Reviewerly/exHarmony huggingface dataset:
A Method for Extracting Plausible Images From EM Leakage Measured at Low Sampling Rates

It has been assumed that attackers with advanced expertise who can use a large setup for precise measurements perform the TEMPEST, which entails eavesdropping on the screen information by measuring and analyzing electromagnetic (EM) waves that leak from video device units (VDUs) [1]. Meanwhile, recently, the possibility that a non-expert attacker might use software to take control of low-cost, small, portable instruments and execute attacks has surfaced [2]. However, the sampling rate of low-cost instrumentation is lower than the pixel clock frequency of the VDU; the pixel information obtainable through leaked EM waves is limited. Therefore, the screen images reconstructed from measured data have pixel information deficits and degraded image quality. Based on the above, we propose a method to reconstruct plausible images from EM leakage, even in measurement environments with low sampling rates and fluctuating clocks. The proposed method focuses on clock jitter, which causes degradation of the quality of the images reconstructed using conventional methods, and measure pixel information over multiple frames using the fluctuation of the sampled pixels caused by clock jitter (Fig. 1). We then reconstruct high-quality images using an optimization problem formulated based on the observed pixel information and timing.
Industry 4.0 Application in the Hospitality and Food Service Industries

The concept of digital transformation, also known as ‘‘Industry 4.0,’’ is making its way onto the agendas of businesses all over the world. The hospitality and food service industry is extremely varied and cutthroat and it is one of the service industries that is expanding at the quickest rate. This sector also has a significant amount of untapped potential for the advancement of digital techniques. This research will investigate the role that digital technique plays in the hospitality industry, as well as the business administration’s adaption to the opportunities or conceptual breakthroughs that the digital revolution suggests. As a result of the research that was carried out, the fundamental conditions that must be met to guarantee a better digital transformation and to reduce the risks associated with digital innovations in hospitality industry businesses were identified.
Dual-Feature Aggregation Network for No-Reference Image Quality Assessment

We propose an effective Dual-Feature Aggregation Network (DFAN) for NR-IQA by using the Convolutional Neural Networks (CNN) called ResNet50 and Vision Transformer (ViT). The proposed DFAN-IQA method consists of three modules: the module of attention feature aggregation (MAFA), the module of semantic feature aggregation (MSFA), and the module of prediction (MP). The MAFA uses the pre-trained ViT to extract attention features of different levels and aggregates these attention features by a novel attention feature aggregation (AFA) block which consists of Full-Connected (FC) layers, Rectified Linear Activation Function (ReLU) and Layer normalization. The MSFA exploits the pre-trained ResNet50 to extract semantic features of different levels and aggregates these semantic features by a novel semantic feature aggregation (SFA) block which consists of convolution, Depth-Wise Convolution (DWConv), ReLU and Batch-Normalization (BN). The MP uses concatenation and FC layers to process the outputs of MAFA and MSFA for calculating quality score. Many experiments on open IQA datasets are done to test the proposed DFAN-IQA method. IQA performance comparisons illustrate that the proposed DFAN-IQA method outperforms some state-of-the-art NR-IQA methods in a whole. Evaluation experiment on across-datasets validates the generalization ability of the proposed DFAN-IQA method.
Adversarial Multi-Teacher Distillation for Semi-Supervised Relation Extraction

The shortage of labeled data has been a long-standing challenge for relation extraction (RE) tasks. Semi-supervised RE (SSRE) is a promising way through annotating unlabeled samples with pseudolabels as additional training data. However, some pseudolabels on unlabeled data might be erroneous and will bring misleading knowledge into SSRE models. For this reason, we propose a novel adversarial multi-teacher distillation (AMTD) framework, which includes multi-teacher knowledge distillation and adversarial training (AT), to capture the knowledge on unlabeled data in a refined way. Specifically, we first develop a general knowledge distillation (KD) technique to learn not only from pseudolabels but also from the class distribution of predictions by different models in existing SSRE methods. To improve the robustness of the model, we further empower the distillation process with a language model-based AT technique. Extensive experimental results on two public datasets demonstrate that our framework significantly promotes the performance of the base SSRE methods.
Task-Aware Dual-Representation Network for Few-Shot Action Recognition

Few-shot action recognition has attracted increasing attention in recent years, but it remains challenging due to the intrinsic difficulty in learning transferable knowledge to generalize to novel classes by using a few labeled samples. Although some successful progress has been made, most few-shot action recognition methods commonly focus on the global characteristics of samples while ignoring the local characteristics of samples, which results in the weak generalization ability of the model. In this paper, we propose a task-aware dual-representation network (TADRNet) for few-shot action recognition, which learns how to adapt video representations to novel tasks in a meta-learning manner. It mainly includes a global relational graph subnetwork (GRG) and a fine-grained local representation subnetwork (FLR). Our method simultaneously considers both global and local characteristics of samples for few-shot action recognition. From a global perspective, we propose GRG to explore the relations across support-query sample pairs by using the relational graph neural network. To facilitate the few-shot visual learning, we propose a novel hybrid semantic attention module (HSA) for enhancing the discriminability of support and query features. From a local perspective, we utilize FLR to fully exploit the local characteristics of samples, which can improve the classification results obtained by GRG and thus guarantee high classification accuracy. Extensive experiments on four challenging benchmarks show that the proposed TADRNet significantly outperforms a variety of state-of-the-art few-shot action recognition methods.
Perfect Match in Video Retrieval

This paper presents the first version of our video search system Perfect Match for the Video Browser Showdown 2023 competition. The system indexes videos from the large V3C video dataset and derives visual content descriptors automatically. Furthermore, it provides an interactive video search user interface (UI), which implements approaches from the domain of critiquing-based recommendation, to enable the user to find the desired video segment as fast as possible.
Research on Chinese short text semantic matching based on lightweight ERNIE

Semantic matching of short texts is a hot research problem in the field of natural language processing, which has a wide range of application needs in information retrieval, dialogue system, text paraphrase questions, and question answering system etc. Short texts have the characteristics of less information and lack of contextual background. The existing semantic matching methods for short texts also generally have the problem of low matching accuracy. With the speedy development of deep learning technology, various models of deep learning have been extensively used in natural language processing and achieved good results. In this paper, we design a FERNIE model based on a lightweight pre-training model ERNIE3.0-medium of BERT architecture, which integrates low-level features and high-level features. Experiments on several datasets show that the FERNIE model has good results in short text semantic matching, and the accuracy is further improved compared to ERNIE3.0-medium.
A Scalable Storage Scheme for On-Chain Big Data using Historical Blockchains

Despite the growing interest in blockchain technology, the scalability of blockchain storage has become a major issue for applications that require large amounts of on-chain data. In this paper, we propose a novel scalable storage scheme for consortium networks to manage the storage capacity required by data-rich blockchain applications. We establish network nodes as super peers or regular peers, where super peers can maintain old blockchain data in the form of historical blockchains. Regular peers maintain only the latest blockchain data stored in the current blockchain, but they can access any data in the historical blockchains by making queries to the super peers. We present procedures to build a historical blockchain and retrieve data from the historical blockchains and the current blockchain in a concurrent manner. Experimental results show that our scalable storage scheme using historical blockchains is feasible and effective in accessing and sharing healthcare data with image files.
Research on Sentiment Analysis of Online Product Reviews Based on Deep Learning

In order to fully extract online users’ product reviews, the deep learning model combining TF-IDF (Term Frequency-Inverse Document Frequency) and LSTM (Long Short-Term Memory) is used to perform sentiment analysis of product reviews. Firstly, it uses Python for data collection. Secondly, applies TF-IDF to eliminate duplicate data and obtain key data, and then converts text into word vectors. Finally, performs sentiment analysis of online reviews by use of LSTM. The group comparison experiments show that the model has good performance with the average precision and average F1 value reaching 0.823 and 0.846 respectively.
Attention-based Intrinsic Reward Mixing Network for Credit Assignment in Multi-Agent Reinforcement Learning

Credit assignment is a critical problem in cooperative Multi-Agent Reinforcement Learning (MARL). To address this problem, current studies mainly rely on the intrinsic reward, which is directly summed with the global reward to generate a total reward. However, such kinds of intrinsic reward functions ignore the dependence among agents and inevitably limit the adaptivity and effectiveness of MARL methods. In this paper, we propose a novel method, Attention-based Intrinsic Reward Mixing Network (AIRMN), for credit assignment in MARL. Specifically, we design a new intrinsic reward network on the basis of the attention mechanism, in order to enhance the effectiveness of teamwork. Besides, we devise a new mixing network that combines the intrinsic and extrinsic rewards in a nonlinear and dynamic manner, so as to adapt the total reward to the variation of the environment. Experimental results on the battle games of StarCraft II demonstrate that AIRMN outperforms the state-of-the-art methods in terms of the average test win rate, and also validate that AIRMN can dynamically return the precise intrinsic reward to each agent based on their contributions to the team cooperation, thereby better dealing with the credit assignment problem.
BiSign-Net: Fine-grained Static Sign Language Recognition based on Bilinear CNN

Sign language (SL) is a type of communication language used by deaf and hard-of-hearing people. Large varieties in different SLs and lack of knowledge in general public to interpret them bring an inevitable necessity for breaking down the communication barriers by automatic sign language recognition (SLR) systems. Despite the existence of numerous approaches with satisfactory performance, they still suffer from severe challenges in dealing with large intra-class and slight inter-class variations, which make them infeasible for real-world applications. To address this issue, a novel end-to-end fine-grained static SLR (SSLR) system is proposed, namely BiSign-Net, based on Bilinear Convolutional Neural Network (Bi-CNN) to efficiently model the variations both in the location and appearance of the hands in the images for enhancing the accuracy, speed, and robustness against the translation. To this end, fine-grained orderless bilinear features are generated by pooled outer product of the extracted features from two identical novel CNN-based feature extractors. Bilinear features pass a normalization module including the signed square root and l <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> normalization through which the accuracy of the model is further improved. A dropout layer is deployed in the classification module to aid the model in dealing with small-scale datasets by preventing overfitting. The number of layers, hyper-parameters, and optimization technique of the proposed CNN are adjusted to achieve high performance and faster convergence with low number of parameters. Experimental results on four datasets of Static ASL, NUS I, Massey, and ArASL from two SLs (i.e. American and Arabic) with an accuracy of 100%, 100%, 99.20%, and 99.35%, respectively, demonstrate that the proposed model surpasses the existing approaches with high robustness and generalization ability.
Prediction When Factors are Weak

In macroeconomic forecasting, principal component analysis (PCA) has been the most prevalent approach to the recovery of factors, which summarize information in a large set of macro predictors. Nevertheless, the theoretical justification of this approach often relies on a convenient and critical assumption that factors are pervasive. To incorporate information from weaker factors, we propose a new prediction procedure based on supervised PCA, which iterates over selection, PCA, and projection. The selection step finds a subset of predictors most correlated with the prediction target, whereas the projection step permits multiple weak factors of distinct strength. We justify our procedure in an asymptotic scheme where both the sample size and the cross-sectional dimension increase at potentially different rates. Our empirical analysis highlights the role of weak factors in predicting inflation, industrial production growth, and changes in unemployment.
Performance analysis of various copy-move forgery detection methods

Analyzing digital images to reveal modifications is called image forensics. Digital images are now becoming incredibly popular due to the availability of several inexpensive image-capturing gadgets. These images are frequently altered, either unintentionally or intentionally, which causes the image to convey false information. Since digital images are frequently utilized as evidence in court proceedings, media, and for preserving visual records, approaches to detecting forgeries in these images should be designed. This paper thoroughly analyzes several image forgery detection strategies, including comparisons of the strategies, advantages, disadvantages, and experimental findings.
Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA

Electrical energy generation with hydrocarbons accounts for about 38% of global CO2 emissions. Solar photovoltaic technology is a reliable alternative to reduce these emissions. To predict the electrical energy generation behavior in a photovoltaic system, we developed an adaptive neuro-fuzzy inference system (ANFIS) model which integrates an optimization through a genetic algorithm (GA). The evolutionary ANFIS-GA uses a geographical area's solar radiation and ambient temperature. This model uses the capacity for classification and identification of data patterns of neural networks, and through fuzzy modeling, it calculates the optimal membership functions and fuzzy rules. The ANFIS-GA model is developed using MATLAB® software and is trained with the acquired data weather station and the electrical power output of the photovoltaic system located in Hermosillo, Sonora, México. The above was compared under the same parameters with an ANFIS model based on a hybrid algorithm. Reach values of RSME of 259.41, MAE of 132.7, MAPE of 4.56 for the ANFIS-GA model; RSME of 295.26, MAE of 149.58, and MAPE of 6.98 for the ANFIS model, respectively. The results indicate that the ANFIS-GA model emulates the power output with better precision, thus providing a valuable planning tool to predict photovoltaic system behavior.
An analysis of how many undiscovered vulnerabilities remain in information systems

Vulnerability management strategy, from both organizational and public policy perspectives, hinges on an understanding of the supply of undiscovered vulnerabilities. If the number of undiscovered vulnerabilities is small enough, then a reasonable investment strategy would be to focus on finding and removing the remaining undiscovered vulnerabilities. If the number of undiscovered vulnerabilities is and will continue to be large, then a better investment strategy would be to focus on quick patch dissemination and engineering resilient systems. This paper examines a paradigm, namely that the number of undiscovered vulnerabilities is manageably small, through the lens of mathematical concepts from the theory of computing. From this perspective, we find little support for the paradigm of limited undiscovered vulnerabilities. We then briefly support the notion that these theory-based conclusions are relevant to practical computers in use today. We find no reason to believe undiscovered vulnerabilities are not essentially unlimited in practice and we examine the possible economic impacts should this be the case. Based on our analysis, we recommend vulnerability management strategy adopts an approach favoring quick patch dissemination and engineering resilient systems, while continuing good software engineering practices to reduce (but never eliminate) vulnerabilities in information systems.
A Fast Algorithm for Privacy-Preserving Utility Mining

Utility mining (UM) is an efficient technique for data mining which aim to discover critical patternsfrom various types of database. However, mining data can reveal sensitive information of individuals. Privacy preserving utility mining (PPUM) emerges as an important research topic in recent years. In the past, integer programming approach was developed to hide sensitive knowledge in a database. This approach required a significant amount of time for preprocessing and formulating a constraint satisfaction problem (CSP). To address this problem, we proposed a new algorithm based on a hash data structure which performs more quickly in itemsets filtering and problem modeling. Experiment evaluations are conducted on real world and synthetic datasets.
Eye movement characteristics and visual fatigue assessment of virtual reality games with different interaction modes

This study aimed to investigate the eye movement characteristics and visual fatigue of virtual reality games with different interaction modes. Eye movement data were recorded using the built-in eye tracker of the VR device and eye movement parameters were calculated from the recorded raw data. The Visual Fatigue Scales and Simulator Sickness Questionnaire were used to subjectively assess visual fatigue and overall discomfort of the VR experience. Sixteen male and 17 female students were recruited for this study. Results showed that both the primary and 360 mode of VR could cause visual fatigue after 30 min of gameplay, with significant differences observed in eye movement behavior between the two modes. The primary mode was more likely to cause visual fatigue, as shown by objective measurements of blinking and pupil diameter. Fixation and saccade parameters also showed significant differences between the two modes, possibly due to the different interaction modes employed in the 360 mode. Further research is required to examine the effects of different content and interactive modes of VR on visual fatigue, as well as to develop more objective measures for assessing it.
Construction of university fire safety facilities management information system based on WebGis

In order to further strengthen the fire safety work in colleges and universities, and improve the degree of public property and personal safety of teachers and students, colleges and universities should actively take measures to improve the level and efficiency of fire safety management. This paper takes fire safety facilities as the research object, innovatively introduces WebGIS technology into daily management and maintenance, and combines ArcGIS Server application platform and Ajax technology to complete the construction of university fire safety facilities management information system in C# language environment. The whole system adopts B/S architecture, and the Web Server is built by ASP.NET framework. It can support the business logic control of functional modules such as user login, basic data management, fire control facilities management and automatic fire alarm, and can also control Oracle database to complete the storage and call of various data. With the help of JSP page and Openlayers framework of the system, the electronic map of campus fire protection produced by WebGIS is displayed, and the image and intuition of the map and the data of fire protection facilities are integrated, which can not only improve the efficiency of campus fire protection management, but also make an attempt for the informatization construction of campus fire safety.
Advancements in Cyber Security and Information Systems in Healthcare from 2004 to 2022: A Bibliometric Analysis

The main goals of the multifaceted healthcare system were to prevent, identify, and treat illnesses or conditions that affect human health. As the usage of IT in healthcare increased, the complexities in managing the IT infrastructure also increase, emphasizing the need of robust cyber security systems. The study aims to emphasize the advancements made in cyber security and information systems in healthcare, based on bibliometric analysis. 5,487 document's metadata was obtained from Scopus and data was analyzed using Vos Viewer. Ranking of articles was done with average yearly citations of the publications. Bibliometric analysis was performed based on “bibliographic coupling of countries”, “co-occurrence of all keywords”, “author-based co-authorship”, and “term co-occurrence based on text data”. It was found that United States had the maximum publications (1337). “Department of Information Systems and Cyber Security, The University of Texas at San Antonio, United States” is the most influential organization with 159 publications. IEEE Access is the most preferred platform for publication related to cyber security and information systems in healthcare (231 publications). 167 publications have received more than 100 citations. Choo K. K.R. is the most influential author with 185 publications.
Question and Answering System For Investment Promotion Based on NLP

Smart Question and Answering System for Investment Promotion is one such software/tool which will enable users (Investors, Investment Promotion Officers, Researchers, Professionals, etc.) to systematically ask the questionnaires related to the Investment and giving answers anytime anywhere. Preparing questions will be categorized according to investment sector based on Natural Processing Language. The levels would be, Knowledge, Comprehension, Application, Analysis, Synthesis and evaluation. Accordingly, Smart Question Generator will be modified to achieve the aspects related to each of the levels in investment promotion concept.Most likely these prepared questions would be of high standard predicting the questions as expected. This will also be a greater advantage for the investors to gain the potential out of this enabling them to invest money for projects and various information accordingly. Time consumption and communication gap is one such major issue which has not yet been practically attempted to. In order to overcome this issue, Smart Question and Answering will be a good assistant.