Reviewerly/exHarmony huggingface dataset:
Prediction of Optical Network Congestion Based on SVM

In this paper, a model based on Support Vector Machine (SVM) is proposed to predict the congestion degree of optical network, which mainly aims at the congestion problem of optical transport network. The model can predict and classify the network congestion degree in the next period of optical network according to the specific information of traffic assignment. The simulation results show that the prediction model of optical network congestion based on SVM has 97.8% classification accuracy, which provides a good strategy for the management and planning of optical network, rational allocation of optical network resources and improving the service quality of optical network.
Narrative Review of the Role of Wearable Devices in Promoting Health Behavior: Based on Health Belief Model

Background: Currently, wearable devices, such as smartwatches, allow people to monitor health-related data, which help a better sense of their health status. But it still has a long way to go to really prove whether the perceptions could translate into health-related behavior which require individual be responsible for their own health. Purpose: The purpose of this paper is to investigate the possibility or effectiveness and limitations of wearable devices in health behavior intervention. Firstly, we explored the factors related to wearable devices and health self-management. Secondly, this paper attempted to get whether these factors related to health self-management could induce health behavior based on the Health Belief Model. Methods: The paper was organized as follow. We first reviewed the literature on wearable devices and then attempted to explain the possibility and effectiveness of wearable devices in promoting health behavior according to the concept in Health Belief Model. Finally, this paper gave a preliminary conclusion about the issues we were discussing.
Learning Personalized Treatment Rules from Electronic Health Records Using Topic Modeling Feature Extraction

To address substantial heterogeneity in patient response to treatment of chronic disorders and achieve the promise of precision medicine, individualized treatment rules (ITRs) are estimated to tailor treatments according to patient-specific characteristics. Randomized controlled trials (RCTs) provide gold standard data for learning ITRs not subject to confounding bias. However, RCTs are often conducted under stringent inclusion/exclusion criteria, and participants in RCTs may not reflect the general patient population. Thus, ITRs learned from RCTs lack generalizability to the broader real world patient population. Real world databases such as electronic health records (EHRs) provide new resources as complements to RCTs to facilitate evidence-based research for personalized medicine. However, to ensure the validity of ITRs learned from EHRs, a number of challenges including confounding bias and selection bias must be addressed. In this work, we propose a matching-based machine learning method to estimate optimal individualized treatment rules from EHRs using interpretable features extracted from EHR documentation of medications and ICD diagnoses codes. We use a latent Dirichlet allocation (LDA) model to extract latent topics and weights as features for learning ITRs. Our method achieves confounding reduction in observational studies through matching treated and untreated individuals and improves treatment optimization by augmenting feature space with clinically meaningful LDA-based features. We apply the method to EHR data collected at New York Presbyterian Hospital clinical data warehouse in studying optimal second-line treatment for type 2 diabetes (T2D) patients. We use cross validation to show that ITRs outperforms uniform treatment strategies (i.e., assigning same treatment to all individuals), and including topic modeling features leads to more reduction of post-treatment complications.
A Hybrid Navigation and Image Processing Model with Dynamic Mobile Manipulation

A mobile manipulator is widely used for many scenarios such as warehouse management, and disaster rescue missions. The autonomy of robots operating in an unknown environment is critical. In this paper, a dynamic mobile manipulation robot platform based on the Pioneer 3-AT robot, equipped with one 6-DOF manipulator and multiple sensors is designed and tested. This autonomous mobile manipulator robot platform functions with mapping, path-planning, obstacle avoidance, image processing, color-based object detecting and manipulation algorithms running in the Matlab-ROS environment. In addition, Arduino-based devices are used to enable manipulation control and other future add-on devices. The platform is operating in an unknown dynamic environment using a local path planning algorithm for map exploration, color-based object detection, and manipulation tasks. The project aims to build a platform that is universally suitable for various task situations, as well as easy and efficient to modify and test algorithms with the advantage of data processing capability provided by Matlab. The modification for integrating Pioneer 3-AT robot and Trossen Widow X robotics arm is introduced on both hardware and software levels.
Tasks Decomposition for Improvement of Genetic Network Programming

Genetic Network Programming is an evolutionary algorithm which can be considered as an extension of Genetic Programming but with a graph-structure instead of tree-structure individuals. This algorithm is mainly used for single/multi-agent decision making. It uses a graph to model a strategy that an agent follows to achieve its goal. However, in this algorithm, crossover and mutation operators repeatedly destroy the structures of individuals and make new ones. Although this can lead to better structures, it may also break suitable structures in elite individuals and increase the time needed to achieve optimal solutions. In this research, we modified the evolution process of Genetic Network Programming so that breaking useful structures will be less likely. In the proposed algorithm, the experiences of the best individuals in successive generations are saved. Then, in some specific generations, these experiences are used to generate offspring. The experimental results of the proposed method were tested on two common agent control problem benchmarks namely Tile-world and Pursuit-domain. The results showed the superiority of our method with respect to standard Genetic Network Programming and some of its versions.
A Group Signature Based Digital Currency System

Digital currency regulation is a hot topic. Traditional privacy-enhanced digital currency system, like the CryptoNote, seeks to protect the privacy of senders and receivers. This paper presents a digital currency system based on the group signature scheme of Boneh et al. The system can protect users’ privacy and enable regulations. The system uses the one-time address technology of the CryptoNote to achieve unlinkability. It uses the group signature in a ring style to achieve untraceability. The group manager in a group signature can open a problematic transaction, restore the real identity of the sender, and revoke the private key of the sender if needed, which makes the digital currency regulatable.
Ultragloves: Lowcost Finger-Level Interaction System for VR-Applications Based on Ultrasonic Movement Tracking

The VR technology is undergoing a series of evolution, including not only the higher video quality but also more fluent human-computer interaction operations. Current solution mainly use camera-based or specified equipment-based hand gesture recognition, where the former suffers from low accuracy and limited interaction capacity while the latter suffers from expensive cost. In this paper, we propose Ultragloves, which is a high accurate finger-level and low cost gesture interaction system for VR and smartphones. Specifically, it is enabled by the gloves implanting multiple microphones with which the ultrasonic signal are played. In the VR or mobile devices, the hand gestures are rebuilt with the recorded FMCW-signal and the hand motion model. Furthermore, to improve the accuracy and calculation speed, we propose a parallel processing algorithm for the signals, which could greatly accelerate our solution to real time manner even in COTS smartphones. The real implementation based experiments shows that, Ultragloves could capture the gestures in the accuracy of 2 cm while the parallel algorithm could accelerate the solution with about 3 times.
Legislating Autonomous Vehicles against the Backdrop of Adversarial Machine Learning Findings

Recent studies on adversarial machine learning <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> made Michael Grossman, a Texas-based injury lawyer, skeptical of the viability of autonomous vehicles. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> These studies had pointed out that adversarial attacks or perturbations on pictures makes it difficult for the algorithm to correctly classify the content of that picture. If this is applied to traffic sign recognition, simple graffiti on the sign could mislead the algorithm that is analyzing the picture of the traffic sign captured by the camera. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> Rather than recognizing the traffic sign for what it is, the algorithm could attribute a different meaning to the traffic sign. The consequences could be disastrous, especially if, for example, a stop sign would be read as a speeding sign. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> When rational car manufacturers know this defect, they will not proceed with the marketing of autonomous vehicles.
Elaborating dependent (co)pattern matching: No pattern left behind

Abstract In a dependently typed language, we can guarantee correctness of our programmes by providing formal proofs. To check them, the typechecker elaborates these programs and proofs into a low-level core language. However, this core language is by nature hard to understand by mere humans, so how can we know we proved the right thing? This question occurs in particular for dependent copattern matching, a powerful language construct for writing programmes and proofs by dependent case analysis and mixed induction/coinduction. A definition by copattern matching consists of a list of clauses that are elaborated to a case tree , which can be further translated to primitive eliminators . In previous work this second step has received a lot of attention, but the first step has been mostly ignored so far. We present an algorithm elaborating definitions by dependent copattern matching to a core language with inductive data types, coinductive record types, an identity type, and constants defined by well-typed case trees. To ensure correctness, we prove that elaboration preserves the first-match semantics of the user clauses. Based on this theoretical work, we reimplement the algorithm used by Agda to check left-hand sides of definitions by pattern matching. The new implementation is at the same time more general and less complex, and fixes a number of bugs and usability issues with the old version. Thus, we take another step towards the formally verified implementation of a practical dependently typed language.
Review on Quantum Computing Tools and Algorithms

Computer performance improvement is one of the biggest challenges. The technology has moved towards increasing the performance by using Quantum computing which improves 20 times to decrypt the code compared to the classical computer. Quantum computing is computing which uses quantum mechanical phenomena. The main purpose of the quantum computing is to find algorithms which are considerably faster than the classical algorithms for solving the same problem. In this paper we are proposing the tools of quantum computing and different quantum algorithms. These ideas are first applied to classical computer and then to the quantum computer. We are also focusing on the architecture of quantum computing which is existing in literature.
Motion Optimization for a Robotic Fish Based on Adversarial Structured Control

This paper proposes a task-based control optimization method for the robotic fish. It is essentially an adversarial structured control consisting of a global control module and a local compensation control module. In detail, the global control module emulates an optimized central pattern generator with Evolutionary Strategy, while the local control module produces targeted compensation control signals with Soft Actor-Critic. The linear summation of two control laws works for the final robotic fish control. Considering that the evolutionary computation optimization algorithms generally have the defect of falling into the local optimum, we propose a method of antagonistic training to improve the optimization performance. The effectiveness of the designed controller is validated by simulation on agents in Mujoco. Noticeably, the simulation results demonstrate that the proposed method teaches the agent fish to move to any target point with a low energy consumption, which lays a good foundation for application of reinforcement learning in real robotic fish control.
Composite values of polynomial power sums

Let (G n (x)) n=0 ∞ be a d-th order linear recurrence sequence having polynomial characteristic roots, one of which has degree strictly greater than the others. Moreover, let m≥2 be a given integer. We ask for n∈ℕ such that the equation G n (x)=g∘h is satisfied for a polynomial g∈ℂ[x] with degg=m and some polynomial h∈ℂ[x] with degh>1. We prove that for all but finitely many n these decompositions can be described in “finite terms” coming from a generic decomposition parameterized by an algebraic variety. All data in this description will be shown to be effectively computable.
Bistable Josephson cell as a single microwave photon sensor

The dissipative dynamics of a bistable Josephson cell in a transmission line resonator in a weakly dissipative regime was studied theoretically and numerically. The bistable cell consists of a quantronium qubit and a nonlinear bifurcation amplifier. Under the rotating wave approximation, the problem of the system evolution in a boson thermostat is reduced to the Pauli equation. Using the master equation, we have numerically studied the dissipative dynamics of the system. It was shown that the nonlinear bifurcation amplifier can determine the qubit states and the system can be used as a detector of single microwave photons.
Multiobjective Particle Swarm Optimization with Improved Selection Strategy for Route Optimization

The Multiobjective Route Optimization (MORO) problem is an extension of the traditional single-object route optimization problem, which aims to find an effective path with several conflicting objectives. Many research studies used multiobjective evolutionary algorithm (MOEA) to solve MORO problem, however, they cannot achieve satisfactory results in both quality and computational speed. In this paper, an improved selection strategy based multiobjective particle swarm optimization (ISSMOPSO) is proposed for MORO. The improved selection strategy tactfully combines the advantages of vector evaluated genetic algorithm (VEGA) and Pareto dominating and dominated relationship based fitness function (PDDR-FF). The selection strategy based on VEGA has a preference for the edge region of the Pareto front, the PDDR-FF-based selection strategy has the tendency converging toward the center area of the Pareto front, which preserve both the convergence rate and the distribution performance. The experimental results show that the convergence of ISSMOPSO is better than comparison algorithms, and the diversity is close to NSGA-II and SPEA2 but better than traditional MOPSO.
CharityChain - Donations Using Blockchain

CharityChain is a decentralized application, built on Ethereum that provides the user with a place in the virtual ocean, where the user can find a cause that he personally can associate with and help fund it in the fairest form using Blockchain technology. Blockchain technolgy is easy to use, lesser cost and fast access in all areas. NPO s enabe the chance for donations from global donars with the help of block chain technology.
Thoughts on Data Science in business and industry

Applied Stochastic Models in Business and IndustryVolume 36, Issue 1 p. 36-40 DISCUSSION Thoughts on Data Science in business and industry David M. Steinberg, Corresponding Author David M. Steinberg dms@tauex.tau.ac.il orcid.org/0000-0001-9112-1809 Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel Correspondence David M. Steinberg, Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel. Email: dms@tauex.tau.ac.ilSearch for more papers by this authorEddie Aronovich, Eddie Aronovich Advanced Technical Center Israel, General Motors, Herzliya, IsraelSearch for more papers by this author David M. Steinberg, Corresponding Author David M. Steinberg dms@tauex.tau.ac.il orcid.org/0000-0001-9112-1809 Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel Correspondence David M. Steinberg, Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, Israel. Email: dms@tauex.tau.ac.ilSearch for more papers by this authorEddie Aronovich, Eddie Aronovich Advanced Technical Center Israel, General Motors, Herzliya, IsraelSearch for more papers by this author First published: 19 January 2020 https://doi.org/10.1002/asmb.2510Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume36, Issue1Special Issue: Statistics and Data Science/Energy Networks and Stochastic OptimizationJanuary/February 2020Pages 36-40 RelatedInformation
Extrinsic Calibration Between a Stereo System and a 3D LIDAR

As the robot gradually develops from indoor to outdoor, multi-sensor fusion is increasingly being applied to the field of robot perception. Among them, LiDAR and binocular sensors are most widely used. Robots use the complementary and redundant data they provide to perform environmental perception and implement certain functions of the robot, such as SLAM, autonomous obstacle avoidance and so on. However, the data obtained by each sensor are relative to its own coordinate system. Data must be converted to a unified coordinate system before data fusion. Therefore, the calibration of the transformation among sensors is the first problem to be solved in data fusion. Moreover, the accuracy of calibration is directly related to the effect of data fusion. Therefore the calibration between sensors is very important and necessary. In this paper, we propose a novel method to find accurate rigid-body transformation for the extrinsic calibration of a 3D LiDAR and a stereo cameras, using two Aruco calibration boards. Also the validity of the algorithm is demonstrated by some experiments.
Identifying Morphology of The Rat Flea Arthropod as a Vector of Plague Disease Based Microscopic Image

Abstract The morphological identification system vector of pes disease is one of teaching aids students of the Faculty of Medicine of Universitas Islam Indonesia. The students needs to study the morphological characteristic under the microscope. In practice, it proof difficult to distinguish the kind of various ordos of flea. Thus, the computer teaching aids on this subject need to be developed. The purposed system identified the morphological characteristic of the insect using their size, shape, and texture features. At the identification stage, ten features selected using the Correlation Features Selection (CFS) with method search Best first. This system can also provide additional information about the taxonomy and general characteristics of parasitic insects. The method used for the classification of parasitic insects is KNN, which provides accuracy for body detection of 97.0377% and head detection of 75%. The system has been validated through the expert and its classification correlation reached Cohen’s Kappa.
Method For Generating Test Data For Detecting SQL Injection Vulnerability in Web Application

SQL injection is among the most dangerous vulnerabilities in web applications that allow attackers to bypass the authentication and access the application database. Security testing is one of the techniques required to detect the existence of SQL injection vulnerability in a web application. However, inadequate test data during testing can affect the effectiveness of security testing. Therefore, in this paper, the new algorithm is designed and developed by applying the Cartesian Product technique in order to generate a set of invalid test data automatically. A total of 624 invalid test data were generated in order to increase the detection rate of SQL injection vulnerability. Finally, the ideas obtained from our method is able to detect the vulnerability of SQL injection in web application.
Effects of Semantic Gaps on Arabic WordNet-Based Similarity Measures

The English WordNet (EnWN) is extensively used by computational linguists as a lexical-semantic resource for Natural Language Processing and Knowledge Engineering. EnWN was extended with multilingual information in several non-English wordnet projects; including the Arabic WordNet (ArWN). ArWN is still limited in terms of semantic structure and coverage of the Arabic language. This paper focuses on the improvement of ArWN semantic structure, the Hypernymy relation. An interactive cross-lingual mapping approach is used to enrich ArWN with new synsets lexicalized in Arabic and mapped to the target synsets in EnWN. The paper also studies to which extent the richness of the semantic structure of the wordnets affects the semantic evidences, which can be utilized in wordnet-based applications. A set of experiments in the context of semantic similarity is conducted on Arabic word pairs dataset to examine the effectiveness and usability of the enriched ArWN resource. The experimental performance measures showed that filling in the semantic gaps significantly improves the quality of the similarity scores between concepts.
The obstacles detection for outdoor robot based on computer vision in deep learning

In this thesis, we propose a guided robot with deep learning techniques and machine vision in order to predict distance and avoid obstacles. The training data images are captured by the stereo camera, and output results are disparity of the single image. Then, the distances are converted by using the triangulation method of computer vision. The back propagation neural network retrains to obtain the actual distance of each pixel in the image. Therefore, the robot with the monocular camera could know the distance between obstacles and itself. Semantic segmentation is utilized to a to distinguish road and obstacles in the image. Fuzzy theory for calculating the area of the road which be cut is designed to avoid walking into the intersection. The obstacle depth and the walkable area are taken as an information for avoiding obstacles control. Because of the robot is proposed to walk on the right side along the road, the edge of road is necessary to navigate the robot. The Hough method is used to find the straight line, and then choose the line we need. The path planning is also achieved in through of navigation and obstacle avoidance control. As a result, the robot can arrive the destination safely and precisely.