Reviewerly/exHarmony huggingface dataset:
Extraction of Family History Information From Clinical Notes: Deep Learning and Heuristics Approach (Preprint)

<sec> <title>BACKGROUND</title> Electronic health records store large amounts of patient clinical data. Despite efforts to structure patient data, clinical notes containing rich patient information remain stored as free text, greatly limiting its exploitation. This includes family history, which is highly relevant for applications such as diagnosis and prognosis. </sec> <sec> <title>OBJECTIVE</title> This study aims to develop automatic strategies for annotating family history information in clinical notes, focusing not only on the extraction of relevant entities such as family members and disease mentions but also on the extraction of relations between the identified entities. </sec> <sec> <title>METHODS</title> This study extends a previous contribution for the 2019 track on family history extraction from national natural language processing clinical challenges by improving a previously developed rule-based engine, using deep learning (DL) approaches for the extraction of entities from clinical notes, and combining both approaches in a hybrid end-to-end system capable of successfully extracting family member and observation entities and the relations between those entities. Furthermore, this study analyzes the impact of factors such as the use of external resources and different types of embeddings in the performance of DL models. </sec> <sec> <title>RESULTS</title> The approaches developed were evaluated in a first task regarding entity extraction and in a second task concerning relation extraction. The proposed DL approach improved observation extraction, obtaining F&lt;sub&gt;1&lt;/sub&gt; scores of 0.8688 and 0.7907 in the training and test sets, respectively. However, DL approaches have limitations in the extraction of family members. The rule-based engine was adjusted to have higher generalizing capability and achieved family member extraction F&lt;sub&gt;1&lt;/sub&gt; scores of 0.8823 and 0.8092 in the training and test sets, respectively. The resulting hybrid system obtained F&lt;sub&gt;1&lt;/sub&gt; scores of 0.8743 and 0.7979 in the training and test sets, respectively. For the second task, the original evaluator was adjusted to perform a more exact evaluation than the original one, and the hybrid system obtained F&lt;sub&gt;1&lt;/sub&gt; scores of 0.6480 and 0.5082 in the training and test sets, respectively. </sec> <sec> <title>CONCLUSIONS</title> We evaluated the impact of several factors on the performance of DL models, and we present an end-to-end system for extracting family history information from clinical notes, which can help in the structuring and reuse of this type of information. The final hybrid solution is provided in a publicly available code repository. </sec>
Permissioned Blockchain Reinforced API Platform for Data Management in IoT-based Sensor Networks

With the rise of IoT-based sensor networks, there is an increasing need for proper security mechanisms and efficient management of crucial resources, such as energy. At the same time, as more services are integrated, more sensors are introduced into the network. As a consequence, the architecture that manages these sensors need to be improved. Blockchains can be a solution in terms of data security. To cater to the inexpensive devices used as sensors in most IoT-based sensor networks, usually, permissioned blockchains are used as its base data management structure. However, due to the diverse collection of sensors that can be incorporated into a network, a static database is not enough. A more sustainable and automative system is needed as its service administrator. Therefore, we chose to integrate smart contracts to enable adaptive and dynamic automation. Lastly, to manage the reception of all the incoming data, a proper interface is required. Therefore, we chose to encapsulate the blockchain within a REST API to enable a systematic allocation of network resources. With these technologies, we propose a low-cost platform to address the management issues of current IoT-based sensor networks. We tested our design against a commercial REST API service and standard socket communication to test its feasibility. The testing metrics were latency and throughput. Based on the results, our platform proved to be the most stable and proficient among the configurations. Therefore, our proposed design shows promise as a low-cost, secure, and systematic means of managing IoT-based sensor networks.
A Study on the Improvement of Juvenile Justice According to the Change of the Investigation Environment — Focusing on the Review of Laws Related to Handling Juvenile Cases —

The so-called adjustment of the investigative powers of the prosecution and the police brought major changes to the roles and functions of the prosecution and police. The key to this adjustment is that the police are given primary investigative power and can conduct investigations independently. Even if there is no suspicion of a crime, the police can close the investigation on their own. On the other hand, the prosecution can directly investigate only six major crimes. Moreover, it is only possible to control the police investigation results later. In addition, the police organization was divided into the national police and local police. The purpose of this study is to examine how the adjustment of investigative powers affects the juvenile justice procedure. To this end, I will first examine and review the current laws and regulations related to the juvenile justice procedure. Next, we will examine what measures can be taken to improve the juvenile case.
Semantic Analysis and Preference Capturing on Attentive Networks for Rating Prediction

Nowadays, people receive an enormous amount of information from day to day. However, they are only interested in information which matches their preferences. Thus, retrieving such information becomes an significant task, in our case, the reviews composed by users. Matrix Factorization (MF) based methods achieve fairly good performances on recommendation tasks. However, there exist several crucial issues with MF - based methods such as cold-start problems and data sparseness. In order to address the above issues, numerous recommendation models are proposed which obtained stellar performances. Nonetheless, we figured that there is not a more comprehensive framework that enhances its performance through retrieving user preference and item trend. Hence, we propose a novel approach to tackle the aforementioned issues. A hierarchical construction with user preference and item trend capturing is employed in this proposed framework. The performance excels in comparison to state-of-the-art models by testing on several real-world datasets. Experimental results verified that our framework can extract useful features even under sparse data.
Prediction on Intent Classification of Java and C# Web queries using Semi-supervision

Proper intent classification of web queries is significant in providing users with accurate search results. For STEM-related searches, the generalized search engine provides some discrete results, and it becomes challenging to find the desired ones. Here, in our work, we have used a semi-supervised process and compared it with supervising approaches. This process has been done on Java and C# Bing web queries. From the performance comparison, we have found that our semi-supervised model has performed better than others according to accuracy and f1-score. We have also analyzed the performance by changing training data size, doing error analysis on all models, and finished by presenting how this prediction can be used on a search data fetching process.
Use of LiDAR for Negative Obstacle Detection: A Thorough Review

Abstract Negative obstacles for field autonomous land vehicles refer to potholes, ditches, cliffs, pits, or any type of obstacles that are on the road but not in a visible way, which bring risks both to the vehicles or potential passengers and even to the environments where autonomous land vehicles are moving along. These negative obstacles can cause severe damage to autonomous land vehicles, from damage to the vehicle’s suspension, rollovers, or even loss of the autonomous vehicle. Obstacle detection is the first step to avoid any risk, and it is extremely important to be able to warn of nearby obstacles, to avoid any type of danger that may arise. In recent years several articles have been published on the different types of hazards or obstacles and different methods of avoiding them. The authors report on ways to better understand the environment so that autonomous land vehicles stay out of harm’s way. Some of the dangers could be, the collision with another vehicle, hitting a pedestrian, not recognizing the traffic signs on the road, leaving the planned routes or lanes, or not braking when they detect an obstacle. The use of image recognition using different types of cameras, as well as the use of ultrasonic sensors and Light Detection and Ranging (LiDAR) sensors, or the combination of some or all of the sensors is widely used in the field of autonomous land vehicles. Most of this research work has been carried out in positive obstacle detection; that is, obstacles that are above the road. However, the detection of negative obstacles and their avoidance remains less explored. Due to this, an exhaustive review is made of the state of the art of use of LiDAR sensors in the detection of negative obstacles applied in autonomous land vehicles and the different techniques that have been used to recognize and classify the different types of potholes, ditches, pits.
Innovative Transformation of Manufacturing Vacuum Equipment at Mechanical Engineering Enterprises

The purpose of the article investigates introducing new products processes at mechanical engineering enterprises based on the innovative transformation of vacuum production equipment. In the context of global competition activities of large Kazakhstan’s enterprises are characterized by an insufficient level of innovation activity. Building the infrastructure and production potential is the main in effective management production activities of engineering enterprises. The heads of enterprises are primarily concerned by the situation since innovation development has a significant impact on competitiveness, industrial enterprises’ export potential and could ensure the balance of the economic sectors. Authors are interested in conducting an economic analysis of the business entities’ activities, which aimed at efficiency increasing and quality of work in all links of industrial production. Authors suppose that practice requires the development of complex measures related to the peculiarities of investigating scientific and practical problems of increasing the production activities efficiency in the economy. In the authors’ opinion, the investment project can become the basis for effective innovation implementation at mechanical engineering enterprises. They analyzed the financial activity of the enterprise and developed measures to reduce the production costs and products sales on the example of JSC «Vostokmashzavod». The result of the study is an investment project that arouses interest from the subjects of the machine-building industry from the point of view of the practical applicability of projects to reduce production costs, and the efficiency of their management.
Twitter after Charlie Hebdo and the Paris Bataclan attacks: identifying and measuring the different types of violent speech on the internet

Much of the impugned speech about terrorism is online, and this feature is accentuated here in order to identify and measure the different types of violent speech on the Internet. It is not difficult to encounter offensive and violent online words, but what is also growing is the assumption that national laws are needed to regulate them to avoid harmful consequences. The chapter approaches the problematic publications not directly through discussion of the criminalization of them, but by empirical analysis in order to identify violent speech and to differentiate its expressions according to axiological criteria related to criminalization. The reality of the phenomenon must be identified before appropriate action can be taken, including by the invocation of precursor crimes.
A pervasive review on biomarker in cervical intraepithelial lesions and carcinoma

Basically, medical diagnosis problems are the most effective component of treatment policies. Recently, significant advances have been formed in medical diagnosis fields using data mining techniques. Data mining or Knowledge Discovery is searching large databases to discover patterns and evaluate the probability of next occurrences. In this research, Bayesian Classifier is used as a Non-linear datamining tool to determine the seriousness of breast cancer. The recorded observations of the Fine Needle Aspiration (FNA) tests that are obtained at the University of Wisconsin are considered as experimental data set in this research. The Tabu search algorithm for structural learning of Bayesian classifier and Genie simulator for parametric learning of Bayesian classifier were used. Finally, the obtained results by the proposed model were compared with actual results. The comparison process indicates that seriousness of the disease in 86.18% of cases are guessed very close to the actual values by proposed model.
Introduction

The popularity of autonomous mobile robots has been rapidly increasing due to the needs that arise with the developing technology and improved application areas.
MACHINE LEARNING-BASED PRONUNCIATION EVALUATION OF SPOKEN ENGLISH DIALOGUES

In order to promote the development of the pronunciation-level evaluation system of spoken English dialogues and enhance the accuracy of the system, this paper studies the pronunciation-level evaluation system of spoken English dialogues on the basis of machine learning technology. The feasibility of the evaluation system of spoken English dialogue pronunciation designed in this paper was verified through the empirical approach. Empirical results indicated that the designed system achieved better results in the uniform rate, adjacent line rate, and Pearson correlation coefficient test than the measured results of the multiple feedback system and big data analysis system. In the test of misjudgment rate, it was found that the misjudgment rate of the designed system remained at about 10% under different mispronunciations, which was significantly lower than the other two common systems. This paper verifies the feasibility of the system designed in this paper and its high evaluation performance.
A robust CNN classification of whole slide thyroid carcinoma images

The objective of this paper is to build a classification system for "Whole Slide Images" (WSIs) based on a Convolutional Neural Network (CNN). Six types of thyroid tumors can be classified by the system: "follicular adenoma" (FA), "papillary carcinoma" (PC), "follicular carcinoma" (FC), "papillary follicular variant" (PFV), "poorly-differentiated follicular carcinoma" (PDFC), and "well-differentiated follicular carcinoma" (WDFC). The proposed custom CNN is compared with the well-known pre-trained Alexnet CNN. The results show the robustness of the proposed CNN, achieving an overall accuracy of 97.07% compared to only 93.81% for the Alexnet.
Assistive signals for deep neural network classifiers

Deep Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are perturbations optimized to improve a model’s confidence score regardless if it’s under attack or not. We analyze some interesting properties of these assistive perturbations and extend the idea to optimize them in the 3D space simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. ‘Assistive Signals’ also illustrate bias of ML models towards certain patterns in real-life objects.
Initial Development of Low-Cost Autonomous Rover for Pursuit of Moving Targets

Abstract Technical progress in the development of autonomous off-road ground vehicles has recently led to the appearance of commercially available compact unmanned rovers. However, most of them remain rather costly. In the present study, an effort is made to develop a low-cost small-scale rover that can be used to pursue moving targets and perform other tasks, as well as be applicable as an educational platform for robotics courses. This paper presents the hardware/software implementation, including Arduino-compatible electronics architecture, GPS-RTK modules, enhanced compass, telemetry modules, and data logging. The vehicle control modes, communication with a moving target, adaptation of software libraries, and recommendations for sensing and steering are described. Three outdoor tests conducted with the autonomous rover are presented in the paper, including trajectory plots and time-dependent kinematic characteristics. In the first trial, the rover successfully reached a set of pre-programmed stationary waypoints. In the second test, the rover autonomously pursued a moving target that had a complicated trajectory. The rover was able to approach and follow the target, while larger deviations of relative distances between the rover and the target and bigger fluctuations of the rover speed were noticed during sudden turns and stops of the target. The last test was carried out with a sonar that can detect obstacles on the rover path, adding a capability for the rover to avoid collisions. The future development steps will include implementation computer vision modules, so more sophisticated autonomous control methods can be tested and optimized on this platform. The reported in this paper information will be useful for researchers, engineers and educators interested in constructing and using low-cost mobile platforms that can be instrumented with a number of sensors and perform a variety of tasks.
Comparative Review of Content Based Image Retrieval Using Deep Learning

Review of literature is important for gaining knowledge on specific area before doing any research. In today’s world wide web, pictures are increasing making it difficult to retrieve a relevant image. CBIR is used to search any image from the corpus of images. It excerpts features from the input image and retrieve the images from the dataset which have similar features. Plentiful techniques have been developed for CBIR, some of which have been illustrated in this paper. These results can be improved by finding significant hidden data from images. Deep learning extracts these hidden features and classify the image. Lot of experiments have been conducted on CBIR using deep learning. Comparative analysis of these experiments was performed to outline the superiority of CBIR using deep learning over traditional CBIR methods. An experiment has been conducted to evaluate the efficacy of CNN over traditionally used machine learning algorithms like FNN and decision tree classifier.
A comparative study on Chinese open domain suggestion mining

As a new research task, suggestion mining increasingly gained attention in recent years. However, it is still open and challenging due to complex semantics, large diversity of domains, and the absence of large labeled and balanced datasets. More importantly, most of the research is focused on English in-domain suggestion mining. But as compared to English, Chinese suggestion has more abundant expression forms, showing many different characteristics, so it is the necessity to carry out suggestions mining research in Chinese environment. In this work, the performances of several classification models for Chinese suggestion mining were compared. Firstly, a Chinese suggestion mining corpus was constructed for open domain, and then trained several models for the suggestion mining, which included both traditional machine learning models (feature engineering-based models) and deep learning models. Our results demonstrated that these models can successfully classify Chinese sentences into two classes: suggestion and non-suggestion. The results of this study can guide future research in Chinese open domain suggestion mining.
RTI Streamline Navigation Systems

Biomedical Safety & Standards: January 15, 2021 - Volume 51 - Issue 1 - p 7-8 doi: 10.1097/01.BMSAS.0000725440.89703.e2
Incorrect Numbers in eTable and Text

An-Kwok Ian Wong, MD, PhD; Marie Charpignon, MS; Han Kim, MSE; Christopher Josef, MD; Anne A. H. de Hond, MSc; Jhalique Jane Fojas, MSE, MSc, MRes, PhD, PhD; Azade Tabaie, MSc; Xiaoli Liu, BS; Eduardo Mireles-Cabodevila, MD; Leandro Carvalho, MD; Rishikesan Kamaleswaran, PhD; R. W. M. A. Madushani, PhD; Lasith Adhikari, PhD; Andre L. Holder, MD, MSc; Ewout W. Steyerberg, PhD; Timothy G. Buchman, PhD, MD; Mary E. Lough, PhD, RN; Leo Anthony Celi, MD, MS, MPH
Rational Robots: A Response to Interesting, Renegotiated, and Relational Robots

The Reasonable Robot is my first book, and it has involved several exciting personal milestones such as receiving my first book review. That excitement was somewhat diminished when the review came out fairly negative, and when it became clear that the reviewer clearly had not read the book, or at least not read it carefully. He took issue, for example, with the book arguing in favor of criminal punishment of robots (the opposite of what the book argues). Yet I have met several people who told me they bought the book after reading that review. I think about this from time to time, and perhaps the lesson is that all press is good press. Or, perhaps that most people read book reviews as carefully as that reviewer read the book. But at least several people have now read the book carefully, for which I am very grateful. Not only are Omri Rachum-Twaig, Ohad Somech, Carys Craig, and Ryan Calo all brilliant and leading scholars, but they have made some very sharp criticisms of the book. Be careful what you wish for, I suppose.
Research on Multi-view 3D Reconstruction of Human Motion Based on OpenPose

With the development of the service robot industry, robot behaviors are more anthropomorphic, and human pose recognition has become an important research direction of machine vision. In the view of this situation, more research in the robot dance field is needed to solve the human-machine interaction, and promote the human-machine cooperative movement, the dance posture recognition and reconstruction in three-dimensional space. At the same time, the collected human actions are used as data for deep learning, so that the computer can learn the artificial solution strategies of the same problem, make more anthropomorphic actions, and solve practical problems on its own. Therefore, this paper proposes a motion capture scheme based on the multi-view recognition to record the posture by using the OpenPose models. It uses multi-view acquisition method to correct the acquisition errors caused by the perspective occlusion, and reconstruct people’s dancing movements by calculating human key-points from different camera views.
A Study of Identifier Resolution Security Strategy Based on Security Domains

The widespread application of industrial Internet identifiers has increased the security risks of industrial Internet and identifier resolution system. In order to improve the security capabilities of identifier resolution system, this paper analyzes the security challenges faced by identifier resolution system at this stage, and in line with the concept of layered security defense in depth, divides the security domains of identifier resolution system and proposes a multi-level security strategy based on security domains by deploying appropriate protective measures in each security domain.