Reviewerly/exHarmony huggingface dataset:
Joint Visualization Diagnosis of Outdoor Insulation Status With Optical and Acoustical Detections

This paper proposes a joint visualization detection method to diagnose outdoor insulation status of high-voltage (HV) equipment in substations, which applies image matching and fusion techniques to accurately combine the results of ultrasonic, ultraviolet, and infrared detection in an image. This image can then be used to quickly and clearly locate the region of corona discharge and overheating of outdoor substation HV equipment. First, ultrasonic localization visualization of corona discharge is proposed, which is based on the designed double-helix array and multisignal classification algorithm. This method provides a more intuitive way for the diagnosis of outdoor insulation status. Then, ultraviolet localization visualization method is detailed, and an adaptive affine matrix is designed for the accurate location of corona discharge. Following this, infrared-visible detection of overheating failure is introduced, and the speeded-up robust features algorithm and random sample consensus algorithm are used to implement image fusion process. Finally, the results of image fusion are provided, and location error evaluations are conducted for each module. With acceptable cost and advantages of three detection method, the proposed visualization method is used for evaluating the insulation status of substation equipment more intuitively and conveniently, and thus, has an extensive application prospect.
Learning Curriculum Policies for Reinforcement Learning

Curriculum learning in reinforcement learning is a training methodology that seeks to speed up learning of a difficult target task, by first training on a series of simpler tasks and transferring the knowledge acquired to the target task. Automatically choosing a sequence of such tasks (i.e., a curriculum) is an open problem that has been the subject of much recent work in this area. In this paper, we build upon a recent method for curriculum design, which formulates the curriculum sequencing problem as a Markov Decision Process. We extend this model to handle multiple transfer learning algorithms, and show for the first time that a curriculum policy over this MDP can be learned from experience. We explore various representations that make this possible, and evaluate our approach by learning curriculum policies for multiple agents in two different domains. The results show that our method produces curricula that can train agents to perform on a target task as fast or faster than existing methods.
Hierarchical Attention Networks for Sentence Ordering

Modeling discourse coherence is an important problem in natural language generation and understanding. Sentence ordering, the goal of which is to organize a set of sentences into a coherent text, is a commonly used task to learn and evaluate the model. In this paper, we propose a novel hierarchical attention network that captures word clues and dependencies between sentences to address this problem. Our model outperforms prior methods and achieves state-of-the-art performance on several datasets in different domains. Furthermore, our experiments demonstrate that the model performs very well even though adding noisy sentences into the set, which shows the robustness and effectiveness of the model. Visualization analysis and case study show that our model captures the structure and pattern of coherent texts not only by simple word clues but also by consecution in context.
Soft Facial Landmark Detection by Label Distribution Learning

Most existing facial landmark detection algorithms regard the manually annotated landmarks as precise hard labels, therefore, the accurate annotated landmarks are essential to the training of these algorithms. However, in many cases, there exist deviations in manual annotations, and the landmarks marked for facial parts with occlusion and large poses are not always accurate, which means that the “ground truth” landmarks are usually not annotated precisely. In such case, it is more reasonable to use soft labels rather than explicit hard labels. Therefore, this paper proposes to associate a bivariate label distribution (BLD) to each landmark of an image. A BLD covers the neighboring pixels around the original manually annotated point, alleviating the problem of inaccurate landmarks. After generating a BLD for each landmark, the proposed method firstly learns the mappings from an image patch to the BLD of each landmark, and then the predicted BLDs are used in a deformable model fitting process to obtain the final facial shape for the image. Experimental results show that the proposed method performs better than the compared state-of-the-art facial landmark detection algorithms. Furthermore, the proposed method appears to be much more robust against the landmark noise in the training set than other compared baselines.
A Low Power Cryptography Solution Based on Chaos Theory in Wireless Sensor Nodes

Unobtrusive personal data collection by wearable sensors and ambient monitoring has increased concerns about user privacy.Applying cryptography solutions to resource constraint wireless sensors as one of the privacy-preserving solutions demand addressing limited memory and energy resources.In this paper, we set up testbed experiments to evaluate the existing cryptographic algorithms for sensors, such as Skipjack and RC5, which are less secure compared to block cipher based on chaotic (BCC) on existing IEEE802.15.4 based SunSPOT sensors.We have proposed modified BCC (MBCC) algorithm, which uses chaos theory characteristics to achieve higher resistance against statistical and differential attacks while maintaining resource consumption.Our comparison observations show that MBCC outperforms BCC in both energy consumption and RAM usage and that both MBCC and BCC outperform RC5 and Skipjack in terms of security measures, such as entropy and characters frequency.Our comparison analysis of MBCC vs BCC suggests 13.44% lower RAM usage for encryption and decryption as well as 6.4 and 6.6 times reduced consumed time and energy for encrypting 32-bit data, respectively.Further analysis is reported for increasing the length of MBCC key, periodical generation of master key on the base station and periodical generation of round key on the sensors to prevent the brute-force attacks.An overall comparison of cipher techniques with respect to energy, time, memory and security concludes the suitability of MBCC algorithm for resource constraint wireless sensors with security requirements.
DeepStar: Detecting Starring Characters in Movies

Recent advances in the film industry have given rise to exponential growth in movie/drama production and adaptation of the Big Data concept. Automatic identification and classification of movie characters have received tremendous attention from researchers due to its applications in video semantic analysis, video summarization, and personalized video retrieval for which several methods have been recently presented. However, these methods cannot detect main characters properly due to their variation in pose and style in different scenes of a movie. To address this problem we present DeepStar, a novel framework for starring character identification based on deep high-level robust features. The proposed framework is threefold: the extraction of shots with clear faces from the input video; face clustering using discriminative deep features; and the occurrence matrix generation, helping in the selection of starring characters. The promising results obtained using representative Hollywood movies demonstrate the effectiveness of our method in detecting starring characters over the state-of-the-art methods.
An Electrooculogram-Based Interaction Method and Its Music-on-Demand Application in a Virtual Reality Environment

Recently, the devices commonly used for interaction with a virtual reality (VR) environment include game controllers, data gloves, and motion tracking systems. However, these devices may limit the immersive experience or cause inconvenience, especially for patients with limb paralysis. This paper proposes a new nonmanual human-computer interface (HCI) based on a single-channel electrooculogram (EOG) signal and enables real-time interactions with the VR environment. The graphical user interface of the EOG-based HCI in VR includes several buttons flashing in a random order. The user needs to blink in synchrony with the corresponding button's flashes to issue a command, while the algorithm detects the eye blinks from the EOG signal and determines the users' target button. Furthermore, with the EOG-based HCI, we developed a music-on-demand system in the VR environment. By blinking, users can search and select target songs in the music library, play the selected songs, switch to the next song, and delete songs from the list. Ten healthy subjects and five patients with spinal cord injuries (SCIs) participated in online experiments. The experimental results demonstrated the effectiveness of the EOG-based HCI as a new method for interacting with the VR environment. Furthermore, the music-on-demand system can be used for entertainment not only by healthy people but also by SCI patients with limb paralysis.
Coupled Nonlinear Delay Systems as Deep Convolutional Neural Networks

Neural networks are currently transforming the field of computer algorithms, yet their emulation on current computing substrates is highly inefficient. Reservoir computing was successfully implemented on a large variety of substrates and gave new insight in overcoming this implementation bottleneck. Despite its success, the approach lags behind the state of the art in deep learning. We therefore extend time-delay reservoirs to deep networks and demonstrate that these conceptually correspond to deep convolutional neural networks. Convolution is intrinsically realized on a substrate level by generic drive-response properties of dynamical systems. The resulting novelty is avoiding vector-matrix products between layers, which cause low efficiency in today's substrates. Compared to singleton time-delay reservoirs, our deep network achieves accuracy improvements by at least an order of magnitude in Mackey-Glass and Lorenz timeseries prediction.
Multi-label learning with multi-label smoothing regularization for vehicle re-identification

Vehicle re-identification (re-ID) is a vital technique to the urban intelligent video surveillance system and smart city. Given a query vehicle image, the vehicle re-ID aims to search and retrieve the images of the same vehicle that have been captured by different surveillance cameras with various viewing angles. Based on the observation that essential vehicle attributes, like vehicle‘s color and types (e.g., sedan, bus, truck, and so on), could be used as important traits to recognize vehicle, an effective multi-label learning (MLL) method is proposed in this paper that can simultaneously learn three labels: vehicle’s ID, type, and color. With three labels, a multi-label smoothing regularization (MLSR) is further proposed, which can allocate a uniform label distribution to the multi-labeled training images to regularize MLL model and improve vehicle re-ID performance. Extensive experiments conducted on the VeRi and VehicleID datasets have demonstrated that the proposed MLL with MLSR approach can effectively improve the performance delivered by the baseline and outperform multiple state-of-the-art vehicle re-ID methods as well.
CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution

Although the accuracy of super-resolution (SR) methods based on convolutional neural networks (CNN) soars high, the complexity and computation also explode with the increased depth and width of the network. Thus, we propose the convolutional anchored regression network (CARN) for fast and accurate single image super-resolution (SISR). Inspired by locally linear regression methods (A+ and ARN), the new architecture consists of regression blocks that map input features from one feature space to another. Different from A+ and ARN, CARN is no longer relying on or limited by hand-crafted features. Instead, it is an end-to-end design where all the operations are converted to convolutions so that the key concepts, i.e., features, anchors, and regressors, are learned jointly. The experiments show that CARN achieves the best speed and accuracy trade-off among the SR methods. The code is available at https://github.com/ofsoundof/CARN .
Part of Speech Tagging in Urdu: Comparison of Machine and Deep Learning Approaches

In Urdu, part of speech (POS) tagging is a challenging task as it is both inflectionally and derivationally rich morphological language. Verbs are generally conceived a highly inflected object in Urdu comparatively to nouns. POS tagging is used as a preliminary linguistic text analysis in diverse natural language processing domains such as speech processing, information extraction, machine translation, and others. It is a task that first identifies appropriate syntactic categories for each word in running text and second assigns the predicted syntactic tag to all concerned words. The current work is the extension of our previous work. Previously, we presented conditional random field (CRF)-based POS tagger with both language dependent and independent feature set. However, in the current study, we offer: 1) the implementation of both machine and deep learning models for Urdu POS tagging task with well-balanced language-independent feature set and 2) to highlight diverse challenges which cause Urdu POS task a challenging one. In this research, we demonstrated the effectiveness of machine learning and deep learning models for Urdu POS task. Empirically, we have evaluated the performance of all models on two benchmark datasets. The core models evaluated in this study are CRF, support vector machine (SVM), two variants of the deep recurrent neural network (DRNN), and a variant of n-gram Markov model the bigram hidden Markov model (HMM). The two variants of DRRN models evaluated include forward long short-term memory (LSTM)-RNN and LSTM-RNN with CRF output.
Conditional entropy based classifier chains for multi-label classification

In many real-world problems, data samples are simultaneously associated with multiple labels, instead of a single label. Multi-label classification deals with such problems, and has extensive applications in many fields. Among the many methods proposed for multi-label classification tasks, classifier chains (CC) is an appealing one. In the classifier chains method, the label order has a strong effect on the classification performance. However, it is difficult to determine a proper order. In this paper, we propose ordering methods based on the conditional entropy of labels. We generate a single order instead of multiple orders. Unlike existing ordering methods, there is no need to train more classifiers than CC. Experimental results on nine benchmark datasets evaluated by eight measures show that the proposed methods achieve good performance.
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA

We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results.UCCA is a crosslinguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation.UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units.The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings.Full results can be found in the task's website https://competitions. codalab.org/competitions/19160.
Whose story is it anyway? Automatic extraction of accounts from news articles

Narratives are comprised of stories that provide insight into social processes. To facilitate the analysis of narratives in a more efficient manner, natural language processing (NLP) methods have been employed in order to automatically extract information from textual sources, e.g., newspaper articles. Existing work on automatic narrative extraction, however, has ignored the nested character of narratives. In this work, we argue that a narrative may contain multiple accounts given by different actors. Each individual account provides insight into the beliefs and desires underpinning an actor's actions. We present a pipeline for automatically extracting accounts, consisting of NLP methods for: (1) named entity recognition, (2) event extraction, and (3) attribution extraction. Machine learning-based models for named entity recognition were trained based on a state-of-the-art neural network architecture for sequence labelling. For event extraction, we developed a hybrid approach combining the use of semantic role labelling tools, the FrameNet repository of semantic frames, and a lexicon of event nouns. Meanwhile, attribution extraction was addressed with the aid of a dependency parser and Levin's verb classes. To facilitate the development and evaluation of these methods, we constructed a new corpus of news articles, in which named entities, events and attributions have been manually marked up following a novel annotation scheme that covers over 20 event types relating to socio-economic phenomena. Evaluation results show that relative to a baseline method underpinned solely by semantic role labelling tools, our event extraction approach optimises recall by 12.22–14.20 percentage points (reaching as high as 92.60% on one data set). Meanwhile, the use of Levin's verb classes in attribution extraction obtains optimal performance in terms of F-score, outperforming a baseline method by 7.64–11.96 percentage points. Our proposed approach was applied on news articles focused on industrial regeneration cases. This facilitated the generation of accounts of events that are attributed to specific actors.
Impact of Network Topology on the Performance of DML: Theoretical Analysis and Practical Factors

To deal with the increasingly larger input data and model sizes, it has become necessary to scale the training of machine learning models to multiple nodes, even a server cluster, which we call distributed machine learning, or DML. However, DML utilizes more computation power at the cost of high communication overhead, which may limit the overall performance in turn. In this paper, we study the impact of network topology on the DML performance both in theory and in practice. We compare two representative network topologies, namely, Fat-Tree which is widely-used in modern data centers, and BCube, which is a low-cost and server-centric network topology, both running on top of RDMA. The results show that Fat-Tree not only has theoretically higher global synchronization time (GST) than BCube, but its practical GST (by NS-3 based simulation) is also considerably larger than the theoretical one. By analyzing the large-scale simulation traces, we find that the root cause for the gap in Fat-Tree comes from the load imbalance among the multiple parallel paths as well as the inevitable PFC frames, both of which do not appear in BCube. For a cluster of around 250 servers, BCube achieves 53%\sim 70% lower GST than Fat-Tree from the simulation. As a result, we suggest using server-centric network topology such as BCube, instead of the common Fat-Tree network, to build a special-purpose DML cluster, due to its parallel synchronization, RDMA friendliness, natural load balance, as well as low economical cost.
Interpreting Recurrent Neural Networks Behaviour via Excitable Network Attractors

Machine learning provides fundamental tools both for scientific research and for the development of technologies with significant impact on society. It provides methods that facilitate the discovery of regularities in data and that give predictions without explicit knowledge of the rules governing a system. However, a price is paid for exploiting such flexibility: machine learning methods are typically black boxes where it is difficult to fully understand what the machine is doing or how it is operating. This poses constraints on the applicability and explainability of such methods. Our research aims to open the black box of recurrent neural networks, an important family of neural networks used for processing sequential data. We propose a novel methodology that provides a mechanistic interpretation of behaviour when solving a computational task. Our methodology uses mathematical constructs called excitable network attractors, which are invariant sets in phase space composed of stable attractors and excitable connections between them. As the behaviour of recurrent neural networks depends both on training and on inputs to the system, we introduce an algorithm to extract network attractors directly from the trajectory of a neural network while solving tasks. Simulations conducted on a controlled benchmark task confirm the relevance of these attractors for interpreting the behaviour of recurrent neural networks, at least for tasks that involve learning a finite number of stable states and transitions between them.
A Parallel FPGA Implementation of the CCSDS-123 Compression Algorithm

Satellite onboard processing for hyperspectral imaging applications is characterized by large data sets, limited processing resources and limited bandwidth of communication links. The CCSDS-123 algorithm is a specialized compression standard assembled for space-related applications. In this paper, a parallel FPGA implementation of CCSDS-123 compression algorithm is presented. The proposed design can compress any number of samples in parallel allowed by resource and I/O bandwidth constraints. The CCSDS-123 processing core has been placed on Zynq-7035 SoC and verified against the existing reference software. The estimated power use scales approximately linearly with the number of samples processed in parallel. Finally, the proposed implementation outperforms the state-of-the-art implementations in terms of both throughput and power.
Detection based visual tracking with convolutional neural network

In this paper, we propose a detection strategy based visual object tracking algorithm. We employ multiple trackers using layers of deep convolutional neural network (CNN) features. Each tracker which is correlation filter based tracking framework tracks an object forwardly and then backwardly. By analyzing the forward and backward trajectories, we measure the robustness of tracking results. A detection strategy which is based on locally adaptive regression kernels (LARK) feature is carried out according to the robustness of tracking result. Target can be located from the provided candidates. Extensive experimental results show that the proposed method improves the accuracy and robustness of tracking, achieving state-of-the-art results on several recent benchmark datasets.