Reviewerly/exHarmony huggingface dataset:
Feature-Critic Networks for Heterogeneous Domain Generalization

The well known domain shift issue causes model performance to degrade when deployed to a new target domain with different statistics to training. Domain adaptation techniques alleviate this, but need some instances from the target domain to drive adaptation. Domain generalisation is the recently topical problem of learning a model that generalises to unseen domains out of the box, and various approaches aim to train a domain-invariant feature extractor, typically by adding some manually designed losses. In this work, we propose a learning to learn approach, where the auxiliary loss that helps generalisation is itself learned. Beyond conventional domain generalisation, we consider a more challenging setting of heterogeneous domain generalisation, where the unseen domains do not share label space with the seen ones, and the goal is to train a feature representation that is useful off-the-shelf for novel data and novel categories. Experimental evaluation demonstrates that our method outperforms state-of-the-art solutions in both settings.
Sentiment-Aware Deep Recommender System With Neural Attention Networks

With the advent of web technology, user-generated textual reviews are becoming increasingly accumulated on many e-commerce websites. These reviews contain not only the user comments on different aspects of the products but also the user sentiments associated with the aspects. Although these user sentiments serve as vital side information for improving the performance of recommender systems, most existing approaches ignore to fully exploit them in modeling the fine-grained user-item interaction for improving recommender system performance. Thus, this paper proposes a sentiment-aware deep recommender system with neural attention network (SDRA), which can capture both the aspects of products and the underlying user sentiments associated with the aspects for improving the recommendation system performance. Particularly, a semi-supervised topic model is designed to extract the aspects of the product and the associated sentiment lexicons from the user textual reviews, which are then incorporated into a long short term memory (LSTM) encoder via an interactive neural attention mechanism for better learning of the user and item sentiment-aware representation. Furthermore, a co-attention mechanism is introduced to better model the fine-grained user-item interaction for improving predictive performance. The extensive experiments on different datasets showed that our proposed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDRA</b> model can achieve better performance over the baseline approaches.
Analysis of Liquid Ensembles for Enhancing the Performance and Accuracy of Liquid State Machines

Liquid state machine (LSM), a bio-inspired computing model consisting of the input sparsely connected to a randomly interlinked reservoir (or liquid) of spiking neurons followed by a readout layer, finds utility in a range of applications varying from robot control and sequence generation to action, speech, and image recognition. LSMs stand out among other Recurrent Neural Network (RNN) architectures due to their simplistic structure and lower training complexity. Plethora of recent efforts have been focused towards mimicking certain characteristics of biological systems to enhance the performance of modern artificial neural networks. It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to better accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lowered number of connections and the freedom to parallelize the liquid evaluation process.
Semi-Automated Heavy-Mineral Analysis by Raman Spectroscopy

A significant amount of information on sedimentary provenance is encoded in the heavy minerals of a sediment or sedimentary rock. This information is commonly assessed by optically determining the heavy-mineral assemblage, potentially followed by geochemical and/or geochronological analysis of specific heavy minerals. The proposed method of semi-automated heavy-mineral analysis by Raman spectroscopy (Raman-HMA) aims to combine the objective mineral identification capabilities of Raman spectroscopy with high-resolution geochemical techniques applied to single grains. The Raman-HMA method is an efficient and precise tool that significantly improves the comparability of heavy-mineral data with respect to both overall assemblages and individual compositions within solid solution series. Furthermore, the efficiency of subsequent analysis is increased due to identification and spatial referencing of the heavy minerals in the sample slide. The method is tested on modern sediments of the Fulda river (central Germany) draining two Miocene volcanic sources (Vogelsberg, Rhön) resting on top of Lower Triassic siliciclastic sediments. The downstream evolution of the volcanic detritus is documented and the capability to analyze silt-sized grains has revealed an additional eolian source. This capability also poses the possibility of systematically assessing the heavy-mineral assemblages of shales, which are often disregarded in sedimentary provenance studies.
An Intelligent approach for UAV and Drone Privacy Security Using Blockchain Methodology

In today's era drones and UAV are being used more and more for spying and warfare. Their excessive use makes them vulnerable to be hacked and used for malicious purposes. There are also security loopholes in this technology like the radio waves which can be exploited by the rivals and can cause a large amount of destruction or loss of data. This paper is written to improve the security of UAV and drones by using blockchain technology. Blockchain is a highly secured technology as it uses private key cryptography and peer to peer network. By incorporating this technology in transmitting signals from controller to drone or UAV, we can achieve an extra amount of security in transmitting of signals also it increases the connectivity.
AnalyticDB

With data explosion in scale and variety, OLAP databases play an increasingly important role in serving real-time analysis with low latency (e.g., hundreds of milliseconds), especially when incoming queries are complex and ad hoc in nature. Moreover, these systems are expected to provide high query concurrency and write throughput, and support queries over structured and complex data types (e.g., JSON, vector and texts). In this paper, we introduce AnalyticDB, a real-time OLAP database system developed at Alibaba. AnalyticDB maintains all-column indexes in an asynchronous manner with acceptable overhead, which provides low latency for complex ad-hoc queries. Its storage engine extends hybrid row-column layout for fast retrieval of both structured data and data of complex types. To handle large-scale data with high query concurrency and write throughput, AnalyticDB decouples read and write access paths. To further reduce query latency, novel storage-aware SQL optimizer and execution engine are developed to fully utilize the advantages of the underlying storage and indexes. AnalyticDB has been successfully deployed on Alibaba Cloud to serve numerous customers (both large and small). It is capable of holding 100 trillion rows of records, i.e., 10PB+ in size. At the same time, it is able to serve 10m+ writes and 100k+ queries per second, while completing complex queries within hundreds of milliseconds.
Learning Multi-Level Density Maps for Crowd Counting

People in crowd scenes often exhibit the characteristic of imbalanced distribution. On the one hand, people size varies largely due to the camera perspective. People far away from the camera look smaller and are likely to occlude each other, whereas people near to the camera look larger and are relatively sparse. On the other hand, the number of people also varies greatly in the same or different scenes. This article aims to develop a novel model that can accurately estimate the crowd count from a given scene with imbalanced people distribution. To this end, we have proposed an effective multi-level convolutional neural network (MLCNN) architecture that first adaptively learns multi-level density maps and then fuses them to predict the final output. Density map of each level focuses on dealing with people of certain sizes. As a result, the fusion of multi-level density maps is able to tackle the large variation in people size. In addition, we introduce a new loss function named balanced loss (BL) to impose relatively BL feedback during training, which helps further improve the performance of the proposed network. Furthermore, we introduce a new data set including 1111 images with a total of 49 061 head annotations. MLCNN is easy to train with only one end-to-end training stage. Experimental results demonstrate that our MLCNN achieves state-of-the-art performance. In particular, our MLCNN reaches a mean absolute error (MAE) of 242.4 on the UCF_CC_50 data set, which is 37.2 lower than the second-best result.
High‐Purity Triggered Single‐Photon Emission from Symmetric Single InAs/InP Quantum Dots around the Telecom C‐Band Window

Abstract The authors demonstrate pure triggered single‐photon emission from quantum dots (QDs) around the telecommunication C‐band window, with characteristics preserved under non‐resonant excitation at saturation, that is, the highest possible, lifetime‐limited emission rates. The direct measurement of emission dynamics reveals photoluminescence decay times in the range of (1.7–1.8) ns corresponding to maximal photon generation rates exceeding 0.5 GHz. The measurements of the second‐order correlation function exhibit, for the best case, a lack of coincidences at zero time delay—no multiple photon events are registered within the experimental accuracy. This is achieved by exploiting a new class of low‐density and in‐plane symmetric InAs/InP QDs grown by molecular beam epitaxy on a distributed Bragg reflector, perfectly suitable for non‐classical light generation for quantum optics experiments and quantum‐secured fiber‐based optical communication schemes.
On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos

The premise of training an accurate 3D human pose estimation network is the possession of huge amount of richly annotated training data. Nonetheless, manually obtaining rich and accurate annotations is, even not impossible, tedious and slow. In this paper, we propose to exploit monocular videos to complement the training dataset for the single-image 3D human pose estimation tasks. At the beginning, a baseline model is trained with a small set of annotations. By fixing some reliable estimations produced by the resulting model, our method automatically collects the annotations across the entire video as solving the 3D trajectory completion problem. Then, the baseline model is further trained with the collected annotations to learn the new poses. We evaluate our method on the broadly-adopted Human3.6M and MPI-INF-3DHP datasets. As illustrated in experiments, given only a small set of annotations, our method successfully makes the model to learn new poses from unlabelled monocular videos, promoting the accuracies of the baseline model by about 10%. By contrast with previous approaches, our method does not rely on either multi-view imagery or any explicit 2D keypoint annotations.
Radical analysis network for learning hierarchies of Chinese characters

Chinese characters have a valuable property, this is, numerous Chinese characters are composed of a compact set of fundamental and structural radicals. This paper introduces a radical analysis network (RAN) that makes full use of this valuable property to implement radical-based Chinese character recognition. The proposed RAN employs an attention mechanism to extract radicals from Chinese characters and to detect spatial structures among the radicals. Then, the decoder in RAN generates a hierarchical composition of Chinese characters based on the knowledge of the extracted radicals and their internal structures. The method of treating a Chinese character as a composition of radicals rather than as a single character category is a human-like method that can reduce the size of the vocabulary, ignore redundant information among similar characters and enable the system to recognize unseen Chinese character categories, i.e., zero-shot learning. Through experiments, we assess the practicality of RAN for recognizing Chinese characters in natural scenes. Furthermore, a RAN framework can be proposed for scene text recognition with the extension of a dense recurrent neural network (denseRNN) encoder, a multihead coverage attention model and HSV representations. The proposed approach achieved the best performance in the ICPR MTWI 2018 competition.
An Edge-Based Distributed Decision-Making Method for Product Design Scheme Evaluation

Successful product development relies on the enterprise system (ES) to manage product lifecycle data and support decision-making in various levels. Since the amount of data generated in the industrial Internet of Things is increasing dramatically, new paradigms of the ES are required to realize the distributed intelligence. Recent progress in edge computing has enabled advances in decentralized decision support systems. Although the product design is a key stage of new product development, none of the recent studies in edge-based ES have shed light on this stage. Therefore, an edge-based ES framework for design scheme evaluation is proposed in this article. It not only accomplishes most of the decision-making tasks within the edge mesh of an enterprise, but also solves the information island of multiple decision makers from different geographical regions. Moreover, a multigroup decision-making algorithm is proposed to enable collaborative design scheme evaluation. The evaluation processes of designers, experts, and customers are analyzed systematically in this article. For one advantage, the trapezium cloud model is applied to convert the qualitative evaluation information given by designers and experts into qualitative values. It decreases the cognitive discrepancy and solves the information distortion. For another, EEG data are utilized to explore the implicit psychological states of customers during the product operation. All the evaluation results of multiple groups of decision makers are integrated by the fuzzy measure and Choquet integral to determine the optimal design scheme. A case study is conducted to illustrate the feasibility of the method proposed in this article.
Use of fuzzy cognitive maps to develop policy strategies for the optimization of municipal waste management: A case study of the land of fires (Italy)

This paper applies an analytical method for developing policy strategies to optimize municipal waste management systems (MWMSs) to the case of the Land of Fires (LoF). The LoF is an area of Italy’s Campania region that is characterized by a legacy of authoritarian environmental governance and the improper dumping and burning of waste. In this paper, we employ the fuzzy cognitive maps (FCM) method, which draws on a participatory approach. Specifically, the complexity of the investigated system was determined from the causal relations identified by relevant stakeholders and experts. The results show that the most effective policy strategies to improve the LoF MWMS, as identified by informants, include: fostering social innovation (e.g. communication and information campaigns); promoting technological innovation (e.g. material and process design); and supporting scientific and technological cooperation among actors. The overall diversity of the identified policy strategies suggests that policy makers must move beyond a simple “best option” approach, given the systemic complexity of the waste management sector.
BEDSR-Net: A Deep Shadow Removal Network From a Single Document Image

Removing shadows in document images enhances both the visual quality and readability of digital copies of documents. Most existing shadow removal algorithms for document images use hand-crafted heuristics and are often not robust to documents with different characteristics. This paper proposes the Background Estimation Document Shadow Removal Network (BEDSR-Net), the first deep network specifically designed for document image shadow removal. For taking advantage of specific properties of document images, a background estimation module is designed for extracting the global background color of the document. During the process of estimating the background color, the module also learns information about the spatial distribution of background and non-background pixels. We encode such information into an attention map. With the estimated global background color and attention map, the shadow removal network can better recover the shadow-free image. We also show that the model trained on synthetic images remains effective for real photos, and provide a large set of synthetic shadow images of documents along with their corresponding shadow-free images and shadow masks. Extensive quantitative and qualitative experiments on several benchmarks show that the BEDSR-Net outperforms existing methods in enhancing both the visual quality and readability of document images.
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation

Due to its robust and precise distance measurements, LiDAR plays an important role in scene understanding for autonomous driving. Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations, which are time-consuming and expensive to obtain. Instead, simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels and transfers the learned model to real scenarios. Existing SRDA methods for LiDAR point cloud segmentation mainly employ a multi-stage pipeline and focus on feature-level alignment. They require prior knowledge of real-world statistics and ignore the pixel-level dropout noise gap and the spatial feature gap between different domains. In this paper, we propose a novel end-to-end framework, named ePointDA, to address the above issues. Specifically, ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning. The joint optimization enables ePointDA to bridge the domain shift at the pixel-level by explicitly rendering dropout noise for synthetic LiDAR and at the feature-level by spatially aligning the features between different domains, without requiring the real-world statistics. Extensive experiments adapting from synthetic GTA-LiDAR to real KITTI and SemanticKITTI demonstrate the superiority of ePointDA for LiDAR point cloud segmentation.
Integration of Speech Separation, Diarization, and Recognition for Multi-Speaker Meetings: System Description, Comparison, and Analysis

Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation. With technical advances in systems dealing with speech separation, speaker diarization, and automatic speech recognition (ASR) in the last decade, it has become possible to build pipelines that achieve reasonable error rates on this task. In this paper, we propose an end-to-end modular system for the LibriCSS meeting data, which combines independently trained separation, diarization, and recognition components, in that order. We study the effect of different state-of-the-art methods at each stage of the pipeline, and report results using task-specific metrics like SDR and DER, as well as downstream WER. Experiments indicate that the problem of overlapping speech for diarization and ASR can be effectively mitigated with the presence of a well-trained separation module. Our best system achieves a speaker-attributed WER of 12.7%, which is close to that of a non-overlapping ASR.
VocGAN: A High-Fidelity Real-Time Vocoder with a Hierarchically-Nested Adversarial Network

We present a novel high-fidelity real-time neural vocoder called VocGAN. A recently developed GAN-based vocoder, MelGAN, produces speech waveforms in real-time. However, it often produces a waveform that is insufficient in quality or inconsistent with acoustic characteristics of the input mel spectrogram. VocGAN is nearly as fast as MelGAN, but it significantly improves the quality and consistency of the output waveform. VocGAN applies a multi-scale waveform generator and a hierarchically-nested discriminator to learn multiple levels of acoustic properties in a balanced way. It also applies the joint conditional and unconditional objective, which has shown successful results in high-resolution image synthesis. In experiments, VocGAN synthesizes speech waveforms 416.7x faster on a GTX 1080Ti GPU and 3.24x faster on a CPU than real-time. Compared with MelGAN, it also exhibits significantly improved quality in multiple evaluation metrics including mean opinion score (MOS) with minimal additional overhead. Additionally, compared with Parallel WaveGAN, another recently developed high-fidelity vocoder, VocGAN is 6.98x faster on a CPU and exhibits higher MOS.
Few-Shot Learning for Opinion Summarization

Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product.The task is practically important and has attracted a lot of attention.However, due to the high cost of summary production, datasets large enough for training supervised models are lacking.Instead, the task has been traditionally approached with extractive methods that learn to select text fragments in an unsupervised or weakly-supervised way.Recently, it has been shown that abstractive summaries, potentially more fluent and better at reflecting conflicting information, can also be produced in an unsupervised fashion.However, these models, not being exposed to actual summaries, fail to capture their essential properties.In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation.We start by training a conditional Transformer language model to generate a new product review given other available reviews of the product.The model is also conditioned on review properties that are directly related to summaries; the properties are derived from reviews with no manual effort.In the second stage, we fine-tune a plug-in module that learns to predict property values on a handful of summaries.This lets us switch the generator to the summarization mode.We show on Amazon and Yelp datasets that our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.