Reviewerly/exHarmony huggingface dataset:
Track Initiation in the Presence of Multipath Effect and Reflecting Point Uncertainties

Track initiation for a low observable target with multipath measurements can be very challenging due to reflecting point uncertainties (e.g., with Over-the-Horizon Radar (OTHR) systems and underwater target tracking). Existing algorithms cannot handle multipath measurements with reflecting point uncertainties. In OTHR tracking, reflecting point uncertainties amount to reflecting height uncertainties. Existing methods assume that this height is exactly known a priori. In practice, however, the reflecting height is time varying slowly. To initiate tracks, initial target parameters and the reflecting height are considered jointly since they depend on each other. Then an approach by embedding joint smoothing (JS) into an expectation-maximization (EM) algorithm is proposed, where the information contained in measurements and the prior information about this height are utilized. An approximate solution is presented, where a closed form is derived for track initiation based on the latest estimated reflecting height and then the current estimated reflecting height is obtained by a smoother, which has a recursive form by using the current estimate of initial target parameters. The performance of our JS-EM approach is further improved through iterative update. Simulation results show that the JS-EM approach outperforms the approach using a specific value of the reflecting height.
Modified Monte Carlo method for integral

Abstract The integral value of a function will be determined by the result of the function of the integral function. In fact many integral functions that are difficult to solve sometimes cannot be solved by exact sciences. There are several ways such as by changing its function toThe approach to the Monte Carlo method uses the principle of generating random numbers to test a function. The use of randoms is very closely related to the value of the error obtained. This study aims to minimize the error value of integration by using the Monte Carlo method. The initial integration of the Monte Carlo method with 10000 random points has a fairly large average error value of 0.078, so that the follow-up is done using pias in the Monte Carlo integration.
Study of Information Literacy Based on Journal Article Data

This thesis takes CNKI database as a platform to make research on information literacy. It makes literature research and applies relevant analytic methods like social network analysis, bibliometrics methods, statistics, and entropy methods to make statistical treatment on journal papers, academic dissertations, conference papers, and newspaper articles related to information literacy, published from 2010 to 2019 in China. Based on the analysis of CNKI quantitative visual retrieval results, this thesis makes an in-depth analysis from six aspects: the number of papers published, the organization and region of the papers, the distribution of research levels, the high-frequency keywords, the distribution of disciplines and the distribution of authors, and puts forward relevant suggestions for the existing problems. The results indicate that in the rapidly developing information society, people only concerns about the mastery of information knowledge in the field of information literacy research, lack of research on information awareness, information ability, and information morality. There should be more improvement and researches on individuals' sensitivity to information, which is the cultivation of information awareness, the collection of information and the moral cultivation of obtaining information.
ANA: a virtual assistant that sees and hears to help tetraplegic online learning

To use a computer, users with spinal cord injury must employ special tools that, in most cases, demand a great effort. In online courses, such tools often become a distraction, which might hinder the learning process. Solutions such as tongue mouses, smart glasses and computer vision systems, although promising, still face considerable usability problems. On the other hand, ANA, our conversational agent that can hear and listen to the student, has proven quite promising [1]. Tests have shown that, while using our assistant, tetraplegia were able to focus more on their studies than on the system's interface. In this paper, we present our latest advances on ANA's architecture, introducing a computational grammar that allows for the insertion in online courses of “learning objects” (LOs) to which students can talk in a natural way. Those LOs respond by performing the requested action. We present the grammar used for processing commands in natural language and show how it is integrated with Google's conversational AI assistant.
A Robust Image-Sequence-Based Framework for Visual Place Recognition in Changing Environments

This article proposes a robust image-sequence-based framework to deal with two challenges of visual place recognition in changing environments: 1) viewpoint variations and 2) environmental condition variations. Our framework includes two main parts. The first part is to calculate the distance between two images from a reference image sequence and a query image sequence. In this part, we remove the deep features of nonoverlap contents in these two images and utilize the remaining deep features to calculate the distance. As the deep features of nonoverlap contents are caused by viewpoint variations, removing these deep features can improve the robustness to viewpoint variations. Based on the first part, in the second part, we first calculate the distances of all pairs of images from a reference image sequence and a query image sequence, and obtain a distance matrix. Afterward, we design two convolutional operators to retrieve the distance submatrix with the minimum diagonal distribution. The minimum diagonal distribution contains more environmental information, which is insensitive to environmental condition variations. The experimental results suggest that our framework exhibits better performance than several state-of-the-art methods. Moreover, the analysis of runtime shows that our framework has the potential to satisfy real-time demands.
Quantifying the Use of English Words in Urdu News-Stories

The vocabulary of Urdu language is a mixture of many other languages including Farsi, Arabic and Sinskrit. Though, Urdu is the national language of Pakistan, English has the status of official language of Pakistan. The use of English words in spoken Urdu as well as documents written in Urdu is increasing with the passage of time.The automatic detection of English words written using Urdu script in Urdu text is a complicated task. This may require the use of advanced machine/deep learning techniques. However, the lack of initial work for developing a fully automatic system makes it a more challenging task. The current paper presents the result of an initial work which may lead to the development of an approach which may detect any English word written Urdu text. First, an approach is developed to preserve Urdu stories from online sources in a normalized format. Second, a dictionary of English words transliterated into Urdu was developed. The results show that there can be different categories of words in Urdu text including transliterated words, words originating from English and words having exactly similar pronunciation but different meaning.
A Loop-Closure-based Inertial Motion Capture, with Application to Sports Swing Measurements

This study presents a system for measuring motions using an inertial measurement unit (IMU) and a camera. IMU-based motion capture systems are advantageous in terms of cost, but measurable motions are limited in length because of rapidly accumulating errors in the process of double integration. We propose the correction of such errors with the assistance of an external monocular camera. Our approach is similar to graph-based simultaneous localization and mapping for mobile robots, where loops in the trajectory are detected and closed to correct the accumulated errors over the trajectory. We apply the idea to motion estimation by developing and integrating three key technologies: pose-graph optimizer, loop detector, and time synchronizer. We evaluate our system through measurements of table tennis swings. The experimental results show that our sensor fusion approach significantly reduces the errors in inertial motion estimations, with an acceptably small additional cost.
A study on Blockchain enabled Applications and its Security Issues

Blockchain technology was first established because of its use in cryptocurrencies, but its applications are not limited just to this field. We have explored other fields like Finance, Auditing, Digital identity, Digital Voting and Healthcare where blockchain can be used to increase efficiency, and how it fares compared to the traditional systems. The paper compares the traditional and blockchain enabled systems based on parameters like privacy, data and application security, consensus algorithms and mining computations. However, like any other technology, this one too might have flaws which can be exploited. This paper also lists the security challenges that need to be kept in mind while implementing these systems.
A Segmentation-driven Handwritten Uighur Word Recognition Algorithm Based on Feedback Structure

Uighur script is cursive in both printed and handwritten forms. For offline handwritten Uighur word, this study proposes a new segmentation-driven recognition algorithm that combines feedback structure and grapheme analysis. Firstly, a handwritten Uighur word is over-segmented into a two-queue grapheme sequence using a MSAC (main segmentation and additional clustering) algorithm. Secondly, a feedback-based grapheme merging strategy is designed to provide the best segmented character sequence and obtain the word recognition result. Three feedback errors accordingly are defined, which are error of grapheme shape, error of character recognition and word matching error. A word recognition rate of 90.82% is obtained during experiments conducted with a database consisting of 11,500 samples.
Top-N Recommendation Using Low-Rank Matrix Completion and Spectral Clustering

In the age of big data, recommendation systems provide users with fast access to interesting information, resulting to a significant commercial value. However, the extreme sparseness of user assessment data is one of the key factors that lead to the poor performance of recommendation algorithms. To address this problem, we propose a spectral clustering recommendation scheme with low-rank matrix completion and spectral clustering. Our scheme exploits spectral clustering to achieve the division of a similar user group. Meanwhile, the low-rank matrix completion is used to effectively predict un-rated items in the sub-matrix of the spectral clustering. With the real dataset experiment, the results show that our proposed scheme can effectively improve the prediction accuracy of un-rated items.
On Monotonic Determinacy and Rewritability for Recursive Queries and Views

A query Q is monotonically determined over a set of views V if Q can be expressed as a monotonic function of the view image. In the case of relational algebra views and queries, monotonic determinacy coincides with rewritability as a union of conjunctive queries, and it is decidable in important special cases, such as for conjunctive query views and queries. We investigate the situation for views and queries in the recursive query language Datalog. We give both positive and negative results about the ability to decide monotonic determinacy, and also about the co-incidence of monotonic determinacy with Datalog rewritability.
Iterative Nonlinear Kalman Filtering via Variational Evidence Lower Bound Maximization

In this paper, the problem of nonlinear Kalman filtering is considered from the viewpoint of variational evidence lower bound maximization, where the posterior distribution is approximated iteratively by a solvable variational distribution. In this way, the hardly intractable integration of the nonlinear posterior probability density function can be converted to the optimization of evidence lower bound. Based on linearization, an iterative nonlinear filter is derived in a closed form. Examples of tracking a moving target by three range-only sensors and univariate nonstationary growth model are presented to demonstrate the efficiency of proposed method compared with several nonlinear filters, as well as the interpretation of ELBO with different iterations and Kullback-Leibler divergence between estimated posterior distribution and true probability density.
INFORMATION-MANAGING EDUCATIONAL ENVIRONMENT AS A TOOL FOR IMPROVING THE QUALITY OF TEACHING INFORMATICS IN THE CONDITIONS OF TOWNS: EXPERIENCE AND PERSPECTIVES

The article presents the experience of developing and implementing a system for improving the quality of informatics training in the framework of the information and educational environment. The main purpose of the system is to improve the quality of education in regional educational institutions in the conditions of shortage of human and material resources.
The Study for Data Mining of Distribution Network Based on Particle Swarm Optimization with Clustering Algorithm Method

In the field of distribution network information system, the research on fault data sorting of feeder is an important subject. The data needed from feeder line can be extracted to lay a foundation for fault prediction. Since the quality of the raw data may be problematic, this paper proposes a method of outlier detection based on clustering. In the method, particle swarm optimization algorithm is used to optimize the clustering center and optimal number of clusters is determined by K-means method. This method can effectively promote the clustering effect, accurately remove the outlier samples and escape from the negative impact on the prediction model caused by the outlier samples. Simulation results show that this method can get good data from fault sorting of feeder, which provides a new idea for distribution network information sorting.
Evaluating the Effects of Different Requirements Representations on Writing Test Cases

[Context and Motivation] One must test a system to ensure that the requirements are met, thus, tests are often derived manually from requirements. However, requirements representations are diverse; from traditional IEEE-style text, to models, to agile user stories, the RE community of research and practice has explored various ways to capture requirements. [Question/problem] But, do these different representations influence the quality or coverage of test suites? The state-of-the-art does not provide insights on whether or not the representation of requirements has an impact on the coverage, quality, or size of the resulting test suite. [Results] In this paper, we report on a family of three experiment replications conducted with 148 students which examines the effect of different requirements representations on test creation. We find that, in general, the different requirements representations have no statistically significant impact on the number of derived tests, but specific affordances of the representation effect test quality, e.g., traditional textual requirements make it easier to derive less abstract tests, whereas goal models yield less inconsistent test purpose descriptions. [Contribution] Our findings give insights on the effects of requirements representation on test derivation for novice testers. Our work is limited in the use of students.
Automatic Handwriting Verification and Suspect Identification for Chinese Characters Using Space and Frequency Domain Features

Automatic handwriting verification is to identify whether the script was written by a person himself or forged. Compared to related works about handwriting verification, the proposed algorithm adopts the features in both the time domain and the frequency domain. Moreover, in addition to distinguishing the forged manuscript from the genuine one, the proposed algorithm can also identify the suspect. The proposed algorithm is robust to writing instruments. In addition to the information of the luminance of the script, we also adopt the energy distribution on the 2-D frequency domain, the Pearson product-moment correlation coefficient (PPMCC) with genuine scripts, and vital information on characterized script points. Simulations show that the proposed method outperforms many advanced methods, including the deep-learning based method and manual identification by human beings. The proposed algorithm can well identify the script even if it is forged after several times of practice.
The Importance of Phase to Texture Discrimination and Similarity

In this article, we investigate the importance of phase for texture discrimination and similarity estimation tasks. We first use two psychophysical experiments to investigate the relative importance of phase and magnitude spectra for human texture discrimination and similarity estimation. The results show that phase is more important to humans for both tasks. We further examine the ability of 51 computational feature sets to perform these two tasks. In contrast with the psychophysical experiments, it is observed that the magnitude data is more important to these computational feature sets than the phase data. We hypothesise that this inconsistency is due to the difference between the abilities of humans and the computational feature sets to utilise phase data. This motivates us to investigate the application of the 51 feature sets to phase-only images in addition to their use on the original data set. This investigation is extended to exploit Convolutional Neural Network (CNN) features. The results show that our feature fusion scheme improves the average performance of those feature sets for estimating humans' perceptual texture similarity. The superior performance should be attributed to the importance of phase to texture similarity.
Securing the Space Data Link Communication Protocol of Earth Observation Satellites

Earth Observation Satellites (EOS) as any remote unmanned vehicle need to communicate with its control ground station (CGS) either to send its housekeeping and payload telemetry data or to receive control commands and this is done through simple lightweight communication protocols which are designed taking into account the special conditions of the space environment. For civilian missions, this communication protocol is not protected and has several security issues that result in critical risks and safety concerns. In this paper, we studied the security vulnerabilities of space data link communication protocols and investigate the applicability of software-based solution to secure these communication protocols that adopt a software-friendly cryptographic algorithm to ensure the protection of exchanged data between the EOS and its CGS to reduce the penalties of added hardware device which affects negatively the spacecraft resources power consumption volume and mass. For validation, a software spacecraft simulator developed to evaluate the impact of different cryptographic algorithms on the space link security protocol. The results showed that ChaCha-poly1305 is a good candidate as authenticated-encryption algorithms used to secure space data link protocol (SDLP) to guarantee its messages confidentiality and authentication, without affecting the resources of the spacecraft.
Analysis of Double Compression Detection in a Video

In the age of 21st century hand holding device such as mobile phone, PDA, digital cameras are easily available and accessible by everyone. Video captured by these devices can be share through internet medium for various purposes like information dissemination, video conferencing, and social platform etc. The video quality can be upgrade and degrade or the video contents can be modified by using various software available freely on internet. It is possible for digital attacker to modify the contents by using these software for malicious purpose and is very difficult to see through the necked eyes. Many of the illegal video alteration cases are identified in the areas like social media, politics, criminal investigation etc. Authentication of a digital video minimizes the false information flow. The content validation of the digital video ranges from an individual, barrier and security setups to law authorization organizations. In this regard forgery detection techniques play important role. In this paper, we are going to take a review of the work done by various researchers previously to identify the forgery in the digital video contents. We also show the comparative study of surveyed techniques and goes for opportunities in the field of forgery detection.
Image Resolution Enhancement Technology Based on Deep Neural Network

Improving image quality is an important subject in the field of image processing. Images have a wide range of important uses in modern society, such as security surveillance, remote conferences, medical images, etc. Different from drawing-based graphics, it is often difficult to obtain images with sufficient accuracy due to the accuracy of the acquisition equipment. Especially in the field of video surveillance, because of the large amount of data storage, the limited bandwidth of the transmission link, and the limitations of the CCD manufacturing process and cost, it is often difficult to improve the resolution of the camera. The purpose of this paper is to study image resolution enhancement techniques based on deep neural networks. In this paper, in order to solve the problem of image resolution enhancement, the related theories and methods of super-resolution are studied. A processing framework for resolution enhancement is designed for real images. The effect of the resolution enhancement method is improved through process. Normalization method. Aiming at image resolution enhancement, a resolution enhancement method based on deep neural networks is proposed. Through the enhancement of various images, the visual effect of the experimental results is effectively improved. The research results show that image resolution enhancement processing can improve the spatial resolution of images under the same hardware conditions to a certain extent, improve image degradation and resolution degradation due to insufficient hardware conditions, and make up for the lack of image resolution to a certain extent to make the image clearer.
Device For Centralized Control

In well-known multi-channel centralized control devices, a sequential interrogation of the controlled parameters of various objects is carried out. In the device under consideration, first of all, the object parameters are examined that have the greatest deviation from the nominal value and only if this deviation exceeds the permissible value, which increases the efficiency of control and the operability of complex objects
Information seeking behaviour of residents towards the work of the architect

Abstract Indonesia is included in the area of the ring of fire so that various regions in Indonesia are vulnerable to disasters, including areas around the capital Jakarta. Unpredictable disasters can occur at any time so the community must equip themselves with knowledge of disasters, especially related to shelter. This study aims to look at the information seeking behaviour of the community in relation to their place of residence and in the context of the work of architects. Is the architect’s work in community dwellings considered safe enough in the face of disasters? The research method used is the Neuroresearch method, mixed method research. The study population is residents of housing complexes in the Greater Jakarta area. The sampling technique used is cluster random sampling. The number of samples was 743 people. Data collection techniques using a questionnaire scale Likert 1 to 5. The results showed a difference in the ability of the community in information seeking behaviour viewed from the area of residence and educational background.