Reviewerly/exHarmony huggingface dataset:
A study of deep learning methods for de-identification of clinical notes in cross-institute settings

De-identification is a critical technology to facilitate the use of unstructured clinical text while protecting patient privacy and confidentiality. The clinical natural language processing (NLP) community has invested great efforts in developing methods and corpora for de-identification of clinical notes. These annotated corpora are valuable resources for developing automated systems to de-identify clinical text at local hospitals. However, existing studies often utilized training and test data collected from the same institution. There are few studies to explore automated de-identification under cross-institute settings. The goal of this study is to examine deep learning-based de-identification methods at a cross-institute setting, identify the bottlenecks, and provide potential solutions.We created a de-identification corpus using a total 500 clinical notes from the University of Florida (UF) Health, developed deep learning-based de-identification models using 2014 i2b2/UTHealth corpus, and evaluated the performance using UF corpus. We compared five different word embeddings trained from the general English text, clinical text, and biomedical literature, explored lexical and linguistic features, and compared two strategies to customize the deep learning models using UF notes and resources.Pre-trained word embeddings using a general English corpus achieved better performance than embeddings from de-identified clinical text and biomedical literature. The performance of deep learning models trained using only i2b2 corpus significantly dropped (strict and relax F1 scores dropped from 0.9547 and 0.9646 to 0.8568 and 0.8958) when applied to another corpus annotated at UF Health. Linguistic features could further improve the performance of de-identification in cross-institute settings. After customizing the models using UF notes and resource, the best model achieved the strict and relaxed F1 scores of 0.9288 and 0.9584, respectively.It is necessary to customize de-identification models using local clinical text and other resources when applied in cross-institute settings. Fine-tuning is a potential solution to re-use pre-trained parameters and reduce the training time to customize deep learning-based de-identification models trained using clinical corpus from a different institution.
Analysis of the Hands in Egocentric Vision: A Survey

Egocentric vision (a.k.a. first-person vision-FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided.
Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed. However, the possibility and feasibility of such attacks against Deep Reinforcement Learning (DRL) are less explored. As DRL has achieved great success in various complex tasks, designing effective adversarial attacks is an indispensable prerequisite towards building robust DRL algorithms. In this paper, we introduce two novel adversarial attack techniques to stealthily and efficiently attack the DRL agents. These two techniques enable an adversary to inject adversarial samples in a minimal set of critical moments while causing the most severe damage to the agent. The first technique is the critical point attack: the adversary builds a model to predict the future environmental states and agent's actions, assesses the damage of each possible attack strategy, and selects the optimal one. The second technique is the antagonist attack: the adversary automatically learns a domain-agnostic model to discover the critical moments of attacking the agent in an episode. Experimental results demonstrate the effectiveness of our techniques. Specifically, to successfully attack the DRL agent, our critical point technique only requires 1 (TORCS) or 2 (Atari Pong and Breakout) steps, and the antagonist technique needs fewer than 5 steps (4 Mujoco tasks), which are significant improvements over state-of-the-art methods.
Context-Transformer: Tackling Object Confusion for Few-Shot Detection

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches.
Blockchain Technologies: Opportunities for Solving Real-World Problems in Healthcare and Biomedical Sciences

Blockchain technology is associated with the financial industry, but it can be applied to other industries. The supporting architecture of blockchain has the immense potential to transform the delivery of healthcare, medical, clinical, and life sciences, due to the extended functionality and distinct features of its distributed ledger. The potential scale of impact is comparable to that seen with the introduction of TCP/IP. Blockchain technology has captured the interest of healthcare providers and biomedical scientists within various healthcare domains such as longitudinal healthcare records, automated claims, drug development, interoperability in population health, consumer health, patient portals, medical research, data security, and reducing costs with supply chain management. It is not yet clear if blockchain is going to disrupt healthcare, but healthcare organizations are monitoring its potential closely for prospective concepts like secure patient IDs. Realistically, the adoption and implementation of blockchains will be a gradual evolution over time, but now is the time to take a fresh look at its possibilities in healthcare and biomedical sciences. Blockchain technology revolutionary solutions are bringing us closer to the possibility of every patient record being able to send updates to an open-source, community-wide trusted ledger that is accessible and understood across organizations with guaranteed integrity.This paper discusses as a review some potential areas of opportunity for blockchain in the health and biomedical sciences fields.This paper describes and synthesizes 20 examples of real-world use-case scenarios for blockchains in healthcare and biomedical practice.
Graph-Propagation Based Correlation Learning for Weakly Supervised Fine-Grained Image Classification

The key of Weakly Supervised Fine-grained Image Classification (WFGIC) is how to pick out the discriminative regions and learn the discriminative features from them. However, most recent WFGIC methods pick out the discriminative regions independently and utilize their features directly, while neglecting the facts that regions' features are mutually semantic correlated and region groups can be more discriminative. To address these issues, we propose an end-to-end Graph-propagation based Correlation Learning (GCL) model to fully mine and exploit the discriminative potentials of region correlations for WFGIC. Specifically, in discriminative region localization phase, a Criss-cross Graph Propagation (CGP) sub-network is proposed to learn region correlations, which establishes correlation between regions and then enhances each region by weighted aggregating other regions in a criss-cross way. By this means each region's representation encodes the global image-level context and local spatial context simultaneously, thus the network is guided to implicitly discover the more powerful discriminative region groups for WFGIC. In discriminative feature representation phase, the Correlation Feature Strengthening (CFS) sub-network is proposed to explore the internal semantic correlation among discriminative patches' feature vectors, to improve their discriminative power by iteratively enhancing informative elements while suppressing the useless ones. Extensive experiments demonstrate the effectiveness of proposed CGP and CFS sub-networks, and show that the GCL model achieves better performance both in accuracy and efficiency.
Tag N’ Train: a technique to train improved classifiers on unlabeled data

A bstract There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets that may not be well modeled in simulation. Therefore, relying on simulations for training will lead to sub-optimal performance in data, but the lack of true class labels makes it difficult to train on real data. To address this challenge we introduce a new approach, called Tag N’ Train (TNT), that can be applied to unlabeled data that has two distinct sub-objects. The technique uses a weak classifier for one of the objects to tag signal-rich and background-rich samples. These samples are then used to train a stronger classifier for the other object. We demonstrate the power of this method by applying it to a dijet resonance search. By starting with autoencoders trained directly on data as the weak classifiers, we use TNT to train substantially improved classifiers. We show that Tag N’ Train can be a powerful tool in model-agnostic searches and discuss other potential applications.
Cybersecurity named entity recognition using bidirectional long short-term memory with conditional random fields

Network texts have become important carriers of cybersecurity information on the Internet. These texts include the latest security events such as vulnerability exploitations, attack discoveries, advanced persistent threats, and so on. Extracting cybersecurity entities from these unstructured texts is a critical and fundamental task in many cybersecurity applications. However, most Named Entity Recognition (NER) models are suitable only for general fields, and there has been little research focusing on cybersecurity entity extraction in the security domain. To this end, in this paper, we propose a novel cybersecurity entity identification model based on Bidirectional Long Short-Term Memory with Conditional Random Fields (Bi-LSTM with CRF) to extract security-related concepts and entities from unstructured text. This model, which we have named XBiLSTM-CRF, consists of a word-embedding layer, a bidirectional LSTM layer, and a CRF layer, and concatenates X input with bidirectional LSTM output. Via extensive experiments on an open-source dataset containing an office security bulletin, security blogs, and the Common Vulnerabilities and Exposures list, we demonstrate that XBiLSTM-CRF achieves better cybersecurity entity extraction than state-of-the-art models.
Demand Characteristics Confound the Rubber Hand Illusion

Reports of experiences of ownership over a fake hand following simple multisensory stimulation (the ‘rubber hand illusion’) have generated an expansive literature. Because such reports might reflect suggestion effects, demand characteristics are routinely controlled for by contrasting agreement ratings for ‘illusion’ and ‘control’ conditions. However, these methods have never been validated, and recent evidence that response to imaginative suggestion (‘phenomenological control’) predicts illusion report prompts reconsideration of their efficacy. A crucial assumption of the standard approach is that demand characteristics are matched across conditions. Here, a quasi-experiment design was employed to test demand characteristics in rubber hand illusion reports. Participants were provided with information about the rubber hand illusion procedure (text description and video demonstration) and recorded expectancies for standard ‘illusion’ and ‘control’ statements. Expectancies for ‘control’ and ‘illusion’ statements in synchronous and asynchronous conditions were found to differ similarly to published illusion reports. Therefore, rubber hand illusion control methods which have been in use for 22 years are not fit for purpose. Because demand characteristics have not been controlled in illusion report in existing studies, the illusion may be, partially or entirely, a suggestion effect. Methods to develop robust controls are proposed. That confounding demand characteristics have been overlooked for decades may be attributable to a lack of awareness that demand characteristics can drive experience in psychological science.
The state of adoption and the challenges of systematic variability management in industry

Abstract Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering—an approach to systematically develop portfolios of products—is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on re-engineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium- to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.
Computational Creativity and Music Generation Systems: An Introduction to the State of the Art

Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors from computers. One of its most prolific subfields is that of Music Generation (also called Algorithmic Composition or Musical Metacreation), that uses computational means to compose music. Due to the multidisciplinary nature of this research field, it is sometimes hard to define precise goals and to keep track of what problems can be considered solved by state-of-the-art systems and what instead needs further developments. With this survey, we try to give a complete introduction to those who wish to explore Computational Creativity and Music Generation. To do so, we first give a picture of the research on the definition and the evaluation of creativity, both human and computational, needed to understand how computational means can be used to obtain creative behaviors and its importance within Artificial Intelligence studies. We then review the state of the art of Music Generation Systems, by citing example for all the main approaches to music generation, and by listing the open challenges that were identified by previous reviews on the subject. For each of these challenges, we cite works that have proposed solutions, describing what still needs to be done and some possible directions for further research.
Sentiment analysis of malayalam tweets using machine learning techniques

Sentiment Analysis of Malayalam Tweets using Machine Learning techniques is done in this paper. The tweets are classified into positive and negative using different machine learning techniques such as Naive Bayes (NB), Support Vector Machine (SVM) and Random Forest (RF). The different features like Bag of Words (BOW), Term Frequency vs. Inverse Document Frequency (TF − IDF), Unigram with Sentiwordnet, and Unigram with Sentiwordnet including negation words are considered for feature vector formation of input dataset. The Random Forest classifier shows higher accuracy while considering Unigram with Sentiwordnet including negation words as a feature.
Multivariate Abnormal Detection for Industrial Control Systems Using 1D CNN and GRU

Currently, most anomaly detection approaches in industrial control systems (ICSs) use network event logs to build models, and current unsupervised machine learning methods rarely use spatiotemporal correlations and other dependencies between multiple variables (sensors/actuators) in a system to detect anomalies. Most of the existing anomaly detection technologies simply compare the current states with the predicted normal range. Due to the highly dynamic characteristic of industrial control systems, it is insufficient to simply compare the current states with the predicted normal range. As a result, these approaches have low detection rates for unknown or new types of attacks. In view of these shortcomings, this paper presents a network model for predicting sensor/controller parameters in industrial control systems. To predict the parameter values of the sensors and controllers more accurately, the 1D convolutional neural network (1D_CNN) and gated recurrent unit (GRU) are combined to fully learn the spatiotemporal correlation and other dependencies between the parameter values of the sensors and controllers at each moment. An abnormal state detection method based on the calculation of the statistical deviation is proposed to realize the anomaly detection of industrial control systems. The model is validated on the Secure Water Treatment (SWaT) dataset. The precision, recall and F1 scores are used to evaluate the effectiveness of this method in anomaly detection on the SWaT dataset. The experimental results show that the average precision and recall of this method are 0.99 and 0.85, respectively, and that the average F1 score is 0.91. The experimental results show that the proposed method can be successfully applied to anomaly detection systems in industrial control systems with lower false positive rates.
RefineFace: Refinement Neural Network for High Performance Face Detection

Face detection has achieved significant progress in recent years. However, high performance face detection still remains a very challenging problem, especially when there exists many tiny faces. In this paper, we present a single-shot refinement face detector namely RefineFace to achieve high performance. Specifically, it consists of five modules: selective two-step regression (STR), selective two-step classification (STC), scale-aware margin loss (SML), feature supervision module (FSM) and receptive field enhancement (RFE). To enhance the regression ability for high location accuracy, STR coarsely adjusts locations and sizes of anchors from high level detection layers to provide better initialization for subsequent regressor. To improve the classification ability for high recall efficiency, STC first filters out most simple negatives from low level detection layers to reduce search space for subsequent classifier, then SML is applied to better distinguish faces from background at various scales and FSM is introduced to let the backbone learn more discriminative features for classification. Besides, RFE is presented to provide more diverse receptive field to better capture faces in some extreme poses. Extensive experiments conducted on WIDER FACE, AFW, PASCAL Face, FDDB, MAFA demonstrate that our method achieves state-of-the-art results and runs at 37.3 FPS with ResNet-18 for VGA-resolution images.
Code and Named Entity Recognition in StackOverflow

There is an increasing interest in studying natural language and computer code together, as large corpora of programming texts become readily available on the Internet.For example, StackOverflow currently has over 15 million programming related questions written by 8.5 million users.Meanwhile, there is still a lack of fundamental NLP techniques for identifying code tokens or software-related named entities that appear within natural language sentences.In this paper, we introduce a new named entity recognition (NER) corpus for the computer programming domain, consisting of 15,372 sentences annotated with 20 fine-grained entity types.We trained indomain BERT representations (BERTOverflow) on 152 million sentences from Stack-Overflow, which lead to an absolute increase of +10 F 1 score over off-the-shelf BERT.We also present the SoftNER model which achieves an overall 79.10 F 1 score for code and named entity recognition on StackOverflow data.Our SoftNER model incorporates a context-independent code token classifier with corpus-level features to improve the BERTbased tagging model. 1