Reviewerly/exHarmony huggingface dataset:
A call for more explainable AI in law enforcement

The use of AI in law enforcement raises several significant ethical and legal concerns. One of them is AI explainability principle, which is mentioned in numerous national and international AI ethical guidelines. This paper firstly analyses what AI explainability principle could mean with relation toAI use in law enforcement, namely, to whom, why and how the explanation about the functioning of AI and its outcomes needs to be provided. Secondly, it explores some legal obstacles in ensuring the desired explainability of AI technologies, namely, the trade secret protection that often applies to AI modules and prevents access to proprietary elements of the algorithm. Finally, the paper outlines and discusses three ways to mitigate this conflict between the AI explainability principle and tradesecret protection. It encourages law enforcement authorities to be more proactive in ensuring that Face Recognition Technology (FRT) outputs are explainable to different stakeholder groups,especially those directly affected.
Incorporating Adjacent User Modeling into Session-based Recommendation with Graph Neural Networks

Recently, session-based recommendation system (SRS) based on anonymous sessions has attracted increasing attention for practical applications. Most existing methods are confined by limited behaviors within a single session while ignoring similar interests between different sessions. In this paper, we treat each session as a virtual user and propose an Adjacent and Target information United Graph Transition Network (ATU-GTN) to activate both user-level and session-level modeling for SRS. The central idea is that we construct adjacent user graph and session graph, and then explore two graph topologies to obtain the target session embedding. Specifically, the adjacent user graph is constructed via the co-occurrence of the last click following the phenomenon that the last click is highly correlated to current user interests. User embedding extracted from the adjacent user graph reveals users’ common interests hidden in homonymous or similar users. Current and global embeddings are extracted from the session graph based on within-session items, which represent current and long-term interests, respectively. By concatenating the three embeddings, the final embedding achieves an explicit and comprehensive representation of the target session. After that, the Neural Tensor Network (NTN) is firstly introduced to learn the sufficient interactions between the target session and candidate items for recommendation. The rationality and effectiveness of ATU-GTN are extensively evaluated on two real-world datasets over state-of-the-art baselines.
Leveraging Ensemble Learning for Side Channel Analysis on Masked AES

Symmetric cryptographic functions are known to leak secret information through power side channels. Although masking provides an effective mitigation to such leakage, these protection designs are vulnerable to advanced side-channel analysis based on machine learning. In this work, we propose a framework to leverage ensemble learning for power analysis on a masked AES implementation. Before the key recovery, we use machine learning techniques including decision tree, random forest, BP nerual network, and ensemble learning to recover the masks. We also implement data set enhancement through oversampling techniques including random oversampling, synthetic minority over-sampling technique (SMOTE), k-means-SMOTE, Borderline-SMOTE, and SMOTENC. In addition, we explore different ensemble learning methods on key recovery of power side-channel attacks. Experimental results on DPA contest v4 power traces show that ensemble learning methods improve the attack success rate by approximately 11.29% on average over neural network attacks. The results also reveal that data set enhancement can help reduce the variations in attack success rates between different key bytes.
FESTH: Visual Tracking with Feature Enhancement and Space-time History Frame Networks

Siamese network is widely used in object tracking. However, it suffers the problem of poor generalization. The existing tracker based on template update mechanism uses complex calculation strategies and time-consuming optimization to achieve good tracking performance, but it does not meet the requirements of real-time tracking. In this paper, we first propose a tracking framework based on space-time history frames, and use feature enhancement to enrich the features of history frames. Then, we propose EnhanceNet which is an offline trained network for performing online data augmentation. It can enhance the tracking accuracy while preserving high speeds of the state-of-the-art online learning. In challenging large-scale datasets, such as LaSOT, VOT2018 and OTB-2015, our framework is better than state-of-the-art real-time trackers and has achieved excellent results while running at 32 FPS.
Reduced-reference Perceptual Discrepancy Learning for Image Restoration Quality Assessment

Image restoration has been receiving extensive attention owing to its widespread applications, and numerous restoration algorithms have been proposed. However, how to accurately evaluate the performances of image restoration algorithms remains largely unexplored. Current image restoration quality metrics make predictions solely based on the restored images without making full use of the original degraded image, which we believe also provides valuable information. For image restoration quality assessment, accurate measurement of the perceptual discrepancy between the degraded image and the restored images is crucial. Motivated by this, this paper presents a perceptual discrepancy learning (PDL) framework for image restoration quality assessment, where the original degraded image is utilized as reduced-reference to achieve reliable predictions. First, a large-scale paired image quality database with weakly annotated labels is built, based on which a prior quality model is trained using Siamese network. Then, based on the prior model, the degraded-restored image pairs (DRIPs) are further used to train the perceptual discrepancy prediction model in an end-to-end manner. Finally, the performances of image restoration algorithms can be obtained based on the predicted relative perceptual discrepancy (RPD) values directly. Experimental results on four image restoration quality databases demonstrate the advantage of the proposed metric over the state-of-the-arts.
Mixed Poisson Regression Models with Varying Dispersion Arising from Non-Conjugate Mixing Distributions

In this article we present a class of mixed Poisson regression models with varying dispersion arising from non-conjugate to the Poisson mixing distributions for modelling overdispersed claim counts in non-life insurance. The proposed family of models combined with the adopted modelling framework can provide sufficient flexibility for dealing with different levels of overdispersion. For illustrative purposes, the Poisson-lognormal regression model with regression structures on both its mean and dispersion parameters is employed for modelling claim count data from a motor insurance portfolio. Maximum likelihood estimation is carried out via an expectation-maximization type algorithm, which is developed for the proposed family of models and is demonstrated to perform satisfactorily.
Knowledge Network and Visual Analysis of Knowledge Graph Research

research-article Knowledge Network and Visual Analysis of Knowledge Graph Research Share on Authors: Qiurong Song Wuhan University, China Wuhan University, ChinaSearch about this author , Xinyue Wang Wuhan University, China Wuhan University, ChinaSearch about this author , Rui Wang Zhengzhou University of Aeronautics, China Zhengzhou University of Aeronautics, ChinaSearch about this author Authors Info & Claims ICVARS 2021: 2021 the 5th International Conference on Virtual and Augmented Reality SimulationsMarch 2021 Pages 61–66https://doi.org/10.1145/3463914.3463924Published:20 March 2021 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
FamSearch: Visual Analysis of Genealogical Data

This paper describes an interactive visualization tool consisting of several views offering different perspectives on genealogical data that is stored and maintained by the Brabants Historical Information Center (BHIC). The dataset consists of several attributes, static as well as dynamic ones, containing information about several million people having lived in the Netherlands. With the tool, we can gain insights in family histories and build, confirm, and reject hypotheses, or search for one’s own relatives having lived in that region. Genealogical, temporal, and geographical relations can be investigated and linked to each other to identify correlations. Moreover, family events such as marriages, divorces, births, deaths, and similar can be explored. To support these tasks the tool provides several algorithms and interactive visualizations that enable data exploration. Visualizations include, amongst others, timeline diagrams, pedigree trees, population pyramids, sunbursts, and word clouds. We illustrate the usefulness of the tool by showcasing which and how patterns and anomalies can be found.
Injecting Sparsity in Anomaly Detection for Efficient Inference

Anomaly detection in the video is a challenging problem in computer vision tasks. Deep networks recently have been successfully applied and achieved competitive performance in anomaly detection. Modern deep networks employ many modules which extract important features. The anomaly detection approaches just developed network architecture and inserted additional networks to improve performance, however, these methods generally require a tremendous amount of computational load and training parameters. Because of limitations in the real world such as field equipment, mobile system, etc., reducing the number of trainable parameters and model capacity is an important issue in anomaly detection. Moreover, the method, which improves the performance of the anomaly detection algorithm, should be developed without additional trainable parameters. In this paper, we propose a sparsity injecting module which reinforces the feature representation of the existing model and presents the abnormality score function using sparsity. In experimental results, our sparsity injecting module improves the performance of state-of-the-art methods without additional trainable parameters.
Multi-task Supervised Learning via Cross-learning

In this paper we consider a problem known as multi-task learning, consisting of fitting a set of classifier or regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task. First, we present a simplified case in which the goal is to estimate the means of two Gaussian variables, for the purpose of gaining some insights on the advantage of the proposed cross-learning strategy. Then we provide a stochastic projected gradient algorithm to perform cross-learning over a generic loss function. If the number of parameters is large, then the projection step becomes computationally expensive. To avoid this situation, we derive a primal-dual algorithm that exploits the structure of the dual problem, achieving a formulation whose complexity only depends on the number of tasks. Preliminary numerical experiments for image classification by neural networks trained on a dataset divided in different domains corroborate that the cross-learned function outperforms both the task-specific and the consensus approaches.
Deep-Learning-based Cross-Modal Luxury Microblogs Retrieval

The current popular Sina microblog application has better support for text retrieval, but limited support for image retrieval. We try to build a cross-modal data set based on the characteristics of Sina microblog data, design a cross-modal retrieval method aiming at luxury official microblog, and implement cross-modal luxury microblog retrieval. In response to the lack of image retrieval on Weibo, a cross-modal graphic information retrieval is constructed in this article, and a cross-modal retrieval and identification method for luxury microblogs based on deep learning is proposed. The retrieval model in this article is composed of a convolutional neural network and a TF-IDF model. The convolutional neural network is used to extract the features of Weibo pictures, and the similarity measurement method is used in the high-level semantic space to perform feature matching to achieve cross-modal retrieval. This research has important theoretical guiding significance for improving the design of microblog multi-modal data retrieval, and has high engineering application value for the design, realization and improvement of future Sina Weibo retrieval methods.
Automatic HMI Structure Exploration Via Curiosity-Based Reinforcement Learning

Discovering the underlying structure of HMI software efficiently and sufficiently for the purpose of testing without any prior knowledge on the software logic remains a difficult problem. The key challenge lies in the complexity of the HMI software and the high variance in the coverage of current methods. In this paper, we introduce the PathFinder, an effective and automatic HMI software exploration framework. PathFinder adopts a curiosity-based reinforcement learning framework to choose actions that lead to the discovery of more unknown states. Additionally, PathFinder progressively builds a navigation model during the exploration to further improve state coverage. We have conducted experiments on both simulations and real-world HMI software testing environment, which comprise a full tool chain of automobile dashboard instrument cluster. The exploration coverage outperforms manual and fuzzing methods which are the current industrial standards.
Resource Management for Blockchain-enabled Internet of Vehicles

With the development of advanced information and communication technology, the Internet of Vehicles (IoV) has been designed to associate with implementing the Intelligent Transportation System (ITS). Meanwhile, the traditional centralized architecture of the cloud services no longer meets the increasing demand for data exchange in IoV. Moreover, the traditional centralized architecture for ITS is vulnerable to the single point of failure, and lack of flexibility due to its dependence on a trusted third party (TTP). The emergence of blockchain technology provides a potential direction to address these problems. However, there are still some problems existing in the construction of an efficient blockchain system in IoV. In this paper, we propose a resource management scheme that improves the performance of blockchain systems by efficiently allocating computing resources. A resource control method and a resource monitoring system are developed to cooperate with the system. The superiority of the proposed method is fully demonstrated by comparing it with the existing baseline method.
Optical Interconnection Network Based on Distributed Switching for Data Center Application

This paper proposes a data center optical interconnection network architecture based on distributed optical switches and the corresponding message scheduling algorithm. The architecture uses a two-dimensional torus topology with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\times 5$</tex> optical switches for each node. In order to adapt to the annular topology, we propose a new configuration of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\times 5$</tex> optical switching module with rearrangement non-blocking property. Network nodes with a dynamically configurable internal structure enable flexible deployment of the number of available wavelengths and the number of optical transceiver modules. Simulation results show that the node configuration of two wavelengths and two transceiver modules can achieve a high network throughput up to approximately 100% of the network capacity. When multiple link failures occur randomly, the throughput close to 90% can still be obtained through different route assignments, verifying the great robustness and high scalability of the proposed scheme.
Versioning temporal characteristics of JSON-based big data via the τJSchema framework

In previous work, we have proposed the use of a framework, named τJSchema (temporal JSON schema), for the definition and validation of temporal JSON documents that conform to a temporal JSON schema. A τJSchema schema is composed of a conventional (i.e., non-temporal) JSON schema, annotated with a set of temporal logical and temporal physical characteristics. Subsequently, we have extended τJSchema to support versioning of conventional JSON schemas. In this work, we complete the picture by extending our framework to also support versioning of temporal characteristics. In fact, we propose a suitable versioning technique and provide a complete set of low-level change operations for the maintenance of these characteristics; for each operation, we define its arguments and its operational semantics. With this extension, τJSchema provides a full support of temporal versioning of JSON-based big data at both instance and schema levels.
General-purpose Abandoned Object Detection Method without Background Modeling

This paper proposes an effective method for detecting abandoned objects in surveillance videos. We use a pedestrian detector trained by YOLO deep learning model to detect surveillance videos. In this process, key frames before and after pedestrians pass through the scene can be obtained. Subsequently, we compare and analyze the key frames to get the position of abandoned objects. It is a general-purpose abandoned object detection method that does not use background modeling. The experimental results obtained based on the ABODA database show that this method is effective for detecting abandoned objects and is more robust to illumination changes than the common methods using background modeling.
A global stochastic optimization particle filter algorithm

Summary We introduce a new online algorithm for expected loglikelihood maximization in situations where the objective function is multimodal or has saddle points. The key element underpinning the algorithm is a probability distribution that concentrates on the target parameter value as the sample size increases and can be efficiently estimated by means of a standard particle filter algorithm. This distribution depends on a learning rate, such that the faster the learning rate the quicker the distribution concentrates on the desired element of the search space, but the less likely the algorithm is to escape from a local optimum of the objective function. In order to achieve a fast convergence rate with a slow learning rate, our algorithm exploits the acceleration property of averaging, which is well known from the stochastic gradient literature. Considering several challenging estimation problems, our numerical experiments show that with high probability, the algorithm successfully finds the highest mode of the objective function and converges to the global maximizer at the optimal rate. While the focus of this work is expected loglikelihood maximization, the proposed methodology and its theory apply more generally to optimization of a function defined through an expectation.
Analyzing COVID-19 Medical Papers Using Artificial Intelligence: Insights for Researchers and Medical Professionals

Since the beginning of the COVID-19 pandemic almost two years ago, there have been more than 700,000 scientific papers published on the subject. An individual researcher cannot possibly get acquainted with such a huge text corpus and, therefore, some help from artificial intelligence (AI) is highly needed. We propose the AI-based tool to help researchers navigate the medical papers collections in a meaningful way and extract some knowledge from scientific COVID-19 papers. The main idea of our approach is to get as much semi-structured information from text corpus as possible, using named entity recognition (NER) with a model called PubMedBERT and Text Analytics for Health service, then store the data into NoSQL database for further fast processing and insights generation. Additionally, the contexts in which the entities were used (neutral or negative) are determined. Application of NLP and text-based emotion detection (TBED) methods to COVID-19 text corpus allows us to gain insights on important issues of diagnosis and treatment (such as changes in medical treatment over time, joint treatment strategies using several medications, and the connection between signs and symptoms of coronavirus, etc.).
Architectural model of the human neuroregulator system based on multi-agent systems and implementation of system-on-chip using FPGA.

The human neuroregulator system is a complex nervous system composed of a heterogeneous group of nerve centres distributed along the spinal cord. These centres act autonomously, communicate through nerve interconnections, and govern and regulate the behaviour of human beings' organs and systems. For over twenty years, our research group has been studying the neuroregulatory system of the lower urinary tract (LUT), which controls the organs and systems involved in the urination process. Based on the study of the behaviour and composition of the LUT, we have succeeded in isolating the centres involved in its functioning. The goal has been to understand the individual role played by each centre in order to create a general model of the neuroregulator system capable of operating at the level of the nerve centre. The model has been created and formalised based on Multi-Agent Systems (MAS) theory: each agent thus models the behaviour of a nerve centre. This latter proposal is a step forward regarding current black box models. Its fine granularity opens up the possibility of acting at the level of the centre, of particular interest to treat dysfunctions. The present study enriches this theoretical model with an architectural model that makes it suitable to implement in hardware. Based on this new model, we propose a System on Chip (SoC) design of a specific processor capable of performing a nerve centre's functions. Although this processor can be entirely configured and programmed to adjust to the functioning of the different centres, the present work aimed at facilitating the understanding and validation of the proposal. We thus focused on the Cortical-Diencephalic (CD) centre, responsible for voluntary micturition. The research adopted an original approach with the aim of creating a configurable chip, capable of developing any neuroregulatory function, implantable in the body and being able to function in a coordinated way with the biological neuroregulator system.