Reviewerly/exHarmony huggingface dataset:
DeepIG: Multi-Robot Information Gathering With Deep Reinforcement Learning

State-of-the-art multi-robot information gathering (MR-IG) algorithms often rely on a model that describes the structure of the information of interest to drive the robots motion.This causes MR-IG algorithms to fail when they are applied to new IG tasks, as existing models cannot describe the information of interest.Therefore, we propose in this paper a MR-IG algorithm that can be applied to new IG tasks with little algorithmic changes.To this end, we introduce DeepIG: a MR-IG algorithm that uses Deep Reinforcement Learning to allow robots to learn how to gather information.Nevertheless, there are IG tasks for which accurate models have been derived.Therefore, we extend DeepIG to exploit existing models for such IG tasks.This algorithm we term it model-based DeepIG (MB-DeepIG).First, we evaluate DeepIG in simulations, and in an indoor experiment with three quadcopters that autonomously map an unknown terrain profile built in our lab.Results demonstrate that DeepIG can be applied to different IG tasks without algorithmic changes, and that it is robust to measurement noise.Then, we benchmark MB-DeepIG against state-of-the-art informationdriven Gaussian-processes-based IG algorithms.Results demonstrate that MB-DeepIG outperforms the considered benchmarks.
A Novel Design of Cryptographic SP-Network Based on Gold Sequences and Chaotic Logistic Tent System

Substitution permutation network (SP-network) is a chain of linked mathematical primitives used in block cipher algorithms. The proposed novel design of cryptographic SP-network consists of three cryptographic primitives: substitution box, permutation box, and random key sequences, including one key whitening operation. A new design is being proposed for each cryptographic primitive. The cryptographic strength of the proposed SP-network is evaluated by employing various standard tests; strict avalanche criterion, differential approximation probability, bit independent criterion, linear approximation probability, nonlinearity test, unified averaged changed intensity, histogram analysis, and coefficient correlation tests. The outcomes of the investigations validate that the designed cryptosystem is stable for secure communication and attains better cryptographic strength as compared with other state-of-the-art techniques.
Secure deep neural networks using adversarial image generation and training with Noise-GAN

Recent advances in artificial intelligence have increased the importance of security issues. Nowadays, deep neural networks (DNNs) are used in many critical applications such as pilot drones and self-driving cars. So, the DNN's malfunction, due to an attack, may cause irreparable damages. The attack may happen either in training phase (poisoning attacks) or testing phase (evasion attacks) by presenting adversarial examples. These samples are maliciously created to deceive DNNs. This paper deals with evasion attacks and aims to immunize DNNs through adversarial examples generation and training. We propose Noise-GAN, a Generative Adversarial Network (GAN) with a multi-class Discriminator for producing a noise that by adding it to the original image adversarial examples can be obtained. In this paper, various types of evasion attacks are considered and performance of the proposed method is evaluated on different victim models under various defensive strategies. Experimental results are based on MNIST and CIFAR10 datasets and the average success rates for different attacks are reported and compared with state-of-the-art methods. The Non-targeted attack success rates on DNNs after training by adversarial examples, generated by Noise-GAN, were declined from 87.7% to 10.41% using MNIST dataset and from 91.2% to 57.66% using CIFAR-10 dataset.
Fractal Residual Network and Solutions for Real Super-Resolution

The degradation function in single image super-resolution (SISR) is usually bicubic with an integer scale factor. However, bicubic is not realistic and a scale factor is not always an integer number in the real world. We introduce some solutions that are appropriate for realistic SR. First, we propose down-upsampling module which allows general SR network to use GPU memory efficiently. With the module, we can stack more convolutional layers, resulting in a higher performance. We also adopt a new regularization loss, auto-encoder loss. That loss generalizes down-upsampling module. Furthermore, we propose fractal residual network (FRN) for SISR. We extend residual in residual structure by adding new residual shells and name that structure FRN because of the self-similarity like the fractal. We show that our proposed model outperforms state-of-the-art methods and demonstrate the effectiveness of our solutions by several experiments on NTIRE 2019 dataset.
SMOTE and Feature Selection for More Effective Bug Severity Prediction

“Severity” is one of the essential features of software bug reports, which is a crucial factor for developers to decide which bug should be fixed immediately and which bug could be delayed to a next release. Severity assignment is a manual process and its accuracy depends on the experience of the assignee. Prior research proposed several models to automate this process. These models are based on textual preprocessing of historical bug reports and classification techniques. Although bug repositories suffer from severity class imbalance, none of the prior studies investigated the impact of implementing a class rebalancing technique on the accuracy of their models. In this paper, we propose a framework for predicting fine-grained severity levels which utilizes an over-sampling technique “SMOTE”, to balance the severity classes, and a feature selection scheme, to reduce the data scale and select the most informative features for training a [Formula: see text]-nearest neighbor (KNN) classifier. The KNN classifier utilizes a distance-weighted voting scheme to predict the proper severity level of a newly reported bug. We investigated the effectiveness of our proposed approach on two large bug repositories, namely Eclipse and Mozilla, and the experimental results showed that our approach outperforms cutting-edge studies in predicting the minority severity classes.
Interacting with Collaborative Robots—A Study on Attitudes and Acceptance in Industrial Contexts

Through combining robots’ power, consistency, and accuracy with humans’ creativity and flexibility, human–robot interactionHuman-Robot interaction offers new ways of manufacturingManufacturing . For the successful introduction of human–robot interactionHuman-Robot interaction in manufacturingManufacturing , it is important not only to consider the necessary change of qualification, but also to create a positive attitudeAttitude toward this new technology as expectationsExpectations trigger behavior and consequently influence the quality of work. The studyStudy reported in this chapter analyzes how attitudes toward collaborative robotsCollaborative robots are influenced through interaction with an industrial robotIndustrial robots .
On the symbol-pair distances of repeated-root constacyclic codes of length <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e20" altimg="si12.svg"><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msup></mml:math>

Let p be an odd prime, and λ be a nonzero element of the finite field Fpm. The λ-constacyclic codes of length 2ps over Fpm are classified as the ideals of quotient ring Fpm[x]〈x2ps−λ〉 in terms of their generator polynomials. Based on these generator polynomials, the symbol-pair distances of all such λ-constacyclic codes of length 2ps are obtained in this paper. As an application, all MDS symbol-pair constacyclic codes of length 2ps over Fpm are established, which produce many new MDS symbol-pair codes with good parameters.
Exploring Word Embedding Techniques to Improve Sentiment Analysis of Software Engineering Texts

Sentiment analysis (SA) of text-based software artifacts is increasingly used to extract information for various tasks including providing code suggestions, improving development team productivity, giving recommendations of software packages and libraries, and recommending comments on defects in source code, code quality, possibilities for improvement of applications. Studies of state-of-the-art sentiment analysis tools applied to software-related texts have shown varying results based on the techniques and training approaches. In this paper, we investigate the impact of two potential opportunities to improve the training for sentiment analysis of SE artifacts in the context of the use of neural networks customized using the Stack Overflow data developed by Lin et al. We customize the process of sentiment analysis to the software domain, using software domain-specific word embeddings learned from Stack Overflow (SO) posts, and study the impact of software domain-specific word embeddings on the performance of the sentiment analysis tool, as compared to generic word embeddings learned from Google News. We find that the word embeddings learned from the Google News data performs mostly similar and in some cases better than the word embeddings learned from SO posts. We also study the impact of two machine learning techniques, oversampling and undersampling of data, on the training of a sentiment classifier for handling small SE datasets with a skewed distribution. We find that oversampling alone, as well as the combination of oversampling and undersampling together, helps in improving the performance of a sentiment classifier.
Mind Your Neighbours: Image Annotation With Metadata Neighbourhood Graph Co-Attention Networks

As the visual reflections of our daily lives, images are frequently shared on the social network, which generates the abundant 'metadata' that records user interactions with images. Due to the diverse contents and complex styles, some images can be challenging to recognise when neglecting the context. Images with the similar metadata, such as 'relevant topics and textual descriptions', 'common friends of users' and 'nearby locations', form a neighbourhood for each image, which can be used to assist the annotation. In this paper, we propose a Metadata Neighbourhood Graph Co-Attention Network (MangoNet) to model the correlations between each target image and its neighbours. To accurately capture the visual clues from the neighbourhood, a co-attention mechanism is introduced to embed the target image and its neighbours as graph nodes, while the graph edges capture the node pair correlations. By reasoning on the neighbourhood graph, we obtain the graph representation to help annotate the target image. Experimental results on three benchmark datasets indicate that our proposed model achieves the best performance compared to the state-of-the-art methods.
Behavioral Biometrics Scheme with Keystroke and Swipe Dynamics for User Authentication on Mobile Platform

Due to the explosive growth of mobile devices worldwide, authentication is receiving increasing attention. Conventionally, explicit authentication methods such as password is employed. However, the system would be breached if the password is stolen. Therefore, there is a continual search for ways to strengthen authentication for mobile platforms. Behavioral biometric information such as keystroke and swipe dynamics can be used for enhancing security. This paper presents an authentication scheme which employs a combination of password, keystroke dynamics, and swipe dynamics for touchscreen mobile devices. Features extracted from swiping pattern and typing pattern were evaluated. Accuracy of the system was enhanced by using combined behavioral biometrics features, as compared with using only a single set of features. The identification accuracies increased significantly from the range of 63.03% - 88.30% to 86.59% -94.05%; while the F1 scores increased from the range of 60.42% - 85.96% to 85.43% - 93.15%.
Barriers and solutions of solar water heaters in Mexican household

In this study, we identify and describe the barriers that limit the installation of solar water heating (SWH) systems in Mexico and the manner in which the Mexican government has been working toward overcoming these challenges. Data regarding architectural, technical, economic, marketing, social, and political barriers were collected from official governmental publications, Mexican journals, and articles, and compared with results from similar research articles. Our results revealed that the governmental programmes have positively motivated the adoption of SWH systems. However, issues related to the structural conditions of buildings, low income, and access to water limit their use. Therefore, these issues should be included in the governmental strategies.
Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective QoE Evaluation

Omnidirectional video (ODV) streaming applications are becoming increasingly popular. They enable a highly immersive experience as the user can freely choose her/his field of view within the 360-degree environment. Current deployments are fairly simple but viewport-agnostic which inevitably results in high storage/bandwidth requirements and low Quality of Experience (QoE). A promising solution is referred to as tile-based streaming which allows to have higher quality within the user's viewport while quality outside the user's viewport could be lower. However, empirical QoE assessment studies in this domain are still rare. Thus, this paper investigates the impact of different tile-based streaming approaches and configurations on the QoE of ODV. We present the results of a lab-based subjective evaluation in which participants evaluated 8K omnidirectional video QoE as influenced by different (i) tile-based streaming approaches (full vs. partial delivery), (ii) content types (static vs. moving camera), and (iii) tile encoding quality levels determined by different quantization parameters. Our experimental setup is characterized by high reproducibility since relevant media delivery aspects (including the user's head movements and dynamic tile quality adaptation) are already rendered into the respective processed video sequences. Additionally, we performed a complementary objective evaluation of the different test sequences focusing on bandwidth efficiency and objective quality metrics. The results are presented in this paper and discussed in detail which confirm that tile-based streaming of ODV improves visual quality while reducing bandwidth requirements.
Binary search algorithm

In In computer science, binary search, also known as half-interval search, [1] logarithmic search, [2] or binary chop, [3] is a search algorithm that finds a position of a target value within a sorted array. [4]Binary search compares the target value to an element in the middle of the array.If they are not equal, the half in which the target cannot lie is eliminated and the search continues on the remaining half, again taking the middle element to compare to the target value, and repeating this until the target value is found.If the search ends with the remaining half being empty, the target is not in the array.Binary search runs in logarithmic time in the worst case, making (log ) comparisons, where is the number of elements in the array, the is 'Big O' notation, and is the logarithm. [5]Binary search is faster than linear search except for small arrays.However, the array must be sorted first to be able to apply binary search.There are specialized data structures designed for fast searching, such as hash tables, that can be searched more efficiently than binary search.However, binary search can be used to solve a wider range of problems, such as finding the nextsmallest or next-largest element in the array relative to the target even if it is absent from the array.1. Set to 0 and to -1. While ≤ , 1. Set (the position of the middle element) to the floor of + 2 , which is the greatest integer less than or equal to + 2 .2. If < , set to + 1. 3. If > , set to -1. 4. Else, = ; return . 3.If the search has not returned a value by the time While > , the search terminates as unsuccessful.This iterative procedure keeps track of the search boundaries with the two variables and .The procedure may be expressed in pseudocode as follows, where the variable names and types remain the same as above, floor is the floor function, and unsuccessful refers to a specific value that conveys the failure of the search.
Fundamental Advantages of Considering Quality of Experience Distributions over Mean Opinion Scores

Due to biased assumptions on the underlying ordinal rating scale in subjective Quality of Experience (QoE) studies, Mean Opinion Score (MOS)-based evaluations provide results, which are hard to interpret and can be little meaningful. This paper proposes to consider the full QoE distribution for evaluating and reporting QoE results instead of only using MOS values. The QoE distribution can be represented in a concise way by using the parameters of a multinomial distribution without losing any information about the underlying QoE ratings, and even keeps backward compatibility with previous, biased MOS-based results. Considering QoE results as a realization of a multinomial distribution allows to rely on a well-established theoretical background, which enables meaningful evaluations also for ordinal rating scales. Exemplary evaluations are described in this work, which demonstrate these fundamental advantages of considering QoE distributions over MOS-based evaluations.
Learning fashion compatibility across categories with deep multimodal neural networks

Fashion compatibility is a subjective sense of human for relationships between fashion items, which is essential for fashion recommendation. Recently, it increasingly attracts more and more attentions and has become a very hot research topic. Learning fashion compatibility is a challenging task, since it needs to consider plenty of factors about fashion items, such as color, texture, style and functionality. Unlike low-level visual compatibility (e.g., color, texture), high-level semantic compatibility (e.g., style, functionality) cannot be handled purely based on fashion images. In this paper, we propose a novel multimodal framework to learn fashion compatibility, which simultaneously integrates both semantic and visual embeddings into a unified deep learning model. For semantic embeddings, a multilayered Long Short-Term Memory (LSTM) is employed for discriminative semantic representation learning, while a deep Convolutional Neural Network (CNN) is used for visual embeddings. A fusion module is then constructed to combine semantic and visual information of fashion items, which equivalently transforms semantic and visual spaces into a latent feature space. Furthermore, a new triplet ranking loss with compatible weights is introduced to measure fine-grained relationships between fashion items, which is more consistent with human feelings on fashion compatibility in reality. Extensive experiments conducted on Amazon fashion dataset demonstrate the effectiveness of the proposed method for learning fashion compatibility, which outperforms the state-of-the-art approaches.
Verifiable Search Meets Blockchain: A Privacy-Preserving Framework for Outsourced Encrypted Data

Outsourcing storage and computation to clouds is popular but also raises security concerns. Most existing solutions mainly focus on an honest-but-curious cloud server, while security designs against a malicious server have not drawn enough attention. Although there are a few works addressing the issue of verifiable designs that enable the data owner to verify the integrity of search results. Unfortunately, these verification schemes are not efficient and or applicable from one scenario to another. Motivated by this, in this paper, we propose a publicly verifiable search framework for outsourced encrypted data based on blockchain. In our framework, we store the encrypted index in a decentralized blockchain (Ethereum) while outsourcing the corresponding encrypted data to the cloud or Interplanetary File System (IPFS). Thus, once a user is authorized, he/she can get the query results and check the query integrity efficiently by the designed smart contract anytime without data owner being online. Besides, to guarantee the privacy of the data user in the Ethereum, we construct a stealth authorization scheme to achieve access authorization delivery. The security analysis and performance evaluation show that the proposed scheme is secure and practical for verification of encrypted data.
Weak economic institutions in Africa: a destiny or design?

Purpose The primacy of institutions for economic progress has been established in the literature. Yet, less research attention is paid to the existence and persistence of weak economic institutions in Africa. Thus, the purpose of this paper is to empirically explore the determinants of the quality of economic institutions in Africa. Design/methodology/approach Hausman–Taylor instrumental variable estimator of panel regression was employed for a sample of 43 Sub-Sahara African countries over the period 1995–2017. Findings The study finds that the existence and persistence of weak economic institutions in Africa is more of design than destiny. That is, weak economic institutions are created and sustained more by bad political institutions rather than cultural diversity and geographical factors. Therefore, strong political institutions need to be entrenched to reverse the equilibrium of weak economic institutions and dismal economic performance in the continent. Practical implications The study provides deep understanding of the determinants of economic institutions. This is imperative for policy makers, development agencies and stakeholders in designing viable economic policies and programs for the continent. Originality/value The novelty of the study is rooted in the examination of the factors responsible for the development and persistence of weak economic institutions in Africa. The idea is original because previous studies focus on political institutions and neglected economic institutions.
Blockchain-based Decentralized Storage Scheme

Abstract Different from the current cloud storage solutions, which are mostly centralized storage providers, this paper proposes a decentralized storage system based on blockchain technology, which can make full use of the remaining space of personal hard disks of users around the world. Storage provider performs a data integrity certificate to the user, and after verifying that the verification is passed, the user pays the storage fee to the storage provider through the lightning network technology. All proofs and payment information are stored in the blockchain, which guarantees the security and credibility of the system. Compared with the current mainstream distributed storage systems, this scheme has been improved in terms of system access and payment methods.