Reviewerly/exHarmony huggingface dataset:
Navigation Path Following Platform for a Greenhouse Shuttle Robot Using the State-flow Method

Authors:
raw_author_name: Donkyu Baek
raw_affiliation_string: Chungbuk National University, Cheongju-si, Republic of Korea
raw_author_name: Seong-gon Choi
raw_affiliation_string: Department of Electrical and Computer Engineering, Chungbuk National University, Chungdae-ro, Seowon-gu, Cheongju-si, Chungcheongbuk-do 28644, Republic of Korea

Abstract: Localization is an important method for autonomous indoor robots to recognize their positions. Generally, the navigation of a mobile robot is conducted using a camera, Lidar, and global positioning system. However, for an indoor environment, GPS is unavailable. Therefore, a, state-trajectory tracking method is utilized based on a Lidar map. This paper presents the path following of an autonomous indoor mobile robot, that is, a shuttle robot, using a state-flow method via a robot operating system network. MATLAB and Linux high-level computers and an inertial measurement unit sensor are used to obtain the Cartesian coordinate information of a bicycle-type mobile robot. The path following problem can be solved in the state-flow block by setting appropriate time and linear and angular velocity variables. After the predetermined time, the linear and angular velocities are set based on the length of the path and radius of the quarter-circle of the left and right turns in the state-flow block, path planning, which can execute the work effectively, is established using the state-flow algorithm. The state-flow block produces time-series data that are sent to Linux system, which facilitates real-time mobile platform path following scenario. Several cases within the path-following problem of the mobile robot were considered, depending on the linear and angular velocity settings: the mobile robot moved forward and backward, turned in the right and left directions on the circular path. The effectiveness of the method was demonstrated using the desktop-based indoor mobile robot control results. Thus, the paper focuses on the application of the state-flow algorithm to the shuttle robot specifically in the narrow indoor environment.
Zhang neural networks: an introduction to predictive computations for discretized time-varying matrix problems

Authors:
raw_author_name: Frank Uhlig

Abstract: Abstract This paper wants to increase our understanding and computational know-how for time-varying matrix problems and Zhang Neural Networks. These neural networks were invented for time or single parameter-varying matrix problems around 2001 in China and almost all of their advances have been made in and most still come from its birthplace. Zhang Neural Network methods have become a backbone for solving discretized sensor driven time-varying matrix problems in real-time, in theory and in on-chip applications for robots, in control theory and other engineering applications in China. They have become the method of choice for many time-varying matrix problems that benefit from or require efficient, accurate and predictive real-time computations. A typical discretized Zhang Neural Network algorithm needs seven distinct steps in its initial set-up. The construction of discretized Zhang Neural Network algorithms starts from a model equation with its associated error equation and the stipulation that the error function decrease exponentially fast. The error function differential equation is then mated with a convergent look-ahead finite difference formula to create a distinctly new multi-step style solver that predicts the future state of the system reliably from current and earlier state and solution data. Matlab codes of discretized Zhang Neural Network algorithms for time varying matrix problems typically consist of one linear equations solve and one recursion of already available data per time step. This makes discretized Zhang Neural network based algorithms highly competitive with ordinary differential equation initial value analytic continuation methods for function given data that are designed to work adaptively. Discretized Zhang Neural Network methods have different characteristics and applicabilities than multi-step ordinary differential equations (ODEs) initial value solvers. These new time-varying matrix methods can solve matrix-given problems from sensor data with constant sampling gaps or from functional equations. To illustrate the adaptability of discretized Zhang Neural Networks and further the understanding of this method, this paper details the seven step set-up process for Zhang Neural Networks and twelve separate time-varying matrix models. It supplies new codes for seven of these. Open problems are mentioned as well as detailed references to recent work on discretized Zhang Neural Networks and time-varying matrix computations. Comparisons are given to standard non-predictive multi-step methods that use initial value problems ODE solvers and analytic continuation methods.
Technologies of literary and methodical teacher education

Authors:
raw_author_name: G. V. Pervova
raw_affiliation_string: Derzhavin Tambov State University

Abstract: Importance. The purpose of the study is to reflect the search for the most productive ways of teaching literature. To achieve this goal, it is necessary to include modern technologies in the methodology of literary reading in primary and secondary schools to prepare teachers for effective teaching. Research Methods. The technologization methods of the educational process are related to the typical features’ understanding of technology, the classification of technologies in pedagogy and, in particular, in didactics, with the development of educational and training technologies that are available to primary school teachers and teachers of philology. Results and Discussion. The technologies of multilevel perception of the text by the teacher, complex analysis of the work in preparation for the lesson, achievement of expressive reading of the text, technology of co-creative reading, discrete reading, immersion in the artistic text, preparation for retelling, carrying out dictionary work, etc. have been developed and tested in teaching practice. Special attention should be paid to the technology of artistic and speech accompaniment of children’s life activities during the day, which has not only an educational, but also a healthsaving, educating character. Each technology cannot be described in detail within the scope of the study, therefore, the list of references contains sources containing detailed theoretical justifications of technologies and multiple examples of applications. Conclusion. The conclusion of the study on the technologization of the educational process is related to the self-education of the teacher, who is currently provided with a variety of opportunities for this.
Formal Security Analysis of ISA100.11a Standard Protocol Based on Colored Petri Net Tool

Authors:
raw_author_name: Tao Feng
raw_affiliation_string: School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China

Abstract: This paper presents a formal security analysis of the ISA100.11a standard protocol using the Colored Petri Net (CPN) modeling approach. Firstly, we establish a security threat model for the ISA100.11a protocol and provide a detailed description and analysis of the identified security threats. Secondly, we use the CPN tool to model the protocol formally and conduct model checking and security analysis. Finally, we analyze and discuss the results of the model checking, which demonstrate that the ISA100.11a standard protocol may have vulnerabilities when certain security threats exist, and provide some suggestions to enhance the security of the protocol. This research provides a certain level of security assurance for the ISA100.11a standard protocol and serves as a reference for similar security research on protocols.
Dual-side Adversarial Learning based Fair Recommendation for Sensitive Attribute Filtering

Authors:
raw_author_name: Yu Zhang
raw_affiliation_string: Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Lingzhi Yi
raw_affiliation_string: School of Information and Safety Engineering, Zhongnan University of Economics and Law, Wuhan, China
raw_author_name: Xianjun Deng
raw_affiliation_string: Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Laurence T. Yang
raw_affiliation_string: Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Bang Wang
raw_affiliation_string: Hubei Key Laboratory of Smart Internet Technology, School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China

Abstract: With the development of recommendation algorithms, researchers are paying increasing attention to fairness issues such as user discrimination in recommendations. To address these issues, existing works often filter users’ sensitive information that may cause discrimination during the process of learning user representations. However, these approaches overlook the latent relationship between items’ content attributes and users’ sensitive information. In this paper, we propose a fairness-aware recommendation algorithm (DALFRec) based on user-side and item-side adversarial learning to mitigate the effects of sensitive information in both sides of the recommendation process. Firstly, we conduct a statistical analysis to demonstrate the latent relationship between items’ information and users’ sensitive attributes. Then, we design a dual-side adversarial learning network that simultaneously filters out users’ sensitive information on the user and item side. Additionally, we propose a new evaluation strategy that leverages the latent relationship between items’ content attributes and users’ sensitive attributes to better assess the algorithm’s ability to reduce discrimination. Our experiments on three real datasets demonstrate the superiority of our proposed algorithm over state-of-the-art methods.
Secure Aggregation Protocol Based on DC-Nets and Secret Sharing for Decentralized Federated Learning

Authors:
raw_author_name: Diogo Pereira
raw_affiliation_string: National Laboratory for Scientific Computing, Petrópolis 25651-075, RJ, Brazil
raw_author_name: Paulo Ricardo Reis
raw_affiliation_string: National Laboratory for Scientific Computing, Petrópolis 25651-075, RJ, Brazil
raw_author_name: Fábio Borges
raw_affiliation_string: National Laboratory for Scientific Computing, Petrópolis 25651-075, RJ, Brazil

Abstract: In the era of big data, millions and millions of data are generated every second by different types of devices. Training machine-learning models with these data has become increasingly common. However, the data used for training are often sensitive and may contain information such as medical, banking, or consumer records, for example. These data can cause problems in people’s lives if they are leaked and also incur sanctions for companies that leak personal information for any reason. In this context, Federated Learning emerges as a solution to the privacy of personal data. However, even when only the gradients of the local models are shared with the central server, some attacks can reconstruct user data, allowing a malicious server to violate the FL principle, which is to ensure the privacy of local data. We propose a secure aggregation protocol for Decentralized Federated Learning, which does not require a central server to orchestrate the aggregation process. To achieve this, we combined a Multi-Secret-Sharing scheme with a Dining Cryptographers Network. We validate the proposed protocol in simulations using the MNIST handwritten digits dataset. This protocol achieves results comparable to Federated Learning with the FedAvg protocol while adding a layer of privacy to the models. Furthermore, it obtains a timing performance that does not significantly affect the total training time, unlike protocols that use Homomorphic Encryption.
An adaptive dynamic window approach for UUV obstacle avoidance planning in 3D environments

Authors:
raw_author_name: Changjian Lin
raw_author_name: Yuhao Liu

Abstract: Abstract Unmanned Underwater Vehicles (UUVs) are essential equipment for Marine development, widely used in Marine scientific research, Marine resource survey, and Marine security. The autonomous navigation planning ability in unknown environments is a critical indicator for UUV intelligence. This paper focuses on the particularity of UUV motion and the complexity of the underwater environment and proposes an adaptive Dynamic Window Approach (DWA) for UUV obstacle avoidance planning. The adaptive DWA introduces novel heading angle evaluation and adaptive dynamic strategies to solve the problems of poor adaptability of traditional DWA to complex environments and unreasonable path selection in encountering dynamic obstacles and approaching targets. Simulations verify the effectiveness and superiority of the proposed method.
Academic community’s use of university digital library and their searching behaviour

Authors:
raw_author_name: Kanwal Ameen
raw_author_name: Alia Arshad

Abstract: Purpose This study aims to explore the evidence-based usage patterns of higher education commission (HEC) subscribed e-journal databases in the university digital library used by the scholarly community and the academics’ online searching behaviour at a higher education institution in Pakistan. Design/methodology/approach The study used an explanatory sequential mixed methods approach. Raw transaction log data were collected for quantitative analysis, and the interview technique was used for qualitative data collection and thematic analysis. Findings Log analysis revealed that HEC subscribed databases were used significantly, and among those, scholarly databases covering various subjects were more frequently used than subject-specific society-based databases. Furthermore, the users frequently accessed the needed e-journal articles through search engines like Google and Google Scholar, considering them sources of free material instead of the HEC subscribed databases. Practical implications It provides practical implications for examining the evidence-based use patterns of e-journal databases. It suggests the need for improving the access management of HEC databases, keeping in view the usage statistics and the demands of the scholars. The study may also help create market venues for the publishers of scholarly databases by offering attractive and economical packages for researchers of various disciplines in developing and underdeveloped countries. The study results also guide the information professionals to arrange orientation and information literacy programs to improve the searching behaviour of their less frequent users and enhance the utilization of these subscribed databases. Originality/value The study is part of a PhD project and, to the best of the authors’ knowledge, is the first such work in the context of a developing country like Pakistan.
Distributed maximum correntropy unscented Kalman filter under hybrid attacks in non‐Gaussian environment

Authors:
raw_author_name: Ting Liu
raw_affiliation_string: Department of Electrical Engineering Shaoxing University  Shaoxing China
raw_author_name: Haiping Ma
raw_affiliation_string: Department of Electrical Engineering Shaoxing University  Shaoxing China
raw_author_name: Jiyuan Huang
raw_affiliation_string: Department of Electrical Engineering Shaoxing University  Shaoxing China
raw_author_name: Mei Yu
raw_affiliation_string: Department of Electrical Engineering Shaoxing University  Shaoxing China
raw_author_name: Dajun Du
raw_affiliation_string: Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation Shanghai University  Shanghai China

Abstract: Abstract This paper addresses a distributed nonlinear filtering issue based on maximum correntropy for dealing with randomly occurring hybrid cyber‐attacks in non‐Gaussian environment. The types of cyber‐attacks include denial of service attacks and deception attacks. First, a modified distributed unscented Kalman filter is proposed, using Cauchy kernel‐based maximum correntropy criterion instead of the traditional mean square error criterion, against cyber‐attacks and non‐Gaussian noise. Then based on fixed‐point iterative rules and information fusion strategy, the update equations of state estimates and covariance matrices of the proposed filter are obtained. Furthermore, the sufficient condition guaranteeing its convergence and computational complexity are also given. Finally, an illustrative example is presented to verify the effectiveness of the proposed filter under hybrid cyber‐attacks in non‐Gaussian environment.
A scoping review of gaze and eye tracking-based control methods for assistive robotic arms

Authors:
raw_author_name: Thomas M. Wendt
raw_affiliation_string: Faculty Economy, Work-Life Robotics Institute, University of Applied Sciences Offenburg, Offenburg, Germany.
raw_author_name: Kristof Van Laerhoven
raw_affiliation_string: Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany.

Abstract: Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.
Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

Authors:
raw_author_name: Yilei Wang
raw_affiliation_string: Research Institute, State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 311152, China
raw_author_name: Wenliang Xu
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
raw_author_name: Futai Zou
raw_affiliation_string: School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Abstract: Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities.
A customized ConvNeXt‐XL network with fusion of deep and handcrafted features for colposcopy image classification

Authors:
raw_author_name: Thendral Natarajan
raw_affiliation_string: Department of Electronics and Communication Engineering, Meenakshi College of Engineering Anna University  Chennai India

Abstract: Abstract Cervical cancer is the second most frequent cancer among women of all age groups worldwide. It occurs due to human papillomavirus. In the premature stages, the symptoms will not be predominant until they reach the final stage of cancer. Detection and classification of cervical cancer always demand gynecologists with the necessary skills and experience. The goal of the proposed work is to develop a deep learning framework to facilitate the automated classification of cervical cancer using colposcopy images. The following Deep Convolutional Neural Network (DCNN) models are proposed to detect cervical cancer and classify cervix‐type images. (i) the pre‐trained DCNNs, namely VGG16, ResNet50, InceptionV3, InceptionResNetV2, and ConvNeXtXLarge (ConvNeXt‐XL) using Softmax classifier based on deep features; (ii) the ConvNeXt‐XL model with classification using Support Vector Machine (SVM), K Nearest Neighbor (KNN) and Decision Tree (DT) based on deep features; (iii) a customized ConvNeXt‐XL network to enhance the classification accuracy using serially concatenated handcrafted and deep features. The research experiment was carried out separately using two datasets: the Cervix‐Type dataset (Type 1, Type 2, and Type 3) and the Real Time Cervical dataset (Normal and Abnormal). The simulation outcome confirms that the customized ConvNeXt‐XL helped to improve the classification accuracy with the Cervix‐Type dataset (&gt;97%) and Real Time Cervical dataset (&gt;98%).
Paguroidea: Fused Parser Generator with Transparent Semantic Actions

Authors:
raw_author_name: Yifan Zhu
raw_affiliation_string: University of Rochester, Rochester, USA

Abstract: Parser generators have long been a savior for programmers, liberating them from the daunting task of crafting correct and maintainable parsers. Yet, this much-needed simplicity often comes at the expense of efficiency.