Reviewerly/exHarmony huggingface dataset:
Rethinking the Value of Just-in-Time Learning in the Era of Industrial Big Data

Just-in-time learning (JITL) has become a widely used industrial process modeling tool. With the advent of the industrial big data era, rich data information has brought new opportunities to JITL. Specifically, the completeness of data samples in the era of big data provides an important premise and support for the JITL method, prompting us to rethink the application value of JITL in the context of industrial big data. At the same time, the huge amount of data causes certain difficulties in data searching, which is a key issue for JITL. In this article, a parallel computing strategy is adopted to divide the entire computational searching task into several subtasks, and assign the subtasks to parallel computing nodes to complete parallel searching. In this way, not only the full advantage of big data information is effectively utilized, but also the search capability and efficiency under big data are improved. In addition, in order to improve the real-time nature of JITL, a model library management (MLM) strategy is adopted, and the query similar samples are used to operate with existing similar models. And by selectively adding new data, the database management (DBM) strategy is also developed, which not only alleviates the problem of information redundancy, but also reduces the search pressure caused by the increasingly large database. Obviously, MLM and DBM are particularly important as JITL auxiliary tools under industrial big data. Combining parallel computing, a parallel JITL (P-JITL) framework is proposed. As an example, the variational Bayesian factor regression model is transformed into the parallel Bayesian-JITL method for big process data modeling, which is further extended to a nonlinear form. To evaluate the feasibility and efficiency of the developed methods, a real industrial case is demonstrated.
Peak-Graph-Based Fast Density Peak Clustering for Image Segmentation

Fuzzy c-means (FCM) algorithm as a traditional clustering algorithm for image segmentation cannot effectively preserve local spatial information of pixels, which leads to poor segmentation results with inconsistent regions. For the remedy, superpixel technologies are applied, but spatial information preservation highly relies on the quality of superpixels. Density peak clustering algorithm (DPC) can reconstruct spatial information of arbitrary-shaped clusters, but its high time complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n^2)$</tex-math></inline-formula> and unrobust allocation strategy decrease its applicability for image segmentation. Herein, a fast density peak clustering method (PGDPC) based on the kNN distance matrix of data with time complexity <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(nlog(n))$</tex-math></inline-formula> is proposed. By using the peak-graph-based allocation strategy, PGDPC is more robust in the reconstruction of spatial information of various complex-shaped clusters, so it can rapidly and accurately segment images into high-consistent segmentation regions. Experiments on synthetic datasets, real and Wireless Capsule Endoscopy (WCE) images demonstrate that PGDPC as a fast and robust clustering algorithm is applicable to image segmentation.
Netpro2vec: A Graph Embedding Framework for Biomedical Applications

The ever-increasing importance of structured data in different applications, especially in the biomedical field, has driven the need for reducing its complexity through projections into a more manageable space. The latest methods for learning features on graphs focus mainly on the neighborhood of nodes and edges. Methods capable of providing a representation that looks beyond the single node neighborhood are kernel graphs. However, they produce handcrafted features unaccustomed with a generalized model. To reduce this gap, in this work we propose a neural embedding framework, based on probability distribution representations of graphs, named Netpro2vec. The goal is to look at basic node descriptions other than the degree, such as those induced by the Transition Matrix and Node Distance Distribution. Netpro2vec provides embeddings completely independent from the task and nature of the data. The framework is evaluated on synthetic and various real biomedical network datasets through a comprehensive experimental classification phase and is compared to well-known competitors.
Be Causal: De-Biasing Social Network Confounding in Recommendation

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called “exposure” perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC ( De-Bias Network Confounding in Recommendation ), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network’s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines.
Melt pool segmentation for additive manufacturing: A generative adversarial network approach

Additive manufacturing (AM) is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. To ensure the stability of the AM process, it is of critical importance to obtain high-quality thermal images by using image processing techniques. In this paper, a novel image processing method is put forward with aim to improve the contrast ratio of the thermal images for image segmentation. To be specific, an image-enhancement generative adversarial network (IEGAN) is developed, where a new objective function is designed for the training process. To verify the superiority and feasibility of the proposed IEGAN, the thermal images captured from an AM process are utilized for image segmentation. Experiment results demonstrate that the developed IEGAN outperforms the original GAN in improving the contrast ratio of the thermal images.
An ecommerce recommendation algorithm based on link prediction

In the field of ecommerce, most recommendation algorithms are based on user-item bipartite graph network (BGN). But this kind of recommendation algorithm is severely lacking in accuracy and diversity. In this paper, a novel ecommerce recommendation algorithm is proposed based on BGN link prediction. Firstly, all the user-item data were imported into distance formula to calculate the similarity between the attributes. Then, the BGN was projected into a single-mode network (SMN), making it more efficient to extract potential links from the BGN. On this basis, the potential links were predicted based on similarity. Through experiments on real ecommerce datasets, it was proved that our algorithm has a higher accuracy and coverage than typical recommendation algorithms.
Accurate Self-Configuration of Rectangular Multiport Interferometers

Multiport interferometers based on integrated beamsplitter meshes are widely used in photonic technologies. While the rectangular mesh is favored for its compactness and uniformity, its geometry resists conventional self-configuration approaches, which are essential to programming large meshes in the presence of fabrication error. Here, we present a configuration algorithm, related to the $2\ifmmode\times\else\texttimes\fi{}2$ block decomposition of a unitary matrix, that overcomes this limitation. Our proposed algorithm is robust to errors, requires no prior knowledge of the process variations, and relies only on external sources and detectors. We show that self-configuration using this technique reduces the effect of fabrication errors by the same quadratic factor observed in triangular meshes. This relaxes a significant limit to the size of multiport interferometers, removing a major roadblock to the scaling of optical quantum and machine-learning hardware.
Probabilistic nonunitary gate in imaginary time evolution

Simulation of quantum matters is a significant application of quantum computers. In contrast to the unitary operation which can be realized naturally on a quantum computer, the implementation of nonunitary operation, widely used in classical approaches, needs special designing. Here, by application of Grover's algorithm, we extend the probabilistic method of implementing nonunitary operation and show that it can promote success probability without fidelity decreasing. This method can be applied to problems of imaginary time evolution and contraction of tensor networks on a quantum computer.
Detection of Fake News on COVID-19 on Web Search Engines

In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which is misleading or even harmful, which has widely spread around the globe. Although social media are increasingly being used as the information source, web search engines, such as Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through search engines. By using a real-world dataset associated with recent COVID-19 pandemic, we first apply re-sampling techniques for class imbalance, and then we use existing machine learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated uniform resource locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URL detection, can improve the efficiency and performance of classifiers. Based on these findings, we suggest that the use of both textual and URL features can improve the effectiveness of fake news detection methods.
Evaluating the Output of a Car-Mounted Photovoltaic Module Under Driving Conditions

In this article, we measured and estimated the outputs of car-mounted PV panels under actual driving conditions and different effective shading angles. On a sunny day, the performance ratio (PR) decreased to 0.99, despite the decrease in module temperature caused by the increased wind speed when driving. The PR decreased because of the effect of partial shading over the PV module. Public infrastructure could have led to dynamic partial shading passing across the PV module at high speeds. Furthermore, the effective shading angle being close to the sun's altitude could also have led to static partial shading. In contrast, on an overcast day the PR value was almost the same ($>$1) under both driving and parking conditions; this was because of the low module temperature and the spectrum effect.
A Deep Learning-Based Approach to Video-Based Eye Tracking for Human Psychophysics

Real-time gaze tracking provides crucial input to psychophysics studies and neuromarketing applications. Many of the modern eye-tracking solutions are expensive mainly due to the high-end processing hardware specialized for processing infrared-camera pictures. Here, we introduce a deep learning-based approach which uses the video frames of low-cost web cameras. Using DeepLabCut (DLC), an open-source toolbox for extracting points of interest from videos, we obtained facial landmarks critical to gaze location and estimated the point of gaze on a computer screen via a shallow neural network. Tested for three extreme poses, this architecture reached a median error of about one degree of visual angle. Our results contribute to the growing field of deep-learning approaches to eye-tracking, laying the foundation for further investigation by researchers in psychophysics or neuromarketing.
Guided Table Structure Recognition Through Anchor Optimization

This paper presents the novel approach towards table structure recognition by leveraging the guided anchors. The concept differs from current state-of-the-art systems for table structure recognition that naively apply object detection methods. In contrast to prior techniques, first, we estimate the viable anchors for table structure recognition. Subsequently, these anchors are exploited to locate the rows and columns in tabular images. Furthermore, the paper introduces a simple and effective method that improves the results using tabular layouts in realistic scenarios. The proposed method is exhaustively evaluated on the two publicly available datasets of table structure recognition: ICDAR-2013 and TabStructDB. Moreover, we empirically established the validity of our method by implementing it on the previous approaches. We accomplished state-of-the-art results on the ICDAR-2013 dataset with an average F1-measure of 94.19% (92.06% for rows and 96.32% for columns). Thus, a relative error reduction of more than 25% is achieved. Furthermore, our proposed post-processing improves the average F1-measure to 95.46% that results in a relative error reduction of more than 35%. Moreover, we surpassed the baseline results on the TabStructDB dataset with an average F1-measure of 94.57% (94.08% for rows and 95.06% for columns).
The cybersecurity labour shortage in Europe: Moving to a new concept for education and training

Recruiting, retaining and maintaining sufficient numbers of cybersecurity professionals in the workplace is a constant battle, not only for the technical side of cybersecurity, but also for the overlooked area of non-technical, managerial-related jobs in the cyber sector. This paper addresses the lack of cybersecurity skills in the European labour force market and the actions taken to improve the education in cybersecurity for meeting the identified needs. The paper analyses what kind of topics are missing within the cybersecurity curricula of the high-level educational institutions in Europe and in the courses provided by the cybersecurity trainers on the market. The findings are based on the data collected by the surveys carried out by the European competence centres on cybersecurity and the European CyberSecurity organization. These findings show that there are missing topics in the context of higher education cybersecurity programmes and within the private courses offered on the market. The problem of common programme accreditation of European higher education institutes (HEI) and the competence certifications for different work profiles in the area of cybersecurity are briefly presented and discussed as well. The actions undertaken to improve the education in both sectors are presented and the emerging educational landscape is proposed based on our findings. Recommendations to the stakeholders and scholars for improving the current state of cybersecurity education and training are explained in the concluding section.
End-to-End Trainable Trident Person Search Network Using Adaptive Gradient Propagation

Person search suffers from the conflicting objectives of commonness and uniqueness between the person detection and re-identification tasks that make the end-to-end training of person search networks difficult. In this paper, we propose a trident network for person search that performs detection, re-identification, and part classification together. We also devise a novel end-to-end training method using adaptive gradient weighting that controls the flow of backpropagated gradients through the re-identification and part classification networks according to the quality of the person detection. The proposed method not only prevents the over-fitting but encourages to exploit fine-grained features by incorporating the part classification branch into the person search framework. Experimental results on the CUHK-SYSU and PRW datasets demonstrate that the proposed method achieves the best performance among the state-of-the-art end-to-end person search methods.
Federated Edge Learning with Misaligned Over-The-Air Computation

Over-the-air computation (OAC) is a promising technique to realize fast model aggregation in the uplink of federated edge learning (FEEL). OAC, however, hinges on accurate channel-gain precoding and strict synchronization among edge devices, which are challenging in practice. As such, how to design the maximum likelihood (ML) estimator in the presence of residual channel-gain mismatch and asynchronies is an open problem. To fill this gap, this paper formulates the problem of misaligned OAC for FEEL and puts forth a whitened matched filtering and sampling scheme to obtain oversampled, but independent samples from the misaligned and overlapped signals. Given the whitened samples, an ML estimator and an aligned-sample estimator are devised to estimate the arithmetic sum of the transmitted symbols. Extensive simulations on the test accuracy versus the average received energy per symbol to noise power spectral density ratio (EsN0) yield two main results: 1) In the low EsN0 regime, the aligned-sample estimator can achieve superior test accuracy provided that the phase misalignment is not severe. In contrast, the ML estimator does not work well due to the error propagation and noise enhancement. 2) In the high EsN0 regime, the ML estimator attains the optimal learning performance regardless of the severity of phase misalignment. On the other hand, the aligned-sample estimator suffers from a test-accuracy loss caused by phase misalignment.
ST-BFL: A Structured Transparency Empowered Cross-Silo Federated Learning on the Blockchain Framework

Federated Learning (FL) relies on on-device training to avoid the migration of devices’ data to a centralized server to address privacy leakage. Moreover, FL is feasible for scenarios (e.g., autonomous cars) where an enormous amount of data is generated every day. Transferring only local model updates in the case of FL is highly communication-efficient compared to transferring all data in the case of centralized machine learning (ML). Although FL offers many advantages, it also has some challenges. A malicious aggregation server can infer device information via local model updates. Another downside of FL is the centralized aggregation server that can malfunction due to an attack or physical damage. To address these issues, we propose a novel Structured Transparency empowered cross-silo Federated Learning on the Blockchain (ST-BFL) framework. In ST-BFL, homomorphic encryption, FL-aggregators, FL-verifiers, and smart contract are employed, which satisfy various structured transparency components, such as input privacy, output privacy, output verification, and flow governance. We present the framework architecture, algorithms, and sequence diagram of our ST-BFL framework to show how different entities interact in ST-BFL for the FL process. We also present a simplified class diagram of ST-BFL’s smart contract for an FL task. Finally, we perform a simulation to analyze our framework from the perspective of aggregation time, accuracy, and storage size. The qualitative and quantitative evaluation shows that ST-BFL has the same accuracy as traditional FL. However, ST-BFL provides input privacy, output privacy, input verification, output verification, and flow governance at the expense of relatively higher computation and communication costs than traditional FL.
An Enhanced Blockchain-Based IoT Digital Forensics Architecture Using Fuzzy Hash

Due to businesses’ growing use of IoT services in their day-to-day operations and the increased use of smart devices, digital forensic investigations involving such systems will need increasingly sophisticated digital evidence collection and processing. The majority of IoT systems are composed of disparate software and hardware components, which may pose security and privacy concerns. Recently, blockchain technology was presented as one of the options for achieving IoT security via the use of an immutable ledger, a decentralized architecture, and strong cryptographic primitives. Integrating blockchain platforms with IoT-based applications, on the other hand, poses a number of difficulties owing to the trustworthiness, integrity, and real-time responsiveness of IoT data. However, certain IoT devices may be incompatible with existing blockchain-based IoT forensic methods for additional digital evidence processing owing to their usage of conventional hash. A critical feature of cryptographic hash functions is that even if just one bit of the input is altered, the output acts pseudo-randomly, making it impossible to identify identical files. However, in the field of computer forensics, it is essential to locate comparable files (e.g., various versions of a file); therefore, we need a hash function that preserves similarity. It is getting more difficult to establish how forensic investigators might utilize traces from such devices. To effectively deal with IoT digital forensics applications, this article presents an improved blockchain-based IoT digital forensics architecture that use fuzzy hash to construct the Blockchain’s Merkle tree in addition to the conventional hash for authentication. Fuzzy hashing enables the identification of potentially damning documents that might otherwise remain undiscovered using conventional hashing techniques. By comparing blocks/files to all nodes in the blockchain network using fuzzy hash similarity, the digital forensics investigator will be able to verify their authenticity. To support the proof of concept, we simulated the suggested model.
Deep learning segmentation of wood fiber bundles in fiberboards

Natural fiber composites and fiberboards are essential components of a sustainable economy, making use of bio-sourced, and also recycled materials. These composites' structure is often complex, and their mechanical behavior is not yet fully understood. A major barrier in comprehending them is the ability to identify the fibers in situ, i.e. embedded in complex fibrous networks such as medium-density fiberboards (MDF). To that end, the first step is to separate individual wood fibers from fiber bundles. Modern material studies on real world, dense fibrous materials using X-ray microtomography and 3D image analysis were always limited in accuracy. However, recent machine learning techniques and particularly deep learning may help to overcome this challenge. In this work, we compare existing segmentation algorithms with the performance of convolutional neural networks (CNNs). We explain the need for network complexity, and demonstrate that our best algorithm, based on the UNet3D architecture, reaches unprecedented accuracy. Moreover, it achieves the first segmentation sufficiently qualitative to extract morphometric measurements of the fiber bundles and accurately estimate their density. Among other applications, the proposed method thus enables the design of more realistic material models of MDF, and is a milestone towards the understanding and improvement of this wood-based product.