Reviewerly/exHarmony huggingface dataset:
Iterative filtering as a direct method for the decomposition of nonstationary signals

The Iterative Filtering method is a technique developed recently for the decomposition and analysis of nonstationary and nonlinear signals. In this work, we propose two alternative formulations of the original algorithm which allows to transform the iterative filtering method into a direct technique, making the algorithm closer to an online algorithm. We present a few numerical examples to show the effectiveness of the proposed approaches.
DSiamMFT: An RGB-T fusion tracking method via dynamic Siamese networks using multi-layer feature fusion

The task of object tracking is very important since its various applications. However, most object tracking methods are based on visible images, which may fail when visible images are unreliable, for example when the illumination conditions are poor. To address this issue, in this paper a fusion tracking method which combines information from RGB and thermal infrared images (RGB-T) is presented based on the fact that infrared images reveal thermal radiation of objects thus providing complementary features. Particularly, a fusion tracking method based on dynamic Siamese networks with multi-layer fusion, termed as DSiamMFT, is proposed. Visible and infrared images are firstly processed by two dynamic Siamese Networks, namely visible and infrared network, respectively. Then, multi-layer feature fusion is performed to adaptively integrate multi-level deep features between visible and infrared networks. Response maps produced from different fused layer features are then combined through an elementwise fusion approach to produce the final response map, based on which the target can be located. Extensive experiments on large datasets with various challenging scenarios have been conducted. The results demonstrate that the proposed method shows very competitive performance against the-state-of-art RGB-T trackers. The proposed approach also improves tracking performance significantly compared to methods based on images of single modality.
Unsupervised X-ray image segmentation with task driven generative adversarial networks

Semantic parsing of anatomical structures in X-ray images is a critical task in many clinical applications. Modern methods leverage deep convolutional networks, and generally require a large amount of labeled data for model training. However, obtaining accurate pixel-wise labels on X-ray images is very challenging due to the appearance of anatomy overlaps and complex texture patterns. In comparison, labeled CT data are more accessible since organs in 3D CT scans preserve clearer structures and thus can be easily delineated. In this paper, we propose a model framework for learning automatic X-ray image parsing from labeled 3D CT scans. Specifically, a Deep Image-to-Image network (DI2I) for multi-organ segmentation is first trained on X-ray like Digitally Reconstructed Radiographs (DRRs) rendered from 3D CT volumes. Then we build a Task Driven Generative Adversarial Network (TD-GAN) to achieve simultaneous synthesis and parsing for unseen real X-ray images. The entire model pipeline does not require any annotations from the X-ray image domain. In the numerical experiments, we validate the proposed model on over 800 DRRs and 300 topograms. While the vanilla DI2I trained on DRRs without any adaptation fails completely on segmenting the topograms, the proposed model does not require any topogram labels and is able to provide a promising average dice of 86% which achieves the same level of accuracy as results from supervised training (89%). Furthermore, we also demonstrate the generality of TD-GAN through quantatitive and qualitative study on widely used public dataset.
Adaptive inertia weight Bat algorithm with Sugeno-Function fuzzy search

Bat algorithm (BA) turns into the most generally utilized meta-heuristic algorithm to solve the different sort of global optimization problems. In the optimization of continuous data, BA experiences one of the prominent difficulties called premature convergence. In order to tackle premature convergence, this study exhibits a new version of BA called Adaptive inertia weight Bat algorithm with Sugeno-Function Fuzzy Search (ASF-BA). The proposed algorithm ASF-BA brings two major adjustments in the standard BA. Firstly, we incorporated an adaptive inertia weight to boost up the velocity rate of bats effectively. Secondly, we replaced the random searching method of standard BA with Sugeno-Function fuzzy search, which used Sugeno-Function decline curves to dynamically adjust the fitness of each bat according to their own experience and experience of their neighbour bats. We compared ASF-BA with several old fashioned and new fashioned optimization algorithms. ASF-BA is also tested against top hybridized and enhanced version of DE algorithms. The CEC 2017 benchmark (30 real parameter numerical optimization problems ), CEC 2017 ( 28 constrained optimization problems) and 19 additional benchmark problems have been used to examine and compare the performance of ASF-BA with other state of the art variants. Contrasted with the existing BA and other leading variants of BA, DE, and PSO on CEC 2017 constrained and numerical benchmarks, the ASF-BA is excellent to the state-of-art variants of BA, DE, and PSO in terms of stability, convergence speed and solution quality. The ASF-BA sets stable support for resolving optimization problems of intelligent and expert systems. Furthermore, we also examined the performance of proposed ASF-BA for the weight optimization of Feed Forward Neural Networks (FFNN) and compared ASF-BA with Back Propagation Algorithm (BPA), BA and PSO. ASF-BA achieved 94 % of maximum accuracy. The experimental outcomes reveal that the suggested algorithm executed especially reliable and effective than the existing state of the art variants.
Using Facial Animation to Increase the Enfacement Illusion and Avatar Self-Identification

Through avatar embodiment in Virtual Reality (VR) we can achieve the illusion that an avatar is substituting our body: the avatar moves as we move and we see it from a first person perspective. However, self-identification, the process of identifying a representation as being oneself, poses new challenges because a key determinant is that we see and have agency in our own face. Providing control over the face is hard with current HMD technologies because face tracking is either cumbersome or error prone. However, limited animation is easily achieved based on speaking. We investigate the level of avatar enfacement, that is believing that a picture of a face is one's own face, with three levels of facial animation: (i) one in which the facial expressions of the avatars are static, (ii) one in which we implement lip-sync motion and (iii) one in which the avatar presents lip-sync plus additional facial animations, with blinks, designed by a professional animator. We measure self-identification using a face morphing tool that morphs from the face of the participant to the face of a gender matched avatar. We find that self-identification on avatars can be increased through pre-baked animations even when these are not photorealistic nor look like the participant.
Large-scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems

Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from recommender systems. However, most LFA-based recommender systems adopt a standard SGD algorithm, which suffers limited scalability when addressing big data. On the other hand, most existing parallel SGD solvers are either under the memory-sharing framework designed for a bare machine or suffering high communicational costs, which also greatly limits their applications in large-scale systems. To address the above issues, this article proposes a distributed alternative stochastic gradient descent (DASGD) solver for an LFA-based recommender. Its training-dependences among latent features are decoupled via alternatively fixing one-half of the features to learn the other half following the principle of SGD but in parallel. It's distribution mechanism consists of efficient data partition, allocation and task parallelization strategies, which greatly reduces its communicational cost for high scalability. Experimental results on three large-scale HiDS matrices generated by real-world applications demonstrate that the proposed DASGD algorithm outperforms state-of-the-art distributed SGD solvers for recommender systems in terms of prediction accuracy as well as scalability. Hence, it is highly useful for training LFA-based recommenders on large scale HiDS matrices with the help of cloud computing facilities.
Blockchain Technology for Smart Grids: Decentralized NIST Conceptual Model

Blockchain technology is showing a significant potential to disrupt a number of information technology domains. One of the especially interesting areas for blockchain applications is smart grid. A number of early papers have been published in this area, however, there is no systematic analysis of the impact of blockchain technology on decentralization of smart grids. In this paper, we analyze the standard NIST conceptual model of smart grid domains with respect to the three critical blockchain features: decentralization, trust and incentive. We integrate our findings in order to produce a fully decentralized blockchain-enabled smart grid considering NIST conceptual model. The results of this paper should help smart grid developers and researchers to obtain a conceptual reference of the overall applicability of blockchain technology in smart grid domains and sub-domains. In addition this research will help to identify and guide smart grid blockchain development and research initiatives.
A Comparative Study on End-to-End Speech to Text Translation

Recent advances in deep learning show that end-to-end speech to text translation model is a promising approach to direct the speech translation field. In this work, we provide an overview of different end-to-end architectures, as well as the usage of an auxiliary connectionist temporal classification (CTC) loss for better convergence. We also investigate on pre-training variants such as initializing different components of a model using pretrained models, and their impact on the final performance, which gives boosts up to 4% in Bleu and 5% in Ter. Our experiments are performed on 270h IWSLT TED-talks En→De, and 100h LibriSpeech Audio-books En→Fr. We also show improvements over the current end-to-end state-of-the-art systems on both tasks.
On Arithmetic Average Fusion and Its Application for Distributed Multi-Bernoulli Multitarget Tracking

This paper addresses the problem of distributed multitarget detection and tracking based on the linear arithmetic average (AA) fusion. We first analyze the conservativeness and Fréchet mean properties of the AA fusion, presenting new analyses based on a literature review. Second, we propose a target-wise fusion rule for tailoring the AA fusion to accommodate the multi-Bernoulli (MB) process, in which only significant Bernoulli components, each represented by an individual Gaussian mixture, are disseminated and fused in a Bernoulli-to-Bernoulli (B2B) manner. For internode communication, both the consensus and flooding schemes are investigated, respectively. At the core of the proposed MB fusion algorithms, Bernoulli components obtained at different sensors are associated via either clustering or pairwise assignment so that the MB fusion problem is decomposed to parallel B2B fusion subproblems, each resolved via exact Bernoulli-AA fusion. Third, two communicatively and computationally efficient cardinality consensus approaches are presented which merely disseminate and fuse target existence probabilities among local MB filters. Finally, the performance of these four approaches in terms of accuracy, computing efficiency, and communication cost is tested in two simulation scenarios.
Intelligent Intersection

Camera-based systems are increasingly used for collecting information on intersections and arterials. Unlike loop controllers that can generally be only used for detection and movement of vehicles, cameras can provide rich information about the traffic behavior. Vision-based frameworks for multiple-object detection, object tracking, and near-miss detection have been developed to derive this information. However, much of this work currently addresses processing videos offline. In this article, we propose an integrated two-stream convolutional networks architecture that performs real-time detection, tracking, and near-accident detection of road users in traffic video data. The two-stream model consists of a spatial stream network for object detection and a temporal stream network to leverage motion features for multiple-object tracking. We detect near-accidents by incorporating appearance features and motion features from these two networks. Further, we demonstrate that our approaches can be executed in real-time and at a frame rate that is higher than the video frame rate on a variety of videos collected from fisheye and overhead cameras.
Unified Binary Generative Adversarial Network for Image Retrieval and Compression

Binary codes have often been deployed to facilitate large-scale retrieval tasks, but not that often for image compression. In this paper, we propose a unified framework, BGAN+, that restricts the input noise variable of generative adversarial networks to be binary and conditioned on the features of each input image, and simultaneously learns two binary representations per image: one for image retrieval and the other serving as image compression. Compared to related methods that attempt to learn a single binary code serving both purposes, we demonstrate that choosing for two codes leads to more effective representations due to less concessions needed when balancing the requirements. The added value of using a unified framework compared to two separate frameworks lies in the synergy in data representation that is beneficial for both learning processes. When devising this framework, we also address another challenge in learning binary codes, namely that of learning supervision. While the most striking successes in image retrieval using binary codes have mostly involved discriminative models requiring labels, the proposed BGAN+ framework learns the binary codes in an unsupervised fashion, yet more effectively than the state-of-the-art supervised approaches. The proposed BGAN+ framework is evaluated on three benchmark datasets for image retrieval and two datasets on image compression. The experimental results show that BGAN+ outperforms the existing retrieval methods with significant margins and achieves promising performance for image compression, especially for low bit rates.
Espresso: A Fast End-to-End Neural Speech Recognition Toolkit

We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit FAIRSEQ. ESRESSO supports distributed training across GPUs and computing nodes, and features various decoding approaches commonly employed in ASR, including look-ahead word-based language model fusion, for which a fast, parallelized decoder is implemented. Espresso achieves state-of-the-art ASR performance on the WSJ, LibriSpeech, and Switchboard data sets among other end-to-end systems without data augmentation, and is 4-11x faster for decoding than similar systems (e.g. ESPNET).
LVPDA: A Lightweight and Verifiable Privacy-Preserving Data Aggregation Scheme for Edge-Enabled IoT

Edge computing is envisioned to be a powerful platform that provides efficient data storage and computation services in the smart Internet-of-Things (IoT) systems. In this data-intensive architecture, protecting user-side data privacy is one of the most critical concerns to prevent privacy leakage from any other untrusted entities. Aiming to resist this concern, many privacy-preserving data aggregation (PPDA) schemes have been proposed for various cloud-enabled IoT applications. However, due to the resource-constrained nature of the smart IoT devices, the conventional PPDA solutions, in terms of both privacy and performance requirements, are unsuitable in edge computing. To address this challenge, we propose a lightweight and verifiable PPDA scheme, named LVPDA, for the edge-computing-enabled IoT system, where the Paillier homomorphic encryption method and an online/offline signature technique are combined to ensure the privacy preserving and integrity verification during the data aggregation process. A detailed security analysis indicates that LVPDA is existentially unforgeable under the chosen message attack (EU-CMA) and the data integrity can be guaranteed with formal proof under q -strong Diffie-Hellman (q -SDH) assumptions. Compared with other PPDA methods, our scheme can achieve lightweight PPDA in terms of less computational complexity and communication overhead.
New fractional-order Legendre-Fourier moments for pattern recognition applications

Orthogonal moments enable computer-based systems to discriminate between similar objects. Mathematicians proved that the orthogonal polynomials of fractional-orders outperformed their corresponding counterparts in representing the fine details of a given function. In this work, novel orthogonal fractional-order Legendre-Fourier moments are proposed for pattern recognition applications. The basis functions of these moments are defined and the essential mathematical equations for the recurrence relations, orthogonality and the similarity transformations (rotation and scaling) are derived. The proposed new fractional-order moments are tested where their performance is compared with the existing orthogonal quaternion, multi-channel and fractional moments. New descriptors were found to be superior to the existing ones in terms of accuracy, stability, noise resistance, invariance to similarity transformations, recognition rates and computational times.
Adjusting the imbalance ratio by the dimensionality of imbalanced data

Class-imbalance extent metrics measure how imbalanced the data are. In pattern classification, it is usually expected that the higher the imbalance extent, the worse the classification performance, and thus an appropriate imbalance extent metric should show a negative correlation with the classification performance. Existing metrics, such as the popular imbalance ratio (IR), only consider the effect of the sample sizes of different classes. However, we note that the dimensionality of imbalanced data also affects the classification performance. Datasets with the same IR can present distinct classification performances when their dimensionalities are different, making IR suboptimal to reflect the imbalance extent for classification. We also observe that the classification performance becomes better with more discriminative features. Inspired by these observations, we propose a new imbalance extent metric, the adjusted IR, by adding a penalty term of the number of discriminative features that is effectively determined by the Pearson correlation test. The adjusted IR adaptively revises the IR when the number of discriminative features varies. The empirical studies demonstrate the effectiveness of the adjusted IR, in terms of its better negative correlation with the classification performance.
Evaluating IoT service composition mechanisms for the scalability of IoT systems

The Internet of Things (IoT) is an emerging paradigm where practically every (physical and virtual) thing will be interconnected through innovative distributed services. Since the number of connected things is rapidly growing, IoT systems will require the composition of plenty of services into complex workflows. Thus, scalability in terms of the size of IoT systems becomes a significant concern. In this paper, we review and evaluate the fundamental semantics of existing IoT service composition mechanisms to determine how well they fulfil the scalability requirements of IoT systems. We identify scalability desiderata and, accordingly, our findings show that dataflows, orchestration and choreography do not fully satisfy such desiderata, unlike a novel composition mechanism called DX-MAN.
Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference

State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multi-grained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channel-wise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency and energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such a trade-off as compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines.
Improved Security of a Pairing-Free Certificateless Aggregate Signature in Healthcare Wireless Medical Sensor Networks

Certificateless aggregate signature (CLAS) schemes reduce the trust on the key generation center of identity-based signatures and thus partially address the inherent key escrow issue in identity-based cryptosystems while retaining the advantage of implementation efficiency. In the past few years, a number of new CLAS schemes were proposed to overcome the communicational and computational limitations of sensors and attain integrality, validity, and availability of patients' medical data in healthcare wireless medical sensor networks (HWMSNs). However, many of these schemes do not provide enough security guarantees. In this article, we first review a most recent CLAS scheme for HWMSNs and show that it is insecure for medical applications by presenting attacks due to a type I adversary and a type II adversary. Then, we put forth an improved construction which is provably secure under the CLAS security model in the random oracle model. Our detailed analyses demonstrate that the proposed scheme not only overcomes the security flaws but also has higher implementation efficiency and lower communication cost.