Reviewerly/exHarmony huggingface dataset:
Pengaruh Penggunaan Mixing Foundation dengan Teknik Bakar Terhadap Kulit Wajah Bekas Jerawat Bopeng

The foundation mixing technique is a development in facial makeup techniques. One technique so that the mixed foundation is evenly mixed is to use the foundation burn technique. This can help maximize foundation mixing and cover problematic parts of the face to make it look more perfect. These two techniques can produce long-lasting and smoother facial makeup results. The aim of this research is to determine the effect and comparison of the results of mixing foundation with the burning technique and the results of mixing foundation without the burning technique. The method used in this research is True Experimental. Data analysis techniques Normality Test, Homogeneity Test, t Test and Duncan Test. The results of the analysis show that there is an effect of using mixed foundation without burning technique on the durability indicator X1 (2.28) which is significantly different from mixing foundation with burning technique X2 (3.85). The results of the analysis of the effect of using mixed foundation without burning techniques on the covering power indicator X1 (2.85) are significantly different from mixing foundations using burning techniques X2 (3.14).
Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network

With the development of technology, mobile robots are increasingly deployed in real-world environments. To enable robots to work safely in a variety of terrain environments, we proposed a ground-type detection method based on the Hilbert–Huang transform (HHT) and attention-based spatiotemporal coupled network. Taking a dataset containing multiple sets of robot signals from a Kaggle competition as an example; we use the proposed method to classify the signals and thus achieve a terrain classification of the robot’s location. Firstly, the signal data were processed using the discrete wavelet transform for noise reduction, and all channels in the dataset were ranked by importance using the permutation importance method. Next, the instantaneous frequencies of the two most important channels were extracted using the HHT and added to the original dataset to expand the feature dimension. Then the features in the expanded dataset were extracted by the convolutional neural network, long short-term memory, and attention module. Afterward, the fully extracted features were passed into the fully connected layer for classification, and an average classification accuracy of 83.14% was obtained. The effectiveness of each part in our method was demonstrated using ablation experiments. Finally, we compared our method with some common methods in the field and found that our method obtained the highest classification accuracy, proving the superiority of the proposed method.
Education Building Space Design in China's New Teaching Model

Under the background of China's new teaching model, many schools in China have changed a lot of teaching methods, teaching models, and teaching skills. Therefore, it is important, creating more and more suitable learning and practical space for students, providing new directions for students' future, and maximizing the overall education level of the school. Therefore, modern schools should be based on the new teaching model. According to the more reasonable design and education building space, they should create a better teaching space for students. Different learning activities have different requirements for space. Curriculum design must be more flexible and practicality, to meet the various needs of students' different learning activities, making students more immersed in classroom teaching. The article outlines the new teaching model in China, and then proposes the transformation method based on the design space design of educational architectural space in the new teaching model, as well as the principles and strategies of education building space design based on the new teaching model.
The Effectiveness Of (Esa) Engage Study Activate Method On Student’s Speaking Ability At The Eight Grade Students Of Islamic Junior High School of Syech Abdul Qadir Al Jailani

The study investigates the effectiveness of the Engage-Study-Activate (ESA) method on the speaking ability of eighth-grade students at Islamic Junior High Schools. A quasi-experimental design was employed, involving a pre-test/post-test control group design. The study took place at Mts Sirajut Thalibin Racek Junior High School, which included two classes with a total of 48 students. Class VIII A, the experimental group, consisted of 24 female students, while Class VIII B, the control group, was made up of 24 male students. In this study, data collection was conducted using observation, tests (including pre-tests and post-tests), and documentation. Descriptive statistical analysis was employed to analyze the data. The statistical analysis aimed to determine the mean, frequency, total score, standard deviation, and percentage. The pre-test values for the experimental and control classes were 57.29 and 48.33, respectively, while the post-test values were 83.96 for the experimental class and 60.00 for the control class. The average scores reveal that the experimental group, taught using the ESA method, outperformed the control group, which did not employ the ESA method. Based on data analysis and research findings, it can be concluded that the ESA method significantly improved students' speaking abilities. This is evidenced by the experimental group's average score of 76.36 compared to the control group's average score of 61.82. The mean value of the experimental group is higher than that of the control group, and the t-test value is 3.754, with a 2-tailed significance of 0.001. This suggests that the ESA method can be effectively integrated into speaking instruction in Islamic Junior High Schools to enhance students' speaking proficiency. The study's implications include the importance of adopting learner-centered approaches in language instruction to foster better communication skills in students.
Shortcut Enhanced Syntactic and Semantic Dual-channel Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained task that predicts the sentiment polarity of different aspects in the same sentence. The main challenge is how to build a strong dependency between aspects and sentiment. Recently, the graph neural network (GNN) has become the mainstream trend to extract syntactic dependency relations from the syntactic dependency tree. However, further improvements are hampered by the inherent mistake on the syntactic dependency tree. Consequently, this article presents a dual-channel model to investigate whether considering both syntactic dependency and semantic relevance can further improve the performance. Specifically, we propose a multi-head syntactic graph convolution network (MHGCN) module in the syntactic channel, focusing on different aspects of the syntactic flow in parallel. We also design a syntactic local attention mechanism (Syn-LFAM) and a semantic local attention mechanism (Sem-LFAM) to fully exploit the crucial local information, respectively. Moreover, we use the cross semantic-syntax interaction module and gate fusion mechanism to control the combination of semantic and syntax dynamically. The experimental results show that we utilize less resource consumption, and the final model outperforms the state-of-the-art methods on three of the four publicly available datasets.
Manipulating Visually Aware Federated Recommender Systems and Its Countermeasures

Federated recommender systems (FedRecs) have been widely explored recently due to their capability to safeguard user data privacy. These systems enable a central server to collaboratively learn recommendation models by sharing public parameters with clients, providing privacy-preserving solutions. However, this collaborative approach also creates a vulnerability that allows adversaries to manipulate FedRecs. Existing works on FedRec security already reveal that items can easily be promoted by malicious users via model poisoning attacks, but all of them mainly focus on FedRecs with only collaborative information (i.e., user–item interactions). We contend that these attacks are effective primarily due to the data sparsity of collaborative signals. In light of this, we propose a method to address data sparsity and model poisoning threats by incorporating product visual information. Intriguingly, our empirical findings demonstrate that the inclusion of visual information renders all existing model poisoning attacks ineffective. Nevertheless, the integration of visual information also introduces a new avenue for adversaries to manipulate federated recommender systems, as this information typically originates from external sources. To assess such threats, we propose a novel form of poisoning attack tailored for visually aware FedRecs, namely image poisoning attacks, where adversaries can gradually modify the uploaded image with human-unaware perturbations to manipulate item ranks during the FedRecs’ training process. Moreover, we provide empirical evidence showcasing a heightened threat when image poisoning attacks are combined with model poisoning attacks, resulting in easier manipulation of the federated recommendation systems. To ensure the safe utilization of visual information, we employ a diffusion model in visually aware FedRecs to purify each uploaded image and detect the adversarial images. Extensive experiments conducted with two FedRecs on two datasets demonstrate the effectiveness and generalization of our proposed attacks and defenses.
Better Understanding Procedural Search Tasks: Perceptions, Behaviors, and Challenges

People often search for information to acquire procedural knowledge–“how to” knowledge about step-by-step procedures, methods, algorithms, techniques, heuristics, and skills. A procedural search task might involve implementing a solution to a problem, evaluating different approaches to a problem, and brainstorming on the types of problems that can be solved with a specific resource. We report on a study ( N =36) that aimed to better understand how people search for procedural knowledge. Much research has investigated how search task characteristics impact people’s perceptions and behaviors. Along these lines, we manipulated procedural search tasks along two orthogonal dimensions: product and goal. The product dimension relates to the main outcome of the task and the goal dimension relates to task’s success criteria. We manipulated tasks across three product categories and two goal categories. The study investigated four research questions. First, we examined the effects of the product and goal on participants’ (RQ1) pre-task perceptions, (RQ2) post-task perceptions, and (RQ3) search behaviors. Second, regardless of the task product and goal, by analyzing participants’ think-aloud comments and screen activities we closely examined how people search for procedural knowledge. Specifically, we report on (RQ4) important relevance criteria, types of information sought, and challenges.
Transcending post-truth: Open educational practices in the information age

This paper discusses operationalization of open educational practices (OEP) using innovative, Internet-influenced pedagogies to expose dangers of post-truth narratives.The first part reviews interpretations of OEP (associated with open-access and tools, collaboration, problem-centered learning, and democratic pedagogy) and explores possibilities for creating educational initiatives where students learn to create problem-solving communities mirroring an informationally healthy society.The second part suggests our society has reached a post-truth crossroads.Post-truth was initially discussed in the 1990s-a reification of critical theorists' pessimism of social structures, controlling narratives and ways members react to critical events, whether through obedience to institutional authority or support for destructive adventurism, creating situations producing cybernetic double binds.The third part contextualizes OEP as ecologically grounded pedagogies through an open source educational processes framework focusing on productive many-to-many online communication and community formation, offering practical examples of open source educational processes curricula implemented in a higher education setting.
Artificial ASMR: A Cyber-Psychological Approach

The popularity of Autonomous Sensory Meridian Response (ASMR) has sky-rockted over the past decade, but scientific studies on what exactly triggered ASMR effect remain few and immature, one most commonly acknowledged trigger is that ASMR clips typically provide rich semantic information. With our attention caught by the common acoustic patterns in ASMR audios, we investigate the correlation between the cyclic features of audio signals and their effectiveness in triggering ASMR effects. A cyber-psychological approach that combines signal processing, artificial intelligence, and experimental psychology is taken, with which we are able to quantize ASMR-related acoustic features, and therewith synthesize ASMR clips with random cyclic patterns but not delivering identifiably scenarios to the audience, which were proven to be effective in triggering ASMR effects.
A Multi-user Deep Semantic Communication System based on Federated Learning with Dynamic Model Aggregation

This paper presents a deep semantic communication system based on federated learning with dynamic model aggregation, namely DeepSC-FedDMA, considering time-varying channel conditions. Each local model is associated with an aggregation indicator reflecting how well the model is trained under the current channel condition. After local training, each local model sends its aggregation indicator and model parameters to the central server that allocates a proper aggregation weight to the local model for global model update. In this way, more effective models contribute more to the dynamic model aggregation while less effective ones maintain appropriate level of diversity. Simulation results demonstrate that the proposed DeepSC-FedDMA overwhelms the semantic communication systems with federated averaging and pure local training in terms of mean square error and peak signal-to-noise ratio, under three kinds of channels.
BehavSniffer: Sniff User Behaviors from the Encrypted Traffic by Traffic Burst Graphs

With the increasing popularity of encryption pro-tocols in application and the rapid development of network applications, traffic classification has become a major challenge for mobile service providers. The failure of traditional classification methods and low classification accuracy are the problems that need to be solved urgently in traffic classification research. Therefore, we propose the scheme of BehavSniffer to sniff user behaviors from the encrypted traffic. The core idea is to propose Traffic Burst Graph (TBG) for extracting multidimensional features from bidirectional interactive data flows, and do feature fusion based on Kernel Principal Component Analysis (KPCA) and Deep Neural Network (DNN). In this way, BehavSniffer can learn both high and low-order combined structural features from traffic patterns of user behavior. Meanwhile, we propose the user behavior dataset, named WWT, from three widely used social media applications (WeChat, WhatsApp, Telegram). Experimental results show that BehavSniffer outperforms stateof-the-art methods, with AUC of 0.987 and accuracy of 99.8%, respectively.
Parallel Execution of Blockchain Transactions with Sharding

Scalability is one of the main problems limiting blockchain applications. Most recent research on blockchain scalability has focused on improving the consensus layer, but the most advanced consensus protocols can only reach a few thousand transactions per second(tps), which is far below the capacity of typical distributed databases. With the use of blockchain sharding technology, the time overhead corresponding to the execution layer and the consensus layer in the blockchain is gradually reduced. Additionally, improving the efficiency of transaction execution could incentivize nodes to actively verify transactions and enhance blockchain security. In this paper, we propose a Sharding Based Parallel Execution of Block Transactions (SPEx-Tran) framework in blockchain, which transforms the sequential execution of intra-block transactions into parallel execution to improve the performance of blockchain. In addition, based on the characteristics of blockchain sharding transactions, we further propose a Blockchain Transaction Scheduling (BCTS) algorithm to improve the performance of blockchain by optimizing the parallel execution process of transactions. In the BCTS algorithm, the mechanism of local multi-processors sharing the Conflict Transactions cache Table (CTT) simplifies the verification process of conflicting transactions, thus improving the blockchain performance. Experimental results show that the throughput of blockchain using the SPExTran framework and BCTS algorithm is significantly higher than that of typical sharding technologies.
Ethical U se of AI in Social Media

As AI algorithms become increasingly integrated into social media platforms, concerns regarding their ethical implications have grown. This research paper examines the ethical use of AI in social media, focusing on three key areas: content moderation, user manipulation, and the spread of misinformation. We analyze the ethical concerns arising from personal, political, social, and ethical biases embedded within AI algorithms. The paper highlights the challenges of striking a balance between human moderation and algorithmic enforcement, as well as the need to address biases in the decision-making process. We delve into the complexities of content moderation, including the unintended consequences of algorithmic enforcement and the ethical debates surrounding content removal. The paper also explores user manipulation tactics employed in social media and the ethical considerations necessary to protect and empower users against manipulation. Furthermore, we examine AI's role in spreading misinformation and the ethical challenges of detecting and combatting false information while avoiding unintended amplification. To address these ethical concerns, we discuss potential solutions such as enhancing transparency, promoting fairness, and empowering users with control over their AI -driven experiences. Additionally, we present case studies and examples to provide practical insights into the complex ethical landscape of AI in social media. The research paper concludes by emphasizing the importance of ethical considerations in AI usage and suggesting future directions for research, collaboration, policy development, and responsible innovation. By addressing the ethical challenges posed by AI algorithms, we can foster an ethical and responsible environment for social media users and ensure that AI serves the best interests of individuals and society.
Toward Efficient and End-to-End Privacy-Preserving Distributed Gradient Boosting Decision Trees

Gradient Boosting Decision Trees (GBDTs) are popular machine learning models due to its simplicity, effectiveness, and interpretability. Recently, to alleviate serious privacy leakages in conventional centralized methods, researchers have proposed several privacy-preserving distributed GBDT solutions. However, those approaches still suffer from either insufficient privacy protection or significant runtime and communication overhead. In this paper, we propose an efficient and end-to-end privacy-preserving distributed GBDT framework, called PPD-GBDT, which uses differential privacy, polynomial approximation, and fully homomorphic encryption to achieve comprehensive privacy protection. Specifically, during the boosting phase, we design a novel model preparation method to improve the efficiency of prediction with acceptably slight accuracy/RMSE loss while preventing data owners' corruption. On the other hand, for the prediction phase, we propose a customized secure prediction method, which effectively prevents the malicious server from stealing private information. Besides, we conduct extensive experiments on six datasets and compare with three prior schemes. Evaluation results show that our privacy-preserving scheme achieves lower runtime and up to 40× less communication overhead compared to the state-of-the-arts.
Malaysian Sign Language Mobile Application Using Deep Learning

This research paper presents the development of a Malaysian Sign Language (MSL) application. The project aims to bridge the communication gap between the deaf community and other Malaysians. The application utilizes the Yolov5 algorithm, chosen for its real-time object detection capabilities and low memory requirements. The waterfall model has been implemented and has been divided into 8 phases. The American Sign Language (ASL) dataset from Roboflow was adapted for MSL due to similar hand gestures. The model achieved a high mAp score of 87.23% after 500 iterations, successfully classifying MSL alphabets with the exception of “J” and “Z”.
Scale Selection Network with Attention Mechanism for Crowd Counting

Crowd counting is an important cornerstone in tasks related to crowd analysis. In recent years, there has been a significant rise in the development of deep learning methods, particularly those based on Convolutional Neural Networks (CNN), in the field of image processing and achieved amazing results. However, there are some challenges waiting to be solved in the current crowd counting task: large-scale variations in crowd sizes and interference from the background. Both of them will lead to poor prediction results. Therefore, we propose a scale selection module to deal with the scale variation problem in images. And for background interference, we proposed an attention module to reduce the interference of background information. Moreover, we evaluated our model on four commonly used datasets and compared the performance with other state-of-the-art methods to demonstrate the competitiveness of our approach. The source code and pretrained models are available at https://git;hub.com/TimLai0307/SSN.
Mobile Edge Adversarial Detection for Digital Twinning to the Metaverse with Deep Reinforcement Learning

Real-time Digital Twinning of physical world scenes onto the Metaverse is necessary for a myriad of applications such as augmented-reality (AR) assisted driving. In AR assisted driving, physical environment scenes are first captured by Internet of Vehicles (IoVs) and are uploaded to the Metaverse. A central Metaverse Map Service Provider (MMSP) will aggregate information from all IoVs to develop a central Metaverse Map. Information from the Metaverse Map can then be downloaded into individual IoVs on demand and be delivered as AR scenes to the driver. However, the growing interest in developing AR assisted driving applications which relies on digital twinning invites adversaries. These adversaries may place physical adversarial patches on physical world objects such as cars, signboards, or on roads, seeking to contort the virtual world digital twin. Hence, there is a need to detect these physical world adversarial patches. Nevertheless, as real-time, accurate detection of adversarial patches is compute-intensive, these physical world scenes have to be offloaded to the Metaverse Map Base Stations (MMBS) for computation. Hence in our work, we considered an environment with moving Internet of Vehicles (IoV), uploading real-time physical world scenes to the MMBSs. We formulated a realistic joint variable optimization problem where the MMSPs' objective is to maximize adversarial patch detection mean average precision (mAP), while minimizing the computed AR scene up-link transmission latency and IoVs' up-link transmission idle count, through optimizing the IoV-MMBS allocation and IoV up-link scene resolution selection. We proposed a Heterogeneous Action Proximal Policy Optimization (HAPPO) (discrete-continuous) algorithm to tackle the proposed problem. Extensive experiments shows HAPPO outperforms baseline models when compared against key metrics.