Reviewerly/exHarmony huggingface dataset:
Content-Sensitive Superpixels Based on Adaptive Regrowth

In this paper, we propose an efficient method to produce content-sensitive superpixels. Our method produces regular superpixels in relatively homogeneous regions and captures object boundaries in content-dense regions. Compared with the existing content-sensitive superpixel methods, a new adaptive regrowth strategy with an explicit boundary constraint is proposed. The boundary constraint limits the shapes and the sizes of superpixels to ensure semantic consistency. The adaptive regrowth strategy generates more superpixels to capture small objects in content-dense regions. Experiments on the BSDS500 benchmark show that our method outperforms the state-of-the-art superpixel methods in terms of content sensitivity and several standard evaluation metrics.
Relevance Feedback For Image Retrieval Using Transfer Learning and Improved MQHOA

Abstract Image retrieval is a challenging technology in multimedia applications where meeting the users’ subjective retrieval needs while achieving high retrieval performance is insufficient for existing methods. In this work, a related feedback image retrieval algorithm based on deep learning and optimization algorithm (CAMQHOA-RF) is proposed. Transfer learning based on the deep convolutional neural network is applied to extract deeper image features to reduce the semantic gap. The multi-scale quantum harmonic oscillator algorithm improved by the idea of “aggregation” is introduced to search the feature space effectively. The covariance matrix is used to strengthen the relationship between feature points at different scales to guide feature points to approach ideal query points faster. Moreover, the query point is reselected based on the feedback information to explore more potential users’ interest areas. Experiments have shown that compared with other algorithms, the proposed algorithm has fewer parameters that need to be set, but higher retrieval accuracy, faster retrieval speed, and stronger robustness are obtained, which can meet users better.
Improved Data Association of Hypothesis-Based Trackers Using Fast and Robust Object Initialization

The tracking of Vulnerable Road Users (VRU) is one of the vital tasks of autonomous cars. This includes estimating the positions and velocities of VRUs surrounding a car. To do this, VRU trackers must utilize measurements that are received from sensors. However, even the most accurate VRU trackers are affected by measurement noise, background clutter, and VRUs’ interaction and occlusion. Such uncertainties can cause deviations in sensors’ data association, thereby leading to dangerous situations and potentially even the failure of a tracker. The initialization of a data association depends on various parameters. This paper proposes steps to reveal the trade-offs between stochastic model parameters to improve data association’s accuracy in autonomous cars. The proposed steps can reduce the number of false tracks; besides, it is independent of variations in measurement noise and the number of VRUs. Our initialization can reduce the lag between the first detection and initialization of the VRU trackers. As a proof of concept, the procedure is validated using experiments, simulation data, and the publicly available KITTI dataset. Moreover, we compared our initialization method with the most popular approaches that were found in the literature. The results showed that the tracking precision and accuracy increase to 3.6% with the proposed initialization as compared to the state-of-the-art algorithms in tracking VRU.
Construction of Scene Tibetan Dataset Based on GAN

Abstract For the research of Tibetan scene text detection and recognition, it is time-consuming and laborious to collect and annotate natural scene data labels manually. Therefore, artificial synthetic data is of great significance for promoting relevant work. This paper focuses on the study of replacing other languages in the scene with Tibetan, while maintaining the style of the original text. We decompose the problem into three sub-networks: text style transfer network, background inpainting network and fusion network. Firstly, the text in the foreground image is replaced by the text style transfer network to generate the foreground image. Then the background inpainting network erases the original text in the style image, and uses the surrounding information around the text to fill the text area to generate the background image. Finally, the generated foreground image and background image are used to generate the target image by the fusion network. We experimented with conversions from English to Tibetan and English to English to verify the generalization and robustness of the network. Experimental results show that its accuracy (SSIM, PSNR) on some datasets (SVT, ICDAR 2013) has been improved to some extent.
A COMPARATIVE ANALYSIS OF DOCUMENT-ORIENTED BIG DATA DATABASES

Abstract: Now-a-days companies are starting to realize the importance of using more data in order to support decision for their strategies. Big Data refers to penta-bytes or exabytes of data. Big Data describes a holistic information management strategy that includes and integrates many data types of data and data management alongside traditional data. Big data is a phrase that defines the large volume of data both structured and unstructured that it is so hard to process using traditional database system performance and software techniques. In the present scenario the big databases have become prominently important because it have facility to generate large amount of data for social media daily analysis and multimedia, etc. big data is an enormous area to be researched. The main objective of this research study, is to evaluate performance of document-oriented Big Data Databases namely CouchDB, Couchbase and MongoDB using YCSB; in term of average runtime and throughput with increasing of number of records and operations. Each database has its own specific use and no one fit for the requirements. The research methodology describes the tool used to compare the document-oriented databases on various features. Most of NoSQL database provide high scalability and high availability at cost compromise consistency.
Executing Graphs with OpenCL

For several decades, graph and dataflow programming models have been niche topics limited to a small number of highly specialized domains. In recent years, however, the machine learning (ML) revolution and the proliferation of ML libraries has made graph programming accessible to even novice programmers. Before, a beginner programmer may have talked about writing a number-guessing game; today the programmer will describe training an off-the-shelf neural network—a type of graph—for handwriting recognition.
Computation Time Optimization on Hashtag Segmentation for Social Media Data

Despite sentiment analysis or contextual mining of text that recognizes and extracts subjective information from a source, it is considered necessary to estimate human behavior. A hashtag is a metadata tag used to classify data into a category. However, there has been little discussion on segmenting hashtags so far. We propose an algorithm to segment hashtags by optimizing computation time. We create candidates according to a given corpus, containing 1-gram (unigram) and 2-gram (bigram) data. The proposed algorithm allows a reduction in the computation time of generating segments by limiting the candidates in a given corpus. The fewer candidates there are, the shorter the calculation is, leading to a decreased duration. In this study, we gather food-related unstructured tweets (N = 951,255) from Twitter. Our results demonstrate that the proposed algorithm allows a computation time reduction of 29.7%. However, if the segment could not be found with the proposed algorithm, the original method for hashtag segmentation, which includes identifying all possible candidates, is used as a fallback method. The proposed approach improves the hashtag segmentation technique, minimizing computation time, which could be utilized in real-time tweet analysis. The result of our study shows that the trend of sentiments for both raw data and segmented data is similar, which also verifies the method's accuracy. This study's discoveries uncover that, despite the fact that computers are getting faster, computational resources should be utilized effectively. Our work also provides a data collection model for future surveys, which could also shorten the data retrieval process with multi-threading programming concepts.
Towards solution-oriented knowledge recommendation

This research identifies three practical problems in the existing scholarly recommendation systems, which leads to three research issues related to solution-oriented scientific knowledge recommendation that have not been addressed in the literature, including the lack of solution-oriented article recommendation, the lack of solution-oriented knowledge repositories, and the lack of weighted bibliometric network for ranking academic articles. Individual research could be conducted to address each issue, however this study proposes a knowledge fusion framework to collectively integrate multiple models which are specifically designed to solve each one of the issues respectively. The framework will be able to generate valuable knowledge repositories containing the solutions proposed in the mass scholarly articles to answer academic questions, and meanwhile it can rank these solutions based on weighted bibliometric networks.
LMI Conditions to Risk-Sensitive Control Design for Linear Discrete-Time Stochastic Systems

In this paper the state feedback risk-sensitive stochastic control problem is analysed to formulate design conditions based on linear matrix inequalities (LMI), with relations to gain matrices of the control law and Kalman predictor. The proposed methodology optimizes the control design step as a priority and concurrently prescribes the prediction error covariance matrix for Kalman gain synthesis. Since the separation principle does not hold in general, the synthesis remains two-step procedure and a regularisation concept is used in LMI construction. The proposed algorithm, solvable by using existing standard numerical tools is illustrated by numerical example to assess the regularity and feasibility of the proposed technique.
Unsupervised Classification of Medical Documents Through Hybrid MWEs Discovery

The automatic processing of medical language represents a clue for computational linguists due to intrinsic feature of these sub-codes: its lexicon comprises a vast number of terms that appear infrequently in texts. In addition, the presence of many sub-domains that can coincide in a single text complicates the collection of the training set for a supervised classification task. This paper will tackle the problem of unsupervised classification of medical scientific papers based on a hybrid Multiword Expression Discovery. We apply a morpho-semantic approach to extract medical domain terms and their semantic tags in addition to the classic MWEs discovery strategies. The collected MWEs will be used to vectorize texts and generate a network of similarities among corpus documents. With this approach, we try to solve both problems caused by the medical domain features. The presence of a vast lexicon of low-frequency terms is dealt with by extracting many semantic tags with a small dictionary; the issues of co-occurring sub-domains are solved by generating clusters of similarity values instead of a rigid classification.
Real-Time Pose Estimation for Human-Robot Interaction

Text-based communication is one of the most popular categories used. Emails, SMS messages while the voice call is better than text. it connects several remote people at once and it is more personal and easier than text. currently, the video call is the best method for communication between people. After transmitting of text, voice, and video successfully the next step is motion transmission. Motion-based communication requires detection of human motion in the sending site and motivation of robot's servo motors in the receiving site. The most important step in motion-based communication is the detection of human pose in real-time and good accuracy because this detection will be driven to a servo motor. If there is some wrong in detection, it will be dangerous because the robot may be damage itself or the motion may be harmful. In this paper, we propose a realtime method for motion transmission by using deep neural networks (DNN) for the detection of human body joints, Then convert the obtained results into commands suitable to work in robot environment. And implement the results on robot so that the human motion corresponds to that of the robot in real-time.
Fractal geometry applied to the analysis of cervix biopsy

the measurement of the spaces of occupation of irregular objects in the context of fractal geometry has had some applications at a cellular morphometric level, where characterizations of normality and disease have been established. The objective of the present study is to apply a fractal methodology to characterize images from cervical colposcopy.a mathematical and geometrical characterization of 67 cell samples was performed by measuring cellular fractal characteristics through the Box-Counting method, being nine normal, eight low-intraepithelial lesions, 16 high-intraepithelial lesions, eight carcinomas in situ, 20 squamous cell carcinomas and six endocervical carcinomas.the values of fractal dimension of the nuclear and cytoplasmic borders with respect to the totality varied between 0.719 to 1128 and 0.81 to 1024 while the occupation spaces in the 2 pixels grid were between 293 to 1606 and 64 to 693 respectively and in the 4 pixels grid oscillated between 153 to 894 and 36 to 361, respectively. Exocervical cells values had sensitivities between 78.3% to 100% in order to differentiate them from different types of cervical lesions.according to the results obtained, the mathematical values found are suggestive of being able to differentiate between normality and some colposcopy-guided cervical biopsy lesions.
Electronic schedule at the university on the basis of 1C:Automated scheduling. University on the example of Omsk State University

Automating scheduling is a classic task in learning management systems. The process of scheduling is, in a sense, the final one in the chain of support of educational activities, and its automation reveals all inconsistencies and shortcomings of the previous processes. In order for the scheduling to be automated as much as possible, a lot of various initial information should be processed in the information systems of the university — curricula, staff, workload, schedule of the educational process, contingent of students, classroom fund. It is especially difficult for universities with educational programs of various orientations — natural science, humanitarian, creative, etc., which have specific principles for organizing and conducting classes.The level of automation of educational activities at Dostoevsky Omsk State University, a classical university with a wide variety of types of educational programs, made it possible to tackle the task of scheduling. However, during the implementation of the automated scheduling system, the project team faced a number of problems.The article describes the results of the project for the transition to an electronic schedule in Dostoevsky Omsk State University using the circulation software product 1C:Automated scheduling. University based on the 1C:Enterprise 8.3 system. Initial data on the individual workload of teachers, the classroom fund, the list of student groups, and the list of disciplines were loaded into the configuration from the existing information system of design of Dostoevsky Omsk State University. Based on the results of the audit of the downloaded reference information, the initial data, including curricula, were normalized. The compiled schedule in two modes (manual and automatic) was published on the official website of the university and is used to operate a chatbot on the VKontakte network to inform students and teachers about upcoming classes.
Research on Target Detection Algorithm of Electronic Components Based on ReD_SSD

In this paper, we design a circuit components testing system for physics teaching based on embedded devices. In order to solve the problem that the computing performance of embedded devices based on NPU processor is weak and the same class samples are not detected due to high overlap, ReD_SSD Mobilenet algorithm based on SSD algorithm is proposed. Depth separable convolution is used to replace the traditional convolution of SSD algorithm to reduce the computational complexity of the model; ReDistance-IOU algorithm is used to complete the screening operation of the model pre-detection frame to reduce the system undetected rate; At the same time, the off-line 8 bit mixed quantization is adopted for the trained network model to further reduce the computational complexity of the model within the acceptable range of accuracy loss. The experimental results show that, compared with the SSD algorithm model, the ReD_SSD Mobilenet algorithm model reduces the average missing rate by 37. 4% on the physical circuit component data set. After adding the depth-separable convolution and off-line 8-bit mixed quantization, the compression rate of the model reaches 95%. The results show that the detection accuracy of ReD_SSD Mobilenet algorithm reaches 76.2%, and the detection frame rate on an embedded device is as high as 33.4 frames/s.
A Hybrid Approximation to the Marginal Likelihood.

Computing the marginal likelihood or evidence is one of the core challenges in Bayesian analysis. While there are many established methods for estimating this quantity, they predominantly rely on using a large number of posterior samples obtained from a Markov Chain Monte Carlo (MCMC) algorithm. As the dimension of the parameter space increases, however, many of these methods become prohibitively slow and potentially inaccurate. In this paper, we propose a novel method in which we use the MCMC samples to learn a high probability partition of the parameter space and then form a deterministic approximation over each of these partition sets. This two-step procedure, which constitutes both a probabilistic and a deterministic component, is termed a Hybrid approximation to the marginal likelihood. We demonstrate its versatility in a plethora of examples with varying dimension and sample size, and we also highlight the Hybrid approximation's effectiveness in situations where there is either a limited number or only approximate MCMC samples available.
A Reliable Cloud Assisted IoT Application in Smart Cities

Internet-of-Things are an amalgamation of multiple devices running on a different platform. They communicate with various instruments of a different calibre. They take the help of the internet to send and receive messages. As these devices do not have enough storage, they employ a cloud to store the sensed readings. The proposal is the inclusion of both the technologies. The recommendation makes sure about correspondence in vehicular organizations. It supports an access scheme without requiring ciphertext re-sign-based encryption mystery keys generation. It doesn’t depend on an intermediary re-encryption worker to execute the strategy update framework. It presents another unquestionable protection saving redistributed ABSC plot that guarantees adaptable access control, information classification, and verification while supporting arrangement refreshes in cloud helped IoT applications. The proposal enhances the work by adding reliability by 3.31% in comparison to the previous contribution. The system provides forward and backward secrecy.
Unburdening Onboarding in Software Product Lines

The number of studies focusing on onboarding in software organizations has increased significantly during the last years. However, current literature overlooks onboarding in Software Product Lines (SPLs). SPLs have been proven effective in managing the increasing variability of industry software and enabling systematic reuse of a product family. Despite these benefits, SPLs are complex and exhibit particular characteristics that distinguish them from traditional software. Due to these peculiarities, SPLs require a tailor-made onboarding process. Assistance tools might help. In this dissertation, we propose assistance tools (i.e., tools built on top of the software project that help learners understand and develop knowledge) as a means for helping newcomers during onboarding in SPLs.
Client-Server Architecture for High-Performance RTK Service

Global Navigation Satellite Systems (GNSS) standard accuracy varies between 2 and 10 m. This accuracy can be improved to centimeter-level in real time, using Real Time Kinematic (RTK). With RTK, a GNSS receiver with known position, a base station (BS), calculates errors and sends corrections to rovers, allowing the receiver to locate with centimeter-level accuracy. RTKLIB is an open source library, that can be included as part of software release in a device, to implement rovers or BSs. The purpose of this paper is to propose a low-cost implementation of a client-server architecture, to provide corrections in real time to rover devices, using RTKLIB and consumer-grade hardware.
An improved DBSCAN algorithm for clustering medical applications

Data clustering is proved effective to discover structures in various medical datasets. In most of the clustering algorithms, we obtain restricted clustering which means a sample should belong to a single cluster only. Most of the real world medical datasets contain the information which may belong to more than one clusters so in order to extract this information and perform clustering we use a new improved DBSCAN clustering algorithm. In this paper, a hybrid method that combines differential evolutions (D.E.) and DBSCAN. The purpose of this algorithm is to use the differential evolutions method to evaluate the clustering results and to use DBSCAN algorithm to initialize the cluster centers. The proposed method is tested by using Precision, recall and accuracy as the metric, which is observed to be the most effective metric to evaluate the clustering regardless the rag bag, homogeneity, cluster size quality, and completeness. According to results from 10 in public offered medical datasets, the planned algorithmic rule outperforms the previous algorithmic rule and may be used as an associate economical technique for clump medical datasets.
Variance reduced value iteration and faster algorithms for solving Markov decision processes

Abstract In this paper we provide faster algorithms for approximately solving discounted Markov decision processes in multiple parameter regimes. Given a discounted Markov decision process (DMDP) with | S | states, | A | actions, discount factor γ ∈ (0, 1), and rewards in the range [− M , M ], we show how to compute an ϵ‐optimal policy, with probability 1 − δ in time (Note: We use to hide polylogarithmic factors in the input parameters, that is, .) This contribution reflects the first nearly linear time, nearly linearly convergent algorithm for solving DMDPs for intermediate values of γ . We also show how to obtain improved sublinear time algorithms provided we can sample from the transition function in O (1) time. Under this assumption we provide an algorithm which computes an ϵ‐optimal policy for with probability 1 − δ in time Furthermore, we extend both these algorithms to solve finite horizon MDPs. Our algorithms improve upon the previous best for approximately computing optimal policies for fixed‐horizon MDPs in multiple parameter regimes. Interestingly, we obtain our results by a careful modification of approximate value iteration. We show how to combine classic approximate value iteration analysis with new techniques in variance reduction. Our fastest algorithms leverage further insights to ensure that our algorithms make monotonic progress towards the optimal value. This paper is one of few instances in using sampling to obtain a linearly convergent linear programming algorithm and we hope that the analysis may be useful more broadly.