Reviewerly/exHarmony huggingface dataset:
From Causal Pairs to Causal Graphs

Causal structure learning from observational data remains a non-trivial task due to various factors such as finite sampling, unobserved confounding factors, and measurement errors. Constraint-based and score-based methods tend to suffer from high computational complexity due to the combinatorial nature of estimating the directed acyclic graph (DAG). Motivated by the ‘Cause-Effect Pair’ NIPS 2013 Workshop on Causality Challenge, in this paper, we take a different approach and generate a probability distribution over all possible graphs informed by the cause-effect pair features proposed in response to the workshop challenge. The goal of the paper is to propose new methods based on this probabilistic information and compare their performance with traditional and state-of-the-art approaches. Our experiments, on both synthetic and real datasets, show that our proposed methods not only have statistically similar or better performances than some traditional approaches but also are computationally faster.
Active Learning with Combinatorial Coverage

Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data labeling but are heavily model-reliant. This has led to the inability of sampled data to be transferred to new models as well as issues with sampling bias. Both issues are of crucial concern in machine learning deployment. We propose active learning methods utilizing combinatorial coverage to overcome these issues. The proposed methods are data-centric, as opposed to model-centric, and through our experiments we show that the inclusion of coverage in active learning leads to sampling data that tends to be the best in transferring to better performing models and has a competitive sampling bias compared to benchmark methods.
A Linguistic Investigation of Machine Learning based Contradiction Detection Models: An Empirical Analysis and Future Perspectives

We analyze two Natural Language Inference data sets with respect to their linguistic features. The goal is to identify those syntactic and semantic properties that are particularly hard to comprehend for a machine learning model. To this end, we also investigate the differences between a crowd-sourced, machine-translated data set (SNLI) and a collection of text pairs from internet sources. Our main findings are, that the model has difficulty recognizing the semantic importance of prepositions and verbs, emphasizing the importance of linguistically aware pre-training tasks. Furthermore, it often does not comprehend antonyms and homonyms, especially if those are depending on the context. Incomplete sentences are another problem, as well as longer paragraphs and rare words or phrases. The study shows that automated language understanding requires a more informed approach, utilizing as much external knowledge as possible throughout the training process.
Dynamic QoS Prediction Based on Attention Mechanism and Recurrent Neural Network

With the growth of science and technology, there are more and more services in the network, and their functions similar. It has become crucial to figure out how to recommend real-time personalized services based on user and service history. Quality of Service (QoS), as a noncritical attribute of Web services, it is a vital criterion to measure the usage performance and utility of service. The dynamic prediction of QoS is a key factor in the recommendation process. In this paper, a new time-aware QoS prediction model is proposed. It extracte global features while considering its dynamics, it capture the implicit features within a time period using Convolutional Neural Networks(CNN) and Attention Mechanisms. The model not only takes into account the impact of time, but also better learns the implicit information between users and services during the time interval, forming global features within time. It study QoS at various times from the entire time period, capture key features, thereby make dynamic QoS prediction. The proposed model has better results by extensive experiments on real dataset.
Graph-based Recommendation using Graph Neural Networks

Graph based recommendation strategies are recently gaining momentum in connection with the availability of new Graph Neural Network (GNN) architectures. In fact, the interactions between users and products in a recommender system can be naturally expressed in terms of a bipartite graph, where nodes corresponding to users are connected with nodes corresponding to products trough edges representing a user action on the product (usually a purchase). GNNs can then be exploited and trained in order to predict the existence of a specific edge between unconnected users and products, highlighting the interest for a potential purchase of a given product by a given user. In the present paper, we will present an experimental analysis of different GNN architectures in the context of recommender systems. We analyze the impact of different kind of layers such as convolutional, attentional and message-passing, as well as the influence of different embedding size on the performance on the link prediction task. We will also examine the behavior of two of such architectures (the ones relying on the presence of node features) with respect to both transductive and inductive situations.
Continuous and Silent User Authentication Through Mouse Dynamics and Explainable Deep Learning

Over the years the number of fraud credential attacks increased drastically. To avoid this type of attack many types of authentication methods have been introduced, in particular, recently taken hold biometric-based such as physical-biometric and behavior-biometric. The idea at the bottom of the latter is that each person has a unique behavior. Starting from the touch dynamics, and keyboard dynamics nowadays, the main topic of study is mouse dynamics. Unlike the other techniques, mouse dynamics require simpler hardware to capture the biometric data without using sensitive data from the users. In this paper, we propose a method based on mouse dynamics and explainable deep learning for continuous and silent user authentication. We propose four different images obtained starting from mouse dynamics and we submit these images to several deep learning models obtaining interesting results in user behavior detection. Moreover, we propose the adoption of the Gradient-weighted Class Activation Mapping to highlight the areas of the images under study that is responsible for a specific categorization to explain the model decision.
Cost-Sensitive Ensemble Learning for Highly Imbalanced Classification

There are a variety of data-level and algorithm-level methods available for treating class imbalance. Data-level methods include data sampling strategies that pre-process training data to reduce levels of class imbalance. Algorithm-level methods modify the learning and inference processes to reduce bias towards the majority class. This study evaluates both data-level and algorithm-level methods for class imbalance using a highly imbalanced healthcare fraud data set. We approach the problem from a cost-sensitive learning perspective, and demonstrate how these direct and indirect cost-sensitive methods can be implemented using a common cost matrix. For each method, a wide range of costs are evaluated using three popular ensemble learning algorithms. Initial results show that random undersampling (RUS) and class weighting are both effective ways to improve classification when the default classification threshold is used. Further analysis using the area under the precision-recall curve, however, shows that both RUS and class weighting actually decrease the discriminative power of these learners. Through multiple complementary performance metrics and confidence interval analysis, we find that the best model performance is consistently obtained when RUS and class weighting are not applied, but when output thresholding is used to maximize the confusion matrix instead. Our contributions include various recommendations related to implementing cost-sensitive ensemble learning and effective model evaluation, as well as empirical evidence that contradicts popular beliefs about learning from imbalanced data.
File system to support secure cloud-based sharing

Cloud storage is a common platform for multiple users to share data over the Internet. It offers a huge storage capacity at low cost and an easy-to-access interface for all users. However, data protection is considered to be an issue, especially for highly secret information. Users usually trust cloud storage providers who do not leak information, but the administrator and tools they use can access arbitrary storage data. One approach against these problems is encrypting files stored in cloud storage. Using encryption, files are kept confidential, but this requires a complicated procedure and understanding of some specific level of cryptography. In this paper, we propose a secure cloud filesystem that enables files to be encrypted and decrypted transparently when storing to or extracting from the filesystem. Each file is protected using a key-based encryption mechanism, and the keys are also stored on the cloud filesystem in secure indexed with the pathname of the target files. This enables users to easily use secure cloud storage that maintains confidentiality and share data using multiple cloud storages without the users needing special knowledge on encryption.
GDSCAN: Pedestrian Group Detection using Dynamic Epsilon

In order to maintain human safety in autonomous vehicles, pedestrian detection and tracking in real-time have become crucial research areas. The critical challenge in this field is to improve pedestrian detection accuracy while reducing tracking processing time. Due to the fact that pedestrians move in groups with the same speed and direction, we can address this challenge by detecting and tracking pedestrian groups. This work focused on pedestrian group detection. Various clustering methods were used in this study to identify pedestrian groups. Firstly, pedestrians were identified using a convolutional neural network approach. Secondly, K-Means and DBSCAN clustering methods were used to identify pedestrian groups based on the coordinates of the pedestrians’ bounding boxes. Moreover, we proposed a modified DBSCAN clustering method named GDSCAN that employs dynamic epsilon to different areas of an image. The experimental results on the MOT17 dataset show that GDSCAN outperformed K-Means and DBSCAN methods based on the Silhouette Coefficient score and Adjusted Rand Index (ARI).
Towards Secure Bilateral Friend Query with Conjunctive Policy Matching in Social Networks

Bilateral friend queries have attracted increasing interest in social networks, as each user has a common requirement to specify a policy for the other. However, existing bilateral friend query schemes either only cannot support conjunctive policy matching, where the match is successful if the policy is a subset of attributes, or compromise user privacy, which reduces users' enthusiasm for friend query services. In this paper, we propose a secure bilateral friend query with conjunctive policy matching named BFQ. Specifically, the matchmaking encryption technique is utilized to achieve bilateral policy specification, where both sender and requester can specify policies for the other. Then, based on the Newton interpolation theorem, we design a novel conjunctive policy matching algorithm, where the match succeeds if and only if user authenticity holds and the policies of both parties are the subset of attributes. Otherwise, nothing is revealed except whether the match occurs or not. Security analysis proves that user authenticity and privacy, i.e., attribute privacy, policy privacy, and match privacy, are well guaranteed under the chosen-plaintext attack. We then implement a real prototype application and demonstrate that BFQ has practical efficiency through theoretical and experimental evaluation.
Uncertainty-based Meta-Reinforcement Learning for Robust Radar Tracking

Nowadays, Deep Learning (DL) methods often overcome the limitations of traditional signal processing approaches. Nevertheless, DL methods are barely applied in real-life applications. This is mainly due to limited robustness and distributional shift between training and test data. To this end, recent work has proposed uncertainty mechanisms to increase their reliability. Besides, meta-learning aims at improving the generalization capability of DL models. By taking advantage of that, this paper proposes an uncertainty-based Meta-Reinforcement Learning (Meta-RL) approach with Out-of-Distribution (OOD) detection. The presented method performs a given task in unseen environments and provides information about its complexity. This is done by determining first and second-order statistics on the estimated reward. Using information about its complexity, the proposed algorithm is able to point out when tracking is reliable. To evaluate the proposed method, we benchmark it on a radar-tracking dataset. There, we show that our method outperforms related Meta-RL approaches on unseen tracking scenarios in peak performance by 16% and the baseline by 35% while detecting OOD data with an F1-Score of 72%. This shows that our method is robust to environmental changes and reliably detects OOD scenarios.
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values

Human activity recognition (HAR) using IMU sensors, namely accelerometer and gyroscope, has several applications in smart homes, healthcare and human-machine interface systems. In practice, the IMU-based HAR system is expected to encounter variations in measurement due to sensor degradation, alien environment or sensor noise and will be subjected to unknown activities. In view of practical deployment of the solution, analysis of statistical confidence over the activity class score are important metrics. In this paper, we therefore propose XAI-BayesHAR, an integrated Bayesian framework, that improves the overall activity classification accuracy of IMU-based HAR solutions by recursively tracking the feature embedding vector and its associated uncertainty via Kalman filter. Additionally, XAI-BayesHAR acts as an out of data distribution (OOD) detector using the predictive uncertainty which help to evaluate and detect alien input data distribution. Furthermore, Shapley value-based performance of the proposed framework is also evaluated to understand the importance of the feature embedding vector and accordingly used for model compression.
Unsupervised Anomaly Detection and Root Cause Analysis for an Industrial Press Machine based on Skip-Connected Autoencoder

We propose an unsupervised-learning-based method for anomaly detection and root cause analysis for an industrial press machine. A skip-connected autoencoder with 55% performance improvement measured by reconstruction root mean square error to vanilla variant in average is used to train the collected multivariant time series data in different schemes. We then conduct a stacked evaluation method for both machine- level anomalies with the root cause localization and anomaly on specific cylinder tracks. Both real-world and synthetic anomalies embedded in real data are used for evaluation. The result shows that the multi-models training scheme and the relatively short window length can gain better performance, i.e., fewer anomaly false alarms and misses.
DeepReject and DeepRoad: Road Condition Recognition and Classification Under Adversarial Conditions

This paper presents the integrated Deep Learning models for camera image semantic segmentation of road conditions with dry, wet, and snow types along with the rejection of adversarial images. For drivers, road conditions are the most important visible factor for safe driving. Also, traffic control through remote cameras is required for sudden changes like dry to snow as well. However, adversarial images like lens reflection, strong light, and fog can impede normal driving and monitoring. Mostly, these are unpredictable to avoid. Using only simple pre- and post-image processing is limiting in alleviating or excluding such adversarial images. Most Deep Learning models have shown their performances in no or less adversarial images, i.e., fine weather in the daytime. Therefore, two Deep Learning models, DeepReject and DeepRoad, have been proposed to overcome such previous issues even under many adversarial events, i.e., strong lights and low light. Using various road conditions from dry to snow, experimental results have proven that the proposed models/methods outperform the previous single Deep Learning models in terms of stability, robustness, and accuracy. The proposed DeepReject and DeepRoad can contribute to warning drivers and supporting road maintainers for safety. Moreover, DeepReject is helpful whenever small image datasets for training many Deep Learning models are available.
Towards Emotion Cause Generation in Natural Language Processing using Deep Learning

Emotion Cause Analysis (ECA) has recently garnered substantial attention from the researcher community. In addition to devising various techniques to solve ECA related problems, researchers also introduced different variants of the ECA tasks such as Emotion Cause Extraction (ECE), Emotion Cause Pair Extraction (ECPE), Emotion Cause Span Extraction (ECSE). These are primarily classification tasks where the cause of the emotion and/or type of the emotion expressed in the text are identified. In this paper, we propose a new ECA related task named Emotion Cause Generation (ECG). This is a generative task that aims to generate meaningful cause for an emotion expressed in a given text. We demonstrate the viability of this newly proposed task with promising early observation.
PerMTL: A Multi-Task Learning Framework for Skilled Human Performance Assessment

Intelligent and complex human motion analysis can help design the next generation IoT and AR/VR systems for automated human performance assessment. Such an automated system can help advocate the interpretability and translatability of complex human motions, intelligent motion feedback, and fine-grained motion skill assessment to design next-generation interactive human-machine teaming systems. Motivated by this, we design a wearable sensing framework for assessing the players’ performance and consider a live badminton game as our use case. Generally, the players on the field try to improve their performance by focusing on fast and synchronous coordination of their limbs’ reflex actions to have the ideal body postures to perform the desired shot. Learning the minute dissimilarities and distinctive traits from each limb of the players simultaneously can help assess the players’ performance and specific skillsets during a game. This paper proposes a multi-task learning framework, PerMTL to learn the shared features from each player’s limb. The PerMTL comprises a task-specific regressor output layer that helps to determine the dissimilarities and distinctive traits between the player’s limbs for collective inference in a body sensor network (BSN) environment. We evaluate the PerMTL framework using publicly available Badminton Activity Recognition (BAR) and Daily and Sports Activities (DSA) datasets. Empirical results indicate that PerMTL achieves R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Score of ≈ 82% in predicting the players’ performance.
Knowledge-aware Graph Attention Network with Distributed &amp; Cross Learning for Collaborative Recommendation

Nowadays, side-information is widely used to rein-force the user-item interaction and helps to handle the sparsity issue and cold start problem of conventional recommendation algorithms. Due to the overlook of the relationship between items and entities and the higher-order connectivity information, most existing approaches are hard to get users' deep preferences. In this paper, we propose KGANCL, a Knowledge-aware Graph Attention Network with Distributed & Cross Learning. It focuses on using different forms of the knowledge graph to strengthen both users' and items' embedding representations, respectively. Firstly, Graph Attention Network is adopted for user embedding learning, which can give different importance scores to different neighbors, and a user-item KG graph is used to integrate adjacent information to enhance the representation. Secondly, a cross module is used for item embedding learning, which shares the high-order interaction between the recommender system and the knowledge graph. We also use the idea of distributed processing for embeddings in different entities to improve the learning efficiency. Experimental results demonstrate that KGANCL can provide better recommendations compared with the state-of-the-art baseline models. Our model can also maintain superior prediction accuracy even in little-known interaction scenarios.
Spatio-Temporal Aware Knowledge Graph Embedding for Recommender Systems

Knowledge Graphs (KGs) have been incorporated into recommender systems as side information to solve the clas-sical data-sparsity and cold-start problems, with explanations for recommended items. Traditional embedding-based recommender systems generally utilize abundant information from KGs directly to enrich the representation of items or users, but the influences of spatial and temporal dependencies are usually ignored among them. In this paper, we propose a Spatio-Temporal Aware Knowledge Graph Embedding (STAKGE) for recommender systems, which incorporates spatio-temporal information with bias when propagating potential preferences of users in knowl-edge graph embedding. Moreover, we construct a multi-source KG-based recommender dataset - YelpST, containing spatio-temporal information. The experiments on YelpST dataset show that our proposed approach can capture comprehensive spatio-temporal correlations and improve the prediction performance as compared to various state-of-the-art baselines.
A Systematic Review: The Role of Multisensory Feedback in Virtual Reality

Most virtual reality (VR) applications rely on auditory and visual stimulation which can be limiting in terms of how well the user experience can be. This systematic review looks at multisensory VR systems that incorporate haptic, olfactory, and/or gustatory cues, in addition to audio-visual. It aims to provide a broad overview of how multisensory stimulation affects VR experiences and to identify which types of sensory stimuli are most commonly used in the multisensory context. The authors found that 90% of the studies they examined showed a positive effect of multisensory VR, with haptics attracting considerable attention (65%). They highlight the need to devote more research and development to smell and taste, as they can add significant value to VR and user experiences.
A Deep Learning based Hand Gesture Recognition on a Low-power Microcontroller using IMU Sensors

In this paper, we demonstrate an inertial measurement unit (IMU) based hand gesture recognition (HGR) on a low-power microcontroller (STM32L476JGY). The focus of this work is to build a reliable hardware prototype by using deep neural networks (DNN) deployed on a resource limited device. To train the DNNs, a dataset was recorded which contains accelerometer and gyroscope readings from three IMUs mounted on the fingertips. With this dataset, various neural networks (NN) were trained and analyzed. The best NN, in terms of accuracy, memory usage and latency, was then selected and ported to the microcontroller. Finally, a runtime analysis of the model has been performed on the controller. The analysis showed that a LSTM is best suited for the detection of hand gestures. The selected model achieves an accuracy of 93% and only takes up around 40KiB of memory. In addition, the model has a throughput time of only 3.52ms, which means that the prototype can be used in real time.