, certificate management, view statistics, view recommendations.
A Novel Hexagonal Psuedo framework for Edge Detection Operators on Hexagonal Framework

Edge detection using a gradient-based detector is a gold-standard method for identifying and analyzing different edge points in an image. A hexagonal grid structure is a powerful architecture dominant for intelligent human-computer vision. This structure provides the best angle resolution, good packing density, high sampling efficiency, equidistant pixels, and consistent connectivity. Edge detection application on hexagonal framework provides more accurate and efficient computations. All the real-time hardware devices available capture and display images in rectangular-shaped pixels. So, an alternative approach to mimic hexagonal pixels using software approaches is modeled in this paper. In this research work, an innovative method to create a pseudo hexagonal lattice has been simulated and the performance is compared with various edge detection operators on the hexagonal framework by comparing the quantitative and qualitative metrics of the grayscale image in both square and hexagonal lattice. The quantitative performance of the edge detection on the hexagonal framework is compared based on the experimental facts. The pseudo-hexagonal lattice structure assures to be aligned toward the human vision.
Cooperative Search Optimization of an Unknown Dynamic Target Based on the Modified TPM

Aiming to address the problem of unknown dynamic target trajectory prediction and search path optimization in unmanned aerial vehicle (UAV) swarm path planning, this paper proposes a target search algorithm based on a modified target probability map (TPM). First, using the TPM, the proposed algorithm generates a high-probability distribution region of a target with directionality to fit the target trajectory and realizes the trajectory prediction of an unknown dynamic target. Then, the distributed ant colony (ACO) algorithm and the artificial potential field (APF) algorithm are combined to generate and optimize the UAV swarm search result and return path with the goal of maximizing task execution efficiency. Finally, the Monte Carlo simulation method is used to analyze the effectiveness of the proposed algorithm, and the results are evaluated from five aspects, including the number of targets captured. The simulation results show that under the condition of an unknown dynamic target trajectory, the average target captured rate and average unknown region search rate of the MTPM method were higher than that of the traditional TPM method, and the performance was improved by 14.6% and 10.7%, respectively.
A Progressive Deep Neural Network Training Method for Image Classification with Noisy Labels

Deep neural networks (DNNs) require large amounts of labeled data for model training. However, label noise is a common problem in datasets due to the difficulty of classification and high cost of labeling processes. Introducing the concepts of curriculum learning and progressive learning, this paper presents a novel solution that is able to handle massive noisy labels and improve model generalization ability. It proposes a new network model training strategy that considers mislabeled samples directly in the network training process. The new learning curriculum is designed to measures the complexity of the data with their distribution density in a feature space. The sample data in each category are then divided into easy-to-classify (clean samples), relatively easy-to-classify, and hard-to-classify (noisy samples) subsets according to the smallest intra-class local density with each cluster. On this basis, DNNs are trained progressively in three stages, from easy to hard, i.e., from clean to noisy samples. The experimental results demonstrate that the accuracy of image classification can be improved through data augmentation, and the classification accuracy of the proposed method is clearly higher than that of standard Inception_v2 for the NEU dataset after data augmentation, when the proportion of noisy labels in the training set does not exceed 60%. With 50% noisy labels in the training set, the classification accuracy of the proposed method outperformed recent state-of-the-art label noise learning methods, CleanNet and MentorNet. The proposed method also performed well in practical applications, where the number of noisy labels was uncertain and unevenly distributed. In this case, the proposed method not only can alleviate the adverse effects of noisy labels, but it can also improve the generalization ability of standard deep networks and their overall capability.
Blockchain in Service of NGOs and Charities

Since its creation, charity and NGOs have been growing exponentially. Between 2019 and 2020, the sector is estimated to have an annual growth of 5.1% from $448 billion to $471 billion. Due to its enormous size, the world's NGO and charity sector has been facing various issues. These issues include fraud, lack of transparency, and high costs. Even more so, due to COVID-19, many forms of donations went utterly digital, resulting in high scams and transaction fees. After in-depth research on blockchain technology, the hypothesis is that this technology can necessarily eliminate many of these concerns. In a world of data and artificial intelligence, blockchain technology has become mature enough for many practical applications outside the world of cryptocurrencies. The characteristics of the blockchain make it a perfect fit for NGOs and Charities applications.
The Shift to Online Teaching of Technical Subjects during the Pandemic

Abstract The aim of the article is to compare the results of the study before and during the pandemic. It is also a reflection on what we experienced and how we undertook pedagogical work with students in critical teaching conditions. The coronavirus pandemic brought about an unexpected and rapid change in all aspects of education. It seems like neither the teachers nor the students were ready. The article describes how the transition to online teaching took place overnight. It describes how technical subjects were taught and examined in a new online environment. This is about how the students evaluated that form of education in the subjects of ‘Heat Treatment and Metal Surface Treatment’ and ‘Material Science I’. The results of the study confirmed that even in the conditions of complete separation in the social and work contact, it is possible to study by conscientious preparation and self-access.
Penerapan Teorema Bayes pada Sistem Pakar Pendeteksi Penyakit Domba

Sheep are one of the livestock that can provide benefits such as meat, milk, and skin which are used as a source of income for the community, especially in the Asahan regency area. However, still, the farmer's knowledge in terms of care and knowledge of sheep diseases can result in the sheep being able to die. The purpose of this study was to create an expert system to diagnose sheep disease using the theory of Bayes. The waterfall is used as a model in this study with stages, analysis, design, implementation, and testing. analysis was carried out to find the required data using observations and interviews. The design of this system consists of Unified Modeling Language (use case ), diagram classes, flowcharts, and interfaces. Testing the system uses a black box which aims to find out the functionality of the system that has been implemented, and data analysis using the theory of Bayes. Our findings resulted in bayes having an expert system for the diagnosis of diseases in sheep. The results of Bayes analysis with the theory of Bayes showed that the disease experienced in sheep is deworming with a probability level of 60.71%. The results of the black box test show that the functionality of this system is already running well
Algoritmich societies and processes of subjectivation

The aim of this article is to indagate the profound changes of the human environments, which arising from the “Digital revolution”. In the first part, the author compares two “ontological” perspectives: respectively, the “Infosphere” and the “Docusphere”. Afterward, taking into account the contemporary processes of subjectivation, he distinguishes between two emerging patterns of political power: “Surveillance capitalism” and “Societies of control”. In the light of the distinction between surveillance and control, the author discusses the elements of each pattern which seem to be useful for understanding the political life in digital societies.
The Strangest Music in the World: Self-Supervised Creativity and Nostalgia for the Future in Robotic Rock Band “The Three Sirens”

The emergence of deep learning since the mid-2010s and its successful application to creative activity challenges long-held anthropocentric conceptions of art and music, bringing back ideas about machine creativity that had been previously explored in the 20th century. Particularly, in the 1990s, some artists, composers, and musicians started working with machine learning and other adaptive computation systems. The work of Nicolas Baginsky is emblematic of that era. In 1992, he created the robot guitar Aglaopheme, which became the first performer of a self-learning robotic band developed throughout the 1990s, soon joined by the robot bass Peisinoe, the robot drum Thelxiepeia, and eventually other artificial agents, forming the autonomous robotic band The Three Sirens. In this review, we describe the technological, musical, and imaginative aspects of Baginsky’s robotic instruments. The unreal and behind-the-scenes story of the mythological three sirens is important in understanding how the robots are designed and what they (are) intend(ed) to do. In the context of artificial intelligence, the concept of seeking a surprising musical effect will push us to reimagine such concepts as musical creativity and improvisation within the algorithmic composition and provide opportunities to discuss nostalgia for the future music and live performance.
Modeling opponent learning in multiagent repeated games

Abstract Multiagent reinforcement learning (MARL) has been used extensively in the game environment. One of the main challenges in MARL is that the environment of the agent system is dynamic, and the other agents are also updating their strategies. Therefore, modeling the opponents’ learning process and adopting specific strategies to shape learning is an effective way to obtain better training results. Previous studies such as DRON, LOLA and SOS approximated the opponent’s learning process and gave effective applications. However, these studies modeled only transient changes in opponent strategies and lacked stability in the improvement of equilibrium efficiency. In this article, we design the MOL (modeling opponent learning) method based on the Stackelberg game. We use best response theory to approximate the opponents’ preferences for different actions and explore stable equilibrium with higher rewards. We find that MOL achieves better results in several games with classical structures (the Prisoner’s Dilemma, Stackelberg Leader game and Stag Hunt with 3 players), and in randomly generated bimatrix games. MOL performs well in competitive games played against different opponents and converges to stable points that score above the Nash equilibrium in repeated game environments. The results may provide a reference for the definition of equilibrium in multiagent reinforcement learning systems, and contribute to the design of learning objectives in MARL to avoid local disadvantageous equilibrium and improve general efficiency.
Security and Interpretability in Automotive Systems

The lack of any sender authentication mechanism in place makes CAN (Controller Area Network) vulnerable to security threats. For instance, an attacker can impersonate an ECU (Electronic Control Unit) on the bus and send spoofed messages unobtrusively with the identifier of the impersonated ECU. To address the insecure nature of the system, this thesis demonstrates a sender authentication technique that uses power consumption measurements of the electronic control units (ECUs) and a classification model to determine the transmitting states of the ECUs. The method's evaluation in real-world settings shows that the technique applies in a broad range of operating conditions and achieves good accuracy. A key challenge of machine learning-based security controls is the potential of false positives. A false-positive alert may induce panic in operators, lead to incorrect reactions, and in the long run cause alarm fatigue. For reliable decision-making in such a circumstance, knowing the cause for unusual model behavior is essential. But, the black-box nature of these models makes them uninterpretable. Therefore, another contribution of this thesis explores explanation techniques for inputs of type image and time series that (1) assign weights to individual inputs based on their sensitivity toward the target class, (2) and quantify the variations in the explanation by reconstructing the sensitive regions of the inputs using a generative model. In summary, this thesis (https://uwspace.uwaterloo.ca/handle/10012/18134) presents methods for addressing the security and interpretability in automotive systems, which can also be applied in other settings where safe, transparent, and reliable decision-making is crucial.
Implementation of interdisciplinary connections of informatics and mathematics in the study of the theme "Processing of numerical information in spreadsheets"

The article describes the possibilities of implementing interdisciplinary connections of informatics and mathematics at school when studying the theme "Processing numerical information in spreadsheets". It is substantiated that interdisciplinary connections in academic disciplines are established through interdisciplinary concepts. The key concepts in the educational theme "Processing numerical information in spreadsheets" of the school informatics course are highlighted. Based on these concepts, examples of solving problems from a school mathematics course in spreadsheets are presented: building a table of squares of two-digit numbers, plotting of functions, solving a system of equations graphically. The examples demonstrate the possibility of implementing interdisciplinary connections of informatics and mathematics in the context of studying the processing of numerical information using spreadsheets: on the one hand, the use of spreadsheets allows you to automate routine tabular calculations in mathematics, on the other hand, solving such problems allows students to better understand the organization of calculations in spreadsheets.
On-Line and Off-Line Teaching Research Based on BP Neural Network

In recent years, due to the influence of the new Crown Epidemic Situation Prevention and control, online teaching has become the mainstream teaching method during the epidemic situation in China.Under this background, a set of Scientific on-line teaching program is designed for the Middle Vocational Department of Guangdong Innovation and Technology Vocational College.Through the establishment of objective and subjective evaluation index system, using BP neural network as a teaching tool, the overall effect of online and offline teaching mode is compared.The results show that offline teaching is helpful to enhance students' practical ability and comprehensive analysis ability, and online teaching is helpful to improve their autonomous learning ability and classroom efficiency.
The Role of Block Particles Swarm Optimization to Enhance The PID-WFR Algorithm

In the conventional Proportional Integral Derivation (PID) controller, the parameters are often adjusted according to the formulas and actual application. However, this empirical method will bring two disadvantages. First, testing the program takes much time and usually needs help to reach the optimal solution. Second, the PID parameters will not adapt to the new environment when the situation changes. This paper proposed a method by employing a Block Particles Swarm Optimization (BPSO) to enhance the conventional Proportional Integral Derivation (PID) algorithm to overcome the mentioned disadvantages. The genetic algorithm (GA) first optimized the PID parameters. However, its optimization time is relatively long. Then, a Block Particle Swarm Optimization (BPSO) algorithm is designed to solve the problem of long optimization time. This method was then applied to the wall-following robot problem by realistically simulating it to confirm the performance. After Compared with conventional methods, the proposed method shows a relatively stable solution.
Immersive animation scene design in animation language under virtual reality

Abstract The study aims to explore a new form of immersive animation scene design and improve the application effect of virtual reality (VR) in immersive animation scene design. First, the research status of VR and the related concepts of animation scene design are analyzed, and the basic characteristics of visual effects in immersive VR animation scenes are summarized. Second, three elements to create immersion in a VR animation scene are color, light and shadow, and digital information technology. Finally, the application of VR technology to an animation scene design is analyzed, the problem of vertigo caused by VR animation scenes is deeply discussed, the vertigo test process in an immersive VR scene is proposed, and an experiment is designed to verify it. The results show that in the design of an immersive VR scene, the relative moving speed of the forward straight line should be maintained less than 6.5 times of the audience’s perceived speed, and the optimal relative moving speed of the scene should be maintained below 5 times of the audience’s perceived speed when they moving backward. In the design of a rotating animation scene, the long-term high-speed rotation should be avoided. If there must be a rotating scene, the rotation speed should be as low as 50°/s. The research provides some guidance for the application of VR to immersive animation scene design. This study discusses immersive animation scene design from dizziness prospective. It is novel. This study introduces relative motion perception illusion and use displacement velocity and relative displacement to analyze VR vertigo. Len motion is divided into forward and reverse linear, horizontal and vertical rotation and camera center rotation to study vertigo source. The dizziness evaluation model is designed and combined with the virtual indoor scene developed to achieve a visual immersion experience.
Source Publication Creation Using Internet Applications to Enrich Archives

Source publications are an important part of archive publication as mandated by the Act Archives Number 43 2009. The principle of good record management must be upheld in the manufacture of source publication so that the results are in accordance with applicable regulations. The steps that must be taken include determining the theme, tracing the archives in various archival institutions, and designing them into source publication books. Internet applications for creating books make source publication creation easier and more efficient. The results of this study indicate that the manufacture of source publications in the form of books is relatively easy and can be achieved using resources at archival institutions. Challenges include adaptation to internet applications and the ability to browse relevant archives.