Reviewerly/exHarmony huggingface dataset:
Mixed effects state-space models with Student-<i>t</i> errors

In this article, mixed-effects state space models (MESSM, [Liu D, Lu T, Niu X-F, et al. Mixed-effects state-space models for analysis of longitudinal dynamic systems. Biometrics. 2011;67(2):476–485.]) are revisited. MESSM can be considered as an alternative to study the HIV dynamic in a longitudinal data environment, defining the mixed-effects component into state-space models setup. As in Liu et al.[Liu D, Lu T, Niu X-F, et al. Mixed-effects state-space models for analysis of longitudinal dynamic systems. Biometrics. 2011;67(2):476–485.], we consider a hierarchical structure to capture possible differences between the immune systems for different patients. We extend MESSM, allowing observational errors to follow a more flexible distribution to take account for heavy tails. Using the Bayesian paradigm, an efficient Markov Chain Monte Carlo (MCMC) algorithm based on McCausland et al. [McCausland WJ, Miller S, Pelletier D. Simulation smoothing for state.space models: A computational efficiency analysis. Comput Stat Data Anal. 2011;55(1):199–212.] is introduced for parameter and latent variables estimation. Moreover, the mixing variables obtained as a by-product of the scale mixture representation can be used to identify outliers. The methodology is illustrated using artificial and real datasets in order to investigate the properties and performance of the proposed model.
Nonlinear l2,p-norm based PCA for Anomaly Network Detection

extraction methods KDDcup99 NSL-KDD Intrusion detection systems are well known for their ability to detect internal and external intrusions, it usually recognizes intrusions through learning the normal behaviour of users or the normal traffic of activities in the network.So, if any suspicious activity or behaviour is detected, it informs the users of the network.Nonetheless, intrusion detection system is usually prone to a high false positive rate & a low detection rate as a consequence of the tremendous amount of meaningless information used in the network traffic utilized to create the intrusion detection system.To overcome that, many techniques like Principal Component Analysis (PCA), L 1 -PCA and 2,p -norm based PCA were suggested.However, these methods are linear and not robust to outliers.This paper introduces the nonlinear variant of the 2,p -norm principal component analysis.Namely, the nonlinear 2,p -norm principal component analysis intends to project the data sets into a more feasible form so that the meaning of the data is damaged as less as possible.The proposed technique is not uniquely robust to outliers but keeps PCA's positive properties as well.Experimental results on the datasets KDDCup99 and NSL-KDD show that the proposed technique is extra effective, robust and outperform PCA, L 1 -PCA and 2,p -norm based PCA algorithms.
Semantic search using Natural Language Processing

Natural Language Processing (NLP) is a computerized way of analyzing texts. NLP involves the acquisition of knowledge on how a person understands and uses language. This is for the development of appropriate tools and techniques that allow computer systems to understand and manipulate natural languages to perform various desirable tasks. This article reviews the literature in the NLP. It also provides information about NLP history. Additionally it provides architectural design for semantic search tool using NLP, and discussed architectural modules, domain and constraints. This work proposes future possibilities for research and its application.
Method for Automatic Segmentation of Vehicles in Digital Images

Introduction . Modern systems for active vehicle safety are designed to significantly reduce the number of road accidents. Sensors based on monocular cameras are increasingly being introduced by the world's leading automakers as an effective tool for improving traffic safety. Modern methods of localisation and classification, combined with semantic segmentation algorithms, allow for image division into independent groups of pixels corresponding to each object. However, the problem of developing segmentation algorithms ensuring improved quality of image segmentation remains to be solved. Aim . To develop an automatic method for segmenting a given object during image analysis. Materials and methods . An automatic method for segmenting vehicles in an image was proposed. The method presented herein allows semantic segmentation of the object of interest, based upon a priori information about the bounding boxes, which frame the objects in the image. Bounding box information is used to transform an image into a polar coordinate system where the pixels of the image act as the edges of a weighted graph. A closed contour is obtained around the object of interest by using the shortest path search algorithm and inverse transformation to the Cartesian coordinate system. Results . The experiments confirmed the correctness of the selected area of interest based on this algorithm. Jacquard’s similarity coefficient for the Carvana open database is 85 %. Furthermore, the proposed method was applied to different classes of images from the Pascal VOC database, thus demonstrating the ability to segment objects of other classes. Conclusion . The main contribution of the proposed method was as follows: 1) segmentation of the object of interest at the level of modern methods, and in some cases in excess thereof; 2) the study presents a new look at the way of tracking object contours.
Traffic Jam Prediction Based on Analysis of Residents Spatial Activities

The prediction of urban traffic congestion has always been one of the important contents in the research of intelligent transportation systems. The difficulty in predicting urban traffic congestion is that urban traffic operation is essentially a collection of spatial activity planning for residents under certain conditions. The huge group of residents themselves has great complexity and uncertainty. Traditional neural networks mostly focus on road characteristic data and road condition data, and lack of in-depth exploration of the fundamental factors of traffic congestion for residents' travel. So we propose a traffic congestion prediction model based on the analysis of residents' spatial activities. Starting from the residents' activities, the simulation of urban traffic operation conditions can more realistically reflect the traffic congestion situation of the city at specific times and roads, and quickly generate model results. The experimental results show that the model analyzed by residents' spatial activities runs fast and has high prediction accuracy. The results are in line with the actual situation and have strong practical value.
Underwater Image Enhancement Using Dual Adversarial Network

Since the degradation of the image has seriously constrained the development of marine research, the underwater image enhancement has been paid more and more attentions. Due to the diversity of underwater images (for example, underwater images show different attenuation and color bias in different scenes) and the lack of underwater datasets, most existing methods usually show satisfactory results on some kinds of underwater types. To solve the problems, we built a novel model, including two adversarial network blocks, to learn the essential content features of multiple underwater types and restore high quality images. We trained the model under the synthetic dataset based on Jerlov underwater type image dataset. Experimental results show that the model not only outperforms most previous methods in PSNR and UIQM but also shows the generalization ability.
The Design of Nutrition Ordering System without Queuing in the College Canteen

In recent years, with the rapid development of information technology and the expansion of its application range, a variety of ordering systems relying on information platforms and mobile technologies have emerged. This paper researches the ordering system in the college canteen. Aiming at improving college students' status quo of diet and efficiency of canteen dining, a nutrition ordering system without queuing in the student canteen is designed, by constructing a database and combining genetic algorithms. The ordering system can generate recommended recipes based on the user's personal physical condition and accept the user's personalized adjustments. Finally, the system forms a completely closed loop based on the evaluative feedback of healthy diet.
Near-lossless Image Compression with Parity Reduction

In our previous work, we introduced a lossless image compression algorithm (called BWIC_I) based on hierarchical prediction, inversion, and context-adaptive coding techniques. We showed that BWIC_I outperformed most commonly used standard color image compression algorithms, including JPEG in lossless mode and JPEG 2000, on a variety of well-known image data sets. In this study, we introduce a near-lossless image compression algorithm that essentially consists of parity reduction by dropping the least significant bit of pixel values of RGB color images and then compressing the resulting data with BWIC_I. The proposed algorithm provides a guaranteed minimum PSNR value. Moreover, it does not require a specialized encoding and decoding algorithm and can be utilized in conjunction with other generic image compression algorithms. The compression performance of the proposed technique is considerably better than JPEG and JPEG 2000, as demonstrated on the standard Kodak image set.
Research on Distrubuted Optimization Control of Multi-Agent System

With the rapid development of science and technology, computer technology and artificial intelligence have entered the public's field of vision. Nowadays, intelligence is reflected in all aspect of life, but with the development of artificial intelligence, centralized control cannot meet the development needs of science and technology. In order to further improve the performance indexes such as the speed and reliability of the system operation, parallel computing and distributed control has more flexibility and convenience in the control method, which leads to the rapid development of distrubuted control. At present, multi-intelligent system, as a relatively complex system, has attracted the attention of computer, artificial intelligence, communication control and other fields. In this paper, the research on distributed optimization control of multi-intelligence systems will be analyzed in depth.
RobustRepStream: Robust stream clustering using self-controlled connectivity graph

A major challenge in stream clustering is the evolution in the statistical properties of the underlying data. As clustering is inherently unsupervised, selecting suitable parameter values is often difficult. Clustering algorithms with sensitive parameters are often not robust to such changes, leadi ng to poor clustering outputs. Algorithms using K-NN graphs face this problem, as they have a sensitive K-connectivity parameter which prohibits them from adapting to stream concept evolution. We address this by controlling the excess of the skewness of edge length distributions in the underlying K-NN graph by introducing novel skewness excess concept. We demonstrate the asymptotic linear dependency of skewness excess against the graph connectivity and propose the novel RobustRepStream algorithm, which extends the RepStream algorithm, and provides improved robustness against stream evolution. By automatically controlling the skewness excess, the user no longer needs to specify the K-connectivity parameter, and RobustRepStream can adjust the graph connectivity locally in order to achieve performance close to when the optimal K value is known. We demonstrate that RobustRepStream’s skewness threshold parameter is insensitive and universal across all data sets. We comprehensively evaluate RobustRepStream on real-world benchmark data sets against previous stream clustering algorithms, and demonstrate that it provides better clustering performance.
Novel Classification of Finite Dimensional Filters with Non-maximal Rank Estimation Algebra

Ever since the technique of Kalman-Bucy filter was popularized, due to its limitations of linear assumption and Gaussian initial condition in model, there has been an intense interest in finding new classes of finite dimensional recursive filters. The idea of using estimation algebra was first proposed to construct finite-dimensional nonlinear filters by Brockett and Mitter independently in the late seventies, and it has rapidly been proven to be an invaluable tool in the study of nonlinear filtering (NLF) problem. For all known finite dimensional estimation algebras (FDEAs), the Wong's matrix has to be constant. However, as shown in this paper, the Wong's matrix is a polynomial when we consider FDEAs with state dimension 4 and linear rank equal to 2. Several mild conditions are established for finding a special class of NLF system. Finally, we give the construction of finite dimensional filters for this class of NLF system by Wei-Norman approach.
Visual saliency mechanism‐based object recognition with high‐resolution remote‐sensing images

Object recognition with remote-sensing image is widely used in many areas. Some objects are smaller and denser in the high-resolution images, such as the oil tank, ship, and aircraft. The recognition of this kind of objects is more difficult than the objects with low-resolution images, for example, bridges and airports. The recognition performance is more dependent on the shallower features. The contour of these objects is obvious, and the characteristics are quite different from background, which satisfies the human visual saliency mechanism. Here, the authors propose a novel theme of object recognition method based on visual saliency mechanism for remote-sensing images with sub-meter resolution. The experimental results show that the proposed method performs best compared with other algorithm.
UAV Fleet Mission Planning Subject to Robustness Constraints

Fleet mission planning for Unmanned Aerial Vehicles (UAVs) involves creating flight plans for a specific set of objectives, which typically, have to be achieved over a specific time period. The key challenge is to develop methods allowing to prototype mission plans, encompassing UAV routes and schedules, that are robust to changing weather conditions and energy constraints. This paper presents a declarative approach to solving UAV mission planning problems subject to weather uncertainty. The approach was tested using several examples, for which we analyzed how the achievement of mission goals depended on parameters, such as UAV fleet size, UAV energy capacity, weather changes, including wind direction and wind speed, as well as the structure of the distribution network and the time horizon.
Scene recognition under special traffic conditions based on deep multi‐task learning

Traffic scene recognition under special conditions is one of the most promising yet challenging tasks for autonomous driving systems. This study presents a deep multi-task classification framework for scene recognition involving special traffic conditions. The framework incorporates four learning tasks where the recognition of special traffic scenes is the chief task and the time of occurrence (daytime or night-time), the weather type and the road attribute are the three auxiliary tasks for improving the recognition performance. The four tasks share the feature map generated by a convolutional neural network followed by task-specific sub-networks which are merged in the end via a joint loss function. Moreover, a small dataset of typical special traffic conditions was built for training and testing the recognition model. Experimental results demonstrate that the proposed framework significantly improves the accuracy of scene recognition under special traffic conditions.
Field Network—A New Method to Detect Directional Object

As the development of object detection technology in computer vision, identifying objects is always an active yet challenging task, and even more efficient and accurate requirements are being imposed on state-of-the-art algorithms. However, many algorithms perform object box regression based on RPN(Region Proposal Network) and anchors, which cannot accurately describe the shape information of the object. In this paper, we propose a new object detection method called Field Network (FN) and Region Fitting Algorithm (RFA). It can solve these problems by Center Field. Center field reflects the probability of the pixel approaching the object center. Different from the previous methods, we abandoned anchors and ROI technologies, and propose the concept of Field. Field is the intensity of the object area, reflecting the probability of the object in the area. Based on the distribution of the probability density of the object center in the visual field perception area, we add the Object Field in the output part. And we abstract it into an Elliptic Field with normal distribution and use RFA to fit objects. Additionally, we add two fields to predict the x,y components of the object direction which contain the neural units in the field array. We extract the objects through these Fields. Moreover, our model is relatively simple and have smaller size, which is only 73 M. Our method improves performance considerably over baseline systems on DOTA, MS COCO and PASCAL VOC datasets, with overall performance competitive with recent state-of-the-art systems.
Integrated YOLO Based Object Detection for Semantic Outdoor Natural Scene Classification

Scene classification facilitates many applications like robotics, surveillance system, image retrieval, etc. Recently, various CNN architectures are designed to attempt the problem of scene classification. This paper proposes two stage model for scene classification using semantic rules. In the first stage, YOLOv2 (You Only Look Once) CNN architecture is trained for seven types of objects: sky, green_land, house, tree, sand_land, water, and windmill. In testing phase, all objects which are present in scene images are detected and recognized through trained network with overall accuracy of 79.35%. Semantic rules are designed using context of objects for the classification of scene image. Objects detected in stage-1 are parsed through the semantic rules to classify the scene image into six classes: barn, beach, desert, windfarm, green_ground, and water_land. Scene classification using semantic rules has achieved overall F-score of 76.81% on scene images from SUN397 dataset. Results of the proposed model using YOLOv2 are compared with results of same model using Faster RCNN which indicate that performance of scene classification depends on performance of object recognition system.
An explanation of the Bernstein‐Vazirani and Deustch‐Josza algorithms with the quantum stabilizer formalism

This work was supported in part by the Ministry of Economy and Competitiveness (“Ministerio de Economia y Competitividad”) from Spain/FEDER under grant MINECO‐16‐TEC2015‐67387‐C4‐3‐R, by the Ministry of Economy, Industry and Competitiveness (“Ministerio de Economia, Industria y Competitividad”) from Spain/FEDER under grant MTM‐2017‐83506‐C2‐2‐P, by Ministry of Science and Innovation (“Ministerio de Ciencia e Innovacion”) from Spain/FEDER under grant RTI2018‐098085‐B‐C44, and by he Regional Ministry of the Principality of Asturias (“Consejeria de Empleo, Industria y Turismo del Principado de Asturias”) under grants FC‐GRUPIN‐IDI/2018/000193 and FC‐GRUPIN‐IDI/2018/000226.
An Efficient LSI Based Multi-keyword Ranked Search Algorithm on Encrypted Data in Cloud Environment

Since the advent of cloud computing, a huge number of data owners are outsourcing their sensitive data to be stored on to the cloud. To maintain privacy requirements, this sensitive data should be encrypted when it is stored on the cloud server which renders simple keyword based document retrieval schemes obsolete. In this paper, we propose a semantic multi-keyword ranked search scheme for document retrieval on cloud data that is encrypted. The proposed scheme returns not only the documents containing terms that match with our query terms but also some more documents that contain terms which are semantically similar to the query keywords. Our experimental result shows that our technique is more precise than TF-IDF/VSM(Vector Space Model) models that focus only keyword matching while simultaneously achieving faster retrieval times than other tree based TF-IDF/VSM approach as our algorithm runs on reduced dimensions.
On the Set of Possible Minimizers of a Sum of Known and Unknown Functions

The problem of finding the minimizer of a sum of convex functions is central to the field of optimization. Thus, it is of interest to understand how that minimizer is related to the properties of the individual functions in the sum. In this paper, we consider the scenario where one of the individual functions in the sum is not known completely. Instead, only a region containing the minimizer of the unknown function is known, along with some general characteristics (such as strong convexity parameters). Given this limited information about a portion of the overall function, we provide a necessary condition which can be used to construct an upper bound on the region containing the minimizer of the sum of known and unknown functions. We provide this necessary condition in both the general case where the uncertainty region of the minimizer of the unknown function is arbitrary, and in the specific case where the uncertainty region is a ball.
Research and Application of Multi-Target Tracking Based on GM-PHD Filter

In recent years, multi-target tracking technology based on Gaussian Mixture-Probability Hypothesis Density (GM-PHD) filtering has become a hot field of information fusion research.This article outlines the generation and development of multi-target tracking methods based on GM-PHD filtering, and the principle and implementation method of GM-PHD filtering are explained, and the application status based on GM-PHD filtering is summarized, and the key issues of the development of GM-PHD filtering technology are analyzed.
Confidence Regions for Simulations with Learned Probabilistic Models

Due to the growing amount of data and processing capabilities, machine learning techniques are increasingly applied for the identification of dynamical systems. Especially probabilistic methods are promising for learning models, which in turn are frequently used for simulations. Although confidence regions around predicted trajectories are of crucial importance in many control approaches, few rigorous mathematical analysis methods are available for learned probabilistic models. Therefore, we propose a novel method to estimate confidence regions for predicted trajectories, and assign them a confidence level based on Monte Carlo random trajectory sampling. Since the confidence level has a strongly nonlinear dependence on the number of Monte Carlo samples, we derive a lower bound on the number of samples that ensures a desired minimum confidence level. The efficiency and flexibility of the proposed method is demonstrated in simulations of a Bayesian hidden Markov model and a Gaussian process state space model.