Reviewerly/exHarmony huggingface dataset:
Proposal and Evaluation of Detour Path Suppression Method in PS Reinforcement Learning

Profit sharing (PS) is well known as a kind of reinforcement learning. In a PS method, a reward is generally distributed with a geometrically decreasing function, and the common ratio of the function is called a discount rate. A large discount rate increases the learning speed, but a non-optimal policy may be learned. On the other hand, a small discount rate improves the performance of the policy, but the learning may not proceed smoothly because of the shallow learning depth. In this paper, in order to cope with these problems, we propose a method that reinforces both the detour path and the non-detour path with different discount rates. Finally, this method is applied to a maze problem and an altruistic multi-agent environment to confirm its effectiveness.
PyODDS: An End-to-End Outlier Detection System

PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches. PyODDS is released under the MIT open-source license, and currently available at (https://github.com/datamllab/pyodds) with official documentations at (https://pyodds.github.io/).
Juridical Status of So-called Smart Contracts against the Background of the Polish Legal Framework

Among substantial advancements challenging contemporary contract law special attention is given to autonomous, cryptographic solutions based on decentralised infrastructure provided by blockchain technology, intended to execute transactions automatically, designated as smart contracts. The need for comprehensive research on legal implications of practical implementation of this technological innovation is triggered particularly by the prognostications declaring it a valid alternative to hitherto contract law framework that is expected to be ultimately replaced by algorithmic mechanisms underpinning smart contracts.A relevant assessment of the impact smart contracts are presumed to have on the contract law domain requires a thorough analysis of their juridical status. The specificity of the category of smart contracts raises doubts whether they comply with the definition criteria inherent to contract law terminology. Additionally, it is of material importance to determine the function smart contracts can perform in the sphere of contractual practice and to confront it with the role and axiology of contract law.The article aims at analysing the peculiarities of smart contracts from the perspective of the Polish private law system with account being also taken of current development tendencies concerning the concept of contract.
A conversation with Danish Contractor

In this series of interviews with innovation leaders, Ubiquity Associate Editor and software engineer, Dr. Bushra Anjum sits down with Danish Contractor, a senior artificial intelligence researcher at IBM Research India. Contractor discusses his work advancing question-answering systems and chatbots for complex human interaction.
A Privacy-preserving Ethereum Lightweight Client Using PIR

By outsourcing most of storage and computational tasks to full nodes, Ethereum lightweight client is able to run on the resource-limited devices such as mobile phones. In the interactions with the lightweight client, the full node obtains considerable information about Ethereum addresses (Ethereum accounts), transactions, and transaction receipts associated with the lightweight client. This behavior obviously violates user privacy. Currently, there is no solution supporting the lightweight client to efficiently retrieve the account state, related transactions, and transaction receipts from the untrust full node without revealing privacy. In this paper, we propose to use private information retrieval (PIR) to design a privacy-preserving Ethereum lightweight client. Since directly using existing PIR scheme significantly increases communication overhead of the lightweight client, we tailor existing PIR schemes to adapt to the lightweight client, which allows the lightweight client to efficiently retrieve account state and transaction data without leaking sensitive query information. Security and performance analysis show that our scheme is capable of efficiently and privately downloading the data of interest to the lightweight client.
Building Quick Resource Index List Using WordNet and High-Performance Computing Resource Ontology towards Efficient Resource Discovery

High-performance computing (HPC) has played a significant role in scientific discovery and technological innovation, but resources distributed in cross-regional supercomputing centers are still used inefficiently and hard to be managed uniformly, due to various challenging issues like the complex resource types, the heterogeneous interfaces of the resource management software, the inefficient coordination of software and hardware, and the lack of available and standardized description specification model. In this paper, we first propose a HPC resource ontology model called HPCRO to define the concepts of hardware resources and software resources, and specifies types, attributes, logical relations, and data transfer among resources. Moreover, based on the proposed model, we further propose a method called WordNet-based Quick Resource Index List (WQRIL) to support efficient HPC resources discovery. Specifically, the method extends and optimizes the existing method with Quick Service Quick List (QSQL) by extracting synonyms of ontology concepts from WordNet. The method not only can provide a unified representation of HPC resources, but also can support semantic-based fuzzy matching. More importantly, it improves the usability and utilization of hardware and software resources effectively in cross-regional high-performance computing centers. Extensive experiments have been conducted to verify the effectiveness and efficiency of our proposals.
Identification of Photo-taking behaviors using Optical Flow Vector

The rate of smartphone ownership has significantly increased all over the world year by year.According to the statistical data by Japanese government, more than 90% of people aged between 20 and 30 own smartphones as of 2017.Smartphones are very useful and it is easy for people to communicate with each other, taking pictures and sharing the pictures on SNS (Social Networking Sites).However, there exists an important social problem related to taking pictures, namely, unintended appearance in photos.When someone is taking a photo in a public place, other people may appear in the photo unintendedly due to the lack of photographer's moral, resulting in a privacy risk of the photographed persons.To avoid such a situation, most of existing studies perform image processing to the photo image, e.g.superimposing pixelated or blurred images around the faces.This is a passive approach for the photographed persons conducted at the photographer side, and thus, there still exists a privacy risk.In this research, an active approach conducted at the photographed person side is proposed, aiming at detecting photo-taking behaviors by smartphone.In the proposed approach, the photographer's behaviors, which show someone is about to take a photo, are focused on.It is assumed that a photographed person (user) wears a small camera like a "life log camera" and monitors his/her surroundings.The final goal of this research is to detect whether the person is about to take a photo or not, based on the video data analysis.In this paper, we analyze the characteristics of photo-taking behaviors using Optical Flow technique, referring to the movement of arms and/or hands of human.The result of evaluation experiments reveals an interesting feature distribution and shows that the detection accuracy of photo-taking behaviors is 67%.
A Literature Review on Image and Emotion Recognition: Proposed Model

This primary focus of this literature review is to build an image detection and emotion detection and recognition. It will analyze image content as well as the facial expression of the input image. Actions, behaviours, facial expressions, poses are considered as channels, and this channel helps to convey human’s emotion. This paper has a prototype system which will automatically detect emotion represented on the face. For classifying the universal emotions, like sadness, anger, happiness, disgust, fear and surprise, an image processing combined with a neural network-based solution will be used. A coloured image containing face of human is given as an input and after the face is detected, image processing based on feature extraction method is used and the set of different values are obtained, after processing and the values which are extracted are given as an input to the neural network for image and emotion detection. We have also applied an evolutionary algorithm to enhance the feature selection of the image.
Russian, Japanese, and Latin Oh My! Using Technology to Catalog Non-English Language Titles

Nearly every library where the dominant language is English also has materials that are written in other languages. These materials can present unique challenges for catalogers. Many non-English language materials are located in the array of collections of the Special Collection Department of Mississippi State University (MSU). To properly process and catalog these materials, the cataloger used online tools which provided a greater understanding of the materials, allowing a higher cataloging standard. The author discusses the various tools and methods that were used to catalog these materials.
Increasing student engagement: Tattoo design competition at Texas State University’s Alkek Library

The Albert B. Alkek Library is part of the University Libraries system that serves Texas State University (TSU) in San Marcos, Texas. University Libraries has a Promotions Team to increase student engagement with the library. A smaller group within the Promotions Team, the Tattoo Design Contest Team, created a contest inviting students to submit original tattoo designs inspired by TSU or the Alkek Library. The team wanted to create an innovative way to increase student engagement with the library while creating a fun promotional tool—free stickers of the winning tattoo designs.
Implementation of TSUNAMI Early Warning Receiver via the EWBS signal

The tsunami is a natural phenomenon that has affected Peru on some occasions, generated after an earthquake with an epicenter close to its coast. Generating human and material losses. Although there are Institutions in charge of alerting the population, this is not enough because of the coverage that they would have, because some public telecommunications services could be disabled during and after an earthquake, and blackouts in many affected areas. Thus, to safeguard more lives, HIRO has been developed, which provides the warning through audio and visual means prior to the arrival of a tsunami in the Peruvian coasts, HIRO takes advantage of the EWBS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> - DTT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> emergency signals issued by a channel authorized by the government of Peru for processing and respective activation of alerts.
A Data Concealing Technique with Random Noise Disturbance and a Restoring Technique for the Concealed Data by Stochastic Process Estimation

Abstract We propose a technique to conceal data on a physical layer by disturbing them with some random noises, and moreover, a technique to restore the concealed data to the original ones by using the stochastic process estimation. Our concealing-restoring system manages the data on the physical layer from the data link layer. In addition to these proposals, we show the simulation result and some applications of our concealing-restoring technique.
Towards practical qubit computation using approximate error-correcting grid states

Encoding a qubit in the continuous degrees of freedom of an oscillator is a significant pursuit of quantum computation. One advantageous way to achieve this is through the Gottesman-Kitaev-Preskill (GKP) grid states, whose symmetries allow for the correction of any small continuous error on the oscillator. Unfortunately, ideal grid states have infinite energy, so it is important to find finite-energy approximations that are realistic, practical, and useful for applications. In the first half of the paper we investigate the impact of imperfect GKP states on computational circuits independently of the physical architecture. To this end we analyze the behaviour of the physical and logical content of normalizable GKP states through several figures of merit, employing a recently-developed modular subsystem decomposition. By tracking the errors that enter into the computational circuit due to the imperfections in the GKP states, we are able to gauge the utility of these states for NISQ (Noisy Intermediate-Scale Quantum) devices. In the second half, we focus on a state preparation approach in the photonic domain wherein photon-number-resolving measurements on some modes of Gaussian states produce non-Gaussian states in the others. We produce detailed numerical results for the preparation of GKP states along with estimating the resource requirements in practical settings and probing the quality of the resulting states with the tools we develop.
Using Gabor Filter Bank with Downsampling and SVM for Visual Sign Language Alphabet Recognition

With the increasing advances in computer vision, the research on automated human gesture and sign language has attracted the attention of many researchers. It has many applications for human-computer interaction helping persons with hearing impairment in smart environments. In this paper, we focus on static hand visual features to build a system for recognizing hand and finger gestures representing different sign language alphabets. After hand segmentation, the proposed method employs texture based features extracted by down-sampling Gabor-transformed images using multiple scales and orientations. Then, a support vector machine is used for multi-class classification. The evaluation of the proposed approach on a benchmark dataset for the American sign language has reported over 95% overall accuracy with several signs perfectly recognized.
Perspectives of Threat Modeling of a Secure Cloud Picture Archiving and Communication System

The Picture Archiving and Communication System (PACS) used in electronic health, is computationally enhanced by the migration into the cloud, which reduces the cost of storage space and equipment. However, cloud-PACS technology is susceptible to threats and vulnerabilities. This paper implements a threat modeling approach on a cloud-PACS framework, using Microsoft Threat Modelling Tools. Security requirements and mitigation strategies were formulated for the implementation of the framework, in order to improve cloud PACS security.
Design and Development of a Novel Symmetric Algorithm for Enhancing Data Security in Cloud Computing

Cloud computing is a kind of computing that depends on shared figuring assets instead of having nearby servers or individual gadgets to deal with applications.Technology is moving to the cloud more and more.It's not just a trend, the shift away from ancient package models to package as service has steadily gained momentum over the last ten years.Looking forward, the following decade of cloud computing guarantees significantly more approaches to work from anyplace, utilizing cell phones.Cloud computing focused on better performances, better scalability and resource consumption but it also has some security issue with the data stored in it.The proposed algorithm intents to come with some solutions that will reduce the security threats and ensure far better security to the data stored in cloud.
Design of a Cascaded Black – Linear Distribution (CBLD) in Circular Aperture and Its Application on Confocal Laser Scanning Microscope (CLSM)

A new model of Cascaded Black -Linear Distribution (CBLD) in circular aperture is suggested.Four different models of CBLD are studied.In the 1 st model, ten strips are considered where half of them are black and the other half has linear distribution, starting with black strip from the center of the circular aperture.In the 2 nd model, twenty strips are considered of equal black and linear zones.In the 3 rd model, a ratio of 2:1 is given for the twenty strips of the CBLD.In the 4 th model, annular aperture of linear distribution is considered.We have computed the Point Spread Function (PSF) corresponding to all arrangements and compared with the corresponding PSF for different apertures of circular, annular, and black and white (B/W) transparent circular apertures.The cut-off spatial frequency which is the indication of resolution is investigated in all the described apertures.The Coherent Transfer Function (CTF) using the CBLD apertures is computed.Application of the CBLD arrangement corresponding to the objective and collector lenses in the CLSM using microscopic input images is shown.The reconstructed images using the described models in the CLSM are investigated.A Mat-Lab code is used for the computation of all images.
Multistep Prediction using Point-Cloud Approximation of Continuous Belief Functions

We consider the problem of quantifying prediction uncertainty in the Dempster-Shafer framework. Our approach assumes a parametric statistical model relating the variable of interest, the parameter and a pivotal random variable with known probability distribution. A predictive belief function is computed using this model and a belief function defined in the parameter space. In the case of multistep prediction, the quantity to be predicted is a vector, and the predictive belief function is defined in a multidimensional space, making its representation and manipulation difficult. To address this issue, we propose to approximate the focal sets of belief functions using point clouds, which allows us to approximate the belief and plausibility of arbitrary events with any accuracy. As an illustration, the approach is applied to the case of a first-order autoregressive process.
Machine Learning, Statistics, and Data Mining for Heliophysics

We present a book entitled “Machine Learning, Statistics, and Data Mining for Heliophysics,” an online and open source book available at helioml.org. This book includes a collection of interactive Jupyter notebooks, written in the programming language Python, that walks the reader through the process of applying machine learning, statistics, and data mining techniques on various kinds of solar and space physics data sets to reproduce published results. We consider this book to be a living document with frequent updates. Please contact us if you’d like to submit a chapter!
Domain-Relevant Embeddings for Medical Question Similarity

The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-question pairs could enable questions to be answered more effectively. While many research efforts have focused on the problem of general question similarity for non-medical applications, these approaches do not generalize well to the medical domain, where medical expertise is often required to determine semantic similarity. In this paper, we show how a semi-supervised approach of pre-training a neural network on medical question-answer pairs is a particularly useful intermediate task for the ultimate goal of determining medical question similarity. While other pre-training tasks yield an accuracy below 78.7% on this task, our model achieves an accuracy of 82.6% with the same number of training examples, and an accuracy of 80.0% with a much smaller training set.
Image encryption algorithm based on modified SC-Hopfield

In order to improve the color in the process of image transmission security and efficiency, sine chaos mapping and Hopfield neural network model is put forward with the combination of image encryption algorithm. Firstly, sinusoidal chaotic mapping is adopted in the image scrambling stage to reduce the high correlation between image pixels; secondly, the key flow generated by iterative Hopfield chaotic neural network is used to complete the image diffusion; then, the image encryption is completed through bit operation; finally, the effectiveness and security of the encryption algorithm are analyzed. It is shown from that result and statistical analysis of the simulation that the algorithm has a variety of anti-attack ability and has high safety performance.
Ground target localization and recognition via descriptors fusion

Abstract Keypoint matching can be defined as locating the position of a particular point in two images precisely. Recently, keypoint descriptors have taken a great effect targeting to be powerfully invariant to rotation, scale and translation for improving target detection. The detection task is carried out by a reference-scene image matching to localize the desired target in the input scene. An innovative approach is proposed in this work to fuse the state-of-the-art feature descriptors ORB, BRISK for the sake of accurate ground target detection in two phases. Firstly, off-line phase, where the fused features are extracted from different perspective, azimuth angles of the desired target to build a comprehensive reference image representation. Secondly, on-line phase, where the fused features extraction task is carried out from the whole scene. Hence, it is matched with the stored reference one to find the keypoints correspondence. The outliers’ problem is eliminated using Random Sample Consensus (RANSAC) algorithm resulting in speeding up the matching procedure. The conducted comparative analysis has revealed the discriminative power of the fused features in localization and recognition tasks while keeping the proposed system works in real-time.
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions

Evolutionary Strategies (ES) are known to be an effective black-box optimization technique for deep neural networks when the true gradients cannot be computed, such as in Reinforcement Learning. We continue a recent line of research that uses surrogate gradients to improve the gradient estimation of ES. We propose a novel method to optimally incorporate surrogate gradient information. Our approach, unlike previous work, needs no information about the quality of the surrogate gradients and is always guaranteed to find a descent direction that is better than the surrogate gradient. This allows to iteratively use the previous gradient estimate as surrogate gradient for the current search point. We theoretically prove that this yields fast convergence to the true gradient for linear functions and show under simplifying assumptions that it significantly improves gradient estimates for general functions. Finally, we evaluate our approach empirically on MNIST and reinforcement learning tasks and show that it considerably improves the gradient estimation of ES at no extra computational cost.