Reviewerly/exHarmony huggingface dataset:
DCT based robust coverless information hiding scheme with high capacity

For a considerable time, individuals have been searching for the most effective way to secretly transmit information, making it challenging for anyone without authorization to decode it. Steganography is a method that conceals confidential information from unauthorized users. The message is encrypted, allowing only the intended recipient to access the message. In the case of image steganography, the message is hidden within a cover image. One technique involves modifying the image, so the receiver can still decode the information, even though it is not visible to the human eye. However, statistical analysis can uncover concealed information, even if it is not noticeable. To counter this issue, researchers developed a technique called Coverless Information Steganography, which sends the recipient an image that already contains a hidden message, thus reducing the possibility of steganalysis. A novel technique based on the direct current (DC) coefficient of the cover image is discussed here, where a hash sequence is created by computing the DC coefficient of each 8×8 sub-block and then XORed with a secret message to form a bit sequence. The capacity and robustness of the method are evaluated to determine its efficiency compared to other implementation strategies.
The KLC Cultures, Tacit Knowledge, and Trust Contribution to Organizational Intelligence Activation

In this paper, the authors address a new approach to three organizational, functional cultures: knowledge culture, learning culture, and collaboration culture, named together the KLC cultures. Authors claim that the KLC approach in knowledge-driven organizations must be designed and nourished to leverage knowledge and intellectual capital. It is suggested that they are necessary for simultaneous implementation because no one of these functional cultures alone is as beneficial for a company as all of them are together. Moreover, there is a risk that organizations with a learning culture developed without collaboration are stuck at the individual level of learning only; and that a knowledge culture developed without a learning culture jeopardizes the organization to be stuck in a passive way where only old, multiply verified knowledge is accepted. As a result, such companies cannot grow. That extreme situation leads to the rejection of new knowledge that is usually rationalized by the need for business safety security - that is nothing more than a ruse for intellectual laziness or personal barriers of fixed-minded managers.Summing up, based on the empirical evidence (640-cases sample, composed of Polish knowledge workers; SEM method of analysis), this paper delivers empirical evidence that knowledge culture rejects mistakes acceptance component of learning culture and that the learning climate component itself is not sufficient for explicit knowledge sharing. Knowledge sharing, organizational intelligence, and innovativeness are key benefits of the synergy that offers the KLC cultures simultaneous implementation and cultivation. The results expand the former studies by Kucharska and Bedford (2020; 2023) and Kucharska (2021a-b) and expose that KLC cultures and TRUST are needed to develop tacit knowledge sharing clearly is an essential ingredient for organizational intelligence development.
Women and Cyberspace: The Challenges and Security in Cyberspace

Information and communication technologies (ICTs) are making everyone's lives easier and more convenient.They are unprecedentedly transforming our lives, especially those of women, by providing a different range of opportunities to people, businesses, and social relations.With the exponential growth of the "social internet", privacy is becoming a blurred concept as women are more eager to share information about their daily experiences, professional gains, and social relations.All these led people and organizations to enjoy parallel virtual lives in cyberspace.Cyberspace means the virtual and dynamic space created by machine clones.It can be compared to a human brain, where the network of computers represents the innumerable neurons and the connections between them.Therefore, it can be considered a link between the physical and the infinite worlds.The main victims of cyberspace are women, who get harassed over social media, and due to a lack of knowledge or sometimes due to fear of defamation, the culprits are not made accountable for their misdeeds, and justice is denied to these victims.Cyberbully is defined as making use of computer networks to impose or threaten violence on another person, which leads to sexual, psychological, or economic harm, and can include the exploitation of situations, qualities, and so on.Additionally, the world witnesses a digital gender divide (i.e., more men than women have smartphones, and men dominate the ICT jobs); as a result, women and girls are more vulnerable to cyber assault.This paper aims to understand the scenario of cyber-crimes and cyber security and analyze the perception of women towards awareness of cyber security.In this paper, we plan to suggest several preventive measures to counter the ever-increasing cybercrimes against women in India.Unless women are being cautious and responsible, choices made in cyberspace can be costly and dangerous, due to the distinct characteristics that render cyber violence's gendered impact.In this paper, we will also examine the various laws that exist to protect women in such cases.We will include various case studies on cyber-crimes.
The Salient360! Toolbox: Processing, Visualising and Comparing Gaze Data in 3D

Eye tracking can serve as a gateway to studying the mind. For this reason it has been adopted by a diverse range of scientific communities. With the improvement of the quality of head-mounted virtual reality devices (HMDs) over the past 10 years, eye tracking has been added to capture gaze in immersive environments. The use of HMDs with eye tracking is increasing significantly and so is the need for a toolbox enabling consensus about eye tracking methods in 3D. We present the Salient360! toolbox: it implements functions to identify saccades and fixations and output gaze characteristics (e.g., fixation duration or saccade directions), to generate saliency maps, fixation maps, and scanpath data. It also implements routines made to compare gaze data that were adapted to 3D. We hope that this toolbox will spark discussions about the methodology of 3D gaze processing, facilitate running experiments, and improve the gaze study in 3D. https://github.com/David-Ef/salient360Toolbox
Transformer in optronic neural networks for image classification

Transformer, with its self-attention mechanism, can acquire global features while parallelizing training. This study proposes an Optronic Vision Transformer (OPViT), which utilizes spatial light modulators (SLMs) and lens group to realize matrix multiplication. This is the first time that Transformer has been introduced into optical neural network, fully exploiting the advantages of photonic computing to minimize its computational cost. Based on the advantages of OPViT, it is reconfigurable, scalable, and has low space complexity. We achieve excellent classification results directly with only one layer of Transformer and two or three layers of optical convolution, achieving 98.70% and 88.93% test accuracy on the MNIST and Fashion-MNIST datasets, respectively. The classification results outperform existing optical convolutional neural networks, and are even comparable to previous optical architectures connected to electronic networks.
Unsupervised Extractive Summarization with BERT

The task of document summarization became more pressing as the information volume increased exponentially from news websites to scientific writings. As such, the necessity for tools that automatically summarize written text, while keeping its meaning and extracting relevant information, increased. Extractive summarization is an NLP task that targets the identification of relevant sentences from a document and the creation of a summary with those phrases. While extensive research and large datasets are available in English, Romanian and other low-resource languages lack such methods and corpora. In this work, we introduce a new approach for summarization using a Masked Language Model for assessing sentence importance, and we research several baselines for the Romanian language including K-Means with BERT embeddings, an MLP considering handcrafted features, and PacSum. In addition, we also present an evaluation corpus to be used for assessing current and future models. The unsupervised methods do not require large datasets for training and make use of low computational power. All of the proposed approaches consider BERT, a state-of-the-art Transformer used for generating contextualized embeddings. The obtained ROUGE score of 56.29 is comparable with state-of-the-art scores and the METEOR average of 51.20 supersedes the most advanced current model.
The application of gesture recognition based on MediaPipe in virtual scene

With the development and application of virtual reality technology, the demand for gesture recognition is increasing. Efficient gesture recognition technology is indispensable in telecommuting and learning. In the research method of this paper, the advanced hand tracking technology is used to construct the human-computer interaction system in the virtual environment. This system uses MediaPipe framework and OpenCV to identify the key points of the hand by collecting real- time image data through monocular camera, and uses Kalman filter algorithm to smooth and optimize the coordinates of the key points of the hand. Through specific gestures in Unity, the experiment of interacting with the building blocks in the virtual environment is carried out to solve the problem of accuracy and stability when taking the building blocks. This method can be applied to multi-scenario (extensible) non-contact human-computer interaction.
A Semi-supervised Learning Application for Hand Posture Classification

The rapid growth of HCI applications results in increased data size and complexity. For this, advanced machine learning techniques and data analysis solutions are used to prepare and process data patterns. However, the cost of data pre-processing, labelling, and classification can be significantly increased if the dataset is huge, complex, and unlabelled. This paper aims to propose a data pre-processing approach and semi-supervised learning technique to prepare and classify a big Motion Capture Hand Postures dataset. It builds the solutions via Tri-training and Co-forest techniques and compares them to figure out the best-fitted approach for hand posture classification. According to the results, Co-forest outperforms Tri-training in terms of Accuracy, Precision, recall, and F1-score.
Hand detection model compression based on channel pruning and knowledge distillation

Gesture recognition is one of the most intuitive and convenient ways of human-computer interaction. Accurate and realtime hand detection is the key to correct gesture recognition. Deep learning is a mainstream solution to visual tasks such as hand detection and recognition, but detection models based on deep learning usually require large storage resources and computing resources, which seriously hinders its actual deployment and application. Therefore, in order to deploy the hand detection model based on deep convolutional neural network to embedded devices with limited storage resources and computing resources, it is necessary to compress and accelerate the hand detection model. In this paper, YOLOv3 was used as the basic model for hand detection, and a channel pruning method based on local sparse rate attenuation training was proposed to compress and accelerate the model. Meanwhile, knowledge distillation was used instead of fine tuning to conduct distillation training on the pruned model, so as to compensate the accuracy loss caused by channel pruning. The model was trained and tested on the Oxford Hand data set. The results show that the compression method of the hand detection model based on the channel pruning and knowledge distillation training of local sparse rate attenuation can realize the compression acceleration of the model while maintaining a high detection accuracy, which provides support for the deployment of embedded devices of the hand detection model.
Microseismic source localization using Fourier Neural Operators

Summary Microseismic event localization involves determining the location of small earthquakes or tremors, which can provide valuable insights into subsurface geological structures, the presence of natural resources, and the potential for future earthquakes. While a number of machine learning approaches have been proposed over the years; however, they are plagued by their generalization ability and in their capability for real-time monitoring. We propose a Fourier Neural Operator (FNO) based method that demonstrates high accuracy with limited training data to address the need for real-time event detection. The FNO model is trained using first arrival traveltimes on observation locations used as the input to predict the traveltime field in the entire computational domain. The minimum of the predicted traveltime field yields the hypocenter location. We demonstrate that the use of FNOs overcomes limitations associated with the recently developed data-driven and physics-based machine learning methods for hypocenter localization, and provides a reliable approach for real-time microseismic monitoring.
Distributed SPARQL queries in collaboration with the routing protocol

In the context of the IoT, energy is a scarce resource. Since the transmission of data is one of the largest energy consumers, the amount of data must be reduced as much as possible. Modern database systems optimize the queries to reduce execution times. Therefore they also consider reducing the network traffic but neglect the network topology. Network topology is critical because it can consume more energy to send a few pieces of data over a long distance than to transmit a large amount of data to a neighboring device. Therefore, we propose new techniques for cooperation between routing and application layers.
A DNN Framework for Learning Lagrangian Drift With Uncertainty

Reconstructions of Lagrangian drift, for example for objects lost at sea, are often uncertain due to unresolved physical phenomena within the data. Uncertainty is usually overcome by introducing stochasticity into the drift, but this approach requires specific assumptions for modelling uncertainty. We remove this constraint by presenting a purely data-driven framework for modelling probabilistic drift in flexible environments. Using ocean circulation model simulations, we generate probabilistic trajectories of object location by simulating uncertainty in the initial object position. We train an emulator of probabilistic drift over one day given perfectly known velocities and observe good agreement with numerical simulations. Several loss functions are tested. Then, we strain our framework by training models where the input information is imperfect. On these harder scenarios, we observe reasonable predictions although the effects of data drift become noticeable when evaluating the models against unseen flow scenarios.
Federated learning with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"><mml:msub><mml:mi>ℓ</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math> regularization

Federated Learning (FL) is a widely adopted deep learning method that does not require the collection of raw training data and solves specific learning tasks by federating distributed devices. Due to the heterogeneous distribution of data across clients, the clients will drift toward the local optimal solutions during local training and result in different local models. The global model after aggregating these different local models may keep away from the global optimal solution. This phenomenon is known as client drift, which often hinders the performance of FL. Parameter regularization methods address this challenge of client drift by controlling the update direction of each client. They consider the global model as both the starting point and a reference for the induction bias in the penalty. However, the existing regularization approaches produce dense solutions so that all parameters need to be updated during local model training. At the same time, we note that some studies on deep learning have found that it is unnecessary to update all parameters at each round. Therefore, in this work, we design a novel FL training approach called Fedℓ1 which can alleviate the performance degradation of FL by updating only part of the parameters at each round. ℓ1 regularization is utilized to control the update direction of each client and avoid unnecessary parameter updates at the same time. To our knowledge, our study is the first to introduce sparse regularization term to correct the local training of individual clients in FL. We design a stochastic subgradient descent algorithm to train the ℓ1-regularized nonsmooth model. The comparison experiments with state-of-the-art baselines verify the superiority of the proposed approach.
Toward Reliable DNN-based Task Partitioning and Offloading in Vehicular Edge Computing

Modern vehicles have become typical consumer electronics with the development of sensing, transmission, and computation technologies. The emerging intelligent transportation systems (ITSs) yield lots of deep neural network (DNN) based tasks, requiring intensive computation. In view of this, this paper makes the first exploration of accelerating the processing of DNN-based tasks while maintaining decent system reliability in vehicular edge computing (VEC) via task partitioning and offloading. Specifically, we present a specific scenario, where vehicles partition and offload their tasks to nearby vehicles and roadside infrastructures, while they may fail to receive results due to unstable vehicular communications. Then, we model the task delay by considering task properties and node capacities. On this basis, the Partitioning and Offloading Problem (POP) is formulated as a bi-objective optimization problem, to maximize both the acceleration ratio and service ratio of DNN-based tasks in VEC. Further, we propose a Distributed Partitioning and Offloading Solution (DPOS), where a delay-priority-oriented offloading strategy is designed to help edge nodes make offloading decisions, and a stacking-based partitioning strategy is designed to assist client vehicles to make partitioning decisions. Finally, we give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution.
Exploration of Optimizing FPGA-based Qubit Controller for Experiments on Superconducting Quantum Computing Hardware

identify some key target areas for optimizing control electronics for superconducting qubit architectures and provide some preliminary results to the resolution of a control pulse waveform. With optimizations targeted at 3D superconducting qubit setups, we hope to bring to light some of the requirements in classical computational methodologies to bring out the full potential of this quantum computing architecture, and to convey the excitement of progress in this research.
Customer Service Opportunities When Troubleshooting Open Access Articles

California State University, Fullerton (CSUF) library makes available its collection on the discovery layer, Primo VE. Users should be able to access all the content seamlessly but, they lack research experience, and the library collection is usually hiding errors that need to be fixed by the Electronic Resources (ER) Librarian. Sometimes, users make false reports to access issues. Students report problems to open access (OA) articles because they do not get linked directly to the desired article. The ER librarian analyzed electronic resource tickets submitted in 2022. These are opportunities to connect with users.
3D Data Security

Three-dimensional data reveals more explicative information and more realistic visualization than that of the two-dimensional ones. This explains the remarkable growth in the use of 3D data in different fields of application, which increases in respect to the risk of illegal use of data and piracy, as well. Since its appearance, digital watermarking has been an essential solution to attest the identity of the owner, control illegal reproduction, and protect the copyright of the 3D object. The current work is about designing a robust 3D object watermarking technique in order to protect copyrights associated with triangular polygon meshes. The approach is based on a watermark's insertion in the spatial domain. The findings are a proof demonstrating that the proposed 3D objects watermarking method does not only meet the need of imperceptibility but also shows, at least, similar or even better robustness, compared with other commonly used 3D objects watermarking approaches in the literature.
The Effects of Barriers to Physical Capital Accumulation on Ghana’s Post-Independence Economic Growth

Ghana’s economy was identical to South Korea’s economy in the 1960s. Since the 1970s, however, Ghana’s economy has substantially lagged. To explain Ghana’s poor long-run economic growth, we build a neoclassical growth model to primarily examine the role of Ghana’s barriers to physical capital accumulation, which is often neglected due to a lack of data. We found that physical capital barriers explain about 10 percent of Ghana’s poor long-run economic growth relative to South Korea from 1970 to 2014. By recalibrating our model to Ghana’s recent macroeconomic data, we found that a reduction in Ghana’s physical capital barriers by almost half (from 0.7 to 0.4) increased Ghana’s real-GDP-per-worker growth rate by about 2.7 and 0.82 percentage points over the periods 2020-2029 and 2030-2049 respectively. Comparably, we found that the policy of reducing Ghana’s physical capital barriers was superior to the policy of raising Ghana’s human capital growth rate (by a realiistic amount) over the short-to-medium term. Overall, our paper sheds some light on the extent of Ghana’s barriers to physical capital accumulation and highlights the need for policy interventions that prioritize the removal of these barriers over the accumulation of human capital.
Application of distributed and decentralized technologies in the management of intelligent transport systems

Shifting focus from the field of distributed and decentralized technologies in the management of intelligent transportation systems (ITS), we now delve into the specific application of blockchain in transportation management. Blockchain is a fundamental component of distributed and decentralized technologies. The research paper discusses the utilization of blockchain technology in managing transportation systems through multi-agent systems. Specifically, the use of blockchain technology is examined in the context of the quick road system (QRS) in ITS to provide a service for obtaining a special fare status. This service aims to establish a decentralized network that facilitates real-time road lane sharing. The study indicates that depending on traffic situations, drivers can share their lane space with other vehicles traveling on the same route by exchanging incentives via blockchain with other private car owners, thereby allowing for faster travel for individuals in a hurry or those requiring priority access to fast lanes. The paper also addresses the increasing number of connected devices in ITS due to the development of the internet of things (IoT) technology. It highlights the importance of efficiently utilizing large datasets and identifies the internet of vehicles (IoV) as a crucial area of integration for existing IoT technologies to address smart traffic within multi-agent systems.