Reviewerly/exHarmony huggingface dataset:
On the Metaphilosophical Conception of G.E. Koryavko

The article examines the metaphilosophical conception of Galina Koryavko (1944–2018), doctor of philosophy, professor, an outstanding scholar and teacher, who worked in Samara in the 1990s. Those years were difficult not only for the country but also for Russian philosophy, it was caused by a radical revision of the attitude to Marxist philosophy, which in the Soviet period was considered as the only true philosophical teaching, whereas in the post-Soviet period, Marxism was mostly passed over in silence. This circumstance initiated the search for new, often very far from Marxism, interpretations of the meaning of philosophical activity, the nature of philosophy, its place in the intellectual culture of modern Russia. The practical significance of such studies was determined, first of all, by the fact that under the new conditions it was necessary, in a short time, to qualitatively restructure the teaching of philosophy courses in Russian universities. The metaphilosophical conception of G.E. Koryavko, developed in the late 1980s, was based, like the vast majority of philosophical works of that time, on the Marxist philosophical paradigm, which, in the spiritual atmosphere that has developed in Russia over the past 30 years, expectedly led this conception to almost complete oblivion. This study identifies the strengths of G.E. Koryavko’s interpretation of philosophy and also identifies the controversial points inherent in that interpretation. The goal of the article to reconsider G.E. Koryavko’s conception in order to indicate the directions of strengthening and modernization, to carry out its new reading. The article’s author comes to the conclusion that this conception represented one of the significant and timely attempts to comprehend the conceptual prerequisites of the crisis of public consciousness in the USSR in the 1980s, which implied a a demand for significant modernization of Soviet Marxism, taking into account the latest scientific achievements for that time, the entry of mankind into the post-industrial and information era. Unfortunately, the author of the conception failed to dialectically reconstruct the interpretation of ideology as “false consciousness” (F. Engels), as well as to substantiate the fundamental need for the presence of qualitatively different forms of false consciousness in any historical types of civilizations.
From real spaces to virtual spaces: The metaverse and decentralized cinema

Developments in computer and communication technologies, which constitute the starting point of concepts such as decentralization, virtuality, simulation, augmented reality and metaverse, have also brought new forms of expression and designs in art to the agenda. In addition to the decentralized data architecture and metaverse areas that emerged in parallel with the development of network technologies, applications that increase the user's interaction and beleaguered experience such as virtual reality, augmented reality and mixed reality have increased their effectiveness in this field. The metaverse spaces that emerge with the cooperation of software, art and architecture offer their users a more similar life simulation of natural life through augmented reality vehicles or screens. Here, users can perform new experiences for artistic production and consumption as well as daily life practices such as socialization and communication. Metaverse spaces, which include the design of a three-dimensional virtual universe that can be supported by augmented reality, are free from all the constraints of the real world as a cinematic plateau. It is seen as a great advantage that the real film set can create a cinematic work without expensive equipment such as cameras, lights, and sound away from all the negativities of the natural shooting conditions. The fact that the production, distribution and screening of cinema works can be realized within this field brings a new understanding of decentralized cinema to the agenda. Decentralized cinema, which has begun to rise in the expanding virtual geography of the metaverse virtual space with its advantages such as virtual characters and scenes and creative space fictions, is an art form worth examining. This study focuses on the possible future transformations of cinema in terms of production and representation in the context of the relationship of virtual and augmented reality technologies with developing metaverse areas. The emergence of a new cinematic ecology; The opportunities and obstacles it provides to producers are examined with the philosophical criticism method through concepts such as virtual and augmented reality, web 3.0, metaverse in terms of audience experiences it offers for screening. As a result of the study, it was concluded that the metaverse area has many advantages in terms of the production of cinema works, democratization of the production and distribution of works, digital privacy and security for metaverse artists, and recognition of ownership for digital works of art.
The main means of criterion assessment and their implementation into the educational process

The modern updated education system requires new innovations, according to this phenomenon, objective, fair assessment based on predetermined standards (criteria) is gaining momentum. Criteria-based assessment in this perspective is a wonderful solution, since criteria-based assessment makes it possible to understand what is assessed in the student's work and how exactly it is assessed. Thus, the criterion assessment has become one of the components of an updated educational process, which is aimed at the development of students' educational and cognitive competence and at assessment, which is designated by certain criteria, without comparing students with each other. However, despite the variety of methods and tools that can demonstrate students' achievements in various areas of development, traditional assessment, in which there are no clear and understandable criteria for evaluating the achievement of planned learning outcomes, has not gone into oblivion and is still actively used by many teachers in educational institutions. This article demonstrates a number of tools that can be used in criteria-based assessment, their main goals, as well as their contents. These tools are aimed at developing critical, creative, and communicative skills of students that are necessary in the modern world.
Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties

This paper introduces a heuristic for multiple sequence alignment aimed at improving real-time object recognition in short video streams with uncertainties. It builds upon the idea of the progressive alignment but is cognitively economical to the extent that the underlying edit distance approach is adapted to account for human working memory limitations. Thus, the proposed heuristic procedure has a reduced computational complexity compared to optimal multiple sequence alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation conducted in real-life settings demonstrated a significant improvement in number recognition accuracy in short video streams under uncertainties caused by noise and incompleteness. The second line of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine learning approaches, which are typically not tailored to the task of object sequence recognition from a limited number of frames of incomplete data recorded in a dynamic scene situation.
MODEL MATEMATIKA PENYEBARAN PENGGUNA NARKOBA DENGAN ADANYA FAKTOR EDUKASI DI SUMATERA UTARA

Drug abuse is using drugs without a doctor's instructions or prescription. Drug abuse is one of Indonesia's health and social problems, not least in the province of North Sumatra. North Sumatra Province currently has the highest number of drug abuse cases. This makes the researchers make a mathematical model of the distribution of drug users with educational factors with the SIRS epidemic model method. The first step is to modify the model, after which the equilibrium and stability points are calculated. The final step is to perform a numerical simulation on the basic reproduction number using the next-generation matrix approach. From the analysis results obtained two equilibrium points, namely the equilibrium point free of drug users and the endemic equilibrium point for drug users, but only one equilibrium point was analyzed. . Based on the numerical simulation, every population are stable near to the drug free equilibrium point at , means the population of the drug addigt constantly decrease and on a certain condition will be non existent, and stable around to drug endemic equilbrium point at , means the drug addict population will constantly increase so the drug epidemic occurred. From the simulation, it is found that the greater the rate of education, the more the number of vulnerable population with education will increase and the number of vulnerable population without education will decrease
Recontextualization practices: A scale of directness

I analyze Israel president Rivlin's 2020 speech delivered against the background of ongoing COVID-19 health threats and a severe political crisis, and its follow-ups in online news articles and in ordinary readers' comments on news sites and on Facebook. I examine the recontextualization practices used in this three-part discourse event, shedding light on their diversity and focusing on the degree of directness they manifest. Recontextualization is conceptualized as the strategic molding of situations and prior texts and their integration into another discourse through discursive practices. The analysis shows that the president recontextualizes the complex political and social crisis through the lens of the COVID-19 pandemic. He frames the pandemic in terms of its morbid, mythic, and moral dimensions, as well as its influence on various aspects of civil and political disorder. This connection is drawn through the juxtaposition of propositions and the shifts between the deliberative and the epidictic keyings, alluding to Jewish tradition, prayers, and blessings. Through the use of the inclusive “we,” he self-positions as a leader on a par with ordinary people, whereas through direct demands formulated in the plural without personal naming he addresses his ratified addressees, the MPs and the ministers, and thus self-positions as an authority demanding accountability from the current leadership. The news articles in leading online media are short and partial, recontextualizing the speech and the situation through their titles, the selection of the extracts they chose to present and the very few evaluations they make. They mostly take up the president's moral framing and some of his explicit demands for political accountability. The commenters mostly follow up on the moral framing and the mythic dimensions proposed by the president but offer a different perspective on these issues. They shift the responsibility for “losing the compass” from the collective “we” advocated by the president to the politicians including the president, and they ironically echo the epidictic keying in order to challenge and even ridicule it. They further add another dimension to the speech event, by framing the president's speech as politically biased. The discursive patterns used all along this thread of discourses by all its participants range in degree of directness and recontextualize the object of talk, perspectives, keying and positionings.
The Role of Internal Audit Computerization in Supporting the Task of External Auditing in Jordanian Insurance Companies

The study aimed to examine the role of the computerization of internal audits in Jordanian insurance companies in supporting the tasks of the external auditor. The study population consisted of managers and heads of internal audit departments, a number which is estimated at 112 in Jordanian insurance companies, of which there were 25 in the year 2021. Using a questionnaire as a primary source for the study data and relevant statistical methods to analyze the respondents’ answers, it is concluded that there is a significant impact for the computerization of internal auditing processes in supporting the tasks of external auditing, and that the computerization of internal auditing processes has a statistical impact on supporting risk assessment, internal control assessment, and the evaluation of audit evidence by the external auditor. The study recommended giving more attention to qualifying both external and internal auditors to work considering the widespread computerized environment, as well as conducting similar studies in different sectors to compare the results from other sectors with those from the Jordanian insurance companies’ sector in this study.
News Stance Discrimination Based on a Heterogeneous Network of Social Background Information Fusion

Media with partisan tendencies publish news articles to support their preferred political parties to guide the direction of public opinion. Therefore, discovering political bias in news texts has important practical significance for national election prediction and public opinion management. Some biased news often has obscure expressions and ambiguous writing styles. By bypassing the language model, the accuracy of methods that rely on news semantic information for position discrimination is low. This manuscript proposes a news standpoint discrimination method based on social background information fusion heterogeneous network. This method expands the judgment ability of creators and topics on news standpoints from external information and fine-grained topics based on news semantics. Multi-attribute features of nodes enrich the feature representation of nodes, and joint representation of heterogeneous networks can reduce the dependence of position discrimination on the news semantic information. To effectively deal with the position discrimination of new news, the design of a multi-attribute fusion heterogeneous network is extended to inductive learning, avoiding the cost of model training caused by recomposition. Based on the Allsides dataset, this manuscript expands the information of its creator's social background and compares the model for discriminating political positions based on news content. In the experiment, the best transductive attribute fusion heterogeneous network achieved an accuracy of 92.24% and a macro F1 value of 92.05%. The effect is improved based purely on semantic information for position discrimination, which proves the effectiveness of the model design.
HBCA: A Toolchain for High-Accuracy Branch-Fused CNN Accelerator on FPGA with Dual-Decimal-Fused Technique

The programmability of FPGA suits the constantly changing convolutional neural network (CNN). However, several challenges arise when the previous FPGA-based accelerators update CNN. Firstly, although the model of RepVGG can balance accuracy and speed, it solely supports two types of kernels. Meanwhile, 8-bit integer-only quantization of PyTorch which can support various CNNs is seldom successfully supported by the FPGA-based accelerators. In addition, Winograd F(4 × 4, 3 × 3) uses less multiplication, but its transformation matrix contains irregular decimals, which could lead to accuracy problems. To tackle these issues, this paper proposes High-accuracy Branch-fused CNN Accelerator (HBCA): a toolchain and corresponding FPGA-based accelerator. The toolchain proposes inception-based branch–fused technique, which can support more types of kernels. Meanwhile, the accelerator proposes Winograd-quantization dual decimal–fuse techniques to balance accuracy and speed. In addition, this accelerator supports multi-types of kernels and proposes Winograd decomposed-part reuse, multi-mode BRAM &amp; DSP and data reuse to increase power efficiency. Experiments show that HBCA is capable of supporting seven CNNs with different types of kernels and more branches. The accuracy loss is within 0.1% when compared to the quantized model. Furthermore, the power efficiency (GOPS/W) of Inception, ResNet and VGG is up to 226.6, 188.1 and 197.7, which are better than other FPGA-based CNN accelerators.
A Query Language for Exploratory Analysis of Video-Based Tracking Data in Padel Matches

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.
MSLCFinder: An Algorithm in Limited Resources Environment for Finding Top-k Elephant Flows

Encrypted traffic accounts for 95% of the total traffic in the backbone network environment with Tbps bandwidth. As network traffic becomes more and more encrypted and link rates increase in modern networks, the measurement of encrypted traffic relies more on collecting and analyzing massive network traffic data that can be separated from the support of high-speed network traffic measurement technology. Finding top-k elephant flows is a critical task with many applications in congestion control, anomaly detection, and traffic engineering. Owing to this, designing accurate and fast algorithms for online identification of elephant flows becomes more and more challenging. Existing methods either use large-size counters, i.e., 20 bit, to prevent overflows when recording flow sizes or require significant space overhead to measure the sizes of all flows. Thus, we adopt a novel strategy, called count-with-uth-level-sampling, in this paper, to find top-k elephant flows in limited resource environments. Moreover, the proposed algorithm, called MSLCFinder, incurs lightweight counter and uth-level multi-sampling with small, constant processing for millions of flows. Experimental results show that MSLCFinder can achieve more than 97% precision with an extremely limited hardware resource. Compared to the state-of-the-art, our method realizes the statistics and filtering of millions of data streams with less memory.
Risk Managed Cloud Adoption: An ANP Approach

To meet the ever-increasing demand for offering a sustainable environment, organizations are beginning to aim for cloud adoption and the migration of their IT infrastructure and operations to the cloud, utilizing various cloud-based technologies. However, cloud adoption has been impeded by the risk of being exposed as a result of a variety of concerns, including performance, security, and privacy concerns, as well as vulnerabilities and data portability. As a result, this study was carried out to investigate and analyse a variety of risks that come with cloud computing adoption by estimating the impact and frequency of these hazards. In this study, the Analytical Network Process (ANP) is used to prioritise these hazards. Prioritization of these risks based on their influence on cloud adoption may be useful for organizations in building a corrective action plan. According to their negative influence on cloud adoption, "business continuity and recovery planning" and "poor availability of services" are shown to be the most prominent hazards. This research also found that the two most common dangers encountered by cloud adopters in India are "poor availability of services" and "slow response rate."
Action Recognition of Human Skeletal Data Using CNN and LSTM

Human action recognition recognizes an action performed by human beings in order to witness the type of action being performed. A lot of technologies have been developed in order to perform this task like GRN, KNN, SVM, depth maps, and two-stream maps. We have used 3 different methods in our research first method is a 2D CNN model, the second method uses an LSTM model and the third method is a combination of CNN+LSTM. With the help of ReLu as an activation function for hidden and input layers. Softmax is an activation function for output training of a neural network. After performing some epochs the results of the recognition of activity are declared. Our dataset is WISDM which recognizes 6 activities e.g., Running, Walking, Sitting, Standing, Downstairs, and Upstairs. After the model is done training the accuracy and loss of recognition of action are described. We achieved to increase in the accuracy of our LSTM model by tuning the hyperparameter by 1.5%. The accuracy of recognition of action is now 98.5% with a decrease in a loss that is 0.09% on the LSTM model, the accuracy of 0.92% and loss of 0.24% is achieved on our 2D CNN model while the CNN+LSTM model gave us an accuracy of 0.90% with the loss of 0.46% that is a stupendous achievement in the path of recognizing actions of a human. Then we introduced autocorrelation for our models. After that, the features of our models and their correlations with each other are also introduced in our research.
Hypernetwork Link Prediction Method Based on Fusion of Topology and Attribute Features

Link prediction aims at predicting missing or potential links based on the known information of complex networks. Most existing methods focus on pairwise low-order relationships while ignoring the high-order interaction and the rich attribute information of entities in the actual network, leading to the low performance of the model in link prediction. To mine the cross-modality interactions between the high-order structure and attributes of the network, this paper proposes a hypernetwork link prediction method for fusion topology and attributes (TA-HLP). Firstly, a dual channel coder is employed for jointly learning the structural features and attribute features of nodes. In structural encoding, a node-level attention mechanism is designed to aggregate neighbor information to learn structural patterns effectively. In attribute encoding, the hypergraph is used to refine the attribute features. The high-order relationship between nodes and attributes is modeled based on the node-attribute-node feature update, which preserves the semantic information jointly reflected by nodes and attributes. Moreover, in the joint embedding, a hyperedge-level attention mechanism is introduced to capture nodes with different importance in the hyperedge. Extensive experiments on six data sets demonstrate that this method has achieved a more significant link prediction effect than the existing methods.