Reviewerly/exHarmony huggingface dataset:
Eye-Controlled Wheelchair

A powered wheelchair is a mobility device for moderate or severe physical disabilities or chronic disorders. Many patients with Amyotrophic Lateral Sclerosis and quadriplegia have to depend on others to move their wheelchairs. Although assistive mobility devices, such as manual and electrical wheelchairs, exist, these options do not suit all individuals who suffer from activity limitations. This project aims to use information technology to assist people with disabilities to move their wheelchairs independently in order to enjoy their lives and integrate into their community. The proposed hands-free wheelchair is based on an eye-controlled system. Different measurement systems for the eye trackers have been evolved, such as search coil, electrooculography, video-oculography, and infrared oculography systems. Usually, the eye-tracking systems add the techniques of eyeball tracking with hardware equipment and software. The movement of eyeballs is used to control the wheelchair movements instead of hands. The infrared eye-tracking system was adopted in this project for several advantages. To achieve this goal, a system consisting of a head-mounted eye-tracker, a powered wheelchair, and an obstacles detector has been designed and tested. The head-mounted eye-tracking system uses infrared light emitters and sensors in gaze detection. By measuring the intensity of the infrared light reflected from the corneal. The changes of the reflected intensity can be translated into signals to determine an eye position. After the gaze direction is determined, it is used to steer the wheelchair accordingly. Furthermore, the chair includes safety features that implement proximity sensors to prevent crashing into objects.
Depth Perception Assessment of 3D Videos Based on Stereoscopic and Spatial Orientation Structural Features

Depth quality of stereoscopic three-dimensional (S3D) videos is a significant factor which directly affects the quality of experience (QoE) associated with 3D video applications and services. Nevertheless, there remain limited reports on the investigation of depth perception and depth quality evaluation of S3D videos, which impedes further advancement and deployment of 3D video technology. This paper reports a series of subjective experiments which have been conducted to investigate the depth perception and its related properties of the human visual system (HVS) using S3D video compressed by the H.264/AVC standard. The experimental results reveal that the HVS response in depth perception varies at different frequencies and in varying orientations, and the distortions introduced by video coding can cause the loss of and/or variation in depth perception. By integration of binocular and monocular features (BM) extracted from left and right views of S3D video with respect to depth perception, a depth quality assessment model, herein referred to as BM-DQAM, is devised by training these stereoscopic and spatial orientation structural features with a support vector regression model. It is shown that the BM-DQAM provides a novel no-reference metric for evaluation of the depth quality of S3D videos. Based on two publicly available 3D video databases and the proposed depth perception assessment database, the experimental results show that the BM-DQAM has demonstrated better performance in assessing the depth quality in S3D video viewing than that of other metrics reported in the published literatures, correlating well with the HVS response in the depth perception assessment experiment.
Experimental Bayesian Calibration of Trapped-Ion Entangling Operations

The performance of quantum gate operations is experimentally determined by how correct operational parameters can be determined and set, and how stable these parameters can be maintained. In addition, gates acting on different sets of qubits require unique sets of control parameters. Thus, an efficient multi-dimensional parameter estimation procedure is crucial to calibrate even medium sized quantum processors. Here, we develop and characterize an efficient calibration protocol to automatically estimate and adjust experimental parameters of the widely used Molmer-Sorensen entangling gate operation in a trapped ion quantum information processor. The protocol exploits Bayesian parameter estimation methods which includes a stopping criterion based on a desired gate infidelity. We experimentally demonstrate a median gate infidelity of $1.3(1)\cdot10^{-3}$, requiring only $1200\pm500$ experimental cycles, while completing the entire gate calibration procedure in less than one minute. This approach is applicable to other quantum information processor architectures with known or sufficiently characterized theoretical models.
Cybersecurity and Fraud Detection in Financial Transactions

Abstract Frauds in financial services are an ever-increasing phenomenon, and cybercrime generates multimillion revenues, therefore even a small improvement in fraud detection rates would generate significant savings. This chapter arises from the need to overcome the limitations of the rule-based systems to block potentially fraudulent transactions. After mentioning the limitations of rule-based approach, this chapter explains how machine learning is able to address many of these limitations and, more effectively, identify risky transactions. A novel AI-based fraud detection system – built over a Data Science and Machine Learning – is presented for the pre-processing of transaction data and model training in a batch layer (to periodically retrain the predictive model with new data) while in a stream layer, the real-time fraud detection is handled based on new input transaction data. The architecture presented makes this solution a valuable tool for supporting fraud analysts and for automating the fraud detection processes.
Expert Finding in Legal Community Question Answering

Expert finding has been well-studied in community question answering (QA) systems in various domains. However, none of these studies addresses expert finding in the legal domain, where the goal is for citizens to find lawyers based on their expertise. In the legal domain, there is a large knowledge gap between the experts and the searchers, and the content on the legal QA websites consist of a combination formal and informal communication. In this paper, we propose methods for generating query-dependent textual profiles for lawyers covering several aspects including sentiment, comments, and recency. We combine query-dependent profiles with existing expert finding methods. Our experiments are conducted on a novel dataset gathered from an online legal QA service. We discovered that taking into account different lawyer profile aspects improves the best baseline model. We make our dataset publicly available for future work.
Deep Learning Based OFDM Channel Estimation Using Frequency-Time Division and Attention Mechanism

In this paper, we propose a frequency-time division network (FreqTimeNet) to improve the performance of deep learning (DL) based OFDM channel estimation. This FreqTimeNet is designed based on the orthogonality between the frequency domain and the time domain. In FreqTimeNet, the input is processed by parallel frequency blocks and parallel time blocks sequentially. By introducing the attention mechanism using the SNR information, an attention based FreqTimeNet (AttenFreqTimeNet) is proposed. Using 3rd Generation Partnership Project (3GPP) channel models, the mean square error (MSE) performance of FreqTimeNet and AttenFreqTimeNet under different scenarios is evaluated. A method for constructing mixed training data is proposed, which could address the generalization problem in DL. It is observed that AttenFreqTimeNet outperforms FreqTimeNet, and FreqTimeNet outperforms other DL networks with reasonable complexity.
Self-Supervised Ego-Motion Estimation Based on Multi-Layer Fusion of RGB and Inferred Depth

In existing self-supervised depth and ego-motion estimation methods, ego-motion estimation is usually limited to only leveraging RGB information. Recently, several methods have been proposed to further improve the accuracy of self-supervised ego-motion estimation by fusing information from other modalities, e.g., depth, acceleration, and angular velocity. However, they rarely focus on how different fusion strategies affect performance. In this paper, we investigate the effect of different fusion strategies for ego-motion estimation and pro-pose a new framework for self-supervised learning of depth and ego-motion estimation, which performs ego-motion estimation by leveraging RGB and inferred depth information in a Multi-Layer Fusion manner. As a result, we have achieved state-of-the-art performance among learning-based methods on the KITTI odometry benchmark. Detailed studies on the design choices of leveraging inferred depth information and fusion strategies have also been carried out, which clearly demonstrate the advantages of our proposed framework. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>
Learning to Adapt Across Dual Discrepancy for Cross-Domain Person Re-Identification

Thanks to the advent of deep neural networks, recent years have witnessed rapid progress in person re-identification (re-ID). Deep-learning-based methods dominate the leadership of large-scale benchmarks, some of which even surpass the human-level performance. Despite their impressive performance under the single-domain setup, current fully-supervised re-ID models degrade significantly when transplanted to an unseen domain. According to the characteristics of the re-ID task, such degradation is mainly attributed to the dramatic variation within the target domain and the severe shift between the source and target domain, which we call dual discrepancy in this paper. To achieve a model that generalizes well to the target domain, it is desirable to take such dual discrepancy into account. In terms of the former issue, a prevailing solution is to enforce consistency between nearest-neighbors in the embedding space. However, we find that the search of neighbors is highly biased in our case due to the discrepancy across cameras. For this reason, we equip the vanilla neighborhood invariance approach with a camera-aware learning scheme. As for the latter issue, we propose a novel cross-domain mixup scheme. It works in conjunction with virtual prototypes which are employed to handle the disjoint label space between the two domains. In this way, we can realize the smooth transfer by introducing the interpolation between the two domains as a transition state. Extensive experiments on four public benchmarks demonstrate the superiority of our method. Without any auxiliary models and offline clustering procedure, it achieves competitive performance against existing state-of-the-art methods. The code is available at https://github.com/LuckyDC/generalizing-reid-improved.
A Variational EM Acceleration for Efficient Clustering at Very Large Scales

How can we efficiently find very large numbers of clusters C in very large datasets N of potentially high dimensionality D? Here we address the question by using a novel variational approach to optimize Gaussian mixture models (GMMs) with diagonal covariance matrices. The variational method approximates expectation maximization (EM) by applying truncated posteriors as variational distributions and partial E-steps in combination with coresets. Run time complexity to optimize the clustering objective then reduces from O(NCD) per conventional EM iteration to O(N'G2D) for a variational EM iteration on coresets (with coreset size N ' ≤ N and truncation parameter G ≪ C). Based on the strongly reduced run time complexity per iteration, which scales sublinearly with NC, we then provide a concrete, practically applicable, parallelized and highly efficient clustering algorithm. In numerical experiments on standard large-scale benchmarks we (A) show that also overall clustering times scale sublinearly with NC, and (B) observe substantial wall-clock speedups compared to already highly efficient recently reported results. The algorithm's sublinear scaling allows for applications at scales where alternative methods cease to be applicable. We demonstrate such very large-scale applicability using the YFCC100M benchmark, for which we realize with a GMM of up to 50.000 clusters an optimization of a data density model with up to 150 M parameters.
Face Mask Alert Detection System For Preventing the Spread of COVID-19

In this current COVID-19 scenario, an effective face mask detection application. The project's major purpose is to put this system in place at college entrances, airlines, hospitals, and offices where the risk of COVID-19 spreading through contagion is highest. According to reports, having a face mask while at work significantly minimizes the chance of transmission. It's an issue of object detection and classification with two classes (Mask and Without Mask). For recognizing face masks, a hybrid model combining deep and traditional machine learning will be shown. This face mask detector is built with Python, OpenCV, TensorFlow, and Keras and is based on a dataset. Everyone should inspect their face before entering the building and make sure they have a mask with them. A beep alert will be triggered if somebody is found without a face mask. As a result, all of the workplaces are reopening, the number of instances of COVID-19 being reported around the country is steadily rising. It can be brought to a close if everyone observes the safety precautions. As a result, we expect that this research will assist in detecting people wearing masks to work.
EDAssistant: Supporting Exploratory Data Analysis in Computational Notebooks with In Situ Code Search and Recommendation

Using computational notebooks (e.g., Jupyter Notebook), data scientists rationalize their exploratory data analysis (EDA) based on their prior experience and external knowledge, such as online examples. For novices or data scientists who lack specific knowledge about the dataset or problem to investigate, effectively obtaining and understanding the external information is critical to carrying out EDA. This article presents EDAssistant, a JupyterLab extension that supports EDA with in situ search of example notebooks and recommendation of useful APIs, powered by novel interactive visualization of search results. The code search and recommendation are enabled by advanced machine learning models, trained on a large corpus of EDA notebooks collected online. A user study is conducted to investigate both EDAssistant and data scientists’ current practice (i.e., using external search engines). The results demonstrate the effectiveness and usefulness of EDAssistant, and participants appreciated its smooth and in-context support of EDA. We also report several design implications regarding code recommendation tools.
Content-Based Feature Extraction and Extreme Learning Machine for Optimizing File Cluster Types Identification

Recent research in digital forensic attempts to classify image clusters into JPEG or non-JPEG clusters before recovering JPEG image files. This issue might improve the recovering JPEG image accuracy and reduce the processing time. In this work, three content-based feature extraction methods are used. The Rate of Change (RoC) is used for tracking relevant bytes in the appropriate groups of their orders. Entropy and Byte Frequency Distribution (BFD) are used to produce an image cluster histogram based on the size of the byte value. Subsequently, we deploy the Extreme Learning Machine (ELM) classifier to evaluate these three features. The ELM identifies the type based on the generated feature vector, whether a JPEG file or a non-JPEG file type. The proposed method is implemented in MATLAB 2017a software and tested and evaluated by using the DFRWS dataset. The test results show that the ELM produces high classification accuracy in identifying the file type. The difference in accuracy between the combinations of the tested features is relatively small. The worst accuracy is generated when the entropy method is used, which is 72.62%, and the best accuracy of 93.46% is generated when using a combination of the three features.
Benchmarking Single-Image Reflection Removal Algorithms

Reflection removal has been discussed for more than decades. This paper aims to provide the analysis for different reflection properties and factors that influence image formation, an up-to-date taxonomy for existing methods, a benchmark dataset, and the unified benchmarking evaluations for state-of-the-art (especially learning-based) methods. Specifically, this paper presents a SIngle-image Reflection Removal Plus dataset "SIR 2+ " with the new consideration for in-the-wild scenarios and glass with diverse color and unplanar shapes. We further perform quantitative and visual quality comparisons for state-of-the-art single-image reflection removal algorithms. Open problems for improving reflection removal algorithms are discussed at the end. Our dataset and follow-up update can be found at https://reflectionremoval.github.io/sir2data/.
Real-Time LiDAR Point Cloud Compression Using Bi-Directional Prediction and Range-Adaptive Floating-Point Coding

Due to the large amount of data involved in the three-dimensional (3D) LiDAR point clouds, point cloud compression (PCC) becomes indispensable to many real-time applications. In autonomous driving of connected vehicles for example, point clouds are constantly acquired along the time and subjected to be compressed. Among the existing PCC methods, very few of them have effectively removed the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal</i> redundancy inherited in the point clouds. To address this issue, a novel lossy LiDAR PCC system is proposed in this paper, which consists of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inter</i> -frame coding and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">intra</i> -frame coding. For the former, a deep-learning approach is proposed to conduct bi-directional frame prediction using an asymmetric residual module and 3D space-time convolutions; the proposed network is called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bi-directional prediction network</i> (BPNet). For the latter, a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">range-adaptive floating-point coding</i> (RAFC) algorithm is proposed for encoding the reference frames and the B-frame prediction residuals in the 32-bit floating-point precision. Since the pixel-value distribution of these two types of data are quite different, various encoding modes are designed for providing adaptive selection. Extensive simulation experiments have been conducted using multiple point cloud datasets, and the results clearly show that our proposed PCC system consistently outperforms the state-of-the-art MPEG G-PCC in terms of data fidelity and localization, while delivering real-time performance.
Camera Response Based Nighttime Image Enhancement Using Concurrent Reflectance

This article proposes a novel scheme that enhances low-light images locally to prevail their details and concurrently adapts camera response models (CRMs) to maintain the naturalness of the image. Low-light images shadow significant information within them, which demands to be unveiled through image enhancement techniques. However, the contemporary methods prove deficient of one or more factors that determine a well-enhanced image. These methods inevitably include intensity distortions or globally enhance the image, which causes loss of local information. The method proposed through this article initially decomposes the image according to retinex theory by utilizing a measured adaption model. A novel CRM is utilized to enhance the illumination of the image, while a reconstructed histogram equalization method enhances the reflectance locally. The method has been devised with complete awareness of its practical compatibility for hardware or software purposes. Extensive quantitative, qualitative, and time complexity analyses confirm the supremacy of the proposed method over various state-of-the-art methods.
Distributed Dimensionality Reduction Fusion Estimation for Stochastic Uncertain Systems With Fading Measurements Subject to Mixed Attacks

In this article, the distributed fusion estimation issue with the dimensionality reduction strategy under DoS attacks and deception attacks is investigated for a class of stochastic uncertain systems with fading measurements. The stochastic uncertainties existed in the system and measurement equations are represented by state-dependent noises. The fading measurements are depicted by stochastic variables with known statistics. Then, a novel attack and compensation model is proposed to display the randomly occurring behaviors of the DoS attacks and the deception attacks within a unified framework. Furthermore, a distributed multisensor fusion estimation (DMSFE) algorithm is presented. An explicit form of dimensionality reduction is designed against attacks. Stability conditions are derived such that the mean square errors (MSEs) of the proposed DMSFE are bounded. A sequential covariance intersection fusion estimator (SCIFE) is designed to prevent the cross fusion covariance matrices calculating, which owns lower accuracy by smaller computation cost than DMSFE. An illustrative example is provided to show the effectiveness and merits of the proposed algorithm.
Design and Evaluation of a Choreography-Based Virtual Reality Authoring Tool for Experiential Learning in Industrial Training

The use of virtual reality (VR) for industrial training helps minimize risks and costs by allowing more frequent and varied use of experiential learning activities, leading to active and improved learning. However, creating VR training experiences is costly and time-consuming, requiring software development experts. Additionally, current authoring tools lack integration with existing data and are desktop-oriented, which detach the pedagogic process of creating the immersive experience from experiencing it in a situated context. In this article, we present a novel interactive approach for immersive authoring of VR-based experiential training by the trainers themselves, from inside the virtual environment and without the support of development experts. The design includes identifying interactable elements, such as 3-D models, equipment, tools, settings, and environment. The trainer also specifies by demonstration the actions to be performed by trainees, as a virtual choreography. During course execution, trainees’ activities are also registered as virtual choreographies and matched to those specified by the trainer. Thus, trainer and trainee are culturally situated within their area semantics and social discourse, rather than adopting concepts of the VR system for the learning content. We conducted a usability case study with professionals from an international wind energy company, using detailed models of wind turbines and real-world procedures. Trainers set up a training course using the immersive authoring tool, and trainees executed the course. The learning experience and usability were analyzed, and the training was certified by comparing real-world task completion between a user who had undergone virtual training and a user who did not.