Reviewerly/exHarmony huggingface dataset:
A Practical Exploration of Cybersecurity Faculty Development With Microteaching

The growth and development of cybersecurity jobs and careers have created a need for new skilled faculty that can effectively teach the appropriate content to students at all levels. Often instructors are hired based on their academic credentials and professional experience without the use of assessment and faculty development methods to discover if these instructors can teach effectively or even improve the way they teach. Effective instructors have the ability constructively adjust teaching approaches when students are excelling or struggling based on skillful observation and constant assessment. If a student learns something with great ease, perhaps that approach would be of benefit to others. Part of what helps novices develop expertise here is their explicit attempt to understand how and why something works for students. The implementation and use of microteaching can provide a quality improvement approach to help cybersecurity instructors on all levels improve their ability to teach effectively.
AI-Based Motorized Appearance Acknowledgement Scheme for an Attendance Marking System

Staff attendance exists as the greatest historical overwhelming chore in each institution. Existing presence scheme is typically grounded in RFID, IRIS, impression, and uniform notepad. Those schemes necessitate corporeal communication. One and all must wait until the preceding worker goes through the queue. The authors develop an appearance acknowledgement-based staff presence scheme by means of AI. With the help of deep learning and datasets, the scheme senses the position and recognizes which appearance goes to which ID and marks attendance in the datasheet. Then it is exported as an Excel sheet. All resemblance and datasets are protected.
Early Experience of the Generation Pattern of Grid Connected Solar PV System in Bangladesh: A SARIMA Analysis

The Engreen solar PV power plant at Shorishabari, Jamalpur, is the first large scale grid connected solar PV plant in Bangladesh, and has been operating for over three years. As the first PV plant of its type, it offers insights into the performance of grid connected solar PV in Bangladesh. In this study, we use the monthly data from the power plant for its operating life until the recent month, and the seasonal autoregressive integrated moving average (SARIMA) model to find out the generation trend of the plant. We find that monthly and seasonal differencing is required, and that the model (1,1,8) x (0,1,0)12 is the most suitable. We conduct a forecast for the model to predict electricity generation for the last eight months of our sample period, and find that the forecast traces the actual generation values, but without having the same extreme values. Further scope for research includes longer time series data as it becomes available in the future, and incorporating the effects of exogenous factors such as solar radiation, temperature and grid outages.
Research on Overcoming about Language Barriers of AI Machine Translation replacing Interpreting under Information Technology

Today, as new technologies continue to iterate, it seems that everything has been given the AI soul. However, despite the rapid development of artificial intelligence, there are still many shortcomings, especially in terms of interpreting The purpose of this paper is to analyze the language barriers that AI machine translation needs to overcome to replace interpreting. Through the literature review method, case analysis method, experience summary method, comparative analysis and other research methods, this paper proposes the language barriers that AI machine translation needs to overcome to analyze. The reason why these obstacles are difficult to break through. By analyzing the impact of AI machine translation on the interpreting industry, some suggestions were given to interpreters, and through questionnaires, people's attitudes towards AI machine translation replacing interpreters were understood. The research results show that the difficulty of AI machine translation to replace interpreting is mainly due to the subjectivity of language, lack of understanding of humor and other emotions in big data, and difficulty in accurately recognizing speech. 38% believe that the main obstacle is the lack of emotion in AI machine translation, 28% think that the main obstacle is the subjectivity of language, and 25% think that the main obstacle is the problem of language recognition. Changes in the working environment of interpreters have put forward new and higher requirements on the capabilities and qualities of interpreters, and they need to learn and master advanced translation technologies to substantially improve the quality and efficiency of interpreting.
An efficient multikeyword fuzzy ciphertext retrieval scheme based on distributed transmission for Internet of Things

As traditional computing and cloud computing integrate, the Internet of Things (IoT) has evolved into a layered and cloud-network-edge-end architecture. However, most searchable encryption models still use triples, in which hierarchical structures are neglected, and insecure intermediate nodes are exposed to external environment. Meanwhile, mainstream schemes adopting accurate retrieval are incompatible with IoT end users' features of differentiation. To address these issues, we innovatively design an efficient and credible search model with an accurate multikeyword fuzzy ciphertext retrieval scheme in the context of IoT. First, based on network coding and key sharing, data are grouped, encoded, and transmitted in parallel to the receiver node through middle-layer nodes, with high efficiency and reliability. Second, to realize fuzzy retrieval of IoT, edit distance is selected as the standard of difference between keywords, and then document index vector and query vector are created based on locality sensitive hashing (LSH) and Bloom Filter. Furthermore, to improve the traditional scheme, query keywords are split into multiple single-word forms, inner products between each trapdoor of single word and encryption index vector are calculated, respectively, for the sum of each inner product and thus top $\mathrm{top}$ - k $k$ sorting search. Ultimately, feasibility, safety, and efficiency of our improved scheme are verified by security analysis, while simulation results support that our scheme has better accuracy and efficiency.
The Impact of Electronic Human Resource Management (E-HRM) Strategies on Organizational Innovation by Knowledge Repository as Mediating Role

The current study is intended to provide the analysis of the direct and indirect relationships between (E-HRM) strategies and organizational innovation through a knowledge repository as a mediation construct. From various previous studies, five (E-HRM) strategies constructs were adopted for this study. These include e-recruitment, e-selection, e-training, e-performance appraisal, and e-compensations, and used the mediating role of a knowledge repository for these constructs to improve organizational innovation. The study applied the Smart Partial Least Square (PLS) software to analyze the underlying relationships amongst the most relevant constructs between (E-HRM) strategies, knowledge repositories, and organizational innovation with a total of seven constructs. As a result of analysis of the findings from middle managers (n = 94) working for the Royal Jordanian airline. It was found that there was a strong and statistically positive significant relationship between several (E-HRM) strategies and organizational innovation by using the knowledge repository as a mediation construct.
Facial Expression Recognition Method Based on Multi-scale Efficient Channel Attention Mechanism

Facial expression recognition is an important research content in the field of computer vision. Aiming at the problems of incomplete feature extraction and weak pertinence of important features in the process of facial expression recognition based on traditional convolutional neural networks, this paper proposes a facial expression recognition method based on multi-scale efficient channel attention mechanism. Firstly, multi-scale feature extraction layers are used to extract more abundant expression feature information; Secondly, the efficient channel attention mechanism is introduced into the multi-scale feature extraction layer to improve the response of the model to important features; Finally, the shallow layer feature information and the deep layer feature information are fused by cross-connection to avoid the loss of shallow layer edge feature information when the network layers are deep. A method to balance the number of different categories of expressions is proposed to reduce the large difference between different types of expressions. The results of the proposed method are comparable to or better than many classical algorithms.
Joint Locality Preservation and Adaptive Combination for Graph Collaborative Filtering

Due to its powerful representation ability, Graph Convolutional Network (GCN) based collaborative filtering (CF), which treats the interaction of user-items as a bipartite graph, has become the upstart in recommender systems. Nevertheless, existing GCNs based recommendation model only compromisingly exploits the shallow relationship (generally less than 4 layers) to represent the user and item with different number of interactions, which limits their performance. To address this problem, we propose a novel recommendation framework named joint Locality preservation and Adaptive combination for Graph Collaborative Filtering (LaGCF), which contains two components: locality preservation and adaptive combination, where locality preservation explicitly integrates local features with high-order features at each propagation layer for obtaining better performance faster, while adaptive combination adds adaptive weight and identity matrix in aggregation to enhance the representing power of deep-level GCN. Finally, extensive experiments are conducted on four publicly available datasets, and the results demonstrate the effectiveness and superior performance of our model from both analytical and empirical perspectives.
A Multi-attribute Decision Handover Strategy for Giant LEO Mobile Satellite Networks

Due to the characteristics of low orbital height and large number of satellites, the giant Low Earth Orbit (LEO) satellite network has more frequent handovers than ordinary LEO satellite communication systems. There are a large number of satellites available for handover, and the handover problem is more prominent. In this study, a handover strategy suitable for all users is designed for the inter-satellite handover in the giant LEO satellite network. This strategy designs two different handover methods for different handover users and new callers. For users who can predict the handover time, a multi-attribute decision handover strategy based on a directed graph is adopted. It uses the satellite service timetable (including satellite coverage at each time and each index value) to construct a satellite handover directed graph, and combine the handover path to reserve channel resources for users. For handover users and new callers who cannot predict the handover time, a multi-attribute decision handover strategy based on combined weights is adopted. The best candidate satellites are obtained by weighting each index combination, distinguishing user types and service types, and using channel queuing strategies to access satellites according to different priorities. The simulation results prove that this strategy can reduce the number of handovers while taking into account the signal strength and system load balance, and can effectively reduce the handover blocking rate.
Trusted 6G-Envisioned Dynamic Spectrum Allocation: A Reference Model, Layered Architecture, and Use-Case

Bhattacharya, Pronaya Patel, Farnazbanu Ralegankar, Vishaka Thakkar, Bhaumik Tanwar, Sudeep Abouhawwash, MohammadSpectrum allocation among multiple telecom providers is challenged with a fair spectrum allocation process and collusion among multiple telecom parties involved in spectrum bids. As spectrum licensing have shifted toward decentralization, blockchain-based spectrum allocation can address the limitations through a fair and trusted bidding process, and spectrum allocation which is transparent to all bidders. Moreover, the spectrum allocation process has shifted to dynamic spectrum allocations, and thus in the coming future, owing to the inherent advantages of sixth-generation (6G) networks, with high network reliability, user bandwidth, high-precision, ultra-high reliability, and extreme flexibility, all presented as networked-in-a-box service, dynamic spectrum allocation in 6G-envisioned networks is a reality in near future. In light of these facts, we present in this paper, a comprehensive examination of the integration of a blockchain-based scheme for dynamic spectrum access in 6G-envisioned communications. We present the layered reference architecture and highlight a case study of future blockchain-based spectrum access for 6G-serviced network access. The paper serves as starting foundation toward the build of effective blockchain-based spectrum allocation schemes with effective networked serviced applications.
Construction and Exploitation of an Algerian Corpus for Opinion and Emotion Analysis

The opinions we form are products of knowledge which are built over the years, from a mixture of our surroundings, culture and traditions. Several methods have been proposed over the years. However, dialects were not given much importance, as most studies focus on the English language. Reviewing the research carried out on North African dialects in general, particularly the Algerian dialect and to compensate for the shortage of documented information, we developed a platform which we called “TWIFIL”, for public data annotation. This resulted in the generation of an annotated corpus and a lexicon for the opinion and emotion analysis of Algerian dialects. The purpose of this work is twofold. Firstly, it addresses the shortage of relevant data for the Algerian dialect’s opinion and emotion analysis. Secondly, it provides a more reliable (the assessment of more than one person) annotated corpus. To validate our corpus, a neural sentiment classifier is created which gives competitive results compared to support vector machines and proves that the proposed corpus can be used for opinion and emotion analysis’s tasks.
Rainbow Differential Privacy

We extend a previous framework for designing differentially private (DP) mechanisms via randomized graph colorings that was restricted to binary functions, corresponding to colorings in a graph, to multi-valued functions. As before, datasets are nodes in the graph and any two neighboring datasets are connected by an edge. In our setting, we assume that each dataset has a preferential ordering for the possible outputs of the mechanism, each of which we refer to as a rainbow. Different rainbows partition the graph of datasets into different regions. We show that if the DP mechanism is pre-specified at the boundary of such regions and behaves identically for all same-rainbow boundary datasets, at most one optimal such mechanism can exist and the problem can be solved by means of a morphism to a line graph. We then show closed form expressions for the line graph in the case of ternary functions. Treatment of ternary queries in this paper displays enough richness to be extended to higher-dimensional query spaces with preferential query ordering, but the optimality proof does not seem to follow directly from the ternary proof.
Coarse-to-Fine Network for Crowd Counting

Crowd counting task aims at predicting the count of people in images or videos. Although recent methods perform well by using CNN, most methods only use the information contained in deep layers to predict the final number. To utilize the rich information existed in layers of different level, we propose a simple framework, i.e., Coarse-to-Fine Network (C2FNet), which stacks a set of blocks. In our method, we predict the density maps not only by the final extracted features, but using the features extracted by middle blocks as well. Therefore, we adopt a coarse-to-fine loss (C2F loss) to supervise the learning progress of coarse density maps so that our network can predict the final density map from coarse to fine. Moreover, there is a multi-scale perception module (MSPM) in each block, which extracts multi-scale features by using convolutional layers with kernels of multiple sizes and different dilation rates. Experimental results demonstrate that C2FNet has achieved great performances on ShanghaiTech Part A, ShanghaiTech Part B, UCF-QNRF and UCF-CC-50 datasets.
Studies on Steganography Images and Videos Using Deep Learning Techniques

Securing information has been very anxious regarding the advancement of data science, technology as well as research fields. To resolve the security issues, information hiding algorithms have developed based on deep learning technique. Steganography refers to the process of hiding information within specific files whereas Steganalysis refers the process of recognition of hidden information within those files. In this chapter, we have reviewed several existing work done by various researchers on steganography videos and images using both machine learning and deep learning algorithms. In addition, we focused on Convolutional Neural Network framework, block diagrams for performing Steganalysis process especially hidden information that referred by several researchers. It has five sections namely image steganography, video steganography, automatic detection, integration of audio and video steganography, and finally state of art using deep learning algorithms have illustrated. This proposed review creates an entire idea on steganography videos and image data hiding based on both ML and DL technique.
An HMM–ensemble approach to predict severity progression of ICU treatment for hospitalized COVID–19 patients

COVID-19-related pneumonia requires different modalities of Intensive Care Unit (ICU) interventions at different times to facilitate breathing, depending on severity progression. The ability for clinical staff to predict how patients admitted to hospital will require more or less ICU treatment on a daily basis is critical to ICU management. For real datasets that are sparse and incomplete and where the most important state transitions (dismissal, death) are rare, a standard Hidden Markov Model (HMM) approach is insufficient, as it is prone to overfitting. In this paper we propose a more sophisticated ensemble–based approach that involves training multiple HMMs, each specialized in a subset of the state transitions, and then selecting the more plausible predictions either by selecting or combining the models. We have validated the approach on a live dataset of about 1,000 patients from a partner hospital. Our results show that rare events, as well as the transitions to the most severe treatments outperform state of the art approaches.
Analysis of Vulnerabilities Associated with Social Engineering Attacks Based on User Behavior

One of the most effective attacks on cybersecurity is Social Engineering, in which the attacker deceives an end-user to steal its credentials and perpetrate cyber-crimes. There are hardware and software countermeasures to deal with these types of attacks. However, people themselves are the most vulnerable link in this security chain. In addition, there are influencing factors in people's behavior, which make them more vulnerable. This study aims to determine the most common characteristics that make users vulnerable, either individually or in groups. For this, we conduct an exploratory and descriptive study on administrative, lecturers, and students of a higher education institution on four scales that consider the following behaviors: risk behavior, conservative behavior, exposure to offense, and perception of risk. The results obtained show that users with risky behavior are the most exposed to a Social Engineering attack. We also concluded that the analyzed groups of lecturers and administrators are less likely to be victims of these attacks than students. Finally, we inferred that people who spend more time in front of a computer and are more permissive of risky behaviors are more vulnerable to these attacks.
High speed fringe projection for dynamic shape measurement using binary phase mask. Part 2: manufacture and test

Dynamic measurement of object shape is now achievable at projection rates of up to 30,000 binary fringe patterns per second from a digital micromirror device (DMD) based projector. This two-part paper describes the design, manufacture and test of a binary phase mask in the projection system that produces high-quality greyscale fringes from the binary patterns, with much reduced phase noise compared to the projector defocus approach that has been used until now. Part 2 describes the experimental verification and characterization of the phase masks, manufactured using photolithography and dry etching from the design methods described in Part 1. The height distribution of two masks with point spread function (PSF) dimensions of 1 × 256 and 4 × 64 pixels were assessed using coherence scanning interferometry and contact surface profilometry. Phase noise was determined using standard temporal unwrapping fringe sequences and found to be 20–50% higher than a traditional 8-bit greyscale approach. The projector lens distortion has been observed, and the corresponding compensation method shown to significantly improve the quality of the projected fringes. Increasing the number of projector frames per camera frame reduced phase noise, to less than one-half the 8-bit values for a 10-slice sequence. Fringe image acquisition rates for the 1-slice and 10-slice sequences are up to 255 × and 25 × faster than for the 8-bit greyscale sequence, respectively. Finally, examples are presented of fringe patterns and wrapped phase maps from a high-speed system used to measure moving objects, with projection and acquisition rates of up to 9515 frames s−1.
Super-Resolution Reconstruction of Cytoskeleton Image Based on A-Net Deep Learning Network

To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of sub-cellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e., ~24 nm thickness of microtubule fiber). In this study, a deep learning network named A-net was developed and shows that the resolution of cytoskeleton images captured by a confocal microscope can be significantly improved by combining the A-net deep learning network with the DWDC algorithm based on a degradation model. Utilizing the DWDC algorithm to construct new datasets and taking advantage of A-net neural network's features (i.e., considerably fewer layers and relatively small dataset), the noise and flocculent structures which originally interfere with the cellular structure in the raw image are significantly removed, with the spatial resolution improved by a factor of 10. The investigation shows a universal approach for exacting structural details of biomolecules, cells and organs from low-resolution images.