Reviewerly/exHarmony huggingface dataset:
Movie Recommendation Using Hybrid-Based Approach

The field of recommendation systems has been rapidly growing due to the increasing amount of data available on the internet. Movie recommendation systems are one of the most widely used applications of recommendation systems. In this paper, we propose a hybrid-based approach to movie recommendation systems. The proposed approach combines content-based filtering and collaborative filtering techniques to provide better recommendations. The content-based filtering technique uses movie features such as genre, director, actors, and plot to recommend similar movies to the users. The collaborative filtering technique uses the user's past behaviour and other users' behaviour to recommend movies to the user. We evaluated the proposed hybrid approach on the Cosine Similarity and SVD dataset to achieve better results compared to the individual content-based and collaborative filtering techniques.
Integrates Spatiotemporal Visual Stimuli for Video Quality Assessment

While feature extraction employing pre-trained models proves effective and efficient for no-reference video tasks, it falls short of adequately accounting for the intricacies of the Human Visual System (HVS). In this study, we proposed a novel approach to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I</i> ntegration of spatio-temporal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> isual <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> timuli into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> ideo <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> uality <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> ssessment (IVS-VQA) for the inaugural time. Exploiting the heightened sensitivity of optic rod cells to edges and motion, along with the capability to track motion via conjugate gaze, our approach affords a distinctive perspective on video quality assessment. To capture significant changes at each timestamp, we incorporate edge information to enhance the feature extraction of the pre-trained model. To tackle pronounced motion across the timeline, we introduce an interactive temporal disparity query employing a dual-branch transformer architecture. This approach adeptly introduces feature biases and extracts comprehensive global attention, culminating in enhanced emphasis on non-continuous segments within the video. Additionally, we integrate low-level color texture information within the temporal domain to comprehensively capture distortions spanning various scales, both higher and lower. Empirical results illustrate that the proposed model attains state-of-the-art performance across all six benchmark databases, along with their corresponding weighted averages.
Sparse Vicious Attacks on Graph Neural Networks

In this study, we introduce SAVAGE, a novel framework for sparse vicious adversarial link prediction attacks in Graph Neural Networks (GNNs). While GNNs have been successful in link prediction tasks, they are susceptible to adversarial attacks where malicious nodes attempt to manipulate recommendations for a target victim. SAVAGE optimizes the attacker's goal to maximize attack effectiveness while minimizing the required malicious resources. Unlike existing methods with static resource-based upper bounds, SAVAGE employs a sparsity enforcing mechanism to reduce the number of malicious nodes needed for the attack. Extensive experiments on real-world and synthetic datasets demonstrate the optimal trade-off achieved by SAVAGE between a high attack success rate and the number of malicious nodes utilized. Furthermore, we demonstrate that SAVAGE can successfully target non-GNN-based link prediction systems, even those unknown at the time of the attack. This showcases the transferability of SAVAGE-generated attacks to other black-box methods for link prediction, highlighting its applicability across different real-world scenarios.
Human Facial Image Generation from Textual Descriptions Using StyleGAN

An unsupervised learning method based on artificial neural networks, deep learning is a branch of machine learning. Generative Adversarial Networks or simply GANs are one of the applications of deep learning methods for generative modeling. GANs are useful for generating realistic images, texts, music and many other forms of art. A lot of research work is being done in developing and improving the performance of GANs because of their potential for many real world applications. GANs are best suited for generating photorealistic images that even humans cannot identify as fake. Many of the existing models worked on only for generating image of birds and flowers from their textual descriptions. But these interesting features of GANs allow us to train the model to generate realistic images of human faces with the desired facial features. StyleGAN is one of the most popular GANs that can generate fake human faces. With the help of such models, we can try to generate images of human faces with specific facial features given as a textual description.
Emotion Detection on Twitter Text Using Machine Learning Techniques with Data Augmentation

Social webs like Instagram, Twitter, and WhatsApp are full of deliberations involving sentiments, feelings, and impressions of human beings worldwide. Moreover, understanding and segregating texts based on emotions is a complex task that could be considered progressive sentiment analysis. As sentiments play a crucial role in human interaction, the skills to perceive it through textual content analysis has numerous applications in natural language processing (NLP) and human–computer interaction (HCI). This paper suggests classifying and examining tweets based on six basic emotions: happiness, fear, anger, disgust, surprise, and sadness. Language translators are used to apply data augmentation. Experimental results show that augmented data provides better results than the original data.
Apply Learning Strategies to Improve grade Speaking Skill Of the Second grade Students at SMAN 4 Sidrap

The objectives of this study were to identify the problems of the second grade students in speaking English and to discover the effective steps of the learning strategy to improve the speaking skill of the second grade students at SMAN 4 Sidrap in the academic year 2021. This study applied a classroom action research and was conducted in two cycles consisting of planning, acting, observing, evaluating, and reflecting. Subjects of the research were the second grade students of SMAN 4 Sidrap in the academic year 2021. Data collecting techniques used were observation, interview, diary and speaking test. Data analysis techniques used were descriptive analysis. Based on the research, there were four strategies applied in cycle I, i.e., the strategies of asking for clarification or verification, grouping, repeating and placing new words into a context. The result of strategies application in cycle I gave positive and negative responses that it did not give much significance to the students‟ speaking skill. The highest speaking score of the students in cycle I was 18. Therefore, revision was done. There were two strategies that must be revised in cycle II, which were the strategies of grouping and repeating. As the revision was done in cycle II, students showed positive response and gave significant improvement to the students‟ speaking skill. The highest speaking score of the students in cycle II was 27. Thus, it can be concluded that the learning strategy is successfully implemented and improves the speaking skill of the second grade students at SMAN 4 Sidrap in the academic year 2021.
A Study on Content Design for Cognitive Function Improvement in VR-based Mild Cognitive Impairment

The increasing societal issue of dementia, resulting from rapid aging, has emphasized the significance of proactive intervention in Mild Cognitive Impairment (MCI), which is a pre-dementia stage. This study examined six cognitive functions of individuals with MCI, including memory, language ability, attention, visual-spatial skills, executive function, and orientation, based on prior research. A case analysis was conducted on three types of VR content: gamified, interactive virtual reality, and social VR. The study identified effective VR content design elements for cognitive improvement. It proposed design directions that focused on storytelling related to everyday life, user-friendly interactive designs for easy communication, a rich communicative environment with virtual avatars, and prioritizing user convenience and accessibility. These directions are expected to contribute to enhancing cognitive functions and preventing dementia.
Dynamic feature analysis of handwritten electronic signatures based on Fourier transform

The utilization of handwritten electronic signatures has expanded in various application scenarios, leading to an increased demand for identification. Unlike handwriting signatures, handwritten electronic signatures offer the advantage of extracting dynamic feature data, including writing pressure, velocity, and acceleration. In this study, the Fourier transform was employed to extract 18 characteristics from the time domain and frequency domain of writing pressure, velocity, and acceleration. The experimental findings revealed distinguishable differences between genuine signatures and random forgeries in writing pressure. However, no statistically significant differences were observed in writing velocity and writing acceleration. Moreover, significant differences were detected in most characteristics when comparing genuine signatures with freehand imitation forgeries and tracing imitation forgeries. The canonical discriminant analysis was performed between the genuine and Non-genuine signatures; the cross-validation estimated the discriminating power of these characteristics with a satisfactory result. The study proposed a new approach to analyzing handwritten electronic signatures using time-domain and frequency-domain characteristics and demonstrated its effectiveness in the examination.
B-142 Development and Clinical Validation of Artificial Intelligence Assisted Flow Cytometry Diagnosis for Acuteleukemia

Abstract Background Flow cytometry is one of the most commonly used diagnostic tools in hematopathology. Manual flow cytometry data analysis is vulnerable to substantial inter-operator variation and typically takes a technologist 20 min to analyze a case. In the last decade, artificial intelligence (AI) technology has advanced dramatically and become widely used in image processing. Previously, we created an AI-assisted workflow for classifying white blood cell subsets in peripheral blood. The goal of this study is to evaluate the automated flow cytometry analysis system for diagnosing acute leukemia. Methods A total of 117 cases were retrieved from the archived diagnostic cases, consisting of three diagnostic categories: 20 normal cases, 37 acute lymphoblastic leukemia and 60 acute myeloid leukemia. Flow cytometry data manually analyzed by a pathologist is the gold standard. The same flow cytometry data was processed by the flow cytometry analysis software DeepFlow® using proprietary AI algorithm. The final diagnosis and blast cell count of AI and manual analysis are compared by Pearson correlation coefficient. Results The AI-assisted blast cell enumeration showed moderate correlation as compared to manual evaluation (r = 0.8, Fig. 1). Manual adjustment for enumeration of CD34 negative blast population with uncommon immunophenotype is required for 7 cases, including 4 CD34−/CD117+ acute myeloid leukemia and 3 CD34−/TDT+ lymphoblastic leukemia. The AI analysis effectively recognized cell populations and provided correct diagnoses on 94% of cases (110/117). With pathologists’ review, all cases were classified into the correct diagnostic categories. Conclusion Our study demonstrated that AI can accurately recognize the flow cytometry pattern of leukemia and make the proper diagnosis. Additional training for CD34 negative blast gating and other uncommon blast immunophenotypes is being conducted. Ultimately, AI will decrease the demand on manual analysis by technologists and pathologists, standardize the analysis process, and provide accurate diagnosis.
Retracted: Swarm Intelligence with Adaptive Neuro-Fuzzy Inference System-Based Routing Protocol for Clustered Wireless Sensor Networks

[This retracts the article DOI: 10.1155/2022/7940895.].
Retracted: Construction of Bilingual Teaching Mode Based on Digital Twinning Technology

[This retracts the article DOI: 10.1155/2022/9003806.].
Lightweight Certificate-less Digital Signature Scheme for WSNs

The sensing technology of Wireless Sensor Networks (WSNs) is pivotal in today’s world, as it has extensive applications that span from surveillance systems in military operations to environmental monitoring. Consequently, WSNs have become a highly sought-after technology. A majority of research efforts in this field aim to enhance the longevity of sensor nodes, given that they operate with limited resources. To be able to protect wireless node communication within a network, certificate-less signature schemes have been adopted as a practical cryptographic tool for maintaining data integrity and authenticity. These schemes eliminate the need for managing cumbersome certificates in certificate-based signature systems, and also resolving the key escrow problem encountered in ID based cryptosystems. However, despite their benefits, many among the certificate-less signature schemes in literature are susceptible to multiple attacks, including public key replacement attacks. Moreover, the schemes that are secure require a significant number of operations to achieve this level of security. This work addresses the security issues and reduces the complexity existing in previous certificateless signature scheme. We introduce a new scheme that is both secure and lightweight with time saving of 28% in comparison with the existing algorithms.
Retracted: A Novel Method of Clinical Nursing under the Medical Internet of Things Technology

[This retracts the article DOI: 10.1155/2021/2234457.].
Research of Blockchain Technology on the Application of Data Sharing Mechanism to the Energy Measurement of Power Demand Side

This article studies the application of data sharing mechanism of blockchain technology in the direction of energy measurement in the demand side of electric power. With the rapid consumption of energy and the increasing awareness of environmental protection, demand-side management has attracted more and more attention. In order to meet the sustainability and security requirements of energy consumption, demand-side management needs to measure energy consumption. However, traditional measurement methods have many problems, such as data opacity, insecurity, and susceptibility to tampering. Therefore, this article proposes a blockchain-based energy measurement scheme for the demand side of electric power. This scheme uses the decentralization, immutability, security, and transparency of blockchain technology to achieve reliability and efficiency in energy metering on the demand side of electric power. At the same time, this article also proposes a data sharing mechanism to solve the related problem, and introduces the advantages and application examples of the mechanism. Finally, this article looks forward to the application prospects and future research directions of the proposal.
Audience Behavior Analysis System Design for Outdoor Advertising based on Machine Vision

In recent years, machine vision technology has been continuously developed and gradually integrated with the advertising and media industry. This study applies machine vision technology to the outdoor advertising scene, with machine vision as the core system, using pedestrian recognition and facial recognition technology to analyze the audience’s response to outdoor advertising, helping advertisers improve the effectiveness and monitoring efficiency of their advertising campaigns. Simulation experiment results show that the machine vision-based outdoor advertising audience behavior analysis system proposed in this study has practical application value.
Emotional Analysis of Short Text Based on Multiscale Improved CNN Model Under Tenorflow

Short text emotion analysis has always been a key area of concern for mastering hot public opinion and understanding public evaluation. However, due to the complex factors involved in emotion analysis such as polysemy, irony, word sequence dependency, the accuracy of some existing short text emotion analysis methods still has room for improvement. Based on this, this paper proposes a short text sentiment analysis method based on multi-scale improved CNN model under Tensorflow. This method builds multi-scale convolution layer on the basis of the original convolution layer to help the model mine more potential function information, and at the same time imposes a lightweight constraint on the network to reduce the probability of over-fitting. Finally, attention mechanism is introduced to give different weights to features according to their importance, and text emotion analysis is carried out. The experimental results show that the average loss value of the improved CNN model is reduced by 38.7%, and the average accuracy is improved by 21.6%.
Hierarchical Braille Layout Detection Model Based on Semantic Assistance

This paper proposes a hierarchical Braille layout detection model based on semantic assistance, BLayoutNet. As research on Braille layout analysis is relatively scarce and there is a lack of relevant datasets, this paper proposes a Braille layout detection dataset to support research in this field. Finally, experimental results demonstrate that the proposed model achieves stable and accurate detection of Braille layout. It is hoped that this model can serve as a strong baseline for Braille layout analysis tasks and promote the development of this field.
Prediction Demonstration of Elman Neural Network

The Elman neural network is used on predicting time series information in this paper, and compared with the prediction outcomes of the BP neural network and the ARIMA model. The conclusion is that the Elman neural network model is better performance on predicting time series data.
Research on Office Automation System Based on Computer Big Data

In this paper, three open-source frameworks Struts, Spring and Hibernate are reasonably integrated together to build a general J2EE office automation system with the advantages of short development cycle, low development cost, loose coupling, easy to expand and migrate. This paper gives the corresponding solutions and ideas to the technical problems such as how to control the document flow, document modification and trace retention, data exchange with relational database, and guarantee the system security. The research shows that the lightweight J2EE architecture improves the system code reuse, development efficiency, and system extension and maintenance.
Innovative Research on Improving Efficiency of LHU Algorithm in Autonomous System

Autonomous system refers to a computer system that can operate independently, control itself and make decisions by itself. It does not rely on the support of external systems or software, and can complete various tasks and operations autonomously. Autonomous systems are often highly intelligent and adaptive in that they can acquire environmental information through sensors, analyze and process that information to make appropriate responses, and perform tasks without human intervention. Autonomous systems have a wide range of applications in artificial intelligence, autonomous systems, automated control and other fields, which can improve production efficiency, reduce costs, improve safety and reliability. However, most of the existing methods of autonomous systems are based on traditional machine learning or reinforcement learning. This paper proposes an algorithm based on deep reinforcement learning to improve the learning efficiency of autonomous systems - LHU algorithm. The simulation dialogue experience is obtained from the interaction between autonomous systems and trainable user models. A posterior-session experience is the synthesis of successful session data by the posterior-module by acting on those failed session fragments and user goals. The dialogue experiences obtained from the user model and the posterior module provide more dialogue examples and positive feedback on dialogue strategy learning respectively. Finally, the reliability of the model is verified by experiments.
Adaptive-Correlation-aware Unsupervised Deep Learning for Anomaly Detection in Cyber-physical Systems

Cyber-Physical System needs high security to ensure the safe operation. Anomaly detection is one of the mainstream security technologies, the core of which is data analysis and learning. Unsupervised Deep-Learning-based Anomaly Detection Methods can be used in the scenarios that collects large amounts of unlabeled data and are more in line with the actual needs of CPS. However, the correlation among data did not attract enough attention to exploring their implicit relationship, and the adaptive training was deficient. Therefore, we propose an Adaptive-Correlation-aware Unsupervised Deep Learning (ACUDL) for anomaly detection in CPS. It constructs a directed graph structure to represent the implicit correlation among data and adaptively updates with dynamic graph; then, designs a dual-autoencoder to extract the original non-correlation, correlation, and reconstruction features, and builds an estimation network using the Gaussian mixture model (GMM) to estimate the anomaly energy. Experimental results on several CPS data scenarios show that ACUDL can be well adapted to many application scenarios with different data characteristics and achieves better overall results than some up-to-date DL-ADMs.
RPAU: Fooling the Eyes of UAVs via Physical Adversarial Patches

Recently, Unmanned Aerial Vehicles (UAVs) deployed with deep learning models have been widely applied both in civil and military. However, the vulnerability of the deployed model to adversarial attacks has raised security concerns. Previous studies have mainly explored adversarial attacks in the digital domain. While physical attacks have posed a more serious threat to UAVs. In this paper, we have explored a novel Robust Physical Attack against UAVs named, which directly threatens the flight safety of UAVs. Specifically, three attacks are proposed in : Hiding Attack (HA), Yaw Attack (YA), and Obstacle Attack (OA). To launch the attacks, we overcome three domain-design challenges, including continuous perturbation, digital-physical domain gap, and optimum perturbation generation. For continuous perturbation, we have introduced a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nested patch</i> that realizes attacks at any distance. Further, a series of transformations are considered to narrow the gap between the digital and physical domains. Then, we proposed a time-dependent mechanism for generating optimum perturbation. We conducted comprehensive experiments in the digital domain, simulation environment, and physical domain. The experimental results validate the robustness of the proposed framework. In the digital domain, outperforms the baseline by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$54.9\%$</tex-math> </inline-formula> average attack success rate (ASR). More importantly, is still effective in both the simulation environment and the physical domain, achieving an average ASR of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100\%$</tex-math> </inline-formula> .