Reviewerly/exHarmony huggingface dataset:
A Practical Website Fingerprinting Attack via CNN-Based Transfer Learning

Website fingerprinting attacks attempt to apply deep learning technology to identify websites corresponding to encrypted traffic data. Unfortunately, to the best of our knowledge, once the total number of encrypted traffic data becomes insufficient, the identification accuracy in most existing works will drop dramatically. This phenomenon grows worse because the statistical features of the encrypted traffic data are not always stable but irregularly varying in different time periods. Even a deep learning model requires good performance to capture the statistical features, its accuracy usually diminishes in a short period of time because the changes of the statistical features technically put the training and testing data into two non-identical distributions. In this paper, we first propose a convolutional neural network-based website fingerprinting attack (CWFA) scheme. This scheme integrates packet direction with the timing sequence from the encrypted traffic data to improve the accuracy of analysis as much as possible on few data samples. We then design a new fine-tuning mechanism for the CWFA (FM-CWFA) scheme based on transfer learning. This mechanism enables the proposed FM-CWFA scheme to support the changes in the statistical patterns. The experimental results in closed-world and open-world settings show that the effectiveness of the CWFA scheme is better than previous researches, with the slowest performance degradation when the number of data decreases, and the FM-CWFA scheme can remain effective when the statistical features change.
Evaluation of Live-Line Work Procedure in Virtual Reality

The paper presents the results of the VR training application in use for electrical technicians. Eight participants were tested, who underwent a virtual training scenario and then provided feedback (in the form of survey study) regarding their subjective impressions of a given solution. The scenario consists of 27 steps and focuses on assembly of a low-voltage connecting cable to an existing installation. For each participant, the time of realization was measured, and the individual stages were controlled and evaluated by the instructor in order to assess the scenario difficulty and potentially identify and introduce any corrections. The paper describes the observations and conclusions that can be drawn by analyzing the entire training procedure and its general reception by users. The scenario has been received favorably by the users and they all managed to finish it successfully. The VR technology has been evaluated as being almost ready for implementation in real classrooms.
Learning hierarchical embedding space for image-text matching

There are two mainstream strategies for image-text matching at present. The one, termed as joint embedding learning, aims to model the semantic information of both image and sentence in a shared feature subspace, which facilitates the measurement of semantic similarity but only focuses on global alignment relationship. To explore the local semantic relationship more fully, the other one, termed as metric learning, aims to learn a complex similarity function to directly output score of each image-text pair. However, it significantly suffers from more computation burden at retrieval stage. In this paper, we propose a hierarchically joint embedding model to incorporate the local semantic relationship into a joint embedding learning framework. The proposed method learns the shared local and global embedding spaces simultaneously, and models the joint local embedding space with respect to specific local similarity labels which are easy to access from the lexical information of corpus. Unlike the methods based on metric learning, we can prepare the fixed representations of both images and sentences by concatenating the normalized local and global representations, which makes it feasible to perform the efficient retrieval. And experiments show that the proposed model can achieve competitive performance when compared to the existing joint embedding learning models on two publicly available datasets Flickr30k and MS-COCO.
Unravelling the effects of dynamic urban thermal environment on utility-scale floating photovoltaic electricity generation

The utility-scale photovoltaic systems have been widely integrated for sustainable urban development. However, the varying photovoltaic conversion efficiency (PVCE) affected by dynamic urban thermal environment causes uncontrollable uncertainty in electricity generation, which challenges installed-capacity planning and load-balancing operations. To tackle this problem, this study develops a PVCE estimation model containing four hierarchical modules. First, photovoltaic surface temperatures (PVSTs) are retrieved from satellite imagery, and meteorological features are collected to represent the dynamic thermal environment. Second, the contribution of each feature to the PVST estimation is evaluated, referring to the Mean Decrease in Impurity, Permutation Importance, and SHapley Addictive exPlanations. Third, machine learning models using Support Vector Machine, Random Forest, and XGBoost are developed to establish robust regressions between PVSTs and the selected features, enabling an accurate estimation of PVST spatio-temporal heterogeneity. Finally, the spatio-temporally corresponding PVCEs are calculated, resulting in a refined estimation of annual electricity generation. The investigation of four floating PV systems in Singapore found that their PVCEs vary insignificantly throughout the year, which is probably because of its stable climate and the cooling effect of water. The proposed model is simple and effective, demonstrating its impact on PV potential estimation when the urban thermal effect becomes significant.
A high throughput pseudo‐random number generator driven by four‐dimensional discrete hyper‐chaotic system

Abstract Pseudo‐random number generators (PRNGs) are the cornerstone of various fields including computer science, cryptography, and scientific simulation. To generate high‐quality and unpredictable pseudo‐random sequences, many works draw attention to the use of chaos theory and dynamical systems. However, most of them focus on continuous or low‐dimensional chaotic systems with strong temporal correlations or insufficient nonlinear dynamics. Thus, in this study, a new PRNG based on a four‐dimensional discrete system is proposed to solve the aforementioned flaws. The PRNG has been implemented on Xilinx Artix‐7 xc7a100tfgg484‐2, which can generate pseudo‐random sequences at a high speed of 12811.53 Mb/s without additional post‐processing. All these sequences have successfully passed the NIST SP800.22 standard test. Both throughput and randomness quality of the proposed PRNG outperform the state‐of‐the‐art.
Graph structure reforming framework enhanced by commute time distance for graph classification

As a graph data mining task, graph classification has high academic value and wide practical application. Among them, the graph neural network-based method is one of the mainstream methods. Most graph neural networks (GNNs) follow the message passing paradigm and can be called Message Passing Neural Networks (MPNNs), achieving good results in structural data-related tasks. However, it has also been reported that these methods suffer from over-squashing and limited expressive power. In recent years, many works have proposed different solutions to these problems separately, but none has yet considered these shortcomings in a comprehensive way. After considering these several aspects comprehensively, we identify two specific defects: information loss caused by local information aggregation, and an inability to capture higher-order structures. To solve these issues, we propose a plug-and-play framework based on Commute Time Distance (CTD), in which information is propagated in commute time distance neighborhoods. By considering both local and global graph connections, the commute time distance between two nodes is evaluated with reference to the path length and the number of paths in the whole graph. Moreover, the proposed framework CTD-MPNNs (Commute Time Distance-based Message Passing Neural Networks) can capture higher-order structural information by utilizing commute paths to enhance the expressive power of GNNs. Thus, our proposed framework can propagate and aggregate messages from defined important neighbors and model more powerful GNNs. We conduct extensive experiments using various real-world graph classification benchmarks. The experimental performance demonstrates the effectiveness of our framework. Codes are released on https://github.com/Haldate-Yu/CTD-MPNNs.
Coping with change: Learning invariant and minimum sufficient representations for fine-grained visual categorization

Fine-grained visual categorization (FGVC) is a challenging task due to similar visual appearances between various species. Previous studies always implicitly assume that the training and test data have the same underlying distributions, and that features extracted by modern backbone architectures remain discriminative and generalize well to unseen test data. However, we empirically justify that these conditions are not always true on benchmark datasets. To this end, we combine the merits of invariant risk minimization (IRM) and information bottleneck (IB) principle to learn invariant and minimum sufficient (IMS) representations for FGVC, such that the overall model can always discover the most succinct and consistent fine-grained features. We apply the matrix-based Rényi’s α-order entropy to simplify and stabilize the training of IB; we also design a “soft” environment partition scheme to make IRM applicable to FGVC task. To the best of our knowledge, we are the first to address the problem of FGVC from a generalization perspective and develop a new information-theoretic solution accordingly. Extensive experiments demonstrate the consistent performance gain offered by our IMS. Code is available at: https://github.com/SYe-hub/IMS.
The Novel Instance Segmentation Method Based on Multi-Level Features and Joint Attention

Instance segmentation is an important task in computer vision.In order to enhance the multilevel features expression ability of the segmentation networks, a novel module is proposed in this paper.Firstly, we design a weighted bi-directional feature fusion way by computing the weight distribution function of bi-directional feature pyramid network.Secondly, we propose a joint attention mechanism to effectively filter different levels of feature information by adopting serial and parallel ways to combine the channel attention and spatial attention modules.At the same time, the module uses dynamic convolution to stabilize the calculation speed while improve the 6.7% mean average precision of segmentation.The experiments on the COCO dataset demonstrate that the module can effectively improve the performance of the existing instance segmentation networks.
Comparative Analysis of ConvNext and Mobilenet on Traffic Vehicle Detection

Traffic vehicle detection plays important role in making decision about maintenance of a road section. However, the method to conduct it still used traditional approach, by means of surveyors being on the road and identifying vehicles for 40 hours, so it takes quite a long time and has the potential for human error to occur when identifying vehicles. In this research, a solution is formulated to identify vehicles using closed-circuit television (CCTV) and object detection methods based on deep learning. The dataset that used to train deep learning model were recorded in some of road section by our CCTV. Then, we annotate each object from given video frame based on defined classes. Then, all of the annotated frame divided in train and validation with percentage of 80% and 20% respectively. Train and validation dataset used for model training and test dataset used for evaluating best model weight and produce Average Precision, while best model weight also tested for show model performance and its Frame Per Second. We then compared the application of Faster-RCNN method with ConvNext v1 and Mobilenet v3 backbone in carrying out vehicle detection. Using 12 classes of vehicle in training and testing phase, test results based on evaluation dataset showed that ConvNext v1 backbone produced an average precision value of 0.81 while Mobilenet v3 backbone obtained a result of 0.3. As for the results of the Frame per Second (FPS) test, Mobilenet v3 backbone obtained an average FPS of 18 while Convnext v1 obtain 7. The results obtained indicated Faster RCNN backbone ConvNext v1 was an effective approach to obtain robust object detection while Faster R-CNN Mobilenet v3 backbone is effective for object detection in real time.
Analysis of Sentiment Towards Artificial Intelligent Industry Using Hybrid Natural Language Processing Technique

Artificial Intelligence (AI) has revolutionized various aspects of human life and transformed how people live, work, and interact. However, the development of AI also poses potential risks and ethical concerns. In this report, we aim to analyze the sentiment toward the AI industry using hybrid natural language processing techniques. To achieve our aim, we propose a model that draws upon a survey of related work. Data collection involves gathering user-generated data from social media platforms. We then use hybrid natural language processing techniques to analyze the sentiment toward the AI industry. Our analysis reveals that the sentiment towards the AI industry is generally positive, with many people recognizing its potential benefits. However, there are also concerns about the potential risks and ethical implications of AI development. Some leading figures in the AI industry have expressed concerns about the potential misuse of AI and the need for ethical guidelines. In conclusion, our analysis highlights the transformative effects of AI on various industries and the potential risks associated with its development. We recommend that policymakers and industry leaders work together to develop ethical guidelines for the development and use of AI. This will help to ensure that the benefits of AI are maximized while minimizing the potential risks and ethical concerns.
A Visualized Hybrid Keyword-Cluster Approach for Extractive Text Summarizer Tool for STEM Education in Malaysia

Summary generated from an automatic text summarization tool has become increasingly relevant due to the shift towards online learning caused by the Covid-19 pandemic. In this paper, we present an extractive educational text summarizer tool with a focus on Science, Technology, Engineering, and Mathematics (STEM) subjects specifically designed for secondary school students in Malaysia. The tool addresses the challenge of understanding complex STEM concepts from lengthy explanations and problem-solving scenarios. We propose a new hybrid method for scoring the keywords by leveraging the word embeddings technique to improve the keyword extraction cluster approach. The generated summaries are displayed with highlighted cluster of keywords and illustrated using word clouds to attract students' interest and engage them with the subject matter. We compare our proposed method to the benchmark and baseline Malay text summarizer using random STEM articles. The ROUGE-l results produced an average F-Score of 56.8%, precision of 56.3% and recall of 58.1%. We also conducted a System Usability Testing with teachers and secondary school students, obtaining high ratings for system simplicity, pleasant interface, and ease of information retrieval. Our study demonstrates the potential of educational text summarization tools to enhance student learning experiences in diverse contexts.
A New SVM-STEG Embedding Model in Steganography

One of the sub-fields in information security is called information hiding and can be applied to protect data and information nowadays. This is a method in where secret-messages are hidden stealthily in an image file. This method has been used in various fields especially in digital image steganography. Most of the techniques proposed to date have various problems i.e., non-random changes will obviously occur especially when the secret message is embedded in an inappropriate area and when the load capacity exceeds the number of bits allowed. This paper proposes a machine learning steganographic method called SVM-Steg model that uses embedding and extracting algorithms by exploiting SVM classification and SVM-Steg embedding to achieve good performance. In addition, the distance of the embedding location is also taken into account so that more pixels can be embedded at more distance locations. The results show a quality cover-image when high peak signal-to-noise ratio (PSNR) values are recorded greater for all types of cover images. In comparison to the other technique, all PSNRs for the proposed technique, SVM-Steg Method achieved 40 or higher. It is not only succeeding in providing a secure embedding position, but also increases the number of secret-bits embedded.
Robust Image Watermarking Using Hessenberg Decomposition and SVD with Integer Wavelet Transform

Preventing unauthorized use and distribution has grown more difficult as a result of the extensive usage of the internet and the simplicity of duplicating and distributing digital media. Watermarking has come to be recognized as a successful remedy for this issue. Image watermarking is the process of adding a distinctive identifier to an image so that it is hidden from view but can still be read to establish ownership. This research proposes Hessenberg Decomposition (HD) and Singular Value Decomposition (SVD) in the Integer Wavelet Transform (IWT) domain to achieve high robustness. The proposed method utilizes the HD feature of the image to enhance the robustness of the watermark against attacks, while the SVD technique is used to achieve high invisibility and security. The IWT domain is employed to make the watermarking process more efficient, leading to a faster and more reliable watermarking algorithm. The results of the experiments demonstrate that the proposed scheme provides a high level of robustness for preserving the ownership of multimedia content and resistant to various image processing attacks. The outcomes demonstrate that, in terms of robustness and invisibility, the suggested approach performs better than the existing watermarking schemes.
The Implementation of Moving Average to Reduce the Photovoltaic System Active Power Fluctuations

The photovoltaic (PV) system works connected to the electricity network, there is a problem of output power fluctuations. The output power of the PV system fluctuates causing system frequency instability. One way to overcome this problem is to use an energy storage system or an electric spring. The electric spring is a system that reduces the ramp rate or changes in the output power of the PV system suddenly, so that the power system perceives the power it produces changes slowly. This paper proposes a simulation of an electric spring using the moving average method to mitigate fluctuations in the output power of a PV system. The moving average (MA) method intends to average a number of PV system active power output data with an adjustable data size range. The active power output value of an average yield PV system is expected to change slowly or have a low ramp rate. The amount of input and output data becomes wider. Changes in the value of the window length adjust the desire and affect the active power output of the PV system that has been refined. The actual output active power of the PV system at that time will most likely not be equal to its average rated power. The energy storage system uses a type of valve regulated lead acid (VRLA) battery. The selection of batteries takes into consideration from the development, research and commercialization side, if all of these stages have been passed, the energy storage can be said to be mature.
11. Crime news, trial by media, and scandal hunting

A rapidly evolving digital media landscape, inhabited by a network of outlets, is amplifying crime consciousness, exploiting crime’s infotainment potential, and reshaping public attitudes towards crime and criminal justice. At the same time, in depth analysis of crime news has dropped off the criminological radar. In this chapter, we argue that because criminologists have not kept pace with transforming news media and markets, crime news remains under-researched and under-conceptualized. We begin by revisiting three classic concepts that continue to dominate crime news research: newsworthiness, moral panic, and penal populism. Though these concepts are still important for understanding crime news, their institutionalization and taxonomical application within criminology has marginalized analysis of dramatic shifts in the production and nature of crime news, the markets in which it circulates, and its power to shape crime consciousness and criminal justice rhetoric and practice. We then explore how ‘trial by media’ and ‘scandal hunting’ are not only redefining crime news, but also exposing institutional failures in public protection and challenging both the efficacy and the legitimacy of the criminal justice system. It is in this context of an unruly networked digital environment that we situate the disruptive challenge posed to criminal justice by the rise of what we define as media justice.
A Model Compression Method for Power Edge Intelligent Inspection via Channel Pruning

In the context of the digital transformation of power equipment in modern power systems, power intelligent inspection is of paramount importance for real-time analysis and diagnostics of power equipment status. However, the constrained computational power and limited memory capacity of edge devices impose limitations on the parameter size and computational demands of object detection algorithms. To overcome this challenge, this study proposes a model compression method for power edge intelligent inspection via channel pruning. Initially, considering the trade-off between speed and accuracy, the YOLOv5 object detection algorithm is chosen as the baseline algorithm. Subsequently, to compress the model size, the ResRep pruning method is employed to compress the YOLOv5 model, and the advantages of ResRep over the conventional BN-λ pruning method are elaborately discussed. Finally, comprehensive experiments are conducted on a dataset comprising transmission line defects including insulator defect, pin missing, and nest. Experimental results demonstrate that the ResRep pruning method can reduce the parameter size of YOLOv5 by 2/3 and decrease computational requirements by 1/3, while only sacrificing a marginal 0.9% of accuracy. Moreover, compared to the BN-λ pruning method, ResRep achieves 0.9% higher accuracy at comparable compression ratios.
Pointer-Type Meter Recognition Algorithm in Complex Substation Scenarios

Substation meter recognition is very significant to status monitoring of instrument equipment in power system. However, meters in substation are various, small and dense distribution. Therefore, in the process of recognizing meter types based on artificial intelligence, there are many problems, such as low pixel, few key features and a great deal of confusion. How to overcome these problems and improve accuracy is critical. Moreover, Algorithm deployment on edge device is also in trouble. In this paper, a lightweight meter recognition Yolov5-based algorithm is proposed. Firstly, Transformer block is selected to enhance the ability of global information extraction and establish long-term dependence. Secondly, convolution block attention mechanism (CBAM) is supplied to strength the power of distinguishing. It can integrate underlying information effectively and be conducive to small object detection. In addition, Mish and E-IoU are used to improve convergence rate, which ensured accuracy and improved model detection speed. Finally, for edge deployment, Ghost module is fused to reduce model parameters and simplify network structure. Experiment results show that the improved method has satisfactory speed and surprising accuracy in the recognition of pointer-type meter in complex substation.