Reviewerly/exHarmony huggingface dataset:
CBR Driven Interactive Explainable AI

Explainable AI (XAI) can greatly enhance user trust and satisfaction in AI-assisted decision-making processes. Numerous explanation techniques (explainers) exist in the literature, and recent findings suggest that addressing multiple user needs requires employing a combination of these explainers. We refer to such combinations as explanation strategies. This paper introduces iSee - Intelligent Sharing of Explanation Experience, an interactive platform that facilitates the reuse of explanation strategies and promotes best practices in XAI by employing the Case-based Reasoning (CBR) paradigm. iSee uses an ontology-guided approach to effectively capture explanation requirements, while a behaviour tree-driven conversational chatbot captures user experiences of interacting with the explanations and provides feedback. In a case study, we illustrate the iSee CBR system capabilities by formalising a real-world radiograph fracture detection system and demonstrating how each interactive tools facilitate the CBR processes.
An Integrated Approach to Predicting the Influence of Reputation Mechanisms on Q&amp;A Communities

The reputation mechanism is a commonly used incentive mechanism to motivate users to participate in Q&A communities, which needs to be carefully reviewed before application. Predicting the impact of reputation mechanisms can help community managers to choose an appropriate mechanism. However, existing methods are difficult to establish the connection between users and reputation mechanisms and their influence in Q&A communities, which may lead to misleading results. Hence, we propose a MASC method combining multi-agent and case-based reasoning systems. We model incentive rules as norms in a multi-agent system composed of heterogeneous agents. We describe the impact of incentive mechanisms from three aspects: user attributes, behaviour, and content structure. Based on past user behaviour data, we present how to predict future behaviour and interaction based on similar user contribution patterns. In addition, we use the developed simulator to reproduce the impact of reputation mechanisms on Q&A communities. We use a new reputation mechanism of the Stack Overflow community to evaluate the performance of MASC. Except for questioning preference, the prediction accuracy for the influence exceeds 65%.
Uncalibrated, Unified and Unsupervised Specular-Aware Photometric Stereo

In this paper we present a variational approach to simultaneously recover the 3D reconstruction, reflectance, lighting and specularities of an object, all of them, from a set of RGB images. The approach works in an uncalibrated, unified and unsupervised manner, without assuming any prior knowledge of the shape geometry or training data to constrain the solution and under general lighting. To this end, the approach exploits a physically-aware image formation model that in combination with a perspective projection one and under spherical harmonics lighting gives a fully interpretable algorithm. Integrability is implicitly ensured as the shape is coded by a depth map rather than normal vectors. As a consequence, a wide variety of illumination conditions and complex geometries can be acquired. Our claims have been experimentally validated on challenging synthetic and real datasets, obtaining a good trade-off between accuracy and computational budget in comparison with competing approaches.
Hybrid Event Memory as a Case Base for State Estimation in Cognitive Agents

State estimation is a fundamental problem in artificial intelligence that involves inferring the state of a system based on available measurements. We investigate the role an integrated event memory can play in tackling state estimation of complex domains. Our approach uses the hybrid event memory developed in our previous work as a case base within a cognitive architecture, which enables agents to incrementally learn and represent large joint probability distributions over states as Bayesian networks. To facilitate near real-time execution of this process for agents, we extended the event memory system with a rule-based representation for encoding large probability distributions in its networks. After a review of our cognitive architecture and its event memory, we describe the representational extension before presenting experimental results demonstrating our system’s ability to scale to large state estimation problems under various partial observability conditions in the Minecraft domain.
Lazy Adaptation Knowledge Learning Based on Frequent Closed Itemsets

This paper focuses on lazy adaptation knowledge learning (LAKL) using frequent closed itemset extraction. This approach differs from eager adaptation knowledge learning (EAKL) by the number of cases used in the learning process and by the moment at which the process is triggered. Where EAKL aims to compute adaptation knowledge once on the whole case base with the idea of solving every future target problem, LAKL computes adaptation knowledge on a subset of cases close to the target problem when a new problem has to be solved. The paper presents experiments on generated datasets from Boolean functions and on a real-world dataset, studying especially how the size of the case base used impacts performance. The results show that LAKL outperforms EAKL in precision and correct answer rate and that it is therefore better not to use the whole case base for adaptation knowledge learning.
Introduction

Business intelligence is a collection of software, technologies, and methods to discover valuable laws and patterns from data, transform data into knowledge, and support enterprises’ decision making, marketing, and services (Chen et al. in Principles and methods of business intelligence, 2nd edn. Electronics Industry Press, Beijing, 2014 [1]). With the rapid development of big data, e-commerce is gradually transitioning to the era of big data, i.e., the new generation of e-commerce. According to eMarketer, a leading global market research firm, global e-commerce sales will be $4.938 trillion in 2021, and according to its forecast predicts that global e-commerce sales will reach $5.542 trillion in 2022, accounting for more than one-fifth of total retail sales. Such rapid growth brings broad prospects for the development of a new generation of e-commerce industry, which means a strong market and broad user demand.
Failure-Driven Transformational Case Reuse of Explanation Strategies in CloodCBR

In this paper, we propose a novel approach to improve problem-solving efficiency through the reuse of case solutions. Specifically, we introduce the concept of failure-driven transformational case reuse of explanation strategies, which involves transforming suboptimal solutions using relevant components from nearest neighbours in sparse case bases. To represent these explanation strategies, we use behaviour trees and demonstrate their usefulness in solving similar problems. Our approach uses failures as a starting point for generating new solutions, analysing the causes and contributing factors to the failure. From this analysis, new solutions are generated through a nearest neighbour-based transformation of previous solutions, resulting in solutions that address the failure. We compare different approaches for reusing solutions of the nearest neighbours and empirically evaluate whether the transformed solutions meet the required explanation intents. Our proposed approach has the potential to significantly improve problem-solving efficiency in sparse case bases with complex case solutions.
The Case for Circularities in Case-Based Reasoning

In this paper, we demonstrate that diverse CBR research contexts share a common thread, in that their origin can be traced to the problem of circularity. An example is where the knowledge of property A requires us to know property B, but B, in turn, is not known unless A is determined. We examine the root cause of such circularities and present fundamental impossibility results in this context. We show how a systematic study of circularity can motivate the quest for novel CBR paradigms and lead to novel approaches that address circularities in traditional CBR retrieval, adaptation, and maintenance tasks. Furthermore, such an analysis can help in extending the solution of one problem to solve an apparently unrelated problem, once we discover the commonality they share deep down in terms of the circularities they address.
Toward Surroundings-Aware Temporal Prediction of 3D Human Skeleton Sequence

Temporal prediction of human pose sequence is vital for robot applications such as human-robot interaction and autonomous control of a robot. Recent methods are based on a 3D human skeleton sequence to predict future skeletons. Even if starting motions of two human skeleton sequences are very similar, their future motions may be different because of the surrounding objects of the human; it is difficult to predict the future skeleton sequences only from a given human skeleton sequence. However, don’t you think the presence of surrounding objects is an important clue for the prediction? This paper proposes a method of predicting future skeleton sequences by incorporating the surrounding information into the skeleton sequence. We assume that the surrounding condition around a target person does not change significantly within a few seconds and use an image feature around the target person as the surrounding information. Through evaluations on a public dataset, performance improvement is confirmed.
Client Selection Based on Diversity Scaling for Federated Learning on Non-IID Data

In a wireless Federated Learning (FL) system, clients train their local models over local datasets on IoT devices. The derived local models are uploaded to the FL server which generates a global model, then broadcasts the model back to the clients for further training. Due to the heterogeneous feature of clients, client selection plays an important role in determining the overall training time. Traditionally, maximum number of clients are selected if they can derive and upload their local models before the deadline in each global iteration. However, selecting more clients not only increases the energy consumption of the clients, but also might not be necessary as having fewer clients in early global iterations and more clients in later iterations have been proved better for model accuracy. To address the issue, this paper proposes a client selection scheme which dynamically adjusts and optimizes the trade-off between maximizing the number of selected clients and minimizing the total communication cost between the clients and the server. By comparing the data diversity of clients, this scheme can select the most suitable clients for global convergence. A Diversity Scaling Node Selection framework (FedDS) is implemented to dynamically change the selecting weights of each node based on the degree of non-i.i.d data diversity. Results has shown that the proposed FedDS can speed up the FL convergence rate compared to FedAvg with random node selection.
Appearance-Independent Pose-Based Posture Classification in Infants

Disruption of motor development in infancy is a risk indicator for a host of developmental delays and disabilities, but screening for such disruptions currently requires expert assessment. Automated assessment of motor development by advanced computer vision algorithms would yield objective screening tools that are inexpensive, highly scalable, and virtually accessible from anywhere. To this end, we present an appearance-independent posture classification framework based on the 2D or 3D poses of the infants. The framework uses pose information extracted with our previously developed models and classifies infant poses into four major postures. Our method is data and label efficient and can work with small amounts of infant pose and posture data. We trained and compared the performance of the infant posture classification model on input RGB images as well as input 2D and 3D posed of our publicly available synthetic and real infant pose (SyRIP) dataset. When applied to a fully novel dataset of images of infants recorded in their interactive natural environments, the infant posture classification performance achieves a classification accuracy of 80.1% with the 2D pose-based inputs and 83.2% with the 3D pose-based inputs (SyRIP dataset and infant posture classification model code available at https://github.com/ostadabbas/Infant-Posture-Estimation .).
Application of Business Big Data Management and Decision Making

The high socialization and ubiquity of emerging e-commerce brings together huge user groups and potential business opportunities. A large number of malicious users gain economic benefits by generating and spreading false opinions and junk information. The analysis and detection of malicious user behavior has become a hot field in the interdisciplinary field of e-commerce.
Unsupervised Brain MRI Anomaly Detection for Multiple Sclerosis Classification

Supervised deep learning has been widely applied in medical imaging to detect multiple sclerosis. However, it is difficult to have perfectly annotated lesions in magnetic resonance images, due to the inherent difficulties with the annotation process performed by human experts. To provide a model that can completely ignore annotations, we propose an unsupervised anomaly detection approach. The method uses a convolutional autoencoder to learn a “normal brain” distribution and detects abnormalities as a deviation from the norm. Experiments conducted with the recently released OASIS-3 dataset and the challenging MSSEG dataset show the feasibility of the proposed method, as very encouraging sensitivity and specificity were achieved in the binary health/disease discrimination. Following the “normal brain” learning rule, the proposed approach can easily generalize to other types of brain diseases, due to its potential to detect arbitrary anomalies.
CBR Assisted Context-Aware Surface Realisation for Data-to-Text Generation

Current state-of-the-art neural systems for Data-to-Text Generation (D2T) struggle to generate content from past events with interesting insights. This is because these systems have limited access to historic data and can also hallucinate inaccurate facts in their generations. In this paper, we propose a CBR-assisted context-aware methodology for surface realisation in D2T that carefully selects important contextual data from past events and utilises a hybrid CBR and neural text generator to generate the final event summary. Through extensive experimentation on a sports domain dataset, we empirically demonstrate that our proposed method is able to accurately generate contextual content closer to human-authored summaries when compared to other state-of-the-art systems.
MTGR: Improving Emotion and Sentiment Analysis with Gated Residual Networks

In this paper, we address the problem of emotion recognition and sentiment analysis. Implementing an end-to-end deep learning model for emotion recognition or sentiment analysis that uses different modalities of data has become an emerging research area. Numerous research studies have shown that multimodal transformers can efficiently combine and integrate different heterogeneous modalities of data, and improve the accuracy of emotion/sentiment prediction. Therefore, in this paper, we propose a new multimodal transformer for sentiment analysis and emotion recognition. Compared to previous work, we propose to integrate a gated residual network (GRN) into the multimodal transformer to better capitalize on the various signal modalities. Our method shows an improvement of the F1 score and the accuracy results on the CMU-MOSI and IEMOCAP datasets compared to the state-of-the-art results.
Sentiment Analysis: Predicting the Position of Islamic Political Parties in Indonesia in the Next Election

The emergence of Islamic political parties was initially thought to represent the voices of the majority of voters in Indonesia, where the majority of voters are Muslim. However, in reality, they could not compete with nationalist political parties. That prompted this study to research Islamic political parties to see opportunities for the next election. This study was conducted using sentiment analysis by relying on the NVivo 12 Plus analysis tool to identify the sentiment results of Twitter users towards Islamic political parties. This study’s findings show negative sentiment results for Islamic political parties. It also indicates that Islamic political parties are not ready to appear as parties that dominate elections and challenge nationalist political parties. It also suggests that the nationalist party will remain a strong pivot for the upcoming election period. The result of negative sentiment is also predicted to influence the decisions of Islamic political parties in the future, especially in forming a coalition axis with nationalist political parties. This will consciously encourage Islamic political parties to ignore their own party’s ideology. In addition, the result of negative sentiment is also predicted to encourage a change in the future campaign model, which is more focused on inclusive nationalist appeals or exclusive appeals to Islam, especially on social media, to change public sentiment. This will likely be done naturally to secure his political position nationally in the upcoming elections.
Synthesis of a Governance Framework for Blockchain Technology: A Meta-synthesis

The governance of blockchain (BC) technology is a topic of continuing study, and a multidisciplinary perspective on blockchain governance is essential for building a formal governance framework for this emerging technology. While there may be some misconceptions surrounding the topic of blockchain governance, it is worth noting that blockchain technology itself has garnered increasing attention from both academic and business communities worldwide. Blockchain stakeholders cannot work or make rational decisions optimally without enough insight into the governance of blockchains. The element that organisations and end-users must consider in their decisions, is the element of regulation for the blockchain technology or network, which regulators and developers still need to grasp this concept. There is a pressing need for meta-synthesis concerning blockchain governance to address the limitations of individual studies, generate new insights and understandings, identify gaps in the existing research, and keep up with the latest developments in this rapidly evolving field. This Meta-synthesis study intends to provide a systematic literature review to evaluate the current Information Systems (IS) literature in search of evidence of governance concerns and frameworks within the blockchain technology environment. In this research, literature shall be used to inform the development and proposition of the synthesized integrated blockchain governance framework model so that there is a common frame of reference for researchers and assists practitioners and business managers of companies in conducting more organised analyses of blockchain governance. As the issue was completely addressed throughout the five-stage assessment, the remaining 30 publications were coded using Nvivo software, 42 codes were created. Each author contributed to summarising and categorising the resultant codes (axial coding). The innovative blockchain governance framework proposes four perspectives: Open-Source System (OSS) Governance Phases, Open-Source System (OSS) Life Cycle Stages, blockchain governance framework dimensions and layers and the Technology-Organisation-Environment (TOE) framework model. It is one of the first research papers in the information systems field to make use of a meta-synthesis approach to consolidate the existing qualitative research and to systematically come up with a blockchain governance framework.
Matching Production and Test Files: A Comparison of Filename and Statement-Based Approaches

Accurate matching of test and production files plays a critical role in the analysis and evaluation of Test-Driven Development (TDD). However, current approaches often yield unsatisfactory results due to their reliance on filename-based matching. The purpose of this study is to compare the performance of a statement-based matching algorithm with the traditional filename-based approach. A comprehensive evaluation was conducted using 500 tests from 16 open-source Java projects, wherein the weighted F1-scores of both methods were assessed. Subsequently, the 95% confidence intervals were determined using a pseudosample size of 500. The statement-based approach achieved a 95% confidence interval of [0.6815, 0.7347], while the filename-based method had a notably lower interval of [0.1931, 0.2459]. These results demonstrate the superior performance of the statement-based matching algorithm, providing a more accurate and reliable solution for matching test and production files in TDD research. In conclusion, the statement-based matching algorithm significantly outperforms the filename-based method, which will benefit TDD research by offering a more accurate method of matching production files to test files.
Preliminary Investigation into a Security Approach for Infrastructure as Code

IaC is relatively a novel technology, with the result that many security frameworks don’t have a clear strategy for risk management or threat modelling for infrastructure when implementing IaC techniques. In DevOps, infrastructure is initialized, prepared, managed, and configured with a left-shift on quality. The DevOps methodology increases the integrity and stability of the deployment. IaC works best with DevOps practices for code quality, scalability, security, and reliability. Infrastructure as Code (IaC) promotes managing knowledge and experience through reusable scripts of infrastructure code, instead of the traditional method of manual labour technique, which is typically slow and time-consuming. This research determines some security risks that should considered during the IaC development process. It further defines the main security practices that should be added into Infrastructure as Code life cycle to fill the gap in the SDLC for IaC. An initial proposal to secure pipelines for IaC is presented.
Development and Implementation of a Scalable and Replicable Industrial Environment at Low Cost to Control an Industrial Process

Automation systems allow the processes to be efficient; however, they are expensive, and an automation process is proposed at low cost, through the use of open-source tools considering all the variables necessary for its optimal operation, and whose results are intended to be replicable and scalable in different processes of an industrial plant. Of course, it contains the design, development, and implementation of three human–machine interfaces that facilitate the control and monitoring of each of the variables and states of the process. These interfaces are segmented as follows. The first one proposes the use of a chatbot that, on the one hand, allows remote control instructions to be sent to the process, while, on the other hand, it can receive information on the status of the process, either by request, or automatically, considering cases of alarms. The second interface is related to the use of a voice assistant allowing to connect with the user bidirectionally with the process through voice commands using natural language. The third interface is related to the use of augmented reality techniques to access process information. In general, the project seeks to present an alternative solution for the implementation of automated industrial processes, focusing its focus, on the one hand, on reducing costs without altering the characteristics of an industrial automatization, while on the other hand, it seeks to incorporate new interfaces, that seek to reduce operator–machine interaction times resulting in greater efficiency and autonomy of the process.