Reviewerly/exHarmony huggingface dataset:
Quantum Two-Mode Squeezing Radar: SNR and Detection Performance

We analyze the signal-to-noise ratio (SNR) metric in the context of quantum two-mode squeezing (QTMS) radar and find that there are actually two SNRs associated with a QTMS radar, one for the received signal and another for a signal retained inside the radar. Definitions for these SNRs are proposed which are simpler than those hitherto used in the quantum radar literature. We plot receiver operating characteristic (ROC) curves for varying values of these two SNRs. These plots show that the quality of the matched filtering performed by the radar, as quantified by the SNR of the retained signal, can have a strong impact on detection performance.
A Situated M-Learning Perspective to Develop Individual Entrepreneurial Competencies

Many individual entrepreneurs start their businesses without preparation and have scarce resources to attend training activities; however, there is a broad range of educational possibilities with the spread of mobile technologies that can support situated learning. This article addresses the following research question: to what extent is mobile learning (m-learning) effective as a means for developing the entrepreneurial competencies of individual entrepreneurs? The authors conducted an action research project that created and tested a mobile learning method to support the development of entrepreneurial competencies from a situated learning perspective. The research results show positive outcomes perceived by 61 individual entrepreneurs in Brazil that tested the method. They have succeeded in developing elements of their entrepreneurial competencies such as financial and operational management skills, feeling encouraged to improve and professionalize their businesses. Some limitations of m-learning for entrepreneurship education are also discussed in the paper.
Posterior contraction rates for support boundary recovery

Given a sample of a Poisson point process with intensity λf(x,y)=n1(f(x)≤y), we study recovery of the boundary function f from a nonparametric Bayes perspective. Because of the irregularity of this model, the analysis is non-standard. We establish a general result for the posterior contraction rate with respect to the L1-norm based on entropy and one-sided small probability bounds. From this, specific posterior contraction results are derived for Gaussian process priors and priors based on random wavelet series.
A real-time traffic environmental perception algorithm fusing stereo vision and deep network1

Object detection and tracking are critical and challenging problems in vehicle environment perception systems, and have received broad attention in recent years. A novel detection and tracking algorithm taking both accuracy and real-time performance into account is proposed in this paper. First, we employ a fusion algorithm based on stereo vision and deep learning in object detection, which achieves high accuracy using two complementary algorithms. Then, a prediction-association algorithm which uses a Kalman filter and Hungarian assignment for multiple object tracking is employed for object tracking. In addition, a detection and tracking framework based on stereo vision improves the robustness of environmental perception system. Experimental results demonstrate that the proposed algorithm has high accuracy and can meet the real-time performance requirement.
Scalable Full Hardware Logic Architecture for Gradient Boosted Tree Training

Gradient Boosted Tree is most effective and standard machine learning algorithm in many fields especially with various type of tabular dataset. Besides, recent industry field and robotics field require high-speed, power efficient and real-time training with enormous data. FPGA is effective device which enable custom domain specific approach to give acceleration as well as power efficiency. We introduce a scalable full hardware implementation of Gradient Boosted Tree training with high performance and flexibility of hyper parameterization. Experimental work shows that our hardware implementation achieved 11–33 times faster than state-of-art GPU acceleration even with small gates and low power FPGA device.
M-ARY Quantized Neural Networks

Parameter quantization is crucial for model compression. This paper generalizes the binary and ternary quantizations to M-ary quantization for adaptive learning of the quantized neural networks. To compensate the performance loss, the representation values and the quantization partitions of model parameters are jointly trained to optimize the resolution of gradients for parameter updating where the non-differentiable function in back-propagation algorithm is tackled. An asymmetric quantization is implemented. The restriction in parameter quantization is sufficiently relaxed. The resulting M-ary quantization scheme is general and adaptive with different M. Training of the M-ary quantized neural network (MQNN) can be tuned to balance the tradeoff between system performance and memory storage. Experimental results show that MQNN is able to achieve comparable image classification performance with full-precision neural network (FPNN), but the memory storage can be far less than that in FPNN.
Uvis: A Formula-Based End-User Tool for Data Visualization

Existing approaches to data visualization are one of these two: accessible to end-user developers but limited in customizability, or inaccessible and expressive. For instance, commercial charting tools are easy to use, but support only predefined visualizations, while programmatic visualization tools support custom visualizations, but require advanced programming skills. We show that it is possible to combine the learnability of charting tools and the expressiveness of visualization tools. Uvis is an interactive visualization and user interface design tool that targets end-user developers with skills comparable to spreadsheet formulas. With Uvis, designers drag and drop visual objects, set visual properties to formulas, and see the result immediately. The formulas are declarative and similar to spreadsheet formulas. The formulas compute the property values and can refer to data from database, visual objects, and end-user input. To substantiate our claim, we compared Uvis with popular visualization tools. Further, we conducted usability studies that test the ability of designers to customize visualizations with our approach. Our results show that end-user developers can learn the basics of Uvis relatively fast.
Hue-Based Gray Coding Method for Three-Dimensional Surface Measurement of Cutlery With Specular Reflection

Many measurement methods have been proposed for use in automated production. Existing methods for measuring three-dimensional surface height data operate by projecting fringe patterns onto a target object and capturing images of them. However, these methods do not work well for cutlery, such as table forks and spoons, because specular reflection causes exposure change on the cutlery surface. To overcome this difficulty, this paper presents a measurement method for cutlery with specular reflection. The proposed method is based on a well-known measurement technique, the gray coding method, which is robust to exposure change because the captured fringe patterns are handled by discretizing their values. Furthermore, the gray coding method is improved by introducing hue-channel information into the captured fringe patterns. The hue-channel information is more robust to exposure change than brightness, which is used in existing methods. Experimental results for real cutlery show that the proposed hue-based gray coding method enables more robust measurement than existing methods.
Fraud Detection Solution for Monetary Transactions with Autoencoders

Fraud has turned into a trillion-dollar industry which may lead to risk of financial loss as well as the loss of customers' and stakeholders' confidence on financial organizations. Nowadays, online transactions, mobile wallets and payment card transactions are becoming more popular within society. With the growth of such cashless transactions, the number of fraudulent activities in the world is also increasing. According to the current global economic context, efforts being made to detect and prevent frauds are also increasing. Having an effective financial transaction fraud detection system could save trillions of dollars from fraudulent activities. Supervised machine learning based fraud detection solution is the trending mechanism used in fraud detection solutions. Nevertheless, such supervised machine learning based solutions need a labelled dataset in order to train the machine learning model. The reason for the existence of current fraudulent actions is that labelled datasets are hard to find in real-world environments, and if such labelled datasets are available, thereafter such fraud detection solutions would detect fraudulent patterns based on the fraudulent patterns of the fraudulent events in the training labelled dataset. Therefore, there is an extensive business requirement of having a fraud detection solution which can be trained using a raw financial transaction dataset, in other words using an unlabelled dataset which is commonly available in financial transaction systems in order to detect accurate fraudulent events. Test results obtained for the synthetically generated dataset shows that autoencoder is able to detect fraudulent transaction events with 83% of AUC score which represents high capability of binary classification as fraudulent or genuine transactions.
Compression Artifact Removal with Ensemble Learning of Neural Networks

We propose to improve the reconstruction quality of DLVC intra coding based on an ensemble of deep restoration neural networks. Different ways are proposed to generate diversity models, and based on these models, the behavior of different integration methods for model ensemble is explored. The experimental results show that model ensemble can bring additional performance gains to post-processing on the basis that deep neural networks have shown great performance improvements. Besides, we observe that both averaging and selection approaches for model ensemble can bring performance gains, and they can be used in combination to pursue better results.
Shared-Memory Parallel Probabilistic Graphical Modeling Optimization: Comparison of Threads, OpenMP, and Data-Parallel Primitives

This work examines performance characteristics of multiple shared-memory implementations of a probabilistic graphical modeling (PGM) optimization code, which forms the basis for an advanced, state-of-the art image segmentation method. The work is motivated by the need to accelerate scientific image analysis pipelines in use by experimental science, such as at x-ray light sources, and is motivated by the need for platform-portable codes that perform well across many different computational architectures. The primary focus of this work and its main contribution is an in-depth study of shared-memory parallel performance of different implementations, which include those using alternative parallelization approaches such as C11-threads, OpenMP, and data parallel primitives (DPPs). Our results show that, for this complex data-intensive algorithm, the DPP implementation exhibits better runtime performance, but also exhibits less favorable scaling characteristics than the C11-threads and OpenMP counterparts. Based upon a set of experiments that collect hardware performance counters on multiple platforms, the reason for the runtime performance difference appears to be due primarily to algorithmic efficiency gains: the reformulation from the traditional C11-threads and OpenMP expression of the solution into that of data parallel primitives results in significantly fewer instructions being executed. This study is the first of its type to do performance analysis using hardware counters for comparing methods based on VTK-m-based data-parallel primitives with those based on more traditional OpenMP or threads-based parallelism. It is timely, as there is increasing awareness of the need for platform portability in light of increasing node-level parallelism and increasing device heterogeneity.
Energy- and Predominant-Period-Dependent P-Wave Onset Picker (EDP-Picker)

Abstract An energy- and predominant-period-dependent (EDP) P-wave onset automatic picking (EDP-picker) algorithm is proposed to deal with the problem of inaccurate P-wave onset picking in cases in which the P-wave onset is hidden in high-amplitude ambient noise or the energy difference between the seismic P-wave and ambient noise is indistinguishable. The algorithm evaluates the energy change using a characteristic variable ΔE, which describes the energy increment of the P wave above ambient noise. The period change is evaluated using two variables with respect to the predominant period, namely Tpd as proposed by Hildyard et al. (2008) and ΔTpd as the gradient of Tpd. The EDP-picker algorithm has two steps: (1) threshold-based cursory P-wave onset picking and (2) precise P-wave onset picking using an Akaike information criterion function, in which both energy information and period information are considered. All three parameters are determined in a 1 s sliding window. The proposed algorithm is verified on a large dataset comprising 13,481 vertical strong ground motion records for 570 events selected from K-NET (Japan) and China Strong Motion Networks Center data. For all records with an epicentral distance of less than 150 km, 93.5% of residuals of manual picks and auto picks are within ±0.5 s. The results demonstrate that EDP-picker is robust and suitable for real-time systems.
THE HEALTHCARE DESIGN DILEMMA: PERILS OF A TECHNOLOGY-DRIVEN DESIGN PROCESS FOR MEDICAL PRODUCTS

Abstract This paper reports an embedded single case study from a globally operating manufacturer for digital healthcare products. Based on nine semi-structured interviews, document analysis, and a diary study among employees, we were able to gain insights on the daily business routines and interactions of the design team, the UX research team, and the product management department. The results revealed several unexpected insights that indicate a practical mismatch between user-centred design processes learned from the textbook and design practice in the healthcare sector that warrant further research.
Scaling Dnn-Based Video Analysis By Coarse-Grained And Fine-Grained Parallelism

Deep neural networks have been widely used in video analysis applications such as automatic metadata generation, action recognition, and video summarization. A fundamental module in the pipeline of such DNN-based applications is feature extraction. However, extracting features for videos is a major bottleneck since it is performed on every frame of each video sequentially. In addition, the long training time of these complex networks also hinders their usability. In this work, we identify fine-grained and coarse-grained parallelism techniques to speed up vital components in video analysis applications through inter-frame and intra-video parallelism. We demonstrate these techniques on the feature extraction and summarization modules. We leverage frame-level parallelism in feature extraction and intra-video parallelism to speed up video summarization and implement them in a distributed environment using Hadoop Map-Reduce framework to get a speed up of 2.67X on a 4-node setup. Furthermore, we show in our results that our approach has similar accuracy to the sequential applications.
Efficient Context-Aware Lossy Image Compression

We present an efficient context-aware lossy image compression system to participate in the Low Rate track of the CLIC 2020 Image Compression challenge. Our method is based on an autoencoder pipeline augmented with a nested hyperprior model, a PixelCNN-based context model and an adversarial loss to remove artefacts.
Overview of contradictions in the presentation of the results of the stages of development of software systems

The article discusses the main contradictions that arise during the transition between the stages of the software system development process. The causes of these contradictions are analyzed. The approaches to the elimination of identified contradictions, in particular, the approach based on the construction of domain-specific models (DSM), are examined. The disadvantages of such solutions based on the DSM approach are described. The substantiation of the relevance of developing a new method for constructing software systems is given.
Security and Performance Implications of BGP Rerouting-Resistant Guard Selection Algorithms for Tor

Tor is the most popular anonymization system with millions of daily users and, thus, an attractive target for attacks, e.g., by malicious autonomous systems (ASs) performing active routing attacks to become man in the middle and deanonymize users. It was shown that the number of such malicious ASs is significantly larger than previously expected due to the lack of security guarantees in the Border Gateway Protocol (BGP). In response, recent works suggest alternative Tor path selection methods prefering Tor nodes with higher resilience to active BGP attacks.In this work, we analyze the implications of such proposals. We show that Counter-RAPTOR and DPSelect are not as secure as thought before: for particular users they allow for leakage of user’s location. DPSelect is not as resilient as widely accepted as we show that it achieves only one third of its originally claimed resilience and, hence, does not protect users from routing attacks. We reveal the performance implications of both methods and identify scenarios where their usage leads to significant performance bottlenecks. Finally, we propose a new metric to quantify the user’s location leakage by path selection. Using this metric and performing large-scale analysis, we show to which extent a malicious middle can fingerprint the user’s location and what kind of confidence it can achieve. Our findings shed light on the implications of path selection methods on the users’ anonymity and the need for further research.
Binary particle swarm optimization as a detection tool for influential subsets in linear regression

An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.
A novel concept of lane-keeping algorithms for mobile robots

In this work, a new concept of lane-keeping motion planning algorithms for mobile robots is introduced. It is called Velocity Lane-keeping (VLK) method because it is an extension of the well-known Velocity Obstacles (VO) method. The goal of the novel algorithm is to select a velocity vector, such that the robot does not leave the lane if collision-free motion is available. Another goal is that the robot has to reach the desired position. The algorithm calculates the velocity and the path profile only for the robot, the obstacles' velocities are supposed to be known (or measurable) at every sampling time. The robot has to execute the motion to the goal position even if there are not only static but also moving obstacles in the workspace. The VLK method can be extended with a cost function resulting an effective target reaching method for the mobile robot in the lane. The novel algorithm was tested in simulations.
Distortion-Free Video Stabilization

User-Generated Content has millions of creators and viewers, with platforms like YouTube allowing amateurs to share casual videos. However, since they are normally recorded with mobile phones by non-professionals, their viewing experience tends to be low due to artifacts such as jitter. In this scenario, Digital Video Stabilization (DVS) has been utilized, to create high quality, professional level videos. In the industry and academia, there are a number of traditional and Deep Learning based DVS systems, however both approaches have limitations: the former struggles to extract and track features in a number of scenarios, and the latter struggles with camera path smoothing, a hard problem to define in this context. In this paper, we utilize the strengths of Deep Learning and traditional methods for video stabilization, by combining Spatial Transformer Networks and Exponentially Weighted Moving Averages. To avoid distortion and blur from 2D transformations, we simulate translations in the x and y axis by padding the edges of the frames. Experimental results show that our system outperforms the state of the art proposals and one commercial solution in a wide variety of scene contents and video categories.
Auto-Generating Neural Networks with Reinforcement Learning for Multi-Purpose Image Forensics

Designing a forensic convolutional neural network (CNN) is usually based on some ad-hoc intuition and domain knowledge. Many methods to automate neural network design have been proposed for computer vision tasks, but they may not be directly applied to image forensic problems, which tend to detect weak traces signals left by image operations rather than strong image content signals. In this paper, we propose an approach to learn an optimal forensic CNN structure with reinforcement learning for detecting multiple image tampering operations. A learning agent is introduced to select CNN layers sequentially in a limited state-action space using Q-learning with an $\epsilon$-greedy strategy and experience replay. The experiments demonstrate that the auto-generated network performs better than other classic image forensic methods and shows more robustness against JPEG compression. To our knowledge, this is the first attempt to design forensic deep neural networks automatically with reinforcement learning.
ArTest: The First Test Collection for Arabic Web Search with Relevance Rationales

The scarcity of Arabic test collections has long hindered information retrieval (IR) research over the Arabic Web. In this work, we present ArTest, the first large-scale test collection designed for the evaluation of ad-hoc search over the Arabic Web. ArTest uses ArabicWeb16, a collection of around 150M Arabic Web pages as the document collection, and includes 50 topics, 10,529 relevance judgments, and (more importantly) a rationale behind each judgment. To our knowledge, this is also the first IR test collection that includes rationales of primary assessors (i.e., topic developers) for their relevance judgments, exhibiting a useful resource for understanding the relevance phenomena. Finally, ArTest is made publicly-available for the research community.