Reviewerly/exHarmony huggingface dataset:
Path Planning of Mobile Robot Based on Improved Wolf Swarm Algorithms

Aiming at solving the shortcomings of traditional wolf swarm algorithm, such as low search efficiency due to fixed search direction and radius, and easy to fall into local optimum when updating rules, an improved wolf swarm algorithm is proposed in this paper. Firstly, the exploding rules of fireworks algorithm are introduced to reduce the exploring steps of wolves near prey and increase the searching direction in order to improve the ability of local exploration, while the wolves far from prey the searching radius are increased and the searching direction are decreased in order to improve the global search ability. Secondly, the moving step size of running behavior is improved automatically to adjust the step size according to the location of each wolf, which can improve the efficiency of running behavior. Finally, the updating rules of wolf swarm algorithm is improved for the individual wolf swarm is selected according to probability to enhance the global optimization ability of the algorithm. The performance test in test function and path planning simulation experiment results show that the improved wolf swarm algorithm has faster convergence speed and higher convergence accuracy.
Sonification to Support the Monitoring Tasks of Security Operations Centres

Sonification (the representation of data as sound) may offer a solution to some of the network-security monitoring challenges faced in security operations centres (SOCs). Prior work has shown that sonification can present network-security information to humans effectively, and indicated that security practitioners foresee potential for sonification to aid in scenarios related to their work. The use of sonification by security practitioners in tasks relevant to SOCs has not been examined, however. To address this gap, we assessed the use of sonification by security practitioners in network-security monitoring tasks in an experimental setting. We report on the results of a study in which we compared the performance of security practitioners using a Security Information and Event Management (SIEM) tool with their performance using a SIEM tool that incorporated sonification, in a primary and a non-primary monitoring task. In both tasks, a number of aspects of the monitoring performance of participants were significantly improved when sonification was used. Our results support the potential for sonification to aid in SOC tasks, and indicate a need to validate the utility of sonification systems by running them in operational SOCs.
Surprise! Predicting Infant Visual Attention in a Socially Assistive Robot Contingent Learning Paradigm

Early intervention to address developmental disability in infants has the potential to promote improved outcomes in neurodevelopmental structure and function [1]. Researchers are starting to explore Socially Assistive Robotics (SAR) as a tool for delivering early interventions that are synergistic with and enhance human-administered therapy. For SAR to be effective, the robot must be able to consistently attract the attention of the infant in order to engage the infant in a desired activity. This work presents the analysis of eye gaze tracking data from five 6-8 month old infants interacting with a Nao robot that kicked its leg as a contingent reward for infant leg movement. We evaluate a Bayesian model of low-level surprise on video data from the infants' head-mounted camera and on the timing of robot behaviors as a predictor of infant visual attention. The results demonstrate that over 67% of infant gaze locations were in areas the model evaluated to be more surprising than average. We also present an initial exploration using surprise to predict the extent to which the robot attracts infant visual attention during specific intervals in the study. This work is the first to validate the surprise model on infants; our results indicate the potential for using surprise to inform robot behaviors that attract infant attention during SAR interactions.
X-Ortho: Fuzzy Rule Based Expert System for Diagnosing Infective Diseases of Hinge Joint Knee

Various Medical expert systems are designed to diagnose the different kinds of diseases by using facts related to disease. Knee is the main weight bearing joint in our body. The proposed work is for the diagnosis of the infective diseases of the knee joint. The major focus is given to Acute Septic Arthritis, Chronic Septic Arthritis and Osteomyelitis of the knee joint. MATLAB fuzzy logic controller is used to handle the vague values. Knowledge acquisition is done with the help of orthopaedic expert. Survey method is used for the data collection and to evaluate the performance of proposed system various defuzzification methods are used. The proposed system is tested on 75 patients and gives 96% efficient results.
RETRACTED: Critical analysis and performance comparison of thirty-eight (38) clear-sky direct irradiance models under the climate of Chilean Atacama Desert

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the Editor-in-Chief, following concerns raised by a reader. The paper is retracted because of: 1. A preliminary release of Meteorological Radiation Model (MRM) has been used rather than the current version (6.1), which affects the results presented in Table 1. Some inputs required by the MRM code are not used. Therefore, the categorization of the models shown in Table 1 is not correct. 2. The use of 1-min radiation values is uncertain in the sense that short time intervals may contain peculiar circumstances, such as, reflection of radiation by clouds, and short passage of Cumulus clouds. If such measurements are not correctly filtered, they may not be well compared to the simulations obtained with a clear-sky radiation model, which is insensitive to such a dynamic atmospheric occurrence. In this regard, the method selected to detect cloud artifacts is not satisfactory. 3. There is a mistake with the maximum value of P registered at the altitude of z = 965 m amsl (i.e., P = 1046 hPa), which is an extremely high pressure. As the authors do not mention any quality-control procedure for the meteorological data, this creates ambiguities about their precision. Furthermore, filtering the meteorological data by their 5-min averages would avoid extreme values. 4. All solar radiation quality-control tests take into account the pyranometers' cosine-effect problem. Therefore, radiation values of solar altitudes lower than 5o (or even 6o or 10o according to the criterion set) must be excluded. The present study accepted all DNI values from sunrise to sunset. This verifies the erroneous behavior of the models in Figs. 3, 4 for solar altitudes close to or less than 10o. 5. In Table 3, the minimum value of the Linke turbidity coefficient, TL, estimated during the experimental period, is indicated as 0.79-a physical impossibility since, by definition, TL must be larger than 1 6. In Table 3, the minimum value of both ß and AOD is 0, which is an abstraction because it would mean a total absence of aerosols. 7. In Table 4, model performance results are deemed "excellent" if their coefficient of determination (R2) is <2.5%, which is unsubstantiated. 8. In Table 5, many R2 values are found >100%, which is impossible. 9. The main problem of the paper is that the method used to obtain the input data to the models is circular, i.e., directly derived from the very same irradiance data then used to "validate" the models' irradiance predictions. This invalidates the method and the results presented. These affect the reliability of the analysis presented in the paper, so the paper is retracted.
Modularizing Cross-Cutting Concerns with Aspect-Oriented Extensions for Solidity

Over the past few years, blockchain technology has attracted much attention. The smart contract is a computer program that implements and executes transactions and business logic on the blockchain. Unlike traditional general-purpose programming languages, the modularization techniques of programming languages for the smart contracts are still immature. Specifically, a suitable modularization mechanism for cross-cutting concerns is still not available. From a software engineering perspective, the modularization of cross-cutting concerns is a fundamental issue that can dramatically affect the contracts' maintainability and readability. As a result, reducing the redundant cross-cutting logic is still a challenging task. This paper proposes a practically viable approach that supports Aspect-Oriented Programming (AOP) for smart contract development on Ethereum-based blockchains. We suggest a set of extension to Solidity, a programming language that runs on the Ethereum-based blockchains, that facilitates AOP semantics and behaviors. As a proof of concept, we also realize the weaving module for the extension. The feasibility of the proposed approach is demonstrated by a case study.
Research on Moving Object Detection and Matching Technology in Multi-Angle Monitoring Video

Prospect detection, extraction and tracking matching of moving objects in surveillance video has always been an important research content in the field of computer vision. It has important practical value in intelligent traffic supervision, video security monitoring, navigation and other aspects. In this paper, the detection and extraction of the same foreground moving object and the matching label in multi-view surveillance videos are studied. A multi-view foreground matching model based on HOG detection and system clustering is established by using background subtraction method, HOG feature detection and least squares of deviations in system clustering. The feature matching of the same foreground object is carried out, thus realizing different foreground objects. Recognition and matching of the same foreground object in multi-angle video. The experimental results show that the model can detect, extract and match foreground moving objects effectively by simultaneously shooting multiple surveillance videos from different angles near the same location.
Predicting Non-Completion of Programming Exercises using Action Logs and Keystrokes

Computing programming skills is gaining importance but yet it is not an easy subject to master as exemplified in the high attribution rates of computer science courses. It is thus critical that we identify learners who struggle with computer programming at opportune moments to sustain and support their learning. In this study, the keystrokes and actions of learners are captured in real-time in a Java programming tutoring system and used to predict the possibility of non-completion of programming exercises on a granular basis. The results indicate that this can be detected at an adequate accuracy with the proposed feature engineering and machine learning techniques. The use of keystrokes and action logs is significant as they are unobtrusive and easy to set up and thus can easily be propagated for use in any learning environment.
Singer quad apertures and super fast decoding

A new class of coded aperture is introduced, the Singer quad aperture, which is the Cartesian product of two unwrapped Singer product apertures. Formulae for the signal-to-noise ratio (SNR) of Singer quad apertures are derived, allowing optimal Singer quad apertures to be identified, and the CPU time required to decode them is quantified. This allows a systematic comparison to be made of the theoretical performance of Singer quad apertures against both conventionally wrapped Singer apertures, and also Singer product apertures. For very large images, equivalently for images at very high resolution, the SNR of Singer quad apertures should be asymptotically as good as either of these other two classes of apertures, but Singer product apertures should decode faster than either, and by a factor which increases with increasing image size. Initial results from numerical simulations indicate that these theoretical predictions are likely to be true in practice.
Quadcopter Obstacle Avoidance and Path Planning Using Flood Fill Method

Unmanned Aerial Vehicles (UAVs) require advanced path planning and obstacle avoidance algorithms for navigation. In this paper, the Flood Fill Method (FFM) was employed for navigating an autonomous quadcopter in a simulated environment created using MATLAB. The proposed method was compared with the traditionally used Potential Field Method (PFM). For a known terrain, Flood Fill Method works computationally faster for a repetitively same path. These methods were compared in terms of the parameters such as avoiding random obstacles for variable starting and destination positions, the time required to complete the journey, and the optimum path selection.
Undecidability of a Newly Proposed Calculus for CatLog3

In his recent papers “Parsing/theorem-proving for logical grammar CatLog3” and “A note on movement in logical grammar”, Glyn Morrill proposes a new substructural calculus to be used as the basis for the categorial grammar parser CatLog3. In this paper we prove that the derivability problem for a fragment of this calculus is algorithmically undecidable.
Training Assistant for Automotive Engineering Through Augmented Reality

This article proposes the development of an augmented reality application for mobile devices with Android OS focused on the visualization and interaction of the user with the components, technical characteristics, location and the processes of disassembly and assembly of engine in a vehicle; facilitating the learning process referring to this automotive system. The application was developed in the graphic engine 3D Unity and the use of Vuforia for the recognition of objects in 3D, being a technological tool that allows vouching the learning in the internal combustion engine, guiding the user and changing the paradigms of the use of physical manuals with the new technological advances as the augmented reality.
Hardware-Software Co-Design of an Image Feature Extraction and Matching Algorithm

Providing low cost and rich information, visual sensors are becoming the top choice for automatic systems. Particularly in the field of navigations or SLAM technologies, extracting and matching features are the basic aspects. This paper addresses the required computational efficiency, computational resources and power-consumption problem of image feature detection and matching algorithm by designing a hardware-software co-design architecture for the implementation on a field-programmable gate array (FPGA) and a Nios II CPU. Given images data from the Nios II, features are extracted and matched by the scale-invariant feature transform (SIFT) algorithm and a linear exhaustive search (LES) method using an Altera DE2i-150 FPGA, respectively. The matched features are subsequently transferred back from the FPGA to Nios II. To show the effectiveness of the proposed approach, two images with affine transformations are provided. An object tracking system is also developed. Experimental results show that, taking the advantages of parallel computing of an FPGA, the overall computational time and the hardware resources usage of the proposed approach are greatly reduced, compared to a full-software implementation and other existing methods.
Predicting How to Test Requirements: An Automated Approach

An important task in requirements engineering is to identify and determine how to verify a requirement (e.g., by manual review, testing, or simulation; also called potential verification method). This information is required to effectively create test cases and verification plans for requirements. [Objective] In this paper, we propose an automatic approach to classify natural language requirements with respect to their potential verification methods (PVM). [Method] Our approach uses a convolutional neural network architecture to implement a multiclass and multilabel classifier that assigns probabilities to a predefined set of six possible verification methods, which we derived from an industrial guideline. Additionally, we implemented a backtracing approach to analyze and visualize the reasons for the network's decisions. [Results] In a 10-fold cross validation on a set of about 27,000 industrial requirements, our approach achieved a macro averaged F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 0.79 across all labels. For the classification into test or non-test, the approach achieves an even higher F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score of 0.94. [Conclusions] The results show that our approach might help to increase the quality of requirements specifications with respect to the PVM attribute and guide engineers in effectively deriving test cases and verification plans.
Prediction of Visual Quality for Lossy Compressed Images

Compression is a typical operation applied in image processing. Lossy compression is widely used although it introduces distortions that might lead to degradation of image visual quality. Then, introduced distortions have to be controlled at compression stage to provide visually lossless compression or appropriate quality according to adequate metrics. Often this should be done without iterations of image compression/decompression. In this paper, we propose an approach to predict visual quality metrics that, for coders based on discrete cosine transform, depend on quantization step or scaling factor. Using such a dependence, it is possible to set quantization step or scaling factor properly (with high accuracy) before carrying out lossy compression. Experiments are performed for optical and remote sensing test images showing that the proposed approach is quite universal.
Cryptocurrencies: Will Machines Replace Your Banker?

Searching the word cryptocurrency on Google Scholar returns over 18,000 results. But of those, only 282 contain the JEL code E42: monetary systems. Such rough exercise suggests that, while the phen...
SAR image compression using optronic processing

SAR image compression is important for space-borne SAR systems. Many compression methods were proposed, conventional methods were based on digital processing. Traditional methods are much computation and storage consumption. A novel SAR image compression method is proposed here. In the method, SAR image compression is based on the discrete cosine transform (DCT), and it is applied using optronic processing. Both the speckle denoising and the processing of cosine spectrum coefficient are presented in authors’ experiments, which are the crucial parts in SAR image compression. The final experimental result verifies the feasibility of the method.
A New Filtering System for Using a Consumer Depth Camera at Close Range

Using consumer depth cameras at close range yields a higher surface resolution of the object, but this makes more serious noises. This form of noise tends to be located at or on the edge of the realistic surface over a large area, which is an obstacle for real-time applications that do not rely on point cloud post-processing. In order to fill this gap, by analyzing the noise region based on position and shape, we proposed a composite filtering system for using consumer depth cameras at close range. The system consists of three main modules that are used to eliminate different types of noise areas. Taking the human hand depth image as an example, the proposed filtering system can eliminate most of the noise areas. All algorithms in the system are not based on window smoothing and are accelerated by the GPU. By using Kinect v2 and SR300, a large number of contrast experiments show that the system can get good results and has extremely high real-time performance, which can be used as a pre-step for real-time human-computer interaction, real-time 3D reconstruction, and further filtering.
Comparative Study on Realtime Data Processing System

This paper will describe the collation of different real time data processing system being used in different companies. We are mainly focusing on real time data processing systems used in facebook, Twitter and LinkedIn. There are two ways to process the data Batched and Streamed. In batched processing, all the data is taken at once and processed. In streaming data is continuously generated and processed in real time. Stream processing systems developed by companies are: Facebook's Puma, Swift, and Stylus; Twitter's Storm and Heron; and LinkedIn's Samza.
Recognizing Micro Actions in Videos: Learning Motion Details via Segment-Level Temporal Pyramid

Recognizing micro actions from videos is a very challenging problem since they involve only subtle motions of body parts. In this paper, we propose a new deep-learning based method for micro action recognition by building a segment-level temporal pyramid to better capture the motion details. More specifically, we first temporally sample the input video for short segments and for each of the video segment, we employ a two-stream convolutional neural networks (CNNs) followed by a temporal pyramid for extracting deep features. Finally, the features derived from all the video segments are combined for action classification. We evaluate the proposed method on a micro-action video dataset, as well as a general-action video dataset, with very promising results.
Contextual weighting approach to compute term weight in layered vector space model

The World Wide Web (WWW) is the largest available repository of information. This huge amount of information put forward the challenges of retrieval of trustworthy information from WWW. It defies researchers with new issues of diversity and complexity while retrieving the web information. Information retrieval from the web demands approaches that span beyond conventional information retrieval. Heterogeneity, complexity and the huge volume of web information requires a unique approach to retrieve information. Besides, end-users introduce some difficulties in the retrieval process. Sometimes queries submitted by the user are subtle and ambiguous. The primary concern in information retrieval is the issue of predicting the relevance of documents. In this article, a new approach is proposed that rationally separates web document into five layers, namely, title, header, hyperlink, meta tag and body layer. The proposed method effectively combines the textual information and structural evidence of web document for retrieving information from Web. In the proposed layered vector space model, each layer has an allocated priority which is used to compute weight factor for these layers. The proposed method deduces equation that effectively combines priority of the layer and length of the layer to calculate the weight of the layer.