Reviewerly/exHarmony huggingface dataset:
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary

We present a novel method of compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters. The proposed method reduces the number of parameters of each convolutional layer by learning a 1D vector termed Filter Summary (FS). The convolutional filters are located in FS as overlapping 1D segments, and nearby filters in FS share weights in their overlapping regions in a natural way. The resultant neural network based on such weight sharing scheme, termed Filter Summary CNNs or FSNet, has a FS in each convolution layer instead of a set of independent filters in the conventional convolution layer. FSNet has the same architecture as that of the baseline CNN to be compressed, and each convolution layer of FSNet has the same number of filters from FS as that of the basline CNN in the forward process. With compelling computational acceleration ratio, the parameter space of FSNet is much smaller than that of the baseline CNN. In addition, FSNet is quantization friendly. FSNet with weight quantization leads to even higher compression ratio without noticeable performance loss. We further propose Differentiable FSNet where the way filters share weights is learned in a differentiable and end-to-end manner. Experiments demonstrate the effectiveness of FSNet in compression of CNNs for computer vision tasks including image classification and object detection, and the effectiveness of DFSNet is evidenced by the task of Neural Architecture Search.
Visual analytics and its use in the NRNU MEPhI “Scientific Visualization” laboratory activities

The article discusses visual analytics, which, according to J. Thomas, is understood as the solution of problems of data analysis using a facilitating interactive visual interface.Today, visual analytics is widely used in various fields -in research, design, management and others, due to the well-known predisposition of people to spatial thinking.Despite the wide usage, according to the authors, the theoretical aspects of visual analytics are not well developed at present, which is certainly a limiting factor in the development of visual analytics tools and its effective use in practice.It is declared in the article, that one of the most common of the forms of visual analytics is solution of data analysis problems using the visualization method.The visualization method is described in details and a number of works done by the "Scientific Visualization" laboratory NRNU MEPhI in this area are presented as an example of usage of the visualization method.The works were performed in different subject areas from physics to finance and monitoring.
Does the Convergence of the Blockchain, the Internet of Things and Artificial Intelligence Changing Our Lives, Education and the Known World of the Internet?! Some Changes and Perspectives for the International Economy

Abstract: Through this paper we try to answer the questions that come to our minds more and more often as the days are passing: Will our lives, education and the known world of the Internet change with this convergence of the blockchain, the Internet of things and artificial intelligence? Will we, people, be able to keep up with these amazing changes? Are companies willing to invest in new technology? What are the advantages and disadvantages for society, in general? Are the managers of companies aware of these phenomena of the new information technologies and are they open to these changes? Through the statistical data that we have accessed by researching various information sources, we have come to some conclusion, but the phenomenon is in full development and until now we do not known all its mysteries.
Evaluation of Hash Rate-based Double-Spending based on Proof-of-Work Blockchain

A Blockchain is a distributed public ledger that hold immutable data in a secure and encrypted way to ensure that a transaction is safe and cannot be altered. It is implemented based on a consensus algorithm called Proof-of-Work (PoW) which confirms transactions and produces new blocks to the chain. With PoW, miners compete against each other to complete transactions on the network and get rewarded. In this paper, we simulate bitcoin, which is a well-known example of a Blockchain, to perform tasks such as network and blockchain simulation, and being the subject of a double spending attacks using a framework established by Arthur Gervais. This framework is utilized to evaluate the doublespending behavior of bitcoin based on an average attacker's hashrate as a proxy measure of the security of processed transactions. This stimulation runs in a discrete-event network simulator called NS-3. The result of this simulation shows that an increase in attacker's hashrate is parallel with an increase in number of double-spending attacks, an increase in attacker's income and the number of stale blocks posing a threat to transaction's security. Stale blocks increase the advantage of attacker in the double spending attacks in the network.
A Study of Inequality in the Ethereum Smart Contract Ecosystem

Ethereum is one of the most popular blockchain platforms and is the second most valuable cryptocurrency. It allows developers to create smart contracts - small computer programs that sit on the blockchain. These programs can be written in programming languages like solidity and are executed by the Ethereum Virtual Machine (EVM). Since Ethereum is a public blockchain, all the data of the blockchain is available publicly. However, getting smart contract data is a tedious process and public data-sets for smart contracts are not available for further analysis.Therefore, in this work, we collect a total of 1.9M smart contracts till the block height of 7.1M, and provide a first of its kind analysis across different parameters like duplicity, ether balance, ether moved, etc. We observe that across all these different parameters, only a small fraction of the smart contracts are dominant. We label such 2900 dominant contracts as the ‘Contracts of Importance’ and use it for further analysis using the various tools available to give us an insight into the vulnerability trends and patterns in smart contracts.
THE EFFECT OF PRODUCTS ATTRIBUTES CUSTOMER SATISFACTION ON BRAND LOYALTY IN THE ELECTRONIC APPLIANCES INDUSTRY: CASE OF JORDAN

This paper shows an analysis of products attributes, customer satisfaction and brand loyalty in the electronic appliances sector. The main goal of the research is to determine the influence of a product’s physical and service-related attributes on customer satisfaction and eventually on brand loyalty. In particular, the objective of this paper is to answer the following question: if the attributes of a product influence in a customer satisfaction and in that way on a brand’s loyalty? We defined Customer satisfaction as the satisfaction of customers with any products. Therefore, the satisfaction was examined through a survey using questionnaires. The population included all customers of two of the branches from SmartBuy (one of the biggest companies in the IT sector in Jordan) in Amman. We designed the questionnaire and distributed it randomly to the respondents in two branches of the mentioned company. The questionnaire conducted was distributed to 150 respondents; we excluded 43 questionnaires from the analysis due to the incomplete data. So (107) questionnaires were valid for the study. The results show a correlation between the main factors, but more extensive researches in terms of the number of companies used in sampling must be conducted to verify this relation due to more factors mostly statistically insignificant.
Multi-branch Body Region Alignment Network for Person Re-identification

Person re-identification (Re-ID) aims to identify the same person images from a gallery set across different cameras. Human pose variations, background clutter and misalignment of detected human images pose challenges for Re-ID tasks. To deal with these issues, we propose a Multi-branch Body Region Alignment Network (MBRAN), to learn discriminative representations for person Re-ID. It consists of two modules, i.e., body region extraction and feature learning. Body region extraction module utilizes a single-person pose estimation method to estimate human keypoints and obtain three body regions. In the feature learning module, four global or local branch-networks share base layers and are designed to learn feature representation on three overlapping body regions and the global image. Extensive experiments have indicated that our method outperforms several state-of-the-art methods on two mainstream person Re-ID datasets.
Reflection on College Informationized Teaching Model under the Background of Educational Informationization

With the rapid development of Internet information technology, an intelligent, networked, digital and multimedia situation of Educational informatization has been formed. Under the new situation, it is imperative to establish a teaching model that is suitable for the process of educational informationization. The informational teaching model emerges spontaneously which must conform to the high-level goal of talent training. However, most colleges and universities are still in the traditional teaching environment. This paper explores the informational teaching model under the background of educational informationization at the present stage, combining with the talents training objectives of high-level cognition and evaluation.
Detecting Asks in Social Engineering Attacks: Impact of Linguistic and Structural Knowledge

Social engineers attempt to manipulate users into undertaking actions such as downloading malware by clicking links or providing access to money or sensitive information. Natural language processing, computational sociolinguistics, and media-specific structural clues provide a means for detecting both the ask (e.g., buy gift card) and the risk/reward implied by the ask, which we call framing (e.g., lose your job, get a raise). We apply linguistic resources such as Lexical Conceptual Structure to tackle ask detection and also leverage structural clues such as links and their proximity to identified asks to improve confidence in our results. Our experiments indicate that the performance of ask detection, framing detection, and identification of the top ask is improved by linguistically motivated classes coupled with structural clues such as links. Our approach is implemented in a system that informs users about social engineering risk situations.
Stratigraphy of the area of Ukrainian Antarctic Akademik Vernadsky station

In 2019 and 2017, the authors performed field geological research on the Graham Coast of the Antarctic Peninsula. The studied area covered the western coast of the Kiev Peninsula and most of the adjacent islands of the Wilhelm Archipelago. The main objective of the study was to specify the stratigraphic column for the Ukrainian Antarctic Station (UAS) area, to determine the petrographic composition and lithological features of strata, to find out the features of their occurrence and the succession of accumulation. Research methods included: large-scale geological surveying and geological mapping. The main result was the development of a new stratigraphic scheme for the UAS area. The authors found that the studied stratigraphic column was composed of volcanic and subordinate terrigenous deposits. Their accumulation lasted in the time interval from late Palaeozoic to Cenozoic inclusive. The conclusions have been made about to the origin of studied strata, their ages and possible analogues in neighbouring territories. Lahille Island Formation (LIF) was recognized as the oldest lithostratigraphic unit of the studied area. Deep-sea turbid origin is assumed for the LIF terrigenous rocks. The TTL sedimentation age is limited by the time interval between the Late Permian and Early Triassic periods. On the basis of lithological features and geological age, TTL correlates with the Paradise Harbour formation belonging to the Trinity Peninsula Group. The most widespread lithostratigraphic unit of the study area is the volcanic strata of the Kiev Peninsula, which is a product island-arc volcanism associated with the Andean orogeny. The age of KPF volcanism is defined as Jurassic. It is found that the volcanic stratum of the Argentine Islands Formation (AIF) differs from the KPF by the petrographic composition, occurrence and features of stratification. It gave grounds to separate AIF into an independent lithostratigraphic unite. The age of AIF volcanism is limited by the time interval between the Jurassic and Cretaceous periods. According to lithological features and geological age, KPF and AIF were identified as the members of Antarctic Peninsula Volcanic Group. An assumption about the possible presence of the latest volcanogenic rocks of Neogene or even Quaternary age on the Argentinean Islands is made.
Defeating Speculative-Execution Attacks on SGX with HyperRace

Speculative-execution attacks, such as SgxSpectre, Foreshadow, and MDS attacks, leverage recently disclosed CPU hardware vulnerabilities and micro-architectural side channels to breach the confidentiality and integrity of Intel Software Guard eXtensions (SGX). Unlike traditional micro-architectural side-channel attacks, speculative-execution attacks extract any data in the enclave memory, which makes them very challenging to defeat purely from the software. However, to date, Intel has not completely mitigated the threats of speculative-execution attacks from the hardware. Hence, future attack variants may emerge. This paper proposes a software-based solution to speculative-execution attacks, even with the strong assumption that confidentiality of enclave memory is compromised. Our solution extends an existing work called HyperRace, which is a compiler-assisted tool for detecting Hyper-Threading based side-channel attacks against SGX enclaves, to thwart speculative-execution attacks from within SGX enclaves. It requires supports from the untrusted operating system, e.g., for temporarily disabling interrupts, but verifies the OS's behaviors. Additional microcode upgrades are required from Intel to secure the attestation flow.
Comparative Analysis of RAM Value Correction Method Based on the Evaluation of the Weapon system Staff

Purpose: This paper presents a correction method to improve the accuracy of the estimated RAM value in the proposed stage of the new weapon system using the SEM(structural equation model).BRMethods: The staff in the field of operation, maintenance, and support of the weapon system evaluates the time required by each field, evaluates the correction between the main variables by using the SEM, and suggests how to use it in estimating the RAM value. Excel and Amos programs are used for descriptive statistical analysis and SEM.BRResults: A comparative analysis on the calibration method using MIL-HDBK-17 and SEM for RAM value correction is proposed to improve the accuracy of RAM value.BRConclusion: RAM data correction items were verified through data verification for the difference of evaluation results according to the staffs in each field, and direct and mediating effects on variables affecting RAM value estimation were verified using SEM.
RCCM: Reinforce Cycle Cascade Model for Image Recognition

Due to the disadvantages that complex network structure, time-consuming training process and the insufficient feature extraction ability existing in deep structures and broad structures, Reinforce Cycle Cascade Model (RCCM) is proposed to offer a novel algorithm for image recognition.In RCCM, the cycle cascade model is used to extract the discriminative features with a small amount of time consumption, where the multi-layer cascade is used to extract low-level and advanced features layer by layer, which provides conditions for features evolution as well.In addition, the cycle mechanism is introduced to reinforce features gradually, which also lighten model complexity by adopting time update instead of space stack.Visualization maps prove the feasibility of RCCM, and the discussion of cascade layers shows the extension of our proposed algorithm.To demonstrate the performance of RCCM, we have conducted extensive experiments on some benchmark image datasets, and the results show that RCCM is significantly better than several state-of-the-art algorithms.Specially, no matter in the training process of testing phase, our method can achieve recognition in real-time even on common PC without GPU, and the maximum testing speed reaches 31073 samples per second.RCCM improves the efficiency and accuracy of image recognition significantly.
Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis

At present, artificial intelligence (AI) contributes to most technological fields. AI has also been introduced in the disaster area to replace humans and contribute to the prevention of disasters and the minimization of damages. So, it is necessary to analyze disaster AI in order to effectively make use of it. In this paper, we analyze the patent documents related to disaster AI technology. We propose Bayesian network modeling and factor analysis for the technology analysis of disaster AI. This is based on probability distribution and graph theory. It is also a statistical model that depends on multivariate data analysis. In order to show how the proposed model can be applied to a real problem, we carried out a case study to collect and analyze the patent data related to disaster AI.
Fine-Grained Exploitation of Mixed Precision for Faster CNN Training

As deep convolutional neural networks (CNNs) have become increasingly popular and successful at an ever-widening number of machine learning tasks specialized hardware has become increasingly available for training and deploying them. NVIDIA's recent Volta architecture includes tensor cores which perform a fused operation reduced and mixed precision (16-bit multiply, 32-bit accumulate). Recent research indicates that, typically, very little is lost (in terms of training accuracy) when half precision is used in place of single precision, and performance gains can be made by doing arithmetic in reduced precision. In this work we demonstrate that making layer-by-layer choices as to the arithmetic/data precision can lead to further performance improvement. In our study of 25,200 CNNs we demonstrate an average speedup (over purely half precision) of 1.27x and speedups as high as 3.64x by appropriately combining single and half precision arithmetic and data types on a layer-by-layer basis.
Machine learning: a long way from implementation in cardiovascular disease

The term ‘machine learning’ (ML) dates back to the 1950s to describe how algorithms and neural network models can assist computer systems in progressively improving their performance. In the last decade, advanced ML algorithms have been increasingly used for phenotypic identification in different cardiovascular diseases (CVDs), driven by two major factors. First, a gap persists between disease definitions from research or consensus guidelines and routine clinical practice. Second, as electronic health records (EHRs) are increasingly adopted within and across countries, there are unprecedented opportunities to investigate disease definitions in a more reproducible and generalisable manner. The objective of ML algorithms in such analyses is to develop replicable EHR-based phenotypical definitions for a given CVD1 and to predict group assignments of new patients.EHRs include diagnostic codes from primary care, secondary care and administrative data. To produce homogeneous subgroups of EHR data, clustering methods are unsupervised ML algorithms which aim to group objects with similar attributes relying on similarity and distance measures. In their Heart paper, Hedman and colleagues2 identify six phenotypes of heart failure with preserved ejection fraction (HFpEF), derived using data from 320 outpatients with HFpEF in the Karolinska-Rennes cohort study. HFpEF is the example par excellence of a disease where disease definitions, and consequently knowledge regarding pathophysiology and management, remain elusive. The team provides a prediction tool for patient assignment of identified subtypes, although this has not been externally validated.The authors provide new insights in a disease lacking a single evidence-based therapy to alter prognosis. They found that two pheno-groups had worst prognosis: those with hypertension and highest prevalence of coronary artery disease, renal disease, anaemia and diabetes (pheno-group 1) and those with atrial fibrillation and high prevalence of chronic obstructive pulmonary disease, old age, kidney dysfunction and anaemia (pheno-group 2). Pheno-group 1 is consistent …
“Feeling the Beat”

Chapter 4 argues that the experience of musical meter (distinguished from musical rhythm) is multimodal: it involves the binding to a common sensory individual of auditory and proprioceptive content. Consideration of this multimodal content offers a novel perspective on the experience of musical movement. Some philosophers have suggested that all perceptual experiences of musical movement must be “metaphorical” (which notion is explained in the chapter), given that musical sounds do not move. But if a given type of musical experience involves more than the awareness of sounds, it does not follow that the experience of movement must be metaphorical in that case. The author claims that since the proprioceptive content involved in beat experience is legitimately movement-involving, it is unnecessary to appeal to “metaphorical perception” to explain the experience of movement in musical meter.
Analytics for Autonomous C4ISR within e-Government: a Research Agenda

e-Government enables big data analytics to support decision processes in governing.C4ISR (Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance) is essentially e-Government scoped to military decision processes.The value of big data and its challenges are common to both. High variety and demand for veracity compel domain expertise-specific data analysis, and increasing volume and velocity hinder data analytics at scale. These conditions challenge even highly automated methods for comprehensive cross-domain analytics, and motivate cognitive approaches such as underlie Autonomous Systems (AS) aimed at C4ISR. A C4ISR framework is examined by parts, linking each C to ISR capability, and a taxonomy of analytics is extended to include cognitive autonomy enablers. Coupling these frameworks, the authors propose an extension of cognitive approaches for autonomy in C4ISR to e-Government in general and outline a research agenda for attaining it.delivery of data products.Finally, the authors further present C4ISR as a functional paradigm whereby different C's enhance ISR capabilities.We note challenges in employing C5ISR solutions and present an array of evaluation approaches when developing AS.Key research questions are provided.