Reviewerly/exHarmony huggingface dataset:
Motion planner for a Tetris-inspired reconfigurable floor cleaning robot

Coverage path planning technique is an essential ingredient in every floor cleaning robotic systems. Even though numerous approaches demonstrate the benefits of conventional coverage motion planning techniques, they are mostly limited to fixed morphological platforms. In this article, we put forward a novel motion planning technique for a Tetris-inspired reconfigurable floor cleaning robot named “hTetro” that can reconfigure its morphology to any of the seven one-sided Tetris pieces. The proposed motion planning technique adapts polyomino tiling theory to tile a defined space, generates reference coordinates, and produces a navigation path to traverse on the generated tile-set with an objective of maximizing the area coverage. We have summarized all these aspects and concluded with experiments in a simulated environment that benchmarks the proposed technique with conventional approaches. The results show that the proposed motion planning technique achieves significantly higher performance in terms of area recovered than the traditional methods.
A Consortium Blockchain-Based Agricultural Machinery Scheduling System

The introduction of a consortium blockchain-based agricultural machinery scheduling system will help improve the transparency and efficiency of the data flow within the sector. Currently, the traditional agricultural machinery centralized scheduling systems suffer when there is a failure of the single point control system, and it also comes with high cost managing with little transparency, not leaving out the wastage of resources. This paper proposes a consortium blockchain-based agricultural machinery scheduling system for solving the problems of single point of failure, high-cost, low transparency, and waste of resources. The consortium blockchain-based system eliminates the central server in the traditional way, optimizes the matching function and scheduling algorithm in the smart contract, and improves the scheduling efficiency. The data in the system can be traced, which increases transparency and improves the efficiency of decision-making in the process of scheduling. In addition, this system adopts a crowdsourcing scheduling mode, making full use of idle agricultural machinery in the society, which can effectively solve the problem of resource waste. Then, the proposed system implements authentication access mechanisms, and allows only authorized users into the system. It includes transactions based on digital currency and eliminates third-party platform to charge service fees. Moreover, participating organizations have the opportunity to obtain benefits and reduce transaction costs. Finally, the upper layers supervision improves the efficiency and security of consensus algorithm, allows supervisors to block users with malicious motives, and always ensures system security.
A Novel Approach of Image Steganography for Secure Communication Based on LSB Substitution Technique

Steganography aims to hide the messages from unauthorized persons for various purposes, e.g., military correspondence, financial transaction data. Securing the data during transmission is of utmost importance these days. The confidentiality, integrity, and availability of the data are at risk because of the emerging technologies and complexity in software applications, and therefore, there is a need to secure such systems and data. There are various methodologies to deal with security issues when utilizing an open system like the Internet. This research proposes a new technique in steganography within RGB shading space to achieve enhanced security compared with existing systems. We evaluate our approach with the help of diverse image quality evaluation techniques including MSE (Mean Square Error), RMSE (Root Mean Square Error), PSNR (Peak Signal-to-Noise Ratio), MAE (Mean Absolute Error), NCC (Normalized Cross-Correlation) and SSIM (Structural Similarity Index). Our experimental results demonstrate improved strength, intangibility, and security when contrasted with existing techniques and vindicate the effectiveness of this exploration work. The proposed approach achieved a 3.6701% average higher score for PSNR Correlation than the next best existing approach. Moreover, in PSNR with a variable amount of cipher embedded in the same images of the same dimensions, the proposed approach attained a 5.22% better score. Embedding the same size of cipher in images of different size resulted a 3.56% better score.
A multi-scale three-dimensional face recognition approach with sparse representation-based classifier and fusion of local covariance descriptors

In this paper, an efficient multi-scale hybrid approach is proposed to tackle two main problems in three-dimensional (3D) face recognition, namely the singularity of scale features representation and underexplored locality in dictionary learning. The multi-scale features space representation is developed based on the new 3D faces generated by the Gaussian filter. The locality-sensitive Riemannian sparse representation-based classifier is also constructed to accurately recognize faces with various expressions, poses and occlusions. Two sets of face recognition experiment, one that includes expression variations, and the another that includes pose and occlusion variations, are conducted to compare the performance of the proposed approach against other benchmark 3D face recognition algorithms. The recognition accuracies of the proposed algorithm to both Neutral vs. Neutral achieved on Face Recognition Grand Challenge (FRGC) v2.0 database and Bosphorus database are 100%.
Measuring Identity Confusion with Uniform Resource Locators

Uniform Resource Locators (URLs) unambiguously specify host identity on the web. URLs are syntactically complex, and although software can accurately parse identity from URLs, users are frequently exposed to URLs and expected to do the same. Unfortunately, incorrect assessment of identity from a URL can expose users to attacks, such as typosquatting and phishing. Our work studies how well users can correctly determine the host identity of real URLs from common services and obfuscated "look-alike" URLs. We observe that participants employ a wide range of URL parsing strategies, and can identify real URLs 93% of time. However, only 40% of obfuscated URLs were identified correctly. These mistakes highlighted several ways in which URLs were confusing to users and why their existing URL parsing strategies fall short. We conclude with future research directions for reliably conveying website identity to users.
Sparse Gaussian Processes with Spherical Harmonic Features

We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations. Our inference scheme is comparable to variational Fourier features, but it does not suffer from the curse of dimensionality, and leads to diagonal covariance matrices between inducing variables. This enables a speed-up in inference, because it bypasses the need to invert large covariance matrices. Our experiments show that our model is able to fit a regression model for a dataset with 6 million entries two orders of magnitude faster compared to standard sparse GPs, while retaining state of the art accuracy. We also demonstrate competitive performance on classification with non-conjugate likelihoods.
Privacy Protection in Real Time HEVC Standard Using Chaotic System

Video protection and access control have gathered steam over recent years. However, the most common methods encrypt the whole video bit stream as unique data without taking into account the structure of the compressed video. These full encryption solutions are time and power consuming and, thus, are not aligned with the real-time applications. In this paper, we propose a Selective Encryption (SE) solution for Region of Interest (ROI) security based on the tile concept in High Efficiency Video Coding (HEVC) standards and selective encryption of all sensitive parts in videos. The SE solution depends on a chaos-based stream cipher that encrypts a set of HEVC syntax elements normatively, that is, the bit stream can be decoded with a standard HEVC decoder, and a secret key is only required for ROI decryption. The proposed ROI encryption solution relies on the independent tile concept in HEVC that splits the video frame into independent rectangular areas. Tiles are used to pull out the ROI from the background and only the tiles figuring the ROI are encrypted. In inter coding, the independence of tiles is guaranteed by limiting the motion vectors of non-ROI to use only the unencrypted tiles in the reference frames. Experimental results have shown that the encryption solution performs secure video encryption in a real time context, with a diminutive bit rate and complexity overheads.
Automatically detecting open academic review praise and criticism

Purpose Peer reviewer evaluations of academic papers are known to be variable in content and overall judgements but are important academic publishing safeguards. This article introduces a sentiment analysis program, PeerJudge, to detect praise and criticism in peer evaluations. It is designed to support editorial management decisions and reviewers in the scholarly publishing process and for grant funding decision workflows. The initial version of PeerJudge is tailored for reviews from F1000Research's open peer review publishing platform. Design/methodology/approach PeerJudge uses a lexical sentiment analysis approach with a human-coded initial sentiment lexicon and machine learning adjustments and additions. It was built with an F1000Research development corpus and evaluated on a different F1000Research test corpus using reviewer ratings. Findings PeerJudge can predict F1000Research judgements from negative evaluations in reviewers' comments more accurately than baseline approaches, although not from positive reviewer comments, which seem to be largely unrelated to reviewer decisions. Within the F1000Research mode of post-publication peer review, the absence of any detected negative comments is a reliable indicator that an article will be ‘approved’, but the presence of moderately negative comments could lead to either an approved or approved with reservations decision. Originality/value PeerJudge is the first transparent AI approach to peer review sentiment detection. It may be used to identify anomalous reviews with text potentially not matching judgements for individual checks or systematic bias assessments.
Better Visual Image Super-Resolution with Laplacian Pyramid of Generative Adversarial Networks

Although there has been a great breakthrough in the accuracy and speed of super-resolution (SR) reconstruction of a single image by using a convolutional neural network, an important problem remains unresolved: how to res... | Find, read and cite all the research you need on Tech Science Press
Human-centric Software Engineering for Next Generation Cloud- and Edge-based Smart Living Applications

Humans are a key part of software development, including customers, designers, coders, testers and end users. In this keynote talk I explain why incorporating human-centric issues into software engineering for next-generation applications is critical. I use several examples from our recent and current work on handling human-centric issues when engineering various `smart living' cloud- and edge-based software systems. This includes using human-centric, domain-specific visual models for non-technical experts to specify and generate data analysis applications; personality impact on aspects of software activities; incorporating end user emotions into software requirements engineering for smart homes; incorporating human usage patterns into emerging edge computing applications; visualising smart city-related data; reporting diverse software usability defects; and human-centric security and privacy requirements for smart living systems. I assess the usefulness of these approaches, highlight some outstanding research challenges, and briefly discuss our current work on new human-centric approaches to software engineering for smart living applications.
REMoDNaV: robust eye-movement classification for dynamic stimulation

Abstract Tracking of eye movements is an established measurement for many types of experimental paradigms. More complex and more prolonged visual stimuli have made algorithmic approaches to eye-movement event classification the most pragmatic option. A recent analysis revealed that many current algorithms are lackluster when it comes to data from viewing dynamic stimuli such as video sequences. Here we present an event classification algorithm—built on an existing velocity-based approach—that is suitable for both static and dynamic stimulation, and is capable of classifying saccades, post-saccadic oscillations, fixations, and smooth pursuit events. We validated classification performance and robustness on three public datasets: 1) manually annotated, trial-based gaze trajectories for viewing static images, moving dots, and short video sequences, 2) lab-quality gaze recordings for a feature-length movie, and 3) gaze recordings acquired under suboptimal lighting conditions inside the bore of a magnetic resonance imaging (MRI) scanner for the same full-length movie. We found that the proposed algorithm performs on par or better compared to state-of-the-art alternatives for static stimulation. Moreover, it yields eye-movement events with biologically plausible characteristics on prolonged dynamic recordings. Lastly, algorithm performance is robust on data acquired under suboptimal conditions that exhibit a temporally varying noise level. These results indicate that the proposed algorithm is a robust tool with improved classification accuracy across a range of use cases. The algorithm is cross-platform compatible, implemented using the Python programming language, and readily available as free and open-source software from public sources.
An Intelligent Recommender and Decision Support System (IRDSS) for Effective Management of Software Projects

The management and estimation of agile projects are challenging tasks for software companies due to their high failure rates. This paper emphasizes how to improve management and estimation challenges in the context of scrum, which is an agile process widely used for the development of small to medium size software projects. The scrum emphasis on code results in spending inadequate time on the estimation process. Mostly, the scrum master, along with the scrum team, estimates the upcoming software projects based on experience or historical data. Many issues can arise in a case where expert judgment is not available or historical data are not properly organized. In this paper, an Intelligent Recommender and Decision Support System (IRDSS) is proposed that can help the scrum master to better estimate an upcoming software project in terms of cost, time, and recommendations of human resources. Formal specification of IRDSS is also performed using the formalism known as Z language. Furthermore, an experiment on fifteen web projects was performed to validate the proposed approach and compared it with Delphi and Planning Poker estimation methods. The overall results indicate that the proposed system can produce better estimation than Planning Poker and Delphi methods by applying MMRE and PRED evaluation. This research opens new directions for the scrum community for the development of software projects within the allocated time and cost.
Blockchain-based Automated Certificate Revocation for 5G IoT

Internet of Things (IoT) is a key topic of interest in modern communication context with the evolution of 5G and beyond ecosystems. 5G will interconnects billions of IoT devices wirelessly. The wireless communication exposes the devices to massive security risks in different dimensions. The Public Key Infrastructure (PKI) is one of the promising solutions to eliminate security risks. It ensures the authentication and communication integrity by using public key certificates. However, the overhead of certificate storage is a significant problem for the resource constrained IoT devices. We propose an application of Elliptic Curve Qu Vanstone (ECQV) certificates, which are lightweight in size for the resource restricted IoT devices. Furthermore, we incorporate the blockchain based smart contracts to handle the certificate related operations. We utilize the smart contracts in the certificate issuance and developed a smart contract based threat scoring mechanism to automatically revoke the certificates. The lightweight nature of ECQV certificates enables the distributed ledger to store, update, and revoke the certificates. We evaluated the proposed solution in Hyperledger Fabric blockchain platform.
Optimizing echo state network through a novel fisher maximization based stochastic gradient descent

Hyperparameter optimization is a challenging process that has the potential to improve machine learning algorithms. Since it creates a remarkable computational burden for machine learning tasks, there have been few works coping with tuning strategies of a specific algorithm. In this paper, an improved Stochastic Gradient Descent (SGD) based on Fisher Maximization is developed for tuning hyperparameters of an Echo State Network (ESN) which has a wide range of applications. The results of the method are then compared with those of traditional Gradient Descent and Grid Search. According to the obtained results; 1) The scale of the data sets greatly affects the reliability of hyperparameter optimization results; 2) Feature selection is critical in terms of mean error of training when hyperparameter optimization is applied on some methods such as ESN; 3) SGD falls in a good local minima if Fisher Maximization is performed to find a good starting point.
Modeling and Analysis of Leftover Issues and Release Time Planning in Multi-Release Open Source Software Using Entropy Based Measure

In Open Source Software (OSS), users report different issues on issues tracking systems. Due to time constraint, it is not possible for developers to resolve all the issues in the current release. The leftover issues which a... | Find, read and cite all the research you need on Tech Science Press
Key questions for the quantum machine learner to ask themselves

Abstract Within the last several years quantum machine learning (QML) has begun to mature; however, many open questions remain. Rather than review open questions, in this perspective piece I will discuss my view about how we should approach problems in QML. In particular I will list a series of questions that I think we should ask ourselves when developing quantum algorithms for machine learning. These questions focus on what the definition of quantum ML is, what is the proper quantum analogue of QML algorithms is, how one should compare QML to traditional ML and what fundamental limitations emerge when trying to build QML protocols. As an illustration of this process I also provide information theoretic arguments that show that amplitude encoding can require exponentially more queries to a quantum model to determine membership of a vector in a concept class than classical bit-encodings would require; however, if the correct analogue is chosen then both the quantum and classical complexities become polynomially equivalent. This example underscores the importance of asking ourselves the right questions when developing and benchmarking QML algorithms.
Evaluation of the CellaVision Advanced RBC Application for Detecting Red Blood Cell Morphological Abnormalities

Background: The Advanced RBC Application of the CellaVision DM9600 system (CellaVision AB, Lund, Sweden) automatically characterizes and classifies red blood cells (RBCs) into 21 morphological categories based on their size, color, shape, and inclusions.We evaluated the diagnostic performance of the CellaVision Advanced RBC Application with respect to the classification and grading of RBC morphological abnormalities in accordance with the 2015 International Council for Standardization in Haematology (ICSH) guidelines.Methods: A total of 223 samples, including 123 with RBC morphological abnormalities and 100 from healthy controls, were included.Seven RBC morphological abnormalities and their grading obtained with CellaVision DM9600 pre-and post-classification were compared with the results obtained using manual microscopic examination.The grading cut-off percentages were determined in accordance with the 2015 ICSH guidelines.The sensitivity and specificity of the CellaVision DM9600 system were evaluated using the manual microscopic examination results as a true positive.Results: In pre-classification, > 90% sensitivity was observed for target cells, tear drop cells, and schistocytes, while > 90% specificity was observed for acanthocytes, spherocytes, target cells, and tear drop cells.In post-classification, the detection sensitivity and specificity of most RBC morphological abnormalities increased, except for schistocytes (sensitivity) and acanthocytes (specificity).The grade agreement rates ranged from 35.9% (echinocytes) to 89.7% (spherocytes) in pre-classification and from 46.2% (echinocytes) to 90.1% (spherocytes) in post-classification.The agreement rate of samples with withinone grade difference exceeded 90% in most categories, except for schistocytes and echinocytes. Conclusions:The Advanced RBC Application of CellaVision DM9600 is a valuable screening tool for detecting RBC morphological abnormalities.
Visual Sentiment Analysis from Disaster Images in Social Media

The increasing popularity of social networks and users' tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people's sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.