Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF THE PROBLEMS OF USING EXISTING WEB VULNERABILITY STANDARDS

In today's digital environment, the security of web resources is of primary importance due to the constant increase in the number of web vulnerabilities. This creates potential risks for users and businesses. In this context, standards and methodologies for detecting web vulnerabilities serve as a key tool in their identification and elimination. The two leading standards in this area, OWASP Top 10 and CWE (Common Weakness Enumeration), provide detailed recommendations and overviews of common vulnerabilities. However, they differ in their approaches to vulnerability classification and assessment. This article focuses on an in-depth analysis and comparison of these standards, identifying their advantages and limitations. The main goal is to develop recommendations to optimize the use of these standards, adapted to the specific needs of organizations, to ensure a higher level of security of web resources.
Application of The Make A Match Model to Improve The Learning Outcomes of Class II Students in Elementary Schools

Purpose of the study: This research aims to improve the learning outcomes of class II students through the Make a Match learning model at State Elementary School 07 Palembang. This type of research is classroom action research carried out over two cycles. The subjects of this research were 30 students.&#x0D; Methodology: The techniques used for data collection are tests, observation, and documentation. The data analysis technique used is qualitative and quantitative data analysis. The research results show that learning outcomes using the Make a Match type cooperative learning model have increased in Indonesian language subjects.&#x0D; Main Findings: This can be seen from before the action. The average student score was only 61.83, with a completion percentage of only 60%. In cycle I, the average student score was 68, 47, with a completion percentage reaching 73%. In cycle II, the average student score was 76.30, and the completion rate reached 83%.&#x0D; Novelty/Originality of this study: The latest research applies the make-a-match learning model to Indonesian language subjects. Thus, using the make-a-match type cooperative learning model can improve the learning outcomes of class 2 students in Indonesian language subjects.
CONSTRUCTION OF AN IMPROVED ENCRYPTION SCHEME ON GENERALIZED SUZUKI 2-GROUPS IN THE MST3 CRYPTOSYSTEM

This paper proposes a method for constructing an improved encryption scheme on generalized Suzuki 2-groups for the MST3 cryptosystem, which improves the security parameters of the original approach. The challenge of improving existing cryptosystem design approaches is driven by advances in building quantum computers with sufficient computing power to render many public-key cryptosystems insecure. In particular, this includes cryptosystems based on the factorization problem or the discrete logarithm problem, such as RSA and ECC. There have been several proposals in the past two decades for using non-commutative groups to create quantum-resistant cryptosystems. The unsolvable word problem is a promising area of research for building cryptosystems. It was formulated by Wagner and Magyarik and lies in the realm of permutation groups. Magliveras proposed logarithmic signatures, which are a special type of factorization that applies to finite groups. The latest version of this implementation, known as MST3, is based on the Suzuki group. In 2008, Magliveras demonstrated a transitive LS limit for the MST3 cryptosystem. Later, Svaba proposed the eMST3 cryptosystem with improved security parameters, achieved by adding a secret homomorphic cover. In 2018, T. van Trung proposed an MST3 approach using strong aperiodic logarithmic signatures for abelian p-groups. Kong and his colleagues conducted an extensive analysis of MST3 and noted that, since there are currently no publications on the quantum vulnerability of the algorithm, it can be considered a candidate for use in the post-quantum era. The first implementation of the cryptosystem on the generalized Suzuki 2-group does not provide encryption of the entire Suzuki 2-group and does not protect against attacks with sequential key recovery by the brute-force method. Further work has developed the idea of public cryptography using non-Abelian refinements of parameters. This paper proposes a method for constructing an encryption scheme on Suzuki 2-groups that improves the security parameters of the existing MST3 cryptosystem and solves its security problems.
THE ISSUES OF FINANCIAL RESOURSES USING ASSESMENT IN UNIVERSITES

By providing educational services, universities pursue the satisfaction of public interests on the one hand, and commercial benefits on the other hand. In this sense, a problem arises to evaluate the effectiveness of the financial resources, spent for the purpose of educational services. The article proposes approaches to evaluating the use of financial resources of universities with a combination of public and commercial interests.
ISSUES OF CHOOSING CLOUD COMPUTING PLATFORMS

Cloud computing has become increasingly popular due to its cost-effectiveness, scalability, and flexibility. However, the selection of the right platform can be challenging, with various factors such as security, pricing, reliability, and functionality, among others, to consider. This study found that while all three platforms offer similar services, the selection of a platform depends on the specifics needs of an organization, and it is crucial to conduct a through evaluation of the different options to make an informed decision.
Quantization: Is It Possible to Improve Classification?

Large-scale data classification poses great challenges to computation and storage. There are two major solutions to address the problem: the data dimension reduction and quantization. In the paper, we study the method of first reducing data dimension by random projection and then quantizing the projections to ternary and binary codes, which has been widely applied in practice. Often, the extreme quantization would degrade the accuracy of classification due to high quantization errors. Interestingly, however, we observe that the quantization could result in performance improvement, rather than degradation, if the data for quantization are preprocessed by sparse transforms. Also, the quantization gain could be obtained with the random projections of the data, if both the data and random projection matrices are sparse enough, such that the resulting projections remain sparse. The intriguing performance is verified and analyzed with extensive experiments.
An Efficient Helmet Wearing Detection Method Based On YOLOv7-Tiny

In high-risk construction environments, ensuring safety is paramount. Accidents, often resulting in casualties, underscore the importance of wearing helmets correctly. To tackle the challenges posed by small, traditional targets and the complexity of such scenarios, this paper proposes an efficient helmet detection method based on improved YOLOv7-Tiny. The method optimizes the feature extraction layer network for the detection effect of small targets; and improves the feature fusion method of PANet by adding a cross-layer densely connected network and proposing an H-Dense feature fusion network, which enhances the information exchange between multi-scale features while reducing the number of parameters in the network; in addition, a lightweight upsampling operator is introduced CARAFE, which improves the upsampling process and thus improves the quality of feature fusion. Experimental results on the public SHWD dataset reveal significant improvements. Compared to SSD and YOLOv4-Tiny, the improved algorithm achieves a performance increase of 5.6% and 14.1%, respectively. In comparison to the original algorithm, there is a 21.9% reduction in parameters, while the mAP improves by 1.5%. These outcomes affirm that the proposed method effectively fulfills the safety helmet detection requirements in real-world scenarios.
SIN OC_SORT: MULTI-OBJECT TRACKING BY RE-CORRECT THE EQUATIONS OF MOTION

With the improvement in detector performance, the Kalman Filter can estimate object trajectories based on the basic assumption of short-term linear motion. However, in scenarios with severe object occlusion, this estimation may suffer from significant biases. This paper leveraged prior knowledge of human walking patterns and analyzed scatter plots from Hailuyuan dataset to understand the motion characteristics of pedestrians. We proposed using a modified motion equation to generate virtual trajectories in situations where objects are heavily occluded. These corrected virtual trajectories contribute to enhancing the accuracy of the Kalman Filter’s estimates in the presence of prolonged occlusion. Evaluating our method against six MOT baseline approaches on Hailuyuan dataset, this study achieved a performance of 0.605 MOTA, 0.652 IDF1, and 25.6 fps, outperforming the baseline models.
A Feature Saliency Based Hybrid Neural Network Model for Object Recognition

Accurate recognition of image targets is a fundamental intelligent perception task and extracting effective features is the prerequisite. However, there are still problems with both hand-crafted features and the ones learned by deep neural networks, such as insufficient generalization ability, limited applicability, and insufficient robustness. This paper first selects representative hand-crafted features and deep convolutional features, analyzing their strengths and weaknesses from the perspective of saliency. Then, inspired by the aforementioned analysis results, a feature saliency extraction process is modeled as the feature coding of the autoencoder based on extreme learning machine (ELM-AE), and further integrated into the object recognition framework composed of a deep convolutional feature extractor and an extreme learning machine classifier. In a consequence, a hybrid neural network model for object recognition based on feature saliency is prposed. Finally, experimental results on the German traffic sign recognition benchmark (GTSRB) show that the proposed model can achieve better performance.
CM: A Cross and Mixture unit for multitask recommendation

With the rapid development of deep recommendation models, Multitask Learning (MTL) enables models to have better generalization ability in the learning process by extracting relevant information from different tasks. Although many studies have focused on extracting task-specific information from expert networks using attention mechanisms, they have failed to fully exploit the rich information among task towers, resulting in the failure of each task to use the information to improve the model's effectiveness. In this paper, design a Cross and Mixture (CM) unit for multitask recommendation to improve the feature exploitation capability of models through feature interaction between task towers. The results show that adding this feature fusion method to the existing multitask model higher the original effect of three common MTL datasets.
U-Net lesion segmentation network combining attention and pyramid fusion

In order to solve the problems of limited feature expression ability and low segmentation accuracy of the lesion segmentation network, CGM-UNet based on Coordinate Attention (CA), Multi-Head Self-Attention (MHSA) and Global Pyramid Guidance (GPG) was proposed. The model uses CA in the encoder to enhance the representation of the region of interest, replaces the convolutional pooling operation with MHSA at the highest stage of the encoder, and exploits the global interaction between encoder features to explicitly model complete contextual information and reduces the model parameter quantity, two kinds of attention are combined to capture long-distance dependencies in images. Use GPG to reconstruct skip connections to provide different levels of global context information. In order to verify the effectiveness of the proposed method, experiments were conducted on the stroke segmentation dataset of the cooperative hospital, the ISIC 2018 dataset and the Kvasir-SEG dataset and compared with advanced algorithms. Experimental results show that CGM-UNet has advantages in multiple segmentation evaluation indicators, can effectively segment multiple types of lesions, and can provide a reliable basis for clinical diagnosis and quantification.
Exploring <i>The Library at Night</i>: A Conversation about Robert Lepage’s VR Experience in Toronto

In this forum piece, three scholars from different disciplinary backgrounds discuss their experience of the Toronto run of Robert Lepage’s The Library at Night, an immersive virtual reality (VR) experience. The show’s Toronto debut was produced by Lighthouse Immersive in spring 2022. Taking their cue from Shannon Jackson’s discussion of how disciplinary training and framing impacts perceptual habits (2–4), the authors explore how their various lenses shape their understanding of the multidisciplinary and difficult-to-categorize experience. They discuss affect, audiences, artistic choices, and the show’s larger context. Examining how the show constructs an aesthetic experience for its audience, and critiquing its limited epistemological frame, they consider how The Library at Night fits within our current moment of experience economy in the arts and culture industry.
Generating layout for complex cave-like levels with schematic maps and Cellular Automata

In this paper an algorithm for creating cave-like, user-guided layout is presented. In applications such as computer games, underground structures offer unique challenges and interesting space for player actions. Preparation of such areas can be time consuming and tiresome, especially during the design process, when many ideas are often scrapped. Presented approach aims at improving this process. Schematic input is used so the user can quickly define the general layout. Cave system is divided into levels and tiles - easily-parallelizable modules for the following method stages. Cellular automata are used to extend initial system sketch with interesting shapes while the diamond-square algorithm spreads the final terrain heights. Each stage uses the results of the previously performed operations as input, providing space for alterations. Input maps can be reused to obtain different variations of the same system. The final structure is represented as a 3D point cloud. Chosen representation supports multilevel systems and can be used either as a base for further algorithms, or as a final mesh. The presented approach can be easily incorporated into game design process, while visualizing initial layouts and speeding up preparation of unique, interesting and challenging game spaces for the players to traverse.
Noisy-Defense Variational Auto-Encoder (ND-VAE): An Adversarial Defense Framework to Eliminate Adversarial Attacks

This paper presents a robust adversarial defense mechanism, Noisy-Defense Variational Auto-Encoder (ND-VAE), that combines the strengths of Nouveau VAE (NVAE) and Defense-VAE to effectively eliminate adversarial attacks from contaminated images. The ND-VAE, trained using noisy images, not only removes adversarial perturbations but also preserves the image details, thereby lowering adversarial training costs. By utilizing advanced NVAE architectures and incorporating a noise filter, the defense system efficiently mitigates both previously seen and unseen adversarial attacks. Our evaluations on both MNIST and Fashion-MNIST datasets confirm the high efficiency of ND-VAE, including better zero-shot performance, demonstrating a balanced approach to model expressivity and noise resistance in image classifiers. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code available at https://github.com/shayan223/ND-VAE
Grounded theory in AI-enhanced women’s image in advertising

This study delves into the critical intersection of artificial intelligence (AI) and women’s representation in advertising, focusing on its impact on gender identity formation. AI has become a powerful tool in the marketing world, enabling advanced image manipulation and content creation in ads. The research investigates how AI imagery affects prevailing beauty norms and gender stereotypes, influencing societal views of women. It examines the ethical aspects of AI-driven personalization in marketing and its effect on consumer perceptions, particularly in Lithuania. The study also explores AI’s potential to foster diversity and inclusion by depicting women from various backgrounds, body types, and abilities. Ethical considerations and responsibilities associated with AI’s influence on women’s portrayal in advertising are emphasized, echoing the views of other marketing experts. This research highlights AI’s transformative role in shaping gender identities in advertising, offering important insights for marketing professionals, advertisers, and academics. It encourages the responsible use of AI to improve women’s representation in advertising.
The use of case forms in Modern Danish – an empirical study

This paper investigates the distribution of the nominative (nom) and the oblique (obl) form in personal pronouns in Modern Danish. As per a primary rule, pronoun NPs functioning as subjects are typically in the nom form, whilst the obl form is used in all other syntactic functions. Nonetheless, this rule is subject to certain exceptions in a number of constructions. In this paper, we present four studies concerning the distribution of nom and obl in selected constructions, where instances of variation are observed. These investigations are based on data from two separate corpora, one representing written language and the other, spoken language. Our findings suggest that both stylistic and structural characteristics, alongside considerations concerning processing, significantly influence the distribution of nom and obl. Furthermore, we note a considerable disparity in distribution when comparing spoken and written data.
Historical development of money: From trade to tokens

The timeless relevance of money points to the need, in addition to its economic functions and importance, to refer to its social and institutional aspects. The fact that it is one of the few categories that has survived during the development of civilization, has influenced money to be the subject of interest not only for economists, but also for historians, philosophers, lawyers, and computer scientists. The origin of money is linked to primitive forms of social life, so that the issue of money in modern conditions of life and work would gain a new context. The concept of money and its evolution are inextricably linked to technological progress, although the same, considering that it is not constrained by legal or cultural restrictions, is incomparably faster. In addition to cryptocurrencies, which today are increasingly created by teams of experts in the field of economics, marketing, programming, supported by large investment funds, tokens are experiencing increasing popularity, especially irreplaceable ones. The irreplaceability of tokens comes from their uniqueness, which is confirmed by the record on the blockchain. Their application is possible for any form of computer-generated file. Seen as a digital 'watermark,' a token can establish the authenticity of a digital file-image, video clip, audio file, or cryptocurrency. The ways in which tokens, through the transactions in which they are used, redefine the relationship between value and objects in the market, have found their application in many areas where authorship is valued. The question is whether their enormous popularity is based on real value. The aim of the research in the paper is to point out the importance of money as a historical and sociological category. In addition to the presentation of the historical development of money, the analysis in the paper seeks an answer to the question of whether cryptocurrencies can play the role of money that existed in forms before their appearance. In the analysis of the origin, history and development of money, a historical and content analysis method was used, which includes the analysis of relevant regulations in the field of money functioning, regardless of its form. The comparative-historical approach to money and the relationships created by the establishment of the commodity-money economy are significant for the analysis of money from a sociological point of view. An important socioeconomic issue when it comes to money is the question of the power it provides, the control of its creation and the struggle that takes place in that field among the state, banks, and debtors (Radonjić, 2010, pp. 45-66).
DATA WAREHOUSE MODELLING INFORMATION SECURITY LOG MANAGEMENT IN BUILDING A SECURITY OPERATION CENTER IN CENTRAL GOVERNMENT AGENCIES WITH KIMBALL METHOD

Central Government, is a government agency that manages important and confidential state data and information. The data that is managed needs to be maintained for reliability and security in order to avoid the risk of loss, leakage and misuse of information. To maintain this data, an optimal information security device is needed. Information security tools used today have a variety of functions resulting in many important logs that must be managed, analyzed and evaluated. The log data from each of these information security devices is still separate and must be processed manually to obtain simpler and more efficient data so that it can be monitored and presented to management. The purpose of this research is to make the right data warehouse modeling in order to assist in the process of presenting information quickly and accurately related to the processing of data logs of information security devices as a report that will be given to management in support of the Zero Tollerance data security policy. The method used in designing this data warehouse is using the Kimball 9 step method. The results obtained are in the form of a starflake schema and a data warehouse log of information security devices consisting of a malware fact table, intrusion facts and attack facts that can be used as centralized data monitoring that will be implemented at the Security Operation Center. Testing is done using Pentaho software tools. This data warehouse is expected to provide a quick, accurate, and continuous summary of information so that it can assist management in the decision-making process and policy making for the future.
From the human mind’s powerful unconscious capacities to its digitally extended possibilities

This commentary elaborates on the themes presented in this special issue, composed of four theoretical reviews and two empirical studies, that all show how much our understanding of cognition and metacognition has advanced during the last fourty years. From the inherent, both conscious and unconscious, capacities of the human mind to its digitally extended possibilities, as well as to the subtle mechanisms of cognitive and metacognitive development, the papers show how much cognitive research has moved on, from approaching the mind as a cognitive system analogous to a computer, which was the dominant perspective in the beginnings of Cognitive Psychology in the 1960s, to approaching the mind as a human function within a dynamic, multifaceted, demanding, and continuously changing sociocultural environment, above and beyond its component processes and the way they interrelate.