Reviewerly/exHarmony huggingface dataset:
Non Holonomic Collision Avoidance under Non-Parametric Uncertainty: A Hilbert Space Approach

We consider the problem of an agent/robot with non-holonomic kinematics avoiding dynamic and static obstacles. Additionally there may be bounds/constraints on the configurational space of the robot in the form of lane/corridor boundaries. State and velocity noise of the robot, the lanes, the obstacles, and the robot’s control noise are modelled as non-parametric distributions as Gaussian assumptions of noise models are violated in real-world scenarios. Under these assumptions, we formulate a robust MPC that samples robotic controls effectively in a manner that aligns the robot to the goal state while avoiding obstacles and staying within the lane bounds under the duress of such non-parametric noise. In particular, the MPC incorporates a distribution matching cost that effectively aligns the distribution of the current collision cone to a certain desired distribution whose samples are collision-free. This cost is posed as a distance function in the Hilbert Space, whose minimization typically results in the collision cone samples becoming collision-free. We show tangible performance gains compared to methods that model the collision cone distribution by linearizing the Gaussian approximations of the original non-parametric state and obstacle distributions. We also show superior performance to methods that pose a chance constraint formulation of the Gaussian approximations of nonparametric noise without subjecting such approximations to further linearizations. The performance gain is shown both in terms of trajectory length and control costs that vindicates the efficacy of the proposed method. Finally we show the proposed method being used to navigate with a non holonomic differential drive robot in real-time in a realistic setting in Gazebo with dynamic and static obstacles. To the best of our knowledge, this is the first presentation of non-holonomic collision avoidance of stationary obstacles, moving obstacles and lane constraints in the presence of non-parametric state, velocity, actuator and lane boundary noise models.
Key-Oblivious Encryption from Isogenies with Application to Accountable Tracing Signatures

Key-oblivious encryption (KOE) is a promising cryptographic primitive that randomizes the public keys of an encryption scheme in an oblivious manner. It has applications in designing accountable tracing signature (ATS) that facilitates the group manager to revoke the anonymity of traceable users in a group signature while preserving the anonymity of non-traceable users. KOE is an independent primitive and may serve as a technical building block in designing privacy preserving protocols.
Semantic Enhanced Cross-modal GAN for Zero-shot Learning

The goal of Zero-shot Learning (ZSL) is to recognize categories that are not seen during the training process. The traditional method is to learn an embedding space and map visual features and semantic features to this common space. However, this method inevitably encounters the bias problem, i.e., unseen instances are often incorrectly recognized as the seen classes. Some attempts are made by proposing another paradigm, which uses generative models to hallucinate the features of unseen samples. However, the generative models often suffer from instability issues, making it impractical for them to generate fine-grained features of unseen samples, thus resulting in very limited improvement. To resolve this, a Semantic Enhanced Cross-modal GAN (SECM GAN) is proposed by imposing the cross-modal association for improving the semantic and discriminative property of the generated features. Specifically, we first train a cross-modal embedding model called Semantic Enhanced Cross-modal Model (SECM), which is constrained by discrimination and semantics. Then we train our generative model based on Generative Adversarial Network (GAN) called SECM GAN, in which the generator generates cross-modal features, and the discriminator distinguishes true cross-modal features from generated cross-modal features. We deploy SECM as a weak constraint of GAN, which makes reliance on GAN get reduced. We evaluate extensive experiments on three widely used ZSL datasets to demonstrate the superiority of our framework.
An analysis of optimization performance on chaotic evolution algorithm using multiple chaotic systems with elite strategy

Chaotic Evolution (CE) is simply a population-based algorithm that applies the ergodic properties of the chaotic system into the search process. With the help of the chaotic motion, the algorithm can visit any arbitrary point with a movement track. However, its search performance should be enhanced to get a better algorithm as CE spends a lot of invalid computation costs when solving the complex optimization problem due to premature convergence and/or slow convergence. In this paper, we introduce, make a comparison, and evaluate the effectiveness of the proposed CE with Elite strategy (ECE), which is based on the heuristic information from the elite individuals of the current population. Moreover, because in CE, the distribution characteristic of the chaotic system plays a very important role in deciding the search power, we also make paired comparison and analyze the optimization performance between our proposed ECE algorithms using different types of chaotic map and the canonical CE by applying 12 well-known Benchmark functions as the test functions and some measurement tests. The investigation process indicates that the ECE algorithm is significantly better on the majority of the test functions.
Hybrid Representation to Locate Vulnerable Lines of Code

Locating vulnerable lines of code in large software systems needs huge efforts from human experts. This explains the high costs in terms of budget and time needed to correct vulnerabilities. To minimize these costs, automatic solutions of vulnerabilities prediction have been proposed. Existing machine learning (ML)-based solutions face difficulties in predicting vulnerabilities in coarse granularity and in defining suitable code features that limit their effectiveness. To addressee these limitations, in the present work, the authors propose an improved ML-based approach using slice-based code representation and the technique of TF-IDF to automatically extract effective features. The obtained results showed that combining these two techniques with ML techniques allows building effective vulnerability prediction models (VPMs) that locate vulnerabilities in a finer granularity and with excellent performances (high precision (&amp;gt;98%), low FNR (&amp;lt;2%) and low FPR (&amp;lt;3%) which outperforms software metrics and are equivalent to the best performing recent deep learning-based approaches.
Span-based Joint Extracting Subjects and Objects and Classifying Relations with Multi-head Self-attention

Entity recognition and relation extraction are two central tasks for extracting information from unstructured text. The current popular models are to perform joint entity and relationship extraction at the span level. However, they do not take full advantage of the uneven distribution of information values in natural languages and their essence of extracting entities before classifying relations leads to entity redundancy. To solve these problems, we propose a novel span-based joint entity and relationship extraction model. We embed the multi-head self-attention layer to the existing span-based entity and relation joint extraction model to leverage valuable information such as semantic and syntactic features in the input sentences. We also propose a novel method to extract subject and object, which can alleviate the entity redundancy problem. In the ablation experiments, we demonstrate the benefits of both improvements for extracting relation triples. The micro f1 of our model on Con1104 is 72.82, the macro f1 is 74.20, and the f1 on SciERC is 52.03, which are better than the currently widely used model.
Credit Strategies for MSMEs : Mathematical solution based on TOPSIS and Fisher models

In recent years, with the rapid development of the Internet era and the surging trend of emerging platforms such as e-commerce, the capital demand of medium, small and micro enterprises(MSMEs) has expanded year by year, and their credit decisions have gradually become a core issue for banks' management. In order to solve the problem in depth in the field, this paper adopts the statistical data related to 123 enterprises with credit records, 302 enterprises without credit records, and the relationship between loan interest rate and customer churn rate in 2019. Based on the strength assessment model and credit rating, the credit risk assessment model is used to determine the annual interest rate and the loan amount.For enterprises with no credit history, the Fisher discriminant method is used to discriminate enterprises with known indicators from those with unknown ratings, and the discriminant credit rating of enterprises with no credit history is obtained. Based on the strength assessment model, this study combines creditworthiness with risk assessment of MSMEs eligible for loans to obtain risk scores and ratings, and eventually determines the annual interest rate and loan amount for each enterprise according to the annual interest rate matrix model.This paper develops a credit decision model for MSMEs with the aim of providing banks with better credit strategies to reduce credit risk and maximize bank profits.
A protection and assurance version for E-authorities Web Services based totally on Cloudlet computing

In the present scenario, Web Administration and cloud computing are the spine architecture of any e-authorities application due to its interoperable and robust nature. Web benefits that are kept up in Cloud computing bring in easy consideration in examination and industry as far as getting the conveyed Web administrations. Consequently, gaining more trust between the imparted Web administrations which has been continuously getting more accessible for Web administrations shoppers, executives, and Web specialist organizations particularly the e-government administrations. Likewise, the proposed architecture is handling the issues that emerge when Web administration purchasers are speaking with most of the e-legislative Web administrations are furnished through a depended on Cloud-based system that is not bound via any administrative authority. Furthermore, Cloudlet computing is used to quantify the supplier of Web benefits' reliability and compliance to the distributed strategy using input from the Web administration customer.
Orientation Estimation in MRI of Prostate Cancer Patients: When Simple Models Perform Better

Magnet Resonance Imaging (MRI) is an important modality in the diagnostic workup of prostate cancer. Poor image quality is critical for detection of tumors and classification according to the Prostate Imaging Reporting and Data System (PI-RADS v2.1). Because of that a precise image acquisition is highly important. Therefore a fully automated quality check is crucial.In this paper we present the first step of an automated multi-step quality check, which consists of deep learning-based orientation estimation for prostate MRI. It is a new field of application in terms of medical quality control. The proposed method achieves a mean absolute error of less than two degree regarding the optimal axial orientation based on the sagittal view. By this means, we achieve values which improve the axial orientation up to 36% on provided examination data. We therefore are able to enable a better setting for the best possible viewing angle and reduce the risk of overlooking a tumor.
Programming to develop reports during online classes generating docx or pdf files including mathematical analysis and graphic expression of remote activities

The programming was designed to develop reports that include mathematical and graphical analysis during the online class modality. The report generator included within the MATLAB software libraries allows the generation of files in pdf, word, power point and html formats to be developed through programming. The software captures and generates dynamic reports of the results and graphical expressions to document the results in a single report, facilitating collaboration with other users to share a template based on professional standards in companies, organizations, educational institutions and universities. Importing libraries like mlreportgen.dom and mlreportgen.report into a script, specifies the structure of objects such as the header, titles and subtitles, image footer and table citation. The report for the delivery of online activities, students are required to enter their personal information, the report includes the equations and the results generated by the template makes it easy for students to deliver the activities instantly. Obtaining a script capable of generating reports with professional standards of remote activities, developed for delivery, thus allowing immediate collaboration and evaluation.
Anomaly Detection in Smart Home Environments using Convolutional Neural Network

The use of smart devices in home environments has been increasing in recent years. The wireless connection of these devices to the internet enables smart homes to be built with less cost and hence, recognition of activities in home environments and the detection of possible anomalies in activities is important for several applications. In this study, we propose a new method based on the changepoint representation of sensor data and variable-length windowing for the recognition of abnormal activities. We present comparative analyses with different representations to demonstrate the efficacy of the proposed scheme. Our results on the WSU performance dataset show that, the use of variable-length windowing improves the anomaly detection performance in comparison to fixed-length windowing.
Implementing Open Source Biometric Face Authentication for Multi-factor Authentication Procedures

This study proposes a solution that extends the capabilities of web information systems with single-factor authentication by introducing an additional authentication factor based on biometric face recognition. The proposed solution design and its main operation steps are presented and discussed. The solution utilizes the standard multimedia functionality of popular web browsers and supports available or built-in image capturing devices (photo and web cameras). Robust program algorithms from the open source computer vision library are used for face image processing and analysis. Experimental testing and validation of the algorithms for face localization and recognition are conducted with image sets produced with consideration of reality. Experimental results demonstrate high effectiveness with a success rate of 80% … 93% for the solution based on the local binary pattern face localization algorithm with the local binary pattern histogram face recognition algorithm.
Study on post-processing algorithms for true random number generators

Randomness is essential for cryptographic applications. The true random number generators (TRNGs) produce true randomness based on unpredictable physical phenomena and offer a stronger security guarantee than pseudorandom number generators (PRNGs). However, hardware entropy sources rarely provide perfect random numbers, postprocessing is required for TRNGs to overcome potential statistical defects. In this paper, we introduce and analyze four conventional lightweight post-processing algorithms. Then, we perform an in-depth study on the hash function based postprocessing from the applicability perspective.
Utilization of Location-Based Services Using Augmented Reality Perception Method For Android Based School Search

Data technology has become the main means for activities in various sectors of life, one of which is location-based services for SMA/SMK schools in the Bangka Belitung area. By using a location-based service application, it is expected to be able to overcome the problem of finding the position of SMA/SMK in the Bangka Belitung area. In this study, we would like to discuss the search for SMA/SMK positions. Through the push, Location-Based Service will be displayed in the smartphone, which will help identify the presence of the school's position on the smartphone. The result of the application that is formed is an Android-based application that can recognize the presence of SMA/SMK positions. This study aims to create a location-based service application that is combined with the concept of overlaying geographic position information from augmented reality perception, which is one type of augmented reality. The overall application quality test results include 1). Based on the Functionality aspect, the percentage of the actual score got a total score of 91.8%. 2). Based on the aspect of reliability, the percentage of the actual score got a total score of 97.6%. 3). Based on the Usability aspect, the percentage of the actual score gets a total score of 92.2%. 4). Based on the aspect of efficiency, the percentage of the actual score gets a total value of 76.5%. 5). From the calculation of the total score in percent is 89.5%. So based on the total score in percent, it can be concluded that the overall application quality test results are 89.5%, so the prototypes produced in this study are included in very good criteria.
Continual Learning with Laplace Operator Based Node-Importance Dynamic Architecture Neural Network

In this paper, we propose a continual learning method based on node-importance evaluation and a dynamic architecture model. Our method determines the important nodes according to the value of Laplace operator of each node. Due to the anisotropy of the important nodes, the sparse sub-networks for the specific task can be constructed by freezing the weights of the important nodes and splitting them with unimportant nodes to reduce catastrophic forgetting. Then we add new nodes in networks to prevent existing nodes from being exhausted after continuously learning many new tasks, and to lessen the negative transfer effects. We have evaluated our method on CIFAR-10, CIFAR-100, MNIST, Fashion-MNIST and CUB200 datasets and it achieves superior results when compared to other traditional methods.
Face Clustering approach for Crime Rate Reduction in Smart City Development

There has been an enormous ascent in the pace of violations in India over the most recent couple of years and reports say that the greater part of these lawlessness are occurred by a gathering of multiple individuals. Subsequently to distinguish and get these lawbreakers, a optimum solution has been proposed. The Emerging field like Convolutional Neural Network is utilized in this arrangement which provides a Face Identification and recognition model. So that the likelihood of relationship of an individual person with other people can be analyzed by this kind of approach. It finds way to monitor a suspected person activity in a social gathering. The framework of this model is the combination of both CNN and AI design. In order to figure out an individual person image, the applications like face identification and face grouping should be done in parallel. This design is suitable for faster compilation and face comparison applications and the results are provided with maximum accuracy compared with eigenfaces approach. The face can be encoded, and it is converted and grouped into a 128-D cluster. And also, the storage of the clusters can be done in the form of array. The confidence relationship can be measured by association rule mining technique that is final step in Grouping algorithm. Application-based interface can be built between the proposed framework and google cloud by which efficient decisions making process is achievable to reduce Crime Occurrence. Because finding the source of Crime is essential in Smart City Development Process.
지식 네트워크 분석을 활용한 비서교육의 연구 동향

The purpose of this study was to provide the trends of research in secretary education area using knowledge network analytic method. For this purpose, 540 keywords were extracted from 195 research papers from the Korea Citation Index (KCI) database during 18 years from 2003 to 2018. The collected data were subjected to a refinement process, and 32 keywords were selected based on the word frequency. The selected keywords were converted to matrix data and analyzed by social network analysis. The results are as follows: first, there were 32 keywords that appeared more than 4 times in the secretary education, and it was the phenomenon of monopolization of majority keywords such as secretary, secretary education, curriculum and secretarial studies. Second, secretarial education research was mainly published in social sciences, business administration, and Journal of Secretarial Studies research. Third, the knowledge network groups in this study were job research, secretary education, and career guidance. The results of this study can be used as a useful information for research trends of secretary education area.
Research of expediency of application of AVX512 for modern digital signature schemes implementations

The third stage of the NIST PQC competition is currently underway, which aims to create new post-quantum standards in cryptography. The vast majority of finalists are representatives of lattice-based cryptography. Electronic signatures include the CRYSTALS-Dilithium schemes. This paper investigates the feasibility of using AVX512 to optimize software implementations of NIST PQC finalists among electronic signatures on algebraic lattices. Since the most expensive operation in such schemes is the multiplication of polynomials, the main attention is paid to the optimization of this operation. In particular, the method of realization of theoretical and numerical transformation using AVX512 for electronic signature schemes CRYSTALS-Dilithium is presented in the work. The increase in speed is shown in comparison with the reference optimized author 's implementations.
A Robotic System Architecture Based on Safety Controller and Priority Module Using Robot Operating System (ROS), Sensor Fusion and Human Robot Interaction for Control and Safety

Since the robotic topic is evolving every day, and humans are in constant interaction with robots that use different sensors and different environment, it is needed to take into consideration the importance of sensor fusion for motion control. The method of sensor fusion consists of merging the data from different sensors to obtain more accurate data for object detection as well as parameters and statements required for the auto location, mapping, path planning, motion control, and motion execution. There are different ways to make possible the human-robot interaction, but no matter the concept implemented the safety of the human and the robot needs to be one of the priorities of the robotic system. In this paper the continuation of the research work is presented from the previously proposed robotic system. The robotic system is extended to include a safety controller and priority module. Also, the improved architecture will be able to receive commands from multiple human inputs, and it will determine which command will have the higher priority to be executed. The paper focuses on the continuation of the research work through detailed implementation, some test case scenarios and their results taking into consideration that the robot can execute a safety measure. The simulations are done using ROS and Gazebo.