Reviewerly/exHarmony huggingface dataset:
The Learning Strategy for Indonesian Grammar: Students’ Perspective on Indonesian Language for Foreign Speakers

Foreign students face challenges in Indonesian language learning, especially grammar. Thus, they can use grammar learning strategies to understand grammar. However, research on grammar learning strategies from BIPA students’ points of view is still limited. In filling this gap, this study aims to analyze (1) strategies for learning Indonesian grammar for BIPA students and (2) the pedagogical implications of learning strategies for grammar for BIPA students. This research is a quantitative descriptive case study. The quantitative data were obtained from Grammar Learning Strategies (GLS) questionnaire. Furthermore, verbal data from respondents was obtained from semi-structured interviews. The research participants were 30 foreign students (BIPA students). Data were analyzed by using Likert-scale descriptive statistics and qualitative analysis consisting of transcription, codification, interpretation, validation, and drawing conclusions. This study found that the strategy most frequently used by BIPA students was social strategy (mean=4.28), followed respectively by metacognitive strategy (mean=4.15), cognitive strategy (mean=4.13), and affective strategy (mean=3.37). The pedagogical implication was that grammar learning needs to be designed by a contextual-communicative approach so that it can accommodate the preference of BIPA students’ learning strategies.
Estimation on Human Motion Posture using Improved Deep Reinforcement Learning

&lt;p&gt;Estimating human motion posture can provide important data for intelligent monitoring systems, human-computer interaction, motion capture, and other fields. However, the traditional human motion posture estimation algorithm is difficult to achieve the goal of fast estimation of human motion posture. To address the problems of traditional algorithms, in the paper, we propose an estimation algorithm for human motion posture using improved deep reinforcement learning. First, the double deep Q network is constructed to improve the deep reinforcement learning algorithm. The improved deep reinforcement learning algorithm is used to locate the human motion posture coordinates and improve the effectiveness of bone point calibration. Second, the human motion posture analysis generative adversarial networks are constructed to realize the automatic recognition and analysis of human motion posture. Finally, using the preset human motion posture label, combined with the undirected graph model of the human, the human motion posture estimation is completed, and the precise estimation algorithm of the human motion posture is realized. Experiments are performed based on MPII Human Pose data set and HiEve data set. The results show that the proposed algorithm has higher positioning accuracy of joint nodes. The recognition effect of bone joint points is better, and the average is about 1.45%. The average posture accuracy is up to 98.2%, and the average joint point similarity is high. Therefore, it is proved that the proposed method has high application value in human-computer interaction, human motion capture and other fields.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Research on Dangerous Area Personnel Detection Algorithm Based on 5G Cloud Edge Collaboration

To strengthen the safety protection of personnel in deep water dangerous areas such as reservoirs and rivers, Relying on the technical characteristics of 5G network technology, such as high speed, low delay, high bandwidth, and high flexibility, Build a video surveillance network architecture; The cloud-edge collaboration technology based on 5G network sinks the cloud intelligent analysis and computing power to the edge side, The edge end realizes the comprehensive perception and real-time analysis of video data, Accelerate the analysis of real-time data, Form a scientific and effective decision-making and intelligent analysis; The cloud is responsible for storing the high-value data uploaded on the edge side, And applied the data to the training of the deep-learning models, Finally, the trained model is distributed to the edge terminal; In this paper, by replacing the SiLU activation function of the YOLO V5s original model as the Mish activation function, Improve the detection and identification rate of remote personnel in the video surveillance area; Improving the weighting factor for non-maximum suppression is for the CIoU function, Further improve the positioning and identification of personnel in the image, Finally, realize the detection and identification of personnel in dangerous areas and remote alarm, Avoid safety accidents.
Cluster-HSFL: A Cluster-Based Hybrid Split and Federated Learning

Federated learning (FL) and split learning (SL) are two machine learning paradigms that can distributedly train artificial intelligence (AI) models while guaranteeing client privacy. FL can parallelly train AI models based on clients’ dataset, but it suffers from considerable communication overhead for model uploading and imposes a significant on-device computation workload. SL can mitigate the computation workload of the devices by offloading part of the AI model training to an edge server, but it suffers from long training delay for a large number of clients. To address these challenges, in this paper we present a cluster-based hybrid split federated learning (Cluster-HSFL) scheme by integrating FL and SL paradigms. This scheme can reduce training latency and alleviate on-device computational constraints while preserving accuracy performance. We establish a testbed for the Cluster-HSFL scheme based on the NVIDIA Jetson series hardware. The experimental results demonstrate that the Cluster-HSFL surpasses FL and SL by 6.3× and 1.8×, respectively, in terms of delay performance. Our results provide insight into the practical performance of the Cluster-HSFL scheme and can help guide future experimental research on SL in resource-limited wireless networks.
Subcarrier Modulation Classification for MIMO-OFDM System with Non-Gaussian Noise

Many existing works have applied distribution test to solve the modulation classification problem and achieved good results. There are various test statistics for distribution test, such as KS test statistic, CvM test statistic, and AD test statistic. Most of the work only considers using one type of test statistic for modulation classification. In addition, most existing literature assumes that the additive noise is Gaussian noise while actual system noise exhibits obvious spike pulse characteristics. This paper proposes a method based on joint distribution test and Bayesian fusion to proceed with subcarrier modulation classification (SMC) of MIMO-OFDM system with non-Gaussian noise. We use these three test statistics together by constructing a joint distribution function for the three test statistics. Using Bayesian theory and maximum likelihood estimation, we find the mode that makes the joint probability maximum as the classification result by solving the posterior probability under different modes. Simulation results show that the proposed method has good results and robustness for SMC of MIMO-OFDM system.
Innovative Teaching Methods and Materials Design in College English Education

In the context of increasing globalization and digitalization, innovations in English teaching methods and materials design play a crucial role in meeting students' needs, improving learning outcomes, and adapting to social and technological developments. This article provides a comprehensive overview of these innovative approaches and designs in university English education, revealing the characteristics and limitations of existing teaching methods and materials, and analyzing them in light of students' and teachers' attitudes and feedback. The article further explores diversified teaching strategies and technology-driven teaching methods, describing their effectiveness and feedback.
Research on Cloud Computing Security Based on Virtualization Security Technology

In order to overcome the problems of high intrusion rate and low encryption depth of traditional cloud computing security research methods, this paper proposes a new cloud computing security research method based on Virtualization security technology. First, analyze the principle of virtualization technology, and build a cloud computing resource transmission model to understand the whole cloud computing process. Secondly, the chaotic mapping method is used to transform cloud computing data to obtain binary chaotic sequence data, and realize the security encryption of cloud computing data to further ensure the security of cloud computing data. Finally, build the cloud computing data encryption and decryption algorithm to complete the cloud computing security research. Experimental results show that compared with traditional methods, the proposed method can effectively improve the security of cloud computing and reduce the intrusion rate.
A Human-Object Interaction Behavior Recognition Algorithm Based on Transformer

Human-object interaction behavior recognition is a high-level computer vision task for computers to understand the content of images in real scenes. Thanks to the introduction of Transformer and DETR, the performance of the algorithms for human-object interaction recognition has improved significantly in the first stage, however, the existing models still suffer from inadequate feature extraction and model bias. Based on this, we optimize and improve the algorithm's performance in terms of both fine-grained contextual information extraction and dynamic weighting. We conducted training and testing on the HICO-DET dataset and found through extensive experiments that (1) our proposed fine-grained contextual information-based Human-object interaction behavior recognition algorithm improves the performance of “Full”, “Rare” and “Non-Rare” mAP by 2.48%, 7.42% and 1.44% respectively compared with the original algorithm. (2) During model training, we introduced dynamic weights to improve the optimization of the model for rare Human-object interaction behavior, which can effectively improve the model performance. These experimental results validate the feasibility, effectiveness and adaptability of our proposed fine-grained module, global context extraction module and dynamic weight training strategy.
A Cluster Comparative Learning Based Abnormal Detection Method for Power IoT Terminals

The widespread application of electric power IoT faces a large number of terminal devices sense access due to the complexity of terminal devices and differences in the threat of attack on the power grid is increasingly severe, in order to better ensure the security of the power IoT underlying terminals, this paper proposes a clustering-based comparative learning method for the detection of abnormal behavior of power IoT terminals. First, the sample data are extracted by the scalable self-organizing mapping network model. The mapping is completed to output the data with corresponding feature dimensions, and the data are downscaled and enhanced by the deep self-encoder. Then, it is input to the clustering comparison learning model (SwAV), and the positive and negative sample detection model is constructed by learning to group sample data with similar characteristics. Finally, the results of this model are input to the encoder for anomaly detection of unknown terminal data. The comparison experiments show that this scheme has good superiority and stability compared with other detection models.
Evaluation Semantic Classification Based on Convolution Neural Network

Convolutional neural network has a good effect on Natural language processing. We extract customer reviews from the hotel reservation system for the past 3 years. Obtained 6000 different evaluation results. We classified these results and obtained three different attitudes: good, bad, and neutral. In the dataset, 70% of them trained in a self-built convolutional network, and 20% used for validation. Train a 4-layer deep network. Test with 10% to achieve the expected results. Compared with English deep networks, Chinese semantic analysis has slightly different processing methods. Following the principle of “cutting sentences into words and fixing coding”, the deep network model well applied in the hotel service system.
PERCEPTION OF HUMAN EXPRESSION, AGE AND GENDER RELATED VARIATIONS OF EMOTIONRECOGNITION BASED ON LIVE CAMERA IMAGES IN DEEP LEARNING USING PYTHON

Now-a-days online marketing is rapidly increasing. As a growth, we capture the customer images through web camera. Depending on their age, gender and expression of mood easily continue our product marketing in online. Automated face detection methods are substantial for facial identification, head position analysis, and human-computer communication. Face detection is a method for detecting and analyzing a human face in a webcam image. Identifying gender and oldness has now established itself as a key step in numerous human-machine communications. It can be used in advertising, security, and all other areas where gender and age recognition help them better to do the task given to them. Gender, oldness and mood of user forecast is the initial tool for any system which utilize it to scale their performance through personalized communication with the user of certain age and gender and mood of customers to improve our business online itself with high preference.
Research on the Cultivation of High School Students' High Order Thinking Ability Based on the Theory of Performance Evaluation

High order thinking abilities in Chinese language for high school students include analysis, evaluation, and creativity, which possess intrinsic and non-visual characteristics. Performance evaluation can visualize thinking through performance tasks and scoring criteria, accurately evaluate and enhance students' high order thinking abilities in Chinese language based on their performance. When evaluating and cultivating high school students' high order thinking abilities in Chinese language using performance evaluation, teachers need to establish clear performance objectives, design performance tasks that align with students' high order thinking, and make the performance evaluation rules transparent to students, encouraging their participation in the development of evaluation criteria.
In search of dissipative time crystals via arrays of self-sustained oscillators. Taming quantum phase diffusion by coherent delay feedback

We show feasibility of time-crystalline states in arrays of self-sustained quantum oscillators. Delayed coherent feedback helps towards this goal, which we investigate with a novel coherent-state representation Langevin formalism that we test against known results.
Teleporting a 15-dimensional state with a nonlinear spatial detector

We present the teleportation of high-dimensional information with a single entangled pair and a nonlinear detector. We demonstrate a 15-dimensional teleportation channel for arbitrary spatial modes, exceeding the classical limit and setting a new state-of- the-art.
Photonic Interference Beyond Two Modes

We propose multipartite Bragg-scattering to perform all-to-all transformation among N frequency modes, realizing a bosonic N-level system. We demonstrate the N = 3 case illustrating a pathway towards scalability for frequency-domain optical quantum information systems.
Photonic Orbital Angular Momentum for Novel Protocols in QKD and Certified Deletion

We have shown the versatility of orbital angular momentum by performing two new quantum communication protocols: a high-dimensional extension to the two-dimensional round robin differential phase shift QKD, and high-dimensional quantum encryption with certified deletion.
Charityhowto.com webinar explores donor engagement with LinkedIn

The nonprofit resources website Charityhowto.com webinar explores donor engagement with LinkedIn will host a webinar titled How To Connect, Engage And Expand Your Donor Pool With LinkedIn: A Step By Step Guide, on Oct. 14 at 1:00 p.m. EST.
FAIR Principles for Research Software Released

Cross-posted on 21 June 2022. FAIR Principles for Research Software Released June 21, 2022 FAIR4RS Working Group Steering Committee We are pleased to announce the release of version 1.0 of the FAIR Principles for Research Software (FAIR4RS Principles), after about 2 years of work by over 500 people. This work has been organized by the FAIR4RS Working Group, which was jointly convened by the Research Data Alliance (RDA), the Research Software Alliance (ReSA), and FORCE11.
FAIR for Research Software (FAIR4RS): A summary

August, 2022 Authors: Daniel S. Katz, Tom Honeyman, Paula Andrea Martinez, Michelle Barker, Leyla Jael Castro, Neil Chue Hong, Morane Gruenpeter, Jennifer Harrow, Anna-Lena Lamprecht, Carlos Martinez-Ortiz, Fotis Psomopoulos Introduction This blog post describes the need for, the work towards, and the recent completion of version 1.0 of the FAIR Principles for Research Software (FAIR4RS Principles v1.0). It also briefly discusses the principles themselves, and how they differ from the FAIR...
The Construction and Application of the SPOC Blended Teaching Model of College English under the Concept of Maker Education

The accelerated development of technological revolution and industrial change has created a new ecological environment for language learning, and the social demand for comprehensive and applied talents has made new demands on university English teaching. Based on the concept of Maker education, this article develops a SPOC blended teaching model of university English and applies it to teaching. 136 learners of an English course in a university are taken as the research object, and the application effect of the developed teaching model is verified and analyzed. The results show that the model has a significant influence on improving students' English language application ability, learning motivation, teacher-student interaction and learning interest.
A brief analysis of the self-therapy documentary

To analyze the self-therapy documentary, the thesis firstly introduces the knowledge of trauma and the healing process based on the theory of psychoanalyst Lacan, which laid the theoretical foundation for the documentary. Then it tracks the originate of documentary and the development of self-therapy film, to see the growth of this subtype film from the prototype. To illustrate the style of the self-healing documentary more, there are two film examples listed to detailed interpretate the making of therapeutic film.
BLOCKCHAIN TECHNOLOGY IN THE INDIA’S FISHERIES INDUSTRY: CURRENT SCENARIO

Blockchain technology has emerged as a transformative tool with the potential to revolutionize industries across the globe, and the fisheries industry is no exception. This abstract provides a concise overview of the current scenario of blockchain technology implementation in the fisheries sector.&#x0D; The fisheries industry is critical in providing food security, livelihoods, and economic growth to numerous communities worldwide. However, the industry has been plagued by illegal, unreported, and unregulated (IUU) fishing, mislabeling, and lack of transparency throughout the supply chain. Blockchain technology offers a decentralized and transparent platform to address these challenges by enabling secure and tamper-proof data storage and sharing.&#x0D; In conclusion, the fisheries industry stands to benefit significantly from the integration of blockchain technology. The current scenario reflects a growing awareness of the potential of blockchain to address longstanding issues of traceability, transparency, and sustainability. As the industry continues to explore and implement blockchain solutions, collaboration among stakeholders and ongoing technological advancements will play a pivotal role in shaping its transformative impact.
Random pruning: channel sparsity by expectation scaling factor

Pruning is an efficient method for deep neural network model compression and acceleration. However, existing pruning strategies, both at the filter level and at the channel level, often introduce a large amount of computation and adopt complex methods for finding sub-networks. It is found that there is a linear relationship between the sum of matrix elements of the channels in convolutional neural networks (CNNs) and the expectation scaling ratio of the image pixel distribution, which is reflects the relationship between the expectation change of the pixel distribution between the feature mapping and the input data. This implies that channels with similar expectation scaling factors ($\delta _{E}$) cause similar expectation changes to the input data, thus producing redundant feature mappings. Thus, this article proposes a new structured pruning method called EXP. In the proposed method, the channels with similar $\delta _{E}$ are randomly removed in each convolutional layer, and thus the whole network achieves random sparsity to obtain non-redundant and non-unique sub-networks. Experiments on pruning various networks show that EXP can achieve a significant reduction of FLOPs. For example, on the CIFAR-10 dataset, EXP reduces the FLOPs of the ResNet-56 model by 71.9% with a 0.23% loss in Top-1 accuracy. On ILSVRC-2012, it reduces the FLOPs of the ResNet-50 model by 60.0% with a 1.13% loss of Top-1 accuracy. Our code is available at: https://github.com/EXP-Pruning/EXP_Pruning and DOI: 10.5281/zenodo.8141065 .
Preface

This volume of Journal of Physics: Conference Series presents the proceedings of the 1 st International Conference on Mechanics, Electronics, Automation and Automatic Control (MEAAC2023). The conference was held in Wuhan, China during May 13-15, welcoming around 50 attendants physical and online from home and abroad. MEAAC2023 organized discussions on hot topics including intelligent manufacturing, robot system control, and smart cities and focused on the challenges during research as well as application. The conference consisted of one physical conference in the morning and one virtual conference in the afternoon, showcasing various sessions such as keynote speeches, oral reports, poster presentations, Q&amp;A, etc. We had the utmost pleasure of having with us experts and scholars from around the globe sharing their latest findings and insights. The MEAAC conference is conceived in the belief of promoting academic exchange within and across disciplines, addressing theoretical and practical challenges and advancing current understanding and application, during the process of which amity is spread, connections established and future collaborations enabled. The MEAAC organizing committee extend their sincerest gratitude to all who have supported the conference in their ways, to the authors who have chosen this platform to publish their works and communicate with peers, to the participants who took an interest and attended the conference in person and/or online, to the chairs and committee members who have been indispensable in lending their professional expertise and judgment, to the keynote speakers who generously shared their vision and passion, and to the reviewers who held up the faith of being a scholar and contributed their experience and honest opinions. It has been a pleasure and honor working alongside them, and we look forward to future cooperation with them at future MEAAC conferences to come. List of Conference Chair, Conference Co-chair, TPC Chair, Technical Program Committee are available in this Pdf.