Reviewerly/exHarmony huggingface dataset:
Making SHAP Rap: Bridging Local and Global Insights Through Interaction and Narratives

The interdisciplinary field of explainable artificial intelligence (XAI) aims to foster human understanding of black-box machine learning models through explanation-generating methods. In practice, Shapley explanations are widely used. However, they are often presented as visualizations and thus leave their interpretation to the user. As such, even ML experts have difficulties interpreting them appropriately. On the other hand, combining visual cues with textual rationales has been shown to facilitate understanding and communicative effectiveness. Further, the social sciences suggest that explanations are a social and iterative process between the explainer and the explainee. Thus, interactivity should be a guiding principle in the design of explanation facilities. Therefore, we (i) briefly review prior research on interactivity and naturalness in XAI, (ii) designed and implemented the interactive explanation interface SHAPRap that provides local and global Shapley explanations in an accessible format, and (iii) evaluated our prototype in a formative user study with 16 participants in a loan application scenario. We believe that interactive explanation facilities that provide multiple levels of explanations offer a promising approach for empowering humans to better understand a model’s behavior and its limitations on a local as well as global level. With our work, we inform designers of XAI systems about human-centric ways to tailor explanation interfaces to end users.
Cross-Domain Recommendation Method Based On Multi-Layer Graph Analysis With Visual Information

In this paper, a cross-domain recommendation (CDR) method based on multi-layer graph analysis with visual information is presented. Although previous graph-based CDR methods have effectively used usersʼ ratings of products and their purchase histories, they lack essential information such as product images that can have an impact on usersʼ decision of whether to purchase products. Then the proposed method newly introduces visual features obtained from product images into the graph-based CDR. For dealing with visual features in multiple domains, we focus on both intra-domain and interdomain relationships through visual features. Specifically, to obtain effective embedding features from users and items in a domain, the proposed method newly introduces visual features into an optimization process of the latest graph neural network that considers only user-item interactions. Consideration of the visual similarity of items within a domain becomes feasible, and embedding features with high representation ability can be estimated. Furthermore, to consider visual information between domains, we construct multi-layer graphs for each domain and introduce visual features into the training of a feature transformer across these graphs. Therefore, consideration of both intra-domain and inter-domain relationships through visual features contributes to the performance improvement. To our best knowledge, this is the first trial to introduce visual features into multi-layer graph-based CDR. The effectiveness of our method is demonstrated by comparing several state-of-the-art methods.
On the Concept of Regulatory Sandboxes

Purpose: The purpose of this work is to study the regulatory sandbox concept and distinguish it from related phenomena.
Multiview Graph Convolutional Hashing for Multisource Remote Sensing Image Retrieval

Recently, hashing has been successfully applied for large-scale remote sensing image retrieval (LSRSIR) due to its advantage in terms of computation and storage. In LSRSIR, existing hashing methods mainly focus on single-source remotely sensed data. They cannot effectively fuse multisource remotely sensed data, which has a large potential for LSRSIR. To fulfill this gap, this letter proposes a novel deep hashing method, dubbed Multiview Graph Convolutional Hashing (MGCH) that can successfully fuse multisource remote sensing image. Since graph convolutional network (GCN) has been applied as an effective means that expresses and integrates relationships into features, MGCH applies a GCN to explore inherent structural similarity among multiview data, which will help to generate discriminative hash codes. An asymmetric scheme is developed that optimizes the proposed deep model in an end-to-end manner to improve training efficiency. We evaluate the proposed method by fusing two different kinds of RS images, i.e., multispectral (MUL) image and panchromatic (PAN) image. The experimental results on the dual-source RS image data set (DSRSID) show that the proposed MGCH outperforms state-of-the-art multiview hashing methods.
AI-terity 2.0: An Autonomous NIME Featuring GANSpaceSynth Deep Learning Model

In this paper we present the recent developments in the AI-terity instrument.AI-terity is a deformable, non-rigid musical instrument that comprises a particular artificial intelligence (AI) method for generating audio samples for real-time audio synthesis.As an improvement, we developed the control interface structure with additional sensor hardware.In addition, we implemented a new hybrid deep learning architecture, GANSpaceSynth, in which we applied the GANSpace method on the GANSynth model.Following the deep learning model improvement, we developed new autonomous features for the instrument that aim at keeping the musician in an active and uncertain state of exploration.Through these new features, the instrument enables more accurate control on GAN latent space.Further, we intend to investigate the current developments through a musical composition that idiomatically reflects the new autonomous features of the AI-terity instrument.We argue that the present technology of AI is suitable for enabling alternative autonomous features in audio domain for the creative practices of musicians.
User-Centered Social Robot Design: Involving Children with Special Needs in an Online World

Robots create a window of opportunity to challenge the barriers that children with special needs face. Robots are physical agents that can be imbued with seemingly "intelligent" behaviours. They can facilitate accessible play by acting as proxies to both children with physical special needs and typically developing children, creating an even playing field. Including target users such as children with special needs in the design process is essential in creating a child-friendly robot that ensures repeated use, engagement and long-term interaction. This paper presents an online approach to involve stakeholders with user-centered design, exemplifying that children can be included in the creation and feedback process even when it is not possible to hold in-person co-design sessions due to COVID-19. We present qualitative findings from a user-centered design study and offer recommendations for designing social robots for accessible play and facilitating child-child interaction.
OO-ABMS: Online/Offline-Aided Attribute-Based Multi-Keyword Search

Recently, Attribute-Based Encryption (ABE) is used as a baseline technology for keyword searching mechanisms to cater users’ security and privacy-related concerns who outsource their data to the powerful and resource-rich cloud server. Almost all the existing Attribute-Based Keyword Searching (ABKS) schemes either operate on a single-keyword or multiple; conjunctive-keyword search setting. These schemes work on the premise of an “all-or-nothing” retrieval mode, which greatly limits users’ searching capabilities. The most recent work incorporates multi-keyword ranked search in attribute-based encryption, which improves user search results accuracy and the flexibility of retrieval mode. However, these schemes demand high computation overhead, which ultimately suffers searching efficiency. This dilemma prevents further research and application of ABKS schemes, especially mobile-resource constrained devices such as mobile phones. This paper proposes an Online/Offline-aided Attribute-Based Multi-keyword Search (OOABMS) scheme to delegate most laborious computation operations to the offline phase before acquiring the attribute-based access control policy or keywords. An online phase then quickly assembles the pre-computed index or trapdoor with the required specifics when it becomes known. Theoretical comparison and simulation results show that our scheme is more efficient and practical in the real world scenario for mobile cloud computing in terms of computation overhead and flexibility.
The effect of the use of computer-aided design (CAD) and a 3D printer on the child’s competence in mathematics

Computer-Aided Design (CAD) and 3D printing are not new technologies but are technologies which, in recent years, have become more accessible to hobbyists and enthusiasts. Engaging with these technologies allows the user to investigate and manipulate virtual and physical 3D shapes, which may affect spatial awareness. The literature suggests that spatial awareness can affect how children perceive and approach Mathematics, an effect which we sought to investigate in this research. This article discusses a case study in which primary school children were given access to CAD software, Tinkercad, and were allowed to make their designs. These designs were printed out using a 3D printer for the children to investigate, manipulate and keep. This article discusses changes in children’s attitudes, confidence and competence in Mathematics that occurred for the case study and how these may have been affected by the use of CAD software and 3D printing in the classroom.
Machine Learning Model for Breast Cancer Data Analysis Using Triplet Feature Selection Algorithm

The machine learning techniques can be used for clinical investigations in breast cancer diagnosis. The researchers investigated various machine learning algorithms, such as Support Vector Machine, Naïve Bayes, Logistic Regression (LR), Random Forest, Decision Tree and K Nearest Neighbor to diagnose the disease. Early detection of breast cancer cells from the features is essential. Feature selection is the process of reducing the input features to improve the performance of the model. This research aims to increase the accuracy, sensitivity, specificity and to reduce the False Positive Rate (FPR) and False Negative Rate (FNR) by feature selection. The proposed feature selection technique is comprised of two phases: feature grouping and feature selection. In the first phase, feature grouping uses the Pearson correlation techniques to identify the correlation among the features and group the features based on high-, medium- and low- level ranking. In the second phase, Triplet Feature Selection (TFS) method has been proposed to avoid collinearity among the features. In this, the features are selected based on the correlation differences in each subset when satisfying the race condition. Finally, select the features in the triplet group and apply LR classification technique to diagnose the disease. The proposed classifier achieved an accuracy (95.4%), FPR (1%), FNR (4%), sensitivity (97%) and specificity (96%) to detect the benign and malign ones. The effects of TFS feature selection with LR classifier were used and the performance of the proposed framework was compared with the existing feature selection methods and classifiers.
Metadata-driven eye tracking for real-time applications

When conducting eye tracking studies, having a mechanism to collect data, build workflows, and validate results in a FAIR (i.e., findable, accessible, interoperable, and reusable) manner, facilitates automation. Given the vast landscape of vendor-specific eye tracking software, adopting FAIR metadata standards for the eye tracking domain is one step towards this. In this paper, we propose an approach to simplify the creation, execution, and validation of eye tracking studies through metadata. Using a metadata format that we developed, we first describe two eye trackers, and two datasets collected using them. Next, we use this metadata to simulate real-time data collection by replaying each dataset. From this replayed data, we analyze eye movements in real-time, and synthesize eye movement data from analytics in real-time. Based on our results, we discuss the utility of metadata in real-time eye tracking studies, and how this idea can be generalized into other applications.
Holistic Explainability Requirements for End-to-End Machine Learning in IoT Cloud Systems

End-to-end machine learning (ML) in Internet of Things (IoT) Cloud systems consists of multiple processes, covering data, model, and service engineering, and involves multiple stakeholders. Therefore, to be able to explain ML to relevant stakeholders, it is important to identify explainability requirements in a holistic manner. In this paper, we present our methodology to address explainability requirements for end-to-end ML in developing ML services to be deployed within IoT Cloud systems. We identify and classify explainability requirements engineering through (i) involvement of relevant stakeholders, (ii) end-to-end data, model, and service engineering processes, and (iii) multiple explainability aspects. We present our work with a case study of predictive maintenance for Base Transceiver Stations (BTS) in the telco domain.
Aggregated Euclidean Distances for a Fast and Robust Real-Time 3D-MOT

Autonomous driving systems must have the ability to monitor the kinematic behaviour of multiple obstacles. Therefore, 3D multi-object tracking (3D-MOT) is one of the crucial modules in autonomous driving to detect the presence of potential hazard movements such as human operated vehicles and pedestrians. In this work, we present a novel online 3D multi-tracking system that uses the Aggregated Euclidean Distances (AED) in data association module instead of using Intersection over Union (IoU) as a new metric. AED is used in order to obtain the relationship between predicted tracks and current object detections. There are several benefits from using AED in data association module. Firstly, it can reduce the system’s complexity so that the execution time can be significantly reduced (as calculating Euclidean distances is much faster than obtaining 3D-IoU). Secondly, AED can provide distance measurement even when there is no overlaps between the predicted tracks and the current detections, while 3D-IoU produces zeros for non-overlapping cases. To demonstrate the validity of our proposed method, we performed extensive experiments on KITTI multi-tracking benchmark and nuScenes validation datasets. The experimental results are compared against the open-sourced state of the art 3D MOTs such as AB3DMOT, FANTrack, and mmMOT. Our method clearly outperforms the AB3DMOT baseline method and other methods in terms of accuracy and/or processing speed.
Machine Learning for Credit Card Fraud Detection

With the development of E-bank, the use of credit cards gets an unprecedented improvement as well as the problem of credit card fraud. To overcome this problem, we need automatic systems to finish the fraud detection. The number of monitored account data is so large that our human resources are unable to detect the whole dataset. Also, since the number of fraudulent transactions is (fortunately) much smaller than the legitimate ones, the data distribution is unbalanced, skewed towards non-fraudulent observations. To solve the problem of unbalanced dataset, there exist many learning algorithms which are used to underperform this kind of problem; to improve the accuracy or predicting, there exist many methods like over or under sampling.
Memory-Friendly Segmentation Refinement for Video-Based Point Cloud Compression

Recently finalized, the MPEG Video-based Point Cloud Compression (V-PCC) standard leverages existing video codecs to compress point clouds. This approach relies on existing video coding hardware accelerators to enable its fast adoption. However, the processing steps to project 3D point clouds into 2D frames still have a considerable complexity. Thus, we propose two modifications to TMC2, V-PCC’s test model, considering the memory access pattern: one reducing unnecessary memory allocation and the other increasing data locality through a set of pre-processing steps. Our approach achieved up to 46.31% encoding self-time reduction without changing the resulting bitstream. This paper also provides an analysis of our implementation that may help future V-PCC codecs achieve real-time encoding.
Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy

Most works on federated learning (FL) focus on the most common frequentist formulation of learning whereby the goal is minimizing the global empirical loss. Frequentist learning, however, is known to be problematic in the regime of limited data as it fails to quantify epistemic uncertainty in prediction. Bayesian learning provides a principled solution to this problem by shifting the optimization domain to the space of distribution in the model parameters. This paper proposes a novel mechanism for the efficient implementation of Bayesian learning in wireless systems. Specifically, we focus on a standard gradient-based Markov Chain Monte Carlo (MCMC) method, namely Langevin Monte Carlo (LMC), and we introduce a novel protocol, termed Wireless Federated LMC (WFLMC), that is able to repurpose channel noise for the double role of seed randomness for MCMC sampling and of privacy preservation. To this end, based on the analysis of the Wasserstein distance between sample distribution and global posterior distribution under privacy and power constraints, we introduce a power allocation strategy as the solution of a convex program. The analysis identifies distinct operating regimes in which the performance of the system is power-limited, privacy-limited, or limited by the requirement of MCMC sampling. Both analytical and simulation results demonstrate that, if the channel noise is properly accounted for under suitable conditions, it can be fully repurposed for both MCMC sampling and privacy preservation, obtaining the same performance as in an ideal communication setting that is not subject to privacy constraints.
Enhancing Adversarial Robustness For Image Classification By Regularizing Class Level Feature Distribution

Recent researches have shown that deep neural networks (DNNs) are vulnerable to adversarial examples. Adversarial training is practically the most effective approach to improve the robustness of DNNs against adversarial examples. However, conventional adversarial training methods only focus on the classification results or the instance level relationship on feature representations for adversarial examples. Inspired by the fact that adversarial examples break the distinguishability of the feature representations of DNNs for different classes, we propose Intra and Inter Class Feature Regularization $(\mathrm{I}^{2}$ FR) to make the feature distribution of adversarial examples maintain the same classification property as clean examples. On the one hand, the intra-class regularization restricts the distance of features between adversarial examples and both the corresponding clean data and samples for the same class. On the other hand, the inter-class regularization prevents the feature of adversarial examples from getting close to other classes. By adding $\mathrm{I}^{2}$ FR in both adversarial example generation and model training steps in adversarial training, we can get stronger and more diverse adversarial examples, and the neural network learns a more distinguishable and reasonable feature distribution. Experiments on various adversarial training frameworks demonstrate that $\mathrm{I}^{2}$ FR is adaptive for multiple training frameworks and outperforms the state-of-the-art methods for classification of both clean data and adversarial examples.
A Pareto Dominance Approach to Multi-criteria Recommender System Using PSO Algorithm

Recommender systems are software tools which are used for dealing with the information overload problem by identifying more relevant items to users based on their past preferences. Single Collaborative Filtering, the most successful recommendation technique, provides appropriate suggestions to users based on their similar neighbours through the utilization of overall ratings given by users. But it can select less representative users as neighbours of the active user, indicating that the recommendations thus made are not sufficiently precise in the context of single-criteria Collaborative Filtering. Incorporating multi-criteria ratings into Collaborative Filtering presents an opportunity to improve the recommendation quality because it represents the user preferences more efficiently. However, learning optimal weights to different criteria for users is a major concern in designing multi-criteria recommendation framework. Our work in this paper is an attempt towards introducing multi-criteria recommendation strategies exploring both the concepts of Pareto dominance and Genetic algorithm, to further enhance their quality of recommendations. The contributions of this paper are two fold: First, we develop a Multi-criteria Recommender system using Pareto dominance methodology (MCRS-PDM). The use of Pareto dominance in our method is to filter out less representative users before the neighbourhood selection process while retaining the most promising ones. Second, we applied Particle Swarm Optimization to our proposed methodology for efficiently learning the weights of various criteria of an item. Effectiveness of our proposed RSs is demonstrated through experimental results in terms of various performance measures using Yahoo! movies dataset.
Muiqa: Image Quality Assessment Database And Algorithm For Medical Ultrasound Images

In the process of medical image acquisition, medical images may be blurred or ghosted due to machine noise, electromagnetic interference, man-made disturbance, etc. This can result in poor image quality and severely affect the diagnosis accuracy and confidence of doctors. IntraVascular UltraSound (IVUS) is an important supplementary method for the diagnosis of coronary angiography. IVUS images can be distorted for many reasons and some severe distortions can affect diagnosis confidence. However, existing manual medical image quality control method is extremely time-consuming and requires a lot of manpower. To solve this problem, we first construct an Medical UltraSound Image Quality Assessment (MUIQA) database, which consists of 10766 IVUS images with quality labels given by professional doctors. Then we propose a deep-learning network to automatically distinguish the low, medium and high level images from each other. We achieve good classification accuracy of 96.34% on the testing set.
Based BERT-BiLSTM-ATT Model of Commodity Commentary on The Emotional Tendency Analysis

In order to realize the analysis of the emotional tendency of the product user reviews. This paper proposes a method for analyzing the sentiment orientation of product reviews based on the BERT-BiLSTM-ATT model. First, use the BERT model to obtain the feature representation of the product review text, and then input the obtained feature representation into the BiLSTM network to extract the emotional features of the product review. Add an Attention layer before the output layer of the normal BiLSTM model to further improve the classification accuracy, and finally combine with Softmax The classifier classifies the extracted features. To validate the algorithm, the design and LSTM, BiLSTM, BiLSTM-ATT, BERTBiLSTM comparative model experiments, experimental results show that the algorithm accuracy on test set are improved 6.17%, 3.75%, 2.83%, 1.03%, to prove the effectiveness of this method in the relevant classification task.