CDYL for infrared and visible light image dense small object detection

Abstract: To address the phenomenon of many small and hard-to-detect objects in infrared and visible light images, we propose an object detection algorithm CDYL (Convolution to Fully Connect-ed-Deformable Convolution You only Look once) based on the CFC-DC (Convolution to Fully Connected-Deformable Convolution) module. The core operator of CDYL is CFC-DC, making our model not only have a large effective receptive field in infrared and visible light images, but also have adaptive spatial aggregation conditioned by input and task information. As a result, the CDYL reduces the strict inductive bias of traditional CNNs and has long-range dependence for large kernel convolution as well as adaptive spatial aggregation, deeply mining of edge and correlation information in images to enhance sensitivity to small objects, thereby improving performance in dense small object detection tasks. In order to improve the ability of the CFC-DC module to perceive the detailed information of the image, we use the Mish activation function, which has a wider minima which improves the generalization. The effectiveness as well as the generalization of CDYL is evaluated on an infrared image dataset and an UAV image dataset, and it is compared with other state-of-the-art object detection algorithms. Compared to the baseline network YOLOv8l, our model achieved a 3.0% improvement in mAP0.5 in infrared image detection tasks and a 1.1% improvement in mAP0.5 in visible light image detection tasks. The experimental results show that the proposed algorithm achieves superior average precision values on both infrared and visible light images, while maintaining a light weight. Code is publicly available at https://github.com/yangzhu1/CDYL .


MTTEGDM: A Moving Target Evolutionary Game Defense Model Based on Three-Way Decisions

Abstract: Aiming at the fact that the moving target defense game model fails to accurately portray attack and defense gains, resulting in bias in attack and defense games and the inability to select effective defense strategies, we construct the moving target three-way evolutionary game defense model (MTTEGDM). Firstly, the model is defined and analyzed theoretically under the premise of uncertainty and irrationality. Then, combined with the three-way decisions, the attack intention is introduced into the target network loss calculation, and a dynamic weight adjustment algorithm based on the three-way decisions is proposed to accurately characterize the attack and defense gains from a multi-attribute perspective. Finally, the evolutionary game model is used to analyze the evolution trend of the multi-stage defense strategy, so as to carry out feasible and effective defense behavior. The simulation results show that the model can accurately predict the optimal defense strategy of moving targets in different stages. Through a Monte Carlo simulation experiment, the proposed algorithm is compared with the traditional evolutionary game model, and the effectiveness and security of the proposed algorithm are verified.


High-Payload Data-Hiding Scheme Based on Interpolation and Histogram Shifting

Abstract: The whole world is quickly transforming into an information world with the setup of smart cities, among other things. Information and other forms of privacy are therefore at risk due to the growing intelligence of hackers. Therefore, to enhance the security of information, a capacity-raising interpolation method based on histogram shifting is proposed. According to the experimental results, the capacity-raising interpolation method based on histogram shifting produces high-quality images with a significantly high embedding capacity, outperforming other methods. Apart from the already-mentioned advantages, the proposed method is reversible and can recover images in a lossless manner.


Beyond the breach: Bitcoin’s response to exchange-related cyberattacks and closures

Abstract: We investigate the influence of digital exchange-related cyberattacks and closures on Bitcoin's returns and volatility. We observe a negative impact on Bitcoin's return of 1.368% on the days when the cyberattacks occur, but no significant effect linked to exchange closures. Conversely, exchange closures increase Bitcoin's volatility, while cyberattacks on digital exchanges do not generate a significant impact. Subsample analysis over the 2012–2018 and 2019–2023 time intervals yields results that are largely consistent with those obtained from the entire sample period.


A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing

Abstract: Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map.


PSMOT: Online Occlusion-Aware Multi-Object Tracking Exploiting Position Sensitivity

Abstract: Models based on joint detection and re-identification (ReID), which significantly increase the efficiency of online multi-object tracking (MOT) systems, are an evolution from separate detection and ReID models in the tracking-by-detection (TBD) paradigm. It is observed that these joint models are typically one-stage, while the two-stage models become obsolete because of their slow speed and low efficiency. However, the two-stage models have naive advantages over the one-stage anchor-based and anchor-free models in handling feature misalignment and occlusion, which suggests that the two-stage models, via meticulous design, could be on par with the state-of-the-art one-stage models. Following this intuition, we propose a robust and efficient two-stage joint model based on R-FCN, whose backbone and neck are fully convolutional, and the RoI-wise process only involves simple calculations. In the first stage, an adaptive sparse anchoring scheme is utilized to produce adequate, high-quality proposals to improve efficiency. To boost both detection and ReID, two key elements-feature aggregation and feature disentanglement-are taken into account. To improve robustness against occlusion, the position-sensitivity is exploited, first to estimate occlusion and then to direct the post-process for anti-occlusion. Finally, we link the model to a hierarchical association algorithm to form a complete MOT system called PSMOT. Compared to other cutting-edge systems, PSMOT achieves competitive performance while maintaining time efficiency.


Exploratory search in information systems: a systematic review

Abstract: Purpose Exploratory search activities are ubiquitous in various information systems. Much potentially useful or even serendipitous information is discovered during the exploratory search process. Given its irreplaceable role in information systems, exploratory search has attracted growing attention from the information system community. Since few studies have methodically reviewed current publications, researchers and practitioners are unable to take full advantage of existing achievements, which, in turn, limits their progress in this field. Through a literature review, this study aims to recapitulate important research topics of exploratory search in information systems, providing a research landscape of exploratory search. Design/methodology/approach Automatic and manual searches were performed on seven reputable databases to collect relevant literature published between January 2005 and July 2023. The literature pool contains 146 primary studies on exploratory search in information system research. Findings This study recapitulated five important topics of exploratory search, namely, conceptual frameworks, theoretical frameworks, influencing factors, design features and evaluation metrics. Moreover, this review revealed research gaps in current studies and proposed a knowledge framework and a research agenda for future studies. Originality/value This study has important implications for beginners to quickly get a snapshot of exploratory search studies, for researchers to re-align current research or discover new interesting issues, and for practitioners to design information systems that support exploratory search.


Manufacturing cloud service recommendation model based on deep feature learning and user preference perception

Abstract: The manufacturing cloud service recommendation model is a key technology for users to quickly discover and obtain the personalized services they need from the increasingly expanding manufacturing cloud; however, the manufacturing cloud service recommendation model faces problems, such as difficulty in representing deep features of services and too sparse user preference models. To address the above problems, this paper proposes a manufacturing cloud service recommendation model framework based on deep feature learning and user preference perception and investigates the key technologies involved. On the one hand, a Convolutional Neural Network (CNN)-based deep feature extraction method for manufacturing cloud services is proposed by combining the Latent Dirichlet Allocation (LDA) topic model and word2vec. On the other hand, the concept of a category is introduced through the method of label clustering to perceive user behavior, efficiently build user preference models, and solve the sparse problem of the user-service matrix. Based on this, the execution process of the recommendation model for manufacturing cloud services is analyzed by fusing the deep features of services and user preference models. The experimental results on the actual dataset prove that the method greatly improves the recommendation effect.


Human Activity Recognition through Smartphone Inertial Sensors with ML Approach

Abstract: Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms’ performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period.


Algorithms in selection decisions: Effective, but unappreciated

Abstract: Abstract Selection decisions are often affected by irrelevant variables such as gender or race. People can discount this irrelevant information by adjusting their predictions accordingly, yet they fail to do so intuitively. In five online studies ( N = 1077), participants were asked to make selection decisions in which the selection test was affected by irrelevant attributes. We examined whether in such decisions people are willing to be advised by algorithms, human advisors or prefer to decide without advice. We found that people fail to adjust for irrelevant information by themselves, and those who received advice from an algorithm or human advisor made better decisions. Interestingly, although most participants stated they prefer advice from human advisors, they tend to rely equally on algorithms in actual selection tasks. The sole exception is when they are forced to choose between an algorithm and a human advisor. In that case, they pick human advisors. We conclude that while algorithms may not be people's preferred source of advice in selection decisions, they are equally useful and can be implemented.


An interdisciplinary framework to optimize the anticipation skills of high-level athletes using virtual reality

Abstract: The ambition of our contribution is to show how an interdisciplinary framework can pave the way for the deployment of innovative virtual reality training sessions to improve anticipation skills in top-level athletes. This improvement is so challenging that some authors say it is like "training for the impossible". This framework, currently being implemented as part of a project to prepare athletes for the 2024 Olympic Games in Paris, based on the ecological-dynamics approach to expertise, is innovative in its interdisciplinary nature, but also and above all because it overcomes the limitations of more traditional training methods in the field designed to optimize anticipation skills in top-level athletes. The ambition is to tackle successive challenges ranging from the design of virtual partners and opponents to the deployment of training programs in virtual reality, while ensuring the acceptability and acceptance of such innovative virtual reality training protocols and measuring associated workloads.


A Comprehensive Survey on Feature Selection with Grasshopper Optimization Algorithm

Abstract: Abstract Recent growth in data dimensions presents challenges to data mining and machine learning. A high-dimensional dataset consists of several features. Data may include irrelevant or additional features. By removing these redundant and unwanted features, the dimensions of the data can be reduced. The feature selection process eliminates a small set of relevant and important features from a large data set, reducing the size of the dataset. Multiple optimization problems can be solved using metaheuristic algorithms. Recently, the Grasshopper Optimization Algorithm (GOA) has attracted the attention of researchers as a swarm intelligence algorithm based on metaheuristics. An extensive review of papers on GOA-based feature selection algorithms in the years 2018–2023 is presented based on extensive research in the area of feature selection and GOA. A comparison of GOA-based feature selection methods is presented, along with evaluation strategies and simulation environments in this paper. Furthermore, this study summarizes and classifies GOA in several areas. Although many researchers have introduced their novelty in the feature selection problem, many open challenges and enhancements remain. The survey concludes with a discussion about some open research challenges and problems that require further attention.


A lightweight and personalized edge federated learning model

Abstract: Abstract As a new distributed machine learning paradigm, federated learning has gained increasing attention in the industry and research community. However, federated learning is challenging to implement on edge devices with limited resources and heterogeneous data. This study aims to realize a lightweight and personalized model through pruning and masking with insufficient resources and heterogeneous data. Particularly, the server first downloads the subnetwork to the client according to the mask, and client prunes the subnetwork with the alternating direction method of multipliers (ADMM), so as to remove the unimportant parameters and reduce the cost of training and communication. At the same time, mask is used to mark the pruning condition of the model. Then, the unpruned parts and masks of local models are transmitted to the server for aggregation. The experimental results showed that the accuracy of the proposed model was improved by 9.36%, and the communication cost was reduced by 1.45 times compared with state-of-the-art models. Last but not least, we deploy flower identification models in Android Studio to illustrate the practicality of the proposed method.


Efficacy of virtual reality-based training programs and games on the improvement of cognitive disorders in patients: a systematic review and meta-analysis

Abstract: Cognitive impairments present challenges for patients, impacting memory, attention, and problem-solving abilities. Virtual reality (VR) offers innovative ways to enhance cognitive function and well-being. This study explores the effects of VR-based training programs and games on improving cognitive disorders.PubMed, Scopus, and Web of Science were systematically searched until May 20, 2023. Two researchers selected and extracted data based on inclusion and exclusion criteria, resolving disagreements through consultation with two other authors. Inclusion criteria required studies of individuals with any cognitive disorder engaged in at least one VR-based training session, reporting cognitive impairment data via scales like the MMSE. Only English-published RCTs were considered, while exclusion criteria included materials not primarily focused on the intersection of VR and cognitive disorders. The risk of bias in the included studies was assessed using the MMAT tool. Publication bias was assessed using funnel plots and Egger's test. The collected data were utilized to calculate the standardized mean differences (Hedges's g) between the treatment and control groups. The heterogeneity variance was estimated using the Q test and I2 statistic. The analysis was conducted using Stata version 17.0.Ten studies were included in the analysis out of a total of 3,157 retrieved articles. VR had a statistically significant improvement in cognitive impairments among patients (Hedges's g = 0.42, 95% CI: 0.15, 0.68; p_value = 0.05). games (Hedges's g = 0.61, 95% CI: 0.30, 0.39; p_value = 0.20) had a more significant impact on cognitive impairment improvement compared to cognitive training programs (Hedges's g = 0.29, 95% CI: -0.11, 0.69; p_value = 0.24). The type of VR intervention was a significant moderator of the heterogeneity between studies.VR-based interventions have demonstrated promise in enhancing cognitive function and addressing cognitive impairment, highlighting their potential as valuable tools in improving care for individuals with cognitive disorders. The findings underscore the relevance of incorporating virtual reality into therapeutic approaches for cognitive disorders.


Face photo-line drawings synthesis based on local extraction preserving generative adversarial networks

Abstract: Facial photo-to-sketch synthesis is crucial for entertainment and criminal investigations, yet challenges persist, including local detail blurring and identity feature loss. To mitigate these problems in face sketch synthesis, We propose a technique for synthesizing photographic sketches of faces using generative adversarial networks that focus on image localization to extract and preserve image identity features – namely LEPGAN. Our method introduces an image local extraction module, an attention mechanism in the generator, and an extended U-Net structure to enhance critical image features and preserve identity information. Multi-scale perceptual and local loss functions further enhance synthetic image quality. LEPGAN outperforms existing methods, producing images with crisper facial details that closely align with human visual perception, effectively preserving unique identity characteristics. Our approach represents a significant advancement in face sketch synthesis for various applications.


Elk herd optimizer: a novel nature-inspired metaheuristic algorithm

Abstract: Abstract This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman’s test post-hocked by Holm’s test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems.


In defense of local descriptor-based few-shot object detection

Abstract: State-of-the-art image object detection computational models require an intensive parameter fine-tuning stage (using deep convolution network, etc). with tens or hundreds of training examples. In contrast, human intelligence can robustly learn a new concept from just a few instances (i.e., few-shot detection). The distinctive perception mechanisms between these two families of systems enlighten us to revisit classical handcraft local descriptors (e.g., SIFT, HOG, etc.) as well as non-parametric visual models, which innately require no learning/training phase. Herein, we claim that the inferior performance of these local descriptors mainly results from a lack of global structure sense. To address this issue, we refine local descriptors with spatial contextual attention of neighbor affinities and then embed the local descriptors into discriminative subspace guided by Kernel-InfoNCE loss. Differing from conventional quantization of local descriptors in high-dimensional feature space or isometric dimension reduction, we actually seek a brain-inspired few-shot feature representation for the object manifold, which combines data-independent primitive representation and semantic context learning and thus helps with generalization. The obtained embeddings as pattern vectors/tensors permit us an accelerated but non-parametric visual similarity computation as the decision rule for final detection. Our approach to few-shot object detection is nearly learning-free, and experiments on remote sensing imageries (approximate 2-D affine space) confirm the efficacy of our model.


Optimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stopping criterion

Abstract: Abstract In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating constitutive models straight from data, capable of describing complex material behavior in a more flexible way than classical constitutive models. Yield functions, which serve as foundation of constitutive models for plasticity, can be properly described in a data-oriented manner using ML methods. However, the quality of these descriptions heavily relies on the availability of sufficient high-quality and representative training data that needs to be generated by fundamental numerical simulations, experiments, or a combination of both. The present paper addresses the issue of data selection, by introducing an active learning approach for Support Vector Classification (SVC) and its application in training an ML yield function with suitable data. In this regard, the Query-By-Committee (QBC) algorithm was employed, guiding the selection of new training data points in regions of the feature space where a committee of models shows significant disagreement. This approach resulted in a marked reduction in the variance of model predictions throughout the active learning process. It was also shown that the rate of decrease in the variance went along with an increase in the quality of the trained model, quantified by the Matthews Correlation Coefficient (MCC). This demonstrated the effectiveness of the approach and offered us the possibility to define a dynamic stopping criterion based on the variance in the committee results.


Correlation enhanced distribution adaptation for prediction of fall risk

Abstract: With technological advancements in diagnostic imaging, smart sensing, and wearables, a multitude of heterogeneous sources or modalities are available to proactively monitor the health of the elderly. Due to the increasing risks of falls among older adults, an early diagnosis tool is crucial to prevent future falls. However, during the early stage of diagnosis, there is often limited or no labeled data (expert-confirmed diagnostic information) available in the target domain (new cohort) to determine the proper treatment for older adults. Instead, there are multiple related but non-identical domain data with labels from the existing cohort or different institutions. Integrating different data sources with labeled and unlabeled samples to predict a patient's condition poses a significant challenge. Traditional machine learning models assume that data for new patients follow a similar distribution. If the data does not satisfy this assumption, the trained models do not achieve the expected accuracy, leading to potential misdiagnosing risks. To address this issue, we utilize domain adaptation (DA) techniques, which employ labeled data from one or more related source domains. These DA techniques promise to tackle discrepancies in multiple data sources and achieve a robust diagnosis for new patients. In our research, we have developed an unsupervised DA model to align two domains by creating a domain-invariant feature representation. Subsequently, we have built a robust fall-risk prediction model based on these new feature representations. The results from simulation studies and real-world applications demonstrate that our proposed approach outperforms existing models.


Dominant‐pole placement for predictor synthesis

Abstract: Abstract This article analyzes the high‐gain prediction approach for nonlinear input‐delay systems. The problem is discussed in the light of weighted homogeneity and input‐to‐state stability. The canonical form for uniformly observable nonlinear systems allows tuning the linear‐part spectrum by multiplicity‐induced dominancy and ensuring closed‐loop system input‐to‐state stability using the descriptor method of the Lyapunov–Krasovskii approach. Due to the trade‐off between delay and gain margin, the limitation of high gain that results for time‐delay systems is overcome using a cascade of subpredictors. A comparative analysis shows that our proposal achieves a better trade‐off between delay and gain margin, resulting in a reduced number of subpredictors.


Autonomous multicopter landing on a moving vehicle based on RSSI

Abstract: Abstract Currently, most of the studies on unmanned aerial vehicle (UAV) automatic landing systems mainly depend on image information to determine the landing location. However, the system requires a camera, a gimbal system and a separate image-processing device, which increases the weight and power consumption of the UAV, resulting in a shorter flight time. In addition, a large amount of computation and slow reaction speed can cause the camera to miss a proper landing moment. To solve these problems, in this study, the moving direction and relative distance between an object and the automatic landing system were measured using a receive signal strength indicator of the radio-frequency (RF) signal. To improve the movement direction and relative distance estimation accuracy, the noise in the RF signal was minimised using a low pass filter and moving average filter. Based on the filtered RF signal, the acceleration of the multicopter to reach the object was estimated by adopting the proportional navigation algorithm. The performance of the proposed algorithm for precise landing on a moving vehicle was demonstrated through experiments.


Unmasking the oligarchs - Using open source data to detect sanctions violations

Abstract: Russia’s invasion of Ukraine in February 2022 led Western governments to impose extensive sanctions on the Russian economy and the people who maintain the regime - the so-called oligarchs. The enforcement of these sanctions and the manoeuvres of these oligarchs to evade them have created new challenges for the authorities, industry and society at large. This study aims to unmask sanctioned oligarchs who are beneficial owners of companies in the UK. The approach is based on the assumption that the actions of oligarchs leave detectable traces in the UK company register. Inspired by simple tools developed by volunteers for citizens doing their own intelligence work, software has been developed to achieve three different research objectives: to develop indicators of sanctions evasion, to demonstrate that it is possible to do useful intelligence work using only the resources available to the average citizen, and to develop a software tool to identify officers in the UK company register who are suspected of being linked to sanctioned oligarchs. The findings underscore the importance of open source investigations, reflecting a burgeoning intelligence phenomenon introduced as ‘citizen intelligence’, which has the potential to enhance the protection of society by building a more democratic and diverse intelligence community. This study opens the door for further research into the actions and tactics of sanctioned individuals, providing insights to protect global financial integrity and security.


A Binary Object Detection Pattern Model to Assist the Visually Impaired in detecting Normal and Camouflaged Faces

Abstract: This study presents a novel Binary Object Detection Pattern Model (BODPM) to detect objects with face key points and recognize them using the KERAS dataset. The proximity and accuracy of the recognized items were evaluated using computer vision techniques. The object recognition time interval and duration were recorded and stored permanently in a database, and the information was communicated to the visually impaired user as voice output. The normal face, without wearing a mask, was identified using binary patterns with proximity detection. Camouflaged objects were detected in a maximum probability range of 100%. The proposed method was tested, calculating accuracy and score, and compared with existing models, showcasing remarkable performance. The proposed method of normal and camouflage detection is a novel prediction with proximity analysis of objects in a frame.


Research and Development of a Traffic Sign Recognition Module in Vietnam

Abstract: Automatic traffic sign recognition is essential in researching and developing driver assistance systems and autonomous vehicles. This paper presents the research and development of an automated traffic sign recognition module in Vietnam. The recognition model is developed based on the deep learning model YOLOv5 and incorporates architectural modifications to reduce computational complexity, increase inference speed, and meet real-time requirements for embedded system applications. The model is trained using a custom dataset collected by the research team from real-world street environments in Vietnam, encompassing diverse locations, times, and weather conditions. The trained recognition model is deployed on the Jetson embedded system, yielding high-quality recognition results and meeting real-time recognition needs.


Analytical framework using remote sensing and water geochemistry to assess changes in landscapes with high ecological and social value

Abstract: Abstract The Brazilian Mata Atlântica coastal rainforest suffered a dramatic reduction in size due to agricultural and mining activities since the 1500s. Most relevant fragments are in mountainous areas assigned for environmental conservation, where there is increasing market pressure to open these areas for mining operations. In this study, we created a broader framework for water basin study on mountainous regions in tropical areas of high ecological importance. Remote sensing and hydrochemical analysis were used to create a more encompassing evaluation and a prognosis of environmental changes caused by mining operations. Our results showed that land use and land cover variations affect the physical and chemical behavior of rivers, and that seasonality plays a compounding role in their levels of particulate and chemical elements. Applying these combined techniques proved to be a good strategy in the evaluation of land cover change on the physical and chemical variations found in smaller watersheds.


Mammography with deep learning for breast cancer detection

Abstract: X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.


AI-based real-time hand gesture-controlled virtual mouse

Abstract: Hand gesture control technology is an emerging alternative to conventional input devices like keyboards and mice, enabling users to interact with computers through natural hand movements. Although, many researchers worked on gesture recognition and its challenges, yet wide range of gestures has not been addressed. In this direction, the author proposed a novel gesture-controlled virtual mouse system that uses hand gestures and hand tip detection with a webcam or internal camera for computer navigation that accurately recognizes various gestures, including cursor movement, left and right clicks, double clicks, scrolling, drag and drop, multiple item selection, volume control. To deal with this real-world problem, the authors proposed a solution for limited space situations and assist individuals with hand impairments who cannot use a physical mouse. Furthermore, it surpasses the limitations of traditional mice, such as mechanical wear and tear, adaptability to different environments, and restricted functionalities. By leveraging hand gestures and tip detection through a camera proposed system offers an efficient and adaptable approach to operating a PC mouse. The proposed methodology uses computer vision and AI for the accurate detection of gestures, even in challenging environments. It also offers a comprehensive set of gestures for various actions and functions.


Application of Preference Selection Index in Recruitment of Search Engine Optimization Specialist

Abstract: Search Engine Optimization Specialist is one of the important factors that can help digital agencies improve services to all their customers. This research aims to develop a decision-making system in the recruitment process of Search Engine Optimization Specialists by applying the Preference Selection Index method. The Search Engine Optimization Specialist recruitment decision-making system in this study uses 5 (five) criteria, namely educational background, ability to use SEO tools, work experience as a Search Engine Optimization Specialist, digital marketing skills, and age. The research sample data collection technique related to the recruitment of Search Engine Optimization Specialists used in this research is Literature Study. After the author succeeds in collecting research sample data, at the next stage the author analyzes the application of the Preference Selection Index method in the recruitment of Search Engine Optimization Specialists. The results of this study show that alternative A02 (0.855641604) has the highest value and gets the first ranking position. Meanwhile, the alternatives that get the second to fifth ranking positions are A05 (0,834620739), A03 (0,741086734), A04 (0,716537597), and A01 (0,708920064). So that the most recommended alternative based on the calculation of the Preference Selection Index on the recruitment of Search Engine Optimization Specialists in this study is alternative A02 (0,855641604).


The long mean-life-time-controlled and potentially scalable qubits composed of electric dipolar molecules based on graphene

Abstract: Abstract We propose a new kind of qubits composed of electric dipolar molecules. The electric dipolar molecules in an external electric field will take simple harmonic oscillations, whose quantum states belonging to the two lowest energy levels act as the states $|0\rangle$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>|</mml:mo> <mml:mn>0</mml:mn> <mml:mo>〉</mml:mo> </mml:math> , $|1\rangle$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>|</mml:mo> <mml:mn>1</mml:mn> <mml:mo>〉</mml:mo> </mml:math> of a qubit. The qubits’ excited states have a very long controlled mean life time about several seconds. We can perform quantum computations by manipulating the qubits of electric dipolar molecules just like those of neutral atoms. When the qubits are used for quantum computations, the dipolar moments’ orientations will harmonically oscillate along an external electric field and they will not change the directions: along or against the electric field, so the qubits can be large-scalely manufactured in graphene system. The radius of Rydberg blockade is about 100 nm.


Interdisciplinary technical perspective: transformation of blockchain and opportunities for organizational training

Abstract: This study explores how blockchain technology can be applied in the area of training in organizations, the benefits it brings, and the management challenges that blockchain technology raises. By exploring the decentralized, transparent, and intelligent contract nature of blockchain technology, we analyzed the application of blockchain technology to organizational training. The results show that organizations can achieve training resource sharing, supply and demand balancing, training certification, and intelligent contract enforcement based on blockchain technology. However, it also presents potential challenges, mainly regarding engagement across sectors, support from all members, global technology rollout, staff competency, data confidentiality, and return on investment. Case and industry analysis further validates the content of our research and demonstrates the potential and challenges of blockchain technology in the field of organizational training.


Identification of Panoramic Photographic Image Composition Using Fuzzy Rules

Abstract: Making panoramic images has gradually become an essential function inside personal intelligent devices because panoramic images can provide broader and richer content than typical images. However, the techniques to classify the types of panoramic images are still deficient. This paper presents novel approaches for classifying the photographic composition of panoramic images into five types using fuzzy rules. A test database with 168 panoramic images was collected from the Internet. After analyzing the panoramic image database, the proposed feature model defined a set of photographic compositions. Then, the panoramic image was identified by using the proposed feature vector. An algorithm based on fuzzy rules is also proposed to match the identification results with that of human experts. The experimental results show that the proposed methods have demonstrated performance with high accuracy and this can be used for related applications in the future.


Multifractal signal generation by cascaded chaotic systems and their analog electronic realization

Abstract: Multifractality, that is, self-similarity where in scaling follows a continuous spectrum of exponents, is a ubiquitous property of the morphology and dynamics of large-scale complex systems. However, to date, the prerequisites for the generation of multifractal time series by dynamical systems remain an open issue. This work demonstrates that multifractality can emerge in the time series spontaneously generated by a small ensemble of cascaded nonlinear oscillators, which are deterministic, autonomous and delayless. Namely, a chain of four Rössler systems with directed couplings is investigated numerically and realized experimentally in the form of analog electronic circuits. The observation of multifractality is established using the detrended fluctuation analysis and confirmed through surrogate tests, wavelet-based analyses and cascade structure visualization. Multifractality consistently arises when the oscillation frequencies of the coupled oscillators span a sufficient range along the chain, and the couplings have an intermediate strength which engenders partial entrainment between the adjacent nodes. These results affirm that neither external entropy injection nor driving are indispensable, and provide a blueprint for the design of self-contained generative circuits toward diverse applications across physiology modeling and unconventional computing.


Hardware and Software Solutions for the Generation of a Database of HSV-Color Characteristics for the Main Ores and Rocks of the Khibiny Massif

Abstract: This article presents developed hardware and software solutions based on the application of machine vision technology. The hardware and software solutions were created in order to generate a database of HSV-color value for the main ore types, host rocks, and minerals to define criteria for the in-process identification of the Khibiny apatite in testing the walls of blasting boreholes. The hardware ensures a multi-parametric assessment of the optical characteristics of samples and minerals located on their surface. The designed software solution allows the user to control the measurement process; systematize a description of the textural and structural features of the sample under study; and process images of the core surface. The resultant database of HSV-color value for the main ores and rocks of the Khibiny massif and their constituent minerals will provide an opportunity to search for criteria for the in-process identification of the Khibiny apatite in a mineral mixture.


Advancing Human Motion Recognition with SkeletonCLIP++: Weighted Video Feature Integration and Enhanced Contrastive Sample Discrimination

Abstract: This paper introduces 'SkeletonCLIP++', an extension of our prior work in human action recognition, emphasizing the use of semantic information beyond traditional label-based methods. The innovation, 'Weighted Frame Integration' (WFI), shifts video feature computation from averaging to a weighted frame approach, enabling a more nuanced representation of human movements in line with semantic relevance. Another key development, 'Contrastive Sample Identification' (CSI), introduces a novel discriminative task within the model. This task involves identifying the most similar negative sample among positive ones, enhancing the model's ability to distinguish between closely related actions. Incorporating the 'BERT Text Encoder Integration' (BTEI) leverages the pre-trained BERT model as our text encoder to refine the model's performance. Empirical evaluations on HMDB-51, UCF-101, and NTU RGB+D 60 datasets illustrate positive improvements, especially in smaller datasets. 'SkeletonCLIP++' thus offers a refined approach to human action recognition, ensuring semantic integrity and detailed differentiation in video data analysis.


Enhanced YOLOv5s-Based Algorithm for Industrial Part Detection

Abstract: In complex industrial environments, accurate recognition and localization of industrial targets are crucial. This study aims to improve the precision and accuracy of object detection in industrial scenarios by effectively fusing feature information at different scales and levels, and introducing edge detection head algorithms and attention mechanisms. We propose an improved YOLOv5-based algorithm for industrial object detection. Our improved algorithm incorporates the Crossing Bidirectional Feature Pyramid (CBiFPN), effectively addressing the information loss issue in multi-scale and multi-level feature fusion. Therefore, our method can enhance detection performance for objects of varying sizes. Concurrently, we have integrated the attention mechanism (C3_CA) into YOLOv5s to augment feature expression capabilities. Furthermore, we introduce the Edge Detection Head (EDH) method, which is adept at tackling detection challenges in scenes with occluded objects and cluttered backgrounds by merging edge information and amplifying it within the features. Experiments conducted on the modified ITODD dataset demonstrate that the original YOLOv5s algorithm achieves 82.11% and 60.98% on mAP@0.5 and mAP@0.5:0.95, respectively, with its precision and recall being 86.8% and 74.75%, respectively. The performance of the modified YOLOv5s algorithm on mAP@0.5 and mAP@0.5:0.95 has been improved by 1.23% and 1.44%, respectively, and the precision and recall have been enhanced by 3.68% and 1.06%, respectively. The results show that our method significantly boosts the accuracy and robustness of industrial target recognition and localization.


Experience in Measuring Productivity of Suffix Morphemes in Chinese Military News Discourse Based on a Universal Measure

Abstract: The article studies the issues of measuring derivational productivity of affixes. It considers the concept of productivity and its various aspects which can be quantified by corpus linguistics methods. In particular, it analyzes the measures of “expanding” and “potential” productivity by H. Baayen, and proposes a new formula for “universal” productivity on the basis of the analysis. These three productivity measures are applied to 80 Chinese suffix morphemes (suffixes and semi-suffixes) within a thematic collection of texts – Chinese media reports on military topics for 2019. The language material comprises 2066 texts (2.852.262 characters). The study resulted in three variants of the rating list for the most productive suffix morphemes of the Chinese military news discourse, which can be used for didactic and applied purposes. H. Baayen’s “expanding productivity” and the “universal productivity” proposed in the work showed a high correlation (0.85), while H. Baayen’s “potential productivity” correlates with them negatively. A meaningful analysis of the results also brings up a question of the appropriateness of using the “potential productivity” measure. Comparison of existing measures and the formula proposed in the work allow us to conclude that for an approximate assessment of the suffix morphemes productivity one can use the formula of “expanding productivity” by H. Baayen, which, despite relying only on the single word token (hapax legomenon) frequency, gives quite plausible results. To measure the suffix morphemes productivity more precisely, it is recommended to use the “universal productivity” formula proposed in this paper which takes into account a larger number of significant parameters (the number of hapax legomena with the estimated affix, the total type count with the given affix, and the average regular word token frequency of the estimated affix) and is more reasonable in this respect.


Three-Dimensional Path Planning Based on Six-Direction Search Scheme

Abstract: In order to solve the problem of how to perform path planning for AUVs with multiple obstacles in a 3D underwater environment, this paper proposes a six-direction search scheme based on neural networks. In known environments with stationary obstacles, the obstacle energy is constructed based on a neural network and the path energy is introduced to avoid a too-long path being generated. Based on the weighted total energy of obstacle energy and path energy, a six-direction search scheme is designed here for path planning. To improve the efficiency of the six-direction search algorithm, two optimization methods are employed to reduce the number of iterations and total path search time. The first method involves adjusting the search step length dynamically, which helps to decrease the number of iterations needed for path planning. The second method involves reducing the number of path nodes, which can not only decrease the search time but also avoid premature convergence. By implementing these optimization methods, the performance of the six-direction search algorithm is enhanced in favor of path planning with multiple underwater obstacles reasonably. The simulation results validate the effectiveness and efficiency of the six-direction search scheme.


Applying Geostatistics to Understand Seismic Activity Patterns in the Northern Red Sea Boundary Zone

Abstract: A comprehensive geostatistical analysis was conducted on a dataset comprising 24,321 seismic events in the Red Sea region, spanning from 1997 to 2020. This analysis involved the creation of a new seismic activity database, incorporating data from both Egyptian and Saudi Seismic Networks. This enriched database provided a robust foundation for a detailed examination of the seismic patterns and activities in the region. Utilizing geographic information systems and various spatial analytic methods, it identifies seismic patterns and tectonic influences. The findings reveal significant seismic clustering along the Central Red Sea axis, indicative of active rifting between the Nubian and Arabian plates. The study demonstrates spatial autocorrelation in seismic activities, with high-high clusters marking zones of elevated seismicity. Kernel Density Estimator analyses highlight concentrated seismic activity in the Gulfs of Aqaba and Suez. Higher magnitude events are shown to localize in areas of greater tectonic stress, aligning with known geological features. This research provides critical insights into the seismic dynamics of the Red Sea, showcasing the effectiveness of geostatistical techniques in analyzing seismic data in tectonically active regions.


Characterization of a Transmon Qubit in a 3D Cavity for Quantum Machine Learning and Photon Counting

Abstract: In this paper, we report the use of a superconducting transmon qubit in a 3D cavity for quantum machine learning and photon counting applications. We first describe the realization and characterization of a transmon qubit coupled to a 3D resonator, providing a detailed description of the simulation framework and of the experimental measurement of important parameters, such as the dispersive shift and the qubit anharmonicity. We then report on a Quantum Machine Learning application implemented on a single-qubit device to fit the u-quark parton distribution function of the proton. In the final section of the manuscript, we present a new microwave photon detection scheme based on two qubits coupled to the same 3D resonator. This could in principle decrease the dark count rate, favoring applications like axion dark matter searches.


Hardware acceleration of DNA pattern matching using analog resistive CAMs

Abstract: DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The remarkable growth in the size of DNA datasets has resulted in challenges in discovering DNA patterns efficiently in terms of run time and power consumption. In this paper, we propose an efficient pipelined hardware accelerator that determines the chance of the occurrence of repeat-expansion diseases using DNA pattern matching. The proposed design parallelizes the DNA pattern matching task using associative memory realized with analog content-addressable memory and implements an algorithm that returns the maximum number of consecutive occurrences of a specific pattern within a DNA sequence. We fully implement all the required hardware circuits with PTM 45-nm technology, and we evaluate the proposed architecture on a practical human DNA dataset. The results show that our design is energy-efficient and accelerates the DNA pattern matching task by more than 100× compared to the approaches described in the literature.


Quantum neural network with privacy protection of input data and training parameters

Abstract: Abstract It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for <?CDATA ${{\rm{R}}}_{{\rm{z}}},$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">z</mml:mi> </mml:mrow> </mml:msub> <mml:mo>,</mml:mo> </mml:math> <?CDATA ${{\rm{R}}}_{{\rm{x}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">x</mml:mi> </mml:mrow> </mml:msub> </mml:math> and <?CDATA ${{\rm{R}}}_{{\rm{y}}}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi mathvariant="normal">R</mml:mi> </mml:mrow> <mml:mrow> <mml:mi mathvariant="normal">y</mml:mi> </mml:mrow> </mml:msub> </mml:math> gate are putting forward. Based on which, we further propose quantum neural network (QNN) with privacy protection of input data and training parameters, where hiding model parameters has hardly mentioned in existing literature. To verify the validity of the proposed scheme, we conducted experiments on MNIST and Iris datasets, and obtained losses and accuracy that are almost identical to the original algorithms. Experiments show that our scheme can protect privacy well in the training and have no effect on accuracy of the algorithm. By comparison, the proposed scheme is more secure than most existing literature and requires less extra complexity O (4n).


The Single-Photon Scattering Properties of Three-Level Giant Atoms under the Interaction of Dissipation and Local Coupling

Abstract: The coupling of three-level giant atoms with one-dimensional waveguides can show interesting phenomena of transmission and reflection. Since the non-waveguide mode can cause the dissipation of external atoms, we consider the effect of the dissipation rate on the scattering of single photons in the system with giant atom–waveguide coupling. We find that as the dissipation rate of giant atoms increases, the transmission rate of a single photon increases and the reflection rate decreases. In addition, by varying the phase difference and decay rate, the giant atoms are able to achieve perfect transmission and total reflection over the entire frequency range. We also find and show the conditions for the conversion of the optimal frequency. When the cumulative phase of photons reaches a certain value, the system can achieve perfect transmission, which is independent of frequency. This model of coupling giant atoms with waveguides has a promising application in quantum communication and quantum information processing.


Prediction Model of Water Abundance of Weakly Cemented Sandstone Aquifer Based on Principal Component Analysis–Back Propagation Neural Network of Grey Correlation Analysis Decision Making

Abstract: At present, in the vast majority of coal mine production processes in China, the degree of hydrogeological exploration often lags behind geological exploration. The main difficulty in evaluating the water richness of coal seam top and bottom water-bearing beds is that the existing evaluation methods often rely on less hydrogeological investigation data. How to utilize the abundant geological exploration data in the mining area to appraise the water-rich distribution of sandstone aquifers is a feasible and challenging methodology. At present, some experts and scholars have tried to use multivariate factor analysis to solve the problem of water-richness evaluation, and they have achieved certain results, but there are some shortcomings: (1) The prediction results are mostly qualitative estimations of the water-richness grade, and there is a lack of quantitative analysis of the units-inflow; and (2) at present, the more advanced prediction methods, such as the back propagation (BP) neural network model, have the disadvantages of low accuracy, requiring many iterations, and slow convergence speed. Therefore, with geological exploration data of the 1503E working face of the Yili No.1 coal mine as the basis., this paper uses grey correlation analysis to screen out the factor indexes suitable for the evaluation of the water richness of a weakly cemented sandstone aquifer, and it combines principal component analysis (PCA) with a BP neural network. Based on the selected factor indexes, a prediction model of the water richness of a weakly cemented sandstone aquifer is established. The results show that compared with the existing methods, the prediction accuracy is higher and has a certain application value.


Pooled Steganalysis via Model Discrepancy

Abstract: Pooled steganalysis aims to discover the guilty actor(s) among multiple normal actor(s). Existing techniques mainly rely on the high-dimension and time-consuming features. Moreover, the minor feature distance between cover and stego is detrimental to pooled steganalysis. To overcome these issues, this paper focuses on the discrepancy of the statistical characteristics of transmitted multiple images and designs a model-based effective pooled steganalysis strategy. Facing the public and monitored channel, without using the feature extractions, pooled steganalysis collects a set of images transmitted by a suspicious actor and use the corresponding distortion values as the statistic representation of the selected image set. Specifically, the normalized distortion of the suspicious image set generated via normal/guilty actor(s) is modelled as a normal distribution, and we apply maximum likelihood estimation (MLE) to estimate the parameter (cluster center) of the distribution by which we can represent the defined model. Considering the tremendous distortion difference between normal and stego image sets, we can deduce that the constructed model can effectively discover and reveal the existence of abnormal behavior of guilty actors. To show the discrepancy of different models, employing the logistic function and likelihood ratio test (LRT), we construct a new detector by which the ratio of cluster centers is turned into a probability. Depending on the generated probability and an optimal threshold, we make a judgment on whether the dubious actor is normal or guilty. Extensive experiments demonstrate that, compared to existing pooled steganalysis techniques, the proposed scheme exhibits great detection performance on the guilty actor(s) with lower complexity.


El léxico coloquial proveniente del lenguaje juvenil en la lengua de ficción española e italiana, versiones originales y meta

Abstract: Abstract Youth lexicon has penetrated and become consolidated in the common colloquial register of adult speakers of any given language. This study uses a comparable bilingual corpus of original Spanish and Italian series, and a parallel trilingual corpus which includes the target Spanish and Italian versions of an analogous French series in order to investigate the use of such voices in national and foreign serial products of both cultures. The frequency of use per minute and the repertoire of terms inserted in the series of the two countries have been compared. The results show that the number of occurrences per minute in the Spanish original fictional language is much higher than in the Italian one, where this kind of words and expressions are often excluded. These data coincide with the number of occurrences per minute recorded in the Spanish and Italian target versions. Therefore, the parallelism between the original and target products of both languages shows that hybridization and domestication have been the predominant strategies in the translation for dubbing.


A Survey of Ethereum Smart Contract Security: Attacks and Detection

Abstract: A smart contract is a computerised transaction agreement that carries out predefined terms without human involvement or third-party intermediaries. It serves as a trust intermediary in several industries, including finance, insurance, and supply chain management, in the blockchain 2.0 era. With the increasing interest in smart contracts, security has become a serious problem. Examining typical vulnerability types and vulnerability detection methodologies is of special importance. In this research, a comprehensive evaluation of common smart contract security vulnerabilities is conducted, and a three-tier threat model is then provided to classify the vulnerabilities. In addition, we examine fourteen existing smart contract analysis tools for finding vulnerabilities and classify them according to the main technique they apply. This paper is designed to serve as a reference for people who wish to analyse deployed code and enhance existing detection techniques. At the conclusion, open issues and future research paths regarding smart contract vulnerability detection are presented.


Good models borrow, great models steal: intellectual property rights and generative AI

Abstract: Abstract Two critical policy questions will determine the impact of generative artificial intelligence (AI) on the knowledge economy and the creative sector. The first concerns how we think about the training of such models—in particular, whether the creators or owners of the data that are “scraped” (lawfully or unlawfully, with or without permission) should be compensated for that use. The second question revolves around the ownership of the output generated by AI, which is continually improving in quality and scale. These topics fall in the realm of intellectual property, a legal framework designed to incentivize and reward only human creativity and innovation. For some years, however, Britain has maintained a distinct category for “computer-generated” outputs; on the input issue, the EU and Singapore have recently introduced exceptions allowing for text and data mining or computational data analysis of existing works. This article explores the broader implications of these policy choices, weighing the advantages of reducing the cost of content creation and the value of expertise against the potential risk to various careers and sectors of the economy, which might be rendered unsustainable. Lessons may be found in the music industry, which also went through a period of unrestrained piracy in the early digital era, epitomized by the rise and fall of the file-sharing service Napster. Similar litigation and legislation may help navigate the present uncertainty, along with an emerging market for “legitimate” models that respect the copyright of humans and are clear about the provenance of their own creations.


Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Abstract: Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.


Assessing primary school preservice teachers’ confidence to apply their TPACK in specific categories of technologies using a self-audit survey

Abstract: Abstract Internationally, university teacher educators have acted on the requirement that practising teachers need to be operational users of technologies. In response, coursework has been restructured to develop preservice teachers’ (PSTs) use and application of educational technologies for teaching and learning purposes. This paper presents the development and use of a self-audit survey designed to guide primary school PSTs’ self-directed learning and assessments in 10 specific categories of technologies, and to improve their confidence to apply their knowledge. The survey was administered to 296 PSTs in a pre- and post-course design and validated post hoc with Rasch analysis. Pre- and post-course comparison of responses showed significant increases in the PSTs’ self-assessed confidence in their abilities across all ability categories with medium to large effect sizes ( r = .42–.82). The survey had utility, providing each PST with a customised report with which to identify their technology learning needs; it also provided PSTs with the agency to be self-directed in their learning. The survey has the potential to provide teacher educators with fine-grained information regarding which technological pedagogical and content knowledge (TPACK) being currently taught can be adopted, adapted, or rejected to maximise PSTs’ TPACK development.


Measuring the motion of the eardrum using Digital Image Correlation

Abstract: Abstract The human eardrum is a translucent curved membrane and is in direct contact with the middle ear bones. Studying this complicated biomechanical system remains challenging. Digital image correlation (DIC) is an optical technique that allows measuring the shape and displacement of curved surfaces in 3D. In this work, we discuss the advantages and disadvantages of using DIC to study the eardrum at frequencies around 1 kHz. Special attention is given to the application of speckle patterns required to perform DIC on the translucent eardrum. Additionally, we detail an alternative approach to high-speed cameras: phase-locked light pulses. Using light pulses synced to the phase of the sound, regular DIC cameras can be used at their full resolution. This enables a high spatial resolution to thoroughly investigate the motion of the eardrum at the relevant frequencies.


An efficient image encryption algorithm using a discrete memory-based logistic map with deep neural network

Abstract: Abstract In the last few years, multimedia technology has made tremendous strides. These days, the Web is frequently used to transfer multimedia content, including audio, video, and photos. However, the Internet is a very vulnerable medium with many security holes. To ensure that multimedia content carried across unprotected channels, like the Internet, is secure and private, several encryption techniques have been proposed. New encryption strategies must be developed because multimedia data streams cannot be encrypted using traditional methods. Therefore, the main goal of the recommended system is to present an analytical research approach for introducing a sophisticated framework wherein the suggested encryption technologies' efficacy is increased through the use of deep neural networks (DNNs). The robustness of the DNN principle is coupled with a discrete memristor-based logistic chaotic map notion for enhanced security performance. In this paper, three distinct encryption algorithms—Arnie cat with an artificial neural network (ANN), Henon map with an ANN, and logistic map with a DNN—are compared for security and performance with the suggested algorithm. Correlation coefficients, information entropy, number of pixels changing rate (NPCR), encryption quality, and encryption duration are the cryptographic analysis parameters examined here. The results show that the recommended implementation enhances security performance without degrading image quality. The proposed algorithm achieves 35.9% of UACI, 99.95% of NPCR, and 7.997231 of entropy.


Traffic sign recognition using CNN and Res-Net

Abstract: In the realm of contemporary applications and everyday life, the significance of object recognition and classification cannot be overstated. A multitude of valuable domains, including G-lens technology, cancer prediction, Optical Character Recognition (OCR), Face Recognition, and more, heavily rely on the efficacy of image identification algorithms. Among these, Convolutional Neural Networks (CNN) have emerged as a cutting-edge technique that excels in its aptitude for feature extraction, offering pragmatic solutions to a diverse array of object recognition challenges. CNN's notable strength is underscored by its swifter execution, rendering it particularly advantageous for real-time processing. The domain of traffic sign recognition holds profound importance, especially in the development of practical applications like autonomous driving for vehicles such as Tesla, as well as in the realm of traffic surveillance. In this research endeavour, the focus was directed towards the Belgium Traffic Signs Dataset (BTS), an encompassing repository comprising a total of 62 distinct traffic signs. By employing a CNN model, a meticulously methodical approach was obtained commencing with a rigorous phase of data pre-processing. This preparatory stage was complemented by the strategic incorporation of residual blocks during model training, thereby enhancing the network's ability to glean intricate features from traffic sign images. Notably, our proposed methodology yielded a commendable accuracy rate of 94.25%, demonstrating the system's robust and proficient recognition capabilities. The distinctive prowess of our methodology shines through its substantial improvements in specific parameters compared to pre-existing techniques. Our approach thrives in terms of accuracy, capitalizing on CNN's rapid execution speed, and offering an efficient means of feature extraction. By effectively training on a diverse dataset encompassing 62 varied traffic signs, our model showcases a promising potential for real-world applications. The overarching analysis highlights the efficacy of our proposed technique, reaffirming its potency in achieving precise traffic sign recognition and positioning it as a viable solution for real-time scenarios and autonomous systems.


Research on YOLOv5 Vehicle Detection and Positioning System Based on Binocular Vision

Abstract: Vehicle detection and location is one of the key sensing tasks of automatic driving systems. Traditional detection methods are easily affected by illumination, occlusion and scale changes in complex scenes, which limits the accuracy and robustness of detection. In order to solve these problems, this paper proposes a vehicle detection and location method for YOLOv5(You Only Look Once version 5) based on binocular vision. Binocular vision uses two cameras to obtain images from different angles at the same time. By calculating the difference between the two images, more accurate depth information can be obtained. The YOLOv5 algorithm is improved by adding the CBAM attention mechanism and replacing the loss function to improve target detection. Combining these two techniques can achieve accurate detection and localization of vehicles in 3D space. The method utilizes the depth information of binocular images and the improved YOLOv5 target detection algorithm to achieve accurate detection and localization of vehicles in front. Experimental results show that the method has high accuracy and robustness for vehicle detection and localization tasks.


Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

Abstract: Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.


High sensitivity strain measurement on a vibrating saxophone reed using stroboscopic digital image

Abstract: Abstract In this paper, we present a study of different optical textures and techniques to apply very fine, non-specular reflecting random speckle texture, which makes it possible to measure surface strains with a sensitivity better than 0.01 % strain on a vibrating object. The method is based on triggered stroboscopic digital image correlation, which is able to obtain time-resolved full-field measurements of displacement and strain fields. The method is demonstrated on reeds of the saxophone to investigate their role in sound production and playability. Such high-sensitivity measurements allow the detection of subtle effects, such as artefacts on synthetic saxophone reeds caused by the production process.


AI and the future of human rights in Bangladesh: a call for robust legal and ethical frameworks

Abstract: Abstract The rise of artificial intelligence (AI) technology presents both opportunities and challenges for the protection of human rights in Bangladesh. While AI has the potential to transform and enhance many areas of society, including healthcare, education, and business, it also raises significant ethical and legal questions that require careful consideration. This paper explores the current state of AI technology in Bangladesh and examines the potential impact on human rights, particularly in the areas of privacy, discrimination, and freedom of expression. The paper argues that while AI can be a valuable tool for promoting human rights, it can also exacerbate existing inequalities and create new forms of discrimination. To ensure that AI is used in a way that upholds human rights, the paper calls for the development of robust legal and ethical frameworks to regulate the use of AI technology. The paper proposes several recommendations for achieving this goal, including the establishment of clear ethical guidelines for the development and deployment of AI systems, the creation of a regulatory body to oversee AI technology in Bangladesh, and the use of impact assessments to identify potential risks and harms associated with AI systems. Overall, the paper emphasizes the need for a collaborative and interdisciplinary approach to addressing the legal and ethical challenges posed by AI technology in Bangladesh. By taking a proactive and holistic approach, Bangladesh can ensure that AI is developed and used in a way that respects and promotes human rights, rather than undermining them. The paper concludes by stressing the importance of ongoing research and dialogue on this critical topic to inform and guide policy and decision-making in the future.


The influence of Ohmic noise on the dynamics of three-spin open quantum system

Abstract: Abstract The dynamics of open quantum systems under decoherence effects remain a hotly debated topic in the case of the practical deployment of quantum information processing. In this regard, we examine how the three-qubit mixed state is affected by changes in the cut-off frequency of the Ohmic spectral density in a harmonic reservoir. Specifically, the degree of quantum memory-assisted entropic uncertainty (QMA-EU), along with the entanglement, coherence, and purity of the system, is to be demonstrated. A thorough exercise is done to investigate any underlying relationship between the three-qubit quantum characteristics. We show that a harmonic reservoir controlled by Ohmic noise prevails a monotonic-like decay in the current case where, sooner or later, the state becomes completely disentangled, decoherent, and mixed. The QMA-EU has always been found to have an increasing function causing the quantum resourcefulness to be reduced. Although, there is no pathway to avoid the Ohmic noise consequences and complete decay, however, we provided parameterization which would lead to prolonged preservation of quantum correlations with time. Finally, we provide various settings for the tuning of cut-off frequency regarding the Ohmic type bath and state parameters on the initial as well as final levels of quantum features.


Initial correlations in open quantum systems are always detectable

Abstract: Consider an open quantum system which interacts with its environment. Assuming that the experimenter has access only to the system, an interesting question is whether it is possible to detect initial correlations between the system and the environment by performing measurements only on the system. Various methods have been proposed to detect correlations by local measurements on the system. After reviewing these methods, we will show that initial correlations between the system and the environment are always detectable. In particular, we will show that one can always find a unitary evolution, for the whole system-environment, such that the trace distance method, proposed to witness correlations locally, succeeds. We also find the condition for existence of the optimal unitary evolution, for which the entire correlation is locally detectable. Next, we address the case where the system and the environment interact through a time-independent Hamiltonian. For this case we will see that if the initial correlation can be detected locally at some time $t$, then it can be detected for almost all the other times too. On the other hand, we see that one can find cases for which initial correlations between the system and the environment always remain undetectable even though the unitary evolution, generated by the Hamiltonian, is not factorized.


Avoided level crossings with exponentially closing gaps in quantum annealing

Abstract: This paper explores the phenomenon of avoided level crossings in quantum annealing, a promising framework for quantum computing that may provide a quantum advantage for certain tasks. Quantum annealing (QA) involves letting a quantum system evolve according to the Schr\"odinger equation, with the goal of obtaining the optimal solution to an optimization problem through measurements of the final state. However, the continuous nature of quantum annealing makes analytical analysis challenging, particularly with regard to the instantaneous eigenenergies. The adiabatic theorem provides a theoretical result for the annealing time required to obtain the optimal solution with high probability, which is inversely proportional to the square of the minimum spectral gap. Avoided level crossings can create exponentially closing gaps, which can lead to exponentially long running times for optimization problems. In this paper, we use a perturbative expansion to derive a condition for the occurrence of an avoided level crossing during the annealing process. We then apply this condition to the MaxCut problem on bipartite graphs. We show that no exponentially small gaps arise for regular bipartite graphs, implying that QA can efficiently solve MaxCut in that case. On the other hand, we show that irregularities in the vertex degrees can lead to the satisfaction of the avoided level crossing occurrence condition. We provide numerical evidence to support this theoretical development, and discuss the relation between the presence of exponentially closing gaps and the failure of quantum annealing.


Phase-error-rate analysis for quantum key distribution with phase postselection

Abstract: Quantum key distribution (QKD) stands as a pioneering method for establishing information-theoretically secure communication channels by utilizing the principles of quantum mechanics. In the security proof of QKD, the phase error rate serves as a critical indicator of information leakage and directly influences the security of the shared key bits between communicating parties, Alice and Bob. In estimating the upper bound of the phase error rate, phase randomization and subsequent postselection mechanisms serve pivotal roles across numerous QKD protocols. However, the nonzero interval of phase postselection will introduce intrinsic errors, leading to an overestimation of phase error rate. Here we make a precise phase-error-rate analysis for QKD protocols with phase postselection, which eliminates error rate associated with nonzero interval and helps us to accurately bound the amount of information an eavesdropper may obtain. We further apply our analysis in sending-or-not-sending twin-field quantum key distribution (SNS-TFQKD) and mode-pairing quantum key distribution (MP-QKD). The simulation results confirm that our precise phase error analysis can noticeably improve the key rate performance especially over long distances in practice. Note that our method does not require alterations to the existing experimental hardware or protocol steps. It can be readily applied within current SNS-TF-QKD and MP-QKD for higher key rate generation.


Minimality, Necessity and Sufficiency for Argumentation and Explanation

Abstract: We discuss explanations for formal (abstract and structured) argumentation – the question whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how selection based on necessity and sufficiency can be captured in our basic framework and discuss a real-life application.


Advances on similarity via transversal intersection of manifolds

Abstract: Let A be an n×n real matrix. As shown in the recent paper “The bifurcation lemma for strong properties in the inverse eigenvalue problem of a graph”, Linear Algebra Appl. 648 (2022), 70–87, by S.M. Fallat, H.T. Hall, J.C.-H. Lin, and B.L. Shader, if the manifolds MA={G−1AG:G∈GL(n,R)} and Q(sgn(A)) (consisting of all real matrices having the same sign pattern as A), both considered as embedded submanifolds of Rn×n, intersect transversally at A, then every superpattern of sgn(A) also allows a matrix similar to A. Those authors introduced a condition on A (in terms of certain linear matrix equations) equivalent to the above transversality, called the nonsymmetric strong spectral property (nSSP). In this paper, this transversality property of A is characterized using an alternative, more direct and convenient condition, called the similarity-transversality property (STP). Let X=[xij] be a generic matrix of order n whose entries are independent variables. The STP of A is defined as the full row rank property of the Jacobian matrix of the entries of AX−XA at the zero entry positions of A with respect to the nondiagonal entries of X. This new approach makes it possible to take better advantage of the combinatorial structure of the matrix A, and provides theoretical foundation for constructing matrices similar to a given matrix while the entries have certain desired signs. In particular, several important classes of zero-nonzero patterns and sign patterns that require or allow this transversality property are identified. Examples illustrating many possible applications (such as diagonalizability, number of distinct eigenvalues, nilpotence, idempotence, semi-stability, eigenvalues and their algebraic and geometric multiplicities, Jordan canonical form, minimal polynomial, and rank) are provided.


A robust phase unwrapping method for phase shifting coding high-frequency color fringe projection profilometry

Abstract: Phase unwrapping is one of the key problems in fringe projection profilometry (FPP). How to achieve unambiguous fringe step order to perform phase unwrapping with few patterns still face a major challenge. We propose a robust phase unwrapping method by using a pair of encoded color fringe patterns, one is a conventional three-step phase-shifting color fringe pattern with equal phase shifts, another is added an additional phase shift valve t(k) to the former, in which t(k) is related to the order of fringe periods. A single-valued solution function for t(k) is constructed and used to determine the unique fringe order. Besides, multiplexing this pair of fringes can help denoise the extracted wrapped phase to improve the accuracy. The simulation and experimental results proved that the proposed method can achieve unambiguous phase unwrapping for high-frequency fringe patterns. The proposed method is also applicable for isolated objects.


Connection-density-aware satellite-ground federated learning via asynchronous dynamic aggregation

Abstract: With the development of space technology, the use of satellites for Earth observation is becoming more and more common. Among them, low-earth orbit (LEO) satellites have received a lot of attention because of their short return period and low cost. However, traditional satellite systems are unable to process data in orbit due to technical limitations and can only transmit massive amounts of data with useless information back to ground stations and then perform calculations, putting a tremendous strain on downstream bandwidth. Additionally, there are data walls between satellite affiliates, which makes it difficult to share and use data. To address the lack of data processing capabilities and data barriers in conventional satellite systems, as well as to effectively use satellite resources and enhance worldwide collaboration, we proposed a connection density-aware satellite-ground federated learning (FL) framework. According to this structure, satellites serve as workers in federated learning, analyzing data and transmitting the parameters to ground stations for aggregation. The ground station senses the satellite’s connection density during the aggregation process and implements a dynamic aggregation approach for the downstream parameters. Experiments based on real image datasets and on a simulated satellite network validate the effectiveness of the method in terms of execution efficiency and accuracy.


Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review

Abstract: In the continuously developing field of cyber security, user authentication and authorization play a vital role in protecting personal information and digital assets from unauthorized use. As the field of cyber security expands, traditional user authentication and authorization approaches are not enough to prevent unauthorized access to personal information. Therefore, Machine Learning and Deep Learning models are introduced in cybersecurity. To assist researchers and cybersecurity experts in their research endeavours, a comprehensive and informative study is required covering the state-of-the-art advancements. Therefore, this research aimed to explore the field of Machine Learning and Deep Learning-based user authentication and authorization. More specifically, this paper intends to explore the diverse application domains of Machine Learning and Deep Learning-based user authentication and authorization. The paper also analyzes the commonly used datasets, pre-processing methods and Machine Learning and Deep Learning algorithms in user authentication and authorization. After that, this study conducts a thorough and detailed examination of some state-of-the-art articles' results and experimental details to enhance comprehension of the present advancements. Finally, the study engages in a comprehensive discussion concerning the various challenges encountered and outlines potential avenues for future research. This systematic review provides an all-encompassing overview of Machine Learning and Deep Learning-based user authentication and authorization, covering its application domains, models, analysis of state-of-the-art results, challenges, and research directions. It serves as a valuable resource for interdisciplinary studies.


Knowledge Distillation via Token-level Relationship Graph based on the Big Data technologies

Abstract: In the big data era, characterized by vast volumes of complex data, the efficiency of machine learning models is of utmost importance, particularly in the context of intelligent agriculture. Knowledge distillation (KD), a technique aimed at both model compression and performance enhancement, serves as a pivotal solution by distilling the knowledge from an elaborate model (teacher) to a lightweight, compact counterpart (student). However, the true potential of KD has not been fully explored. Existing approaches primarily focus on transferring instance-level information by big data technologies, overlooking the valuable information embedded in token-level relationships, which may be particularly affected by the long-tail effects. To address the above limitations, we propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) that leverages token-wise relationships to enhance the performance of knowledge distillation. By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model, resulting in improved performance and mobile-friendly efficiency. To further enhance the learning process, we introduce a dynamic temperature adjustment strategy, which encourages the student model to capture the topology structure of the teacher model more effectively. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches. Empirical results demonstrate the superiority of TRG across various visual tasks, including those involving imbalanced data. Our method consistently outperforms the existing baselines, establishing a new state-of-the-art performance in the field of KD based on big data technologies.


Investigating the behavioural intentions of museum visitors towards VR: A systematic literature review

Abstract: This study investigates the behavioural intentions of museum visitors towards Virtual Reality (VR) technology, spanning the years 2010–2023. It evaluates literature quality, explores research theories, and identifies trends. Findings indicate that most museum visitors hold positive attitudes towards VR, necessitating further research on generational perspectives. Quantitative research methods dominate (77.8%), with 38.9% employing the Technology Acceptance Model (TAM) or its extensions. Key influencing factors include perceived usefulness, information quality, perceived ease of use, and psychological cognition. The study underscores the importance of comprehending generational differences among museum visitors and advocates for adopting comprehensive theoretical models in future research within the museum domain. This research contributes to understanding how museum visitors engage with VR technology, emphasising nuanced approaches considering generational variations and recommending more inclusive theoretical frameworks.


Fractional-order quantum game chaotic map and its synchronization with application

Abstract: In this paper, according to the knowledge of discrete fractional calculus and quantum game chaotic map, we first get the fractional quantum game chaotic map, which describes the dynamics of a quantum duopoly game. Then, the phase space, maximum Lyapunov exponent, and bifurcation diagram of the proposed map are obtained for the purpose of observing dynamic behaviors compared with the original quantum game chaotic map. After that, the synchronous controller is designed and the chaos synchronization of the fractional quantum game chaotic map is observed. Finally, we develop the fractional quantum game chaotic map into the chaotic sequence generator and image encryption scheme.


Multi-view universum support vector machines with insensitive pinball loss

Abstract: Multi-view learning (MVL) is a promising field that seeks to make the most of information shared across different views, ultimately improving generalization performance. Numerous multi-view support vector machines (SVMs) have been proposed and shown great success. However, hingle loss is widely used by existing methods, which makes these SVMs sensitive to noise and unstable when re-sampling. Furthermore, many researchers have demonstrated the effectiveness of universum data for supervised learning. This paper proposes a novel MVL method called multi-view universum support vector machine with insensitive pinball loss (Pin-MvUSVM). Specifically, we first integrate the pinball loss into the multi-view learning framework, which is dependent on the quantization distance and is robust to noise. Then, we utilize universum data to explore prior knowledge for MVL, which improves the generalization performance. Moreover, we develop multi-view universum twin support vector machine with insensitive pinball loss (Pin-MvUTSVM) as a low-complexity alternative to improve computational efficiency. It solves a pair of smaller-scale quadratic programming problems (QPPs) and utilizes the dual form of the model to solve two convex optimization problems for obtaining the prediction classifier. We conduct classification experiments on four multi-view datasets, and our results show that the proposed convex models outperform several state-of-the-art models.