Reviewerly/exHarmony huggingface dataset:
Recognition of Opinion Leaders in Social Networks Using Text Posts’ Trajectory Scoring and Users’ Comments Sentiment Analysis

Identifying opinion leaders in social networks, particularly in social media, is a crucial marketing strategy. These individuals have a considerable influence on the purchasing decisions of their communities. Companies can benefit from collaborating with relevant opinion leaders in their market as this can increase their visibility, establish their credibility, and gain consumer trust, leading to increased sales, improved brand perception, and an expanded market share. Additionally, by gaining a comprehensive understanding of opinion leaders, companies can better comprehend the trends and preferences of their target audience. This allows them to tailor their marketing and product strategies more effectively. Identifying suitable influencers to endorse their products or services is a significant challenge for companies. The identification of opinion leaders is complicated by their informal and unstructured nature, as well as the varying selection criteria depending on the marketing campaign’s goals. While numerous research studies have focused on detecting opinion leaders in social networks based on content, interactions, or a combination of both, few have explored sentiment analysis of post content, received interactions, and user comments in relation to published posts. The purpose of this paper is to present an hybrid approach to detect opinion leaders in Facebook. This approach involves analyzing the trajectory of post content by examining interactions on the post, as well as mining the text content of the post itself and analyzing the users’comments sentiments.
Effect of Auto-Erased Sketch Cue in Multiuser Surgical Planning Virtual Reality Collaboration System

Many researchers have introduced Virtual Reality (VR) systems for various medical applications, such as training, education, and collaboration. In this research, we explore the effect of using 3-second sketch communication cue in a collaborative VR system for two surgeons planning surgery, when a head-gaze pointer and hand-gesture are available as the baseline cues. We defined five steps of surgical planning for a liver cancer, based on the proposal from surgeons, and conducted a user study with the task including the five steps. The results demonstrated that the 3-second sketch cue increased the level of usability and message understanding between two collaborators, and decreased the required task load. Additionally, we propose six design principles for the interface of a surgical planning VR system. First, a 3D reconstructed body model should be supported based on real-world data, such as CT and MRI images. Second, the cross-sectional view at any angle and position should be supported for examining inside the body and organs. Third, visual communication cues, such as sketches, pointers, and hand gestures, are crucial for communication. Fourth, the erasing sketch should occur when the next sketch starts being drawn. Fifth, the system should support permanent annotations, especially attached to the 3D body, together with the volatile visual communication cues, such as hand gesture, erased sketch, and head-gaze pointer. Sixth, when both collaborators focus on the same object or area, the system should control the transparency of the hand gestures and head-gaze pointers to mitigate the effect of occlusions.
Evaluating Kernel Functions in Software Effort Estimation: A Comparative Study of Moving Window and Spectral Clustering Models Across Diverse Datasets

This study embarks on an in-depth analysis of the performance of various kernel functions, namely uniform, epanechnikov, triangular, and gaussian, in window-based and spectral clustering-based models. Employing seven distinct datasets, our approach evaluated both window sizes (25%, 50%, 75%, and 100%) and clustering clusters (ranging from 1 to 4). The kernel functions served as weighting functions for regression models, leading to the creation of 192 window-based and 192 clustering-based models. Our analysis underscores the dominance of the uniform kernel function. In most models where the Pred(0.25) was maximal and the Mean Absolute Error was minimal, the uniform kernel function was predominantly utilized. Further, our results exhibit varying outcomes between moving windows and spectral clustering across datasets. For instance, in the fpa_china dataset, while moving windows with a 50% size displayed no significant superiority over spectral-clustering with 1 cluster, spectral-clustering (1 cluster) demonstrated a significantly enhanced performance. However, in datasets like fpa_kitchenham, neither approach proved to be significantly better. This comprehensive exploration into the efficiency of kernel functions in moving windows and spectral-clustering models provides valuable insights for future research and applications in data modelling and analysis.
Blockchain-Based Multidimensional Trust Management in Edge Computing

Due to the resource limitations of Internet of Things (IoT) terminals and the distributed characteristics of edge computing architecture, trustworthy services management in dynamic edge computing is a very large challenge. A general and extensible blockchain-based multidimensional trust management (BMDTM) model suitable for edge computing is proposed in this paper. First, probabilistic linguistic terms sets (PLTSs) are adopted as a trust scaling method to integrate the multicriteria evaluation data of the whole domain to measure the performance of the service provider, and the stability degree of each attribute performance is calculated based on information entropy theory, which enables us to measure the dynamic performance accurately and precisely. Second, the dual characteristic of the associated criteria is utilized to filter out malicious or unprofessional evaluation information of requesting nodes and avoid malicious user collusion, ensuring the credibility of trust management. Third, blockchain technology and smart contracts (SCs) are adopted to store trust evidence, share trust information across domains, and execute multisource trust fusion automatically, avoiding the problems of information opacity and the single point of failure of the traditional centralized trust model. The experimental results demonstrate that our model can well manage trust problems in a dynamically hostile edge computing environment. The first finding is that the introduction of the domain trust value significantly improves the quality of service (QoS) compliance ratio due to an accurate description of the dynamic performance of services. The second finding is that our model performs better in attack resistance by leveraging blockchain technology and the dual characteristic of the associated criteria.
Mixed Attention Mechanism Generative Adversarial Network Painting Image Conversion Algorithm Based on Multi-Class Scenes

With the rapid development of computer vision and artificial intelligence, image conversion technology has been widely applied in the fields of digital media and art. Based on the Generative adversarial network, this paper proposes a painting image conversion algorithm for multi class scenes. It performs deep feature extraction in the residual module, divides the encoding and decoding parts of the generator into functional parts, and designs them separately. The mixed attention module is inserted into the decoder and encoder to preserve the texture details of the image. The deep network interpolation is incorporated to achieve smooth and continuous conversion of the painting image. The experiment showed that the loss value of the research method decreases to 0.008 after 400 iterations during the loss value testing. Its maximum peak signal-to-noise ratio is 34.9dB when the bit rate increases to 1000kb/s. In the SAR image conversion dataset, the F1 value increases to 97.4 after 200 iterations. The pixel loss when it reaches 100% conversion in outdoor images is 5.38k. The data indicates that the research method has good performance in painting image conversion and can provide effective technical references for image conversion.
A New Scheme to Solve the Compatibility Conundrum for CPU(TMSC320C32) Used as a Controller for Industrial Facilities

Texas Instruments (TI)’s SMQ320C32x is used in various fields such as military, nuclear facilities, and aviation. However, this CPU has difficulties in compatibility such as discontinued parallel port with Computer & Emulator for Joint Test Action Group (JTAG) Cable and security & availability problem such discontinued Windows OS. For development and maintenance of devices using this CPU, JTAG connection is essential, and parallel port (LPT) connection is essential now. However, PCs with built-in parallel port are not produced. And, for connecting Programmable Logic Controller (PLC) composed of this CPU in nuclear power plants or other industrial facilities to the computer, the XDS510PP-MPSD product in Spectrum Digital Inc must be used, and it is difficult to secure it. Also, you must use Windows XP or Windows 2000 with Code Composer program provided by TI, which has security problems and has been discontinued. According to the regulatory requirements in the nuclear field, these compatibility and security issues must be resolved before use. This paper presents the world’s first solution to these difficulties.
Designing and Evaluating User Experience of an AI-Based Defense System

In recent years, artificial intelligence (AI) has been applied in various fields, with rapid expansion of the scope of AI-human interactions. However, most AI technologies continue to exhibit black-box characteristics, i.e., their decisions and actions are not explainable, degrading user experience (UX). Recently, research on explainable AI (XAI), focusing on both AI performance and UX, has garnered significant attention. However, development of generalizable UX evaluation tools for AI and improvement of AI UX have not been investigated adequately. In this study, a UX evaluation tool is developed for AI based on a systematic literature review and verified using exploratory and confirmatory factor analyses. Subsequently, based on identified AI UX factors, UX of an AI defense system is upgraded. Based on user evaluation, significant improvements are confirmed in eight out of nine factors. The proposed evaluation tool is expected to serve as a cornerstone for future evaluation of AI UX advancement.
Causality-Guided Counterfactual Debiasing for Anomaly Detection of Cyber-Physical Systems

Machine learning has become a promising technology for anomaly detection of cyber-physical systems (CPSs). However, the trained anomaly detection models always suffer from bias due to the scarcity of anomaly data in CPSs and the biased data collection process, which may poison the models' generalization ability. Recent debiasing methods are proposed to deal with the bias via resampling the training dataset, reweighting during the training phase, or adjusting the classification threshold. However, they may lose valuable information, need extra knowledge of the models, or lead to overfitting. Especially, they lack a causal understanding of the debiasing process, so they cannot point out the source and propagation of the bias and, thus, cannot deal with it in an explainable way. In this article, we propose a counterfactual debiasing framework to mitigate the bias in a well-trained model. First, we formalize the model's training and inference processes using causal graphs. Thus, we can understand the source and propagation of the model's bias through causal inference. Then, we use counterfactual inference to estimate the bias's detrimental causal effect on the prediction and remove it from the total causal effect. Therefore, we can conduct unbiased inferences with a biased model. The proposed method can remove the bias in an explainable way by incorporating causal graphs. Comprehensive experiments are conducted on seven real-world CPS datasets, i.e., IDA, MFP, ACS, SPF, UNS, NSL, and ICS. The results demonstrate the effectiveness, compatibility, and unbiasedness of the proposed approach.
Track-Before-Detect Algorithm Based on Cost-Reference Particle Filter Bank for Weak Target Detection

Detecting weak target is an important and challenging problem in many applications such as radar, sonar etc. However, conventional detection methods are often ineffective in this case because of low signal-to-noise ratio (SNR). This paper presents a track-before-detect (TBD) algorithm based on an improved particle filter, i.e. cost-reference particle filter bank (CRPFB), which turns the problem of target detection to the problem of two-layer hypothesis testing. The first layer is implemented by CRPFB for state estimation of possible target. CRPFB has entirely parallel structure, consisting amounts of cost-reference particle filters with different hypothesized prior information. The second layer is to compare a test metric with a given threshold, which is constructed from the output of the first layer and fits GEV distribution. The performance of our proposed TBD algorithm and the existed TBD algorithms are compared according to the experiments on nonlinear frequency modulated (NLFM) signal detection and tracking. Simulation results show that the proposed TBD algorithm has better performance than the state-of-the-arts in detection, tracking, and time efficiency.
Channel Pruning Method for Signal Modulation Recognition Deep Learning Models

Automatic modulation recognition (AMR) plays an important role in communication system. With the expansion of data volume and the development of computing power, deep learning framework shows great potential in AMR. However, deep learning models suffer from the heavy resource consumption problem caused by the huge amount of parameters and high computational complexity, which limit their performance in scenarios that require fast response. Therefore, the deep learning models must be compressed and accelerated, where channel pruning is an effective method to reduce the amount of computation and can speed up models inference. In this paper, we propose a new channel pruning method suitable for AMR deep learning models. We consider both the channel redundancy of the convolutional layer and the channel importance measured by the γ scale factor of the batch normalization (BN) layer. Our proposed method jointly evaluates the model channels from the perspectives of structural similarity and numerical value, and generates evaluation indicators for selecting channels. This method can prevent cutting out important convolutional layer channels. And combined with other strategies such as one-shot pruning strategy and local pruning strategy, the model classification performance can be guaranteed further. We demonstrate the effectiveness of our approach on a variety of different AMR models. Compared with other classical pruning methods, the proposed method can not only better maintain the classification accuracy, but also achieve a higher compression ratio. Finally, we deploy the pruned network model to edge devices, validating the significant acceleration effect of our method.
A Proposed Quantum Framework for Low-Complexity Quantum Simulation and Spectrum Estimation of Hankel-Patterned Systems

The structured matrix completion problem (SMCP) is ubiquitous in several signal processing applications. In this work, we consider a fixed pattern, namely Hankel-structure for the SMCP under quantum formalism. By exploiting its structure, a lower-gate-complexity quantum circuit realisation of a Hankel system is demonstrated. Further, we propose a quantum simulation algorithm for the Hankel-structured Hamiltonian with an advantage in quantum gate-operation complexity in comparison with the standard quantum Hamiltonian simulation technique. We show its application in eigenvalue spectrum estimation for signal processing applications. An error bound associated with this proposed quantum evolution is proposed with the consideration of spectrum estimation, and measurement uncertainty. Numerical results are reported adopting random matrix theory in its fold to evaluate the efficacy of the proposed architecture and algorithm for large dimensional systems including an example application in delay estimation for ranging operation in a wireless communication system.
Enhancing Semantically Masked Transformer With Local Attention for Semantic Segmentation

Transformer-based semantic segmentation has been applied to various visual recognition applications and achieved outstanding performance in recent years. Since most of these approaches adopt a pretrained backbone and finetune it for semantic segmentation, they are not efficient in capturing semantic contextual information during the encoding stage, leading to sub-optimal segmentation performance. To address this problem, SeMask proposes a semantic attention operation to incorporate the semantic contextual information of an image during the encoding stage and improves the segmentation performance. However, the architecture of SeMask is entirely based on the attention mechanisms of Transformers and has some limitations to fully exploit the local details, which are important for more accurate segmentation. In this paper, we introduce a novel semantic layer into the encoder side of a Transformer-based segmentation model. The proposed semantic layer consists of depthwise convolutions with different kernel sizes to capture multi-scale local details. It is integrated at different stages of a hierarchical Transformer backbone to acquire multi-scale semantic contextual information on the encoder side to improve the overall segmentation performance, especially for more accurate segmentation of small objects. Our proposed method can be integrated with common segmentation models such as Semantic-FPN and Mask Transformers. Experimental results show that our proposed method can achieve state-of-the-art performance on the ADE20K dataset with 58.24% mIoU and the Cityscapes dataset with 84.97% mIoU.
Context-Aware Region-Dependent Scale Proposals for Scale-Optimized Object Detection Using Super-Resolution

Image scaling techniques such as Super-Resolution (SR) are useful for object detection, especially for detecting small objects. However, we found that scaling by an inappropriate factor tends to induce false-positive detections. This paper presents a Region-Dependent Scale-Proposal (RDSP) network that estimates the appropriate scale factors for each image region depending on its contextual information. In our detection framework, images are appropriately scaled by SR according to the estimations of the RDSP network, and fed into the scale-specific object detectors. While previous works have proposed models for scale proposal, our RDSP extracts regions where objects could potentially exist based on scene structure, regardless of whether actual objects are present, because small objects are often too small to determine their presence accurately. Additionally, while existing approaches have fused object detection and SR in an end-to-end manner, scale proposals for SR are not provided or are performed independently. Qualitative and quantitative experiments show that our RDSP network provides appropriate SR scales and improve detection accuracy on highly challenging dataset, captured by real car-mounted cameras with size-varied objects, including extremely small objects. Our code is available at https://github.com/kakitamedia/RDSP.
Augmenting the Robustness and Efficiency of Violence Detection Systems for Surveillance and Non-Surveillance Scenarios

Violence detection holds immense significance in ensuring public safety, security, and law enforcement in various domains. With the increasing availability of video data from surveillance cameras and social media platforms, the need for accurate and efficient violence detection algorithms has become paramount. Automated violence detection systems can aid law enforcement agencies in identifying and responding to violent incidents promptly, thereby preventing potential threats and ensuring public protection. This research focuses on violence detection in large video databases, proposing two keyframe-based models named DeepkeyFrm and AreaDiffKey. The keyframes selection process is critical in violence detection systems, as it reduces computational complexity and enhances accuracy. EvoKeyNet and KFCRNet are the proposed classification models that leverage feature extraction from optimal keyframes. EvoKeyNet utilizes an evolutionary algorithm to select optimal feature attributes, while KFCRNet employs an ensemble of LSTM, Bi-LSTM, and GRU models with a voting scheme. Our key contributions include the development of efficient keyframes selection methods and classification models, addressing the challenge of violence detection in dynamic surveillance scenarios. The proposed models outperform existing methods in terms of accuracy and computational efficiency, with accuracy results as follows: 98.98% (Hockey Fight), 99.29% (Violent Flow), 99% (RLVS), 91% (UCF-Crime), and 91% (ShanghaiTech). The ANOVA and Tukey tests were performed to validate the statistical significance of the differences among all models. The proposed approaches, supported by the statistical tests, pave the way for more effective violence detection systems, holding immense promise for a safer and secure future. As violence detection technology continues to evolve, our research stands as a crucial stepping stone towards achieving improved public safety and security in the face of dynamic challenges.
Time-Shifted Transformers for Driver Identification Using Vehicle Data

A modern vehicle contains a large number of electronic control units and sensors that are connected to cloud environments. These electronic units generate a huge amount of data that can be leveraged to identify the current driver and adapt to their behavior. For example, driver identification can improve the accuracy of range estimation in battery electric vehicles. This study focuses on identifying drivers based on their behaviour by using multivariate time series data acquired by the sensors available in the vehicle. We propose two different classifiers: one based on Bi-directional stacked Long Short-Term Memory with Attention mechanism (BiLSTM-A) and another based on a Modified Time Series Transformer (MTST). We perform driver identification in two different ways: 1) by applying a classifier on a single time segment; and 2) by using our proposed time-shift ensembles to combine predictions made from multiple time segments. We experimentally evaluate the proposed techniques on a publicly available dataset that comprises 10 drivers. By using all appropriate features, the proposed BiLSTM-A and MTST classifiers identify the driver with an accuracy of 55% and 73%, respectively, from a single time segment of 60 seconds duration. Time-shift ensembles increase the accuracy substantially, to 92% for BiLSTM-A and 97% for MTST when a 460-second time period is used for each prediction. We conclude that the proposed MTST classifier outperforms BiLSTM-A and existing classifiers in the driver identification task, and time-shift ensembles substantially improve the accuracy.