Reviewerly/exHarmony huggingface dataset:
Social Media through Voice

With advances in expressive speech synthesis and conversational understanding, an ever-increasing amount of digital content---including social and personal content---can be consumed through voice. Voice has long been known to convey personal characteristics and emotional states, both of which are prominent aspects of social media. Yet, no study has investigated voice design requirements for social media platforms. We interviewed 15 active social media users about their preferences on using synthesized voices to represent their profiles. Our findings show that participants want to have control over how a voice delivers their content, such as the personality and emotion with which the voice speaks, because these prosodic variations can impact users' online personas and interfere with impression management. We report motivations behind customizing or not customizing voice characteristics in different scenarios, and uncover key challenges around usability and the potential for stereotyping. We argue that synthesized speech for social media should be evaluated not only on listening experience and voice quality but also on its expressivity, degree of customizability, and ability to adapt to contexts (e.g., social media platforms, groups, individual posts). We discuss how our contribution confirms and extends knowledge of voice technology design and online self-presentation, and offer design considerations for voice personalization related to social interactions.
<i>d</i>-BAME: Distributed Blockchain-Based Anonymous Mobile Electronic Voting

Electronic voting (e-voting) presents a convenient and cost-effective alternative to current paper ballot-based voting. It provides many benefits such as increased voter turnout and accuracy in the decision-making process. While presenting many improvements, e-voting still faces serious security challenges that hinder its adoption, especially when designed to be run over mobile devices. In this article, we propose a novel remote e-voting model for large-scale elections by proposing the participation of two conflicting parties to ensure election integrity and accountability. Our scheme can be implemented in IoT devices such as smartphones, which we believe can significantly increase voter turnout of the election process. Our proposed work is secure and preserves voter privacy through secure multiparty computations performed by parties of differing allegiances. It also leverages a blockchain running smart contracts as a publicly accessible and tamper-resistant bulletin board to permanently store votes and prevent double voting. In our security and privacy analysis, we show that our proposed scheme is secure against potential security threats and provides voter anonymity. We show orthogonality between universal verifiability and coercion resistance in our proposed scheme, allowing an election to favor one over the other. Our performance analysis and smartphone simulation results show that the proposed scheme is practical for large-scale elections.
Accelerating graph sampling for graph machine learning using GPUs

Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and Graph-SAGE, sample the graph to produce mini-batches that are suitable for training a DNN. However, sampling time can be a significant fraction of training time, and existing systems do not efficiently parallelize sampling.
A Survey on Human Activity Recognition using Sensors and Deep Learning Methods

One of the most difficult challenges in the field of computer vision is human activity recognition (HAR). The main purpose of intelligent video system is to determine the actions and activities of the individual. This action monitoring system can be used in a variety of settings, which include human-computer interaction, tracking, security, and health monitoring. Detecting activity in an uncircumscribed territory remains a challenging task with multiple challenges, despite ongoing efforts in this area. Throughout this article, some recent research papers that include different approaches are analyzed to detect different human activities. Wearable devices and phone sensors are required for this task. The vision-based approach has become a prevalent HAR technique, according to the researchers.
Computer-aided autism diagnosis based on visual attention models using eye tracking

An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.
An optimized transaction verification method for trustworthy blockchain-enabled IIoT

The blockchain technology is one of the hottest research fields in recent years, and it can provide a new security solution for data transmission and storage under trust-free environments. The distributed structure of blockchain is naturally suitable for the Industrial Internet of Things (IIoT), which can be used to build distributed trustworthy IIoT with high security. The transaction database is the most important security component in the blockchain-enabled IIoT systems. Each valid transaction must be recorded in the database on the long connected blockchain. Merkle tree is designed for the transaction verification and it can guarantee the data integrity and security. Therefore, the Merkle tree has the same and fixed verification time for every transaction. Due to the human elements, only a few transactions will be verified frequently in some real scenarios. The current Merkle tree storage structure can not improve the verification efficiency for transactions that require frequent verification in IIoT systems with heterogeneous Devices. To tackle the issue, this paper proposes an optimized Merkle tree structure for efficient transaction verification in trustworthy blockchain-enabled IIoT systems. This work first analyzes the current building method and verification mechanism of the Merkle tree structure. Then we propose the optimized Merkle tree structure and its construction and verification. Finally, the superiority of our proposed method is proved through extensive experiments. The new Merkle tree structure is more efficient for verifying blockchain transactions for trustworthy blockchain-enabled IIoT systems.
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 20 important graph problems. We also present the interfaces, optimizations, and graph processing techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We have made the implementations developed in this work publicly-available as the Graph Based Benchmark Suite (GBBS).
Executing Spark BigDL for Leukemia Detection from Microscopic Images using Transfer Learning

Acute Leukemia disease is the bone marrow problem common both in children and adults. Medical image analytics is applied in the field of Digital Image Processing (DIP) and Deep Learning (DL). The role of deep learning in medical research with Big Data has been a huge benefit, opening new doors and possibilities for disease diagnostics procedures. Now the medical specialists like pathologists, hematologists, mammalogists and researchers are working in deep learning area. The proposed methodology is Leukemia detection by implementing apache spark BigDL library from the microscopic images of human blood cells using Convolutional Neural Network (CNN) architecture GoogleNet deep transfer learning. The proposed system is an efficient enough to detect 4 types of leukemia Acute Myeloid Leukemia (AML), Actuate Lymphocytic Leukemia (ALL), Chronic Myeloid Leukemia (CML) and Chronic Lymphocytic Leukemia (CLL) and normal from the microscopic images of human blood sample. The proposed methodology after using Spark BigDL framework with Google Net architecture, we achieved 97.33% accuracy in case of training and 94.78% of validation respectively. Moreover we are also compared our model without BigDL GoogleNet. The accuracy of training and validation accuracy are 96.42% and 92.69% respectively. The BigDL model outperformed the Keras model with more efficient and accurate results.
Joint Face Image Restoration and Frontalization for Recognition

In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. To address these challenges, previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. However, most of these methods are stage-wise, which is sub-optimal and deviates from the reality. In this paper, we address all these challenges jointly for unconstrained face recognition. We propose an Multi-Degradation Face Restoration (MDFR) model to restore frontalized high-quality faces from the given low-quality ones under arbitrary facial poses, with three distinct novelties. First, MDFR is a well-designed encoder-decoder architecture which extracts feature representation from an input face image with arbitrary low-quality factors and restores it to a high-quality counterpart. Second, MDFR introduces a pose residual learning strategy along with a 3D-based Pose Normalization Module (PNM), which can perceive the pose gap between the input initial pose and its real-frontal pose to guide the face frontalization. Finally, MDFR can generate frontalized high-quality face images by a single unified network, showing a strong capability of preserving face identity. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks demonstrate the superiority of MDFR over state-of-the-art methods on both face frontalization and face restoration.
Underwater image enhancement and marine snow removal for fishery based on integrated dual-channel neural network

Computer vision technology can reduce the intensity and difficulty of marine aquaculture. Nonetheless, the degradation problems of marine fishery underwater images hamper further interpretation and analysis of underwater information by computer vision technology. In order to comprehensively solve the problems of degradation in marine fishery underwater images, this paper proposes an end-to-end integrated dual-channel network model, which uses the underwater image enhancement module based on the residual dense network to perform color correction and dehazing for the low-frequency layer of images, and the marine snow removal module based on local residual learning strategy to remove the white marine snow in the high-frequency layer of images. Moreover, the addition of refinement module further improves the textual details and colors of images. Experimental results on marine fishery dataset indicate that the approach proposed in this paper performs better than several state-of-the-art methods in quantitative metrics, including underwater image quality measure (UIQM), blur index and smooth index, effective in improving the contrast of underwater fishery images and reducing marine snow noise, while achieving better visual quality in qualitative evaluation such as color correction, dehazing, detail and feature restoration. Furthermore, the generalization tests and application tests have proved its effectiveness in underwater scenarios, which means it can meet the practical needs in the field of marine aquaculture.
Security concerns on machine learning solutions for 6G networks in mmWave beam prediction

6G – sixth generation – is the latest cellular technology currently under development for wireless communication systems. In recent years, machine learning (ML) algorithms have been applied widely in various fields, such as healthcare, transportation, energy, autonomous cars, and many more. Those algorithms have also been used in communication technologies to improve the system performance in terms of frequency spectrum usage, latency, and security. With the rapid developments of ML techniques, especially deep learning (DL), it is critical to consider the security concern when applying the algorithms. While ML algorithms offer significant advantages for 6G networks, security concerns on artificial intelligence (AI) models are typically ignored by the scientific community so far. However, security is also a vital part of AI algorithms because attackers can poison the AI model itself. This paper proposes a mitigation method for adversarial attacks against proposed 6G ML models for the millimeter-wave (mmWave) beam prediction using adversarial training. The main idea behind generating adversarial attacks against ML models is to produce faulty results by manipulating trained DL models for 6G applications for mmWave beam prediction. We also present a proposed adversarial learning mitigation method’s performance for 6G security in mmWave beam prediction application a fast gradient sign method attack. The results show that the defended model under attack’s mean square errors (i.e., the prediction accuracy) are very close to the undefended model without attack.
On Designing Programming Error Messages for Novices: Readability and its Constituent Factors

Programming error messages play an important role in learning to program. The cycle of program input and error message response completes a loop between the programmer and the compiler/interpreter and is a fundamental interaction between human and computer. However, error messages are notoriously problematic, especially for novices. Despite numerous guidelines citing the importance of message readability, there is little empirical research dedicated to understanding and assessing it. We report three related experiments investigating factors that influence programming error message readability. In the first two experiments we identify possible factors, and in the third we ask novice programmers to rate messages using scales derived from these factors. We find evidence that several key factors significantly affect message readability: message length, jargon use, sentence structure, and vocabulary. This provides novel empirical support for previously untested long-standing guidelines on message design, and informs future efforts to create readability metrics for programming error messages.
Multi-scale relation reasoning for multi-modal Visual Question Answering

The goal of Visual Question Answering (VQA) is to answer questions about images. For the same picture, there are often completely different types of questions. Therefore, the main difficulty of VQA task lies in how to properly reason relationships among multiple visual objects according to types of input questions. To solve this difficulty, this paper proposes a deep neural network to perform multi-modal relation reasoning in multi-scales, which successfully constructs a regional attention scheme to focus on informative and question-related regions for better answering. Specifically, we firstly design regional attention scheme to select regions of interest based on informative evaluation computed by a question-guided soft attention module. Afterwards, features computed by regional attention scheme are fused in scaled combinations, thus generating more distinctive features with scalable information. Due to designs of regional attention and multi-scale property, the proposed method is capable to describe scaled relationships from multi-modal inputs to offer accurate question-guided answers. By conducting experiments on VQA v1 and VQA v2 datasets, we show that the proposed method has superior efficiencies than most of the existing methods.
Temporal Binary Representation for Event-Based Action Recognition

In this paper we present an event aggregation strategy to convert the output of an event camera into frames processable by traditional Computer Vision algorithms. The proposed method first generates sequences of intermediate binary representations, which are then losslessly transformed into a compact format by simply applying a binary-to-decimal conversion. This strategy allows us to encode temporal information directly into pixel values, which are then interpreted by deep learning models. We apply our strategy, called Temporal Binary Representation, to the task of Gesture Recognition, obtaining state of the art results on the popular DVS128 Gesture Dataset. To underline the effectiveness of the proposed method compared to existing ones, we also collect an extension of the dataset under more challenging conditions on which to perform experiments.
Bloch-type photonic skyrmions in optical chiral multilayers

Magnetic skyrmions are topological quasiparticles in magnetization. Recently, as one of their photonic counterparts, Néel-type photonic skyrmions were discovered in evanescent electromagnetic waves. The deep-subwavelength features of the photonic skyrmions suggest their potential in optical imaging quantum technologies and data storage. Here, by exploiting the photonic quantum spin Hall effect of a plasmonic vortex in a trilayered structure, we predict the existence of photonic twisted-Néel- and Bloch-type skyrmions in chiral materials. Their chirality-dependent features can be considered as additional degrees of freedom for future chiral sensing, information processing, and storage technologies. In particular, our findings enrich the formations of photonic skyrmions and reveal a remarkable resemblance of the feature of chiral materials in two seemingly distant fields: photonic skyrmions and magnetic skyrmions.2 MoreReceived 16 December 2020Accepted 9 April 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023109Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasChiralityLight-matter interactionNanophotonicsNear-field opticsPlasmonicsSkyrmionsTopological effects in photonic systemsAtomic, Molecular & Optical
Hyper-parameter tuned light gradient boosting machine using memetic firefly algorithm for hand gesture recognition

Hand gesture is considered as one of the natural ways to interact with computers. The utility of hand gesture-based application is a recent trend and is an effective method for human–computer interaction. Though many static and other intelligent approaches using Machine learning (ML) are developed, still there is a marginal tradeoff between the computational cost and accuracy. In this paper, a Lightboost based Gradient boosting machine (LightGBM) is proposed for efficient hand gesture recognition. The hyper-parameters of the LightGBM are optimized with an improved memetic firefly algorithm. We have introduced a perturbation operator and incorporated it in the proposed memetic firefly algorithm for avoiding the local optimal solution in the traditional firefly algorithm. With comparative analysis among the proposed method and other competitive ML methods, the performance of the proposed method is found to be better in terms of various performance metrics such as accuracy, precision, recall, F1 score, and ROC–AUC. The proposed memetic firefly-based boosting approach is dominant over all the other considered methods with an accuracy of 99.36% and is robust for accurate hand gesture recognition.
Joint blockchain service vendor-platform selection using social network relationships: A multi-provider multi-user decision perspective

Blockchain technology has been widely touted for building and supporting supply chain management capabilities. The technology has substantial potential to enhance operational effectiveness and profitability. Blockchain platform evaluation and selection still requires investigation. Blockchain platform selection and adoption decision making in a multi-organizational supply chain context is complex. Effective blockchain adoption and operations requires consideration of multiple vendors—service providers—and platforms while satisfying multiple users and stakeholders. This study introduces a managerial decision support method to assist organizations evaluate and select joint blockchain service vendor and platforms for multiple organizational functions and organizations. Using literature and practice, we introduce blockchain service vendor and platform attributes from user, vendor, and platform perspectives. A social network theory lens sets the foundation for an innovative group decision-making method—a DEMATEL-based hierarchical best-worst method—integrating characteristics from this decision environment. The technological and distributed network nature of blockchain technology requires evaluation by decision makers from various levels of a supply chain network. These decision makers will likely have varying blockchain technology knowledge and subjective preferences that need integration. The proposed method helps to operationalize social network relationships to comprehend partial and idiosyncratic expert opinions about blockchain technology. An illustrative example and various scenarios are presented to identify managerial and research implications. Methodological limitations and future research are presented for this emergent managerial and technological concern across supply chains.