Reviewerly/exHarmony huggingface dataset:
Virtual Study Space: A Novel Way of Focus Management

Abstract: Spark motivation, productivity, and focus with aesthetic background study spaces along with a Pomodoro timer to keep study sessions productive using Virtual Study Space. In Virtual Study Space, students can create their custom environment from highly curated minimalist themes, and work with the ability to control background noises to reduce distraction that provides the perfect background companion while one is working away at a lengthy to-do list. The virtual environment also features a video call for students to work together as a team to promote learning using collaboration to support the provision of organizational activities and the exchange of information and educational material. Thus, the implementation of this website becomes a practical depiction of the relations between rational beliefs about studying, academic procrastination, academic life satisfaction, and academic achievement. Tasks in the creation of the website include the development of the study environment, which is setting up web elements and embedding videos as workspaces. The student can choose user-friendly backgrounds according to the study moods such as cafes, nature and libraries. Next is the building of the timer where the user can work along with break time scheduled according to the Pomodoro timer. Lastly, the website has a feature of tracking tasks inside the study space by making a list of priorities to enable efficient planning of tasks to be accomplished in the correct time frame to keep the studentorganized with their work.
A Novel Approach for Detection of Malicious Websites using Machine Learning Techniques

When an unsuspecting victim visits a malicious website, it infects her machine to steal valuable information, redirects her to malicious targets, or compromises her system to launch future attacks. While current approaches have. There are still open issues in effectively and efficiently addressing: filtering of web pages from the wild, coverage of a wide range of malicious characteristics to capture the big picture, continuous evolution of web page features, systematic combination of features, semantic implications of feature values on characterizing web pages, ease and cost of flexibility and scalability of analysis and detection technology. In this position paper, we highlight our ongoing efforts towards effective and efficient analysis and detection of malicious websites, with a particular emphasis on broader feature space and attack-payloads, technique flexibility with changes in malicious characteristics and web pages, and, most importantly, technique usability in defending users against malicious websites.
Triple Encryption of Images based on RC4, Zigzag, and Elliptic Curve Algorithms for Enhanced Security

The security of digital images is of paramount importance in today's digital world. Encryption is a technique used to secure digital images from unauthorized access. The encryption technique transforms the original image into a scrambled form, which is unreadable to anyone without the proper decryption key. Several encryption techniques have been proposed to secure digital images, including DES, AES, RSA, and ECC. These methods, however, are susceptible to side-channel and brute-force attacks. Hence, new encryption methods that offer more security are required. The new multi-encryption algorithm for secure digital image encryption is presented in this research. The suggested approach combines the RC4, Zigzag transformation, and Elliptic Curve Cryptography (ECC) algorithms to increase the security of picture encryption. Data is encrypted using the RC4 stream cipher, image data is scrambled using the Zigzag method, and the key used in the transformation is encrypted using the ECC technique. The proposed algorithm was tested using several standard metrics, and the results show that it outperforms existing encryption techniques in terms of security.
Internet of things and efficient use to management information systems: A field study at the University of Baghdad

The aim the research at the resulting: To identify the perception of the Internet of Things and its components, and to clarify the concepts and components of the efficiency of management information systems and their measurement, as well as to measure the influence of the Internet of equipment on the efficiency of management information systems at the University of Baghdad. The descriptive analytical approach has been used in the search, and a questionnaire, consisting of 184 forms, was planned and dispersed to a sample of administrative workers at the University of Baghdad at the higher and medium administrative levels. The documents been composed and analyzed by SPSS program. The most significant of which is the next: There is a direct impact of the Internet of things on raising the efficiency of management information systems at the University of Baghdad.&#x0D; Internet of things (IoT) components are moderately available at the University of Baghdad. Also, the human element efficiency is moderate at the University of Baghdad. Therefore, they must have more training programs and their capabilities and skills must be improved for technical transformation and fitting up with the use of modern technologies.&#x0D; Moreover, the high efficiency of the senior and middle administrative staff at the university, the efficiency of electronic and technical human resources, equipment and technologies which are available, and the efficiency of workers in the financial department and their use of techniques in financial transactions. The Search reached a set of results and recommendations, the most important of which are: There is a direct influence of the Internet of Things on raising the efficiency of management information systems at the University of Baghdad. Improving the reality of the Internet of Things at the University of Baghdad. This can be done according to the following methods: By focusing on infrastructure and providing the necessary material capabilities.
Ship Target Detection in Remote Sensing Image Based on Improved RetinaNet

Ship image target detection has important applications for ship management. In recent years, target detection based on deep learning has been widely studied in visual ship target detection. However, due to the difference and overlap of ship targets, the target loss rate is high. In order to solve the problem, this paper proposes a target detection algorithm based on improved RetinaNet for ship image target detection. The cyclical focal loss function and CIOU loss function are used to increase the training times of negative samples in the middle of training, which effectively increases ship detection precision by 2.5%.
Research on Application Technology of Multi-source Fusion of 3D Digital Resources in Power Grid

In view of the technical requirements of 3D model resource fusion in power grid infrastructure and operation and inspection, this paper studies the spatial transformation of multi-media heterogeneous models, pose registration, and comprehensive reconstruction algorithms and analyzes the multi-source 3D digital resource fusion analysis of variable time and space technology. A fusion registration analysis method of 3D digital resources oriented to multi-medium and changeable space–time is proposed, forming a management scheme for updating 3D digital models of infrastructure and operation inspection. Relevant achievements support the integration analysis and interaction of 3D digital resources and provide a technical basis for the fusion application of multi-source and polymorphic 3D models.
Research on Mass Image Data Storage Method for Data Center

With the advancement of technology and the development of the electric power business, power enterprises have generated a large amount of image data, which contains rich potential information, and it is urgent to store massive-scale image data for further mining and analysis. Starting from the storage requirements of power grid image data, this paper expounds on the drawbacks of direct storage of small files according to the characteristics of power grid image files. Then, the architecture design and function design of the grid image small file storage are carried out, and the function implementation is carried out. Finally, the grid image small file storage method based on the SequenceFile file format is briefly summarized.
Real-Time Passable Area Segmentation With Consumer RGB-D Cameras for the Visually Impaired

Passable area segmentation is an essential component of navigation assistance for the visually impaired people. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting informative features from depth modality. Simple fusion, however, may not lead to satisfactory results as actual depth data are generally noisy, which may worsen the accuracy as the networks go deeper. In this paper, we construct a wearable system with a novel Lightweight Multi-Modality Collaborative Network (LM-MCN), which is performing real-time passable area segmentation to assist people walking alone more safely. Firstly, a normal map estimation (NME) module, which can convert dense depth to a normal map with high accuracy and efficiency, is introduced. Moreover, we propose a multi-modality feature fusion module (M-MFFM), which can recalibrate and fuse the features from both RGB images and the inferred depth information for accurate passable area segmentation. Furthermore, we publish a new passable area segmentation dataset, named SensingAI-Easy-to-Walk (SETW), collected from sidewalk views in urban areas. Extensive experiments demonstrate that depth information is more appealing since it provides a geometric counterpart to the RGB representation. In addition, the experimental results report our method achieves a 96.50% MaxF with real-time inference speed of 50 FPS for KITTI road, which demonstrates the validity and superiority of LM-MCN.
Bibliometric Analysis of Virtual Reality in School and University Contexts

Bibliometric analysis has become an extremely popular research method with the dramatic increase in scientific publications and other textual and numerical online data. Using bibliometric analysis in combination with qualitative content analysis, it is possible to discover important characteristics of scientific articles and to gain insights into the progress of other researchers’ work, scientific trends, most cited authors, the relationships between keywords recurring in publications, and other important parameters of scientific texts. Our study aims to explore the topic of virtual reality in education using bibliometric analysis of scientific publications with the aid of visualization tools and keyword co-occurrence analysis. The WoS and Scopus databases were chosen because of their popularity and wide use in bibliometric studies. Data from 2012 to 2022 were collected and analyzed using the search query “School” OR “University” as the topic AND “Virtual Reality” as author keywords, with a focus on journal articles and no restriction on the scientific field. A qualitative content analysis of the ten most cited articles was also done. We found that since 2016, there has been an intense growth of publications on this topic. Four main themes have emerged in research on virtual reality in education over the last decade: research on virtual reality technologies and innovative solutions for education, research on the impact of virtual reality on learners of different ages, research on the adaptation of learning materials to virtual reality, and research on learning with virtual reality. The results of the qualitative content analysis revealed the main topics of the most cited publications. The bibliometric analysis provides insights into the diversity of research on the chosen topic. These insights allow researchers and practitioners to understand virtual reality's landscape in education better and choose less explored areas when planning their new research. This study also demonstrates the combination of bibliometric analysis and qualitative content analysis methods.
Information Transparency and Market Efficiency in Blockchain-enabled Marketplaces: Role of Traders’ Analytical Ability

Classic economic theory asserts that full information transparency entails information symmetry and, thus, market efficiency in trading. We test if this theory still holds in a blockchain-enabled marketplace where full information transparency is accomplished via blockchain. We leverage the data from EnjinX, a non-fungible token (NFT) marketplace, where the entire historical NFT transactions are recorded on the blockchain and are symmetrically accessible to all buyers and sellers. We surprisingly observe substantial market inefficiencies, measured by the percentage of unexploited flipping opportunities. To explain this paradox that inefficiencies persist even in a fully information-transparent environment, we propose that not all traders are proficient at analyzing such transparent information for trading purposes. We argue that it is the limited analytical ability of traders, rather than information asymmetry, that ultimately drives market inefficiencies. We quantify traders’ analytical ability by examining whether traders’ performance can be augmented by machine learning algorithms. And we further show how traders’ analytical ability influences market efficiency and how the effect of blockchain amount of information on market efficiency is moderated by analytical ability. We find that having ten more historical transactions of an NFT increases market efficiency by 1.10%. However, market efficiency could decrease by 69.02% when traders are incapable of effectively consuming the available information. Our findings contribute to the literature by quantifying analytical ability and highlighting the phenomenon of the analytical-ability divide: Due to this divide of ability, market efficiency may not be attained even when information is symmetrically transparent to all.
Post-Digital University Students and Faculty Members Learning Lives Entanglement: A Collaborative Autoethnographic Case Study

This paper addresses some of the tensions associated with the proliferation of screens in the university environment. Advances in the digitalisation of the university have precipitated a series of pressures and problems that remain unresolved. These issues are linked to agentivity in learning, the precariousness of relationships, and the economy of attention, among many others. Our participation in the TRAY-AP research project on the learning of young university students encouraged us to explore the unforeseen effects of the intensive incorporation of digital devices in higher education. To do so, we adopted a methodological approach based on collaborative autoethnography, from which we confront not only challenges associated with the advance of university technologisation but also its effects on the construction of personal and professional identities. We follow a polyphonic approach in three voices, in which we bring to our reflections the experiences of the teachers and students with whom we have been in dialogue during this project. The contribution focuses on three tensions associated with the consumption of attention by colonising digital devices. We also address the conflict linked to the degradation of personal relationships, which has been caused by over-exposure to communication through screens. Finally, with the rise of digital public profiles, university professors’ contributions and social and institutional commitments seem to have been simplified to quantifying their research results. This paper aims to reveal some of the perverse effects that the growing incorporation of digital technology in the university is having on the academic and personal lives of those who are part of it.
Memory Network With Pixel-Level Spatio-Temporal Learning for Visual Object Tracking

Making full use of temporal and spatial information is critical to cope with the appearance changes of objects in visual object tracking. However, existing methods in the tracking field, which employ a memory network at frame level to learn this information, bring redundancy and cannot build long-term relationships among historical frames due to the limited memory size. In this paper, we propose a novel memory network, Pixel-level Spatio-Temporal Memory (PSTM), which organizes object features in an efficient way to leverage temporal and spatial context information. Specifically, PSTM is constructed and updated by a memory writer, which includes a pixel-level updating strategy to maintain the temporal consistency and dynamically memorize the noteworthy variations. Furthermore, in order to exploit relationships between the object and search region and precisely estimate the state of the object, we propose a memory reader, Pixel-wise Matching and Refinement module (PMR), and model spatial context without a complex manual-designed mechanism. Comprehensive experiments and comparisons on challenging large-scale benchmarks, including GOT-10k, TrackingNet, LaSOT, OTB2015, VOT2020, and NfS, have demonstrated the effectiveness of our proposed method, which performs favorably against state-of-the-art trackers.
Algorithm-Hardware Co-Design of Split-Radix Discrete Galois Transformation for KyberKEM

KyberKEM is one of the final round key encapsulation mechanisms in the NIST post-quantum cryptography competition. Number theoretic transform (NTT), as the computing bottleneck of KyberKEM, has been widely studied. Discrete Galois Transformation (DGT) is a variant of NTT that reduces transform length into half but requires more multiplication operations than the latest NTT algorithm in theoretical analysis. This paper proposes the split-radix DGT, a novel DGT variant utilizing the split-radix method, to reduce the computing complexity without compromising the transform length. Specifically, for length-128 polynomial, the split-radix DGT algorithm saves at least 10% multiplication operations compared with the latest NTT algorithm in theoretical analysis. Furthermore, we proposed a unified split-radix DGT processor with the dedicated stream permutation network for KyberKEM and implemented it on the Xilinx Artix-7 FPGA. The processor achieves at least 49.4% faster transformation and 65.3% faster component-wise multiplication, with at most 87% and 32% LUT-NTT area-time product and LUT-CWM area-time product, compared with the state-of-the-art polynomial multipliers in KyberKEM with the same BFU setting on similar platforms. Lastly, we designed a highly efficient KyberKEM architecture using the proposed split-radix DGT processor. The implementation results on Artix-7 FPGA show significant performance improvements over the state-of-the-art KyberKEM designs.
IRA-FSOD: Instant-Response and Accurate Few-Shot Object Detector

Aiming at recognizing and localizing objects of novel categories with just a few reference samples, few-shot object detection (FSOD) is quite a challenging task. Previous works rely heavily on the fine-tuning process to transfer their models to the novel categories. They are flawed in the real application since the fine-tuning process is time-consuming and it suffers from serious deterioration on the low-quality support set. Based on the observation, this paper proposes an instant-response and accurate few-shot object detector (IRA-FSOD) that can detect the objects from novel categories without fine-tuning. We carefully analyze the limitations of widely-used Faster R-CNN and transform it to IRA-FSOD. Specifically, we first propose a novel semi-supervised Region Proposal Network (SS-RPN) module and a switch classifier module to precisely recognize the potential foreground instances from novel categories without fine-tuning. Moreover, we introduce two explicit inference strategies into the localization module, including explicit localization score and semi-explicit box regression, to alleviate over-fitting towards the base categories. Extensive experiments demonstrates that the proposed IRA-FSOD not only accomplish few-shot object detection with the instant-response, but also reaches state-of-the-art performance under various FSOD protocols and settings.
InconSeg: Residual-Guided Fusion With Inconsistent Multi-Modal Data for Negative and Positive Road Obstacles Segmentation

Segmentation of road obstacles, including negative and positive obstacles, is critical to the safe navigation of autonomous vehicles. Recent methods have witnessed an increasing interest in using multi-modal data fusion (e.g., RGB and depth/disparity images). Although improved segmentation accuracy has been achieved by these methods, we still find that their performance could be easily degraded if the two modalities have inconsistent information, for example, distant obstacles that can be viewed in RGB images but cannot be viewed in depth/disparity images. To address this issue, we propose a novel two-encoder-two-decoder RGB-depth/disparity multi-modal network with Residual-Guided Fusion modules. Different from most existing networks that fuse feature maps in encoders, we fuse feature maps in decoder. We also release a large-scale RGB-depth/disparity dataset recorded in both urban and rural environments with manually-labeled ground truth for both negative- and positive-obstacles segmentation. Extensive experimental results demonstrate that our network achieves state-of-the-art performance compared with other networks.
Dubai and UAE’S Context of Happiness as a Government Policy in 2021 and 2022 Content Analysis of Government Communication

We have been studying Dubai and the UAE government’s communication focusing on the concepts of happiness, positivity and well-being as government policy for several years. Our qualitative studies have demonstrated that it is possible to implement successfully initiatives related to abstract concepts such as happiness, positivity and well-being. In 2016, Dubai and the UAE appointed a Happiness Minister. In the May 2020 government reshuffle, the ‘Quality of Life and Happiness’ portfolio was moved to the Ministry of Community Development. This study’s main objective is to understand the impact of the extinction of the Ministry of Happiness on policies for happiness, positivity, and well-being. We also want to understand the presence of these topics in government communication. We retrieved news published online in English from January 2021 to the end of November 2022 (in English) and information from the government website. We listed the news headlines and conducted a content analysis to the Ministry of Community Development mission statement using webQDA software. Qualitative methodology and content analysis using webQDA software were of utmost importance in validating our conclusions, thus adding scientific value to the studies. The results show that policies for happiness, positivity, and well-being are no longer frequently mentioned in the news. However, the concepts of happiness and well-being still play an important role in that statement. The study allowed to conclude that there are two distinct realities: in one hand a significant decline in initiatives; and the government’s commitment to continue working for a happy society on the other hand. We chose the qualitative methodology and content analysis with the webQDA software was of utmost importance for validating our conclusions and thus added scientific value to the studies.