Reviewerly/exHarmony huggingface dataset:
Cybersecurity Attacks on Academic Data and Personal Information and the Mediating Role of Education and Employment

The cyberspace has simultaneously presented opportunities and challenges alike for personal data security and privacy, as well as the process of research and learning.Moreover, information such as academic data, research data, personal data, proprietary knowledge, complex equipment designs and blueprints for yet to be patented products has all become extremely susceptible to Cybersecurity attacks.This research will investigate factors that affect that may have an influence on perceived ease of use of Cybersecurity, the influence of perceived ease of use on the attitude towards using Cybersecurity, the influence of attitude towards using Cybersecurity on the actual use of Cybersecurity and the influences of job positions on perceived ease of use of Cybersecurity and on the attitude towards using Cybersecurity and on the actual use of Cybersecurity.A model was constructed to investigate eight hypotheses that are related to the investigation.An online questionnaire was constructed to collect data and results showed that hypotheses 1 to 7 influence were significant.However, hypothesis 8 turned out to be insignificant and no influence was found between job positions and the actual use of Cybersecurity.
Polynomial-Time Targeted Attacks on Coin Tossing for Any Number of Corruptions

Consider a coin tossing protocol in which n processors \(P_1,\dots ,P_n\) agree on a random bit b in n rounds, where in round i \(P_i\) sends a single message \(w_i\). Imagine a full-information adversary who prefers the output 1, and in every round i it knows all the finalized messages \(w_1,\dots ,w_{i-1}\) so far as well as the prepared message \(w_i\). A k-replacing attack will have a chance to replace the prepared \(w_i\) with its own choice \(w'_i \ne w_i\) in up to k rounds. Taking majority protocol over uniformly random bits \(w_i=b_i\) is robust in the following strong sense. Any k-replacing adversary can only increase the probability of outputting 1 by at most \(O(k/\sqrt{n})\). In this work, we ask if the above simple protocol is tight.For the same setting, but restricted to uniformly random bit messages, Lichtenstein, Linial, and Saks [Combinatorica’89] showed how to achieve bias \(\varOmega (k/\sqrt{n})\) for any \(k \in [n]\). Kalai, Komargodski, and Raz [DISC’18, Combinatorica’21] gave an alternative polynomial-time attack when \(k \ge \varTheta (\sqrt{n})\). Etesami, Mahloujifar, and Mahmoody [ALT’19, SODA’20] extended the result of KKR18 to arbitrary long messages. It hence remained open to find any attacks of bias \(\varOmega (k/\sqrt{n})\) in the few-corruption regime \(k=o(\sqrt{n})\) when the messages are of arbitrary length, and to find such polynomial-time (and perhaps tight) attacks when messages are uniformly random bits. In this work, we resolve both of these problems. For arbitrary length messages, we show that k-replacing polynomial-time attacks can indeed increase the probability of outputting 1 by \(\varOmega (k/\sqrt{n})\) for any k, which is optimal up to a constant factor. By plugging in our attack into the framework of Mahloujifar Mahmoody [TCC’17] we obtain similar data poisoning attacks against deterministic learners when adversary is limited to changing \(k=o(\sqrt{n})\) of the n training examples. For uniformly random bits \(b_1,\dots ,b_n\), we show that whenever \(\Pr [b=1]= \Pr [\sum b_i \ge t] = \mathsf {\beta }^{(t)}_n\) for \(t \in [n]\) is the probability of a Hamming ball, then online polynomial-time k-replacing attacks can increase \(\Pr [b=1]\) from \(\mathsf {\beta }^{(t)}_n\) to \(\mathsf {\beta }^{(t-k)}_n \), which is optimal due to the majority protocol. In comparison, the (information-theoretic) attack of LLS89 increased \(\Pr [b=1]\) to \(\mathsf {\beta }^{(t-k)}_{n-k}\), which is optimal for adaptive adversaries who cannot see the message before changing it. Thus, we obtain a computational variant of Harper’s celebrated vertex isoperimetric inequality.
A Robust blind watermarking with integer wavelets and Hadamard transform using Gray Wolf Optimization algorithm

In this work, Gray-Wolf optimization (GWO) is applied to decrease the trade-off between robustness and imperceptibility. Initially, the cover image is applied with integer wavelet transform (IWT) and later the whole image partitioned into $4 \times 4$ blocks. After this, the Hadamard transform (HT) is applied for obtaining transform coefficients. The first three pixel intensities in the first rows of each $4 \times 4$ block HT coefficients are quantized with the watermark coefficients. These three-pixel intensities are randomized with a vector, and the threshold parameters are obtained from GWO. When these optimized parameters applied, and the GWO algorithm improves the transparency. The results show the proposed algorithm has high imperceptibility and robust to attacks than existing works in literature.
Interactive Augmented Reality Application Design based on Mobile Terminal

Augmented Reality could realize the integration of virtuality and reality and bring users an enhanced information space feeling. In this context, the design of interactive Augmented Reality application based on mobile terminal is described. Under the integrated development environment of Unity3D, ARFoundation and TensorFlowSharp are adopted to integrate virtual information with markers, and an application with functions of command interaction, plane recognition, image recognition and object recognition are implemented. The design scheme is introduced, and the realization processes are discussed in detail. The test shows that the Augmented Reality application provide rich information and natural way of interacting in three-dimensional virtual-real fusion mode, which improves the interactive experience and brings novelty and immersion to users. The immersive and interactive space improves the interactive experience and strengthens the effect of information transmission, which has certain application significance.
ICT infrastructure in the college libraries of Dooars under North Bengal University

Eniac Computer the first substantial computer was the giant ENIAC machine developed by John W. Mauchly and J. Presper Eckert at the University of Pennsylvania. Development of computers gone through the five generations, and came to the present position. Computer is such a machine which can calculate, process, store and retrieve information very first which human mind cannot do. The real revolution took place when IT came into existence. It is a combination use of computer, communication technology, multimedia technology, optical storage, networking, and barcode technology. Development of IT made easy to get information of any form sitting at home in just one click. Development of IT proves to be the boon for the library. Benefits of ICT use in the library are seen in library management and services. UGC has made provision of special grants for university and colleges to develop ICT infrastructure in the libraries.Present study is conducted on ICT infrastructure in the college libraries of Dooars region under North Bengal University to discover how ICT has affected the overall performance; the results of the study have not found encouraging.
On Disabling Prefetcher to Amplify Cache Side Channels

Side-channel attacks exploit the hardware implementation of processors to extract sensitive data. Attacks that target shared resources between the victim and the attacker are prominent. A shared cache (available in today’s multi-core systems) between the attacker and victim has been shown to create a covert channel capable of leaking the private key. An attacker can extract the key by tracking the victim’s memory access pattern through the shared cache. Prefetchers introduce noise in the attack by speculatively bringing data into the cache that the victim may not use. This paper presents a denial of service (DoS) attack on the prefetcher to prevent it from generating memory accesses that interfere with the side-channel attack. The proposed attack aims to significantly reduce the number of prefetches generated to enable faster key retrieval. Our results show that the private key can be extracted with only 21% additional time in the presence of a prefetcher when the proposed attack is run (compared to a system with no prefetcher).
CASR: A Corpus for Albanian Speech Recognition

This research paper introduces a Corpus for Albanian Speech Recognition (CASR), aims for training and evaluating Automatic Speech Recognition models for Albanian language. The corpus is based on bible' audiobook, comprising 20 hours of transcribed audio data, where transcripts and audios are in the Albanian standard language. An end-to-end speech recognition model based on deep learning is implemented to test and evaluate this corpus. It shows that acoustic models trained on CASR gives satisfactory results. The corpus will be freely available for independent research and provides a valuable resource for research on Albanian ASR.
A Study on Relative Importance of Institutional Mentorship on the development of positive personality among students.

Various studies have shown importance of past academic performance and social environments as factors influencing a student’s personality. With a student spending considerable number of hours in an educational institution, we believe social interactions with peer groups as well as teachers plays a significant role in developing the personality of a student. In the study, 6 personality traits have been evaluated - Ability to continue relationships in peer groups, Comfort Level in Peer Group, Ability to influence Peer Group, Ability to interact in a peer group, Ability to lead a group of peers and Confidence. The study develops a personality score on the basis of eigenvalues and uses a threshold value to predict a positive personality. The study aims at proving the relevance of institutional mentorship provided by colleges in developing a positive personality in students. The demographic and educational factors have also been considered in the study. The study leverages statistical methods – Bayes generalized linear model, Naïve Bayes model and Random Forest for classification and Learning Vector Quantization (LVQ) to determine importance and effect on personality score.
Smart Education Platform to Enhance Student Learning Experience during COVID-19

The community of learners of these days are highly motivated to use the latest trends in education due to the current demand of the education sector. When the Smart education scenario is observed, various advancements in Smart cities play a vital role. Situations like the current pandemic of COVID-19 has forced the entire community of learners to learn through online mode and most of the educational institutions are opting for this mode for a safe learning environment. Several tools are available to support this of which Zoom is one of the most popular online tools and many institutions are using Zoom for online activities related to teaching and learning practice. This paper shows the results of one of such practice implemented during the current situation including the infrastructure of Video Streaming Server utilizing recorded zoom meetings and Moodle. Data is collected by means of a survey conducted among the staff and students. Results have revealed that this practice has shown a good impact on flexibility, accessibility and innovation on learners. The engagement has a scope for improvement to motivate the learners.
Analyzing Security and Privacy Concerns of Contact Tracing Applications

The spread of COVD-19 has affected normal life like no other pandemic in the 21 st century. This has seen the evolution and adoption of digital contact tracing applications, majority of which rely on google and apple exposure notification and can easily be downloaded for use in any smartphone. It is imperative to protect personal health information transmitted in these apps. Developers have been criticized for slacking in protecting personal health information and on being non-compliant to HIPAA. Using MobSF, we interact with these apps to detect security vulnerabilities and demonstrate whether they are complying with their privacy policies. Our analysis showed that contact tracing applications have poor security features and not safe.
Experimental Evaluation and Accuracy Study of Free Offline English Handwritten Character Recognition Tools and Android Applications

Many research fields and real-time applications have the necessity to extract and process handwritten text from documents or images. Handwritten character recognition (HCR)/intelligent character recognition (ICR) is an extended technology of optical character recognition (OCR) which is utilized to convert scanned document/image into editable text form. Offline handwritten character recognition emerged as challenging research topic as it is more favorable to recognize the data years after the document is created. The primary objective of the work presented here is comparison of various offline HCR tools in terms of character and word recognition accuracy. Experimental evaluation of offline tools and Android apps like Google Docs, ABBYY FineReader, CamScanner, Pen to Print, etc., is carried out and recognition accuracy is deduced. This analysis assists researchers to determine which tool is efficient for HCR in terms of recognition rate and also helps in using the tool/app in various real-time scenarios like recognizing bank check amount (written in words), recognizing postal address, captcha reading, healthcare sector, digitize handwritten notes, evaluation of answer scripts, etc.
The Use of Big Data in Digital Economy and Related Legal Aspects

This article examines a new type of crime that was not widely known previously-criminal attacks on Big Data in the digital economy. A definition of the concept of cybercrime is also proposed in the paper. The article examines global criminal law practices of fighting crimes related to the use of Big Data in the digital economy, studied by the author. Typically, such crimes are committed by groups. The main types of criminal activity in the field of IT concern the application and use of Big Data in the digital economy are considered in the study. Detailed statistical data on the considered crimes committed in Russia in the IT sphere are provided. The article examines the features of cybercrimes in Russia and substantiates the need for legislative expansion Chap. 28 of the Criminal Code of the Russian Federation, which punishment for digital crimes by introducing new norms into it that regulate liability for group crimes in the application and use of Big Data in the digital economy.
A User Experience Model for Designing Educational Mobile Application

The use of mobile application facilitates an interactive and innovative learning experience for students. While there are many studies emphasizing on the use of mobile application but less focuses on the student's experience in using those applications. Mobile application designers tend focus on user interfaces but neglects user experience (UX) as their approach in designing educational application for children. Thus, this paper aims to present a model called EduMobile UX model, a user experience model that designers can adopt when designing educational applications for children. To develop the model, an extensive literature was performed to determine the dimensions for the model. The elements for each dimension of the model were identified through semi-structured interviews and observation respondents from two public school in Tronoh, Perak. This study contributes to the development of a UX model that designers can use to develop education mobile applications based on UX principles.
Sparse Neural Networks with Topologies Inspired by Butterfly Structures

In this paper we propose sparse neural networks with topologies inspired by fast computational butterfly structures of selected linear transforms, namely: fast discrete cosine transform, and Beneš network like topologies. We demonstrate that sparse neural networks allow to obtain high reduction in the number of arithmetic operations and weights needed by neural structures while preserving good efficiency in selected tasks where dense neural networks find their applications. In order to verify the efficiency of the considered structures we conducted a series of experiments in data compression and image recognition. The obtained experimental results confirm good efficiency of the considered sparse neural structures and reveal that such topologies allow for significant reduction in the number of arithmetic operations and weights that must be trained and stored in order to re-use trained neural networks.
Discovering Latent Representations of Relations for Interacting Systems

Systems whose entities interact with each other are common. In many interacting systems, it is difficult to observe the relations between entities which is the key information for analyzing the system. In recent years, there has been increasing interest in discovering the relationships between entities using graph neural networks. However, existing approaches are difficult to apply if the number of relations is unknown or if the relations are complex. We propose the DiScovering Latent Relation (DSLR) model, which is flexibly applicable even if the number of relations is unknown or many types of relations exist. The flexibility of our DSLR model comes from the design concept of our encoder that represents the relation between entities in a latent space rather than a discrete variable and a decoder that can handle many types of relations. We performed the experiments on synthetic and real-world graph data with various relationships between entities, and compared the qualitative and quantitative results with other approaches. The experiments show that the proposed method is suitable for analyzing dynamic graphs with an unknown number of complex relations.
Research on Vehicle Detection Algorithm Based on Improved YOLO

Object detection algorithm has high recognition accuracy and fast detection speed, but there are also obvious problems. The positioning is inaccurate because the network uses an end-to-end model that generates the resulting final framework directly after regression without matching and preselecting the main framework. The YOLO network can resize the entire image to a fixed size and divide it into a grid. When there is more than one object in a grid, it is likely to cause missed detection. The DenseNet network can combine the information of the front feature layer and adopt a new network structure, which improves the detection accuracy of objects to a certain extent. Therefore, THE YOLO-D algorithm is proposed, which combines the information of the front characteristic layer to improve the vehicle detection accuracy and positioning accuracy while maintaining the vehicle detection speed.
Photonic spiking neuron with coupled degenerate optical parametric oscillators

We demonstrated a photonic spiking neuron that can represent the bio-realistic spiking modes of Class-I and Class-II neurons. In network experiments, we observed spontaneous modification of spiking mode depending on the synchronization of clustered neurons.
Building Knowledge Graph End to End: Data Integration with Semantic Web

The data are evolved from heterogeneous sources as structured, unstructured, and semi-structured in varied formats from various platforms and applications, extending data integration difficulty. Data heterogeneity in varied formats and representation leads to a standardization gap that has to be addressed on enabling common data representation. Semantic web technologies are used to address the gap of data interoperability by representing data in a machine-understandable format for knowledge inference defined as taxonomy, thesaurus, and ontology to infer knowledge. An end-to-end knowledge graph is proposed to integrate diverse data generated from heterogeneous data sources in this work. A brief view of the data integration process and NLP techniques are discussed to identify concepts from unstructured data inputs to the knowledge representation ontology. The concepts for knowledge modeling are described as a logistic domain for shipping processes. Logistic information is extracted for conceptualization and represented as taxonomy. Ontology is modeled for managing logistic shipping containers to infer knowledge of containers used in the logistic shipping process. The proposed knowledge base is evaluated with a specific use case to infer knowledge for cargo tracking in shipment processes.
Slide Hatch: Smart Slide Generator

With the current situation of Covid-19 and people are moving into online learning for their studies, lecturers and teachers should find a way in which they could make online learning effective for students. The radical shift towards online learning has surged the use of various online tools and methods. The most common tool being used is the presentation slide. Due to the fact that most teachers and lecturers have less knowledge on creating presentations which helps retain student concentration levels during online learning, a system to help them to auto generate these lecture materials would be worthwhile. Previous studies have been conducted to look into generating slides for research and academic papers. However, there is no system which is fine-tuned to help teachers in generating slides automatically just by them uploading images from the lecture material they use. The main objective of this research will be to minimize the overhead and to have a smart way of generating slides for teaching to help students retain their concentration levels.
A Decentralized Runtime Environment for Service Collaboration: the Architecture and a Case Study

In this paper, we describe PACOL (Panoramic-Collaboration), a decentralized and multi-layered architecture of runtime environment for service collaboration. We illustrate by a case study why it is designed as a decentralized control architecture, and why it is designed to support multi-tenants and service solution evolution and continuous optimization. Preliminary case analysis indicates that PACOL could be a feasible proposition to realise cross-domain, reliable and optimized service collaboration for service collaboration towards Internet of Services.
A Pilot Study on Navigation for Information Acquisition Using Eye Tracking

With the increasing number of opportunities to search for the desired content from a large amount of information, it is required to improve the efficiency of information search. Therefore, we propose a support method for information search using eye movement. In information search, the gaze moves quickly during the search, and the gaze stops when it is judged whether the content is the desired content or something close to it. From this, it was thought that by coloring the stopped part in real time, it would be possible to read back quickly, and it would be easier for the gaze to go to similar contents. A pilot experiment was conducted on four subjects and it was found that the total distance of eye movements and elapsed time to complete information search were shorter and more efficient.