Reviewerly/exHarmony huggingface dataset:
Enhancing Remote Work Security: A Multi-Key Biometric Authentication Scheme for Virtual Workspaces

Due to the COVID-19 pandemic, people have been using personal computers, smartphones, and tablets more frequently to work from home and access company-owned networks, data, and applications remotely. However, this increased use also brings with it security risks. To address these risks, a study has proposed a Multiple key biometric authentication scheme for virtual workspaces. This scheme involves using three types of keys: group, pairwise, and individual. Group keys are shared with all workstations in the virtual workspace, pairwise keys are used for communication with other workstations, and individual keys are used for communication with the server. The study evaluated the effectiveness of this authentication scheme on various attack models and found that it was very effective in protecting against attacks. The study suggests that using multiple keys is more secure than using a single key in virtual workspace authentication.
Dual Band CPW Pentagonal Island with Modified Sierpinski Gasket Structure at 3.5 GHz and 5.8 GHz

In this work, a dual-band pentagonal island antenna is proposed for WiMAX 3.5 GHz and WLAN 5.8 GHz applications. Firstly, the basic CPW pentagonal island antenna, Antenna A, is constructed on a 1.6 mm thick FR-4 substrate with a dielectric constant of 4.3 and a copper thickness of 0.035 mm. This antenna contains a pentagonal island with five mini structures of pentagonal island. Then, for Antenna B, a modification is made to the shading made of the Sierpinski gasket structure in the mini structure of the pentagonal island. For simulation Antenna B, it shows the simulation performance of 3.52 GHz and 5.93 GHz with return loss of - 24.84 dB and – 27.94 dB. For the measurement result of Antenna B at desired frequencies of WiMAX 3.5 GHz and WLAN 5.8 GHz, it shows a good performance of – 17.25 dB and – 19.35 dB, respectively.
Factors Influencing the Success of Database Forensic Investigation in Organisations

Database Forensics Investigation (DFBI) plays a crucial role in organizations by safeguarding information from potential intrusions and facilitating investigations by qualified practitioners when such incidents occur. This empirical research examines the factors influencing DBFI success in organizations, including data privacy, database management systems, database forensic models, and lack of knowledge. A proposed theoretical model integrates a conceptual framework called the Operational Data Platform (ODP) with Dempster-Shafer theory. Interview results revealed that 80% of respondents believed data privacy negatively impacted DBFI, leading to hindrance and prevarication of data. Sixty percent attributed the lack of DBFI knowledge to inconsistencies in applying its principles, while 40% considered ignorance a significant factor. Automation of DBFI emerged as a positive factor, with practitioners emphasizing the need for consolidation and simplification of different data models. Automation-as-a-solution was also proposed. The conclusion highlighted that data collected across database sources for investigative purposes leads to data landscapes that introduce challenges in business adoption, data duplication, and integrity. Thus, organizations must establish automated mechanisms as indisputable sources of truth, promoting commonality in DBFI practices.
Flood mitigation in urban areas: Adopting governance approach

Even though flood disasters are becoming more frequent on an annual basis, this category of disaster is still one of the problems that has not shown any significant signs of progress toward a better direction. In the meantime, a number of studies have arrived at the conclusion that an appropriate policy approach can reduce the severity of disasters of this kind. We are making an effort to gain an understanding of the degree to which the policy direction of the government is moving toward flood management, particularly in urban areas. In order to accomplish this, we are examining a wide range of written materials and documents that are connected to the administration of flood control in Indonesia. Our review, which examines the implementation of the most recent flood mitigation, leads us to make recommendations regarding how flood management can take a governance approach to the process of flood mitigation. These recommendations will be based on the findings of our review. In order to lessen the likelihood of flooding, the government will need to lay a solid foundation from both strategic and tactic perspective.
CkanFAIR: a digital tool for assessing the FAIR principles

Over the last decade, the importance of FAIR principles as a reference for data reusability and openness has increased constantly. Various tools for FAIR data assessment exist, including manual approaches like the FAIR Data Self-Assessment Tool (SAT) and automated tools as the FAIR Evaluation Services and Generic Automatic Tool (GAT). However, subjectivity in manual assessment and limited guidance in automated tools represent significant drawbacks. In such a context, in 2020, the European Commission introduced the “European Data Strategy” to create a unified data market, preserving European competitiveness and data sovereignty. Common European data spaces facilitate access to data in various sectors and promote convergence of data infrastructures and regulations, with a focus on interconnection and interoperability, complying to international standards, such as INSPIRE and FAIR principles, to achieve a unified EU data space. In this work, we propose a tool called CkanFAIR for the automatic assessment of dataset FAIRness in PA portals. The tool incorporates the European Data Quality Guidelines for a more comprehensive dataset evaluation and implements the euFAIR metrics. With this tool, we aim to improve the quality and sharing of data in a reliable environment that can pave the way to the creation of a data space fully compliant with FAIR principles, as well as conforming to community standards in PAs portals. From a wider perspective, it could also signify the initial phase in embracing FAIR Digital Twins (FDT). To validate our results, we compared CkanFAIR with corresponding SAT and GAT, highlighting significant differences.
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities

Self-supervised learning (SSL) is a growing torrent that has recently transformed machine learning and its many real world applications, by learning on massive amounts of unlabeled data via self-generated supervisory signals. Unsupervised anomaly detection (AD) has also capitalized on SSL, by self-generating pseudo-anomalies through various data augmentation functions or external data exposure. In this vision paper, we first underline the importance of the choice of SSL strategies on AD performance, by presenting evidences and studies from the AD literature. Equipped with the understanding that SSL incurs various hyperparameters (HPs) to carefully tune, we present recent developments on unsupervised model selection and augmentation tuning for SSL-based AD. We then highlight emerging challenges and future opportunities; on designing new pretext tasks and augmentation functions for different data modalities, creating novel model selection solutions for systematically tuning the SSL HPs, as well as on capitalizing on the potential of pretrained foundation models on AD through effective density estimation.
Quality Ranking for Synthetically Generated Images

This paper proposes a novel metric for evaluating the quality of images of the same category generated by a synthetic image generator. The proposed method assumes that the points representing synthetic images in a feature space lie on a manifold, and it aims to determine the curvatures of the manifold at those points. The lower the angle, the higher the quality of the generated images is considered to be. Let $\mathcal{F}$ be the set of data points in a D-dimensional feature space where each point represents one synthetically generated image. Let $\mathbb{M}$ be the corresponding manifold to match $\mathcal{F}$. For each data point $y\in\mathbb{R}^{D}$ on $\mathbb{M}$, a number of neighboring points are determined. A local subspace $\mathbb{S}$ is matched for y and a local subspace is also matched for each of its neighboring points. Then, a set of weighted angles between $\mathbb{S}$ and each neighboring local subspace are computed. The average of those weighted angles is used as a measure of curvature of $\mathbb{M}$ at y. Experimental results demonstrate the effectiveness of the proposed metric in evaluating the quality of synthetically generated images.
Privacy-preserving Approximate Query Processing with Differentially Private Generative Models

The increasing importance of data-driven decision-making across various sectors, coupled with the need for efficient large-scale data analysis while upholding privacy, prompts our exploration. Accordingly, approaches like synthetic data generation and approximate query processing have arisen. In this study, we combined differential privacy with approximate query processing for machine learning to enhance privacy. Our approach has been implemented through differentially private generative models within an approximate query processing framework, all of which safeguard data privacy. We provide assessments of synthetic data quality concerning sensitive data and the relative error in approximate query processing utilizing synthetic data.
Mining Large-Scale Knowledge Graphs for Chemical Reaction Fingerprints

Knowledge graphs have become a popular method for representing large, relational data. Similar to citation networks and social networks, relationships in chemical reaction data can also be uniquely captured using a knowledge graph. However, relatively few studies exist concerning the application of knowledge graph mining techniques for numerical representation of chemical reactions. In this study, we develop a pipeline for transforming large-scale relational databases of chemical reactions into heterogeneous graphs, in which reactions and their reactants and products are all characterized as nodes with connecting edges. We create nodes for reaction templates, each of which links to multiple reactions to enhance the connectivity of the graph, and then employ graph representation learning methods (Node2Vec and RotatE) to generate an embedding (or fingerprint) for each reaction node. To evaluate the efficacy of this method, we construct classifiers to label the mechanisms of reactions based on these fingerprints. Experimental results show that our graph learning approach outperforms the state-of-the-art reaction fingerprints, specifically when class labels are not available during the representation learning process. When the representations can be fine-tuned for the subsequent classification task, our approach achieves comparable accuracy to a recent Transformer-based algorithm, but with a significantly lower computational cost.
Efficient Estimation of Local Causal Effects in Graphs via Neighborhood Pooling

In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes. Furthermore, the relational time-series causal inference models are able to estimate local effects for individual nodes by exploiting local node-centric temporal dependencies and topological/structural dependencies. We show that simpler causal models that do not consider the graph topology are recovered as special cases of the proposed relational time-series causal inference model. We describe how the DurbinWu-Hausman test of specification can be used to test for the consistency of the proposed estimator from data. Empirically, we demonstrate the effectiveness of the causal inference models on both synthetic data with known ground-truth and a largescale observational relational time-series data set collected from Wikipedia.
HierAct: a Hierarchical Model for Human Activity Recognition in Game-Like Educational Applications

Constantly evolving landscape of modern education makes the integration of technology crucial to enable more interactive and immersive learning experiences. One of the key problems in this domain is Human Activity Recognition (HAR), which uses standard smartphone sensors to understand and recognize user movements. While HAR exhibits high potential in this area, its persistent challenge is to differentiating between similar activities, whose occurrences can often lead to misclassification and reduced quality of applications. To tackle this problem, we employ specialized classifiers and use them in a hierarchical manner instead of using monolith multi-class models. We propose HierAct - a Hierarchical model that solves the multi-class human activity recognition problem. We also release a new HAR dataset - EduAct - (https://github.com/iitis/HierAct-Dataset) focused on activities that could be used to create game-like educational applications.Our results show that the performance of the proposed model is substantially better than other state-of-the-art machine learning models. Our work shows the potential of hierarchical classifiers in HAR applications for education, by offering educators and students a more accurate, reliable, and engaging interactive experience.
Causal Associations between Temporal Events

Causal inference from observational data has been widely studied to infer causal relations between causes and effects. Due to the popularity of event-based data, causal inference from event datasets has attracted increasing interest. However, inferring causalities from observational event sequences is challenging because of the heterogeneous and irregular nature of event-based data. Existing work on causal inference for temporal events disregards the event durations, and is thus unable to capture their impact on the causal relations. In the present paper, we overcome this limitation by proposing a new modeling approach for temporal events that captures and utilizes event durations. Based on this new temporal model, we propose a set of novel Duration-based Event Causality (DEC) scores, including the Duration-based Necessity and Sufficiency Trade-off score, and the Duration-based Conditional Intensity Rates scores that take into consideration event durations when inferring causal associations between temporal events. We conduct an extensive experimental evaluation using both synthetic datasets and real-world event datasets in the environmental domains to evaluate our proposed scores, and compare them against the closest baseline. The experimental results show that our proposed scores outperform the baseline with a large margin using the popular evaluation metric Hits@K.
The Ethics of Making Information Matter

What ethos informs engagement, and what possibilities for activity are realized in making? What material effects play out in informationalization? What matters? When we theorize and study different information practices one thing becomes clear: in this dance of information, infrastructures, tools, and ourselves, it makes a difference what matter gains liveliness and how. What information is generated, how matter matures, decomposes and re-emerges, and at what point it stops being a possibility (Steyerl, 2013) creates effects for everyone involved. Ethics is central to the ensemble of this ‘spiral dance’ (Haraway, 1991: 181). Or to put it differently: what matters is ultimately an ethical concern. Ethics is present making: it implies the act of doing something consciously. It involves the ethicality of making something of consequence, contributing to something that matters. As a result of that, ethics is also present in matter. It is embodied in the very materiality that emerges and exhibits agency. In our case, the ethicality of materials can refer to what kind of information appears and how a tool performs normativity, values, and sometimes very concrete understandings of the world. This ethicality is not merely about morality and responsibilities, but about co-creative values and ‘ethical forces that operate like analytic frames for ongoing experiments with intensities that need to be enacted collectively’ (Braidotti, 2019: 158). This book’s argument is thus also relevant to ethical considerations: the ethics of making matter are performed in collective, embodied material practices that acknowledge passing (Braidotti, 2019), and transience, but also make anew.
Application of Immersive Technology in Heritage Tourism: A Literature Review

Due to digital growth and the need to discover new ways to com­municate, promote, and maintain cultural heritage resources, the use of im­mersive technology to promote heritage tourism is becoming a more wide­spread practice. Based on a literature review and bibliometric analysis, this study provides insight into the development of this topic and potential direc­tions for future research. The articles considered in this literature review were selected from the Web of Science (WoS) database and published in journals and conference proceedings in a period time from 2017 to 2022. The results obtained from analysis contribute to a comprehensive understanding of the application of immersive heritage tourism experience. Further studies are needed to track the development of these topics and their future application in the heritage tourism context. Directions for further studies are suggested to address the identified gaps.
Effectiveness Of E-Learning To Student Learning Satisfaction: A Literature Review

Introduction: E-learning is acquiring knowledge and skills using the Internet, offering learners a versatile and individualized medium for education. It is a novel method of providing high-quality educational services to students through electronic means to enhance their knowledge, abilities, and consistency. Higher education institutions globally are increasingly using several forms of e-learning. Conversely, through engagement, e-learning has secured the improvement of learning abilities, knowledge, and excellence. Material and Methods: This study constitutes a comprehensive examination of the current body of literature. The subject matter, comprising publications published between 2018 and 2023, was selected from various internet sources, such as Science Direct, PubMed, and Google Scholar. We employed the PRISMA methodology to analyze the literature review systematically. Results: Following a comprehensive evaluation of 14 suitable papers, only 10 of the 183 articles chosen for analysis could be analyzed. Evidence suggests that e-learning can facilitate a fresh comprehension of the essential aspects of effective learning methodologies. Conclusion: According to the literature research findings, it was discovered that the e-learning model affects learning satisfaction, which is simultaneous with its influence. The level of contentment that students have with the outcomes of their e-learning experiences can be a primary measure of the quality of education provided in higher education.
Role of Political Economy in Solving Problems of Researches of Contradictions in the Contemporary World and Prospects of Economical Education Correction (review of the VII International Political Economy Congress)

An overview of the VIIth International Political and Economic Congress «Russia in a Changing World: Political and Economic Justifications for the Development of a New Scientific and Educational Program» which took place in June 2023 at Lomonosov Moscow State University is presented here.
RESEARCH ON THE CONSTRUCTION AND PRACTICE OF NEW TUTOR SYSTEM FOR APPLIED UNDERGRADUATE PROGRAMS BASED ON “THREE REPAIRS AND THREE SUCCESSES”

In order to further improve the quality of talent cultivation, promote the construction of a new mentor system for applied undergraduates based on “three repairs and three successes”, build a new pattern of “whole-person training”, deepen the reform of the undergraduate talent cultivation mode, and explore the implementation of a new mentor system. This paper analyzes the current situation of research at home and abroad, combines the actual situation of the school, analyzes the undergraduate mentorship system to adhere to the “three repair and three success” and “four synergistic” in-depth integration of the model, the implementation of the teacher to return to the teaching of the original heart, to build the “full coverage and the whole process”. The “full coverage, full participation”, innovative undergraduate mentorship teaching mode, clear undergraduate mentorship teaching responsibilities, standardize the form of undergraduate mentorship guidance, strengthen undergraduate mentorship incentives and other aspects of the undergraduate mentorship system to explore the “three repairs, three successes” under the background of the undergraduate full mentorship teaching mode exploration and practice, the undergraduate mentorship system. In order to further improve the quality of undergraduate teaching, we are going to explore the teaching mode of undergraduate mentorship in the context of “three studies and three successes”, with a view to providing reference for the similar colleges and universities and the same majors.
A Design Methodology of an Embedded Motion-Detecting Video Surveillance System

Malaysia urbanization rate has been growing fast due to rapid development and modernization (Economy Planning Unit, 2015) (World Bank, 2015). With the fast growing urban population, the property crimes associated also rises at an alarming rate (United Nations Human Settlements Program, 2007) ( Mohit Mohammad Abdul, Elsawahli H. Mohamed Hassan, 2015) (Ministry of Home Affairs, 2015). Video surveillance system is one of the trusted and efficient security systems to protect people and property against criminal (Varij Ken, 2015). Three problems and issues regarding current video surveillance system are functionality, flexibility and efficiency. The aim of this project is to design and develop an embedded motion detecting video surveillance system and to produce a functioning prototype as the final result to solve these problems. In order to achieve the aim of this project, three objectives are set to be attained. These objectives are to design and develop a motion detection algorithm based on OpenCV library functions using camera as sensor, to process and execute the algorithm using an embedded micro-computer and to compare its processing performance with a PC and to enable wireless user alert using LAN and internet connection. The first objective is achieved as the motion detection algorithm designed and developed based on background subtraction method using camera sensor and openCV library function is proven to functioning well in detecting for motion changes. The algorithm performs successfully both in BeagleBone Black module and PC and is able to deliver outputs required for embedded motion detection video surveillance system. The second objective is achieved as BeagleBone Black (BBB) module, a micro-computer embedded system is used as main processor for the embedded motion-detecting video surveillance system. The BBB module is able to process and execute the motion detection algorithm designed with image processing functions from OpenCV library. The third objective is achieved as embedded motion-detecting video surveillance system is equipped with wireless user alert function using local area network and internet connection. The system is able to send real-time alert to the user via email attached with the image captured and detected as a threat by the system.