Reviewerly/exHarmony huggingface dataset:
Predicting the readability of physicians’ secure messages to improve health communication using novel linguistic features: Findings from the ECLIPPSE study

Low literacy skills impact important aspects of communication, including health-related information exchanges. Unsuccessful communication on the part of physician or patient contributes to lower quality of care, is associated with poorer chronic disease control, jeopardizes patient safety and can lead to unfavorable healthcare utilization patterns. To date, very little research has focused on digital communication between physicians and patients, such as secure messages sent via electronic patient portals.The purpose of the current study is to develop an automated readability formula to better understand what elements of physicians' digital messages make them more or less difficult to understand. The formula is developed using advanced natural language processing (NLP) to predict human ratings of physician text difficulty.The results indicate that NLP indices that capture a diverse set of linguistic features predict the difficulty of physician messages better than classic readability tools such as Flesch Kincaid Grade Level. Our results also provide information about the textual features that best explain text readability.Implications for how the readability formula could provide feedback to physicians to improve digital health communication by promoting linguistic concordance between physician and patient are discussed.
Guaranteed set-membership state estimation of an octorotor's position for radar applications

In the context of state estimation of dynamical systems subject to bounded perturbations and measurement noises, this paper proposes an application of a guaranteed ellipsoidal-based set-membership state estimation technique to estimate the linear position of an octorotor used for radar applications. The size of the ellipsoidal set containing the real state is minimized at each sample time taking into account the measurements performed by the drone's sensors. Three case studies highlight the efficiency of the estimation technique in finding guaranteed bounds for the octorotor's linear position. The computed guaranteed bounds in the linear trajectory are exploited to find the maximum operating frequency of the radar, a necessary information in radar applications.
Selective blockchain system for secure and efficient D2D communication

With the advent of a hyper-connected society, expectations for blockchain technology and its potential have increased. Blockchain technology is emerging as a method to solve security vulnerabilities in the IoT and mobile devices that are already used in various industrial fields. However, the security problem is still one of the most critical issues. The conventional blockchain has a problem in which the performance decreases as the number of participants increases because all of the users participate in the verification. As a result, it is not suitable for traditional IoT and mobile environments in which resources are limited. This work presents an approach of applying a blockchain system in a D2D (Device-to-Device) environment and proposes solutions for the problems of conventional mobile device communication and blockchain consensus algorithms. After a blockchain system in a wireless network environment is separated into an off-blockchain and an on-blockchain, the divided blockchains are processed in three steps. In the first stage of 0ff-blockchain, our proposal confirms the safety of IoT devices that transfer confidential data. In the next stage, the devices reduce the risk of eavesdropping from a third party by calculating the security capacity using the resources of the mobile devices which register for membership on a private blockchain. In the last stage, the devices join the blockchain consensus process. The reliability of the consensus and the performance of the system are improved through calculation of the real participation probability of the participants by the communication elements and by determining whether each node is a selfish participant. As a result, our work proposes a consensus algorithm suitable for D2D communication and a SBC(Selective Blockchain) system considering environmental characteristics.
Mech-Elites: Illuminating the Mechanic Space of GVG-AI

This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate the mechanic space for four different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals. With this system, we can generate playable levels that contain different combinations of most of the possible mechanics. These levels can later be used to populate game tutorials that teach players how to use the mechanics of the game.
Sequence labelling and sequence classification with gaze: Novel uses of eye‐tracking data for Natural Language Processing

Abstract Eye‐tracking data from reading provide a structured signal with a fine‐grained temporal resolution which closely follows the sequential structure of the text. It is highly correlated with the cognitive load associated with different stages of human, cognitive text processing. While eye‐tracking data have been extensively studied to understand human cognition, it has only recently been considered for Natural Language Processing (NLP). In this review, we provide a comprehensive overview of how gaze data are being used in data‐driven NLP, in particular for sequence labelling and sequence classification tasks. We argue that eye‐tracking may effectively counter one of the core challenges of machine‐learning‐based NLP: the scarcity of annotated data. We outline the recent advances in gaze‐augmented NLP to discuss how the gaze signal from human readers can be leveraged while also considering the potentials and limitations of this data source.
Video representation and suspicious event detection using semantic technologies

Storage and analysis of video surveillance data is a significant challenge, requiring video interpretation and event detection in the relevant context. To perform this task, the low-level features including shape, texture, and color information are extracted and represented in symbolic forms. In this work, a methodology is proposed, which extracts the salient features and properties using machine learning techniques and represent this information as Linked Data using a domain ontology that is explicitly tailored for detection of certain activities. An ontology is also developed to include concepts and properties which may be applicable in the domain of surveillance and its applications. The proposed approach is validated with actual implementation and is thus evaluated by recognizing suspicious activity in an open parking space. The suspicious activity detection is formalized through inference rules and SPARQL queries. Eventually, Semantic Web Technology has proven to be a remarkable toolchain to interpret videos, thus opening novel possibilities for video scene representation, and detection of complex events, without any human involvement. The proposed novel approach can thus have representation of frame-level information of a video in structured representation and perform event detection while reducing storage and enhancing semantically-aided retrieval of video data.
Two-stream encoder–decoder network for localizing image forgeries

This paper proposes a novel two-stream encoder–decoder network that utilizes both the high-level and the low-level image features for precisely localizing forged regions in a manipulated image. This is motivated by the fact that the forgery creation process generally introduces both the high-level artefacts (e.g., unnatural contrast) and the low-level artefacts (e.g., noise inconsistency) to the forged images. In the proposed two-stream network, one stream learns the low-level manipulation-related features in the encoder side by extracting noise residuals through a set of high-pass filters in the first layer. In the second stream, the encoder learns the high-level image manipulation features from the input image RGB values. The coarse feature maps each encoder are upsampled by the corresponding decoder network to produce the dense feature maps. The dense feature maps of the two streams are concatenated and fed to a final convolutional layer with sigmoidal activation to produce the pixel-wise prediction. We have carried out experimental analyses on multiple standard forensics datasets to evaluate the performance of the proposed method. The experimental results show the efficacy of the proposed method with respect to the state-of-the-art.
Ensemble Learning Using Bagging And Inception-V3 For Anomaly Detection In Surveillance Videos

The prevalent use of surveillance cameras in public places and advancements in computer vision warrants most sought-after research in the domain of anomalous activity detection. Several approaches have been proposed for the detection of an anomaly in videos. Spatio-temporal features using 3D Convolutional Network (C3D) is a state-of-the-art approach for this problem where deep multiple instance ranking framework is being investigated. However, this approach requires the segmentation of videos before feature extraction that can produce unstable segmentation results and can have a large memory footprint. In this paper, we extract video features using the Inception-v3 deep learning network which eliminates segmentation. Moreover, to improve the robustness of the backbone classifier we propose to use a homogeneous bagging ensemble of the 3-Layer Fully Connected (FC) Network. Experiments are conducted on the UCF-Anomaly detection dataset and exhibit improved performance over existing approaches.
Joint Multiclass Debiasing of Word Embeddings

Bias in Word Embeddings has been a subject of recent interest, along with efforts for its reduction. Current approaches show promising progress towards debiasing single bias dimensions such as gender or race. In this paper, we present a joint multiclass debiasing approach that is capable of debiasing multiple bias dimensions simultaneously. In that direction, we present two approaches, HardWEAT and SoftWEAT, that aim to reduce biases by minimizing the scores of the Word Embeddings Association Test (WEAT). We demonstrate the viability of our methods by debiasing Word Embeddings on three classes of biases (religion, gender and race) in three different publicly available word embeddings and show that our concepts can both reduce or even completely eliminate bias, while maintaining meaningful relationships between vectors in word embeddings. Our work strengthens the foundation for more unbiased neural representations of textual data.
Conquering the extensional scalability problem for value-flow analysis frameworks

Modern static analyzers often need to simultaneously check a few dozen or even hundreds of value-flow properties, causing serious scalability issues when high precision is required. A major factor to this deficiency, as we observe, is that the core static analysis engine is oblivious of the mutual synergy among the properties being checked, thus inevitably losing many optimization opportunities. Our work is to leverage the inter-property awareness and to capture redundancies and inconsistencies when many properties are considered at the same time. We have evaluated our approach by checking twenty value-flow properties in standard benchmark programs and ten real-world software systems. The results demonstrate that our approach is more than 8× faster than existing ones but consumes only 1/7 of the memory. Such substantial improvement in analysis efficiency is not achieved by sacrificing the effectiveness: at the time of writing, thirty-nine bugs found by our approach have been fixed by developers and four of them have been assigned CVE IDs due to their security impact.
Zoom in to the Details of Human-Centric Videos

Presenting high-resolution (HR) human appearance is always critical for the human-centric videos. However, current imagery equipment can hardly capture HR details all the time. Existing super-resolution algorithms barely mitigate the problem by only considering universal and low-level priors of image patches. In contrast, our algorithm is under bias towards the human body super-resolution by taking advantage of high-level prior defined by HR human appearance. Firstly, a motion analysis module extracts inherent motion pattern from the HR reference video to refine the pose estimation of the low-resolution (LR) sequence. Furthermore, a human body reconstruction module maps the HR texture in the reference frames onto a 3D mesh model. Consequently, the input LR videos get super-resolved HR human sequences are generated conditioned on the original LR videos as well as few HR reference frames. Experiments on an existing dataset and real-world data captured by hybrid cameras show that our approach generates superior visual quality of human body compared with the traditional method.
SiliconToaster: A Cheap and Programmable EM Injector for Extracting Secrets

Electromagnetic Fault Injection (EMFI) is considered as an effective fault injection technique for the purpose of conducting physical attacks against integrated circuits. It enables an adversary to inject errors on a circuit to gain knowledge of sensitive information or to bypass security features. The aim of this paper is to highlight the design and validation of SiliconToaster, which is a cheap and programmable platform for EM pulse injection. It has been designed using low-cost and accessible components that can be easily found. In addition, it can inject faults with a programmable voltage up to 1.2kV without the need to an external power supply as it is powered by the USB. The second part of the paper invests the SiliconToaster in order to bypass the firmware security protections of STM32F2 microcontroller. Two security levels were bypassed sequentially for the first time in a non-invasive way (without chip decapsulation).
Flow-Based Deformation Guidance for Unpaired Multi-contrast MRI Image-to-Image Translation

Image synthesis from corrupted contrasts increases the diversity of diagnostic information available for many neurological diseases. Recently the image-to-image translation has experienced significant levels of interest within medical research, beginning with the successful use of the Generative Adversarial Network (GAN) to the introduction of cyclic constraint extended to multiple domains. However, in current approaches, there is no guarantee that the mapping between the two image domains would be unique or one-to-one. In this paper, we introduce a novel approach to unpaired image-to-image translation based on the invertible architecture. The invertible property of the flow-based architecture assures a cycle-consistency of image-to-image translation without additional loss functions. We utilize the temporal information between consecutive slices to provide more constraints to the optimization for transforming one domain to another in unpaired volumetric medical images. To capture temporal structures in the medical images, we explore the displacement between the consecutive slices using a deformation field. In our approach, the deformation field is used as a guidance to keep the translated slides realistic and consistent across the translation. The experimental results have shown that the synthesized images using our proposed approach are able to archive a competitive performance in terms of mean squared error, peak signal-to-noise ratio, and structural similarity index when compared with the existing deep learning-based methods on three standard datasets, i.e. HCP, MRBrainS13 and Brats2019.
A Deformable CRF Model for Histopathology Whole-Slide Image Classification

To detect abnormality from histopathology images in a patch-based convolutional neural network (CNN), spatial context is an important cue. However, whole-slide image (WSI) is characterized by high morphological heterogeneity in the shape and scale of tissues, hence a simple visual span to a larger context may not well capture the information associated with the central patch or disease of interest. In this paper, we propose a Deformable Conditional Random Field (DCRF) model to learn the offsets and weights of neighboring patches in a spatial-adaptive manner. Additionally, rather than using regularly tessellated or overlapped patches, we localize patches with more powerful feature representations by the adaptively adjusted offsets in a WSI. Both the employment of DCRF for better feature extraction from spatial sampling patches, as well as utilization of the auto-generated patches as training input, can achieve performance improvement in the target task. This model is feasible to the widespread annotation strategies in histopathology images, either with a contoured region of interest (ROI) or patch-wise multi-tissue labels. The proposed model is validated on the patient cohorts from The Cancer Genome Atlas (TCGA) dataset and the Camelyon16 dataset for performance evaluation. The experimental results demonstrate the advantage of the proposed model in the classification task, by the comparison against the baseline models.
Controlled False Negative Reduction of Minority Classes in Semantic Segmentation

In semantic segmentation datasets, classes of high importance are oftentimes underrepresented, e.g., humans in street scenes. Neural networks are usually trained to reduce the overall number of errors, attaching identical loss to errors of all kinds. However, this is not necessarily aligned with human intuition. For instance, an overlooked pedestrian seems more severe than an incorrectly detected one. One possible remedy is to deploy different decision rules by introducing class priors that assign more weight to underrepresented classes. While reducing the false negatives of the underrepresented class, at the same time this leads to a considerable increase of false positive indications. In this work, we combine decision rules with methods for false positive detection. Therefore, we fuse false negative detection with uncertainty based false positive meta classification. We present the efficiency of our method for the semantic segmentation of street scenes on the Cityscapes dataset based on predicted instances of the "human" class. In the latter we employ an advanced false positive detection method using uncertainty measures aggregated over instances. We, thereby, achieve improved trade-offs between false negative and false positive samples of the underrepresented classes.
Graph-based non-maximal suppression for detecting products on the rack

Identification of stacked retail products from the images of racks of supermarket is a challenging computer vision problem. Regions with convolutional neural network features (R-CNN) generate (mostly overlapped) region proposals around the products on the rack. Subsequently, the region proposals are classified using a convolutional neural network. In the end, R-CNN implements a greedy non-maximal suppression (greedy-NMS) for disambiguating the overlapping proposals. Greedy-NMS discards the proposals (with lower classification scores) that are overlapped with the proposal with higher classification score. This greedy approach often eliminates the (geometrically) better fitted region proposals with (marginally) lower classification scores. This paper introduces a novel graph-based non-maximal suppression (G-NMS) that removes this critical bottleneck of greedy-NMS by looking not only at the classification scores but also at the product classes of the overlapping region proposals. G-NMS first determines the potential confidence scores (pc-scores) of the region proposals by defining the groups of overlapping regions. Subsequently, a directed acyclic graph (DAG) is strategically constructed with the proposals utilizing their pc-scores and overlapping groups. Eventually the maximum weighted path of the DAG provides the products that are present in the rack. The results of our extensive experiments confirm that the proposed scheme is better up to around 7% on one large In-house and three benchmark datasets of retail products. Additionally, the efficacy of our proposed GNMS is also analyzed on four benchmark datasets for detecting generic objects.
Multi-scale Gradational-Order Fusion Framework for Breast Lesions Classification Using Ultrasound Images

Predicting malignant potential of breast lesions based on breast ultrasound (BUS) images is crucial for computer-aided diagnosis (CAD) system for breast cancer. However, since breast lesions in BUS images have various shapes with relatively low contrast and the textures of breast lesions are often complex, it still remains challenging to predict the malignant potential of breast lesions. In this paper, a novel multi-scale gradational-order fusion (MsGoF) framework is proposed to make full advantages of features from different scale images for predicting malignant potential of breast lesions. Specifically, the multi-scale patches are first extracted from the annotated lesions in BUS images as the multi-channel inputs. Multi-scale features are then automatically learned and fused in several fusion blocks that armed with different fusion strategies to comprehensively capture morphological characteristics of breast lesions. To better characterize complex textures and enhance non-linear modeling capability, we further propose isotropous gradational-order feature module in each block to learn and combine different-order features. Finally, these multi-scale gradational-order features are utilized to perform prediction for malignant potential of breast lesions. The major advantage of our framework is embedding the gradational-order feature module into a fusion block, which is used to deeply integrate multi-scale features. The proposed model was evaluated on an open dataset by using 5-fold cross-validation. The experimental results demonstrate that the proposed MsGoF framework obtains the promising performance when compared with other deep learning-based methods.
Discriminative Clip Mining for Video Anomaly Detection

Real-world anomalous events are complicated, diverse, and rarely occurred. The main challenge to anomaly detection is to learn normal and anomalous patterns accurately. In this work, we propose discriminative clip mining for anomaly detection and classification: firstly, by introducing clip-level class activation mapping, an efficient Discriminative Anomalous Clip Miner (DACM) is developed to mine discriminative anomalous clips from a large number of normal ones; secondly, with the mined discriminative clips, an attentive ranking loss is designed to increase the anomalous instances hit rate of the traditional Multiple Instance Learning (MIL) model. Furthermore, by integrating the DACM and attentive MIL, one novel anomaly detection framework is proposed to learn more contrastive anomalous and normal patterns, and thus higher recognition performance can be achieved. Our experimental results on the widely-used UCF-Crime dataset show that, as compared to the state-of-the-art approaches, the proposed method achieves competitive performance both in anomaly detection and anomalous activity classification.