Reviewerly/exHarmony huggingface dataset:
From Multilingual to Multimodal: The Evolution of CLEF over Two Decades

This introductory chapter begins by explaining briefly what is intended by experimental evaluation in information retrieval in order to provide the necessary background for the rest of this volume. The major international evaluation initiatives that have adopted and implemented in various ways this common framework are then presented and their relationship to CLEF indicated. The second part of the chapter details how the experimental evaluation paradigm has been implemented in CLEF by providing a brief overview of the main activities and results obtained over the last two decades. The aim has been to build a strong multidisciplinary research community and to create a sustainable technical framework that would not simply support but would also empower both research and development and evaluation activities, while meeting and at times anticipating the demands of a rapidly evolving information society.
Free evolution of a mixture of quantum states with classical space and momentum uncertainties

This article continues and completes a recent one regarding a quantum version of van Cittert–Zernike's theorem (Fabre et al 2018 Eur. J. Phys. 39 015401). In that paper, we considered a mixture of quantum states that only differed in their initial positions, but not on their momenta. Here, we relax this nonphysical assumption and analyse how the inclusion of both classical uncertainties in the initial position and momentum of a mixture of—otherwise identical—free quantum states which do not interact with an environment, affects its quantum coherence length. We show that the mathematical development and the final result are simple enough to make possible the incorporation of this subject into a basic course of quantum physics. Furthermore, we demonstrate that these results provide a comprehensive solution to a recent controversy in atomic collision physics.
Deep Learning Based Container Text Recognition

Traditional character segmentation has low accuracy for container scene text recognition. Convolutional recurrent neural network (CRNN) and connectionist text proposal network (CTPN) methods cannot extract container text features effectively. This paper proposes a novel Container Text Detection and Recognition Network (CTDRNet) for accurately detecting and recognizing container scene text. The CTDRNet consists of three components: (1) CTDRNet text detection enables to improve detection accuracy for single words; (2) CTDRNet text recognition has faster convergence speed and detection accuracy; (3) CTDRNet post-processing improves detection and recognition accuracy. In the end, the CTDRNet is implemented and evaluated with an accuracy of 96% and processing rate of 2.5 fps.
Toward human motion capturing with an ultra-wide fisheye camera on the chest

We are interested in utilizing egocentric view from a wearable camera and are working on MonoEye system, a novel system to estimate the wearer's motion using a chest-mounted camera equipped with an ultra-wide fisheye lens. Because our system has a wide field of view, it provides a balanced capacity of recognizable pose types and broad egocentric view. The prototype deep neural network estimates camera wearer's 3D pose and acquires motion without complex configuration like conventional motion capture systems.
Online Variational Learning for Medical Image Data Clustering

Data mining is an extensive area of research involving pattern discovery and feature extraction which is applied in various critical domains. In clinical aspect, data mining has emerged to assist the clinicians in early detection, diagnosis, and prevention of diseases. Advances in computational methods have led to implementation of machine learning in multi-modal clinical image analysis. One recent method is online learning where data become available in a sequential order, thus sequentially updating the best predictor for the future data at each step, as opposed to batch learning techniques which generate the best predictor by learning the entire data set at once. In this chapter, we have examined and analysed multi-modal medical images by developing an unsupervised machine learning algorithm based on online variational inference for finite inverted Dirichlet mixture model. Our prime focus was to validate the developed approach on medical images. We do so by implementing the algorithm on both synthetic and real data sets. We test the algorithm’s ability to detect challenging real world diseases, namely brain tumour, lung tuberculosis, and melanomic skin lesion.
A Spark-based Gaussian Bare-bones Cuckoo Search with dynamic parameter selection

Cuckoo search algorithm (CS), as a new heuristic algorithm, has been paid increasing attention and studied by many scholars because of its efficient performance. However, premature convergence is a defect of CS. Recently, some heuristic algorithms have been successfully applied to some high performance computing frameworks, effectively overcome the premature convergence problem of the algorithm, which provides us with a new idea to enhance CS. Therefore, a novel CS variant, called Spark-based gaussian bare-bones cuckoo search with dynamic parameter selection (SparkGDCS), which combines a novel CS variant with the efficient Spark framework, is proposed in this paper. In SparkGDCS, GDCS is a new variant which combines Gaussian bare-bones strategy and dynamic parameter selection, whose purpose is to enhance the search ability of CS. Finally, by testing the benchmark functions proposed by the 2010 and 2013 IEEE Congress on Evolutionary Computation special session (CEC 2010 and CEC 2013), comprehensive experiments prove the effectiveness of SparkGDCS.
Dense 3D Visual Mapping via Semantic Simplification

Dense 3D visual mapping estimates as many as possible pixel depths, for each image. This results in very dense point clouds that often contain redundant and noisy information, especially for surfaces that are roughly planar, for instance, the ground or the walls in the scene. In this paper we leverage on semantic image segmentation to discriminate which regions of the scene require simplification and which should be kept at high level of details. We propose four different point cloud simplification methods which decimate the perceived point cloud by relying on class-specific local and global statistics still maintaining more points in the proximity of class boundaries to preserve the infra-class edges and discontinuities. 3D dense model is obtained by fusing the point clouds in a 3D Delaunay Triangulation to deal with variable point cloud density. In the experimental evaluation we have shown that, by leveraging on semantics, it is possible to simplify the model and diminish the noise affecting the point clouds.
Deep Residual Network for Single Image Super-Resolution

This paper proposes a Deep Residual Network for Single Image Super-Resolution (DRSR). We build a deep model using residual units that remove unnecessary modules. We can build deeper network at the same computing resources with the modified residual units. Experiments shows that deepening the network structure can fully utilize the image contextual information to improve the image reconstruction quality. The network learns both global residuals and local residuals, making the network easier to train. Our network directly extracts features from Low-Resolution (LR) images to reconstruct High-Resolution (HR) images. Computational complexity of the network is dramatically reduced in this way. Experiments shows that our network not only performs well in subjective visual effect but also achieves a high level in objective evaluation index.
Blockchain in Smart Park

The blockchain characteristics of decentralization and trust mechanism match with the development demand of smart parks. Based on a practical park planning project, this paper studies the requirements of smart parks, proposes an innovative blockchain application scheme. The service model (entity model, data model, transaction model) of the blockchain smart park is proposed, and the system architecture, DAPP modules and process flow are designed, and the integration of blockchains is analyzed as well. The scheme provides reference to the construction and operation of smart parks running on the blockchain, to realize the opening and autonomy of the park ecosystem.
An Improved Non-local Means Denoising Technique for Brain MRI

Noise artifacts introduced in Magnetic Resonance Imaging (MRI) due to imperfection in radio-frequency coils typically deteriorate the performance of automated analysis of a brain MRI. Thus, it is necessary to eliminate the noise for effective analysis of MRI. The denoising techniques that work on pixels vicinity (e.g., Averaging filter and Gaussian filter) suffer in denoising due to loss of salient information in brain MRI. As a result, these techniques gain poor Peak Signal-to-Noise Ratio (PSNR) in denoising of an MRI. However, the methods that denoise an image based on non-local correlation of patches show significant improvement in the performance. In this work, we proposed an improved denoising method for brain MRI based on non-local correlation of patches. The input image is decomposedSarkar, Saumyadipta into two components: periodic component and smooth component using Fast Fourier Transform (FFT). These two components of the image are denoised separatelyChandra Tripathi, Prasun using non-local based averaging. The filtered image is obtained by reconstructing these two components. Quantitative and qualitative results on a T1-w simulated brain MRI datasetBag, Soumen and a local brain MRI dataset indicate the effectiveness of the proposed method.
Text-Based Event Detection: Deciphering Date Information Using Graph Embeddings

Event detection is increasingly gaining attention within the fields of natural language processing and social network analysis. Graph models have always been integral to social media analysis literature. Owing to the long processing time and time complexities of graph-based algorithms, these models were initially very difficult to improve upon. Over the past few years, researchers proposed many approaches to create representations such as word2vec and doc2vec [11]. With the emergence of graph embedding techniques in recent years using deep learning techniques such as node2vec, it is possible to extract node embeddings that can be used to embed graph information into machine learning methods. We introduce SnakeGraph, a new model which uses the sequences of words making up each body of text along with key representations such as the user and the date. These representations can help us learn about the main ideas communicated via written language. However, our method not only looks at both the content of text and how it links to other key information, but also factors the relationship between words in our text as they appear in sequence and overlap as they appear across different bodies of text. We believe that date and user embeddings can especially shed light on event detection literature.
A proximity moving horizon estimator based on Bregman distances and relaxed barrier functions

In this paper, we introduce a general proximity-based formulation for moving horizon estimation (MHE) of constrained discrete-time linear systems. The cost function of the underlying optimization problem consists of convex stage costs as well as Bregman distances centered around the a priori estimates. The Bregman term allows to incorporate constraints into the cost function by means of relaxed barrier functions and to formulate the moving horizon estimator as an unconstrained optimization problem. The stability properties of the proposed estimator are investigated and the obtained results are illustrated by means of a numerical example.
A Free Stale Synchronous Parallel Strategy for Distributed Machine Learning

With the machine learning applications processing larger and more complex data, people tend to use multiple computing nodes to execute the machine learning tasks in distributed way. However, in real world, people always encounter a problem that a few nodes in system exhibit poor performance and drag down the efficiency of the whole system. In existing parallel strategies such as bulk synchronous parallel and stale synchronous parallel, these nodes with poor performance may not be monitored and found out in time. To address this problem, we proposed a free stale synchronous parallel (FSSP) strategy to free the system from the negative impact of those nodes. Our experimental results on some classical machine leaning algorithms and datasets demonstrated that FSSP strategy outperformed other existing parallel computing strategy.
Speeded-Up Robust Feature Descriptor for Endochromoscopy Images

With the help of recent computer-aided diagnosis systems, cancer in the gastrointestinal track can be detected and classified into different stages. However, there exists some problems, such as the dependence of the image features and the experience of physicians that negatively affect the diagnosing results. To avoid these problems, this study proposes using Speeded-Up Robust Features (SURF) as descriptor for chromoendoscopy (CH) images. Experimental results show that by combining with Support Vector Machine (SVM) classifier, the proposed descriptor has a classification accuracy of 85% and an AUC of 0.92 on CH dataset obtained using an Olympus CV-180 endoscope at the Portuguese Institute of Oncology (IPO) Hospital in Porto, Portugal.
Passing cash from bank notes to bitcoin: standardizing money

This essay is about the modern bank note and bitcoin and how each standardizes money. Whether constituted by paper or protocol, whether performing (or failing) as a store of wealth, a medium of exchange, or unit of account, bank notes and bitcoins enact interchangeable equivalence with themselves, every one-pound note or bitcoin the same as another, and their mode of passing hand-to-hand or peer-to-peer also conscripts their holders as readily interchangeable. This essay examines the different ways that these two types of currency engineered this standardization, and I show how the new blockchain technology of Bitcoin innovated upon the standardizing process that made bank notes interchangeable. As I recount in the essay’s first half, standardizing physical bank notes involved re-imagining them detached from the temporality of any specific contractual transaction so as to be, like coin, grasped as immediately physically interchangeable, and the bank note used print and the act of reading to do so. In the essay’s second half, I discuss how, by contrast, Bitcoin standardizes money not by operating on any physical object, not even a digital one, but by creating a sense of immediately fungible value through maintaining the unique historicity of every transaction in the system.
A Comparison Study of Different Privacy Preserving Techniques in Collaborative Filtering Based Recommender System

Recommender System is a productive tool which has been used in various fields for personalizing the applications so that we get recommended items such as e-commerce sites, shopping sites like Amazon, Netflix, research journals, mobile-based platforms and TV-based platforms. Collaborative Filtering (CF) is the most popular widely used tool on the Internet for recommender systems to search for new items that fit users’ interest by finding similar users’ opinion expressed on other similar items. However, there are many disadvantages for CF algorithm, which are data privacy risk and data sparsity. In this paper, we compare and analyze different privacy preserving collaborating filtering techniques that have greatly captured the attention of researchers i.e. Matrix Factorization (MF) and Singular Value Decomposition (SVD) with finding accuracy. Compared with SVD and MF, WBSVD achieves better recommendation quality and is more accurate from experimental results.
Analyzing the demand for user information and patent support at scientific libraries

The authors substantiate the importance and the need for the study of the functional character of information needs (further referred as IN) of scientists at research institutes and, in particular, the Federal Research Center in Pushchino Scientific Center of the Russian Academy of Sciences (PSC RAS) in order to improve information services. Much attention was paid to the study of IN of scientists searching for patent information. The authors summarize the results of the analysis of information needs of researchers specializing in physicochemical biology. They substantiate the need for user patent information support comprising information and analytical services, consulting, methodical work and patent studies. The study findings demonstrate that patent information services hold a special place in the modern information infrastructure and are highly demanded by the PSC RAS users. Based on the study results, the authors introduce the concept of patent information support of PSC RAS library users. This model envisages priority development of online and analytical services for the patent research. Such information support of PSC RAS researchers would facilitate profitability and benefits and increase interest in the library services.
Profile-guided code identification and hardening using return oriented programming

Protecting code from a security breach needs a systematic defense. The most promising solution in this task is to protect the control flow of the code. A majority of the existing approaches for protecting the code using control flow information suffer from high performance overhead. In this paper, we propose a method for assisted discovery of profile-guided techniques for the cold-code identification and low-overhead software security hardening. Our method proceeds with identifying cold-code and hot-code by performing static and dynamic analysis on the input code. Furthermore, develops heuristics to identify security-sensitive code. The outcome of this research is the identification of a minimum number of security sensitive points with minimal overhead. This research develops a common framework for profile-guided code self-verification based on predetermined security and performance requirements. We use Return Oriented Programming (ROP) which is used mostly by adversaries for the defensive purpose to build self-verifying ROP chain. The ROP chain inserted into the code diversifies and obfuscates execution of the program hence, making it harder to breach.
Geographic Dependence of the Solar Irradiance Spectrum at Intermediate to High Frequencies

Temporal fluctuations in solar irradiance are important in energy harvesting and the study of ecological systems and climate. As solar irradiance is not stationary, analysis is often made in the frequency domain, rather than the time domain, and the power-law spectrum is often attributed to atmospheric effects, such as dust and clouds. The authors show that the power-law spectrum of solar irradiance appears even for a clear sky, due to modulation of the diurnal oscillations caused by varying daylight duration. Thus the power law depends on geographical location, and one must account for the multiplicative effect of atmospheric factors on the clear-sky power-law spectrum.
Similarity Preserved Camera-to-Camera Gan for Person Re-Identification

Person re-identification (re-ID) is a challenge task, which suffers from detector errors and background variations. The existing works target the problem by generating persons images with GANs for data augmentation against over-fitting. However, the previous approaches still arise the person appearance changes during transferring. To circumvent these issues, we propose a similarity preserved camera-to-camera GAN (SPCGAN) for learning foreground-related and background-unrelated representation. It is a novel structure based on StarGAN and used specifically for camera domain transferring in re-ID with similarity preserved. We introduce augmented datasets with transferred pedestrian images, whose foregrounds are basically unchanged, to different camera domains. Our augmented and synthetic datasets effectively improve the performance of deep learning models for re-ID. The proposed method is evaluated on Market-1501 and DukeMTMC-reID datasets. Experimental results show that our method is effective and achieve outstanding re-ID accuracy compared with state-of-the-art GAN based person re-ID approaches.
[DC] Learning Tornado Formation via Collaborative Mixed Reality

With the rise of attention to global warming which brings in more extreme weather and climate conditions, the earth science education would be one of the crucial topics for the next generation. Mixed-Reality has been shown to offer more engaging and effective learning solutions on essential science topics, such as math, physics, and chemistry. However, there are few augmented reality and mixed reality applications on earth science subject. Also, collaborative learning has been shown to be beneficial for student learning by aspiring student curiosity, and the ability of cooperation. In this paper, we propose a Mixed Reality Tornado Simulator which offers an earth science education intervention in a collaborative mixed reality setting. Students and their instructor can wear see-through head-mounted displays to cooperate on learning the knowledge of the formation and its damage cause on human-built structures, farming, and vegetation by using our proposed mixed reality application. Also, for evaluating the learning performance in this mixed reality setting, we will study the students cognitive load using standard survey instruments. We will conduct a controlled study with two conditions to compare the proposed intervention in the head-mounted-display setting, versus a desktop setting to test usability and knowledge gain of the students in those settings.
EEG Investigation of Brain Bioelectrical Activity (Regarding Perception of Multimodal Polycode Internet Discourse)

This paper considers the pilot experiment regarding the perception functioning of various brain areas channels in respect of multimodal polycode Internet discourses. The investigation focuses on three variants of EEG-analysis: solely auditory, exclusively visual and integrative auditory-visual perception. Regarding functional properties, it has been found that brain bioelectrical activity and all brain rhythm power values are dependent on the type of mental modality: visualization, audition and integrative stimuli perception. The experiment included a number of special tests involving perception of polycode Internet discourse, where subjects’ brain bioelectrical activity dynamics were evaluated on the basis of EEGs. The frequency spectra were calculated in the special “Typology” program. The primary aim of this experiment was to find out what kind of Internet stimuli (auditory, visual and complex ones) have the greatest influence on all brain rhythm power values. The interpretation of the results may help in studying the influence on the brain areas observed during participation of young users (18–25 years old) in polycode Internet discourses. In the first series of experiments, differences in the brain bioelectrical activity dynamics were studied. The purpose was to evaluate how the brain performs auditory, visual and auditory-visual complex analyses with two indicators – positive and negative ones – of the brain area activity.
Leader-Follower Formations on Real Terrestrial Robots

Controlling robot formations represents one of the biggest open challenges for the Robotics Community. Its importance is remarked in a wide range of applications from truck platooning to autonomous robot-based parcel delivery. In this paper we propose a lightweight decentralized solution for controlling a queue shaped leader-follower turtlebot formation. In order to test our implementation, we perform tests both in simulation and on real turtlebots1. The complexity of the challenge we are tackling further increases due the fact that the robots we use to evaluate our work are non-holonomic, meaning they have limited controllable degrees of freedom.