Reviewerly/exHarmony huggingface dataset:
Epidemic Disease Expert System

The growth in the epidemic disease has created threat to human population. Many of the epidemic diseases are known to be sensitive to changes in the climate and short-term fluctuations in the weather. Recent outbreaks of Dengue, Chikungunya in India and Ebola in Africa have shown the importance of monitoring and understanding the public sentiments on disease outbreaks. These expert systems are fed with relevant knowledge and techniques to infer the result to make decisions. The paper focuses on extracting the web content like e-newspapers, Health related articles specific to the study of the disease occurrence and provide prior information of the outbreaks. The knowledge base is fed with the authenticated data collected from the field of medicine that provides information about the relationships between the diseases, symptoms, and medications. The system uses WebCrawler to crawl across the initial seed URL's. The developed crawler supports the feature of multithreading crawling, content extraction and duplicate links detection. Around 3000 links are crawled by the WebCrawler. The dynamicity of the DOM structure of each e-newspaper is also taken care. Out of which few of the potential links related to the epidemic disease are taken for the study. Sentiment analysis of the curated newspaper article is demonstrated with real time data. The summary of the article, article published date, article title that are related to the epidemic disease is extracted. Pre-processing of data is done which includes parsing, tokenizing, lemmatizing and chinking of data. The Naïve Bayes Classifier is applied which results in the generation of score. The detailed view of the Disease related information is displayed to the user.
Privacy-Preserving Data Mining in Spatiotemporal Databases Based on Mining Negative Association Rules

In the real world, most of the entities are involved with space and time, from any starting point to the end point of the space. The conventional data mining process is extended to the mining knowledge of the spatiotemporal databases. The major knowledge is to mine the association rules in the spatiotemporal databases; the traditional approaches are not sufficient to do mining in the spatiotemporal databases. While mining the association rules, the privacy is the main concern. This paper proposed privacy preserved data mining technique for spatiotemporal databases based on the mining negative association rules and cryptography with low storage and communication cost. In the proposed approach first, the partial support for all the distributed sites is calculated, and then finally, the actual support was calculated to achieve privacy preserve data mining. The mathematical calculation was done and proved that this approach is best for mining association rules for spatiotemporal databases.
Code Inspection Support for Recurring Changes with Deep Learning in Evolving Software

Developers often make recurring changes, similar but different changes across multiple locations. They inspect such code changes per source file (i.e., a diff patch) during code reviews; however, diff patches represent low-level code modification without summarizing recurring changes, leading to tedious and error-prone code inspection. To address this problem, we propose a novel code review approach, Recurring Code Changes Inspection with Deep Learning (RIDL) that leverages change patterns of an edit script by learning code clones, identical or nearly similar code fragments. To train a classifier, RIDL learns 13,940 clones with four different clone types (e.g., Type-1, Type-2, Type-3, and Type-4 clones) from a clone database mined from 25,000 subject programs. Our approach then leverages the classifier to (1) interactively summarize recurring changes and (2) detect change mistakes, potential anomalies in a given codebase. In the evaluation, after 2 hours of training, RIDL analyzes code changes in four open source projects. It summarizes recurring changes with 95.1% accuracy and detects change anomalies with 93.1% accuracy. Our results show that RIDL should help developers effectively inspect recurring changes during code reviews.
FPGA-based Accurate Pedestrian Detection with Thermal Camera for Surveillance System

A surveillance system requires to achieve high accuracy of object detection at all times and to meet real-time processing requirements (30 frames per second) with high energy-efficiency. Since thermal cameras allow to see even in darkness unlike a RGB camera, object detection with a thermal camera obtains higher accuracy in the night, and thereby it has attracted much attention. However, since it is challenging to extract informative features from a thermal image, implementation challenges of an object detection with high accuracy remain. To meet the requirements, we present a sparse YOLOv2-based pedestrian detector with a thermal camera on an FPGA. For high accuracy, we propose a preprocessing that concatenates a thermal image with the background subtracted one for a detector to extract more informative features. Also, we develop a zero weight skipping architecture dedicated to our detector that contains a vectorizing unit that packs successive valid values into the same memory address to realize high parallel degree calculation. It leads to meet the real-time processing requirement with high energy-efficiency. Compared with a conventional one, F-score was 29 points higher, and speed was 3.3 times faster. Therefore, our system is more suitable for surveillance systems.
RBF neural network PID trajectory tracking based on 6-PSS parallel robot

Aiming at the problems of trajectory tracking of six-degree-of-freedom parallel robots, this paper proposes a control strategy combining RBF neural network and traditional PID. According to the nonlinear characteristics of parallel robot, RBF neural network is used to approximate the characteristics of arbitrary nonlinear function. The RBF neural network is optimized by gradient descent algorithm. The neural network PID controller is designed to find the optimal PID parameters through the adaptive self-learning ability of the neural network, thus achieving better trajectory tracking effect.
A Feedback Motion Plan for Vehicles with Bounded Curvature Constraints

The use of a feedback motion plan instead of the decoupled scheme consisting of separate plan and control phases can facilitate the task of proving the properties of an autonomous system. The advantage of using a feedback motion plan is the possibility to validate the whole plan offline before its execution, which means that trajectories having different initial states can be tested simultaneously. In this paper, we formulate a feedback motion plan based on the extension of the wavefront expansion to the case of vehicles having bounded curvature. Additionally, the use of a transition function and a Gaussian filter limits undesired oscillations in the resultant trajectories. The method is suitable for both single goal missions and path following. The paper illustrates the algorithm for the generation of the plan and presents simulation data containing example trajectories and analysis of tuning parameters. Finally, future developments are discussed.
An Improved method for sharing medical images for Privacy Preserving Machine Learning using Multiparty Computation and Steganography

Digital data privacy is one of the main concerns in today's world. When everything is digitized, there is a threat of private data being misused. Privacy-preserving machine learning is becoming a top research area. For machines to learn, massive data is needed and when it comes to sensitive data, privacy issues arise.With this paper, we combine secure multiparty computation and steganography helping machine learning researchers to make use of a huge volume of medical images with hospitals without compromising patients' privacy. This also has application in digital image authentication. Steganography is one way of securing digital image data by secretly embedding the data in the image without creating visually perceptible changes. Secret sharing schemes have gained popularity in the last few years and research has been done on numerous aspects.
Recurrence with prescribed number of residues

In this paper we show that for every positive integer m there exist positive integers x1,x2,M such that the sequence (xn)n=1∞ defined by the Fibonacci recurrence xn+2=xn+1+xn, n=1,2,3,…, has exactly m distinct residues modulo M. As an application we show that for each integer m⩾2 there exists ξ∈R such that the sequence of fractional parts {ξφn}n=1∞, where φ=(1+5)/2, has exactly m limit points. Furthermore, we prove that for no real ξ≠0 it has exactly one limit point.
Facial Expression Recognition: Residue Learning Using SVM

Residue learning using SVM is exploited to recognize facial expression in this paper. A facial expression consists of neutral component and expressive one(residue), which contains most of the expression information. Firstly, a cGAN is trained to generate neutral face image from an input face image. The intermediate layers record the information during this procedure. So secondly, kernel PCA and SVMs are exploited to analyze the residue in these intermediate layers. Results of experiments on five facial expression databases including BP4D, CK+, JAFFE, Oulu-CASIA and RAF show considerable performance compared with the latest methods.
On the Emerging Area of Biocybersecurity and Relevant Considerations

Biocybersecurity is a novel space for the 21st century that meets our innovations in biotechnology and computing head on. Within this space, many considerations are open for and demand consideration as groups endeavor to develop products and policies that adequately ensure asset management and protection. Herein, simplified and brief exploration is given followed by some surface discussion of impacts. These impacts concern the end user, ethical and legal considerations, international proceedings, business, and limitations. It is hoped that this will be helpful in future considerations towards biocybersecurity policy developments and implementations.
Quantum-Inspired DMATT-BiGRU for Conversational Sentiment Analysis

Conversational sentiment analysis (CSA) is emergent research field in natural language processing (NLP). This brings a lot of new issues worth studying and directions worth exploring. At the same time, there are many difficulties that need to be overcome. There are lot of challenges, such as lacking of effective deep learning models and being short of appropriate datasets. Inspired by the concept of density matrix in quantum mechanics, we propose a novel attention mechanism called DMATT and apply it to conversational sentiment analysis tasks. In the experiment, we find that deep learning model combined with DMATT has a great improvement in test results compared to the model with traditional attention mechanism. Recurrent neural networks (RNN) and their variants LSTM and GRU are very effective choices in solving time series problem such as conversational sentiment analysis tasks. In this paper, we propose a new model combining GRU and DMATT called DMATT-BiGRU. We experiment in multiple datasets, one of which is called ScenarioSA collected by ourselves.
Data Modeling for a NoSQL Database Service

Cloud-hosted NoSQL database services, such as AWS DynamoDB, offer significant advantages, including low start-up costs, high performance and availability, wide scalability, and ease of deployment and management. These advantages have led to their rapid adoption and growth. However, the data storage, querying, and modification features supported by such NoSQL services are very rudimentary in comparison with those of relational and object database systems. Further, data modeling decisions made to map application requirements to the supported NoSQL model have very significant impact on not only performance but also financial cost incurred in using the services. Unlike the well developed body of work for relational-and object- database design, there is a great dearth of systematic procedures for NoSQL database design. This paper addresses this design problem by providing methods that map standard data models to the typically idiosyncratic and rudimentary models supported by NoSQL database services, using AWS DynamoDB as a specific instance.
Fast AES Implementation Using ARMv8 ASIMD Without Cryptography Extension

While the ARMv8-A ISA allows for hardware accelerated cryptographic instructions, such extension is not available for every device, being added at the discretion of the CPU manufacturer. Prime examples of ARMv8 devices without this support are the low cost Raspberry Pi 3B/3B+/4 single board computers. This work presents an optimized AES implementation targeting CPUs without Cryptography Extension instructions, relying only on ASIMD operations. We show a new implementation that processes four blocks at the same time, which requires block permutations and modified versions of the main layers. In particular, we provide a new efficient formula for computing the MixColumns layer. The time performance our AES implementation outperforms the current ASIMD implementation found in the Linux Kernel by about 5%.
Private Authentication: Optimal Information Theoretic Schemes

The main security service in the connected world of cyber physical systems necessitates to authenticate a large number of nodes privately. In this paper, the private authentication problem is considered, that consists of a certificate authority, a verifier, many legitimate users (prover) and any arbitrary number of illegitimate users. Each legitimate user wants to be authenticated (using his personal key) by the verifier, while simultaneously wants to stay completely anonymous (even to the verifier and the CA). On the other hand, an illegitimate user must fail to authenticate himself. We analyze this problem from an information theoretical perspective. First, we propose a general interactive information-theoretic model for the problem. As a metric to measure the reliability, we consider the authentication key rate whose rate maximization has a trade-off with establishing privacy. Then, we analyze the problem in two different regimes: finite size regime (i.e., the variables are elements of a finite field) and asymptotic regime (i.e., the variables are considered to have large enough length). For both regimes, we propose schemes that satisfy the completeness, soundness and privacy properties. In finite size regime, the idea is to generate the authentication keys according to a secret sharing scheme. In asymptotic regime, we use a random binning based scheme which relies on the joint typicality to generate the authentication keys. Moreover, providing the converse proof, we show that our scheme achieves capacity in the asymptotic regime. For finite size regime our scheme achieves capacity for large field size.
Modeling The Thermal Prediction Using The Fuzzy Rule Classifier

The comfort of the indoor environment greatly affects the work efficiency of the personnel. Although “sensation” is a fuzzy conceptualization of `satisfaction with the thermal environment', it is frequently used as a measure for evaluating thermal comfort. Therefore, this is an innovation that the fuzzy rule classifier is used for modeling the thermal prediction based on the ambiguity of the data itself. This paper applies the fuzzy rule classifier to model the thermal prediction based on the PR-821 dataset. The simulation results show that the model has over-fitting but it can be solved by processing the data by SMOTEENN algorithm. The prediction accuracy of the improved model is greatly improved. Compared with other algorithms (CART, C4.5, Adaboost), the results show that the fuzzy rule classifier has a better predictive effect on thermal sensation.
Vertex-reinforced Random Walk for Network Embedding

In this paper, we study the fundamental problem of random walk for network embedding. We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW), to fully use the history of a random walk path. To solve the getting stuck problem of VRRW, we introduce an exploitation-exploration mechanism to help the random walk jump out of the stuck set. The new random walk algorithms share the same convergence property of VRRW and thus can be used to learn stable network embeddings. Experimental results on two link prediction benchmark datasets and three node classification benchmark datasets show that our proposed approach reinforce2vec can outperform state-of-the-art random walk based embedding methods by a large margin.MSC codesRandom WalkVertex-reinforced Random WalkNetwork Embedding
High Precision Canthus Alignment for Visible-Spectrum Gaze Tracking System

The visible-spectrum gaze tracker gains the advantages of good user experience and makes superior usability of consumer electronics applications. Still, the system accuracy as well as the precision of the gaze tracker appears to highly depend on the accuracy of the eye position. This paper addresses the issues of eye alignment and presents a robust algorithm for positioning the inner eye corner.
Advanced Density Peak and K-means Clustering on Image Segmentation

K-means is a classical and widely-used data clustering algorithm. Despite its effectiveness, the drawbacks are obvious that it needs to know k value previously and not suitable for complex situations. Density Peak clustering can practice on irregular data sets with a higher accuracy and better performance than K-means and doesn't need to get prior knowledge. However, few concentrated on their performances on image segmentation. In this paper, we propose novel image segmentation approaches based on K-means and Density Peak clustering which greatly reduce running time. Compared with current methods, our methods have improved aspects as following: 1) The methods could have much shorter run time performance than other current normal methods. 2) Unlike other current image segmentation methods, our method could save the original colors of the pictures and provide a rather real image segments. Experiments on test data will testify the validity of the methods and a detailed description based on empirical results will be provided as conclusions.
A Novel Approach Based on Gesture Recognition through Video Capturing for Sign Language

Communication is an important life skill which helps us to understand and provides a way to interact with the outside world. Communication language consists of facial expressions, hand gestures, and tone of speech for interpreting the actions and words spoken by a person. Sign language is the only source of communication for deaf and dumb people to communicate with the outside world. But very few normal people can understand the sign language properly. Sign language changes with the change of geographical regions, language and religions. So, almost every country of the world has its own sign language. India also has its sign language which is used by the physically impaired community (Indian Sign Language (ISL)). The work discussed here is a novel approach for recognition of ISL from continuous video captured from the web camera in real life condition.
First Order and Second Order Learning Algorithms on the Special Orthogonal Group to Compute the SVD of Data Matrices

The present paper deals with neural algorithms to learn the singular value decomposition (SVD) of data matrices. The neural algorithms utilized in the present research endeavor were developed by Helmke and Moore (HM) and appear under the form of two continuous-time differential equations over the special orthogonal group of matrices. The purpose of the present paper is to develop and compare different numerical schemes, under the form of two alternating learning rules, to learn the singular value decomposition of large matrices on the basis of the HM learning paradigm. The numerical schemes developed here are both first-order (Euler-like) and second-order (Runge-like). Moreover, a reduced Euler scheme is presented that consists of a single learning rule for one of the factors involved in the SVD. Numerical experiments performed to estimate the optical-flow (which is a component of modern IoT technologies) in real-world video sequences illustrate the features of the novel learning schemes.
Path Planning for USV with FG-DA-RRT Algorithm

Rapidly exploring Random Tree (RRT) algorithm for path planning has some shortcomings, such as a slow resolution for nearest node, polyline path, and so on. A Fibonacci initial trimming and Geometry trimming for the path of a Diagonal distance and Angle RRT (FG-DA-RRT) is proposed to plan the path for Unmanned Surface Vessels (USV). FG-DA-RRT has two characteristics, one is that the diagonal distance with the angle value is instead of the traditional Euclidean distance to calculate the nearest neighbors, which improves the speed of the neighboring nodes. The other is, a smooth path for FG-DA-RRT is modified by Fibonacci search method and geometric principle. Finally, FG-DA-RRT is applied to plan the path for USV in simulations. Simulation results indicate FG-DA-RRT has a super performance than RRT and RRT-Connect.
API Misuse Detection in C Programs: Practice on SSL APIs

Libraries offer reusable functionality through Application Programming Interfaces (APIs) with usage constraints such as call conditions or orders. Constraint violations, i.e. API misuses, commonly lead to bugs and security issues. Although researchers have developed various API misuse detectors in the past few decades, recent studies show that API misuse is prevalent in real-world projects, especially for secure socket layer (SSL) certificate validation, which is completely broken in many security-critical applications and libraries. In this paper, we introduce SSLDoc to effectively detect API misuse bugs, specifically for SSL API libraries. The key insight behind SSLDoc is a constraint-directed static analysis technique powered by a domain-specific language (DSL) for specifying API usage constraints. Through studying real-world API misuse bugs, we propose ISpec DSL, which covers majority types of API usage constraints and enables simple but precise specification. Furthermore, we design and implement SSLDoc to automatically parse ISpec into checking targets and employ a static analysis engine to identify potential API misuses and prune false positives with rich semantics. We have instantiated SSLDoc for OpenSSL APIs and applied it to large-scale open-source programs. SSLDoc found 45 previously unknown security-sensitive bugs in OpenSSL implementation and applications in Ubuntu. Up to now, 35 have been confirmed by the corresponding development communities and 27 have been fixed in master branch.
Generating infrared image from visible image using Generative Adversarial Networks

Infrared images have properties that are unaffected by illumination compared to visible images, object can be clearly recognized at day or night. Therefore, it is a better choice to use infrared images when training deep learning algorithms in unmanned systems. However, how to obtain a large number of infrared images makes it possible to train deep learning algorithm, which is the focus of our attention. In contrast to difficulties in direct shooting, computer simulations can generate large infrared images from visible images. But models that are trained on simulated data usually do not translate well to real scenarios. To bridge the domain gap between simulated and real infrared images, we exploit recent advances in paired image-to-image translation. We extend Pixel2Pixel to generate infrared image from visible image and propose a novel way of multilayer semantic information fusion, which significantly improves the quality of generated images. Finally, the image generated by our proposed PixelPro network is almost consistent with the real image distribution, achieved the purpose of expanding the dataset.