Reviewerly/exHarmony huggingface dataset:
Applying Robust Gradient Difference Compression to Federated Learning

Nowadays, federated learning has been a prevailing paradigm for large-scale distributed machine learning, which is faced with the problem of communication bottleneck. To solve this problem, recent works usually apply different compression techniques such as sparsification and quantization compressors. However, such approaches are all lossy compression and have two drawbacks. First, they could lead to information loss of the global parameter. Second, compressed parameters carrying less information would be more likely to be attacked by malicious workers than full parameters, leading to a Byzantine failure of the model. In this paper, to avoid information loss, mitigate the communication bottleneck, and at the same time tolerate popular Byzantine attacks, we propose FedGraD, which leverages gradient difference compression and combines robust aggregation rules in federated learning settings. Our experimental results on three different datasets a9a, w8a and mushrooms show good performance of our method.
An Improved LSTM based Method for User Behavior Classification in VR Scenes

Time series classification is a common machine learning problem. With the rapid growth of the virtual reality (VR) industry, the number of users participating in VR is increasing rapidly, and analyzing user behavior in VR scenes is becoming increasingly important. Compared to general time series classification tasks, the input sources in VR are more complex and diverse, and traditional time series classification models struggle to handle this complexity. In this study, we propose a multi-source time series classification network that utilizes an attention mechanism to combine multiple input sources such as left and right controllers, head-mounted displays, and buttons. By focusing on key features using an attention mechanism, we can provide more accurate information and improve the overall performance. Our approach achieved high accuracy in user action classification tasks in VR scenes.
BIFLC: A Blockchain and IPFS-Based Multi-Consensus Federated Learning Framework

Federated learning is an efficient technology that implements distributed model training among multiple data sources with local data, and can realize data privacy protection and data sharing computing .However, existing federated learning models may involve a large number of external attacks that can reconstruct the original training data using the acquired model, resulting in possible global model or user privacy data attacks. To address the above problem, we propose a new decentralized multiconsensus federated learning model by combining blockchain and interplanetary file system (IPFS), named as BIFLC. To be specific, we firstly design an on-chain consensus process based on a blockchain hybrid consensus mechanism by introducing a proof- of-work (PoW) and a proof-of-stake (PoS) mechanism, which can ensure the integrity of the on-chain consensus process and provide a chained data hash index for data. Furthermore, we introduce the interplanetary file system to reduce the cost of storing data on the chain and employ its distributed content delivery mechanism to save bandwidth. Extensive experiments demonstrate that our proposed scheme has higher accuracy and lower IPFS transmission time.
An Aspect Level Sentiment Analysis Method for Graph Convolutional Networks based on Improved Dependencies

Aspect-level sentiment analysis can be more delicate to grasp the user’s emotional tendency, has a wide range of applications in various fields. It has been proved that the method of combining dependency relationship with graph convolutional neural network can be used to improve the effect of aspect-level sentiment analysis. However, user comments are generally conducted from multiple perspectives, resulting in multiple aspects in sentences, and longer comments usually contain more effective information. This will make parsing sentence dependencies more complicated, and generate a large number of parameters in the convolution process, resulting in noise and other problems. Therefore, it is necessary to choose and choose the information and leave the effective information for sentiment analysis. This paper proposes a graph convolution network joint model to improve the dependency relationship(AIDE-GCN). By improving the dependency tree, this model builds multiple matrices and vectors for effectively screening sentences, so that the weight matrix is constructed with the aspect word as the center. When realizing the sentiment analysis of multi-aspect words, only the target dependence is kept, so that the model magnitude is smaller and the accuracy is guaranteed. Through experiments on public datasets SemEval series, Twitter series and more challenging MAMS series, it is proved that the proposed model has obvious advantages.
A Framework Using Absolute Compression Hard-Threshold for Improving The Robustness of Federated Learning Model

Nowadays, with the popularity of the federated learning, it becomes crucial for us to tackle the challenges, communication cost and model robustness. And targeting at the communication bottleneck, data compression is widely used to solve the problem. Besides, the usage of variance reduction for achieving robustness and communication compression for reducing costs has been studied. The Byz-VR-MARINA pro- posed before uses random-sparsification. In this paper, we adopt the absolute compressors hard-threshold and propose a robust compressed framework Byz-VR-BARRY. Experimental results on w8a and a9a datasets have shown the effectiveness of our method, which can decrease the optimality gap obviously.
A New Targeted Online Password Guessing Algorithm Based on Old Password

Password authentication is a widely used identity authentication method for computer supported cooperative systems. However, the frequent occurrence of password leakage incidents has become a universal problem, and the leaked passwords seriously threaten the security of users’ unleaked passwords. In order to gain a deeper understanding of the relationship between users’ old passwords and new passwords and help users choose a securer new password when their old passwords are leaked, we propose a new targeted online guessing algorithm, Targuess-II <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> , based on old password in this article. As a new probabilistic algorithm, Targuess-II <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> not only supports the application of strong transformation rules at any positions in a password, but also shows the transformation process from one password to another. Our analysis and experimental results have demonstrated that Targuess-II <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> obtains better performance in terms of crack rate and efficiency compared with other existing algorithms.
Electrical Fault Diagnosis via Text Mining: A Weakly-Supervised Learning Model

To automatically and accurately identify electrical fault categories by means of data-driven approaches has attracted much attention in recent years. However, the research on fault diagnosis by mining unstructured data such as text data is far from being perfect. One problem lies in that a large number of human-labeled samples is often costly and difficult to obtain in real applications. In this study, we present a novel Label Embedding joint with Weakly-supervised Classification model (LEMWEC) for predicting fault category given the fault-descriptive corpus. Our model leverages a few labeled documents and a good number of pseudo-labeled documents to learn both sentence embeddings and the multi-label classifier. Then, the model will be iteratively refined by the newly generated pseudo documents. Experiments on a real-life dataset demonstrate the LEMWEC can improve the accuracy of electrical fault classification remarkably compared with a comprehensive set of baselines.
Understanding Military Collaboration in Wargames

The military has developed wargames to explore their doctrines and train operational concepts. Wargames are often designed to teach educational objectives and share and produce knowledge. The success of a wargame design depends on how it achieves these objectives. Players are the main actors in wargames and often group into teams. The final result of the wargame depends on the collaboration and interaction between the members of each team. Therefore, this work aims to understand how collaboration occurs in wargames. Moreover, we observed the activities and processes during a large-scale wargame to gather how the players collaborate. A better understanding of collaboration in wargames may provide insights for game designers to improve the wargames design process to consider how the players collaborate.
Implementation and analysis of Adaptive Neural Fuzzy Inference System based Brushless DC motor

This paper aims to model the brushless DC motor (BLDC) and design the adaptive neuro-fuzzy controller (ANFIS) to provide speed control of the brushless DC motor (BLDC). The method applied in this work is first to simulate the PID controller (proportional, integral, and derivative) and then design the neuro-fuzzy adaptive controller using the ANFIS toolkit for the BLDC motor. According to the simulation carried out on Matlab/Simulink, the response of the PID-controlled system does not reach the required performance whereas the ANFIS controller allows for appropriate control parameters and more straightforward calculations. Indeed, the PID involves significant oscillations and an overshoot of the setpoint. Contrary to the ANFIS controller, which smoothly follows the setpoint without overshooting. Therefore, the ANFIS-based intelligent controller gives more relevant results than conventional controllers.
Multi-Feature Fusion Based Approach for Classifying Encrypted Mobile Application Traffic

With rapid development of mobile Internet, a great number of mobile applications has emerged, presenting a great explosion in mobile Internet traffic. Therefore, accurate classification of application traffic is necessary to more effectively manage mobile Internet traffic. However, the encryption of mobile application traffic gradually eliminates traditional classification approaches based on specific signatures, greatly increasing the difficulty of the classification of mobile application traffic. Therefore, we propose a novel multi-feature fusion (MFF)- based approach to enhance the accuracy of mobile application traffic classification. We also extract packet length sequence, byte sequence, statistical feature, etc. Then, we perform weighted fusions of features based on Relief-F algorithm to achieve the best set of features. Finally, we use machine learning techniques for application classification. Compared to several other feature extraction methods, MFF achieves an excellent performance with an accuracy of 97.6% for 16 mobile applications and a F1-score of over 99% for VPN-nonVPN.
Inspecting Traffic in Residential Networks with Opportunistically Outsourced Middleboxes

Home networks lack the powerful security tools and trained personnel available in enterprise networks. This complicates efforts to address security risks in residential settings. While prior efforts explore outsourcing network traffic to cloud or cloudlet services, such an approach exposes that network traffic to a third party, which introduces privacy risks, particularly where traffic is decrypted (e.g., using Transport Layer Security Inspection (TLSI)). To enable security screening locally, home networks could introduce new physical hardware, but the capital and deployment costs may impede deployment. In this work, we explore a system to leverage existing available devices, such as smartphones, tablets and laptops, already inside a home network to create a platform for traffic inspection. This software-based solution avoids new hardware deployment and allows decryption of traffic without risk of new third parties. Our investigation compares on-router inspection of traffic with an approach using that same router to direct traffic through smartphones in the local network. Our performance evaluation shows that smartphone middleboxes can substantially increase the throughput of communication from around 10 Mbps in the on-router case to around 90 Mbps when smartphones are used. This approach increases CPU usage at the router by around 15%, with a 20% CPU usage increase on a smartphone (with single core processing). The network packet latency increases by about 120 milliseconds.
Teacher Competency Model in Human Resource Management: International Economic and Legal Experience in Chinese Universities under Digitalization

The article deals with the aim to demonstrate the necessity of the implementation the teacher competency model as the innovative tool in human resource management.The authors achieved this goal by researching the international legal and economic experience in the context of digitalization, as well as its application in the Chinese educational sphere.Various methods of scientific research were used by the authors to establish universal scientific standards for the selection, evaluation, training and management of teachers' work.Using the survey method, the interview method and the expert survey method, the authors identified and summarized certain terms regarding the competencies of university teachers.Using the statistical research method, the reliability of the model was evaluated.Factor analysis was used to verify the certainty of the structural model.The application of the Delphi method provided the expert group with 30 important competencies.Respondents to the expert survey were teachers from the local universities in 8 provinces in the Eastern, Central and Western China.The method of factor analysis of data allowed the authors to form their own model of competence of teachers at the Chinese universities.The model proposed by the authors has: 1) four dimensions: basic qualities, pedagogical competence, research competence and competence of social service; 2) contains 22 indicators of second-level competence, which, unlike existing models, takes into account all research results and doesn't ignore the possibility of employees' professional development, even in the case of low rates of every person.The applying of the proposed model on practice will allow HR professionals not only to pay attention to the current assessment of the performance of university teachers, but also to be the effective tool for comprehensive assessment of teachers' competencies under conditions of digitalization.
Action recognition algorithm from multimodal images for human-robot collaboration systems

Automation of production processes using robots is a priority for developing many industrial enterprises. Human-machine interaction is a key component of such control infrastructure. The proposed algorithm is a four-stage procedure: (a) fusion information from multimodal sensors based on the quaternion model, (b) image preprocessing using a 3D Gabor filter, (c) a descriptor calculation using 3D local binary dense micro-block difference with skeleton points, and (d) classification. The proposed algorithm is based on capturing 3D sub-volumes located inside a video sequence patch and calculating the difference in intensities between these sub-volumes; for intensified motion, used the convolution with a bank of 3D arbitrarily oriented Gabor filters and calculating 3D local binary dense micro-block difference. A program was developed for transmitting information about the target points of the robot's movement through a virtual TCP / IP port and a script for working out the target points in the simulation environment. To test the effectiveness of the proposed algorithm, we simulate an action recognition system for Human-robot collaboration in the RoboGuide environment.
THE MYTH OF METAPHOR SYMBOLS IN SPEECH I HAVE A DREAM

The speech I Have A Dream is Martin Luther King's political speech, delivered in front of the Lincoln Memorial on August 28, 1963. In the text of speech there are several metaphorical symbols (language style) in conveying his message and protest attitude against the American legal and political system which is considered unfair. To fully understand the meaning and message of the speech, the research question is "What is the meaning of the mythical metaphorical symbol in Martin Luther King’s I Have A Dream?" In analyzing the metaphorical symbols in the speech, the researcher used Roland Barthes' semiotic approach and the research procedure used descriptive qualitative form. The results of this study found that the metaphorical symbols in I Have A Dream's speech used various syntactic patterns, namely: 1) using adjective word patterns such as symbolic shadow, beacon light, flames of withering, long night; 2) using noun patterns such as manacles, chains, island, ocean, languished, land, shameful, valley, hill, mountain, crooked place; 3) use a verb pattern as in the phrase to blow off steam, a rude awakening, returns to business
PithaNet: A transfer learning-based approach for traditional pitha classification

&lt;span lang="EN-US"&gt;Pitha, pithe, or peetha are all Bangla words referring to a native and traditional food of Bangladesh as well as some areas of India, especially the parts of India where Bangla is the primary language. Numerous types of pithas exist in the culture and heritage of the Bengali and Bangladeshi people. Pithas are traditionally prepared and offered on important occasions in Bangladesh, such as welcoming a bride grooms, or bride, entertaining guests, or planning a special gathering of family, relatives, or friends. The traditional pitha celebration and pitha culture are no longer widely practiced in modern civilization. Consequently, the younger generation is unfamiliar with our traditional pitha culture. In this study, an effective pitha image classification system is introduced. convolutional neural network (CNN) pre-trained models EfficientNetB6, ResNet50, and VGG16 are used to classify the images of pitha. The dataset of traditional popular pithas is collected from different parts of Bangladesh. In this experiment, EfficientNetB6 and ResNet50 show nearly 90% accuracy. The best classification result was obtained using VGG16 with 92% accuracy. The main motive of this study is to revive the Bengali pitha tradition among young people and people worldwide, which will encourage many other researchers to pursue research in this domain.&lt;/span&gt;
Thematic analysis and development of a conceptual framework for agile meetings' problems

For Agile methodologies, it is extremely important to have a well-established communication system. In addition to the daily meetings of the project team members, there are also meetings before the start and after the end of each development cycle, when the client needs to express his opinion, proposals, and suggestions based on the delivered part of the product. Due to the great importance of having meetings, it is necessary to analyze the problems related to meetings. In this paper, inductive thematic analysis of the data obtained through a systematic review of the literature was carried out. The analysis was carried out using the software tool QDA Miner Lite by creating codes to determine the themes that represent problems in Agile meetings. Through thematic analysis, we want to show which problems occur at meetings in an Agile environment. Finally, we develop and present a conceptual framework encompassing the problems found in Agile meetings. This literature review offers a conceptual framework with identified key themes found in the literature: Meetings last too long, information is not shared with all team members, negative attitudes toward meetings, and less time is spent on tasks. The conceptual framework aims to help researchers and industry practitioners by presenting the issues. Developing a conceptual framework is important because if the problems presented in the mentioned framework are noticed in time, we believe that it is possible to intervene in time and solve a certain problem, which would increase the efficiency of work. Researchers can see how to create a conceptual framework based on the thematic analysis of data as well as extend this conceptual framework with more problems that may potentially arise. Based on the research findings, we discuss research implications and propose directions for future research.
POLITICAL ACTIVIST COMMUNITY PORTRAIT IN LEILA S. CHUDORI’S NOVEL LAUT BERCERITA

In the process of democratization in a state, the position of university students is decisive. Every student must be responsible to the society, able to see what is happening and to provide the best solutions to the societal problems as well as being a mouthpiece from the common people to the government, nation and state. In addition to developing their cognitive abilitie0s, students are required to participate actively in everything that is social in nature to foster a social spirit in the community. This picture is reflected in the political activist community in the novel Laut Bercerita by Leila S. Chudori. The research was conducted using a literary sociology approach related to the author's relationship with society and texts revealing problems in society. The focus of the research is related to the disclosure of social phenomena. Holistically, a qualitative descriptive research method is implemented. The data source is the novel and data collection is based on documentation techniques, obtained from the novel in the forms text referring to the attitudes of the students in their struggle for reform in all areas of public life and also based on the content analysis techniques with an objective approach. The results of the study show that the attitudes reflected in the political activist movement are social awareness, hard work and tolerance.