Reviewerly/exHarmony huggingface dataset:
Registration method for maintenance-work support based on augmented-reality-model generation from drawing data

Abstract The importance of operations and maintenance (O&amp;M) and piping inspection in shipbuilding and offshore plant industries has significantly increased recently. Therefore, this study proposes a system that uses augmented reality (AR) to support these operations. AR in O&amp;M and inspection systems can increase work comprehension and efficiency by utilizing 3D graphics, instead of drawings, to describe specific work functions. To realize this improvement, the augmented model should correspond to the reality; if accurate registration is not achieved, it can disrupt the work functions. Therefore, marker-based AR is used to generate specific recognition objects and correct the location of the augmented model. However, owing to certain characteristics of the shipbuilding and offshore plant industries, the markers are likely to be damaged, and thus the cameras fail to clearly detect them. In this study, a 3D camera was used to generate a point cloud based on 3D image information. Accordingly, the area around the model to be detected was designated as the region of interest (ROI). Furthermore, it is difficult to support a high-end device environment because it is tested as a portable device that can be used in a working environment; therefore, superfluous data were removed by detecting the 3D edges in the ROI, thereby minimizing data operation. The scalability of this work was enhanced using the computer-aided design (CAD) file information extracted from the CAD tool (used in the shipbuilding industry) and converting it into a point cloud. The proposed system is expected to eliminate the issues related to understanding the O&amp;M work by addressing registration errors that might occur in constrained environments and to improve the torsional phenomenon of the augmented models.
How to transform graph states using single-qubit operations: computational complexity and algorithms

Abstract Graph states are ubiquitous in quantum information with diverse applications ranging from quantum network protocols to measurement based quantum computing. Here we consider the question whether one graph ( source ) state can be transformed into another graph ( target ) state, using a specific set of quantum operations (LC + LPM + CC): single-qubit Clifford operations (LC), single-qubit Pauli measurements (LPM) and classical communication (CC) between sites holding the individual qubits. This question is of interest for effective routing or state preparation decisions in a quantum network or distributed quantum processor and also in the design of quantum repeater schemes and quantum error-correction codes. We first show that deciding whether a graph state | G ⟩ can be transformed into another graph state | G ′⟩ using LC + LPM + CC is <?CDATA $\mathbb{N}\mathbb{P}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="double-struck">N</mml:mi> <mml:mi mathvariant="double-struck">P</mml:mi> </mml:math> -complete, which was previously not known. We also show that the problem remains NP-complete even if | G ′⟩ is restricted to be the GHZ-state. However, we also provide efficient algorithms for two situations of practical interest. Our results make use of the insight that deciding whether a graph state | G ⟩ can be transformed to another graph state | G ′⟩ is equivalent to a known decision problem in graph theory, namely the problem of deciding whether a graph G ′ is a vertex-minor of a graph G . The computational complexity of the vertex-minor problem was prior to this paper an open question in graph theory. We prove that the vertex-minor problem is <?CDATA $\mathbb{N}\mathbb{P}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi mathvariant="double-struck">N</mml:mi> <mml:mi mathvariant="double-struck">P</mml:mi> </mml:math> -complete by relating it to a new decision problem on 4-regular graphs which we call the semi-ordered Eulerian tour problem.
An application of frequency domain approach to the causal nexus between information, communication and technology infrastructure and financial development in selected countries in Africa

Abstract Earlier studies on the causal relationship between information, communication and technology (ICT) and financial development have relied on a time domain approach, which does not distinguish among the various forms of causality at the different time periods. In this study, we re‐examine the causality nexus between ICT infrastructure and domestic level of financial development in 19 selected countries in Africa using a frequency domain spectral causality test technique that corrects the weaknesses eminent in the existing studies. Our findings suggest that, for most frequency levels, ICT infrastructure and domestic level of financial development in several countries are completely independent confirming the neutrality hypothesis. This holds irrespective of the measure of ICT and finance. We also find evidence to support the demand‐following hypothesis, supply leading hypothesis and feedback hypothesis at different time periods for the different countries under study. Given the findings, we argue that the causal relationship between ICT and financial development is not straightforward as suggested by the existing studies. In addition to indicator of ICT and finance, the extent to which countries' ICT infrastructure relates with their domestic financial sector development is contingent on whether the country is operating in the short, intermediate or long run.
Visual Analysis of Class Separations With Locally Linear Segments

High-dimensional labeled data widely exists in many real-world applications such as classification and clustering. One main task in analyzing such datasets is to explore class separations and class boundaries derived from machine learning models. Dimension reduction techniques are commonly applied to support analysts in exploring the underlying decision boundary structures by depicting a low-dimensional representation of the data distributions from multiple classes. However, such projection-based analyses are limited due to their lack of ability to show separations in complex non-linear decision boundary structures and can suffer from heavy distortion and low interpretability. To overcome these issues of separability and interpretability, we propose a visual analysis approach that utilizes the power of explainability from linear projections to support analysts when exploring non-linear separation structures. Our approach is to extract a set of locally linear segments that approximate the original non-linear separations. Unlike traditional projection-based analysis where the data instances are mapped to a single scatterplot, our approach supports the exploration of complex class separations through multiple local projection results. We conduct case studies on two labeled datasets to demonstrate the effectiveness of our approach.
Triple-based graph neural network for encoding event units in graph reasoning problems

Nowadays graph neural networks have achieved excellent performance on many graph-based tasks such as abstract meaning representation (AMR) text generation and graph reasoning. Graph-based models often calculate the information flow by nodes and their associated edges. But node-based or edge-based calculation can not reflect the strong relation between every two nodes as triples, which actually act as event units in graph reasoning tasks. Considering triples (a triple means an edge and its two nodes in our model) as basic calculation units is more suitable for relation-based graphs and event-reasoning tasks. We thus propose a novel structure called triple-based graph neural network and directly perform triple-based neural calculation on interrelated triples. Experimental results on babi 16, babi 19, wikisql and our own proposed genealogy dataset show that our model significantly outperforms other strong alternatives.
Assessing introspective linguistic judgments quantitatively: the case of The Syntax of Chinese

The informal judgments of the well-formedness of phrases and sentences have long been used as the primary data source for syntacticians. In recent years, the reliability of data based on linguists’ introspective intuitions is increasingly subject to scrutiny. Although a number of studies were able to replicate a vast majority of English judgments published in a textbook and in peer-reviewed journal articles, the status of data in many non-English languages has yet to be experimentally examined. In this work, we employed formal quantitative methods to evaluate the reliability of judgments in the widely used textbook, The Syntax of Chinese (Huang et al. 2009). We first assessed example sentences based on the acceptability ratings from 148 native Mandarin Chinese speakers. Using a target forced-choice task, we further explored the potentially problematic sentence pairs. Results of the two experiments suggest an eminently successful replication of judgments in the book: out of the 557 data samples tested, only five sentence pairs require further investigation. This large-scale study represents the first attempt to replicate the judgments in a non-English syntax textbook, in hopes to bridge the gap between the informal data-collection in Chinese linguistic research and the protocols of experimental cognitive science.
A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories

We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories, and validate its effectiveness in a principled way. To model and generate scenarios of trajectories with different lengths, we develop two approaches. First, we adapt the Recurrent Conditional Generative Adversarial Networks (RC-GAN) by conditioning on the length of the trajectories. This provides us the flexibility to generate variable-length driving trajectories, a desirable feature for scenario test case generation in the verification of autonomous driving. Second, we develop an architecture based on Recurrent Autoencoder with GANs to obviate the variable length issue, wherein we train a GAN to learn/generate the latent representations of original trajectories. In this approach, we train an integrated feed-forward neural network to estimate the length of the trajectories to be able to bring them back from the latent space representation. In addition to trajectory generation, we employ the trained autoencoder as a feature extractor, for the purpose of clustering and anomaly detection, to obtain further insights into the collected scenario dataset. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection.
Development of Africa Wild Dog Optimization Algorithm for Optimize Freight Coordination for Decreasing Greenhouse Gases

The phenomenon of increasing greenhouse gases is the main problem causing climate change, in which emissions from transport vehicles contribute a significant part. Therefore, a research onto an optimization of transport activities and minimizing their impact has an important significance for scientific and practical. This study developed the African Wild Dog Optimization algorithm (AWDO) to optimize freight coordination with automatic updating of route data. Comparing results with previous studies highlights the effectiveness of the proposed method with high accuracy. The novel optimization algorithm promises to bring the most optimal solution to the government in coordinating traffic as well as supporting to manage cargo transport in the most scientific and fuel-efficient way.
Multi-Colony Collaborative Ant Optimization Algorithm Based on Cooperative Game Mechanism

Aiming at the problem that ant colony algorithm is easy to fall into local optima and slow convergence, a multi-colony collaborative ant optimization algorithm based on cooperative game mechanism(CCACO) is proposed. First of all, this article presents pheromone matrix adaptive matching strategy and improved generative adversarial nets (GAN) model according to cooperative game mechanism. The pheromone matrix adaptive matching strategy allocates pheromone matrix for each colony to maximize the overall benefit of the colonies by establishing a cooperative game model. The improved GAN model is built according to the game between the global optimal solution and the new solution of each colony, which improves the convergence speed of the algorithm and the quality of solutions. In addition, collaborative optimization mechanism is proposed to improve the quality of the solution. In this mechanism, cloning strategy binds the cities on each common path together, which increases the pheromone concentration of the common path to accelerate the algorithm convergence; central diffusion strategy diffuses pheromones from central cities to nearby cities, which increases the diversity of solutions; forward and backward propagators adjust the amount of pheromone release to regulate the convergence rate of the algorithm. Finally, information entropy is used to measure the diversity of CCACO. When the value of information entropy is less than threshold value, cooperative game mechanism and collaborative optimization mechanism to regulate the relationship between the convergence speed and the quality of the solution. Experimental results in the TSPLIB standard library demonstrate that the proposed algorithm outperforms other state-of-the-art multi-colony ant colony optimization algorithms. The algorithm is applied to robot path planning, which reflects the practicality of the algorithm.
Wearable-sensors Based Activity Recognition for Smart Human Healthcare Using Internet of Things

With the growing of chronic diseases, strain on public healthcare systems becomes a critical problem of our society. Internet of Things (IoT) technology [1]–[3], as a hot topic, has attracted more and more attention recently due to its potential ability to reduce the strain on public healthcare systems. In IoT based healthcare system, the information of patients can be automatically obtained via various sensors and the physical condition of patients can be analyzed via those obtained data. In this work, we present a IoT and blockchain based healthcare system for human activity recognition via monitoring vital/non-vital signals collected from wearable-sensors remotely. On the one hand, the IoT technology is used for data acquisition and transmission [4]. On the other hand, the blockchain technology is used for data encryption. An incremental learning strategy based on covariance matrix is designed to recognize the activity done by a patient under care and determine whether there is an emergency for the patient under care. In such a manner, necessary feedback or programmable alarms can be made during or after the activity.
Side-Channel Gray-Box Attack for DNNs

Deep neural networks are becoming increasingly popular. However, they are also vulnerable to adversarial attacks. The existing attack methods include white-box attack and black-box attack. The white-box attack assumes full model knowledge while the black-box one assumes none. In this brief, we propose a novel attack method between these two. Specifically, we have made the following contributions: (1) we propose the gray-box attack, which utilizes the side-channel attack to predict the model structure based on a pre-trained classifier and (2) we validate our method on real-world experiments. The experimental results show that our gray-box attack can significantly outperform the existing techniques.
Threshold-Based New Segmentation Model to Separate the Liver from CT Scan Images

The liver is considered as one of the complicated organs in human body. It has close proximity to the neighboring organs in abdomen with numerous anatomical variations. It is difficult to find out the severity of disease connected to the liver unless the scanned image is subjected to segmentation process. The difficulty level also varies with the diseases that affect the liver. Any of the disease alters its density, homogeneity, color and texture. Liver image segmentation is necessary to identify the complexity and severity of the disease and it remains as an open challenge to researchers. Among all liver segmentation algorithms, threshold segmentation is fastest, simplest and numerically less complex. The accuracy of threshold-based segmentation lies in the selection of threshold values which separates foreground and background. This paper proposes a novel multi-threshold liver segmentation model based on “Slope Difference Distribution” (SDD) of image histogram. It consists of three stages. In the first stage, the noise in Computed Tomography (CT) scan image is reduced using a median filter. In the second stage, automatic threshold values are obtained from SDD of image histogram. These threshold values separate the liver image accurately from abdominal CT scan image. In the third stage, seed points are selected automatically which grow outwardly using the Fast Marching Method (FMM) discovering liver border in CT scan image. The proposed model is tested on 55 CT scan images and it is providing satisfactory results.
ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks

Steganography is the art of embedding a confidential message within a host message. Modern steganography is focused on widely used multimedia file formats, such as images, video files, and Internet protocols. Recently, cyber attackers have begun to include steganography (for communication purposes) in their arsenal of tools for evading detection. Steganalysis is the counter-steganography domain which aims at detecting the existence of steganography within a host file. The presence of steganography in files raises suspicion regarding the file itself, as well as its origin and receiver, and might be an indication of a sophisticated attack. The JPEG file format is one of the most popular image file formats and thus is an attractive and commonly used carrier for steganography embedding. State-of-the-art JPEG steganalysis methods, which are mainly based on neural networks, are limited in their ability to detect sophisticated steganography use cases. In this paper, we propose ASSAF, a novel deep neural network architecture composed of a convolutional denoising autoencoder and a Siamese neural network, specially designed to detect steganography in JPEG images. We focus on detecting the J-UNIWARD method, which is one of the most sophisticated adaptive steganography methods used today. We evaluated our novel architecture using the BOSSBase dataset, which contains 10,000 JPEG images, in eight different use cases which combine different JPEG's quality factors and embedding rates (bpnzAC). Our results show that ASSAF can detect stenography with high accuracy rates, outperforming, in all eight use cases, the state-of-the-art steganalysis methods by 6% to 40%.
Investigating the dynamics of illegal online activity: The power of reporting, dark web, and related legislation

This paper aims to shed light into the operation of SafeLine, the only Greek Hotline for illegal online content, and its seventeen years of successful operation as a member of INHOPE, the International Association of Internet Hotlines. The operation of SafeLine is introduced and an analysis of the received reports during its operation is attempted, in order to reveal hidden trends over the seventeen years. Furthermore, a comparison between the reports of SafeLine and the reports of the other 48 national hotlines, members of the International Association INHOPE, operating in 43 different countries spanning six continents worldwide is presented. Another main contribution of this paper is a correlation analysis between SafeLine's reports and dark web data. Specifically, SafeLine's reports are compared against the ALTAS dataset of the Voyager system of Web-IQ, drilled from the dark web, revealing a correlation of more than 50% between the reports received by SafeLine and the domains of illegal sites discussed in the dark web. Last, but not least, an analysis of the legislative framework concerning Child Sexual Abuse Material (CSAM) in all country members of INHOPE, European and outside Europe is attempted, revealing similarities as well as differences in what is considered illegal, the ways CSAM is tackled, and the penalty limits prescribed in various countries.
Generalizations of Runs and Patterns Distributions for Sequences of Binary Trials

In the present paper we study the distributions of families of patterns which generalize runs and patterns distributions extensively examined in the literature during the last decades. In our analysis we assume that the sequence of outcomes under investigation includes independent, but not necessarily identically distributed trials. An illustration is also provided how our new results could be exploited to enrich a new system, still in research, related to patients' weaning from mechanical ventilation.
Visual Question Generation from Radiology Images

Visual Question Generation (VQG), the task of generating a question based on image contents, is an increasingly important area that combines natural language processing and computer vision.Although there are some recent works that have attempted to generate questions from images in the open domain, the task of VQG in the medical domain has not been explored so far.In this paper, we introduce an approach to generation of visual questions about radiology images called VQGR, i.e. an algorithm that is able to ask a question when shown an image.VQGR first generates new training data from the existing examples, based on contextual word embeddings and image augmentation techniques.It then uses the variational auto-encoders model to encode images into a latent space and decode natural language questions.Experimental automatic evaluations performed on the VQA-RAD dataset of clinical visual questions show that VQGR achieves good performances compared with the baseline system.The source code is available at https://github.com/sarrouti/vqgr.
A Programmable Approach to Neural Network Compression

Deep neural networks (DNNs) frequently contain far more weights, represented at a higher precision, than are required for the specific task, which they are trained to perform. Consequently, they can often be compressed using techniques such as weight pruning and quantization that reduce both the model size and inference time without appreciable loss in accuracy. However, finding the best compression strategy and corresponding target sparsity for a given DNN, hardware platform, and optimization objective currently requires expensive, frequently manual, trial-and-error experimentation. In this article, we introduce a programmable system for model compression called CONDENSA. Users programmatically compose simple operators, in Python, to build more complex and practically interesting compression strategies. Given a strategy and user-provided objective (such as minimization of running time), CONDENSA uses a novel Bayesian optimization-based algorithm to automatically infer desirable sparsities. Our experiments on four real-world DNNs demonstrate memory footprint and hardware runtime throughput improvements of 188× and 2.59×, respectively, using at most ten samples per search.