Reviewerly/exHarmony huggingface dataset:
Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization

Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to “track the moving optima”, rather than solving the changed problems from scratch. Its hidden assumption is that the problems are slightly changed in general, and it is expected that the search process can be accelerated by learning from past search experiences. However, the hidden assumption that the current search process can always benefit from previous search experiences might not be true for certain cases. For example, if the current problem is not similar to any of its previous problems, the historical solutions might not be helpful for the current search. To reduce such negative transfer, the following issues are worthy of study: how to choose which historical problems to learn from, and how to determine the degree of learning from historical problems. To solve the above problems, we propose a dynamic processing framework based on fuzzy rules from the perspective of incorporating human knowledge for optima tracking. The experimental results on the DOPF problems show that the SSA with the proposed optima tracking strategy outperforms other comparative algorithms. We open up the code and data of our algorithm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> https://github.com/DMiC-Lab-HFUT/SMDE-Transfer.
Soft Robotic Mastication System Inducing Misperception of Food Texture During Human Oral Processing

The perception of food texture in humans heavily relies on the way of biting and the condition and arrangement of teeth. Consequently, changes in dentures or biting speed can create an illusion of different textures for the same food. Like-wise, in mastication robots, variations in control parameters and probe geometry can lead to differences in acquired data, resulting in texture misidentification. This study aims to bridge the gap in texture perception between humans and machines, and make their misperception of texture more similar. To achieve this, the possibility of replicating human texture perception by machines was investigated using a highly accurate texture discriminator called “Gel Biter”, which comprises multiple soft materials. The findings indicate that misperception due to variations in mastication speed leads to misdiscrimination with a tendency similar to that in humans. Additionally, it was discovered that different contact between the tooth-shaped probes can cause a completely different texture perception. The potential contributions to food development using human dental techniques and food texture illusion are also discussed.
Exemplar-Free Continual Learning in Vision Transformers via Feature Attention Distillation

In this paper, we propose a new approach for continual learning based on the Visual Transformers (ViTs). The purpose of continual learning is to address the catastrophic forgetting problem. One method for preventing forgetting previous tasks is exemplar replay. However, exemplar replay has limitations such as limited memory capacity, large storage requirements, and difficulty adapting to new tasks, which restrict its practical application. Therefore, we adopted knowledge distillation to implement exemplar-free continual learning. This approach transfers the knowledge of old tasks to the model's prior knowledge, helping the model to learn new tasks better while not increasing the storage burden. Based on this, we propose a new method for training the ViT model called Feature Attention Distillation (FAD). Specifically, we introduced a feature attention mechanism into the knowledge distillation model to help the student model better retain meaningful feature representations and attention distributions during the learning process of new tasks by passing attention information between the teacher and student models, thereby improving the learning efficiency and performance of the model. In addition, we also introduced a new normalization method called Continual Normalization (CN) into the student model. This method dynamically calculates the sample mean and variance of the current and historical tasks, allowing the student network to adapt better to the feature distribution changes between different tasks, thus improving the model's generalization ability and robustness. Extensive experiments on the CIFAR100 and ImageNet-32 datasets show that our exemplar-free method is competitive in performance compared to rehearsal-based ViT methods.
MAPPO-Based Optimal Reciprocal Collision Avoidance for Autonomous Mobile Robots in Crowds

This paper proposes an improved Optimal Reciprocal Collision Avoidance (ORCA) algorithm for robot navigation in crowds using a deep reinforcement learning algorithm, Multi-agent Proximal Policy Optimization (MAPPO). The original ORCA algorithm allows the robot to compute escape velocities to escape from collisions with humans and then gets a set of collision-free velocities based on the reciprocal assumption. But humans usually don't follow the assumption, and thus ORCA performs badly. In this paper, we propose a MAPPO-based method to explore the optimal escape velocity for the robot to each human and use an estimation module to decide the effect of each escape velocity on the robot's new velocity. The proposed method is evaluated in simulated crowds of humans, and the original ORCA algorithm with different configurations are used as baselines. Simulation results show the proposed method achieves a significantly higher navigating success rate with almost the same time consumption compared with the original ORCA algorithm in robot navigation among crowds.
Training Minimal Complexity Support Vector Machines with Multiple Kernels

The minimal complexity support vector machine (ML1 SVM), which is a fusion of the standard support vector machine (L1 SVM) and the minimal complexity machine (MCM), works to improve the generalization ability over the L1 SVM. In this paper, we introduce a three-kernel structure into the ML1 SVM. We call it ML1 SVM (MK). We speed up model selection by three-stage cross-validation: In the first stage, we determine the hyperparameters for the L1 SVM (MK) fixing the hyperparameters for multiple kernels. In the second stage we determine the hyperparameters for the multiple kernels, and in the third stage, we determine the hyperparameter for the ML1 SVM (MK) for controlling the maximum margin. We also discuss a method for accelerating the second stage of model selection. In the computer experiment using two-class and multiclass problems, the ML1 SVM (MK) showed statistically comparable to, or better than the L1 SVM (MK), ML1 SVM, and L1 SVM.
An Overview of Blockchain-Based Application in Internet of Things (IoT)

The Internet of Things (IoT) is a very crucial aspect of Computing, and it fosters the interconnection of physical nodes on the Internet via enabling them to interact and share data. However, the potentials for security and privacy breaches increase as the number of connected devices/nodes (in the network) rises. Blockchain technology, with its capacity to provide secure and tamper-proof data storage, possess the potentials to mitigate the aforementioned vulnerabilities in the IoT. Hence, this paper gives a detailed review of the present state of blockchain-based applications in the IoT as well as the prospective benefits of employing blockchain technology with the aim/goal of securing IoT devices and its infrastructure. Also mentioned in this paper are the obstacles, research gaps, etc., currently impeding the implementation of some blockchain-based technologies; and the potential solutions toward surmounting these challenges.
An Augmented Cooperative Setting for Training the Embodiment of an Artificial Lower Limb

Literature highlights how virtual and augmented settings offer engaging solutions to improve one's feeling of an artificial limb embodiment. In this paper, we explored the potential of a setting for Spatial Augmented Reality (SAR, where a display augments a surface without making the user wear any visor) in two conditions of a lower limb ownership training involving subjects without disabilities. In the first condition, the subject must contract the quadriceps of a leg for commanding (through electromyography, EMG) a virtual leg (a 3D model of the Hybrid Knee prosthesis) to kick a virtual wall: each collision corresponds to a vibratory feedback on the thigh (a position defined for upcoming tests with transfemural amputees). The second condition adds a social context to engage the user: the subject is asked to cooperate with another (fictional) player to kick on the same virtual wall. Subjective (through questionnaires) and objective (according to the number of kicks as a performance index, and the proprioceptive drift as an embodiment index) assessments have been performed before a rubber leg illusion test. Overall, we observed how the cooperative task can engage the subject to be more active. However, this condition can reduce the impact of the training on the embodiment itself, probably because the social task generates a distraction. Nevertheless, such findings suggest the possibility to alternate these two tasks in the same session to increase the duration of a prosthetic embodiment training.
A Social Recommendation Model Based on Mining Timing Information and Enhancing Item Neighborhood Relationships

With the advancement of the Internet, Graph Neural Networks based recommendation systems have become a topic of great concern in the research field. However, the current recommendation systems still have the following problems. First, it focuses on modeling users but ignores the problems of missing values of item rating vectors and non-corresponding positions of item rating vectors in the process of solving associated items; second, it focuses on the association relationship between users but pays less attention to the association relationship between items; third, there is insufficient research on the short-term attractiveness of items and the users' temporary preferences. To address the above problems, this study proposes the following solutions to better construct the item social graph and extract the short-term interest/attraction of users/items. Firstly, for problem one, this study reconstructs the item rating vector innovatively based on whether users have interaction with the items; secondly, for problem two, this study proposes to use Pearson similarity to calculate the association relationship between items so as to construct the item social graph. Again, for problem three, This paper investigates temporal information features and extracts short-term user preferences and item attractiveness. To achieve this, an attention network that focuses on temporal information features is constructed by combining channel attention and bidirectional long-term and short-term memory networks. Finally, it involves using a multilayer perceptron with a residual connection structure to combine user and item factors, leading to more accurate predictions. In this study, two publicly available datasets, Epinions and Ciao, were used in a comparative experiment. This model outperformed other baseline models in the experiment, resulting in a reduction of 1.42% and 1.24% in MAE values, and 1.47% and 1.38% in RMSE values, respectively. These findings suggest that incorporating short-term preferences and reconstructing item social graphs can enhance the precision of social recommendations.
Surpass Teacher: Enlightenment Structured Knowledge Distillation of Transformer

It is difficult to train a trustworthy transformer model on a small image classification dataset. This research proposes a sophisticated structured knowledge distillation algorithm that uses CNNs as Transformer's sophisticated teachers, significantly lowering the number of training datasets needed. To better to develop the potential for CNN tutors, this research configures a public data set for CNN teaching as an enlightenment textbook to guide Transformer's training and avoid falling into local optimization prematurely. The distillation process then employs a “learn-digest-self-distillation” learning strategy to enable the Transformer to assimilate CNN knowledge in a structured manner. Sufficient experiments show that the proposed method is significantly better than the direct training Transformer under the condition of limited data sets. Moreover, in order to show the practical application value, this research contributed a practical data set for the classification of smoking and calling. The corresponding code and dataset will be released at https://gitee.com/wustdch/surpass-teacher if this paper is accepted.
Statement-Level Software Bug Localization Based on Information Retrieval and Spectrum

According to whether the program under test is executed, software bug localization methods can be divided into static bug localization and dynamic bug localization. Among them, Information Retrieval-based Bug Localization (IRBL) and Spectrum-based Fault Localization (SFL) are widely used static and dynamic bug localization methods, respectively. But the localization granularity of IRBL is coarse and the localization accuracy of SFL is easily reduced by the information which is unrelated to the bug. In order to refine the localization granularity of IRBL and improve the localization accuracy of SFL, this paper proposes ISBL (Combine Information Retrieval and Spectrum for Bug Localization), a statement-level software bug localization method based on information retrieval and spectrum. Firstly, the suspicious files are filtered using information retrieval technique, and then the suspicious files are used to reduce spectrum information for statement-level bug localization. To evaluate the performance of ISBL, experiments were conducted on the Defects4J dataset, and MRR and TOP@N were used as metrics for evaluation. As the experimental results show, for MRR, ISBL increased 3.0% and 3.1% compared to Ochiai and DStar, respectively; for TOP@1, ISBL locates 4 more bug statements than Ochiai and DStar.
Two-Stage Aspect Sentiment Quadruple Prediction Based on MRC and Text Generation

In recent years, aspect sentiment quadruple prediction (ASQP) has become popular in aspect-based sentiment analysis (ABSA). Its purpose is to decode a given sentence into aspect sentiment quadruples (aspect category, aspect term, opinion term, and sentiment polarity). When trying to efficiently extract aspect sentiment quadruples, the following problems are often encountered: Firstly, the intrinsic relationships between aspect terms and opinion terms are usually ignored, thus failing to address the correlation between establishing aspect-opinion pairs and ignoring the mutual interference between different sentiment quadruples; Secondly, the semantic information contained in the sentiment elements of comment utterances is often underutilized, thus increasing the risk of obtaining inaccurate predictions. We propose a two-stage framework to address these issues by enhancing the correlations between aspects and opinions and fully utilizing the semantic information of sentiment elements. Specifically, in the first stage, we treat the extraction task as a machine reading comprehension (MRC) problem, employ a span-based labeling scheme, and construct a question-and-answer-based MRC task to efficiently extract aspect-opinion pairs. In the second stage, we view the classification of aspect categories and sentiment polarities as a text generation task, where the semantics of sentiment elements can be leveraged by learning to generate them in natural language form. Finally, the two stages are combined with our proposed template generator, and the aspect sentiment quadruples can be decoded. We conducted experiments on two datasets, and the experimental results were superior to those of the comparison approaches, with our model demonstrating excellent performance in terms of processing complex sentences containing multiple quaternary groups. Additionally, in sub-task experiments, our model achieved good results, further proving its effectiveness. We will upload the specific code to https://github.com/SlienceLZJ/ASQP.
Creation of Delicious Mixed Juices for Multiple Users based on Distributed Interactive Genetic Algorithm

Creating media content suited to many users' feelings is essential in a situation with multiple users' participation. Interactive Evolutionary Computation (IEC) is a search method that finds good solutions; however, it is originally for each user. Previous studies proposed that IEC creates mixed juices for two users. Allowing many users' participation contributes to finding good solutions suited to many users and will support developing good products. This study aims to propose an IEC in which trio of users can participate. The elite Genetic Algorithm individuals were exchanged between the users in this method. A concrete system based on the proposed method was constructed, and the experiment was conducted with the system. Fifteen students participated in the experiment. Changes in the fitness values and search space were investigated. Furthermore, the exchanged individuals were compared with the individuals created by the examinee's population by their fitness values. The statistical analyses showed that the proposed method could find good mixed juices for the trio of examinees, and the exchange of the elite individuals had the potential for an efficient search.
Intelligent Genetic Crossover Algorithm for Improving State Estimation in Particle Filtering

Particle filtering (PF) is a Monte Carlo (MC) method widely employed to sequentially approximate the time-varying posterior PDF of states or values of variables of interest conditional to noisy observation data from non-linear/non-Gaussian dynamic systems. In many applications, we also infer (or estimate) the hidden state from the obtained approximated posterior PDF. Particles are randomly drawn samples of state vectors that form the approximation of the posterior PDF. However, representation of the approximated posterior PDF may not always be good because sometimes there can be only a few particles with low likelihoods (or importance weights), while there can be many particles with near-zero (or negligible) weights. New state vectors with high weights then should be introduced to the swarm in order to reshape the approximated posterior PDF. This paper introduces an intelligent genetic crossover algorithm (IGCA) that assists PF by applying crossover schemes employed in genetic algorithms (GAs) to reshape the approximated posterior PDF. Experimental results shown that the proposed algorithm improved the accuracy and performance in nonlinear state estimation.
Location Independent Automated Dual-Axis Solar Tracking Based on Astronomical Calculations

Solar power is among the cleanest forms of renewable energy, and various methods have been proposed to increase its energy output. Among these proposals are control methods for the solar panel to maximize energy output. This paper presents the design of a dual-axis solar tracking system to maximize the power output at different times of day and geographical locations. The system calculates the optimal solar tracking angles ahead of time using the astronomical and geometrical relationships. These pre-calculated angles are stored and used to position the panel every 30 minutes without the need for photocells. The system uses brush-less DC motors with PID controllers for precise tracking. Calculations are done for three cities in Iran to showcase the system’s adaptability to the location. Results show the smooth tracking responses and flexibility across variable solar angles throughout the day and year.
Evaluating Usability and Immersion Experiences in Virtual Reality Platforms: A Case Study Focused on the Ancient City of Chiang Saen

This academic study evaluates the usability and immersion factor with the use of Virtual Reality (VR) platforms; computer as a WebGL and the Oculus Quest 2 as VR device on Chiang Saen Ancient City’s historical VR environment. Wat Pa Sak, known as the impressive architecture and design, displaying the craftsmanship. To implement the VR application involved the process of research the related application, consulting with experts in the historical field, design characters and environments, development application, and design the evaluation. Therefore, this research focused on evaluating the user experience in three factors: presence, usability, and immersion. The experiment was conducted on two groups of elementary school students. Group 1 experienced the virtual world with WebGL platform, while group 2 experienced the virtual environment with Oculus Quest 2 device. The study’s findings indicate that utilizing Virtual Reality through the Oculus Quest 2 platform resulted in a good user experience in terms of usability. Furthermore, users reported an enhanced feeling of immersion within the Virtual environment compared to their experiences with WebGL platform.
Decrypting Criminal Transaction Patterns in Cryptocurrency

This paper introduces an approach to detect a thief’s wallet by analyzing linked transactions connected to centralized exchanges mandating KYC verification. The proposed methods are designed to provide valuable insights into the intricate flow of funds and the behavioral patterns of malicious actors, explicitly focusing on thieves. By inputting a thief’s wallet address, our analysis examines associated transactions, capturing vital details such as the amount of Ethereum (ETH) transferred and all related transactions, including those involving ERC-20 tokens. The primary objective is comprehending the money flow among different addresses, emphasizing wallets interacting with centralized exchanges. By monitoring the movements of ETH and ERC-20 tokens, this analysis aims to identify suspicious transactions and uncover behavioral patterns indicative of illicit activities. Leveraging this analytical approach, we can track the thief’s identity, making a substantial contribution to the prevention and detection of cryptocurrency theft, thus ensuring the security of blockchain ecosystems.
Visual Navigation for Obstacle Avoidance Using Deep Reinforcement Learning with Policy Optimization

In order for mobile robots to navigate through indoor environments without colliding, they must be able to detect and avoid obstacles. In recent years, memory-based Deep Reinforcement Learning approaches have gained increasing popularity as they enable robots to navigate more safely as a result of a long sequence of information acquired over time. As the indoor environment has a partial observability problem due to its complexity, obtaining sufficient information from its properties remains an ongoing challenge. To retain relevant information about the structure of the environment over time, LSTM has been incorporated into a network architecture to implement a memory-based Deep Reinforcement Learning method. As part of this method, LiDAR data are fused with grayscale images captured by a monocular camera to obtain the information necessary to identify obstacles, and the final features are used to develop an effective obstacle avoidance policy. According to the assessment results, the approach demonstrated remarkable performance in terms of average accumulated reward and success rate.