Reviewerly/exHarmony huggingface dataset:
Byzantine Resilient Distributed Multi-Task Learning

Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models simultaneously.However, distributed algorithms for learning relatedness among tasks are not resilient in the presence of Byzantine agents. In this paper, we present an approach for Byzantine resilient distributed multi-task learning. We propose an efficient online weight assignment rule by measuring the accumulated loss using an agent's data and its neighbors' models. A small accumulated loss indicates a large similarity between the two tasks. In order to ensure the Byzantine resilience of the aggregation at a normal agent, we introduce a step for filtering out larger losses. We analyze the approach for convex models and show that normal agents converge resiliently towards the global minimum.Further, aggregation with the proposed weight assignment rule always results in an improved expected regret than the non-cooperative case. Finally, we demonstrate the approach using three case studies, including regression and classification problems, and show that our method exhibits good empirical performance for non-convex models, such as convolutional neural networks.
Conceptual model of personality's information competence

Analysis of scientific literature, generalization of information about information competence of the individual gives grounds to define it as the orientation of the individual to ensure the effectiveness of life in the information society, which is an integral quality of personality, holistic, dynamic system of personal abilities that allow consciously and creatively define and carry out . Based on the analysis of the scientific literature, expert data and taking into account the idea of the component composition of information competence (N. Balovsyak, A. Khutorsky, I. Kisla, R. Pavlyuk, etc.), nineteen bipolar indicators of information competence of the individual are determined. It is established that the set of their typical characteristics reflect the level of manifestation of components in a particular person, which allows to determine the general level of information competence of the individual. Cluster analysis allowed to group indicators into the component composition of information competence of the individual, which includes motivation, direction of action, subjective, personal, group and business orientation, project-behavioral, emotional-volitional components, personality abilities, life values, communication, professionally important qualities , eco- and legal culture. Relevant components are grouped into blocks: motivational-motivational, cognitive-activity, personal-value, meaningful. These components are closely interconnected and together ensure that the individual performs its functions: motivational and directional; cognitive-perceptual; communicative; regulatory and developmental; operational orientation. They determine the trajectory of the path of formation of personality, taking into account the development of its qualities, professional self-determination, motivational sphere, motivations, awareness of the essence of life and so on. It is established that the presence of reflection presupposes the ability to analyze, realize one’s qualities, personal qualities, know oneself, correct one’s behavior and way of life (M. Dyachenko, L. Kandybovych, etc.). Given the results of the study of V. Romenets and A. Furman, we believe that reflection is a phenomenon of aftereffect, which expresses the essence of the action. A person’s ability to reflect combines the characteristics of behavior and his ability to control their actions, and in interpersonal relationships supports the morality of behavior. Using a system-structural approach that takes into account the complexity of the whole personality as a set of interdependent substructures (S. Maksymenko and others), personality attitudes, attitudes, value orientations, communicative properties (O. Vakulenko, etc.), a structural-functional model is proposed information competence of the individual.
Conditional Mixture Model for Modeling Attributed Dyadic Data

Dyadic data contains co-occurrences of objects, which is often modeled by finite mixture model which in turn is learned by expectation maximization (EM) algorithm. Objects in traditional dyadic data are identified by names, causing the drawback which is that it is impossible to extract implicit valuable knowledge under objects. In this research, I propose the so-called attributed dyadic data (ADD) in which each object has an informative attribute and each co-occurrence of two objects is associated with a value. ADD is flexible and covers most of structures / forms of dyadic data. Conditional mixture model (CMM), which is a variant of finite mixture model, is applied into learning ADD. Moreover, a significant feature of CMM is that any co-occurrence of two objects is based on some conditional variable. As a result, CMM can predict or estimate co-occurrent values based on regression model, which extends applications of ADD and CMM.
Perturbation-based exploration methods in deep reinforcement learning

Recent research on structured exploration placed emphasis on identifying novel states in the state space and incentivizing the agent to revisit them through intrinsic reward bonuses. In this study, we question whether the performance boost demonstrated through these methods is indeed due to the discovery of structure in exploratory schedule of the agent or is the benefit largely attributed to the perturbations in the policy and reward space manifested in pursuit of structured exploration. In this study we investigate the effect of perturbations in policy and reward spaces on the exploratory behavior of the agent. We proceed to show that simple acts of perturbing the policy just before the softmax layer and introduction of sporadic reward bonuses into the domain can greatly enhance exploration in several domains of the arcade learning environment. In light of these findings, we recommend benchmarking any enhancements to structured exploration research against the backdrop of noisy exploration.
The Application of Three Order Cumulants Coupling Features in Fault Diagnosis

Although mechanical vibration is extremely complicated, each fault signal produced by it has its own inherent features, The distinction may be most prominent between the certain components hidden in those features and the same components of normal signals.Three-order cumulant can reduce the Gaussian background noise automatically and its complex formal includes different coupling information of its signal.In the experiment, through these different coupling modes, the same coupling components are fetched from specific fault signal and normal signal, then these components are used to diagnose that certain fault.The results show that the method can fetch the most prominent distinction between normal signal and the specific fault signal, so the specific fault diagnosis by this method is satisfactory.
Channel Pruning Guided by Spatial and Channel Attention for DNNs in Intelligent Edge Computing

Deep Neural Networks (DNNs) have achieved remarkable success in many computer vision tasks recently, but the huge number of parameters and the high computation overhead hinder their deployments on resource-constrained edge devices. It is worth noting that channel pruning is an effective approach for compressing DNN models. A critical challenge is to determine which channels are to be removed, so that the model accuracy will not be negatively affected. In this paper, we first propose Spatial and Channel Attention (SCA), a new attention module combining both spatial and channel attention that respectively focuses on "where" and "what" are the most informative parts. Guided by the scale values generated by SCA for measuring channel importance, we further propose a new channel pruning approach called Channel Pruning guided by Spatial and Channel Attention (CPSCA). Experimental results indicate that SCA achieves the best inference accuracy, while incurring negligibly extra resource consumption, compared to other state-of-the-art attention modules. Our evaluation on two benchmark datasets shows that, with the guidance of SCA, our CPSCA approach achieves higher inference accuracy than other state-of-the-art pruning methods under the same pruning ratios.
The impact of data breaches: The organizational security measures and the individual perception of an organization’s security attempts

As people and organizations make themselves more vulnerable on the internet, there is a significant increase in cybersecurity threats. Due to this, organizations and individuals think more about their actions online. However, organizations are constantly improving their methods, when handling confidential data and are developing an understanding of protecting consumers from such exposure. This research has looked into previous literature, regarding the concepts of cybercrime, cybersecurity, and handling of personal data. The empirical data consists of two semi-structured interviews with two individuals working at security departments, within respective organizations. From the interviews, we identified some clear similarities and some that were less obvious. To get a better understanding of the individual perspective, a survey questionnaire was conducted and sent to a mixed group of individuals. The survey results are presented and discussed with the theoretical and empirical findings. The study identifies several conclusions. Including organizational security measures and individuals perceptions of the information provided by organizations, regarding their data handling. (Less)
The Method of Detecting Online Password Attacks Based on High-level Protocol Analysis and Clustering Techniques

Although there have been many solutions applied, the safety challenges related to the password security mechanism are not reduced.The reason for this is that while the means and tools to support password attacks are becoming more and more abundant, the number of transaction systems through the Internet is increasing, and new services systems appear.For example, IoT also uses password-based authentication.In this context, consolidating password-based authentication mechanisms is critical, but monitoring measures for timely detection of attacks also play an important role in this battle.The password attack detection solutions being used need to be supplemented and improved to meet the new situation.In this paper we propose a solution that automatically detects online password attacks in a way that is based solely on the network, using unsupervised learning techniques and protected application orientation.Our solution therefore minimizes dependence on the factors encountered by host-based or supervised learning solutions.The certainty of the solution comes from using the results of in-depth analysis of attack characteristics to build the detection capacity of the mechanism.The solution was implemented experimentally on the real system and gave positive results.
General theory of quantum fingerprinting network

The purpose of fingerprinting is to compare long messages with low communication complexity. Compared with its classical version, quantum fingerprinting can realize exponential reduction in communication complexity. Recently, multiparty quantum fingerprinting is studied to determine whether the messages from many parties are all the same. However, the complete relationships among them, including which ones are the same, are more general for future applications. Here, we propose the relationship state $\mathcal{G}$ to describe all possible relationships among $N$ messages. Based on this, we provide a general quantum fingerprinting network and then take the four-party quantum fingerprinting protocol as an example for detailed analysis. We take the full parameter optimization for the quantum fingerprinting protocol through asymmetric channel. The method of encoding more than one bit on each coherent state is used to further improve the performance of our protocol. Compared with other protocols, our protocol provides more relationships among different parties with only linear increase in communication complexity.
Determine and explain confidence in predicting violations on inland ships in the Netherlands

For real-world problems even the most complex machine learning models can only achieve a certain accuracy. This makes it important to understand why a specific prediction is made. Explanations can provide human decision support by allowing human experts to assess the reasoning of the model as well as the correctness. Specifically, in this thesis, we consider the problem of predicting violations on inland ships in the Netherlands to help inspectors in the Dutch government deciding which ship to inspect. The main contribution is determining confidence in a prediction separately from probability and using this confidence estimation for deciding which prediction of violation to select as well as to explain. With the limited number of inspectors and a large number of inland ships in the Netherlands, the global performance on all ships is less relevant. Instead, deciding the most qualitative predictions is more useful. Therefore, a measure of model confidence is determined to improve upon the traditional ranking based on probability. In the evaluation of this approach, no significant difference is found between the ranking based on probability for complex ensemble models. However, for simpler, more interpretable models, there is a significant improvement in using model confidence to re-rank. The determination of confidence is further used to create explanations from the context of confidence. The goal of these explanations is to help an inspector in deciding whether to inspect an inland ship. This novel explanation approach justifies the confidence in a prediction by expressing features contributing towards the confidence. We perform a human-grounded user study evaluation to identify the task effectiveness, perceived usefulness and user trust compared to the explanations from the traditional context of probability. The results of the user study suggest the explanations of the confidence to be particularly useful for problems with a lower accuracy.
The SecureElect - Blockchain based Electronic Voting System to Enable Online

In democratic systems, voting is a central piece as it gives individuals in a community to voice their opinion. E-voting was acquainted to address few concerns, such as decrease in number of voters, security and accessibility of current voting systems. However, it isn’t financially savvy and still requires full supervision by a central authority. In view of the high security and verifiability, e-voting system has been a popular expression in the continuous years. The blockchain is an emerging, decentralized, and distributed technology that promises to enhance different aspects of many industries. The blockchain with the smart contracts, rises as a decent possibility to use in improvements of more secure, less expensive, more straightforward, and ease to-utilize e-voting frameworks. This paper intends to assess prerequisites of building electronic voting frameworks and distinguishes the lawful and technological limitations of utilizing blockchain as an administration for acknowledging such systems. The paper begins by assessing a portion of the popular blockchain structures that offer blockchain as an administration followed by discussing different consensus algorithms and finally choosing the right infrastructure and the algorithm for the problem. We have built a web platform which is used to simulate the voting process with blockchain and the results of the system for transparency, security and immunity to data tampering is presented. Additionally, we have discussed the possibilities of Aadhar card as an authentication mechanism for e-voting frameworks.
РОЗРОБКА СИСТЕМИ АВТОМАТИЗОВАНОГО ОБЛІКУ ДОСЯГНЕНЬ СТУДЕНТІВ НА NODE.JS З ІНТЕГРАЦІЄЮ FIREBASE

The introduction of information technology occurs in almost all industries, but the solution to this issue in the education system and particularly in the organization of management at facultiesof higher educational institutions is the most acute. The article is devoted to developing a software prod uct for automated control of studentsachievement with statistics, reporting, convenient and fast format of interaction, which will replace the manual paper format of document processing. Service’s Server Platform is Node.js with TypeScript programming language in integration with Google Firebase services. The client side is created using Vue.js (frontend framework for creating SPA - one-page applications), Nuxt.js (framework for SSR - server rendering), Typescript (strictly typed web application programming language). The server interacts with the REST API. The main implementation is in a webapplication. All the necessary reports on faculties, groups, specific students, courses, years, and the level of competitions are automatically generated and issued in a form convenient for analysis.The product is designed for deans’ offices of the Donetsk State University of Management, but can flexibly adapt to the individual needs of other educational institutions of all levels of accreditation. Automation of students' knowledge control with statistics, reporting, a convenient and fast interaction format, using the most advanced and most effective tools for creating web applications, will significantly affect the competitive advantages of an educational institution in the educationalservices market. And increasing the speed and quality of work with the student body will be able to ensure the reliability and integrity of management in general.
On the Way to the Ontology of Test and Diagnostics Systems for Blading Power Plants

The article is an attempt to create ontology of test and diagnostics systems for blading power plants.Ontology is considered as a formal construction that combines understanding of the object model (more adequately -the subject area) from a logical point of view (as interpretation of a formal system that satisfies the axioms), from an algebraic point of view (as a set of objects belonging to certain classes with certain relations), in general scientific terms (as a simplified representation of objects, phenomena and processes of the real world).The prospective use of the model is associated with support for the development of test and diagnostics systems for blading power plants.The support is interpreted as a process of meeting the technical requirements, certain methods implemented by certain means.
Methodology of scientific researches: methods of searching and processing of scientific information

The methodology as the doctrine of the organization of professional activity, which is characterized by a minimum content and maximum coverage of essential connections and relationships, is characterized in the article. The valuable significance of the methodology of scientific researches, which consists in the delineation of the established features, the formation of the structure, the identification of forms, mechanisms and conditions for the implementation of scientific research, is indicated. The effectiveness of scientific research, which largely depends on the timely provision of information about new advances in science and the effective use of this information for scientific and research purposes, is revealed. The typology of division of all sources of information used in the preparation of written works into printed sources of information is revealed and specialized informative-retrieval systems, electronic sources of information is determined. The most important technological procedure in the methodology of scientific research is the collection of sources of information and their systematization. The main task of systematization is to organize and group all the collected material on the content and sequence of its use in the preparation of scientific work. The main functions of the systematic analysis are characterized: implementation of careful check of completeness of selection of literary sources; implementation of verification of compliance of the source data of the used sources. It is revealed that the selection and evaluation of factual material for writing a scientific work is a scientific work that requires purposeful individual effort, personal perseverance and motivated desire. Scientific creativity is manifested in the selection and processing of a significant amount of basic and additional information, its generalization and presentation in a form that is convenient for analysis and writing conclusions. It is concluded that the proposed methodological activity facilitates the methods of scientific research and is necessary throughout the time of writing a scientific paper.
A Novel Image Retrieval Method with Improved DCNN and Hash

In large-scale image retrieval, deep features extracted by Convolutional Neural Network (CNN) can effectively express more image information than those extracted by traditional manual methods.However, the deep feature dimensions obtained by Deep Convolutional Neural Network (DCNN) are too high and redundant, which leads to low retrieval efficiency.We propose a novel image retrieval method, which combines deep features selection with improved DCNN and hash transform based on high-dimension features reduction to gain lowdimension deep features and realizes efficient image retrieval.Firstly, the improved network is based on the existing deep model to build a more profound and broader network by adding multiple groups of different branches.Therefore, it is named DFS-Net (Deep Feature Selection Network).The adaptive learning deep features of the Network can effectively alleviate the influence of over-fitting and improve the feature expression of image content.Secondly, the information gain rate method is used to filter the extracted deep features to reduce the feature dimension and ensure the information loss is small.The last step of the method, hash Transform, sparsifies and binarizes this representation to reduce the computation and storage pressure while maintaining the retrieval accuracy.Finally, the scheme is based on the distinguished ResNet50, InceptionV3, and MobileNetV2 models, and studied and evaluated deeply on the CIFAR10 and Caltech256 datasets.The experimental results show that the novel method can train the deep features with stronger recognition ability on limited training samples, and improve the accuracy and efficiency of image retrieval effectively.
Menstimulasi Kemampuan Berbicara Pada Anak Usia Dini Berbasis Media Dongeng

The early childhood education is the first spearhead for children to enjoy education.The need for synergy between teachers and students. The language skills possessed by students require stiulation to improve these abilities. The One of language skill that need to be improved namely the ability to speak. The One method that is considered effective is storytelling. Storytelling is active activity carried out by the teacher to stimulate students speaking abilities.Based on the result of dedication that has been done it can be conclude that the method of storytelling can be used teacher to stimulate students speaking ability at the age of 5-6 years