Reviewerly/exHarmony huggingface dataset:
An investigation on turnover intention antecedents amongst the academician in universities

Employees’ decision to quit their present job or company is a turnover intention. The number of employees joining and leaving an organization in a specific period is regarded as employee turnover. This study aims to examine the factors influencing faculty members’ intentions to leave universities in the Kingdom of Saudi Arabia. The population includes academicians from various universities in Saudi Arabia. The primary data were gathered using random sampling methods; 140 responses investigating various demographics and other factors were obtained from academicians of various universities. The present study chose five constructs: job satisfaction, job stress, work engagement, organizational commitment, and turnover intention. The statistical tools were used to conduct statistics examinations. The mean, correlation, and regressions were used to analyze the data gathered. The results showed a significant association between work engagement, job stress, job satisfaction, organizational commitment, and turnover intention. Job stress and turnover intention were not shown to be significantly correlated. Other variables showed significant relationships with the turnover intention of university academicians. AcknowledgmentThis project was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University under research project No 2021/02/18733.
Quantum k-Uniform States From Quantum Orthogonal Arrays

The quantum orthogonal arrays define remarkable classes of multipartite entangled states called $k$-uniform states whose every reductions to $k$ parties are maximally mixed. We present constructions of quantum orthogonal arrays of strength 2 with levels of prime power, as well as some constructions of strength 3. As a consequence, we give infinite classes of 2-uniform states of $N$ systems with dimension of prime power $d\geq 2$ for arbitrary $N\geq 5$; 3-uniform states of $N$-qubit systems for arbitrary $N\geq 6$ and $N\neq 7,8,9,11$; 3-uniform states of $N$ systems with dimension of prime power $d\geq 7$ for arbitrary $N\geq 7$.
CDIEA: Chaos and DNA Based Image Encryption Algorithm

A proposal for an image encryption algorithm called Chaos and DNA Based Image Encryption Algorithm (CDIEA) has been put forward. CDIEA is a combination of block cipher algorithms, permutations, chaotic keys, and DNA operations. It leverages the strong structures of modern cryptography and the properties of chaotic systems, rather than relying solely on chaos. The permutations used in CDIEA are constructed using a strategy called the wide trail design, which makes it resilient to many forms of cryptanalysis. CDIEA operates as a byte-oriented SP-network and has been confirmed to have high security for practical image encryption through both theoretical analysis and computer experiments.
Privacy-Preserving Location-Based Task Allocation

Chapter 3 discusses privacy-preserving task allocation based on task content. In mobile crowdsensing (MCS), since tasks are usually closely related to locations, data requesters may wish to find sensing users located within a certain geometric range to complete the task. Therefore, this chapter will study the privacy-preserving task allocation based on geometric range. Existing privacy-preserving geometric range query schemes either cannot achieve query in arbitrary geometric range or rely on trusted data centers to process location data. To address the challenges, this chapter proposes a privacy-preserving task allocation scheme GPTA that supports arbitrary geometric range queries. Specifically, this chapter firstly proposes GPTA-L with linear query efficiency based on the techniques of polynomial fitting and random matrix multiplication. In order to further improve query efficiency, this chapter studies the query history of data requesters and designs a nonlinear query efficiency scheme GPTA-F based on geometric properties. Security analysis proves that the proposed scheme can effectively preserve the user’s location privacy and query privacy. Experiments based on the mobile crowdsensing applications verify that the proposed scheme has high computational and communication efficiency.
A short text topic classification method based on feature expansion and bi-directional neural network

Short text classification has a pivotal role in many applications such as sentiment analysis and recommender systems. However, the traditional classification models work poorly on short texts due to data sparsity issue. The objective of this research is to determine whether fused topic features will extend short text features and achieve better classification performance. We propose a novel short text topic classification approach and put the topic features into consideration. Firstly, LDA topic model is used to obtain short text topic features. Next, we use BiLSTM(Bi-directional Long Short-Term Memory), CNN(Convolutional Neural Networks) and attention mechanism to extended short text feature extraction. Finally, a SoftMax classifier is introduced to obtain the topic classification results of the short texts. Compared with the state-of-the-art short text topic classification approaches, the experimental results demonstrate that our method achieves better performance.
DiffGAR: Model-Agnostic Restoration from Generative Artifacts Using Image-to-Image Diffusion Models

Recent generative models show impressive results in photo-realistic image generation. However, artifacts often inevitably appear in the generated results, leading to downgraded user experience and reduced performance in downstream tasks. This work aims to develop a plugin post-processing module for diverse generative models, which can faithfully restore images from diverse generative artifacts. This is challenging because: (1) Unlike traditional degradation patterns, generative artifacts are non-linear and the transformation function is highly complex. (2) There are no readily available artifact-image pairs. (3) Different from model-specific anti-artifact methods, a model-agnostic framework views the generator as a black-box machine and has no access to the architecture details. In this work, we first design a group of mechanisms to simulate generative artifacts of popular generators (i.e., GANs, autoregressive models, and diffusion models), given real images. Second, we implement the model-agnostic anti-artifact framework as an image-to-image diffusion model, due to its advantage in generation quality and capacity. Finally, we design a conditioning scheme for the diffusion model to enable both blind and non-blind image restoration. A guidance parameter is also introduced to allow for a trade-off between restoration accuracy and image quality. Extensive experiments show that our method significantly outperforms previous approaches on the proposed datasets and real-world artifact images.
BACO: A Bi-Ant-Colony-Based Strategy for UAV Trajectory Planning with Obstacle Avoidance

Trajectory planning for a logistic delivery using an unmanned aerial vehicle (UAV) involves a typical traveling salesman problem (TSP), in which the turning of the UAV to avoid obstacles can cause significant energy consumption. The obstacles in the airspace and the angle constraints of the UAV must also be considered in the delivery. To address the low precision of UAV trajectory searches, and the serious impact of flight angles on UAV energy consumption, we propose a UAV trajectory planning strategy called bi-ant-colony optimization (BACO). BACO consists of two phases: path planning and track planning. By applying the guidance layer ant colony optimization (GuLACO) algorithm, the path planning phase eliminates the problem of ant colony deadlock that arises in multi-target point environments, and reopens the ant tabu table to search for a guidance path. Following this, the track planning phase employs the general layer ant colony optimization (GeLACO) algorithm to build the guidance path in segments. Furthermore, the precision of the flight heading for the UAV is optimized by adjusting the flight step in an adaptive manner, and obtaining fine-grained UAV flight tracks to control the turning angle of the logistics UAV. Our simulation results show that compared with the use of the greedy algorithm and the classical ACO algorithm, UAV trajectory planning using BACO can not only obtain shorter flight paths that take into account obstacle avoidance, but can also reduce the energy consumption of the UAV by finely controlling the amplitudes of the flight angles to ensure the safety and energy efficiency of UAV while in flight.
Product feature extraction from Chinese online reviews: application to product improvement

Online product reviews are valuable resources to collect customer preferences for product improvement. To retrieve consumer preferences, it is important to automatically extract product features from online reviews. However, product feature extraction from Chinese online reviews is challenging due to the particularity of the Chinese language. This research focuses on how to accurately extract and prioritize product features and how to establish product improvement strategies based on the extracted product features. First, an ensemble deep learning based model (EDLM) is proposed to extract and classify product features from Chinese online reviews. Second, conjoint analysis is conducted to calculate the corresponding weight of each product feature and a weight-based Kano model (WKM) is proposed to classify and prioritize product features. Various comparative experiments show that the EDLM model achieves impressive results in product feature extraction and outperforms existing state-of-the-art models used for Chinese online reviews. Moreover, this study can help product managers select the product features that have significant impact on enhancing customer satisfaction and improve products accordingly.
Block Switching: A Stochastic Approach for Deep Learning Security

Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models. That is, subtly crafted perturbations of the input can make a trained network with high accuracy produce arbitrary incorrect predictions, while maintain imperceptible to human vision system. In this paper, we introduce Block Switching (BS), a defense strategy against adversarial attacks based on stochasticity. BS replaces a block of model layers with multiple parallel channels, and the active channel is randomly assigned in the run time hence unpredictable to the adversary. We show empirically that BS leads to a more dispersed input gradient distribution and superior defense effectiveness compared with other stochastic defenses such as stochastic activation pruning (SAP). Compared to other defenses, BS is also characterized by the following features: (i) BS causes less test accuracy drop; (ii) BS is attack-independent and (iii) BS is compatible with other defenses and can be used jointly with others.
A new multi-subpopulation co-evolutionary genetic algorithm for cloud resource scheduling

Cloud resource scheduling has become a hot spot in the field of cloud computing. The common method to deal with resource scheduling problem is to build an optimization model and then use intelligent optimization algorithm to solve it. However, only tenant task execution time and the consumed resources in the built models are considered, the improvement of cloud resource scheduling performance is limited. To this end, this paper proposes a new Multi-subpopulation Co-evolutionary Genetic Algorithm called MC-GA for cloud resource scheduling, which comprehensively considers tenant task execution time, task transmission time and resources consumed by task execution. The MC-GA algorithm selects individuals with large fitness values into the selected sub-population through the elitism strategy, and randomly shrinks the size of the sub-population. Experimental results show that the proposed algorithm has the characteristics of higher solution efficiency and stronger optimization ability than other genetic algorithms.
Framework of degraded image restoration and simultaneous localization and mapping for multiple bad weather conditions

Image degradation caused by bad weather, such as haze, rain, and snow, significantly degrades a vision system’s performance, imposing challenges to the autonomous motion of mobile robots. Therefore, it is crucial to restore the degraded images for mobile robots operating outdoors. Spurred by this concern, a framework that restores degraded images and affords simultaneous localization and mapping (SLAM) under multiple bad weather conditions is presented. Specifically, the developed architecture combines the advantages of convolutional neural network features and weather features. It improves the accuracy of identifying the degradation type by developing a weather inference module based on ensemble learning. According to the internal mechanism of image degradation, a degraded image restoration method is proposed utilizing physical models, with a subsequent operation refining the preliminary restored results. To improve our method’s adaptability to real scenes, unpaired real-world weather images are introduced into the degradation removal algorithm through generative adversarial networks. The proposed weather persistent assumption combines weather inference and degradation restoration modules in the SLAM system to capture multiple weather conditions, which improves the system’s accuracy and running speed. Comprehensive experiments evaluating the developed framework and its components highlight that the proposed weather inference and degraded image restoration methods achieve a highly appealing effect. The final experimental results demonstrate that our framework autonomously identifies weather types, triggers the corresponding restoration, and realizes accurate localization in multiple bad weather conditions. It affords SLAM accuracy under multiple bad weather conditions that is close to raw SLAM in clear weather.
Cryptocurrency: An Overview and Analysis on the Awareness

This paper analysis delves into a wide range of aspects of crypto currency, with the goal of giving answers to queries like "Can crypto-currency be the dominant money in future?" Is it financially advantageous to dabble in digital currencies? Is utilizing digital cash safe? It focuses mostly on how Crypto currency’s function, making it easier to evaluate whether virtual currency exchanges are secure. It investigates crypto currency’s effect on the world economy, as well as its potential future impact. It presents a concise review of the legislations in numerous countries that support and prohibit cryptocurrencies. Also, in this paper, we look at the risks and benefits of cryptocurrency. In addition, the results of a brief survey we performed on cryptocurrency and bitcoins provide insight into how well people are aware about the digital currencies.
Challenges of Wartime: The Work of the Central State Archive of Science and Technology (TsDNTA) of Ukraine during the Russian Invasion

The article focuses on the activities of the Central State Archive of Science and Technology (TsDNTA) of Ukraine from the start of the full-scale Russian invasion on February 24, 2022 and until October 2022. The author gives an overview of the types of scientific and technical documentation and composition and contents of the archive’s holdings and considers the principal areas of the archive’s work, aimed at enriching the National Archive Fund with socially valuable materials, ensuring the preservation of documents, popularizing archival resources, and promoting their wider use. Particular attention is paid to important cooperation with legal entities that serve as key sources of documentation for the National Archive Fund, as well as to the provision of practical and methodological assistance to employees of state archival institutions. Implementing the Strategy for the Development of Archives until 2025 and maintaining the priority of ensuring unhindered access to information and satisfying people’s information needs by providing equal access to archival resources, the TsDNTA of Ukraine elicits feedback from users, services requests, issues electronic copies of documents, offers online consultations, etc. Despite the difficult wartime conditions and remote work, the archivists of the TsDNTA of Ukraine continue to pursue large-scale undertakings launched earlier, particularly the creation of a search system for the electronic identification of objects of preservation (TOPAZ) and development of a comprehensive 3D tour of all the premises of the archive. These endeavors not only significantly expand the nomenclature of remote services the archive provides, but also have given rise to new projects introducing innovative technologies into archival work, in particular the development of an object-geographical catalog integrated into an interactive map. These measures are aimed at adapting the TsDNTA of Ukraine to wartime conditions and preserving the country’s scientific and cultural heritage. Since the Russian armed aggression is still ongoing and archival institutions are functioning under the constant threat of destruction and seizure by the invaders, the article is far from the last word in the study of the efforts by the archivists of the TsDNTA of Ukraine to safeguard the nation’s cultural treasures. The subject has a lot of potential for further research.
Probabilistic Fingermark Quality Assessment with Quality Region Localisation

The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions.
Gradient-less Federated Gradient Boosting Tree with Learnable Learning Rates

The privacy-sensitive nature of decentralized datasets and the robustness of eXtreme Gradient Boosting (XGBoost) on tabular data raise the needs to train XGBoost in the context of federated learning (FL). Existing works on federated XGBoost in the horizontal setting rely on the sharing of gradients, which induce per-node level communication frequency and serious privacy concerns. To alleviate these problems, we develop an innovative framework for horizontal federated XGBoost which does not depend on the sharing of gradients and simultaneously boosts privacy and communication efficiency by making the learning rates of the aggregated tree ensembles learnable. We conduct extensive evaluations on various classification and regression datasets, showing our approach achieves performance comparable to the state-of-the-art method and effectively improves communication efficiency by lowering both communication rounds and communication overhead by factors ranging from 25x to 700x.
Cyber Blackmail between Threats and Protection

This research paper discusses the threats and the legal protection of the Cyber Blackmail in both the Egyptian and American Legislations. The growth of the Internet has led to the overlap between blackmail and Cybercrime where Cyber Blackmail is increasing, in light of the growing number of social media users, and the acceleration witnessed in the number of various conservation programs. With the phenomenon widely increasing in the Egyptian society in recent times, the researcher, after comparing the Egyptian legislation to the American legislation, seeks to identify the potential threats by analyzing the articles in the Egyptian legislation and identifying the needs to address and provide legal protection for this phenomenon.
Digital Transformation to Advance High-Quality Development of Higher Education

Digitization will bring unlimited potential and a new vision for education, and the digitization of education is a historical process and an inevitable trend of education reform. This paper first puts forward the connotation, foundation, core of education digital transformation and the typical stages of development. Then extracts a series of practices launched in China to cope with the demand in digital transformation from four aspects: platform construction, curriculum supply, teaching methods and evaluation methods. Finally, the exploration and practice carried out by three typical universities are presented where the author has served as president, namely Central China Normal University, Xidian University and Wuhan University of Technology. The purpose is to summarize their development patterns and characteristics to provide reference for the subsequent comprehensive transformation of higher education digitalization. Overall, digital transformation will promote paradigmatic changes in higher education research and practice, build a high-quality higher education system, and promote human-centered development caring for comprehensive, holistic and personalized needs.
Word Sense Disambiguation by Refining Target Word Embedding

Word Sense Disambiguation (WSD) which aims to identify the correct sense of a target word appearing in a specific context is essential for web text analysis. The use of glosses has been explored as a means for WSD. However, only a few works model the correlation between the target context and gloss. We add to the body of literature by presenting a model that employs a multi-head attention mechanism on deep contextual features of the target word and candidate glosses to refine the target word embedding. Furthermore, to encourage the model to learn the relevant part of target features that align with the correct gloss, we recursively alternate attention on target word features and that of candidate glosses to gradually extract the relevant contextual features of the target word, refining its representation and strengthening the final disambiguation results. Empirical studies on the five most commonly used benchmark datasets show that our proposed model is effective and achieves state-of-the-art results.