Reviewerly/exHarmony huggingface dataset:
Integration of artificial intelligence of things (AIoT) in the public sector: drivers, barriers and future research agenda

Purpose With the development of information technology (IT), governments around the globe are using state-of-the-art IT interfaces to implement the so-called 3E’s in public service delivery, that is, economy, efficiency and effectiveness. Two of these IT interfaces relate to Artificial Intelligence (AI) and Internet of Things (IoT). While AI focuses on providing a “human” garb for computing devices, thereby making them “intelligent” devices, IoT relies on interfaces between sensors and the environment to make “intelligent” decisions. Recently, the convergence of AI and IoT – also referred to as Artificial Intelligence of Things (AIoT) – is seen as a real opportunity to refurbish the public service delivery formats. However, there is limited understanding as to how AIoT could contribute to the improvisation of public service delivery. This study aims to create a modular framework for AIoT in addition to highlighting the drivers and barriers for its integration in the public sector. Design/methodology/approach This descriptive-explanatory study takes a qualitative approach. It entails a thorough examination of the drivers and barriers of integrating AI and IoT in the public sector. A review of literature has led to the development of a conceptual framework outlining the various factors that contribute to creating public value. Findings Value creation occurs when AI and IoT coalesce in the public service delivery mechanisms. Originality/value AIoT is a cutting-edge technology revolutionizing health care, agriculture, infrastructure and all other industrial domains. This study adds to the growing body of knowledge on the public sector's use of AI and IoT. Understanding these disruptive technologies is critical to formulating policies and regulations that can maximize the potential benefits for the public-sector organizations.
Hierarchical Means Clustering

Abstract In the cluster analysis literature, there are several partitioning (non-hierarchical) methods for clustering multivariate objects based on model estimation. Distinct to these methods is the use of a system of n nested statistical models and the optimization of a loss function to best-fit a clustering model to observed data. Many hierarchical clustering methods are not model-based where hierarchy is obtained using a divisive or agglomerative greedy procedure. This paper aims to fill this gap by proposing a novel hierarchical cluster analysis methodology called Hierarchical Means Clustering. HMC produces a set of nested partitions with a centroid-based model estimated via least-squares by minimizing the total within-cluster deviance of the n partitions in the hierarchy. Hierarchical Means Clustering produces a hierarchy formed by n -1 nested partitions from 2 to n clusters with minimal total cluster deviance. Six real data examples are featured, and key links to k -means, Ward’s method, Bisecting k -means and model-based hierarchical agglomerative clustering methods are discussed.
ECKM: An improved K-means clustering based on computational geometry

A modified version of traditional k-means clustering algorithm applying computational geometry for initialization of cluster centers has been presented in this paper. It is well known that the quality of k-means clustering depends on its random initialization of centers. This paper shows that when the initial cluster centers are selected from the list of circumference points of the empty circles sorted in non-increasing order of their corresponding radii, it can significantly improve the performance. The proposed algorithm is named as Empty Circles based k-means (ECKM) Clustering, which was successfully implemented with PYTHON. Extensive experimentation was carried out on various benchmark data sets that includes both artificial and real data sets having different shapes, sizes and features. In order to minimize the size of this paper, we presented the results for seven of such benchmark datasets, viz., iris, wine, hepta, flame compound, breast_cancer and DS577 although in reality we experimented for more than fifteen data sets in order to validate our ECKM. Results establish the superiority of the ECKM clustering over standard techniques.
A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory

Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the apparent volatility and chaotic nature of the non-ferrous metal price sequence, forecasting it remains a difficult challenge. While prior research employed a variety of methodologies to forecast metal prices, they overlooked the critical role of chaos feature analysis and the necessity of error analysis, severely limiting prediction accuracy. This paper designs a novel non-ferrous metal price ensemble prediction system that incorporates data decomposition, phase space reconstruction, multi-objective optimization, point prediction, and interval prediction. A combined kernel extreme learning machine based on the improved multi-objective lion swarm optimization algorithm is developed and theoretically explained to improve prediction accuracy and reliability. Additionally, the appropriate creation of the prediction interval based on the best-fit distribution of the point prediction error enabled the examination of various levels of uncertainty. In an empirical experiment using copper and aluminum prices from the London Metal Exchange, the proposed system demonstrated benefits in point and interval prediction, providing decision makers with useful prediction references.
A coupled WRF-PV mesoscale model simulating the near-surface climate of utility-scale photovoltaic plants

To simulate the local or regional climate of utility-scale photovoltaic (PV) plants, a new PV-associated energy balance model was developed for the Weather Research and Forecasting Model (WRF). The resultant tool can be used to improve our understanding of the interaction between utility-scale PV plants and land-surface processes. The effects of PV panels were represented by transferring the solar radiation into electricity and heat flux and imposing an extra drag force on the mean flow in the model domain. The proposed PV model is more advanced than previous models in the following ways: (1) The model is universal based on its analytical expression of the sensible heat of PV panels and the influence of the physical shielding of the panels on the upward longwave radiation. (2) The photoelectric conversion efficiency (ε) in the model is temperature dependent and restrained between seasons. (3) The model includes the soil and latent heat flux parameterizations under observation constraints in a novel way. (4) The model bases the dynamic roughness length (Z0) on the eddy covariance flux observation. This new PV model was then incorporated with the WRF mesoscale meteorological model, and the model evaluation in two utility-scale PV plants demonstrated acceptable performance of the new WRF-PV mesoscale model in simulating the max/mean daily surface temperature, downward shortwave radiation, and wind speed with correlation coefficients of 0.61–0.99, and the hourly sensible/latent heat fluxes, with correlation coefficients of 0.59–0.93 during different seasons. The consistency of the performance of the model across these near-surface meteorological variables confirms the versatility and reliability of the new WRF-PV model as a suitable tool for simulating the climate of utility-scale PV plants.
BIECS: A Blockchain-based Intelligent Edge Cooperation System for Latency-Sensitive Services

Although the emerging edge computing paradigm offers a promising approach to overcoming some limitations of conventional cloud computing, the heterogeneous edge nodes with highly diverse system capacities bring new challenges to service provisioning especially for latency-sensitive services. Cooperative edge computing (CEC) has been proposed for facing such challenges through resource sharing among edge nodes. However, some technical issues must be fully addressed to make CEC effective, among which incentive and trust mechanisms and performance optimization are crucial for latency-sensitive service provision. In this paper, we design a novel blockchain-based intelligent edge cooperation system named BIECS to tackle these challenges systematically. BIECS provides incentive to edge nodes for resource sharing and enables trust among cooperative nodes upon a distributed platform leveraging the blockchain technology. In order to optimize system performance for meeting the requirements of latency-sensitive services, we propose a two-stage strategy for node selection in BIECS that chooses the most appropriate edge nodes for executing offloaded tasks and recording related transactions in the blockchain. We also implemented a prototype of BIECS based on Hyperledger Fabric and conducted extensive experiments for evaluating the performance of BIECS. The obtained experiment results verify that the proposed BIECS achieves better performance in system delay and throughput compared to the state-of-the-art methods for edge cooperation.
Channel exchange and adversarial learning guided cross-modal person re-identification

The extensive progress of Re-ID has been obtained in the visible modality. Because the security monitoring system automatically switches from visible modality to infrared modality in the dark situation, the research of the infrared-visible modality-based cross-modal person Re-ID (IV-Re-ID) task increases much attention. However, the existing heterogeneous physical properties result in a substantial semantic gap between the visible and infrared modality data, further increasing the challenge of IV-Re-ID. This paper proposes a Cross-modal Channel Exchange Network (CmCEN) for the task of IV-Re-ID. First, a non-local attention mechanism and a semi-weight share mechanism are utilized in the backbone network to enhance the discriminative capability of both local and global data representations. Then, a channel exchange module is designed to measure each channel’s significance effectively, and the useless channels could be replaced by the critical channels from the other modality data. Finally, a discriminator with adversarial loss is designed in the generative adversarial module. It can generate a similar distribution of two different modality images with the same person identity and a different distribution of two different modality images with different identities under the supervision of the adversarial loss to further improve the robustness of the learned latent feature space. The evaluation results on two cross-modal datasets demonstrated that the CmCEN achieves competitively and even higher performance comparing the SOTA models in terms of accuracy and mAP of IV-Re-ID.
Constructing polytomous knowledge structures from fuzzy skills

The dichotomous knowledge structure theory was introduced to polytomous items, which is proved meaningful in representing individuals' partial mastery of items. Given that different proficiencies in skills may be required to solve polytomous items, fuzzy skills can be applied to represent latent cognitive abilities of individuals. Then we consider how to construct polytomous knowledge structures by fuzzy skills. To characterize relationships between polytomous items and fuzzy skills related to solving these items, we introduce the concept of fuzzy skill function on polytomous items. Given a set Q×L of polytomous items and a set S of skills, a fuzzy skill function (Q×L,S,σ) assigns each response category (q,l)∈Q×L with a family σ(q,l) of fuzzy sets on S, where each element in σ(q,l) represents an approach for solving q to the level l. Notice that there are two special kinds of fuzzy skill functions: one is disjunctive, while the other is conjunctive. The former delineates polytomous knowledge spaces, while the latter delineates polytomous closure spaces. Based on these facts, we design algorithms for delineating the two kinds of polytomous knowledge structures. For any fuzzy skill function on polytomous items, all competencies and all weak competencies are enough to delineate a polytomous knowledge structure. Thus, we design an algorithm for delineating polytomous knowledge structures via fuzzy skill functions on polytomous items. This algorithm only depends on competencies and weak competencies of fuzzy skill functions on polytomous items. Besides, we take "Addition and subtraction of fractions" as knowledge domain to illustrate the process of constructing polytomous knowledge structures from fuzzy skills.
Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

Recently, Internet of Things (IoT) has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track applications.Wireless sensor network (WSN) is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management, intelligent transportation, home automation, smart buildings, and other such IoT-based application.The routing approaches were extremely-utilized energy efficient approaches with an initial drive that is, for balancing the energy amongst sensor nodes.The clustering and routing procedures assumed that Non-Polynomial (NP) hard problems but bio-simulated approaches are utilized to a recognized time for resolving such problems.With this motivation, this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning (BCEHGS-RP) scheme for IoT assisted WSN.The presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supported WSN environment.In addition, an effective multihop route planning approach was designed by the use of EHGS technique.The proposed EHGS technique is derived from the concept of Hill Climbing strategy (HCS) and HGS algorithm.Moreover, a fitness function with two parameters namely residual energy (RE) and intercluster distance to elect optimal routes.The performance validation of the BCEHGS-RP model is experimented with under diverse number of nodes.934 CMC, 2023, vol.74, no.1 Extensive experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.
An Empirical Comparison of Pen-Testing Tools for Detecting Web App Vulnerabilities

Today, one of the most popular ways organizations use to provide their services, or broadly speaking, interact with their customers, is through web applications. Those applications should be protected and meet all security requirements. Penetration testers need to make sure that the attacker cannot find any weaknesses to destroy, exploit, or disclose information on the Web. Therefore, using automated vulnerability assessment tools is the best and easiest part of web application pen-testing, but these tools have strengths and weaknesses. Thus, using the wrong tool may lead to undetected, expected, or known vulnerabilities that may open doors for cyberattacks. This research proposes an empirical comparison of pen-testing tools for detecting web app vulnerabilities using approved standards and methods to facilitate the selection of appropriate tools according to the needs of penetration testers. In addition, we have proposed an enhanced benchmarking framework that combines the latest research into benchmarking and evaluation criteria in addition to new criteria to cover more ground with benchmarking metrics as an enhancement for web penetration testers and penetration testers in real life. In addition, we measure the tool’s abilities using a score-based comparative analysis. Moreover, we conducted simulation tests of both commercial and non-commercial pen-testing tools. The results showed that Burp Suite Professional scored the highest out of the commercial tools, while OWASP ZAP scored the highest out of the non-commercial tools.
LRPC Codes with Multiple Syndromes: Near Ideal-Size KEMs Without Ideals

We introduce a new rank-based key encapsulation mechanism (KEM) with public key and ciphertext sizes around 3.5 Kbytes each, for 128 bits of security, without using ideal structures. Such structures allow to compress objects, but give reductions to specific problems whose security is potentially weaker than for unstructured problems. To the best of our knowledge, our scheme improves in size upon all the existing unstructured post-quantum lattice or code-based algorithms such as FrodoKEM or Classic McEliece. Our technique, whose efficiency relies on properties of rank metric, is to build upon existing Low Rank Parity Check (LRPC) code-based KEMs and to send multiple syndromes in one ciphertext, allowing to reduce the parameters and still obtain an acceptable decoding failure rate. Our system relies on the hardness of the Rank Support Learning problem, a well-known variant of the Rank Syndrome Decoding problem. The gain on parameters is enough to significantly close the gap between ideal and non-ideal constructions. It also enables to choose an error weight close to the rank Gilbert-Varshamov bound, which is a relatively harder zone for algebraic attacks.
Multi-Objective Evolution of Strong S-Boxes Using Non-Dominated Sorting Genetic Algorithm-II and Chaos for Secure Telemedicine

There exist several performance criteria for cryptographically strong substitution boxes which are often conflicting with each other.Constructing S-boxes that satisfy multiple criteria with optimal tradeoffs is one of the challenging tasks for cryptographers.In practice, the existing S-box designing algorithms are used to optimize only a single performance criterion, mainly the nonlinearity, which usually resulted in weak scores of other equally significant criteria.To overcome this problem, a multi-objective optimization based method is presented in this paper which constructs 8×8 S-boxes satisfying multiple criteria of balancedness, high nonlinearity, low differential uniformity, and low auto-correlation.The fulfillment of multiple objectives is done by applying the chaos-assisted non-dominated sorting genetic algorithm-II to evolve S-boxes.The performance assessment of a proposed method and comparative analysis with available optimization based and other state-of-the art algorithms demonstrate its proficiency to generate significantly better S-box solutions with good Pareto-optimal security features.Eventually, the S-boxes with minimum NL of 110, DU as low as 8, and ACF as low as 80, are obtained after the optimization.Furthermore, the obtained Pareto-optimal S-box is utilized to put forward a medical image encryption algorithm for secure telemedicine services.The suggested encryption algorithm makes use of Sbox for performing the required permutation and diffusion of images.The encryption performance assessment and comparison analyses validate its effectiveness for securing medical imagery data in telemedicine networks.
A Survey on Explainable Anomaly Detection for Industrial Internet of Things

Anomaly detection techniques in the Industrial Internet of Things (IIoT) are driving traditional industries towards an unprecedented level of efficiency, productivity and performance. They are typically developed based on supervised and unsupervised machine learning models. However, some machine learning models are facing “black box” problems, namely the rationale behind the algorithm is not understandable. Recently, several models on explainable anomaly detection have emerged. The “black box” problems have been studied by using such models. But few works focus on applications in the IIoT field, and there is no related review of explainable anomaly detection techniques. In this survey, we provide an overview of explainable anomaly detection techniques in IIoT. We propose a new taxonomy to classify the state-of-the-art explainable anomaly detection techniques into two categories, namely intrinsic based explainable anomaly detection and explainer based explainable anomaly detection. We further discuss the applications of explainable anomaly detection across various IIoT fields. Finally, we suggest future study options in this rapidly expanding subject.
High-order manifold regularized multi-view subspace clustering with robust affinity matrices and weighted TNN

Multi-view subspace clustering achieves impressive performance for high-dimensional data. However, many of these models do not sufficiently mine the intrinsic information among samples and consider the robustness problem of the affinity matrices, resulting in the degradation of clustering performance. To address these problems, we propose a novel high-order manifold regularized multi-view subspace clustering with robust affinity matrices and a weighted tensor nuclear norm (TNN) model (termed HMRMSC) to characterize real-world data. Specifically, all the similarity matrices of different views are first stacked into a third-order tensor. However, the constructed tensor may contain an additional inter-class representation since the data are usually noisy. Then, we use a technique similar to tensor principal component analysis (TPCA) to obtain a more robust similarity tensor, which is constrained by the so-called weighted TNN since the original TNN treats each singular value equally and usually considers no prior information of singular values. In addition, a high-order manifold regularized term is also added to utilize the manifold information of data. Finally, all the steps are unified into a framework, which is resolved by the augmented Lagrange multiplier (ALM) method. Experimental results on six representative datasets show that our model outperforms several state-of-the-art counterparts.
Why people keep falling for phishing scams: The effects of time pressure and deception cues on the detection of phishing emails

Lack of personalisation and poor mechanics (e.g., grammar, spelling and punctuation) are commonly cited as cues of deception that people can use to identify phishing emails. However, in an online email classification experiment (N=472), we found no empirical evidence that the presence of these features was associated with better phishing email discrimination. We also manipulated time pressure and found that it significantly reduced detection accuracy. Participants rarely inspected the URLs associated with links in the phishing emails but, when they did, their detection performance improved. Better performance in distinguishing between genuine and phishing emails was linked to lower levels of an intuitive decision-making style and relatively lower education levels amongst a highly educated sample. Older participants and those with greater computer proficiency and stronger email habit showed a slight increase in tendency to judge emails as suspicious. The results are discussed in terms of intervention strategies such as cyber security training to improve resilience to phishing attacks.