Reviewerly/exHarmony huggingface dataset:
Cases Are King: A User Study of Case Presentation to Explain CBR Decisions

From the early days of case-based reasoning research, the ability of CBR systems to explain their decisions in terms of past cases has been seen as an important advantage. However, there have been few studies on the factors affecting the effectiveness of explaining CBR decisions by cases. This paper presents results from a human subjects study that examined how alternative retrieval processes (one-shot or conversational) and case presentation approaches affect the perceived goodness of case-based explanations for explaining system behavior, their convincingness, and the trust they engender. The study corroborates that cases are well received as explanations, with some benefit for providing information to support similarity comparison, and suggests that elucidating the retrieval process has little effect on explanatory effectiveness.
Examining the Impact of Network Architecture on Extracted Feature Quality for CBR

Classification accuracy for case-based classifiers depends critically on the features used for case retrieval. Feature extraction from deep learning classifier models has proven a useful method for generating case-based classifier features, especially for domains in which manual feature engineering is costly or difficult. Previous work has explored how the quality of extracted features is influenced by structural choices such as the number of features extracted and the location/depth of extraction. This paper investigates how feature quality is influenced by another factor: the choice of the network model itself. We consider a selection of deep learning models for a computer vision classification task and test the accuracy of a case-based classifier using features extracted from them, both as the sole feature source and in combination with a supplementary set of knowledge-engineered features. Results suggest that feature quality reflects a trade-off between model complexity and training data requirements and provide lessons for the selection of deep learning architectures for feature extraction to support case-based classification.
An Overview and Comparison of Case-Based Reasoning Frameworks

Case-Based Reasoning (CBR) is a methodology with many applications in industrial and scientific domains. Over the past decades, various frameworks have been developed to facilitate the development of CBR applications. For practitioners and researchers, it is challenging to overview the landscape of existing frameworks with their specific scope and features. This makes it difficult to choose the most suitable framework for specific requirements. To address this issue, this work provides an overview and comparison of CBR frameworks, focusing on five recent, open-source CBR frameworks: CloodCBR, eXiT*CBR, jColibri, myCBR, and ProCAKE. They are compared by supported CBR types, knowledge containers, CBR phases, interfaces, and special features.
An overview of ensemble and feature learning in few-shot image classification using siamese networks

Abstract Siamese Neural Networks (SNNs) constitute one of the most representative approaches for addressing Few-Shot Image Classification. These schemes comprise a set of Convolutional Neural Network (CNN) models whose weights are shared across the network, which results in fewer parameters to train and less tendency to overfit. This fact eventually leads to better convergence capabilities than standard neural models when considering scarce amounts of data. Based on a contrastive principle, the SNN scheme jointly trains these inner CNN models to map the input image data to an embedded representation that may be later exploited for the recognition process. However, in spite of their extensive use in the related literature, the representation capabilities of SNN schemes have neither been thoroughly assessed nor combined with other strategies for boosting their classification performance. Within this context, this work experimentally studies the capabilities of SNN architectures for obtaining a suitable embedded representation in scenarios with a severe data scarcity, assesses the use of train data augmentation for improving the feature learning process, introduces the use of transfer learning techniques for further exploiting the embedded representations obtained by the model, and uses test data augmentation for boosting the performance capabilities of the SNN scheme by mimicking an ensemble learning process. The results obtained with different image corpora report that the combination of the commented techniques achieves classification rates ranging from 69 % to 78 % with just 5 to 20 prototypes per class whereas the CNN baseline considered is unable to converge. Furthermore, upon the convergence of the baseline model with the sufficient amount of data, still the adequate use of the studied techniques improves the accuracy in figures from 4 % to 9 % .
Exploring latent weight factors and global information for food-oriented cross-modal retrieval

Food-oriented cross-modal retrieval aims to retrieve relevant recipes given food images or vice versa. The modality semantic gap between recipes and food images (text and image modalities) is the main challenge. Though several studies are introduced to bridge this gap, they still suffer from two major limitations: 1) The simple embedding concatenation only can capture the simple interactions rather than complex interactions between different recipe components. 2) The image feature extraction based on convolutional neural networks only considers the local features and ignores the global features of an image, as well as the interactions between different extracted features. This paper proposes a novel method based on Latent Component Weight Factors and Global Information (LCWF-GI) to learn the robust recipe and image representations for food-oriented cross-modal retrieval. This proposed method integrates the textual embeddings of different recipe components into a compact embedding to represent the recipes with the latent component-specific weight factors. A transformer encoder is utilised to capture the intra-modality interactions and the importance of different extracted image features for enhanced image representations. Finally, the bi-directional triplet loss is further used to perform retrieval learning. Experimental results on the Recipe 1M dataset show that our LCWF-GI method achieves competent improvements.
Class relationship‐based knowledge distillation for efficient human parsing

Abstract In computer vision, human parsing is challenging due to its demand for accurate human region location and semantic partitioning. This dense prediction task needs powerful computation and high‐precision models. To enable real‐time parsing on resource‐limited devices, the authors introduced a lightweight model using ResNet18 as a core network . The authors simplified the pyramid module, improving context clarity and reducing complexity. The authors integrated a spatial attention fusion strategy to counter precision loss in the light‐weighting process. Traditional models, despite their segmentation precision, are limited by their computational complexity and extensive parameters. The authors implemented knowledge distillation (KD) techniques to enhance the authors’ lightweight network's accuracy. Traditional methods can fail to learn useful knowledge with significant network differences. Hence, the authors used a novel distillation approach based on inter‐class and intra‐class relations in prediction outcomes, noticeably improving parsing accuracy. The authors’ experiments on the Look into Person (LIP) dataset show that their lightweight model significantly reduces parameters while maintaining parsing precision and enhancing inference speed.
Stress, motivation, and performance in global software engineering

Abstract The objective of this study is to analyze the current perspective as regards knowledge related to what causes stress or motivates developers, how these two aspects are related to each other, and how this in turn affects their performance in the sphere of Global Software Development and how these can be controlled. This paper presents the results obtained after conducting a systematic mapping study of literature in order to analyze how stress, motivation, and performance affect the project members in Global Software Development teams. We carried out a systematic mapping of published studies dealing with stress, motivation, and performance in global software engineering. A total of 118 papers dealing with this subject were found. The literature analyzed provided a relatively significant quantity of data referring to the impact that the characteristics of distributed software development projects have on the performance and productivity of teams, along with the actions taken to improve that performance. However, when focusing on the analysis of the impact of this type of projects on team members' motivation, and on the actions that can be taken to improve that motivation, we discovered that the number of works decreases considerably and that works referring to the impact of this kind of development on developers' stress were virtually non‐existent, as were those concerning ways in which to improve that stress. We are, therefore, of the opinion that it is necessary to carry out in‐depth research into the aspects of working in distributed teams that may have a negative impact on developers' levels of motivation and stress, along with what could be beneficial in order to improve levels of motivation and decrease levels of stress.
Efficient distributional reinforcement learning with Kullback-Leibler divergence regularization

In this article, we address the issues of stability and data-efficiency in reinforcement learning (RL). A novel RL approach, Kullback-Leibler divergence-regularized distributional RL (KL-C51) is proposed to integrate the advantages of both stability in the distributional RL and data-efficiency in the Kullback-Leibler (KL) divergence-regularized RL in one framework. KL-C51 derived the Bellman equation and the TD errors regularized by KL divergence in a distributional perspective and explored the approximated strategies of properly mapping the corresponding Boltzmann softmax term into distributions. Evaluated not only by several benchmark tasks with different complexity from OpenAI Gym but also by six Atari 2600 games from the Arcade Learning Environment, the proposed method clearly illustrates the positive effect of the KL divergence regularization to the distributional RL including exclusive exploration behaviors and smooth value function update, and demonstrates an improvement in both learning stability and data-efficiency compared with other related baseline approaches.
Measuring the indication error of the Fizeau interferometer and evaluating its uncertainty

The indication error, as a characteristic of the interferometer, can be obtained through verification and calibration, and the uncertainty during measurement represents the reliability of the indication error. This paper employed the modified absolute measurement method to gain the indication error. First, the improved absolute algorithm was derived. Then, the absolute measurements were conducted with a 150-mm-aperture Fizeau interferometer. The measured indication error conforms to the maximum allowable error of the interferometer. Finally, the measurement uncertainty evaluation was carried out based on the method to characterize the indication error's dependability, which was much less than one-third of the maximum allowable error.
Underwater power compensated white light source based on synthetic white laser

The semiconductor white laser light source is used as a light source for underwater illumination. The required standard color temperature of white light is obtained at the underwater target surface. We studied the power compensation of a synthetic white laser source and its application to underwater illumination. First, the power ratios of the red (638 nm), green (520 nm), and blue (450 nm) lasers at a color temperature of 6500 K were obtained by using chromaticity theory. Next, the three-color and synthetic white laser parameters were obtained with transmission distance, according to the exponential attenuation characteristics of different light in clear water and seawater medium. The three-color laser power at the output was compensated, and the underwater target illumination surface reached the standard 6500 K color temperature of the white laser, improving the illumination. Finally, an experimental system for underwater white laser illumination based on power compensation was established. The errors between experimental and theoretical results of color temperature and illuminance are no more than 0.43% and 22.15%. This power-compensated synthetic white laser light source has both the advantages of long-range underwater detection and the spectral advantages of LED white light sources. The white laser light source meets specific requirements by compensating for power and optimizing white light characteristics for underwater lighting applications.
Investigating, quantifying and controlling the co-location attack’s conditional value at risk of VM placement strategies

Co-location attacks jeopardize data privacy and performance of virtual machines (VMs) in cloud computing, which can result in unexpected monetary losses that are not supported or covered by the investments made by users and providers. The uncertainty about the VM type (attacker, target or neutral) exacerbates the difficulty of the problem. However, existing methods, particularly VM placement strategies, fail to capture the risk of significant losses. The measures of risk have a crucial role in coping with the losses that might be incurred by the involved parties in finance or the insurance industry. In this paper, we introduce the notion of risk to the co-location resistant VM placement strategies. We develop a novel framework that assesses the co-location attack risk of an assignment returned by a specific VM placement strategy. A thorough analysis of multiple existing VM placement algorithms highlights the significance of incorporating this measure as it reveals a discernible correlation pattern with respect to co-location attack coverage and resource consumption. Based on these findings, we propose a new Approximate Co-Location Resistant Placement Strategy with Risk Constraints (ACLRPS-RC) to protect the involved parties from overwhelming security losses. The simulations on CloudSim using real workloads show the ability of the new strategy to meet a given level of risk while maintaining an adequate number of used servers.
Deep steerable pyramid wavelet network for unified JPEG compression artifact reduction

Although numerous methods have been proposed to remove blocking artifacts in JPEG-compressed images, one important issue not well addressed so far is the construction of a unified model that requires no prior knowledge of the JPEG encoding parameters to operate effectively on different compression-level images (grayscale/color) while occupying relatively small storage space to save and run. To address this issue, in this paper, we present a unified JPEG compression artifact reduction model called DSPW-Net, which employs (1) the deep steerable pyramid wavelet transform network for Y-channel restoration, and (2) the classic U-Net architecture for CbCr-channel restoration. To enable our model to work effectively on images with a wide range of compression levels, the quality factor (QF) related features extracted by the convolutional layers in the QF-estimation network are incorporated in the two restoration branches. Meanwhile, recursive blocks with shared parameters are utilized to drastically reduce model parameters and shared-source residual learning is employed to avoid the gradient vanishing/explosion problem in training. Extensive quantitative and qualitative results tested on various benchmark datasets demonstrate the effectiveness of our model as compared with other state-of-the-art deblocking methods.
Enabling efficient and secure health data sharing for Healthcare IoT systems

The Healthcare Internet of Things (HIoT) system enables remote monitor patient health state and feedback on medical treatments timely. The success of HIoT relies on how to share health data securely and efficiently. Existing solutions either fail to ensure the security or incur significant overheads. In this paper, we put forward an efficient and secure health data sharing scheme for HIoT systems, named ESDS, to solve this dilemma. In ESDS, we first signcrypt the health data collected by IoT devices to protect patient privacy and ensure data authenticity. Next, we aggregate N signatures for N pieces of different health data into a short signature, which significantly reduces storage space and transmission size, i.e., from O(N) to O(1). Then, we apply local verifiability to verify one piece of health data under the aggregated signature. With this property, a doctor only needs one auxiliary message generated by the patient to check the target message without obtaining whole health data, which improves system verification efficiency. Extensive security analysis and performance evaluation show that ESDS is secure and efficient.
MD-SGT: Multi-dilation spherical graph transformer for unsupervised medical image registration

Deformable medical image registration is an essential preprocess step for several clinical applications. Even though the existing convolutional neural network and transformer based methods achieved the promising results, the limited long-range spatial dependence and non-uniform attention span of these models prohibit further improving the registration performance. To deal with this issue, we proposed a multi-dilation spherical graph transformer (MD-SGT), in which the encoder combined the advantages of convolutional and graph transformer blocks to distinguish effectively the differences between the reference and the template images at various scales. Specifically, the features of each voxel were obtained by aggregating the information from its neighbors sampled from different spherical regions with different dilation rates. The implicit convolution inductive bias and long-range uniform attention span induced by such information aggregation manner made the features more representative for registration. Through the qualitative and quantitative comparisons with state-of-the-art methods on two datasets, we demonstrated that combining long-range uniform attention span and inductive bias are beneficial for promoting the image registration performance, with the Dice score, ASD and HD95 being improved at least by 0.5%, 2.2% and 1.1%, respectively.
Predicting Alzheimer’s Disease: A Stroke-Based Handwriting Analysis Approach Based on Machine Learning

Alzheimer’s disease (AD) causes most of dementia cases. Although currently there is no cure for this disease, predicting the cognitive decline of people at the first stage of the disease allows clinicians to alleviate its burden. Recently, machine learning based approaches have been developed to automatically analyze handwriting to support early diagnosis of AD. In this context, features are extracted from the coordinates of the handwriting traits, recorded using digital devices. For a given task used for data collection, typically these features take into account the whole handwriting making up the task. In this paper, we present a novel approach to predict Alzheimer’s Disease based on machine learning that analyses the single elementary traits making up handwriting, named strokes. The experimental results confirmed the effectiveness of the proposed approach.
An Empirical Study on Model Pruning and Quantization

In machine learning, model compression is vital for resource-constrained Internet of Things (IoT) devices, such as unmanned aerial vehicles (UAVs) and smart phones. Currently there are some state-of-the-art (SOTA) compression methods, but little study is conducted to evaluate these techniques across different models and datasets. In this paper, we present an in-depth study on two SOTA model compression methods, pruning and quantization. We apply these methods on AlexNet, ResNet18, VGG16BN and VGG19BN, with three well known datasets, Fashion-MNIST, CIFAR-10, and UCI-HAR. Through our study, we draw the conclusion that, applying pruning and retraining could keep the performance (average less than $$0.5\%$$ degrade) while reducing the model size (at $$10\times $$ compression rate) on spatial domain datasets (e.g. pictures); the performance on temporal domain datasets (e.g. motion sensors data) degrades more (average about $$5.0\%$$ degrade); the performance of quantization is related with the pruning rate and the network architecture. We also compare different clustering methods and reveal the impact on model accuracy and quantization ratio. Finally, we provide some interesting directions for future research.
Selecting Explanation Methods for Intelligent IoT Systems: A Case-Based Reasoning Approach

The increasing complexity of intelligent systems in the Internet of Things (IoT) domain makes it essential to explain their behavior and decision-making processes to users. However, selecting an appropriate explanation method for a particular intelligent system in this domain can be challenging, given the diverse range of available XAI (eXplainable Artificial Intelligence) methods and the heterogeneity of IoT applications. This paper first presents a novel case base generated from an exhaustive literature review on existing explanation solutions for AIoT (Artificial Intelligence of the Things) systems. Then, a Case-Based Reasoning (CBR) approach is proposed to address the challenge of selecting the most suitable XAI method for a given IoT domain, AI task, and model. Both the case base and the CBR process are evaluated, showing their effectiveness in selecting appropriate explanation methods for different AIoT systems. The paper concludes by discussing the potential benefits and limitations of the proposed approach and suggesting avenues for future research.
Weighted Voting Stacking Ensemble Method for Highly Skewed Binary Data Distribution

Weighted voting stacking ensemble method is an important technique that combines various classifier models to predict the output of a credit card transaction. The Artificial Neural Network, Support Vector Machine, Naïve Bayesian, and k-Nearest Neighbors algorithms are powerful classifier models. However, in a highly skewed binary classification problem, the binary classification models were observed to misclassify an adequate number of minority class observations (fraudulent transactions). The proposed optimized weighted stacking ensemble model, which was evaluated using the confusion matrix, was found to perform better than the base models in terms of accuracy and error rate. Furthermore, the performances of the weighted stacking ensemble model and the optimized weighted stacking ensemble model were compared using Artificial Neural Network, Support Vector Machine, Naïve Bayesian, and k-Nearest Neighbors base models, and it was observed that the optimized weighted stacking ensemble model performs better than the weighted stacking ensemble model. The optimized model managed to reduce the total misclassified fraudulent transactions from an average of 7818 transactions to an average of 367 transactions. The optimized model can be applied in financial transactions, loan applications, account transaction, loan repayment, and credit card activities.