Reviewerly/exHarmony huggingface dataset:
A Multiview Model for Detecting the Inappropriate Use of Prescription Medication: Machine Learning Approach

Background The inappropriate use of prescription medication has recently garnered worldwide attention, but most national policies do not effectively provide for early detection or timely intervention. Objective This study aimed to develop and assess the validity of a model that can detect the inappropriate use of prescription medication. This effort combines a multiview and topic matching method. The study also assessed the validity of this approach. Methods A multiview extension of the latent Dirichlet allocation algorithm for topic modeling was chosen to generate diagnosis-medication topics, with data obtained from the Chinese Monitoring Network for Rational Use of Drugs (CMNRUD) database. Topic mapping allowed for calculating the degree to which diagnoses and medications were similarly distributed and, by setting a threshold, for identifying prescription misuse. The Beijing Regional Prescription Review Database (BRPRD) database was used as the gold standard to assess the model’s validity. We also conducted a sensitivity analysis using random samples of validated prescriptions and evaluated the model’s performance. Results A total of 44 million prescriptions were used to generate topics using the diagnoses and medications from the CMNRUD database. A random sample (15,000 prescriptions) from the BRPRD was used for validation, and it was found that the model had a sensitivity of 81.8%, specificity of 47.4%, positive-predictive value of 14.5%, and negative-predictive value of 96.0%. The model showed superior stability under different sampling proportions. Conclusions A method that combines multiview topic modeling and topic matching can detect the inappropriate use of prescription medication. This model, which has mediocre specificity and moderate sensitivity, can be used as a primary screening tool and will likely complement and improve the process of manually reviewing prescriptions.
Data Publishing Techniques and Privacy Preserving

What is Privacy?-According to the definition from Cambridge dictionary, "Someone's right to keep their personal matters and relationships secret".The term privacy is defined as an action where the data is kept hidden from either anonymous user, server to avoid use of malpractice of the data [2], [3].Healthcare data is considered as a most significant but sensitive data in the world, since it has all private information about patient such as diseases, treatment, prescription, name, address etc.The volume of the data generated in healthcare industry is rapidly growing.In this patient centric world, to get effective results, we need to increase healthcare data utility.With increasing data utility, the privacy of the same data is compromised which is another important challenge that users and healthcare data publishers are facing, since there is no monitory control on data which is published on internet.Hiding sensitive healthcare data from either untrusted users or thirdparty data publishers is an important concern today.Healthcare Data Publishing is the process where certain transformation (such as anonymization, generalization, suppression etc.) can be applied before publishing healthcare data online.From the available research, it is seen that such transformations are not susceptible to certain attacks like background knowledge, homogeneity etc.This review paper studies all existing Privacy Preserving Data Publishing (PPDP) schemes using data generalization.The literature review also touches recent researches on ARX tool-which is an open source data de-identification tool for analyzing risk and utility factor of healthcare data.The paper finally concludes with feasible research gaps from available literature survey.
Group Key Exchange from CSIDH and Its Application to Trusted Setup in Supersingular Isogeny Cryptosystems

In this paper, we propose a multi-party (group) key exchange protocol based on CSIDH (Commutative Supersingular Isogeny Diffie–Hellman), which is a post-quantum Diffie-Hellman type key exchange protocol from a commutative group action. The proposed group key exchange protocol called G-CSIDH uses the same size prime modulus p as that in CSIDH for the same security level, and the security of G-CSIDH is reduced to the security of CSIDH. In addition, we propose the trusted protocol of generating public parameters of supersingular isogeny cryptosystems by using the proposed G-CSIDH. Trust in the setup based on G-CSIDH is reduced to the security of G-CSIDH, and then that of CSIDH. The trusted protocol can be applied to any supersingular isogeny cryptosystem, which uses a supersingular elliptic curve as a public parameter.
Scale invariant learning from trapezoidal data streams

Trapezoidal data streams pervasively exist in many applications, where the number of features keeps increasing over time with the continuous arrival of new instances. Extensive learning solutions on such data streams with a doubly streaming nature make a strong assumption that all features have similar scales. However, this is impractical in many applications where features may vary with time. When applying on streaming data with different scaled features, they may cause a poor convergence and be unable to rescale these features under streaming conditions. In this paper, we propose two effective online learning algorithms that can maintain feature scale-invariants even with arbitrary scaling of features in the incoming trapezoidal data streams. Specifically, two algorithms are designed to learn a classifier by keeping track of the cumulative sum of squared gradients, the negative cumulative sum of gradients, maximum feature values occurred and the cumulative sum of occurrences of features. The scaled gradients are employed to update their corresponding classifier weights. We also conduct experiments on nine UCI datasets to evaluate the effectiveness of our proposed algorithms. The experimental results demonstrate that the features in the incoming trapezoidal data streams indeed have different scales and our proposed solutions can significantly outperform the state-of-the-art solution in terms of prediction accuracy.
Improvement in the Effectiveness of Cutting Skill Practice for Paper-Cutting Creations Based on the Steering Law

To improve the cutting skills of learners, we developed a method for improving the skill involved in creating paper cuttings based on a steering task in the field of human-computer interaction. TaWe made patterns using the white and black boundaries that make up a picture. The index of difficulty (ID) is a numerical value based on the width and distance of the steering law. First, we evaluated novice and expert pattern-cutters, and measured their moving time (MT), error rate, and compliance with the steering law, confirming that the MT and error rate are affected by pattern width and distance. Moreover, we quantified the skills of novices and experts using ID and MT based models. We then observed changes in the cutting skills of novices who practiced with various widths and evaluated the impact of the difficulty level on skill improvement. Patterns considered to be moderately difficult for novices led to a significant improvement in skills.
Area-Efficient Fault Tolerant Design for Finite State Machines

The finite state machine (FSM) controls the operation of the entire system. Therefore, if the errors occur in the FSM, it causes serious problem of the system. The fault tolerant state machine is proposed to deal with the errors in the FSM. In this paper, we propose a selective fault-tolerant finite state machine that increases fault-tolerance by applying the fault-tolerant circuit selectively according to the importance of state. The proposed SFT-FSM is 39% smaller when the redundancy factor <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pmb{N}$</tex> is 9, compared to the FSM with NMR technique.
Maybe tainted data: Theory and a case study

Dynamic taint analysis is often used as a defense against low-integrity data in applications with untrusted user interfaces. An important example is defense against XSS and injection attacks in programs with web interfaces. Data sanitization is commonly used in this context, and can be treated as a precondition for endorsement in a dynamic integrity taint analysis. However, sanitization is often incomplete in practice. We develop a model of dynamic integrity taint analysis for Java that addresses imperfect sanitization with an in-depth approach. To avoid false positives, results of sanitization are endorsed for access control (aka prospective security), but are tracked and logged for auditing and accountability (aka retrospective security). We show how this heterogeneous prospective/retrospective mechanism can be specified as a uniform policy, separate from code. We then use this policy to establish correctness conditions for a program rewriting algorithm that instruments code for the analysis. These conditions synergize our previous work on the semantics of audit logging with explicit integrity which is an analogue of noninterference for taint analysis. A technical contribution of our work is the extension of explicit integrity to a high-level functional language setting with structured data, vs. previous systems that only address low level languages with unstructured data. Our approach considers endorsement which is crucial to address sanitization. An implementation of our rewriting algorithm is presented that hardens the OpenMRS medical records software system with in-depth taint analysis, along with an empirical evaluation of the overhead imposed by instrumentation. Our results show that this instrumentation is practical.
Augmented Shared Spaces: An Application for Exposure Psychotherapy

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span&gt;The latest advances in computational power of small portable devices enables the development of new forms of Augmented Realities, namely Augmented Reality Shared Spaces. In this work, we will explore this technolo- gy to improve the collaborative work between people in the same physical space allowing them to share and visualise virtual contents. The idea is to have a 3D scene shared between several people where everyone can see and interact with and explore its use in psychotherapy context. These live interactions can also be used to improve the quality of collaborative work by presenting in real time ideas and thoughts of any participant directly on top of the 3D model. In the case of a psychotherapy session typically patients describe their feelings and fears to the therapist. This may be centred on virtual representations of their phobic elements, supporting interactions with them, being those actions and re- sults observable by both in their shared space. Our main goal is to apply and evaluate this concept, specially in exposure-based therapies, where it is im- portant for the therapist to control and manipulate the scene that the patient is exposed to in order to obtain the desired emotional activations.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Design of Anti-interference System for Fully Autonomous UAV Based on ADRC- EKF Algorithm

In order to solve the short-time drift problem of height data caused by environmental and noisy pollution, a multi-rotor aircraft control system was designed to prevent the aircraft from falling rapidly in a short time during the autonomous cruise. The problem of data loss due to strong interference on the UAV, which is equipped with the Real-time kinematic (RTK), can be well solved by introducing an inertial measurement unit (IMU) for high resolution, and using an Extended Kalman filter and IMU to calculate system acceleration (ACC) as well as altitude predictions for eliminating height drift due to sensor errors. In addition, the ADRC control algorithm is also introduced to control the attitude of the aircraft, to realize the control compensation, and finally to control the height of the drone through the three- ring cascade PID algorithm. The experimental results show that the proposed algorithm can make the UAV get better height and attitude control even though the external disturbance is unknown.
A Bayesian approach to disease clustering using restricted Chinese restaurant processes

Identifying disease clusters (areas with an unusually high incidence of a particular disease) is a common problem in epidemiology and public health. We describe a Bayesian nonparametric mixture model for disease clustering that constrains clusters to be made of adjacent areal units. This is achieved by modifying the exchangeable partition probability function associated with the Ewen’s sampling distribution. We call the resulting prior the Restricted Chinese Restaurant Process, as the associated full conditional distributions resemble those associated with the standard Chinese Restaurant Process. The model is illustrated using synthetic data sets and in an application to oral cancer mortality in Germany.
Quadrotor Modeling and a PID Control Approach

Since there has been an important increase in unmanned vehicles systems research such as quadrotors, a mathematical model and PID control laws are studied. Based on some dynamic variables, PID control is applied to compute a controller to be then use in autopilot simulations. As this kind of VTOL vehicle seems to be unstable, the aim of this work is to change even other flight mechanics parameters and control gains to study attitude and altitude variations. A well-known computational tool is used for simulation purposes, performance analysis and validation.
A Modular Extension for a Computer Algebra System

Computer algebra systems are complex software systems that cover a wide range of scientific and practical problems. However, the absolute coverage cannot be achieved. Often, it is required to create a user extension for an existing computer algebra system. In this case, the extensibility of the system should be taken into account. In this paper, we consider a technology for extending the SymPy computer algebra system with a low-level module that implements a random number generator.
A Variational Approach to Image Despeckling under Varied Blur

Restoring blurred and speckle corrupted images have been a traditional problem in the area of image processing. In this paper, we introduce a new variational Bayesian approach for deblurring and despeckling of images corrupted by multilevel noise and different types of blur. We introduce a L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> norm based data-fidelity term and coupled with Total Variational (TV) regularization filters in our optimization problem. Such an approach, as is evident through experimental result, is capable of retaining finer details in the image while producing high quality despeckled and deblurred image. Comparison with existing state-of-the-art shows the high-quality visual and quantitative results, justifying the superiority of the proposed approach.
Parallel Watershed method for Medical modality Image segmentation

Medical image processing on the GPU has become quite popular recently, since this technology makes it possible to apply more advanced algorithms and to perform computationally demanding tasks quickly in clinical context. Image segmentation in medical imaging is often used to segment brain structures, organs, blood vessels and bones. Combined interactive segmentation and visualization are impeccably suited to the GPU. The data already in GPU memory can be extracted very efficiently. As a consequence, the segmentation process often becomes more complex and time-consuming. This paper proposes the ways to improve the computational speed of watershed segmentation algorithm using GPU Computing. GPUs are used to solve a wide variety modality of problems in the field of medical imaging.
An Analysis of Distributed Programming Models and Frameworks for Large-scale Graph Processing

In recent years, processing and analysing large graphs has become a major need in many research areas. Distributed graph processing programming models and frameworks arised as a natural solution to process linked data of large volumes, such as data originating from social media. These solutions are distributed by design and help developers to perform operations on the graph, sometimes reaching almost real-time performance even on huge graphs. Some of the available graph processing frameworks exploit generic data processing models, like MapReduce, while others were specifically built for graph processing, introducing techniques such as vertex or edge partitioning and graph-oriented programming models. In this work, we analyse the properties of recent and widely popular frameworks – from the perspective of the adopted programming model – designed to process large-scale graphs with the goal of assisting software developers/designers in choosing the most adequate tool.
An Explorative Paper on Speculative Approaches to Smart Contracts

The trend of cryptocurrencies has stirred interest in the underlying technology that qualifies cryptocurrencies as a secure structure with speedy, timely and cheap transactions. The aforementioned technology, the blockchain, in brief terms is a decentralized ledger technology that attains an immutable characteristic through consensus and timestamp mechanics. The model also sets the stage for transparency in transactions, which renders the technology applicable to a myriad of scenarios that involve financial instruments. This research puts forth an argumentative approach to the applicability of blockchain technology and specifically studies the prospect of utilizing smart contracts. This approach probes the feasibility of introducing smart contracts to everyday financial transactions and settlements. An opposing perspective, by taking a devil's advocate standpoint, invokes the impractical or implausible aspects of implementing the blockchain in certain scenarios. Difficulty in auditing is a prominent example among those impracticalities. Research methodology is qualitative in nature and takes the form of exploratory research by examining existing literature on the topic.
Application of DenseNets for Classification of Breast Cancer Mammograms

In this study, we focus on the problem of a breast cancer diagnosis using mammography images by classifying them as belonging either to a negative or to a malignant mass class. We explore the potential of densely connected convolutional neural network (DenseNet) architectures by comparing its three different variants that were trained to classify the abnormalities in breast tissue. The models have been tested in a series of systematic experiments. With a limited dataset (2247 images per class), it was necessary to perform tests to verify whether the amount of data used in this work is sufficient to allow for the conclusion that the experimental results are not dependent on the subset of the data. The training was conducted using stratified 10-fold cross-validation to obtain statistically reliable metrics estimates. DenseNet-201 was found to be the best model achieving: 0.96 value for area under the curve (AUC), 0.92 for precision, 0.90 for recall, and 91% for accuracy.
A Comparative Study of Various Approaches to Adaptive Web Scraping

Web Scraping is a coming of age technology which has been gaining traction quite recently. Data is the currency of web-based activities and for quite a while had the webpages been stagnant pools of data waiting to be analyzed. Fully automated retrieval of data over a context-based environment has led to cutting edge technologies being developed using the constantly updated comparative analysis of data provided by this technique. This paper explores all the aspects of web crawling and scraping starting from the technology itself to the techniques and methods used. It also highlights the prospective applications of the technology and the legality surrounding its practice.
Ranking Hotels Based on Online Hotel Attribute Ratings Using Neutrosophic AHP and Stochastic Dominance

Electronic word-of-mouth (EWOM) is playing an important part in the consumer’s purchasing decision making across various shopping platforms. This is especially true in service sector, where the feedbacks covering the guest experience influence the adoption intention of potential consumers. The online ratings of various service aspects of the hotels posted by earlier users provide a comprehensive evaluation of their opinions and assessments on a standardized scale. In order to maximize utility, customers look for various candidate alternatives as well as the online ratings corresponding to it. This requires making a mathematical justification for precise decision making. Moreover, purchasing decisions incorporate hesitation among customers. To take care of these problems, this paper proposes a model applying multi criteria decision making (MCDM) techniques with online ratings of hotel. The objective of the paper is to rank hotels on the basis of online ratings of their attributes available on Tripadvisor. Firstly, the priorities of the service attributes, in the form of weights, are obtained using Neutrosophic Analytic Hierarchy Process (NAHP). The Neutrosophic set are an extension of fuzzy concept and has the advantage of handling hesitation in decision making process. We apply stochastic dominance along with the preference weights on the available ratings to obtain a comparison matrix of alternative. Finally, we make use of PROMETHEE technique to get the overall ranking of hotels. Future studies may include the role of online review text in the present model.
Automatic Key Structure Extraction

Studying original cipher keys constructed throughout history gives important insights into encryption methods and cipher systems.We can study the type of encryption used, the code structure and their corresponding plaintext entities, be it letters, morphemes, words, or named entities.The insights can lead us to better decryption methods, and the understanding of the development of historical ciphers.In this paper, we present a tool for automatic key structure extraction that describes the symbol system and the code structure along with the encoded plaintext features and the mapping between the two.The tool is aimed at the empirical study of historical keys given transcribed keys.
Meta-Learning for Black-Box Optimization

Recently, neural networks trained as optimizers under the “learning to learn” or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free black-box function optimization. Recurrent neural networks (RNNs) trained to optimize a diverse set of synthetic non-convex differentiable functions via gradient descent have been effective at optimizing derivative-free black-box functions. In this work, we propose RNN-Opt: an approach for learning RNN-based optimizers for optimizing real-parameter single-objective continuous functions under limited budget constraints. Existing approaches utilize an observed improvement based meta-learning loss function for training such models. We propose training RNN-Opt by using synthetic non-convex functions with known (approximate) optimal values by directly using discounted regret as our meta-learning loss function. We hypothesize that a regret-based loss function mimics typical testing scenarios, and would therefore lead to better optimizers compared to optimizers trained only to propose queries that improve over previous queries. Further, RNN-Opt incorporates simple yet effective enhancements during training and inference procedures to deal with the following practical challenges: (i) Unknown range of possible values for the black-box function to be optimized, and (ii) Practical and domain-knowledge based constraints on the input parameters. We demonstrate the efficacy of RNN-Opt in comparison to existing methods on several synthetic as well as standard benchmark black-box functions along with an anonymized industrial constrained optimization problem.