Reviewerly/exHarmony huggingface dataset:
Towards Automated Microservices Extraction Using Muti-objective Evolutionary Search

We introduce in this paper a novel approach, named MSExtractor, that formulate the microservices identification problem as a multi-objective combinatorial optimization problem to decompose a legacy application into a set of cohesive, loosely-coupled and coarse-grained services. We employ the non-dominated sorting genetic algorithm (NSGA-II) to drive a search process towards optimal microservices identification while considering structural dependencies in the source code. We conduct an empirical evaluation on a benchmark of two open-source legacy software systems to assess the efficiency of our approach. Results show that MSExtractor is able to find relevant microservice candidates and outperforms recent three state-of-the-art approaches.
Assessing Postural Instability and Cybersickness Through Linear and Angular Displacement

To examine the hypothesis that constant speed is more comfortable than variable speed profiles and may minimize cybersickness.Current best practices for virtual reality (VR) content creation suggest keeping any form of acceleration as short and infrequent as possible to mitigate cybersickness.In Experiment 1, participants experienced repetitions of simulated linear motion, and in Experiment 2, they experienced repetitions of a circular motion. Three speed profiles were tested in each experiment. Each trial lasted 2 min while standing. Cybersickness was measured using the Simulator Sickness Questionnaire (SSQ) and operationally defined in terms of total severity scores. Postural stability was measured using a Wii Balance Board and operationally defined in terms of center of pressure (COP) path length. Postural measures were decomposed into anterior-posterior and medial-lateral axes and subjected to detrended fluctuation analysis.For both experiments, no significant differences were observed between the three speed profiles in terms of cybersickness or postural stability, and none of the baseline postural measures could predict SSQ scores for the speed profile conditions. An axis effect was observed in both experiments such that normalized COP movement was significantly greater along the anterior-posterior axis than the medial-lateral axis.Results showed no convincing evidence to support the common belief that constant speed is more comfortable than variable speed profiles for scenarios typical of VR applications.The present findings offer guidelines for the design of locomotion techniques involving traversal in VR environments.
Grasping VR: Presence of Pseudo-Haptic Interface Based Portable Hand Grip System in Immersive Virtual Reality

This study proposes a pseudo-haptic interface optimized for a novel portable hand grip haptic system, providing simple and realistic interfaces in various situations using the hand grips of users in immersive virtual reality. The proposed haptic system accurately determines the force of the user grip through an Arduino-based system design using portable low-cost electromyography (EMG) sensors. Using this system, a visual illusion-based pseudo-haptic interface is designed to maximize presence in virtual reality. This involves processes that use a grasping illusion to enable the user to quickly become familiar with the system when tactile sensation is excluded, which maximizes immersion. Based on this process, an immersive virtual reality application is produced to survey and analyze user presence for the provided hand grip. Finally, the positive experience and improved presence for hand grip in immersive virtual reality are statistically analyzed and verified through user surveys.
Separated Variational Hashing Networks for Cross-Modal Retrieval

Cross-modal hashing, due to its low storage cost and high query speed, has been successfully used for similarity search in multimedia retrieval applications. It projects high-dimensional data into a shared isomorphic Hamming space with similar binary codes for semantically-similar data. In some applications, all modalities may not be obtained or trained simultaneously for some reasons, such as privacy, secret, storage limitation, and computational resource limitation. However, most existing cross-modal hashing methods need all modalities to jointly learn the common Hamming space, thus hindering them from handling these problems. In this paper, we propose a novel approach called Separated Variational Hashing Networks (SVHNs) to overcome the above challenge. Firstly, it adopts a label network (LabNet) to exploit available and nonspecific label annotations to learn a latent common Hamming space by projecting each semantic label into a common binary representation. Then, each modality-specific network can separately map the samples of the corresponding modality into their binary semantic codes learned by LabNet. We achieve it by conducting variational inference to match the aggregated posterior of the hashing code of LabNet with an arbitrary prior distribution. The effectiveness and efficiency of our SVHNs are verified by extensive experiments carried out on four widely-used multimedia databases, in comparison with 11 state-of-the-art approaches.
Self-calibration approach to stereo cameras with radial distortion based on epipolar constraint

In this paper, we propose a self-calibration approach to stereo cameras with radial distortion from stereo image pairs of a common 3D scene. Based on the epipolar constraint in the stereo image pair, the intrinsic and extrinsic parameters of stereo cameras are estimated synchronously with a minimum number of nine image point correspondences. It is significant within a random sample consensus (RANSAC) scheme to cope with the outliers of feature matches efficiently and robustly. Then the inliers of the stereo image pair that have been determined after RANSAC are used to optimize the calibration parameters of stereo cameras. Furthermore, more accurate calibration results can be achieved with the joint optimization of multiple stereo image pairs. Both synthetic and real data are used to evaluate the performance of the proposed method, demonstrating that our method can calibrate stereo cameras with radial distortions efficiently and accurately.
Subject Review : Key Generation in Different Cryptography Algorithm

The importance of encryption in most organizations, therefore, it became necessary to encrypt data because data security is an essential component in order to maintain the security of data and information in various institutions. Encryption also helps to ensure the confidentiality and integrity of data during transmission through communication channels. Given the importance of the keys used in encryption as a key part in the strength of the algorithm and increase its security in most encryption algorithms, thus generating the key in many research the most important part in data encryption and its importance lies in the non-duplication of keys to ensure better results and theoretically impossible to break. In this paper, we try to describe and review many of the papers that were used to create the keys and compare them with each other.
Efficient Single Image Super-Resolution via Hybrid Residual Feature Learning with Compact Back-Projection Network

Deep learning methods have achieved state-of-the-art accuracy in single image super-resolution (SISR). Yet, how to achieve good balance between efficiency and accuracy in SISR is still an open issue. While most existing methods learn residual features only in low resolution (LR) space in order for higher efficiency, recent studies show that jointly learning residual features in LR and high resolution (HR) space is more preferred for accurate SISR. In this paper, we propose an efficient SISR method via learning hybrid residual features, based on which the residual HR image can be reconstructed. To fulfill hybrid residual feature learning, we propose a compact back-projection network that can simultaneously generate features in both LR and HR space by cascading up-and down-sampling layers with small-sized filters. Extensive experiments on four benchmark databases demonstrate that our proposed method can achieve high efficiency (i.e., small number of parameters and operations) while preserving state-of-the-art SR accuracy.
TraceChain: A blockchain‐based scheme to protect data confidentiality and traceability

Summary The risk of sharing data in cloud computing has gathered increasing attention. After the owner of some confidential data outsources the data to cloud storage services and shares it with others, the data owner lost the control to the data to a large extent. To achieve data sharing while keeping data confidentiality, attribute‐based encryption (ABE) can be employed by cloud storage services. However, ABE can only guarantee that outsourced data on the cloud is decrypted by attribute‐satisfying users but cannot restrict data from being accessed by dishonest users whose attributes also satisfy the access‐control policy. It is impossible for the data owner to control the shared data after it has been decrypted by dishonest users, especially when a set of attribute‐satisfying dishonest users may collude. To address this concern, we propose a traceable data sharing scheme called TraceChain. In TraceChain, data is encrypted over a new CP‐ABE scheme called E‐CP‐ABE. Furthermore, the system parameters for generating the private key in E‐CP‐ABE are uploaded to the private blockchain and transactions are performed on the chain. The data owner can obtain the identity of users by monitoring system parameters simultaneously and control data sharing on the blockchain. To prove the security of our scheme, the security analysis is given in this paper. Meanwhile, experimental results also show that our system is viable and efficient.
Traceable attribute-based signature

Traceable attribute-based signature (TABS) is a useful cryptographic tool to protect the real signers' identities while tracing the real signers under some conditions. Traceable attribute-based signature inherits the merits of attribute-based signature, where each signer can sign any messages with fine-grained control over identity information. Since attribute-based signature can hide the identities of real signers to protect their privacy, it can bring about another security issue. For example, some signers abuse attribute-based signatures to sign any messages, and the abusing behaviour cannot be traced. To solve this problem, traceable attribute-based signature is proposed to balance the usage of attribute-based signatures. Traceable attribute-based signature may protect the privacy of real signers, and it may trace the identities of real signers by trusted third party under some conditions. Currently few traceable attribute-based signature schemes are proposed. Escala et al. proposed a traceable attribute-based signature scheme in the standard model, which can trace the identities of real signers. However, their scheme only supports that the attribute predicates are solely conjunction of attributes. Okamoto et al. proposed an attribute-based signature scheme in the standard model, their scheme can support generalized non-monotone predicates. However, their scheme does not have the traceability. In this paper, we present a traceable attribute-based signature scheme for monotone predicates in the standard model. Compared with other traceable attribute-based signature schemes, our proposed scheme is efficient.
Using machine learning to selectively highlight patient information

• Clinicians seek relevant patient data in EMR systems to accomplish clinical tasks. • We developed and evaluated a learning electronic medical record (LEMR) system. • LEMR uses machine learning models to identify and highlight relevant patient data. • Machine learning models are built from information-seeking behavior of clinicians. Electronic medical record (EMR) systems need functionality that decreases cognitive overload by drawing the clinician’s attention to the right data, at the right time. We developed a Learning EMR (LEMR) system that learns statistical models of clinician information-seeking behavior and applies those models to direct the display of data in future patients. We evaluated the performance of the system in identifying relevant patient data in intensive care unit (ICU) patient cases. To capture information-seeking behavior, we enlisted critical care medicine physicians who reviewed a set of patient cases and selected data items relevant to the task of presenting at morning rounds. Using patient EMR data as predictors, we built machine learning models to predict their relevancy. We prospectively evaluated the predictions of a set of high performing models. On an independent evaluation data set, 25 models achieved precision of 0.52, 95% CI [0.49, 0.54] and recall of 0.77, 95% CI [0.75, 0.80] in identifying relevant patient data items. For data items missed by the system, the reviewers rated the effect of not seeing those data from no impact to minor impact on patient care in about 82% of the cases. Data-driven approaches for adaptively displaying data in EMR systems, like the LEMR system, show promise in using information-seeking behavior of clinicians to identify and highlight relevant patient data.
Direct Validation of the Information Bottleneck Principle for Deep Nets

The information bottleneck (IB) has been suggested as a fundamental principle governing performance in deep neural nets (DNNs). This idea sparked research on the information plane dynamics during training with the cross-entropy loss, and on using the IB of some "bottleneck" layer as a loss function. However, the claim that reaching the maximal value of the IB Lagrangian in each layer leads to optimal performance, was in fact never directly confirmed. In this paper, we propose a direct way of validating this hypothesis, using layer-by-layer training with the IB loss. In accordance with the original theory, we train each DNN layer explicitly with the IB objective (and without any classification loss), and freeze it before moving on to train the next layer. While mutual information (MI) is generally hard to estimate in high dimensions, we show that in the case of MI between DNN layers, this can be done quite accurately using a modification of the recently proposed mutual information neural estimator. Interestingly, we find that layer-by-layer training with the IB loss leads to accuracy which is on-par with end-to-end training with the cross entropy loss. This is, thus, the first direct experimental illustration of the link between the IB value in each layer, and a net's performance.
Practical Key Recovery Model for Self-Sovereign Identity Based Digital Wallets

Recent years have seen an increased interest in digital wallets for a multitude of use cases including online banking, cryptocurrency, and digital identity management. Digital wallets play a pivotal role in the secure management of cryptographic keys and credentials, and for providing certain identity management services. In this paper, we examine a proof-of-concept digital wallet in the context of Self-Sovereign Identity and provide a practical decentralized key recovery solution using Shamir's secret sharing scheme and Hyperledger Indy distributed ledger technology.
A Vector Quantized Variational Autoencoder (VQ-VAE) Autoregressive Neural $F_0$ Model for Statistical Parametric Speech Synthesis

Recurrent neural networks (RNNs) can predict fundamental frequency (F0) for statistical parametric speech synthesis systems, given linguistic features as input.However, these models assume conditional independence between consecutive F0 values, given the RNN state.In a previous study, we proposed autoregressive (AR) neural F0 models to capture the causal dependency of successive F0 values.In subjective evaluations, a deep AR model (DAR) outperformed an RNN.Here, we propose a Vector Quantized Variational Autoencoder (VQ-VAE) neural F0 model that is both more efficient and more interpretable than the DAR.This model has two stages: one uses the VQ-VAE framework to learn a latent code for the F0 contour of each linguistic unit, and other learns to map from linguistic features to latent codes.In contrast to the DAR and RNN, which process the input linguistic features frame-by-frame, the new model converts one linguistic feature vector into one latent code for each linguistic unit.The new model achieves better objective scores than the DAR, has a smaller memory footprint and is computationally faster.Visualization of the latent codes for phones and moras reveals that each latent code represents an F0 shape for a linguistic unit.
A Secure Decentralized Trustless E-Voting System Based on Smart Contract

E-voting plays a significant role in social activities. The trust of the voting results and the privacy of each voter are always the most important concerns in designing secure e-voting system. In this paper, we design a decentralized trustless e-voting system based on the smart contract. To guarantee the correctness of the voting result, the smart contract on Blockchain is used to provide a trusted public bulletin board and trusted computing environment. To hide the identity of each voter as well as avoid multiple voting, linkable ring signature is used for each voter to group a signing ring. In addition, to make sure that all voters will see the voting result at the same time, or nobody can get it, threshold encryption without trusted third party is used to make secret-key secretly before tally stage. Moreover, the trust (power) of the voting system is separated to all voters. Even though some of them are malicious, the final result will not be influenced. The contract is deployed on the Ethereum private network. Some feasibility and cost analysis on money and time are also provided.
Secure Data Streaming to Untrusted Road Side Units in Intelligent Transportation System

The paper considers data security issues in vehicle-to-infrastructure communications, where vehicles stream data to a road side unit. We assume aggregated data in road side units can be stored or used for data analytics. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicle layer, where a group leader is assigned to communicate with group devices and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality of sensory data.
Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance

This paper analyzes the gender representation in four major corpora of French broadcast. These corpora being widely used within the speech processing community, they are a primary material for training automatic speech recognition (ASR) systems. As gender bias has been highlighted in numerous natural language processing (NLP) applications, we study the impact of the gender imbalance in TV and radio broadcast on the performance of an ASR system. This analysis shows that women are under-represented in our data in terms of speakers and speech turns. We introduce the notion of speaker role to refine our analysis and find that women are even fewer within the Anchor category corresponding to prominent speakers. The disparity of available data for both gender causes performance to decrease on women. However, this global trend seems to be counterbalanced when sufficient amount of data per speaker is available.
Better and Faster: Exponential Loss for Image Patch Matching

Recent studies on image patch matching are paying more attention on hard sample learning, because easy samples do not contribute much to the network optimization. They have proposed various hard negative sample mining strategies, but very few addressed this problem from the perspective of loss functions. Our research shows that the conventional Siamese and triplet losses treat all samples linearly, thus make the training time consuming. Instead, we propose the exponential Siamese and triplet losses, which can naturally focus more on hard samples and put less emphasis on easy ones, meanwhile, speed up the optimization. To assist the exponential losses, we introduce the hard positive sample mining to further enhance the effectiveness. The extensive experiments demonstrate our proposal improves both metric and descriptor learning on several well accepted benchmarks, and outperforms the state-of-the-arts on the UBC dataset. Moreover, it also shows a better generalizability on cross-spectral image matching and image retrieval tasks.
Fast in-memory CRIU for docker containers

Server systems with large amounts of physical memory can benefit from using some of the available memory capacity for in-memory snapshots of the ongoing computations. In-memory snapshots are useful for services such as scaling of new workload instances, debugging, during scheduling, etc., which do not require snapshot persistence across node crashes/reboots. Since increasingly more frequently servers run containerized workloads, using technologies such as Docker, the snapshot, and the subsequent snapshot restore mechanisms, would be applied at granularity of containers. However, CRIU, the current approach to snapshot/restore containers, suffers from expensive filesystem write/read operations on image files containing memory pages, which dominate the runtime costs and impact the potential benefits of manipulating in-memory process state.
Summarizing User-generated Textual Content: Motivation and Methods for Fairness in Algorithmic Summaries

As the amount of user-generated textual content grows rapidly, text summarization algorithms are increasingly being used to provide users a quick overview of the information content. Traditionally, summarization algorithms have been evaluated only based on how well they match human-written summaries (e.g. as measured by ROUGE scores). In this work, we propose to evaluate summarization algorithms from a completely new perspective that is important when the user-generated data to be summarized comes from different socially salient user groups, e.g. men or women, Caucasians or African-Americans, or different political groups (Republicans or Democrats). In such cases, we check whether the generated summaries fairly represent these different social groups. Specifically, considering that an extractive summarization algorithm selects a subset of the textual units (e.g. microblogs) in the original data for inclusion in the summary, we investigate whether this selection is fair or not. Our experiments over real-world microblog datasets show that existing summarization algorithms often represent the socially salient user-groups very differently compared to their distributions in the original data. More importantly, some groups are frequently under-represented in the generated summaries, and hence get far less exposure than what they would have obtained in the original data. To reduce such adverse impacts, we propose novel fairness-preserving summarization algorithms which produce high-quality summaries while ensuring fairness among various groups. To our knowledge, this is the first attempt to produce fair text summarization, and is likely to open up an interesting research direction.