Reviewerly/exHarmony huggingface dataset:
The Urban Last Mile Problem: Autonomous Drone Delivery to Your Balcony

Drone delivery has been a hot topic in the industry in the past few years. However, existing approaches either focus on rural areas or rely on centralized drop-off locations from where the last mile delivery is performed. In this paper we tackle the problem of autonomous last mile delivery in urban environments using an off-the-shelf drone. We build a prototype system that is able to fly to the approximate delivery location using GPS and then find the exact drop-off location using visual navigation. The drop-off location could, e.g., be on a balcony or porch, and simply needs to be indicated by a visual marker on the wall or window. We test our system components in simulated environments, including the visual navigation and collision avoidance. Finally, we deploy our drone in a real-world environment and show how it can find the drop-off point on a balcony. To stimulate future research in this topic we open source our code.
Toward an interpretable Alzheimer’s disease diagnostic model with regional abnormality representation via deep learning

In this paper, we propose a novel method for magnetic resonance imaging based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches into a unified framework. Specifically, we parcellate the brain into predefined regions based on anatomical knowledge (i.e., templates) and derive complex nonlinear relationships among voxels, whose intensities denote volumetric measurements, within each region. Unlike existing methods that use cubical or rectangular shapes, we consider the anatomical shapes of regions as atypical patches. Using complex nonlinear relationships among voxels in each region learned by deep neural networks, we extract a "regional abnormality representation." We then make a final clinical decision by integrating the regional abnormality representations over the entire brain. It is noteworthy that the regional abnormality representations allow us to interpret and understand the symptomatic observations of a subject with AD or MCI by mapping and visualizing these observations in the brain space. On the baseline MRI dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, our method achieves state-of-the-art performance for four binary classification tasks and one three-class classification task. Additionally, we conducted exhaustive experiments and analysis to validate the efficacy and potential of our method.
Can we PASS beyond the Field of View? Panoramic Annular Semantic Segmentation for Real-World Surrounding Perception

Pixel-wise semantic segmentation unifies distinct scene perception tasks in a coherent way, and has catalyzed notable progress in autonomous and assisted navigation, where a whole surrounding perception is vital. However, current mainstream semantic segmenters are normally benchmarked against datasets with narrow Field of View (FoV), and most vision-based navigation systems use only a forward-view camera. In this paper, we propose a Panoramic Annular Semantic Segmentation (PASS) framework to perceive the entire surrounding based on a compact panoramic annular lens system and an online panorama unfolding process. To facilitate the training of PASS models, we leverage conventional FoV imaging datasets, bypassing the effort entailed to create dense panoramic annotations. To consistently exploit the rich contextual cues in the unfolded panorama, we adapt our real-time ERF-PSPNet to predict semantically meaningful feature maps in different segments and fuse them to fulfill smooth and seamless panoramic scene parsing. Beyond the enlarged FoV,we extend focal length-related and style transfer-based data augmentations, to robustify the semantic segmenter against distortions and blurs in panoramic imagery. A comprehensive variety of experiments demonstrates the qualified robustness of our proposal for realworld surrounding understanding.
Crossbow

Deep learning models are trained on servers with many GPUs, and training must scale with the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel synchronous stochastic gradient descent: they process a batch of training data at a time, partitioned across GPUs, and average the resulting partial gradients to obtain an updated global model. To fully utilise all GPUs, systems must increase the batch size, which hinders statistical efficiency. Users tune hyper-parameters such as the learning rate to compensate for this, which is complex and model-specific. We describe Crossbow, a new single-server multi-GPU system for training deep learning models that enables users to freely choose their preferred batch size---however small---while scaling to multiple GPUs. Crossbow uses many parallel model replicas and avoids reduced statistical efficiency through a new synchronous training method. We introduce SMA, a synchronous variant of model averaging in which replicas independently explore the solution space with gradient descent, but adjust their search synchronously based on the trajectory of a globally-consistent average model. Crossbow achieves high hardware efficiency with small batch sizes by potentially training multiple model replicas per GPU, automatically tuning the number of replicas to maximise throughput. our experiments show that Crossbow improves the training time of deep learning models on an 8-GPU server by 1.3--4X compared to TensorFlow.
E-Mobility Systems Architecture: a model-based framework for managing complexity and interoperability

The future of e-mobility will consist of a large number of connected electric vehicles, smart charging stations and information systems at the intersection of electricity and mobility sector. When engineering and integrating the multitude of systems into even more complex systems-of-systems for e-mobility, interoperability and complexity handling are vital. Model-based system architectures support the engineering process of information systems with the concepts of abstraction, reduction and separation of concerns. In this paper, we contribute to the research body, by extracting requirements for managing complexity and interoperability of these systems. Further, a comparative analysis of the state-of-the-art in existing architecture models and frameworks for e-mobility is conducted. Based on the identified gaps in existing research, we propose the E-Mobility Systems Architecture (EMSA) Model, a three-dimensional systems architecture model for the e-mobility sector. Its structure originates from the well-established Smart Grid Architecture Model. We further allocate all relevant entities from the e-mobility sector to the EMSA dimensions, including a harmonized role model, functional reference architecture, component and systems allocation, as well as a mapping of data standards and communication protocols. The model then is validated qualitatively and quantitatively against the requirements with a case study approach. Our evaluation shows that the EMSA Model fulfills all requirements regarding the management of complexity and ensuring interoperability. From the case study, we further identify gaps in current data model standardization for e-mobility.
Convolutional relation network for skeleton-based action recognition

In the skeleton-based action recognition, mining information from the joints and limbs of human skeletons plays a key role. Previous studies treated the skeleton data as vectors and could not explicitly capture the joint interactions (e.g., RNN-based methods), or modeled the joint interactions in a local manner and may lose important cues without global response mapping (e.g., CNN and GCN (Graph Convolution Network) based methods). In this work, we address these problems by considering the potential relations of all the node pairs and edge pairs on the skeleton graphs. A dilation group-specific convolution module is proposed to aggregate relation messages of all the unit pairs on the skeleton graphs. By enumerating all the pair relations, the joint interactions could be learned explicitly and globally. It is then enhanced by introducing the attention pooling including temporal attention, spatial attention and channel attention. By stacking such several blocks, the relation messages of the node pairs are augmented by multi-layer propagation. Finally, the late fusion of four streams is used to combine the predictions of different inputs including node pairs, edge pairs and corresponding frame differences. The proposed method, termed conv-relation network, achieves competitive performance on two large scale datasets, NTU RGB+D and Kinetics.
Linear $$\ell $$-intersection pairs of codes and their applications

In this paper, a linear $$\ell $$-intersection pair of codes is introduced as a generalization of linear complementary pairs of codes. Two linear codes are said to be a linear $$\ell $$-intersection pair if their intersection has dimension $$\ell $$. Characterizations and constructions of such pairs of codes are given in terms of the corresponding generator and parity-check matrices. Linear $$\ell $$-intersection pairs of MDS codes over $${\mathbb {F}}_q$$ of length up to $$q+1$$ are given for all possible parameters. As an application, linear $$\ell $$-intersection pairs of codes are used to construct entanglement-assisted quantum error correcting codes. This provides a large number of new MDS entanglement-assisted quantum error correcting codes.
Distributional semantics as a source of visual knowledge

In PNAS Kim et al. (1) detail congenitally blind individuals’ extensive knowledge of the visual appearance of animals. This is exciting and important work speaking directly to long-standing questions about the role of direct perceptual experience in semantic knowledge. Despite lacking visual input, blind people show substantial alignment with one another and with sighted people in judging animal shape, skin texture, size, and, to a much lesser extent, color. Where does this knowledge come from? One possibility, advanced by the authors, is inferential reasoning. Knowing that birds have feathers and that ostriches are birds allows blind people to infer that ostriches have feathers despite never having seen an ostrich (or feathers). Another possibility is the distributional structure of language. The authors reject the “obvious idea … that blind individuals learn from sighted people’s verbal descriptions” on the grounds that the lowest … [↵][1]1To whom correspondence may be addressed. Email: mollyllewis{at}gmail.com. [1]: #xref-corresp-1-1
CODDLE: Code-Injection Detection With Deep Learning

Code Injection attacks such as SQL Injection and Cross-Site Scripting (XSS) are among the major threats for today's web applications and systems.This paper proposes CODDLE, a deep learning-based intrusion detection systems against web-based code injection attacks.CODDLE's main novelty consists in adopting a Convolutional Deep Neural Network and in improving its effectiveness via a tailored pre-processing stage which encodes SQL/XSS-related symbols into type/value pairs.Numerical experiments performed on real-world datasets for both SQL and XSS attacks show that, with an identical training and with a same neural network shape, CODDLE's type/value encoding improves the detection rate from a baseline of about 75% up to 95% accuracy, 99% precision, and a 92% recall value.
Super-resolution MRI and CT through GAN-CIRCLE

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are widely used for screening, diagnosis and imageguided therapeutics. Due to physical, technical and economical limitations, it is impossible for MRI and CT scanners to target ideal image resolution. Given the nominal imaging performance, how to improve image resolution has been a hot topic, and referred to as super-resolution research. As a promising method for super-resolution, over recent years deep learning has shown a great potential especially in deblurring natural images. In this paper, based on the neural network model termed as GAN-CIRCLE (Constrained by the Identical, Residual, Cycle Learning Ensemble), we adapt this neural network for achieving super-resolution for both MRI and CT. In this study, we demonstrate two-fold resolution enhancement for MRI and CT with the same network architecture.
A No-Reference Image Quality Assessment Metric by Multiple Characteristics of Light Field Images

Evaluation of light field image (LFI), especially micro-lens camera light field (LF), is a new and challenging work. The development of image quality assessment (IQA) metric of LFIs relies on the subjective quality assessment database. In this paper, we establish a perceptual quality assessment dataset consisting of 240 distorted images from 8 source images with five distortion types. Furthermore, a no-reference IQA metric is proposed by combining 2D and 3D characteristics of LFI with the Support Vector Regression (SVR) model. The performance of the proposed metric is demonstrated by comparing with some classical full reference IQA metrics both on the presented dataset and a third-party dataset. The experiment results show that our method has a better performance than others.
More Practical Single-Trace Attacks on the Number Theoretic Transform

Single-trace side-channel attacks are a considerable threat to implementations of classic public-key schemes. For lattice-based cryptography, however, this class of attacks is much less understood, and only a small number of previous works show attacks. Primas et al., for instance, present a single-trace attack on the Number Theoretic Transform (NTT), which is at the heart of many efficient lattice-based schemes. They, however, attack a variable-time implementation and also require a rather powerful side-channel adversary capable of creating close to a million multivariate templates. Thus, it was an open question if such an attack can be made practical while also targeting state-of-the-art constant-time implementations. In this paper, we answer this question positively. First, we introduce several improvements to the usage of belief propagation, which underlies the attack. And second, we change the target to encryption instead of decryption; this limits attacks to the recovery of the transmitted symmetric key, but in turn, increases attack performance. All this then allows successful attacks even when switching to univariate Hamming-weight templates. We evaluate the performance and noise resistance of our attack using simulations, but also target a real device. Concretely, we successfully attack an assembly-optimized constant-time Kyber implementation running on an ARM Cortex M4 microcontroller while requiring the construction of only 213 templates.
Addressing delayed feedback for continuous training with neural networks in CTR prediction

One of the challenges in display advertising is that the distribution of features and click through rate (CTR) can exhibit large shifts over time due to seasonality, changes to ad campaigns and other factors. The predominant strategy to keep up with these shifts is to train predictive models continuously, on fresh data, in order to prevent them from becoming stale. However, in many ad systems positive labels are only observed after a possibly long and random delay. These delayed labels pose a challenge to data freshness in continuous training: fresh data may not have complete label information at the time they are ingested by the training algorithm. Naive strategies which consider any data point a negative example until a positive label becomes available tend to underestimate CTR, resulting in inferior user experience and suboptimal performance for advertisers. The focus of this paper is to identify the best combination of loss functions and models that enable large-scale learning from a continuous stream of data in the presence of delayed labels. In this work, we compare 5 different loss functions, 3 of them applied to this problem for the first time. We benchmark their performance in offline settings on both public and proprietary datasets in conjunction with shallow and deep model architectures. We also discuss the engineering cost associated with implementing each loss function in a production environment. Finally, we carried out online experiments with the top performing methods, in order to validate their performance in a continuous training scheme. While training on 668 million in-house data points offline, our proposed methods outperform previous state-of-the-art by 3% relative cross entropy (RCE). During online experiments, we observed 55% gain in revenue per thousand requests (RPMq) against naive log loss.
Fake News Detection in Social Media using Blockchain

Blockchain technology has opened the gate of creating decentralized applications, where security is a big concern. Here, any transaction ever held is recorded permanently. Over the years, some non-reputable sources have been publishing fake and attractive news stories. Due to the lack of any regulatory systems, this news cannot be verified. Hence, these unreliable sources can publish whatever they want, and even in some cases, it makes chaos in society. In recent times due to the ease in internet availability and social media, inappropriate news can spread more quickly than ever before. In some cases, fake news is more attractive than the real one. Thus, people become misguided. Using the advantages of Blockchain's peer-to-peer network concepts, we will discuss a way to detect fake news in social media.
Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.
LSTM-CRF Neural Network With Gated Self Attention for Chinese NER

Named entity recognition (NER) is an essential part of natural language processing tasks. Chinese NER task is different from the many European languages due to the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually regarded as the first step of processing Chinese NER. However, the word-based NER models relying on CWS are more vulnerable to incorrectly segmented entity boundaries and the presence of out-of-vocabulary (OOV) words. In this paper, we propose a novel character-based Gated Convolutional Recurrent neural network with Attention called GCRA for Chinese NER task. In particular, we introduce a hybrid convolutional neural network with gating filter mechanism to capture local context information and a highway neural network after LSTM to select characters of interest. The additional gated self-attention mechanism is used to capture the global dependencies from different multiple subspaces and arbitrary adjacent characters. We evaluate the performance of our proposed model on three datasets, including SIGHAN bakeoff 2006 MSRA, Chinese Resume, and Literature NER dataset. The experiment results show that our model outperforms other state-of-the-art models without relying on any external resources like lexicons and multi-task joint training.
Exploration of Chinese Sign Language Recognition Using Wearable Sensors Based on Deep Belief Net

In this paper, deep belief net (DBN) was applied into the field of wearable-sensor based Chinese sign language (CSL) recognition. Eight subjects were involved in the study, and all of the subjects finished a five-day experiment performing CSL on a target word set consisting of 150 CSL subwords. During the experiment, surface electromyography (sEMG), accelerometer (ACC), and gyroscope (GYRO) signals were collected from the participants. In order to obtain the optimal structure of the network, three different sensor fusion strategies, including data-level fusion, feature-level fusion, and decision-level fusion, were explored. In addition, for the feature-level fusion strategy, two different feature sources, which are hand-crafted features and network generated features, and two different network structures, which are fully-connected net and DBN, were also compared. The result showed that feature level fusion could achieve the best recognition accuracy among the three fusion strategies, and feature-level fusion with network generated features and DBN could achieve the best recognition accuracy. The best recognition accuracy realized in this study was 95.1% for the user-dependent test and 88.2% for the user-independent test. The significance of the study is that it applied the deep learning method into the field of wearable sensors-based CSL recognition, and according to our knowledge it's the first study comparing human engineered features with the network generated features in the correspondent field. The results from the study shed lights on the method of using network-generated features during sensor fusion and CSL recognition.
Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density in the image plane. While useful for this purpose, this image-plane density has no immediate physical meaning because it is subject to perspective distortion. This is a concern in sequences acquired by drones because the viewpoint changes often. This distortion is usually handled implicitly by either learning scale-invariant features or estimating density in patches of different sizes, neither of which accounts for the fact that scale changes must be consistent over the whole scene. In this paper, we explicitly model the scale changes and reason in terms of people per square-meter. We show that feeding the perspective model to the network allows us to enforce global scale consistency and that this model can be obtained on the fly from the drone sensors. In addition, it also enables us to enforce physically-inspired temporal consistency constraints that do not have to be learned. This yields an algorithm that outperforms state-of-the-art methods in inferring crowd density from a moving drone camera especially when perspective effects are strong.