Reviewerly/exHarmony huggingface dataset:
Glımpse On Blockchaın Technology And Applıcatıons

Abstract The term blockchain pasteurizes the chain of blocks like linked lists which will be linked to the previous block. A blockchain is a distribted ledger of digital transactions which is decentralized and no individual, team or company has its controllability over it. However, with time, the use of blockchain has come into existence. It has wide range of applications including risk management, credit money, Internet of Things ( IoT), social networks, and financial resources. Blockchain has many benefits like decentralization, persistency, anonymity, audit ability. Blockchain is trustable one and it is very efficient. In particular, this paper gives us the detailed information about the blockchain and its applications.
Five Things You Should Not Use Blockchain For

The Bitcoin fever notwithstanding, the underlying blockchain technology cannot solve all data exchange and product needs, as some seem to believe. This paper provides examples of problems that we believe are poorly suited to a blockchain solution.
Multi-view content-based mammogram retrieval using dynamic similarity and locality sensitive hashing

Content-Based Mammogram Retrieval (CBMR) methods using Multi-View Information Fusion (MVIF) have triggered a growing interest in the last years given their ability to help radiologists make the right breast-cancer related decision. To further improve the retrieval performance, this paper introduces an efficient MVIF-CBMR method based on late fusion that combines retrieval result-level of Medio-Lateral Oblique (MLO) and Cranio-Caudal (CC) views. The proposed method adopts a coupled multi-index with a dynamic distance to evaluate the similarity between mammograms, which allows to fully exert the discriminative power of the complementary of MLO-CC features. Furthermore, the ROI dataset signature indexing step uses a hashing technique to optimize the computational time for retrieving relevant images. Thus, the proposed method takes two query ROIs corresponding to two different views (MLO and CC) as input and displays the most similar ROIs to each view using a dynamic similarity assessment. The retrieved ROIs can therefore be analyzed according to their clinical cases for the final decision-making relative to the query ROIs. The experiments realized on the challenging Digital Database for Screening Mammography (DDSM) dataset have proved the effectiveness and the efficiency of the proposed method.
Semi-supervised kernel matrix learning using adaptive constraint-based seed propagation

In this paper, we propose semi-supervised kernel matrix learning (SS-KML) using adaptive constraint-based seed propagation (ACSP). Conventional SS-KML methods such as pairwise constraint propagation (PCP) and kernel propagation (KP) have achieved outstanding performance in data classification. However, they are likely to distort the global data structure because of using hard constraints in their semi-definite problems (SDPs) for constraint propagation. Moreover, given a large number of pairwise constraints and a large amount of samples, they tend to be incredibly complex, thus being hard to be applied to real-life complex problems such as internet-scale image categorization. To address this problem, we utilize adaptive constraints to effectively maintain the inherent coherence of samples and successfully propagate constraint information into all samples. Moreover, we adopt seed propagation to remarkably reduce the computational complexity of SS-KML. Experimental results demonstrate that ACSP achieves a significant improvement in performance over PCP and KP in terms of both effectiveness and efficiency.
SMARTCARE: On the Design of an IoT Based Solution for Assisted Living

In the wake of the Covid-19 pandemic, Ambient Assisted Living (AAL) and Home Monitoring System (HMS) frameworks came into focus as an alternative to classic healthcare services for the ageing world population. Both overwhelmed healthcare systems and the susceptibility of older adults to develop severe complications are indicators that in the near future there will be a shift to a decentralized monitoring paradigm for chronic disease patients.This paper introduces the SMARTCARE system, a solution that integrates both AAL and HMS features in order to provide maximum flexibility in defining monitoring scenarios through the use of IoT devices. More, the system tackles the lack of uniformity that exists in standardization across the IoT field through the use of a modular architecture.
Role of Interactive Space in Social Relationshops through Actor-Network Theory

This paper aims to express the role of interactive space in social relationships through Actor-network theory. It considers interactive space as an active participant of social relationships, an actor within a heterogeneous network of human and non-human components. Actor-network theory is applied as a methodological tool to understand how does society communicate with interactive space, what signals are used for it, and what are the stages of involvement into this communication. The article describes using a full-scale interactive space prototype as a proof-of concept to test and analyze various ways of realtime communication between visitors and the space, assess their responsiveness to different types of signals, and how participants behave. In order to analyze participants' experience, they participated in a survey that has been quantified and expressed in a table. Obtained data allows to understand the most effective ways in shaping visitor's behavior and communication with interactive space.
Working from home: Self-assessment computer workstation set-up

The purpose of this study was to help employees and computer users who have recently transitioned to working from home self-evaluate setting up computer workspace at home and provide some recommended actions about adjusting workstation set-up and tips on changes can be made to be more ergonomically correct. Participants filled an online survey and were then provided with the ‘Computer Workstation Set-up: Self-Assessment Checklist’. A brief follow-up satisfaction survey was sent to all participants. A total of 146 employees and computer users from different countries participated in this study and filled the online survey. Participants were satisfied and reported that the Computer Workstation Set-up: Self-Assessment Checklist is a useful tool. It has helped maintain comfort, well-being, safety, and maximised performance, and included useful tips related to key computer workstation areas, such as the chair, the keyboard and mouse, the monitor, the computer table, and the work surface size and lighting.
A Cloud Model-Based Risk Assessment Methodology for Tunneling-Induced Damage to Existing Tunnel

This study presents a cloud model-based approach for risk assessment of existing tunnels in tunneling construction environments where the cloud model provides a basis for uncertainty transformation between its qualitative concepts and quantitative expressions. An evaluation index system is established for risk assessment of existing tunnels based on the tunnel-induced failure mechanism analysis. The assessment result is obtained through the correlation with the cloud model of each risk level. Risk assessment for existing Guangzhou-Shenzhen-Hong Kong Railway Tunnel in the tunneling environment of Shenzhen Metro Line 6 is shown in a case study. Comparisons between Fuzzy Analytic Hierarchy Process (FAHP) methods are further discussed according to results. The proposed evaluation method is verified to be more competitive as the fuzziness and randomness of uncertainties in the risk assessment system can be considered comprehensively. This method can serve as a decision-making tool for other similar project risk assessment methods to increase the likelihood of a successful project in an uncertain environment.
Can Appliances Understand the Behavior of Elderly Via Machine Learning? A Feasibility Study

Over the last half decade, fast development of the Internet of Things and machine learning (ML) made it feasible to leverage the power of artificial intelligence to facilitate a variety of intelligent systems in smart home. Nevertheless, the studies on designing specific computing technologies for helping elderly to enjoy a comfortable, convenient, and independent daily life are extremely limited. On the one hand, there are increasingly growing demands from the ageing society to implement the cutting edge technology enabling a better life quality for the elderly. On the other hand, there is still a lack on fundamental investigations, applicable infrastructures, and advanced data-driven frameworks. To this end, we propose a novel machine framework for analyzing the daily life behavior of elderly-all in this study are living alone-by the data collected from their home appliances, i.e., television and refrigerator. First, the interevent intervals for the use of the appliances collected in one month from 76 elderly are the raw data to describe the behaviors. Then, three ML paradigms are investigated and compared, which include “classic” ML methods and the state-of-the-art deep learning approaches. Finally, we indicate the current findings and limitations in this feasibility study. Experimental results demonstrate that, our proposed method can reach performance peak at an unweighted average recall of 58.7% (chance level: 50.0%) in a subject-independent test for classifying symptom/nonsymptom days.
Cross-Domain Lithology Identification Using Active Learning and Source Reweighting

Cross-domain lithology identification (CDLI) is a common case in lithology identification, which aims to train a machine learning model using the logging data of an interpreted well to predict the lithology of another uninterpreted well. Compared with the general lithology identification problem, the CDLI problem is more challenging for two reasons: the data distribution shift between the wells, and the expensive label acquisition on the uninterpreted well. To tackle these issues, we propose a novel framework that embeds active learning (AL) and domain adaptation into lithology identification. The proposed framework is composed of two components: an AL algorithm that selects the most uncertain and diverse target samples to query their real labels, and a source reweighting method that leverages the target labels to reduce data distribution discrepancy. Experimental results on two real-world data sets demonstrate that the proposed method can more effectively suppress the performance degradation caused by the data distribution shift than the baselines, with fewer target label queries.
Hand Gesture Localization and Classification by Deep Neural Network for Online Text Entry

Hand gesture recognition is an important aspect of human-computer interaction. A proper hand gesture recognizing system can be used to build a robust text entry system for human-computer interface. This work proposes a real-time hand localization and recognition system. For hand localization, YOLOv3 is used that predicts a bounding box around the hand, and for hand gestures classification, a pretrained VGG16 network is employed. The bounding box regression technique helped localize the ROI (region of interest) and reduced the complexity, that aided in the classification task. The experimental results show that the proposed method is capable of recognizing the gestures with high testing accuracy on three benchmark datasets, namely, ASL (American sign language), Libras and NUS (National University of Singapore) datasets.
BLE-GSpeed: A New BLE-Based Dataset to Estimate User Gait Speed

To estimate the user gait speed can be crucial in many topics, such as health care systems, since the presence of difficulties in walking is a core indicator of health and function in aging and disease. Methods for non-invasive and continuous assessment of the gait speed may be key to enable early detection of cognitive diseases such as dementia or Alzheimer’s disease. Wearable technologies can provide innovative solutions for healthcare problems. Bluetooth Low Energy (BLE) technology is excellent for wearables because it is very energy efficient, secure, and inexpensive. In this paper, the BLE-GSpeed database is presented. The dataset is composed of several BLE RSSI measurements obtained while users were walking at a constant speed along a corridor. Moreover, a set of experiments using a baseline algorithm to estimate the gait speed are also presented to provide baseline results to the research community.
Persuasive dialogue understanding: The baselines and negative results

Persuasion aims at forming one's opinion and action via a series of persuasive messages containing persuader's strategies. Due to its potential application in persuasive dialogue systems, the task of persuasive strategy recognition has gained much attention lately. Previous methods on user intent recognition in dialogue systems adopt recurrent neural network (RNN) or convolutional neural network (CNN) to model context in conversational history, neglecting the tactic history and intra-speaker relation. In this paper, we demonstrate the limitations of a Transformer-based approach coupled with Conditional Random Field (CRF) for the task of persuasive strategy recognition. In this model, we leverage inter- and intra-speaker contextual semantic features, as well as label dependencies to improve the recognition. Despite extensive hyper-parameter optimizations, this architecture fails to outperform the baseline methods. We observe two negative results. Firstly, CRF cannot capture persuasive label dependencies, possibly as strategies in persuasive dialogues do not follow any strict grammar or rules as the cases in Named Entity Recognition (NER) or part-of-speech (POS) tagging. Secondly, the Transformer encoder trained from scratch is less capable of capturing sequential information in persuasive dialogues than Long Short-Term Memory (LSTM). We attribute this to the reason that the vanilla Transformer encoder does not efficiently consider relative position information of sequence elements.
Compact class-conditional domain invariant learning for multi-class domain adaptation

Neural network-based models have recently shown excellent performance in various kinds of tasks. However, a large amount of labeled data is required to train deep networks, and the cost of gathering labeled training data for every kind of domain is prohibitively expensive. Domain adaptation tries to solve this problem by transferring knowledge from labeled source domain data to unlabeled target domain data. Previous research tried to learn domain-invariant features of source and target domains to address this problem, and this approach has been used as a key concept in various methods. However, domain-invariant features do not mean that a classifier trained on source data can be directly applied to target data because it does not guarantee that data distribution of the same classes will be aligned across two domains. In this paper, we present novel generalization upper bounds for domain adaptation that motivates the need for class-conditional domain invariant learning. Based on this theoretical framework, we then propose a class-conditional domain invariant learning method that can learn a feature space in which features in the same class are expected to be mapped nearby. We empirically experimented that our model showed state-of-the-art performance on standard datasets and showed effectiveness by visualization of latent space.
Segmented Generative Networks: Data Generation in the Uniform Probability Space

Recent advancements in generative networks have shown that it is possible to produce real-world-like data using deep neural networks. Some implicit probabilistic models that follow a stochastic procedure to directly generate data have been introduced to overcome the intractability of the posterior distribution. However, the ability to model data requires deep knowledge and understanding of its statistical dependence-which can be preserved and studied in appropriate latent spaces. In this article, we present a segmented generation process through linear and nonlinear manipulations in the same-dimensional latent space where data are projected to. Inspired by the known stochastic method to generate correlated data, we develop a segmented approach for the generation of dependent data, exploiting the concept of copula. The generation process is split into two frames: one embedding the covariance or copula information in the uniform probability space, and the other embedding the marginal distribution information in the sample domain. The proposed network structure, referred to as a segmented generative network (SGN), also provides an empirical method to sample directly from implicit copulas. To show its generality, we evaluate the presented approach in three application scenarios: a toy example, handwritten digits, and face image generation.
Katz and COVID-19: How a Pandemic Changed the Reasonable Expectation of Privacy

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Optimal Opponent Stealth Trajectory Planning Based on an Efficient Optimization Technique

In principle, the Automatic Identification System (AIS) makes covert rendezvous at sea, such as smuggling and piracy, impossible; in practice, AIS can be spoofed or simply disabled. Previous work showed a means whereby such deviations can be spotted. Here we play the opponent's side, and describe the least-detectable trajectory that the elusive vessel can take. The opponent's route planning problem is formalized as a non-convex optimization problem capitalizing the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. The velocity of the vessel is modeled with an Ornstein-Uhlenbeck (OU) mean reverting stochastic process, and physical and practical requirements are accounted for by enforcing several constraints at the optimization design stage. To handle the resulting non-convex optimization problem, we propose a globally-optimal and computationally-efficient technique, called the Non-Convex Optimized Stealth Trajectory (N-COST) algorithm. The N-COST algorithm consists amounts to solving multiple convex problems, with the number proportional to the number of segments of the piecewise OU trajectory. The effectiveness of the proposed approach is demonstrated through case studies and a real-world example.
Bitcoin price manipulation: evidence from intraday orders and trades

We analyse 519.4 million Bitcoin orders placed on Gemini Exchange during January 2016-August 2019 and find limit orders dominate at 99.92%. We document order-based evidence of price manipulation during the Bitcoin bubble in late 2017, when the daily number of market orders during the bubble period more than triples the overall daily average. The changes in both prices and liquidity satisfy two criteria specified in Kyle and Viswanathan (2008) for the price manipulation definition. Moreover, we find a significant increase in market order imbalance associated with price manipulations modelled in Jarrow, Protter and Roch (2012).
Data classification based on fractional order gradient descent with momentum for RBF neural network

The weight-updating methods have played an important role in improving the performance of neural networks. To ameliorate the oscillating phenomenon in training radial basis function (RBF) neural network, a fractional order gradient descent with momentum method for updating the weights of RBF neural network (FOGDM-RBF) is proposed for data classification. Its convergence is proved. In order to speed up the convergence process, an adaptive learning rate is used to adjust the training process. The Iris data set and MNIST data set are used to test the proposed algorithm. The results verify the theoretical results of the proposed algorithm such as its monotonicity and convergence. Some non-parametric statistical tests such as Friedman test and Quade test are taken for the comparison of the proposed algorithm with other algorithms. The influence of fractional order, learning rate and batch size is analysed and compared. Error analysis shows that the algorithm can effectively accelerate the convergence speed of gradient descent method and improve its performance with high accuracy and validity.
Multi-task Learning-Based Spoofing-Robust Automatic Speaker Verification System

Abstract Spoofing attacks posed by generating artificial speech can severely degrade the performance of a speaker verification system. Recently, many anti-spoofing countermeasures have been proposed for detecting varying types of attacks from synthetic speech to replay presentations. While there are numerous effective defenses reported on standalone anti-spoofing solutions, the integration for speaker verification and spoofing detection systems has obvious benefits. In this paper, we propose a spoofing-robust automatic speaker verification system for diverse attacks based on a multi-task learning architecture. This deep learning-based model is jointly trained with time-frequency representations from utterances to provide recognition decisions for both tasks simultaneously. Compared with other state-of-the-art systems on the ASVspoof 2017 and 2019 corpora, a substantial improvement of the combined system under different spoofing conditions can be obtained.
rust-code-analysis: A Rust library to analyze and extract maintainability information from source codes

The literature proposes many software metrics for evaluating the source code non-functional properties, such as its complexity and maintainability. The literature also proposes several tools to compute those properties on source codes developed with many different software languages. However, the Rust language emergence has not been paired by the community’s effort in developing parsers and tools able to compute metrics for the Rust source code. Also, metrics tools often fall short in providing immediate means of comparing maintainability metrics between different algorithms or coding languages. We hence introduce rust-code-analysis, a Rust library that allows the extraction of a set of eleven maintainability metrics for ten different languages, including Rust. rust-code-analysis, through the Abstract Syntax Tree (AST) of a source file, allows the inspection of the code structure, analyzing source code metrics at different levels of granularity, and finding code syntax errors before compiling time. The tool also offers a command-line interface that allows exporting the results in different formats. The possibility of analyzing source codes written in different programming languages enables simple and systematic comparisons between the metrics produced from different empirical and large-scale analysis sources.