Reviewerly/exHarmony huggingface dataset:
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection

Personal health mention detection deals with predicting whether or not a given sentence is a report of a health condition.Past work mentions errors in this prediction when symptom words, i.e., names of symptoms of interest, are used in a figurative sense.Therefore, we combine a state-of-the-art figurative usage detection with CNN-based personal health mention detection.To do so, we present two methods: a pipeline-based approach and a feature augmentation-based approach.The introduction of figurative usage detection results in an average improvement of 2.21% F-score of personal health mention detection, in the case of the feature augmentation-based approach.This paper demonstrates the promise of using figurative usage detection to improve personal health mention detection.
Deep Modular Co-Attention Networks for Visual Question Answering

Visual Question Answering (VQA) requires a fine-grained and simultaneous understanding of both the visual content of images and the textual content of questions. Therefore, designing an effective `co-attention' model to associate key words in questions with key objects in images is central to VQA performance. So far, most successful attempts at co-attention learning have been achieved by using shallow models, and deep co-attention models show little improvement over their shallow counterparts. In this paper, we propose a deep Modular Co-Attention Network (MCAN) that consists of Modular Co-Attention (MCA) layers cascaded in depth. Each MCA layer models the self-attention of questions and images, as well as the guided-attention of images jointly using a modular composition of two basic attention units. We quantitatively and qualitatively evaluate MCAN on the benchmark VQA-v2 dataset and conduct extensive ablation studies to explore the reasons behind MCAN's effectiveness. Experimental results demonstrate that MCAN significantly outperforms the previous state-of-the-art. Our best single model delivers 70.63$\%$ overall accuracy on the test-dev set. Code is available at https://github.com/MILVLG/mcan-vqa.
iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images

Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation in aerial imagery that combines instance-level object detection and pixel-level segmentation tasks. In comparison to instance segmentation in natural scenes, aerial images present unique challenges e.g., a huge number of instances per image, large object-scale variations and abundant tiny objects. Our large-scale and densely annotated Instance Segmentation in Aerial Images Dataset (iSAID) comes with 655,451 object instances for 15 categories across 2,806 high-resolution images. Such precise per-pixel annotations for each instance ensure accurate localization that is essential for detailed scene analysis. Compared to existing small-scale aerial image based instance segmentation datasets, iSAID contains 15$\times$ the number of object categories and 5$\times$ the number of instances. We benchmark our dataset using two popular instance segmentation approaches for natural images, namely Mask R-CNN and PANet. In our experiments we show that direct application of off-the-shelf Mask R-CNN and PANet on aerial images provide suboptimal instance segmentation results, thus requiring specialized solutions from the research community. The dataset is publicly available at: this https URL
Robustness properties of Facebook's ResNeXt WSL models

We investigate the robustness properties of ResNeXt class image recognition models trained with billion scale weakly supervised data (ResNeXt WSL models). These models, recently made public by Facebook AI, were trained with ~1B images from Instagram and fine-tuned on ImageNet. We show that these models display an unprecedented degree of robustness against common image corruptions and perturbations, as measured by the ImageNet-C and ImageNet-P benchmarks. They also achieve substantially improved accuracies on the recently introduced "natural adversarial examples" benchmark (ImageNet-A). The largest of the released models, in particular, achieves state-of-the-art results on ImageNet-C, ImageNet-P, and ImageNet-A by a large margin. The gains on ImageNet-C, ImageNet-P, and ImageNet-A far outpace the gains on ImageNet validation accuracy, suggesting the former as more useful benchmarks to measure further progress in image recognition. Remarkably, the ResNeXt WSL models even achieve a limited degree of adversarial robustness against state-of-the-art white-box attacks (10-step PGD attacks). However, in contrast to adversarially trained models, the robustness of the ResNeXt WSL models rapidly declines with the number of PGD steps, suggesting that these models do not achieve genuine adversarial robustness. Visualization of the learned features also confirms this conclusion. Finally, we show that although the ResNeXt WSL models are more shape-biased than comparable ImageNet-trained models in a shape-texture cue conflict experiment, they still remain much more texture-biased than humans, suggesting that they share some of the underlying characteristics of ImageNet-trained models that make this benchmark challenging.
Polyp Tracking in Video Colonoscopy Using Optical Flow With an On-The-Fly Trained CNN

Colonoscopy has been widely applied as a common practice to inspect the inside of large bowel for colon cancer screening. However, missing polyps in such procedure could happen and thus preventing early disease detection and treatment. In this paper, we propose an algorithm for automatic polyp detection and localization in colonoscopy video. The method initially detects and localizes polyps based on single frame object detection or segmentation network such as U-Net. Then it utilizes optical flow to track polyps and fuse temporal information. To overcome tracking failure caused by motion effects, a motion regression model and an efficient on-the-fly trained CNN have been deployed. The proposed algorithm achieves the highest scores in both polyp detection task and polyp localization task in the MICCAI 2018 Endoscopic Vision Challenge on “Gastrointestinal Image Analysis”.
Forensic Detection Using Bit-Planes Slicing of Median Filtering Image

For the detection of median filtering (MF) forensics, this paper proposes the feature vector extracted from the bit-planes slicing of the forged image.The assembled feature vector is trained in a support vector machine (SVM) classifier for the MF detection (MFD) of the forged images.The performance of the proposed MFD scheme is measured with several types of forged images: unaltered, Gaussian filtering (3×3), averaging filtering (3 × 3), downscaling (0.9), upscaling (1.1), and post-frame-up, respectively, in a block size 32 × 32 and 64 × 64 pixels.Subsequently, in experimental items, a classification ratio, Area Under the Curve (AUC), P TP at P FP = 0.01, and Pe (a minimum average decision error) are estimated.The result in terms of AUC shows that the estimation of the proposed MFD scheme is graded as 'Excellent (A)'.
Detection of weak seismic sequences based on arrival time coherence and empiric network detectability: an application at a near fault observatory

SUMMARY Microseismic monitoring is a primary tool for understanding and tracking the progress of mechanical processes occurring in active rock fracture systems. In geothermal or hydrocarbon fields or along seismogenic fault systems, the detection and location of microseismicity facilitates resolution of the fracture system geometry and the investigation of the interaction between fluids and rocks, in response of stress field perturbations. Seismic monitoring aims to detect locate and characterize seismic sources. The detection of weak signals is often achieved at the cost of increasing the number of false detections, related to transient signals generated by a range of noise sources, or related to instrumental problems, ambient conditions or human activity that often affect seismic records. A variety of fast and automated methods has been recently proposed to detect and locate microseismicity based on the coherent detection of signal anomalies, such as increase in amplitude or coherent polarization, at dense seismic networks. While these methods proved to be very powerful to detect weak events and to reduce the magnitude of completeness, a major problem remains to discriminate among weak seismic signals produced by microseismicity and false detections. In this work, the microseimic data recorded along the Irpinia fault zone (Southern Apennines, Italy) are analysed to detect weak, natural earthquakes using one of such automated, migration-based, method. We propose a new method for the automatic discrimination of real vs false detections, which is based on empirical data and information about the detectability (i.e. detection capability) of the seismic network. Our approach allows obtaining high performances in detecting earthquakes without requiring a visual inspection of the seismic signals and minimizing analyst intervention. The proposed methodology is automated, self-updating and can be tuned at different success rates.
A serious game to explore human foraging in a 3D environment

Traditional search tasks have taught us much about vision and attention. Recently, several groups have begun to use multiple-target search to explore more complex and temporally extended "foraging" behaviour. Many of these new foraging tasks, however, maintain the simplified 2D displays and response demands associated with traditional, single-target visual search. In this respect, they may fail to capture important aspects of real-world search or foraging behaviour. In the current paper, we present a serious game for mobile platforms, developed in Unity3D, in which human participants play the role of an animal foraging for food in a simulated 3D environment. Game settings can be adjusted, so that, for example, custom target and distractor items can be uploaded, and task parameters, such as the number of target categories or target/distractor ratio are all easy to modify. We are also making the Unity3D project available, so that further modifications can also be made. We demonstrate how the app can be used to address specific research questions by conducting two human foraging experiments. Our results indicate that in this 3D environment, a standard feature/conjunction manipulation does not lead to a reduction in foraging runs, as it is known to do in simple, 2D foraging tasks.
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module

The ability to generalize quickly from few observations is crucial for intelligent systems. In this paper we introduce APL, an algorithm that approximates probability distributions by remembering the most surprising observations it has encountered. These past observations are recalled from an external memory module and processed by a decoder network that can combine information from different memory slots to generalize beyond direct recall. We show this algorithm can perform as well as state of the art baselines on few-shot classification benchmarks with a smaller memory footprint. In addition, its memory compression allows it to scale to thousands of unknown labels. Finally, we introduce a meta-learning reasoning task which is more challenging than direct classification. In this setting, APL is able to generalize with fewer than one example per class via deductive reasoning.
How Mobile Augmented Reality Is Applied in Education? A Systematic Literature Review

Mobile Augmented Reality (MAR) approaches have been applied as an instructional strategy in educational contexts.In this respect, it is important to identify how these approaches have been developed and evaluated with the purpose to identify their quality and benefits.The goal of this article is to identify the state-of-the-art on how the MAR has been used for educational purposes.Therefore, we performed a systematic literature review with a sample of 57 articles.Based on the selected studies, we analysed which knowledge areas the MAR approaches are used in the educational contexts.We also analysed how the approaches have been developed, and evaluated.As a result, we identify that the technical requirements used in the development of the MAR approaches are defined based on the expertise of the development team.Most evaluations are carried out without an appropriate scientific rigor in terms of research design.This shows that there is a need to more consistent and uniform patterns in terms of methods for systematically develop and evaluation of MAR approaches.And, thus, obtain valid results that can be used as a basis for a decision on the application of such approaches and/or their continuous improvement for educational purposes.
Assessing the Relationship between Cognitive Load and the Usability of a Mobile Augmented Reality Tutorial System: A Study of Gender Effects

In this study, the relationship between the usability of a mobile Augmented Reality (AR) tutorial system and cognitive load was examined. In this context, the relationship between perceived usefulness, the perceived ease of use, and the perceived natural interaction factors and intrinsic, extraneous, germane cognitive load were investigated. In addition, the effect of gender on this relationship was investigated. The research results show that there was a strong relationship between the perceived ease of use and the extraneous load in males, and there was a strong relationship between the perceived usefulness and the intrinsic load in females. Both the perceived usefulness and the perceived ease of use had a strong relationship with the germane cognitive load. Moreover, the perceived natural interaction had a strong relationship with the perceived usefulness in females and the perceived ease of use in males. This research will provide significant clues to AR software developers and researchers to help reduce or control cognitive load in the development of AR-based instructional software.
Fast Algorithm for K-Truss Discovery on Public-Private Graphs

In public-private graphs, users share one public graph and have their own private graphs. A private graph consists of personal private contacts that only can be visible to its owner, e.g., hidden friend lists on Facebook and secret following on Sina Weibo. However, existing public-private analytic algorithms have not yet investigated the dense subgraph discovery of k-truss, where each edge is contained in at least k-2 triangles. This paper aims at finding k-truss efficiently in public-private graphs. The core of our solution is a novel algorithm to update k-truss with node insertions. We develop a classification-based hybrid strategy of node insertions and edge insertions to incrementally compute k-truss in public-private graphs. Extensive experiments validate the superiority of our proposed algorithms against state-of-the-art methods on real-world datasets.
Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns

Many real-world problems, such as robot control and soccer game, are naturally modeled as sparse-interaction multi-agent systems. Reutilizing single-agent knowledge in multi-agent systems with sparse interactions can greatly accelerate the multi-agent learning process. Previous works rely on bisimulation metric to define Markov decision process (MDP) similarity for controlling knowledge transfer. However, bisimulation metric is costly to compute and is not suitable for high-dimensional state space problems. In this work, we propose more scalable transfer learning methods based on a novel MDP similarity concept. We start by defining the MDP similarity based on the N-step return (NSR) values of an MDP. Then, we propose two knowledge transfer methods based on deep neural networks called direct value function transfer and NSR-based value function transfer. We conduct experiments in image-based grid world, multi-agent particle environment (MPE) and Ms. Pac-Man game. The results indicate that the proposed methods can significantly accelerate multi-agent reinforcement learning and meanwhile get better asymptotic performance.
Building Trust in Deep Learning System towards Automated Disease Detection

Though deep learning systems have achieved high accuracy in detecting diseases from medical images, few such systems have been deployed in highly automated disease screening settings due to lack of trust in how well these systems can generalize to out-of-datasets. We propose to use uncertainty estimates of the deep learning system’s prediction to know when to accept or to disregard its prediction. We evaluate the effectiveness of using such estimates in a real-life application for the screening of diabetic retinopathy. We also generate visual explanation of the deep learning system to convey the pixels in the image that influences its decision. Together, these reveal the deep learning system’s competency and limits to the human, and in turn the human can know when to trust the deep learning system.
Universal Adversarial Audio Perturbations

We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios. We propose two methods for finding such perturbations. The first method is based on an iterative, greedy approach that is well-known in computer vision: it aggregates small perturbations to the input so as to push it to the decision boundary. The second method, which is the main contribution of this work, is a novel penalty formulation, which finds targeted and untargeted universal adversarial perturbations. Differently from the greedy approach, the penalty method minimizes an appropriate objective function on a batch of samples. Therefore, it produces more successful attacks when the number of training samples is limited. Moreover, we provide a proof that the proposed penalty method theoretically converges to a solution that corresponds to universal adversarial perturbations. We also demonstrate that it is possible to provide successful attacks using the penalty method when only one sample from the target dataset is available for the attacker. Experimental results on attacking various 1D CNN architectures have shown attack success rates higher than 85.0% and 83.1% for targeted and untargeted attacks, respectively using the proposed penalty method.
Modified RSA-based algorithm: a double secure approach

Security algorithms like RSA are becoming increasingly important for communications to provide companies, organizations, and users around the world, secure applications who rely heavily on them in their daily work. Security algorithms use different acquaintances among companies which might belong to various countries or even cities. Such data should essentially be encrypted to make sure that there is security in transportation. Thus, the current research paper leads to the novel system of security for the safe transfer of data. This paper examines the general principles of encryption and focuses on the development of RSA and the complexity of the encryption key so that it becomes more secure in the applications used. In this project, we will work on the RSA algorithm by adding some complexity to the 3keys (3k). This addition will increase the security and complexity of the algorithm's speed while maintaining encryption and decryption time. The paper also presents an approach by means of public key encryption to enhance cryptographic security. Moreover, double security is provided by the algorithm of RSA. This novel RSA algorithm was investigated in MATLAB. Numerical results for the various parameters such as Mean Square Error (MSE), correlation and Bit Error Ratio (BER) were implemented for the encryption of the message. The experimental results demonstrated that the proposed algorithm for 3 keys has small error rate in the retrieval of the encoded text.
A controlled protocol for asymmetric cyclic $(A\Rightarrow B\Rightarrow C\Rightarrow A)$ quantum state transfer between three parties

Cyclic teleportation scheme was introduced in a recent work by Chen et al (2017 Quantum Inf. Process. 16 201). Also certain protocols for asymmetric exchange of states have appeared in some recent works. In this paper, combining the above two concepts, we design a protocol for teleporting three different states cyclically between three parties. These three states are certain single-qubit, two-qubit and three-qubit states. The protocol is overseen by a controller. The communication scheme presented here falls in the category of quantum multi-tasking in which multiple tasks are performed using a single quantum resource.
A modern mold room: Meshing 3D surface scanning, digital design, and 3D printing with bolus fabrication

This case series represents an initial experience with implementing 3-dimensional (3D) surface scanning, digital design, and 3D printing for bolus fabrication for patients with complex surface anatomy where traditional approaches are challenging.For 10 patients requiring bolus in regions with complex contours, bolus was designed digitally from 3D surface scanning data or computed tomography (CT) images using either a treatment planning system or mesh editing software. Boluses were printed using a fused deposition modeling printer with polylactic acid. Quality assurance tests were performed for each printed bolus to verify density and shape.For 9 of 10 patients, digitally designed boluses were used for treatment with no issues. In 1 case, the bolus was not used because dosimetric requirements were met without the bolus. QA tests revealed that the bulk density was within 3% of the reference value for 9 of 12 prints, and with more judicious selection of print settings this could be increased. For these 9 prints, density uniformity was as good as or better than our traditional sheet bolus material. The average shape error of the pieces was less than 0.5 mm, and no issues with fit or comfort were encountered during use.This study demonstrates that new technologies such as 3D surface scanning, digital design and 3D printing can be safely and effectively used to modernize bolus fabrication.
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity Recognizers

Most state-of-the-art models for named entity recognition (NER) rely on the availability of large amounts of labeled data, making them challenging to extend to new, lowerresourced languages.However, there are now several proposed approaches involving either cross-lingual transfer learning, which learns from other highly resourced languages, or active learning, which efficiently selects effective training data based on model predictions.This paper poses the question: given this recent progress, and limited human annotation, what is the most effective method for efficiently creating high-quality entity recognizers in under-resourced languages?Based on extensive experimentation using both simulated and real human annotation, we find a dualstrategy approach best, starting with a crosslingual transferred model, then performing targeted annotation of only uncertain entity spans in the target language, minimizing annotator effort.Results demonstrate that cross-lingual transfer is a powerful tool when very little data can be annotated, but an entity-targeted annotation strategy can achieve competitive accuracy quickly, with just one-tenth of training data.The code is publicly available here.