Reviewerly/exHarmony huggingface dataset:
COVICT: an IoT based architecture for COVID-19 detection and contact tracing

The world we live in has been taken quite surprisingly by the outbreak of a novel virus namely SARS-CoV-2. COVID-19 i.e. the disease associated with the virus, has not only shaken the world economy due to enforced lockdown but has also saturated the public health care systems of even most advanced countries due to its exponential spread. The fight against COVID-19 pandemic will continue until majority of world's population get vaccinated or herd immunity is achieved. Many researchers have exploited the Artificial intelligence (AI) knacks based IoT architecture for early detection and monitoring of potential COVID-19 cases to control the transmission of the virus. However, the main cause of the spread is that people infected with COVID-19 do not show any symptoms and are asymptomatic but can still transmit virus to the masses. Researcher have introduced contact tracing applications to automatically detect contacts that can be infected by the index case. However, these fully automated contact tracing apps have not been accepted due to issues like privacy and cross-app compatibility. In the current study, an IoT based COVID-19 detection and monitoring system with semi-automated and improved contact tracing capability namely COVICT has been presented with application of real-time data of symptoms collected from individuals and contact tracing. The deployment of COVICT, the prediction of infected persons can be made more effective and contaminated areas can be identified to mitigate the further propagation of the virus by imposing Smart Lockdown. The proposed IoT based architecture can be quite helpful for regulatory authorities for policy making to fight COVID-19.
TextConvoNet: a convolutional neural network based architecture for text classification

This paper presents, TextConvoNet, a novel Convolutional Neural Network (CNN) based architecture for binary and multi-class text classification problems. Most of the existing CNN-based models use one-dimensional convolving filters, where each filter specializes in extracting n-grams features of a particular input word embeddings (Sentence Matrix). These features can be termed as intra-sentence n-gram features. To the best of our knowledge, all the existing CNN models for text classification are based on the aforementioned concept. The presented TextConvoNet not only extracts the intra-sentence n-gram features but also captures the inter-sentence n-gram features in input text data. It uses an alternative approach for input matrix representation and applies a two-dimensional multi-scale convolutional operation on the input. We perform an experimental study on five binary and multi-class classification datasets and evaluate the performance of the TextConvoNet for text classification. The results are evaluated using eight performance measures, accuracy, precision, recall, f1-score, specificity, gmean1, gmean2, and Mathews correlation coefficient (MCC). Furthermore, we extensively compared presented TextConvoNet with machine learning, deep learning, and attention-based models. The experimental results evidenced that the presented TextConvoNet outperformed and yielded better performance than the other used models for text classification purposes.
Explainable artificial intelligence for cybersecurity: a literature survey

Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essential in the development of cybersecurity solutions. However, sharing the same fundamental limitation with other DL application domains, such as computer vision (CV) and natural language processing (NLP), AI-based cybersecurity solutions are incapable of justifying the results (ranging from detection and prediction to reasoning and decision-making) and making them understandable to humans. Consequently, explainable AI (XAI) has emerged as a paramount topic addressing the related challenges of making AI models explainable or interpretable to human users. It is particularly relevant in cybersecurity domain, in that XAI may allow security operators, who are overwhelmed with tens of thousands of security alerts per day (most of which are false positives), to better assess the potential threats and reduce alert fatigue. We conduct an extensive literature review on the intersection between XAI and cybersecurity. Particularly, we investigate the existing literature from two perspectives: the applications of XAI to cybersecurity (e.g., intrusion detection, malware classification), and the security of XAI (e.g., attacks on XAI pipelines, potential countermeasures). We characterize the security of XAI with several security properties that have been discussed in the literature. We also formulate open questions that are either unanswered or insufficiently addressed in the literature, and discuss future directions of research.
Fusion of Remote Sensing, Magnetometric, and Geological Data to Identify Polymetallic Mineral Potential Zones in Chakchak Region, Yazd, Iran

Exploration geologists are urged to develop new, robust, and low-cost approaches to identify high potential zones related to underground/unexplored mineral deposits because of increased depletion of ore deposits and high consumption of basic metal production industries. Fusing remote sensing, geophysical and geological data has great capability to provide a complete range of prerequisite data to accomplish this purpose. This investigation fuses remote sensing data, such as Sentinel-2 and Landsat 7, aerial magnetic geophysical data, and geological data for identifying polymetallic mineralization potential zones in the Chakchak region, Yazd province, Iran. Hydrothermal alteration mineral zones and surface and deep intrusive masses, hidden faults and lineaments, and lithological units were detected using remote sensing, aerial magnetic, and geological data, respectively. The exploratory/information layers were fused using fuzzy logic modeling and the multi-class index overlap method. Subsequently, mineral potential maps were generated for the study area. Some high potential zones of polymetallic mineralization were identified and verified through a detailed field campaign and drilling programs in the Chakchak region. In conclusion, the fusion of remote sensing, geophysical, and geological data using fuzzy logic modeling and the multi-class index overlap method is a robust, reliable, and low-cost approach for mining companies to explore the frontier areas with identical geologic conditions that are alleged to indicate polymetallic mineralization potential.
Using immersive reality technologies to increase a physical education teacher's health-preserving competency

The article discusses the findings of study targeted at enhancing the methodology of augmented reality use for the development of a physical education teacher's competence in health preservation under post-graduate educational settings. According to Umwelt phenomenology, augmented reality is distinguished by its cognitive, metaphorical, varied, interactive, and anthropomorphic characteristics. The article examines the potential applications of augmented reality in a physical education teacher's line of work, notably in the area of health promotion. The software that could be implemented for this function has been described. The research determined that physical education teachers had a favorable attitude toward using immersive reality to protect students' health and foster the growth of their motor skills, intelligence, and creativity. The survey's findings indicate that the majority of teachers had a favorable response to the notion of using augmented reality in their professional work. However, in certain instances, there was a lack of a completely developed knowledge of this issue. The introduction of augmented technology techniques into the post-graduate education process, taking into account the anthropological, ethical, and cultural settings as well as instructor involvement in the mentioned process, could be one way to solve the problem as described.The software application "Virtual Model of Identifying the Risks for Locomotor Apparatus Caused by Ligament Stretching" has been developed based on the usage of immersive reality technologies and comprises of 5 models. Specifically, the anthropological-spatial systems that simulate joints and are utilized to help teachers grasp the dangers to the locomotor apparatus. The positive dynamics of physical education teachers' educational outcomes are characterized as a consequence of study into the effectiveness of applying virtual models as a component of a technique for developing the health-preserving competence of physical education teachers.
Portfolio optimization with digitized counterdiabatic quantum algorithms

We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete mean-variance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.
Lidar Point Cloud Guided Monocular 3D Object Detection

Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in monocular 3D detection, which is an interesting and counterintuitive finding. Using rough labels that are randomly disturbed, the detector can achieve very close accuracy compared to the one using the ground-truth labels. We delve into this underlying mechanism and then empirically find that: concerning the label accuracy, the 3D location part in the label is preferred compared to other parts of labels. Motivated by the conclusions above and considering the precise LiDAR 3D measurement, we propose a simple and effective framework, dubbed LiDAR point cloud guided monocular 3D object detection (LPCG). This framework is capable of either reducing the annotation costs or considerably boosting the detection accuracy without introducing extra annotation costs. Specifically, It generates pseudo labels from unlabeled LiDAR point clouds. Thanks to accurate LiDAR 3D measurements in 3D space, such pseudo labels can replace manually annotated labels in the training of monocular 3D detectors, since their 3D location information is precise. LPCG can be applied into any monocular 3D detector to fully use massive unlabeled data in a self-driving system. As a result, in KITTI benchmark, we take the first place on both monocular 3D and BEV (bird’s-eye-view) detection with a significant margin. In Waymo benchmark, our method using 10% labeled data achieves comparable accuracy to the baseline detector using 100% labeled data. The codes are released at https://github.com/SPengLiang/LPCG .
Shaping future low-carbon energy and transportation systems: Digital technologies and applications

Digitalization and decarbonization are projected to be two major trends in the coming decades.As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern.This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles.This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems.
Machine Learning based Education System with Sentiment Analysis for Students

After a deep questionnaire among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies.
How Practical Are Fault Injection Attacks, Really?

Fault injection attacks (FIA) are a class of active physical attacks, mostly used for malicious purposes such as extraction of cryptographic keys, privilege escalation, attacks on neural network implementations.There are many techniques that can be used to cause the faults in integrated circuits, many of them coming from the area of failure analysis.In this paper we tackle the topic of practicality of FIA.We analyze the most commonly used techniques that can be found in the literature, such as voltage/clock glitching, electromagnetic pulses, lasers, and Rowhammer attacks.To summarize, FIA can be mounted on most commonly used architectures from ARM, Intel, AMD, by utilizing injection devices that are often below the thousand dollar mark.Therefore, we believe these attacks can be considered practical in many scenarios, especially when the attacker can physically access the target device.
Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Scientific workflow scheduling is a well-known problem that involves the allocation of workflow tasks to particular computational resources.The generation of optimal solutions to reduce runtime, cost, and energy consumption, as well as ensuring proper load balancing, remains a major challenge.Therefore, this work presents a Multi-Swarm based Particle Swarm Optimization (MS-PSO) algorithm to improve the scheduling of scientific workflows in cloud-fog environments.In particular, MS-PSO seeks to address the canonical PSO's problem of premature convergence, which leads it to suboptimal solutions.In MS-PSO, particles are divided into several swarms, with each swarm having its own cognitive and social learning coefficients.This work also develops a weighted sum objective function for the workflow scheduling problem, based on four objectives: makespan, cost, energy and load balancing for cloud and fog tiers.The FogWorkflowSim Toolkit is used in the evaluation process, with the objectives serving performance metrics.The MS-PSO approach is compared with the canonical PSO, Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO.The following scientific workflows are used in the simulations: Montage, Cybershake, Epigenomics, LIGO and SIPHT.MS-PSO outperforms the canonical PSO on all scientific workflows and under all performance metrics.It competes fairly well against the other approaches and it is more stable and reliable.It only ranks second to PSO, in terms of execution time.In future, multiple species, incorporating population update mechanisms from several algorithmic frameworks (MS-PSO, DE, GA), will be used for scientific workflow scheduling.Hybdridization of the realized algorithm with dynamic approaches will also be investigated.
Metaverse: A Potential Virtual-Physical Ecosystem for Innovative Blended Education and Training

Recently online education has become quite popular, especially during the recent COVID-19 pandemic situations when mostly few remote synchronous S/W platforms were usually used across the globe. However, those platforms cannot replicate the physical classroom/training room environments and lack to provide hands-on support for laboratories or corporate development centers. Again, Metaverse has recently emerged as a useful platform for various types of useful applications. Metaverse has attracted the academic community for some time and this can be well utilized in education and training. Digital Twins (DT) can help in implementing the ideas and concepts of the natural/physical world in Metaverse-based education to make education more realistic and user-friendly. No work or literature review, to the best of the knowledge, of the author, is available that has considered the application of metaverse in training or developing skills for industry professionals along with academics in a holistic manner through an integrated environment. This paper aims to explore the potential of Metaverse for the development of an innovative virtual-physical blended teaching-learning. The objective is to overcome the limitations of current online and physical (offline) education and training systems of both academics and the corporate world.
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data

Recent deep face recognition models proposed in the literature utilized large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks, achieving state-of-the-art performance on mainstream benchmarks. Recently, many of these datasets, e.g., MS-Celeb-1M and VGGFace2, are retracted due to credible privacy and ethical concerns. This motivates this work to propose and investigate the feasibility of using a privacy-friendly synthetically generated face dataset to train face recognition models. Towards this end, we utilize a class-conditional generative adversarial network to generate class-labeled synthetic face images, namely SFace. To address the privacy aspect of using such data to train a face recognition model, we provide extensive evaluation experiments on the identity relation between the synthetic dataset and the original authentic dataset used to train the generative model. Our reported evaluation proved that associating an identity of the authentic dataset to one with the same class label in the synthetic dataset is hardly possible. We also propose to train face recognition on our privacy-friendly dataset, SFace, using three different learning strategies, multi-class classification, label-free knowledge transfer, and combined learning of multi-class classification and knowledge transfer. The reported evaluation results on five authentic face benchmarks demonstrated that the privacy-friendly synthetic dataset has a high potential to be used for training face recognition models, achieving, for example, a verification accuracy of 91.87% on LFW using multi-class classification and 99.13% using the combined learning strategy. The training code and the synthetic face image dataset are publicly released <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/fdbtrs/SFace-Privacy-friendly-and-Accurate-Face-Recognition-using-Synthetic-Data.
Levels of technological competence in the use of social networks among teachers in Santa Marta

Social networks are elements that have had a great reception in the educational context, providing students with the possibility of interaction with each other in real time, both synchronously and asynchronously. Deficiencies in competencies in the management of social networks have an impact on educators with little knowledge in technology and therefore on students with deficits in this area. The factors that influence these types of phenomena may be related to the lack of programs and training by institutions or independently by teachers. The objective was to analyze the levels of technological competence of teachers during the pandemic with respect to the use of social networks in tenth grade. An inductive, non-experimental empirical research was proposed, with a cross-sectional design, in the city of Santa Marta, Colombia, at the El Parque District Educational Institution, focusing on tenth grade. The sample was made up of 15 teachers. A data collection instrument was used, composed of 15 closed Likert-type questions. These questions respond to the exploratory, integrative, and innovative levels that underlie the pentagon of competencies in the causal of technological competencies established by the Ministry of National Education of Colombia. Once the interpretation was carried out, it is appreciated that the hypothesis was refuted and that the instrument achieved its purpose; it was evidenced that teachers do have an adequate level of competencies regarding the use of mechanisms such as social networks. However, training should be carried out with the objective of taking advantage of the tacit potential in these tools, which allows for recommendations not only for implementation with the objective of sharing information, but also for evaluating and monitoring, as well as constant updating by the teaching staff so as not to fall behind technological advances that may take place in the future.
IoT &amp; Cloud-based Smart Attendance Management System using RFID

Attendance management is an essential process for organizations, particularly in the education and corporate sectors.Conventional attendance management systems are prone to errors and inefficiencies.The recent advent of IoT and cloud computing technologies has revolutionized the way attendance is managed, leading to more accurate and efficient systems.In this research paper, we propose architecture for an attendance management system that utilizes IoT, AWS, and an RFID module with an Arduino Uno board.The proposed system aims to automate the attendance management process and eliminate the drawbacks of traditional systems.The proposed system has two main components: the hardware and the software.The hardware component includes an RFID module connected to an Arduino Uno board, which is used to capture attendance data.The software component is built using Python Django and hosted on the AWS cloud, responsible for storing and processing the attendance data.The system provides real-time attendance tracking and reporting and can be accessed from anywhere using a web or mobile application.The proposed architecture was implemented and tested in a real-world scenario using an RFID-enabled tag or card for attendance, and the results show that it is more accurate and efficient than traditional attendance management systems.The system provides a reliable and cost-effective solution for attendance management, which can be implemented in different organizations.The proposed system provides real-time attendance tracking and reporting accessible through a web or mobile application.The expected results of this proposed architecture are more accurate and efficient than traditional attendance management systems, making it a cost-effective and reliable solution for attendance management in various organizations.