Reviewerly/exHarmony huggingface dataset:
Lens imaging opposition-based learning for differential evolution with cauchy perturbation

Opposition-based learning (OBL) is an effective optimization strategy that enhances the performance of various global optimization algorithms. Among these algorithms, differential evolution (DE) based on OBL has received the attention of many researchers. However, OBL is a strategy with relative symmetry between the target point and the opposite point, which does not contribute significantly to the population in the later stage of the algorithm, and tends to disregard more meaningful points around the opposite points. Therefore, we propose a novel opposition-based differential evolution based on the principle of convex lens (LensOBLDE), which utilizes the distance relationship between the target point and the focal length of the lens, and dynamically adjusts the search radius by controlling the parameters, thus further improving the search ability. In addition, to avoid the algorithm from falling into a local optimum, a small-scale and low-probability cauchy perturbation strategy is applied to the elite individuals in the population, which ensures the exploration and exploitation of the algorithm. Finally, LensOBLDE evaluates the performance of several advanced algorithms on the CEC2017 benchmark suite and conducts a statistical test to evaluate the difference between algorithms. The results indicate that LensOBLDE outperforms several algorithms with a significant difference between them. This verifies the effectiveness of the strategies newly added in this study.
MIMR: Modality-Invariance Modeling and Refinement for unsupervised visible-infrared person re-identification

Compared to visible modality person re-identification that handles only the intra-modality discrepancy, visible-infrared person re-identification (VI-ReID) aims to achieve the match between visible modality and infrared modality. However, supervised VI-ReID methods are limited in flexibility due to their dependence on labeled data. Although several unsupervised VI-ReID methods have been developed, they usually ignore the impact of hard samples and noisy labels. In this paper, we propose the modality-invariance modeling and refinement (MIMR) framework for unsupervised VI-ReID. For the first issue, we first conduct cross-modality image translation, then enable the encoder to extract modality-invariant feature representation by aligning translated versions with original samples, and finally use cross-modality clustering and hard sample contrastive loss to handle the hard samples. For the second issue, we believe that ambiguous samples are usually assigned noisy labels, so we design ambiguity-oriented pseudo label refinement (APLR), which evaluates ambiguity from both the sample itself and the corresponding translated version, rather than discards hard samples as existing methods. Extensive experiments demonstrate that MIMR achieves superior performance compared to state-of-the-art unsupervised methods and even surpasses early supervised methods.
NQFL: Nonuniform Quantization for Communication Efficient Federated Learning

Federated learning (FL), as a potential machine learning framework for privacy preservation, has gained significant attention. However, the considerable communication overhead associated with FL remains a prominent challenge. To mitigate this issue, a nonuniform quantization scheme based on Lloyd-Max algorithm is introduced in this letter. By employing this approach, less communication resources are consumed to achieve the same performance. Through performance analysis and numerical simulations, we verify the convergence and effectiveness of the proposed algorithm. It demonstrates the potential of our approach in reducing communication overhead while maintaining reliable performance in FL systems.
Enhancing Gesture Recognition in Low-Light Conditions Using Laser-Induced Graphene Flexible Strain Sensors

Gesture recognition using machine learning methods is valuable in the development of healthcare and human-computer interaction, and typically relies on images or video. To improve recognition accuracy, these visual data can be combined with data from other sensors, but this approach, known as data fusion, is limited by the quality of sensor data and incompatible datasets. Here, we propose a flexible strain sensor based on laser-induced graphene technology, which is easy to manufacture and has superior performance. The sensor is attached to the finger and effectively captures finger bending information as an additional gesture information channel, constructing a bimodal gesture interaction interface for strain and vision. In particular, a deep convolutional neural network based on machine learning is used to acquire features in monocular camera image data. The data fusion method of feature layer fusion is selected to construct a bimodal gesture recognition system based on image data and strain sensor data. Experimental tests show that the strain and vision bimodal gesture interaction interface developed based on this fusion recognition model can still achieve more than 90% real-time gesture recognition accuracy under an illumination level of 20 lux, which effectively solves the problem of the traditional monocular vision’s gesture interaction interface with a significant decrease in recognition accuracy under dark light conditions, and also further expands the application of LIG flexible sensor devices in human-computer interaction system.
Enhancing Life Still Sketch Skills Through Virtual Reality Technology

This study addresses a significant gap in research concerning the utilization of virtual reality (VR) technology within higher education in China. Focusing on Mianyang Teachers' College in Sichuan, the research explores the potential integration of VR into the practice of life still sketching. Using a qualitative case study approach, ten experts, comprising five in life still sketching and five in VR technology, were interviewed. Thematic analysis of the data revealed that life still sketching is viewed as a highly subjective mode of artistic expression, emphasizing adherence to fundamental aesthetic principles. The study underscores the importance of factors such as the art's current status, exploratory nature, instructional objectives, and technical aspects for effective life still sketching. VR technology, with devices like PICO and QUEST2, emerges as a promising tool to enhance this artistic practice, offering innovative solutions to its challenges. This research contributes valuable insights to the intersection of VR and art education in the Chinese higher education context.
Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey

Sentiment analysis is a computational technique that analyses the subjective information conveyed within a given expression. This encompasses appraisals, opinions, attitudes or emotions towards a particular subject, individual, or entity. Conventional sentiment analysis solely considers the text modality and derives sentiment by identifying the semantic relationship between words within a sentence. Despite this, certain expressions, such as exaggeration, sarcasm and humor, pose a challenge for automated detection when conveyed only through text. Multimodal sentiment analysis incorporates various forms of data, such as visual and acoustic cues, in addition to text. By utilizing fusion analysis, this approach can more precisely determine the implied sentiment polarity, which includes positive, neutral, and negative sentiments. Thus, the recent advancements in deep learning have boosted the domain of multimodal sentiment analysis to new heights. The research community has also shown significant interest in this topic due to its potential for both practical application and educational research. In light of this fact, this paper aims to present a thorough analysis of recent ground-breaking research studies conducted in multimodal sentiment analysis, which employs deep learning models across various modalities such as text, audio, image, and video. Furthermore, the article dives into a discussion of the multiple categories of multimodal data, diverse domains in which multimodal sentiment analysis can be applied, a range of operations that are integral to multimodal sentiment analysis, deep learning architectures, a variety of fusion methods, challenges associated with multimodal sentiment analysis, and the benchmark datasets in addition to the state-of-the-art approaches. The ultimate goal of this survey is to indicate the success of deep learning architectures in tackling the complexities associated with multimodal sentiment analysis.
Asynchronous evolution of deep neural network architectures

Many evolutionary algorithms (EAs) take advantage of parallel evaluation of candidates. However, if evaluation times vary significantly, many worker nodes (i.e., compute clients) are idle much of the time, waiting for the next generation to be created. Evolutionary neural architecture search (ENAS), a class of EAs that optimizes the architecture and hyperparameters of deep neural networks, is particularly vulnerable to this issue. This paper proposes a generic asynchronous evaluation strategy (AES) that is then adapted to work with ENAS. AES increases throughput by maintaining a queue of up to K individuals ready to be sent to the workers for evaluation and proceeding to the next generation as soon as M<<K individuals have been evaluated. A suitable value for M is determined experimentally, balancing diversity and efficiency. To showcase the generality and power of AES, it was first evaluated in eight-line sorting network design (a single-population optimization task with limited evaluation-time variability), achieving an over two-fold speedup. Next, it was evaluated in 11-bit multiplexer design (a single-population discovery task with extended variability), where a 14-fold speedup was observed. It was then scaled up to ENAS for image captioning (a multi-population open-ended-optimization task), resulting in an over two-fold speedup. In all problems, a multifold performance improvement was observed, suggesting that AES is a promising method for parallelizing the evolution of complex systems with long and variable evaluation times, such as those in ENAS.
Universal domain adaptation from multiple black-box sources

Black-box domain adaptation (B2DA) is a practical unsupervised domain adaptation (UDA) setting, in which only an interface to source model is available due to privacy or security concerns. It aims to transfer source knowledge with no source data or model, but only queries for target samples from the source interface. Previous B2DA methods commonly assume a single source interface and shared label space across domains. Whereas in real tasks, there are usually multiple source interfaces with label shift from the target domain, how to effectively combine them without target ground-truth has not been fully investigated yet so far. We term such setting as Universal Domain Adaptation from Multiple Black-box sources (Uni-MBDA), and propose a new Universal multi-source Black-Box method (Um2B) through Dual-Attention on both source domains and classes. In Um2B, adaptive domain attention is first introduced to seek the optimal combination of source interfaces, whose performance is no worse than the single best one. Second, to address the label shift across domains, adaptive class attention is adopted in adaptation to suppress the private source classes, then automatically identify target unknowns and separate them apart from the known classes. Empirical results over benchmark datasets show the effectiveness of Um2B, and in most cases, it performs no worse than the best single-source.
Pragmatic degradation learning for scene text image super-resolution with data-training strategy

Super-resolution of scene text images represents a formidable computational problem, marred by a myriad of intricate challenges. This paper focuses on the specific hurdles that have impeded significant advancements in this domain, and introduces the Higher-Order Degradation-Based Super-Resolution Network (HDSN) as a novel solution to address these intricate issues. The challenges in super-resolving scene text images are manifold. Firstly, the semantic ambiguity inherent to text in natural scenes often leads to degraded results, as standard super-resolution techniques struggle to preserve meaningful textual content. Additionally, the uncertainty surrounding font variability exacerbates this issue, as different fonts require distinct treatment for optimal super-resolution. Furthermore, scene text images often exhibit long trailing shadows, artifacts, and strong noise, rendering conventional methods inadequate in producing satisfactory results. To tackle these intricate challenges, we propose a pragmatic higher-order degradation modeling process. This process takes into account the nuanced characteristics of scene text images, including the diverse forms of noise such as Gaussian, Poisson, speckle, and JPEG compression noise, as well as varying levels of blurring. By meticulously considering these real-world scenarios, our approach significantly enhances the robustness and adaptability of super-resolution for scene text images. In addition to addressing these challenges, we recognize the issues arising from sparse datasets and the lack of corresponding paired images for training. To surmount this limitation, we introduce a text image pre-training strategy, which proves to be highly effective in improving recognition accuracy. The experimental results on TextZoom affirm the effectiveness of our approach, demonstrating substantial improvements over existing methods. Notably, our HDSN achieves average recognition rates of 67.2% on ASTER, 63.2% on MORAN, and 58.0% on CRNN, surpassing the performance of available approaches. Our source code is available at https://github.com/syyang2022/HDSN.
HFA-GTNet: Hierarchical Fusion Adaptive Graph Transformer network for dance action recognition

Dance action recognition is a hot research topic in computer vision. However, current skeleton-based action recognition methods face difficulties in capturing the adequate spatial structure and temporal variations of dance actions, resulting in lower recognition accuracy. In this paper, we propose a Hierarchical Fusion Adaptive Graph Transformer network (HFA-GTNet) for dance action recognition. A Hierarchical Spatial Attention (HSAtt) module is designed to extract different levels of spatial feature information from joint to parts to group, it can effectively learn high-order dependency relationships from local joints to global poses in dance actions. Secondly, to extract the joint variations in dance actions at different speeds, we have designed a Temporal Fusion Attention (TFAtt) module. This module learns the short-term and long-term temporal dependencies among joints across frames. Additionally, to capture the variations in motion patterns and dance styles among different dancers, we introduce an Adaptive Component (AdaptC). Finally, we evaluate our model on two self-built dance datasets, MSDanceAction and InDanceAction, and demonstrate its superior performance compared to other state-of-the-art methods in dance action recognition.
Locality sensitive hashing scheme based on online-learning

Locally Sensitive Hashing (LSH) algorithms are classical algorithms commonly used on the c-Approximate Nearest Neighbor (c-ANN) search problem. When using Euclidean distance to measure sample similarity and solve the c-ANN problem, the traditional approach is to utilize the Exact Euclidean Locality Sensitive Hashing (E2LSH) algorithm based on the p-stable distribution. However, the uncertainty of the p-stable distribution causes the hash buckets constructed by the E2LSH algorithm to vary in queries. Therefore, this paper proposes the OLLSH algorithm based on the Weighted Majority algorithm in the Online-Learning framework, which selects the hash buckets with more stable query accuracy by weighted voting on the hash buckets generated by the E2LSH algorithm. Then, we conduct simulation experiments on synthetic dataset and four real data sets and conclude that the proposed OLLSH algorithm improves the accuracy compared to the original algorithm with the same memory usage.
Evolution of the random subset feature selection algorithm for classification problem

Datasets often include excessive or irrelevant data that affect the performance and complexity of the machine learning model. Feature selection is one of the most effective dimension-reduction technics in datasets to solve the problem. Feature reduction can lead to increased classification accuracy and decreased computational costs. Wrapper feature selection algorithms are a popular and effective category of these methods which considering the learning method's feedback. One of the well-known algorithms in this area is the random subset feature selection algorithm (RSFS). This study proposes an improved version that has higher convergence speed, lower feature selection rate, and higher classification accuracy. Improvements include considering the features' interaction in the selective subsets, continuous evaluation of the selected features to avoid being stuck in the local optima and enhancing the method's evolution phase. To comprehensively evaluate the proposed algorithm, its performance was applied to 20 standard and well-known datasets from public resources and compared with various recent related methods. Experimental results demonstrate that the proposed method reduced classification errors using fewer features compared to other methods, achieving the highest ranking in the Friedman and Wilcoxon rank-sum tests.
SDCS-CF: Saliency-driven localization and cascade scale estimation for visual tracking

Discriminative correlation filtering (DCF)-based trackers have demonstrated remarkable results in the field of visual tracking. Nevertheless, in most DCF-based trackers: (i) they apply correlation operations across the entire search area features without discriminative weights, rendering them highly susceptible to background interference; and (ii) the fixed aspect ratio scale search strategy is inadequate for accurate scale estimation under irregular scale variations. To overcome these challenges, this study proposes a new correlation filter through saliency-driven localization and cascaded scale estimation (SDCS-CF). Specifically, a U-like network is devised to generate a pixel-wise saliency map. This map is then multiplied element-wise with search area features to accentuate the target attribute, mitigating distractors and increasing localization confidence. Furthermore, a cascaded scale estimation model consisting of three one-dimensional filters is designed to refine the scale estimation process and improving the robustness. Extensive experimental results on three public datasets demonstrate that the proposed SDCS-CF outperforms most DCF-based trackers.
Wasserstein adversarial learning based temporal knowledge graph embedding

Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate temporal and structural information. However, these methods only employ a uniformly random sampling to construct negative facts. As a consequence, the corrupted samples are often too simplistic for training an effective model. In this paper, we propose a new temporal knowledge graph embedding framework by introducing adversarial learning to further refine the performance of traditional TKGE models. In our framework, a generator is utilized to construct high-quality plausible quadruples and a discriminator learns to obtain the embeddings of entities and relations based on both positive and negative samples. Meanwhile, we also apply a Gumbel-Softmax relaxation and the Wasserstein distance to prevent vanishing gradient problems on discrete data; an inherent flaw in traditional generative adversarial networks. Through comprehensive experimentation on temporal datasets, the results indicate that our proposed framework can attain significant improvements based on benchmark models and also demonstrate the effectiveness and applicability of our framework.
EFL Learners' Digitalized Practices in Promoting Vocabulary Learning at the Higher Education Level

Technology has become an integral part of education; and language instruction, which is communicative in nature, has a share in the whole unit. The widespread use of portable devices has also transformed language learning through diverse accessible facilities that can alternatively be evaluated in an out-of-school context. This paved the way for self-directed learning practices that enable learners to advance their language skills and vocabulary. This study aimed to investigate these self-directed practices for learning vocabulary in English. 120 EFL learners at diverse departments at a state university in Turkiye participated in the study. For data collection, a survey was employed that interrogated the self-directed vocabulary learning practices of the participants in digital contexts. The results of the study showed that learners use mobile technology for English language practices and adopt different strategies in vocabulary learning supplied by a variety of digital applications and facilities. The results are discussed and related implications are addressed based on the findings.
Role of AR and VR in the Context of Technical Education

In recent years, the primary research subjects for improving the lives of future generation youngsters have been excellence and creativity in the existing pedagogical system. Virtual reality plays an important part in introducing new educational ideas. To enhance the quality of teaching in aspects of relevant and interactive learning, the recent trend in educational technology is mostly centered on e - learning and mobile-based learning approaches. Virtual reality is now widely utilized in a variety of fields, including education, healthcare, training, and entertainment. This study paper examines the influence of virtual reality on the pedagogical sector, as well as its current and future.
EFL Learners' Views About the Use of Artificial Intelligence in Giving Corrective Feedback on Writing

This chapter presents a qualitative study the aim of which is to explore EFL learners' views about the use of artificial intelligence for giving corrective feedback on their written works. Artificial intelligence use was addressed through one of recent and popular chatbots: ChatGPT. The data were gathered through semi-structured and stimulated-recall interviews, also videorecording of the participants' ChatGPT use for writing feedback. In the interviews, the participants reflected on their individualized language studies, and also having practiced the use of artificial intelligence in receiving corrective feedback on their writing assignments via ChatGPT for five weeks, they expressed their thoughts about this experience. The findings indicated codes and themes about learner characteristics and preferences, and benefits and pitfalls of artificial intelligence use for corrective feedback, which leads to several language teaching implications specifically concerning writing to be proposed in the chapter.
Emerging Tech Analysis of Intellectual Property and Data Ownership in Smart Factories

Integrating emerging technologies in smart factories has ushered in a new manufacturing era. These technologies have transformed how data is generated, stored, and utilised, raising critical questions about intellectual property (IP) and data ownership. This study comprehensively analyses the challenges and implications associated with IP and data ownership within the context of smart factories. By reviewing the existing literature and conducting empirical research, this project aims to shed light on the multifaceted issues surrounding these concepts, offering valuable insights for industry professionals and policymakers.