Reviewerly/exHarmony huggingface dataset:
Using indication embeddings to represent patient health for drug safety studies

The electronic health record is a rising resource for quantifying medical practice, discovering the adverse effects of drugs, and studying comparative effectiveness. One of the challenges of applying these methods to health care data is the high dimensionality of the health record. Methods to discover the effects of drugs in health data must account for tens of thousands of potentially relevant confounders. Our goal in this work is to reduce the dimensionality of the health data with the aim of accelerating the application of retrospective cohort studies to this data.Here, we develop indication embeddings, a way to reduce the dimensionality of health data while capturing information relevant to treatment decisions. We evaluate these embeddings using external data on drug indications. Then, we use the embeddings as a substitute for medical history to match patients and develop evaluation metrics for these matches.We demonstrate that these embeddings recover the therapeutic uses of drugs. We use embeddings as an informative representation of relationships between drugs, between health history events and drug prescriptions, and between patients at a particular time in their health history. We show that using embeddings to match cohorts improves the balance of the cohorts, even in terms of poorly measured risk factors like smoking.Unlike other embeddings inspired by word2vec, indication embeddings are specifically designed to capture the medical history leading to the prescription of a new drug. For retrospective cohort studies, our low-dimensional representation helps in finding comparator drugs and constructing comparator cohorts.
Efficient Implementation of a Crypto Library Using Web Assembly

We implement a cryptographic library using Web Assembly. Web Assembly is expected to show better performance than Javascript. The proposed library provides comprehensive algorithm sets including revised CHAM, Hash Message Authentication Code (HMAC), and ECDH using the NIST P-256 curve to provide confidentiality, data authentication, and key agreement functions. To optimize the performance of revised CHAM in the proposed library, we apply an existing method that is a four-round combining method and additionally propose the precomputation method to CHAM-64/128. The proposed revised CHAM showed an approximate 2.06 times (CHAM-64/128), approximate 2.13 times (CHAM-128/128), and approximate 2.63 times (CHAM-128/256) performance improvement in Web Assembly compared to JavaScript. In addition, CHAM-64/128 applying the precomputation method showed an improved performance by approximately 1.2 times more than the existing CHAM-64/128. For the ECDH using P-256 curve, the naive implementation of ECDH is vulnerable to side-channel attacks (SCA), e.g., simple power analysis (SPA), and timing analysis (TA). Thus, we apply an SPA and TA resistant scalar multiplication method, which is a core operation in ECDH. We present atomic block-based scalar multiplication by revising the previous work. Existing atomic blocks show a performance overhead of 55%, 23%, and 37%, but atomic blocks proposed to use only P=(X,Y,Z) show 18%, 6%, and 11% performance overhead. The proposed Web Assembly-based crypto library provides enhanced performance and resistance against SCA thus, it can be used in various web-based applications.
BAS Optimized ELM for KUKA iiwa Robot Learning

In this brief, an enhanced robotic learning interface has been investigated using Beetle Antennae Search (BAS) and Extreme Learning Machine (ELM). The initial values of learning weights and bias of the network have significant effect on the performance of the ELM, hence, BAS algorithm was employed to optimize the initial values of learning weights and bias. Kinect v2 camera sensor was applied to obtain the endpoint's position of the upper limb, MYO armband was used to measure the corresponding joint angle values. Those aforementioned data formed the dataset to be trained by ELM and after training the ELM model was able to generate angle values by only giving position as input without a need to carry out kinematic calculations. The proposed method has been validated by conducting series of experimental studies on a KUKA iiwa robot.
Quantum-over-Classical Advantage in Solving Multiplayer Games

We study the applicability of quantum algorithms in computational game theory and generalize some results related to Subtraction games, which are sometimes referred to as one-heap Nim games.In quantum game theory, a subset of Subtraction games became the first explicitly defined class of zero-sum combinatorial games with provable separation between quantum and classical complexity of solving them. For a narrower subset of Subtraction games, an exact quantum sublinear algorithm is known that surpasses all deterministic algorithms for finding solutions with probability 1.Typically, both Nim and Subtraction games are defined for only two players. We extend some known results to games for three or more players, while maintaining the same classical and quantum complexities: \(\varTheta \left( n^2\right) \) and \(\tilde{O}\left( n^{1.5}\right) \) respectively.
Speed Planning for Autonomous Driving in Dynamic Urban Driving Scenarios

Trajectory planning is essential for autonomous vehicles when operating in dynamic traffic environments. A layered approach usually separates out into path planning and speed planning. In the work reported in this paper, speed profile planning over a given path, which is defined by a trajectory planner, is proposed. The relevant information is provided by vehicle-to-vehicle (V2V) communication. First, a speed planning optimization algorithm which considers safety, time efficiency, smoothness and comfort constraints is presented. This strategy can provide a safe, comfortable and feasible speed profile for autonomous driving via a S-T graph under a complex traffic environment. Secondly, a conventional non-convex optimization problem is translated into a quadratic programming problem. This has the advantage of a low computation requirement because it uses a CFS (convex feasible set) algorithm. The effectiveness of the proposed scheme is verified through simulation studies in various urban driving scenarios. This holistic approach provides a more effective approach to speed and trajectory planning.
Semantic Evidential Grid Mapping based on Stereo Vision

Accurately estimating the current state of local traffic scenes is a crucial component of automated vehicles. The desired representation may include static and dynamic traffic participants, details on free space and drivability, but also information on the semantics. Multi-layer grid maps allow to include all these information in a common representation. In this work, we present an improved method to estimate a semantic evidential multi-layer grid map using depth from stereo vision paired with pixel-wise semantically annotated images. The error characteristics of the depth from stereo is explicitly modeled when transferring pixel labels from the image to the grid map space. We achieve accurate and dense mapping results by incorporating a disparity-based ground surface estimation in the inverse perspective mapping. The proposed method is validated on our experimental vehicle in challenging urban traffic scenarios.
Rating Prediction Based on Merge-CNN and Concise Attention Review Mining

Online review websites provide an open platform for users to write reviews or give ratings on items (business services) as well as share their consumption experience.However, the volume of reviews is large, while the rating scores provide users with a quick picture of the items without reading all reviews.Recommendation systems can help users find items of interest by predicting user's ratings on unrated items.Review contents contain more personalized preference features than simply user ratings.Therefore, it is important to consider both ratings and review contents when making rating predictions.This research proposes a novel approach that combines deep learning and review mining with attention mechanism for rating predictions.Review mining with attention mechanism is adopted to extract concise attention reviews with important words and sentences.A merge convolutional neural network (merge-CNN) model is proposed to consider both the target user's preference features and performance features of target business for rating prediction.This method extracts quality business performance features from the quality reviews written by elite (credible) users.Moreover, the proposed method uses the concise attention reviews of target user's neighbors to simulate target users' reviews on unrated target business.Experiments were conducted on Yelp data sets to evaluate our proposed methods.The results show that the proposed method, i.e. considering concise attention reviews and quality reviews written by elite users, outperforms traditional methods in improving prediction accuracy.The experiment result also shows that our review simulation methods can well simulate target user's reviews on unrated target business.
Software Requirements Modeling: A Systematic Literature Review

Software requirements modeling (SRM) is a subprocess of requirements engineering (RE) which is used to elicit and represent the need of the stakeholders. Different systematic literature reviews (SLR) have been performed in different areas of RE like requirements elicitation, stakeholder identification, requirements prioritization, use case models, etc. Despite the availability of different SRM techniques, less attention is given to synthesize the existing SRM techniques in the context of the unified modeling language (UML) and goal oriented techniques like “ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Knowledge </sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Acquisition </sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">for </sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Automated </sub> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Specifications</sub> ” (KAOS), I* framework, non-functional requirements (NFR) framework, and Tropos, etc. Therefore, to address this issue, in this paper we present the SLR by analysing the existing SRM techniques based on the following formulated research questions (RQs): (a) how UML and goal oriented techniques were evolved? (b) which modeling techniques are appropriate for modeling the NFRs? (c) what are the tools available for modeling the different types of the software requirements, i.e., functional and nonfunctional requirements? Search items were extracted from the RQs to identify the primary studies from the Journals, Conferences, Workshops, and Symposium. Our SLR has identified 56 distinct studies which have been published from 2008 to 2019. Selected studies were assessed according to the formulated RQs for their quality and coverage to specific SRM technique thus identifying some gaps in the literature. We observed that there is need to develop the SRM techniques for representing the different types of the NFRs; and also to strengthen the UML by integrating the NFRs and multi-criteria decision making techniques.
Ship Detection from X-Band SAR Images Using M2Det Deep Learning Model

Synthetic aperture radar (SAR) images have been used in many studies for ship detection because they can be captured without being affected by time and weather. In recent years, the development of deep learning techniques has facilitated studies on ship detection in SAR images using deep learning techniques. However, because the noise from SAR images can negatively affect the learning of the deep learning model, it is necessary to reduce the noise through preprocessing. In this study, deep learning vessel detection was performed using preprocessed SAR images, and the effects of the preprocessing of the images on deep learning vessel detection were compared and analyzed. Through the preprocessing of SAR images, (1) intensity images, (2) decibel images, and (3) intensity difference and texture images were generated. The M2Det object detection model was used for the deep learning process and preprocessed SAR images. After the object detection model was trained, ship detection was performed using test images. The test results are presented in terms of precision, recall, and average precision (AP), which were 93.18%, 91.11%, and 89.78% for the intensity images, respectively, 94.16%, 94.16%, and 92.34% for the decibel images, respectively, and 97.40%, 94.94%, and 95.55% for the intensity difference and texture images, respectively. From the results, it can be found that the preprocessing of the SAR images can facilitate the deep learning process and improve the ship detection performance. The results of this study are expected to contribute to the development of deep learning-based ship detection techniques in SAR images in the future.
A Collision-Free Person-Following Approach Based on Path Planning

Person following is a crucial capability for mobile robots in tasks of human assistance and cooperation. In this paper, a person-following approach based on path planning is proposed for mobile robots, which takes person following and obstacle avoidance into account in a unified framework. The proposed approach is comprised of a sensing module and a planning module. The sensing module with a camera and a 3D LiDAR is employed to locate the target person and perceive obstacles. For the planning module, the combination of global planner and local planner effectively outputs collision-free paths and control commands for a mobile robot to keep following the target person. Experiments were conducted to evaluate the performance of the proposed approach.
The stability of learning disabilities among emergent bilingual children: A latent transition analysis.

This study investigated the prevalence and stability of latent classes among elementary-aged English learning (EL) children whose first language is Spanish. To this end, EL children (N = 267) in Grades 1, 2, and 3 at Wave 1 (Year 1) were administered a battery of vocabulary, reading, math, and cognitive measures (short-term memory, working memory, rapid naming, inhibition) in both Spanish and English. These same measures were also administered one year later (Wave 2). Four important findings occurred. First, four latent classes (balanced bilinguals-average achievers, unbalanced bilinguals-average achievers, children at risk for learning disabilities, English dominant) at both testing waves emerged. Second, probability estimates indicated that 20% of the total sample was at risk for learning disabilities at Wave 1, with late-emerging academic difficulties increasing the learning disabilities latent class by 5% at Wave 2. Third, the incidence of late-emerging children at risk for learning disabilities was higher among balanced bilingual average achievers, especially for those children transitioning to or from Grade 3. Finally, the cognitive measures for predicting the odds of children being correctly classified in the final wave of testing included measures of naming speed and working memory. The results support the notion that statistically distinct latent classes emerge under the umbrella of language and academic performance and that children at risk for learning disabilities can be separated among a heterogeneous sample of children who are English language learners. (PsycInfo Database Record (c) 2021 APA, all rights reserved)
Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System

With recent viruses across the world affecting millions and millions of people, the self-healthcare information systems show an important role in helping individuals to understand the risks, self-assessment, and self-educating to avoid being affected. In addition, self-healthcare information systems can perform more interactive tasks to effectively assist the treatment process and health condition management. Currently, the technologies used in such kind of systems are mostly based on text crawling from website resources such as text-searching and blog-based crowdsourcing applications. In this research paper, we introduce a novel Artificial Intelligence (AI) framework to support interactive and causality reasoning for a Chatbot application. The Chatbot will interact with the user to provide self-healthcare education and self-assessment (condition prediction). The framework is a combination of Natural Language Processing (NLP) and Knowledge Graph (KG) technologies with added causality and probability (uncertainty) properties to original Description Logic. This novel framework can generate causal knowledge probability neural networks to perform question answering and condition prediction tasks. The experimental results from a prototype showed strong positive feedback. The paper also identified remaining limitations and future research directions.
Exploring Rare Pose in Human Pose Estimation

We tackle the issue of data imbalance between different poses in the human pose estimation problem.We explore unusual poses that are rare which occupy a small portion in a pose dataset.In order to identify a rare pose without additional learning, a simple K -means clustering algorithm is applied to a given dataset.Experimental results on MPII and COCO datasets show that outliers which are far from the nearest cluster center can be defined as rare poses and the accuracy decreases as the distance between the data point and the cluster center increases.In order to improve the performance on the rare poses, we proposed three methods for the problem of data scarcity, which are addition of rare pose duplicates, addition of synthetic rare pose data and weighted loss based on the distance from the cluster.In the proposed methods, the highest increasing score is 13.5 mAP at the rare pose data.
Cyber Taxi: A Taxonomy of Interactive Cyber Training and Education Systems

The lack of guided exercises and practical opportunities to learn about cybersecurity in a practical way makes it difficult for security experts to improve their proficiency. Capture the Flag events and Cyber Ranges are ideal for cybersecurity training. Thereby, the participants usually compete in teams against each other, or have to defend themselves in a specific scenario. As organizers of yearly events, we present a taxonomy for interactive cyber training and education. The proposed taxonomy includes different factors of the technical setup, audience, training environment, and training setup. By the comprehensive taxonomy, different aspects of interactive training are considered. This can help trainings to improve and to be established successfully. The provided taxonomy is extendable and can be used in further application areas as research on new security technologies.
Implementation of Smart Contracts based on Hyperledger Fabric Blockchain for the Purpose of Insurance Services

Blockchain technology has been evolved into many applications, such as banking, financial markets, accounting and supply chains. The insurance business is other huge economics sector, where the Blockchain solutions minimize time to process claims for losses and the operational costs. The main advantages of Blockchain technology are process identification, transaction security and speed of payment. The paper presents an implementation of smart contracts for insurance services, based on Hyperledger Fabric Blockchain. The experimental results are presented.
Supporting Remote Participation when Designing with People with Dementia

Exploring accessible remote design methods has become the need of the hour for supporting participation in research and collaborative design with individuals with dementia. Existing remote design approaches face specific challenges when facilitating best practices for co-design with participants with dementia. These challenges include, enabling sensory engagement with physical design materials and prototypes and observing these interactions in a natural manner. We present a system architecture and use cases for a portable system with a range of connected devices that support real-time, embodied design activities with individuals with dementia.
Large scale “speedtest” experimentation in Mobile Broadband Networks

Characterizing and evaluating the performance of Mobile Broadband (MBB) networks is a vital need for today’s societies. Testbed-based measurements are of great significance in this context, since they allow for controlled and longitudinal experimentation. In this work, we focus on “speed” as an important Quality of Service (QoS) indicator for MBB networks, and work with MONROE-Nettest, an open source speedtest tool running as an Experiment as a Service (EaaS) on the Measuring Mobile Broadband Networks in Europe (MONROE) testbed. We conduct an extensive longitudinal measurement campaign spanning 2 countries over 2 years, and provide our experiment results together with rich metadata as an open dataset. We characterize this open dataset in detail, as well as derive insights from it regarding the impact of network context, spatio-temporal effects, roaming, and mobility on network performance. We describe our experiences about conducting speedtest measurements in MBB, and discuss the challenges associated with large scale testbed experimentation in operational MBB networks. Tackling one of the said challenges further, we introduce the notion of adaptive speedtest duration, and leverage a Machine Learning (ML) based algorithm to provide a proof-of-concept implementation called “Speedtest++”. Finally, we describe the lessons we have learned, as well as provide an overall discussion of how open datasets can support MBB research, and comment on open challenges, in the hope that these can serve as discussion points for future work.
ParaGlyder: Probe-driven Interactive Visual Analysis for Multiparametric Medical Imaging Data

Multiparametric imaging in cancer has been shown to be useful for tumor detection and may also depict functional tumor characteristics relevant for clinical phenotypes. However, when confronted with datasets consisting of multiple values per voxel, traditional reading of the imaging series fails to capture complicated patterns. These patterns of potentially important imaging properties of the parameter space may be critical for the analysis, but standard approaches do not deliver sufficient details. Therefore, in this paper, we present an approach that aims to enable the exploration and analysis of such multiparametric studies using an interactive visual analysis application to remedy the trade-offs between details in the value domain and in spatial resolution. This may aid in the discrimination between healthy and cancerous tissue and potentially highlight metastases that evolved from the primary tumor. We conducted an evaluation with eleven domain experts from different fields of research to confirm the utility of our approach.
A Novel Based 3D Facial Expression Detection Using Recurrent Neural Network

People periodically have diverse facial expressions and disposition changes in this way. Human facial expression acknowledgment plays a really energetic part in social relations. The acknowledgment of feelings has been an dynamic breakdown point from early age. The real-time location of facial expressions like appall, upbeat, pitiful, irate, anxious, astonish. The proposed framework can recognize 6 diverse facial expression. A facial expression acknowledgment framework needs to perform location and change to 3D image, then the facial highlight extraction, and facial expression classification is worn. Out proposed strategy we should be utilizing Recurrent Neural Network (RNN). This RNN show is prepared on JAFEE and Yale database dataset. This framework has capacity to screen individuals' feelings, to segregate between feelings and name them fittingly.