Reviewerly/exHarmony huggingface dataset:
Grid neighbourhood based three way clustering (3WC)

Authors:
raw_author_name: Tamleek Ali Tanveer
raw_affiliation_string: Institute Of Managemant Sciences, Peshawar, Paksitan
raw_author_name: Bahar Ali
raw_affiliation_string: National University of Computer and Emerging Sciences, Peshawar Campus, Pakistan
raw_author_name: Bashir Hayat
raw_affiliation_string: Institute Of Managemant Sciences, Peshawar, Paksitan

Abstract: The key idea behind three-way clustering (3WC) is to group items into three regions to reveal patterns that might not be apparent using two-way clustering. Three-way clustering, contrasting with two-way, considers data across three regions instead of two regions. The three regions are referred to as inside, outside, and partial regions, contrary to the two-way clustering that uses inside and outside regions. The objects in the inside region are members of a cluster, whereas those in the outside region are not, and partial objects may be members of a cluster. Three-way clustering encounters two challenges: 1) Investigation of sophisticated evaluation functions. 2) Finding an optimized pair of thresholds used to identify three regions for a cluster. In the context of Three-Way Clustering (3WC), applying conditional probability can be beneficial for assessing the likelihood of patterns or relationships within each region. In this study, the first challenge is addressed by employing a grid-based approach for a sophisticated evaluation function based on conditional probability. Whereas the second challenge is addressed by employing the elbow-based method to determine a pair of thresholds within a grid-based histogram. Further, two approaches namely the OGN3 and RGN3 are introduced to discover clear and crisp clusters. Experimental results on UCI and Kaggle datasets indicate improvements for commonly used F1 measures for OGN3 and RGN3 up to 4.97% and 12.88%, respectively
A knowledge-guided graph attention network for emotion-cause pair extraction

Authors:
raw_author_name: Peican Zhu
raw_affiliation_string: School of Computer Science and Engineering, Northwestern Polytechnical University (NWPU), Xi'an, 710072, China
raw_author_name: Botao Wang
raw_affiliation_string: School of Computer Science and Engineering, Northwestern Polytechnical University (NWPU), Xi'an, 710072, China
raw_author_name: Keke Tang
raw_affiliation_string: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China
raw_author_name: Haifeng Zhang
raw_affiliation_string: School of Mathematical Science, Anhui University, Hefei, 230601, China
raw_author_name: Xiaodong Cui
raw_affiliation_string: School of Marine Science and Technology, Northwestern Polytechnical University (NWPU), Xi'an, 710072, China
raw_author_name: Zhen Wang
raw_affiliation_string: School of Cybersecurity, Northwestern Polytechnical University (NWPU), Xi'an, 710072, China

Abstract: Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE.
Blockchain-based access control architecture for multi-domain environments

Authors:
raw_author_name: Li Yunliang
raw_affiliation_string: School of Computer Science and Engineering, Xi'an Technological University, Xi'an, 710021, China
raw_author_name: Du Zhiqiang
raw_affiliation_string: College of Computer and Data Science/College of Software, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Fu Yanfang
raw_affiliation_string: School of Computer Science and Engineering, Xi'an Technological University, Xi'an, 710021, China

Abstract: Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring authentication and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.
Memory-enhanced appearance-motion consistency framework for video anomaly detection

Authors:
raw_author_name: Zhiyuan Ning
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Yang Liu
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China; Department of Computer Science, University of Toronto, Ontario, M5S 1A1, Canada; Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou, 215316, China
raw_author_name: Jing Liu
raw_affiliation_string: Department of Electrical and Computer Engineering, UBC, Vancouver, V6T 1Z4, BC, Canada; Division of Natural and Applied Sciences, Duke Kunshan University, Suzhou, 215316, China; School of Information Science and Technology, Fudan University, Shanghai, 200433, China
raw_author_name: Liang Song
raw_affiliation_string: Academy for Engineering & Technology, Fudan University, Shanghai, 200433, China; Innovation Platform for Academicians of Hainan Province, Haikou, 570228, Hainan, China

Abstract: Modern network communication systems extensively utilize video data for various applications, creating a pressing need for efficient Video Anomaly Detection (VAD) mechanisms. The essence of VAD lies in the detection of discrepancies in the appearance-motion dynamics of normal and abnormal events. However, existing methods primarily focus on the isolated analysis of appearance or motion anomalies, thereby overlooking the crucial aspect of appearance-motion consistency semantics. To this end, we introduce a Memory-enhanced Appearance-Motion Consistency (MAMC) framework, which is focused on understanding complex appearance-motion consistency patterns in video data for the discernment of anomalies. The first stage of our model involves the design of an Appearance-Motion Fusion (AMF) module, engineered to generate a robust representation of scene dynamics that captures appearance-motion consistency. This consistency data is then processed through the memory module, augmenting the distinction between normal and anomalous events. Experimental results on three benchmark datasets validate the effectiveness of our approach, which achieves AUCs of 96.7%, 87.6%, and 71.5% on three benchmark datasets (UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets). Additional experiments and visualization analysis confirm the effectiveness of the proposed MAMC framework in anomaly detection scenarios.
Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection

Authors:
raw_author_name: Jinrui Gao
raw_affiliation_string: Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
raw_author_name: Ziqian Wang
raw_affiliation_string: Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
raw_author_name: Ting Jin
raw_affiliation_string: School of Science, Nanjing Forestry University, Nanjing, 210037, China
raw_author_name: Jiujun Cheng
raw_affiliation_string: Ministry of Education, Key Laboratory of Embedded System and Service Computing, Tongji University, Shanghai, China
raw_author_name: Zhenyu Lei
raw_affiliation_string: Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan
raw_author_name: Shangce Gao
raw_affiliation_string: Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan

Abstract: Feature selection is a critical preprocessing step in machine learning with significant real-world applications. Despite the widespread use of particle swarm optimization (PSO) for feature selection, owing to its robust global search capabilities, developing an effective PSO method for this task is still a substantial challenge. This study introduces a novel PSO variant, ISPSO, which integrates the information gain ratio for assessing feature importance. ISPSO's feature selection process involves partitioning features into distinct groups to establish the initial population. Recognizing that feature selection tasks are inherently binary, ISPSO replaces the traditional PSO velocity concept with a probabilistic approach. In addition, introducing a penalty term enhances the algorithm's ability to achieve superior results. Experimental evaluations on 16 datasets consistently show that ISPSO surpasses compared algorithms, highlighting its efficiency in eliminating redundant and irrelevant features.
Functional Mixed Membership Models

Authors:
raw_author_name: Damla Şentürk
raw_affiliation_string: Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA
raw_author_name: Shafali Jeste
raw_affiliation_string: Division of Neurology and Neurological Institute, Children’s Hospital Los Angeles, Los Angeles, CA
raw_author_name: Donatello Telesca
raw_affiliation_string: Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA

Abstract: Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this article, we propose a Bayesian mixed membership model for functional data. By using the multivariate Karhunen-Loève theorem, we are able to derive a scalable representation of Gaussian processes that maintains data-driven learning of the covariance structure. Within this framework, we establish conditional posterior consistency given a known feature allocation matrix. Compared to previous work on mixed membership models, our proposal allows for increased modeling flexibility, with the benefit of a directly interpretable mean and covariance structure. Our work is motivated by studies in functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). In this context, our work formalizes the clinical notion of "spectrum" in terms of feature membership proportions. Supplementary materials, including proofs, are available online. The R package BayesFMMM is available to fit functional mixed membership models.
Recovering a clean background: A parallel deep network architecture for single-image deraining

Authors:
raw_author_name: Nanrun Zhou
raw_affiliation_string: Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China; School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
raw_author_name: Meng Pang
raw_affiliation_string: School of Mathematics and Computer Sciences, Nanchang University, Nanchang 330031, China

Abstract: Deep convolutional neural networks have been popularly applied in single-image deraining recently. Nevertheless, as the network becomes deeper, it is easy to cause training over-fitting and performance saturation, particularly in the case of insufficient training data. In this paper, we report the design of a new network, namely parallel deraining convolutional neural network (PARDNet), for single-image deraining. Specifically, PARDNet adopts two parallel residual sub-networks based on different receptive fields to extract more comprehensive characteristics of the rain streaks, as well as decrease the depth of the network. The hybrid dilated convolution is employed to enlarge the sub-network's receptive field to capture more context information. The efficient channel attention module is integrated into the proposed PARDNet to capture rain streaks more effectively and preserve more background details. Furthermore, to facilitate the network training, the residual learning is also fused into PARDNet in a holistic way. Extensive experiments on synthetic and real-world rainy image datasets demonstrate the superiority of PARDNet for single-image deraining.
Knowledge-based dynamic prompt learning for multi-label disease diagnosis

Authors:
raw_author_name: Jing Xie
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
raw_author_name: Xin Li
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
raw_author_name: Ye Yuan
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
raw_author_name: Yi Guan
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
raw_author_name: Jingchi Jiang
raw_affiliation_string: Faculty of Computing, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
raw_author_name: Xitong Guo
raw_affiliation_string: School of Management, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China

Abstract: Pretrained language models (PLMs) have been developed rapidly which establish impressive performance on many open-domain downstream tasks. However, conducting these pretrained models directly without additional network architectures on special domain tasks like multi-label disease diagnosis cannot perform well. Recently, prompt learning has been a new paradigm in PLM field which is more convenient and well-performed than the traditional fine-tuning approach for different domain tasks. However, prompt engineering is challenging because it takes time and experience. In this paper, we propose a new prompt learning method named Knowledge-based Dynamic PrompT (KBDPT) to deal with these problems. Firstly, we import medical knowledge into PLMs by prompt templates which make results of the disease diagnosis more reasonable and qualified. Compared with the fine-tuning approach, this method needs fewer trainable parameters and less training data but achieve better performance. Secondly, unlike most existing pre-defined prompt methods, KBDPT dynamically generates prompts based on personal medical information and a large-scale medical knowledge graph, which can provide more valuable guidance information for disease diagnosis. Lastly, the proposed model also ensembles multiple prompts from all possible diseases to introduce more knowledge and obtain differential diagnosis results. Experiments of multi-label disease diagnosis are conduct on three real-world EMR datasets. Results demonstrate that our model can be used in various pretrained models and outperform both classical deep learning methods and fine-tuning PLMs. The source code of our proposed model has been released at: https://github.com/loxs123/KBDPT.
Multiscale cascaded domain-based approach for Arabic fake reviews detection in e-commerce platforms

Authors:
raw_author_name: Arwa A. Albelaihi
raw_affiliation_string: Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia

Abstract: Fake reviews in e-commerce can lead to customer deception and financial losses. Despite the importance of fake reviews detection, studies for Arabic language are scarce due to the lack of comprehensive datasets. This study addresses this gap by introducing a full-gold standard dataset, the Arabic Fake Reviews Detection (AFRD), across hotels, restaurants, and product domains. To identify the most effective model for each domain in the context of fake review detection, this research employed Bi-LSTM, Bi-GRU, CNN+Bi-LSTM, and CNN+Bi-GRU models. These models were then used in a cascading approach called Multiscale Cascaded domain-based (MCDB), which transfers knowledge from one domain to enhance results in other domains. Experimental results demonstrated that the MCDB approach improved the results of the models by 2.09% to 7.8% in terms of accuracy. The introduced dataset can be used to build effective models for Arabic e-commerce platforms, in addition to further Natural Language Processing applications. This study demonstrates that leveraging domain-specific datasets in a cascading manner can significantly improve performance, holding substantial implications for future research in problems with limited-size datasets.
Treatment of non-Markovian effects to investigate non-locality, dense coding and non-local information

Authors:
raw_author_name: C. Seida
raw_affiliation_string: ESMaR, Faculty of Sciences, Mohammed V University in Rabat, Morocco; Laboratory of R&D in Engineering Sciences, Faculty of Sciences and Techniques Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco
raw_author_name: K. El Anouz
raw_affiliation_string: Laboratory of R&D in Engineering Sciences, Faculty of Sciences and Techniques Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco
raw_author_name: A. El Allati
raw_affiliation_string: Laboratory of R&D in Engineering Sciences, Faculty of Sciences and Techniques Al-Hoceima, Abdelmalek Essaadi University, Tetouan, Morocco; Max Planck Institute for Physics of Complex Systems, Nöthnitzer Str. 38, D-01187 Dresden, Germany

Abstract: In this contribution, we explore the time-evolution of Bell non-locality, dense coding capacity, local and non-local information with an exact solution in a two-qubit system, for the case in which the second qubit is coupled to a Markovian reservoir. Our results reveal a significant relationship between dense coding capacity and the behavior of Bell non-locality. In particular, we observe that optimal dense coding occurs when the violation of Bell non-locality is at its maximum. Furthermore, we study the dynamics of both local and non-local information, considering different initial states, including maximally/partially entangled states. However, our outcomes indicate that all quantities are sensitive to temperature variations, where a temperature threshold is obtained. Beyond this threshold, all proposed quantities exhibit unstable behavior. Specifically, Bell non-locality and dense coding capacity stabilize eventually to steady values below their classical limits. Moreover, the non-Markovian behavior is more prominent when using partially entangled states compared to those used maximally.
A novel approach for breast cancer detection using optimized ensemble learning framework and XAI

Authors:
raw_author_name: Lucia Cascone
raw_affiliation_string: Department of Computer Science, University of Salerno, Fisciano, Italy
raw_author_name: Nazik Alturki
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
raw_author_name: Oumaima Saidani
raw_affiliation_string: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
raw_author_name: Muhammad Umer
raw_affiliation_string: Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Abstract: Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading contributor to cancer-related deaths in women worldwide. The timely identification of this condition is of utmost importance in mitigating mortality rates. This research paper presents a novel framework for the precise identification of BC, utilising a combination of image and numerical data features with explainable Artificial Intelligence (XAI). The utilisation of the U-NET transfer learning model is employed for image-based prediction. Additionally, an ensemble model is constructed by integrating characteristics from a customised convolutional neural network (CNN) model with an ensemble comprising random forest (RF) and support vector machine (SVM). The experiments aim to evaluate the influence of original features compared to convoluted features. A comparative analysis is carried out to assess the efficacy of various classifiers in accurately detecting BC, utilising the Wisconsin dataset. The model under consideration exhibits promising capabilities in enhancing BC diagnosis, with a remarkable accuracy rate of 99.99%. The present study contributes to the advancement of BC diagnosis by introducing a novel strategy based on machine learning and discussing the interpretation of the variables using XAI. The primary objective of this approach is to get a notable level of precision, hence facilitating the early and reliable identification of BC. Ultimately, the implementation of this approach is expected to enhance patient outcomes.
An iterated greedy heuristic for collaborative Human-UAV search of missing tourists

Authors:
raw_author_name: Ying Xu
raw_affiliation_string: Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, 315211, Zhejiang, China
raw_author_name: Xiaobo Li
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China
raw_author_name: Weipeng Zhang
raw_affiliation_string: College of Digital Technology and Engineer, Ningbo University of Finance & Economics, Ningbo, 315175, Zhejiang, China

Abstract: This paper investigates a problem of collaborative human-unmanned aerial vehicle (UAV) search for missing tourists. A mathematical model including detection stage and rescue stage is constructed to describe practical operation scenarios. The objective function is formulated using conditional probability theory by introducing the weighted probability that tourists being rescued at an allowable time limit. To solve this combinatorial optimization problem, an iterated greedy heuristic (IGH) is designed to search solution space. IGH starts with an initialization procedure based on problem-specific knowledge, then a neighborhood perturbation-based local search method is implemented on perturbed solution to intensify exploitation capability. Meanwhile, an acceptance criterion is presented to avoid premature convergence. Ultimately, Taguchi method is utilized to calibrate parameters of IGH for best performance. Comparison with other algorithms on objective function value and search performance validates the effectiveness of our proposed algorithm.