Reviewerly/exHarmony huggingface dataset:
Cloud accounting adoption in Thai SMEs amid the COVID-19 pandemic: an explanatory case study

The COVID-19 pandemic has altered the way small and medium-sized businesses (SMEs) function. To maintain business efficiency and reduce operating costs in the context of the constraints imposed by the pandemic, SMEs have been forced to embrace new digital technologies. Cloud accounting is becoming an increasingly important business operating tool for SMEs. By adopting cloud-based accounting, SMEs can become more efficient, financially organized, and flexible. This study aims to investigate the factors that have a pervasive influence on cloud accounting adoption among SMEs in Thailand, following the pandemic's effects. This study integrates three technology adoption theories-the Technology-Organization-Environment framework, Diffusion of Innovation theory, and Institutional Theory-and studies them alongside SMEs' unique characteristics. The research employs a qualitative case study method and triangulated sources of evidence. The findings provide important implications for the research community, policymakers, cloud accounting vendors, and SME owners aiming to formulate better approaches to cloud accounting adoption after the pandemic. The results suggest that vendors should focus on SMEs' particular characteristics and needs. By contrast, SMEs should determine the organizational fit of the cloud accounting platform and integrate cloud accounting with other aspects of their operations.
DSLA: Dynamic smooth label assignment for efficient anchor-free object detection

Anchor-free detectors basically formulate object detection as dense classification and regression. For popular anchor-free detectors, it is common to introduce an individual prediction branch to estimate the quality of localization. The following inconsistencies are observed when we delve into the practices of classification and quality estimation. Firstly, for some adjacent samples which are assigned completely different labels, the trained model would produce similar classification scores. This violates the training objective and leads to performance degradation. Secondly, it is found that detected bounding boxes with higher confidences contrarily have smaller overlaps with the corresponding ground-truth. Accurately localized bounding boxes would be suppressed by less accurate ones in the Non-Maximum Suppression (NMS) procedure. To address the inconsistency problems, the Dynamic Smooth Label Assignment (DSLA) method is proposed. Based on the concept of centerness originally developed in FCOS, a smooth assignment strategy is proposed. The label is smoothed to a continuous value in [0,1] to make a steady transition between positive and negative samples. Intersection-of-Union (IoU) is predicted dynamically during training and is coupled with the smoothed label. The dynamic smooth label is assigned to supervise the classification branch. Under such supervision, quality estimation branch is naturally merged into the classification branch, which simplifies the architecture of anchor-free detector. Comprehensive experiments are conducted on the MS COCO benchmark. It is demonstrated that, DSLA can significantly boost the detection accuracy by alleviating the above inconsistencies for anchor-free detectors. Our codes are released at https://github.com/YonghaoHe/DSLA.
Text Style Transfer

The stylistic properties of text have intrigued computational linguistics researchers in recent years. Specifically, researchers have investigated the text style transfer task (TST), which aims to change the stylistic properties of the text while retaining its independent content of style. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. The field of TST research has developed because of this symbiosis. This article aims to provide a comprehensive review of recent research efforts on text style transfer. More concretely, we create a taxonomy to organize the TST models, and provide a comprehensive summary of the state of the art. We review existing evaluation methodologies for TST tasks and conduct a large-scale reproducibility study in which we experimentally benchmark 19 state-of-the-art TST algorithms on two publicly available datasets. Finally, we expand on current trends and provide new perspectives on the new and exciting developments in the TST field.
COVID-19 pandemic and the cyberthreat landscape: Research challenges and opportunities

Although cyber technologies benefit our society, there are also some related cybersecurity risks. For example, cybercriminals may exploit vulnerabilities in people, processes, and technologies during trying times, such as the ongoing COVID-19 pandemic, to identify opportunities that target vulnerable individuals, organizations (e.g., medical facilities), and systems. In this paper, we examine the various cyberthreats associated with the COVID-19 pandemic. We also determine the attack vectors and surfaces of cyberthreats. Finally, we will discuss and analyze the insights and suggestions generated by different cyberattacks against individuals, organizations, and systems.
DeepFedWT: A federated deep learning framework for fault detection of wind turbines

Data-driven fault detection of wind turbines has gained increasingly attention. Currently, most existing methods require sufficient labeled data to train a reliable model in a centralized way. However, it is difficult to collect enough labeled data due to data privacy and strict confidentiality of wind farm owners. To this end, we propose a federated deep learning framework (DeepFedWT), which allows multiple decentralized WTs to collaboratively build a fault detection model using their local private data. Specifically, we designed a multi-scale residual attention network (MSRAN) model to extract informative features from raw multivariate sensor data, which first integrates a multiscale residual learning block to extract spatial features among different sensor variables at multiple scales and adopts a feature attention block to highlight important features highly associated with faults, and finally enables an enhanced fault detection. Experimental results on two real WT datasets demonstrate the effectiveness of our proposed DeepFedWT framework.
Query driven-graph neural networks for community search

Given one or more query vertices, Community Search (CS) aims to find densely intra-connected and loosely inter-connected structures containing query vertices. Attributed Community Search (ACS), a related problem, is more challenging since it finds communities with both cohesive structures and homogeneous vertex attributes. However, most methods for the CS task rely on inflexible pre-defined structures and studies for ACS treat each attribute independently. Moreover, the most popular ACS strategies decompose ACS into two separate sub-problems, i.e., the CS task and subsequent attribute filtering task. However, in real-world graphs, the community structure and the vertex attributes are closely correlated to each other. This correlation is vital for the ACS problem. In this vein, we argue that the separation strategy cannot fully capture the correlation between structure and attributes simultaneously and it would compromise the final performance. In this paper, we propose Graph Neural Network (GNN) models for both CS and ACS problems, i.e., Query Driven-GNN (QD-GNN) and Attributed Query Driven-GNN (AQD-GNN). In QD-GNN, we combine the local query-dependent structure and global graph embedding. In order to extend QD-GNN to handle attributes, we model vertex attributes as a bipartite graph and capture the relation between attributes by constructing GNNs on this bipartite graph. With a Feature Fusion operator, AQD-GNN processes the structure and attribute simultaneously and predicts communities according to each attributed query. Experiments on real-world graphs with ground-truth communities demonstrate that the proposed models outperform existing CS and ACS algorithms in terms of both efficiency and effectiveness. More recently, an interactive setting for CS is proposed that allows users to adjust the predicted communities. We further verify our approaches under the interactive setting and extend to the attributed context. Our method achieves 2.37% and 6.29% improvements in F1-score than the state-of-the-art model without attributes and with attributes respectively.
DeepTEA

In this paper, we study anomalous trajectory detection, which aims to extract abnormal movements of vehicles on the roads. This important problem, which facilitates understanding of traffic behavior and detection of taxi fraud, is challenging due to the varying traffic conditions at different times and locations. To tackle this problem, we propose the &lt;u&gt;deep&lt;/u&gt; -probabilistic-based &lt;u&gt;t&lt;/u&gt;ime-d&lt;u&gt;e&lt;/u&gt;pendent &lt;u&gt;a&lt;/u&gt;nomaly detection algorithm ( DeepTEA ). This method, which employs deep-learning methods to obtain time-dependent outliners from a huge volume of trajectories, can handle complex traffic conditions and detect outliners accurately. We further develop a fast and approximation version of DeepTEA, in order to capture abnormal behaviors in real-time. Compared with state-of-the-art solutions, our method is 17.52% more accurate than seven competitors on average, and can handle millions of trajectories.
Chaotic signal denoising based on simplified convolutional denoising auto-encoder

Chaos is a ubiquitous phenomenon in nature, but the observed chaotic signals are often contaminated by noises. In this work, we consider chaotic signal denoising from the perspective of deep learning, and propose a chaotic signal denoising method referred to as Simplified Convolutional Denoising Auto-Encoder (SCDAE). The method consists of an encoder and a decoder with 13 layers in total, and requires minimal preprocessing steps. Our simulation results show that the proposed method can achieve smaller root mean square errors and better proliferation exponents than conventional denoising techniques.
Security intrusion detection using quantum machine learning techniques

Conventional machine learning approaches applied for the security intrusion detection degrades in case of big data input ( $$10^6$$ and more samples in a dataset). Model training and computing by traditional machine learning executed on big data at a common computing environment may produce accurate outputs but take a long time, or produce poor accuracy by quick training, both disparate to malicious activity. The paper observes the quantum machine learning (QML) methods overcoming the barriers of big data and the computing abilities of common hardware for the purpose of high performance intrusion detection. Quantum support vector machine (QSVM) and quantum convolution neural network (QCNN) as concurrent methods are discussed and evaluated comparing to the conventional intrusion detectors running on the traditional computer. The QML-based intrusion detection utilizes our own dataset that implements the grouping of the network packets into the input streams eatable for the QML. We have developed the software solution that encodes the network traffic streams ready to the quantum computing. Experimental results show the ability of the QML-based intrusion detection for processing big data inputs with high accuracy (98%) providing a twice faster speed comparing to the conventional machine learning algorithms utilized for the same task.
Distributed Filtering for Multi-sensor Systems with Missing Data

This paper studies distributed estimation problems for multi-sensor systems with missing data. Missing data may occur during sensor measuring or data exchanging among sensor nodes due to unreliability of communication links or external disturbances. Missing data include random missing measurements of sensor itself and random missing estimates of neighbor nodes. Three distributed Kalman filter (DKF) algorithms with the Kalman-like form are designed for each sensor node. When it is available whether a datum is missing or not at each time, an optimal DKF (ODKF) dependent on the knowledge of missing data is presented, where filter gains and covariance matrices require online computing. To reduce online computational cost, a suboptimal DKF (SDKF) is presented, where filter gains and covariance matrices dependent on missing probabilities can be computed offline. When it is unavailable whether a datum is missing or not, a probability-based DKF (PDKF) dependent on missing probabilities is presented. For each DKF algorithm, an optimal Kalman filter gain for measurements of sensor itself and different optimal consensus filter gains for state estimates of its neighbor nodes are designed in the linear unbiased minimum variance (LUMV) sense, respectively. Mean boundedness of covariance matrix of the proposed ODKF is analyzed. Stability and steady-state properties of the proposed SDKF and PDKF are analyzed. Also, the performance of three DKF algorithms is compared. Simulation examples demonstrate effectiveness of the proposed algorithms.
Exploration of Mobile Device Behavior for Mitigating Advanced Persistent Threats (APT): A Systematic Literature Review and Conceptual Framework

During the last several years, the Internet of Things (IoT), fog computing, computer security, and cyber-attacks have all grown rapidly on a large scale. Examples of IoT include mobile devices such as tablets and smartphones. Attacks can take place that impact the confidentiality, integrity, and availability (CIA) of the information. One attack that occurs is Advanced Persistent Threat (APT). Attackers can manipulate a device's behavior, applications, and services. Such manipulations lead to signification of a deviation from a known behavioral baseline for smartphones. In this study, the authors present a Systematic Literature Review (SLR) to provide a survey of the existing literature on APT defense mechanisms, find research gaps, and recommend future directions. The scope of this SLR covers a detailed analysis of most cybersecurity defense mechanisms and cutting-edge solutions. In this research, 112 papers published from 2011 until 2022 were analyzed. This review has explored different approaches used in cybersecurity and their effectiveness in defending against APT attacks. In a conclusion, we recommended a Situational Awareness (SA) model known as Observe-Orient-Decide-Act (OODA) to provide a comprehensive solution to monitor the device's behavior for APT mitigation.
Evaluating algorithms for anomaly detection in satellite telemetry data

Detecting anomalies in telemetry data captured on-board a spacecraft is critical to ensure its safe operation. Although there exist various techniques for automatically detecting point, contextual, and collective anomalies from time-series data, quantifying their performance remains under-researched. In this paper, we thoroughly validate our approach for the task of anomalous event detection that is built upon a two-stage technique, in which the telemetry signal is predicted using a long short-term memory network based on the historical data, and then the prediction is compared with the actual (captured) data. If the difference between those two is sufficiently large, we can infer that an anomalous event has happened. To evaluate the capabilities of such detection techniques over the simulated and benchmark time-series data, we investigate a set of commonly used metrics obtained for a range of anomaly detection approaches, and present their shortcomings, especially related to their inability of capturing the temporal aspects of the detectors. We tackle this issue by introducing new quality metrics which enable us to objectively verify if the detectors can timely spot the anomalies in sequential data. The experimental study showed that inferring the conclusions based on a subset of metrics can lead to biased observations, as the best algorithms determined based on the overlap metrics, including the Dice coefficient, do not necessarily correspond to the algorithms that offer the fastest detection. Finally, we discuss the Antelope Toolbox—our software tool for simulating nominal telemetry data of given characteristics, alongside well-defined anomalous events, and to perform the quantitative and qualitative analysis of anomaly detection algorithms over such simulated events.
Proactivity or passivity? An investigation of the effect of service robots’ proactive behaviour on customer co-creation intention

The implementation of robots in tourism services is transforming how companies interact with their customers and create value. Although corporate initiatives are beginning to focus on proactive robot services, little is known about the impact of this behavioural characteristic of service robots on customer value co-creation. Through two experiments and one field survey, this research demonstrates that customers’ co-creation intention is higher when a robot’s service proactivity is high (vs. low), and that this relationship is mediated by perceived robotic empathy. Furthermore, customer task orientation negatively moderates the mediating effect of empathy perception that underlies the relationship between service proactivity and customers’ co-creation intention. These findings provide new insights into the effect of service robots’ behavioural characteristics on customer–robot interactions.
Impact of artificial intelligence in breast cancer screening with mammography

To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or "continuous BI-RADS 100". Cohen's kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528-0.571) without AI and κ = 0.626, 95% CI (0.607-0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.
Stress, Burnout, and Security Fatigue in Cybersecurity: A Human Factors Problem

Abstract Stress, burnout, and security fatigue continue as slight destroyers of strong cybersecurity and significant human factors concerns. The persistence of these human performance issues is concerning given the lack of mitigation and integration of human factors practitioners to mitigate these adverse risk circumstances. Security fatigue is not a new phenomenon but the evolving nature of cybersecurity results in various sub-categories of security fatigue; thus, making it a difficult problem to solve. Stress and burnout are major causes of short tenures in senior roles for security executives. Business decision-makers lack the expertise to explore the negative influences of stress, burnout, and security fatigue on cybersecurity. Technology-led cycles are organizations’ primary course of action to mitigate cybersecurity threats, resulting in complexity debt and making businesses more vulnerable to attacks. Human factors professionals can identify high-friction areas that degrade human performance and implement initiatives to reduce the risk. Human performance degradation in cybersecurity is a critical risk factor and requires immediate attention, given that cybercriminals continue to exploit human weaknesses to gain access to sensitive and critical infrastructure.
Creating and Comparing Dictionary, Word Embedding, and Transformer-Based Models to Measure Discrete Emotions in German Political Text

Abstract Previous research on emotional language relied heavily on off-the-shelf sentiment dictionaries that focus on negative and positive tone. These dictionaries are often tailored to nonpolitical domains and use bag-of-words approaches which come with a series of disadvantages. This paper creates, validates, and compares the performance of (1) a novel emotional dictionary specifically for political text, (2) locally trained word embedding models combined with simple neural network classifiers, and (3) transformer-based models which overcome limitations of the dictionary approach. All tools can measure emotional appeals associated with eight discrete emotions. The different approaches are validated on different sets of crowd-coded sentences. Encouragingly, the results highlight the strengths of novel transformer-based models, which come with easily available pretrained language models. Furthermore, all customized approaches outperform widely used off-the-shelf dictionaries in measuring emotional language in German political discourse.
Extended reality technologies in small and medium-sized European industrial companies: level of awareness, diffusion and enablers of adoption

Augmented reality (AR) and virtual reality (VR), collectively referred to as "extended reality" (XR), have begun to diffuse in industry. However, the current levels of awareness, perceived limitations, and use of AR and VR, as well as the potential differences on these aspects between these technologies are still not well known. Moreover, it is unknown whether small and medium-sized enterprises (SMEs) differ from large companies on these issues. This research employed a mixed methods research design to address this gap by carrying out a cross-sectional survey (n = 208) to gauge European industrial companies' level of AR and VR awareness and adoption, and by interviewing 45 companies in nine European countries in order to identify critical enabling factors in the adoption of XR for SMEs. Results show no statistical difference between the respondents' perceptions toward AR and VR or in their use levels. Thus, examining AR and VR under the umbrella term XR seems justified, especially in the context of their organizational use. However, larger companies were found to be using XR more than SMEs. Analysis of interviews based on the technology-organization-environment framework also yielded several enabling factors affecting XR adoption and specified whether they are particularly highlighted in the SME context. Overall, this paper contributes to XR research by providing a holistic multi-country overview that highlights key issues for managers aiming to invest in these technologies, as well as critical organizational perspectives to be considered by scholars.