Reviewerly/exHarmony huggingface dataset:
Multi-hidden target detection of transmission line based on improved YOLOv5s and its hardware implementation

Aiming at the problems of poor accuracy of insulator defects, bird’s nests and foreign objects detection in transmission lines, and the difficulty of algorithm hardware deployment, this paper proposes an improved YOLOv5s multi-hidden target detection algorithm for transmission lines, firstly, in ba ckbone, the CA attention(Coordinate attention) mechanism is integrated into the C3 module to form the C3CA module, which replaces the C3 module of the sixth and the eighth layers, and enhances the feature fusion capability; secondly, in the neck, the GSConv convolution and VoVGSCSP modules are used to replace the standard convolution and C3 modules to form a BiFPN network, which reduces the floating-point operations of the network; finally, the improved algorithm is deployed into Raspberry Pi and accelerated by OpenVINO to realize the hardware deployment of the algorithm, which is demonstrated by experiments that: the mAP value of the algorithm is comparable to that of YOLOv3, YOLOv5 and YOLOv7 by 4.7%, 1.1%, and 1.2%, respectively. The model size is 14.2MB, and the average time to detect an image in Raspberry Pi is 78.2 milliseconds, which meets the real-time detection requirements.
Constrained incomplete argumentation frameworks: Expressiveness, complexity and enforcement

Operations like belief change or merging have been adapted to the context of abstract argumentation. However, these operations may require to express some uncertainty or some disjunction in the result, which is not representable in classical AFs. For this reason, some of these earlier works require a set of AFs or a set of extensions as the outcome of the operation, somehow to represent a “disjunction” of AFs or extensions. In parallel, the notion of Incomplete AFs (IAFs) has been developed recently. It corresponds to AFs where the existence of some arguments or attacks may be uncertain. Each IAF can be associated with a set of classical AFs called completions, that correspond to different ways of resolving the uncertainty. While these IAFs could be good candidates for a compact representation of a disjunction of AFs, we prove that this model is not expressive enough. Then we introduce Constrained IAFs, that include a propositional formula allowing to select the set of completions used for reasoning. We prove that this model is expressive enough for representing any set of AFs, or any set of extensions. Moreover, we study the complexity of various decision problems related to the verification of extensions and the acceptability of arguments. While some of them are one level higher in the polynomial hierarchy (compared to their counterpart with standard IAFs), most of them have the same complexity than in the case of IAFs. Finally, we show that CIAFs can be used to model a new form of extension enforcement, where the possible evolutions of an AF are taken into account and modeled by the completions of the CIAF.
Sentiment classification on online product reviews using dwarf mongoose optimization with attention based deep learning model

Opinion Mining and Sentiment Analysis acts as a pivotal role in facilitating businesses to actively operate on enhancing the business strategies and accomplish detailed insights of the consumer’s feedback regarding the products. In recent times, deep learning (DL)technique has been used for many se ntiment analysis tasks and has attained effective outcomes. Huge quantity of product reviews is being posted by the customer on different e-commerce and social networking platforms which can assist the developers to improve the quality of the products. The study focuses on the design of Sentiment Classification on Online Product Reviews using Dwarf Mongoose Optimization with Attention based Deep Learning (DMO-ABDL) model. The proposed DMO-ABDL technique analyzes the product reviews for the identification of sentiments. To accomplish this, the DMO-ABDL technique performs different stages of preprocessing to transform the actual data into suitable format. Furthermore, the Glove technique is employed for word embedding process. Moreover, attention based long short-term memory (ALSTM) approach was exploited for sentiment classification and its hyperparameters can be optimally chosen by the DMO technique. A comprehensive set of experiments were performed in order to guarantee the enhanced sentiment classification performance of the DMO-ABDL algorithm. A brief comparative study highlighted the supremacy of the DMO-ABDL technique over other existing approaches under different measures.
FPGA-based microsystematic design of Gaussian modulation for continuous-variable quantum communication

Continuous-variable (CV) quantum communication is an important branch of quantum information technology and promises information-theoretical secure communication. High-performance and lightweight CV quantum devices are one of the keys to realizing quantum networks. Among them, the Gaussian-modulation module still has problems such as cumbersome photoelectric calibration and inconvenient use in the experimental stage, which introduces modulation noise and brings additional excess noise to the system, reducing the signal-to-noise ratio of the system and thus reducing system performance. To this end, by designing a field-programmable gate array and integrating all optical components (except the laser source), we achieved a miniaturized and low-cost Gaussian-modulation unit compatible with the existing fiber-optic communication infrastructure, realizing quantum signal modulation under adaptive photoelectric calibration. Taking CV quantum key distribution as the verification object, we analyzed the signal characteristics and the stability of the unit experimentally. The experimental results show that the proposed scheme exhibits quality Gaussian properties and correlations. Moreover, the temporal stability of the device is relatively high (the mean fluctuation is on the order of ${10}^{\ensuremath{-}31}$ and the variance fluctuation is 0.0363), which provides more possibilities for the subsequent popularization of quantum communication and the development of quantum information networks.
From beginner to over champion

In the game of chess the notion of beginner has three meanings. In this contribution they are used as a framework for the history of computer chess. The natural line of reasoning will then bring us to main question: Will the game of Chess (soon) be Solved? A positive answer will certainly be seen as a stepping stone for the next question: Can a computer play perfect chess? Of course, this question will have a natural follow-up in the form of an even more challenging question: Is perfect thinking by a computer possible? The audacious answer “yes” will lead to the world-wide prevailing question of all current AI scientists: Will our intelligence be replaced by AI? The article finds its way (not discussed by us) via machine learning and autonomous adaptation to deep learning and thereafter via Explainable AI and Interpretable AI to solving the Game of Chess. Cornerstones of this historical development will be given in the next three articles by Richard Pijl, Dap Hartmann and Jon Edwards.
Application-Aware Resource Allocation Based on Benefit–Cost Ratio in Computing Power Network with Heterogeneous Computing Resources

The computing power network (CPN) is expected to realize the efficient provisioning of heterogeneous computing power through the collaboration between cloud computing and edge computing. Heterogeneous computing resources consist of CPU, GPU, and other types of computing power. Different types of applications may have diverse requirements for heterogeneous computing resources, such as general applications, CPU-intensive applications, and GPU-intensive applications. Service providers are concerned about how to dynamically provide heterogeneous computing resources for different applications in a cost-effective manner, and how to deploy more applications as much as possible with limited resources. In this paper, the concept of the benefit–cost ratio (BCR) is proposed to quantify the usage efficiency of CPU and GPU in CPNs. An application-aware resource allocation (AARA) algorithm is designed for processing different types of applications. With massive simulations, we compare the performance of the AARA algorithm with a benchmark. In terms of blocking probability, resource utilization, and BCR, AARA achieves better performance than the benchmark. The simulation results indicate that more computing tasks can be accommodated by reducing 3.7% blocking probability through BCR-based resource allocation.
Parameter selection of suppressed relative entropy fuzzy c-means clustering algorithm

The relative entropy fuzzy c-means (REFCM) clustering algorithm improves the robustness of the fuzzy c-means (FCM) algorithm against noise. However, its increased complexity results in slower convergence. To address this issue, we have proposed a suppressed REFCM (SREFCM) algorithm, in which a cons tant suppression rate, α, is selected. However, in cases where external factors, such as changes in the data structure, are present, relying on a fixed α value may result in a decline in algorithm performance, which is clearly unsuitable. Therefore, the adaptive selection of parameters is a critical step. Based on the data structure itself, this paper proposes an algorithm for adaptive parameter selection utilizing partition entropy coefficient and alternating modified partition coefficient, and compares it to six parameter selection algorithms based on generalized rules: θ′ type, ρ type, β type, τ type, σ type and ξ type. Empirical findings indicate that adapting parameters can enhance the partitioning capability of the algorithm while ensuring a rapid convergence rate.
Research on the Effectiveness of Virtual Reality Technology for Locomotive Crew Driving and Emergency Skills Training

With the continuous expansion of road networks and the rise in railway transportation capacity, the scale of locomotive crew needs has increased sharply. However, the current training of locomotive crews is inefficient and costly and cannot meet the needs of the industry. This project adopted virtual reality technology to develop the driving and emergency skills of harmonious electric locomotive crews. The trainees can learn the composition and structure of the locomotive, master the working principles of the locomotive and receive training in relevant emergency skills in the virtual environment. After completion of the system development, we carried out a series of research studies and experiments; the results show that the use of this system to train personnel can lead to them quickly mastering the practical training content. This new training mode can effectively solve the outstanding problems with the current training system for locomotive crews.
On the links between belief merging, the Borda voting method, and the cancellation property1

In this work, we explore the links between the Borda voting rule and belief merging operators. More precisely, we define two families of merging operators inspired by the definition of the Borda voting rule. We also introduce a notion of cancellation in belief merging, inspired by the axiomatization of the Borda voting rule proposed by Young. This allows us to provide a characterization of the drastic merging operator and of a family of merging operators defined in a way which is similar to the Borda rule.
The evaluation method for the completion degree of sports training action based on deep residual network

The completion degree of sports training can not reach the corresponding standard, and the training effect will be greatly weakened. In order to improve the effect of sports training, the evaluation method of sports training completion degree based on deep residual network is studied. The image collector based on ARM is used to collect the action images of athletes in sports training, and the collected action images are preprocessed based on spatial scale filtering and regression factors. Construct a depth residual network, learn the implicit relationship between athletes’ state and the dynamic change process of sports training actions through off-line training, and train the model; In the online application process, the preprocessed action images will be input into the trained evaluation model to evaluate the athletes’ sports training action completion in real time. At the same time, residual shrinkage unit and attention mechanism are used to optimize the depth residual network, which improves the training efficiency and evaluation performance of the network. The experimental results show that this method has good evaluation performance under the condition of setting parameters, and can effectively improve the effect of physical training.
Color Remote Sensing Image Restoration through Singular-Spectra-Derived Self-Similarity Metrics

Color remote sensing images have key features of pronounced internal similarity characterized by numerous repetitive local patterns, so the capacity to effectively harness these self-similarity features plays a key role in the enhancement of color images. The main novelty of this study lies in that we utilized an unusual technique (singular spectrum) to derive brand-new similarity metrics inside the quaternion representation of color images and then incorporated these metrics into denoising algorithms. Color image denoising experiments demonstrated that compared with seven mainstream image restoration algorithms (homomorphic filtering (HPF), wavelet transforms (WT), non-local means (NLM), non-local total variation (NLTV), the color adaptation of non-local means (NLMC), quaternion Euclidean metric (QNLM), and quaternion Euclidean metric total variation (QNLTV)), our algorithms with two novel self-similarity metrics achieved maximum peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), average gradient (AG), and information entropy index (IE) values, with average increases of 1.98 dB /2.12 dB, 0.1168/0.1244, 1.824/1.897, and 0.158/0.135. Moreover, for a complex, mixed-noise scenario, two versions of our algorithms also achieved average increases of 0.382 dB/0.394 dB and 0.0207/0.0210 under Motion and Gaussian mixed noise and average increases of 0.129 dB/0.154 dB and 0.0154/0.0158 under Average and Gaussian mixed noise compared with three quaternion-based restoration algorithms (QNLM, QNLTV, and quantization weighted nuclear norm minimization (QWNNM)).
Beyond i.i.d. in the Resource Theory of Asymmetry: An Information-Spectrum Approach for Quantum Fisher Information

Energetic coherence is indispensable for various operations, including precise measurement of time and acceleration of quantum manipulations. Since energetic coherence is fragile, it is essential to understand the limits in distillation and dilution to restore damage. The resource theory of asymmetry (RTA) provides a rigorous framework to investigate energetic coherence as a resource to break time-translation symmetry. Recently, in the independent and identically distributed (i.i.d.) regime where identical copies of a state are converted into identical copies of another state, it was shown that the convertibility of energetic coherence is governed by a standard measure of energetic coherence, called the quantum Fisher information (QFI). This fact means that QFI in the theory of energetic coherence takes the place of entropy in thermodynamics and entanglement entropy in entanglement theory. However, distillation and dilution in realistic situations take place in regimes beyond i.i.d., where quantum states often have complex correlations. Unlike entanglement theory, the conversion theory of energetic coherence in pure states in the non-i.i.d. regime has been an open problem. In this Letter, we solve this problem by introducing a new technique: an information-spectrum method for QFI. Two fundamental quantities, coherence cost and distillable coherence, are shown to be equal to the spectral QFI rates for arbitrary sequences of pure states. As a consequence, we find that both entanglement theory and RTA in the non-i.i.d. regime are understood in the information-spectrum method, while they are based on different quantities, i.e., entropy and QFI, respectively.
Improved graph representation learning based on neighborhood aggregation and interaction fusion

The adaptive fusion module with an attention mechanism functions by employing a dual-channel graph convolutional network to aggregate neighborhood information. The resulting embeddings are then utilized to calculate interaction terms, thereby incorporating additional information. To enhance the relevance of fusion information, an adaptive fusion module with an attention mechanism is constructed. This module selectively combines the neighborhood aggregation and interaction terms, prioritizing the most pertinent information. Through this adaptive fusion process, the algorithm effectively captures both neighborhood features and other nonlinear information, leading to improved overall performance. Neighborhood Aggregation Interaction Graph Convolutional Network Adaptive Fusion (NAIGCNAF) is a graph representation learning algorithm designed to obtain low-dimensional node representations while preserving graph properties. It addresses the limitations of existing algorithms, which tend to focus solely on aggregating neighborhood features and overlook other nonlinear information. NAIGCNAF utilizes a dual-channel graph convolutional network for neighborhood aggregation and calculates interaction terms based on the resulting embeddings. Additionally, it incorporates an adaptive fusion module with an attention mechanism to enhance the relevance of fusion information. Extensive evaluations on three citation datasets demonstrate that NAIGCNAF outperforms other algorithms such as GCN, Neighborhood Aggregation, and AIR-GCN. NAIGCNAF achieves notable improvements in classification accuracy, ranging from 1.0 to 1.6 percentage points on the Cora dataset, 1.1 to 2.4 percentage points on the Citeseer dataset, and 0.3 to 0.9 percentage points on the Pubmed dataset. Moreover, in visualization tasks, NAIGCNAF exhibits clearer boundaries and stronger aggregation within clusters, enhancing its effectiveness. Additionally, the algorithm showcases faster convergence rates and smoother accuracy curves, further emphasizing its ability to improve benchmark algorithm performance.
A Task-Driven Visualization Framework for Diabetes Management

Diabetes are chronic diseases that require patients to regularly manage their own health conditions. The goal is to have their blood glucose levels under control by making frequent changes to their diabetes therapy, diet, and lifestyle behaviour. As a result, patients are advised to use personal health devices such as glucometers and blood pressure meters to collect their own health data. These multi-source health data are presented by patients to their health care providers during clinical visits in separate visualizations and are often complicated by the variety of data sources and formats. To address this issue, TaskVisDMa conceptual task-driven visualizations framework for diabetes management—is proposed in this paper to produce advanced visualizations that can provide both informative and actionable insights for health care providers to make correct reasoning for treatment plan adjustments and to better communicate with patients. The framework is composed of a collection of data pre-processing, design choices and visualization strategies to handle multi-source and multi-dimensional patient-generated health data, and is divided into five modules: an input module that receives multiple health datasets, a data pre-processing module that processes and integrates the input datasets, an data-idiom-task linking module that uses a task base to determine the data attributes and visualization idioms that best represent the data-analysis tasks identified from the pre-processed dataset, a visualization generation module that uses a strategy base to generate a holistic visual representation of the multi-source and multi-dimensional datasets, and an output layer that presents an interactive and holistic visual representation to users. Our future work will focus on: (1) expanding the task base and strategy base to formulate if–then rules for the automated selection of design choices and visual and analytic strategies; and (2) advancing the framework as a tool for creating visualizations from patient-generated health data for diabetes management.
Fusion of label attention and mask attention for unsupervised and self-supervised anomaly detection

Abstract Many uncontrollable factors during industrial flow lead to products’ unforeseen defects. Anomaly detection for the defects is inclined to use unsupervised frameworks to formulate one-class classification tasks, as well as segmentation tasks. The attention mechanism has proved to be effective in defect classification and segmentation. However, previous works with attention mechanisms have not investigated the combination of the two kinds of attention for the above tasks in depth. In this paper, we propose a framework that fuses Label Attention and Mask Attention (LAMA). Specifically, the LAMA is implemented by a loss prediction module and a decoder in a transform-based segmentation model. Moreover, a network based on normalizing flow is introduced as the architecture of anomaly detection, which integrates with the LAMA. The experimental results on MVTecAD demonstrate that the proposed method outperforms the contrast algorithms. The proposed framework achieves an overall image-wise AUC of 99.6% and 98.6% on pixel-wise AUC.
Study of Energy Efficient Technologies for Workload Processing in Data Centers

The trend of software implementation of hardware functions in modern communication networks has contributed to the development of technologies and concepts such as NFV, SDN, Network Slicing, Edge Computing, bDDN. Implementing these concepts requires significant computational effort, resulting in a load on data centers and significant energy consumption for processing this load. The analysis of publications with proposals for increasing the energy efficiency of workload processing in distributed data centers made it possible to determine the advantages of a number of approaches that provide an increase in the energy efficiency of load processing in communication networks. But all these approaches do not provide a simultaneous increase in energy efficiency and productivity of load processing while meeting SLA requirements. To ensure energy-efficient data processing and improve its processing productivity, while maintaining the quality of SLA requirements, an approach was proposed, the essence of which is to take into account the daily load and allocate data processing resources in such a way as to minimize energy consumption and not lose productivity during processing. The study of the proposed approach revealed its energy efficiency gain of 9.953% in comparison with Backfill and 26.382% energy efficiency gain in comparison with the Round Robin input load scheduling algorithm.
Sign Language Recognition Using Long Short-Term Memory Deep Learning Model

This paper aims to present an easy way of communication for speech impaired and hearing impaired people. This paper focuses on the use of neural network techniques, specifically MediaPipe Holistic and the Long Short-Term Memory (LSTM) module, to recognize sign language for individuals with disabilities. The utilization of MediaPipe Holistic, which integrates pose, hand, and face keypoints with precise levels, was used due to its low latency and high tracking accuracy in real-world scenarios. This paper deals with commonly used words of American Standard Sign Language. The result obtained shows that the proposed model can detect American Standard Sign Language with accuracy of 98.50 percentage.