Reviewerly/exHarmony huggingface dataset:
Routing of curves with piecewise constant curvature applied to routing of preformed hoses

We present a method for routing of a preformed hose in a static surrounding of obstacles. Routing of a preformed hose is the process of finding a preformed design of the hose that connects a start and a goal point in a collision-free way. A preformed hose design is in this paper represented by a center curve on the form of a sequence of straight line segments joined by circular arcs. The method generates a set of center curves that are collision-free and satisfy minimum allowed straight length and radius of curvature constraints. Each center curve takes a topologically different path with respect to the surrounding geometry and is locally of minimum length. The main scientific contribution is the coupling of a generalized bi-directional grid search with local curve optimization and curve filtering. The method was applied to a set of benchmark cases from the automotive industry and relevant routing solutions were generated in the magnitude of seconds.
A framework for 3D tracking of frontal dynamic objects in autonomous cars

Both recognition and 3D tracking of frontal dynamic objects are crucial problems in an autonomous vehicle, while depth estimation as an essential issue becomes a challenging problem using a monocular camera. Since both camera and objects are moving, the issue can be formed as a structure from motion (SFM) problem. In this paper, to elicit features from an image, the YOLOv3 approach is utilized beside an OpenCV tracker. Subsequently, to obtain the lateral and longitudinal distances, a nonlinear SFM model is considered alongside a state-dependent Riccati equation (SDRE) filter and a newly developed observation model. Additionally, a switching method in the form of switching estimation error covariance is proposed to enhance the robust performance of the SDRE filter. The stability analysis of the presented filter is conducted on a class of discrete nonlinear systems. Furthermore, the ultimate bound of estimation error caused by model uncertainties is analytically obtained to investigate the switching significance. Simulations are reported to validate the performance of the switched SDRE filter. Finally, real-time experiments are performed through a multi-thread framework implemented on a Jetson TX2 board, while radar data is used for the evaluation.
A genetic algorithm with an elitism replacement method for solving the nonfunctional web service composition under fuzzy QoS parameters

The nonfunctional (QoS-aware) web Service Composition (QSC) problem, which is a strong NP-hard optimization one, is widely addressed by considering the advertised Quality of Service (QoS) values of web services as non-ambiguous. However, in real world environments, and due to some of their unconditional factors like network architectures changes, communications congestion and economic policies, the QoS values ambiguity should be undertaken in formulating the QSC problem. In this paper, we present a genetic algorithm that integrates an elitism replacement method for solving the QoS problem under fuzzy QoS parameters, which have been expressed as generalized trapezoidal fuzzy numbers. The addressed QSC problem is formulated as a fuzzy nonlinear integer constrained single-objective optimization model through adapting the well-known simple additive weighting method. To illustrate the performance and the efficiency of the proposed algorithm, we present the experimental comparisons to a fuzzy approach of an existing Particle Swarm Optimization (PSO)-based web service selection algorithm over a fuzzy extended version of the real-world QWS dataset.
Where to Handle an Exception? Recommending Exception Handling Locations from a Global Perspective

Exception handling is an effective mechanism to guarantee software reliability in modern programming languages. An exception interrupts the program execution and propagates backwards along the call chain until the exception is caught by an exception handler. In software development practices, developers may be confused in determining where to place the exception handler in the call chain. The reason is that exception handling requires a developer to take a comprehensive consideration from a global perspective of the software project. In this paper, we propose an automatic approach EHAdvisor, which recommends exception handling locations from the global perspective of the project. EHAdvisor first trains a binary classification model based on four types of features, including architectural features, project features, functional features, and exception features. Then, for a new code snippet with exceptions, EHAdvisor predicts the exception catching probability for each method in the call chain based on the classification model and recommends Top-K exception handling locations based on the probability ranking. We conducted experiments on a dataset from 29 high-quality open source projects. Experimental results show that EHAdvisor achieves an average Top-1 recommendation success rate of 70.83% for across-project location recommendation and an average Top-1 accuracy of 86.21% for intra-project recommendation. Experiments on the importance scores show that global features, such as project features and architectural features, are evidently important to the recommendation of exception handling locations.
Crowd Violence Detection from Video Footage

Surveillance systems currently deploy a variety of devices that can capture visual content (such as CCTV, body-worn cameras, and smartphone cameras), thus rendering the monitoring of video footage obtained from multiple such devices a complex task. This becomes especially challenging when monitoring social events that involve large crowds, particularly when there is a risk of crowd violence. This paper presents and demonstrates a crowd violence detection system that can process, analyze, and alert potential stakeholders, when violence-related content is identified in crowd-based video footage. Based on deep neural networks, the proposed end-to-end framework utilizes a 3D Convolutional Neural Network (CNN) to deal with the (near) real-time analysis of video streams and video files for crowd violence detection. The framework is trained, evaluated, and demonstrated using the Violent Flows dataset, a dataset related to crowd violence that is widely used for research. The presented framework is provided as a standalone application for desktop environments and can analyze both video streams and video files.
Efficient Control Flow in Dataflow Systems: When Ease-of-Use Meets High Performance

Modern data analysis tasks often involve control flow statements, such as iterations. Common examples are PageRank and K-means. To achieve scalability, developers usually implement data analysis tasks in distributed dataflow systems, such as Spark and Flink. However, for tasks with control flow statements, these systems still either suffer from poor performance or are hard to use. For example, while Flink supports iterations and Spark provides ease-of-use, Flink is hard to use and Spark has poor performance for iterative tasks. As a result, developers typically have to implement different workarounds to run their jobs with control flow statements in an easy and efficient way.We propose Mitos, a system that achieves the best of both worlds: it achieves both high performance and ease-of-use. Mitos uses an intermediate representation that abstracts away specific control flow statements and is able to represent any imperative control flow. This facilitates building the dataflow graph and coordinating the distributed execution of control flow in a way that is not tied to specific control flow constructs. Our experimental evaluation shows that the performance of Mitos is more than one order of magnitude better than systems that launch new dataflow jobs for every iteration step. Remarkably, it is also up to 10.5 times faster than Flink, which has native iteration support, while matching the ease-of-use of Spark.
The Roles of Computing Terminology in Non-Computing Disciplines

Aspects of computing are used in nearly all professions. Regardless of discipline, graduates require ever-improving computing competencies-applied in their own disciplinary contexts-to be proficient experts and informed citizens. However, teaching and learning contextually relevant computing competencies to non-computing students is fraught with challenges. One of those is the effective use of computing terminology. Terminology is fundamental to most human activities and is particularly critical where disciplines meet. We seek to investigate the nature of, and barriers presented by, computing terminology used in non-computing disciplines.
Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm

Artificial intelligence chips (AICs) are the intersection of integrated circuits and artificial intelligence (AI), involving structure design, algorithm analysis, chip fabrication and application scenarios. Due to their excellent ability in data processing, AICs show a long-term industrial prospect in big data services, cloud centers, etc. However, with the conceivable exhaustion of Moore’s Law, the size of traditional electronic AICs (EAICs) is gradually approaching the limit, and an architectural update is highly required. Photonic artificial intelligence chips (PAIC) utilize light beam propagation in the silicon waveguide, contributing to a high parallelism configuration, fast calculation speed and low latency. Due to light manipulation, PAICs perform well in anti-electromagnetic interference and energy conservation. This invited paper summarized the recent research on PAICs. The characteristics of different hardware structures are discussed. The current widely used training algorithm is given and the Photonic Design Automatic (PDA) simulation platform is introduced. In addition, the authors’ related work on PAICs is presented and we believe that PAICs may play a critical role in the deployment of data processing technology.
Sky-Preserved Image Dehazing and Enhancement for Outdoor Scenes

Outdoor imaging systems are vulnerable to severe weather including haze and fog. Image dehazing methods can be used to improve image quality without replacing cameras or using additional devices. However, it is not easy to effectively remove haze from outdoor images while preserving color fidelity and reducing artifacts, especially for sky regions due to their large depth of field. To solve these problems, we propose a novel sky-preserved image dehazing and enhancement approach based on region segmentation. First, regions of the sky and white objects are separated efficiently from other areas. Second, contrast limited adaptive histogram equalization (CLAHE) is processed for the sky region and multi-scale Retinex with color restoration (MSRCR) is carried out for non-sky and non-white areas, respectively. Finally, a region-ratio-based adaptive Gamma transformation is performed to correct the overall darkness and color distortions introduced by the original MSRCR. Experiments on diverse real-world hazy outdoor images show that the proposed approach outperforms the traditional MSRCR algorithm as well as three other state-of-the-art methods, in terms of contrast, image entropy, average gradient, and visual effects.
Cybersecurity professionals information sharing sources and networks in the U.S. electrical power industry

This article describes an exploratory qualitative case study of information sharing among cybersecurity professionals in the critical U.S. electrical power industry. Drawing on organizational information processing theory, this study examined how information related to cybersecurity related threats and response alternatives was accessed and shared in the participants’ organizations and within their broader industry network. In-depth interviews of 13 participants from 10 organizations were conducted to identify the nature of information sharing, the networks used to access the information, and changes that could improve information sharing between cybersecurity professionals and their organizations. The study found that information sharing networks exist at interpersonal, company-to-company, and company-to-multicompany levels of analysis. The role of trust in forming these networks was notable, as was the role of certain communication media, particularly threat briefings, testing exercises, and cybersecurity mutual assistance planning. The study found that certain types of network actors, such as law enforcement agencies, had strong relationship ties with the utility companies, while other actor types had weak or nonexistent ties. The implications for critical infrastructure protection are discussed.
Parallel differential evolution applied to interleaving generation with precedence evaluation of tentative solutions

This paper proposes a method to improve the CPU utilization of parallel differential evolution (PDE) by incorporating the interleaving generation mechanism. Previous research proposed the interleaving generation evolutionary algorithm (IGEA) and its improved variants (iIGEA). IGEA reduces the computation time by generating new offspring, which parents have been determined even when all individuals have not evaluated. However, the previous research only used a simple EA method, which is not suitable for practical use. For this issue, this paper explores the applicability of IGEA and iIGEA to practical EA methods. In particular, we choose differential evolution (DE), which is widely used in real-world applications, and propose IGDE and its improved variant, iIGDE. We conduct experiments to investigate the effectiveness of IGDE with several features of the evaluation time on a simulated parallel computing environment. The experimental results reveal that the IGDE variants have higher CPU utilization than a simple PDE and reduce the computation time required for optimization. Besides, iIGDE outperforms the original IGDE for all features of the evaluation time.
Predicting and Estimating the Major Nutrients of Soil Using Machine Learning Techniques

The prediction of soil properties leads to a better understanding of the soil ecosystem dynamics and effective soil management practices which will eventually lead to sustainable agricultural and environmental management. Using machine learning, the classification of soil will be easy and efficient. This paper presents an approach to design simple linear regression and multiple linear regression-based models to analyze basic soil macronutrients and micronutrients. The models were used to check the interdependency of the most important nutrients, namely, nitrogen (N), phosphorus (P), and potassium (K), also known as primary nutrients of soils, and also to evaluate the impact of N content over other vital soil nutrients. The results obtained by these three models justified the interdependency of these vital nutrients over each other.
On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection

Recent studies have shown the promising direction of deep learning based bug detection, which relieves human experts from the tedious and subjective task of manually summarizing features. Simple one-statement bugs (i.e., SStuBs), which occur relatively often in Java projects, cannot be well spotted by existing static analysis tools. In this paper, we make effort to empirically analyze whether deep learning based techniques could be used to detecting SStuBs. We have re-implemented two state-of-the-art techniques in approximately 3,000 lines of code and adopted them to detecting Java SStuBs. Experiments on large-scale datasets suggest that although deep vulnerability detectors can achieve much better results than existing static analyzers, the SStuBs cannot be well flagged when comparing with traditional complex vulnerabilities. We further look in detail on the per bug category basis, observing that deep learning based methods perform better when detecting some specific types of bugs (e.g., "Same Function Change Caller"), which have strong data flow and control flow semantic. Our observations could offer implications on the automated detection and repair of SStuBs.
REACT: Real-Time Contact Tracing and Risk Monitoring via Privacy-Enhanced Mobile Tracking

Contact tracing is an essential public health tool for controlling epidemic disease outbreaks such as the COVID-19 pandemic. Digital contact tracing using real-time locations or proximity of individuals can be used to significantly speed up and scale up contact tracing. In this demonstration, we present our system, REACT, for REAl-time Contact Tracing and risk monitoring via privacy-enhanced tracking of users' locations. With privacy enhancement that allows users to control and refine the precision with which their information will be collected and used, REACT will enable: 1) contact tracing of individuals who are exposed to infected cases and identification of hot-spot locations, 2) individual risk monitoring based on the locations they visit and their contact with others. In this paper, we demonstrate the procedure of contact tracing using our application and the utility of contact tracing given the protected locations.
Stochastic Analysis of the Time Continuum

This paper considers the time continuum of Brouwer in terms of the complex system physics. It is based upon a processual definition of real numbers which concern the measurement problem. The multiresolution hierarchy of the measurement process is represented by the time operator acting on continuous signals. The wavelet domain hidden Markov model, which recapitulates statistical properties of the hierarchy, is verified experimentally on a wide range of signal ensembles. It indicates a novel method that has already been proved to be tremendously useful in applied mathematics.
Bitcoin: A Digital Cryptocurrency

Bitcoin is the largest cryptocurrency ever created and traded using a decentralized ledger known as the blockchain. Altogether, Bitcoin is a network in which every computing node is responsible to service the others and allows shared access to the data, known peer to peer (P2P) network, and provides an adaption of electronic-cash that supports e-payments. Such payments are transferred directly from transacting parties to the receiver without the requirement of any intermediary monetary body. Satoshi Nakamoto introduced bitcoin in 2009 and since then it has emerged as the most thriving cryptocurrency. Bitcoin is a globally accepted and immutable e-payment system of digital currency. All the electronic transactions performed using bitcoin are verified by the intermediate nodes called miners and then added as a block in the distributed ledger. Bitcoin blockchains are maintained by the miners running Bitcoin software. Bitcoin depends on Proof-of-Work (PoW) to confront double-spending by a distributed timestamping service. To ensure the operations and security of Bitcoin, all the transactions and their execution order must be available to all Bitcoin users. In addition to its security robustness, anonymity is the key attribute for its success. There are several factors like market-cap, the marketplace, miners-revenue, etc. which causes the rise and fall of the price of Bitcoin. This chapter focus on the factors that are responsible for the rise and fall of Bitcoin Price with a comparison with other Cryptocurrencies.
Perceptual Image Compression using Relativistic Average Least Squares GANs

In this work, we provide a detailed description on our submitted methods ANTxNN and ANTxNN_SSIM to Workshop and Challenge on Learned Image Compression (CLIC) 2021. We propose to incorporate Relativistic average Least Squares GANs (RaLSGANs) into Rate-Distortion Optimization for end-to-end training, to achieve perceptual image compression. We also compare two types of discriminator networks and visualize their reconstructed images. Experimental results have validated our method optimized by RaLSGANs can achieve higher subjective quality compared to PSNR, MS-SSIM or LPIPS-optimized models.
Clouseau: Blockchain-based Data Integrity for HDFS Clusters

As the volume of produced data is exponentially increasing, companies tend to rely on distributed systems to meet the surging demand for storage capacity. With the business workflows becoming more and more complex, such systems often consist of or are accessed by multiple independent, untrusted entities, which need to interact with shared data. In such scenarios, the potential conflicts of interest incentivize malicious parties to act in a dishonest way and tamper the data to their own benefit. The decentralized nature of the systems renders verifiable data integrity a strenuous but necessary task: The various parties should be able to audit changes and detect tampering when it happens.In this work, we focus on HDFS, the most common storage substrate for Big Data analytics. HDFS is vulnerable to malicious users and participating nodes and does not provide a trustful lineage mechanism, thus jeopardizing the integrity of stored data and the credibility of extracted insights. As a remedy, we present Clouseau, a blockchain-based system that provides verifiable integrity over HDFS, while it does not incur significant overhead at the critical path of read/write operations. During the demonstration, the attendees will have the chance to interact with Clouseau, corrupt data themselves, and witness how Clouseau detects malicious actions.
Color Image Watermarking with Watermark Authentication against False Positive Detection Using SVD

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Deep Insights of Erroneous Bengali–English Code-Mixed Bilingual Language

Code-switching, the combination of more than one language within a single utterance is popular in social media sites for informal communication, generating a huge amount of data, though not fully analysed for knowledge extraction due to lack of documentation. Moreover, the code-mixed data is erroneous, and to detect and correct different types of errors, the model requires large erroneous code-mixed language data, not publicly available. This paper first defines generic rules to write Bengali–English code-mixed language in English script, considering the inherent complexity of the Bengali language. Different types of typographical and cognitive errors are induced to obtain the huge erroneous data, based on human behaviour and perception as consulted with the language experts. The errors considered here are applicable to other code-mixed Indic languages and would be beneficial for the researchers. To demonstrate the applicability of the model, we have included the errors in the Hindi–English code-mixed Indic language. An attention-based two-level deep network architecture (uses LSTM as a basic unit) is used for error detection, correction, and translation of code-mixed sentences into a monolingual sentence. Results are reported in terms of accuracy, ROUGE score, and BLEU scores at word level and sentence level for both Bengali–English and Hindi–English code-mixed languages.