Reviewerly/exHarmony huggingface dataset:
Application of exploratory and Spatial Data Analysis (SDA), singularity matrix analysis, and fractal models to delineate background of potentially toxic elements: A case study of Ahvaz, SW Iran

It is essential to study spatial distribution of Potentially Toxic Elements (PTEs) in surface soil, and separate PTEs geochemical background from their human sources, and also determine their correlation with other environmental factors in order to assess their impacts on humans, provide realistic environmental geochemical maps, and carry out soil management. For this purpose, this study was designed to investigate the surface soil in Ahvaz, Southwest of Iran. The applied methods were exploratory data analysis (EDA), including boxplot, Q-Q plot, probability plot (PP), concentration-area (C-A), number-size (N-S) fractal model, and singularity index (SI) model. The obtained results revealed that the fractal models led to a more realistic distinction among the geochemical population compared to the EDA methods such as probability plot. Furthermore, the C-A model was found to be more effective on the separation of subpopulation compared to the N-S and PP models. The studied elements exhibited a similar pattern implying that pollution is a function of geochemical dispersion regarding the surface soil in Ahvaz (Zn ≫ Pb > Cu > As). The studied metals-major elements plot also indicated that there was no meaningful relationship between Pb, Zn, Cu, and major elements in the study area. Plots of association of Pb, Zn, Cu, and As distinctly showed two general geogenic and anthropogenic populations. Moreover, the results of SI revealed that the highly contaminated area was consistent with the main defined hotspots and anthropogenic sources of elements as well as places affected by the contaminated area that have not been reported in previous studies. Furthermore, a combination of geochemical and geographical model comprising different statistical models was developed to more effectively separate geogenic from anthropogenic sources. Also, the geochemical background for the studied elements (Pb 180 mg/kg; Cu 200 mg/kg; Zn 90 mg/kg; As 65 mg/kg) was shown to be higher than the Iranian soil quality guideline with Pb, Zn, Cu, and As of 100, 80, 200, and 18 mg/kg, respectively.
Undirected Graphical Models as Approximate Posteriors

The representation of the approximate posterior is a critical aspect of effective variational autoencoders (VAEs). Poor choices for the approximate posterior have a detrimental impact on the generative performance of VAEs due to the mismatch with the true posterior. We extend the class of posterior models that may be learned by using undirected graphical models. We develop an efficient method to train undirected approximate posteriors by showing that the gradient of the training objective with respect to the parameters of the undirected posterior can be computed by backpropagation through Markov chain Monte Carlo updates. We apply these gradient estimators for training discrete VAEs with Boltzmann machines as approximate posteriors and demonstrate that undirected models outperform previous results obtained using directed graphical models. Our implementation is available at this https URL .
The stability of demand for money in the proposed Southern African Monetary Union

Purpose The purpose of this paper is to investigate the stability of demand for money in the proposed Southern African Monetary Union (SAMU). Design/methodology/approach The study uses annual data for the period 1981 to 2015 from ten countries making-up the Southern African Development Community. A standard function of demand for money is designed and estimated using a bounds testing approach to co-integration and error-correction modeling. Findings The findings show divergence across countries in the stability of money. This divergence is articulated in terms of differences in cointegration, CUSUM (cumulative sum) and CUSUMSQ (CUSUM squared) tests, short run and long-term determinants and error correction in event of a shock. Policy implications are discussed in the light of the convergence needed for the feasibility of the proposed SAMU. Originality/value This study extends the debate in scholarly and policy circles on the feasibility of proposed African monetary unions.
Voronoi-based image representation applied to binary visual cryptography

A common processing scheme of Visual Cryptography (VC) consists of generating two ciphered images from a secret image. The sender transmits these key images via two different channels. The receiver collects the key images and recovers the secret image by superimposing them. In this article, Voronoi tessellation is exploited in Visual Cryptography for the first time to the best of our knowledge. The proposed Voronoi-based Visual Cryptography (VVC) technique permits to drastically reduce the amount of encoded and transmitted information by comparison with a traditional VC scheme. Hence, the technique can be used for transmissions of secret images with enforcement of the transmission reliability by enhancing the redundancy of information or with communication networks and technologies having low bandwidth and memory. Moreover, the random arrangement of the Voronoi polygons makes the transmission of the secret image more secure since the sender and the recipient use a private generator of random points. In addition, shape reconstruction properties of the image-based Voronoi representation are stated.
Improving the Quality and Design of Retrospective Clinical Outcome Studies that Utilize Electronic Health Records

Description Electronic health records (EHRs) are an excellent source for secondary data analysis. Studies based on EHR-derived data, if designed properly, can answer previously unanswerable clinical research questions. In this paper we will highlight the benefits of large retrospective studies from secondary sources such as EHRs, examine retrospective cohort and case-control study design challenges, as well as methodological and statistical adjustment that can be made to overcome some of the inherent design limitations, in order to increase the generalizability, validity and reliability of the results obtained from these studies.
Brain CT image segmentation based on 3D slicer

This article focuses on CT images of human brain. Based on the characteristics of CT, 3D Slicer software was used to segment the brain CT images. First, the functions and features of 3D Slicer software are briefly introduced. Second, the principles of threshold segmentation and FCM algorithm are described. Using the Segment Editor module of 3D Slicer software to perform image segmentation, the threshold segmentation method and FCM algorithm are compared.
A Certificateless Aggregate Arbitrated Signature Scheme for IoT Environments

The Internet of Things (IoT) environment consists of numerous devices. In general, IoT devices communicate with each other to exchange data, or connect to the Internet through a gateway to provide IoT services. Most IoT devices participating in the IoT service are lightweight devices, in which the existing cryptographic algorithm cannot be applied to provide security, so a more lightweight security algorithm must be applied. Cryptographic technologies to lighten and provide efficiency for IoT environments are currently being studied a lot. In particular, it is necessary to provide efficiency for computation at a gateway, a point where many devices are connected. Additionally, as many devices are connected, data authentication and integrity should be fully considered at the same time, and thus digital signature schemes have been proposed. Among the recently studied signature algorithms, the certificateless signature (CLS) based on certificateless public key cryptography (CL-PKC) provides efficiency compared to existing public key-based signatures. However, in CLS, security threats, such as public key replacement attacks and signature forgery by the malicious key generation center (KGC), may occur. In this paper, we propose a new signature scheme using CL-PKC in generating and verifying the signature of a message in an IoT environment. The proposed scheme is a certificateless aggregate arbitrated signature, and the gateway aggregates the signatures of messages generated by the device group to reduce the size of the entire signature. In addition, it is designed to be safe from security threats by solving the problems caused by public key replacement attacks and malicious KGC, and adding arbitrated signatures of the gateway to strengthen non-repudiation.
IMPROVING FAKE PRODUCT DETECTION USING AI-BASED TECHNOLOGY

ResearchAndMarkets wrote in their report on May 15, 2018, that up to 1.2 Trillion USD in 2017 of products are counterfeited goods.The report estimated this damage globally at 1.82 Trillion USD in 2020 (RESEARCH AND MARKETS, 2018).This paper does not consider copyrights or digital piracy, counterfeiting, fraudulent documents but rather investigates the prevention of counterfeiting on a technological basis.The presence of counterfeit products on the European market is on the increase, therefore the intervention of inspection bodies and authorities alone is not sufficient, consumers can make their contribution and support this process.In this paper, we research the possibility to reduce counterfeit products using machine learning-based technology.Image and text recognition and classification based on machine learning have the potential to be a key technology in the fight against counterfeiting.The automatic image and text recognition and the classification of product information enable end customers to detect counterfeits precisely and quickly by checking them against trained models.The goal of this paper is to create an easy to use applications in which the end-user identifies the counterfeit product and contribute to the fight against product piracy.
Prototype Implementation and Evaluation of a Blockchain Component on IoT Devices

With the greater availability and reduced dimensions of complex electronics, an Internet of Things (IoT) will emerge in the future, in which machines use sensors to collect and evaluate information about the physical world and share it with other machines without having to rely on human interaction. Blockchain technology encompasses features such as trustworthiness and decentralization and these features can be advantageous in the IoT context, as they can help to ensure data integrity in an Internet of Things unmonitored by humans. In the course of this paper, we investigate to what extent the use of blockchain technology is feasible for autonomous devices in IoT. For this purpose, a prototypical client blockchain component is built using the single-board computer Raspberry Pi and a simulated Ethereum blockchain. This prototype will be examined for aspects like proper functionality, response times and performance.
Comparative performance study of classification models for image-splicing detection

Recently, images have been manipulated for malicious activities, rather than to enhance their quality. This allows malicious users to make changes to images to create forged versions using digital processing tools. Therefore, the authenticity of digital images has become an important research area because humans cannot observe image forgery processes. The objective of this study was to detect spliced images using three classification techniques—support vector machine (SVM), naïve Bayes, and K nearest neighbors (KNN)—to identify the most suitable one. Their classification quality was evaluated using accuracy, sensitivity, and specificity as performance measures. The experimental results showed that the KNN classifier achieved the highest accuracy and sensitivity among the three classifiers. However, naïve Bayes achieved the highest specificity among the classifiers.
Impact of optical coherence on the performance of large-scale spatiotemporal photonic reservoir computing systems

Large-scale spatiotemporal photonic reservoir computer (RC) systems offer remarkable solutions for massively parallel processing of a wide variety of hard real-world tasks. In such systems, neural networks are created by either optical or electronic coupling. Here, we investigate the impact of the optical coherence on the performance of large-scale spatiotemporal photonic RCs by comparing a coherent (optical coupling between the reservoir nodes) and incoherent (digital coupling between the reservoir nodes) RC systems. Although the coherent configuration offers significant reduction on the computational load compared to the incoherent architecture, for image and video classification benchmark tasks, it is found that the incoherent RC configuration outperforms the coherent configuration. Moreover, the incoherent configuration is found to exhibit a larger memory capacity than the coherent scheme. Our results pave the way towards the optimization of implementation of large-scale RC systems.
Reasoning about Durations

A set of assertions is consistent provided they can all be true at the same time. Naive individuals could prove consistency using the formal rules of a logical calculus, but it calls for them to fail to prove the negation of one assertion from the remainder in the set. An alternative procedure is for them to use an intuitive system (System 1) to construct a mental model of all the assertions. The task should be easy in this case. However, some sets of consistent assertions have no intuitive models and call for a deliberative system (System 2) to construct an alternative model. Formal rules and mental models therefore make different predictions. We report three experiments that tested their respective merits. The participants assessed the consistency of temporal descriptions based on statements using "during" and "before." They were more accurate for consistent problems with intuitive models than for those that called for deliberative models. There was no robust difference in accuracy between consistent and inconsistent problems. The results therefore corroborated the model theory.
A Robust Zero-Watermarking Algorithm for Medical Images Using Curvelet-Dct and RSA Pseudo-random Sequences

With the popularization of digital medicine, the protection of patients’ privacy has become extremely important in the medical field. Embedding watermarks into medical images is one of the effective ways to protect patients’ privacy. However, the existing watermarking algorithms have poor robustness under geometric attacks. To solve this problem, aiming at the particularity of medical image, a robust zero-watermarking algorithm using Curvelet-Dct and RSA pseudo-random sequence is proposed in this paper. First, extract the visual feature vectors of medical images where the energy is most concentrated via perform DCT transformation on the Coarse layer of Curvelet transformation. Then, the pseudo-random sequence generated by the RSA algorithm is used to encrypt the watermark which improved the security of the patients’ privacy. Combined with the concept of zero-watermark, the experimental results show that the proposed algorithm has good robustness in both conventional attacks and geometric attacks.
Robust Neural Machine Translation for Clean and Noisy Speech Transcripts

Neural machine translation models have shown to achieve high quality when trained and fed with well structured and punctuated input texts. Unfortunately, the latter condition is not met in spoken language translation, where the input is generated by an automatic speech recognition (ASR) system. In this paper, we study how to adapt a strong NMT system to make it robust to typical ASR errors. As in our application scenarios transcripts might be post-edited by human experts, we propose adaptation strategies to train a single system that can translate either clean or noisy input with no supervision on the input type. Our experimental results on a public speech translation data set show that adapting a model on a significant amount of parallel data including ASR transcripts is beneficial with test data of the same type, but produces a small degradation when translating clean text. Adapting on both clean and noisy variants of the same data leads to the best results on both input types.
Copyright Certificate Storage and Trading System Based on Blockchain

In the era of digitalization and networking, the high cost and long time of traditional centralized copyright protection methods make the copyright of digital works cannot be well protected and the infringement phenomenon be serious. Blockchain, as a decentralized distributed database, can store the copyright information of works. This paper researches and analyzes the advantages of blockchain in copyright protection and the current research status, and finally a copyright certificate storage and trading system based on blockchain is designed. In this system, registration confirmation, transaction certificate, and dispute protection are researched as a complete process. And the certificate storage data on the blockchain are precisely designed, which greatly reduces the time and cost of rights protection. What's more, the copyright of digital works is divided by studying laws related to copyright in China, which makes this system more suitable for practical applications.
Lossiness and Entropic Hardness for Ring-LWE

The hardness of the Ring Learning with Errors problem (RLWE) is a central building block for efficiency-oriented lattice-based cryptography. Many applications use an “entropic” variant of the problem where the so-called “secret” is not distributed uniformly as prescribed but instead comes from some distribution with sufficient min-entropy. However, the hardness of the entropic variant has not been substantiated thus far. For standard LWE (not over rings) entropic results are known, using a “lossiness approach” but it was not known how to adapt this approach to the ring setting. In this work we present the first such results, where entropic security is established either under RLWE or under the Decisional Small Polynomial Ratio (DSPR) assumption which is a mild variant of the NTRU assumption. In the context of general entropic distributions, our results in the ring setting essentially match the known lower bounds (Bolboceanu et al., Asiacrypt 2019; Brakerski and Döttling, Eurocrypt 2020).
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline Generation

News headline generation aims to produce a short sentence to attract readers to read the news.One news article often contains multiple keyphrases that are of interest to different users, which can naturally have multiple reasonable headlines.However, most existing methods focus on the single headline generation.In this paper, we propose generating multiple headlines with keyphrases of user interests, whose main idea is to generate multiple keyphrases of interest to users for the news first, and then generate multiple keyphrase-relevant headlines.We propose a multi-source Transformer decoder, which takes three sources as inputs: (a) keyphrase, (b) keyphrase-filtered article, and (c) original article to generate keyphrase-relevant, highquality, and diverse headlines.Furthermore, we propose a simple and effective method to mine the keyphrases of interest in the news article and build a first large-scale keyphraseaware news headline corpus, which contains over 180K aligned triples of news article, headline, keyphrase .Extensive experimental comparisons on the real-world dataset show that the proposed method achieves state-of-theart results in terms of quality and diversity 1 .
Blockchain and Cryptoassets: Insights from Practice*

ABSTRACT Blockchain and cryptoassets are transforming how business is conducted. Leaders from Canadian practice provide insights on how consultants, standard setters, auditors, and regulators have been getting involved to support business and work through the challenges to raise funds, report to investors, and protect the public interest in a blockchain‐enabled world. These leaders share the challenges and opportunities they see, and the role accountants can play, including how educators and researchers can help.
The Value Equivalence Principle for Model-Based Reinforcement Learning

Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separate the learning of the model from its use, by constructing a model of the environment's dynamics that correctly predicts the observed state transitions. In this paper we argue that the limited representational resources of model-based RL agents are better used to build models that are directly useful for value-based planning. As our main contribution, we introduce the principle of value equivalence: two models are value equivalent with respect to a set of functions and policies if they yield the same Bellman updates. We propose a formulation of the model learning problem based on the value equivalence principle and analyze how the set of feasible solutions is impacted by the choice of policies and functions. Specifically, we show that, as we augment the set of policies and functions considered, the class of value equivalent models shrinks, until eventually collapsing to a single point corresponding to a model that perfectly describes the environment. In many problems, directly modelling state-to-state transitions may be both difficult and unnecessary. By leveraging the value-equivalence principle one may find simpler models without compromising performance, saving computation and memory. We illustrate the benefits of value-equivalent model learning with experiments comparing it against more traditional counterparts like maximum likelihood estimation. More generally, we argue that the principle of value equivalence underlies a number of recent empirical successes in RL, such as Value Iteration Networks, the Predictron, Value Prediction Networks, TreeQN, and MuZero, and provides a first theoretical underpinning of those results.
Trash and Recycled Material Identification using Convolutional Neural Networks (CNN)

The aim of this research is to improve municipal trash collection using image processing algorithms and deep learning technologies for detecting trash in public spaces. This research will help to improve trash management systems and help to create a smart city. Two Convolutional Neural Networks (CNN), both based on the AlexNet network architecture, were developed to search for trash objects in an image and separate recyclable items from the landfill trash objects, respectively. The two-stage CNN system was first trained and tested on the benchmark TrashNet indoor image dataset and achieved great performance to prove the concept. Then the system was trained and tested on outdoor images taken by the authors in the intended usage environment. Using the outdoor image dataset, the first CNN achieved a preliminary 93.6% accuracy to identify trash and non-trash items on an image database of assorted trash items. A second CNN was then trained to distinguish trash that will go to a landfill from the recyclable items with an accuracy ranging from 89.7% to 93.4% and overall 92%. A future goal is to integrate this image processing based trash identification system in a smart trash can robot with a camera to take real-time photos that can detect and collect the trash all around it.