Reviewerly/exHarmony huggingface dataset:
ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption

Authors:
raw_author_name: Donghua Jiang
raw_affiliation_string: School of Information Engineering, Chang’an University, Xi’an 710064, China
raw_author_name: Nestor Tsafack
raw_affiliation_string: Research Unit of Laboratory of Energy and Artificial Intelligence, Electrical Engineering Department and Industrial Computing of ISTAMA, University of Douala, P.O. Box 3223, Douala, Cameroon
raw_author_name: Wadii Boulila
raw_affiliation_string: Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia
raw_author_name: Jawad Ahmad
raw_affiliation_string: School of Computing, Edinburgh Napier University, Edinburgh, United Kingdom

Abstract: Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.
Role-oriented binary grey wolf optimizer using foraging-following and Lévy flight for feature selection

Authors:
raw_author_name: Yong Wang
raw_affiliation_string: School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
raw_author_name: Songjie Ran
raw_affiliation_string: School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
raw_author_name: Gai-Ge Wang
raw_affiliation_string: School of Computer Science and Technology, Ocean University of China, Qingdao 266100, China

Abstract: Feature selection can effectively define the feature subset, remove redundant, irrelevant, and noisy features. In order to adapt the feature selection problem, this paper adopts role-oriented modeling paradigm to make up for the defects of binary grey wolf optimizer such as premature, declining diversity, and insufficient convergence, then comes up with novel V-shaped linear transfer functions. Ultimately, a role-oriented binary grey wolf optimizer is proposed. The top grey wolves combine the foraging-following to enhance convergence, while the ordinary grey wolves integrate Lévy flight to avoid premature and improve diversity. The outstanding ability of role-oriented binary grey wolf optimizer in balance of exploitation and exploration is verified by benchmarks and the effectiveness in solving feature selection is demonstrated through feature selection tasks. The results of benchmark and feature selection task have comprehensively demonstrated the competitiveness of role-oriented modeling paradigm in improving metaheuristic algorithms.
Emotional expression by artificial intelligence chatbots to improve customer satisfaction: Underlying mechanism and boundary conditions

Authors:
raw_author_name: Junbo Zhang
raw_affiliation_string: School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, PR China
raw_author_name: Qi Chen
raw_affiliation_string: School of Economics and Management, Dalian University of Technology, Dalian, Liaoning, 116081, PR China
raw_author_name: Xiaolei Wang
raw_affiliation_string: School of Information Technology and Management, University of International Business and Economics, Beijing, 100029, PR China
raw_author_name: Luning Liu
raw_affiliation_string: School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, PR China
raw_author_name: Yuqiang Feng
raw_affiliation_string: School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, PR China

Abstract: Artificial intelligence chatbots have invaded the tourism industry owing to their low cost and high efficiency. However, the influence of emotional expressions of chatbots on service outcomes has not received much attention from researchers. Drawing upon expectancy violations theory, we explored how emotional expressions of chatbots affect customer satisfaction using three experiments in the context of tourist attraction recommendations. Chatbots' expressions of concern for customers can improve customer satisfaction by reducing expectancy violations. In particular, customer's goal orientation, the human-likeness of chatbot's avatars, and the relationship type between customers and chatbots can moderate the negative relationship between emotional expression and expectancy violation. These findings advance research on the emotional expressions of chatbots and provide critical insights for deploying chatbots in customer service in the tourism industry.
Collaborative contrastive learning for hypergraph node classification

Authors:
raw_author_name: Hanrui Wu
raw_affiliation_string: Jinan University, Guangzhou, China; Pazhou Lab, Guangzhou, China
raw_author_name: Nuosi Li
raw_affiliation_string: Jinan University, Guangzhou, China
raw_author_name: Jia Zhang
raw_affiliation_string: Jinan University, Guangzhou, China; Pazhou Lab, Guangzhou, China
raw_author_name: Sentao Chen
raw_affiliation_string: Shantou University, Shantou, China
raw_author_name: Michael K. Ng
raw_affiliation_string: Hong Kong Baptist University, Hong Kong, China
raw_author_name: Jinyi Long
raw_affiliation_string: Guangdong Key Lab of Traditional Chinese Medicine Information Technology, Guangzhou, China; Jinan University, Guangzhou, China; Pazhou Lab, Guangzhou, China

Abstract: Plenty of models have been presented to handle the hypergraph node classification. However, very few of these methods consider contrastive learning, which is popular due to its great power to represent instances. This paper makes an attempt to leverage contrastive learning to hypergraph representation learning. Specifically, we propose a novel method called Collaborative Contrastive Learning (CCL), which incorporates a generated standard graph with the hypergraph. The main technical contribution here is that we develop a collaborative contrastive schema, which performs contrast between the node views obtained from the standard graph and hypergraph in each network layer, thus making the contrast collaborative. To be precise, in the first layer, the view from the standard graph is used to augment that from the hypergraph. Then, in the next layer, the augmented features are adopted to train a new representation to augment the view from the standard graph conversely. With this setting, the learning procedure is alternated between the standard graph and hypergraph. As a result, the learning on the standard graph and hypergraph is collaborative and leads to the final informative node representation. Experimental results on several widely used datasets validate the effectiveness of the proposed model.
A cross Transformer for image denoising

Authors:
raw_author_name: Chunwei Tian
raw_affiliation_string: National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi'an, Shaanxi, 710129, China; School of Software, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, China
raw_author_name: Menghua Zheng
raw_affiliation_string: School of Software, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, China
raw_author_name: Wangmeng Zuo
raw_affiliation_string: Peng Cheng Laboratory, Shenzhen, Guangdong, 518055, China; School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
raw_author_name: Shichao Zhang
raw_affiliation_string: School of Computer Science and Engineering, Central South University, Changsha, Hunan, 410083, China
raw_author_name: Yanning Zhang
raw_affiliation_string: National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Xi'an, Shaanxi, 710129, China; School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710129, China
raw_author_name: Chia-Wen Lin
raw_affiliation_string: Department of Electrical Engineering and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu, 30013, Taiwan

Abstract: Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://github.com/hellloxiaotian/CTNet.
Big data analytics deep learning techniques and applications: A survey

Authors:
raw_author_name: Hoda K. MOHAMED
raw_affiliation_string: Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt
raw_author_name: Walaa Medhat
raw_affiliation_string: Information Technology and Computer Science, Nile University/Faculty of Computer and Artificial Intelligence, Benha University, Cairo, Egypt

Abstract: Deep learning (DL), as one of the most active machine learning research fields, has achieved great success in numerous scientific and technological disciplines, including speech recognition, image classification, language processing, big data analytics, and many more. Big data analytics (BDA), where raw data is often unlabeled or uncategorized, can greatly benefit from DL because of its ability to analyze and learn from enormous amounts of unstructured data. This survey paper tackles a comprehensive overview of state-of-the-art DL techniques applied in BDA. The main target of this survey is intended to illustrate the significance of DL and its taxonomy and detail the basic techniques used in BDA. It also explains the DL techniques used in big IoT data applications as well as their various complexities and challenges. The survey presents various real-world data-intensive applications where DL techniques can be applied. In particular, it concentrates on the DL techniques in accordance with the BDA type for each application domain. Additionally, the survey examines DL benchmarked frameworks used in BDA and reviews the available benchmarked datasets, besides analyzing the strengths and limitations of each DL technique and their suitable applications. Further, a comparative analysis is also presented by comparing existing approaches to the DL methods used in BDA. Finally, the challenges of DL modeling and future directions are discussed.
Learning Flow-Based Disentanglement

Authors:
raw_author_name: Jen-Tzung Chien
raw_affiliation_string: Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; degree in electrical and computer engineering from National Central University, Taoyuan, Taiwan, in 2018,
raw_author_name: Sheng-Jhe Huang
raw_affiliation_string: Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan; degree in electrical and computer engineering from National Central University, Taoyuan, Taiwan, in 2018,

Abstract: Face reenactment aims to generate the talking face images of a target person given by a face image of source person. It is crucial to learn latent disentanglement to tackle such a challenging task through domain mapping between source and target images. The attributes or talking features due to domains or conditions become adjustable to generate target images from source images. This article presents an information-theoretic attribute factorization (AF) where the mixed features are disentangled for flow-based face reenactment. The latent variables with flow model are factorized into the attribute-relevant and attribute-irrelevant components without the need of the paired face images. In particular, the domain knowledge is learned to provide the condition to identify the talking attributes from real face images. The AF is guided in accordance with multiple losses for source structure, target structure, random-pair reconstruction, and sequential classification. The random-pair reconstruction loss is calculated by means of exchanging the attribute-relevant components within a sequence of face images. In addition, a new mutual information flow is constructed for disentanglement toward domain mapping, condition irrelevance, and condition relevance. The disentangled features are learned and controlled to generate image sequence with meaningful interpretation. Experiments on mouth reenactment illustrate the merit of individual and hybrid models for conditional generation and mapping based on the informative AF.
ArtCap: A Dataset for Image Captioning of Fine Art Paintings

Authors:
raw_author_name: Yue Lu
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Chao Guo
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Xingyuan Dai
raw_affiliation_string: State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China
raw_author_name: Fei-Yue Wang
raw_affiliation_string: Institute of Science and Art, National Museum of China, Beijing, China

Abstract: The image captioning of fine art paintings aims at generating content descriptions for the paintings. Due to the complexity of modeling both image and language, this task usually needs sufficient training data. However, different from photographic image captioning, there are few satisfactory datasets for painting captioning. In this article, we introduce a painting captioning dataset (named the ArtCap dataset), which contains 3606 paintings and five descriptions for each painting. We present the carefully designed construction pipeline of our dataset and further evaluate our dataset from two aspects of annotation quality and application effectiveness, respectively. For the annotation quality, we compare the global characteristics, annotation content, and annotation consistency of our dataset with other painting descriptions datasets. For application effectiveness, we employ our dataset and other painting descriptions datasets to train image captioning models and analyze the captioning performances. The results demonstrate the promising annotation quality and application effectiveness of our dataset.
An Emotion Recognition Method Based on Eye Movement and Audiovisual Features in MOOC Learning Environment

Authors:
raw_author_name: Jindi Bao
raw_affiliation_string: School of Computer Science and Engineering and the School of Software, Guangxi Normal University, Guilin 541000, China; School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China
raw_author_name: Xiaomei Tao
raw_affiliation_string: School of Computer Science and Engineering and the School of Software, Guangxi Normal University, Guilin 541000, China; School of Information Science and Engineering, Guilin University of Technology, Guilin 541004, China

Abstract: In recent years, more and more people have begun to use massive online open course (MOOC) platforms for distance learning.However, due to the space-time isolation between teachers and students, the negative emotional state of students in MOOC learning cannot be identified timely.Therefore, students cannot receive immediate feedback about their emotional states.In order to identify and classify learners' emotions in video learning scenarios, we propose a multimodal emotion recognition method based on eye movement signals, audio signals, and video images.In this method, two novel features are proposed: feature of coordinate difference of eyemovement (FCDE) and pixel change rate sequence (PCRS).FCDE is extracted by combining eye movement coordinate trajectory and video optical flow trajectory, which can represent the learner's attention degree.PCRS is extracted from the video image, which can represent the speed of image switching.A feature extraction network based on convolutional neural network (CNN) (FE-CNN) is designed to extract the deep features of the three modals.The extracted deep features are inputted into the emotion classification CNN (EC-CNN) to classify the emotions, including interest, happiness, confusion, and boredom.In single modal identification, the recognition accuracies corresponding to the three modals are 64.32%,74.67%, and 71.88%.The three modals are fused by feature-level fusion, decision-level fusion, and modellevel fusion methods, and the evaluation experiment results show that the method of decision-level fusion achieved the highest score of 81.90% of emotion recognition.Finally, the effectiveness of FCDE, FE-CNN, and EC-CNN modules is verified by ablation experiments.
Assessment of Wealth Distribution in Blockchain Online Social Media

Authors:
raw_author_name: Barbara Guidi
raw_affiliation_string: Department of Computer Science, University of Pisa, Pisa, Italy
raw_author_name: Andrea Michienzi
raw_affiliation_string: Department of Computer Science, University of Pisa, Pisa, Italy
raw_author_name: Laura Ricci
raw_affiliation_string: Department of Computer Science, University of Pisa, Pisa, Italy

Abstract: Online social networks (OSNs) revolutionized how people interact with each other, and nowadays, thanks to blockchain technology, new solutions are being considered, giving birth to blockchain online social media (BOSMs). BOSMs use the blockchain to redistribute with their users the wealth generated by the platform through a rewarding system, assigning better rewards to socially impactful users. Thus, these new systems are characterized by highly intertwined economical and social aspects and constitute a new scenario in the world of social networks. Many scenarios, economic and social alike, show a phenomenon known as “the rich-get-richer”, which states that the richest actors of a system tend to become richer over time. To the best of our knowledge, in the scenario of BOSMs, where users can acquire cryptocurrency through their social actions, this type of phenomenon was not yet studied. In this article, we propose a methodological framework composed of three hypotheses that can help study the rich-get-richer phenomenon through a set of measures and indices. In addition, we apply the proposed framework to the Steem case study, showing how unevenly wealth is distributed on its blockchain and comparing our results to other scenarios.
MTBullyGNN: A Graph Neural Network-Based Multitask Framework for Cyberbullying Detection

Authors:
raw_author_name: Krishanu Maity
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India
raw_author_name: Tanmay Sen
raw_affiliation_string: Ericsson, Kolkata, India
raw_author_name: Sriparna Saha
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India
raw_author_name: Pushpak Bhattacharyya
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, India

Abstract: Cyberbullying is a malady of social media, and its automatic detection is critically important considering its virulence, velocity of spreading, and the scale of the havoc it can wreak. However, the problem is challenging due to its disguised behavior, noise in the content, and, in recent times, introduction of code-mixing. In this work, we propose MTBullyGNN a novel graph neural network (GNN)-based multitask (MT) framework that solves sentiment-aided cyberbullying detection (CD) from code-mixed language. The GNN helps detect unlabelled or noisy label nodes (sentences) accurately by aggregating information from similarly labeled nodes. To connect nodes, we apply cosine similarity between sentences and create a single text graph for a benchmark code-mixed cyberbullying corpus, BullySent. Experimental results illustrate that MTBullyGNN outperforms the state-of-the-art (SOTA) methods for both the single (CD) and MT (CD and sentiment) settings by up to 4.46% and 4.92% in classification accuracy, respectively. Furthermore, another benchmark Hindi–English code-mixed single-task dataset has also been considered to illustrate the robustness of our proposed model. The code will be made publicly available in the camera-ready version.