Reviewerly/exHarmony huggingface dataset:
Invariant multiscale triangle feature for shape recognition

Shape is an important visual characteristic in representing an object, and it is also an important part of human visual information. Shape recognition is an important research direction in pattern recognition and image understanding. However, it is a difficult problem to extract discriminative and robust shape descriptors in shape recognition. This is because there are very large deformations in the shape, such as geometric changes, intra-class variations, and nonlinear deformations. These factors directly influence the accuracy of shape recognition. To deal with the influence of these factors on the performance of shape recognition and enhance the accuracy of recognition, we present a novel shape description method called invariant multiscale triangle feature (IMTF) for robust shape recognition. This method uses two types of invariant triangle features to obtain the shape features of an object, and it can effectively combine the boundary and the interior characteristics of a shape, and hence can increase the distinguish ability of the shape. We conducted an extensive experimental analysis on some shape benchmarks. The results indicate that our method can achieve high recognition accuracy. The superiority of our method has been further demonstrated in comparison to the state-of-the-art shape descriptors.
Fast physical repetitive patterns generation for masking in time-delay reservoir computing

Albeit the conceptual simplicity of hardware reservoir computing, the various implementation schemes that have been proposed so far still face versatile challenges. The conceptually simplest implementation uses a time delay approach, where one replaces the ensemble of nonlinear nodes with a unique nonlinear node connected to a delayed feedback loop. This simplification comes at a price in other parts of the implementation; repetitive temporal masking sequences are required to map the input information onto the diverse states of the time delay reservoir. These sequences are commonly introduced by arbitrary waveform generators which is an expensive approach when exploring ultra-fast processing speeds. Here we propose the physical generation of clock-free, sub-nanosecond repetitive patterns, with increased intra-pattern diversity and their use as masking sequences. To that end, we investigate numerically a semiconductor laser with a short optical feedback cavity, a well-studied dynamical system that provides a wide diversity of emitted signals. We focus on those operating conditions that lead to a periodic signal generation, with multiple harmonic frequency tones and sub-nanosecond limit cycle dynamics. By tuning the strength of the different frequency tones in the microwave domain, we access a variety of repetitive patterns and sample them in order to obtain the desired masking sequences. Eventually, we apply them in a time delay reservoir computing approach and test them in a nonlinear time-series prediction task. In a performance comparison with masking sequences that originate from random values, we find that only minor compromises are made while significantly reducing the instrumentation requirements of the time delay reservoir computing system.
Joint ASR and Language Identification Using RNN-T: An Efficient Approach to Dynamic Language Switching

Conventional dynamic language switching enables seamless multilingual interactions by running several monolingual ASR systems in parallel and triggering the appropriate downstream components using a standalone language identification (LID) service. Since this solution is neither scalable nor cost- and memory-efficient, especially for on-device applications, we propose end-to-end, streaming, joint ASR-LID architectures based on the recurrent neural network transducer framework. Two key formulations are explored: (1) joint training using a unified output space for ASR and LID vocabularies, and (2) joint training viewed as multi-task optimization. We also evaluate the benefit of using auxiliary language information obtained on-the-fly from an acoustic LID classifier. Experiments with the English-Hindi language pair show that: (a) multi-task architectures perform better overall, and (b) the best joint architecture surpasses monolingual ASR (6.4–9.2% word error rate reduction) and acoustic LID (53.9–56.1% error rate reduction) baselines while reducing the overall memory footprint by up to 46%.
RDNet: Regression Dense and Attention for Object Detection in Traffic Symbols

Identification and detection of traffic identifiers are some of the most challenging tasks in the field of autonomous driving. For object detection, how to obtain deep semantic information and shallow spatial information has always been a permanent subject. This paper proposes a dense network structure for the detection of traffic identifiers; the deep structure of the residual network is used to obtain the high-level semantic information of the detection target; the regression operation is used to obtain the underlying spatial information; the detection structure is higher than the previous The accuracy of the network structure and the generalization ability of the network has been improved; ASPP using the attention mechanism; the main function of ASPP is to obtain a continuous feature map; using the ASPP of the attention mechanism, the prior balance between the receptive field and the feature resolution map is solved; In the paper, a collection of residual networks is used to obtain a variety of acceptance domains; through a regression operation, an effective interactive connection between the receptive field and the acceptance domain is obtained; a deep network using regression residuals allows geometric transformation of the model during data training The ability to improve has been improved; at the same time, the use of a dense network structure has realized the diversity of different receptive fields in the process of multi-scale feature extraction.
Multidiscriminator Sobolev Defense-GAN Against Adversarial Attacks for End-to-End Speech Systems

This paper introduces a defense approach against end-to-end adversarial attacks developed for cutting-edge speech-to-text systems. The proposed defense algorithm has four steps. First, we use the short-time Fourier transform to represent speech signals with 2D spectrograms. Second, we iteratively find a safe vector using a spectrogram subspace projection operation. This operation minimizes the chordal distance adjustment between spectrograms with an additional regularization term. Third, we synthesize a spectrogram with such a safe vector using a novel GAN architecture trained with Sobolev integral probability metric. We impose an additional constraint on the generator network to improve the model’s performance in terms of stability and the total number of learned modes. Finally, we reconstruct the signal from the synthesized spectrogram and the Griffin-Lim phase approximation technique. We evaluate the proposed defense approach against six strong white and black-box adversarial attacks on DeepSpeech, Kaldi, and Lingvo models. The experimental results show that our algorithm outperforms other state-of-the-art defense algorithms in terms of accuracy and signal quality.
Toward Aspect-Level Sentiment Modification Without Parallel Data

This article takes the lead to study aspect-level sentiment modification (ALSM) without parallel data. Given a sentence, the task of ALSM needs to reverse the sentiment with respect to the given aspect while preserving other content. The main challenge is reversing the sentiment of the given aspect without affecting the sentiments of other aspects in the sentences. To handle this problem, we propose a joint aspect-level sentiment modification (called JASM) model. JASM is a multitask system, which jointly trains two coupled modules: aspect-specific sentiment words extraction and aspect-level sentiment transformation. Besides, we propose a novel memory mechanism to learn aspect-aware sentiment representation and a gating mechanism to dynamically select aspect-aware sentiment information or content information for generating the next words. Experiments show that the proposed model substantially outperforms the compared methods in both aspect-level sentiment transformation and content preservation. For applications, we conduct data augmentation for aspect-based sentiment analysis (ABSA) through generating plausible training data with the trained ALSM model. Experiments show that data augmentation with generated data boosts the performance of a broad range of ABSA models.
A Combined Extractive With Abstractive Model for Summarization

Aiming at the difficulties in document-level summarization, this paper presents a two-stage, extractive and then abstractive summarization model. In the first stage, we extract the important sentences by combining sentences similarity matrix (only used for the first time) or pseudo-title, which takes full account of the features (such as sentence position, paragraph position, and more.). To extract coarse-grained sentences from a document, and considers the sentence differentiation for the most important sentences in the document. The second stage is abstractive, and we use beam search algorithm to restructure and rewrite these syntactic blocks of these extracted sentences. Newly generated summary sentence serves as the pseudo-summary of the next round. Globally optimal pseudo-title acts as the final summarization. Extensive experiments have been performed on the corresponding data set, and the results show our model can obtain better results.
A comparison of features in a crowdsourced phishing warning system

Abstract Initial research on using crowdsourcing as a collaborative method for helping individuals identify phishing messages has shown promising results. However, the vast majority of crowdsourcing research has focussed on crowdsourced system components broadly and understanding individuals' motivation in contributing to crowdsourced systems. Little research has examined the features of crowdsourced systems that influence whether individuals utilise this information, particularly in the context of warnings for phishing emails. Thus, the present study examined four features related to warnings derived from a mock crowdsourced anti‐phishing warning system that 438 participants were provided to aid in their evaluation of a series of email messages: the number of times an email message was reported as being potentially suspicious, the source of the reports, the accuracy rate of the warnings (based on reports) and the disclosure of the accuracy rate. The results showed that crowdsourcing features work together to encourage warning acceptance and reduce anxiety. Accuracy rate demonstrated the most prominent effects on outcomes related to judgement accuracy, adherence to warning recommendations and anxiety with system use. The results are discussed regarding implications for organisations considering the design and implementation of crowdsourced phishing warning systems that facilitate accurate recommendations.
Smart Microgrid Energy Market: Evaluating Distributed Ledger Technologies for Remote and Constrained Microgrid Deployments

The increasing strain on ageing generation infrastructure has seen more frequent instances of scheduled and unscheduled blackouts, rising reliability on fossil fuel based energy alternatives and a slow down in efforts towards achieving universal access to electrical energy in South Africa. To try and relieve the burden on the National Grid and still progress electrification activities, the smart microgrid model and secure energy trade paradigm is considered—enabled by the Industrial IoT (IIoT) and distributed ledger technologies (DLTs). Given the high availability requirements of microgrid operations, the limited resources available on IIoT devices and the high processing and energy requirements of DLT operations, this work aims to determine the effect of native DLT algorithms when implemented on IIoT edge devices to assess the suitability of DLTs as a mechanism to establish a secure, energy trading market for the Internet of Energy. Metrics such as the node transaction time, operating temperature, power consumption, processor and memory usage are considered towards determining possible interference on the edge node operation. In addition, the cost and time required for mining operations associated with the DLT-enabled node are determined in an effort to predict the cost to end users—in terms of fees payable and mobile data costs—as well as predicting the microgrid’s growth and potential blockchain network slowdown.
Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning

This paper studies the problem of learning the conditional distribution of a high-dimensional output given an input, where the output and input may belong to two different domains, e.g., the output is a photo image and the input is a sketch image. We solve this problem by cooperative training of a fast thinking initializer and slow thinking solver. The initializer generates the output directly by a non-linear transformation of the input as well as a noise vector that accounts for latent variability in the output. The slow thinking solver learns an objective function in the form of a conditional energy function, so that the output can be generated by optimizing the objective function, or more rigorously by sampling from the conditional energy-based model. We propose to learn the two models jointly, where the fast thinking initializer serves to initialize the sampling of the slow thinking solver, and the solver refines the initial output by an iterative algorithm. The solver learns from the difference between the refined output and the observed output, while the initializer learns from how the solver refines its initial output. We demonstrate the effectiveness of the proposed method on various conditional learning tasks, e.g., class-to-image generation, image-to-image translation, and image recovery. The advantage of our method over GAN-based methods is that our method is equipped with a slow thinking process that refines the solution guided by a learned objective function.
Combined i-Vector and Extreme Learning Machine Approach for Robust Speaker Identification and Evaluation with SITW 2016, NIST 2008, TIMIT Databases

In this article, a novel combined i-vector and an Extreme Learning Machine (ELM) is proposed for speaker identification. The ELM is chosen because it is fast to train and has a universal approximator property. Four combinations of features based on Mel Frequency Cepstral Coefficient and Power Normalized Cepstral Coefficient are used. Besides, seven fusion methods are exploited. The system is evaluated with three different databases, namely: the SITW 2006, NIST 2008, and the TIMIT database. This work employs the 2016 SITW database for the first time for speaker identification using the integration between the ELM and i-vector approach. From each database, 120 speakers with 1200 speech utterances are used (overall 360 speakers with 3600 speech utterances). Furthermore, comprehensive evaluations are exploited with a wide range of realistic background noise types (Stationary noise AWGN and Non-Stationary Noise types) with the handset effect. The proposed system is compared with the Gaussian Mixture Model-Universal Background Model (GMM-UBM) and other states of the art approaches. The results show that the i-vector method outperforms the GMM-UBM approach and other state- of-the-art methods under specific conditions, and that fusion techniques can be used to improve robustness to noise and handset effects.
A high-performance native approach to adaptive blockchain smart-contract transmission and execution

We are presenting a continuously adaptive approach that significantly reduces the storage requirements of smart-contracts, an essential part of programmable blockchains, aimed towards increasing the throughput of the blockchain overall. Our approach proposes a semantically-different blockchain architecture, which introduces a new type of nodes whose purpose is to enhance the storage used by smart-contracts, alongside a highly efficient storage model combined with a hybrid compression mechanism. We then perform real-world experiments through our custom native blockchain and evaluate the performance impact of the approach, as well as the gains observed. We also assess an important advantage lying in backwards compatibility with major blockchains today.
App-Aware Response Synthesis for User Reviews

Hundreds of thousands of mobile app users post their reviews online. Responding to user reviews promptly and satisfactorily improves application ratings, which is key to application popularity and success. The proliferation of such reviews makes it virtually impossible for developers to keep up with responding manually. To address this challenge, recent work has shown the possibility of automatic response generation by training a seq2seq model with a large collection of review-response pairs. However, because the training review-response pairs are aggregated from many different apps, it remains challenging for such models to generate app-specific responses, which, on the other hand, are often desirable as appwes have different features and concerns. Solving the challenge by simply building an app-specific generative model per app (i.e., training the model with review-response pairs of a single app) may be insufficient because individual apps have limited review-response pairs, and such pairs typically lack the relevant information needed to respond to a new review.To enable app-specific response generation, this work proposes AARSYNTH: an app-aware response synthesis system. The key idea behind AARSYNTH is to augment the seq2seq model with information specific to a given app. Given a new user review, AARSYNTH first retrieves the top-K most relevant app reviews and the most relevant snippet from the app description. The retrieved information and the new user review are then fed into a fused machine learning model that integrates the seq2seq model with a machine reading comprehension model. The latter helps digest the retrieved reviews and app description. Finally, the fused model generates a response that is customized to the given app. We evaluated AARSYNTH using a large corpus of reviews and responses from Google Play. The results show that AARSYNTH outperforms the state-of-the-art system by 22.2% on BLEU-4 score. Furthermore, our human study shows that AARSYNTH produces a statistically significant improvement in response quality compared to the state-of-the-art system.
Towards Optimizing Task Scheduling Process in Cloud Environment

The cloud computing paradigm offers many advantages over traditional self-hosting computing solutions by abstracting computations from infrastructure. An essential task for any cloud provider is task scheduling, wherein tasks are assigned to computing resources within the cloud system by a broker. Numerous cloud task scheduling algorithms exist including Min-Min, Max-Min, and Sufferage-though each is defined by performance trade-offs. BSufferage is proposed as a novel cloud task scheduling algorithm that augments the performance of the classical Sufferage algorithm. Modeling the algorithm using the open source CloudSim package and comparing it to its precursor yields performance results for BSufferage-demonstrating decreased completion times, increased throughput, and improved load balancing of resources.
Unsupervised Multiple Network Alignment with Multinominal GAN and Variational Inference

Network alignment techniques, which aim to identify the same entities across multiple networks, often suffer challenges from feature inconsistency to transitivity law preservation. This paper presents a purely unsupervised network alignment method, KEMINA, with three original contributions. First, in order to address the feature inconsistency issue, an adversarial kernel embedding technique is proposed to extract network-invariant information among multiple networks without prior alignment knowledge, and project them into the common embedding space. Second, a multinomial generative adversarial network (GAN) model is developed to train multiple network alignment tasks simultaneously in an unsupervised manner with preserving the transitivity law property. Third but last, a variational inference model is designed to alleviate the data sparsity and inadequate training issues by filling realistic detail for vertices with sparse features and generating real-looking supplementary vertex samples within limited training opportunity of each pair of source and target networks.
Plug-and-Play gradient-based denoisers applied to CT image enhancement

Blur and noise corrupting Computed Tomography (CT) images can hide or distort small but important details, negatively affecting the consequent diagnosis. In this paper, we present a novel gradient-based Plug-and-Play (PnP) algorithm and we apply it to restore CT images. The plugged denoiser is implemented as a deep Convolutional Neural Network (CNN) trained on the gradient domain (and not on the image one, as in state-of-the-art works) and it induces an external prior onto the restoration model. We further consider a hybrid scheme which combines the gradient-based external denoiser with an internal one, obtained from the Total Variation functional. The proposed frameworks rely on the Half-Quadratic Splitting scheme and we prove a general fixed-point convergence theorem, under weak assumptions on both the denoisers. The experiments confirm the effectiveness of the proposed gradient-based approach in restoring blurred noisy CT images, both in simulated and real medical settings. The obtained performances outperform the achievements of many state-of-the-art methods.
Research on volleyball action standardization based on 3D dynamic model

We propose a volleyball action standardization method based on 3D dynamic model. Firstly, optical motion capture technology and 3D scanning technology are used to collect the volleyball motion images. Then, the human skeleton model and the appearance model are constructed. Finally, the distance between each joint of the volleyball player's posture and the standard posture is calculated. The results show that the wrong actions made by the players can be accurately recognized by the proposed method. This study also gives some standardized suggestions and may improve players' action learning and training.
Sliding window based deep ensemble system for breast cancer classification

Breast cancer is a severe problem for women around the world especially in developing countries, according to recent reports from the World Health Organization (WHO). High accuracy and early detection of breast cancer reduces the mortality rate, in the other hand, recognition of breast cancer is a complicated issue. Various studies and methods have been carried out to overcome this problem and to obtain accurate screening of breast cancer. One of the most recent methods with high performance is deep learning; it has been used to classify breast cancer using mammograms or histopathological images. This paper proposes a new using the concept of sliding window, and using the ensemble of four pre-trained convolutional neural networks (CNN) in order to classify breast cancer into eight classes. In this study, each image produces 4 non-overlapped sliding windows which are fed to GoogleNet, AlexNet, ResNet50, and DenseNet-201 CNNs, and an ensemble is then done to find the major class of each window, the ensemble is then applied again to find the class of the whole histopathological image. Breast Cancer Histopathological Database (BreakHis) database has been employed in this paper with eight classes (Adenosis, Ductal Carcinoma, Fibroadenoma, Lobular Carcinoma, Mucinous Carcinoma Papillary Carcinoma, Phyllodes Tumour, Tubular Adenoma). The proposed method is applied to four magnification cases: 40x, 100x, 200x, and 400x images. The proposed ensemble technique achieved an accuracy of 99.3325%. The results of the proposed system are comparable to recent studies results.