Reviewerly/exHarmony huggingface dataset:
Medical Data Compression and Sharing Technology Based on Blockchain

Cloud service provides distributed storage spaces and avoids computing bottlenecks of centralized database storage, but it cannot provide secure data storage and sharing. How to store and share medical data efficiently and reliably is an important issue to ensure the safety of medical data in the process of medical data management of multi-regional hospitals. The public access of blockchains and the non-modifiable characteristics of the stored data make it an effective implementation scheme for medical data sharing. Meanwhile, with the continuous transactions in blockchains, the data of the blockchains are bound to be larger and larger, which leads to serious issues for the use and storage of data. Based on the LZW (Lemple-Ziv-Welch) algorithm, this paper presents a lossless compression technology for Chinese text compression with a compression storage and sharing scheme for medical data using blockchains to provide safer and more efficient access services for medical data.
Mobile Applications in Cultural Heritage Context

As mobile technology sustains exponential growth and spread to all aspects of our everyday life and smartphone computational power increases, new promises arise for cultural institutions and citizens to use these tools for promoting cultural heritage. This survey proposes to review available smartphone applications (apps) relating to cultural heritage in three different contexts: cities, street art, and museums. Apps were identified by searching two app stores: Apple's App Store and Google Play (Android). A data search was undertaken using keywords and phrases relating to cities, street art, and museums. A total of 101 apps were identified (Google Play only= 7, Apple App Store only = 26, both Google Play and Apple App Store = 61, Apple Web Store and Web App = 6). Apps were categorized into the following categories: museums (39), street art (30), and cities (32). The most popular features are photos (96%) and maps (79%), and the most uncommon the 360 (4% – only in museums apps), games (6%), and video (15%).
REALational perspectives

This article analyzes mobile AR installations in order to demonstrate different strategies for producing a relational sense of place and time. These installations combine multiple narratives in situ; thus, they reshape existing perceptions and influence national identities. By means of exposing actual environments as constructed and therefore as virtual landscapes, mobile AR art exposes our situatedness and becomes a strong tool for activism as it encourages us to think beyond familiar, material reality. As such, it rejects an absolute perception of reality and reconfigures it as a relational domain.
Classification of authors for an automatic recommendation process for criminal responsibility

One problem in classifying tasks is the handling of features that characterize classes. When the list of features is long, a noise resistant algorithm of irrelevant features can be used, or these features can be reduced. Authorship attribution is a task that assigns an anonymous text to a subject on a list of possible authors, has been widely addressed as an automatic text classification task. In it, n-grams can produce long lists of features even in small corpora. Despite this, there is a lack of research exposing the effects of using noise-resistant algorithms, reducing traits, or combining both options. This paper responds to this lack by using contributions to discussion forums related to organized crime. The results show that the classifiers evaluated, in general, benefit from feature reduction, and that, thanks to such reduction, even classical algorithms outperform state-of-the-art classifiers considered highly noise resistant.
MatchingRef

Debugging compiler errors is essential to programming and can be challenging for novice programmers. In introductory computer science courses, challenging errors can discourage students. One reason these errors are difficult to resolve is that most online help systems do not match a student's code. For example, online reference pages use different variable names, identifiers, and method names compared with a student's particular code. To utilize existing resources, students must wade through other people's code (which is often too advanced for novices to comprehend). This is time-consuming and does not provide novices with solutions.
Pattern Combination and Variation Syntax for Case Similarity

This paper aims to explain a new way of seeking case similarities to Case Based Reasoning question and answering system. A new concept used is to create a combination of sentence patterns with the variations of syntax and relation between words in sentences. More precisely, we improve the similarity from a resource-poor source language. The combination of pattern was made for the variation of the fridge using Predicate Argument Structure (PAS) analysis, whereas the combination of pattern with the relation between words was by forming Sub Tree (ST) and Subset Tree (SST) from parsing tree. The result of forming a combination of these patterns was then selected to obtain an appropriate answer. Accuracy resulted by using the combination of this pattern resulted in a significant value equal to 88.64% of 132 question sentences used as data testing.
Arbitrary-Centered Discrete Gaussian Sampling over the Integers

Discrete Gaussian sampling over the integers, which is to sample from a discrete Gaussian distribution $$\mathcal {D}_{\mathbb {Z},\sigma ,\mu }$$ over the integers $$\mathbb {Z}$$ with parameter $$\sigma >0$$ and center $$\mu \in \mathbb {R}$$ , is one of fundamental operations in lattice-based cryptography. The sampling algorithm should support a varying center $$\mu $$ and even a varying parameter $$\sigma $$ , when it is used as one of the subroutines in an algorithm for sampling trapdoor lattices, or sampling from Gaussian distributions over a general n-dimensional lattice $$\varLambda $$ . In this paper, combining the techniques in Karney’s algorithm for exactly sampling the standard normal distribution, we present an exact sampling algorithm for $$\mathcal {D}_{\mathbb {Z},\sigma ,\mu }$$ with an integer-valued parameter $$\sigma $$ . This algorithm requires no pre-computation storage, uses no floating-point arithmetic, supports centers of arbitrary precision, and does not have any statistical discrepancy. Applying the convolution-like property of discrete Gaussian distributions, we also present an approximated sampling algorithm for $$\mathcal {D}_{\mathbb {Z},\sigma ,\mu }$$ with a real-valued parameter $$\sigma $$ . It also supports centers of arbitrary precision, and we show that the distribution it produces has a smaller max-log distance to the ideal distribution, as compared to Micciancio-Walter sampling algorithm, which was introduced by Micciancio et al. in Crypto 2017 for discrete Gaussian distributions with varying $$\sigma $$ and $$\mu $$ over the integers.
Behaviour of Outsourced Employees as Sources of Information System Security Threats

There is an increased need for information systems to be protected against unauthorized access and retrieval, particularly from legitimate ‘insider’ outsourced employees. While most studies have focused on organisations’ employees as threats, only a few have focused on the role the outsourced employees’ play as a potential threat. The study seeks to investigate the insider threat behaviour of an outsourced employee in developing countries as security threats to information systems by virtue of their privileged access. The study is quantitative and adopts social bond and involvement theories for this purpose. The research sample was chosen from organisations in Nigeria and South Africa which are the largest two national economies in Africa. Close-ended questionnaires were used and the data were analysed using factor analysis. The study found that outsourced employees exploit information systems vulnerabilities because they are not actively involved in the organisation and lack moral values and beliefs. The findings of this study will assist organisations in developing countries to mitigate the information security threats posed by outsourced employees.
ORTIA: An Algorithm to Improve Quality of Experience in HTTP Adaptive Bitrate Streaming Sessions

Adaptive Bitrate (ABR) is used at large scale in online video streaming to improve viewer perception. The advanced online streaming process utilizes adaptive bitrate adaptation algorithms that works in video-players. To improve viewer’s quality of experience, ABR algorithm has to select high quality video as per the available network throughput and transmit with minimal stops and low-bitrate fluctuation. Although, current ABR algorithms suffers from stops, and low bitrate fluctuations because they don’t consider the influence of the underlying atypical value also known as outlier in statistics during throughput prediction. Here we propose a new ABR Outlier-Removal-based Throughput Improvement Algorithm (OTRIA), that provides a realistic forecast of throughput in changing network circumstances, thus minimizing stops and bitrate fluctuations. We used various statistical methods to detect outliers. We used the method of Inter-Quartile-Range to identify a range of throughput values that differs significantly from the other values in dataset and utilized this tool to maximize the precision for the predictions of throughput. Results from real-time experimentation have shown the elimination of stops during the implementation of our algorithm. In addition, a comparison is made with the latest generation ABR algorithm named DYNAMIC to exhibit that the ORTIA-algorithm is superior to the current ABR algorithm. Simply put, our algorithm delivers an excellent viewer’s Quality of Experience and aperture for adaptive transmission of real video. It is noteworthy that our algorithm beats the DYNAMIC, which is now officially the part of DASH-IF reference player and is used by video content supplier in production atmosphere.
General Remote Control Based on Hand Patterns Detection Using Convolutional Neural Network

Abstract In this paper, the system is made to be able to recognize hand patterns to be able to control electronic devices, as a substitute for the conventional remote. This system consists of a computer containing a program with a trained Convolutional Neural Network (CNN) model and an MCU node containing a remote infrared signal database whose function we want to replace. The camera functions as a reader of hand patterns which will be classified using trained CNN models. Each hand gesture represents the infrared pulse in the database program n Node MCU. There are 4 hand patterns that represent commands in this proposed system. To be able to handle much more commands, this proposed system is designed to change mode according to the combination of hand patterns detected by the system algorithm. The changing of mode results in the changing of the infrared pulse which is represented by each hand pattern in the database. Node MCU will transmit infrared pulse corresponds to hand gesture from CNN classification after receiving a command from the computer through TCP/IP communication.
Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.
Coarse-Grained vs. Fine-Grained Lithuanian Dependency Parsing

In this paper we present the dependency parsing experiments done on the Lithuanian treebank Alksnis v2.1. This treebank contains 84 different fine-grained syntactic labels (and can be generalized to 10 coarse-grained), is annotated with lemmas, parts-of-speech and other fine-grained morphological information (cases, numbers, tenses, etc.). During our experiments with the transition-based MaltParser system we have explored an impact of different morphological features. The best results were achieved when the following features were taken into account: case, verb form, named entity, word type, mood on the coarse-grained dependency labels and case, verb form, aspect, named entity and person on the fine-grained. The increase in LAS (UAS) over the baseline was $$\sim $$ 7.16% ( $$\sim $$ 4.77%) and $$\sim $$ 8.03% ( $$\sim $$ 5.19%) on the coarse-grained and the fine-grained reaching $$\sim $$ 71.90% ( $$\sim $$ 75.45%) and $$\sim $$ 71.14% ( $$\sim $$ 76.20%), respectively.
A MULTILEVEL USER VALIDATION FRAMEWORK FOR ACCESSING CLOUD SERVICES

Cloud computing is a paradigm that offers enormous advantages to cloud users for example,huge memory, adaptable processing abilities, security to the data and boundless registering capabilities. Toutilize the maximum capacity of Cloud computing, information is moved, handled and stored by Cloud ServiceProviders (CSP). Be that as it may, information owners are suspicious to put their information in cloud that isoutside their own control. The main issue with Cloud computing is the manner by which to provide the securityand protection of cloud user information being prepared and additionally stored in a Cloud computing condition.With regards to cloud information assurance, the techniques utilized can be fundamentally the same as ensuringinformation inside a conventional server group. To perform user validation for accessing cloud services, a strongcryptography method is introduced along with a multi factor verification process. Moving information into thecloud implies that the clients have less authority over their information, this implies the cloud clients mustbelieve the CSP to secure the information from both outside and inside attacks. In the proposed work, a MultiLevel User Authentication (MLUA) framework is introduced for accessing cloud services. The unauthorizedusers are not allowed to access cloud resources. The cloud user, data owner and cloud service providers areinvolved in multi factor authentication. The CSP will generate a Digital Unique Authentication Identity (DUAI)number along with general verification for undergoing multi level verification process. The proposed method iscompared with the traditional methods and the results show that the proposed method is better in performanceand accuracy in identification of valid cloud users.
Motion and Navigation Control System of a Mobile Robot as A Prototype of An Autonomous Vehicle

Abstract Autonomous vehicle is a vehicle that can automatically navigate both long-range and short-range navigation. In this paper, we designed a motion and navigation control system of an autonomous vehicle that is implemented on a mobile robot. The remote navigation process involves using the global positioning system (GPS) sensor as a reference to navigate, and for short-range navigation using the proximity and force sensor. The robot task is to reach several coordinate points that are determined and marked with a traffic-cone at each point. In the control of short-range navigation, the robot uses five ultrasonic and three switch sensors to read the environment as input to the description of the distance between the robot and the object. The micro-controller then compares it to the robot’s safe distance instructions in navigating with surrounding objects. GPS sensor is used to provide remote navigation path information. To compensate for the accuracy of the wide GPS sensor readings, several waypoints are designed to guide the robot to the specified traffic-cone. From the implementation, the robot movement can go along the navigation area and can avoid short-range objects in the robot trajectory and get to the traffic-cone point with the percentage of success reaching the final destination above 86% both in sunny and cloudy weather.
Estimating the Dimension of the Subfield Subcodes of Hermitian Codes

In this paper, we study the behavior of the true dimension of the subfield subcodes of Hermitian codes. Our motivation is to use these classes of linear codes to improve the parameters of the McEliece cryptosystem, suchas key size and security level. The McEliece scheme is one of the promising alternative cryptographic schemes to the current public key schemes since in the last four decades, they resisted all known quantum computing attacks. By computing and analyzing a data collection of true dimensions of subfield subcodes, we concluded that they can be estimated by the extreme value distribution function.
Web Service for Academic Information Systems

Abstract This research has the goal of creating an academic information system based on web service or Application Programming Interface (API). We use API, so that information systems are easily accessed and applied to cross platforms such as websites, desktop applications, mobile applications, and others. The descriptive analysis method was used in this study to analyze the requirements contained in the built system. Other than that, we used the waterfall method as a model approach in software development. The final results of this study can help in the management of academic data. These results can be achieved because all information is processed and presented online and accessed through cross platforms. Besides, if universities are advanced, this system is more comfortable to develop because it uses the API. Hopefully, this research result can contribute to the Ministry of Research, Technology, and Higher Education data management regulation.
CanvasMirror: Secure Integration of Third-Party Libraries in a WebVR Environment

Web technology has evolved to offer 360-degree immersive browsing experiences. This new technology, called WebVR, enables virtual reality by rendering a three-dimensional world on an HTML canvas. Unfortunately, there exists no browser-supported way of sharing this canvas between different parties. As a result, third-party library providers with ill intent (e.g., stealing sensitive information from end-users) can easily distort the entire WebVR site. To mitigate the new threats posed in WebVR, we propose CanvasMirror, which allows publishers to specify the behaviors of third-party libraries and enforce this specification. We show that CanvasMirror effectively separates the third-party context from the host origin by leveraging the privilege separation technique and safely integrates VR contents on a shared canvas.
Distributed Team Cognition

Throughout the last 25 years there has been much research generated in the area of team cognition and distributed cognition, often independent from each other. However more recently these two areas have congealed in a way that makes them interrelated in myriad ways. Our book in 2001 (McNeese, Salas, & Endsley, 2001) among many others focused more distinctively on team cognition and in many ways was predicated on the study of teams, teamwork, and team performance that educed from a long history within the tradition of industrial/organizational psychology. This chapter provides an introduction to the Handbook of Distributed Team Cognition and provides a broader array of worldviews, histories, methodologies, measures, and applications than what was possible in the time of the first book. As part of this milieu is the important role that information, computation, and technology has played in defining multiple perspectives that have melded together distributed and team cognition, often in unique and innovative ways. This chapter hence provides an initial foray into the intermingling of what distributed means in the context of real-world environments where technology is highly active and present. The use of the autobiographic method is applied in the second part of the chapter to show how interdisciplinary science came into play to shape the first editor’s worldview and research prowess within distributed team cognition.
Automated Extraction of Paradigmatic Relationships from Natural Language Texts on the Basis of the Complex of Heterogeneous Features

The paper presents the experience of applying a two-stage semantic analysis of texts to extract paradigmatic relationships and concepts of the subject area. An approach to machine understanding of the text based on the joint application of the methods of linguistic and distributive analysis to form a heterogeneous attribute space, including syntactic, statistical, and semantic features of the text, is considered. Examples of the implementation of heuristic and neural-network analyzers are presented, an assessment of their effectiveness is given, and prospects for the development of the approach are indicated.
Matrix Multiplication Unit Scalability Investigation for InP SOA-based Photonic Deep Neural Network

We investigate the scalability of a SOA-based photonic DNN with WDM inputs. For a 3-layer DNN, a quasi-linear dependence between the prediction accuracy and errors/layer is found for a NRMSE <; 0.09. Optimized passive losses can enable synaptic function at half of the power consumption.
Big Data Visualisation - An Update until Today

Visualisation is a psychological picture that is like a visual observation. Visualisation methods have the ability to fortify intelligence by giving realistic and intuitive information explore interface. Information visualisation can demonstrate the aftereffects of information investigation in an instinctive way, which has a vital handy noteworthiness. Data visualisation improves individuals' consciousness of conceptual data by means of supporting through computer intelligently and the visual portrayal of theoretical information. Besides, visualisation changes the data into a visual frame and takes focal points of individuals' normal capacity that can rapidly recognize visual examples to observe, peruse, segregate and comprehend data. Visualisation gives an incredible way to information examination, however to be practical, visual investigation instruments must support smooth and adaptable utilization of representations at a quick rate. This paper examines a scope of current visualisation perception tools to recognize a few issues in existing procedures and prescribes some legitimate logical solutions for fixing the issue with existing data visualisation tools.