Reviewerly/exHarmony huggingface dataset:
A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications

Authors:
raw_author_name: Dieudonné Tchuente
raw_affiliation_string: TBS Business School, Department of Information, Operations and Management Sciences, 1 Place Alphonse Jourdain - CS 66810, 31068 TOULOUSE Cedex 7, France
raw_author_name: Jerry Lonlac
raw_affiliation_string: IMT Nord Europe, University of Lille, Center for Digital Systems, Cité scientifique, Rue Guglielmo Marconi, BP 20145, 59653 Villeneuve d′Ascq Cedex, France
raw_author_name: Bernard Kamsu-Foguem
raw_affiliation_string: Université de Toulouse, Laboratoire de Génie de Production (LGP), EA 1905, 47 Avenue d׳Azereix, BP 1629, 65016 Tarbes Cedex, France

Abstract: Artificial Intelligence (AI) is becoming fundamental in almost all activity sectors in our society. However, most of the modern AI techniques (e.g., Machine Learning – ML) have a black box nature, which hinder their adoption by practitioners in many application fields. This issue raises a recent emergence of a new research area in AI called Explainable artificial intelligence (XAI), aiming at providing AI-based decision-making processes and outcomes to be easily understood, interpreted, and justified by humans. Since 2018, there has been an exponential growth of research studies on XAI, which has justified some review studies. However, these reviews currently focus on proposing taxonomies of XAI methods. Yet, XAI is by nature a highly applicative research field, and beyond XAI methods, it is also very important to investigate how XAI is concretely used in industries, and consequently derive the best practices to follow for better implementations and adoptions. There is a lack of studies on this latter point. To fill this research gap, we first propose a holistic review of business applications of XAI, by following the Theory, Context, Characteristics, and Methodology (TCCM) protocol. Based on the findings of this review, we secondly propose a methodological and theoretical framework in six steps that can be followed by all practitioners or stakeholders for improving the implementation and adoption of XAI in their business applications. We particularly highlight the need to rely on domain field and analytical theories to explain the whole analytical process, from the relevance of the business question to the robustness checking and the validation of explanations provided by XAI methods. Finally, we propose seven important future research avenues.
Age, gender and handedness prediction using handwritten text: A comprehensive survey

Authors:
raw_author_name: Chinu Singla
raw_affiliation_string: Department of Computer Science and Engineering, Punjabi University Patiala, Punjab, India
raw_author_name: Raman Maini
raw_affiliation_string: Department of Computer Science and Engineering, Punjabi University Patiala, Punjab, India
raw_author_name: Munish Kumar
raw_affiliation_string: Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

Abstract: Handwriting recognition is widely acknowledged as one of the most challenging tasks in pattern recognition and machine learning. This study explores the intriguing field of age, gender and handedness prediction through handwriting analysis by offering valuable insights into its potential applications in forensics, psychology and education. A comprehensive survey has been conducted encompassing an introduction, motivational aspects, background information, sources of data, analysis schemes, survey protocols, reported findings and methodologies used for predicting age, gender and handedness in both Indic and non-Indic scripts. The investigation highlights research gaps and concludes with an in-depth analysis of unresolved issues accompanied by a roadmap for future advancements in this domain. The survey focuses on a systematic examination of eight Indic scripts (Assamese, Bangla, Devanagari, Gurumukhi, Kannada, Malayalam, Tamil and Telugu) and six non-Indic scripts (Arabic, Chinese, Japanese, Persian, Roman and Thai) and it can be concluded that non-Indic scripts attain more accuracy in comparison with Indic scripts. Furthermore, the study mostly focuses on providing a catalog of publicly accessible online datasets featuring diverse handwriting samples from various scripting languages by giving a roadmap for further research in this area.
Emojifying chatbot interactions: An exploration of emoji utilization in human-chatbot communications

Authors:
raw_author_name: Shubin Yu
raw_affiliation_string: Department of Communication and Culture, BI Norwegian Business School, Room number C4I-021, Nydalsveien 37, 0484 Oslo, Norway
raw_author_name: Luming Zhao
raw_affiliation_string: School of Journalism, Fudan University, 400 Guoding Road, Shanghai 200433, China

Abstract: The prevalence of chatbots in human–computer communication has significantly increased. Emojis, as a form of emotional disclosure, have gained significant attention for their potential to boost chatbot service satisfaction. However, how and when emoji usage can increase satisfaction toward chatbots is not fully examined. This paper aims to fill this gap and contribute to the rapidly evolving field of human-chatbot communication research. Through three experiments, this paper investigates and explores the role of emojis in enhancing chatbot interactions. The results reveal that emojis heighten chatbot's perceived warmth but do not necessarily augment their competence. This warmth promoting effect leads to boosted service satisfaction and is more apparent when chatbots serve hedonic purposes and are pre-programmed rather than highly autonomous. However, the warmth upshot of emojis is not as potent for chatbots as it is for humans. While this study unravels the intricate pathway of how emojis augment service satisfaction, it also extends the dialogue of the Stereotype Content Model (SCM) and propels the new wave of the Computers Are Social Actors (CASA) paradigm. Thus, this research lays down pathways for further studies in understanding the role of emotionally simulated interactions in automated technologies.
A partition-based problem transformation algorithm for classifying imbalanced multi-label data

Authors:
raw_author_name: Jicong Duan
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
raw_author_name: Xibei Yang
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
raw_author_name: Shang Gao
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
raw_author_name: Hualong Yu
raw_affiliation_string: School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, China

Abstract: Multi-label learning has garnered much research interest due to its wide range of real-world applications. Many multi-label learning methods have been proposed; however, few have addressed the class imbalance problem existing in multi-label data. Even though some studies have taken this issue into account, most of them have ignored the label correlations or only considered random correlations between them. In this study, we propose a novel partition-based imbalanced multi-label learning algorithm, named Multi-label Learning based on Hierarchical Clustering (MLHC), to tackle this problem. MLHC first carries out hierarchical clustering on the original label space to divide it into several disconnected subspaces, each of which contains several labels that are strongly correlated with each other. Then, for each label subspace, we use the problem transformation strategy to convert it into a multi-class problem by binary coding. Any multi-class imbalance learning algorithm can be applied to the transformed multi-class data. Finally, the classification results will be decoded to retrieve the corresponding label subspace, and all label subspace results are combined to show the predicted label vector in the original label space. We conducted experiments not only on thirteen benchmark multi-label datasets but also carried out them on XJTU-SY which is a multi-label engineering application dataset, and the results indicated that our proposed MLHC learning algorithm outperforms several state-of-the-art class imbalance multi-label learning algorithms, demonstrating the effectiveness and necessity of discovering label correlations and transforming the original imbalanced multi-label learning problem into multiple strongly correlated multi-class imbalanced learning problems.
Arithmetic Average Density Fusion-Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion

Authors:
raw_author_name: Tiancheng Li
raw_affiliation_string: Key Laboratory of Information Fusion Technology (Ministry of Education), School of Automation, Northwestern Polytechnical University, Xi&#x0027;an, China
raw_author_name: Kai Da
raw_affiliation_string: Laboratory of Information Fusion Technology (Ministry of Education), School of Automation, Northwestern Polytechnical University, Xi'an 710129, China,
raw_author_name: Hongqi Fan
raw_affiliation_string: Laboratory of Information Fusion Technology (Ministry of Education), School of Automation, Northwestern Polytechnical University, Xi'an 710129, China,

Abstract: This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need.These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets.Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities.To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged.In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered.Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.
Manipulating hidden-Markov-model inferences by corrupting batch data

Authors:
raw_author_name: William N. Caballero
raw_affiliation_string: United States Air Force Academy, Colorado Springs, CO, USA
raw_author_name: Tahir Ekin
raw_affiliation_string: McCoy College of Business, Texas State University, San Marcos, TX, USA
raw_author_name: Roi Naveiro
raw_affiliation_string: CUNEF Universidad, Madrid, Spain

Abstract: Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker's inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov model inferences via corrupted data. In particular, we provision a suite of corruption problems for filtering, smoothing, and decoding inferences leveraging an adversarial risk analysis approach. Multiple stochastic programming models are set forth that incorporate realistic uncertainties and varied attacker objectives. Three general solution methods are developed by alternatively viewing estimation from frequentist and Bayesian perspectives. The efficacy of each method is illustrated via extensive, empirical testing. The developed methods are characterized by their solution quality and computational effort, resulting in a stratification of techniques across varying problem-instance architectures. This research highlights the weaknesses of hidden Markov models under adversarial activity, thereby motivating the need for robustification techniques to ensure their security.
MixCam-attack: Boosting the transferability of adversarial examples with targeted data augmentation

Authors:
raw_author_name: Sensen Guo
raw_affiliation_string: School of Cybersecurity, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China; State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, Shaanxi, 710071, China
raw_author_name: Peican Zhu
raw_affiliation_string: School of Artificial Intelligence, Optics and Electronics (iOPEN), NWPU, Xi'an, Shaanxi, 710072, China
raw_author_name: Baocang Wang
raw_affiliation_string: State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, Shaanxi, 710071, China
raw_author_name: Zhiying Mu
raw_affiliation_string: Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, Guangdong, 518057, China
raw_author_name: Jinxiong Zhao
raw_affiliation_string: Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, Guangdong, 518057, China

Abstract: Many black-box adversarial attack algorithms perform attacks on machine learning models based on the transferability of adversarial examples, and the input transformation-based attack is one of the most effective methods. However, existing input transformation methods ignore that each pixel contributes differently to the output of the model, and the focus regions of different models on the same images are similar. Therefore, this paper proposes a targeted data augmentation-based adversarial attack algorithm named MixCam, which augments the input data based on the contribution of each pixel to the prediction result. This is done to enhance the transferability of the crafted adversarial example from the perspective of shifting the regions to which the model pays most of its attention. In addition, this paper proposes further boosting the transferability of the crafted adversarial examples by fusing the class activation maps of multiple models for the input image. Furthermore, the MixCam can integrate other input transformation methods to further boost the transferability of crafted adversarial examples. Extensive experiments on ImageNet demonstrate that MixCam outperforms other state-of-the-art methods in black-box attacks against considered adversarially trained models, with an average increase of 11.7% and 10.7% in attack success rates for single and ensemble attack settings, respectively.
Progressive network based on detail scaling and texture extraction: A more general framework for image deraining

Authors:
raw_author_name: Jiehui Huang
raw_affiliation_string: Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, Guangdong, China
raw_author_name: Zhenchao Tang
raw_affiliation_string: Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, Guangdong, China
raw_author_name: Xuedong He
raw_affiliation_string: School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China
raw_author_name: Jun Zhou
raw_affiliation_string: Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, Guangdong, China
raw_author_name: Defeng Zhou
raw_affiliation_string: Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, Guangdong, China
raw_author_name: Calvin Yu-Chian Chen
raw_affiliation_string: AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzhen, 518055, Guangdong, China; Artificial Intelligence Medical Research Center School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, Guangdong, China; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan; Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan

Abstract: Many feature extraction components have been proposed for image deraining tasks, aiming to improve feature learning. However, few models have addressed the integration of multi-scale features from derain images. The fusion of multiple features at different scales in one model has the potential to significantly enhance the authenticity and detail of rainy images restoration. This study introduces a migratable multi-scale feature blending model, which is a progressive learning model based on detail dilation and texture extraction. First, the degraded image is sent to the detail dilation module, which is designed to increase the detailed outline and obtain the coarse image features. Second, the extracted feature maps are sent to the multi-scale feature extraction (MFE) module and the multi-scale hybrid strategy (MHS) module for improved texture restoration. Third, the simple convolution modules are replaced by an optimized transformer model to more efficiently extract contextual features and multi-scale information in images. Finally, a progressive learning strategy is employed to incrementally restore the degraded images. Empirical results show that our proposed module for progressive restoration achieves near state-of-the-art performance in several rain removal tasks. In particular, our model exhibits better rain removal realism compared to state-of-the-art models. The source code is available at https://github.com/JackAILab/DTPNet.
Collaborative Planning for Catching and Transporting Objects in Unstructured Environments

Authors:
raw_author_name: Liuao Pei
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Junxiao Lin
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Zhichao Han
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Lun Quan
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Yanjun Cao
raw_affiliation_string: Huzhou Institute, Zhejiang University, Huzhou, China
raw_author_name: Chao Xu
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
raw_author_name: Fei Gao
raw_affiliation_string: State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China

Abstract: Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation. In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments. For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form. This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state. Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints. We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance. Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality.
An automatic Darknet-based immunohistochemical scoring system for IL-24 in lung cancer

Authors:
raw_author_name: Zihao He
raw_affiliation_string: College of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China
raw_author_name: Dongyao Jia
raw_affiliation_string: College of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China
raw_author_name: Chuanwang Zhang
raw_affiliation_string: College of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China
raw_author_name: Ziqi Li
raw_affiliation_string: College of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China
raw_author_name: Nengkai Wu
raw_affiliation_string: College of Electronics and Information Engineering, Beijing Jiaotong University, Beijing, China

Abstract: Immunohistochemical (IHC) detection is of critical importance in the pathological diagnosis of lung cancer. Interleukin-24 (IL-24) is a significant predictive and prognostic marker in IHC detection, which can help to characterize the tumor and predict the clinical course. To determine the score of the IL-24 expression, pathologists will assess the positive area and staining intensity through a microscope and then use a semi-quantitative assessment method to assign a score for each IHC image of lung cancer. However, this process is a time-consuming, imprecise, and subjective process, which can result in inter- and intra-observer discrepancies. Meanwhile, The performance of computer-aided diagnosis (CAD) systems for IHC scoring depends on the quality of manually extracted features. For example, many advanced methods require precise extraction of features of cell nuclei and membranes. However, the complex background and overlapping cells in lung cancer IHC images present significant challenges for segmentation, which can affect the accuracy of the final system's predicted scoring results. In this paper, an automatic Darknet-based IHC scoring system for IL-24 in lung cancer is proposed. Firstly, the original IHC images of lung cancer are blocked and the features are concentrated by a "block attention mechanism, which can reduce the computational burden of the analysis of millions of pixels in IHC images. The blocked images are then inputted into a Darknet-based scoring network, which incorporates a novel feature extraction backbone and loss function to obtain the final scores. To the best of our knowledge, this is the first end-to-end system that directly outputs a clinical score using lung cancer IL-24 IHC images as input. We have constructed a dataset of 5000 manually annotated IL-24 IHC images of lung cancer obtained from the Institute of Life Science and Bioengineering at Beijing Jiaotong University. We will present experimental results to demonstrate the feasibility of our proposed method, which can greatly assist with the clinical diagnosis and treatment of lung cancer.