Reviewerly/exHarmony huggingface dataset:
Binomial Distribution Assisted Individual Selection for Differential Evolution

Mutation plays a crucial role in assisting differential evolution (DE) to effectively solve optimization problems. The key to mutation lies in the selection of parent individuals participating in the mutation. Along this road, this paper devises a binomial distribution-assisted individual selection strategy for DE. Specifically, this paper takes advantage of the probability distribution function of the binomial distribution to assign weights to individuals based on their fitness rankings. In this way, the selection of individuals focuses more on medium better individuals instead of the top best ones. Therefore, high mutation diversity can be preserved and thus it is likely that falling into local regions can be effectively avoided. Embedding this selection strategy into DE, a novel DE variant called binomial distribution assisted DE (BDDE) is developed. Experiments conducted on the CEC2017 benchmark suite have verified the effectiveness of BDDE in solving optimization problems. Particularly, BDDE gains much better performance against the well-known and representative mutation strategies.
Efficient Policy Learning Based on Pretrained Vision-Language Model Representations

Reinforcement learning is a powerful framework for training intelligent agents to make optimal decisions in various environments. Several recent studies have analyzed and discussed the efficacy of utilizing pretrained language model features within the reinforcement learning paradigm. In this paper, we examine and investigate the impact of employing pretrained vision-language models, specifically the contrastive language image pretraining (CLIP) approach, in the policy gradient method. It is found that the intrinsic rewards will have a negative impact on learning with pretrained model features in some complex environments. To address this issue, we introduce a self-imitation guided policy gradient (SGPG) method to avoid using intrinsic rewards while enhancing learning efficiency and performance. This approach involves self-imitation learning to train a guiding policy, employs policy gradient techniques to train the target policy, and utilizes a KL-divergence to ensure the action distribution of the target policy closely approximates that of the guiding policy. The rationale behind learning two distinct policies is to prevent the simultaneous optimization of disparate objectives within a single policy learning process, which could result in suboptimal performance. In our experiments, we assess the proposed model using several sparse reward procedurally generated tasks and partial observations as inputs. The results show the effectiveness of incorporating pretrained model features in the policy gradient approach, and empirically validate the superior performance of our proposed method compared to baseline techniques and some recent methods. Some limitations of using pretrained model feature are also found in high-dimensional environments, which is discussed and points out the further work.
Multi-Granularity Interest Learning for Click-Through Rate Prediction

Embedding&MLP paradigm based on deep learning has been widely used in click-through rate prediction tasks. Such approaches compress user features into a fixed-length vector, which causes the bottleneck of interest learning and makes it difficult to express diverse interests of users. To improve the accuracy and diversity of recommendation, more comprehensive user interest learning is required. We present Multi-Granularity Interest Learning(MGIL)—combining the memorization of user's historical preferences with generalization of user's interests. Our fine-grained interest module learns interests at the granularity of items, preserving the memory of users' historical preferences as much as possible. The coarse-grained interest module, on the other hand, extracts multiple high-level interests from user behavior sequences to characterize multiple aspects of user interest, which are more generalized and abstract. The two modules complement each other from different granularities. We conduct extensive experiments on three real-world datasets, Movielens, Taobao and Amazon. Experimental results demonstrate that our method significantly improves the accuracy and diversity of recommendation compared to state-of-the-art models.
Optimal Sharding for Dynamic Throughput Optimization in Blockchain Systems with Deep Reinforcement Learning

The rapid advancement in blockchain technology has enabled its applications across wide spectrum of fields. The blockchain throughput, which is usually measured by Transactions Per Second (TPS), is one of the key metrics to reflect the performance of the blockchain systems. However, current blockchain systems have low TPS rates that makes them unsuitable for latency critical applications like Vehicle-to-vehicle (V2V) communication. To address the above issue, the sharding technology, which divides the network into multiple disjoint groups so that transactions can be processed in parallel, is applied to the blockchain systems as a promising solution to improve TPS. This paper considers the Optimal Blockchain Sharding (OBCS) problem which is formulated as a Markov Decision Process (MDP) where the decision variables are the number of shards, block size and block interval. Previous works solved the OBCS problem via Deep Reinforcement Learning (DRL) based methods where the action space has to be discretized such that it is not too large for tractability. However, the discretization degrades the solution quality since the optimal solution usually lies between discrete values. In this paper, we treat the block size and block interval as continuous decision variables and propose a sharding control algorithm based on Parametrized Deep Q-Networks (P-DQN) to efficiently handle the discrete-continuous hybrid action space without the scalability issue. Experimental results show that our Parametrized Deep Q-Networks Blockchain Sharding (P-DQNBS) method can effectively improve the TPS by up to 20%.
An Adaptive Real Coded Population-Based Incremental Learning Algorithm for Design Optimizations in Continuous Space

In a traditional evolutionary algorithm, the selection, crossover, and mutation are commonly used to evolve its searching procedure. For a robust and feasible evolutionary algorithm, these operators must be properly designed. Nevertheless, the proper design of these operators is not an easy task. On the other hand, it would be preferable for an algorithm that the previously explored solutions can be used to help the creation of new solutions or states. In this point of view, evolutionary methods using probabilistic models are deserved further attentions. The Population-Based Incremental Learning (PBIL) algorithm can be categorized into this type of algorithms. The PBIL is initially developed as a binary coded algorithm, and is awkward to some extent in applying to solve a design problem with continuous variables. In this respect, an adaptive continuous PBIL is introduced. In the newly improved PBIL method, an automatic mechanism is presented to update the probability matrix, which is adopted to stochastically produce the offspring, to obtain a balance among the fast convergence and the high quality final solution. Two examples are numerically solved by the introduced PBIL method to highlight its advantages and deficiencies.
Thyroid Nodule Classification in Ultrasound Videos by Combining 3D CNN and Video Transformer

Diagnosing thyroid nodules with computer-aided techniques remains a challenging task. Using ultrasound videos for the classification of benign and malignant nodules can provide valuable timing and change information that is consistent with clinical diagnosis. In this paper, we propose a novel thyroid nodule classification model based on ultrasound video. To capture different semantic information, we sample video frames at various time intervals and extract local and global features using two different feature extraction branches. Our experimental results show that our method outperforms existing state-of-the-art methods, demonstrating its effectiveness in accurately diagnosing thyroid nodules with ultrasound videos.
Enhancing Graph Structures for Node Classification: An Alternative View on Adversarial Attacks

Recently, graph neural networks (GNNs) have become a popular approach to deal with machine learning tasks for graph-structured data. To achieve reliable performance with a GNN-based approach, obtaining high-quality graph structures is crucial. However, the graph data in the real-world often contain noise from data themselves or during the collecting procedure, which leads to the performance degradation of GNNs. In this paper, we propose a novel approach to enhance graph structures for performance improvement of GNNs by reversely applying the concept of adversarial attacks on graph data. Experimental results demonstrate the effectiveness of our method in improving performance of GNNs. Furthermore, we investigate the changes in the graph structure induced by our method, taking into account the connectivity of both interclass and intra-class edges and measuring the extent of over-smoothing.
Tree-Structured Gaussian Mixture Models and Their Variational Inference

In this paper, Gaussian mixture models with tree structure and their variational inference methods are proposed for non-parametric Bayesian clustering. In this model, the tree structure is included as an unobservable random variable. The number of leaf nodes corresponds to the number of mixture components. This model is expected to capture not only the number of clusters but also tree structure from data.
Possible Prospects and Emerging Challenges for Use of AI for Educational Purposes

The paper presents the results of research on the integration of artificial intelligence (AI) into the educational system, the pedagogical process and training programs. The introduction of AI technologies at all levels in education creates new opportunities for optimizing the management of the learning process, facilitating its monitoring and evaluating the final results, analyzing and introducing innovative teaching methods. Ethical issues are becoming relevant again, raising a number of questions related to the admissibility of the use and regulation of these systems, the impact of ethical norms on the decision-making process of the program, questions of the legal personality of AI. The results proved the practical value and viability of the concept of using intelligent systems and applications based on AI. Ethical conflicts and possible problems in the interaction of AI with the object and subjects of the pedagogical process are established, various options are proposed to prevent such disagreements and eliminate the negative consequences of integrating artificial intelligence.
Sparse inference Type II fuzzy models optimized by Q-leaming algorithm

This paper presents a sparse inference method for improving the structure of Type II fuzzy systems, which can extract fuzzy rules from data structures without any assumptions and has advantages in solving modelling problems for such non-linear systems as industrial production. Typically, the fuzzy rule base extracted from the data is often incomplete, resulting in reduced stability of data-driven fuzzy systems. At the same time, we also propose a method to automatically adjust the fuzzy set based on Q-learning algorithm and a self-learning strategy to adjust the parameters of fuzzy rules, which can effectively avoid forgetting the old knowledge. Finally, we use the proposed method for fitting functions and predicting the residual resistance of sailing boats as well as compare the results with those obtained by other methods in the literature. The experimental results show that the data forecast using the Type II fuzzy system model are in satisfactory agreement with the actual data.
A Systematic Mapping Study of Machine Learning Techniques Applied to Software Traceability

Context: Software traceability (ST) refers to capturing associations in various artifacts. A growing interest has been in applying machine learning (ML) techniques to ST. Objective: The purpose of this work is to present a comprehensive review of the state-of-the-art progress on the intersection of ML and ST. Method: A systematic mapping study (SMS) is conducted. A total of 965 citations are retrieved from 2013 to 2022, among which 37 studies are selected as primary studies. Result: 32 ML technologies and 9 enhancement strategies for generating trace links have been identified. Besides, 90 datasets and 16 measures have been summarized, which are applied to evaluate the efficacy of the ML-based tracing techniques. The overall reproducibility of these primary studies is at a medium level. Conclusion: We have found that ML is playing a positive role in improving the accuracy and efficiency of ST. However, there are still some challenges such as reproducibility. Hence, researchers are suggested to pay more attention to standardization to improve the reproducibility of studies.
Transformation of Education in the Context of Modern Informational and Cultural Realities of Artificial Intelligence ChatGPT Application

The article is intended to provide an idea of the role of artificial intelligence in education in the postmodern situation. The emergence of ChatGPT with artificial intelligence has caused concern in society about possible threats and negative consequences, in particular, for education. Publications abound with warnings that artificial intelligence is capable of surpassing human intelligence. Therefore, the article substantiates the inability of ChatGPT to generate innovative ideas of the level of social intelligence. The generation of ideas by artificial intelligence burdened by formal logic does not accommodate innovation. The provision on the priority of authentic intelligence over its artificial prototype is substantiated. The role of integrity, responsibility and other personal qualities of the acquirer of knowledge in the application of the capabilities of artificial intelligence is revealed. The result of penetration of artificial intelligence into the area of postmodern cultural transformations is the humanitarianization of education in general and engineering in particular, overcoming its alienation from the value demands of society. The representation of reality in professional creativity is the parity of rational and irrational forms of knowledge. Professional competence involves systematic knowledge based on the integration of natural, mathematical, technical and social sciences, rethinking the very philosophy of education. The conducted experiment proved that in modern projecting as a mutual condition of divergent and convergent thinking, the advantage in the selection of ideas for innovation belongs to artificial intelligence. It is noted that with proper organization, the involvement of ChatGPT will lead to the intensification of the educational process, will become a personalized assistant for the student, a means of his release from routine work. It is mentioned that the expressed moderate optimism regarding the prospects of education in the realities of artificial intelligence conditions is based on the philosophical understanding of the priority of social creativity over its machine organization.
The Case Study Method as the Means of Improving Engineering Students’ Critical Thinking Skills

The basic objective of the research presented argues for a case study approach to teaching critical thinking skills to engineering students. It has been proved that even in the settings of the distance-learning, the case study method fosters students’ critical thinking, improves their motivation and learning outcomes. These results are obtained only if a) the material responds to the current students’ interests and conditions them for pre-class inquisitive preparation and active emotional in-class participation, and b) the case study procedure is thoroughly thought out with each step clearly described. The target group of the research is fifty-two bachelor year students of S. Kuznets Kharkiv National University of Economics learning the same discipline. The study demonstrates that guided problematic classroom discussions facilitate a deeper understanding of complex engineering issues, encourage active engagement and participation, develop students’ ability to think independently and logically, as well as enhance their communication and teamwork skills. The research outcomes may be of significant value and can be applied by lecturers of other specialties and HEIs which dramatically lacked practical constituent in education earlier under conditions of quarantine and nowadays under martial law.
MorphoLander: Reinforcement Learning Based Landing of a Group of Drones on the Adaptive Morphogenetic UAV

This paper focuses on a novel robotic system MorphoLander representing heterogeneous swarm of drones for exploring rough terrain environments. The morphogenetic leader drone is capable of landing on uneven terrain, traversing it, and maintaining horizontal position to deploy smaller drones for extensive area exploration. After completing their tasks, these drones return and land back on the landing pads of MorphoGear. The reinforcement learning algorithm was developed for a precise landing of drones on the leader robot that either remains static during their mission or relocates to the new position. Several experiments were conducted to evaluate the performance of the developed landing algorithm under both even and uneven terrain conditions. The experiments revealed that the proposed system results in high landing accuracy of 0.5 cm when landing on the leader drone under even terrain conditions and 2.35 cm under uneven terrain conditions. MorphoLander has the potential to significantly enhance the efficiency of the industrial inspections, seismic surveys, and rescue missions in highly cluttered and unstructured environments.
Decoupled Adversarial Network and Self-training with Weighted Pseudo-labels for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation(UDA) in semantic segmentation reduces the dependence on pixel level labeling. It uses labeled source domain datasets and unlabeled target domain images to learn to segment the network. This article proposes a domain adaptive framework that combines decoupled adversarial network and self-training. The problem of over fitting the source domain in domain adaptation and the inability of the network to focus on segmentation tasks has been solved. Considering the impact of the long tailed distribution of data, the Rare Class Sampling (RCS) module is introduced. In order to make full use of pseudo-labels, we designed a self-training UDA scheme using weighted pseudo-labels. At the same time, the RCS module for rare class sampling improves the quality of pseudo-labels by reducing the recognition bias of self-training on public classes. The LoveDA dataset is the latest domain adaptive dataset for land cover mapping. In urban-to-rural and rural-to-urban scenarios, our proposed UDA method has significant advantages.
MOBTAG: Multi-Objective Optimization Based Textual Adversarial Example Generation

Natural language processing (NLP) models are vulnerable to adversarial examples. Generating high-quality adversarial examples, which expose the vulnerability of NLP models and can be used to evaluate and improve their robustness, deserves further research. Existing techniques of generating adversarial examples in the NLP field are typically based on greedy synonym replacements, which may result in out-of-context and unnatural perturbations, and are easily identifiable by humans. In this paper, we present MOB-TAG, a Multi-objective Optimization based Textual Adversarial Example Generation method, which includes three types of perturbations, and utilizes pre-trained models such as BERT and RoBERTa to generate high-quality adversarial examples. MOBTAG generates fluent and grammatical output through a mask-then-infill procedure, with introducing multi-objective optimization and genetic algorithm to pursue a high attack success rate while maintaining a high level of similarity and readability. Experimental results show that compared with methods such as TextFooler, BERTAttack, and CLARE, MOBTAG improves the attack success rate and the textual similarity by at least 11.8% and 0.09 on average, respectively.
Research on the QR Code Mobile Payment System based on Passive IoT

Mobile payment via QR (Quick Response) code is the main payment method in current days which faces with many potential payment risks. To tackle with the fraudulent transactions of QR code, the study presents a secure mobile payment system based on passive IoT, and a payment instrument featured by lifecycle energy supply is also designed. The experimental results show that the remote fraudulent transactions via QR code can be well detected. Besides, the properties of interaction time and success rate of the proposed mobile payment system perform well, especially in the low payment frequency. Besides, the payment instrument can gain sufficient energy to meet the demand of energy consumption. This paper is aimed at providing helpful solutions for IoT payments in the area of specially-engaged commercial units and government-issued vouchers.
Federated Learning with Common Representation Learning Criterion and Personalized Predictor

Federated learning (FL) enables model training on decentralized devices while preserving data privacy. However, data heterogeneity poses a significant challenge to FL, and various approaches have been proposed to address it. Existing research has mainly focused on either enhancing global models or customizing personalized models for clients. This paper proposes a novel approach, FedCRC, that decouples the machine learning model into a representation extractor and predictor. This enables us to enhance both generalization and personalization, thereby addressing the challenge of data heterogeneity in FL. The approach employs a stable global predictor to unify the representation learning criterion during the training of the representation extractor. Additionally, a personalized predictor is trained for each client to achieve a personalized model tailored to the local data distribution. Our FedCRC algorithm was evaluated on multiple benchmark datasets with varying distributions, covering diverse settings. Extensive experimental results demonstrate the effectiveness of our method.
Anatomy-guided Weakly Supervised Breast Lesion Segmentation Fusing Contour and Semantic Information

Accurate lesion segmentation on breast ultrasound (BUS) images is a crucial procedure in computer-aided ultrasonic diagnosis. Owing to the privacy of BUS data and the complexity of acquiring pixel-level labels, numerous researches attempt to achieve breast lesion segmentation with predefined feature-based and deep learning-based methods in a unsuper-vised or weakly supervised scenario. Although the former can typically extract more reliable contour information of the lesion, it is severely interfered by irrelevant tissues due to its inability to capture any semantic information. Furthermore, the weakly supervised deep learning segmentation based on class activation map (CAM) can explore the semantic information while failing to provide precise contour information. In view of the above observation, we present a weakly supervised framework merging complementary contour and semantic information for early lesion segmentation in BUS images. Specifically, guided by the prior knowledge of breast anatomy, we first extract and filter the contour information of suspected lesions located in breast parenchyma layer by clustering and morphological characteristic, respectively. Afterward, semantic information extraction is performed by a classification network to automatically explore the category information of the lesion. Finally, we selectively fuse the complementary information to facilitate lesion segmentation performance with more comprehensive features. Extensive experiments are conducted on the public dataset BUSI, and the results confirm the validity of our approach.