Reviewerly/exHarmony huggingface dataset:
A Multi-Data Driven Hybrid Learning Method for Weekly Photovoltaic Power Scenario Forecast

This paper proposes a multi-data driven hybrid learning method for weekly photovoltaic (PV) power scenario forecast that is coordinately driven by weather forecasts and historical PV power output data. Patterns of historical data and weather forecast information are simultaneously captured to ensure the quality of the generated scenarios. By combining bicubic interpolation and bidirectional long-short term memory (BiLSTM), a super resolution algorithm is first presented to enhance the time resolution of weather forecast data from three hours to one hour and increase the precision of weather forecasting. A weather process-based weekly PV power classification strategy is proposed to capture the coupling relationships between meteorological elements, continuous weather changes and weekly PV power. A gated recurrent unit (GRU)-convolutional neural network (CNN)-based scenario forecast method is developed to generate weekly PV power scenarios. Evaluation indices are presented to comprehensively assess the quality of the generated weekly scenarios of PV power. Finally, the PV power, weather observation and weather forecast data collected from five PV plants located in Northeast Asia are used to verify the effectiveness and correctness of the proposed method.
How Much Communication Resource is Needed to Run a Wireless Blockchain Network?

Blockchain is built on a peer-to-peer network that relies on frequent communications among distributively located nodes. In particular, the consensus mechanisms (CMs), which play a pivotal role in blockchain, are communication resource-demanding and largely determine blockchain security bounds (i.e., fault tolerances) and other key performance metrics such as transaction throughput, latency and scalability. Most blockchain systems are designed in a stable wired communication network running in advanced devices under the assumption of sufficient communication resource provision. However, it is envisioned that the majority of blockchain node peers will be connected through the wireless network in the future. Constrained by the highly dynamic wireless channel and scarce frequency spectrum, communication can significantly affect blockchain's key performance metrics. Hence, in this article, we present wireless blockchain networks (WBN) under various commonly used CMs and we answer the question of how much communication resource is needed to run such a network. We first present the role of communication in the four stages of the blockchain procedure. We then discuss the relationship between the communication resource provision and the WBNs performance, for three of the most used blockchain CMs, namely, Proof-of-Work (PoW), practical Byzantine Fault Tolerant (PBFT) and Raft. Finally, we provide analytical and simulated results to show the impact of the communication resource provision on block-chain performance.
RNN-based deep learning for physical activity recognition using smartwatch sensors: A case study of simple and complex activity recognition

Currently, identification of complex human activities is experiencing exponential growth through the use of deep learning algorithms. Conventional strategies for recognizing human activity generally rely on handcrafted characteristics from heuristic processes in time and frequency domains. The advancement of deep learning algorithms has addressed most of these issues by automatically extracting features from multimodal sensors to correctly classify human physical activity. This study proposed an attention-based bidirectional gated recurrent unit as Att-BiGRU to enhance recurrent neural networks. This deep learning model allowed flexible forwarding and reverse sequences to extract temporal-dependent characteristics for efficient complex activity recognition. The retrieved temporal characteristics were then used to exemplify essential information through an attention mechanism. A human activity recognition (HAR) methodology combined with our proposed model was evaluated using the publicly available datasets containing physical activity data collected by accelerometers and gyroscopes incorporated in a wristwatch. Simulation experiments showed that attention mechanisms significantly enhanced performance in recognizing complex human activity.
Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing

Semantic segmentation is a promising machine learning (ML) method for highly precise fine-scale defect detection and part qualification in additive manufacturing (AM). Most existing segmentation methods utilize convolutional neural network architectures that require large quantities of training data. However, obtaining sufficient data—both in quality and quantity—to train such models is expensive and time-consuming for individual AM practitioners, which severely limits the deployment of semantic segmentation in a data-scarce production environment. Similar data may be readily available with other AM practitioners that cannot be pooled together for conventional centralized learning (CL) due to its sensitive nature or conflicts of interest. This paper develops a federated learning (FL)-based method to simultaneously alleviate the constraints of data availability and data privacy. A U-Net architecture is created for semantic segmentation and is trained under the FL framework. The effectiveness of the developed FL-based semantic segmentation approach is demonstrated using case studies on layer-wise images from the laser powder bed fusion process. Results show that the proposed technique achieves a comparable defect detection performance with CL, which shares data among manufacturers/clients but does not preserve data privacy, and significantly outperforms individual learning, where each manufacturer trains a model using its own data. Additionally, the impact of data distribution across clients, incentives to participate in FL, and the learning dynamics of FL are discussed in detail. It is found that data diversity within and across clients improves FL performance, and FL does not involve a significantly higher training cost compared to CL. Lastly, transfer learning is shown to enhance FL generalizability, thus allowing more manufacturers with heterogeneous machines or technologies to benefit from participating in a data federation. Overall, this work puts forth FL as a promising paradigm for privacy-preserving collaborative ML in AM process control.
Incentive techniques for the Internet of Things: A survey

The Internet of Things (IoT) has remarkably evolved over the last few years to realize a wide range of newly emerging services and applications empowered by the unprecedented proliferation of smart devices. The quality of IoT networks heavily relies on the involvement of devices for undertaking functions from data sensing, computation to communication and IoT intelligence. Stimulating IoT devices to actively participate and contribute to the network is a practical challenge, where incentive techniques such as blockchain, game theory, and Artificial Intelligence (AI) are highly desirable to build a sustainable IoT ecosystem. In this article, we present a systematic literature review of the incentive techniques for IoT, aiming to provide general readers with an overview of incentive-enabled IoT from background, motivations, and enabling techniques. Particularly, we first present the fundamentals of IoT data network infrastructure, and several key incentive techniques for IoT are described in details, including blockchain, game theory, and AI. We next provide an extensive review on the use of these incentive techniques in a number of key IoT services, such as IoT data sharing, IoT data offloading and caching, IoT mobile crowdsensing, and IoT security and privacy. Subsequently, we explore the potential of incentives in important IoT applications, ranging from smart healthcare, smart transportation to smart city and smart industry. The research challenges of incentive techniques in IoT networks are highlighted, and the potential directions are also pointed out for future research of this important area.
Fast and scalable search of whole-slide images via self-supervised deep learning

The adoption of digital pathology has enabled the curation of large repositories of gigapixel whole-slide images (WSIs). Computationally identifying WSIs with similar morphologic features within large repositories without requiring supervised training can have significant applications. However, the retrieval speeds of algorithms for searching similar WSIs often scale with the repository size, which limits their clinical and research potential. Here we show that self-supervised deep learning can be leveraged to search for and retrieve WSIs at speeds that are independent of repository size. The algorithm, which we named SISH (for self-supervised image search for histology) and provide as an open-source package, requires only slide-level annotations for training, encodes WSIs into meaningful discrete latent representations and leverages a tree data structure for fast searching followed by an uncertainty-based ranking algorithm for WSI retrieval. We evaluated SISH on multiple tasks (including retrieval tasks based on tissue-patch queries) and on datasets spanning over 22,000 patient cases and 56 disease subtypes. SISH can also be used to aid the diagnosis of rare cancer types for which the number of available WSIs is often insufficient to train supervised deep-learning models.
Semantic-Aware Domain Generalized Segmentation

Deep models trained on source domain lack generalization when evaluated on unseen target domains with different data distributions. The problem becomes even more pro-nounced when we have no access to target domain samples for adaptation. In this paper, we address domain generalized semantic segmentation, where a segmentation model is trained to be domain-invariant without using any target domain data. Existing approaches to tackle this problem standardize data into a unified distribution. We argue that while such a standardization promotes global normalization, the resulting features are not discriminative enough to get clear segmentation boundaries. To enhance separation between categories while simultaneously promoting domain invariance, we propose a framework including two novel modules: Semantic-Aware Normalization (SAN) and Semantic-Aware Whitening (SAW). Specifically, SAN focuses on category-level center alignment between features from different image styles, while SAW enforces distributed alignment for the already center-aligned features. With the help of SAN and SAW, we encourage both intra-category compactness and inter-category separability. We validate our approach through extensive experiments on widely-used datasets (i.e. GTAV, SYNTHIA, Cityscapes, Mapillary and BDDS). Our approach shows significant improvements over existing state-of-the-art on various backbone networks. Code is available at https://github.com/leolyj/SAN-SAW
Fast vehicle logo detection in complex scenes

Vehicle logo detection has received considerable attention due to its many applications, such as brand reputation measurement and vehicle monitoring. Deep-learning algorithms, such as you only look once (YOLO) and faster-regional convolutional neural network, have obtained excellent performance for object detection. However, it is not easy to exploit these methods to detect small objects. This work presents a novel method for fast and accurate vehicle logo detection (VLD) in complex scenes. First, we modify the original YOLOv3 model for the VLD task and solve the small object detection problem by hard example training. Second, we construct a new VLD dataset known as VLD-30, which encourages us to develop a data-driven training method and improve the detection accuracy. Experimental results demonstrate that the proposed data-training method is useful and the modified YOLOv3 is effective for fast and accurate vehicle logo detection in complex scenes.
A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification

Cervical cancer is the second most common cancer affecting women worldwide but is curable if diagnosed early. Routinely, expert pathologists visually examine histology slides for assessing cervix tissue abnormalities. A localized, fusion-based, hybrid imaging and deep learning approach is explored to classify squamous epithelium into cervical intraepithelial neoplasia (CIN) grades for a dataset of 83 digitized histology images. Partitioning the epithelium region into 10 vertical segments, 27 handcrafted image features and rectangular patch, sliding window-based convolutional neural network features are computed for each segment. The imaging and deep learning patch features are combined and used as inputs to a secondary classifier for individual segment and whole epithelium classification. The hybrid method achieved a 15.51% and 11.66% improvement over the deep learning and imaging approaches alone, respectively, with a 80.72% whole epithelium CIN classification accuracy, showing the enhanced epithelium CIN classification potential of fusing image and deep learning features.
People teach with rewards and punishments as communication, not reinforcements.

Carrots and sticks motivate behavior, and people can teach new behaviors to other organisms, such as children or nonhuman animals, by tapping into their reward learning mechanisms. But how people teach with reward and punishment depends on their expectations about the learner. We examine how people teach using reward and punishment by contrasting two hypotheses. The first is evaluative feedback as reinforcement, where rewards and punishments are used to shape learner behavior through reinforcement learning mechanisms. The second is evaluative feedback as communication, where rewards and punishments are used to signal target behavior to a learning agent reasoning about a teacher's pedagogical goals. We present formalizations of learning from these 2 teaching strategies based on computational frameworks for reinforcement learning. Our analysis based on these models motivates a simple interactive teaching paradigm that distinguishes between the two teaching hypotheses. Across 3 sets of experiments, we find that people are strongly biased to use evaluative feedback communicatively rather than as reinforcement. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus

Abstract Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and Indian languages are morphologically rich. In this paper, we propose a neural machine translation (NMT) system for four language pairs: English–Malayalam, English–Hindi, English–Tamil, and English–Punjabi. We also collected sentences from different sources and cleaned them to make four parallel corpora for each of the language pairs, and then used them to model the translation system. The encoder network in the NMT architecture was designed with long short-term memory (LSTM) networks and bi-directional recurrent neural networks (Bi-RNN). Evaluation of the obtained models was performed both automatically and manually. For automatic evaluation, the bilingual evaluation understudy (BLEU) score was used, and for manual evaluation, three metrics such as adequacy, fluency, and overall ranking were used. Analysis of the results showed the presence of lengthy sentences in English–Malayalam, and the English–Hindi corpus affected the translation. Attention mechanism was employed with a view to addressing the problem of translating lengthy sentences (sentences contain more than 50 words), and the system was able to perceive long-term contexts in the sentences.
Deep learning for named entity recognition on Chinese electronic medical records: Combining deep transfer learning with multitask bi-directional LSTM RNN

Specific entity terms such as disease, test, symptom, and genes in Electronic Medical Record (EMR) can be extracted by Named Entity Recognition (NER). However, limited resources of labeled EMR pose a great challenge for mining medical entity terms. In this study, a novel multitask bi-directional RNN model combined with deep transfer learning is proposed as a potential solution of transferring knowledge and data augmentation to enhance NER performance with limited data. The proposed model has been evaluated using micro average F-score, macro average F-score and accuracy. It is observed that the proposed model outperforms the baseline model in the case of discharge datasets. For instance, for the case of discharge summary, the micro average F-score is improved by 2.55% and the overall accuracy is improved by 7.53%. For the case of progress notes, the micro average F-score and the overall accuracy are improved by 1.63% and 5.63%, respectively.
Machine learning topological defects of confined liquid crystals in two dimensions

Supervised machine learning can be used to classify images with spatially correlated physical features. We demonstrate the concept by using the coordinate files generated from an off-lattice computer simulation of rodlike molecules confined in a square box as an example. Because of the geometric frustrations at high number density, the nematic director field develops an inhomogeneous pattern containing various topological defects as the main physical feature. We describe two machine-learning procedures that can be used to effectively capture the correlation between the defect positions and the nematic directors around them and hence classify the topological defects. First is a feedforward neural network, which requires the aid of presorting the off-lattice simulation data in a coarse-grained fashion. Second is a recurrent neural network, which needs no such sorting and can be directly used for finding spatial correlations. The issues of when to presort a simulation data file and how the network structures affect such a decision are addressed.
The Limitations of Adversarial Training and the Blind-Spot Attack

The adversarial training procedure proposed by Madry et al. (2018) is one of the most effective methods to defend against adversarial examples in deep neural networks (DNNs). In our paper, we shed some lights on the practicality and the hardness of adversarial training by showing that the effectiveness (robustness on test set) of adversarial training has a strong correlation with the distance between a test point and the manifold of training data embedded by the network. Test examples that are relatively far away from this manifold are more likely to be vulnerable to adversarial attacks. Consequentially, an adversarial training based defense is susceptible to a new class of attacks, the blind-spot attack, where the input images reside in (low density regions) of the empirical distribution of training data but is still on the ground-truth data manifold. For MNIST, we found that these blind-spots can be easily found by simply scaling and shifting image pixel values. Most importantly, for large datasets with high dimensional and complex data manifold (CIFAR, ImageNet, etc), the existence of blind-spots in adversarial training makes defending on any valid test examples difficult due to the curse of dimensionality and the scarcity of training data. Additionally, we find that blind-spots also exist on provable defenses including (Wong & Kolter, 2018) and (Sinha et al., 2018) because these trainable robustness certificates can only be practically optimized on a limited set of training data.
Novelty Detection for Multispectral Images with Application to Planetary Exploration

In this work, we present a system based on convolutional autoencoders for detecting novel features in multispectral images. We introduce SAMMIE: Selections based on Autoencoder Modeling of Multispectral Image Expectations. Previous work using autoencoders employed the scalar reconstruction error to classify new images as novel or typical. We show that a spatial-spectral error map can enable both accurate classification of novelty in multispectral images as well as human-comprehensible explanations of the detection. We apply our methodology to the detection of novel geologic features in multispectral images of the Martian surface collected by the Mastcam imaging system on the Mars Science Laboratory Curiosity rover.
Random forest implementation and optimization for Big Data analytics on LexisNexis’s high performance computing cluster platform

In this paper, we comprehensively explain how we built a novel implementation of the Random Forest algorithm on the High Performance Computing Cluster (HPCC) Systems Platform from LexisNexis. The algorithm was previously unavailable on that platform. Random Forest’s learning process is based on the principle of recursive partitioning and although recursion per se is not allowed in ECL (HPCC’s programming language), we were able to implement the recursive partition algorithm as an iterative split/partition process. In addition, we analyze the flaws found in our initial implementation and we thoroughly describe all the modifications required to overcome the bottleneck within the iterative split/partition process, i.e., the optimization of the data gathering of selected independent variables which are used for the node’s best-split analysis. Essentially, we describe how our initial Random Forest implementation has been optimized and has become an efficient distributed machine learning implementation for Big Data. By taking full advantage of the HPCC Systems Platform’s Big Data processing and analytics capabilities, we succeed in enhancing the data gathering method from an inefficient Pass them All and Filter approach into an effective and completely parallelized Fetching on Demand approach. Finally, based upon the results of our learning process runtime comparison between these two approaches, we confirm the speed up of our optimized Random Forest implementation.
Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting

A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio under model and true distributions. When the likelihood ratio is unknown, it can be estimated by training a probabilistic classifier to distinguish samples from the two distributions. We employ this likelihood-free importance weighting method to correct for the bias in generative models. We find that this technique consistently improves standard goodness-of-fit metrics for evaluating the sample quality of state-of-the-art deep generative models, suggesting reduced bias. Finally, we demonstrate its utility on representative applications in a) data augmentation for classification using generative adversarial networks, and b) model-based policy evaluation using off-policy data.