Reviewerly/exHarmony huggingface dataset:
Impact of Inclusive Leadership on Innovation Performance During Coronavirus Disease 2019 Outbreak: Mediating Role of Employee Innovation Behavior and Moderating Role of Psychological Empowerment

This study investigates the effect of inclusive leadership on innovation performance with a mediating role of employee innovation behavior and the moderating role of psychological empowerment (PE). Supervisors and employees of Saudi manufacturing firms are the participants of this study. This study used a quantitative research technique with a cross-sectional approach and a self-administrative survey questionnaire to collect the data. The data were analyzed by using the Smart PLS 3 software. The results depict that inclusive leadership has a significant positive impact on the firm's innovation performance. Employees' innovation behavior has a significant mediating effect on the association of inclusive leadership and innovation performance. Findings revealed that PE has an important moderating role in the association of inclusive leadership and innovation performance. The findings of this study contribute to the body of knowledge by finding that inclusive leadership has a significant effect on the firm's innovative performance and PE is crucial to enhance innovation performance.
Investigation into Phishing Risk Behaviour among Healthcare Staff

A phishing attack is one of the less complicated ways to circumvent sophisticated technical security measures. It is often used to exploit psychological (as as well as other) factors of human users to succeed in social engineering attacks including ransomware. Guided by the state-of-the-arts in a phishing simulation study in healthcare and after deeply assessing the ethical dilemmas, an SMS-based phishing simulation was conducted among healthcare workers in Ghana. The study adopted an in-the-wild study approach alongside quantitative and qualitative surveys. From the state-of-the-art studies, the in-the-wild study approach was the most commonly used method as compared to laboratory-based experiments and statistical surveys because its findings are generally reliable and effective. The attack results also showed that 61% of the targeted healthcare staff were susceptible, and some of the healthcare staff were not victims of the attack because they prioritized patient care and were not susceptible to the simulated phishing attack. Through structural equation modelling, the workload was estimated to have a significant effect on self-efficacy risk (r = 0.5, p-value = 0.05) and work emergency predicted a perceived barrier in the reverse direction at a substantial level of r = −0.46, p-value = 0.00. Additionally, Pearson’s correlation showed that the perceived barrier was a predictor of self-reported security behaviour in phishing attacks among healthcare staff. As a result, various suggestions including an extra workload balancing layer of security controls in emergency departments and better security training were suggested to enhance staff’s conscious care behaviour.
Diverse Feature Learning Network With Attention Suppression and Part Level Background Suppression for Person Re-Identification

In this paper, we propose a Diverse Feature Learning Network with Attention Suppression and Part Level Background Suppression (DFLN) for person re-identification (ReID). DFLN includes two key components: attention suppression mechanism (ASM) and part level background suppression mechanism (PLBSM). Firstly, despite attention mechanism has made great progress in current state-of-the-art ReID methods, they can only pay attention to the most salient region but ignore other discriminative information limiting the diversity of networks, which is not optimal for ReID due to the models tend to match persons by diverse clues (e.g., legs, arms, body, logo of clothes). To tackle the limitation above, we propose the ASM to assist the network to make full use of the most salient features and capture the other sub-salient features, so as to attain diverse features to improve the network performance. Secondly, we adopt a novel PLBSM to develop the part-based method which is proved effective for enhancing the diversity of ReID network. The PLBSM consists of a part feature refined module and a background suppression loss function, and aims to attain pure part level feature by filtering background clutter. Our DFLN integrates the ASM and part-based method developed by PLBSM into an end-to-end network and is able to extract robust diversity feature representations leading to higher performance. Extensive experimental results demonstrate the effectiveness of each component and our method achieves state-of-the-art results on mainstream person re-identification datasets.
Multi Global Information Assisted Streaming Session-Based Recommendation System

Streaming Session-Based Recommendation (SSBR) is a challenging problem as user preferences in sessions are continually drifting with sessions generated chronologically. In recent years, some SSBR models have been proposed to address this problem by reservoir technique and Graph Neural Networks (GNN) which help to preserve a representative sketch of the historical data and extract item transition information in sessions. However, there are two critical problems in existing methods: (1) most existing methods only focus on the local session information without exploiting the information of other sessions and users; (2) GNN models in existing SSBR methods are unable to capture the importance of different user features. To address the problems mentioned above, we propose a novel architecture named <b>G</b>lobal <b>I</b>tem and <b>U</b>ser embedding <b>A</b>ssisted <b>G</b>raph <b>N</b>eural <b>N</b>etwork (<b>GIUA-GNN</b>) for combining the global user and item information in an attentional manner with local session information for the recommendation. We also propose a novel architecture of graph neural network which utilizes the attention mechanism for better extracting the importance of different features of user embeddings named <b>B</b>i-directed <b>A</b>ttentional <b>G</b>raph <b>C</b>onvolutional <b>N</b>etwork (<b>BA-GCN</b>). Extensive experiments on three different sizes of real-world datasets have been conducted to demonstrate the superiority of our model on metrics MRR and Recall.
Is Assertion Roulette still a test smell? An experiment from the perspective of testing education

Test smells are commonly perceived as having a negative impact on software maintainability and correctness. Research has shown that Assertion Roulette is the most pervasive smell in industrial and open-source systems. However, some recent studies argue that the impact of Assertion Roulette is not as severe as previously believed, and developers usually consider it acceptable.The controversy over the impact of Assertion Roulette also exists in the area of testing education. To assess the impact of Assertion Roulette, we conducted a controlled empirical study with 42 CS students. We recruited participants from two populations, CS1 and a graduate testing course, to see what role experience may have in terms of this test smell&#x2019;s impact. Participants were tasked with implementing a project in Java that passes provided JUnit tests. Through analysis of student-authored source code, we measured the impact of Assertion Roulette using code quality measures and testing behavior measures. Our findings show that the impact of Assertion Roulette on students in this study was minimal. Though students with exposure to the test smell began testing significantly later, they performed similarly in terms of programming quality measures. Thus, it would seem the Assertion Roulette smell is no longer a smell at all, even for less experienced populations like students.
Feature Engineering and Resampling Strategies for Fund Transfer Fraud With Limited Transaction Data and a Time-Inhomogeneous Modi Operandi

Detecting financial fraud to profile crimes and pinpoint system vulnerabilities is an essential issue in the financial industry.Because of interpretability requirements and the lack of mass transaction data due to privacy regulations, sophisticated handcrafted features have been adopted in much of the literature for fraud detection.In addition to established recency, frequency, monetary, and anomaly features, we propose behavior-and segmentation-type features based on statistical characteristics belonging solely to (non-)fraudulent accounts informed by financial expertise.Our proposed features are difficult for automatic feature generators to synthesize, and provide transparent cause-effect relationships and good prediction results.Features with time-inhomogeneous properties cause popular boosting classifiers such as XGBoost and LGBM to produce unstable detection results.We use the Kolmogorov-Smirnov test to detect and remove these features to improve XGBoost and LGBM detection performance and robustness.The resulting performance shown in our experiments is better than that of other classifiers, such as SVM and random forests.We examine the advantage of our technique by comparing it with several feature engineering works on fraud detection and automatic feature generation methods.On the other hand, we also find that generating training/testing sets with random sampling falsely eliminates such time inhomogeneity and results in misleading assessments of the robustness of machine learning models.These time-inhomogeneous phenomena also entail various modus operandi patterns, which influence the performance of different resampling methods for addressing data imbalance in fraud detection.Improper linear interpolation of SMOTE-related approaches leads to poor performance due to varying patterns of modi operandi.However, synthesizing fraudulent samples with simple oversampling and GANs mitigates this problem.
Spatial Steganalysis Based on Non-Local Block and Multi-Channel Convolutional Networks

Image steganalysis aims to detect whether secret information is hidden in images and is a means to solve the communication security. Recently, a series of convolutional neural network-based steganalysis models has been proposed and has achieved remarkable performance. Given that the steganalysis task is different from other computer vision tasks, the series of models is inevitably restricted by manual factors, and some other useful residual information will be missed in the process of feature extraction. To solve this problem, this paper proposes a network model based on non-local operations and multi-channel convolution as part of the basic block of feature extraction for spatial grayscale image steganalysis. The preprocessing layer is improved to compress the image content, further using the introduced non-local operations and multi-channel convolution modules to enhance the residual information in high-frequency regions and extract diverse steganographic features effectively. Extensive ablation study shows that the introduced extraction module can improve the steganographic detection accuracy effectively. Our method is generally better than other methods in terms of detection accuracy, especially in low payload detection.
Dynamic Representation Learning via Recurrent Graph Neural Networks

A large number of real-world systems generate graphs that are structured data aligned with nodes and edges. Graphs are usually dynamic in many scenarios, where nodes or edges keep evolving over time. Recently, graph representation learning (GRL) has received great success in network analysis, which aims to produce informative and representative features or low-dimensional embeddings by exploring node attributes and network topology. Most state-of-the-art models for dynamic GRL are composed of a static representation learning model and a recurrent neural network (RNN). The former generates the representations of a graph or nodes from one static graph at a discrete time step, while the latter captures the temporal correlation between adjacent graphs. However, the two-stage design ignores the temporal dynamics between contiguous graphs during the learning processing of graph representations. To alleviate this problem, this article proposes a representation learning model for dynamic graphs, called DynGNN. Differently, it is a single-stage model that embeds an RNN into a graph neural network to produce better representations in a compact form. This takes the fusion of temporal and topology correlations into account from low-level to high-level feature learning, enabling the model to capture more fine-grained evolving patterns. From the experimental results on both synthetic and real-world networks, the proposed DynGNN yields significant improvements in multiple tasks compared to the state-of-the-art counterparts.
Multi-View Subspace Clustering by Joint Measuring of Consistency and Diversity

In multi-view subspace clustering, it is significant to find a common latent space in which the multi-view datasets are located. A number of multi-view subspace clustering methods have been proposed to explore the common latent subspace and achieved promising performance. However, previous multi-view subspace clustering algorithms seldom consider the multi-view consistency and multi-view diversity, let alone take them into consideration simultaneously. In this paper, we propose a novel multi-view subspace clustering by joint measuring the consistency and diversity, which is able to exploit these two complementary criteria seamlessly into a holistic design of clustering algorithms. The proposed model first searches a pure graph for each view by detecting the intrinsic consistent and diverse parts. A consensus graph is then obtained by fusing the multiple pure graphs. Moreover, the consensus graph is structurized to contain exactly <inline-formula><tex-math notation="LaTeX">$c$</tex-math></inline-formula> connected components where <inline-formula><tex-math notation="LaTeX">$c$</tex-math></inline-formula> is the number of clusters. In this way, the final clustering result can be obtained directly since each connected component precisely corresponds to an individual cluster. Extensive experimental studies on various datasets manifest that our model achieves comparable performance than the other state-of-the-art methods.
Latency-Efficient Wireless Federated Learning With Quantization and Scheduling

Federated learning (FL) protects data privacy through local training and parameter aggregation. However, there is no need that all users are required to train their local models, and the parameter needs to be quantized via wireless channels in practice. In this letter, we investigate and analyze how to improve the model prediction accuracy with the system latency guarantee. Specifically, our goal is to minimize the loss function under the latency constraint by taking the parameter quantization, user scheduling, and channel bandwidth and transmit power into account. To make the optimization problem tractable, we first derive an upper bound on the loss function with joint quantization and scheduling and an upper bound on the number of bits for parameter aggregation, and then solve the reformulated problem based on the derived upper bounds to obtain closed-form expressions for the quantization level, the scheduling number, the optimized bandwidth and power allocation. Simulation results confirm the convergence and the effectiveness of the proposed algorithm.
Eye-tracking training improves the learning and memory of children with learning difficulty

Children who experience difficulty in learning at mainstream schools usually are provided with remediation classes after school to facilitate their learning. The present study aims to evaluate an innovative eye-tracking training as possible alternative remediation. Our previous findings showed that children who received eye-tracking training demonstrated improved attention and inhibitory control, and the present randomized controlled study aims to evaluate if eye-tracking training can also enhance the learning and memory of children. Fifty-three primary school students with learning difficulty (including autism spectrum disorder, attention-deficit/hyperactivity disorder, specific learning disorder, specific language impairment and borderline intellectual functioning) were recruited and randomly assigned to either the Eye-tracking Training group or the after-school remediation class. They were assessed on their learning and memory using the Hong Kong List Learning Test before and after 8-month training. Twenty weekly parallel sessions of training, 50 min per session, were provided to each group. Children who received the eye-tracking training, not those in the control group, showed a significant improvement in memory as measured by the delayed recall. In addition, the Eye-Tracking Training group showed significantly faster learning than the control group. Also, the two groups showed a significant improvement in their reading abilities. In sum, eye-tracking training may be effective training for enhancing the learning and memory of children with learning difficulties.
RF Fingerprinting Based on Reservoir Computing Using Narrowband Optoelectronic Oscillators

Radiofrequency (RF) fingerprinting refers to a range of technologies that recognize transmitters by their intrinsic hardware-level characteristics. These characteristics are often introduced during the fabrication process and form a unique fingerprint of the transmitter that is very hard to counterfeit. RF fingerprinting often serves as a security measure at the physical-layer of communication networks against potentials attacks. In recent years, neuromorphic computing techniques such as convolutional neural networks (CNNs) have been explored as classifiers for RF fingerprinting. However, in radiofrequency communication networks, the transmitted signals are I/Q modulated on multi-GHz carriers while most conventional machine learning algorithms operate at the baseband. Therefore, the I/Q modulated signals have to be demodulated and converted into compatible formats before applying to these platforms – a procedure that inevitably slows down the processing speed. Moreover, the deep learning technologies often require a large amount of data to train the artificial neural networks (ANNs) while in practice, the available amount of data for a new transmitter is limited. Reservoir computing (RC) provides a relatively simple yet powerful structure that is capable of reaching state-of-the-art performance on several benchmarks. However, traditional digital RC also operates at baseband, which is not suitable for directly processing the I/Q modulated signals. In this article, we propose a reservoir computer based on narrowband optoelectronic oscillator (OEO) that can be utilized to directly classify I/Q modulated signals without the need for demodulation. We successfully train and test our narrowband OEO-based RC on three publicly available benchmarks, namely the FIT/CorteXlab RF fingerprinting dataset, the ORACLE RF fingerprinting dataset, and the AirID RF fingerprinting dataset. We show that for all three datasets, the narrowband OEO-based RC demonstrates competing accuracy with much less training data comparing to CNNs, and achieves an accuracy as high as 97%.
Separable Reversible Data Hiding in Encrypted AMBTC Images Using Hamming Code

Data hiding is a field widely used in copyright, annotation, and secret communication for digital content, and has been continuously studied for more than 20 years. In general, data hiding uses the original image as a cover image to hide data, but recently, the research area has expanded to research on improving the security and privacy protection of image contents by encrypting the image. This research is called separable reversible data hiding in an encrypted image (SRDH-EI). In this paper, we proposed a more efficient SRDH-EI method based on AMBTC. AMBTC can guarantee very good network transmission efficiency for applications that do not require a particularly high image quality because the compression time is short and calculation is simple compared to other existing compression methods. The SRDH-EI method presented here divides AMBTC into non-overlapping 4×4 blocks and then performs image encryption on them. Thereafter, data can be hidden using a Hamming code for each block. The proposed method has an advantage in that the quality of the cover image and the hiding capacity can be adjusted by appropriately using the value T of the difference between the two quantization levels. The experimental results proved the efficiency and superiority of our proposed model.
Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud

The increasing popularity of cloud computing has forced cloud providers to build economies of scale to meet the growing demand. Nowadays, data-centers include thousands of physical machines, each hosting many virtual machines (VMs), which share the main system resources, causing interference that can significantly impact on performance. Frequently, these data-centers run latency-critical workloads, whose performance is determined by tail latency, which is very sensitive to the interference of co-running workloads. To prevent QoS violations, cloud providers adopt overprovisioning strategies but they reduce the server utilization and increase the costs. A mechanism that accurately estimates performance degradation dynamically in a production system would allow cloud providers to improve the servers' utilization. In this work we propose Cloud White, an approach that is able to detect the inter-VM interference in scenarios with multiple co-located latency-critical VMs and estimate the performance degradation using multi-variable regression models. Unlike previous proposals, Cloud White is built taking into account the limitations of a public cloud production system. Experimental results show that Cloud White is able to estimate performance degradation with a small overall prediction error of 5%.
Non-Local Color Compensation Network for Intrinsic Image Decomposition

Single image-based intrinsic image decomposition attempts to separate one input image into several intrinsic components, which is inherently an under-constrained problem. Some recent works have been proposed to estimate the intrinsic components using encoder-decoder structures. However, they generally lack exploration of the different component-oriented feature constraints and feature selection processes. In this paper, a non-local color compensation network (NCCNet) is proposed. Firstly, the hue and value channels of HSV color space are used as the complementary information for RGB images for the estimation of albedo and shading, respectively. The color space representation serves as an external constraint, which does not require expensive sensors or complicated computations. Secondly, an integrated non-local attention scheme is proposed to describe the relations of non-adjacent regions with a lower computational complexity compared to traditional methods. Then the non-local and local attention are combined to describe correlations among features and used as feature selectors between the encoder and decoder. Thirdly, the mutual constraint between albedo and shading is also explored in the network to further optimize the process. In order to train the network, a unified mutual exclusion loss function is proposed. Extensive experiments are conducted on several popular datasets, and the proposed NCCNet achieves improved performance with comparable computational cost compared to competing methods.