Reviewerly/exHarmony huggingface dataset:
A human pose estimation algorithm based on the integration of improved convolutional neural networks and multi-level graph structure constrained model

This work introduces a novel convolutional feature for the task of human pose estimation, which is a framework of fusing a convolutional neural network into a multi-level graph structure model so as to improve the pose estimation results from body-part detection and human spatial structure. In the stage of part detection, the probability vectors corresponding to human body parts in the whole image are analyzed by a convolutional neural network. And then, a novel multi-level graph structure model is designed in this method, which contains the whole body, human body parts and joints, and realizes the coarse-to-fine establishment of a more constrained human spatiTal constraint model from levels of the structures, the edges, to the pixels. The obtained probability vectors are put into the multi-level graph structure model to compute the location coordinates for each joint, successfully achieving a framework of combining the deep learning network and the multi-level graph structure model. A large number of qualitative and quantitative experimental results show that compared with other state-of-art methods, the integration of the deep learning network and the multi-level pictorial structure model can improve the accuracy of human pose estimation to a greater extent.
Supporting Creation of FAQ Dataset for E-Learning Chatbot

Recently, many universities provide e-learning systems for supporting classes. Though the system is an effective and efficient learning environment, it usually lacks a dynamic user support systems. A chatbot is a good choice to support a dynamic Q&A system; however, it is difficult to collect the large number of Q&A data or high-quality datasets required to train the chatbot model to obtain high accuracy. In this paper, we propose a novel framework for supporting dataset creation. This framework provides two recommendation algorithms: creating new questions and aggregating semantically similar answers. We evaluated our framework and confirmed that the framework can improve the quality of an FAQ dataset.
A Study on Local Search Meta-heuristics for Ontology Alignment

Ontologies are one of the most suitable methodologies to provide formalized representations of the real-world data in several contexts such as the emerging paradigm of the Internet of Things. In spite of their key capability of providing an abstract representation of the information captured by different sources, the variety of ways that a domain can be conceptualized results in the development of heterogeneous ontologies with overlapping parts. In order to address this problem, a so-called ontology alignment process is required. This process allows generating a set of correspondences between semantically similar entities of two ontologies and, as a consequence, enabling system interoperability. Unfortunately, this process is a complex and time-consuming task. Therefore, recently, meta-heuristics are appearing as a suitable methodology to implement it. However, no meta-heuristics based on local search optimization have been applied until now. This paper bridges this gap by performing a comparison among some popular local search-based algorithms for generating an alignment between two ontologies. As shown by the results involving well-known benchmarks, Tabu search results to be the best performer in terms of precision, recall and F-measure.
Research and Analysis of Blockchain Data

Abstract Blockchain technology is characterized by anti-counterfeiting, non-tampering and easy to implement smart contracts, and is known as a new technology that will lead to social change. Therefore, the study of data in blockchain has important theoretical and practical significance. The author proposes a three-layer model from the perspective of data analysis; on the basis of this model, the data structure and data type of blockchain, as well as the data structure and operation principle of smart contract are studied. Finally, the seven problems and correlations of blockchain data analysis are summarized.
Quantum on the money [quantum computing in financial services sector]

Opinion is divided over when quantum computers will enter the commercial mainstream: IBM reckons four to five years; more conservative forecasts suggest not for a decade, at least. Whichever timeframe proves correct, it's probable that the financial services sector will be foremost to adopt quantum tech for line-of-business applications - and to bear the brunt of the challenges that early adopters always face. Quantum promises pros but also cons, especially in respect to the cyber-security liabilities that its power gives rise to. The article considers the impact it could have on cyber-risk exposure.
Ques-Chain: An Ethereum Based E-Voting System

Ethereum is an open-source, public, block chain-based distributed computing platform and operating system featuring smart contract functionality. In this paper, we proposed an Ethereum based electronic voting (e-voting) protocol, Ques-Chain, which can ensure the authentication can be done without hurting confidentiality and the anonymity can be protected without problems of scams at the same time. Furthermore, the authors considered the wider usages Ques-Chain can be applied on, pointing out that it is able to process all kinds of messages and can be used in all fields with similar needs.
Introduction to Discourse Relation Parsing and Treebanking (

This overview summarizes the main contributions of the accepted papers at the 2019 workshop on Discourse Relation Parsing and Treebanking (DISRPT 2019). Co-located with NAACL 2019 in Minneapolis, the workshop’s aim was to bring together researchers working on corpus-based and computational approaches to discourse relations. In addition to an invited talk, eighteen papers outlined below were presented, four of which were submitted as part of a shared task on elementary discourse unit segmentation and connective detection.
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-domain

Gaussian process (GP) audio source separation is a time-domain approach that circumvents the inherent phase approximation issue of spectrogram based methods. Furthermore, through its kernel, GPs elegantly incorporate prior knowledge about the sources into the separation model. Despite these compelling advantages, the computational complexity of GP inference scales cubically with the number of audio samples. As a result, source separation GP models have been restricted to the analysis of short audio frames. We introduce an efficient application of GPs to time-domain audio source separation, without compromising performance. For this purpose, we used GP regression, together with spectral mixture kernels, and variational sparse GPs. We compared our method with LD-PSDTF (positive semi-definite tensor factorization), KL-NMF (Kullback-Leibler non-negative matrix factorization), and IS-NMF (Itakura-Saito NMF). Results show that the proposed method outperforms these techniques.
Studying Cultural Differences in Emoji Usage across the East and the West

Global acceptance of Emojis suggests a cross-cultural, normative use of Emojis. Meanwhile, nuances in Emoji use across cultures may also exist due to linguistic differences in expressing emotions and diversity in conceptualizing topics. Indeed, literature in cross-cultural psychology has found both normative and culture-specific ways in which emotions are expressed. In this paper, using social media, we compare the Emoji usage based on frequency, context, and topic associations across countries in the East (China and Japan) and the West (United States, United Kingdom, and Canada). Across the East and the West, our study examines a) similarities and differences on the usage of different categories of Emojis such as People, Food & Drink, Travel & Places etc., b) potential mapping of Emoji use differences with previously identified cultural differences in users’ expression about diverse concepts such as death, money emotions and family, and c) relative correspondence of validated psycho-linguistic categories with Ekman’s emotions. The analysis of Emoji use in the East and the West reveals recognizable normative and culture specific patterns. This research reveals the ways in which Emojis can be used for cross-cultural communication.
The Shift Bound for Abelian Codes and Generalizations of the Donoho-Stark Uncertainty Principle

Let G be a finite abelian group. If f : G → C is a nonzero function with Fourier transform f, the Donoho-Stark uncertainty principle states that |supp(f)||supp(f̂)| ≥ |G|. The purpose of this paper is twofold. First, we present the shift bound for abelian codes with a streamlined proof. Second, we use the shifting technique to prove a generalization and a sharpening of the Donoho-Stark uncertainty principle. In particular, if f : G → F is a non-zero function from G to a field F, and if f has a Fourier transform f̂, the sharpened uncertainty principle states that |supp(f)||supp(f̂)| ≥ |G|+|supp(f)|-|H(supp(f))|, where H(supp(f)) is the stabilizer of supp(f) in G.
Certificateless Deniable Authenticated Encryption for Location-Based Privacy Protection

Deniable authenticated encryption (DAE) is a cryptographic primitive that supports data confidentiality with deniable authentication in an efficient manner. The DAE plays a significant role in location-based service systems for privacy protection. In this paper, we construct a certificateless DAE (CLDAE) scheme. The CLDAE is based on certificateless cryptosystems (CLCs), which avoids the need to manage public key certificates in public key infrastructure (PKI)-based cryptosystems and key escrow problems in identity-based cryptosystems (IBCs). Our design utilizes hybrid methods: tag-key encapsulation mechanism (TKEM) and data encapsulation mechanism (DEM). This technique is more suitable for location-based applications. We show how to construct a CLDAE scheme utilizing a certificateless deniable authenticated tag-KEM (CLDATK) and a DEM. We also design a CLDATK scheme and provide formal security proof using the random oracle model (ROM). We conduct a comprehensive performance analysis, which shows that CLDAE is highly efficient in terms of communication overhead. We also provide an application of the CLDAE for a location-based service (LBS) system.
Joint Learning of Pre-Trained and Random Units for Domain Adaptation in Part-of-Speech Tagging

Sara Meftah, Youssef Tamaazousti, Nasredine Semmar, Hassane Essafi, Fatiha Sadat. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Vernam Cipher with Complement Method and Optimization Key with Genetic Algorithm

Abstract Integrity is important aspect on the process of exchange data, because the originality of the data sent must be kept confidentiality from the irresponsible attacker. One of the algorithm on cryptography is Vernam Cipher which main focus on XOR operation for encryption and decryption process. The key length and the complexity of algorithm for key determination are very influential to avoid attacks. This aim of study is to modify Vernam Cipher with addition 2’s complement operation on encryption, decryption and key optimizing. Modified Vernam cipher generate random key by genetic approach to improve level of security and complexity of the algorithm.
Rebuilding racket on chez scheme (experience report)

We rebuilt Racket on Chez Scheme, and it works well&amp;mdash;as long as we're allowed a few patches to Chez Scheme. DrRacket runs, the Racket distribution can build itself, and nearly all of the core Racket test suite passes. Maintainability and performance of the resulting implementation are good, although some work remains to improve end-to-end performance. The least predictable part of our effort was how big the differences between Racket and Chez Scheme would turn out to be and how we would manage those differences. We expect Racket on Chez Scheme to become the main Racket implementation, and we encourage other language implementers to consider Chez Scheme as a target virtual machine.
Sports Highlights Generation using Decomposed Audio Information

Visual features are generally used in video summarization including sports videos. However, audio features are also important in summarization as they characterize the exciting events of a sport, especially when external thing e.g. raining effects the visual feature. The commentator's voice, referee's whistle, and audience-voice are strongly correlated to the exciting events e.g. goal, penalty shootout, red card, etc. in a soccer game. The existing methods using original audio signals could not provide expected summary due to the various noises from the audience such as horn and sound from various musical instruments. In this paper, we propose a novel approach to extract audio features using Empirical Mode Decomposition (EMD) that can filter out noises. We have conducted a set of experiments on four real soccer videos. The experimental results demonstrate that the proposed method can detect goal events with 96% accuracy which outperforms the existing state-of-the-art method by 42%.
Path Planning Based Navigation Using LIDAR for an Ackerman Unmanned Ground Vehicle

Path planning techniques for UGVs has been studied to reach valuable results of performance in avoiding obstacles and recognizing the best path to displace when it is trying to approach from the point A to the B. The path is estimated by computer algorithms that take data from the environment of the UGV in terms of space and depth in function of its actual position. But one problem that must be affronted is to recognize the orientation of the UGV on a specific time. This can be solved by a correctly mathematic modeling of the UGV’s cinematic. In this paper we expose a path planning algorithm for an UGV of Ackermann displacement geometry to avoid nearby obstacles using a LIDAR sensor, considering limitations of movement of the UGV cause of its mechanism. The work proposed is to develop a path planning algorithm and simulate it based on the mathematical model of cinematic displacement and considering mechanical constraints of an existing UGV.
Mixture Integral Attacks on Reduced-Round AES with a Known/Secret S-Box

In this work, we present new low-data secret-key distinguishers and key-recovery attacks on reduced-round AES. The starting point of our work is "Mixture Differential Cryptanalysis" recently introduced at FSE/ToSC 2019, a way to turn the "multiple-of-8" 5-round AES secret-key distinguisher presented at Eurocrypt 2017 into a simpler and more convenient one (though, on a smaller number of rounds). By reconsidering this result on a smaller number of rounds, we present as our main contribution a new secret-key distinguisher on 3-round AES with the smallest data complexity in the literature (that does not require adaptive chosen plaintexts/ciphertexts), namely approximately half of the data necessary to set up a 3-round truncated differential distinguisher (which is currently the distinguisher in the literature with the lowest data complexity). For a success probability of 95%, our distinguisher requires just 10 chosen plaintexts versus 20 chosen plaintexts necessary to set up the truncated differential attack. Besides that, we present new competitive low-data key-recovery attacks on 3- and 4-round AES, both in the case in which the S-box is known and in the case in which it is secret.
A Fractional-Order Variational Residual CNN for Low Dose CT Image Denoising

In this paper, a Fractional-order Variational Residual CNN (FVCNN) for Low Dose CT (LDCT) denoising is proposed. Recently, the trade-off between the radiation dose and the quality of CT image has become a challenging study. To this end, inspired on the rapid development and on the application of deep learning and fractional calculus, this paper combines CNN and fractional-order total variation to propose a new algorithm for LDCT image denoising. At first, a Fractional-order Total Variational (FTV) item is introduced into the loss function for texture enhancement. Secondly, a FVCNN with residual connection is constructed aiming at denoising. Finally, the capability of removing noise and preserving texture details of the proposed method is validated by experimental analysis. According to the experimental analysis, the fractional order regularization item can preserve the critical texture details while suppressing noise, generating high-quality CT image for radiologists’ judgment. Compared with the state-of-the-art method, our method achieved better details preserving in vision as well as in numerical calculation. The promising results obtained by our extensive experiments will significantly improve the availability of LDCT images.
A Study of Utilizing Backend as a Service (BaaS) Space for Mobile Applications

With the advancement of semiconductor and information and communication technologies, our computing environments are evolving into complex, large-scale, and high-performance platforms. Especially, this movement is characterized as hyper-connected society, in which IoT and cloud computing are core technologies. The Parse Server is a full feature, production ready Parse Platform on the Cloud. It could be an optimal platform for IoT service by providing open-source back-end services for developers with a set of tools to enable front-end development focus. In this paper, Parse Server is interoperable with an open source based game and a single board computer to develop use cases for the purpose of IoT programming education. This research aims to get insights of methodology for IoT programming with implementation of game ranking service and remote sensing systems. Implementation results were confirmed with Parse Dashboard and Rasberry Pi.
How Good is SGD with Random Shuffling

We study the performance of stochastic gradient descent (SGD) on smooth and strongly-convex finite-sum optimization problems. In contrast to the majority of existing theoretical works, which assume that individual functions are sampled with replacement, we focus here on popular but poorly-understood heuristics, which involve going over random permutations of the individual functions. This setting has been investigated in several recent works, but the optimal error rates remain unclear. In this paper, we provide lower bounds on the expected optimization error with these heuristics (using SGD with any constant step size), which elucidate their advantages and disadvantages. In particular, we prove that after $k$ passes over $n$ individual functions, if the functions are re-shuffled after every pass, the best possible optimization error for SGD is at least $\Omega\left(1/(nk)^2+1/nk^3\right)$, which partially corresponds to recently derived upper bounds. Moreover, if the functions are only shuffled once, then the lower bound increases to $\Omega(1/nk^2)$. Since there are strictly smaller upper bounds for repeated reshuffling, this proves an inherent performance gap between SGD with single shuffling and repeated shuffling. As a more minor contribution, we also provide a non-asymptotic $\Omega(1/k^2)$ lower bound (independent of $n$) for the incremental gradient method, when no random shuffling takes place. Finally, we provide an indication that our lower bounds are tight, by proving matching upper bounds for univariate quadratic functions.
Introduction to Statistics Using R

Introduction to Statistics Using R is organized into 13 major chapters. Each chapter is broken down into many digestible subsections in order to explore the objectives of the book. There are many real
Efficient Search-Based Weighted Model Integration.

Weighted model integration (WMI) extends Weighted model counting (WMC) to the integration of functions over mixed discrete-continuous domains. It has shown tremendous promise for solving inference problems in graphical models and probabilistic programming. Yet, state-of-the-art tools for WMI are limited in terms of performance and ignore the independence structure that is crucial to improving efficiency. To address this limitation, we propose an efficient model integration algorithm for theories with tree primal graphs. We exploit the sparse graph structure by using search to performing integration. Our algorithm greatly improves the computational efficiency on such problems and exploits context-specific independence between variables. Experimental results show dramatic speedups compared to existing WMI solvers on problems with tree-shaped dependencies.
The Resource Theory of Entanglement

In this chapter, we discuss the phenomena of quantum entanglement within the framework of quantum resource theories. In particular, we study entanglement witnesses and their properties. This chapter incorporates two main results on the theory of entanglement, namely, ultrafine entanglement witnessing and measurement-device-independent quantification of entanglement. The first result presents a new approach for improving standard witnessing techniques by using additional information obtained within witnessing experiments and thus, cope with unexpected experimental imperfections. The second result illustrates the use of entanglement witnessing in construction of measures of entanglement that are independent of the specific description of measurement devices similar to a Bell scenario.
Regulating Digital Currency: Taming the Unruly

Regulation of digital currency is still at its infancy as authorities around the world grapple with its mechanics, and study its impact and the best method to regulate it. Significant increase in the use of digital cryptocurrency based on Blockchain technology post-Bitcoin phenomenon had challenged the conventional idea of central bank monopoly in currency issuance. This had also raised concern that digital currency being used as an instrumentality of crime given its anonymity feature that allows for the flow of funds without tracing and the fact that it is built on trustless system that provides security of transaction. This concern, plus other consideration including the prospect of issuing central bank digital currency, had driven some authorities around the world to adopt countermeasures either via an outright ban or a regulatory regime that suits the nature of digital currency, which is purely virtual and anonymous. However, in coming out with an appropriate legal regime, authorities faced multiple difficulties especially when the pace of legal development does not sync congruently with the rapid progress of technology. In addition, given the growing prominence of Islamic finance around the world, questions also arise pertaining to the legality of digital cryptocurrency from the Islamic perspective. Through a qualitative study of relevant literatures as well as legislations in different countries, this chapter discusses the various categories of digital currency, its position from the Islamic perspective, regulatory regimes of digital cryptocurrency in selected jurisdictions and challenges faced by authorities around the world in regulating this new medium of exchange.