Reviewerly/exHarmony huggingface dataset:
Saliency Detection Based Adaptive Streaming Method for Panoramic Videos

The demand for the extensive application of panoramic video makes the viewpoint-aware adaptive streaming methods attract more and more attention. Studies found that the users’ viewpoints, under the guidance of the visual attention mechanism and the behavior inertia, are more inclined to focus on the targets that conform to their interest and motion characteristic.Therefore, in this paper, we take the widely used intra prediction mode into consideration and propose an improved saliency detection method to identify regions of user interest based on the content features extracted from compressed domain. By combining the detected saliency with the viewpoint movement characteristics of the user in the past period of time, the viewing probability of each tile is calculated. On this basis, an adaptive streaming scheme is presented to maximize the overall quality of all users under the delay constraint. Simulation results show that, our scheme can give more accuracy prediction for user's viewpoint and further improve the average visual quality of all users while guaranteeing the smooth playback.
Conditional proxy pool re-encryption scheme against collusion attacks

Proxy re-encryption is an encryption scheme used to authorize the decryption authority. For the proxy re-encryption algorithm using a single proxy node, there are security problems of collusion attack and single point failure of the proxy. This paper proposes a conditional proxy pool re-encryption scheme, which uses the security model definition of the encryption scheme of the security identity, and designs a unidirectional, single-hop conditional proxy pool re-encryption scheme using the randomly selected proxy method. Using multiple proxy nodes to form a group of proxies (proxy pool) that do not trust each other, a collusion attack becomes difficult. By pooling proxy nodes to construct a proxy pool, plus a strong signature function, random filling, and one-way, collision-resistant hash function, the threat of proxies being attacked by collusion attack is reduced. Theoretically, the scheme satisfies the security of selected ciphertext attacks under the standard model and the security under the application scenario. Compared with other proxy re-encryption schemes, this scheme solves the problem of proxy single-point failure, improves the operation time and efficiency, greatly reduces the computational cost, and only increases the proxy selection time. It can be combined with distributed systems to solve the problem of multi-node deployment.
Time series intrusion warning with GAN for missing data in CPS

Attacks on Cyber-Physical Systems (CPS) are extremely costly to repair, which is why the early warning is critical for industrial control system security. Previous warning methods have relied heavily on statistical analysis, but such methods struggle to adequately capture the nonlinear relationship between time series data and describe its changing trend. It has limitations in the face of long-term forecasts. This paper analyses the main characteristics of intrusion data in CPS: high dimensionality, data imbalance, and missing data due to few failure samples and sensor failures in highly reliable equipment. Based on the above problem, we propose an intrusion warning system based on missing data, which consists of three modules: data collection module, data interpolation module and intrusion warning module. The improved Generative Adversarial Networks (GAN) model is used to interpolate the missing data and improve the quality of the interpolated data, which is then handed over to the downstream model for intrusion warning. Experiments are conducted on two intrusion datasets collected from CPS. The experimental results show that the model produces more accurate interpolated data and has a high detection rate in intrusion warning work. The proposed data repair and warning method are more applicable to CPS.
Will the Student Get an A Grade? Machine Learning-based Student Performance Prediction in Smart Campus

As an important component in the structure of smart campus, student performance prediction can help to observe the academic progress of students and make timely decisions towards improving the overall learning process. In order to achieve accurate performance prediction, it is required to extract useful data such as the past performance of students from the student information system and employ the extracted data to train an appropriate machine learning model. In this paper, different machine learning models are utilized to predict whether or not a student will get an A grade (i.e, 90 or higher). The work utilizes a real dataset that is extracted from three different courses that require computer and programming skills. The dataset includes three features about the student past performance, namely, high school grade, course midterm grade, and absence percentage. The dataset is then used to train different machine learning models, specifically, linear discriminant, logistic regression, Naive Bayes, support vector machine, decision tree, K-nearest neighbors, and bagged trees. In order to highlight the effectiveness of these classifiers, different metrics were used to evaluate the classification performance such as accuracy, precision, recall, and F1-score. Besides, these models are tested considering one, two, and three features from the dataset to evaluate the significance of each feature in the classification process. After comparing the performance of these machine learning models, it is shown that predicting student performance is indeed applicable with an accuracy that reached 99%. It is also shown that the bagged trees and K-nearest neighbors succeeded to achieve the highest classification accuracy compared to the other models.
Phish-identifier: Machine Learning based classification of Phishing attacks

Phishing attack is one of the most common online attacks that aims to steal important data. It includes sending fake and fraudulent links via emails, text messages, or an instant message. These links are sent from a masquerading attacker posing as a trusted party to the victims. As malicious link is opened at the victim's end, sensitive information can be revealed to the attacker including login credentials. It is an example of highly effective online fraud activity. Correct identification of phishing attack is an important task that can save from revelation of important information of an organization, person, intelligence and/or a government body. This paper shows machine-learning based classification methods based on data collected from 5000 malicious, 5000 legitimate web-pages, and 48 different attributes. The classification is done using Random Tree (RT), Random Forest (RF), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Radial Based Function (RBF), and K-Nearest Neighbors (KNN) using different percentages of training and testing data for MLP and K-values for KNN. The results are evaluated using precision, recall, f-measure, true-positive, false positive rate, and ROC curves. Results conclude that RF and MLP (with 80% division of train to test data) yields highest accuracy of 98.37% and 96.95%, respectively. However, SVM classified lowest number of (91%) instances correctly.
SubTempora: A Hybrid Approach for Optimising Subgraph Searching

Subgraph searching is the problem of determining the presence of a given query graph in either a single or multiple data graph. Due to the wide adoption of graphs in various domains for dataset representation, studies related to graph database management have evolved over the last decades. The subgraph containment query has vast applications in multiple disciplines, particularly for biological datasets that support molecular searching. The classical solution performs expensive one-to-one mapping of the vertices between the query graph and data graph. In the case of multiple data graph settings, also called transactional graph database, the filter-then-verify framework (FTV) adopts specific index structures to represent graph features and to reduce the run-time overheads associated with the one-to-one mapping of the vertices. However, the state-of-the-art approaches mainly suffer from large indexing sizes. In this paper, we study the problem of subgraph searching in a transactional graph database. We present a new compact representation and faster algorithm to reduce the search space by using (1) a compact data structure for indexing the subgraph patterns, and (2) state-of-the-art compressed inverted and bitmap indexes for maintaining the graph occurrences information. Finally, the candidate graph set, generated after the intersection operation, was verified using the subgraph isomorphism algorithm. Extensive experiments with real datasets show that our compressed inverted and bitmap-based indexes outperform the state-of-the-art algorithm regarding memory usage and filtering time.
Combining Image and Text Matching for Product Classification in Retail$$^*$$

The enormous variety of products and their diverse attributes result in a large amount of product data that needs to be managed by retail companies. A single product can have several hundred attributes which are often entered manually. In addition, products have to be classified by hand in many cases by grouping them into categories based on their properties and their relationships to other products. This is a very labor-intensive, time-consuming and error-prone task. In this paper, we present a hybrid approach for automated product classification, which assigns products automatically to the corresponding product category based on the information on their product images. For this purpose, graphical and textual information is extracted from the product images and matched with already classified data using machine learning methods. Our hybrid approach for automated product classification is based on the Global Product Classification (GPC) standard. Our experiments show that the combination of text-based and image-based classification leads to better results and is a promising approach to reduce the manual effort for product classification in retail.
A Classification Method for “Kawaii” Images Using Four Feature Filters

The Japanese word “kawaii”, similar to but slightly different from “cute”, has many varied words. Images expressed by these words can be classified based on human intuitive judgements. However, in computational image classification, conventional image classification methods like CNN (Convolutional Neural Network) can classify ordinary images like human faces with high accuracy, but it is considered to be quite difficult to classify images that do not have common objects and concepts like “kawaii”. In this paper, we propose a method for classifying “kawaii” images by extracting color, shape, and other latent features of “kawaii” images using four proposed feature filters, quantitatively representing them, and then comparing classification accuracy using various classifiers based on machine learning. In the experiments, the RGB color, SIFT (Scale Invariant Feature Transform), and line features of the images are extracted using such filters and compared using NN (Neural Network), Random Forests, AdaBoost, and SVM (Support Vector Machine). The experimental results show the effectiveness of the proposed feature filters and the predominance of “Random Forests” as a classifier for “kawaii” images.
RKTUP Framework: Enhancing Recommender Systems with Compositional Relations in Knowledge Graphs

Advances in joint recommendation and knowledge graph completion (KGC) learning have enhanced the performance and explainability of recommendations. Recent studies have established that taking the incomplete nature of knowledge graphs (KG) into consideration can lead to enhancements in recommender systems’ (RS) performance. The existing models depend on translation-based knowledge graph embedding (KGE) methods for KGC. They cannot capture various relation patterns, including composition relations, even though the composition relationships are prevalent in real-world KG. This study proposes a simple and effective approach to enhance the KGC task while training it with the RS. Our approach, rotational knowledge-enhanced translation-based user preference (RKTUP), is an advanced variant of the knowledge-enhanced translation-based user preference model (KTUP), an existing MTL model. To enhance KTUP, we use the rotational-based KGE techniques (RotatE or HRotatE) to model and infer various relation patterns, such as symmetry/asymmetry, composition, and inversion. Unlike earlier MTL models, RKTUP can model and infer diverse relation patterns while learning more robust representations of the entities and relations in the KGC task, leading to improved recommendations for users. Using RotatE improved the recommender system’s performance while using HRotatE enhanced the model’s efficiency. The experimental results reveal that RKTUP outperforms existing methods and achieves state-of-the-art performance on the recommendation and KGC tasks. Specifically, it shows a 13.7% and 11.6% improvement in the F1 score, as well as a 12.8% and 13.6% increase in the hit ratio on DBbook2014 and MovieLens-1m, respectively.
FedSC: A federated learning algorithm based on client-side clustering

&lt;abstract&gt;&lt;p&gt;In traditional centralized machine learning frameworks, the consolidation of all data in a central data center for processing poses significant concerns related to data privacy breaches and data sharing complexities. In contrast, federated learning presents a privacy-preserving paradigm by training models on local devices, thus circumventing the need for data transfer. However, in the case of non-IID (non-independent and identically distributed) data distribution, the performance of federated learning will drop. Addressing this predicament, this study introduces the FedSC algorithm as a remedy. The FedSC algorithm initially partitions clients into clusters based on the distribution of their data types. Within each cluster, clients exhibit comparable local optimal solutions, thus facilitating the aggregation of a superior global model. Moreover, the global model trained by the previous cluster serves as the initial model parameter for subsequent clusters, enabling the incorporation of data contributions from each cluster to foster the development of an enhanced global model. Experimental results corroborate the superiority of the FedSC algorithm over alternative federated learning approaches, particularly in non-IID data distributions, thereby establishing its capacity to achieve heightened accuracy.&lt;/p&gt;&lt;/abstract&gt;
DOCENTES E BRECHA DIGITAL ANTES E DURANTE A PANDEMIA POR COVID19: UM ESTUDO NA AMÉRICA LATINA

Before COVID19´s health emergency, teachers had different technologies in their teaching processes: while some incorporated the digital dimension in their classes through platforms, software and digital meetings, others simply saw it distant. With the arrival of the pandemic, teachers had to anticipate their learning under the requirement to fulfill the school cycle. In turn, Higher Education Institutions intensified training courses, assuming that this resulted in their efficient use. In addition to the stress that this educational process brought to teachers, the emotional and family spaces were transformed, among others by schedules and work activities. Moreover, the costs of intensive use of technology were transferred to teachers (internet, light, equipment, digital resources, among others), which deepened the precariousness in their work. This study is part of a comparative education research, with a mixed approach, which aims to analyze the technological strategies of teachers for the development of educational proposals that support the reduction of the digital gap in their work as well as social processes, perceived by them during the pandemic by COVID19. The methodological design comprises three stages. In this article, we present part of the findings of the first stage, which includes the construction and validation of the analysis instrument (questionnaire). Specifically, the pilot test results are discussed. Twenty-four responses were obtained from Lasallistian professors of Higher Education Institutions from Mexico (15), Brazil (5), Colombia (3) and Peru (1). Among the conclusions, there were semantic differences in linguistic terms depending on the geographic regions of the Lasallistian Higher Education Institutions. Regarding the appropriation and management of technology, it was found that teachers applied different strategies to reduce these digital differences and that their family environment was deeply modified by virtuality. The pandemic required school classroom to be transferred to the homes of both teachers and students, which generated some problems or malaise, such as the lack of necessary technology, the sharing of spaces with other people, the lack of knowledge of different tools, and the need to acquire resources such as computers, broadband, among others.
Fundamental Law of Active Management (FLAM) Under Fundamental Factor Models: Application in R Based Software Application

In this paper we derive the fundamental law of active management (FLAM) based on cross-section fundamental factor model for residual returns and its application in R. Our focus is using the fundamental factor model to forecast asset expected returns from risk factors instead of predicting and controlling portfolio risk and then use those expected returns in active quadratic utility optimization with constraints to obtain optimal portfolio active weights which we use to calculate information coefficient and transfer coefficient for each time-period. We show that by setting a desired target risk and expected return one can obtain a desired optimal portfolio by maximizing expected return and minimizing forward-looking active risk. The results also show how multi-factor models can be extended to include the framework of the fundamental law of active management and R can be used as a software for the application of the law under factor risk models.
Applying mind mapping methodology in practical studies at a technical university

The paper deals with mind mapping methodology using specifics in practical classes at a technical university. The features of this technique are analyzed, its choice during classes is substantiated, its relevance to modern educational system is shown.
How Do 'Trilemmas' Affect Economic Globalization?

Conventional wisdom holds that globalization has peaked and is now in retreat. Dani Rodrik argued in 2000 that economic integration conflicted with national sovereignty and that this tension would, or at least should, put a brake on economic globalization. Has that view been vindicated? This article explores that question and others by examining four trilemmas, each of which claims that only two of three conditions can hold at once. Rodrik saw a fundamental conflict across the nation-state, mass politics, and integrated national economies. He was inspired by an earlier “impossible trinity” that stressed the inconsistency of an independent national monetary policy, free capital movement, and a fixed exchange rate. The financial crisis beginning in 2007 led to another trilemma claim: the conflict among independent national financial regulation, open capital markets, and economic stability. The experience of the EU generated a fourth claim: a “social trilemma” that identifies the clash of deep economic integration, high levels of social protection, and autonomous national welfare states with very different per capita incomes. This paper argues that only the logic of the last trilemma, related to what is here called “fiscal nationalism,” provides a red line brake on economic globalization because it rests on conflicts of national interest. The other three trilemmas have been worked around, and recent changes in both public and private integration practices likely signal a new globalization rather than a general retreat.
Creativity as one of the ways to cultivate graphic culture

This article discusses motivational aspects of independent creative activity of students of State Fire Academy of EMERCOM of Russia. An analysis of competition works created by cadets using the AutoCAD graphics editor within the framework of celebrating the 90th anniversary of the institution is provided. Competitions and contests in engineering graphics serve as a tool for evaluating knowledge and skills acquired, and provide a basis for correlating methods of solving graphic tasks with the topics of the discipline, which becomes a foundation for scientific research on problems in the organization of the educational process, effectiveness of pedagogical activity, and the education system. The aim of this article is to find new forms of motivation for the creative activity of students in the process of studying graphic disciplines.
Process-Based Cost Management Approach: Enhancing Sustainability of Enterprise Development in Modern Conditions

This contribution explores the process-based approach to cost management as a crucial tool for enhancing the sustainability and competitiveness of businesses in modern conditions. The work outlines the fundamental principles and tools of process-based cost management and presents several successful examples of its implementation across various industries. The process-based approach to cost management involves a comprehensive and systematic analysis of an organization’s business processes to identify areas of improvement and optimize resource utilization. By focusing on process efficiency and effectiveness, this approach can lead to significant cost savings while maintaining or improving the quality of products and services. Moreover, the process-based approach to cost management is a valuable tool for businesses to adapt to changing market conditions, regulatory requirements, and customer expectations. By continuously analyzing and improving their processes, organizations can remain agile and competitive, even in uncertain and dynamic environments. The author also discusses the importance of collaboration and communication across different departments and functions within an organization to successfully implement process-based cost management. Additionally, it highlights the significance of employee engagement and empowerment in achieving sustainable cost reductions and improving overall business performance. In conclusion, this author emphasizes that the process-based approach to cost management is a necessary tool for businesses to enhance their sustainability, competitiveness, and adaptability in the modern economy. By continuously improving their processes, organizations can achieve cost savings, maintain or improve product and service quality, and remain agile and competitive in the face of changing market conditions.
Q&amp;amp;A Interactions: Giving Investors a Voice and Managers’ Withholding of Information

In response to growing demands from retail investors, firms are increasing their engagement with investors through Q&As. We examine how giving investors a voice in the Q&A process (i.e., giving them the opportunity to express an opinion about the content covered during the Q&A) interacts with managers’ answer type to affect investors’ judgments. Using experimental methods, we find that giving investors a voice increases trust and mitigates investors’ negative reactions if managers later withhold (vs. provide) information. However, this mitigative effect is contingent on who requests the information that managers withhold. Investors grow more frustrated and react more negatively when managers give them a voice but withhold information they specifically requested compared to information someone else requested. Our results have important implications for managers considering whether and how to engage with investors using Q&As.