Reviewerly/exHarmony huggingface dataset:
Decisions and influences-the premises of restrictive economic and banking policies in the current economic and social context

Purpose- This scientific approach aims to review the decisions taken by the European Union regarding the sanctions applied to Russia and analyze the impact they have in the economies of the European Union countries and on companies in Romania in particular. The regional development policy, which has as its main objective the achievement of an inter- and intra-regional distribution within the activities and their results as efficient and equitable as possible, is one of the most important and complex policies of the European Union. The steps taken so far, regarding the removal of the economies of the European states from the economic crisis they are in, have the role of helping the various sectors of activity to overcome the difficulties they are going through and to relaunch the areas of activity that are at a standstill due to the restrictions in regarding the export and import of raw materials, materials and goods. Thus, the study wants to highlight the needs that the European economies and implicitly the sectors of the Romanian economy have at this moment. Methodology- In this study, the research methodology was carried out using the random effects model, analyzing the data series from the panel. The descriptive analysis provided an assessment of the main characteristics of the data series: average, standard deviation, minimum and maximum values. The generated results highlight a good distribution of resources, thus determining a better financial performance, measured by the composite index, ROA, ROE and EPS. The practical implications are relevant for the proper functioning of the entire economic system of societies and for the continuity of the activity itself. Findings- From an econometric point of view, economic variables have been studied in terms of the influences they have exerted on other economic variables and the influences that other variables have exerted on them. The links that were identified between the variables were established both in terms of causality and by the intensity established between them. In addition to the specific stages of the regression analysis, it was also important to determine the intensity of the connection between the variables included in the econometric model. Thus, the determination of the intensity of the connection is the object of the analysis. Conclusion- The research is intended to be an X-ray of the existing situation in the economy of the European Union and Romania, Therefore, it becomes a useful tool for the Romanian business environment in terms of short-term adaptation to current market conditions and long-term decision making. Keywords: Economic indicators, instability, crisis, sanctions and restrictions, economic policies JEL Codes: C20, C23, C33
PCCA-Model: An Attention Module for Medical Image Segmentation

Convolutional neural networks have been increasingly employed in the field of medical image segmentation. Based on this idea that the human visual cortex differs in terms of the size of the receptive field and can sense the stimulus location., we proposed the pyramid channel coordinate attention (PCCA) module to fuse multiscale features in the channel direction, aggregate local and global channel information, combine them with the location information in the spatial direction, and then integrate them into the existing semantic segmentation network. We conducted numerous experiments on the datasets, namely LiTS, ISIC-2018, and CX, and obtained state-of-the-art results.
An overview on key digital assets of decentralized finance: cryptocurrency and NFT

Purpose – The emergence of blockchain and decentralized financial technology have been reshaping various aspects of economies, opening a wider spectrum for research communities. In finance, Blockchain has provided effective applications such as tokens, initial coin offerings, crowdfunding, smart contracts, prediction market systems, and digital currencies such as Bitcoin and Ethereum. This article aims to provide a brief overview of these two types of digital assets (cryptocurrency and NFT) and their potential. It gives brief information about these and on the other hand, tries to convey the prominent concerns of the financial markets. Methodology - This study reviews digital assets through the literature. Findings - The growing popularity and prevalence of digital assets such as cryptocurrencies and non-fungible tokens reveals that existing financial ecosystems are in many cases unable to respond to citizens' needs and concerns. The capabilities of digital assets in financial markets are increasing day by day and they are entering the game from simple to complex financial transactions. Cryptocurrencies stand out especially with their ability to handle more efficient online transactions, lower costs and simplified payment processes. Conclusion – Cryptocurrencies meet an increased level of efficiency in commerce and value exchange over the Internet. Non-fungible tokens, on the other hand, stand out with their ability to acquire a presence in the digital world, to be unique, and to be easily transferred. In particular, the financial indicators cited in this article give the impression that the interest in decentralized finance technologies will continue to increase, based on the potential of these two digital assets. Keywords: Digital finance, blockchain, decentralized finance, information systems JEL Codes: F00, F30, M00, O10
Industrial Internet of Things (IIoT) Forensics: Challenges, Opportunities, And Future Directions

The rapid growth of Industrial Internet of Things (IIoT) technology has not only enabled new possibilities for industrial operations, but it also introduces new cybersecurity risks that can lead to data breaches, loss of control, and even physical damage. This has led to increased complexity and interconnectedness of IIoT systems which further presents difficulties in collecting and analyzing digital evidence. In order to mitigate these risks and investigate incidents, forensic techniques must be applied to IIoT systems. As a result, this paper examines the Industrial Internet of Things (IIoT) forensics by identifying existing challenges, opportunities, and future research challenges. The paper begins by identifying relevant IIoT case scenarios, where these scenarios have been used as a baseline for exploring existing challenges from existing literature. Based on the outcome of this study, it is the author’s opinion that the field of IIoT forensics has the potential to greatly impact not only the field of digital forensics, but also the security and integrity of the industrial systems that are becoming increasingly reliant on the IIoT.
Research on the Construction of the Ecological Chain of Intelligent English Teaching Based on Artificial Intelligence

The rapid development of artificial intelligence has brought new development opportunities and challenges to foreign language teaching. The combination of English teaching and artificial intelligence is also one of the key directions of the development of the Internet in the new era, and promotes the development of English teaching in the direction of intelligence, precision, differentiation and individualization. The combination of English teaching and artificial intelligence can improve the slow development of online foreign language teaching. This article proposes to build an intelligent ecological teaching model of English under the background of artificial intelligence. The application of artificial intelligence technology and network resources to English teaching provides a strategy for the construction of an English ecological teaching system.
Penelitian penerapan SLiMS dalam pengolahan perpustakaan pada database Google Scholar: sebuah narrative literature review

Electronic catalogs have brought about a change in cataloging patterns in libraries. The existence of a catalog based on SLiMS is a choice of electronic catalogs that are easy to uses in libraries. This study aims to determine the research on the application of SLiMS in library processing on the Google Scholar database which analyzes the themes/topics, research methods, and research results used. The research method uses an qualitative approach through a narrative literature review on eight references indexed by the Google Scholar database with a range of publications from 2017 to 2022. The results shows that research on the application of SLiMS in a library processing on the Google Scholar database is a research that is mostly carried out in various libraries. According to the research theme/topic, the SLiMS studied were from the field of university libraries and public/city libraries. As for the research analysis method, SLiMS has been widely studied using qualitative and quantitative methods. The eight references analyzed have research results showing that SLiMS provides convenience and benefits in the continuity of collection processing activities in the library. The SLiMS electronic catalog has helped librarians in collection processing and librarian development.
ARTIFICIAL INTELLIGENCE BIAS AND NEURAL MACHINE TRANSLATION: TRANSLATING HEAVILY_LOADED IDEOLOGICAL ENGLISH→ARABIC MESSAGES

Bias, "an inclination, prejudice, preference or tendency towards or against a person, group, thing, idea or belief" (Murphy 2021), raises ethical questions whether in human or machine communication and it can have detrimental impacts on individuals and societies, e.g.criminal judgments.The deployment of AI systems in real-world settings has exposed various kinds of bias against certain social groups.The big number of research on bias in AI applications generally and the few studies on bias in neural machine translation (NMT) particularly have given the present study the momentum to further investigate the issue.It aims to examine bias in NMT through exploring the translation of some heavily-loaded ideological messages from English into Arabic.A multidisciplinary perspective deriving its tenets from translation studies, political sciences and computer science is utilized to explore bias in the translation of ideology by Google Translate API.It employs a qualitative approach using analysis, comparison and deduction as tools of research.It has reached some interesting findings, which came contrary to my initial expectations.
Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

The escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data, demandingthe deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data needs to be managed, preserved, and shared. Its heterogeneity calls for highly flexible data models to represent data from fabrication workflows, measurements, and simulations. We propose employing a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we have created an EMMO-based ontology, where EMMO stands for European Materials Modelling Ontology. The Python framework Django is used to allow for the intuitive integration into the virtual materials intelligence platform, VIMI. The Django framework relies on the Object-Graph-Mapper (OGM) neomodel to create a mapping between database classes and python objects. The model can store the whole bandwidth of data, from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research.
EXPERIMENTAL STUDY OF THE EFFECTIVENESS OF THE PROCESS OF TRAINING SCHOOL PRINCIPALS TO IMPLEMENT QUALITY MANAGEMENT OF SCHOOL EDUCATION

The process of preparing education managers for the implementation of the quality management model of school education has an important feature. This is its managerial orientation, which has the following aspects: on the one hand, the explicit management of the pedagogical process of the school; on the other, the design and construction of management of high-quality educational activities of the school based on appropriate approaches. In order to ensure the proper implementation of school education quality management, this article has developed a training module "School education quality management", provides analytical information on the organization and conduct of a special experiment with the participation of 74 school principals who undergo scheduled training.
Hopfield Neural Network With Negative Weights

&lt;p&gt;Hopfield and Tank have shown that a neural network can find solutions for complex optimization problems although it can be trapped in a local minimum of the objective function returning a suboptimal solution. When the problem has constraints they can be added to the objective function as penalty terms using Lagrange multipliers. In this paper, we introduce an approach inspired by the work of Andrew, Chu, and Kate to implement a neural network to obtain solutions satisfying the linear equality constraints using the Moore- Penrose pseudo inverse matrix to construct a projection matrix to send any configuration to the subspace of configuration space that satisfies all the constraints. The objective function of the problem is modified to include Lagrange-multipliers terms for the equations of constraints. Furthermore, we have found that such condition makes the network converge to a set of stable states even if some diagonal elements of the weight matrix are negative. If after several steps the network does not converge to a stable state, we just solve the problem using simulated annealing. We use this technique to solve the NP-hard Light Up puzzle.&lt;/p&gt;
Why Smart Contracts Reported as Vulnerable were not Exploited?

&lt;p&gt;Smart contract security is essential for blockchain applications. Studies show that few of the reported vulnerabilities are exploited. However, no follow-up study is performed to why the reported vulnerabilities are not exploited. We aim to understand the reasons for the low exploitation rate to help improve vulnerability detection practices. We first collect 136,969 unique real-world smart contracts and analyze them using seven vulnerability detectors. Then, we apply Strauss’ grounded theory approach to understand if they are exploitable. In addition, we analyze the transaction logs of the exploitable vulnerabilities to understand their exploitations in history. Among the 4,364 smart contracts reported as vulnerable by the vulnerability detectors, 75.27% of them are unexploitable. Only 66 (0.015%) exploitable contracts have been exploited. We uncover 11 reasons for making the detectors misidentify unexploitable vulnerabilities and six reasons that may lower the possibility of exploitable contracts being exploited by attackers. We illustrate that: beyond treating the smart contracts as yet another Object Oriented (OO) application, it is essential to consider the Solidity programming language’s design principle, smart contracts' application scenarios, and their execution environments. Based on the study's insights, we provide several suggestions to improve smart contract vulnerability detection, prioritization, and mitigation. &lt;/p&gt;
A Practical Scheme to Improve Memorability of System-assigned Random Password

Most users follow common strategies and patterns while choosing passwords, which makes them easier to remember but often very weak in terms of security. System-assigned random passwords can be an answer to this problem. However, these random passwords are difficult to remember and hardly used by the users through their strong security guarantee. Recently researchers have been trying on devising techniques to remember random passwords. However, state-of-the-art methods have noticeable limitations such as - no upper case or special characters were considered, which is not practical for any good password. This paper proposes a novel scheme to aid users in remembering random passwords that do not suffer from these limitations. Users can select both graphical and text-based hints and associate them with systemassigned random passwords. Detailed user surveys were performed and the results showed that the proposed method can help users to remember random passwords with high accuracy. Using the proposed method, participants could recollect random passwords with an accuracy of 90.41% (average), which becomes 95% if case sensitivity is ignored. DUJASE Vol. 7(1) 29-37, 2022 (January)
­Nonverbal Behavior Analysis for Instructors ­With Deep Semantic Segmentation and Pose Classification

In typical instructional situations, teachers’ nonverbal behavior such as body language and eye contact are useful cues for efficient teacher-student communication, and many efforts have been devoted for automatic nonverbal behavior capture and analyses. Existing vision-based methods mostly rely on motion tracking which are limited to scenes with static and simple background. To tackle this problem, we proposed a deep learning approach for instructor’s nonverbal behavior analysis, which used a two-stage architecture with an encoder-decoder network for semantic scene segmentation, and passed the segmented image to a classification network to recognize the teaching pose from six pre-defined categories. We also collected and annotated two datasets, i.e., Classroom-Seg and Classroom-Pose for the training and testing of data-driven methods for behavior understanding in the classroom. Our classroom scene segmentation algorithm achieved an optimal average precision of 84.95% on the classroom-Seg dataset, which exceeded DeepLabV3+ by 1.9% with significant less computational resources. The pose classification part achieved an average accuracy of 87.83% on the Classroom-Pose dataset. Our method and constructed datasets can serve as a building block for intelligent scene understanding and behavior analyses in the classroom.
Penerapan Information Gain Dan Algoritma K-Means Untuk Klasterisasi Kedisiplinan Pegawai Menggunakan Rapidminer

One aspect of the discipline of an employee in an agency can be seen from the side of attendance. The level of employee attendance is closely related to an employee's disciplinary assessment. The level of employee discipline can be seen by looking at the hours of attendance or check in attendance, so that with these parameters you will get early, on time and late entry. This study explores data on attendance by using the k-means clustering algorithm. Before calculating the k-means clustering algorithm, attribute selection using information gain is expected to reduce attributes with small weights. The calculations are performed using Rapidminer software. The results showed that the attribute that had the greatest influence was the percentage of late entry with a weight of 0.783. Clustering using the k-means algorithm produces three clusters with the performance value of the Davies Bouldin Index (DBI) -0.645. Cluster zero has fifteen members, cluster one has thirty-six members, and cluster two has fifty-two members. Cluster zero is a cluster that has a low level of discipline, cluster one is a cluster that has a high level of discipline, while cluster two is a cluster that has a moderate level of discipline .
A Traversal Multi-Target Path Planning Method for Multi-Unmanned Surface Vessels in Space-Varying Ocean Current

The multi-USVs cooperation is widely used. Multi-target traversing is an important aspect on the research on the multi-USVs cooperation in space-varying ocean current environment. Therefore, this paper proposes a traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current. The proposed algorithm is divided into two steps: First, a task allocation method for multi-USVs traveling multi-target based on improved Hungary algorithm is proposed for multi-target task allocation. The improved Hungary algorithm is used to allocate tasks of multi-target. Second, to solve the path planning problem, a virtual field RRT* (VF-RRT*) algorithm is proposed. The virtual field sampling algorithm and the ocean current constraint function are introduced in the RRT* algorithm. In order to verify the effectiveness of the algorithm, three groups of simulation experiments are implemented. The results show that the proposed algorithm has good effectiveness in path planning and task allocation.
OBJECT CLUSTERIZATION METHOD IN PICTURES BASED ON FEATURE SELECTION

The article describes the development of a method that allows you to create clusters based on selecting feature features. In todayʼs world, the entertainment industry on the Internet is developing rapidly, creating a demand for better products. This factor has led to the use of artificial intelligence not only in science but also in entertainment. Currently, applications that allow you to create animations of objects in photos are gaining popularity. This article presents an approach to solving the problem of defining objects for animation. To classify and further identify objects, their characteristics should be determined. This is one of the options for abstraction, in which the input set of properties of the object is reduced to the minimum required number of features by which you can identify the object. The algorithm can be used to determine the main features of objects, such as area and perimeter, radii of inscribed and circumscribed circles, sides of the described rectangle, number and relative position of angles, gradient of the object histogram. Based on these features clustering and classification of the image are implemented. The artificial neural network was trained on image samples, each class contained from 2528 to 16185 images of 64×64 pixels. 1000 images of objects of each class were then selected for testing. The success of recognition based on a convolutional neural network was evaluated. According to the results, we can conclude that the smaller the invariance of the class, the greater the accuracy of recognition. The amount of data in the training sample has little effect on the accuracy of the algorithm. After calculating the intensity gradient, you should divide the image into a cell and build a histogram of the gradient object for each pocket of cell; the histogram module corresponds to the intensity gradient at the point.
Risk-Taking Behavior During Downturn: Evidence of Loss-Chasing and Realization Effect in the Cryptocurrency Market

This paper examines risk-taking behavior of cryptocurrency traders in Thailand. We find that, on average, people engage in loss-chasing behavior, taking on more risk after paper loss hoping to recover their investment. Although the literature has documented this loss-chasing behavior in casino gambling, we are the first to reveal this behavior in cryptocurrency market. An increase in paper loss by one standard deviation would raise risk-taking by 13.79%. The loss-chasing behavior is more severe for high-risk individuals and during downturn. In the most extreme case, one-standard-deviation increase in paper loss would increase risk-taking by 48.99%. Our study also adds new insights for the realization effect literature as we take individual’s risk-aversion level into consideration. We find some evidence of realization effect for low-risk individuals. Our study is useful for monitoring and policy recommendations as loss-chasing behavior may result in people irrationally taking on more risk than what they can afford.