Reviewerly/exHarmony huggingface dataset:
TV shows popularity prediction of genre-independent TV series through machine learning-based approaches

Authors:
raw_author_name: Alfonso Guarino
raw_affiliation_string: Computer Science Department, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Italy
raw_author_name: Delfina Malandrino
raw_affiliation_string: Computer Science Department, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Italy
raw_author_name: Rocco Zaccagnino
raw_affiliation_string: Computer Science Department, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, Italy

Abstract: Abstract The use of social media has grown exponentially in recent years up to become a reflection of human social attitudes and to represent today the main channel for conducting discussions and sharing opinions. For this reason, the vast amount of information generated is often used for predicting outcomes of real-world events in different fields, including business, politics, and health, as well as in the entertainment industry. In this paper, we focus on how data from Twitter can be used to predict ratings of a large set of TV shows regardless of their specific genre. Given a show, the idea is to exploit features concerning the pre-release hype on Twitter for rating predictions. We propose a novel machine learning-based approach to the genre-independent TV show popularity prediction problem. We compared the performance of several well-known predictive methods, and as a result, we discovered that LSTM and Random Forest can predict the ratings in the USA entertainment market, with a low mean squared error of 0.058. Furthermore, we tested our model by using data of “ never seen ” shows, by deriving interesting results in terms of error rates. Finally, we compared performance against relevant solutions available in the literature, with discussions about challenges arousing from the analysis of shows in different languages.
Optimization of multi-vehicle obstacle avoidance based on improved artificial potential field method with PID control

Authors:
raw_author_name: Xi Wu
raw_affiliation_string: School of Energy and Power Engineering, China
raw_author_name: Guanghong Liang
raw_affiliation_string: School of Energy and Power Engineering, China

Abstract: In the context of multi-vehicle formation, obstacle avoidance in unknown environments presents a number of challenges, including obstacles near the target, susceptibility to local minima, and dynamic obstacle avoidance. To address these issues in multi-vehicle formation control and obstacle avoidance within unknown environments, this paper uses PID control to optimize the potential field function of the artificial potential field method and conducts simulation experiments. The results demonstrate that the proposed algorithm achieves reductions of 39.7%, 41.9%, 24.8% and 32.0% in four efficiency functions (total iteration times, formation efficiency function value, energy consumption and standard deviation of iteration times) compared to other algorithms. The improved algorithm more effectively addresses the challenge of slow obstacle avoidance when vehicles approach the target and can handle unexpected situations such as local minima and dynamic obstacles. It achieves energy-efficient optimization for multi-vehicle obstacle avoidance in complex environments.
The predictive robustness of organizational and technological enablers towards blockchain technology adoption and financial performance

Authors:
raw_author_name: Nadia A. Abdelmegeed Abdelwahed
raw_author_name: Mohammed A. Al Doghan
raw_author_name: Bahadur Ali Soomro

Abstract: Purpose Blockchain technology has brought about significant transformation among organizations worldwide. This study aimed to explore the effects of organizational and technological factors on blockchain technology adoption (BTA) and financial performance (FP) in Pakistan. Design/methodology/approach This is a co-relational study which used the cross-sectional data. We gathered the data from the managers of Pakistan’s small and medium-sized enterprises (SMEs), which functioned their industries with blockchain technology. We applied convenience sampling to identify the respondents. Finally, we based this study’s findings on 274 valid cases. Findings We used structural equation modeling (SEM) in this study, to exert a positive and significant impact on organizational factors such as organizational innovativeness (OI), organizational learning capability (OLC), top management support (TMS) and organizational work climate (OWC) on BTA. In addition, the technological factors, such as complexity (CTY), technology readiness (TR), compatibility (CBTY) and technology capability (TC), have a positive and significant effect on BTA. Finally, this study’s findings show that BTA positively and significantly impacts FP. Practical implications This study’s findings will help policymakers and planners to design policies to adopt other blockchain technologies to improve SMEs’ operations. Moreover, this study’s findings will inspire policymakers and planners to actively seek new ideas, knowledge and skills through acquiring new knowledge to assist with their IT-related decisions. Originality/value This study empirically confirms the role of organizational and technology factors toward BTA and FP among Pakistan's SME managers.
Blockchain technology adoption for collaborative emission reduction considering carbon information asymmetry in supply chains

Authors:
raw_author_name: Xue-Yan Wu
raw_author_name: Xujin Pu

Abstract: Purpose Collaborative emission reduction among supply chain members has emerged as a new trend to achieve climate neutrality goals and meet consumers’ low-carbon preferences. However, carbon information asymmetry and consumer mistrust represent significant obstacles. This paper investigates the value of blockchain technology (BCT) in solving the above issues. Design/methodology/approach A low-carbon supply chain consisting of one supplier and one manufacturer is examined. This study discusses three scenarios: non-adoption BCT, adoption BCT without sharing the supplier’s carbon emission reduction (CER) information and adoption BCT with sharing the supplier’s CER information. We analyze the optimal decisions of the supplier and the manufacturer through the Stackelberg game, identify the conditions in which the supplier and manufacturer adopt BCT and share information from the perspectives of economic and environmental performance. Findings The results show that adopting BCT benefits supply chain members, even if they do not share CER information through BCT. Furthermore, when the supplier’s CER efficiency is low, the manufacturer prefers that the supplier share this information. Counterintuitively, the supplier will only share CER information through BCT when the CER efficiencies of both the supplier and manufacturer are comparable. This diverges from the findings of existing studies, as the CER investments of the supplier and the manufacturer in this study are interdependent. In addition, despite the high energy consumption associated with BCT, the supplier and manufacturer embrace its adoption and share CER information for the sake of environmental benefits. Practical implications The firms in low-carbon supply chains can adopt BCT to improve consumers’ trust. Furthermore, if the CER efficiencies of the firms are low, they should share CER information through BCT. Nonetheless, a lower unit usage cost of BCT is the precondition. Originality/value This paper makes the first move to discuss BCT adoption and BCT-supported information sharing for collaborative emission reduction in supply chains while considering the transparency and high consumption of BCT.
AI‐ALOE: AI for reskilling, upskilling, and workforce development

Authors:
raw_author_name: Ashok Goel
raw_affiliation_string: Interactive Computing Georgia Institute of Technology  Atlanta Georgia USA
raw_author_name: Chris Dede
raw_affiliation_string: Harvard University  Cambridge Massachusetts USA
raw_author_name: Chaohua Ou
raw_affiliation_string: Georgia Institute of Technology  Atlanta Georgia USA

Abstract: Abstract The National AI Institute for Adult Learning and Online Education (AI‐ALOE) develops AI learning and teaching assistants to enhance the proficiency of adult reskilling and upskilling, and thereby transform workforce development. The AI assistants both address known problems in online education for reskilling/upskilling and help personalize adult learning for workforce development. AI‐ALOE develops new AI models and techniques for self‐explanation, machine teaching, and mutual theory of mind to make the AI assistants usable, learnable, teachable, and scalable. AI‐ALOE is also developing a data architecture for deploying and evaluating the AI assistants, collecting and analyzing data, and personalizing learning at scale.
Parameterized complexity of weighted team definability

Authors:
raw_author_name: Juha Kontinen
raw_author_name: Yasir Mahmood
raw_author_name: Arne Meier
raw_author_name: Heribert Vollmer

Abstract: Abstract In this article, we study the complexity of weighted team definability for logics with team semantics. This problem is a natural analog of one of the most studied problems in parameterized complexity, the notion of weighted Fagin-definability, which is formulated in terms of satisfaction of first-order formulas with free relation variables. We focus on the parameterized complexity of weighted team definability for a fixed formula $\varphi$ of central team-based logics. Given a first-order structure $\mathcal{A}$ and the parameter value $k\in \mathbb N$ as input, the question is to determine whether $\mathcal{A},T\models \varphi$ for some team T of size k . We show several results on the complexity of this problem for dependence, independence, and inclusion logic formulas. Moreover, we also relate the complexity of weighted team definability to the complexity classes in the well-known W-hierarchy as well as paraNP.
A modified shuffled frog leaping algorithm with inertia weight

Authors:
raw_author_name: Zhuanzhe Zhao
raw_affiliation_string: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, Anhui, China
raw_author_name: Mengxian Wang
raw_affiliation_string: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, Anhui, China
raw_author_name: Yongming Liu
raw_affiliation_string: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, Anhui, China
raw_author_name: Kang He
raw_affiliation_string: School of Mechanical and Electronic Engineering, Suzhou University, Suzhou, Anhui, China
raw_author_name: Zhibo Liu
raw_affiliation_string: School of Mechanical Engineering, Anhui Polytechnic University, Wuhu, Anhui, China

Abstract: Abstract The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.
Hate speech detection in the Arabic language: corpus design, construction, and evaluation

Authors:
raw_author_name: Ashraf Ahmad
raw_affiliation_string: Department of Computer Science, Princess Sumaya University for Technology (PSUT), Jordan
raw_author_name: Mohammad Azzeh
raw_affiliation_string: Department of Data Science, Princess Sumaya University for Technology (PSUT), Jordan
raw_author_name: Eman Alnagi
raw_affiliation_string: Department of Computer Science, Princess Sumaya University for Technology (PSUT), Jordan
raw_author_name: Qasem Abu Al-Haija
raw_affiliation_string: Department of Cybersecurity, Faculty of Computer and Information Technology, Jordan University of Science and Technology, Jordan
raw_author_name: Dana Halabi
raw_affiliation_string: SAE Institute, Luminus Technical University College (LTUC), Jordan
raw_author_name: Abdullah Aref
raw_affiliation_string: Department of Computer Science, Princess Sumaya University for Technology (PSUT), Jordan
raw_author_name: Yousef AbuHour
raw_affiliation_string: Department of Basic Sciences, Princess Sumaya University for Technology (PSUT), Jordan

Abstract: Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.
The construction of smart city community environment under the application of animation immersion technology integrated into southern Fujian Folk Culture

Authors:
raw_author_name: Sichao Wu
raw_affiliation_string: Faculty of Social Sciences &amp; Liberal Arts, UCSI University, Kuala Lumpur, Malaysia; Xiamen Academy of Arts and Design, FuZhou University, Fuzhou, China
raw_author_name: Wee Hoe Tan
raw_affiliation_string: International Institute of Science Diplomacy &amp; Sustainability, UCSI University, Kuala Lumpur, Malaysia

Abstract: With the rapid development of global information technology, data resources play an increasingly prominent role in society. Countries have issued ‘smart city’ strategic plans one after another, taking the sustainable construction of smart cities as an important means to enhance national competitiveness and seize the commanding heights of the new round of the information technology revolution. This paper selects southern Fujian for experiments to develop modern communication methods in some areas of China that have not undergone smart city upgrades. First, the animation immersion method under smart city technology is taken as the starting point. The folk culture of southern Fujian is integrated to analyze the importance of developing the smart city community under big data. Second, a combined system dynamics model and structural equation modeling are established. It is applied to intelligent, sustainable urban construction to explore the possibility of new development paths. The cities that apply the model proposed here for intelligent, sustainable construction have increased their output value in all aspects within five years. The maximum annual growth rate has reached 17.11%. Planners and designers base the smart city community environment on animation immersion technology. From the perspective of animation immersion planning, integrating the animation immersion technology of southern Fujian folk culture into the sustainable construction and development of the smart city community environment will help enhance the comprehensive competitiveness of the region.
Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices

Authors:
raw_author_name: Cemile İnce
raw_affiliation_string: Computer Engineering Department, Faculty of Engineering, İnönü University, 44200, Malatya, Turkey
raw_author_name: Davut Hanbay
raw_affiliation_string: Computer Engineering Department, Faculty of Engineering, İnönü University, 44200, Malatya, Turkey

Abstract: Abstract In the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.
Improving Semi-Supervised Text Classification with Dual Meta-Learning

Authors:
raw_author_name: Shujie Li
raw_affiliation_string: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
raw_author_name: Min Yang
raw_affiliation_string: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
raw_author_name: Ying Shen
raw_affiliation_string: School of Intelligent Systems Engineering, Sun Yat-sen University, Guangdong, China
raw_author_name: Chengming Li
raw_affiliation_string: School of Intelligent Systems Engineering, Sun Yat-sen University, Guangdong, China
raw_author_name: Ruifeng Xu
raw_affiliation_string: Harbin Institute of Technology (Shenzhen), Shenzhen, China
raw_author_name: Xiaoyan Zhao
raw_affiliation_string: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Abstract: The goal of semi-supervised text classification (SSTC) is to train a model by exploring both a small number of labeled data and a large number of unlabeled data, such that the learned semi-supervised classifier performs better than the supervised classifier trained on solely the labeled samples. Pseudo-labeling is one of the most widely used SSTC techniques, which trains a teacher classifier with a small number of labeled examples to predict pseudo labels for the unlabeled data. The generated pseudo-labeled examples are then utilized to train a student classifier, such that the learned student classifier can outperform the teacher classifier. Nevertheless, the predicted pseudo labels may be inaccurate, making the performance of the student classifier degraded. The student classifier may perform even worse than the teacher classifier. To alleviate this issue, in this paper, we introduce a dual meta-learning ( DML ) technique for semi-supervised text classification, which improves the teacher and student classifiers simultaneously in an iterative manner. Specifically, we propose a meta-noise correction method to improve the student classifier by proposing a Noise Transition Matrix (NTM) with meta-learning to rectify the noisy pseudo labels. In addition, we devise a meta pseudo supervision method to improve the teacher classifier. Concretely, we exploit the feedback performance from the student classifier to further guide the teacher classifier to produce more accurate pseudo labels for the unlabeled data. In this way, both teacher and student classifiers can co-evolve in the iterative training process. Extensive experiments on four benchmark datasets highlight the effectiveness of our DML method against existing state-of-the-art methods for semi-supervised text classification. We release our code and data of this paper publicly at https://github.com/GRIT621/DML.
Perceptual Image Quality Prediction: Are Contrastive Language–Image Pretraining (CLIP) Visual Features Effective?

Authors:
raw_author_name: Chibuike Onuoha
raw_affiliation_string: Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
raw_author_name: Jean Flaherty
raw_affiliation_string: Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
raw_author_name: Truong Cong Thang
raw_affiliation_string: Department of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan

Abstract: In recent studies, the Contrastive Language–Image Pretraining (CLIP) model has showcased remarkable versatility in downstream tasks, ranging from image captioning and question-answering reasoning to image–text similarity rating, etc. In this paper, we investigate the effectiveness of CLIP visual features in predicting perceptual image quality. CLIP is also compared with competitive large multimodal models (LMMs) for this task. In contrast to previous studies, the results show that CLIP and other LMMs do not always provide the best performance. Interestingly, our evaluation experiment reveals that combining visual features from CLIP or other LMMs with some simple distortion features can significantly enhance their performance. In some cases, the improvements are even more than 10%, while the prediction accuracy surpasses 90%.