Reviewerly/exHarmony huggingface dataset:
Understanding plasticity in neural networks

Plasticity, the ability of a neural network to quickly change its predictions in response to new information, is essential for the adaptability and robustness of deep reinforcement learning systems. Deep neural networks are known to lose plasticity over the course of training even in relatively simple learning problems, but the mechanisms driving this phenomenon are still poorly understood. This paper conducts a systematic empirical analysis into plasticity loss, with the goal of understanding the phenomenon mechanistically in order to guide the future development of targeted solutions. We find that loss of plasticity is deeply connected to changes in the curvature of the loss landscape, but that it often occurs in the absence of saturated units. Based on this insight, we identify a number of parameterization and optimization design choices which enable networks to better preserve plasticity over the course of training. We validate the utility of these findings on larger-scale RL benchmarks in the Arcade Learning Environment.
Detection of Cyber-Bullying Through Sentimental Analysis

social media is being notably used these days. This has reflected in a sort of coercion known as cyberbullying. Bullies use vivid community spots to assault victims with obnoxious Feedback and posts. This has been so ruinous that numerous youngsters suffer despair, commit self-murder, lose their tone of confidence, and plenty less. With obscurity and a deficit of Supervision this form of bullying has advanced exponentially. It is also veritably delicate and tough to show similar times. This leads us to discover a way to help mortal beings out and shield them from similar vulnerable assaults. Machine Learning has vivid algorithms that help us in detecting cyber-bullying with many Algorithms outperforming the others there through abecedarian us to the First- class set of regulations.
Multi-Source Soft Pseudo-Label Learning with Domain Similarity-based Weighting for Semantic Segmentation

This paper describes a method of domain adaptive training for semantic segmentation using multiple source datasets that are not necessarily relevant to the target dataset. We propose a soft pseudo-label generation method by integrating predicted object probabilities from multiple source models. The prediction of each source model is weighted based on the estimated domain similarity between the source and the target datasets to emphasize contribution of a model trained on a source that is more similar to the target and generate reasonable pseudo-labels. We also propose a training method using the soft pseudo-labels considering their entropy to fully exploit information from the source datasets while suppressing the influence of possibly misclassified pixels. The experiments show comparative or better performance than our previous work and another existing multi-source domain adaptation method, and applicability to a variety of target environments.
Retrieval for Extremely Long Queries and Documents with RPRS: a Highly Efficient and Effective Transformer-based Re-Ranker

Retrieval with extremely long queries and documents is a well-known and challenging task in information retrieval and is commonly known as Query-by-Document (QBD) retrieval. Specifically designed Transformer models that can handle long input sequences have not shown high effectiveness in QBD tasks in previous work. We propose a Re-Ranker based on the novel Proportional Relevance Score (RPRS) to compute the relevance score between a query and the top-k candidate documents. Our extensive evaluation shows RPRS obtains significantly better results than the state-of-the-art models on five different datasets. Furthermore, RPRS is highly efficient since all documents can be pre-processed, embedded, and indexed before query time which gives our re-ranker the advantage of having a complexity of O(N) where N is the total number of sentences in the query and candidate documents. Furthermore, our method solves the problem of the low-resource training in QBD retrieval tasks as it does not need large amounts of training data, and has only three parameters with a limited range that can be optimized with a grid search even if a small amount of labeled data is available. Our detailed analysis shows that RPRS benefits from covering the full length of candidate documents and queries.
Lightweight Audio-Based Human Activity Classification Using Transfer Learning

This paper employs the acoustic modality to address the human activity recognition (HAR) problem.The cornerstone of the proposed solution is the YAMNet deep neural network, the embeddings of which comprise the input to a fully-connected linear layer trained for HAR.Importantly, the dataset is publicly available and includes the following human activities: preparing coffee, frying egg, no activity, showering, using microwave, washing dishes, washing hands, and washing teeth.The specific set of activities is representative of a standard home environment facilitating a wide range of applications.The performance offered by the proposed transfer learning-based framework surpasses the state of the art, while being able to be executed on mobile devices, such as smartphones, tablets, etc.In fact, the obtained model has been exported and thoroughly tested for real-time HAR on a smartphone device with the input being the audio captured from its microphone.
How far Generated Data Can Impact Neural Networks Performance?

The success of deep learning models depends on the size and quality of the dataset to solve certain tasks. Here, we explore how far generated data can aid real data in improving the performance of Neural Networks. In this work, we consider facial expression recognition since it requires challenging local data generation at the level of local regions such as mouth, eyebrows, etc, rather than simple augmentation. Generative Adversarial Networks (GANs) provide an alternative method for generating such local deformations but they need further validation. To answer our question, we consider noncomplex Convolutional Neural Networks (CNNs) based classifiers for recognizing Ekman emotions. For the data generation process, we consider generating facial expressions (FEs) by relying on two GANs. The first generates a random identity while the second imposes facial deformations on top of it. We consider training the CNN classifier using FEs from: real-faces, GANs-generated, and finally using a combination of real and GAN-generated faces. We determine an upper bound regarding the data generation quantity to be mixed with the real one which contributes the most to enhancing FER accuracy. In our experiments, we find out that 5-times more synthetic data to the real FEs dataset increases accuracy by 16%.
Clipaha: A Scheme to Perform Password Stretching on the Client

Password security relies heavily on the choice of password by the user but also on the one-way hash functions used to protect stored passwords.To compensate for the increased computing power of attackers, modern password hash functions like Argon2, have been made more complex in terms of computational power and memory requirements.Nowadays, the computation of such hash functions is performed usually by the server (or authenticator) instead of the client.Therefore, constrained Internet of Things devices cannot use such functions when authenticating users.Additionally, the load of computing such functions may expose servers to denial of service attacks.In this work, we discuss client-side hashing as an alternative.We propose Clipaha, a client-side hashing scheme that allows using high-security password hashing even on highly constrained server devices.Clipaha is robust to a broader range of attacks compared to previous work and covers important and complex usage scenarios.Our evaluation discusses critical aspects involved in client-side hashing.We also provide an implementation of Clipaha in the form of a web library 1 and benchmark the library on different systems to understand its mixed JavaScript and WebAssembly approach's limitations.Benchmarks show that our library is 50% faster than similar libraries and can run on some devices where previous work fails.
Multi-Phase Relaxation Labeling for Square Jigsaw Puzzle Solving

We present a novel method for solving square jigsaw puzzles based on global optimization. The method is fully automatic, assumes no prior information, and can handle puzzles with known or unknown piece orientation. At the core of the optimization process is nonlinear relaxation labeling, a well-founded approach for deducing global solutions from local constraints, but unlike the classical scheme here we propose a multi-phase approach that guarantees convergence to feasible puzzle solutions. Next to the algorithmic novelty, we also present a new compatibility function for the quantification of the affinity between adjacent puzzle pieces. Competitive results and the advantage of the multi-phase approach are demonstrated on standard datasets.
Evaluation of a Tool to Increase Cybersecurity Awareness Among Non-experts (SME Employees)

Humans are the weak link in cybersecurity, hence, this paper considers the human factor in cybersecurity and how the customer journey approach can be used to increase cybersecurity awareness.The Customer Journey Modelling Language (CJML) is used to document and visualise a service process.We expand the CJML formalism to encompass cybersecurity and develop an easy-to-use web application as a supporting tool for training and awareness.We present the results from the usability test with ten persons in the target group and report on usability and feasibility.All participants managed to finish the test, and most participants indicated that the tool was easy to use.By using the tool, non-expert users can make user journey diagrams showing basic conformance in a short time without professional training.For the threat diagram, half of the users achieved full conformance.In conclusion, the tool can serve as low-threshold cybersecurity awareness training for SME employees.We discuss the limitations and validity of the results and future work to improve the tool's usability.
Creating a Math Expression Filter to Extract Concise Math Expression Images

Even though the web is an effective resource to search for math expressions, finding appropriate ones among the obtained documents is time-consuming.Therefore, we propose a math expression filter that presents appropriate images for such searches.We call an appropriate math expression a concise math expression, such as fh, written in a compact form whose content can be interpreted by the math expression itself.We determined the conditions satisfied by a concise math expression and developed classifiers that discriminate the images of concise math expressions from web images using supervised machine learning methods based on these conditions.We performed two experiments: Experiment 1 used methods other than deep learning, and Experiment 2 used deep learning.A convolutional neural network (CNN) with transfer learning and fine tuning by VGG16 shows high performance with an obtained F-measure of 0.819.We applied this filter to a task that presents math expression images by entering mathematical terms into a web search engine as queries.All of the evaluation metrics outperformed the previous study, including F-measure, MAP, and MRR.
Temporal Constraints in Online Dating Fraud Classification

A number of automated systems attempt to combat online fraud through the application of classifiers created using machine learning techniques.However, online fraud is a moving target, and cybercriminals alter their strategies over time, causing a gradual decay in the effectiveness of classifiers designed to detect them.In this paper, we demonstrate the existence of this concept drift in an online dating fraud classification problem.Working with a dataset of real and fraudulent dating site profiles spread over 6 years, we measure the extent to which dating fraud classification performance may be expected to decay, finding substantial decay in classifier F1 over time, amounting to a decrease of more than 0.2 F1 by the end of our evaluation period.We also evaluate strategies for keeping fraud classification performance robust over time, suggesting mitigations that may be deployed in practice.
The HORM Diagramming Tool: A Domain-Specific Modelling Tool for SME Cybersecurity Awareness

Improving security posture while addressing human errors made by employees are among the most challenging tasks for SMEs concerning cybersecurity risk management.To facilitate these measures, a domainspecific modelling tool for visualising cybersecurity-related user journeys, called the HORM Diagramming Tool (HORM-DT), is introduced.By visualising SMEs' cybersecurity practices, HORM-DT aims to raise their cybersecurity awareness by highlighting the related gaps, thereby ultimately informing new or updated cyber-risk strategies.HORM-DT's target group consists of SMEs' employees with various areas of technical expertise and different backgrounds.The tool was developed as part of the Human and Organisational Risk Modelling (HORM) framework, and the underlying formalism is based on the Customer Journey Modelling Language (CJML) as extended by elements of the CORAS language to cover cybersecurity-related user journeys.HORM-DT is a fork of the open-source Diagrams.netsoftware, which was modified to facilitate the creation of cybersecurity-related diagrams.To evaluate the tool, a usability study following a within-subject design was conducted with 29 participants.HORM-DT achieved a satisfactory system usability scale score of 80.69, and no statistically significant differences were found between participants with diverse diagramming tool experience.The tool's usability was also praised by participants, although there were negative comments regarding its functionality of connecting elements with lines.
Examining the Potential for Conversational Exploratory Search Using a Smart Speaker Digital Assistant

Online Digital Assistants, such as Amazon Alexa, Google Assistant, Apple Siri are very popular and provide a range or services to their users, a key function is their ability to satisfy user information needs from the sources available to them. Users may often regard these applications as providing search services similar to Google type search engines. However, while it is clear that they are in general able to answer factoid questions effectively, it is much less obvious how well they support less specific or exploratory type search tasks. We describe an investigation examining the behaviour of the standard Amazon Alexa for exploratory search tasks. The results of our study show that it not effective in addressing these types of information needs. We propose extensions to Alexa designed to overcome these shortcomings. Our Custom Alexa application extends Alexa's conversational functionality for exploratory search. A user study shows that our extended Alexa application both enables users to more successfully complete exploratory search tasks and is well accepted by our test users.
Visual Analysis of Multi-Labelled Temporal Noise Data from Multiple Sensors

Environmental noise pollution is a problem for cities' inhabitants, that can be especially severe in large cities.To implement measures that can alleviate this problem, it is necessary to understand the extent and impact of different noise sources.Although gathering data is relatively cheap, processing and analyzing the data is still complex.Besides the lack of an automatic method for labelling city sounds, maybe more important is the fact that there is not a tool that allows domain experts to analytically explore data that has been manually labelled.To solve this problem, we have created a visual analytics application that facilitates the exploration of multiplelabelled temporal data captured at four different corners of a crossing in a populated area of Barcelona, the Eixample neighborhood.Our tool consists of a series of linked interactive views that facilitate top-down (from noise events to labels) and bottom-up (from labels to time slots) exploration of the captured data.
Debiasing Sentence Embedders Through Contrastive Word Pairs

Over the last years, various sentence embedders have been an integral part in the success of current machine learning approaches to Natural Language Processing (NLP). Unfortunately, multiple sources have shown that the bias, inherent in the datasets upon which these embedding methods are trained, is learned by them. A variety of different approaches to remove biases in embeddings exists in the literature. Most of these approaches are applicable to word embeddings and in fewer cases to sentence embeddings. It is problematic that most debiasing approaches are directly transferred from word embeddings, therefore these approaches fail to take into account the nonlinear nature of sentence embedders and the embeddings they produce. It has been shown in literature that bias information is still present if sentence embeddings are debiased using such methods. In this contribution, we explore an approach to remove linear and nonlinear bias information for NLP solutions, without impacting downstream performance. We compare our approach to common debiasing methods on classical bias metrics and on bias metrics which take nonlinear information into account.
Educators Strategies: Overcoming Students’ Speaking Difficulties

The purpose of the study to know how teachers actually put their "strategies in teaching speaking class" into practice. This study employed a descriptive qualitative methodology. The result of the study show that The teacher at SMA Negeri 1 Selayar employs a number of methods to help her students overcome their speech impairments, including role playing as a means of encouraging students to express and expand upon their own ideas in relation to a given theme, group discussions to increase students' fluency in English and comprehension of assigned material, and the use of games to expand students' vocabularies in the language. Role playing is the most popular tactic, and The positive reaction from pupils at SMA Negeri 1 Selayar suggests that the methods being used to help them are effective.
Multimedia technologies as a means of professional training of higher education students in master studies

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Self-determination in Culture as a Result of Emotional Living Through Situations of Value Choice Based on Personal Potential

Abstract: The relevance of the study is determined by the special significance of the process of creating conditions for high school students to make multiple choices, since modern young people cannot imagine their existence without constant situations of bifurcation. The main goal set by the authors is to develop an algorithm necessary for understanding the logic of self-determination of a high school student in culture, which can also be used as a basis for the practical activities of a teacher. Based on this algorithm (working scheme), the teacher can offer specific options for situations of choice for high school students. The article provides examples of such situations, tested in literature classes. It is shown that the process of choosing opportunities for realization of personal potential by high school students can be more effective if the teacher is able to create pedagogical conditions for such actions as individual refraction of the norms and values of society, identification and justification of the value-semantic foundations of life activity; choice of goals and means of self-realization based on personal potential, choice of ways and forms of life, responsible design and implementation of individual trajectories of personal/professional formation and development. One of the important conclusions of the article is the one that the leading way of self-determination of a student in culture is a play activity (real or imaginary), age-appropriate and fixed in the mind as a cultural norm. This justifies the need to use such a method of pedagogical support as adaption for the stage (from expressive reading of a literary text to a school theater performance). It is shown that a high school student’s living through events by the medium of a literary work is often a preventive measure in relation to possible undesirable situations in the future, contributes to the self-determination of a person in culture, which is relatively permanent and proceeds throughout almost a person’s entire life.
Ways to improve the management of an educational institution in the conditions of the development of market relations

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Irbis: static taint analyzer for vulnerabilities detection in C/C++

Static taint analysis can be used to find various security weaknesses and vulnerabilities in programs by discovering dataflow paths from taint sources to taint sinks. In most cases the data is called ”tainted” if it was obtained from an untrusted source without proper sanitization. In this paper we present a static taint analyzer Irbis. It implements analysis based on IFDS (Interprocedural Finite Distributive Subset) dataflow problem, as well as various extensions aimed at improving accuracy and completeness of the analysis. It supports different definitions of tainted data, which enables it to find such weaknesses as out of buffer access, use of freed memory, hardcoded passwords, data leaks and discover dataflow paths between user-defined sources and sinks. All sources, sinks and propagators definitions are stored in JSON format and can be adjusted to meet the users’ needs. We compare analysis results on Juliet Test Suite for C/C++ with several other analyzers, such as Infer, Clang Static Analyzer and Svace. Irbis manages to demonstrate 100% coverage on taint-related subset of tests for implemented CWEs, while suppressing all the false positives using heuristics. We also show performance and false positive rate on real projects, with examples of real vulnerabilities, which can be detected by Irbis.
Modification of the Method of Object Contours Extraction in Intelligent Systems

This research paper focuses on the use of computer vision in intelligent systems to analyze human contours. With the growth of technology in various industries, there is a need to improve the efficiency of human-computer systems. The proposed method uses a video camera and computer software to detect a person in the image and process it using the OpenCV library and C++ programming language. The paper reviews existing human detection methods, analyzes an alternative method that uses computer vision, and develops a new method for human detection. Modifications include the use of the Kuwahar filter for image blurring and the Sobel algorithm for outline extraction. Applications for this technology include security at transportation hubs and crowded areas, remote health monitoring, enhanced control at borders and secure facilities, and interactive advertising and entertainment.
Virtual laboratories as a means of improving the efficiency of research activities of students in physics lessons

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.
Issues of integration of science and education in improving the quality of education

International scientific conference «Information technologies and management in higher education and sciences» : conference proceedings (November 28, 2022. Fergana, the Republic of Uzbekistan). Riga, Latvia : “Baltija Publishing”, 2022. Part 3. 432 pages.