Reviewerly/exHarmony huggingface dataset:
Sand-Dust Image Enhancement Using Successive Color Balance With Coincident Chromatic Histogram

Outdoor images in sand-dust environments play an adverse role in various remote-based computer vision tasks because captured sand-dust images have severe color casts, low contrast, and poor visibility. However, although sand-dust image restoration is as important as haze removal and underwater image enhancement, it has not been sufficiently studied. In this paper, we present a novel color balance algorithm for sand-dust image enhancement. The aim of the proposed enhancement method is to obtain a coincident chromatic histogram. First, we introduce a pixel-adaptive color correction method using the mean and standard deviation of chromatic histograms. Pixels of each color component are adjusted based on the statistical characteristics of the green component. Second, a green-mean-preserving color normalization technique is presented. However, using the mean of red and blue components as the mean of the green can result in an undesirable output because the red or blue components of many sand-dust images have a narrow histogram with a high peak. To address this problem, we propose a histogram shifting algorithm that makes the red and blue histograms overlap the green histogram as much as possible. Based on this algorithm, bluish or reddish artifacts of the enhanced image can be reduced. Finally, image adjustment is exploited to improve the brightness of the sand-dust image. We performed intensive experiments for various sand-dust images and compared the performance of the proposed method with those of state-of-the-art enhancement methods. The simulation results indicate that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities.
Secured by Blockchain: Safeguarding Internet of Things Devices

Blockchain is a disruptive technology that has been characterized as the next big thing and has already gained broad recognition from experts in diverse fields. In this article, we consider the possible use cases and applications of the blockchain for the consumer electronics (CE) industry and its interplay with the Internet of Things (IoT). Instead of discussing how the blockchain can revolutionize the supply chain, we focus on how it could be used to enhance the security of networked CE devices. This work was motivated by the many recent attacks in which easily hackable devices were used as weaponry. We also present privacy and data-protection aspects of blockchain solutions and link them to regulatory framework provisions. Information on existing blockchain solutions is also provided.
Towards evaluating the robustness of deep diagnostic models by adversarial attack

Deep learning models (with neural networks) have been widely used in challenging tasks such as computer-aided disease diagnosis based on medical images. Recent studies have shown deep diagnostic models may not be robust in the inference process and may pose severe security concerns in clinical practice. Among all the factors that make the model not robust, the most serious one is adversarial examples. The so-called “adversarial example” is a well-designed perturbation that is not easily perceived by humans but results in a false output of deep diagnostic models with high confidence. In this paper, we evaluate the robustness of deep diagnostic models by adversarial attack. Specifically, we have performed two types of adversarial attacks to three deep diagnostic models in both single-label and multi-label classification tasks, and found that these models are not reliable when attacked by adversarial example. We have further explored how adversarial examples attack the models, by analyzing their quantitative classification results, intermediate features, discriminability of features and correlation of estimated labels for both original/clean images and those adversarial ones. We have also designed two new defense methods to handle adversarial examples in deep diagnostic models, i.e., Multi-Perturbations Adversarial Training (MPAdvT) and Misclassification-Aware Adversarial Training (MAAdvT). The experimental results have shown that the use of defense methods can significantly improve the robustness of deep diagnostic models against adversarial attacks.
Intelligent cyber-phishing detection for online

Phishing attacks are on the increase, resulting in financial loss and theft of sensitive information to online services and users. Anti-phishing approaches have concentrated on blacklist-based approaches that use manually verified Unified Resource Locators (URLs); or content-based methods that utilise heuristics-based machine learning (ML) classifiers. However, online deception is still on the rise. In this study, we introduce a novel methodology combining blacklist-based, web content-based and heuristic based approaches, using ML algorithms with comprehensive features to allow more accurate phishing attack detection. Extensive evaluation was carried out based on Adaptive neuro-fuzzy inference system (ANFIS), Naïve Bayes (NB), PART, J48, and JRip with features, using evaluation methods (metrics) to measure the proposed method performance. All the classifiers achieved over 99% - 99.33% accuracy. PART attained 99.33% accuracy with 0.006 seconds (secs) speed, which is the best performance. We experimentally demonstrate that the proposed methodology can detect phishing websites with a high accuracy in real-time and generalise well to new phishing attacks. The proposed approach has the best performance compared to related approaches in the field.
A survey on extraction of causal relations from natural language text

Abstract As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps in building causal networks for predictive tasks. Existing causality extraction techniques include knowledge-based, statistical machine learning (ML)-based, and deep learning-based approaches. Each method has its advantages and weaknesses. For example, knowledge-based methods are understandable but require extensive manual domain knowledge and have poor cross-domain applicability. Statistical machine learning methods are more automated because of natural language processing (NLP) toolkits. However, feature engineering is labor-intensive, and toolkits may lead to error propagation. In the past few years, deep learning techniques attract substantial attention from NLP researchers because of its powerful representation learning ability and the rapid increase in computational resources. Their limitations include high computational costs and a lack of adequate annotated training data. In this paper, we conduct a comprehensive survey of causality extraction. We initially introduce primary forms existing in the causality extraction: explicit intra-sentential causality, implicit causality, and inter-sentential causality. Next, we list benchmark datasets and modeling assessment methods for causal relation extraction. Then, we present a structured overview of the three techniques with their representative systems. Lastly, we highlight existing open challenges with their potential directions.
Contact-tracing apps help reduce COVID infections, data suggest

Evaluations find apps are useful, but would benefit from better integration into health-care systems. Evaluations find apps are useful, but would benefit from better integration into health-care systems.
An Improved Equilibrium Optimizer with Application in Unmanned Aerial Vehicle Path Planning

The unmanned aerial vehicle (UAV) path planning problem is a type of complex multi-constraint optimization problem that requires a reasonable mathematical model and an efficient path planning algorithm. In this paper, the fitness function including fuel consumption cost, altitude cost, and threat cost is established. There are also four set constraints including maximum flight distance, minimum flight altitude, maximum turn angle, and maximum climb angle. The constrained optimization problem is transformed into an unconstrained optimization problem by using the penalty function introduced. To solve the model, a multiple population hybrid equilibrium optimizer (MHEO) is proposed. Firstly, the population is divided into three subpopulations based on fitness and different strategies are executed separately. Secondly, a Gaussian distribution estimation strategy is introduced to enhance the performance of MHEO by using the dominant information of the populations to guide the population evolution. The equilibrium pool is adjusted to enhance population diversity. Furthermore, the Lévy flight strategy and the inferior solution shift strategy are used to help the algorithm get rid of stagnation. The CEC2017 test suite was used to evaluate the performance of MHEO, and the results show that MHEO has a faster convergence speed and better convergence accuracy compared to the comparison algorithms. The path planning simulation experiments show that MHEO can steadily and efficiently plan flight paths that satisfy the constraints, proving the superiority of the MHEO algorithm while verifying the feasibility of the path planning model.
Research on Dynamic Path Planning Based on the Fusion Algorithm of Improved Ant Colony Optimization and Rolling Window Method

This paper focuses on the problem that the current path planning algorithm is not mature enough to achieve the expected goal in a complex dynamic environment. In light of the ant colony optimization (ACO) with good robustness and strong search ability, and the rolling window method (RWM) with better planning effect in local path planning problems, we propose a fusion algorithm named RACO that can quickly and safely reach the designated target area in a complex dynamic environment. This paper first improves the ant colony optimization, which greatly improves the convergence performance of the algorithm and shortens the global path length. On this basis, we propose a second-level safety distance determination rule to deal with the special problem of the research object encountering obstacles with unknown motion rules, in order to perfect the obstacle avoidance function of the fusion algorithm in complex environments. Finally, we carry out simulation experiments through MATLAB, and at the same time conduct three-dimensional simulation of algorithm functions again on the GAZEBO platform. It is verified that the algorithm proposed in this paper has good performance advantages in path planning and dynamic obstacle avoidance.
Classification and feature transformation with Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) are considered a soft computing technique combining elements of fuzzy logic and recurrent neural networks. They found multiple application in such domains as modeling of system behavior, prediction of time series, decision making and process control. Less attention, however, has been turned towards using them in pattern classification. In this work we propose an FCM based classifier with a fully connected map structure. In contrast to methods that expect reaching a steady system state during reasoning, we chose to execute a few FCM iterations (steps) before collecting output labels. Weights were learned with a gradient algorithm and logloss or cross-entropy were used as the cost function. Our primary goal was to verify, whether such design would result in a descent general purpose classifier, with performance comparable to off the shelf classical methods. As the preliminary results were promising, we investigated the hypothesis that the performance of d-step classifier can be attributed to a fact that in previous d−1 steps it transforms the feature space by grouping observations belonging to a given class, so that they became more compact and separable. To verify this hypothesis we calculated three clustering scores for the transformed feature space. We also evaluated performance of pipelines built from FCM-based data transformer followed by a classification algorithm. The standard statistical analyzes confirmed both the performance of FCM based classifier and its capability to improve data. The supporting prototype software was implemented in Python using TensorFlow library.
A Ladder Spherical Evolution Search Algorithm

In 2019, a completely new algorithm, spherical evolution (SE), was proposed. The brand new search style in SE has been proved to have a strong search capability. In order to take advantage of SE, we propose a novel method called the ladder descent (LD) method to improve the SE' population update strategy and thereafter propose a ladder spherical evolution search (LSE) algorithm. With the number of iterations increasing, the range of parent individuals eligible to produce offspring gradually changes from the entire population to the current optimal individual, thereby enhancing the convergence ability of the algorithm. Experiment results on IEEE CEC2017 benchmark functions indicate the effectiveness of LSE.
The token economy as a key driver for tourism: Entering the next phase of blockchain research

This is a research note in which I briefly outline the advantages of discussing tokens in tourism research rather than referring to the rather unspecific terms “cryptocurrencies” or “blockchain”.
Role of blockchain in HR's response to new-normal

Purpose This study aims to broaden the understanding of the blockchain for human resource (HR) managers through use cases. The study presents a plausible solution for HR professionals to effectively manage some of the core processes to focus on more strategic work and be a true HR business partner for the organization. Design/methodology/approach The study adopted a case research strategy. The case research strategy is well-suited to capture the practitioner’s knowledge, mainly when focusing on contemporary events (such as COVID-19). Data collected from 12 tech organizations through telephonic conversations and the interviews were recorded and transcribed using NoNotes call recording. Findings This study identifies five use cases to streamline the critical processes, helping HR professionals such as certificates verification, skill mapping, payroll processing, data protection and performance management. These early use cases offer a plausibly superior alternative in managing critical HR functions and associated business processes with blockchain technology. Research limitations/implications Despite the growing number of blockchain applications, its usage in HR activities is limited. By extensive qualitative case study and data triangulation, the study integrates a resource-based view and unified theory of acceptance by explaining how blockchain adoption helps organizations use their internal resources and capabilities to gain a competitive advantage. The study presents five use cases and propositions that can act as building blocks for the HR department in adopting blockchain applications. Lack of empirical validation (quantitative rigor) of the propositions is the limitation and can be a future research scope. Practical implications Adopting new technologies is not new for HR managers. However, most of the technologies are disjointed applications, and therefore, the need for an all-pervasive solution assumes significance. Several of the blockchain concepts are still in the nascent stage. Thus, the study highlights the need for HR leaders to work alongside technical architects to create blockchain applications. Unlike other HR applications, blockchain can integrate all the employees, clients, vendors and businesses seamlessly. This study proposes research propositions that provide research directions for future research. Originality/value Academic literature on connecting blockchain technology with HR functions and applications is notably absent. This research can be considered one of the first academic articles connecting blockchain and HR processes.
Embedded Supervision: How to Build Regulation into Blockchain Finance

The spread of distributed ledger technology (DLT) in finance could help to improve the efficiency and quality of supervision.This paper makes the case for embedded supervision, i.e., a regulatory framework that provides for compliance in tokenised markets to be automatically monitored by reading the market's ledger, thus reducing the need for firms to actively collect, verify and deliver data.After sketching out a design for such schemes, the paper explores the conditions under which distributed ledger data might be used to monitor compliance.To this end, a decentralised market is modelled that replaces today's intermediary-based verification of legal data with blockchain-enabled data credibility based on economic consensus.The key results set out the conditions under which the market's economic consensus would be strong enough to guarantee that transactions are economically final, so that supervisors can trust the distributed ledger's data.The paper concludes with a discussion of the legislative and operational requirements that would promote low-cost supervision and a level playing field for small and large firms.
On the Large-scale Graph Data Processing for User Interface Testing in Big Data Science Projects

In functional User Interface testing, test scenarios are written with respect to the requirements that are specified by test analysts. Usually, a test analyst focuses on base URLs and HTML components while collecting requirements of User Interface test scenarios. A base URL is essentially a unit segment of large scale graph data. It has mostly dynamic shape and is used to navigate pages amongst application's pages. We argue that even though dynamic URLs have additional important information about the content of the page, they are not being utilized in generating User Interface test scenarios. In this study, we address this lack of capability and focus on the development of a methodology that can support the usage of large-scale dynamic URL datasets in UI test script generation. Our proposed methodology is designed as an add-on tool that can be used on the top of the existing UI test automation tools to improve testing quality. We introduce a higher quality testing methodology to make the results more accurate, and we discuss the proposed methodology and give an overview of the implementation details followed by the evaluation results. We perform various performance evaluations to investigate how well the proposed algorithms scale under increasing data sizes. The results are promising and show the usability of the proposed methodology.
Programmable Switches for in-Networking Classification

Deploying accurate machine learning algorithms into a high-throughput networking environment is a challenging task. On the one hand, machine learning has proved itself useful for traffic classification in many contexts (e.g., intrusion detection, application classification, and early heavy hitter identification). On the other hand, most of the work in the area is related to post-processing (i.e., training and testing are performed offline on previously collected samples) or to scenarios where the traffic has to leave the data plane to be classified (i.e., high latency). In this work, we tackle the problem of creating simple and reasonably accurate machine learning models that can be deployed into the data plane in a way that performance degradation is acceptable. To that purpose, we introduce a framework and discuss issues related to the translation of simple models, for handling individual packets or flows, into the P4 language. We validate our framework with an intrusion detection use case and by deploying a single decision tree into a Netronome SmartNIC (Agilio CX 2x10GbE). Our results show that high-accuracy is achievable (above 95%) with minor performance degradation, even for a large number of flows.
Accurate Scene Text Detection Via Scale-Aware Data Augmentation and Shape Similarity Constraint

Scene text detection has attracted increasing concerns with the rapid development of deep neural networks in recent years. However, existing scene text detectors may overfit on the public datasets due to the limited training data, or generate inaccurate localization for arbitrary-shape scene texts. This paper presents an arbitrary-shape scene text detection method that can achieve better generalization ability and more accurate localization. We first propose a Scale-Aware Data Augmentation (SADA) technique to increase the diversity of training samples. SADA considers the scale variations and local visual variations of scene texts, which can effectively relieve the dilemma of limited training data. At the same time, SADA can enrich the training minibatch, which contributes to accelerating the training process. Furthermore, a Shape Similarity Constraint (SSC) technique is exploited to model the global shape structure of arbitrary-shape scene texts and backgrounds from the perspective of the loss function. SSC encourages the segmentation of text or non-text in the candidate boxes to be similar to the corresponding ground truth, which is helpful to localize more accurate boundaries for arbitrary-shape scene texts. Extensive experiments have demonstrated the effectiveness of the proposed techniques, and state-of-the-art performances are achieved over public arbitrary-shape scene text benchmarks (e.g., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CTW1500</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Total-Text</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ArT</i> ).
A lossless DWT-SVD domain watermarking for medical information security

The goal of this work is to protect as much as possible the images exchanged in telemedicine, to avoid any confusion between the patient's radiographs, these images are watermarked with the patient's information as well as the acquisition data. Thus, during the extraction, the doctor will be able to affirm with certainty that the images belong to the treated patient. The ultimate goal of our completed work is to properly integrate the watermark with as little distortion as possible to typically retain the medical information in the image. In this innovative approach used DWT decomposition is appropriately applied to the image which allows a remarkably satisfactory adjustment during the insertion. An SVD is then applied to the three subbands LL, LH and HL, which ideally allows retaining the maximum energy of the used image in a guaranteed minimum of singular values. A specific combination of the three resulting singular value matrices is then performed for watermark integration. The proposed approach ensures data integrity, patient confidentiality when sharing data, and robustness to several conventional attacks.