Reviewerly/exHarmony huggingface dataset:
A feasibility study of a new method to enhance the augmented reality navigation effect in mandibular angle split osteotomy

The advantage of augmented reality (AR) is the combination of virtual reality, three-dimensional display and real–time interaction. Traditional augmented reality navigation systems lack the solution for deep perception and display errors. This study is designed to compare the different situation between the traditional image guide pattern and deviation-reminder pattern in the model trials. The deviation-reminder pattern is adding the position and rotation deviation in the scene. The AR system can track the model and surgical tools at the same time. Five craniofacial surgeons and engineers participated in this study. The navigation points were ensured with the traditional image guide pattern and deviation-reminder pattern for each one. Then, the position error, orientation error and time requirement were recorded for statistical analyses in the study. There was a statistically significant difference between the deviation-reminder pattern and traditional image guide pattern for both engineers and surgeons in orientation error and navigation time (p < 0.05). Additionally, there were no significant differences between engineers and surgeons in errors (p = 0.117) and time requirements by the team (p = 0.913). Our results suggest that the deviation-reminder pattern is better than traditional image-guided pattern for both engineers and surgeons. In addition, the extra information can improve the performance of surgeons in navigating position assurance.
Hippocampal subfields segmentation in brain MR images using generative adversarial networks

Segmenting the hippocampal subfields accurately from brain magnetic resonance (MR) images is a challenging task in medical image analysis. Due to the small structural size and the morphological complexity of the hippocampal subfields, the traditional segmentation methods are hard to obtain the ideal segmentation result.In this paper, we proposed a hippocampal subfields segmentation method using generative adversarial networks. The proposed method can achieve the pixel-level classification of brain MR images by building an UG-net model and an adversarial model and training the two models against each other alternately. UG-net extracts local information and retains the interrelationship features between pixels. Moreover, the adversarial training implements spatial consistency among the generated class labels and smoothens the edges of class labels on segmented region.The evaluation has performed on the dataset obtained from center for imaging of neurodegenerative diseases (CIND) for CA1, CA2, DG, CA3, Head, Tail, SUB, ERC and PHG in hippocampal subfields, resulting in the dice similarity coefficient (DSC) of 0.919, 0.648, 0.903, 0.673, 0.929, 0.913, 0.906, 0.884 and 0.889 respectively. For the large subfields, such as Head and CA1 of hippocampus, the DSC was increased by 3.9% and 9.03% than state-of-the-art approaches, while for the smaller subfields, such as ERC and PHG, the segmentation accuracy was significantly increased 20.93% and 16.30% respectively.The results show the improvement in performance of the proposed method, compared with other methods, which include approaches based on multi-atlas, hierarchical multi-atlas, dictionary learning and sparse representation and CNN. In implementation, the proposed method provides better results in hippocampal subfields segmentation.
Online Distilling from Checkpoints for Neural Machine Translation

Hao-Ran Wei, Shujian Huang, Ran Wang, Xin-yu Dai, Jiajun Chen. 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.
Contribution of Artificial Intelligence to Risk Assessment of Railway Accidents

In the design, development, and operation of a rail transport system, all the actors involved use one or more safety methods to identify hazardous situations, the causes of hazards, potential accidents, and the severity of the consequences that would result. The main objective is to justify and ensure that the design architecture of the transportation system is safe and presents no particular risk to users or the environment. As part of this process of certification, domain experts are responsible for reviewing the safety of the system, and are being brought in to imagine new scenarios of potential accidents to ensure the exhaustiveness of such safety studies. One of the difficulties in this process is to determine abnormal scenarios that could lead to a particular potential accident. This is the fundamental point that motivated the present work, whose objective is to develop tools to assist certification experts in their crucial task of analyzing and evaluating railway safety. However, the type of reasoning (inductive, deductive, by analogy, etc.) used by certification experts as well as the very nature of the knowledge manipulated in this certification process (symbolic, subjective, evolutionary, empirical, etc.) justify that conventional computer solutions cannot be adopted; the use of artificial intelligence (AI) methods and techniques helps to understand the problem of safety analysis and certification of high-risk systems such as guided rail transport systems. To help experts in this complex process of evaluating safety studies, we decided to use AI techniques and in particular machine learning to systematize, streamline, and strengthen conventional approaches used for safety analysis and certification.
Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent

Sequential Minimal Optimization (SMO) is the traditional training algorithm for Support Vector Machines (SVMs). However, SMO does not scale well with the size of the training set. For that reason, Stochastic Gradient Descent (SGD) algorithms, which have better scalability, are a better option for massive data mining applications. Furthermore, even with the use of SGD, training times can become extremely large depending on the data set. For this reason, accelerators such as Field-programmable Gate Arrays (FPGAs) are used. This work describes an implementation in hardware, using FPGA, of a fully parallel SVM using Stochastic Gradient Descent. The proposed FPGA implementation of an SVM with SGD presents speedups of more than 10,000× relative to software implementations running on a quad-core processor and up to 319× compared to state-of-the-art FPGA implementations while requiring fewer hardware resources. The results show that the proposed architecture is a viable solution for highly demanding problems such as those present in big data analysis.
Real-Time Online Multi-Object Tracking in Compressed Domain

Recent online multi-object tracking (MOT) methods have achieved desirable tracking performance. However, the tracking speed of most existing methods is rather slow. Inspired from the fact that the adjacent frames are highly relevant and redundant, we divide the frames into key and non-key frames and track objects in the compressed domain. For the key frames, the RGB images are restored for detection and data association. To make data association more reliable, an appearance convolutional neural network (CNN) which can be jointly trained with the detector is proposed. For the non-key frames, the objects are directly propagated by a tracking CNN based on the motion information provided in the compressed domain. Compared with the state-of-the-art online MOT methods, our tracker is about $6\times $ faster while maintaining a comparable tracking performance.
DocFace+: ID Document to Selfie Matching

Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators. However, this process is slow, labor intensive and unreliable. As such, an automated system for matching ID document photographs to live face images (selfies <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ) in real time and with high accuracy is required. In this paper, we propose DocFace+ to meet this objective. We first show that gradient-based optimization methods converge slowly (due to the underfitting of classifier weights) when many classes have very few samples, a characteristic of existing ID-selfie datasets. To overcome this shortcoming, we propose a method, called dynamic weight imprinting, to update the classifier weights, which allows faster convergence and more generalizable representations. Next, a pair of sibling networks with partially shared parameters are trained to learn a unified face representation with domain-specific parameters. Cross-validation on an ID-selfie dataset shows that while a publicly available general face matcher (InsightFace) only achieves a true accept rate (TAR) of 88.78 ± 1.30% at a false accept rate of 0.01% on the problem, DocFace+ improves the TAR to 95.95 ± 0.54%. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Technically, the word “selfies” refers to self-captured photos from mobile phones. But here, we define “selfies” as any self-captured live face photos, including those from mobile phones and kiosks.
Lightweight and Privacy-Preserving Medical Services Access for Healthcare Cloud

With the popularity of cloud computing technology, the healthcare cloud system is becoming increasingly perfect, which reduces the time of disease diagnosis and brings great convenience to people’s lives. But meanwhile, the healthcare cloud system usually involves users’ privacy information, and there is still a challenge on how to ensure that the sensitive information of users is not disclosed. Attribute-based signature (ABS) is a very useful technique for the privacy protection of users and is very suitable for anonymous authentication and privacy access control. However, general ABS schemes usually contain heavy computation overhead in signing and verification phases, which is not conducive for resource-limited devices to access healthcare cloud system. To address the above issues, we propose a lightweight and privacy-preserving medical services access scheme based on multi-authority ABS for healthcare cloud, named LPP-MSA. By using online/offline signing and server-aided verification mechanisms, the proposed scheme can greatly reduce the calculation overhead. In addition, LPP-MSA achieves unforgeability and anonymity and can resist collision attack. The comparisons of computational cost and storage overhead between LPP-MSA and the other existing schemes show that LPP-MSA is more efficient in both signing and verification phases. Therefore, it could be well applied to the scenarios where users access the healthcare cloud system for large scale remote medical services via resource-constrained mobile devices.
An Extreme-Learning-Machine-Based Hyperspectral Detection Method of Insulator Pollution Degree

The on-line detection of insulator pollution degree of transmission lines is important to the prevention and control of flashover. This paper proposed a non-contact detection method of insulator pollution degree based on hyperspectral technique. Firstly, hyperspectral images of the samples with different pollution degrees were obtained by hyper-spectrometer. Secondly, after original hyperspectral images were corrected by black-and-white correction and multiplicative scatter correction, hyperspectral curves from the region of interest (ROI) of corrected images were obtained. Finally, a multiclassification model of extreme learning machine (ELM) was built to realize the pollution degree classification of test samples. The results show that the absorption peak, the position of reflection peak, amplitude and the change trend of the hyperspectral curve obviously change with different kinds of pollution on the surface of silicone rubber, whereas only the amplitude obviously changes with same kind of pollution on the surface of silicone rubber; and the ELM-classification model can accurately and rapidly classify the pollution degree, with the pollution degree classification accuracy of NaCl, CaSO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> and mixed NaCl-CaSO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> respectively reaching 95%, 97.5% and 97.5%; and finally The ELM model based on hyperspectral curves data of the artificial pollution samples can classify the surface of insulator umbrellas with different pollution degrees, and the classification accuracy of CaSO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> and mixed NaCl-CaSO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> samples respectively are 87.5% and 90%. Consequently, the results of this study prove that hyperspectral technique has considerable potential for the non-contact detection of insulator pollution degree.
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models

Generative models often use human evaluations to measure the perceived quality of their outputs. Automated metrics are noisy indirect proxies, because they rely on heuristics or pretrained embeddings. However, up until now, direct human evaluation strategies have been ad-hoc, neither standardized nor validated. Our work establishes a gold standard human benchmark for generative realism. We construct Human eYe Perceptual Evaluation (HYPE) a human benchmark that is (1) grounded in psychophysics research in perception, (2) reliable across different sets of randomly sampled outputs from a model, (3) able to produce separable model performances, and (4) efficient in cost and time. We introduce two variants: one that measures visual perception under adaptive time constraints to determine the threshold at which a model's outputs appear real (e.g. $250$ms), and the other a less expensive variant that measures human error rate on fake and real images sans time constraints. We test HYPE across six state-of-the-art generative adversarial networks and two sampling techniques on conditional and unconditional image generation using four datasets: CelebA, FFHQ, CIFAR-10, and ImageNet. We find that HYPE can track model improvements across training epochs, and we confirm via bootstrap sampling that HYPE rankings are consistent and replicable.
Is dependency distance experiencing a process of minimization? A diachronic study based on the State of the Union addresses

Dependency distance has been considered a valid measure of syntactic complexity, and it has been extensively investigated in various contexts. One interesting line of research focuses on the issue of dependency distance minimization. Studies have shown that dependency distance has been experiencing a process of minimization in order to adapt to the limited resource of human working memory. However, little is known of whether dependency distance also tends to minimize across a long span of time. If dependency distance tends to minimize, then it should be hypothesized that the minimization process may also occur across a long span of time. To this end, this study investigates the possible change of dependency distance in a diachronic dataset, i.e., the State of the Union addresses from 1790 to 2017. Results of the study confirm the hypothesis that dependency distance has been decreasing across the examined 200 years. Besides, the correlation of mean dependency distance and normalized dependency distance suggests that these two algorithms may serve well as measures of the same linguistic construct, i.e. syntactic complexity.
Cryptocurrencies in institutional investors’ portfolios: Evidence from industry stop-loss rules

Cryptocurrencies are characterized by very high volatility, which makes them unlikely candidates for institutional investors’ portfolios. Our paper examines whether the application of industry stop-loss rules makes investment in cryptocurrencies more feasible for institutional investors. We show that volatility and returns on investment in a single cryptocurrency or a portfolio of cryptoassets are nearly halved when stop-loss rules are in place. Moreover, our analysis shows that the survival rate of long positions in cryptocurrencies does not exceed 35%. We conclude that the application of stop-loss rules may help in managing the risk of investment in cryptoassets. Our results also highlight the importance of the development of cryptoasset-specific stop-loss rules to deal with the low survival rate.
Normalization of Indonesian-English Code-Mixed Twitter Data

Twitter is an excellent source of data for NLP researches as it offers a tremendous amount of textual data.However, processing tweet to extract meaningful information is very challenging, at least for two reasons: (i) using nonstandard words as well as informal writing manner, and (ii) code-mixing issues, which is combining multiple languages in single tweet conversation.Most of the previous works have addressed both issues in isolated different task.In this study, we work on normalization task in code-mixed Twitter data, more specifically in Indonesian-English language.We propose a pipeline that consists of four modules, i.e tokenization, language identification, lexical normalization, and translation.Another contribution is to provide a gold standard of Indonesian-English code-mixed data for each module.
Language, gender and parenthood online: negotiating motherhood in Mumsnet Talk

"Language, gender and parenthood online: negotiating motherhood in Mumsnet Talk." Journal of Gender Studies, 29(2), pp. 234–235
Automatic Waypoint Generation to Improve Robot Navigation Through Narrow Spaces

In domestic robotics, passing through narrow areas becomes critical for safe and effective robot navigation. Due to factors like sensor noise or miscalibration, even if the free space is sufficient for the robot to pass through, it may not see enough clearance to navigate, hence limiting its operational space. An approach to facing this is to insert waypoints strategically placed within the problematic areas in the map, which are considered by the robot planner when generating a trajectory and help to successfully traverse them. This is typically carried out by a human operator either by relying on their experience or by trial-and-error. In this paper, we present an automatic procedure to perform this task that: (i) detects problematic areas in the map and (ii) generates a set of auxiliary navigation waypoints from which more suitable trajectories can be generated by the robot planner. Our proposal, fully compatible with the robotic operating system (ROS), has been successfully applied to robots deployed in different houses within the H2020 MoveCare project. Moreover, we have performed extensive simulations with four state-of-the-art robots operating within real maps. The results reveal significant improvements in the number of successful navigations for the evaluated scenarios, demonstrating its efficacy in realistic situations.
Machine Learning based System for Prediction of Breast Cancer Severity

Breast cancer is one of the most common diseases and the leading cause of death to mostly females all over the world. Early detection can provide higher treatment efficiency and better healing chances. Even though mammography screening is handy in diagnosing breast cancer at an early stage, Computer-Aided Diagnosis (CAD) systems can help to reduce the cancer death-rate. Radiologists, physicians, and doctors, in general, make use of these CAD systems to diagnose, detect, analyze and make decisions whether the patient is benign or malignant. The present paper presents some data mining techniques used in the diagnosis of cancer such as Artificial Neuron Network (ANN), K-Nearest Neighbors (KNN), Binary Support Vector Machine (Binary SVM), and Decision Tree (DT). Within this framework, the database utilized is the Mammographic Mass dataset. This database contains data of probabilistic breast cancer patients and the advanced results by experts in the field. The paper adopts a confusion matrix for binary prediction as a method of data analysis. The present paper provides a comparison between the different Computer-Aided diagnosis systems techniques regarding accuracy, specificity, and sensitivity amidst many other criteria to find the most accurate alternative among ANN, KNN, Binary SVM, and DT.
Custos: Practical Tamper-Evident Auditing of Operating Systems Using Trusted Execution

System auditing is a central concern when investigating and responding to security incidents.Unfortunately, attackers regularly engage in anti-forensic activities after a breakin, covering their tracks from the system logs in order to frustrate the efforts of investigators.While a variety of tamper-evident logging solutions have appeared throughout the industry and the literature, these techniques do not meet the operational and scalability requirements of system-layer audit frameworks.In this work, we introduce CUSTOS, a practical framework for the detection of tampering in system logs.CUSTOS consists of a tamper-evident logging layer and a decentralized auditing protocol.The former enables the verification of log integrity with minimal changes to the underlying logging framework, while the latter enables near real-time detection of log integrity violations within an enterprise-class network.CUSTOS is made practical by the observation that we can decouple the costs of cryptographic log commitments from the act of creating and storing log events, without trading off security, leveraging features of off-the-shelf trusted execution environments.Supporting over one million events per second, we show that CUSTOS' tamper-evident logging protocol is three orders of magnitude (1000×) faster than prior solutions and incurs only between 2% and 7% runtime overhead over insecure logging on intensive workloads.Further, we show that CUSTOS' auditing protocol can detect violations in near realtime even in the presence of a powerful distributed adversary and with minimal (3%) network overhead.Our case study on a real-world APT attack scenario demonstrates that CUSTOS forces anti-forensic attackers into a "lose-lose" situation, where they can either be covert and not tamper with logs (which can be used for forensics), or erase logs but then be detected by CUSTOS.
Trajectories of Repeated Readmissions of Chronic Disease Patients: Risk Stratification, Profiling, and Prediction

The problem of recurrent, unplanned readmissions, where some patients return shortly after discharge from the hospital and are readmitted for the same or a related condition, has become a challenge worldwide due to care quality, health outcomes, and finan
An economic analysis of ransomware and its welfare consequences

We present in this work an economic analysis of ransomware, a relatively new form of cyber-enabled extortion. We look at how the illegal gains of the criminals will depend on the strategies they use, examining uniform pricing and price discrimination. We also explore the welfare costs to society of such strategies. In addition, we present the results of a pilot survey which demonstrate proof of concept in evaluating the costs of ransomware attacks. We discuss at each stage whether the different strategies we analyse have been encountered already in existing malware, and the likelihood of them being implemented in the future. We hope this work will provide some useful insights for predicting how ransomware may evolve in the future.