Reviewerly/exHarmony huggingface dataset:
Probability in reliability assessments of electric power complexes

Electric power complexes today rightfully belong to the category of complex systems that become integral structural elements of the environment. Any electric power complex created by engineers has to satisfy the corresponding needs of a person, because this is what it is created for. From this point of view, the electric power complex should be considered endowed with appropriate consumer properties, which must show stability during the period of its operation, determined by the reliability indicators, which for the electric power complex must be supplemented by the corresponding safety indicators. It is these indicators that make it possible to conclude about the possibility of manifestation of the electric power complex during the operation of its consumer properties in full and without a threat to people and to the environment, that is, they make it possible to make a decision about the possibility and feasibility of operating this electric power complex. The analysis of the essence of the concept of probability as a tool to overcome the incompleteness and inaccuracy of information about the events under study, along with various concepts (interpretations) of probability from the point of view of their theoretical status and practical application in the problems of calculating the reliability and safety indicators of electric power complexes. Analyzed the classical, statistical and axiomatic concepts of probability, revealed the features of each of these concepts, conditions and reservations regarding their application in engineering practice. Reveals the reasons for the discrepancy between the reliability indicators, calculated at the design stage of devices on the basis of reference data on the failure rate of individual components, indicators that are observed during the operation of these devices, as well as the need to be careful with the processing of observation results in cases where such that differ significantly from the majority. It is noted that the limitations associated with statistical probability can be to a greater or lesser extent leveled by using the concept of hyper-random events, but the widespread implementation of this concept is constrained by the lack of an appropriate experimental database. On the basis of the analysis carried out, urgent tasks are formulated related to increasing the efficiency of calculating the reliability and safety indicators of electric power complexes.
Equivalent Causal Models

The aim of this paper is to offer the first systematic exploration and definition of equivalent causal models in the context where both models are not made up of the same variables. The idea is that two models are equivalent when they agree on all "essential" causal information that can be expressed using their common variables. I do so by focussing on the two main features of causal models, namely their structural relations and their functional relations. In particular, I define several relations of causal ancestry and several relations of causal sufficiency, and require that the most general of these relations are preserved across equivalent models.
Optimising Cost vs Accuracy of Decentralised Analytics in Fog Computing Environments

The exponential growth of devices and data at the edges of the Internet is rising scalability and privacy concerns on approaches based exclusively on remote cloud platforms. Data gravity, a fundamental concept in Fog Computing, points towards decentralisation of computation for data analysis, as a viable alternative to address those concerns. Decentralising AI tasks on several cooperative devices means identifying the optimal set of locations or Collection Points (CP for short) to use, in the continuum between full centralisation (i.e., all data on a single device) and full decentralisation (i.e., data on source locations). We propose an analytical framework able to find the optimal operating point in this continuum, linking the accuracy of the learning task with the corresponding <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">network</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computational</i> cost for moving data and running the distributed training at the CPs. We show through simulations that the model accurately predicts the optimal trade-off, quite often an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intermediate</i> point between full centralisation and full decentralisation, showing also a significant cost saving w.r.t. both of them. Finally, the analytical model admits closed-form or numeric solutions, making it not only a performance evaluation instrument but also a design tool to configure a given distributed learning task optimally before its deployment.
IMPROVE EFFICIENCY AND RELIABILITY OF SUPPLY CHAINS USING SMART CONTRACTS

Digital transformation of the world economy and the rapid growth of information flows have led to the implementation of innovative technologies in the processes of business management. Data analysis, risk assessment technologies and decision support systems enable companies to adapt to modern challenges.In the process of globalization, a consumer has the priority due to which quality of service and timely delivery of the required products determine the competition among the companies. All participants of the supply process, including logistics service providers, have traditionally focused on optimizing process management and minimizing supply chain costs [1]. The determining factor of supply efficiency from the perspective of procurement and logistics is the uninterrupted supply of raw materials and semi-finished goods that depends on the selection of a reliable supplier, as well as the continuous improvement of the quality of materials and the reliability of the customer-supplier relationship. The purpose of this article is to analyze use of blockchain technology to improve reliability of supply chains, whereas the rationale behind the management of business processes and the contractual obligation between the parties are verified cryptographically, using a smart contract.
Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search

Conflict-Based Search (CBS) is a state-of-the-art algorithm for multi-agent path finding. At the high level, CBS repeatedly detects conflicts and resolves one of them by splitting the current problem into two subproblems. Previous work chooses the conflict to resolve by categorizing the conflict into three classes and always picking a conflict from the highest-priority class. In this work, we propose an oracle for conflict selection that results in smaller search tree sizes than the one used in previous work. However, the computation of the oracle is slow. Thus, we propose a machine-learning framework for conflict selection that observes the decisions made by the oracle and learns a conflict-selection strategy represented by a linear ranking function that imitates the oracle's decisions accurately and quickly. Experiments on benchmark maps indicate that our method significantly improves the success rates, the search tree sizes and runtimes over the current state-of-the-art CBS solver.
Examining the interplay between privacy by design (PbD) schemes and privacy patterns

Privacy is a vague concept. This vagueness makes it difficult to understand what it means. Software engineering is an area such ambiguity creates a significant problem. For example, if the software developers do not understand privacy properly, they are not going to develop the software in a privacy-protected manner. To address this issue, as a community, over the last two decades, many researchers have proposed a few different Privacy by Design (PbD) schemes. Typically, PbD schemes comprise a set of instructions. These instructions are also referred to as guidelines, principles, strategies. Another way to address this problem of ambiguity is privacy patterns. Inspired by design patterns in software engineering domain, researchers and privacy experts have developed privacy patterns. Each Privacy pattern is designed to improve privacy in particular application design by eliminating certain privacy risks in a certain way. For our analysis, we identified ten (10) different PhD schemes. We analyse them against 74 different privacy patterns (\url{privacypatterns.eu}, \url{privacypatterns.org}). In this report, we examine the interplay between Privacy by Design (PbD) schemes and privacy patterns. This document contains the raw outcome of our analysis. Please refer to our research paper to read about insights we generated through this analysis.
Mapping of radioactive contamination with predetermined confidence level

Standard approaches to the construction of maps of radioactive contamination do not provide errors in map data, so such maps do not, in fact, guarantee the accuracy of the map information. In this paper, based on the fact that the characteristics of radioactive contamination at a particular point in the territory have a lognormal probability distribution, a methodology for creating maps with a guaranteed confidence level of the provided information has been proposed and tested. There are considered two ways of creating maps, based on the results of "direct" measurements of radioactive contamination characteristics and in the combination of "direct" and "indirect" measurements of values statistically related to the mapping characteristic. The approaches and use of kriging methods proposed in the article allow to create maps with a given level of confidence and, accordingly, to take into account the risks caused by the uncertainty of measurements of radioactive contamination characteristics and uncertainty of their approximation.
Penetapan Harga Pokok Produksi Dengan Metode Full Costing Sebagai Dasar Penentuan Harga Jual Kain Tenun Songket Melayu

Songket Melayu weaving craftsmen in determining the cost of production as the basis for determining the selling price are still inaccurate because they are based on estimates and have not classified costs in the calculation of the cost of production. This study aims to assist in determining the cost of goods manufactured as a basis for determining the selling price using the full costing method. This research is descriptive qualitative research. The results of this study indicate the difference in the cost of production between the weaving craftsmen's calculations with the full costing method. The result of the calculation of the cost of production according to the weaving craftsman is IDR 483,333.33 per piece, while the full costing method is IDR 528,833.00 per piece. The difference in production costs is IDR 45,500.00 lower than using the full costing method. The determination of the selling price of the product is based on the cost of goods sold per piece of woven fabric plus the desired profit of the company of 35% in order to cover the production costs that have been incurred. The result of the calculation of the selling price according to the weaving craftsman is IDR 113,925.00 per piece lower than using the full costing method.
Airborne Network Traffic Identification Method under Small Training Samples

Due to the high cost and difficulty of traffic data set acquisition and the high time sensitivity of traffic distribution, the machine learning-based traffic identification method is difficult to be applied in airborne network environment. Aiming at this problem, a method for airborne network traffic identification based on the convolutional neural network under small traffic samples is proposed. Firstly, the pre-training of the initial model for the convolutional neural network is implemented based on the complete data set in source domain, and then the retraining of the convolutional neural network is realized through the layer frozen based fine-tuning learning algorithm of convolutional neural network on the incomplete dataset in target domain, and the convolutional neural network model based feature representing transferring(FRT-CNN) is constructed to realize online traffic identification. The experiment results on the actual airborne network traffic dataset show that the proposed method can guarantee the accuracy of traffic identification under limited traffic samples, and the classification performance is significantly improved comparing with the existing small-sample learning methods.
E-learning formation retrospective analysis in the theory and practice of education

The article identifies and describes the retrospective analysis stages of the E-learning tools use problem in the theory and practice of education. Based on the analysis of trajectory "infrormatization - open education - information and education environment - E-learning - Elearning tools" and retrospective analysis and scientific output methods the actuality and objectivity of teachers#x2019; training e-learning use in professional activity problem is substantiated
Implementasi Algoritma Clustering Partitioning Around Medoid (PAM) dalam Clustering Virus MERS-Cov

&lt;p&gt;&lt;em&gt;Th&lt;/em&gt;&lt;em&gt;e Middle East Respiratory Coronavirus (MERS-CoV) is a disease caused by a coronavirus. This virus is contagious, but its transmission is not as easy as the common cold, MERS-CoV virus is better susceptible to transmitting through direct contact, for example in people who care about the MERS-CoV virus without the need for virus protection. To determine the characteristics, the MERS-CoV disease virus can be identified by identifying DNA (deoxyribonucleic acid). One technique in understanding the characteristics of life is by grouping. Grouping can be done by grouping DNA into groups that have attributes and functions. The Clustering algorithm used in this study is Partitioning Around Medoid (PAM). This algorithm has the advantage that the results of the grouping process are not by following the order of entering the dataset and overcome sensitivity to noise and outliers. The purpose of this study is to implement the Partitioning Around Medoid (PAM) clustering algorithm in clustering the MERS-CoV virus. This research was conducted through a quantitative descriptive literature study. The implementation of the PAM algorithm on the MERS-CoV DNA sequence obtained 2 clusters with the highest silhouette coefficient value on the number of clusters 2, namely 0.61534. The number of members in Cluster 1 is 84 MERS-CoV DNA sequences and the number of members in Cluster 2 is 16 MERS-CoV DNA sequences.&lt;/em&gt;&lt;/p&gt;
"BORED MAN": SELF-PRESENTATION IN THE WORLD OF VIRTUAL REALITY

The authors analyse in the article the phenomenon of “bored person”, show that for the first time a person finds himself in a state when his being in the modern world changes dramatically, and this entails changes in a person’s self-identification. A brief historical and philosophical excursion is given to the change in the place and role of the man from the ancient picture of the world, where man is all outward, to the commoner in the Middle Ages, a crowd man in the 20th century to the appearance of a “bored man” in the 21th century. “Bored person” is defined through the identification of such basic characteristics as the loss of inner meanings and values, the inability to go beyond the crowd, to be different. The constant state of immersion in the media space changes not only the nature of a person’s communication with other people, but also with himself.
Efficient Attribute-Based Proxy Re-Encryption with Constant Size Ciphertexts

Attribute-based proxy re-encryption (ABPRE) allows a semi-trusted proxy to transform an encryption under an access-policy into an encryption under a new access policy, without revealing any information about the underlying message. Such a primitive facilitates fine-grained secure sharing of encrypted data in the cloud. In its key-policy flavor, the re-encryption key is associated with an access structure that specifies which type of ciphertexts can be re-encrypted. Only two attempts have been made towards realising key-policy ABPRE (KP-ABPRE), one satisfying replayable chosen ciphertext security (RCCA security) and the other claiming to be chosen ciphertext secure (CCA secure). We show that both the systems are vulnerable to RCCA and CCA attacks respectively. We further propose a selective CCA secure KP-ABPRE scheme in this work. Since we demonstrate attacks on the only two existing schemes in the literature, our scheme becomes the first KP-ABPRE scheme satisfying selective CCA security. Moreover, our scheme has an additional attractive property, namely collusion resistance. A proxy re-encryption scheme typically consists of three parties: a delegator who delegates his decryption rights, a proxy who performs re-encryption, and a delegatee to whom the decryption power is delegated to. When a delegator wishes to share his data with a delegatee satisfying an access-policy, the proxy can collude with the malicious delegatee to attempt to obtain the private keys of the delegator during delegation period. If the private keys are exposed, security of the delegator’s data is completely compromised. Hence, achieving collusion resistance is indispensable to real-world applications. In this paper, we show that our construction satisfies collusion resistance. Our scheme is proven collusion resistant and selective CCA secure in the random oracle model, based on Bilinear Diffie-Hellman exponent assumption.
Method for Improving the Efficiency of IT Project Management Using a Genetic Algorithm

The article is devoted to solving the current problem of improving the efficiency of IT project management processes. When managing IT projects, managers are faced with the problem of formation teams and distributing tasks among project participants in the face of the need to minimize costs and completion dates of an IT project. The lack of necessary methods and software doesn't allow the IT project Manager to adequately assess competences and skills of participants, their personal qualities, which leads to a decrease in the effectiveness of project management. The article proposes a method of improving the efficiency of IT project management, which differs by using a genetic algorithm to form project commands and assign team participants to project tasks. The efficiency criterion is the complexity and duration of the project and individual tasks using the COCOMO II method. When forming project teams, takes into account the level of technologies proficiency, experience with technologies, the coherence of the project team members, and the experience of similar developments of project participants. The level of technologies proficiency affects the level of labor input multiplier, experience with technologies — at the level of the multiplier, the coherence of the project team members — on the level of scale factor, the experience of similar development — on the level of the scale factors of the COCOMO II methodology. Taking into account the personal and psychological qualities of project participants reduces the risk of interpersonal conflicts within the team, which also reduces the duration of projects and the labor input of solving tasks. Research of personal and psychological qualities is carried out on the basis of automated tests. The test suite includes Rosenzweig, Belbin, Myers-Briggs, Thomas and Honey-Mumford tests. The developed method is implemented in a software complex for multilevel IT project management. Testing of the method and software complex was carried out within the framework of the students' learning practice of the specialty "Software engineering" of the Belarusian-Russian University. The use of the proposed method allowed to reduce the labor input of solving the tasks of training projects by 19.2 %, to reduce the project realization term by 10 %.
Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning

In this study, we investigate self-supervised representation learning for speaker verification (SV). First, we examine a simple contrastive learning approach (SimCLR) with a momentum contrastive (MoCo) learning framework, where the MoCo speaker embedding system utilizes a queue to maintain a large set of negative examples. We show that better speaker embeddings can be learned by momentum contrastive learning. Next, alternative augmentation strategies are explored to normalize extrinsic speaker variabilities of two random segments from the same speech utterance. Specifically, augmentation in the waveform largely improves the speaker representations for SV tasks. The proposed MoCo speaker embedding is further improved when a prototypical memory bank is introduced, which encourages the speaker embeddings to be closer to their assigned prototypes with an intermediate clustering step. In addition, we generalize the self-supervised framework to a semi-supervised scenario where only a small portion of the data is labeled. Comprehensive experiments on the Voxceleb dataset demonstrate that our proposed self-supervised approach achieves competitive performance compared with existing techniques, and can approach fully supervised results with partially labeled data.
Deep multi‐level up‐projection network for single image super‐resolution

Most convolutional neural network-based single image super-resolution (SR) methods do not take full account of the hierarchical features of the original low-resolution (LR) images, including the intra-channel spatial feature information and the inter-channel feature information, which decreases the representational capacity of the network. A deep multi-level upprojection network (DMUN) is proposed to solve this problem. Local feature up-projection unit is adopted in DMUN to obtain high-resolution (HR) feature of different levels and then to reconstruct the SR image. Residual up-projection group in DMUN mines the hierarchical LR feature information and its corresponding HR residual information recursively. A residual recorrection mechanism is further introduced, which adopts HR residual information to re-correct HR features and enrich the details of the output image. Finally, the original residual block with spatial-and-channel attention mechanism is improved, which adaptively recalibrates features by considering the intra-channel spatial relationships and the inter-channel pixel-wise interdependencies simultaneously. Experiments on benchmark datasets show that DMUN achieves favourable performance against state-of-the-art methods.