Reviewerly/exHarmony huggingface dataset:
Design of Aquila Optimization Heuristic for Identification of Control Autoregressive Systems

Swarm intelligence-based metaheuristic algorithms have attracted the attention of the research community and have been exploited for effectively solving different optimization problems of engineering, science, and technology. This paper considers the parameter estimation of the control autoregressive (CAR) model by applying a novel swarm intelligence-based optimization algorithm called the Aquila optimizer (AO). The parameter tuning of AO is performed statistically on different generations and population sizes. The performance of the AO is investigated statistically in various noise levels for the parameters with the best tuning. The robustness and reliability of the AO are carefully examined under various scenarios for CAR identification. The experimental results indicate that the AO is accurate, convergent, and robust for parameter estimation of CAR systems. The comparison of the AO heuristics with recent state of the art counterparts through nonparametric statistical tests established the efficacy of the proposed scheme for CAR estimation.
SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection

With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people's lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.
Interactive methods in blended learning of the fundamentals of UI/UX design by pre-service specialists

The problems of blended learning implementation in the context of university students training in the subject domain of UI/UX design are discussed in the paper. Based on the literature review and educational practice analysis, it is revealed that the elaboration of the proper learning activities and methods is underestimated and overseen by the researchers and practitioners of blended learning implementation. It is admitted the urgency of finding out proper learning techniques (or their combination), which are necessary to involve the university students into interactive forms of work in order to overcome the core challenges of the blended learning implementation, and to add classroom atmosphere into the tutorial process in terms of online studying. In the progress of work, there were analyzed the core features and challenges of blended learning, and covered the possibilities of interactive methods in blended learning implementation into the tutorial process of students' mastering of UI/UX design basics, according to authors' approach to their curriculum building. The experience of interactive methods practical realization in the process of the fundamentals of UI/UX design learning by university students of different specialties is presented. It was demonstrated how the considered leaning techniques which are typically used at blended learning in its flex model (flipped learning, gamification, storytelling etc.) can be enhanced and enriched via using different interactive methods. Exact examples of their applications in real blended learning process are given and discussed. The prospects of the research are outlined.
DEAR

The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. We present DEAR, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or multiple consecutive statements in one or multiple hunks of code. We first design a novel fault localization (FL) technique for multi-hunk, multi-statement fixes that combines traditional spectrum-based (SB) FL with deep learning and data-flow analysis. It takes the buggy statements returned by the SBFL model, detects the buggy hunks to be fixed at once, and expands a buggy statement s in a hunk to include other suspicious statements around s. We design a two-tier, tree-based LSTM model that incorporates cycle training and uses a divide-and-conquer strategy to learn proper code transformations for fixing multiple statements in the suitable fixing context consisting of surrounding subtrees. We conducted several experiments to evaluate DEAR on three datasets: Defects4J (395 bugs), BigFix (+26k bugs), and CPatMiner (+44k bugs). On Defects4J dataset, DEAR outperforms the baselines from 42%--683% in terms of the number of auto-fixed bugs with only the top-1 patches. On BigFix dataset, it fixes 31--145 more bugs than existing DL-based APR models with the top-1 patches. On CPatMiner dataset, among 667 fixed bugs, there are 169 (25.3%) multi-hunk/multi-statement bugs. DEAR fixes 71 and 164 more bugs, including 52 and 61 more multi-hunk/multi-statement bugs, than the state-of-the-art, DL-based APR models.
Radar-Based Air-Writing Gesture Recognition Using a Novel Multistream CNN Approach

Hand gestures, being a convenient and natural way of communication, is getting huge attention for human–computer interface designs. Among these gestures, detecting mid-air writing is one of the most promising applications. Existing radar-based solutions often perform the mid-air writing recognition by tracking the hand trajectory using multiple monostatic or bistatic radars. This article presents a multistream convolutional neural network (MS-CNN)-based in-air digits recognition method using a frequency-modulated continuous-wave (FMCW) radar. With one FMCW radar comprising of two receiving channels, a novel three-stream CNN network with range-time, Doppler-time, and angle-time spectrograms as inputs is constructed and the features are fused together in the later stage before making a final recognition. Unlike the traditional CNN, MS-CNN with multiple independent input layers enables the creation of a multidimensional deep-learning model for FMCW radars. Twelve human volunteers were invited to writing the digits from zero to nine in the air in both home and lab environments. The three-stream CNN architecture-based air writing for digits has shown a promising accuracy of 95%. A comparison of the proposed MS-CNN system was made with 45 different variants of CNN and preliminary results shows that MS-CNN outperforms the other traditional CNN architectures for air-writing application. The gestures radar data have also been made available to the research community.
Two Contrasting Data Annotation Paradigms for Subjective NLP Tasks

Labelled data is the foundation of most natural language processing tasks.However, labelling data is difficult and there often are diverse valid beliefs about what the correct data labels should be.So far, dataset creators have acknowledged annotator subjectivity, but rarely actively managed it in the annotation process.This has led to partly-subjective datasets that fail to serve a clear downstream use.To address this issue, we propose two contrasting paradigms for data annotation.The descriptive paradigm encourages annotator subjectivity, whereas the prescriptive paradigm discourages it.Descriptive annotation allows for the surveying and modelling of different beliefs, whereas prescriptive annotation enables the training of models that consistently apply one belief.We discuss benefits and challenges in implementing both paradigms, and argue that dataset creators should explicitly aim for one or the other to facilitate the intended use of their dataset.Lastly, we conduct an annotation experiment using hate speech data that illustrates the contrast between the two paradigms.
IHAOAVOA: An improved hybrid aquila optimizer and African vultures optimization algorithm for global optimization problems

Aquila Optimizer (AO) and African Vultures Optimization Algorithm (AVOA) are two newly developed meta-heuristic algorithms that simulate several intelligent hunting behaviors of Aquila and African vulture in nature, respectively. AO has powerful global exploration capability, whereas its local exploitation phase is not stable enough. On the other hand, AVOA possesses promising exploitation capability but insufficient exploration mechanisms. Based on the characteristics of both algorithms, in this paper, we propose an improved hybrid AO and AVOA optimizer called IHAOAVOA to overcome the deficiencies in the single algorithm and provide higher-quality solutions for solving global optimization problems. First, the exploration phase of AO and the exploitation phase of AVOA are combined to retain the valuable search competence of each. Then, a new composite opposition-based learning (COBL) is designed to increase the population diversity and help the hybrid algorithm escape from the local optima. In addition, to more effectively guide the search process and balance the exploration and exploitation, the fitness-distance balance (FDB) selection strategy is introduced to modify the core position update formula. The performance of the proposed IHAOAVOA is comprehensively investigated and analyzed by comparing against the basic AO, AVOA, and six state-of-the-art algorithms on 23 classical benchmark functions and the IEEE CEC2019 test suite. Experimental results demonstrate that IHAOAVOA achieves superior solution accuracy, convergence speed, and local optima avoidance than other comparison methods on most test functions. Furthermore, the practicality of IHAOAVOA is highlighted by solving five engineering design problems. Our findings reveal that the proposed technique is also highly competitive and promising when addressing real-world optimization tasks. The source code of the IHAOAVOA is publicly available at https://doi.org/10.24433/CO.2373662.v1.
Information security risk assessments following cybersecurity breaches: The mediating role of top management attention to cybersecurity

Information Systems (IS) research on managerial response to cybersecurity breaches has largely focused on externally oriented actions such as customer redressal and crisis response. Within the firm itself, a breach may be a symptom of systematic problems, and a narrow, siloed focus on only fixing immediate issues through technical fixes and controls might preclude other managerial actions to ensure future cybersecurity. Towards this end, Information Security Risk Assessments (ISRA) can help surface other vulnerabilities following a breach. While the role of governance in such exercises is emphasized in standards, it is undertheorized in IS research and lacks empirical evidence. We draw on the attention-based view to theorize that the principles of focus of attention, structural distribution of attention, and situated attention can lead to the top management team (TMT) according greater attention to cybersecurity following relatively high breach costs. Using firm level data, we find that high breach costs result in greater TMT attention to cybersecurity, while also making it more likely that firms will carry out an ISRA. Moreover, TMT attention to cybersecurity partially mediates the relation between breach costs and the decision to carry out an ISRA. We theorize that this is because the TMT is best positioned to oversee resource allocation, consider business implications, and centrally orchestrate an ISRA. Our findings stress the need for the cybersecurity function to work with the TMT in managing breach response.
Explainable AI for clinical and remote health applications: a survey on tabular and time series data

Abstract Nowadays Artificial Intelligence (AI) has become a fundamental component of healthcare applications, both clinical and remote, but the best performing AI systems are often too complex to be self-explaining. Explainable AI (XAI) techniques are defined to unveil the reasoning behind the system’s predictions and decisions, and they become even more critical when dealing with sensitive and personal health data. It is worth noting that XAI has not gathered the same attention across different research areas and data types, especially in healthcare. In particular, many clinical and remote health applications are based on tabular and time series data, respectively, and XAI is not commonly analysed on these data types, while computer vision and Natural Language Processing (NLP) are the reference applications. To provide an overview of XAI methods that are most suitable for tabular and time series data in the healthcare domain, this paper provides a review of the literature in the last 5 years, illustrating the type of generated explanations and the efforts provided to evaluate their relevance and quality. Specifically, we identify clinical validation, consistency assessment, objective and standardised quality evaluation, and human-centered quality assessment as key features to ensure effective explanations for the end users. Finally, we highlight the main research challenges in the field as well as the limitations of existing XAI methods.
Educational Horizons of the Metaverse: Vision, Opportunities, and Challenges

The onset of the COVID-19 pandemic prompted a profound reconsideration of our approaches to teaching and learning. The metaverse, blending 3D digital realms with the intersection of reality and the virtual landscape, has emerged as a prospective frontier in education, offering substantial promise. Despite its potential, scant attention has been devoted in current research to examining the metaverse through the lens of education, marking it as a relatively unexplored domain. In this article, we commence by presenting the conceptualizations of the metaverse, delving into its inception, definitions, and common attributes. Subsequently, a precise delineation of the metaverse in an educational context is articulated, accompanied by an elaborate framework that undergoes thorough exploration of its distinctive features. We expound upon four conceivable applications of the metaverse in education, elucidating the rationale and providing illustrative cases for each: blended learning, language acquisition, competence-based education, and inclusive educational practices. Our primary discoveries indicate that the terms "education," "application," and "metaverse" appeared with the highest frequency and demonstrated interconnectedness. Upon analysis, it becomes evident that concepts like "challenge," "teaching," and "knowledge" have not received exhaustive research attention. Concluding our exploration, we put forth a spectrum of research topics that are pertinent to the intersection of the metaverse and education, serving as potential avenues for future scholarly investigations.
A novel steganography method using transliteration of Bengali text

In this paper, we present a simple and novel approach for steganography through transliteration. A phonetic keyboard layout is very popular for writing languages having non-roman alphabets. Bengali, a language spoken by 230 million people, is a fair example and in this work we utilize Bengali digital text for data hiding by the proposed technique. For several characters in the Bengali alphabet, there are multiple options to represent a character in it’s equivalent roman form using a phonetic keyboard layout. The main idea of the proposed method is to exploit this special feature of Bengali phonetic keyboard layouts to hide secret information in form of bits. One of these options can be used to represent the bit ‘0’ and the other option can represent the bit ‘1’ in a document without any risk of understanding by any intermediate user. The results show that the proposed method is very prominent to be a successful steganography technique. Steganalysis results show that the capacity of the method is 1.2%, which is adequate for a text steganography system with very low risk of machine detection. This method can be easily adapted and applied for any other language having non-roman alphabet.
The spatial-temporal patterns of land cover changes due to mining activities in the Darling Range, Western Australia: A Visual Analytics Approach

The Darling Range in Western Australia is a major bauxite producing region. Clearing, excavation and rehabilitation activities related to bauxite mining have influenced land cover within this region since mining commenced in the 1960s. This paper presents a study that used machine learning and time series visualisation to analyse the land cover changes of the Darling Range using time-lapse multispectral images, with the aim of understanding the impact of the mining activities and land rehabilitation patterns of the region. Land cover changes were analysed using 14 Landsat Thematic Mapper (TM) images between 1988 and 2014. The spatial distribution of land cover was classified automatically using machine learning algorithms, and their temporal changes were visualized for analysis. Supervised classification was carried out based on the six spectral features of the images using three machine learning algorithms, namely support vector machine (SVM), random forest (RF) and Naïve Bayes (NB). The results showed that the RF algorithm achieved overall accuracy, (ratio of correctly classified samples divided by the total number of samples), greater than 95% for all years. The temporal changes of land cover distribution over the study period were visualized using a change map. These changes were compared with land clearing and rehabilitation records of a mining company operating in that region. A close correlation was observed between the automated analysis outputs and the company’s records. This work demonstrates the potential use of machine analysis in improving the accuracy of spatial data related to land clearing; and in monitoring vegetation recovery of closed and rehabilitated mines.
Object detection with class aware region proposal network and focused attention objective

In this paper, we propose a novel deep CNN-based framework to improve object detection performance. First, we introduce the Class Aware Region Proposal Network (CARPN) to produce high quality region proposals by using a new strategy for anchor generation, and by training the network with both bounding boxes and category labels of the objects. Instead of learning a binary object/non-object classifier for generating region proposals, we assign the class label to each anchor, and train the region proposal network with a multi-class loss. Second, we introduce the Focused Attention (FA) objective to encourage the network to learn features mainly from objects of interest while suppressing those features from the background region. As a result, false positive proposals caused by strong background features can be reduced to a large extent. Comprehensive experimental evaluations reveal that the proposed CARPN & FA framework remarkably outperforms the baseline Faster R-CNN method up to 4.1% mAP with a shallower network and 2.8% mAP with a deeper network, and achieves a better mAP than most of the latest state-of-the-art methods.
Information asymmetry in KL-regularized RL

Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time. In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning. We start from the KL regularized expected reward objective which introduces an additional component, a default policy. Instead of relying on a fixed default policy, we learn it from data. But crucially, we restrict the amount of information the default policy receives, forcing it to learn reusable behaviors that help the policy learn faster. We formalize this strategy and discuss connections to information bottleneck approaches and to the variational EM algorithm. We present empirical results in both discrete and continuous action domains and demonstrate that, for certain tasks, learning a default policy alongside the policy can significantly speed up and improve learning.
Real-Time Path Planning for Multi-copters flying in UTM -TCL4

NASA's UAS Traffic management (UTM) -TCL-4 focuses on safely enabling large scale small UAS oper- ations in low altitude airspace in dense urban regions. This paper presents an operational architecture of an autonomous unmanned aerial vehicle operating in TCL4. An on-line path planning scheme is proposed which can effectively plan for feasible paths in real time with TCL-4 constraints. An end to end system is designed and tested in high fidelity Reflection architecture which demonstrates the feasibility of the approach.
Probabilistic prediction of direct normal irradiance derived from global horizontal irradiance over the Korean Peninsula by using Monte-Carlo simulation

Solar resource assessment is carried out in a feasibility study using reliable meteorological elements including solar irradiance. In concentrating solar power plants, the direct normal irradiance is the key variable in the system operation. However, direct normal irradiance is rarely measured as compared to global horizontal irradiance. There are several models that can be used to derive the direct normal irradiance from global horizontal irradiance. In this study, the Engerer model is used as a decomposition model, then evaluated against in situ observations at three ground stations: Seoul, Buan, and Jeju ground stations. The relative root mean square errors between the observed and direct normal irradiance estimated by the Engerer model are 15.0%, 19.4%, and 17.1% at Seoul, Buan, and Jeju ground stations, respectively. The uncertainty of estimates is represented by the prediction interval from probabilistic prediction through Monte-Carlo simulation that employs the bias between estimation and ground truth for training datasets. The prediction interval for 90% confidence level is 117.9 W m−2 at the Seoul station, resulting from Monte-Carlo simulation. The prediction interval coverage probability is 92.8%, implying that the probability that observed DNI is not included in the prediction interval is 7.2%. The error metrics for probabilistic prediction indicates that Monte-Carlo simulation provides both valid and more informative estimations.