Reviewerly/exHarmony huggingface dataset:
ORTHROS: non-autoregressive end-to-end speech translation With dual-decoder

Fast inference speed is an important goal towards real-world deployment of speech translation (ST) systems. End-to-end (E2E) models based on the encoder-decoder architecture are more suitable for this goal than traditional cascaded systems, but their effectiveness regarding decoding speed has not been explored so far. Inspired by recent progress in non-autoregressive (NAR) methods in text-based translation, which generates target tokens in parallel by eliminating conditional dependencies, we study the problem of NAR decoding for E2E-ST. We propose a novel NAR E2E-ST framework, Orthros, in which both NAR and autoregressive (AR) decoders are jointly trained on the shared speech encoder. The latter is used for selecting better translation among various length candidates generated from the former, which dramatically improves the effectiveness of a large length beam with negligible overhead. We further investigate effective length prediction methods from speech inputs and the impact of vocabulary sizes. Experiments on four benchmarks show the effectiveness of the proposed method in improving inference speed while maintaining competitive translation quality compared to state-of-the-art AR E2E-ST systems.
Evaluating and comparing memory error vulnerability detectors

Memory error vulnerabilities have been consequential and several well-known, open-source memory error vulnerability detectors exist, built on static and/or dynamic code analysis. Yet there is a lack of assessment of such detectors based on rigorous, quantitative accuracy and efficiency measures while not being limited to specific application domains. Our study aims to assess and explain the strengths and weaknesses of state-of-the-art memory error vulnerability detectors based on static and/or dynamic code analysis, so as to inform tool selection by practitioners and future design of better detectors by researchers and tool developers. We empirically evaluated and compared five state-of-the-art memory error vulnerability detectors against two benchmark datasets of 520 and 474 C/C++ programs, respectively. We conducted case studies to gain in-depth explanations of successes and failures of individual tools. While generally fast, these detectors had largely varied accuracy across different vulnerability categories and moderate overall accuracy. Complex code (e.g., deep loops and recursions) and data (e.g., deeply embedded linked lists) structures appeared to be common, major barriers. Hybrid analysis did not always outperform purely static or dynamic analysis for memory error vulnerability detection. Yet the evaluation results were noticeably different between the two datasets used. Our case studies further explained the performance variations among these detectors and enabled additional actionable insights and recommendations for improvements. There was no single most effective tool among the five studied. For future research, integrating different techniques is a promising direction, yet simply combining different classes of code analysis (e.g., static and dynamic) may not. For practitioners to choose right tools, making various tradeoffs (e.g., between precision and recall) might be inevitable.
Multitree Genetic Programming With New Operators for Transfer Learning in Symbolic Regression With Incomplete Data

Lack of knowledge is a common consequence of data incompleteness when learning from real-world data. To deal with such a situation, this work utilizes transfer learning (TL) to reuse knowledge from different (yet related) but complete domains. Due to its powerful feature construction ability, genetic programming (GP) is used to construct feature-based transformations that map the feature space of the source domain to that of the target domain such that their differences are reduced. Particularly, this work proposes a new multitree GP-based feature construction approach to TL in symbolic regression with missing values. It transfers knowledge related to the importance of the features and instances in the source domain to the target domain to improve the learning performance. Moreover, new genetic operators are developed to encourage minimizing the distribution discrepancy between the transformed domain and the target domain. A new probabilistic crossover is developed to make the well-constructed trees in the individuals more likely to be mated than the other trees. A new mutation operator is designed to give more probability for the poorly constructed trees to be mutated. The experimental results show that the proposed method not only achieves better performance compared with different traditional learning methods but also advances two recent TL methods on real-world data sets with various incompleteness and learning scenarios.
Let’s Frets! Assisting Guitar Students During Practice via Capacitive Sensing

Learning a musical instrument requires regular exercise. However, students are often on their own during their practice sessions due to the limited time with their teachers, which increases the likelihood of mislearning playing techniques. To address this issue, we present Let's Frets - a modular guitar learning system that provides visual indicators and capturing of finger positions on a 3D-printed capacitive guitar fretboard. We based the design of Let's Frets on requirements collected through in-depth interviews with professional guitarists and teachers. In a user study (N=24), we evaluated the feedback modules of Let's Frets against fretboard charts. Our results show that visual indicators require the least time to realize new finger positions while a combination of visual indicators and position capturing yielded the highest playing accuracy. We conclude how Let's Frets enables independent practice sessions that can be translated to other musical instruments.
An End-to-end Multi-task Object Detection using Embedded GPU in Autonomous Driving

Autonomous driving has gained popularity due to its high reliability compared to human drivers. Autonomous vehicles combine variety of sensors to perceive their surroundings, and use deep learning (DL) to extract complicated information from the sensing data. However, there are several challenges: Many DL models have explosive model sizes, and therefore not only time consuming but also power consuming when implementing on embedded systems on vehicles, further degrading the battery life-cycle. The current on-board AI treats lane detection and car location separately. In this paper, we propose an end-to-end multi-task environment detection framework. We fuse the 3D point cloud object detection model and lane detection model, with model compression technique applied. As on-board sensors forward information to the multi-task network, it not only parallel two detection tasks to extract combination information, but also reduces entire running time of the DL model. Experiments show by adding the model compression technique, the running speed of multi-task model improves more than 2×. Also, running time of lane detection model on Nvidia Jetson TX2 is almost 6× less comparing with running on CPU, which shows reasonableness of using embedded AI computing device on autonomous vehicle. Index Terms-3D Object Detection, LIDAR Point Cloud, Lane Detection, Weight Pruning, Embedded Computing Device.
An Iterative Framework for Self-Supervised Deep Speaker Representation Learning

In this paper, we propose an iterative framework for self-supervised speaker representation learning based on a deep neural network (DNN). The framework starts with training a self-supervision speaker embedding network by maximizing agreement between different segments within an utterance via a contrastive loss. Taking advantage of DNN’s ability to learn from data with label noise, we propose to cluster the speaker embedding obtained from the previous speaker network and use the subsequent class assignments as pseudo labels to train a new DNN. Moreover, we iteratively train the speaker network with pseudo labels generated from the previous step to bootstrap the discriminative power of a DNN. Speaker verification experiments are conducted on the VoxCeleb dataset. The results show that our proposed iterative self-supervised learning framework outperformed previous works using self-supervision. The speaker network after 5 iterations obtains a 61% performance gain over the speaker embedding model trained with contrastive loss.
Traffic Monitoring System Based on Deep Learning and Seismometer Data

Currently, vehicle classification in roadway-based techniques depends mainly on photos/videos collected by an over-roadway camera or on the magnetic characteristics of vehicles. However, camera-based techniques are criticized for potentially violating the privacy of vehicle occupants and exposing their identity, and vehicles can evade detection when they are obscured by larger vehicles. Here, we evaluate methods of identifying and classifying vehicles on the basis of seismic data. Vehicle identification from seismic signals is considered a difficult task because of interference by various noise. By analogy with techniques used in speech recognition, we used different artificial intelligence techniques to extract features of three, different-sized vehicles (buses, cars, motorcycles) and seismic noise. We investigated the application of a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) to classify vehicles on the basis of vertical-component seismic data recorded by geophones. The neural networks were trained on 5580 unprocessed seismic records and achieved excellent training accuracy (99%). They were also tested on large datasets representing periods as long as 1 month to check their stability. We found that CNN was the most satisfactory approach, reaching 96% accuracy and detecting multiple vehicle classes at the same time at a low computational cost. Our findings show that seismic methods can be used for traffic monitoring and security purposes without violating the privacy of vehicle occupants, offering greater efficiency and lower costs than current methods. A similar approach may be useful for other types of transportation, such as vessels and airplanes.
SE-VFC: Secure and Efficient Outsourcing Computing in Vehicular Fog Computing

Fog-aided computing is an emerging computing paradigm developed for providing various computation services to end users in vehicular fog computing. However, fog vehicles may be untrusted, and results from malicious operations may cause serious accidents. Therefore, we propose a secure and efficient outsourcing computing scheme in vehicular fog computing (SE-VFC), which performs outsourcing computing through fog vehicles with computing resources, and combines lightweight Boneh-Lynn-Shacham (BLS) signature and group signature to achieve batch anonymous authentication of fog vehicles while protecting their privacy in multiple outsourcing tasks. We also verify the correctness of the outsourcing computing results. Security analysis shows that our scheme can authenticate the fog vehicles in the outsourcing computation and preserve their privacy, it can also trace the malicious ones if necessary. Compared with the existing schemes, our scheme has relatively low communication and computation overhead, thus it is quite efficient for batch authentication especially in multiple computing tasks. Extensive simulation results verify the effectiveness and practicality of our proposed scheme in vehicular fog computing.
An Xception Based Convolutional Neural Network for Scene Image Classification with Transfer Learning

Over the past decade, image classification, which can provide assistance to address complex tasks such as planetary exploration and unmanned driving, has become a hot topic. As a subproblem of image classification, scene image classification has received increasing attention. Based on previous studies, the Xception model achieved superior performance on image classification tasks in comparison with the original Inception model. The Xception model is advantageous at processing image classification, yet it has not been used for scene image classification. To tackle this issue, this paper proposed an Xception based transfer learning, and analyzed the model performance by comparing it with the Inception-V3 model. We found that the Xception based transfer learning significantly outperforms other methods such as Inception-V3, which is nicely demonstrated by the experimental results on the Intel Image Classification Challenge dataset. Furthermore, the Xception has shown greater robustness and ability in generalization with less overfitting problems.
Dual Face Alignment Learning Network for NIR-VIS Face Recognition

As the most important topic in Heterogeneous Face Recognition (HFR), Near-InfraRed and VISual (NIR-VIS) face recognition has attracted increasing research attentions owing to its potential application in the field of criminal detective cases and multimedia information retrieval. However, due to its dramatic intra-class variations, including modality, pose, occlusion, blurry, lighting, distance, expression, etc, it is very challenging to retain inherent identity information. To address the above issue, we propose a novel Dual Face Alignment Learning (DFAL) algorithm to explore the potential domain-invariant neutral face representations of the cross-modal images. Our model contains three effective components including Feature-level Face Alignment (FFA), Image-level Face Alignment (IFA) and Cross-domain compact Representation (CdR). Firstly, Teacher-Encoder CNNs (TeEn-CNNs) and Student-Encoder CNNs (StEn-CNNs) are designed to encode features for VIS neutral face images and non-neutral face images, and the FFA is introduced to learn neutral face representations by performing feature-level alignment between non-neutral face and VIS neutral face. Secondly, Student-Decoder CNNs (StDe-CNNs) is developed to decode features to restore face images, and the IFA is designed to reconstruct neutral face image by imposing image-level alignment. Notably, the FFA acts as the primary target to learn VIS neutral face representations for cross-view data, while the IFA plays a role in the icing on the cake, i.e., further disentangling domain and residual information through the synthesis process. Finally, the CdR dispels modality features and distills identity features by mining inter-class information, inter-domain information and inter-semantic relationship. The joint scheme enables the elimination of intra-class variations and the purification of identity information. We carry out comprehensive experiments to illustrate the effectiveness of the DFAL approach on three challenging NIR-VIS databases.
The Design Space of Wearables for Sports and Fitness Practices

The growing interest in wearables for sports and fitness calls for design knowledge and conceptualizations that can help shape future designs. Towards that end, we present and discuss a design space of wearables for these practices, based on a survey of previous work. Through a thematic analysis of 47 research publications in the domain, we surface core design decisions concerning wearability, technology design, and wearable use in practice. Building on these, we show how the design space takes into account the goals of introducing technology, that design decisions can be either directly designed, or left open for appropriation by end-users; and the social organization of the practice. We characterize prior work based on the design space elements, which yields trends and opportunities for design. Our contributions can help designers think about key design decisions, exploit trends and explore new areas in the domain of wearables for sports and fitness practices.
Predicting confirmation times of Bitcoin transactions

We study the distribution of confirmation times of Bitcoin transactions, conditional on the size of the current memory pool. We argue that the time until a Bitcoin transaction is confirmed resembles the time to ruin in a corresponding Cramer-Lundberg process. This well-studied model gives mathematical insights in the mempool behaviour over time. Specifically, for situations where one chooses a fee, such that the total size of incoming transactions with higher fee is close to the total size of transactions leaving the mempool (heavy traffic), a diffusion approximation leads to an inverse Gaussian distribution for the confirmation times. The results of this paper are particularly interesting for users that want to make a Bitcoin transaction during heavy-traffic situations, as evaluation of the well-known inverse Gaussian distribution is computationally straightforward.
Distributed temporal graph analytics with GRADOOP

Abstract Temporal property graphs are graphs whose structure and properties change over time. Temporal graph datasets tend to be large due to stored historical information, asking for scalable analysis capabilities. We give a complete overview of Gradoop , a graph dataflow system for scalable, distributed analytics of temporal property graphs which has been continuously developed since 2005. Its graph model TPGM allows bitemporal modeling not only of vertices and edges but also of graph collections. A declarative analytical language called GrALa allows analysts to flexibly define analytical graph workflows by composing different operators that support temporal graph analysis. Built on a distributed dataflow system, large temporal graphs can be processed on a shared-nothing cluster. We present the system architecture of Gradoop , its data model TPGM with composable temporal graph operators, like snapshot, difference, pattern matching, graph grouping and several implementation details. We evaluate the performance and scalability of selected operators and a composed workflow for synthetic and real-world temporal graphs with up to 283 M vertices and 1.8 B edges, and a graph lifetime of about 8 years with up to 20 M new edges per year. We also reflect on lessons learned from the Gradoop effort.
HaRT - The Virtual Reality Hand Redirection Toolkit

Past research has proposed various hand redirection techniques for virtual reality (VR). Such techniques modify a user's hand movements and have been successfully used to enhance haptics and 3D user interfaces. Up to now, however, no unified framework exists that implements previously proposed techniques such as body warping, world warping, and hybrid methods. In this work, we present the Virtual Reality Hand Redirection Toolkit (HaRT), an open-source framework developed for the Unity engine. The toolkit aims to support both novice and expert VR researchers and practitioners in implementing and evaluating hand redirection techniques. It provides implementations of popular redirection algorithms and exposes a modular class hierarchy for easy integration of new approaches. Moreover, simulation, logging, and visualization features allow users of the toolkit to analyze hand redirection setups with minimal technical effort. We present the architecture of the toolkit along with the results of a qualitative expert study.
Trust Prediction for Online Social Networks with Integrated Time-Aware Similarity

Online social networks gain increasing popularity in recent years. In online social networks, trust prediction is significant for recommendations of high reputation users as well as in many other applications. In the literature, trust prediction problem can be solved by several strategies, such as matrix factorization, trust propagation, and <?TeX $K$?> -NN search. However, most of the existing works have not considered the possible complementarity among these mainstream strategies to optimize their effectiveness and efficiency. In this article, we propose a novel trust prediction approach named iSim : an integrated time-aware similarity-based collaborative filtering approach leveraging on user similarity, which integrates three kinds of factors to measure user similarity, including vector space similarity, time-aware matrix factorization, and propagated trust. This article is the first work in the literature employing time-aware matrix factorization and propagated trust in the study of similarity. Additionally, we use several methods like adding inverted index to reduce the time complexity of iSim , and provide its theoretical time bound. Moreover, we also provide the detailed overview and theoretical analysis of the existing works. Finally, the extensive experiments with real-world datasets show that iSim achieves great improvement for both efficiency and effectiveness over the state-of-the-art approaches.
Iterative Bounding Box Annotation for Object Detection

Manual annotation of bounding boxes for object detection in digital images is tedious, and time and resource consuming. In this paper, we propose a semi-automatic method for efficient bounding box annotation. The method trains the object detector iteratively on small batches of labeled images and learns to propose bounding boxes for the next batch, after which the human annotator only needs to correct possible errors. We propose an experimental setup for simulating the human actions and use it for comparing different iteration strategies, such as the order in which the data is presented to the annotator. We experiment on our method with three datasets and show that it can reduce the human annotation effort significantly, saving up to 75% of total manual annotation work.
Multi-Dialect Speech Recognition in English Using Attention on Ensemble of Experts

In the presence of a wide variety of dialects, training dialect-specific models for each dialect is a demanding task. Previous studies have explored training a single model that is robust across multiple dialects. These studies have used either multi-condition training, multi-task learning, end-to-end modeling, or ensemble modeling. In this study, we further explore using a single model for multi-dialect speech recognition using ensemble modeling. First, we build an ensemble of dialect-specific models (or experts). Then we linearly combine the outputs of the experts using attention weights generated by a long short-term memory (LSTM) network. For comparison purposes, we train a model that jointly learns to recognize and classify dialects using multi-task learning and a second model using multi-condition training. We train all of these models with about 60,000 hours of speech data collected in American English, Canadian English, British English, and Australian English. Experimental results reveal that our best proposed model achieved an average 4.74% word error rate reduction (WERR) compared to the strong baseline model.