Reviewerly/exHarmony huggingface dataset:
Memristive hyperchaotic system-based complex-valued artificial neural synchronization for secured communication in Industrial Internet of Things

In this article, a memristive hyperchaotic system-based complex-valued Artificial Neural Network (ANN) synchronization for secured communication between Industrial Internet of Things (IIoT) nodes is proposed. The IIoT, an extension of the Internet of Things (IoT), has significant promise for resource use in the industrial sector and intelligent transformation. Security and privacy issues exist when industrial data is sent over an open network via sensor devices. For this, a secure key exchange protocol is necessary. Synchronized key exchange using an ANN is one of the solutions. Additionally, there is a dearth of research on the reciprocating training of ANNs and the use of a robust Pseudo-Random Number Generator (PRNG) to generate a shared input. This study explains how to use a hyperchaotic environment to swiftly assess how well ANNs finalize their coordination and synchronize for session key switch over. Reciprocating learning is used to synchronize two neural networks and transfer the ANN's output across a public channel. When the ANNs have produced the same outputs in previous iterations, coordination is tested using that function. The proposed technique has lot of benefits, like: (1) using a hyperchaotic-guided PRNG to produce the ANN input sequences; (2) generating the session key via the public network using a reciprocal neuronal alignment of complex ANNs; and (3) allowing two communicating partners to identify complete synchronization more quickly when compared to earlier methodologies. The tested proposed technique outperforms comparable strategies in the literature, according to the results.
DADRnet: Cross-domain image dehazing via domain adaptation and disentangled representation

Recent years have witnessed remarkable progress of learning-based methods in single image dehazing. Among them, dehazing methods trained on the synthetic images cannot adapt to real hazy ones due to the domain gap, and domain adaptation methods only concentrate on creating a mapping or extracting shared features regardless of representations of deep features. In this paper, we propose an innovative cross-domain dehazing architecture that integrates domain adaptation and disentangled representation. Specifically, we first construct a shared encoder to map synthetic and real hazy images to a latent space and narrow their domain gap at the feature level. Then we utilize a separator to separate the hazy image features into haze-free representations and haze ones. We introduce feature consistency loss and orthogonal loss to further guide the disentanglement process. And then, we utilize a decoder to produce clean images from haze-free features and introduce both supervised and unsupervised losses to guide the training process. Moreover, we also propose to reconstruct the images from both domains by recombining the separated features, which guarantees information completeness. Experiments demonstrate that our method is on par with state-of-the-art methods. Codes are available at https://github.com/lixiaopeng123456/DADRnet.
A multi-disciplinary review of enablers and barriers to Cloud ERP implementation and innovation outcomes

Purpose Due to the unprecedented disruptions in business operations, many organisations are turning to Cloud ERP implementation to ensure employees can access real-time business information from anywhere, enabling the continuity of business activities. As a result, over the past decades, literature on Cloud ERP implementation has seen significant growth across different subject areas. This paper aims to present a systematic literature review (SLR) that consolidates the literature scattered across various multidisciplinary subject areas, explores recent developments and identifies knowledge gaps for more impactful future research. Design/methodology/approach An SLR approach has been applied to a sample of 73 articles published until 1 February 2022. Findings Our SLR identifies and consolidates a set of critical enablers and barriers to the implementation of Cloud ERP. What is particularly interesting is that this study established a link between these enablers and barriers and four key innovation outcomes: product, service, process and business model innovations. A rigorous framework has been devised that demonstrates the nexus between enablers and barriers to Cloud ERP implementation and innovation outcomes in an organisation. In addition, this study has recognised several organisational theories from information systems literature that have the potential for future research in this emerging area. Research limitations/implications This SLR makes several theoretical contributions to the literature on Cloud ERP implementation and its impact on innovation outcomes. Practical implications The review consolidates a wide range of literature to provide decision-makers with an integrated understanding of the most influential factors in Cloud ERP implementation. Originality/value SLR provides a comprehensive analysis of the current state of research on the topic, offering unique perspectives on developments in theory and knowledge gaps, as well as identifying future research opportunities in the area.
Vehicle detection systems for intelligent driving using deep convolutional neural networks

Abstract In the paper, a vision-based vehicle identification system is proposed for autonomous intelligent car driving. The accurate detection of obstacles (vehicles) during intelligent car driving allows avoiding crashes, preventing accidents, saving people’s lives and reducing harm. The vehicle detection system, which uses low-quality images captured by a monocular video camera mounted at the front of the car, is based on convolutional neural networks (CNN). The CNN can extract global features of the images using convolutional layers and achieves more accurate, and faithful contours of vehicles. The CNN structure proposed in the paper provides high-accuracy detection of vehicle images. The experiments that have been performed using GTI dataset demonstrate that the CNN-based vehicle detection system achieves very accurate results and is more robust to different variations of images.
Multi-step histogram based outlier scores for unsupervised anomaly detection: ArcelorMittal engineering dataset case of study

Anomaly detection is the task of detecting samples that behave differently from the rest of the data or that include abnormal values. Unsupervised anomaly detection is the most common scenario, which implies that the algorithms cannot train with a labeled input and do not know the anomaly behavior beforehand. Histogram-based methods are one of the most approaches in unsupervised anomaly detection, remarking a good performance and a low runtime. Despite the good performance, histogram-based anomaly detectors are not capable of processing data flows while updating their knowledge and cannot deal with a high amount of samples. In this paper, we propose a new histogram-based approach for addressing the aforementioned problems by introducing the ability to update the information inside a histogram. We have applied these strategies to design a new algorithm called Multi-step Histogram Based Outlier Scores (MHBOS), including five new histogram update mechanisms. The results have shown the performance and validity of MHBOS as well as the proposed strategies in terms of performance and computing times.
MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN

Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed (MTC). In order to make up for the Transformer's lack of local detail feature extraction capability and the 1D-CNN's shortcoming of ignoring long-distance correlation information when processing traffic sequences, the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block. This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences. The model simultaneously handles multiple tasks, which lowers the cost of training. Experiments reveal that on the ISCX VPN-nonVPN dataset, the model achieves an average F1 score of 98.25% and an average recall of 98.30% for the task of identifying applications, and an average F1 score of 97.94%, and an average recall of 97.54% for the task of traffic characterization. When advanced models on the same dataset are chosen for comparison, the model produces the best results. To prove the generalization, we applied MTC to CICIDS2017 dataset, and our model also achieved good results.
Hybrid Graph Partitioning with OLB Approach in Distributed Transactions

Online Transaction Processing (OLTP) gets support from data partitioning to achieve better performance and scalability. The primary objective of database and application developers is to provide scalable and reliable database systems. This research presents a novel method for data partitioning and load balancing for scalable transactions. Data is efficiently partitioned using the hybrid graph partitioning method. Optimized load balancing (OLB) approach is applied to calculate the weight factor, average workload, and partition efficiency. The presented approach is appropriate for various online data transaction applications. The quality of the proposed approach is examined using OLTP database benchmark. The performance of the proposed methodology significantly outperformed with respect to metrics like throughput, response time, and CPU utilization.
Complementary Gray Code Fourfold-N Step Phase Shift Grating Fringe Projection Profilometry

Complementary Gray code <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -step phase shift algorithm has been widely used because of its high detection accuracy. In order to solve its low detection efficiency and real-time detection, this article proposes a grating projection profilometry based on the combination of complementary Gray code and fourfold- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> step phase shift algorithm. When the number of phase shift steps is an integral multiple of four, divide images into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> /4 groups according to the same phase difference, then get the wrapped phase of each group image, and fuse them. Finally, unwrap phase with complementary Gray code. The algorithm in this article can quickly eliminate periodic phase error and obtain a high-precision unwrapped phase. In order to prove the effectiveness of this algorithm, the algorithm in this article is compared with the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -step phase shift algorithm of complementary Gray code and the double <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -step phase shift algorithm of complementary Gray code with different <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> -values. The experimental result shows that the proposed algorithm can achieve high-precision phase unwrapping and improve the efficiency by 5%–20%.
The security mindset: characteristics, development, and consequences

Abstract The world is facing a cybersecurity skills gap as cybercrime and cyberwarfare grow in importance. One often-discussed quality that is potentially relevant to cybersecurity recruitment and education is the so-called “security mindset”: a way of thinking characteristic of some security professionals that they believe to be especially advantageous in their work. Although some employers express a desire to hire people with a security mindset, and initiatives to cultivate the security mindset are being implemented, it has no common definition and little is known about its characteristics, its development, and its consequences. We interviewed 21 cybersecurity professionals who strongly identified as having a security mindset based on a minimal description drawn from existing literature. Thematic analysis of the interview data suggests that the security mindset can be conceptualized as consisting of three interconnected aspects—“monitoring” for potential security anomalies, “investigating” anomalies more deeply to identify security flaws, and “evaluating” the relevance of those flaws in a larger context. These three aspects develop in different ways and have different personal and professional consequences. Participants mostly spoke positively of the security mindset, but they also mentioned several disadvantages not mentioned by existing security-mindset literature, such as mental health pressures, workplace tensions, and negative effects on personal relationships. We discuss the implications of these findings for future study of the security mindset and suggest practical implications for cybersecurity management, education, and recruitment.
Lessons from COVID-19 for rescalable data collection

Novel data and analyses have had an important role in informing the public health response to the COVID-19 pandemic. Existing surveillance systems were scaled up, and in some instances new systems were developed to meet the challenges posed by the magnitude of the pandemic. We describe the routine and novel data that were used to address urgent public health questions during the pandemic, underscore the challenges in sustainability and equity in data generation, and highlight key lessons learnt for designing scalable data collection systems to support decision making during a public health crisis. As countries emerge from the acute phase of the pandemic, COVID-19 surveillance systems are being scaled down. However, SARS-CoV-2 resurgence remains a threat to global health security; therefore, a minimal cost-effective system needs to remain active that can be rapidly scaled up if necessary. We propose that a retrospective evaluation to identify the cost-benefit profile of the various data streams collected during the pandemic should be on the scientific research agenda.
Profiling and Monitoring Deep Learning Training Tasks

The embarrassingly parallel nature of deep learning training tasks makes CPU-GPU co-processors the primary commodity hardware for them. The computing and memory requirements of these tasks, however, do not always align well with the available GPU resources. It is, therefore, important to monitor and profile the behavior of training tasks on co-processors to understand better the requirements of different use cases. In this paper, our goal is to shed more light on the variety of tools for profiling and monitoring deep learning training tasks on server-grade NVIDIA GPUs. In addition to surveying the main characteristics of the tools, we analyze the functional limitations and overheads of each tool by using a both light and heavy training scenario. Our results show that monitoring tools like nvidia-smi and dcgm can be integrated with resource managers for online decision making thanks to their low overheads. On the other hand, one has to be careful about the set of metrics to correctly reason about the GPU utilization. When it comes to profiling, each tool has its time to shine; a framework-based or system-wide GPU profiler can first detect the frequent kernels or bottlenecks, and then, a lower-level GPU profiler can focus on particular kernels at the micro-architectural-level.
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data for Cyber-Physical Systems

Uncertainty in the predictions of learning enabled components hinders their deployment in safety-critical cyber-physical systems (CPS). A shift from the training distribution of a learning enabled component (LEC) is one source of uncertainty in the LEC's predictions. Detection of this shift or out-of-distribution (OOD) detection on individual datapoints has therefore gained attention recently. But in many applications, inputs to CPS form a temporal sequence. Existing techniques for OOD detection in time-series data for CPS either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We propose using deviation from the in-distribution temporal equivariance as the non-conformity measure in conformal anomaly detection framework for OOD detection in time-series data for CPS. Computing independent predictions from multiple conformal detectors based on the proposed measure and combining these predictions by Fisher's method leads to the proposed detector CODiT with bounded false alarms. We illustrate the efficacy of CODiT by achieving state-of-the-art results in autonomous driving systems with perception (or vision) LEC. We also perform experiments on medical CPS for GAIT analysis where physiological (non-vision) data is collected with force-sensitive resistors attached to the subject's body. Code, data, and trained models are available at https://github.com/kaustubhsridhar/time-series-OOD
A novel method for 3D reconstruction in tight and restricted space based on improved line-structured light

Three-dimensional (3D) reconstruction techniques have been widely employed in many fields. But precise endoscopic 3D measurement inside of objects, especially in tight and restricted spaces, is still a challenge since there is no easy access for measurement tools. Endoscope with 2D-view function has been widely used. However, the precise measurement technology of the inner surface of small hollow objects has not been widely researched. The components of the existing endoscope three-dimensional measuring probe are complex, expensive and not performed well in accuracy. Thus, a novel method and a miniaturized endoscopic 3D sensor is introduced. To be suit in tight and restricted space, the sensor probe prototype has a maximum 6 mm in thickness. The miniatured sensor prototype is mainly based on improved line-structured light, which performs better in robustness and measurement accuracy than coded projection. Especially, the components are inexpensive and easily available, which reduces the manufacturing cost thus making it easily to popularize in industry. Correspondingly, to ensure the sensor measurement accuracy, we also proposed a calibration method for a miniature line-structured light system based on specially designed planar targets. It should be noted that the 3D extension of the line-structured light system relies on the movement of the translation stage. Taking full account of the characteristics of the sensor prototype, a high-precision direction calibration method of the translation stage based on joint multi-calibration position noise estimation is proposed. Finally, the accuracy experiment is carried out on the gauge block to verify that the measurement method proposed can achieve high-precision 3D reconstruction in a restricted space, and that the 3D reconstruction error is less than 10µm.
An improved 3D KiU-Net for segmentation of liver tumor

It is a challenging task to accurately segment liver tumors from Computed Tomography (CT) images. The widely used U-Net and its variants generally suffer from the issue to accurately segment the detailed edges of small tumors, because the progressive down sampling operations in the encoder module will gradually increase the receptive fields. These enlarged receptive filed have limited ability to learn the information about tiny structures. KiU-Net is a newly proposed dual-branch model that can effectively perform image segmentation for small targets. However, the 3D version of KiU-Net has high computational complexity, which limits its application. In this work, an improved 3D KiU-Net (named TKiU-NeXt) is proposed for liver tumor segmentation from CT images. In TKiU-NeXt, a Transformer-based Kite-Net (TK-Net) branch is proposed to build the over-complete architecture to learn more detailed features for small structures, and an extended 3D version of UNeXt is developed to replace the original U-Net branch, which can effectively reduce computational complexity but still with superior segmentation performance. Moreover, a Mutual Guided Fusion Block (MGFB) is designed to effectively learn more features from two branches and then fuse the complementary features for image segmentation. The experimental results on two public CT datasets and a private dataset demonstrate that the proposed TKiU-NeXt outperforms all the compared algorithms, and it also has less computational complexity. It suggests the effectiveness and efficiency of TKiU-NeXt.
Using 360-Degree Virtual Reality Technology for Training Construction Workers about Safety Challenges of Drones

Integrating drones into already dangerous construction sites could expose workers to additional risks and make construction sites more dangerous than before. Several studies have identified various safety challenges of using drones on jobsites. However, limited studies have focused on educating construction workers about the safety challenges of the drone presence in their working environment. This study is the first effort to develop a 360-degree virtual reality (360VR) environment providing a semi-immersive training experience about drone applications in construction, potential safety challenges of working with or near drones, and proposed countermeasures for their safe integration. A repeated measures study design was used to assess the effectiveness of the 360VR training, followed by a system usability assessment. The results indicate that the developed 360VR training significantly improved users’ safety knowledge about drones and that the 360VR platform proved to be a sufficiently usable delivery mechanism for this training. The main contribution of this study is to provide a better understanding of 360VR capabilities, as an emerging learning technology, to create virtual learning environments for construction education and training, and, in particular, enhancing trainees’ knowledge about working safely with drones.
Copyright Safety for Generative AI

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