Reviewerly/exHarmony huggingface dataset:
Generative Adversarial Networks: Applications, Challenges, and Open Issues

Generative Adversarial Networks (GANs) represent an emerging class of deep generative models that have been attracting notable interest in recent years. These networks are unique in their capacity to train high-dimensional distributions spanning a range of data types. Conventional GANs encounter problems related to model collapse, convergence, and instability. These issues can be primarily attributed to suboptimal network architecture design, misuse of objective functions, and inappropriate parameter optimisation methods. Several studies have made efforts to tackle these issues, to varying degrees of success. This research aims to offer an exhaustive review of contemporary techniques utilised in GANs, the persisting problems they face, applications of these techniques and performance evaluation metrics across various sectors. Comprehensive searches were performed using selected publications from 2014 to 2022 and out of 260 publications retrieved, 20 publications (7.69%) were deemed eligible. The result using Comprehensive Meta-Analysis (CMA) tool shows the mean effect size is −0,537 with a 95% confidence interval of −1205 to 0,132 having a p-value &gt;0.05. This analysis will equip researchers with deeper insights into the potential applications of GANs and how they can help address current challenges in various domains.
Implementing Deep Learning and CNNs for Vehicle Detection to Alleviate Highway Traffic Congestion

Traffic jams are a common problem in urban areas, and detecting them in real time can help alleviate congestion and improve traffic flow. This project, propose CNN-based method for detecting traffic jams from live video feeds. Our system involves capturing video streams from cameras placed in strategic locations and processing them using a deep-learning model. The study aims to develop a system for detecting traffic jams using (CNN) Convolutional Neural Networks. The system analyzes real-time traffic images to identify congested areas and provides insights to traffic management authorities for timely intervention. The study examines the accuracy of the system by testing it on a dataset of traffic videos. We evaluated our system on a dataset of traffic videos and obtained an average accuracy 89.05.11%, the error average 0.10%., and average F-score 89.61% indicating that our approach can effectively detect traffic jams. Our experiments show that our CNN-based approach outperforms traditional methods of traffic jam detection and can provide accurate and real-time information to drivers and traffic management systems.
Secure Federations: Addressing the Security Challenges in Federated Learning and Privacy-Preserving AI

Federated Learning (FL) and Privacy-Preserving AI present a promising frontier for fostering collaborative machine learning while upholding data privacy at individual levels. Yet, these advancements are not without challenges, notably opening new corridors for adversarial incursions. In this paper, we delve into the intricate security impediments inherent within FL and privacy-centric AI. We cast a spotlight on dominant attack vectors, notably model poisoning, and data injection, with an emphasis on healthcare applications [1][4]. Through a meticulous exploration of their operational modalities, repercussions, and viable defenses, we craft security protocols tailored for FL infrastructures [2]. Venturing further, we evaluate the delicate balance between ensuring model robustness and sustaining optimal performance, underpinned by rigorous empirical studies. Our discourse accentuates not just the vulnerabilities lurking within FL, but also charts a structured path towards fortifying distributed machine learning ecosystems [6].
ChatGPT for generating stories and mind-maps in storytelling

This study explores the potential synergy between ChatGPT and mind mapping in storytelling to enhance children's storytelling and comprehension skills. ChatGPT's natural language processing capabilities and adaptive nature offer personalized feedback and recommendations, making it a promising tool for education. Mind mapping, known for organizing information visually, enhances learning performance. Four research questions are addressed: Can ChatGPT generate stories fitting different children? Can ChatGPT generate mind maps for stories? Can ChatGPT generate test questions to evaluate children's understanding? Can ChatGPT-generated mind maps facilitate children's storytelling and understanding of stories? Preliminary analysis with two participants showed that ChatGPT-generated stories were tailored to different age groups, and mind maps improved comprehension. Test questions effectively assessed children's understanding. The integrated use of ChatGPT and mind maps holds promise for creating engaging and personalized learning experiences, revolutionizing education with artificial intelligence technology.
How do Economic Inequality and Urban-rural Inequality Affect Individual Psychology? An Analysis Based On WEIBO Big Data

Economic inequality adversely affects all aspects of people's lives, and the urban-rural gap perpetuated by China's persistent household registration system is exacerbating this trend in the country. The rise of social media has created an extensive database, which can be leveraged to delve deeper into individual psychology and behaviour. This research analyzed over 20 million Weibo blog posts, one of the major social media platforms in China, and investigated the linguistic patterns of users in inequality environment at the province level. The findings reveal a positive correlation between the degree of inequality in the environment and the usage of expressions related to death. Furthermore, these expressions may not be self-directed. And different facets of economic inequality appear to have a comparable impact on users' language usage.
Open Access Database of Industry 4.0 Tasks for the Development of AI-Based Classifier

Robots and humans coworkers are sharing more and more portions of the smart manufacturing globally, meeting the need for high flexibility and rapid changes in the production layout. To be fully effective, however, such transition from classic robotics to the so-called collaborative robotics has to address several open problems, mostly related with safety and task optimization. Promising answers are coming from the motion capture technology, where wearable and optoelectronic sensing devices are deployed to gather human centric data to provide the robots with some form of awareness respect with the human activity and position. Tracking the hand of the operator, in particular, offers many advantages as we use our hands to explore and interact with the surroundings and to communicate. This has been highlighted by the several works focusing on gesture hand configuration recognition. This work present HANDMI4, a new open access database of hand motion tracking data, which includes a wide range of static hand grasp configurations and some classic dynamic industry tasks. Such database was generated using two of the most mature technologies for motion capture: IMU-based data glove and camera-based triangulation. To test the capability of such dataset to foster AI-based task classifier, a set of machine learning techniques were implemented and tested. In particular, KNN weighted reached 94,4% and 100% of task classification accuracy for the data glove and the camera system, respectively. With this open access database we aim to boost the research around task classification through motion capture technology to enable the next revolution in smart manufacturing.
A Survey Study on Secure Image Transmission and Noncoherent Chaos Communication System

Security of image signal is required to be transmitted reliably through a communication channel or when information is managed by computing units. Security issues are becoming problematic in multimedia applications and network systems. Thus, providing a secure transmission of data is an important theme tackled by researchers. The pace at which data is sent is an important consideration. Wireless communications are dominated by coherent communications at this time. To decode received data, such systems need exact knowledge of the receiver’s channel condition. In chaotic communication systems, noncoherent detection, which doesn’t need a lot of complicated the synchronization of the transmitter and receiver, has been very interesting to many people. Many systems that don’t make sense are being shown in this section, this covers measures like as complexity, data throughput, and security. Non-coherent chaos has received a lot of attention in order to leverage chaotic signals and noncoherent detection while avoiding chaotic synchronization, which has poor performance in the presence of additive noise. This offers the reader an idea of what is being done currently and what research is still required in the future.
Local Outlier Detection Based on K-distance Variation to Enhancing Imaging-aided Diagnosis

Local outlier detection is a critical task in data mining, which identifies outliers by estimating the anomaly score for each data point. The traditional density-based methods assign large k-distances to boundary data, which leads to the "boundary bias issue". Due to this issue, the anomaly scores of the boundary data are overestimated while the scores of their neighboring data points are underestimated, limiting the performance of local outlier detection. In this paper, we propose a novel local outlier detection method based on k-distance variation. This technique replaces the typical k-distance by measuring the k-nearest neighbor distance variations, eliminating the boundary bias by using adjacent nearest neighbors to transfer distances. To evaluate the performance of our proposed approach, we conduct plenty of experiments based on synthetic dataset, and also transform local anomaly scores into attention maps in the breast cancer detection. Experiments demonstrate that the proposed method not only outperforms other state-of-the-art methods in terms of the area under the receiver operating characteristic curve and precision on various synthetic datasets, but also generates more accurate attention maps to focus on multiple lesion areas for medical imaging-based diagnosis.
Dynamic Hand Gesture Recognition Using Effective Feature Extraction and Attention Based Deep Neural Network.

Skeleton-based dynamic hand gesture recognition has become the most attractive and popular research domain due to its wide range of applications. Sign language recognition, mixed reality environments, touchless public kiosks, controlling various electronic devices used in daily life, entertainment systems, and more are numerous applications of hand gesture recognition. However, many previous hand gesture recognition methods may fail to achieve satisfactory accuracy due to the lack of spatio-temporal features and noisy skeleton data. In the deployment stage, some hand gesture recognition systems suffer from low execution speed because of the large number of parameters. In this study, we propose a skeleton-based lightweight hand gesture recognition system using a multi-stream feature fusion approach to address these challenges. We extract three different features: y-axis distance, y-axis angle, and global motion features including slow motion and fast motion from the selected 10 joint coordinates. Next, we employ a spatial convolutional neural network (CNN) module to enhance the spatial contextual information for each of the three feature branches. By fusing the three feature branches, robust features are generated and then input into a multi-head attention module, which is subsequently fed into the classification module. Our proposed model achieves high accuracy after evaluation with the DHGD dataset, which demonstrates the superiority of the proposed study.
Selective Pruning of Sparsity-Supported Energy-Efficient Accelerator for Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are widely used in various fields with the rapid development of Deep Learning (DL). However, the massive amount of parameters and huge models severely limit the calculation speed and performance of the model. To address this, model compression has become one of the most popular methods, and quantization and pruning are two of the most common techniques. Quantization reduces the bit width in exchange for lower power consumption and computing time, while pruning reduces the number of parameters to reduce memory access and operation time. However, the existing hardware architecture is still unable to satisfy real-time and low power consumption requirements simultaneously. Therefore, scholars have been paying attention to Application Specific Integrated Circuits (ASICs) and low-bit quantization co-optimization. This paper aims to co-design sparsity and quantization energy-saving accelerators. It uses the characteristics of selective pruning to design low-cost sparsity hardware and employs low-cost comparison circuits to solve the problem of energy-saving data flow dependencies and improve traditional low-efficiency non-real-time methods. In terms of hardware, compared to Dynamic Region-based Quantization (DRQ), one of the state-of-the-art hardware accelerators, the proposed network reduces the area by 43.22%, power consumption by 52.17%, and computing time by 25.36%. Additionally, it increases power efficiency by 1.29 times.
Deep Learning-Driven Video Summarization on the Cloud: A Pathway to Efficient Storage and Quick Access

Cloud-based video summarization is an increasingly popular technique for managing large video repositories more efficiently. In this research, we present a novel approach for cloud-based video summarization that focuses on reducing storage costs and improving accessibility by automatically extracting critical information from videos and generating brief summaries. Our approach leverages cloud computing technology and deep learning algorithms for video analysis and processing. We evaluate the effectiveness of our approach using a real-world dataset of video files and demonstrate that it can generate high-quality video summaries while significantly reducing storage requirements. Our proposed approach has potential applications in areas such as security monitoring, video surveillance, and multimedia content management. This paper contributes to the existing literature by presenting a comprehensive study of cloud-based video summarization and proposing a novel solution that addresses the limitations of existing approaches, thereby improving the efficiency and cost-effectiveness of video management.
Classification of crimes committed using information and telecommunication networks (technologies) under the criminal legislation of the Russian Federation and Turkmenistan

In the criminal law doctrines of the Russian Federation and Turkmenistan, as well as in the legal acts and official documents adopted in these states, there are different approaches to the classification of crimes committed using information and telecommunication networks (technologies). These differences lead to a decrease in the effectiveness of criminal law counteraction of the corresponding type of illegal acts. In this regard, the author analyzes alternative approaches to the classification of crimes committed using information and telecommunication networks (technologies). It is concluded that based on the structural features of the Criminal Codes of the Russian Federation and Turkmenistan these types of socially dangerous acts should be classified depending on the characteristics of the object and subject of these unlawful attacks, the nature and degree of public danger of the act, the form of guilt, as well as the methods and means of committing these crimes. The conclusions and recommendations made in this article have a certain theoretical significance and can be used to further study of the classification of crimes committed using information and telecommunication networks (technologies) under the criminal legislation of the Russian Federation and Turkmenistan.
Parallel JSO-Based Approach to Task Scheduling Problems in Cloud Systems

Task scheduling in cloud systems is essential for both the customer and the service provider. One of the efficient solution methods for task scheduling problems, which is an NP-hard problem type, is the metaheuristic approach. On the other hand, due to its nature, two handicaps can often arise in metaheuristic solutions based on a random search. The first is the possibility of getting stuck in local minima due to randomness, and the second is that the solution time may increase with the increase in the problem size. Multithread /multi-process approaches can provide significant advantages in overcoming these two obstacles. For this purpose, in this study, a parallel Jellyfish Search optimizer-based approach is proposed for task scheduling in cloud systems. In the proposed approach, metaheuristic algorithms working in parallel can share the best solution with each other and shorten the solution time. Both multi-thread and multi-process versions of the proposed approach are used in the study. All versions are tested separately in CloudSim for scenarios with different task sizes and the results were shared.
Universal 32/64-bit CGRA for Lightweight Cryptography in Securing IoT Data Transmission

National Institute of Standards and Technology (NIST) has standardized ten lightweight cryptography (LWC) algorithms for efficient data protection in IoT, often implemented in hardware to optimize speed and power for resource-constrained devices. However, previous hardware architectures face challenges in providing flexible support for different LWC algorithms with high speed and low power consumption to meet the diverse tasks of gateway and edge devices. Therefore, this paper presents LiCGRA, the first 32/64-bit coarse-grained reconfigurable architecture (CGRA) for LWC, focusing on high flexibility, fast processing, and low power consumption. Our LiCGRA includes three proposed optimizations: a one-dimensional 4×4 processing element array (PEA) to enhance parallel processing capabilities, a multi-bit processing arithmetic logic unit (ALU) to support various LWC operations, and a multi-level data forwarding mechanism to minimize memory access. The successful implementation and verification of the LiCGRA on the ZCU102 FPGA demonstrated its superior speed and energy efficiency compared to existing research on LWC implementation using the iMX233. Experiment on ASIC with a 45nm CMOS technology yields that LiCGRA archives an area of 0.309 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , a power consumption of 88.506 mW, and a throughput of 1,948 Mbps, offering comparable throughput to dedicated single LWC architectures while providing greater flexibility.
A Novel Supervised Distributed Dictionary Learning based on Learned K-SVD for Image Denoising

With limited computational resources and storage space on SoCs, deploying large deep-learning networks (DNNs) is challenging. However, dictionary learning (DicL) has lower complexity and storage space requirements while improving the interpretability of the model. In addition, deep unfolding techniques can construct high-performance end-to-end networks based on DicL. Therefore, in this paper, we apply Deep KSVD (LKSVD), a deep unfolding network based on the classical K-SVD algorithm, with a distributed framework and propose a distributed dictionary learning (DDL) method called DDL-LKSVD. We experimentally validate DDL-LKSVD on the classical and fundamental image denoising problem, and the experimental results show that the average PSNR values we achieve in the DDL-LKSVD proposed in this paper on the Set12 dataset with the noise level 25 are 2.58 dB, 2.74 dB, 0.29 dB, and 0.03 dB higher than those of OMP, ISTA, K-SVD, and LKSVD, respectively.
Generating Realistic Images with NeRF for Training of Autonomous Driving Network

We present method of constructing dataset of training NeRF for synthesizing a realistic autonomous training dataset. Since the realism of NeRF depends on the image set used for training, it is necessary to use an appropriate image set. Therefore, to generate realistic synthesized images in the autonomous vehicle racing environment, we aim to improve the realism with images from in-vehicle viewpoints in addition to images obtained by the photography technique widely used in the research on NeRF. We experiment with a 1/10 scale vehicle to evaluate the driving performance of the autonomous driving models trained with synthesized images by NeRF. In this experiment, we compare the model’s driving performance using our proposed NeRF training image set with an image set obtained by a widely used photography technique. The experimental results show that the autonomous driving model trained with our proposed method drives better on a real racetrack than the model trained with the conventional method.
An adaptive Particle Swarm Optimization to Solve Modern Security System

Nonlinear stream ciphers hold a vital position in ensuring confidentiality within contemporary cryptographic systems. In this context, we interduce a new approach known as the Adaptive Three Sub-Swarm PSO based on Regrouping Strategy (ATSPSO-RG) to enhance the effectiveness of deciphering nonlinear stream ciphers, particularly those based on the Bruer generator system. Our method significantly reduces the complexity of cipher-only attacks, making the extraction of the correct secret key more efficient. The proposed system dividing the population size into three distinct subswarm, each incorporating its evolving strategies, namely the single score, four score, and nine score strategies. In the single score strategy, we employed, the traditional PSO evolving process. In the four scores strategy, we used three additional scores in addition to the single score of the traditional PSO to update the velocity and position of particles. lastly, the nine score strategy employed five scores to update the velocity and position, as well as the four scores of the previous score. To counteract the issue of premature convergence, we employ a regrouping strategy that periodically reinitializes the population. Experimental results vividly demonstrate the success of the ATSPSO-RG method in deciphering the Bruer cipher, even when dealing with a varying number of Linear Feedback Shift Registers (LFSRs) and polynomial degrees, spanning up to 15, and secret initial values extending up to 39. The method proves its efficiency in cryptanalysis, offering a potent solution for nonlinear stream ciphers with substantial key sizes while ensuring a reasonable timeframe for analysis. The research outcomes underscore the efficacy of ATSPSO-RG, highlighting both its accuracy and time efficiency in deciphering nonlinear stream ciphers.
Increasing Artificial Intelligence’s Context-Dependent Grasping, through Cloninger’s Planes of Being Theory

The ability to understand the true meaning and intentions behind statements and events is crucial for any thinking system. In human interactions, sentences may carry both obvious literal meanings and hidden interpretations based on context. Understanding and deciphering these hidden meanings require attention to specific behavioral and interactional cues. This paper explores the importance of context and meaning behind words and actions in enhancing communication and behavior. It delves into the power of human abstraction, emphasizing the role of context in understanding and how artificial intelligence, particularly chatbots, can be used to discern human concerns based on linguistic cues. The Cloninger’s theory of planes of being is proposed as a framework to identify and categorize individual concerns and thoughts. The paper advocates for a deeper understanding of human concerns and intentions to improve AI systems and make them capable of perceiving the nuances of human interaction.