Reviewerly/exHarmony huggingface dataset:
A Secure Multi Factor User Authentication Framework for Electronic Payment System

In the growth of financial industries, the electronic payments system is a newest topic, which is to be replaced in the near future by electronic or online transaction. With the advancement of the technology, there is a strong need to build and enforce safe authentication schemes to protect user sensitive information against security threats. Protection is becoming increasingly important for companies today, and so the need for authentication is more essential than before. In single-factor authentication, there are many security problems such as password schemes. Additionally, invaders will try various ways of stealing passwords including, dictionary attacks, brute force attack, password divination, shoulder surfing, etc. This paper provides a multi-authentication system for electronic payments to address the problem. The proposed technique here combines password, biometric and OTP verification for a more reliable user authentication using a multi-factor authentication. The proposed system has three phases, namely: registration phase, an authentication phase, and transaction phase. Our proposed approach has been found to boost security efficacy for various forms of assault and authentication layers dependent on password based attacks.
Storing and sharing: Everyday relationships with digital material

Solid-state drives, Bluetooth capabilities, smartphones, “the cloud,” social media platforms, and other digital technologies have fundamentally altered how we share and store digital materials. This article sets an agenda for understanding how people manage the proliferation of digital material in their everyday lives through a close examination of the strategies and rituals different people around the world employ to organize, curate, or delete digital materials. Drawing upon findings from 11 articles, it contextualizes contemporary forms with digital media and technologies with existing cultural practices of sharing and storing in relation to wider social and cultural systems and infrastructures such as household and family, logistics, health, government, and governance. We argue that the proliferation of platforms, changes in temporalities, and the emergence of different forms of digital labor have fundamentally shaped the ways in which we share and store digital material.
Approximate <i>k</i>-NN Graph Construction: A Generic Online Approach

Nearest neighbor search and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph construction are two fundamental issues that arise from many disciplines such as multimedia information retrieval, data-mining, and machine learning. They become more and more imminent given the big data emerge in various fields in recent years. In this paper, a simple but effective solution both for approximate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor search and approximate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph construction is presented. These two issues are addressed jointly in our solution. On one hand, the approximate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph construction is treated as a search task. Each sample along with its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbors is joined into the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph by performing the nearest neighbor search sequentially on the graph under construction. On the other hand, the built <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor graph is used to support <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor search. Since the graph is built online, the dynamic update on the graph, which is not possible for most of the existing solutions, is supported. This solution is feasible for various distance measures. Its effectiveness both as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor construction and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -nearest neighbor search approaches is verified across different types of data in different scales, various dimensions, and under different metrics.
Machine learning applied to rock geochemistry for predictive outcomes: The Neapolitan volcanic history case

In this paper we explore the efficiency of various machine learning techniques to determine the volcano source, the eruptive formation and the eruption period of volcanic rocks when their chemical contents are known. With this aim, we assembled a data set of 9800 volcanic rocks from the open-access literature. The rocks belong to eruptive formations from Somma-Vesuvius, Campi Flegrei, Ischia and Procida volcanoes, in the Neapolitan region of Italy. The data set includes content of major oxides and trace elements, as well as Sr and Nd isotope ratios, eruptive periods, eruption formations and volcano source. Some discrete numerical variables are missing in certain samples resulting in data exclusion and measurement inhomogeneity. Our results indicate that, despite such issues, some machine learning algorithms have a very high prediction ability, i.e., at >70%. The achieved results are interesting in order to facilitate the managing of new data for volcanological reconstruction and tephrostratigraphic studies.
Summarize Dates First: A Paradigm Shift in Timeline Summarization

Timeline summarization aims at presenting long news stories in a compact manner. State-of-the-art approaches first select the most relevant dates from the original event timeline then produce per-date news summaries. Date selection is driven by either per-date news content or date-level references. When coping with complex event data, characterized by inherent news flow redundancy, this pipeline may encounter relevant issues in both date selection and summarization due to a limited use of news content in date selection and no use of high-level temporal references (e.g., the past month). This paper proposes a paradigm shift in timeline summarization aimed at overcoming the above issues. It presents a new approach, namely Summarize Date First, which focuses on first generating date-level summaries then selecting the most relevant dates on top of summarized knowledge. In the latter stage, it performs date aggregations to consider high-level temporal references as well. The proposed pipeline also supports frequent incremental timeline updates more efficiently than previous approaches. We tested our unsupervised approach both on existing benchmark datasets and on a newly proposed benchmark dataset describing the COVID-19 news timeline. The achieved results were superior to state-of-the-art unsupervised methods and competitive against supervised ones.
Implementation of quantum measurements using classical resources and only a single ancillary qubit

We propose a scheme to implement general quantum measurements, also known as Positive Operator Valued Measures (POVMs) in dimension $d$ using only classical resources and a single ancillary qubit. Our method is based on the probabilistic implementation of $d$-outcome measurements which is followed by postselection of some of the received outcomes. We conjecture that the success probability of our scheme is larger than a constant independent of $d$ for all POVMs in dimension $d$. Crucially, this conjecture implies the possibility of realizing arbitrary nonadaptive quantum measurement protocol on a $d$-dimensional system using a single auxiliary qubit with only a \emph{constant} overhead in sampling complexity. We show that the conjecture holds for typical rank-one Haar-random POVMs in arbitrary dimensions. Furthermore, we carry out extensive numerical computations showing success probability above a constant for a variety of extremal POVMs, including SIC-POVMs in dimension up to 1299. Finally, we argue that our scheme can be favourable for the experimental realization of POVMs, as noise compounding in circuits required by our scheme is typically substantially lower than in the standard scheme that directly uses Naimark's dilation theorem.
A Blockchain based Cost effective Digital Voting System using SideChain and Smart Contracts

Democracy plays a significant part in voting. If the voting process is not clear, stable and tamper proof, the integrity and legitimacy of the entire system is at risk. In recent years, neither civilians nor elected leaders have appreciated traditional ballots. Elections are surrounded by vote falsification, bribery and other voting problems. Often an individual must stand in a long line of people while casting votes and the procedure is quite lengthy. There has to be an exceptional feature of modern technologies to upgrade the existing system. Block chain provides the various features that will alter the scenario. But to implement ethereum based blockchain is expensive as for each transaction there is a computation cost. So we have used the sidechain concept to provide a cost effective blockchain based voting mechanism as sidechain extends the capabilities of blockchain by performing some operation besides it using duplicate currency and returns the result to mainchain for its use.
A Framework for Behavioral Biometric Authentication Using Deep Metric Learning on Mobile Devices

Mobile authentication using behavioral biometrics has been an active area of research. Existing research relies on building machine learning classifiers to recognize an individual’s unique patterns. However, these classifiers are not powerful enough to learn the discriminative features. When implemented on the mobile devices, they face new challenges from the behavioral dynamics, data privacy and side-channel leaks. To address these challenges, we present a new framework to incorporate training on battery-powered mobile devices, so private data never leaves the device and training can be flexibly scheduled to adapt the behavioral patterns at runtime. We re-formulate the classification problem into deep metric learning to improve the discriminative power and design an effective countermeasure to thwart side-channel leaks by embedding a noise signature in the sensing signals without sacrificing too much usability. The experiments demonstrate authentication accuracy over 95 percent on three public datasets, a sheer 15 percent gain from multi-class classification with less data and robustness against brute-force and side-channel attacks with 99 and 90 percent success, respectively. We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer. Finally, we profile memory, energy and computational overhead. Our results indicate that training consumes lower energy than watching videos and slightly higher energy than playing games.
WebSocket Adoption and the Landscape of the Real-Time Web

Developers are increasingly deploying web applications which require real-time bidirectional updates, a use case which does not naturally align with the traditional client-server architecture of the web. Many solutions have arisen to address this need over the preceding decades, including HTTP polling, Server-Sent Events, and WebSockets. This paper investigates this ecosystem and reports on the prevalence, benefits, and drawbacks of these technologies, with a particular focus on the adoption of WebSockets. We crawl the Tranco Top 1 Million websites to build a dataset for studying real-time updates in the wild. We find that HTTP Polling remains significantly more common than WebSockets, and WebSocket adoption appears to have stagnated in the past two to three years. We investigate some of the possible reasons for this decrease in the rate of adoption, and we contrast the adoption process to that of other web technologies. Our findings further suggest that even when WebSockets are employed, the prescribed best practices for securing them are often disregarded. The dataset is made available in the hopes that it may help inform the development of future real-time solutions for the web.
Robust Optical and SAR Image Registration Based on OS-SIFT and Cascaded Sample Consensus

Although several algorithms have achieved automatic registration on optical and synthetic aperture radar (SAR) images, it is still a challenge to establish enough reliable correspondences between such images due to their different imaging mechanisms. For this purpose, we propose a robust point-feature-based registration method. Considering the inherent properties of optical image and SAR image, two different gradient operators are utilized to construct scale spaces and extract features. A new gradient operator is defined for SAR image, yielding a more consistent gradient with optical image gradient calculated by the multiscale Sobel operator. Then, a novel cascaded matching method called cascaded sample consensus (CSC) is put forward to increase the number of correct correspondences. In the first matching, a simple but effective scale constraint strategy is used to remove outliers for a robust initial transformation model. Considering the spatial location relationship in each correct matching pair, the second matching constructs precise search spaces of the best matching points for more correspondences. Experimental results finally verify the robustness and accuracy of the proposed algorithm.
Learning Discriminative Neural Representations for Event Detection

Retrieving event instances from texts is pivotal to various natural language processing applications (e.g., automatic question answering and dialogue systems), and the first task to perform is event detection. There are two related sub-tasks therein-trigger identification and type classification, and the former is considered to play a dominant role. Nevertheless, it is notoriously challenging to predict event triggers right. To handle the task, existing work has made tremendous progress by incorporating manual features, data augmentation and neural networks, etc. Due to the scarcity of data and insufficient representation of trigger words, however, they still fail to precisely determine the spans of triggers (coined as trigger span detection problem). To address the challenge, we propose to learn discriminative neural representations (DNR) from texts. Specifically, our DNR model tackles the trigger span detection problem by exploiting two novel techniques: 1) a contrastive learning strategy, which enlarges the discrepancy between representations of words inside and outside triggers; and 2) a Mixspan strategy, which better trains the model to differentiate words nearby triggers' span boundaries. Extensive experiments on benchmarks-ACE2005 and TAC2015-demonstrate the superiority of our DNR model, leading to state-of-the-art performance.
Towards estimating affective states in Virtual Reality based on behavioral data

Abstract Inferring users’ perceptions of Virtual Environments (VEs) is essential for Virtual Reality (VR) research. Traditionally, this is achieved through assessing users’ affective states before and after being exposed to a VE, based on standardized, self-assessment questionnaires. The main disadvantage of questionnaires is their sequential administration, i.e., a user’s affective state is measured asynchronously to its generation within the VE. A synchronous measurement of users’ affective states would be highly favorable, e.g., in the context of adaptive systems. Drawing from nonverbal behavior research, we argue that behavioral measures could be a powerful approach to assess users’ affective states in VR. In this paper, we contribute by providing methods and measures evaluated in a user study involving 42 participants to assess a users’ affective states by measuring head movements during VR exposure. We show that head yaw significantly correlates with presence, mental and physical demand, perceived performance, and system usability. We also exploit the identified relationships for two practical tasks that are based on head yaw: (1) predicting a user’s affective state, and (2) detecting manipulated questionnaire answers, i.e., answers that are possibly non-truthful. We found that affective states can be predicted significantly better than a naive estimate for mental demand, physical demand, perceived performance, and usability. Further, manipulated or non-truthful answers can also be estimated significantly better than by a naive approach. These findings mark an initial step in the development of novel methods to assess user perception of VEs.
Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

Graph neural networks (GNNs) have achieved tremendous success in graph mining. However, the inability of GNNs to model substructures in graphs remains a significant drawback. Specifically, message-passing GNNs (MPGNNs), as the prevailing type of GNNs, have been theoretically shown unable to distinguish, detect or count many graph substructures. While efforts have been paid to complement the inability, existing works either rely on pre-defined substructure sets, thus being less flexible, or are lacking in theoretical insights. In this paper, we propose GSKN1, a GNN model with a theoretically stronger ability to distinguish graph structures. Specifically, we design GSKN based on anonymous walks (AWs), flexible substructure units, and derive it upon feature mappings of graph kernels (GKs). We theoretically show that GSKN provably extends the 1-WL test, and hence the maximally powerful MPGNNs from both graph-level and node-level viewpoints. Correspondingly, various experiments are leveraged to evaluate GSKN, where GSKN outperforms a wide range of baselines, endorsing the analysis.
Multi-sensory research of singing visualization in pre-school music education

The research aim was to develop a visual training simulator with real-time audio correction using the Python programing language and reference audio files classified library. Depending on the melody perception and playback level, the learning pace increases from the minimum. The desired result is affected by the repetitions number. The designed simulator having real-time visualization option allows estimating results and errors. The proposed methodology practical value can be effective for preschool educational institutions music teaching and other music schools and universities music subjects. Further research will be devoted to this technique improvement using virtual reality methods.
Completing Missing Prevalence Rates for Multiple Chronic Diseases by Jointly Leveraging Both Intra- and Inter-Disease Population Health Data Correlations

Population health data are becoming more and more publicly available on the Internet than ever before. Such datasets offer a great potential for enabling a better understanding of the health of populations, and inform health professionals and policy makers for better resource planning, disease management and prevention across different regions. However, due to the laborious and high-cost nature of collecting such public health data, it is a common place to find many missing entries on these datasets, which challenges the utility of the data and hinders reliable analysis and understanding. To tackle this problem, this paper proposes a deep-learning-based approach, called Compressive Population Health (CPH), to infer and recover (to complete) the missing prevalence rate entries of multiple chronic diseases. The key insight of CPH relies on the combined exploitation of both intra-disease and inter-disease correlation opportunities. Specifically, we first propose a Convolutional Neural Network (CNN) based approach to extract and model both of these two types of correlations, and then adopt a Generative Adversarial Network (GAN) based prevalence inference model to jointly fuse them to facility the prevalence rates data recovery of missing entries. We extensively evaluate the inference model based on real-world public health datasets publicly available on the Web. Results show that our inference method outperforms other baseline methods in various settings and with a significantly improved accuracy (from 14.8% to 9.1%).
Accelerated Training via Device Similarity in Federated Learning

Federated Learning is a privacy-preserving, machine learning technique that generates a globally shared model with in-situ model training on distributed devices. These systems are often comprised of millions of user devices and only a subset of available devices can be used for training in each epoch. Designing a device selection strategy is challenging, given that devices are highly heterogeneous in both their system resources and training data. This heterogeneity makes device selection very crucial for timely model convergence and sufficient model accuracy. Existing approaches have addressed system heterogeneity for device selection but have largely ignored the data heterogeneity. In this work, we analyze the impact of data heterogeneity on device selection, model convergence, model accuracy, and fault tolerance in a federated learning setting. Based on our analysis, we propose that clustering devices with similar data distributions followed by selecting the devices with the best processing capacity from each cluster can significantly improve the model convergence without compromising model accuracy. This clustering also guides us in designing policies for fault tolerance in the system. We propose three methods for identifying groups of devices with similar data distributions. We also identify and discuss rich trade-offs between privacy, bandwidth consumption, and computation overhead for each of these proposed methods. Our preliminary experiments show that the proposed methods can provide a 46% - 58% reduction in training time compared to existing approaches in reaching the same accuracy.
A real-time, continuous pedestrian tracking and positioning method with multiple coordinated overhead-view cameras

This paper proposes a real-time, continuous pedestrian tracking and positioning method with multiple coordinated overhead-view cameras. First, a discriminative correlation filter with detection and space reliability (DSR-DCF) is presented for continuous, real-time pedestrian tracking with single cameras. Then, the frame difference method is employed to detect moving pedestrians when they walk from one camera’s view to another’s. Motion-area images are cropped to establish a pedestrian image library (PIL). Finally, this PIL is relied upon to reidentify pedestrians. Pedestrian positioning is realized based on the pinhole imaging model. Tracking and positioning experiments showed the following. DSR-DCF is stable with a high success rate. Its tracking speed exceeds 24 fps. It produces a success rate higher than 90% for cross-camera pedestrian tracking and misses no pedestrians. It yields stable pedestrian positioning results with an accuracy better than 0.2 m. The proposed algorithm will be used in airport terminals.