Reviewerly/exHarmony huggingface dataset:
The Role of Virtual Reality Professional Experiences in Initial Teacher Education

The adoption of virtual reality professional experiences in initial teacher education programs has been expedited over the last few years. Virtual reality has provided alternative professional experiences for preservice teachers unable to attend traditional classrooms due to school closures and visitor restrictions due to COVID-19. Virtual reality has been used as a substitute for traditional professional experiences and to create valuable online professional experiences for preservice teachers. This chapter discusses the advantages and disadvantages of adopting these virtual reality professional experiences. These experiences can involve microteaching with virtual classrooms or responding to critical incidents. Immersing in classroom scenarios and critical incidents in virtual reality environments can help preservice teachers prepare for the challenges of their future face-to-face classrooms and for teaching online. There are, however, a number of challenges to the adoption of virtual professional experiences in initial teacher education programs. These challenges include the acceptance of virtual reality as a valuable experience, addressing potential short-term health risks, and ensuring access to relevant technologies. Teacher educators also need time to develop the skills needed to design realistic virtual reality classroom scenarios and incidents that can extend preservice teachers professional experiences. Future research needs to address these challenges.
Contextures of hate: Towards a systems theory of hate communication on social media platforms

We inquire into different perspectives and patterns of problematizing online hate speech within the social sciences from a systems-theoretical perspective. Our results identify five different research perspectives adopted by studies on the issue: (1) systematic perspectives on problems of operationalizing (online) hate speech; (2) intentionalist perspectives on actors and their motives; (3) consequentialist perspectives on victims of online hate speech; (4) perspectives on media affordances, infrastructures, and strategies of online hate speech; and finally, (5) normative perspectives on the consequences of online hate speech. Additionally, we want to propose a functionalist perspective on hate communication and, for this purpose, develop a systems-theoretical and media-sociological framework for analyzing online hate speech. A systems-theoretical perspective connects to a process-oriented paradigm of doing hate speech. Instead of asking what hate speech is, a systems-theoretical framework focuses on how different communicative contextures empirically produce different understandings of hate communication. We will make four research proposals: We will (1) conceptualize hate as hate communication, then proceed to (2) analyze different communicative contextures, (3) develop media archeology of negation and conflict communication, and finally (4) focus on the function of conflict and hate communication for the emergence of (counter-)publics.
An Improved Backward Smoothing Method Based on Label Iterative Processing

Effective target detection and tracking has always been a research hotspot in the field of radar, and multi-target tracking is the focus of radar target tracking at present. In order to effectively deal with the issue of outlier removal and track initiation determination in the process of multi-target tracking, this paper proposes an improved backward smoothing method based on label iterative processing. This method corrects the loophole in the original backward smoothing method, which can cause estimated target values to be erroneously removed due to missing detection, so that it correctly removes outliers in target tracking. In addition, the proposed method also combines label iterative processing with track initiation determination to effectively eliminate invalid target short-lived tracks. The results of simulation experiments and actual data verification showed that the proposed method correctly removed outliers and invalid short-lived tracks. Compared with the original method, it improved the accuracy of target cardinality estimation and tracking performance to a certain extent. Moreover, without affecting the algorithm performance, the method’s processing efficiency could be improved by increasing the track pruning threshold. Finally, the proposed method was compared with existing methods, verifying that its tracking performance was better than that of existing methods.
Detection of Jamming Attacks via Source Separation and Causal Inference

Jamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing.
Investigations on meta-heuristic algorithms for intelligent speed regulation of mobile robot

With technical advances in robotics and technology, mobile robots (MRs) have become more significant in medical applications that require great subtlety, accuracy and efficiency. MRs are primarily time-varying and nonlinear systems that require intelligent controller rather conventional controllers. When artificial intelligence (AI) is employed in MRs, they become intelligent or autonomous. So, metaheuristic algorithms firefly algorithm (FFA), genetic algorithm (GA), particle swarm optimization (PSO) and grey wolf optimization (GWO) have been investigated in this paper to get the optimal outcome and improve the performance of the MR controller, resulting in an overall system improvement. The study exhibit that GWO control is superior for time-varying and nonlinear MR than conventional control in terms of time response parameters and accuracy assessment.
Cuckoo search algorithm based on cloud model and its application

Cuckoo search algorithm is an efficient random search method for numerical optimization. However, it is very sensitive to the setting of the step size factor. To address this issue, a new cuckoo search algorithm based on cloud model is developed to dynamically configure the step size factor. More specifically, the idea of giving consideration to both fuzziness and randomness of cloud model is innovatively introduced into cuckoo search algorithm, and the appropriate step size factor can be determined according to the membership degree and an exponential function, so as to realize the adaptive adjustment of the control parameter. After that, simulation experiments are conducted on 25 benchmark functions with different dimensions and two chaotic time series prediction problems to comprehensively evaluate the superiority of the proposed algorithm. Numerical results demonstrate that the developed method is more competitive than the other five CS and several non-CS algorithms.
Revolutionizing Smart Contracts

The current study examines the challenges associated with smart contracts and their overall concept and offers a resolution that can effectively address all the prevailing issues. Moreover, it advocates for a shift in the current architecture employed in smart contracts and blockchain. The study identifies the problems, conducts an analysis, and presents a solution that expounds on how the proposed changes can enhance the performance of smart contracts based on a new architecture, boost their scalability, change the IoT world, and promote an eco-friendly environment by reducing pollution of natural environment and conserving energy.
Nearest neighbor-based approaches for multi-instance multi-label classification

Nearest neighbor-based methods are classic techniques that, due to their efficiency, still are widely used today. However, they have not been broadly applied to solve the multi-instance multi-label (MIML) problem, a supervised learning paradigm that combines multi-instance (MI) and multi-label (ML) learning. This work presents new neighbor-based approaches for solving MIML problems. On the one hand, MIML data are transformed into ML data and ML nearest neighbor algorithms are used. On the other hand, algorithms that directly address MIML data and use a bag-based distance are proposed. A comprehensive study and an overall comparison have been conducted to study the performance of these methods using different configurations. Experiments included 16 datasets and 8 performance metrics. The results and statistical tests showed that the problem transformation applied and the distance function used impacted the performance and that the approaches that do not transform the problem obtained the best predictive results. Furthermore, most of the proposed algorithms outperformed the MIMLkNN algorithm, the state-of-art algorithm for MIML learning based on nearest-neighbor. Therefore, the relevance and capabilities of neighbor-based approaches to obtain competitive results in MIML learning are shown. Finally, all the algorithms developed in this paper have been included in the MIML library to facilitate the comparison with other future proposals.
Structuring superconductor data with ontology: reproducing historical datasets as knowledge bases

Applying historical human-readable databases to recent data-driven science is a natural concept. However, this cannot be realized by simply converting a database into a tabular format because the meaning of each table column and the relationships between columns need to be rewritten in a machine-readable format. In particular, eliminating implicit notations that can only be understood by experts in the fields covered by each database and making them machine-readable under a unified academic system is necessary when integrating data across fields with a view to solving specific social issues and social implementation. In this study, we constructed a superconducting materials ontology for SuperCon, a legacy superconductor database that was recently republished as a datasheet, based on the well-known Basic Formal Ontology (BFO) top ontology, and designed a schema for material composition, structure, properties, and processes, among others. Using this schema, we constructed and published the Resource Description Framework (RDF) for each instance in the SuperCon datasheet. We also discuss the machine-readable format of data common to materials science discovered in this process.
IGAN: A collaborative filtering model based on Improved Generative Adversarial Networks for recommendation

In recent years, Generative Adversarial Networks (GANs) have been successfully applied to collaborative filtering for implicit feedback. However, the ability of GAN to learn user interest distributions is greatly reduced due to its difficulty in characterizing the features. In this paper, a collaborative filtering model based on Improved Generative Adversarial Networks (IGAN) is proposed to address the above issue. In IGAN, we first introduce the independent encoder and generator to learn features representation during adversarial training. Then, the Kullback–Leibler (KL) loss and reconstruction loss are added as the penalty term to further fit the interest distribution of users and achieve higher recommendation accuracy. Finally, this paper conducts extensive experiments on three real public datasets, showing that the IGAN method has a maximum improvement of 5.26% and 5.43% in P@5 and NDCG@5 compared with the current GAN-based methods.
A new hybrid path planning method for the sailboat architecture wave glider in the wind field environment

To ensure efficient sailing of the SWG in complex marine environments and maintain its controllability in upwind or downwind conditions, a hybrid path planning method is proposed aiming at path time consumption and safety in this paper. The method includes a weight heuristic A* algorithm and a WAPF algorithm. The kinematic and dynamic models of the SWG are established, and the rotary diameter, velocity polar diagram, rotation constraint and dangerous wind angle constraint are obtained. To make better use of the fixed wind field environment and improve the efficiency of the algorithm, a weight heuristic A* algorithm is proposed for global path planning. The algorithm uses the form of weight-heuristic and reduces useless search directions. To ensure navigation safety and overcome the shortcomings of traditional APF algorithm, a WAPF algorithm for local path planning is proposed by introducing kinematic constraints and wind potential field into the algorithm model. Simulations are carried out and the results show that the proposed weight heuristic A* algorithm and WAPF algorithm are more suitable for the SWG navigation in the wind field environment than traditional path planning algorithms. The effectiveness of the WAPF algorithm is verified by sea trial and simulation comparison.
FedraTrees: A novel computation-communication efficient federated learning framework investigated in smart grids

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing energy consumption information to perform centralised learning may compromise data privacy and make it vulnerable to misuse, in addition to incurring high transmission overhead on communication resources. This study addresses these issues by utilising federated learning (FL), an emerging technique that performs ML model training at the user/substation level, where data resides. We introduce FedraTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based FL stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedraTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.
Application of neuro-fuzzy ensembles across domains: A systematic review of the two last decades (2000–2022)

Neuro-fuzzy systems have received considerable attention from academia in the last decade. They can provide a tradeoff between the performance of artificial neural networks and the interpretability of fuzzy inference systems expressed through fuzzy rules. Single techniques do not always provide the optimal performance, especially when the results are susceptible to changes in the data or hyperparameters. Therefore, ensemble learning can be used to build a more stable and robust model. In this paper, a systematic literature review of studies evaluating neuro-fuzzy ensembles was performed to highlight the importance of ensemble learning and its role in improving the performance of neuro-fuzzy systems. Many aspects are highlighted, including publication years, sources, contribution types, application domains, single neuro-fuzzy systems, ensemble techniques, and the overall performance and interpretability. A total of forty-eight articles published from 2000 to March 2022 addressing the use of neuro-fuzzy ensembles in different engineering applications were selected and analyzed. Results show that: (i) Takagi–Sugeno–Kang neuro-fuzzy systems are the most evaluated category, especially the ANFIS single. (ii) Homogeneous​ ensembles, in particular boosting, are the most investigated. (iii) Logical, relational and ANFIS ensembles are outperforming. And (iv) The interpretability of neuro-fuzzy ensembles was not thoroughly investigated, but based on the number of rules, logical systems were the best.
Communication Analysis and Privacy in CAI Based on Data Mining and Federated Learning

Due to the fact that client data do not need to leave the local area, a distributed machine learning framework can aggregate training from several clients while preserving data privacy. In this essay, the development of CAI both domestically and internationally is reviewed and summarized, and the current state of CAI is examined. Communication analysis has so far been a key academic and theoretical area in federated learning, and some theoretical contributions have become the crucial theoretical foundations for understanding, defending, and guiding various human social behaviors. The major objective of knowledge distillation based on model responses is to provide students the ability to rapidly replicate the teacher’s model’s output. The experimental results demonstrate that the optimized Smith Regan model adopts the “Smith Logan” teaching design model, selects the courseware structure and record preservation as the teaching content in the fundamental CAI courseware design, and optimizes the teaching design from the perspectives of learning environment analysis, learner characteristics analysis, etc. Based on this, the model’s accuracy and robustness are increased by 7.34%.
Imperfect-Information Game AI Agent Based on Reinforcement Learning Using Tree Search and a Deep Neural Network

In the field of computer intelligence, it has always been a challenge to construct an agent model that can be adapted to various complex tasks. In recent years, based on the planning algorithm of Monte Carlo tree search (MCTS), a new idea has been proposed to solve the AI problems of two-player zero-sum games such as chess and Go. However, most of the games in the real environment rely on imperfect information, so it is impossible to directly use the normal tree search planning algorithm to construct a decision-making model. Mahjong, which is a popular multiplayer game with a long history in China, attracts great attention from AI researchers because it contains a large game state space and a lot of hidden information. In this paper, we utilize an agent learning approach that leverages deep learning, reinforcement learning, and dropout learning techniques to implement a Mahjong AI game agent. First, we improve the state transition of the tree search based on the learned MDP model, the player position variable and transition information are introduced into the tree search algorithm to construct a multiplayer search tree. Then, the model training based on a deep reinforcement learning method ensures the stable and sustainable training process of the learned MDP model. Finally, we utilize the strategy data generated by the tree search and use the dropout learning method to train the normal decision-making agent. The experimental results demonstrate the efficiency and stability performance of the agent trained by our proposed method compared with existing agents in terms of test data accuracy, tournament ranking performance, and online match performance. The agent plays against human players and acts like real humans.
Erasure of Unaligned Attributes from Neural Representations

Abstract We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which erases information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example. Our algorithm works by alternating between two steps. In one, it finds an assignment of the input representations to the information to be erased, and in the other, it creates projections of both the input representations and the information to be erased into a joint latent space. We test our algorithm on an extensive array of datasets, including a Twitter dataset with multiple guarded attributes, the BiasBios dataset, and the BiasBench benchmark. The latter benchmark includes four datasets with various types of protected attributes. Our results demonstrate that bias can often be removed in our setup. We also discuss the limitations of our approach when there is a strong entanglement between the main task and the information to be erased.1
Knowledge Graph Completion with Triple Structure and Text Representation

Abstract Knowledge Graphs (KGs) describe objective facts in the form of RDF triples, each triple contains sufficient semantic information and triple structure information. Knowledge Graph Completion (KGC) is to acquire new knowledge by predicting hidden relationships between entities and adding the new knowledge to the KG. At present, the mainstream KGC approaches only applied the triple structure information or only utilized the semantic information of the text. This paper proposes an approach (TSTR) using BERT and deep neural networks to fully extract the semantic information of knowledge, and designs an aggregated re-ranking scheme that incorporates existing graph embedding approach to learn the structural information of triples. In experiments, the approach achieves state-of-the-art performance on three benchmark datasets, and outperforms recent KGC approaches on sparsely connected datasets.
Adversarial Attack for Deep Steganography Based on Surrogate Training and Knowledge Diffusion

Deep steganography (DS), using neural networks to hide one image in another, has performed well in terms of invisibility, embedding capacity, etc. Current steganalysis methods for DS can only detect or remove secret images hidden in natural images and cannot analyze or modify secret content. Our technique is the first approach to not only effectively prevent covert communications using DS, but also analyze and modify the content of covert communications. We proposed a novel adversarial attack method for DS considering both white-box and black-box scenarios. For the white-box attack, several novel loss functions were applied to construct a gradient- and optimizer-based adversarial attack that could delete and modify secret images. As a more realistic case, a black-box method was proposed based on surrogate training and a knowledge distillation technique. All methods were tested on the Tiny ImageNet and MS COCO datasets. The experimental results showed that the proposed attack method could completely remove or even modify the secret image in the container image while maintaining the latter’s high quality. More importantly, the proposed adversarial attack method can also be regarded as a new DS approach.