Reviewerly/exHarmony huggingface dataset:
Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey

Authors:
raw_author_name: Ananya Pandey
raw_affiliation_string: Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi 110042, India
raw_author_name: Dinesh Kumar Vishwakarma
raw_affiliation_string: Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi 110042, India

Abstract: 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

Authors:
raw_author_name: Hormoz Shahrzad
raw_affiliation_string: Cognizant AI Labs, United States of America
raw_author_name: Risto Miikkulainen
raw_affiliation_string: Cognizant AI Labs, United States of America; The University of Texas at Austin, United States of America

Abstract: 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

Authors:
raw_author_name: Yunyun Wang
raw_affiliation_string: Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210046, China; School of Computer Science and Engineering, Nanjing University of Posts & Telecommunications, Nanjing 210046, China
raw_author_name: Jian Mao
raw_affiliation_string: Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210046, China; School of Computer Science and Engineering, Nanjing University of Posts & Telecommunications, Nanjing 210046, China
raw_author_name: Cong Zou
raw_affiliation_string: Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210046, China; School of Computer Science and Engineering, Nanjing University of Posts & Telecommunications, Nanjing 210046, China
raw_author_name: Xinyang Kong
raw_affiliation_string: Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210046, China; School of Computer Science and Engineering, Nanjing University of Posts & Telecommunications, Nanjing 210046, China

Abstract: 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

Authors:
raw_author_name: Shengying Yang
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, PR China
raw_author_name: Lifeng Xie
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, PR China
raw_author_name: Jingsheng Lei
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, PR China
raw_author_name: Xiaohong Qian
raw_affiliation_string: School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou, 310023, PR China

Abstract: 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.
Locality sensitive hashing scheme based on online-learning

Authors:
raw_author_name: Jingjian Zhang
raw_affiliation_string: School of Mathematics and Statistics, Xidian University, Xi’an, 710126, PR China
raw_author_name: Youlong Yang
raw_affiliation_string: School of Mathematics and Statistics, Xidian University, Xi’an, 710126, PR China
raw_author_name: Yuanyuan Liu
raw_affiliation_string: School of Mathematics and Statistics, Xidian University, Xi’an, 710126, PR China

Abstract: 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

Authors:
raw_author_name: Hamid Saadatfar
raw_affiliation_string: Department of Computer Engineering, University of Birjand, Birjand, Iran
raw_author_name: Mahdi Khazaiepoor
raw_affiliation_string: Department of Computer Engineering, Islamic Azad University, Birjand Branch, Birjand, Iran

Abstract: 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

Authors:
raw_author_name: Qinghui Wang
raw_affiliation_string: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China
raw_author_name: Jie Dou
raw_affiliation_string: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China
raw_author_name: Lei Dou
raw_affiliation_string: National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, China

Abstract: 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

Authors:
raw_author_name: Yuanfei Dai
raw_affiliation_string: Department of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China
raw_author_name: Wenzhong Guo
raw_affiliation_string: Department of Computer and Big Data, Fuzhou University, Fuzhou 350108, China
raw_author_name: Carsten Eickhoff
raw_affiliation_string: E-Health and Medical Data Science, University of Tübingen, Tübingen 72074, Germany

Abstract: 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

Authors:
raw_author_name: Fatma Kimsesiz
raw_affiliation_string: Kırşehir Ahi Evran University, Turkey

Abstract: 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

Authors:
raw_author_name: Dharmesh Dhabliya
raw_affiliation_string: Vishwakarma Institute of Information Technology, India
raw_author_name: Ankur Gupta
raw_affiliation_string: Vaish College of Engineering, India
raw_author_name: Sukhvinder Singh Dari
raw_affiliation_string: Symbiosis International University, India
raw_author_name: Sabyasachi Pramanik
raw_affiliation_string: Haldia Institute of Technology, India

Abstract: 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

Authors:
raw_author_name: Ümran Üstünbaş
raw_affiliation_string: Zonguldak Bülent Ecevit University, Turkey

Abstract: 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

Authors:
raw_author_name: Subir Gupta
raw_affiliation_string: Dept. of CSE., Dr. B.C. Roy Engineering College, Durgapur, India

Abstract: 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.
Natural Language Processing Applications in Language Assessment

Authors:
raw_author_name: Tuğba Elif Toprak-Yıldız
raw_affiliation_string: Izmir Democracy University, Turkey & University of Hamburg, Germany

Abstract: Natural language processing is a subfield of artificial intelligence investigating how computers can be utilised to understand and process natural language text or speech to accomplish useful things in various areas, and it draws on various disciplines, such as computer science, linguistics, and robotics. Natural language processing applications, including automated speech recognition and scoring, have several exciting prospects for language testing and assessment practices. These prospects include addressing practical constraints associated with test administration and scoring, securing standardisation in test delivery, ensuring objectiveness and reliability in scoring procedures, and providing personalised feedback for learning. This chapter focuses on automated speech scoring and its applications in language testing and assessment and discusses how these systems can be employed in assessment contexts. The chapter also discusses the potential benefits and drawbacks of automated speech scoring while focusing on construct-related and practical challenges surrounding such systems.
Exploring the Benefits of Virtual Reality (VR) in Manufacturing Training

Authors:
raw_author_name: Ipseeta Satpathy
raw_affiliation_string: KIIT School of Management, KIIT University, India
raw_author_name: B. C. M. Patnaik
raw_affiliation_string: KIIT School of Management, KIIT University, India
raw_author_name: Subir Gupta
raw_affiliation_string: Swami Vivekananda University, India

Abstract: Manufacturing necessitates a delicate balance of productivity and safety, and factory training must reflect this. Leaders must be able to swiftly grow the workforce to guarantee high productivity. Through off-the-line mental repetitions, virtual reality training assists industrial coworkers in acquiring task execution abilities, boosting situational awareness, and comprehending safety standards. Manufacturers are embracing virtual reality training because it has been shown to drastically cut training times, safety issues, and worker time to profitability. By generating engaging, genuine teaching settings, virtual reality (VR) technology revolutionizes human learning by giving hands-on experiences, improving safety, increasing productivity, and lowering costs. Virtual reality (VR) technology enhances hands-on experiences, reliability, efficiency, and affordability by creating immersive, genuine instructional settings. The chapter presents an overview of the major advantages that VR offers in industrial training programs.