Reviewerly/exHarmony huggingface dataset:
Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network

Crowd counting has recently attracted increasing interest in computer vision but remains a challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet) for crowd counting, which focuses on generating high-quality density estimation maps. The major contributions are four-fold. First, we develop a new trellis architecture that incorporates multiple decoding paths to hierarchically aggregate features at different encoding stages, which can handle large variations of objects. Second, we design dense skip connections interleaved across paths to facilitate sufficient multi-scale feature fusions and to absorb the supervision information. Third, we propose a new combinatorial loss to enforce local coherence and spatial correlation in density maps. By distributedly imposing this combinatorial loss on intermediate outputs, gradient vanishing can be largely alleviated for better back-propagation and faster convergence. Finally, our TEDnet achieves new state-of-the art performance on four benchmarks, with an improvement up to 14% in terms of MAE.
NHL Pathological Image Classification Based on Hierarchical Local Information and GoogLeNet-Based Representations

Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL).There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result.The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998.The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images.
Temporal Convolution for Real-time Keyword Spotting on Mobile Devices

Keyword spotting (KWS) plays a critical role in enabling speech-based user interactions on smart devices. Recent developments in the field of deep learning have led to wide adoption of convolutional neural networks (CNNs) in KWS systems due to their exceptional accuracy and robustness. The main challenge faced by KWS systems is the trade-off between high accuracy and low latency. Unfortunately, there has been little quantitative analysis of the actual latency of KWS models on mobile devices. This is especially concerning since conventional convolution-based KWS approaches are known to require a large number of operations to attain an adequate level of performance. In this paper, we propose a temporal convolution for real-time KWS on mobile devices. Unlike most of the 2D convolution-based KWS approaches that require a deep architecture to fully capture both low- and high-frequency domains, we exploit temporal convolutions with a compact ResNet architecture. In Google Speech Command Dataset, we achieve more than \textbf{385x} speedup on Google Pixel 1 and surpass the accuracy compared to the state-of-the-art model. In addition, we release the implementation of the proposed and the baseline models including an end-to-end pipeline for training models and evaluating them on mobile devices.
Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.
A Substructural Logic for Inconsistent Mathematics

A logic for inconsistent mathematics must be strong enough to support reasoning in proofs, while weak enough to avoid paradoxes. We present a substructural logic intended to meet the needs of a working dialetheic mathematician—specifically, by adding a de Morgan negation to light linear logic, and extending the logic with a relevant conditional. The logic satisfies a deduction theorem. Soundness and completeness is established, showing in particular that contraction is invalidated. This opens the way for a robust naive set theory; we conclude by showing how the set theory provides a foundation for induction.
Distributed sampling, quantum communication witnesses, and measurement incompatibility

We study prepare-and-measure experiments where the sender (Alice) receives trusted quantum inputs but has an untrusted state-preparation device and the receiver (Bob) has a fully untrusted measurement device. A distributed-sampling task naturally arises in this scenario, where the goal is for Alice and Bob to reproduce the statistics of his measurements on her quantum inputs using a fixed communication channel. Their performance of this task can certify quantum communication (QC), and this is formalized by measurement-device-independent QC witnesses. Furthermore, we prove that QC can provide an advantage (over classical communication) for distributed sampling if and only if Bob's measurements are incompatible. This gives an operational interpretation to measurement incompatibility and motivates a generalized notion of it related to a subset of quantum states. Our findings have both fundamental and applied implications.
Digital figurations in the online trade of illicit drugs: A qualitative content analysis of darknet forums

This article analyses the factors of trust, logistics, and conflict on darknet markets (DNMs) and forums that are dedicated to selling illicit drugs. The analysis is conducted by utilising Elias (1978) theoretical concept of ‘figuration,’ which refers to the communicative constellations that eventuate between actors. A qualitative content analysis was applied to a sample collected from darknet forums related to darknet markets, independent forums with trade sections, and separate vendor shops. The main categories trust, logistics, and conflict are analysed, and certain subcategories are also explored, including operational security (opsec), vouching, and shilling. These terms are commonly used to describe the trading of drugs on the darknet. Users of DNMs and forums discuss vendors, their products, and security-related features. Vouching and shilling are attempts to strengthen trust, logistics are used to secure the trade, while conflicts reflected in forum discussion threads often arise when logistics fail. Logistics may fail due to different reasons and actors involved in the associated figurations. The (anticipated) presence of actors in disguise is crucial for all of the main categories. Notions of trust, logistics, and conflict express attempts to cope with or circumvent risks imposed on users of DNMs by other users (e.g., scamming) or law enforcement (e.g., the interception of packages). Due to the illicit nature of the trade, these notions have to be constantly negotiated through digital communication.
PaperRobot: Incremental Draft Generation of Scientific Ideas

We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively.
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices. To achieve this goal, we introduce a novel network reparameterization based on the Kronecker-factored eigenbasis (KFE), and then apply Hessian-based structured pruning methods in this basis. As opposed to existing Hessian-based pruning algorithms which do pruning in parameter coordinates, our method works in the KFE where different weights are approximately independent, enabling accurate pruning and fast computation. We demonstrate empirically the effectiveness of the proposed method through extensive experiments. In particular, we highlight that the improvements are especially significant for more challenging datasets and networks. With negligible loss of accuracy, an iterative-pruning version gives a 10$\times$ reduction in model size and a 8$\times$ reduction in FLOPs on wide ResNet32.
Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing

Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In Spider, a recently-released text-to-SQL dataset, new and complex DBs are given at test time, and so the structure of the DB schema can inform the predicted SQL query. In this paper, we present an encoder-decoder semantic parser, where the structure of the DB schema is encoded with a graph neural network, and this representation is later used at both encoding and decoding time. Evaluation shows that encoding the schema structure improves our parser accuracy from 33.8% to 39.4%, dramatically above the current state of the art, which is at 19.7%.
Improving Event Coreference Resolution by Learning Argument Compatibility from Unlabeled Data

Yin Jou Huang, Jing Lu, Sadao Kurohashi, Vincent Ng. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Combating Label Noise in Deep Learning Using Abstention

We introduce a novel method to combat label noise when training deep neural networks for classification. We propose a loss function that permits abstention during training thereby allowing the DNN to abstain on confusing samples while continuing to learn and improve classification performance on the non-abstained samples. We show how such a deep abstaining classifier (DAC) can be used for robust learning in the presence of different types of label noise. In the case of structured or systematic label noise -- where noisy training labels or confusing examples are correlated with underlying features of the data-- training with abstention enables representation learning for features that are associated with unreliable labels. In the case of unstructured (arbitrary) label noise, abstention during training enables the DAC to be used as an effective data cleaner by identifying samples that are likely to have label noise. We provide analytical results on the loss function behavior that enable dynamic adaption of abstention rates based on learning progress during training. We demonstrate the utility of the deep abstaining classifier for various image classification tasks under different types of label noise; in the case of arbitrary label noise, we show significant improvements over previously published results on multiple image benchmarks. Source code is available at https://github.com/thulas/dac-label-noise
LeTS-Drive: Driving in a Crowd by Learning from Tree Search

Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is an unsolved challenge for robotics.The robot vehicle must contend with a dynamic and partially observable environment, noisy sensors, and many agents.A principled approach is to formalize it as a Partially Observable Markov Decision Process (POMDP) and solve it through online belief-tree search.To handle a large crowd and achieve realtime performance in this very challenging setting, we propose LeTS-Drive, which integrates online POMDP planning and deep learning.It consists of two phases.In the offline phase, we learn a policy and the corresponding value function by imitating the belief tree search.In the online phase, the learned policy and value function guide the belief tree search.LeTS-Drive leverages the robustness of planning and the runtime efficiency of learning to enhance the performance of both.Experimental results in simulation show that LeTS-Drive outperforms either planning or imitation learning alone and develops sophisticated driving skills.
Enlightenment Demands of Russians

The article presents the results of a large-scale survey conducted by Russian “Znanie” Society in 2017. The survey measured citizen awareness of local and national enlightenment initiatives, interest in specific topics and formats of educational events, motivation for learning and development, and willingness to pay for educational products, and it also tested the concept of an online enlightenment portal. We analyze the current globalization trends affecting the formats, topics and technology of enlightenment as well as educational niches that could be of public interest and of benefit to the digital future. Conclusions are made about the most in-demand fields of study and format preferences in learning and development.
A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&amp;E-Stained Tissues

Nuclei segmentation is an important but challenging task in the analysis of hematoxylin and eosin (H&E)-stained tissue sections. While various segmentation methods have been proposed, machine learning-based algorithms and in particular deep learning-based models have been shown to deliver better segmentation performance. In this work, we propose a novel approach to segment touching nuclei in H&E-stained microscopic images using U-Net-based models in two sequential stages. In the first stage, we perform semantic segmentation using a classification U-Net that separates nuclei from the background. In the second stage, the distance map of each nucleus is created using a regression U-Net. The final instance segmentation masks are then created using a watershed algorithm based on the distance maps. Evaluated on a publicly available dataset containing images from various human organs, the proposed algorithm achieves an average aggregate Jaccard index of 56.87%, outperforming several state-of-the-art algorithms applied on the same dataset.
Common digital space of scientific knowledge

The article attempts a comprehensive analysis of the concept and phenomenon of the common digital space of scientific knowledge (CDSSK) as a narrower wording of the concept of the common Russian electronic space of knowledge, which is proposed in a number of government documents. Definitions of basic concepts are proposed in relation to this phenomenon. Goals, objectives, composition, and structure are considered to be the boundaries and the principles of creating CDSSK. A brief overview of the existing scientific information resources and services that have the features of the elements of space of scientific knowledge follows. It is concluded that the existing information resources can be sources of CDSSK but not its components. The paper proposes a structure for CDSSK, including static and dynamic components (base and add-in), various levels of knowledge representation, and core and periphery. This should include formalized knowledge over which a set of operations and the periphery – the sources of this knowledge (including documents) – are selected on the basis of quality. The logical structure of CDSSK must submit its ontology, formed as it ought to be on the basis of information languages and metadata of existing scientific information resources. A number of steps are proposed to organize the work of creating CDSSK, as well as its content and support.
Quantification of Student Radiographic Patient Positioning Using an Immersive Virtual Reality Simulation

Introduction Immersive virtual reality (VR) simulation environments facilitate novel ways for users to visualize anatomy and quantify performance relative to expert users. The ability of software to provide positional feedback before a practitioner progresses with subsequent stages of examinations has broad implications for primary and allied healthcare professionals, particularly with respect to health and safety (eg, exposing to x-rays). The effect of training student-radiographers (radiology technicians), with a VR simulation environment was quantitatively assessed. Methods Year 1 radiography students (N = 76) were randomly split into 2 cohorts, each of which were trained at performing the same tasks relating to optimal hand positioning for projection x-ray imaging; group 1 was trained using the CETSOL VR Clinic software, whereas group 2 was trained using conventional simulated role-play in a real clinical environment. All participants completed an examination 3 weeks after training. The examination required both posterior-anterior and oblique hand x-ray positioning tasks to be performed on a real patient model. The analysis of images from the examination enabled quantification of the students' performance. The results were contextualized through a comparison with 4 expert radiographers. Results Students in group 1 performed on average 36% ( P &lt; 0.001) better in relation to digit separation, 11% ( P ≤ 0.001) better in terms of palm flatness and 23% ( P &lt; 0.05) better in terms of central ray positioning onto the third metacarpal. Conclusion A significant difference in patient positioning was evident between the groups; the VR clinic cohort demonstrated improved patient positioning outcomes. The observed improvement is attributed to the inherent task deconstruction and variety of visualization mechanisms available in immersive VR environments.
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks

Light field imaging has recently known a regain of interest due to the availability of practical light field capturing systems that offer a wide range of applications in the field of computer vision. However, capturing high-resolution light fields remains technologically challenging since the increase in angular resolution is often accompanied by a significant reduction in spatial resolution. This paper describes a learning-based spatial light field super-resolution method that allows the restoration of the entire light field with consistency across all angular views. The algorithm first uses optical flow to align the light field and then reduces its angular dimension using low-rank approximation. We then consider the linearly independent columns of the resulting low-rank model as an embedding, which is restored using a deep convolutional neural network (DCNN). The super-resolved embedding is then used to reconstruct the remaining views. The original disparities are restored using inverse warping where missing pixels are approximated using a novel light field inpainting algorithm. Experimental results show that the proposed method outperforms existing light field super-resolution algorithms, achieving PSNR gains of 0.23 dB over the second best performing method. The performance is shown to be further improved using iterative back-projection as a post-processing step.
Distant Supervision for Relation Extraction with Linear Attenuation Simulation and Non-IID Relevance Embedding

Distant supervision for relation extraction is an efficient method to reduce labor costs and has been widely used to seek novel relational facts in large corpora, which can be identified as a multi-instance multi-label problem. However, existing distant supervision methods suffer from selecting important words in the sentence and extracting valid sentences in the bag. Towards this end, we propose a novel approach to address these problems in this paper. Firstly, we propose a linear attenuation simulation to reflect the importance of words in the sentence with respect to the distances between entities and words. Secondly, we propose a non-independent and identically distributed (non-IID) relevance embedding to capture the relevance of sentences in the bag. Our method can not only capture complex information of words about hidden relations, but also express the mutual information of instances in the bag. Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method.
Cheaper Private Set Intersection via Differentially Private Leakage

Abstract In this work we demonstrate that allowing differentially private leakage can significantly improve the concrete performance of secure 2-party computation (2PC) protocols. Specifically, we focus on the private set intersection (PSI) protocol of Rindal and Rosulek (CCS 2017), which is the fastest PSI protocol with security against malicious participants. We show that if differentially private leakage is allowed, the cost of the protocol can be reduced by up to 63%, depending on the desired level of differential privacy. On the technical side, we introduce a security model for differentially-private leakage in malicious-secure 2PC. We also introduce two new and improved mechanisms for “differentially private histogram overestimates,” the main technical challenge for differentially-private PSI.