Reviewerly/exHarmony huggingface dataset:
Declarative Approaches to Counterfactual Explanations for Classification

Abstract We propose answer-set programs that specify and compute counterfactual interventions on entities that are input on a classification model. In relation to the outcome of the model, the resulting counterfactual entities serve as a basis for the definition and computation of causality-based explanation scores for the feature values in the entity under classification, namely responsibility scores . The approach and the programs can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus of this study is on the specification and computation of best counterfactual entities, that is, those that lead to maximum responsibility scores. From them one can read off the explanations as maximum responsibility feature values in the original entity. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints. Several examples of programs written in the syntax of the DLV ASP-solver, and run with it, are shown.
WITHDRAWN: Breast cancer images classification by clustering of ROI and mapping of features by CNN with XGBOOST learning

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Privacy in Data Service Composition

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services. However, data integration is often hindered by the lack of a trusted entity, often called a mediator, with which the services can share their data and delegate the enforcement of their privacy policies. In this paper, we propose a flexible privacy-preserving data integration approach for answering data integration queries without the need for a trusted mediator. In our approach, services are allowed to enforce their privacy policies locally. The mediator is considered to be untrusted, and only has access to encrypted information to allow it to link data subjects across the different services. Services, by virtue of a new privacy requirement, dubbed k-Protection, limiting privacy leaks, cannot infer information about the data held by each other. End-users, in turn, have access to privacy-sanitized data only. We evaluated our approach using an example and a real dataset from the healthcare application domain. The results are promising from both the privacy preservation and the performance perspectives.
ICU Data Management - A Permissioned Blockchain Approach

Since its origin in finance, blockchain have been revolutionizing data storage and sharing in many other sensitive areas. Being the focus of Permissioned Blockchains around privacy, confidentiality, immutability, interoperability and reliability, it fits perfectly within the data requisites of healthcare. Even more, with the surge of new iterations of more recent implementations based on smart-contracts/chaincode that has its focus on increasing efficiency and usability and ease of implementation. Intensive Medicine an area with such high data complexity and throughput, and high incidence of medical error and patient injury. As such, it’s imperative the continuous research and implementation of new technologies that can make pertinent knowledge available through reliable and accurate data, thus providing appropriate problem-solving skills to physicians. This paper presents a solution, as part of the Intelligence Decision Support Systems for Intensive Medicine (ICDS4IM) project, which objective is to increase accuracy, confidentiality and value to data from vital sensors and monitors by assuring its immutability and controlled oversee.
A Gaussian Process based Method for Multiple Model Tracking

Manoeuvring target tracking faces the challenge caused by the target motion model uncertainty, i.e., unknown model types or uncertain model parameters. Multiple-model (MM) methods have been generally considered to deal with this challenge, in which a bank of elemental filters is run simultaneously to provide a joint decision and estimation of motion model and localisation. However, if the uncertainty of the target trajectory increases, such as the target moves under mixed manoeuvring behaviours with time-varying parameters, more filters with different model assumptions have to be taken into account to match the motion of the target, which may lead to prohibitive computational complexity. To address this problem, we establish a training based algorithm which can learn the actual motion model as a Gaussian process (GP) regression. Then, by integrating the trained GP into the particle filter (PF), a GP-PF based tracking method is developed to track the manoeuvring targets in real-time. Monte Carlo experiments show that the proposed method had much lower tracking root mean square error (RMSE) and robustness compared with the most commonly used MM methods.
3D Human Pose Estimation With Generative Adversarial Networks

3D human pose estimation from a monocular RGB image is a challenging task in computer vision because of depth ambiguity in a single RGB image. As most methods consider joint locations independently which can lead to an overfitting problem on specific datasets, it's crucial to consider the plausibility of 3D poses in terms of their overall structures. In this paper, we present Generative Adversarial Networks (GANs) for 3D human pose estimation, which learn plausible 3D human body representations by adversarial training. In GANs, the generator regresses 3D joint positions from a 2D input and the discriminator aims to distinguish the ground-truth 3D samples from the predicted ones. We leverage Graph Convolutional Networks (GCNs) in both generator and discriminator to fully exploit the spatial relations of input and output coordinates. The combination of GANs and GCNs promotes the network to predict more accurate 3D joint locations and learn more reasonable human body structures at the same time. We demonstrate the effectiveness of our approach on standard benchmark datasets (i.e. Human3.6M and HumanEva-I) where it outperforms state-of-the-art methods. Furthermore, we propose a new evaluation metric distance-based Pose Structure Score (dPSS) for evaluating the structural similarity degree between the predicted 3D pose and its ground-truth.
Image-to-Voxel Model Translation for 3D Scene Reconstruction and Segmentation

Objects class, depth, and shape are instantly reconstructed by a human looking at a 2D image. While modern deep models solve each of these challenging tasks separately, they struggle to perform simultaneous scene 3D reconstruction and segmentation. We propose a single shot image-to-semantic voxel model translation framework. We train a generator adversarially against a discriminator that verifies the object's poses. Furthermore, trapezium-shaped voxels, volumetric residual blocks, and 2D-to-3D skip connections facilitate our model learning explicit reasoning about 3D scene structure. We collected a SemanticVoxels dataset with 116k images, ground-truth semantic voxel models, depth maps, and 6D object poses. Experiments on ShapeNet and our SemanticVoxels datasets demonstrate that our framework achieves and surpasses state-of-the-art in the reconstruction of scenes with multiple non-rigid objects of different classes. We made our model and dataset publicly available ( http://www.zefirus.org/SSZ ).
Breast Cancer Prediction Based on K-Means and SOM Hybrid Algorithm

Abstract Breast cancer is one of the most serious diseases that threaten women’s health, affecting about 12.5% of women worldwide. Early detection of breast cancer is critical to saving lives. Therefore, If the physical examination indicators related to the human body can be extracted and the breast cancer can be analyzed through machine learning, which will play a key role in predicting and preventing breast cancer. As the high complexity and low precision of SOM neural network algorithm and shortcomings of K-means clustering algorithm needs to determine the number of clustering advanced and randomly select initial clustering centers, a hybrid algorithm combining K-means and SOM neural network is proposed in this study. The results show that the hybrid algorithm can accurately cluster the data sets, and compared with K means model and SOM neural network model, the performance of the hybrid algorithm model is better in clustering accuracy and computing speed.
A deep neural architecture based meta-review generation and final decision prediction of a scholarly article

Peer reviews form an essential part of scientific communications. Research papers and proposals are reviewed by several peers before they are finally accepted or rejected for publication and funding, respectively. With the steady increase in the number of research domains, scholarly venues (journal and/or conference), researchers, and papers, managing the peer review process is becoming a daunting task. Application of recommender systems to assist peer reviewing is, therefore, being explored and becoming an emerging research area. In this paper, we present a deep learning network based Meta-Review Generation considering peer review prediction of the scholarly article (MRGen). MRGen is able to provide solutions for: (i) Peer review prediction (Task 1) and (ii) Meta-review generation (Task 2). First, the system takes the peer reviews as input and produces a draft meta-review. Then it employs an integrated framework of convolution layer, long short-term memory (LSTM) model, Bi-LSTM model, and attention mechanism to predict the final decision (accept/reject) of the scholarly article. Based on the final decision, the proposed model MRGen incorporates Pointer Generator Network-based abstractive summarization to generate the final meta-review. The focus of our approach is to give a concise meta-review that maximizes information coverage, coherence, readability and also reduces redundancy. Extensive experiments conducted on the PeerRead dataset demonstrate good consistency between the recommended decisions and original decisions. We also compare the performance of MRGen with some of the existing state-of-the-art multi-document summarization methods. The system also outperforms a few existing models based on accuracy, Rouge scores, readability, non-redundancy, and cohesion.
A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package

Nowadays, interest is growing in automating KDD processes. Thanks to the increasing power and decreasing costs of computation devices, the search for the best features and model parameters can be conducted with different meta-heuristics. Thus, researchers can focus on other important tasks like data wrangling or feature engineering. This article details a comparative study of a GAparsimony R Package with six model complexity metrics. The objective was to identify an adequate model complexity measure for searching for accurate parsimonious solutions by combining feature selection, hyperparameter optimization, and parsimonious evaluation. This study also includes a regression code example to address some recommended precautions and considerations to find robust parsimonious models. This code can be easily adapted to other problems, databases, or algorithms.
Impact of the Impairment in 360-Degree Videos on Users VR Involvement and Machine Learning-Based QoE Predictions

Current extended virtual reality (VR) applications use 360-degree video to boost viewers' sense of presence and immersion. The quality of experience (QoE) effectiveness of 360-degree video in VR has often been related to many aspects. The four significant aspects to take into account when evaluating QoE in the VR are a sense of presence and immersion, acceptability, reality judgment, and attention captivated. In this manuscript, we subjectively investigate the impact of 360-degree videos QoE-affecting factors, including quantization parameters (QP), resolutions, initial delay, and different interruptions (single interruption and two interruptions) on these QoE-aspects. We then design a Decision Tree-based (DT) prediction models that predict users' VR immersion, acceptability, reality judgment, and attention captivated based on subjective data. The accuracy performance of the DT-based model is then analyzed with respect to mean absolute error (MAE), precision, accuracy rate, recall, and f1-score. The DT-based prediction model performs well with a 91% to 93% prediction accuracy, which is in close agreement with the subjective experiment. Finally, we compare the performance accuracy of the proposed model against existing Machine learning methods. Our DT-based prediction model outperforms state-of-the-art QoE prediction methods.
Leveraging machine learning for software redocumentation—A comprehensive comparison of methods in practice

Abstract Source code comments contain key information about the underlying software system. Many redocumentation approaches, however, cannot exploit this valuable source of information. This is mainly due to the fact that not all comments have the same goals and target audience and can therefore only be used selectively for redocumentation. Performing a required classification manually, for example, in the form of heuristics, is usually time‐consuming and error‐prone and strongly dependent on programming languages and guidelines of concrete software systems. By leveraging machine learning (ML), it should be possible to classify comments and thus transfer valuable information from the source code into documentation with less effort but the same quality. We applied classical ML techniques but also deep learning (DL) approaches to legacy systems by transferring source code comments into meaningful representations using, for example, word embeddings but also novel approaches using quick response codes or a special character‐to‐image encoding. The results were compared with industry‐strength heuristic classification. As a result, we found that ML outperforms the heuristics in number of errors and less effort, that is, we finally achieve an accuracy of more than 95% for an image‐based DL network and even over 96% for a traditional approach using a random forest classifier.
High Throughput Novel Architecture of SIT Cipher for IoT Application

The advancement in network connectivity and data handling capabilities shows the tremendous growth of Internet of things (IoT) in physical life. The number of connected devices has been increasing in IoT applications, left severe security concerns. The problem of providing security solutions to resource-constrained devices leads to lightweight cryptographic algorithms. Secure IoT (SIT) is one of the lightweight cryptographic algorithm follows SPN and Feistel structure. SIT cipher works on 64-bit block and 64-bit key size. In this chapter, an efficient pipelined architecture of SIT lightweight block cipher has been proposed. Operating frequency of this proposed work has been improved by 17% and also throughput improved by more than 180% in comparison with LEA lightweight block cipher.
An eigenspace divide-and-conquer approach for large-scale optimization

Divide-and-conquer-based (DC-based) evolutionary algorithms (EAs) have achieved notable success in dealing with large-scale optimization problems (LSOPs). However, the appealing performance of this type of algorithms generally requires a high-precision decomposition of the optimization problem, which is still a challenging task for existing decomposition methods. This study attempts to address the above issue from a different perspective and proposes an eigenspace divide-and-conquer (EDC) approach. Different from existing DC-based algorithms that perform decomposition and optimization in the original solution space, EDC first establishes an eigenspace by conducting singular value decomposition on a set of high-quality solutions selected from recent generations. Then it transforms the optimization problem into the eigenspace, and thus significantly weakens the dependencies among the corresponding eigenvariables. Accordingly, these eigenvariables can be efficiently grouped by a simple random decomposition strategy and each of the resulting subproblems can be addressed more easily by a traditional EA. To verify the efficiency of EDC, comprehensive experimental studies were conducted on two sets of benchmark functions. Experimental results indicate that EDC is robust to its parameters and has good scalability to the problem dimension. The comparison with several state-of-the-art algorithms further confirms that EDC is pretty competitive and performs better on complicated LSOPs.
Open quantum systems integrable by partial commutativity

This article provides a framework to solve linear differential equations based on partial commutativity, which is introduced by means of the Fedorov theorem. The framework is applied to specific types of three-level and four-level quantum systems. The efficiency of the method is evaluated and discussed. The Fedorov theorem appears to answer the need for methods that allow us to study dynamical maps corresponding with time-dependent generators. By applying this method, one can investigate countless examples of dissipative systems such that the relaxation rates depend on time.
CoinBot: A Covert Botnet in the Cryptocurrency Network

Cryptocurrencies are a new form of digital asset and are being widely used throughout the world. A variety of cryptocurrency-based botnets have been proposed and developed to utilize cryptocurrencies as new command and control (C&C) platforms. Most existing cryptocurrency-based botnets are bonded with the cryptocurrency client, which generates abnormal P2P traffic that can be easily detected and blocked. In addition, the commands embedded in transaction records can be easily traced, since the transaction records in a cryptocurrency network are usually publicly available. In this paper, we propose CoinBot, a novel botnet that based on the cryptocurrency networks. CoinBot is characterized by low cost, high resilience, stealthiness, and anti-traceability. Different from other cryptocurrency-based botnet, CoinBot utilizes Web2.0 services to achieve a dynamic addressing service for obtaining commands. As such, there is no need to run a cryptocurrency wallet application and hardcode a botmaster’s sensitive information in CoinBot, and the communications between the botmaster and the bots are hidden under legitimate HTTP/S traffic. Furthermore, we propose a cleaning scheme to prevent commands from being permanently recorded in the blockchain, thereby decreasing the risk of channel exposure. CoinBot is a generic model that can be applied to different kinds of cryptocurrency networks. We believe this model will be highly attractive to botmasters and could pose a considerable threat to cybersecurity. Therefore, we provide defensive suggestions to mitigate similar threats in the future.
Characterizing the Scale-Up Performance of Microservices using TeaStore

Cloud-based applications architected using microservices are becoming increasingly common. While recent work has studied how to optimize the performance of these applications at the data-center level, comparatively little is known about how these services utilize end-server compute resources. Major advances have been made in recent years in terms of the compute density offered by cloud servers, thanks to the emergence of mainstream, high-core count CPU designs. Consequently, it has become equally important to understand the ability of microservices to “scale up” within a server and make effective use of available resources. This paper presents a study of a publicly available microservice based application on a state-of-the-art x86 server supporting 128 logical CPUs per socket. We highlight the significant performance opportunities that exist when the scaling properties of individual services and knowledge of the underlying processor topology are properly exploited. Using such techniques, we demonstrate a throughput uplift of 22% and a latency reduction of 18% over a performance-tuned baseline of our microservices workload. In addition, we describe how such microservice-based applications are distinct from workloads commonly used for designing general-purpose server processors. This paper presents a study of a publicly available microservice based application on a state-of-the-art x86 server supporting 128 logical CPUs per socket. We highlight the significant performance opportunities that exist when the scaling properties of individual services and knowledge of the underlying processor topology are properly exploited. Using such techniques, we demonstrate a throughput uplift of 22% and a latency reduction of 18% over a performance-tuned baseline of our microservices workload. In addition, we describe how such microservice-based applications are distinct from workloads commonly used for designing general-purpose server processors.
A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification

User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user intents, this paper proposes a BERT-Cap hybrid neural network model with focal loss for user intent classification to capture user intents in dialogue. The model uses multiple transformer encoder blocks to encode user utterances and initializes encoder parameters with a pre-trained BERT. Then, it extracts essential features using a capsule network with dynamic routing after utterances encoding. Experiment results on four publicly available datasets show that our model BERT-Cap achieves a F1 score of 0.967 and an accuracy of 0.967, outperforming a number of baseline methods, indicating its effectiveness in user intent classification.