Reviewerly/exHarmony huggingface dataset:
The Impact of Motion and Delay on Selecting Game Targets with a Mouse

All real-time computer games, particularly networked computer games, have a delay from when a player starts an action (e.g., clicking the mouse) until the game renders the result (e.g., firing a projectile). This delay can degrade both player performance (e.g., reduced game score) and quality of experience (e.g., the game is less fun). While previous work has studied the effects of delay on commercial games and individual game actions, a more detailed understanding is needed of the effects of delay on moving target selection with realistic target motion, a common scenario in many games. This paper presents an in-depth study of the effects of delay on the fundamental game action of selecting a moving target with a mouse while varying two parameters for the target motion – turn frequency and turn angle. We design and implement a custom game where players select moving targets using a mouse, while the game controls both the target motion and input delay. Analysis of data gathered in a 56-person user study shows both target selection time and accuracy degrade with delay. However, both selection time and accuracy increase with the frequency and angle of changes in the target’s movement, because turning slows targets down even while making them less predictable. We set these results in the context of other studies of delay and target selection by comparing our findings to those in seven other previously published papers that investigated the effects of delay on target selection.
Do Individual Differences in Cognition and Personality Predict Retrieval Practice Activities on MOOCs?

Online quizzes building upon the principles of retrieval practice can have beneficial effects on learning, especially long-term retention. However, it is unexplored how interindividual differences in relevant background characteristics relate to retrieval practice activities in e-learning. Thus, this study sought to probe for this research question on a massive open online course (MOOC) platform where students have the optional possibility to quiz themselves on the to-be-learned materials. Altogether 105 students were assessed with a cognitive task tapping on reasoning, and two self-assessed personality measures capturing need for cognition (NFC), and grittiness (GRIT-S). Between-group analyses revealed that cognitively high performing individuals were more likely to use the optional quizzes on the platform. Moreover, within-group analyses (n = 56) including those students using the optional quizzes on the platform showed that reasoning significantly predicted quiz performance, and quiz processing speed. NFC and GRIT-S were unrelated to each of the aforementioned retrieval practice activities.
A Novel Bayesian Network Structure Learning Algorithm: Best Parents-Children

Bayesian network structure-learning represents a principal research technique in the domain of knowledge discovery. However, the construct of the correct structure is considered as an NP-hard problem due to the amount of nominee graphs, which is exponentially-related to the variables' number. To overcome this challenge, several methods had been proposed like the score and search method. In this paper, we provide an improvement of the K2 algorithm based on Best parents and children research (IK2-BN). Compared to the other algorithms in the field, and based on the obtained results, our algorithm has a better performance in terms of correct structure detection.
Privacy-Preserving Netting in Local Energy Grids

The production of renewable energies by individual households typically is small-scale and not profitable without public subsidies, yet a critical factor in preventing further global warming. Unlike market-based peer-to-peer trading solutions, which require households to engage in costly peer-to-peer trading activities, we propose a community-based approach where households in a local distribution grid share the energy they produce in a netting process to maximize internal consumption. The technical instantiation of this idea in real-world energy grids comes with several challenges. Households within a community do not necessarily trust each other or electric utilities. Furthermore, energy consumption data is highly sensitive and must be protected. Further idiosyncrasies of national energy markets, regulatory frameworks, and current grid infrastructure exist. Addressing all these challenges, we propose a blockchain-based system that leverages zero-knowledge off-chain computations to facilitate automated energy sharing within a community in a trustless and privacy-preserving way. We provide a proof- of-concept implementation using the ZoKrates framework for verifiable off-chain computations and the Ethereum Blockchain. To support our claims, we provide evaluation results obtained in the context of a major German national research project on blockchain-based energy networks.
Building Hybrid DApps using Blockchain Tactics -The Meta-Transaction Example

Building blockchain-based applications and deciding which elements of an architecture should employ blockchain technologies poses several challenges. Architectural design decisions have a strong impact on quality attributes such as privacy, operational cost, transparency, risk and user experience (UX). To deal with these challenges, we propose a structured approach using existing architectural concepts such as strategies, tactics and design patterns and illustrate their application using the Meta-Transaction design pattern. Meta-Transactions are cryptographically signed function calls (i.e. transactions) a user sends to a backend. The backend submits the transaction to the blockchain and pays the fees on behalf of the user. Due to the cryptographic signature, the backend is not able to manipulate the function name or its parameters, thus acting as a trustless proxy. Several other design patterns exist in the area of blockchain-oriented applications but it remains unclear how to decide which are suitable for a given use case and how quality attributes of the resulting system are affected. By using the Meta-Transaction design pattern as an example, this paper motivates why Blockchain Tactics and corresponding design patterns are necessary and help to structure best practices and common solutions for challenges of using blockchain technology.
Jackknife empirical likelihood for the mean difference of two zero-inflated skewed populations

In constructing a confidence interval for the mean difference of two independent populations, we may encounter the problem of having a low coverage probability when there are many zeros in the data, and the non-zero values are highly positively skewed. The violation of the normality assumption makes parametric methods inefficient in such cases. In this paper, jackknife empirical likelihood (JEL) and adjusted jackknife empirical likelihood (AJEL) methods are proposed to construct a nonparametric confidence interval for the mean difference of two independent zero-inflated skewed populations. The JEL and AJEL confidence intervals are compared with the confidence intervals by normal approximation and empirical likelihood proposed by Zhou and Zhou (2005). Simulation studies are performed to assess the new methods. Two real-life datasets are also used as an illustration of the proposed methodologies.
Reactive Temporal Logic-Based Precursor Detection Algorithm for Terminal Airspace Operations

The air traffic management system is one of the most complex manmade systems, with stringent standards for safety and operational performance. Modern surveillance systems make available detailed flight and airport information through onboard and ground recording systems. These recorded datasets can be used for detecting and/or predicting anomalies that hinder safe and efficient operations. The prediction of an anomaly is performed by identifying events that precede the occurrence of an anomaly, which are called precursors. In this paper, we propose a precursor detection algorithm that can identify precursors for flight anomalies through data-driven models designed with surveillance data recorded in the terminal airspace. The proposed algorithm is demonstrated to detect precursors of flight anomalies in the terminal airspace around LaGuardia Airport in New York City using real traffic data obtained from the Airport Surface Detection Equipment–Model X and the terminal automation information service surveillance datasets.
Accurate and Practical Calibration of Multiple Pan-Tilt-Zoom Cameras for Live Broadcasts

We aim to realize novel services for live broadcasts, including visualizing the 3D position of a subject, using multiple pan-tilt-zoom (PTZ) cameras. For this purpose, a camera calibration technique with high accuracy over a wide pan-tilt-zoom range and high operability is required. Herein, we propose an accurate and practical method to calibrate multiple PTZ cameras for live broadcasts. This method models the PTZ camera system and accurately pre-estimates unknown parameters in the model. After the pre-estimation, accurate camera parameters can be calculated in real time using the pan-tilt angles and zoom values acquired from the sensors in the camera system. The proposed method accurately estimates the unknown parameters for a high-magnification zoom lens by making repeated initial guesses and nonlinear optimizations for high accuracy, and consists of a two-stage camera calibration for operability. We demonstrated the effectiveness of the proposed method by conducting experiments on computer graphics data and real data. We also applied it to a live sports graphics system.
A survey of knowledge‐based sequential decision‐making under uncertainty

Reasoning with declarative knowledge (RDK) and sequential decision-making (SDM) are two key research areas in artificial intelligence. RDK methods reason with declarative domain knowledge, including commonsense knowledge, that is either provided a priori or acquired over time, while SDM methods (probabilistic planning and reinforcement learning) seek to compute action policies that maximize the expected cumulative utility over a time horizon; both classes of methods reason in the presence of uncertainty. Despite the rich literature in these two areas, researchers have not fully explored their complementary strengths. In this paper, we survey algorithms that leverage RDK methods while making sequential decisions under uncertainty. We discuss significant developments, open problems, and directions for future work.
Virtual Try-On With Generative Adversarial Networks

This chapter elaborates on using generative adversarial networks (GAN) for virtual try-on applications. It presents the first comprehensive survey on this topic. Virtual try-on represents a practical application of GANs and pixel translation, which improves on the techniques of virtual try-on prior to these new discoveries. This survey details the importance of virtual try-on systems and the history of virtual try-on; shows how GANs, pixel translation, and perceptual losses have influenced the field; and summarizes the latest research in creating virtual try-on systems. Additionally, the authors present the future directions of research to improve virtual try-on systems by making them usable, faster, more effective. By walking through the steps of virtual try-on from start to finish, the chapter aims to expose readers to key concepts shared by many GAN applications and to give readers a solid foundation to pursue further topics in GANs.
Weighted Multi-View Data Clustering via Joint Non-Negative Matrix Factorization

In recent years, datasets which exist in present world are comprising of various representations of the data or in multiview environment, which frequently give the important data to each other. Multi-view clustering based on Non-negative matrix factorization (NMF) has turned to be a very hot direction of research in the field of Pattern Reognition, Machine Learning (ML), and data mining. and data mining due to unsupervised confuse information of Numerous Views. The main problem of employing NMF to multi-view clustering is how to define the factorizations to give significant and commensurate clustering solutions. Specially, multi-view clustering based NMF has achieved extensive attention due to its dimensionality reduction property. Existing methods based on NMF barely produced meaningful clustering solution from heterogeneous numerous views due to their complementary behaviors. To address this issue, we design a innovative NMF technique based Multiview clustering approach, which gives the more meaningful and compatible clustering solution over Numerous Views. The main outcome of the work, is to a design combined NMF method with view weight and constraint co-efficient which will bring the clustering solution to a common point for each view. The effectiveness of propose method is validated by conducting the experiments on real-world datasets.
Monte Carlo Approximation of Bayes Factors via Mixing With Surrogate Distributions

By mixing the target posterior distribution with a surrogate distribution, of which the normalizing constant is tractable, we propose a method for estimating the marginal likelihood using the Wang–Landau algorithm. We show that a faster convergence of the proposed method can be achieved via the momentum acceleration. Two implementation strategies are detailed: (i) facilitating global jumps between the posterior and surrogate distributions via the multiple-try Metropolis (MTM); (ii) constructing the surrogate via the variational approximation. When a surrogate is difficult to come by, we describe a new jumping mechanism for general reversible jump Markov chain Monte Carlo algorithms, which combines the MTM and a directional sampling algorithm. We illustrate the proposed methods on several statistical models, including the log-Gaussian Cox process, the Bayesian Lasso, the logistic regression, and the g-prior Bayesian variable selection. Supplementary materials for this article are available online.
Image Enhancement Using Convolutional Neural Networks

This paper presented a Convolutional Neutral Network (CNN) based technique to perform image enhancement the contrast of images can be adaptively improved. At the point when an image is caught under inadequate dimensions, the pixel values are in a low dynamic range, which will cause image quality to descend apparently. Hence, it is important to improve the nature of images. So as to safeguard image surfaces however much as could reasonably be expected, and the use SSIM loss. DnCNN grasps the advancement in profound engineering, learning calculation and regularization technique into image denoising. DnCNN certainly evacuates the inactive clean image in the shrouded unit layers. Finally, this CNN method has increased the PSNR value and reduce the SSIM value.
Robot Path Planning Method Based on Deep Reinforcement Learning

Aiming at the excessive dependence of traditional path planning methods on map information and the lack of self-learning and self-adaptive capabilities, a path planning method for robot based on deep reinforcement learning is proposed. The paper takes lidar data of Gazebo simulation environments built on the ROS platform as input. Learn direct action control from environment information through end-to-end learning, adopt neural network to fit value-based non-model time difference Q learning algorithm, reasonably design environment models, and the number of state spaces, the optimal decision strategy is learned by maximizing robot and dynamic environment interaction of cumulative reward. Simulation results show the method can meet the requirements of intelligent perception and decision-making by only relying on some map information.
Temporally Coherent General Dynamic Scene Reconstruction

Abstract Existing techniques for dynamic scene reconstruction from multiple wide-baseline cameras primarily focus on reconstruction in controlled environments, with fixed calibrated cameras and strong prior constraints. This paper introduces a general approach to obtain a 4D representation of complex dynamic scenes from multi-view wide-baseline static or moving cameras without prior knowledge of the scene structure, appearance, or illumination. Contributions of the work are: an automatic method for initial coarse reconstruction to initialize joint estimation; sparse-to-dense temporal correspondence integrated with joint multi-view segmentation and reconstruction to introduce temporal coherence; and a general robust approach for joint segmentation refinement and dense reconstruction of dynamic scenes by introducing shape constraint. Comparison with state-of-the-art approaches on a variety of complex indoor and outdoor scenes, demonstrates improved accuracy in both multi-view segmentation and dense reconstruction. This paper demonstrates unsupervised reconstruction of complete temporally coherent 4D scene models with improved non-rigid object segmentation and shape reconstruction and its application to various applications such as free-view rendering and virtual reality.
Catalysis Clustering with GAN by Incorporating Domain Knowledge

Clustering is an important unsupervised learning method with serious challenges when data is sparse and high-dimensional. Generated clusters are often evaluated with general measures, which may not be meaningful or useful for practical applications and domains. Using a distance metric, a clustering algorithm searches through the data space, groups close items into one cluster, and assigns far away samples to different clusters. In many real-world applications, the number of dimensions is high and data space becomes very sparse. Selection of a suitable distance metric is very difficult and becomes even harder when categorical data is involved. Moreover, existing distance metrics are mostly generic, and clusters created based on them will not necessarily make sense to domain-specific applications. One option to address these challenges is to integrate domain-defined rules and guidelines into the clustering process. In this work we propose a GAN-based approach called Catalysis Clustering to incorporate domain knowledge into the clustering process. With GANs we generate catalysts, which are special synthetic points drawn from the original data distribution and verified to improve clustering quality when measured by a domain-specific metric. We then perform clustering analysis using both catalysts and real data. Final clusters are produced after catalyst points are removed. Experiments on two challenging real-world datasets clearly show that our approach is effective and can generate clusters that are meaningful and useful for real-world applications.
Modified Decision Tree Learning for Cost-Sensitive Credit Card Fraud Detection Model

Credit card fraudulent transactions are cost-sensitive in nature, where the cost differs in each misclassification transaction. Generally, the classification methods do not work on the cost factor. It considers a constant cost factor for each misclassification. In this paper, it proposes a modified instance-based cost-sensitive decision tree algorithm which reflects on different cost factor for each misclassified transactions. In the proposed algorithm, it implements different instance-based costs into the cost-based impurity measure as well as cost-based pruning approach. Experimentally, it shows that the proposed algorithm performs better in terms of cost savings as compared against classical decision tree algorithms. Additionally, it observes that the smaller trees are generated in minimum computational time.
Privacy-enhanced robust image hashing with bloom filters

Robust image hashes are used to detect known illegal images, even after image processing. This is, for example, interesting for a forensic investigation, or for a company to protect their employees and customers by filtering content. The disadvantage of robust hashes is that they leak structural information of the pictures, which can lead to privacy issues. Our scientific contribution is to extend a robust image hash with privacy protection. We thus introduce and discuss such a privacy-preserving concept. The approach uses a probabilistic data structure - known as Bloom filter - to store robust image hashes. Bloom filter store elements by mapping hashes of each element to an internal data structure. We choose a cryptographic hash function to one-way encrypt and store elements. The privacy of the inserted elements is thus protected. We evaluate our implementation, and compare it to its underlying robust image hashing algorithm. Thereby, we show the cost with respect to error rates for introducing a privacy protection into robust hashing. Finally, we discuss our approach's results and usability, and suggest possible future improvements.
CodNN – Robust Neural Networks From Coded Classification

Deep Neural Networks (DNNs) are a revolutionary force in the ongoing information revolution, and yet their intrinsic properties remain a mystery. In particular, it is widely known that DNNs are highly sensitive to noise, whether adversarial or random. This poses a fundamental challenge for hardware implementations of DNNs, and for their deployment in critical applications such as autonomous driving.In this paper we construct robust DNNs via error correcting codes. By our approach, either the data or internal layers of the DNN are coded with error correcting codes, and successful computation under noise is guaranteed. Since DNNs can be seen as a layered concatenation of classification tasks, our research begins with the core task of classifying noisy coded inputs, and progresses towards robust DNNs.We focus on binary data and linear codes. Our main result is that the prevalent parity code can guarantee robustness for a large family of DNNs, which includes the recently popularized binarized neural networks. Further, we show that the coded classification problem has a deep connection to Fourier analysis of Boolean functions.In contrast to existing solutions in the literature, our results do not rely on altering the training process of the DNN, and provide mathematically rigorous guarantees rather than experimental evidence.
Federated Learning in Side-Channel Analysis

Recently introduced federated learning is an attractive framework for the distributed training of deep learning models with thousands of participants. However, it can potentially be used with malicious intent. For example, adversaries can use their smartphones to jointly train a classifier for extracting secret keys from the smartphones' SIM cards without sharing their side-channel measurements with each other. With federated learning, each participant might be able to create a strong model in the absence of sufficient training data. Furthermore, they preserve their anonymity. In this paper, we investigate this new attack vector in the context of side-channel attacks. We compare the federated learning, which aggregates model updates submitted by N participants, with two other aggregating approaches: (1) training on combined side-channel data from N devices, and (2) using an ensemble of N individually trained models. Our first experiments on 8-bit Atmel ATxmega128D4 microcontroller implementation of AES show that federated learning is capable of outperforming the other approaches.
Cooperative Software-hardware Acceleration of K-means on a Tightly Coupled CPU-FPGA System

We consider software-hardware acceleration of K-means clustering on the Intel Xeon+FPGA platform. We design a pipelined accelerator for K-means and combine it with CPU threads to assess performance benefits of (1) acceleration when data are only accessed from system memory and (2) cooperative CPU-FPGA acceleration. Our evaluation shows that the accelerator is up to 12.7×/2.4× faster than a single CPU thread for the assignment/update step of K-means. The cooperative use of threads and FPGA is roughly 1.9× faster than CPU threads alone or the FPGA by itself. Our approach delivers 4×–5× higher throughput compared to existing offload processing approaches.