Reviewerly/exHarmony huggingface dataset:
Studying the organizational structure effectiveness on the basis of internal communication analysis

Digital technologies are changing corporate communication due to the opportunity to make contact via e-mail, social networks and messengers, which leads to the transformation of a company’s organizational structure. At the same time, the organizational structure should guarantee the effective implementation of communicative functions. Methodologically, the research relies on organizational theory when declaring communication to be one of the main functions of an organization that allows managing the organization and ensuring low transaction costs of internal communications. The present article aims to assess the possibility of using traces of internal corporate communication to gauge the organizational structure’s effectiveness. The authors adopt statistical and mathematical methods, such as the construction of a communication graph, clustering and comparative analysis. We process server data on intra-domain communications of the organization, build a communication graph, and evaluate various parameters reflecting its stability and flexibility and influencing the organizational structure’s effectiveness. Based on the proposed indicators, the study assesses the level of communication efficiency of each element of the company’s structure. The results of the research exploiting non-traditional sources of information and approaches allow us to evaluate the correspondence between the organizational structure and the cluster structure of communications, as well as the sustainability of internal and external communication of each element.
Early earthquake detection capabilities of different types of future-generation gravity gradiometers

SUMMARY Since gravity changes propagate at the speed of light, gravity perturbations induced by earthquake deformation have the potential to enable faster alerts than the current earthquake early warning systems based on seismic waves. Additionally, for large earthquakes (Mw &amp;gt; 8), gravity signals may allow for a more reliable magnitude estimation than seismic-based methods. Prompt elastogravity signals induced by earthquakes of magnitude larger than 7.9 have been previously detected with seismic arrays and superconducting gravimeters. For smaller earthquakes, down to Mw ≃ 7, it has been proposed that detection should be based on measurements of the gradient of the gravitational field, in order to mitigate seismic vibration noise and to avoid the cancelling effect of the ground motions induced by gravity signals. Here we simulate the five independent components of the gravity gradient signals induced by earthquakes of different focal mechanisms. We study their spatial amplitude distribution to determine what kind of detectors is preferred (which components of the gravity gradient are more informative), how detectors should be arranged and how earthquake source parameters can be estimated. The results show that early earthquake detections, within 10 s of the rupture onset, using only the horizontal gravity strain components are achievable up to about 140 km distance from the epicentre. Depending on the earthquake focal mechanism and on the detector location, additional measurement of the vertical gravity strain components can enhance the detectable range by 10–20 km. These results are essential for the design of gravity-based earthquake early warning systems.
Asymmetric Interaction between HMD Wearers and Spectators with a Large Display

Head-mounted displays (HMDs) provide the wearer with a highly immersive virtual reality experience, but they obstruct communication with the outside world. This makes it difficult to communicate or share the virtual experience with viewers outside of the headset. The aim of this project is to develop a better spectator solution for collaborative tasks and demonstrations. We propose an approach which allows the spectator to take control of their viewing experience and interact with the HMD user in a natural way. They are able to control their own camera and point in the virtual world. This interaction enables spectators to effectively communicate, collaborate, and feel semi-immersed without the need to wear a HMD.
Utilization of the cyclic interferometer in polarization phase-shifting technique to determine the thickness of transparent thin-films

An alternative polarization phase-shifting technique is proposed to determine the thickness of transparent thin-films. In this study, the cyclic interferometric configuration is chosen to maintain the stability of the operation against external vibrations. The incident light is simply split by a non-polarizing beam splitter cube to generate test and reference beams, which are subsequently polarized by a polarizing beam splitter. Both linearly polarized beams are orthogonal and counter-propagating within the interferometer. A wave plate is inserted into the common paths to introduce an intrinsic phase difference between the orthogonal polarized beams. A transparent thin-film sample, placed in one of the beam tracks, modifies the output signal in terms of the phase retardation in comparison with the reference beam. The proposed phase-shifting technique uses a moving mirror with a set of “fixed” polarizing elements, namely, a quarter-wave retarder and a polarizer, to facilitate phase extraction without rotating any polarizing devices. The measured thicknesses are compared with the measurements of the same films acquired using standard equipment such as the field-emission scanning electron microscope and spectroscopic ellipsometer. Experimental results with the corresponding measured values are in good agreement with commercial measurements. The system can be reliably utilized for non-destructive thickness measurements of transparent thin-films.
Regularized Evolution for Macro Neural Architecture Search

Neural Architecture Search is becoming an increasingly popular research field and method to design deep learning architectures. Most research focuses on searching for small blocks of deep learning operations, or micro-search. This method yields satisfactory results but demands prior knowledge of the macro architecture's structure. Generally, methods that do not utilize macro structure knowledge perform worse but are able to be applied to datasets of completely new domains. In this paper, we propose a macro NAS methodology which utilizes concepts of Regularized Evolution and Macro Neural Architecture Search (DeepNEAT), and apply it to the Fashion-MNIST dataset. By utilizing our method, we are able to produce networks that outperform other macro NAS methods on the dataset, when the same post-search inference methods are used. Furthermore, we are able to achieve 94.46% test accuracy, while requiring considerably less epochs to fully train our network.
Quaternion quantum neurocomputing

Since the introduction of quaternion by Hamilton in 1843, quaternions have been used in a lot of applications. One of the most interesting qualities is that we can use quaternions to carry out rotations and operate on other quaternions; this characteristic of the quaternions inspired us to investigate how the quantum states and quantum operator work in the field of quaternions and how we can use it to construct a quantum neural network. This new type of quantum neural network (QNN) is developed in the quaternion algebra framework that is isomorphic to the rotor algebra [Formula: see text] of the geometric algebra and is based on the so-called qubit neuron model. The quaternion quantum neural network (QQNN) is tested and shows robust performance.
Independent validation of paige prostate: Assessing clinical benefit of an artificial intelligence tool within a digital diagnostic pathology laboratory workflow.

e14076 Background: The most common approach to diagnose prostate cancer is the “whole gland biopsy procedure,” in which numerous cores (≥12) are taken from different regions of the gland to maximize the chances of detecting small cancers; the presence of cancer in any of these cores is significant to the patient. If concerning features that are not fully diagnostic of cancer are identified, the pathologist may defer the final diagnosis until additional studies (e.g. immunohistochemistry) have been performed. We recently developed an artificial intelligence (AI)-based system for the assessment of cancer in prostate biopsies. Here, we investigated the performance of this test in an independent dataset of prostate cancers consecutively accrued. Methods: Two board-certified pathologists retrospectively reviewed 600 digitized hematoxylin-and-eosin (H&amp;E) stained diagnostic prostate core needle biopsy slides from 100 consecutive patients, originally diagnosed at an independent hospital. Pathologists’ assessments were based on the H&amp;E image alone; if further testing would be preferred, it was noted in the review notes. All images were assessed by Paige Prostate 1.0, an AI-based diagnostic tool; based on its outputs (either suspicious for cancer or not), the discordant images were re-reviewed by the pathologists and, in parallel, adjudicated with additional testing (e.g. ancillary immunohistochemical markers). Results: Paige Prostate's slide-level sensitivity was 98.9% and its specificity was 93.3% (100% and 78.0%, respectively, at the subject-level). The pathologists' average slide-level sensitivity and specificity without Paige Prostate was 90.9% and 98.6%, respectively. The sensitivity with their consensus read and Paige Prostate increased by 5.7% to 96.6% with only 0.8% decrease in specificity. In addition to new slide-level findings, benefits were also observed at the subject-level; with Paige, three new prostate cancer cases were discovered that were initially missed. Conclusions: The study reflects the potential benefits of the Paige Prostate system in the hands of experienced pathologists and validates the algorithm in a completely independent dataset. Paige Prostate can improve pathologists' sensitivity when reviewing digitized H&amp;E prostate needle biopsy images with a minor impact on specificity.
Policy agenda proposals from text mining analysis of patents and news articles

The purpose of this study is to explore the trend of blockchain technology through analysis of patents and news articles using text mining, and to suggest the blockchain policy agenda by grasping social interests. For this purpose, 327 blockchain-related patent abstracts in Korea and 5,941 full-text online news articles were collected and preprocessed. 12 patent topics and 19 news topics were extracted with latent dirichlet allocation topic modeling. Analysis of patents showed that topics related to authentication and transaction accounted were largely predominant. Analysis of news articles showed that social interests are mainly concerned with cryptocurrency. Policy agendas were then derived for blockchain development. This study demonstrates the efficient and objective use of an automated technique for the analysis of large text documents. Additionally, specific policy agendas are proposed in this study which can inform future policy-making processes.
Optimal Quinary Cyclic Codes with Minimum Distance Four

Chinese Journal of ElectronicsVolume 29, Issue 3 p. 515-524 ArticleFree Access Optimal Quinary Cyclic Codes with Minimum Distance Four Jinmei Fan, Jinmei Fan College of Science, Guilin University of Technology, Guilin, 541004 ChinaSearch for more papers by this authorYanhai Zhang, Corresponding Author Yanhai Zhang zhang.yanhai@foxmail.com College of Science, Guilin University of Technology, Guilin, 541004 ChinaSearch for more papers by this author Jinmei Fan, Jinmei Fan College of Science, Guilin University of Technology, Guilin, 541004 ChinaSearch for more papers by this authorYanhai Zhang, Corresponding Author Yanhai Zhang zhang.yanhai@foxmail.com College of Science, Guilin University of Technology, Guilin, 541004 ChinaSearch for more papers by this author First published: 01 May 2020 https://doi.org/10.1049/cje.2020.02.011Citations: 1AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The necessary and sufficient condition for the quinary cyclic codes with generator polynomial (x + 1)mα(x)mαe (x) to have parameters [5m - 1,5m - 2m - 2,4] is provided by analyzing solutions of certain equations over the finite field 5m. And thus several classes of new optimal quinary cyclic codes with the same parameters and generator polynomial are constructed based on analyzing irreducible factors of certain polynomials with low degrees over finite fields. Citing Literature Volume29, Issue3May 2020Pages 515-524 RelatedInformation
Privacy-preserving machine learning using EtC images

In this paper, we propose a novel privacy-preserving machine learning scheme with encrypted images, called EtC (Encryption-then-Compression) images. Using machine learning algorithms in cloud environments has been spreading in many fields. However, there are serious issues with it for end users, due to semi-trusted cloud providers. Accordingly, we propose using EtC images, which have been proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is considered under the use of z-score normalization. It is demonstrated that the use of EtC images allows us not only to protect visual information of images, but also to preserve both the Euclidean distance and the inner product between vectors. In addition, dimensionality reduction is shown to can be applied to EtC images for fast and accurate matching. In an experiment, the proposed scheme is applied to a facial recognition algorithm with classifiers for confirming the effectiveness of the scheme under the use of support vector machine (SVM) with the kernel trick.
Compressed Sparse FM-Index: Fast Sequence Alignment Using Large K-Steps

The FM-index is a data structure used in genomics for exact search of input sequences over large reference genomes. Algorithms based on the FM-index show an irregular memory access pattern, resulting in a memory bound problem. We analyze a recent implementation of the FM-index and highlight existing throughput-memory trade-offs, showing that memory requirements limit implementation of large k-steps. We propose COFI, a COmpressed FM-Index for large K-steps. COFI enables a 15-step FM-index using less than 16 GB for a human genome reference of 3 giga base pairs. An algorithm based on this new layout is evaluated on both a Knights Landing (KNL) and an Skylake-based system (SKX). We achieve average speed-ups of 1.46× and 1.39×, respectively, with respect to an state-of-the-art FM-index implementation that is already well optimized.
Abstraction and Genericity in Why3

The benefits of modularity in programming—abstraction barriers, which allow hiding implementation details behind an opaque interface, and genericity, which allows specializing a single implementation to a variety of underlying data types—apply just as well to deductive program verification, with the additional advantage of helping the automated proof search procedures by reducing the size and complexity of the premises and by instantiating and reusing once-proved properties in a variety of contexts In this paper, we demonstrate the modularity features of WhyML, the language of the program verification tool Why3. Instead of separating abstract interfaces and fully elaborated implementations, WhyML uses a single concept of module, a collection of abstract and concrete declarations, and a basic operation of cloning which instantiates a module with respect to a given partial substitution, while verifying its soundness. This mechanism brings into WhyML both abstraction and genericity, which we illustrate on a small verified Bloom filter implementation, translated into executable idiomatic C code.
A Secure Cloud Password and Secure Authentication Protocol for Electronic NFC Payment Between ATM and Smartphone

NFC (Near Field Communication) is a radio frequency wireless communication technology for less distance (less than 10cm).It operates at a frequency of 13.56 MHz recently, it has been used for electronic payment between an Automated Teller Machine (ATM) and a Smartphone.It can be menaced by attacks which stole personal data like the user password, the user bank account number ant its amount.So, it must be protected and secured.In this paper, we present a cloud secured password, a simple secured authentication protocol, a simple proposed hash function and a simple test of intrusion to secure the NFC payment between an ATM and a Smartphone against eleven attacks.The analysis of our solution proves that it defends against eleven attacks; it is cost-effectiveness in terms of hardware, cost of calculation, storage space and cost of communication.The proposed technique of password and the protocol use simple cryptography operations, a simple hash function and simple operators.
Cyber crimes in the banking sector: Case study of Vietnam

The technological revolution 4.0 brings great opportunities, but also cybercrimes to economic sectors, especially to banks. Using secondary data and survey results of 305 bank clients, the main findings of this paper are: (i) there are several types of cybercrimes in the banking sector; (ii) Vietnam is one of the top countries worldwide having hackers and being attacked by hackers, especially the banking sector. Three most common attacks are skimming, hacking and phishing. Number of cybercrime attacks in Vietnam are increasing rapidly over years; (iii) Vietnamese customers are very vulnerable to cybercrime in banking, as more than 58% seem to hear about cybercrimes, and how banks provide services to let them know about their transactions. However, more than 50% do not have any deep knowledge or any measures for preventing cybercrime; (iii) Customers believe in banks, but do not think that banks can deal with cybercrime issues well. They still feel traditional transactions are more secure than e-transactions; (iv) the reasons for high cybercrimes come from commercial banks (low management and human capacity), supporting environment (inadequate), legal framework (not yet strong and strict enough on cybercrimes), and clients (low level of financial literacy). Therefore, several solutions should be carried out, from all stakeholders, for improving the cybersecurity in Vietnamese banks.
The Behavioral Sign of Account Theft: Realizing Online Payment Fraud Alert

As a matter of fact, it is usually taken for granted that the occurrence of unauthorized behaviors is necessary for the fraud detection in online payment services. However, we seek to break this stereotype in this work. We strive to design an ex-ante anti-fraud method that can work before unauthorized behaviors occur. The feasibility of our solution is supported by the cooperation of a characteristic and a finding in online payment fraud scenarios: The well-recognized characteristic is that online payment frauds are mostly caused by account compromise. Our finding is that account theft is indeed predictable based on users' high-risk behaviors, without relying on the behaviors of thieves. Accordingly, we propose an account risk prediction scheme to realize the ex-ante fraud detection. It takes in an account's historical transaction sequence, and outputs its risk score. The risk score is then used as an early evidence of whether a new transaction is fraudulent or not, before the occurrence of the new transaction. We examine our method on a real-world B2C transaction dataset from a commercial bank. Experimental results show that the ex-ante detection method can prevent more than 80\% of the fraudulent transactions before they actually occur. When the proposed method is combined with an interim detection to form a real-time anti-fraud system, it can detect more than 94\% of fraudulent transactions while maintaining a very low false alarm rate (less than 0.1\%).
ARTIFICIAL INTELLIGENCE AND CYBER SECURITY – FACE TO FACE WITH CYBER ATTACK – A MALTESE CASE OF RISK MANAGEMENT APPROACH

The work paper aims to underline the benefits of using Artificial Intelligence to improve the business productivity, and in the same time to address awareness in order to overcome fear in exploring new technology, because of cyber-attacks. How vulnerable are businesses to computerization? 100%. Internet is a virtual space available for everyone. Storing data on any device that can be connected to the internet can become vulnerable in any given second. This article comes to show how we can use cybersecurity to protect our business, presenting in the same time cases of risk management form Malta.
Straight to the Point: Fast-Forwarding Videos via Reinforcement Learning Using Textual Data

The rapid increase in the amount of published visual data and the limited time of users bring the demand for processing untrimmed videos to produce shorter versions that convey the same information. Despite the remarkable progress that has been made by summarization methods, most of them can only select a few frames or skims, which creates visual gaps and breaks the video context. In this paper, we present a novel methodology based on a reinforcement learning formulation to accelerate instructional videos. Our approach can adaptively select frames that are not relevant to convey the information without creating gaps in the final video. Our agent is textually and visually oriented to select which frames to remove to shrink the input video. Additionally, we propose a novel network, called Visually-guided Document Attention Network (VDAN), able to generate a highly discriminative embedding space to represent both textual and visual data. Our experiments show that our method achieves the best performance in terms of F1 Score and coverage at the video segment level.
A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks

Many high-level procedural tasks can be decomposed into sequences of instructions that vary in their order and choice of tools. In the cooking domain, the web offers many, partially-overlapping, text and video recipes (i.e. procedures) that describe how to make the same dish (i.e. high-level task). Aligning instructions for the same dish across different sources can yield descriptive visual explanations that are far richer semantically than conventional textual instructions, providing commonsense insight into how real-world procedures are structured. Learning to align these different instruction sets is challenging because: a) different recipes vary in their order of instructions and use of ingredients; and b) video instructions can be noisy and tend to contain far more information than text instructions. To address these challenges, we use an unsupervised alignment algorithm that learns pairwise alignments between instructions of different recipes for the same dish. We then use a graph algorithm to derive a joint alignment between multiple text and multiple video recipes for the same dish. We release the Microsoft Research Multimodal Aligned Recipe Corpus containing ~150K pairwise alignments between recipes across 4262 dishes with rich commonsense information.