Reviewerly/exHarmony huggingface dataset:
An overview of deep learning in medical imaging

Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential growth in recent years. The scientific community has focused its attention on DL due to its versatility, high performance, high generalization capacity, and multidisciplinary uses, among many other qualities. In addition, a large amount of medical data and the development of more powerful computers has also fostered an interest in this area. This paper presents an overview of current deep learning methods, starting from the most straightforward concept but accompanied by the mathematical models that are behind the functionality of this type of intelligence. In the first instance, the fundamental concept of artificial neural networks is introduced, progressively covering convolutional structures, recurrent networks, attention models, up to the current structure known as the Transformer. Secondly, all the basic concepts involved in training and other common elements in the design of the architectures are introduced. Thirdly, some of the key elements in modern networks for medical image classification and segmentation are shown. Subsequently, a review of some applications realized in the last years is shown, where the main features related to DL are highlighted. Finally, the perspectives and future expectations of deep learning are presented.
A self-supervised domain-general learning framework for human ventral stream representation

Abstract Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find that category information implicitly emerges in the local similarity structure of this feature space. Further, these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world.
A critical review of robot research and future research opportunities: adopting a service ecosystem perspective

Purpose Given the recent growth of service robot research in hospitality and tourism management (HTM), the purpose of this study is to identify a research agenda by conducting a systematic and holistic review of service robot research published in both HTM and broader business management (BM) journals. Design/methodology/approach Adopting a service ecosystem perspective, 38 HTM articles and 13 highly cocited BM articles out of 126 BM articles were qualitatively reviewed to analyze the intellectual structures and foundations of robotics research. Findings The relationships between service robots and the four multilevel actors of the service ecosystem were analyzed: the consumer, employee, management and society. Twenty-eight specific research questions were proposed for the robotics-customer relationship, robotics-employee relationship, robotics-management relationship and robotics-society relationship. Research limitations/implications This study contributes to understanding the intellectual structures and evolution of rapidly growing HTM robotics research in terms of the holistic relationships among the four service ecosystem actors of robotics. Future research needs to identify other actors and their activities to examine the service ecosystem of robotics. Originality/value This study provides a pathway for future hospitality and tourism research by helping to focus on important robotics issues and further develop the theoretical and empirical knowledge of robotics. This work informs practitioners of key issues associated with the industrial adoption of robots.
Split Multiplicative Multi-View Subspace Clustering

Various subspace clustering methods have been successively developed to process multi-view datasets. Most of the existing methods try to obtain a consensus structure coefficient matrix based on view-specific subspace recoveries. However, since view-specific structures contain individualized components that are intrinsically different from the consensus structure, directly adopting view-specific subspace structures might not be a reasonable choice. In this paper, with this concern in mind, our goal is to seek novel strategies to extract valuable components from view-specific structures that are consistent with the consensus subspace structure. To this end, we propose a novel multi-view subspace clustering method named split multiplicative multi-view subspace clustering (SM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SC) with the joint strength of a multiplicative decomposition scheme and a variable splitting scheme. Specifically, the multiplicative decomposition scheme effectively guarantees the structural consistency of the extracted components. Then, the variable splitting scheme takes a step further via extracting the structural consistent components from view-specific structures. Furthermore, an alternating optimization algorithm is proposed to optimize the resulting optimization problem, which is non-convex and constrained. We prove that this algorithm could converge to a critical point. Finally, we provide empirical studies on real-world datasets that speak to the practical efficacy of our proposed method. The source code is released on GitHub https://github.com/joshuaas/SM2SC.
Chip-based measurement-device-independent quantum key distribution

Modern communication strives towards provably secure systems which can be widely deployed. Quantum key distribution provides a methodology to verify the integrity and security of a key exchange based on physical laws. However, physical systems often fall short of theoretical models, meaning they can be compromised through uncharacterized side-channels. The complexity of detection means that the measurement system is a vulnerable target for an adversary. Here, we present secure key exchange up to 200 km while removing all side-channels from the measurement system. We use mass-manufacturable, monolithically integrated transmitters that represent an accessible, quantum-ready communication platform. This work demonstrates a network topology that allows secure equipment sharing which is accessible with a cost-effective transmitter, significantly reducing the barrier for widespread uptake of quantum-secured communication.
Real-Time Radar-Based Gesture Detection and Recognition Built in an Edge-Computing Platform

In this paper, a real-time signal processing frame-work based on a 60 GHz frequency-modulated continuous wave (FMCW) radar system to recognize gestures is proposed. In order to improve the robustness of the radar-based gesture recognition system, the proposed framework extracts a comprehensive hand profile, including range, Doppler, azimuth and elevation, over multiple measurement-cycles and encodes them into a feature cube. Rather than feeding the range-Doppler spectrum sequence into a deep convolutional neural network (CNN) connected with recurrent neural networks, the proposed framework takes the aforementioned feature cube as input of a shallow CNN for gesture recognition to reduce the computational complexity. In addition, we develop a hand activity detection (HAD) algorithm to automatize the detection of gestures in real-time case. The proposed HAD can capture the time-stamp at which a gesture finishes and feeds the hand profile of all the relevant measurement-cycles before this time-stamp into the CNN with low latency. Since the proposed framework is able to detect and classify gestures at limited computational cost, it could be deployed in an edge-computing platform for real-time applications, whose performance is notedly inferior to a state-of-the-art personal computer. The experimental results show that the proposed framework has the capability of classifying 12 gestures in real-time with a high F1-score.
Pyramid: A Layered Model for Nested Named Entity Recognition

This paper presents Pyramid, a novel layered model for Nested Named Entity Recognition (nested NER).In our approach, token or text region embeddings are recursively inputted into L flat NER layers, from bottom to top, stacked in a pyramid shape.Each time an embedding passes through a layer of the pyramid, its length is reduced by one.Its hidden state at layer l represents an l-gram in the input text, which is labeled only if its corresponding text region represents a complete entity mention.We also design an inverse pyramid to allow bidirectional interaction between layers.The proposed method achieves state-of-the-art F1 scores in nested NER on ACE-2004, ACE-2005, GENIA, and NNE, which are 80.27, 79.42, 77.78, and 93.70 with conventional embeddings, and 87.74, 86.34, 79.31, and 94.68 with pre-trained contextualized embeddings.In addition, our model can be used for the more general task of Overlapping Named Entity Recognition.A preliminary experiment confirms the effectiveness of our method in overlapping NER.
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains

Recently proposed methods for weakly-supervised semantic segmentation have achieved impressive performance in predicting pixel classes despite being trained with only image labels which lack positional information. Because image annotations are cheaper and quicker to generate, weak supervision is more practical than full supervision for training segmentation algorithms. These methods have been predominantly developed to solve the background separation and partial segmentation problems presented by natural scene images and it is unclear whether they can be simply transferred to other domains with different characteristics, such as histopathology and satellite images, and still perform well. This paper evaluates state-of-the-art weakly-supervised semantic segmentation methods on natural scene, histopathology, and satellite image datasets and analyzes how to determine which method is most suitable for a given dataset. Our experiments indicate that histopathology and satellite images present a different set of problems for weakly-supervised semantic segmentation than natural scene images, such as ambiguous boundaries and class co-occurrence. Methods perform well for datasets they were developed on, but tend to perform poorly on other datasets. We present some practical techniques for these methods on unseen datasets and argue that more work is needed for a generalizable approach to weakly-supervised semantic segmentation. Our full code implementation is available on GitHub: https://github.com/lyndonchan/wsss-analysis.
Variational-state quantum metrology

Abstract Quantum technologies exploit entanglement to enhance various tasks beyond their classical limits including computation, communication and measurements. Quantum metrology aims to increase the precision of a measured quantity that is estimated in the presence of statistical errors using entangled quantum states. We present a novel approach for finding (near) optimal states for metrology in the presence of noise, using variational techniques as a tool for efficiently searching the high-dimensional space of quantum states, which would be classically intractable. We comprehensively explore systems consisting of up to 9 qubits and find new highly entangled states that are not symmetric under permutations and non-trivially outperform previously known states up to a constant factor 2. We consider a range of environmental noise models; while passive quantum states cannot achieve a fundamentally superior scaling (as established by prior asymptotic results) we do observe a significant absolute quantum advantage. We finally outline a possible experimental setup for variational quantum metrology which can be implemented in near-term hardware.
The acceptability and uptake of smartphone tracking for COVID-19 in Australia

In response to the COVID-19 pandemic, many Governments are instituting mobile tracking technologies to perform rapid contact tracing. However, these technologies are only effective if the public is willing to use them, implying that their perceived public health benefits must outweigh personal concerns over privacy and security. The Australian federal government recently launched the ‘COVIDSafe’ app, designed to anonymously register nearby contacts. If a contact later identifies as infected with COVID-19, health department officials can rapidly followup with their registered contacts to stop the virus’ spread. The current study assessed attitudes towards three tracking technologies (telecommunication network tracking, a government app, and Apple and Google’s Bluetooth exposure notification system) in two representative samples of the Australian public prior to the launch of COVIDSafe. We compared these attitudes to usage of the COVIDSafe app after its launch in a further two representative samples of the Australian public. Using Bayesian methods, we find widespread acceptance for all tracking technologies, however, observe a large intention-behaviour gap between people’s stated attitudes and actual uptake of the COVIDSafe app. We consider the policy implications of these results for Australia and the world at large.
Digital proximity tracing on empirical contact networks for pandemic control

Abstract Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide.
IoT Inspector

The proliferation of smart home devices has created new opportunities for empirical research in ubiquitous computing, ranging from security and privacy to personal health. Yet, data from smart home deployments are hard to come by, and existing empirical studies of smart home devices typically involve only a small number of devices in lab settings. To contribute to data-driven smart home research, we crowdsource the largest known dataset of labeled network traffic from smart home devices from within real-world home networks. To do so, we developed and released IoT Inspector, an open-source tool that allows users to observe the traffic from smart home devices on their own home networks. Between April 10, 2019 and January 21, 2020, 5,404 users have installed IoT Inspector, allowing us to collect labeled network traffic from 54,094 smart home devices. At the time of publication, IoT Inspector is still gaining users and collecting data from more devices. We demonstrate how this data enables new research into smart homes through two case studies focused on security and privacy. First, we find that many device vendors, including Amazon and Google, use outdated TLS versions and send unencrypted traffic, sometimes to advertising and tracking services. Second, we discover that smart TVs from at least 10 vendors communicated with advertising and tracking services. Finally, we find widespread cross-border communications, sometimes unencrypted, between devices and Internet services that are located in countries with potentially poor privacy practices. To facilitate future reproducible research in smart homes, we will release the IoT Inspector data to the public.
Question Answering as an Automatic Evaluation Metric for News Article Summarization

Matan Eyal, Tal Baumel, Michael Elhadad. 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.
Time-series forecasting of Bitcoin prices using high-dimensional features: a machine learning approach

Bitcoin is a decentralized cryptocurrency, which is a type of digital asset that provides the basis for peer-to-peer financial transactions based on blockchain technology. One of the main problems with decentralized cryptocurrencies is price volatility, which indicates the need for studying the underlying price model. Moreover, Bitcoin prices exhibit non-stationary behavior, where the statistical distribution of data changes over time. This paper demonstrates high-performance machine learning-based classification and regression models for predicting Bitcoin price movements and prices in short and medium terms. In previous works, machine learning-based classification has been studied for an only one-day time frame, while this work goes beyond that by using machine learning-based models for one, seven, thirty and ninety days. The developed models are feasible and have high performance, with the classification models scoring up to 65% accuracy for next-day forecast and scoring from 62 to 64% accuracy for seventh–ninetieth-day forecast. For daily price forecast, the error percentage is as low as 1.44%, while it varies from 2.88 to 4.10% for horizons of seven to ninety days. These results indicate that the presented models outperform the existing models in the literature.
High-Speed Autonomous Drifting With Deep Reinforcement Learning

Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled precisely. We propose a robust drift controller without explicit motion equations, which is based on the latest model-free deep reinforcement learning algorithm soft actor-critic. The drift control problem is formulated as a trajectory following task, where the error-based state and reward are designed. After being trained on tracks with different levels of difficulty, our controller is capable of making the vehicle drift through various sharp corners quickly and stably in the unseen map. The proposed controller is further shown to have excellent generalization ability, which can directly handle unseen vehicle types with different physical properties, such as mass, tire friction, etc.
APWG: Phishing Activity Trends Report Q4 2018

Phishing remains one of the most pernicious forms of attack. The technical requirements are modest – no exotic zero-day vulnerabilities, no clever hacking techniques. And it leverages one of the hardest weaknesses to fix in the technology environment – human frailty.
Zero Shot Detection

As we move toward large-scale object detection, it is unrealistic to expect annotated training data, in the form of bounding box annotations around objects, for all object classes at sufficient scale; therefore, the methods capable of unseen object detection are required. We propose a novel zero-shot method based on training an end-to-end model that fuses semantic attribute prediction with visual features to propose object bounding boxes for seen and unseen classes. While we utilize semantic features during training, our method is agnostic to semantic information for unseen classes at test-time. Our method retains the efficiency and effectiveness of YOLOv2 for objects seen during training, while improving its performance for novel and unseen objects. The ability of the state-of-the-art detection methods to learn discriminative object features to reject background proposals also limits their performance for unseen objects. We posit that, to detect unseen objects, we must incorporate semantic information into the visual domain so that the learned visual features reflect this information and lead to improved recall rates for unseen objects. We test our method on PASCAL VOC and MS COCO dataset and observed significant improvements on the average precision of unseen classes.
Ant Colony Stream Clustering: A Fast Density Clustering Algorithm for Dynamic Data Streams

A data stream is a continuously arriving sequence of data and clustering data streams requires additional considerations to traditional clustering.A stream is potentially unbounded, data points arrive online and each data point can be examined only once.This imposes limitations on available memory and processing time.Furthermore, streams can be noisy and the number of clusters in the data and their statistical properties can change over time.This paper presents an online, bio-inspired approach to clustering dynamic data streams.The proposed ant colony stream clustering (ACSC) algorithm is a density-based clustering algorithm, whereby clusters are identified as high-density areas of the feature space separated by low-density areas.ACSC identifies clusters as groups of micro-clusters.The tumbling window model is used to read a stream and rough clusters are incrementally formed during a single pass of a window.A stochastic method is employed to find these rough clusters, this is shown to significantly speeding up the algorithm with only a minor cost to performance, as compared to a deterministic approach.The rough clusters are then refined using a method inspired by the observed sorting behavior of ants.Ants pick-up and drop items based on the similarity with the surrounding items.Artificial ants sort clusters by probabilistically picking and dropping microclusters based on local density and local similarity.Clusters are summarized using their constituent micro-clusters and these summary statistics are stored offline.Experimental results show that the clustering quality of ACSC is scalable, robust to noise and favorable to leading ant clustering and stream-clustering algorithms.It also requires fewer parameters and less computational time.