Reviewerly/exHarmony huggingface dataset:
Towards Efficient Analysis of Variation in Time and Space

Variation is central to today's software development. There are two fundamental dimensions to variation: Variation in time refers to the fact that software exists in numerous revisions that typically replace each other (i.e., a newer version supersedes an older one). Variation in space refers to differences among variants that are designed to coexist in parallel. There are numerous analyses to cope with variation in space (i.e., product-line analyses) and others that cope with variation in time (i.e., regression analyses). The goal of this work is to discuss to which extent product-line analyses can be applied to revisions and, conversely, where regression analyses can be applied to variants. In addition, we discuss challenges related to the combination of product-line and regression analyses. The overall goal is to increase the efficiency of analyses by exploiting the inherent commonality between variants and revisions.
Multi-Scale Inception Based Super-Resolution Using Deep Learning Approach

Single image super-resolution (SISR) aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) image. In order to address the SISR problem, recently, deep convolutional neural networks (CNNs) have achieved remarkable progress in terms of accuracy and efficiency. In this paper, an innovative technique, namely a multi-scale inception-based super-resolution (SR) using deep learning approach, or MSISRD, was proposed for fast and accurate reconstruction of SISR. The proposed network employs the deconvolution layer to upsample the LR image to the desired HR image. The proposed method is in contrast to existing approaches that use the interpolation techniques to upscale the LR image. Primarily, interpolation techniques are not designed for this purpose, which results in the creation of undesired noise in the model. Moreover, the existing methods mainly focus on the shallow network or stacking multiple layers in the model with the aim of creating a deeper network architecture. The technique based on the aforementioned design creates the vanishing gradients problem during the training and increases the computational cost of the model. Our proposed method does not use any hand-designed pre-processing steps, such as the bicubic interpolation technique. Furthermore, an asymmetric convolution block is employed to reduce the number of parameters, in addition to the inception block adopted from GoogLeNet, to reconstruct the multiscale information. Experimental results demonstrate that the proposed model exhibits an enhanced performance compared to twelve state-of-the-art methods in terms of the average peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) with a reduced number of parameters for the scale factor of 2 × , 4 × , and 8 × .
Nodest: feedback-driven static analysis of Node.js applications

Node.js provides the ability to write JavaScript programs for the server-side and has become a popular language for developing web applications. Node.js allows direct access to the underlying filesystem, operating system resources, and databases, but does not provide any security mechanism such as sandboxing of untrusted code, and injection vulnerabilities are now commonly reported in Node.js modules. Existing static dataflow analysis techniques do not scale to Node.js applications to find injection vulnerabilities because small Node.js web applications typically depend on many third-party modules. We present a new feedback-driven static analysis that scales well to detect injection vulnerabilities in Node.js applications. The key idea behind our new technique is that not all third-party modules need to be analyzed to detect an injection vulnerability. Results of running our analysis, Nodest, on real-world Node.js applications show that the technique scales to large applications and finds previously known as well as new vulnerabilities. In particular, Nodest finds 63 true positive taint flows in a set of our benchmarks, whereas a state-of-the-art static analysis reports 3 only. Moreover, our analysis scales to Express, the most popular Node.js web framework, and reports non-trivial injection vulnerabilities.
Transferable AutoML by Model Sharing Over Grouped Datasets

Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure are becoming important. This paper presents a so-called transferable AutoML approach that Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure are becoming important. This paper presents a so-called transferable AutoML approach that leverages previously trained models to speed up the search process for new tasks and datasets. Our approach involves a novel meta-feature extraction technique based on the performance of benchmark models, and a dynamic dataset clustering algorithm based on Markov process and statistical hypothesis test. As such multiple models can share a common structure while with different learned parameters. The transferable AutoML can either be applied to search from scratch, search from predesigned models, or transfer from basic cells according to the difficulties of the given datasets. The experimental results on image classification show notable speedup in overall search time for multiple datasets with negligible loss in accuracy.
Research on Resource Prediction Model Based on Kubernetes Container Auto-scaling Technology

Cloud computing provides a new way for computing resource acquisition and brings about changes in software development deployment. Docker is a lightweight alternative technology of virtualization, which is simple, convenient and practical when developing and deploying applications. Kubernetes is an open source container management system based on Docker container technology. This paper studies the existing auto-scaling strategy of Kubernetes and proposes an auto-scaling optimization strategy which can solve the response delay problem in the expansion phase. This strategy uses a combination of empirical modal decomposition and ARIMA models to predict the load of Pods and adjust the number of Pods in advance according to the prediction result. Our experiment proves that the strategy can achieve the purpose of capacity expansion before the peak load and reduce the application request response time.
Dynamic Risk Density for Autonomous Navigation in Cluttered Environments without Object Detection

In this paper, we examine the problem of navigating cluttered environments without explicit object detection and tracking. We introduce the dynamic risk density to map the congestion density and spatial flow of the environment to a cost function for the agent to determine risk when navigating that environment. We build upon our prior work, wherein the agent maps the density and motion of objects to an occupancy risk, then navigate the environment over a specified risk level set. Here, the agent does not need to identify objects to compute the occupancy risk, and instead computes this cost function using the occupancy density and velocity fields around them. Simulations show how this dynamic risk density encodes movement information for the ego agent and closely models the object-based congestion cost. We implement our dynamic risk density on an autonomous wheelchair and show how it can be used for navigating unstructured, crowded and cluttered environments.
Autonomous Waypoint Planning, Optimal Trajectory Generation and Nonlinear Tracking Control for Multi-rotor UAVs

A framework for autonomous waypoint planning, trajectory generation through waypoints, and trajectory tracking for multi-rotor unmanned aerial vehicles (UAVs) is proposed in this work. Safe and effective operations of these UAVs is a problem that demands obstacle avoidance strategies and advanced trajectory planning and control schemes for stability and energy efficiency. To address this problem, a two-level optimization strategy is used for trajectory generation, then the trajectory is tracked in a stable manner. The framework given here consists of the following components: (a) a deep reinforcement learning (DRL)-based algorithm for optimal waypoint planning while minimizing control energy and avoiding obstacles in a given environment; (b) an optimal, smooth trajectory generation algorithm through waypoints, that minimizes a combination of velocity, acceleration, jerk and snap; and (c) a stable tracking control law that determines a control thrust force for an UAV to track the generated trajectory.
Multi-Ray Jumping: Comprehensible Group Navigation for Collocated Users in Immersive Virtual Reality

The collaborative exploration of virtual environments benefits from joint group navigation capabilities. In this paper, we focus on the design and evaluation of a short-range teleportation technique (jumping) for a group of collocated users wearing head-mounted displays. In a pilot study with expert users, we tested three naïve group jumping approaches and derived the requirements for comprehensible group jumping. We propose a novel Multi-Ray Jumping technique to meet these requirements and report results of two formal user studies, one exploring the effects of passive jumping on simulator sickness symptoms (N=20) and a second one investigating the advantages of our novel technique compared to naïve group jumping (N=22). The results indicate that Multi-Ray Jumping decreases spatial confusion for passengers, increases planning accuracy for navigators, and reduces cognitive load for both.
NullAway: practical type-based null safety for Java

NullPointerExceptions (NPEs) are a key source of crashes in modern Java programs. Previous work has shown how such errors can be prevented at compile time via code annotations and pluggable type checking. However, such systems have been difficult to deploy on large-scale software projects, due to significant build-time overhead and / or a high annotation burden. This paper presents NullAway, a new type-based null safety checker for Java that overcomes these issues. NullAway has been carefully engineered for low overhead, so it can run as part of every build. Further, NullAway reduces annotation burden through targeted unsound assumptions, aiming for no false negatives in practice on checked code. Our evaluation shows that NullAway has significantly lower build-time overhead (1.15×) than comparable tools (2.8-5.1×). Further, on a corpus of production crash data for widely-used Android apps built with NullAway, remaining NPEs were due to unchecked third-party libraries (64%), deliberate error suppressions (17%), or reflection and other forms of post-checking code modification (17%), never due to NullAway's unsound assumptions for checked code.
Understanding community structure in layered neural networks

A layered neural network is now one of the most common choices for predicting high-dimensional practical data sets, where the relationship between input and output data is complex and cannot be represented well by simple conventional models. Its effectiveness is shown in various tasks, such as image recognition and natural language processing, however, the lack of interpretability of the trained result by a layered neural network has limited its application area. In our previous studies, we proposed methods for extracting a simplified global structure of a trained layered neural network by applying a network analysis method and by classifying the units into communities according to their connection patterns with adjacent layers. These methods provided us with knowledge about the strength of the relationship between communities from the existence of bundled connections, which are determined by the threshold processing of the connection ratio between pairs of communities. However, it has been difficult to understand the role of each community in detail or quantitatively by observing the resulting modular structure with these previous methods. We could only know to which sets of the input and output dimensions each community was mainly connected by tracing the bundled connections from the community to the input and output layers. Another problem is that the finally obtained modular structure is highly dependent on the setting of the threshold hyperparameter used for determining bundled connections, leading to a different result to the discussion about the role of each community. In this paper, we propose a new method CA-MNN (Community Analysis for Modular Neural Networks), for interpreting quantitatively the role of each community as regards inference, which we extracted using our previously reported methods, by defining the effect of each input dimension on a community, and the effect of a community on each output dimension. We show experimentally that the proposed CA-MNN can reveal the role of each part of a layered neural network by applying the neural networks to three types of data sets, extracting communities from the trained network, and applying the CA-MNN to the community structure.
AVC, HEVC, VP9, AVS2 or AV1? — A Comparative Study of State-of-the-Art Video Encoders on 4K Videos

4K, ultra high-definition (UHD), and higher resolution video contents have become increasingly popular recently. The largely increased data rate casts great challenges to video compression and communication technologies. Emerging video coding methods are claimed to achieve superior performance for high-resolution video content, but thorough and independent validations are lacking. In this study, we carry out an independent and so far the most comprehensive subjective testing and performance evaluation on videos of diverse resolutions, bit rates and content variations, and compressed by popular and emerging video coding methods including H.264/AVC, H.265/HEVC, VP9, AVS2 and AV1. Our statistical analysis derived from a total of more than 36,000 raw subjective ratings on 1,200 test videos suggests that significant improvement in terms of rate-quality performance against the AVC encoder has been achieved by state-of-the-art encoders, and such improvement is increasingly manifest with the increase of resolution. Furthermore, we evaluate state-of-the-art objective video quality assessment models, and our results show that the SSIMplus measure performs the best in predicting 4K subjective video quality. The database will be made available online to the public to facilitate future video encoding and video quality research.
Computationally Efficient Visibility Graph-Based Generation Of 3D Shortest Collision-Free Path Among Polyhedral Obstacles For Unmanned Aerial Vehicles

Autonomous unmanned aerial vehicles (UAVs) need to dynamically re-plan paths online to avoid newly detected obstacles and no-fly zones. Existing 3D path planning methods are either too computationally intensive for online use or have practical limitations for actual applications. We propose a new method based on visibility graphs that is both computationally efficient for online use and is suitable for actual applications. We consider the 3D space to be composed of many 2D planes that all pass through the current position of the UAV and the destination point. Finding the shortest collision-free path in each plane is a 2D path planning problem which can be solved by using existing visibility graph algorithms. We then collect all the shortest paths generated from each 2D plane and find the shortest path in the whole 3D space. We present the results of the proposed 3D path planning algorithm for two cases to demonstrate that the proposed method is effective.
Future Event Prediction: If and When

We consider the problem of future event prediction in video: if and when a future event will occur. To this end, we propose a number of representations and loss functions tailored to this problem. These include several probabilistic formulations that also model the uncertainty of the prediction. We train and evaluate the approach on two entirely different prediction scenarios: if and when a car will stop in the BDD100k car driving dataset; and if and when a player is going to shoot a basketball towards the basket in the NCAA basketball dataset. We show that (i) we are able to predict events far in the future, up to 10 seconds before they occur; and (ii) using attention, we can determine which areas of the image sequence are responsible for these predictions, and find that they are meaningful, e.g. traffic lights are picked out for predicting when a vehicle will stop.
Visualization Assessment: A Machine Learning Approach

Researchers assess visualizations from multiple aspects, such as aesthetics, memorability, engagement, and efficiency. However, these assessments are mostly carried out through user studies. There is a lack of automatic visualization assessment approaches, which hinders further applications like visualization recommendation, indexing, and generation. In this paper, we propose automating the visualization assessment process with modern machine learning approaches. We utilize a semi-supervised learning method, which first employs Variational Autoencoder (VAE) to learn effective features from visualizations, subsequently training machine learning models for different assessment tasks. Then, we can automatically assess new visualization images by predicting their scores or rankings with the trained model. To evaluate our method, we run two different assessment tasks, namely, aesthetics and memorability, on different visualization datasets. Experiments show that our method can learn effective visual features and achieves good performance on these assessment tasks.
Fast and Robust 3D Person Detector and Posture Estimator for Mobile Robotic Applications

Due to recent deep learning techniques, person detection seems to be solved in the computer vision domain, however, it is still an issue in mobile robotics. On a robot only limited computing capacities are available. The challenge gets even more difficult when operating in an environment, with people in poses different from the standard upright ones. In this work the environment of a supermarket is considered. Unlike most scenarios targeted by the community, persons not only occur in standing postures, but also grasping into the shelves or squatting in front of them. Furthermore, people are heavily occluded, e.g. by shopping carts. In such a challenging environment, it is important to perceive people early enough and in real-time in order to enable a socially aware navigation. Classical person detectors often suffer from a high posture variance or do not achieve acceptable real-time detection rates. For this reason, different components from the 3D object detection domain have been used to create a new robust person detector for mobile application. Operating on 3D point clouds allows fast detections in real-time up to our goal distance of ten meters and above using the Kinect2 depth sensor. The detector can even differentiate between typical postures of customers who stand or squat in front of shelves.
AM Modulation Signal Estimation Allowing Further Research on Sources of Voltage Fluctuations

This article presents the preliminary research results on a novel method of amplitude modulation demodulation that allows estimation of the modulating signal at a frequency higher than that of the carrier signal. The need for a new method from the perspective of research on sources of voltage fluctuations is first explained. Then, a comparison is carried out between the estimation of the modulating signal based on the currently used demodulator in the IEC flickermeter signal chain, the finite-impulse response Hilbert filter, and the proposed method with the carrier signal estimation. The correctness of the recreation of the frequency of the modulating signal, as well as, the signal amplitude and shape were considered in the comparison. The influence of the deformation of the carrier signal on the estimation error of individual demodulation methods was investigated. The metrological interpretation of the results obtained from the numerical simulation and experimental research is discussed, and the conclusions are presented.
CloudDet: Interactive Visual Analysis of Anomalous Performances in Cloud Computing Systems

Detecting and analyzing potential anomalous performances in cloud computing systems is essential for avoiding losses to customers and ensuring the efficient operation of the systems. To this end, a variety of automated techniques have been developed to identify anomalies in cloud computing. These techniques are usually adopted to track the performance metrics of the system (e.g., CPU, memory, and disk I/O), represented by a multivariate time series. However, given the complex characteristics of cloud computing data, the effectiveness of these automated methods is affected. Thus, substantial human judgment on the automated analysis results is required for anomaly interpretation. In this paper, we present a unified visual analytics system named CloudDet to interactively detect, inspect, and diagnose anomalies in cloud computing systems. A novel unsupervised anomaly detection algorithm is developed to identify anomalies based on the specific temporal patterns of the given metrics data (e.g., the periodic pattern). Rich visualization and interaction designs are used to help understand the anomalies in the spatial and temporal context. We demonstrate the effectiveness of CloudDet through a quantitative evaluation, two case studies with real-world data, and interviews with domain experts.
Design of a Robust RF Fingerprint Generation and Classification Scheme for Practical Device Identification

Radio frequency (RF) fingerprint has gained wide attention as it takes advantages of inherent characteristics in hardware for identification and verification. However, performance unreliability with long-ago training data and channel fading interference are two open problems that restrict the development of RF fingerprint identification. To address the former issue, we propose a long-term stacking of repetitive symbols algorithm to turn the measurement noise toward standard Gaussian distribution, which contributes to both classification accuracy and long-term stability. For the latter issue, we propose an artificial noise adding algorithm in training stage to enhance the identification robustness. At last, we implement a robust RF fingerprint generation and classification scheme for practical device identification. In the experiment, we used 50 CC2530 ZigBee devices to verify the performance of proposed RF identification scheme. Our scheme achieved an accuracy of 100% with both training and testing data measured in the same day, and enabled reliable node recognition with the accuracy over 99% for testing data collected 18 months later. Further exploration shows that the scheme is both robust in additive white Gaussian noise channel and slight non-line-of-sight channel scenarios. The performance in multipath scenarios has also demonstrated the reliability and feasibility of our scheme.