Reviewerly/exHarmony huggingface dataset:
Tendrils of Crime: Visualizing the Diffusion of Stolen Bitcoins

The first six months of 2018 have seen cryptocurrency thefts of $761 million, and the technology is also the latest and greatest tool for money laundering. This increase in crime has caused both researchers and law enforcement to look for ways to trace criminal proceeds. Although tracing algorithms have improved recently, they still yield an enormous amount of data of which very few datapoints are relevant or interesting to investigators, let alone ordinary bitcoin owners interested in provenance. In this work we describe efforts to visualize relevant data on a blockchain. To accomplish this we come up with a graphical model to represent the stolen coins and then implement this using a variety of visualization techniques.
Cold-start solutions for recommendation systems

Recommendation systems are essential tools to overcome the choice overload problem by suggesting items of interest to users. However, they suffer from a major challenge which is the so-called cold-start problem. The cold-start problem typically happens when the system does not have any form of data on new users and on new items. In this chapter, we describe the cold-start problem in recommendation systems. We mainly focus on collaborative filtering systems which are the most popular approaches to build recommender systems and have been successfully employed in many real-world applications. Moreover, we discuss multiple scenarios that cold start may happen in these systems and explain different solutions for them.
An effective crossing minimisation heuristic based on star insertion

We present a new heuristic method for minimising crossings in a graph. The method is based upon repeatedly solving the so-called {\em star insertion problem} in the setting where the combinatorial embedding is fixed, and has several desirable characteristics for practical use. We introduce the method, discuss some aspects of algorithm design for our implementation, and provide some experimental results. The results indicate that our method compares well to existing methods, and also that it is suitable for dense instances.
Vibration monitoring of an open-type one-way tensioned membrane structure based on stereovision

Membrane structures are lightweight and flexible, and the responses of them are of great importance to evaluate the dynamic characteristics. However, when observing the dynamic responses by using conventional measurements (e.g., laser displacement meter), there are several disadvantages, such as high-cost, low sampling efficiency, and only one-dimensional data being obtained, especially for the membrane structures in the field of civil engineering. Therefore, a stereovision measurement is organized by proposing a series of algorithms which are based on a newly built mathematical model. The calibration toolbox based on OpenCV is adopted for the camera calibration. After that, edge detection, determination of central pixel coordinates, continuous tracking, and matching of circular feature points between different images are solved in the process of image processing. Finally, the new-formed stereovision measurement system is adopted to observe the responses of an open-type one-way tensioned membrane structure in free-vibration and aeroelastic wind tunnel test. After the feasibility and validity of this measurement system are verified, the full field displacement distributions of this tensioned structure under different wind speeds are presented. This successful application implies that such stereovision measurement can make up the disadvantages of the conventional measurements. Such stereovision measurement can be used to measure the responses of the structures in the field of civil engineering.
A Novel Image-Restoration Method Based on High-Order Total Variation Regularization Term

This paper presents two new models for solving image the deblurring problem in the presence of impulse noise. One involves a high-order total variation (TV) regularizer term in the corrected total variation L1 (CTVL1) model and is named high-order corrected TVL1 (HOCTVL1). This new model can not only suppress the defects of the staircase effect, but also improve the quality of image restoration. In most cases, the regularization parameter in the model is a fixed value, which may influence processing results. Aiming at this problem, the spatially adapted regularization parameter selection scheme is involved in HOCTVL1 model, and spatially adapted HOCTVL1 (SAHOCTVL1) model is proposed. When dealing with corrupted images, the regularization parameter in SAHOCTVL1 model can be updated automatically. Many numerical experiments are conducted in this paper and the results show that the two models can significantly improve the effects both in visual quality and signal-to-noise ratio (SNR) at the expense of a small increase in computational time. Compared to HOCTVL1 model, SAHOCTVL1 model can restore more texture details, though it may take more time.
Impact of ASR Performance on Spoken Grammatical Error Detection

Computer assisted language learning (CALL) systems aid learners to monitor their progress by providing scoring and feedback on language assessment tasks. Free speaking tests allow assessment of what a learner has said, as well as how they said it. For these tasks, Automatic Speech Recognition (ASR) is required to generate transcriptions of a candidate's responses, the quality of these transcriptions is crucial to provide reliable feedback in downstream processes. This paper considers the impact of ASR performance on Grammatical Error Detection (GED) for free speaking tasks, as an example of providing feedback on a learner's use of English. The performance of an advanced deep-learning based GED system, initially trained on written corpora, is used to evaluate the influence of ASR errors. One consequence of these errors is that grammatical errors can result from incorrect transcriptions as well as learner errors, this may yield confusing feedback. To mitigate the effect of these errors, and reduce erroneous feedback, ASR confidence scores are incorporated into the GED system. By additionally adapting the written text GED system to the speech domain, using ASR transcriptions, significant gains in performance can be achieved. Analysis of the GED performance for different grammatical error types and across grade is also presented.
Virtual Training on Pumping Stations for Drinking Water Supply Systems

This article offers the development of a realistic and intuitive virtual environment for training on pumping stations for drinking water supply systems. The environment has structures and equipment found in pumping stations that simplify the user interaction with the industrial process, instrumentation, monitoring and control of the station. The environment has been created using photogrammetry techniques, CAD tools and the UNITY 3D graphic engine that combined with MATLAB exchanges information in real time to execute the simulation of the process, achieving a high degree of realistic immersion. Experimental tests allow the operator to interact with the virtual environment and achieve maximum experience in the integrated station. Through experimental tests, the immersion of the training environment system is verified.
A Novel Dataset For Intelligent Indoor Object Detection Systems

Indoor Scene understanding and indoor objects detection is a complex high-level task for automated systems applied to natural environments. Indeed, such a task requires huge annotated indoor images to train and test intelligent computer vision applications. One of the challenging questions is to adopt and to enhance technologies to assist indoor navigation for visually impaired people (VIP) and thus improve their daily life quality. This paper presents a new labeled indoor object dataset elaborated with a goal of indoor object detection (useful for indoor localization and navigation tasks). This dataset consists of 8000 indoor images containing 16 different indoor landmark objects and classes. The originality of the annotations comes from two new facts taken into account: (1) the spatial relationships between objects present in the scene and (2) actions possible to apply to those objects (relationships between VIP and an object).This collected dataset presents many specifications and strengths as it presents various data under various lighting conditions and complex image background to ensure more robustness when training and testing objects detectors. The proposed dataset, ready for use, provides 16 vital indoor object classes in order to contribute for indoor assistance navigation for VIP.
Multi-Factor Authentication Modeling

The work defines a multi-factor authentication model in case the application supports multiple authentication factors. The aim of this modeling is to find acceptable authentication methods sufficient to access specifically qualified information. The core of the proposed model is risk-based authentication. Results of simulations of some key scenarios often used in practice are also presented.
Improvement of Mask-RCNN Object Segmentation Algorithm

Semantic maps play a key role in tasks such as navigation of mobile robots. However, the visual SLAM algorithm based on multi-objective geometry does not make full use of the rich semantic information in space. The map point information retained in the map is just a spatial geometric point without semantics. Since the algorithm based on convolutional neural network has achieved breakthroughs in the field of target detection, the target segmentation algorithm MASK-RCNN is combined with the SLAM algorithm to construct the semantic map. However, the MASK-RCNN algorithm easily treats part of the background in the image as foreground, which results in inaccuracy of target segmentation. Moreover, Grubcut segmentation algorithm is time-consuming, but it’s easy to take foreground as background, which leads to the excessive edge segmentation. Based on these, our paper proposes a novel algorithm which combines MASK-RCNN and Grubcut segmentation. By comparing the experimental results of MASK-Rcnn, Grubcut and the improved algorithm on the data set, it is obvious that the improved algorithm has the best segmentation effect and the accuracy of image target segmentation is significantly improved. These phenomenons demonstrate the effectiveness our proposed algorithm.
Real Object Detection Using TensorFlow

Distinguishing and perceiving objects in unstructured and in addition organized situations is a standout amongst the most difficult undertakings in computer vision and man-made reasoning exploration. This paper presents another computer-based vision hindrance recognition technique for versatile innovation and its applications. Every individual picture pixel is delegated having a place either with an impediment dependent on its appearance. The technique utilizes a solitary focal point webcam camera that performs progressively, and furthermore gives a twofold hindrance picture at high goals. In the versatile mode, the framework continues taking in the presence of the snag amid activity. The framework has been tried effectively in an assortment of situations, inside and outside, making it reasonable for a wide range of obstacles. It likewise reveals to us the kind of impediment which has been distinguished by the framework.
When can unlabeled data improve the learning rate

In semi-supervised classification, one is given access both to labeled and unlabeled data. As unlabeled data is typically cheaper to acquire than labeled data, this setup becomes advantageous as soon as one can exploit the unlabeled data in order to produce a better classifier than with labeled data alone. However, the conditions under which such an improvement is possible are not fully understood yet. Our analysis focuses on improvements in the minimax learning rate in terms of the number of labeled examples (with the number of unlabeled examples being allowed to depend on the number of labeled ones). We argue that for such improvements to be realistic and indisputable, certain specific conditions should be satisfied and previous analyses have failed to meet those conditions. We then demonstrate examples where these conditions can be met, in particular showing rate changes from $1/\sqrt{\ell}$ to $e^{-c\ell}$ and from $1/\sqrt{\ell}$ to $1/\ell$. These results improve our understanding of what is and isn't possible in semi-supervised learning.
A novel image super-resolution reconstruction algorithm based on improved GANs and gradient penalty

Purpose Conventional image super-resolution reconstruction by the conventional deep learning architectures suffers from the problems of hard training and gradient disappearing. In order to solve such problems, the purpose of this paper is to propose a novel image super-resolution algorithm based on improved generative adversarial networks (GANs) with Wasserstein distance and gradient penalty. Design/methodology/approach The proposed algorithm first introduces the conventional GANs architecture, the Wasserstein distance and the gradient penalty for the task of image super-resolution reconstruction (SRWGANs-GP). In addition, a novel perceptual loss function is designed for the SRWGANs-GP to meet the task of image super-resolution reconstruction. The content loss is extracted from the deep model’s feature maps, and such features are introduced to calculate mean square error (MSE) for the loss calculation of generators. Findings To validate the effectiveness and feasibility of the proposed algorithm, a lot of compared experiments are applied on three common data sets, i.e. Set5, Set14 and BSD100. Experimental results have shown that the proposed SRWGANs-GP architecture has a stable error gradient and iteratively convergence. Compared with the baseline deep models, the proposed GANs models have a significant improvement on performance and efficiency for image super-resolution reconstruction. The MSE calculated by the deep model’s feature maps gives more advantages for constructing contour and texture. Originality/value Compared with the state-of-the-art algorithms, the proposed algorithm obtains a better performance on image super-resolution and better reconstruction results on contour and texture.
New self-dual and formally self-dual codes from group ring constructions

In this work, we study construction methods for self-dual and formally self-dual codes from group rings, arising from the cyclic group, the dihedral group, the dicyclic group and the semi-dihedral group. Using these constructions over the rings $ &nbsp;\mathbb{F}_2+u \mathbb{F}_2 $ and $ &nbsp;\mathbb{F}_4+u \mathbb{F}_4 $, we obtain 9 new extremal binary self-dual codes of length 68 and 25 even formally self-dual codes with parameters $ [72,36,14] $.
Aerial image mosaicking based on the 6-DoF imaging model

Image mosaicking technology is widely used in remote sensing and imaging of unmanned aerial vehicles (UAVs) for its superiority of generating large seamless images with high resolutions. Existing methods generally adopt the 8-DoF (degrees of freedom) homography camera model for image mosaicking, which require large computation cost in parameter optimization and cause distortions in the mosaicked images because of the accumulation errors. In view of this, this paper introduces an efficient mosaicking method based on 6-DoF imaging model to improve the quality of aerial image mosaicking. This derived model reduces the number of unknown variables for parameter optimization, hence improves the time efficiency. Meanwhile, a constraint term is merged into the objective function of optimization by analysing the characteristic of UAV imaging to eliminate the accumulation error in the mosaicking process. The experimental results on multiple datasets demonstrate that the proposed method achieves more accurate mosaicking results efficiently and is robust to different reference images relative to the state-of-the-art method.
Smart Exploration: Innovative ways of exploring for the raw materials in the EU

Summary Europe has a favourable geology for a wide variety of commodities necessary for improving our modern life sustainably and environmentally friendly. The Smart Exploration initiative answered one of the seven societal challenges offered by the European Innovation Partnership on Raw Materials on new solutions for sustainable production of raw materials – new sensitive exploration technologies. The project involves 27 partners from nine European countries comprising of 11 research institutions, 11 small and medium enterprises and 5 stakeholders. It primarily focuses on developing cost-effective, environmentally friendly tools and methods for geophysical exploration in highly challenging brownfield areas to meet the ever-increasing community (social acceptance) and environmental issues, as well as reduce the return time (from exploration to production). The aim is to not only generate new technological and methodological markets, but also to create results that will also allow for improved exploration in the EU countries and beyond. Planned prototypes and their potential to impact the market as well as methodological developments will be introduced. Furthermore, the value of legacy data through the use of both traditional and innovative approaches, reviving these datasets and illustrating their potential for deep (+500 m) targeting of mineralized bodies will be discussed.
Learning Word Embeddings without Context Vectors

Most word embedding algorithms such as word2vec or fastText construct two sort of vectors: for words and for contexts. Naive use of vectors of only one sort leads to poor results. We suggest using indefinite inner product in skip-gram negative sampling algorithm. This allows us to use only one sort of vectors without loss of quality. Our “context-free” cf algorithm performs on par with SGNS on word similarity datasets
Improving Textual Network Learning with Variational Homophilic Embeddings

The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on textual networks. Different from most existing methods that optimize a discriminative objective, we introduce Variational Homophilic Embedding (VHE), a fully generative model that learns network embeddings by modeling the semantic (textual) information with a variational autoencoder, while accounting for the structural (topology) information through a novel homophilic prior design. Homophilic vertex embeddings encourage similar embedding vectors for related (connected) vertices. The VHE encourages better generalization for downstream tasks, robustness to incomplete observations, and the ability to generalize to unseen vertices. Extensive experiments on real-world networks, for multiple tasks, demonstrate that the proposed method achieves consistently superior performance relative to competing state-of-the-art approaches.
Adaptive Inference Using Hierarchical Convolutional Bag-of-Features for Low-Power Embedded Platforms

Using early exits provide a straightforward way to implement models that can adapt on-the-fly to the available computational resources. However, early exits in many cases suffer from significant limitations, which often prohibit their practical application, especially when placed on convolutional layers with narrow receptive fields. In this work, we propose a method capable of overcoming these limitations by a) using a Bag-of-Features (BoF)-based pooling approach, that allows for keeping more information regarding the distribution of the extracted feature vectors, while also maintaining more spatial information and b) employing a simple, yet effective, hierarchical approach for designing the exits, allowing for efficiently re-using the information that was already extracted by the previous layers. It is experimentally demonstrated that the proposed approach leads to significant performance improvements, allowing early exits to be a more practical tool that can be used in many real-world embedded applications.
Take a Ramble into Solution Spaces for Classification Problems in Neural Networks

Solving a classification problem for a neural network means looking for a particular configuration of the internal parameters. This is commonly achieved by minimizing non-convex object functions. Hence, the same classification problem is likely to have several, different, equally valid solutions, depending on a number of factors like the initialization and the adopted optimizer. In this work, we propose an algorithm which looks for a zero-error path joining two solutions to the same classification problem. We witness that finding such a path is typically not a trivial problem; however, our heuristics is able to succeed in such a task. This is a step forward to explain why simple training heuristics (like SGD) are able to train complex neural networks: we speculate they focus on particular solutions, which belong to a connected solution sub-space. We work in two different scenarios: a synthetic, unbiased and totally-uncorrelated (hard) training problem, and MNIST. We empirically show that the algorithmically-accessible solutions space is connected, and we have hints suggesting it is a convex sub-space.
Experimental study on hybrid compensation testing of an off-axis convex ellipsoid surface

Aspherical surfaces can provide significant benefits to a wide variety of optical systems, but manufacturing high-precision aspherical surfaces has historically been limited by the ability to measure them. Null testing has always been the ideal method in aspherical measurement. However, in many cases, it is hard to realize null testing for complex surfaces, especially for convex surfaces in complicated forms. In this paper, we propose a hybrid compensation method combining a spherical mirror and a computer generated hologram (CGH) to achieve the null testing of the convex aspherical surface. Firstly, we introduce our self-developed mathematical models in the hybrid compensation method, including optics alignment model, distortion correction model and spherical surface error removing model. Then the performance of our proposed method is analyzed by a null testing experiment of an off-axis convex ellipsoid mirror. The experimental result shows that the proposed method can accomplish the hybrid compensation testing of convex aspherical surfaces effectively, and it can also bring much to the application of our method in convex aspherical surface testing.
The Digital Copyright Management System Based on Blockchain

The rapid development of the Internet provides a shortcut for the dissemination of digital content. A large amount of text, images, audio, and content are digitally placed on the Internet, how to optimize the traditional copyright transaction and rights protection methods to better protect the rights of content creators has become an urgent problem to be studied and solved. Blockchain as an emerging encryption technology in recent years.It has seen a new opportunity for the protection of digital rights. This study combines blockchain technology and digital copyright registration technology to design a complete copyright registration protection application system. Highlights the copyrighted data storage and the security and trustworthiness of blockchain technology. At the software level, the data is absolutely credible and has broad application prospects in the future.