Reviewerly/exHarmony huggingface dataset:
Supervised line attention for tumor attribute classification from pathology reports: Higher performance with less data

We aim to build an accurate machine learning-based system for classifying tumor attributes from cancer pathology reports in the presence of a small amount of annotated data, motivated by the expensive and time-consuming nature of pathology report annotation. An enriched labeling scheme that includes the location of relevant information along with the final label is used along with a corresponding hierarchical method for classifying reports that leverages these enriched annotations.Our data consists of 250 colon cancer and 250 kidney cancer pathology reports from 2002 to 2019 at the University of California, San Francisco. For each report, we classify attributes such as procedure performed, tumor grade, and tumor site. For each attribute and document, an annotator trained by an oncologist labeled both the value of that attribute as well as the specific lines in the document that indicated the value. We develop a model that uses these enriched annotations that first predicts the relevant lines of the document, then predicts the final value given the predicted lines. We compare our model to multiple state-of-the-art methods for classifying tumor attributes from pathology reports.Our results show that across colon and kidney cancers and varying training set sizes, our hierarchical method consistently outperforms state-of-the-art methods. Furthermore, performance comparable to these methods can be achieved with approximately half the amount of labeled data.Document annotations that are enriched with location information are shown to greatly increase the sample efficiency of machine learning methods for classifying attributes of pathology reports.
Binarizing Super-Resolution Networks by Pixel-Correlation Knowledge Distillation

Convolutional neural networks (CNNs) have been widely used in single image super-resolution (SR) and obtained remarkable performance. However, most CNN-based SR models require heavy computation, which limits their real-world applications. In this paper, we address the computation problem of SR by network binarization, which converts the full-precision network into the binary network, thus intensively reducing computation. We propose the pixel-correlation distillation for SR network binarization, which distills the knowledge of pixel relationship from the original full-precision network to the binary network. In addition, we further reduce the quantization errors of the binary network by introducing trainable scaling factors to replace the fixed scaling factors in most existing binarization methods. We carry out extensive experiments on SRResNet [1] and VDSR [2], which are two commonly used SR networks. It is shown that the proposed method generates more visually pleasing SR images, and consistently outperforms other state-of-the-art methods in PSNR and SSIM.
Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science. Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence. Knowledge graphs (KGs) can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently. Here, we introduce a novel framework that can extract the COVID-19 public health evidence knowledge graph (CPHE-KG) from papers relating to a modelling study. We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process. We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset (CPHIE). We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++ based on the dataset. Leveraging the model on the new corpus, we construct CPHE-KG containing 60,967 entities and 51,140 relations. Finally, we seek to apply our KG to support evidence querying and evidence mapping visualization. Our SS-DYGIE++(SpanBERT) model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks. It has also shown high performance in the relation identification task. With evidence querying, our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions. The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic. Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.
Neural Event Semantics for Grounded Language Understanding

Abstract We present a new conjunctivist framework, neural event semantics (NES), for compositional grounded language understanding. Our approach treats all words as classifiers that compose to form a sentence meaning by multiplying output scores. These classifiers apply to spatial regions (events) and NES derives its semantic structure from language by routing events to different classifier argument inputs via soft attention. NES is trainable end-to-end by gradient descent with minimal supervision. We evaluate our method on compositional grounded language tasks in controlled synthetic and real-world settings. NES offers stronger generalization capability than standard function-based compositional frameworks, while improving accuracy over state-of-the-art neural methods on real-world language tasks.
Integrating Security Behavior into Attack Simulations

The increase of cyber-attacks raised security concerns for critical assets worldwide in the last decade. Leading to more efforts spent towards increasing the cyber security among companies and countries. For the sake of enhancing cyber security, representation and testing of attacks have prime importance in understanding system vulnerabilities. One of the available tools for simulating attacks on systems is the Meta Attack Language (MAL), which allows representing the effects of certain cyber-attacks. However, only understanding the component vulnerabilities is not enough in securing enterprise systems. Another important factor is the 'human', which constitutes the biggest 'insider threat'. For this, Security Behavior Analysis (SBA) helps understanding which system components that might be directly affected by the 'human'. As such, in this work, the authors present an approach for integrating user actions, so called "security behavior", by mapping SBA to a MAL-based language through MITRE ATT&CK techniques.
Leveraging Frequency Based Salient Spatial Sound Localization to Improve 360° Video Saliency Prediction

Virtual and augmented reality (VR/AR) systems dramatically gained in popularity with various application areas such as gaming, social media, and communication. It is therefore a crucial task to have the knowhow to efficiently utilize, store or deliver 360° videos for end-users. Towards this aim, researchers have been developing deep neural network models for 360° multimedia processing and computer vision fields. In this line of work, an important research direction is to build models that can learn and predict the observers' attention on 360° videos to obtain so-called saliency maps computationally. Although there are a few saliency models proposed for this purpose, these models generally consider only visual cues in video frames by neglecting audio cues from sound sources. In this study, an unsupervised frequency-based saliency model is presented for predicting the strength and location of saliency in spatial audio. The prediction of salient audio cues is then used as audio bias on the video saliency predictions of state-of-the-art models. Our experiments yield promising results and show that integrating the proposed spatial audio bias into the existing video saliency models consistently improves their performance.
Dual Image Deblurring Using Deep Image Prior

Blind image deblurring, one of the main problems in image restoration, is a challenging, ill-posed problem. Hence, it is important to design a prior to solve it. Recently, deep image prior (DIP) has shown that convolutional neural networks (CNNs) can be a powerful prior for a single natural image. Previous DIP-based deblurring methods exploited CNNs as a prior when solving the blind deburring problem and performed remarkably well. However, these methods do not completely utilize the given multiple blurry images, and have limitations of performance for severely blurred images. This is because their architectures are strictly designed to utilize a single image. In this paper, we propose a method called DualDeblur, which uses dual blurry images to generate a single sharp image. DualDeblur jointly utilizes the complementary information of multiple blurry images to capture image statistics for a single sharp image. Additionally, we propose an adaptive L2_SSIM loss that enhances both pixel accuracy and structural properties. Extensive experiments show the superior performance of our method to previous methods in both qualitative and quantitative evaluations.
High tech crime, high intellectual crime? Comparing the intellectual capabilities of cybercriminals, traditional criminals and non-criminals

In our highly digitalized society, cybercrime has become a common crime. However, because research into cybercriminals is in its infancy, our knowledge about cybercriminals is still limited. One of the main considerations is whether cybercriminals have higher intellectual capabilities than traditional criminals or even the general population. Although criminological studies clearly show that traditional criminals have lower intellectual capabilities, little is known about the relationship between cybercrime and intelligence. The current study adds to the literature by exploring the relationship between CITO-test scores and cybercrime in the Netherlands. The CITO final test is a standardized test for primary school students - usually taken at the age of 11 or 12 - and highly correlated with IQ-scores. Data from Statistics Netherlands were used to compare CITO-test scores of 143 apprehended cybercriminals with those of 143 apprehended traditional criminals and 143 non-criminals, matched on age, sex, and country of birth. Ordinary Least Squares regression analyses were used to compare CITO test scores between cybercriminals, traditional criminals, and non-criminals. Additionally, a discordant sibling design was used to control for unmeasured confounding by family factors. Findings reveal that cybercriminals have significantly higher CITO test scores compared to traditional criminals and significantly lower CITO test scores compared to non-criminals.
Face Forgery Detection Based On Segmentation Network

Recent progress in facial manipulation technologies have made it hard to distinguish the sophisticated face swapped images/videos. Due to the diversity of generation software and data sources, it is extremely challenging to devise an efficient generality framework. Instead of regarding the detection process as a vanilla binary classification task, we proposed a detection framework based on pixel-level classification. Considering that the acquisition of real pixel-level ground-truth is somehow expensive or even impractical, we proposed a pseudo ground-truth generation pipeline with prior knowledge of facial manipulation. Besides, we added a new module into the neural network to capture frequency clues, while the ablation experiment verified the effectiveness of this module. The experimental results on several public datasets demonstrated that our proposed framework is effective and superior to other existing similar detection networks.
The Assessment of Cognitive and Physical Well-Being Through Ambient Sensor Measures of Movement Towards Longitudinal Monitoring of Activities of Daily Living

Ubiquitous sensing technologies are a well recognized concept these days. An individual with a rudimentary understanding of technology intuitively understands that their cell phone can track their whereabouts, detect their activity and communicate with their home devices. With the increased capability of technology, measurements have become finer and communication between devices has become faster and more efficient [1]. With such sensitive, co-operative systems it seems natural to extend this technology to measures of health and particularly measures of health at home and in other enclosed environments. This paper examines the role of sensor systems in the assessment of cognitive and physical well-being by first establishing the connections between well-being, cognition and mobility before discussing the sensors used to measure these concepts.
Face Mask Detection on Real-World Webcam Images

The COVID-19 pandemic has been one of the biggest health crises in recent memory. According to leading scientists, face masks and maintaining six feet of social distancing are the most substantial protections to limit the virus's spread. Experimental data on face mask usage in the US is limited and has not been studied in scale. Thus, an understanding of population compliance with mask recommendations may be helpful in current and future pandemic situations. Knowledge of mask usage will help researchers answer many questions about the spread in various regions. One way to understand mask usage is by monitoring human behavior through publicly available webcams. Recently, researchers have come up with abundant research on face mask detection and recognition but their experiments are performed on datasets that do not reflect real-world complexity. In this paper, we propose a new webcam-based real-world face-mask detection dataset of over 1TB of images collected across different regions of the United States, and we implement state-of-the-art object detection algorithms to understand their effectiveness in such a real-world application.
Semi-Supervised Object Detection With Sparsely Annotated Dataset

When training an anchor-based object detector with a sparsely annotated dataset, the effort required to locate positive examples can cause performance degradation. Because anchor-based object detection models collect positive examples under IoU between anchors and ground-truth bounding boxes, in a sparsely annotated image, some objects that are not annotated can be assigned as negative examples, such as backgrounds. We attempt to solve this problem with two approaches: 1) using an anchor-less object detector and 2) using a single-object tracker for semi-supervised learning-based object detection. The proposed technique performs bidirectional single-object tracking from sparsely annotated bounding boxes as starting points in videos to obtain dense annotations. On applying our method to the EPIC-KITCHENS-55 dataset, we were able to achieve runner-up performance in the Unseen section, while achieving the first place in the Seen section of the EPIC-KITCHENS 2020 object detection challenge under IoU > 0.5 on the EPIC-KITCHENS 2020 object detection challenge.
Research on a Vehicle-Mounted Intelligent TCM Syndrome Differentiation System Based on Deep Belief Network

This article is oriented to the research of the vehicle-mounted intelligent traditional Chinese medicine (TCM) syndrome differentiation system to provide a quick and accurate diagnosis suggestion for smart and mobile medical vehicles.Nowadays, TCM modernization has attached more and more attention due to its remarkable clinical effect.The typical TCM diagnosis process is to induces the syndrome types of a patient from the four-diagnosis symptoms based on syndrome differentiation theory, that is, given a set of symptoms to treat, an overall syndrome representation is learned by effectively fusing all the symptoms in the set to mimic how a doctor induce the syndromes.Therefore, we believe that an overall description of symptoms of a patient is very important for the follow-up treatment and should be handled carefully and properly.However, due to the complexity and diversity of syndromes, most recommended prescription of a patient lacks the explicit ground-truth of syndrome.Therefore, in this article, a new TCM syndrome differentiation method based on multi-label classification method and deep learning method was proposed to learn the implicit symptoms induction process in the real diagnosis, A Deep Belief Network (DBN) was used to reconstruct the TCM diagnosis model based on the Rrestricted Boltzman Machine (RBM) mechanism.Towards symptoms-syndrome groups learning, a symptom vector representing the symptom collection of a patient is constructed as the input of DBN, which was used to train the symptom data and labeled samples to build an unsupervised model at first.And then the parameters of DBN was adjusted gradually based on back propagation method.Secondly, binary classification algorithm was used to convert the multi-label classification problem into several corresponding labels.Each binary classifier model corresponds to a binary classifier to complete the classification from symptoms to corresponding syndrome types.Finally, Experiments were conducted on a self-built TCM clinical records database, demonstrating obvious improvements on the classification accuracy and convergence rate.Further studies should focus on the promotion of effectiveness of the proposed model.
I am the metre: The representation of one’s body size affects the perception of tactile distances on the body

Humans must ground the perception of one's body in a mental representation to move in space and interact with objects. This representation can be temporarily altered artificially. In the full-body illusion (FBI), participants see a virtual (or filmed) body receiving a tactile stimulation. When participants receive touches on their body similarly to the seen one (i.e., homologous location and synchronous timing), they embody the seen alien body. While the subjective embodiment of alien bodies of different sizes has been already manipulated with the FBI, it remains unexplored whether the body-metric perception is impacted too. We first developed a new setup for the FBI using 360° videos to favour the embodiment. The FBI was induced for bodies of three sizes adopting anatomical and non-anatomical viewpoints, and we measured the subjective embodiment. The results suggest that humans can embody normal size or bigger bodies seen from anatomical viewpoints, but not smaller ones. We then investigated if the FBI modulates the body-metric representation. We found that the resized bodies' vision affects the perception of one's body-metric representation, but this was independent of the embodiment, suggesting that the FBI alters the body representation at different levels with a specific impact.
Multi-Encoder Parse-Decoder Network for Sequential Medical Image Segmentation

Deep learning models, especially U-Net and its derivate models, have been widely used in medical image segmentation. These approaches have achieved promising results in many medical image segmentation tasks with a limited number of training samples. We aim on enhancing medical image segmentation by using spatial continuity information in a proposed Multi-Encoder Parse-Decoder Network (MEPDNet) based on the fact that most of the medical images are sampled continuously. Sequential images are input into parameter shared encoders for getting feature maps, which are then fused by a fusion block. A V$\Lambda$-block is structured to parse the fused feature map to extract the hidden continuity information. The reconstructed feature map is fed into a decoder for generating segmentation masks. Experiments on three datasets show MEPDNet outperforms other state-of-the-art segmentation models while using the least parameters.
Cybersecurity Curricula Designer

The paper aims at minimizing the skills gaps and skills shortages on the cybersecurity job market by empowering education and training institutions during the process of creation of new cybersecurity study programs. We provide a complex cybersecurity skills framework based on standardized definitions that helps with the identification of skills and knowledge necessary for cybersecurity work positions. Furthermore, we practically implement the framework in the form of an interactive web application for cybersecurity curricula design. The app, called Curricula Designer, is built upon the framework and allows intuitive design of higher-education curricula and their analysis with respect to requirements of work roles already defined in widely-accepted standards. Using the analytical functions, it is easy to identify missing content in the courses and precisely structure the study program so that the graduates are well-prepared to enter the job market. The Curricula Designer is described in details in this paper, including user interface and technical background, and a link for public free access is provided to serve all education and training institutions.
A web tool for analyzing FIDO2/WebAuthn Requests and Responses

Passwords are a problem in today's digital world. FIDO2, through WebAuthn, brought alternative password-less authentication that is more usable and secure than classic password-based systems, for web applications and services. In this work, we give a brief overview of FIDO2, and we present WebDevAuthn, a novel FIDO2/WebAuthn requests and responses analyser web tool. This tool can be used to help developers understand how FIDO2 works, aid in the development processes by speeding debugging using the WebAuthn traffic analyser and to test the security of an application through penetration testing by editing the WebAuhn requests or responses.
A salient object detection framework using linear quadratic regulator controller

In this paper, a novel salient object detection framework based on Linear Quadratic Regulator (LQR) controller is proposed. The major goal of this research is to take advantage of optimal control theory for improving the performance of detecting salient objects in images. In this regard, for the sake of detection of salient and non-salient regions, two LQR-based control systems are employed. In the proposed framework, for the initialization of the control systems, background and foreground estimations have been done with two different strategies. Doing so, we would ultimately have more effective distinction between those regions. After the initialization step, the control systems refine both estimations in parallel until reaching a steady state for each of them. Within the mentioned process, by using optimal control concept, specifically LQR controller (for the first time in the field), control signals which are in charge of determining saliency values, would be constantly optimized. At the end, the raw saliency map will be generated by combination of background and foreground optimized initial maps. Finally, the integrated saliency map will be refined by using angular embedding method. The experimental evaluations on three benchmark datasets shows that the proposed framework performs well and introduces comparable results with some deep learning based methods.
Animal behavior missing from data archives

An investigation into animal behavior data archiving practices revealed low rates of data archiving, frequent issues with archived data, and a near absence of multimedia data from data archives. Increasing archiving of animal behavior data will improve the integrity of current studies and enable new avenues of research.