Reviewerly/exHarmony huggingface dataset:
Provable Reactive Navigation of Mobile Robots to a Moving Target in Unpredictable Dynamic Scenes

A speed-limited robot travels in a dynamic environment cluttered with arbitrarily shaped moving obstacles. There also is an unpredictably moving target in the scene. The robot measures the heading angle to the target and the distance to the nearest obstacle along any ray emitted from the robot. A simple reactive navigation strategy is presented that autonomously drives the robot to the target in a finite time provided that the algorithm is properly tuned. This is shown via a mathematically rigorous global convergence result under minor and partly unavoidable technical assumptions. Feasible closed-form recommendations on controller tuning are offered. Theoretical results are confirmed by computer simulation tests.
Research on Super-resolution Reconstruction Algorithm of Remote Sensing Image Based on Generative Adversarial Networks

Due to natural conditions and hardware manufacturing processes, the resolution of remote sensing images is generally low. Obtaining high-definition remote sensing images by simply improving hardware and manufacturing processes is not only costly and technically challenging but also cannot be deployed on a large scale. Aiming at the limitations of the traditional methods, this paper studies the image super-resolution reconstruction method for improving the generated anti-network. Firstly, the generator network is optimized, and an RRDB (Residual-in-Residual Dense without BN (Batch Normalization) is used. Block) module; secondly, the related idea of relativistic GAN (relativistic generative adversarial network) is introduced, the relative value of the discriminator is not the absolute value; finally, the sensation loss is improved, and the feature is used before the function is activated. The test results show that the proposed algorithm is better than SRGAN (Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network), SRCNN (Super-Resolution Convolutional Neural Network) and FSRCNN (Fast Super-Resolution Convolutional Neural Network). The clarity of the reconstructed image is improved, and the reconstructed image quality is significantly improved.
A visualization monitoring technique in camera surveillance system

Surveillance camera system is gradually becoming familiar with the popular of cameras and camera installation services. Usually these systems only support separated camera screens without establishing a panoramic view to track and assess an interested area. Overcoming that, the paper presents an object locating technique for visual monitoring in surveillance camera system with a combination of detecting the object's position on each specific camera and building the map to the physical space, then analyzing errors based on camera settings.
Classification of expressions in Indian Classical Dance using LBP

Face analysis has been a topic of interest since the past few decades and a lot of research has been done in the domains of face detection-recognition and facial expression recognition. Amongst the numerous applications that use face analysis, the application that is not much explored is an analysis of expressions in Indian classical dance. The expressions that are peculiarly used in Indian classical dance strictly follow the ancient literature on drama and dance. Also, the expressions are deliberate as opposed to real-life expressions. This is the motivation behind carrying out a separate analysis for dance-specific expressions. The dataset of facial images with dance-specific expressions is created. The images of trained classical dancers with and without makeup are captured in a controlled environment. The local binary pattern, a widely used texture descriptor, is used for feature extraction. The classification of the expressions into 7 classes viz. happy, sad, angry, fearful, surprised, disgusted, and neutral is carried out using linear SVM with 81.38% recognition rate. The expressions in Indian Classical Dance that are difficult to differentiate from a neutral (due to the nature of expression) need to be identified. To do so, the differentiability of the expression from neutral face is calculated using template matching and SVM. This experiment is carried out on our dataset successfully and more than 90% recognition rate is achieved for most of the expressions. "Anger" is identified as the expression that is difficult to differentiate from neutral in the dance specific dataset.
Research and Implementation of Object Tracking

Abstract Aiming at the problems that the traditional MeanShift has a singleness feature, this paper introduces depth feature based on binocular vision, thus proposes a depth feature based real-time MeanShift tracking algorithm. Firstly, a similarity measurement method based on advanced quadratic-form distance is put forward. Secondly, the self-adaptive adjustment mechanism of feature weight coefficient and updating strategy of object model are improved based on the color and depth features. Finally, the algorithm is simulated by Matlab. Results show that the proposed algorithm performs well in tracking accuracy, robustness.
Graph-Based Visualization of Stochastic Dominance in Statistical Comparisons

In this paper, a graph visualization scheme and methodology is proposed for representing, understanding, and interpreting the statistical comparison of means and the resulting stochastic dominance. The practicality and applicability of the visualization scheme and the methodology is illustrated through a case study, with data coming from higher education institutes in the United States of America (U.S.A.). The objective of the research is to make statistical results more accessible and readable, enabling the visual derivation of actionable insights.
Aggregation for Sensitive Data

In many modern applications, considerations like privacy, security and legal doctrines like the GDPR put limitations on data storage and sharing with third parties. Specifically, access to individual level data points is restricted and machine learning models need to be trained with aggregated versions of the datasets. Learning with aggregated data is a new and relatively unexplored form of semi-supervision. We tackle this problem by designing aggregation paradigms that conform to certain kinds of privacy or non-identifiability requirements. We further develop novel learning algorithms that can nevertheless be used to learn from only these aggregates. We motivate our framework for the case of Gaussian regression, and subsequently extend our techniques to subsume arbitrary binary classifiers and generalised linear models. We provide theoretical results and empirical evaluation of our methods on real data from healthcare and telecom.
Control Strategy of Mobile Robots Using Fuzzy-Gravitational Search Method and Review of Other Techniques

A systematic research methodology has been adapted using fuzzy-gravitational search algorithm for solving a complex navigational control problem of a mobile robot from source point to destination point while negotiating with obstacles. Inputs to the hybrid method are target angle, right obstacle distance, left obstacle distance, front obstacle distance. The interim output is interim steering angle. These interim inputs along with LOD, ROD, FOD are applied to gravitational search algorithm, and the outputs are final steering angle (SA). It is observed from the simulation and experimental results that the proposed technique is well suited for navigational control of robots in a densely populated environment. Keeping in view of the methodology used for control of robots, several artificial intelligence techniques are also discussed in the current paper.
An Application of Polymorphic Models for the purpose of Threat Assessment in Swarm-based Recognition System

In Information Fusion, like in other branches of The Applied Informatics there is an observable interest of the researchers in finding solutions of the known problems, exploring the potential of the existing approaches, and application of new techniques. The aim of this paper is to provide a concept of application of the new techniques: Polymorphic Models in Swarm-based Recognition System in order to resolve efficiently the well-known problem of Threat Assessment.
Genetic optimization of a trading algorithm based on pattern recognition

In the present paper, a trading strategy based on pattern recognition is optimized by means of a genetic algorithm. The genetic algorithm is used to determine decisions of buy/sell based on the patterns found through time for a portfolio in the stock market. The predominant algorithms used in this work were the K-means clustering algorithm to find the patterns in different time lapses, and the genetic algorithm for optimization. The results are supported by simulations using a selected shares of the Mexican stock market.
Performance Analysis of Feature Extraction Methods Based on Genetic Expression for Clustering Video Dataset

Clustering of high-dimensional video datasets consists of thousands of features, which encounter problems related to feature selection, computational complexities, and cluster quality. Features are extracted from key frames of videos using feature extraction techniques. Feature selection improves cluster quality by removing irrelevant features from the dataset before applying clustering algorithm. This paper compares the implementation of four feature extraction methods namely texture, speeded up robust feature (SURF), wavelet transform, and scale-invariant feature transform (SIFT). For detecting clusters of different sizes and shapes, density-based clustering algorithm (DBSCAN) is used. It becomes unstable when dealing with high-dimensional data. DBSCAN based on feature selection, new methodology like genetic expression has been adopted. The performance of this approach has been verified on approximately 100 real-world high-dimensional video datasets in terms of accuracy and purity. It has been ascertained that applying genetic expression for feature selection outperforms compared to other feature selection method.
A Multi-stage Strategy for Chinese Discourse Tree Construction

Building discourse tree is crucial to improve the performance of discourse parsing. There are two issues in previous work on discourse tree construction, i.e., the error accumulation and the influence of connectives in transition-based algorithms. To address above issues, this paper proposes a tensor-based neural network with the multi-stage strategy and connective deletion mechanism. Experimental results on both CDTB and RST-DT show that our model achieves the state-of-the-art performance.
Automated Detection of Brain Abnormalities Using Multi-Directional Features and Randomized Learning

Automated detection of brain abnormalities through magnetic resonance imaging (MRI) has made a significant stride in the past decade. The feature extractors exploited in the literature suffer from issues like limited directional selectivity and high dimensionality, and the classifiers used have critical drawbacks like slow learning speed, poor computational scalability, and trivial human intervention. The fast curvelet transform (FCT) and ripplet-II transform (R2T) provides improved discriminant ability and high directional selectivity. Extreme learning machine (ELM), a randomized learning algorithm for single layer feed-forward neural network, has received significant attention as it provides good generalization performance at much faster speed. In this chapter, the authors compare the effectiveness of two feature extractors based on FCT and R2T along with different ELM algorithms. These schemes have been evaluated on three brain MR datasets and comparative analyses have been made on several combinations of methods. Finally, the potential of the best scheme is compared to the state of the art.
TrustZone for Supervised Asymmetric Multiprocessing Systems

Many modern forms of asymmetric multiprocessing (AMP) architecture use hypervisors to increase software security by isolating the system software in virtual machines. However, efficient virtualisation depends on hardware support that is not available across all products. Within modern ARM architectures, the aforementioned software isolation can also be implemented using ARM TrustZone technology. This paper presents a TrustZone-based AMP architecture (TZ-AMP) that can consolidate multiple system software environments securely on devices that lack hardware virtualisation support. We evaluate our prototype on the ARMv7-A architecture, and demonstrate TrustZone-based context-switching performance in the order of microseconds, confirming that TZ-AMP maintains high performance while also achieving hardware-backed software security.
A Simple Model for Reasoning about Limits on Coupling in Object-Oriented Software

Given that all of a program's code must be reachable from its main method, and that type-safety causes this reachability to substantially manifest as compilation dependencies among the program's classes, a simple model for reasoning about limits on coupling among a program's classes-a long-standing question in the software design literature-is proposed. The model takes the form of a directed graph with classes as nodes and four distinct forms of compilation dependencies as labeled edges. As manifested in compilation dependencies, the model suggests that there is a trade-off between direct and transitive coupling, and that certain forms of coupling in a class are prerequisites for the existence of other forms in that same class.
Hypersphere Embedding and Additive Margin for Query-by-example Keyword Spotting

Query-by-example (QbE) keyword spotting is convenient for users to define their own keywords, so it is useful in device control. However, conventional regular softmax, which is commonly used for training QbE models, has two limitations. First, the learned features are not discriminative enough. Second, norm variations of the unnormalized features affect computing cosine similarities. To address these issues, this paper introduces normalization and additive margin into residual networks for QbE keyword spotting. Features and weights are normalized on a hypersphere of fixed radius. Additive margin further helps to reduce the intra-class variations and increase inter-class differences. Based on public datasets AISHELL-1 and HelloNPU, we design three different test sets, namely in-vocabulary, out-of-vocabulary, and cross-corpus, to evaluate our proposed method. Experiments show that our proposed method can learn more discriminative embedding features. For totally unseen situation, our proposed method achieves a relative false rejection rate reduction of 46.60% when the false alarm rate is 2% in cross-corpus evaluation, compared with regular softmax.
Medical image segmentation using modified fuzzy c mean based clustering

Locating disease area in medical images is one of the most challenging task in the field of image segmentation. This paper presents a new approach of image-segmentation using modified fuzzy c-means(MFCM) clustering. Considering low illuminated medical images, the input image is firstly enhanced using histogram equalization(HE) technique. The enhanced image is now segmented into various regions using the MFCM based approach. The local information is employed in the objective-function of MFCM to overcome the issue of noise sensitivity. After that membership partitioning is improved by using fast membership filtering. The observed result of the proposed scheme is found suitable in terms of various evaluating parameters for experimentation.
Text Classification Method Based on Co-Occurrence Events

In view of the problem that emergency events are to be reported only following with the time sequence and theme types tend to be difficult to be distinguished from the texts. This paper first attempt to describe the reporting texts through events, in accordance with co-occurrence events appear in the texts, then endeavors to explore the properties such as distance and correlation between co-occurrence events in the text on the basis of the semantic relations between events to cluster the high similarity texts and eventually achieves text classification. Experiment shows that combining semantic relations between events with the relationship between co-occurrence events are more effective for text categorization.
Four Epistemic Traditions of Evaluation

On what grounds do we judge whether a theory for design is useful, valuable or successful? What is validity in constructive design research? What is the role of theory produced from design? Chapter 4 dealt with ways of construing hypotheses and how the K-R model may help map the constituent parts of a research process. Chapter 5 presented the typology of ways of drifting explaining how design experiments inform and urges design researchers to drift. This chapter turns to how design researchers can evaluate and justify their claims about knowledge. And completes our core trilogy of dialectic activities serving the dual ambition of relevance and knowledge production in constructive design research.
A New Framework Approach to Predict the Wine Dataset Using Cluster Algorithm

Examination, plan, and usage of programming frameworks for online administrations are a repetitive and testing. Amazon programming gives item proposals, Yahoo! powerfully suggests web pages, afflux makes proposals for films, and Google makes ads on the Internet. Things are suggested in view of the inclinations, needs, attributes, and conditions of clients. The wine informational index has been used in examine for quite a while, and still, it stays as the benchmark informational collection. Nature of wines is hard to characterize as there are numerous variables that impact the apparent quality. This paper exhibits a basic survey of research slants on wine quality and client-driven closeness measures also. A novel client-driven likeness measure in item bunching is proposed to assess the prominent wine informational index named red wine dataset. The test results got in this work can give preferable proposals to item purchasers over the current frameworks. The proposed approach is skilled to gather the red wine dataset into requested gatherings of favored wine variations and can judge the wine quality in view of these client inclination gatherings.
Computed Tomography Medical Image Compression using Conjugate Gradient

Image compression which is a subset of data compression plays a crucial task in medical field. The medical images like CT, MRI, PET scan and X-Ray imagery which is a huge data, should be compressed to facilitate storage capacity without losing its details to diagnose the patient correctly. Now a days artificial neural network is being widely researched in the field of image processing. This paper examines the performance of a feed forward artificial neural network with learning algorithm as conjugate gradient. This work performs a comparison between Conjugate gradient technique and Gradient Descent algorithm is done. MSE and PSNR are used as quality metrics. The investigation is carried on CT scan of lower abdomen medical image.
A Cognitive ML Agent for Airborne Networking

This conference note describes a Deep Reinforcement Learning architecture specifically designed to improve wireless network performance over a heterogeneous airborne wireless network consisting of multiple waveforms, antennas, platforms, link protocols, frequencies, spatial transmission, and codes. The cooperative optimization of this high dimensional space is a difficult problem which obviously has a highly correlated characterization where human network operators cannot possibly capture these correlations. Model-free Reinforcement Learning techniques represent a potential solution to our problem. Specifically, we use Deep Q-Learning Networks (DQN) to improve networking performance. We have developed a high-fidelity network simulation tool we call Tactical Airborne Network Simulator (TANS) which we use to train our neural network before deploying to the field where the asset is deployed to some mission which is hopefully somewhat similar to the scenarios used for training. By utilizing the model developed under the TANS training scenarios for the target mission scenario our learning technique gets a head start, rather than using a truly model-free approach. Our technique is codified in the ML community as “Deep Transfer Learning” [4] where terms and metrics have been examined. This paper represents an initial investigation into both the decision support agent architecture and the ML technique. Upcoming research will be described below including our vision for an expanded agent architecture as well as ideas for improved ML techniques which ultimately will result in better wireless network performance. Here we demonstrate a minor throughput performance improvement of 4% using a proof of concept agent over the use of a standard unassisted network. We improved the throughput from 309kbps to 324 kbps.
Houses Bombing in Ravixe: a Bench for High Level Fusion Evaluation

Many benchmarks exist for the evaluation of algorithms in fields such as numerical optimization, automatic planning, SAT solving, Natural Language Processing, automatic code parallelization, signal processing, machine learning and low level fusion. To the best of our knowledge, however, there is no such bench existing for the evaluation of high level information fusion. In this paper, we describe a benchmark that we developed with the aim of evaluating high level fusion algorithms. The bench is related to a house bombing scenario in a fictive world. The scenario is fictional and takes place in a city, within a context of instability in the population between the members of two different communities. We describe the scenario itself, as well as the different sources of information and the kind of information they provide. The whole set of information available in the bench is described. Starting from a complete version of the bench that contains all the information items exchanged during the scenario, we derived several other benches that contain less or altered information items. This will enable evaluating the robustness of the high level information fusion systems, according to the quality of the information they are provided with and without the imperfection of information extractors exhibiting pieces of information from raw data.
Lung Cancer Patient’s Survival Prediction Using GRNN-CP

Published results for cancer patients have been previously estimated by applying various machine learning techniques to large. especially, for lung cancer, it is not well known to the time, which sorts of techniques would generate more imminent information, and which data attributes should be employed in order to prepare this information. In this study, a supervised learning technique is implemented to analyze lung cancer patients in terms of survival, the purpose of this study is to predict lung cancer and to compose an aiding model that will help form a more reliable prediction as a factor that is vital for advancing survival time evaluation. We utilize general regression neural networks (GRNN) for replacing the regular predictions with prediction periods to achieve a moderate percentage of confidence. The mechanism applied here employs a machine learning system called conformal prediction (CP), to assign consistent confidence measures to predictions, which are combined with GRNN. We apply the resulting algorithm to the problem of lung cancer diagnosis of supervised learning techniques is applied to the NCI database to classify lung cancer patients. Experimental results confirm that the prediction formed by this method is feasible and could be useful in clinical institutions.