Reviewerly/exHarmony huggingface dataset:
Random Features and Random Neurons for Brain-Inspired Big Data Analytics

With the explosion of Big Data, fast and frugal reasoning algorithms are increasingly needed to keep up with the size and the pace of user-generated contents on the Web. In many real-time applications, it is preferable to be able to process more data with reasonable accuracy rather than having higher accuracy over a smaller set of data. In this work, we leverage on both random features and random neurons to perform analogical reasoning over Big Data. Due to their big size and dynamic nature, in fact, Big Data are hard to process with standard dimensionality reduction techniques and clustering algorithms. To this end, we apply random projection to generate a multi-dimensional vector space of commonsense knowledge and use an extreme learning machine to perform reasoning on it. In particular, the combined use of random multi-dimensional scaling and randomly-initialized learning methods allows for both better representation of high-dimensional data and more efficient discovery of their semantic and affective relatedness.
Virtual View Synthesis for 3DoF+ Video

The paper reports a new view synthesis method for omnidirectional video with the ability to slightly displace a virtual viewpoint, i.e. the paper describes a novel synthesis method for 3DoF+ 360 video. This new method is noteworthy because of its high versatility and reliability: the method is appropriate for both perspective and omnidirectional input views, is able to render both perspective and omnidirectional views, and the produced synthetic views differ from the respective ground truth images less than with other view synthesis methods. These important features result from several innovations, e.g., prioritization of input views, efficient inpainting adapted to equirectangular projections, and efficient color correction. The experimental results demonstrate that the new method outperforms the state-of-the-art methods used hitherto.
Real-Time 3D Reconstruction of Thin Surface Based on Laser Line Scanner

The truncated signed distance field (TSDF) has been applied as a fast, accurate, and flexible geometric fusion method in 3D reconstruction of industrial products based on a hand-held laser line scanner. However, this method has some problems for the surface reconstruction of thin products. The surface mesh will collapse to the interior of the model, resulting in some topological errors, such as overlap, intersections, or gaps. Meanwhile, the existing TSDF method ensures real-time performance through significant graphics processing unit (GPU) memory usage, which limits the scale of reconstruction scene. In this work, we propose three improvements to the existing TSDF methods, including: (i) a thin surface attribution judgment method in real-time processing that solves the problem of interference between the opposite sides of the thin surface; we distinguish measurements originating from different parts of a thin surface by the angle between the surface normal and the observation line of sight; (ii) a post-processing method to automatically detect and repair the topological errors in some areas where misjudgment of thin-surface attribution may occur; (iii) a framework that integrates the central processing unit (CPU) and GPU resources to implement our 3D reconstruction approach, which ensures real-time performance and reduces GPU memory usage. The proposed results show that this method can provide more accurate 3D reconstruction of a thin surface, which is similar to the state-of-the-art laser line scanners with 0.02 mm accuracy. In terms of performance, the algorithm can guarantee a frame rate of more than 60 frames per second (FPS) with the GPU memory footprint under 500 MB. In total, the proposed method can achieve a real-time and high-precision 3D reconstruction of a thin surface.
Color image enhancement with high saturation using piecewise linear gamut mapping

Most of the color image enhancement algorithms are implemented in two stages: gray scale image enhancement, which finds the target intensity, and then gamut mapping of the original color coordinates to the target. Therefore, hue preserving gamut mapping is an essential and crucial step, which influences colorfulness. In conventional color mapping methods, color saturation is reduced after intensity modification, which deteriorates subjective image quality. In this paper, a new color enhancement algorithm resulting in high color saturation is proposed. The proposed method employs multiplicative and additive color mapping to improve color saturation without clipping of a color component for increased target intensity as well as decreased cases. This new scheme is fast and effective, therefore, it can be employed to real time applications such as video signal processing.
Resources for Reproducibility of Experiments in Empirical Software Engineering: Topics Derived From a Secondary Study

Background: Replication is a recurrent issue in empirical software engineering (ESE).Although it is a foundation of science, replication is hard to execute despite the many supporting tools meant to facilitate reproducibility.For example, in an experiment, which is the most used method in ESE, the number of replications is not enough compared to other sciences.Objective: In this study, we aim to identify tools that maximize reproducibility in software engineering experiments and how they are applied.Methods: We performed a Systematic Mapping Study and complementary strategies to analyze replication from three concerns (communication, knowledge management, and motivation).We analyzed more than 2,600 studies to get 40 primary studies, using a qualitative analytical tool (Atlas.ti) to create semantic maps for synthesizing our results.Result: We found that tools and practices depend on the experiment domain.Human-oriented experiments tend to use an informal mechanism that is costly and time-consuming.On the other hand, technology-oriented experiments are automated, domain-centric, and specialized so they require a learning process and are not transferable to other domains.Conclusion: Tools and practices still lack acceptation and usability among the ESE research community.Therefore, reproducibility is mostly relegated to internal replication, at which time and costs can be assumed within research groups.A focus on new alternatives should be considered to broaden replication.
Machine Perception Platform for Safe Human-Robot Collaboration

Speed and separation monitoring, operation defined in safety standards for collaborative robots, is meant for real-time collision avoidance. Laser scanners are safety-certified devices and a traditional sensor choice for this application. Unfortunately, the limited amount of target information they provide restricts their use in realistic collaborative robot scenarios, in which knowledge about the nature of the detected targets is required. We propose a machine perception platform for safe human-robot collaboration based on a broadband W-band radar, a 3D camera, and a laser scanner. Besides computing range and angle-of-arrival information, we use the micro-Doppler signatures of the radar echo signals to distinguish between humans and objects.
User Authentication Using Keystroke Dynamics via Crowdsourcing

An increasing number of security breaches in North America are the result of stolen or weak credentials yet many businesses have not adapted their user authentication strategies to account for this vulnerability. This paper presents a preliminary study on a purely statistical keystroke dynamics authentication system that provides an additional layer of security on top of traditional username and password authentication. This form of authentication will reduce the threat of stolen or weak credentials for virtually any system which uses a standard keyboard for authentication. Our model produced an FRR and FAR as low as 2.54% and 0% respectively which is an improvement over other statistical keystroke dynamics authentication models.
Text Generation Service Model Based on Truth-Guided SeqGAN

The Generative Adversarial Networks (GAN) has been successfully applied to the generation of text content such as poetry and speech, and it is a hot topic in the field of text generation. However, GAN has been facing the problem of training and convergence. For the generation model, this paper redefines on the loss function. The truth-guided method has been added to make the generated text closer to the real data. For the discriminant model, this paper designs a more suitable network structure. The self-attention mechanism has been added to the discrimination network to obtain richer semantic information. Finally, some experiments under different model structures and different parameters indicates the model with truth-guided and self-attention mechanism gets better results.
Pose Comparison Based on Part Affinity Fields

Inspired by the massive demand for pose-comparison technology in today's society especially athletes and police; A real-time human body pose comparison method based on part affinity fields neural network is proposed. The neural network is used to extract the feature map. The CNN network and the L2 loss calculation are used to extract the detected confidence map and the part affinity field. The maximum weight bipartite graph is used to calculate the optimal connection between the limb parts. The example shows that the method can extract the angles of various parts of the human body more accurately as the reference basis for the pose comparison.
Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection

To construct a safe and sound autonomous driving system, object detection is essential, and research on fusion of sensors is being actively conducted to increase the detection rate of objects in a dynamic environment in which safety must be secured. Recently, considerable performance improvements in object detection have been achieved with the advent of the convolutional neural network (CNN) structure. In particular, the YOLO (You Only Look Once) architecture, which is suitable for real-time object detection by simultaneously predicting and classifying bounding boxes of objects, is receiving great attention. However, securing the robustness of object detection systems in various environments still remains a challenge. In this paper, we propose a weighted mean-based adaptive object detection strategy that enhances detection performance through convergence of individual object detection results based on an RGB camera and a LiDAR (Light Detection and Ranging) for autonomous driving. The proposed system utilizes the YOLO framework to perform object detection independently based on image data and point cloud data (PCD). Each detection result is united to reduce the number of objects not detected at the decision level by the weighted mean scheme. To evaluate the performance of the proposed object detection system, tests on vehicles and pedestrians were carried out using the KITTI Benchmark Suite. Test results demonstrated that the proposed strategy can achieve detection performance with a higher mean average precision (mAP) for targeted objects than an RGB camera and is also robust against external environmental changes.
HCI in the Garden

Multiple research areas within HCI have examined opportunities for the design and evaluation of interactive technologies for the garden. In this paper we present a mapping review of HCI papers on the topic of gardening to map out themes that span across these research areas and to identify gaps and opportunities for future research. A review of 28 papers has highlighted the following themes: discovery in the garden, connecting through gardening, bringing the garden inside, coordinating gardens, sustainability through gardening and learning new gardening skills. Based on the review, we present opportunities for future research to 1) design for specific user groups, including older adults; 2) connect gardeners to the world from their gardens (i.e., technologies for gardeners to share their discoveries with the world); and to 3) explore activities beyond gardening, e.g., playing, relaxing, admiring nature and citizen science.
Hololens AR - Using Vuforia-Based Marker Tracking Together with Text Recognition in an Assembly Scenario

Many real world applications for Microsoft HoloLens*-based applications suffer the problem of reliably recognizing and identifying movable objects within an environment. While the HoloLens is perfectly able to discern already known rooms, it still has troubles with reflecting surfaces or identically shaped objects. Using dedicated recognition libraries for each task poses the issue of shared resource access in the rather controlled HoloLens environment-In this poster we present a solution for scenario with hard to track objects and similarly shaped objects for an electrical cabinet assembly task, where the reflective cabinet is tagged with a marker and the prefabricated cables are differentiated by text-based labels.
Feature fusion adversarial learning network for liver lesion classification

The number of training data is the key bottleneck in achieving good results for medical image analysis and especially in deep learning. Due to small medical training data, deep learning models often fail to mine useful features and have serious over-fitting problems. In this paper, we propose a clean and effective feature fusion adversarial learning network to mine useful features and relieve over-fitting problems. Firstly, we train a fully convolution autoencoder network with unsupervised learning to mine useful feature maps from our liver lesion data. Secondly, these feature maps will be transferred to our adversarial SENet network for liver lesion classification. Our experiments on liver lesion classification in CT show an average accuracy as 85.47% compared with the baseline training scheme, which demonstrate our proposed method can mime useful features and relieve over-fitting problem. It can assist physicians in the early detection and treatment of liver lesions.
Emojis-Based Recurrent Neural Network For Chinese Microblogs Sentiment Analysis

As a novel graphical expression derived from emoticons, emojis have a wide application in social networks. Emojis can assist people in expressing stronger sentiment or show subtler sentiment indirectly which are helpful to sentiment analysis. In this paper, we propose an emojis-based recurrent neural network for sentiment analysis in Chinese microblogs. Firstly, we differentiate ambiguous emojis and explicit emojis by using pre-trained word embedding and a new sentiment lexicon. Then we verify that users' information can eliminate the ambiguity of ambiguous emojis to some extent and confirm the sentiment polarity of ambiguous emojis. On the basis, we obtain emoji representations by utilizing the position vector, semantic vector and sentiment vector of emojis, then put the emoji representations into Bi-directional gated recurrent unit(BiGRU) neural network model to conduct sentiment analysis. The experimental results on a Chinese microblog dataset demonstrate that compared with other baselines, the proposed model can improve the accuracy significantly in sentiment analysis.
Navigation of Semi-autonomous Service Robots Using Local Information and Anytime Motion Planners

SUMMARY This paper deals with the problem of navigating semi-autonomous mobile robots without global localization systems in unknown environments. We propose a planning-based obstacle avoidance strategy that relies on local maps and a series of short-time coordinate frames. With this approach, simple odometry and range information are sufficient to make the robot to safely follow the user commands. Different from reactive obstacle avoidance strategies, the proposed approach chooses a good and smooth local path for the robot. The methodology is evaluated using a mobile service robot moving in an unknown corridor environment populated with obstacles and people.
3-D Human Pose Estimation Using Iterative Conditional Squeeze and Excitation Networks

We propose a new method for single-camera real-world 3-D human pose estimation. Our method uses multitask training together with iterative pose refinement using a novel conditional attention mechanism. For iterative pose refinement, the output of each convolutional layer is conditioned on the latest pose estimate, using a conditioned squeeze-and-excitation network architecture that incorporates novel feedback connections. Multitask training on both an in-the-wild 2-D pose dataset and a controlled 3-D pose dataset allows for real-world 3-D pose estimation without the need for a large-scale in-the-wild 3-D pose dataset, which is unavailable. Experiments are performed on several real-world datasets, as well as the Human 3.6 Million and HumanEva-I datasets, to show that the combined attention mechanism, iterative refinement scheme, and multitask training allow us to achieve robust and competitive performance with only a simple network architecture. In addition, we show that our method is efficient enough to run on commodity hardware, producing pose estimates in real time.
Content-based echo chamber detection on social media platforms

"Echo chamber" is a metaphorical description of a situation in which beliefs are amplified inside a closed network, and social media platforms provide an environment that is well-suited to this phenomenon. Depending on the scale of the echo chamber, a user's judgment of different opinions may be restricted. The current study focuses on detecting echoing interaction between a post and its related comments to then quantify the predominating degree of echo chamber behavior on Facebook pages. To enable such detection, two content-based features are designed; the first aids stance representation of comments on a particular discussion topic, and the second focuses on the type and intensity of emotion elicited by a subject. This work also introduces data-driven semi-supervised approaches to extract such features from social media data.
Prediction of Discomfort due to Egomotion in Immersive Videos for Virtual Reality

We consider the problem of automatic assessment of visually induced motion sickness in virtual reality applications. In particular, we study the impact on visual discomfort due to camera motion or egomotion present in the video displayed through a head mounted display. We develop a database of 100 short duration videos with different camera trajectories, speeds and shake levels and conduct a large scale subjective study by collecting more than 4000 human ratings of discomfort levels. The videos are generated synthetically by applying different camera trajectories. We then use the subjective study to learn to predict discomfort by designing features describing the camera motion. The features are based on the ground truth camera trajectory and estimate the camera velocity and shake and depth of the visual scene. We show that these features can be effectively used to predict discomfort by obtaining a high correlation with the subjective discomfort scores provided by humans.
Comparative Study on Trajectory Outlier Detection Algorithms

This paper explores, reviews, and evaluates the existing outlier trajectory detection using small, large and big databases. We divide existing solutions into two main categories: similarity-based, and clustering-based approaches. The first category groups solutions employing distance and neighborhoods computation to derive local density estimates. The second category explores the correlation between the trajectory data by using clustering techniques. Solutions in each of these categories are explained, implemented and compared using different trajectory database sizes. Compared to state-of-the-art survey papers, the contribution of this paper is in providing a deep analysis of all kinds of trajectory outlier detection methods. In this context, we can better understand the intuition, limitations, and benefits of the existing outlier trajectory detection algorithms. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.
Depth Image-Based Obstacle Avoidance for an In-Door Patrol Robot

Image-based obstacle avoidance has been studied for decades. One weak point of image-based approaches is that the performance usually depends on the lighting condition. That is, the performance can be very poor in dark environments. In this research, we investigate the possibility of the depth image-based approach for full-time indoor patrolling. As the first step, we consider a 3-class problem. Each depth image is classified as “danger” if some obstacle is too close, as “notice” if the obstacle is close, and as “normal” if there is no obstacle in the vicinity. The label of each depth image is defined based on the RGB image captured at the same time, and an AlexNet, which is a well-trained convolutional neural network, is retrained via transfer learning, and used for classification. In our primary experiment, we collected 102,776 image data in the Research Quadrangle of the University of Aizu. Test results show that the performance of the depth image-based approach is good during both day and night, and in most cases, it is better than the RGB image-based approach. This result can provide new insights when designing more practical full-time patrol robots.
Datachain

Fueled by the wide societal interest for decentralized services, blockchain has emerged into a highly desired paradigm extending well beyond financial transactions. The next generation of blockchain services are now storing more and more data on distributed ledgers. Therefore, the need to perform analytic queries over blockchains is more evident than ever. However, despite the wide public interest and the release of several frameworks, efficiently accessing and processing data from blockchains is challenging. This paper introduces Datachain, a lightweight, flexible and interoperable framework deliberately designed to ease the extraction of data hosted on distributed ledgers. Through high-level query abstractions, users connect to underlying blockchains, perform data requests, extract transactions, manage data assets and derive high-level analytic insights. Most importantly, due to the inherent interoperable nature of Datachain, queries and analytic jobs are reusable and can be executed without alterations on different underlying blockchains. To illustrate the wide applicability of Datachain, we present a realistic use-case on top of Hyperledger and BigchainDB.
Causal Models for Dynamical Systems

chapter Share on Causal Models for Dynamical Systems Authors: Jonas Peters University of Copenhagen University of CopenhagenSearch about this author , Stefan Bauer MPI Tübingen MPI TübingenSearch about this author , Niklas Pfister University of Copenhagen University of CopenhagenSearch about this author Authors Info & Claims Probabilistic and Causal Inference: The Works of Judea PearlFebruary 2022 Pages 671–690https://doi.org/10.1145/3501714.3501752Online:04 March 2022Publication History 0citation28DownloadsMetricsTotal Citations0Total Downloads28Last 12 Months28Last 6 weeks10 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access