Reviewerly/exHarmony huggingface dataset:
A Blind Video Quality Assessment Method via Spatiotemporal Pyramid Attention

As social media communication develops, reliable multimedia quality evaluation indicators have become a prerequisite for enriching user experience services. In this paper, we propose a multiscale spatiotemporal pyramid attention (SPA) block for constructing a blind video quality assessment (VQA) method to evaluate the perceptual quality of videos. First, we extract motion information from the video frames at different temporal scales to form a feature pyramid, which provides a feature representation with multiple visual perceptions. Second, an SPA module, which can effectively extract multiscale spatiotemporal information at various temporal scales and develop a cross-scale dependency relationship, is proposed. Finally, the quality estimation process is completed by passing the extracted features obtained from a network of multiple stacked spatiotemporal pyramid blocks through a regression network to determine the perceived quality. The experimental results demonstrate that our method is on par with the state-of-the-art approaches. The source code necessary for conducting groundbreaking scientific research is accessible online https://github.com/Land5cape/SPBVQA.
Enhancing Assistive Communication: An EOG-Based Continuous Eye-Writing Recognition Deep Learning Model

Modern human-computer interfaces (HCI) are crucial for individuals facing severe neurological diseases such as amyotrophic lateral sclerosis, brainstem stroke, and multiple sclerosis [1]. In many cases where voluntary movements are restricted to eye control, eye movements are critical for them to perform different tasks, like writing and communication[2][3]. Therefore, HCI systems that capture their gaze direction are pivotal in enhancing their communication and have a better quality of life.
Towards a More Extensible Machine Learning Demonstration Tool

The Institute of Signal and Information Processing (ISIP) Machine Learning Demo (IMLD) is an educational tool designed to introduce the basics of machine learning through an easy-to-use graphical user interface (GUI) [1]. IMLD was first developed as part of a suite of Java applets in the late-1990’s when Java applets were envisioned as the future of interactive computing [2]–[4]. IMLD visualizes the performance and results of various machine learning algorithms and techniques in a simple, intuitive GUI. A typical IMLD screen is shown in Figure 1. This paper introduces the recent enhancements to IMLD, which now hosts many new customizations, where its adaptability allows users to access more complex machine learning algorithms and the ability to implement their own algorithms as needed. IMLD provides benefits to the realm of medical signal processing as users are exposed to machine learning and the benefits that it provides in classifying data through various algorithms.
Addressing Psychological Needs in Designing for a Sustainable Circular Economy

Approaches and methods on designing for a sustainable circular economy are developing solutions for sustainable development. For such solutions to succeed, market and social acceptance of circular products and services must improve. Currently, there is a mixed reception among consumers and hence a stagnating market share of circular solutions. In this chapter, the authors discuss these limitations and relate them to psychological aspects of consumer and user experience and behavior. Research on user experience and experience design has delivered theories, approaches and methods on how to design for people as they experience and behave in the world. One core finding is that psychological needs play an important role in human-product interaction. The fulfillment of such needs results in positive experiences, and can raise acceptance of products and services. We illustrate how psychological needs can be addressed in a sustainable circular design. They present a fictitious case to illustrate how specific non-instrumental qualities of offerings can address psychological needs and hence fundamentally influence overall judgments and behavior in interaction with sustainable circular solutions. Finally, we discuss how such needs-based experience design can be implemented in design processes.
Automatic Generation of Frontend Code from Design Interface

Internet technology is constantly evolving, and application software plays a crucial role in various fields. The development of various software relies heavily on the design of user interfaces and the corresponding code implementation. However, implementing front-end code is a highly repetitive and tedious task. Prior to designing the front-end interface based on the requirements document, designers usually create a simpler interface to assist in designing and implementing the actual interface. The specific focus of this study is to generate a domain-specific language (DSL) file based on a screenshot of the front-end interface using deep learning. The pix2code project was the first to propose using deep learning for DSL text generation. However, the pix2code project has several issues: firstly, the DSL language designed in the project lacks an adequate description vocabulary to meet the basic requirements of describing actual web pages; secondly, the project has poor scalability for the DSL language; and finally, the project's generation effectiveness using deep learning models is average. This study expands on the pix2code project by extending the design of the domain-specific language, introducing new descriptive vocabulary, and developing an algorithm to generate a new dataset. These enhancements allow the DSL to better describe various components and their combinations within the interface. Additionally, three improvements are made to the original deep learning model. Firstly, the convolutional network used for image feature extraction is deepened to enhance feature extraction capabilities. Secondly, a text encoder based on Transformer is designed for text feature extraction. Finally, a bidirectional LSTM network is used for decoding. Experimental results show that the proposed model in this study improves the BLEU score from 0.81 to 0.85 on a publicly available dataset, and from 0.547 to 0.575 on a newly created dataset.
Gender Classification of Twitter Users Using Convolutional Neural Network

Social media has become a place for social media analysts to obtain data to gain deeper insights and understanding of user behavior, trends, public opinion, and patterns associated with social media usage. Twitter is one of the most popular social media platforms where users can share messages or ”tweets” in a short text format. However, on Twitter, user information such as gender is not shown, but without realizing it or not, there is information about it in an unstructured manner. In social media analytics, gender is one of the important data that someone likes, so this research was conducted to determine the best accuracy for gender classification. The purpose of this study was to determine whether using combined data can improve the accuracy of gender classification using data from Twitter, tweets, and descriptions. The method used was word vector representation using word2vec and the application of a 2D Convolutional Neural Network (CNN) model. Word2vec was used to generate word vector representations that take into account the context and meaning of words in the text. The 2D CNN model extracted features from the word vector representation and performed gender classification. The research aimed to compare tweet data, descriptions, and a combination of tweets and descriptions to find the most accurate. The result of this study was that combined data between tweets and
Computing offloading policy for training effectiveness of space-air-ground integrated network

Edge computing has become indispensable in the context of wireless network communication, particularly in scenarios with strained computing resource such as remote areas or areas where ground communication disruptions are prevalent. It significantly enhances the service quality of ground terminal devices. This paper proposes a Space-Air-Ground Integrated Network(SAGIN) edge computing model. However, the limited computational resources pose challenges to crucial aspects such as edge server deployment, edge server computing resource allocation, and offloading strategies in such scenarios, becoming pivotal in boosting network service capabilities. To address this, we explore different deep reinforcement learning algorithms to train the model towards optimizing system objectives. Among them, the Soft-Actor-Critic (SAC) deep reinforcement learning algorithm demonstrates superior applicability to such model problems, improving training effectiveness by approximately 18% compared to the DDPG algorithm (calculated using average reward value). The edge computing offloading technique also serves as a crucial foundation for future edge computing cross-domain interconnections and distributed task collaboration.
PENGARUH MODEL PEMBELAJARAN COOPERATIVELEARNING TIPE TEAMS GAMES TOURNAMENTS DIINTEGRASIKAN DENGAN MEDIA PUZZLE TERHADAP HASIL BELAJAR IPS SISWA SDN GUGUS II BENGKULU UTARA

ABSTRACTLearning Models of Teams Games Tournaments Type Integrated with Media Puzzles on student learning outcomes in social studies subjects of class V SDN Gugus II Bengkulu Utara. The type used is quantitative research. The method used is quasy experiment with research design The Matching Only Pretest-Postest Group Design. The population is SDN Gugus IIBengkulu Utara. The sampling technique uses cluster random sampling. The sample is B-accredited SDN and KTSP curriculum. Class V at SDN 090 with the number of students as many as 15 students as the experimental class and class V students at SDN 099 with the number of students as many as 15 students as the control class. The t-test results get tcount (4.132)&gt; t table (1.666). The tcount obtained is higher than ttable, it can be concluded that there is a significant influence of the Cooperative Learning learning model Teams Games Tournaments Type Integrates With Media Puzzle on Social Studies learning outcomes of students in class V SDN Gugus II Bengkulu Utara.Keywords: Cooperative Learning Teams Games Tournaments Type, Media Puzzle, Social Studies Learning Outcomes
Audio-Video Analysis Method of Public Speaking Videos to Detect Deepfake Threat

Aim: The purpose of the article is to present the hypothesis that the use of discrepancies in audiovisual materials can significantly increase the effectiveness of detecting various types of deepfake and related threats. In order to verify this hypothesis, the authors proposed a new method that reveals inconsistencies in both multiple modalities simultaneously and within individual modalities separately, enabling them to effectively distinguish between authentic and altered public speaking videos. Project and methods: The proposed approach is to integrate audio and visual signals in a so-called fine-grained manner, and then carry out binary classification processes based on calculated adjustments to the classification results of each modality. The method has been tested using various network architectures, in particular Capsule networks – for deep anomaly detection and Swin Transformer – for image classification. Pre-processing included frame extraction and face detection using the MTCNN algorithm, as well as conversion of audio to mel spectrograms to better reflect human auditory perception. The proposed technique was tested on multimodal deepfake datasets, namely FakeAVCeleb and TMC, along with a custom dataset containing 4,700 recordings. The method has shown high performance in identifying deepfake threats in various test scenarios. Results: The method proposed by the authors achieved better AUC and accuracy compared to other reference methods, confirming its effectiveness in the analysis of multimodal artefacts. The test results confirm that it is effective in detecting modified videos in a variety of test scenarios which can be considered an advance over existing deepfake detection techniques. The results highlight the adaptability of the method in various architectures of feature extraction networks. Conclusions: The presented method of audiovisual deepfake detection uses fine inconsistencies of multimodal features to distinguish whether the material is authentic or synthetic. It is distinguished by its ability to point out inconsistencies in different types of deepfakes and, within each individual modality, can effectively distinguish authentic content from manipulated counterparts. The adaptability has been confirmed by the successful application of the method in various feature extraction network architectures. Moreover, its effectiveness has been proven in rigorous tests on two different audiovisual deepfake datasets. Keywords: analysis of audio-video stream, detection of deepfake threats, analysis of public speeches
A Novel Planning and Tracking Approach for Mobile Robotic Arm in Obstacle Environment

In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.
Research on hybrid synchronization methods in multi‐user collaborative <scp>VR</scp> simulation medical surgery training system

Summary The simulation training system based on VR technology has been applied in clinical medicine due to its immersive interactive experience, economy, and safety. In response to the limitation that most medical surgery training systems can only achieve single user operations, a hybrid synchronous model based multi‐user collaborative framework design is proposed to meet the needs of collaborative training in virtual reality teaching processes. Based on the characteristics of frame synchronization and state synchronization, the synchronization method has been improved. Based on frame synchronization, specific state synchronization is used on interactive events to improve the consistency of operation details and real‐time action. On this basis, a system prototype has been implemented using Unity 3D, and compared with traditional schemes, analysis shows that it has faster response speed for disconnected reconnection and more stable interaction state.
Forecasting The Amount Of Oil and Non-Oil and Gas Exports In Indonesia Using The Box-Jenkins Method

Indonesia is one of the countries in Southeast Asia that has diversity and abundance of natural resources, both oil and gas and non-oil and gas. Export is an international trade activity that is profitable for economic growth in Indonesia. The purpose of this study is to determine the best results and models for oil and gas and non-oil and gas export data. The Box-Jenkins method (ARIMA) is a forecasting method that does not require or ignores independent variables. The stages used in the box-Jenkins method are identification of the model to see whether the model is stationary or not, for a model that is already stationary, a parameter estimation test will be carried out by looking at the significance value of the parameter, then a diagnostic check will be carried out using residual white noise and residual tests. normal distribution. After that, the initial prediction model is obtained and to get the best ARIMA model, it can be done by looking at the smallest MAPE value of each model. The best model obtained in this study to predict the amount of oil and gas and non-oil and gas exports in Indonesia uses the Box-Jenkins or ARIMA method, namely model (5,1,3) with the smallest MAPE of 8.142%. Forecasting results for the number of oil and gas and non-oil and gas exports in Indonesia in January - December 2023, namely: January 22194.9 million dollars, February 22795.6 million dollars, March 24796.0 million dollars, April 24106 million dollars, May 22281.6 million dollars, June 23186.6 million dollars, July 25184.3 million dollars, August 24325.8 million dollars, September 22456.3 million dollars, October 23565.6 million dollars, November 25602.2 million and December 24540.3 million dollars. Actual data or actual data for January – April 2023, namely January 22,323.8 million dollars, February 21,321.3 million dollars, March 23,416 million dollars, April 19,290.5 million dollars. It can be concluded that the results of the forecasting value of the box-Jenkins method are close to the actual value.
Pengaruh Etika Pustakawan Terhadap Pelayanan Prima Pada Perpustakaan

Librarian ethics in providing acceptable service is very important, where there are many impacts that are given to users when the services provided are deemed not good. Therefor the importance of librarians in having good ethics, solely to provide good service with good quality to users, so that users will feel satisfied with these services as explained by Prakoso (2015: 44) regarding ethics, explaining that This ethic for a person or a group of people makes the handle to regulate their behavior in accordance with the values and norms contained therein. Meanwhile, what is contained in the Law No. 47 of 2007 concerning Libraries. Explaining that 'library services are carried out in an excellent manner and are oriented towards the interests of the visitors. So excellent service in customer-oriented is the key to the quality of library services. 'Therefor the importance of having good ethics to provide good service with good quality.
Multi-Domain Feature Fusion Based Radar Deception Jamming Recognition Method

Considering the challenges posed by low recognition probability, high computational complexity, and difficult engineering implementation in radar deception jamming recognition under a low jamming-to-noise ratio (JNR), a method for recognizing radar deception jamming based on multi-domain feature fusion is proposed. This method analyzes the mathematical models of three types of deception jamming-intermittent sampling repeater jamming (ISRJ), dense false target jamming (DFTJ), and distance deception jamming (DDJ) -to extract two features: envelope fluctuation and waveform similarity in the time and frequency domains, respectively. Jamming classification and recognition are achieved using a multi-class support vector machine (multi-class SVM) model. Simulation results demonstrate that as JNR increases, there is a gradual improvement in the recognition rate which reaches 90%at ldB.
Verifiable Multi-Key Privacy Data Computing System Based on Blockchain

Secure storage and computing of ciphertext data is an important research area in cloud computing. Homomorphic encryption (HE) is a popular ciphertext computing technique, but most homomorphic computing requires encryption using the same public key. This restricts the application scenarios and compromises the security of the system. To address this issue, we propose a verifiable multi-key secure computing system for off-line non-real-time data, which solves the multi-key issue with a double-trapdoor HE scheme. Additionally, we propose a lightweight commitment algorithm(LC) that generates commitments using the existing operators of the encryption algorithm, reducing computation. Our proposed system employs two types of servers for ciphertext outsourcing calculation, with the blockchain acting as a supervisor to prevent the servers' collusion. Furthermore, the LC algorithm is used to verify the accuracy of data addition and multiplication results. Security analysis shows that the blockchain can improve system security by detecting information exchanged by servers. We also present simulation results that demonstrate the feasibility and accuracy of the system, as well as the low time consumption of the LC algorithm.
Pengaruh Model Discovery Learning Berbantuan Media Kartu Kata Bergambar Terhadap Hasil Belajar Tematik

This study aims to determine the effect of discovery learning models assisted by pictorial word card media on Thematic learning on student learning outcomes. This research is quantitative research. The research method used was Quasi Experiment with the type of design The Matching Only Pretest-Posstest Control Group Design. The population of this study was first-grade students of SDN Gugus 10 Lebong Regency. The sample in this study was class I SDN 54 Lebong and Class I SDN 16 Lebong. The sampling technique uses Cluster Random Sampling. The total sample of 52 students consisted of class I SDN 54 Lebong totaling 25 students as the experimental class and class I SDN 16 Lebong totaling 27 students as the control class. The research instrument used was in the form of a knowledge learning test in the form of Multiple Choices given through Pretest and Posttest, for the learning outcomes aspects of attitudes and skills the instruments used were observation guidelines. The data analysis technique in this study is quantitative analysis using descriptive statistics and inferential statistics. Based on the results of inferential statistical analysis using the t-test. Based on the results of this study it is known that the value of t-count on SBDP subjects is 5.43 and in Indonesian Language subjects is 2.65 greater than t-table at a 5% significance level of 1.675. Thus the Ha hypothesis is proven and accepted. This shows that there are influences on student learning outcomes in Thematic learning that use discovery learning models assisted with pictorial word card media.