Reviewerly/exHarmony huggingface dataset:
Cybersecurity for railways – A maturity model

With the advancements in and widespread adoption of information and communication technologies in infrastructures, cyber-attacks are becoming more frequent and more severe. Advanced cybersecurity threats with automated capabilities are increasing in such sectors as finance, health, grid, retail, government, telecommunications, transportation, etc. Cyber-attacks are also increasing in railways with an impact on railway stakeholders, e.g. threat to the safety of employees, passengers, or the public in general; loss of sensitive railway information; reputational damage; monetary loss; erroneous decisions; loss of dependability, etc. There is a need to move towards advanced security analytics and automation to identify, respond to, and prevent such security breaches. The objective of this research is to reduce cyber risks and vulnerabilities and to improve the cybersecurity capabilities of railways by evaluating their cybersecurity maturity levels and making recommendations for improvements. After assessing various cybersecurity maturity models, the Cybersecurity Capability Maturity Model (C2M2) was selected to assess the cybersecurity capabilities of railway organizations. The contributions of this research are as follows. First, a new maturity level MIL4 (Maturity Indicator Level 4) is introduced in the C2M2 model. Second, the C2M2 model is adapted by adding advanced security analytics and threat intelligence to develop the Railway-Cybersecurity Capability Maturity Model (R-C2M2). The cybersecurity maturity of three railway organizations is evaluated using this model. Third, recommendations and available standards &amp; guidelines are provided to the three railway organizations to improve maturity levels within different domains. In addition, they are given an action plan to implement the recommendations in a streamlined way. The application of this model will allow railway organizations to improve their capability to reduce the impacts of cyber-attacks and eradicate vulnerabilities. The approach can also be extended to other infrastructures with necessary adaptations.
Real-time trajectory planning based on joint-decoupled optimization in human-robot interaction

In order to perform safe and natural interactions with humans, robots are required to adjust their motions quickly according to human behaviors. Performing the complex calculation and updating the trajectories in real-time is a particular challenge. In this paper, we present a real-time optimization-based trajectory planning method for serial robots. We encode the trajectory planning problem into a series of optimization problems. To solve the high-dimensional complex non-linear optimization problems in real-time, we provide a joint-decoupling method that transforms the original joint-coupled optimization problem into multiple joint-independent optimization problems, with much lower computational complexity. We implement and validate our method in a specific human-robot interaction case. Experimental results show that the computational feasibility and efficiency of optimization solution were greatly improved by the joint-decoupling transformation. Smooth, safe, and rapid motion of the robot was generated in real-time, establishing a basis for safe and reactive human-robot interactions.
Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors

Background subtraction is one of the key pre-processing steps necessary for obtaining relevant information from a video sequence. The selection of a background subtraction algorithm and its parameters is also important for achieving optimal detection performance, especially in night environments. The research contribution presented in this paper is the identification of the optimal background subtractor algorithm in indoor night-time environments, with a focus on the detection of human falls. 30 background subtraction algorithms are analyzed to determine which has the best performance in indoor night-time environments. Genetic algorithms have been applied to identify the best background subtraction algorithm, to optimize the background subtractor parameters and to calculate the optimal number of pre- and post-processing operations. The results show that the best algorithm for fall-detection in indoor, night-time environments is the LBAdaptativeSOM, optimal parameters and processing operations for this algorithm are reported.
SmartHandwriting: Handwritten Chinese Character Recognition With Smartwatch

Most existing systems use portable devices or image processing techniques for handwritten Chinese character recognition (HCCR), which are unable to detect character when writing on a paper or sensitive to lighting conditions. In this article, we present the design, implementation, and evaluation of a smartwatch-based HCCR system, called SmartHandwriting. To segment each Chinese character, we further analyze the hand movement between the handwriting gesture and the wrist movement gesture and propose a novel algorithm to distinguish the two types of gestures. Due to too many Chinese characters for classification, we utilize the data augmentation method for avoiding overfitting. Then, we build the HCCR model using the deep convolutional neural network (DCNN) method. The recognition accuracy of the Chinese characters is 96.0%, and extensive experiments confirm its effectiveness and robustness. Moreover, we also explore adverse factors that affect the recognition performance, which can be avoided in the future.
Counting crowds using a scale-distribution-aware network and adaptive human-shaped kernel

Intelligent bus system plays a key role in the modern smart city. The number of passengers in the buses or at the stations is necessary for making an optimal scheduling policy of public buses. We develop a crowd counting algorithm to provide the counting information for a bus dispatch system in a human–machine system. In consideration of the challenges (e.g., pedestrian occlusions, non-uniform crowd distributions, and scale variations) existed in hand-crafted features based crowd counting, a scale-distribution-aware multi-column convolutional neural network (SDA-MCNN) is presented to count crowds by summing up the output (denoted as the density map) of the SDA-MCNN. The SDA-MCNN is robust to scale variations by processing a crowd image with multiple convolutional neural network (CNN) columns and minimizing the per-scale loss. A weighted Euclidean loss is proposed to handle non-uniform crowd distributions. The loss can increase activations in dense regions and restrain activations in backgrounds. A new approach to estimate perspective maps of dense crowds is put forward to offer necessary information for generating density maps with human-shaped kernels. Evaluations on benchmarks are performed with other state-of-the-art counting approaches using deep neural networks. Comparative results verify the accuracy of our counting approach in challenging crowds. Evaluations on the real world BUS data reveal the accuracy of the proposed approach in counting passengers in spite of the complex environment.
Identify Topic Relations in Scientific Literature Using Topic Modeling

Over the past five years, topic models have been applied to bibliometrics research as an efficient tool for discovering latent and potentially useful content. The combination of topic modeling algorithms and bibliometrics has generated new challenges of interpreting and understanding the outcome of topic modeling. Motivated by these new challenges, this paper proposes a systematic methodology for topic analysis in scientific literature corpora to face the concerns of conducting post topic modeling analysis. By linking the corpus metadata with the discovered topics, we feature them with a number of topic-based analytic indices to explore their significance, developing trend, and received attention. A topic relation identification approach is then presented to quantitatively model the relations among the topics. To demonstrate the feasibility and effectiveness of our methodology, we present two case studies, using big data and dye-sensitized solar cell publications derived from searches in World of Science. Possible application of the methodology in telling good stories of a target corpus is also explored to facilitate further research management and opportunity discovery.
Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms

Recent development of automated closed-loop (CL) insulin delivery systems, the so-called artificial pancreas (AP), improved the quality of type 1 diabetes (T1D) therapy. As new technologies emerge, patients put increasing trust in their therapeutic devices; therefore, it becomes increasingly important to detect malfunctioning affecting such devices. In this work, we explore a new paradigm to detect insulin pump faults (IPFs) that use unsupervised anomaly detection.We generated CL data corrupted with IPFs using the latest version of the T1D Padova/UVA simulator. From the data, we extracted several features capable to describe the patient dynamics and making more apparent suspicious data portions. Then, a feature selection is performed to determine the optimal feature set. Finally, the performance of several popular unsupervised anomaly detection algorithms is analyzed and compared on the identified optimal feature set.Using the identified optimal configuration, the best performance is obtained by the Histogram-Based Outlier Score (HBOS) algorithm, which detected 87% of the IPF with only 0.08 false positives per day on average. Isolation forest is the best algorithm that offers more conservative performances, detection of 85% of the faults but only 0.06 false positives per day on average.Unsupervised anomaly detection algorithms can be used effectively to detect IPFs and improve the safety of the AP. Future studies will be dedicated to test the presented method inside dedicated clinical trials.
Nonlocal quantum correlations under amplitude damping decoherence

Different nonlocal quantum correlations of entanglement, steering and Bell nonlocality are defined with the help of local hidden state and local hidden variable models. Considering their unique roles in quantum information processing, it is of importance to understand the individual nonlocal quantum correlation as well as their relationship. Here, we investigate the effects of amplitude damping decoherence on different nonlocal quantum correlations. In particular, we have theoretically and experimentally shown that the entanglement sudden death phenomenon is distinct from those of steering and Bell nonlocality. In our scenario, we found that all the initial states present sudden death of steering and Bell nonlocality, while only some of the states show entanglement sudden death. These results suggest that the environmental effect can be different for different nonlocal quantum correlations and, thus, it provides distinct operational interpretations of different quantum correlations.
Virtual Walking Sensation by Prerecorded Oscillating Optic Flow and Synchronous Foot Vibration

This article reports the first psychological evidence that the combination of oscillating optic flow and synchronous foot vibration evokes a walking sensation. In this study, we first captured a walker's first-person-view scenes with footstep timings. Participants observed the naturally oscillating scenes on a head-mounted display with vibrations on their feet and rated walking-related sensations using a Visual Analogue Scale. They perceived stronger sensations of self-motion, walking, leg action, and telepresence from the oscillating visual flow with foot vibrations than with randomized-timing vibrations or without vibrations. The artificial delay of foot vibrations with respect to the scenes diminished the walking-related sensations. These results suggest that the oscillating visual scenes and synchronous foot vibrations are effective for creating virtual walking sensations.
Real-time object detection algorithm based on improved YOLOv3

Object detection is a challenging computer vision problem with numerous practical applications. Due to low accuracy and slow detection speed in object detection, we propose a real-time object detection algorithm based on YOLOv3. First, to solve the problem that features are likely to be lost in the feature extraction process of YOLOv3, a DB-Darknet-53 feature extraction network embedded in inception structure is designed, which effectively reduces the loss of features. Second, the detection network of YOLOv3 and the reuse of deep features in multiscale detection network are improved. Finally, the numbers and sizes of anchor boxes are selected by <italic>K</italic>-means clustering analysis, and the detection model is obtained by means of multiscale training. The improved algorithm has a mean average precision of 0.835 on the PASCAL VOC data set and a detection speed of 35.8 f / s, which is better than YOLOv3.
Feature-based synchronization correction for multilevel watermarking of medical images

Desynchronization attacks cause the global geometric displacements such as Rotation, Scaling, and Translation (RST) of the watermarked image. One of promising way is to embed hiding information in the stable to RST attacks regions defined by feature points. However, a multilevel protection of medical images supposes the embedding of Region Of Interest (ROI), electronic patient record, and fragile watermark. Volume of embedding information is too large to be stored in the restricted regions robust to attacks. First, a preliminary step is required for intensity equalization and detection of the cropped regions. Second, we use a synchronization correction of the global geometric attacks with the following compensation of possible local warping. The applied frequency transform such as digital wavelet transform or digital shearlet transform with single value decomposition is chosen due to a changeable volume of embedded information. Arnold’s transform is additional procedure increasing a security. Our experiments show that the proposed synchronization correction provides accuracy of the extracted watermarks up to 92%.
Learning Structural Node Representations using Graph Kernels

Many applications require identifying nodes that perform similar functions in a graph. For instance, identifying structurally equivalent nodes can provide insight into the structure of complex networks. Learning latent representations that capture such structural role information about nodes has recently gained a lot of attention. Existing techniques for learning such representations typically rely on manually engineered features or are very expensive in terms of time and memory requirements. In this paper, we propose SEGK, a powerful framework for computing structural node representations. SEGK learns node representations by generating (or approximating) and decomposing a kernel matrix that incorporates structural similarity between nodes. To compute the similarity between two nodes, the proposed framework builds on well-established concepts from graph mining. Specifically, it compares the neighborhood subgraphs of increasing size of two nodes using graph kernels. SEGK is very flexible, and besides unlabeled graphs, it can also handle node-labeled and node-attributed graphs. We evaluate the proposed framework on several synthetic and real-world datasets, and compare its performance to state-of-the-art techniques for learning structural node embeddings. In almost all cases, the instances of the proposed framework outperform the competing methods, while their time complexity remains very attractive.
Duplicate Pull Request Detection

In open source communities (e.g., GitHub), developers frequently submit pull requests to fix bugs or add new features during development process. Since the process of pull request is uncoordinated and distributed, it causes massive duplication. Usually, only the first pull request qualified by reviewers can be merged to the main branch of the repository, and the others are regarded as duplication by maintainers. Since the duplication largely aggravates workloads of project reviewers and maintainers, the evolutionary process of open source repositories is delayed. To identify the duplicate pull requests automatically, Ren et al. proposed a state-of-the-art approach that models a pull request by nine features and determine whether a given request is duplicate with the other existing requests or not. Nevertheless, we notice that their approach overlooked the time factor which is a significant feature for the task. In this study, we investigate the influence of time factor and improve the pull request representation. We assume that two pull requests are more likely duplicate when their created time are close to each other. We verify the assumption based on 26 open source repositories from GitHub with over 100,000 pairs of pull requests. We integrate the time feature to the nine features proposed by Ren et al. and the experimental results show that it can substantially improve the performance of Ren et al.'s work by 14.36% and 11.93% in terms of [email protected] and [email protected], respectively.
Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms

This article presents two novel methodologies for solving the motion planning problem through retained experience. Both approaches employ AI’s case-based reasoning (CBR) technique. Case-based reasoning is an expert system development methodology which reuses past solutions to solve new problems. The first approach uses CBR to retain K similar cases to solve the motion planning problem by merging those solutions into a set. Afterwards, it picks from this set based on a heuristic function to assemble a final solution. Regarding the second approach, it employs the retained K similar cases differently. It uses those solution to build a graph which can be queried using traditional graph search algorithms. Results prove the success of such approaches concerning solution quality and success rate compared to different experience-based algorithms. Such utilization for CBR systems develops new research directions for building systems that can solve NP problems based on retained experiences exclusively.
Towards Adaptive Information Visualization - A Study of Information Visualization Aids and the Role of User Cognitive Style

Information Visualization systems have traditionally followed a one-size-fits-all model, whereby the same visualization is shown to each user, without taking into consideration an individual user’s preferences, abilities, or context. By contrast, given the considerable cognitive effort involved in using Information Visualizations, this paper investigates the effect of an individual user’s cognitive style on Information Visualization performance. In addition, this paper studies several interactive “visualization aids” (i.e. interactive overlays that can aid in visualization comprehension), as well as the effect of cognitive style on aid choices and preferences. The results from a user study show that cognitive style plays a significant role when performing tasks with Information Visualizations in general, and that there are clear differences in terms of individual aid choices and preferences. These findings also provide motivation for the development of adaptive and personalized Information Visualization systems that could better assist users according to their individual cognitive style.
Ontology-Based Model Abstraction

In recent years, there has been a growth in the use of reference conceptual models to capture information about complex and critical domains. However, as the complexity of domain increases, so does the size and complexity of the models that represent them. Over the years, different techniques for complexity management in large conceptual models have been developed. In particular, several authors have proposed different techniques for model abstraction. In this paper, we leverage on the ontologically well-founded semantics of the modeling language OntoUML to propose a novel approach for model abstraction in conceptual models. We provide a precise definition for a set of Graph-Rewriting rules that can automatically produce much-reduced versions of OntoUML models that concentrate the models' information content around the ontologically essential types in that domain, i.e., the so-called Kinds. The approach has been implemented using a model-based editor and tested over a repository of OntoUML models.
A Visual Similarity Recommendation System using Generative Adversarial Networks

The goal of content-based recommendation system is to retrieve and rank the list of items that are closest to the query item. Today, almost every e-commerce platform has a recommendation system strategy for products that customers can decide to buy. In this paper we describe our work on creating a Generative Adversarial Network based image retrieval system for e-commerce platforms to retrieve best similar images for a given product image specifically for shoes. We compare state-of-the-art solutions and provide results for the proposed deep learning network on a standard data set.
Toward Fairness in Face Matching Algorithms

Automated face matching algorithms are used in a wide variety of societal applications ranging from access authentication, to criminal identification, to application customization. Hence, it is important for such algorithms to be equitable in their performance for different demographic groups. If the algorithms work well only for certain racial or gender identities, they would adversely affect others. Recent efforts in algorithmic fairness literature (typically not focused on multimedia or computer vision tasks such as face matching) have argued for designing algorithms and architectures to tackle such bias via trade-offs between accuracy and fairness. Here, we show that adopting an adversarial deep learning-based approach allows for the model to maintain the accuracy at face matching while also reducing demographic disparities compared to a baseline (non-adversarial deep learning) approach at face matching. The results motivate and pave way for more accurate and fair face matching algorithms.
Turning Digitised Material into a Diachronic Corpus

In this paper, we argue that exploitation of historical corpus data requires text metadata which metadata accompanying digital objects from digital libraries, archives or other electronic text collections, do not provide. Most text collections describe in their metadata the object (book, newspaper) containing the text. To do research on the style of an author, or study the language of a certain time period, or a phenomenon through time, correct metadata is needed for each word in the text, which leads to a very intricate metadata scheme for some text collections. We focus on the Nederlab corpus. Nederlab is a research environment that gives access to a large diachronic corpus of Dutch texts from the 6th - 21st century, of more than 10 billion words. The corpus has been compiled using existing digitised text material from researchers, research organisations, archives and libraries. The aim of Nederlab is to provide tools and data to enable researchers to trace long-term changes in Dutch language, culture and society. This type of research sets high-level requirements on the metadata accompanying the texts. Since the Nederlab corpus consists of different collections, each with their own metadata, the task of adding the appropriate metadata was not straightforward, all the more so because of the difference in perspective content providers and corpus builders have. We will describe the desired metadata scheme and how we tried to realize this for a corpus of the size of Nederlab.