Reviewerly/exHarmony huggingface dataset:
Scale‐wise interaction fusion and knowledge distillation network for aerial scene recognition

Abstract Aerial scene recognition (ASR) has attracted great attention due to its increasingly essential applications. Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR. However, the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features, leading to a limited ability to deal with challenges of large‐scale variation and complex background in aerial scene images. In addition, existing methods may suffer from poor generalisations due to millions of to‐be‐learnt parameters and inconsistent predictions between global and local features. To tackle these problems, this study proposes a scale‐wise interaction fusion and knowledge distillation (SIF‐KD) network for learning robust and discriminative features with scale‐invariance and background‐independent information. The main highlights of this study include two aspects. On the one hand, a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tackle the large‐scale variation problem in aerial scene images. Specifically, a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features. On the other hand, a scale‐wise knowledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training. Comprehensive experimental results show the proposed SIF‐KD network achieves the best overall accuracy with 99.68%, 98.74% and 95.47% on the UCM, AID and NWPU‐RESISC45 datasets, respectively, compared with state of the arts.
The Utilization of Blockchain Technology on Remote Audit to Ensure Audit Data Integrity in Detecting Potential Fraudulent Financial Reporting

The rapid evolution of technology has massively changed and shaped audit work culture. At the same time, the COVID-19 pandemic outbreak significantly changed audit and control activities, encouraging auditors to audit clients remotely and forcing auditors to adopt advanced digital technology, especially blockchain-based technology. Blockchain is one decentralized, transparent, and immutable technology that plays an essential role and has an aggressive impact in the audit field. The implementation and adoption of cognitive technologies such as blockchain in the remote audit process allow auditors to access various client data and information in real-time, ensure audit data integrity, review and process data documentation quickly, obtaining data directly without third parties that make it easier for auditors to obtain audit evidence instantly and accurately in detecting any anomalies. This research aims to identify and navigate how blockchain assists auditors in performing remote audits, ensuring data integrity, detecting fraudulent financial reporting, elaborating on factors that influence the auditor's success in using blockchain, and identifying challenges auditors face during blockchain utilization in their remote audits. This research used a descriptive qualitative approach by obtaining primary data from profound interview results with several auditors experienced in using blockchain. We also used secondary data from journals, papers, articles, books, and relevant websites. The research findings showed that blockchain's unique characteristics greatly assist auditors in conducting remote audits, ensuring audit data integrity, and detecting fraudulent financial reporting in various ways. Furthermore, various factors influence blockchain utilization, such as human resources aspects, the effectiveness of the IT control environment, and the CPA size firm. Lastly, auditors should face several challenges in the present and future.
An Enhanced Interval‐Based Approach for Anomaly Detection in Time Series

Since anomalies are usually caused by changes in shape and amplitude in time series data, and most of the interval‐based methods utilized appear in the literature from a global perspective to detect anomalies. Nevertheless, the anomalies are also usually caused by local changes in shape and amplitude. Following these limitations, an enhanced interval‐based approach based on the interval information granules with the principle of justifiable granularity, viz. , the EIA method, is formulated in this study for anomaly detection. First, the interval information granules are produced separately from the global and local perspectives in an abstracted manner on the basis of this principle. An interval‐based similarity measurement algorithm is then designed based on the abstraction results to calculate the anomaly scores from the various interval information granules for anomaly detection. Compared with classical methods, the proposed approach exhibits better performance in terms of data anomaly resolution and detection accuracy, which have been verified and supported in much of real‐world data. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
More than Money: Examining the Potential Exposure of Romance Fraud Victims to Identity Crime

Romance fraud occurs when an offender uses the guise of a genuine relationship to defraud an individual for financial gain. Known statistics indicate that millions are lost each year to this form of fraud. However, the potential for romance fraud victims to also experience identity crime as a consequence of their interactions and communications with an offender has yet to be explored. This article begins to address this gap by examining the potential exposure for identity crime in the context of romance fraud using reports lodged with Scamwatch, an Australian online fraud reporting portal. The findings demonstrate few behavioural and demographic characteristics associated with the potential exposure of those targeted by romance fraud to identity crime. However, the article provides insights into how this analysis should inform prevention messaging and victim support services for those targeted by romance fraud.
CCM-FL: Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing, to handle the numerous data collected by the sensors and provide the users with precise and customized services. Meanwhile, how to protect the private information of users in federated learning has become an important research topic. Compared with the differential privacy (DP) technique and secure multiparty computation (SMC) strategy, the covert communication mechanism in federated learning is more efficient and energy-saving in training the machine learning models. In this paper, we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks. Different from the previous works about covert communication in federated learning, most of which are considered in a centralized framework and experimental-based, our work in this paper firstly proposes a centralized covert communication mechanism for federated learning among n learning agents, the time complexity of which is O(log n)-approximating to the optimal solution. Secondly, for the federated learning without parameter server, which is a harder case, we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log log Δ log n) times approximating to the optimal solution. Δ is the maximum distance between any pair of learning agents. Theoretical analysis and numerical simulations are presented to show the performance of our covert communication mechanisms. We hope that our covert communication work in this paper can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.
Multi-view semantic understanding for visual dialog

Visual dialog, as a challenging cross-media task, requires answering a sequence of questions based on a given image and dialog history. Hence the key problem becomes how to answer visually grounded questions based on ambiguous reference information from dialog. In this work, we propose a novel method called Multi-View Semantic Understanding for Visual Dialog (MVSU) to resolve the visual coreference resolution problem. The model consists of two main textual processing modules, SRR (Semantic Retention RNN) and CRoT (Coreference Resolution on Text). Specifically, the SRR module generates word features that have semantical meaning by considering contextual information. The CRoT module is from a textual perspective to divide all useful nouns and pronouns into different clusters that serve as the supplement of the detailed information for semantic understanding. In experiments, we demonstrate that MVSU enhances the ability to understand the semantical information on the VisDial v1.0 dataset.
Privacy-enhanced and non-interactive linear regression with dropout-resilience

Linear regression, the most basic and widely used machine learning algorithm, has played an essential role in many areas such as healthcare, economics, and weather prediction. However, in practice, regression training requires a large dataset, usually stored in a distributed form from multiple data owners. Moreover, it is difficult to federate data among users to train a global model because of privacy issues and communication limitations. To address these challenges, we propose a non-interactive privacy-enhanced training scheme for linear regression based on functional encryption - FELR. In particular, based on the secure aggregation scheme and Paillier inner-product functional encryption, two cloud servers can train the model on the ciphertext. The solution does not require any interaction between the cloud servers and data owners. Throughout the training period, the cloud servers do not know any local private data. A detailed security analysis demonstrates that FELR can provide high security for data owners. The experimental conclusions also demonstrate that the performance of the training scheme on the data owners' side is approximately 25% higher than that of the existing solution, which significantly decreases the computational costs.
Analysis of Hybridized Techniques with Class Imbalance Learning for Predicting Software Maintainability

Software maintainability is a vital concern of organizations that develop and maintain large software products. The models that assess the maintainability of software systems at initial development stages play a significant role. In the Software Maintainability Prediction (SMP), a prevalent issue that needs to be taken care of is imbalanced data problem. For SMP, imbalanced data problem arises when the software classes that require high maintenance effort are less in number than classes that require low maintenance effort. In this paper, we dealt with the imbalanced data problem by the data resampling. With the imbalanced data, efficient machine learning algorithms are unable to predict the data points of both classes competently. Therefore, we examine the effectiveness of hybridized (HYB) techniques. The HYB techniques aid in finding an optimal solution for a problem by judging the goodness of multiple solutions. As per the results of the study, Adaptive synthetic minority oversampling technique (Adasyn) and Safe level synthetic minority oversampling technique (SafeSMOTE) are the best techniques of imbalanced data. Also, among the investigated HYB techniques, Fuzzy LogitBoost (GFS-LB) and Particle Swarm Optimization with Linear Discriminant Analysis (PSOLDA) emerged as the best techniques to predict maintainability.
A Formal Approach for Consistency Management in UML Models

Consistency is a significant indicator to measure the correctness of a software system in its lifecycle. It is inevitable to introduce inconsistencies between different software artifacts in the software development process. In practice, developers perform consistency checking to detect inconsistencies, and apply their corresponding repairs to restore consistencies. Even if all inconsistencies can be repaired, how to preserve consistencies in the subsequent evolution should be considered. Consistency management (consistency checking and consistency preservation) is a challenging task, especially in the multi-view model-driven software development process. Although there are some efforts to discuss consistency management, most of them lack the support of formal methods. Our work aims to provide a framework for formal consistency management, which may be used in the practical software development process. A formal model, called a Structure model, is first presented for specifying the overall model-based structure of the software system. Next, the definition of consistency is given based on consistency rules. We then investigate consistency preservation under the following two situations. One is that if the initial system is inconsistent, then the consistency can be restored through repairs. The other is that if the initial system is consistent, then the consistency can be maintained through update propagation. To demonstrate the effectiveness of our approach, we finally present a case study with a prototype tool.
A supervised method to enhance distance-based neural network clustering performance by discovering perfect representative neurons

Abstract Distance-based neural network clustering requires the intrinsic assumption that a particular neuron in the network represents a cluster centroid. However, not all these neurons can perfectly represent the training data; these neurons can only represent part of the training samples. This paper proposes an effective training data splitting method (TDSM) to find perfect representative neurons and improve the clustering results in a distance-based neutral network without changing the original network’s internal algorithm or the training data quality. The method allows a network with N neurons to be enlarged to a new network with $$m\times N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>m</mml:mi> <mml:mo>×</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> neurons. These neurons represent m subnetworks, and each subnetwork perfectly represents a part of the training set, where the clustering qualification indicators (the purity, normalized mutual information, and adjusted rand index measures) all equal 1. The results are statistically validated with a t test, and we demonstrate that the TDSM performs better than the original clustering paradigm on some real datasets.
Robots and Human Librarians for Delivering Library Services to Patrons

ABSTRACTABSTRACTThe study examined if students would prefer robots to human librarians in the delivery of library services. The study used a descriptive survey research design. Data were analyzed using descriptive statistics. Of the 1009 students surveyed, findings indicated that most students would prefer human librarians to robots in the delivery of library services. However, students would like to see robots introduced into library services because they believe they would be effective in giving directions, welcoming users, ensuring library security, answering library users’ questions, monitoring library users’ activities, library users’ registrations, charging and discharging of books, and maintaining library quietness. The study concluded that robots are necessary for providing library services and therefore recommends that robots be bought to do difficult and time-consuming jobs such as library security, monitoring library user activities, and providing directions.KEYWORDS: Robotslibrarianslibrary servicespatronsInformation technology Disclosure statementNo potential conflict of interest was reported by the author.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.
A graph model with integrated pattern and query-based technique for extracting answer to questions in community question answering system

This paper presents an extension of the community question-answering (CQA) system we developed previously. This graph-based system method that builds ranked answers for related questions using nKullback–Leibler (KL) divergence. The process of extracting answers to questions in this work involves; question core, building question query, query-based answer extraction (QBAE), pattern-based answer extraction (PBAE), and combined answer extraction. The source data for this work were existing data from ResearchGate, a socio-academic networking website that provides researchers the platform to collaborate, ask questions, and offer answers to questions. The performance for answer extraction for 2786 questions shows that when 80% of patterns and keywords were considered, QBAE and PBAE extracted 2765 and 2766 correct answers, respectively, while the QBAE + PBAE method extracted 2782 correct answers. Also, when 90% of patterns and keywords were utilized, QBAE and PBAE extracted 2782 and 2784 correct answers, whereas the QBAE + PBAE method extracted 2786 correct answers. Our method was able to identify 229 questions without answers. Finally, the evaluation of our model reveals high-performance accuracy and precision.
Towards Transparency and Security: An Analysis of The Application of Blockchain in Digital Journalism

Blockchain technology has been regarded as a disruptive new technology from its very beginning. Various blockchain platforms, such as one featuring a public chain, or a private chain, or an alliance chain, are built for the news industry. With its unique technological features, it helps the journalism industry rebuild a transparent production chain and solve the problems in news production. Finally, the incentive mechanism of crypto tokens creates a virtuous cycle of content output, source management and fact checking to protect copyrights and strengthen the accountability mechanism. However, the existing block technology is not mature. Due to technological demands, energy cost and potential economic bubbles in token trading, applications of blockchain in journalism require further technological tweaks and updates.
React example viability for efficient API learning (REVEAL): A tool to help programmers utilize incompatible code examples in React.js

Programmers typically learn APIs on-the-fly through web examples. Incompatibilities and omissions in copied example code can create barriers for these learners. We present an analysis of example usage barriers programmers faced in a previous study of React.js novices. We show that a small set of errors prevented programmers from using most found code examples. In response, we built REVEAL to detect and repair the common errors we identified in copied code. We describe the formative evaluation of REVEAL and show that REVEAL users were more likely to successfully integrate code examples than participants in the previous study.
Logistics 4.0 in the agri-food supply chain with blockchain: a case study

Blockchain is an emerging technology with the potential to radically transform logistics in the highly complex environment of the agri-food supply chain. The key challenges of the agri-food sector include overcoming the limitations (time, cost and quality) to developing global and efficient transparency and traceability of food, eliminating expensive intermediaries, and ensuring no disruption between suppliers and retailers. This paper describes a practical approach by not only using smart contracts to integrate various stakeholders but also incorporating a Public Participation Geographic Information System platform to ensure traceability of the operations conducted in an agri-food supply chain. As a factor in improving distribution and minimising intermediaries, third-party logistics participation is included in this research. Through a case study, the entities and interactions between processes within a blockchain system based on a smart contract are identified. The findings reveal an innovative blockchain approach to logistics in the agri-food supply chain.
Significance of pyrophyllite parameters in shortwave infrared spectroscopy: A case study of the Guihu super-large pyrophyllite deposit

Shortwave infrared (SWIR) spectroscopy can effectively identify various clay minerals, and has been widely used in alteration mapping and exploration vectoring toward hydrothermal center(s). The SWIR parameters (e.g., pyrophyllite spectral maturity (PSM), full width at half maximum of 1400 nm (FWHM1400), and absorption peak at ∼2160 nm (Pos2160)) of alteration minerals are influenced by factors such as temperature, pressure, and pH. Accordingly, SWIR parameters of alteration minerals can be used to determine the ore-fluid physicochemical conditions and their migration pathways. Pyrophyllite is present in the lithocaps of many porphyry-epithermal systems. However, due to the limited distribution of pyrophyllite compared with other alteration minerals (e.g., sericite, alunite, and chlorite), SWIR studies of pyrophyllite are still inadequate. We present a SWIR study on the Guihu pyrophyllite deposit, by combining microscope petrography, EPMA, and XRD analyses. Our results indicate that with increasing temperature, PSM increases. The FWHM1400 absorption peak decreases, and the Pos2160 shifts to longer wavelength. Our results also show that the pyrophyllite spectral maturity increases with increasing coherent scattering domain thickness, AlVI content, and Na/(Na + K) ratio and decreasing XRD-FWHM of pyrophyllite. At Guihu, the intrusion-proximal pyrophyllite and illite show elevated spectral maturity. Pyrophyllite and illite at the bottom of drill-holes also show high spectral maturity, indicating high-hydrothermal conditions and possible occurrence of concealed intrusions at depth.
CoolTeD: A tool for co-labeling and visual analysis of textual dataset

High-quality labeled textual data are reported as an important type of research data in data-driven requirements engineering (RE), especially in automatic mining and analysis of massive textual data produced by software systems. Several tools have been designed to facilitate manual labeling of textual data at different levels of granularity. However, these tools neither aim to provide visualized statistics and analysis of labeled textual data, nor support collaboration among the coders to reduce the time cost in manual labeling and enhance the quality of labeling results. Besides, these tools seldom explicitly serve RE researchers. In this paper, we developed a Web-based labeling tool named CoolTeD (available at http://williamsriver.cn) for collaborative labeling of the textual datasets for RE purposes. Specifically, CoolTeD can be used to: (1) label textual data with the tag category based on ISO 25010 or other user-defined tag categories in a collaborative way; (2) review the labeling results with different confidence levels and contradictory labels, (3) identify contradictory labels and disagreements online; (4) automatically calculate the Cohen's Kappa coefficient of multiple coders, and (5) visualize the labeling results. The tool demo is available at https://youtu.be/KTVrLLenvLE.