Reviewerly/exHarmony huggingface dataset:
Evaluation of large plastic deformation for metals by a non-contacting technique using digital image correlation with laser speckles

The full-field deformation characterizations of a metal undergoing large plastic strains are difficult to be obtained from the conventional true stress-strain curves. In this study, we proposed using a simple non-contacting technique, digital image correlation (DIC) with laser speckles, to evaluate large plastic deformations of metals, especially for thin metal foils and for the forming at high temperatures. The feasibility and accuracy of this DIC technique were confirmed by conducting uniaxial tensile tests using different materials. As results, the measured strains indicated a good accuracy with an error range of 6% in measuring the large plastic deformation at room temperature and 10% in measuring a large strain up to 0.6 at a high temperature of 400 °C. The strain distribution measured by DIC with laser speckles and with artificial speckles showed good agreement, confirming the feasibility of this DIC technique for measuring large plastic deformations. By using this technique, the evolution of plastic zone was successfully investigated for thin metal sheets with a thickness of 0.5 mm. Furthermore, the strain fields involving localizations of 50 μm thick metal foils forming at room temperature and a large plastic deformation of metal tubes forming at 400 °C were evaluated qualitatively and quantitatively.
Working with a Social Robot in School

Interactive learning technologies, such as robots, increasingly find their way into schools. However, more research is needed to see how children might work with such systems in the future. This paper presents the unsupervised, four month deployment of a Robot-Extended Computer Assisted Learning (RECAL) system with 61 children working in their own classroom. Using automatically collected quantitative data we discuss how their usage patterns and self-regulated learning process developed throughout the study.
Compact optical asymmetric cryptosystem based on unequal modulus decomposition of multiple color images

In this paper, a silhouette-free and crosstalk-free multiple-color-image cryptosystem is presented based on unequal modulus decomposition (UMD) in the gyrator transform (GT) domains. To the best of our knowledge, it is the first time that the use of cascaded GT, UMD, and inverse-GT operations is demonstrated which features four-orders-of-magnitude higher sensitivity relative to the known GT-UMD cryptosystems. This significantly enlarges its key space. It is demonstrated that this method eliminates the silhouette and cross-talk problems which are well-known in previous interference-based and multiple-image cryptosystems. The polarization and time degrees of freedom (DoFs) are harnessed to realize the proposed cryptosystem using a compact optical system. Also for more compactness, the inverse-GT operation is performed by propagating the beam carrying the image in the reverse direction through the optical GT system. The cryptosystem is verified robust against occlusion, noise, statistical attacks, as well as the chosen-plaintext attack. The quality of the output images is found unaffected by the number of encrypted images which offers ultimately high multiplexing capacity.
Blockchain-Based Certification for Education, Employment, and Skill with Incentive Mechanism

Nowadays, more and more companies become employee centric enterprises and have a data-driven culture. Employees, as a unique capital value, is linked to the company’s profitability. Hiring the appropriate employee and decreasing hiring liability workplace violence are two main tasks in the employment process. Most companies are outsourcing background screening to third parties to ensure the efficiency and professional compare with the traditional in-house check. However, background screening still is a pain point in human resource industry, especially for employment, education and skill verification. Sometimes, background screening is prolonged and inaccurate. For overseas recruitment, background screening will be more troublesome due to the information asymmetry. Therefore, we propose a novel E2C-Chain, which is a two-stage blockchain to improve education, employment, and skill verification system. The first stage aims to create new blocks when a trust organization verifies the education and employment information of the candidate. In the second stage, we comprehensively address the challenge of how to encourage the participants in the blockchain platform. We employ a Vickrey-Clarke-Groves (VCG) incentive mechanism to find the Nash Equilibrium and ensure social cost minimization. We also present the theoretical proofs and extensive simulations to demonstrate beneficial properties and efficiency of our proposed system.
Coterie

In this paper, we study how to support high-quality immersive multiplayer VR on commodity mobile devices. First, we perform a scaling experiment that shows simply replicating the prior-art 2-layer distributed VR rendering architecture to multiple players cannot support more than one player due to the linear increase in network bandwidth requirement. Second, we propose to exploit the similarity of background environment (BE) frames to reduce the bandwidth needed for prefetching BE frames from the server, by caching and reusing similar frames. We find that there is often little similarly between the BE frames of even adjacent locations in the virtual world due to a "near-object" effect. We propose a novel technique that splits the rendering of BE frames between the mobile device and the server that drastically enhances the similarity of the BE frames and reduces the network load from frame caching. Evaluation of our implementation on top of Unity and Google Daydream shows our new VR framework, Coterie, reduces per-player network requirement by 10.6X-25.7X and easily supports 4 players for high-resolution VR apps on Pixel 2 over 802.11ac, with 60 FPS and under 16ms responsiveness.
Blockchain Security Threats, Attacks and Countermeasures

This paper is about the information security and solution to it by the use of blockchain implementation in data security. Blockchain security implementations mostly witnessed in sensitive information systems, like the financial database. Furthermore, in this research paper, threats to computers and information systems are highlighted and analyzed as well as solutions to them are elaborated. The paper majorly based on the step-by-step solution to the threats to the information systems by blockchain technology. This paper systematically covers various risks aspects, demonstrates different attacks and discusses the challenges faced while incorporating blockchain technology for securing the system.
Assessment of QoE for Video and Audio in WebRTC Applications Using Full-Reference Models

WebRTC is a set of standard technologies that allows exchanging video and audio in real time on the Web. As with other media-related applications, the user-perceived audiovisual quality can be estimated using Quality of Experience (QoE) measurements. This paper analyses the behavior of different objective Full-Reference (FR) models for video and audio in WebRTC applications. FR models calculate the video and audio quality by comparing some original media reference with the degraded signal. To compute these models, we have created an open-source benchmark in which different types of reference media inputs are sent browser to browser while simulating different kinds of network conditions in terms of packet loss and jitter. Our benchmark provides recording capabilities of the impairment WebRTC streams. Then, we use different existing FR metrics for video (VMAF, VIFp, SSIM, MS-SSIM, PSNR, PSNR-HVS, and PSNR-HVS-M) and audio (PESQ, ViSQOL, and POLQA) recordings together with their references. Moreover, we use the same recordings to carry out a subjective analysis in which real users rate the video and audio quality using a Mean Opinion Score (MOS). Finally, we calculate the correlations between the objective and subjective results to find the objective models that better correspond with the subjective outcome, which is considered the ground truth QoE. We find that some of the studied objective models, such as VMAF, VIFp, and POLQA, show a strong correlation with the subjective results in packet loss scenarios.
An improved particle swarm optimization based on the reinforcement of the population initialization phase by scrambled Halton sequence

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Towards automatic content analysis of social presence in transcripts of online discussions

This paper presents an approach to automatic labeling of the content of messages in online discussion according to the categories of social presence. To achieve this goal, the proposed approach is based on a combination of traditional text mining features and word counts extracted with the use of established linguistic frameworks (i.e., LIWC and Coh-metrix). The best performing classifier obtained 0.95 and 0.88 for accuracy and Cohen's kappa, respectively. This paper also provides some theoretical insights into the nature of social presence by looking at the classification features that were most relevant for distinguishing between the different categories. Finally, this study adopted epistemic network analysis to investigate the structural construct validity of the automatic classification approach. Namely, the analysis showed that the epistemic networks produced based on messages manually and automatically coded produced nearly identical results. This finding thus produced evidence of the structural validity of the automatic approach.
Beyond the online offline distinction: Entry points to digital discourse

Research in the field of digital discourse has increasingly highlighted the relevance of the blurring of borders between online and offline, and the convergence of different modes for the ways individuals use resources, including language, to engage in various socio-cultural practices. More recently some of this work has turned to focus on the methodological implications of these developments. Building on existing scholarship, this paper reviews a range of literature to probe the methodological implications of shifts in possible sites or "entry points" (Heller, Pietkäinen & Pujolar, 2018, 47) to digital discourse and changes in the scope of what counts as data. While we position our paper as a review on methodology, we argue for the importance of engaging with these questions as intricately related to epistemology, theory and the study of language more generally.
Graph Attention Topic Modeling Network

Existing topic modeling approaches possess several issues, including the overfitting issue of Probablistic Latent Semantic Indexing (pLSI), the failure of capturing the rich topical correlations among topics in Latent Dirichlet Allocation (LDA), and high inference complexity. In this paper, we provide a new method to overcome the overfitting issue of pLSI by using the amortized inference with word embedding as input, instead of the Dirichlet prior in LDA. For generative topic model, the large number of free latent variables is the root of overfitting. To reduce the number of parameters, the amortized inference replaces the inference of latent variable with a function which possesses the shared (amortized) learnable parameters. The number of the shared parameters is fixed and independent of the scale of the corpus. To overcome the limited application of amortized inference to independent and identically distributed (i.i.d) data, a novel graph neural network, Graph Attention TOpic Network (GATON), is proposed to model the topic structure of non-i.i.d documents according to the following two observations. First, pLSI can be interpreted as stochastic block model (SBM) on a specific bi-partite graph. Second, graph attention network (GAT) can be explained as the semi-amortized inference of SBM, which relaxes the i.i.d data assumption of vanilla amortized inference. GATON provides a novel scheme, i.e. graph convolution operation based scheme, to integrate word similarity and word co-occurrence structure. Specifically, the bag-of-words document representation is modeled as a bi-partite graph topology. Meanwhile, word embedding, which captures the word similarity, is modeled as attribute of the word node and the term frequency vector is adopted as the attribute of the document node. Based on the weighted (attention) graph convolution operation, the word co-occurrence structure and word similarity patterns are seamlessly integrated for topic identification. Extensive experiments demonstrate that the effectiveness of GATON on topic identification not only benefits the document classification, but also significantly refines the input word embedding.
Collective Multi-type Entity Alignment Between Knowledge Graphs

Knowledge graph (e.g. Freebase, YAGO) is a multi-relational graph representing rich factual information among entities of various types. Entity alignment is the key step towards knowledge graph integration from multiple sources. It aims to identify entities across different knowledge graphs that refer to the same real world entity. However, current entity alignment systems overlook the sparsity of different knowledge graphs and can not align multi-type entities by one single model. In this paper, we present a Collective Graph neural network for Multi-type entity Alignment, called CG-MuAlign. Different from previous work, CG-MuAlign jointly aligns multiple types of entities, collectively leverages the neighborhood information and generalizes to unlabeled entity types. Specifically, we propose novel collective aggregation function tailored for this task, that (1) relieves the incompleteness of knowledge graphs via both cross-graph and self attentions, (2) scales up efficiently with mini-batch training paradigm and effective neighborhood sampling strategy. We conduct experiments on real world knowledge graphs with millions of entities and observe the superior performance beyond existing methods. In addition, the running time of our approach is much less than the current state-of-the-art deep learning methods.
Machine learning algorithms, applications, and practices in data science

Data science is an umbrella term used for referring to concepts and practices of subset of the topics under artificial intelligence (AI) methodologies. AI is actually a framework to define notion of intelligence in software systems or devices in terms of knowledge representation and reasoning methodologies. There are two main types of reasoning methods deductive and inductive over data. The major class of machine learning and deep learning methods come under inductive reasoning where essentially, missing pieces of information are interpolated based on existing data through numerical transformations. However, today AI is mostly identified with deduction systems while it is actually a comprehensive school of thought and formal framework. The AI framework offers rigor and robustness to the solutions developed and there is still scope for onboarding today's deep learning solutions and reap benefits of sturdiness. Data science is about end to end development of a smart solution that involves creation of pipelines for activities for data generation, business decision making and solution maintenance with humans in loop. Data generation is a cycle of activities involving collection, refinement, feature transformations, devising more insightful heuristic measures based on domain peculiarities and iterations to enhance quality of data driven decisions. Business decision making is pipeline of activities involving designing mappers from data to business decisions. The mappers are typically machine learning methods which are fine tuned to give best possible performance in a given period of study subject to business constraints. The mappers are fine tuned based on quality and magnitude of data and subdata. Solution maintenance is a critical component that involves setting up alarms to detect when a given decision maker model no longer works as desired. The maintenance work calls for repair actions such as identifying data to gather, comparative metrics of different models and monitoring the patterns and trends in the input data.
Interpretable CNNs for Object Classification

This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific object part.Our method does not require additional annotations of object parts or textures for supervision.Instead, we use the same training data as traditional CNNs.Our method automatically assigns each interpretable filter in a high conv-layer with an object part of a certain category during the learning process.Such explicit knowledge representations in conv-layers of the CNN help people clarify the logic encoded in the CNN, i.e., answering what patterns the CNN extracts from an input image and uses for prediction.We have tested our method using different benchmark CNNs with various architectures to demonstrate the broad applicability of our method.Experiments have shown that our interpretable filters are much more semantically meaningful than traditional filters.
High performance logistic regression for privacy-preserving genome analysis

Abstract Background In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand. Methods Our setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance. Results For our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition. Conclusions In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.
Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions

Virtual assistants are involved in the daily activities of humans such as managing calendars, making appointments, and providing wake-up calls. They provide a conversational service to customers around-the-clock and make their daily life manageable. With this emerging trend, many well-known companies launched their own virtual assistants that manage the daily routine activities of customers. In the healthcare sector, virtual medical assistants also provide a list of relevant diseases linked to a specific symptom. Due to low accuracy and uncertainty, these generated recommendations are untrusted and may lead to hypochondriasis. In this study, we proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user’s chief complaint and predicts a specific disease. Instead of informing about the medical condition, the user is referred to a nearby appropriate medical specialist. We designed an architecture for MIRA that considers the limitations of existing virtual medical assistants such as weak authentication, lack of understanding multiple intent statements about a specific medical condition, and uncertain diagnosis recommendations. To implement the designed architecture, we collected the chief complaints along with the dialogue corpora of real patients. Then, we manually validated these data under the supervision of medical specialists. We then used these data for natural language understanding, disease identification, and appropriate response generation. For the prototype version of MIRA, we considered the cases of glaucoma (eye disease) and diabetes (an autoimmune disease) only. The performance measure of MIRA was evaluated in terms of accuracy (89%), precision (90%), sensitivity (89.8%), specificity (94.9%), and F-measure (89.8%). The task completion was calculated using Cohen’s Kappa ( k = 0.848 ) that categorizes MIRA as ‘Almost Perfect’. Furthermore, the voice-based authentication identifies the user effectively and prevent against masquerading attack. Simultaneously, the user experience shows relatively good results in all aspects based on the User Experience Questionnaire (UEQ) benchmark data. The experimental results show that MIRA efficiently predicts a disease based on chief complaints and supports the user in decision making.
Privacy-Preserving distributed deep learning based on secret sharing

Distributed deep learning (DDL) naturally provides a privacy-preserving solution to enable multiple parties to jointly learn a deep model without explicitly sharing the local datasets. However, the existing privacy-preserving DDL schemes still suffer from severe information leakage and/or lead to significant increase of the communication cost. In this work, we design a privacy-preserving DDL framework such that all the participants can keep their local datasets private with low communication and computational cost, while still maintaining the accuracy and efficiency of the learned model. By adopting an effective secret sharing strategy, we allow each participant to split the intervening parameters in the training process into shares and upload an aggregation result to the cloud server. We can theoretically show that the local dataset of a particular participant can be well protected against the honest-but-curious cloud server as well as the other participants, even under the challenging case that the cloud server colludes with some participants. Extensive experimental results are provided to validate the superiority of the proposed secret sharing based distributed deep learning (SSDDL) framework.