Reviewerly/exHarmony huggingface dataset:
Fundamentals of organization and trends of academic professional music education in the Ukraine

This publication deals with the structure of modern musical schooling, as well as school as a complex of didactic, theoretical and practical aspects of learning.This came to light because of new concepts of musical education, which became the flagship of children's schooling, and the practical achievements in pedagogical endeavours of Ukrainian composer Bohdan Kotyuk: "Through studying folklore to the formation of students' national consciousness".
Visual and Inertial Data-Based Virtual Localization for Urban Combat

The present work of investigation presents a system of estimation of position and orientation based on algorithms of artificial vision and inertial data taken from the unit of inertial measurement incorporated in a smartphone device. The implemented system realizes the estimation of position and orientation in real time. An application was developed for android operating systems that allows capturing the images of the environment and executes the algorithms of artificial vision. In the implementation of the system, the detectors of feature points were tested, Harris, Shi-Tomasi, FAST and SIFT, with the objective of finding the detector that allows to have an optimized system so that it can be executed by the processor of a system embedded as are smartphones. To calculate the displacement of the camera adhered to a mobile agent, the optical flow method was implemented. Additionally, gyroscope data incorporated in the smartphone was used to estimate the orientation of the agent. The system incorporates a simulation of estimated movement within a three-dimensional environment that runs on a computer. The position and orientation data are sent from the smartphone to the computer wirelessly through a Wi-Fi connection. The three-dimensional environment is a digital version of the central block of the Universidad de la Fuerzas Armadas ESPE where the tests of the implemented system were carried out.
Research Infrastructures and Karst Science

Research data management planning is a key practice in science today, with a fundamental role in the organisation, use and reuse of research data, as well as in the verification of results.
Research and Design of a Key Technology for Accelerating Convolution Computation for FPGA-Based CNN

Convolutional Neural Network is an important network model of deep learning algorithm. It is widely used in handwriting recognition, Natural Language Processing, and so on. It is also a hot topic in the research of machine learning and computer image vision, so it has a certain research significance and value. This paper first proposes a simple convolution neural network model—SpNet model, and analyzes the different types of parallelism in the training process of convolution neural network. In view of the extensive use of convolution computation, from the perspective of software and hardware, a scheme to speed up convolution computation is designed.
Neighborhood Pixel-Based Approach for Arbitrary-Oriented Multilingual Text Localization

The textual information present in an image provides the semantic information and prominent source of an event. The text localization process is a primary stage for extracting the textual information from the image. But still, the text localization process is challenging and interesting research work in the field of computer vision due to illumination, alignments, complex background and variation size, color, fonts of the text. The multilingual text consists of different geometrical structures with respect to languages. In this paper, a simple and yet effective neighborhood pixel-based approach is presented to locate arbitrary-oriented multilingual text in images. The proposed method introduced the pixel difference method to locate the text region. The neighborhood pixel difference concept generates edges for all components with the help of standard deviation. Finally, the double-line structure concept is performed to determine the actual text region. The proposed method is evaluated on three datasets such as Hua’s dataset, arbitrarily oriented dataset, Multi-script Robust Reading Competition (MRRC) dataset with performance measure precision, recall and f-measure. The results show that the proposed method is promising and encouraging.
QR Based Filing System For Hospitals

We are making a management system for hospitals which will be based on QR-Code. In this, QR-Code will be used as a tag or an identification of the patient, through which the vast amount of information of the patient will be accessed on application which will be install on a mobile or tablet. The QR-code is used to scan the information of the patient when is required by using mobile or tablet’s camera.We used QR-Code because it is cheap and fast. For Data Feeding we will make a Web application through which the details will be stored on the cloud. For scan and update patient’s data we will use a mobile application which will be stored on doctor’s mobile or tablet.
Application of Kalman Fixed Interval Smoothing in Trajectory Correction

A Kalman fixed interval trajectory smoothing algorithm is established combining with the particle trajectory model, which is used to find a way to resolve the contradictions of one-dimensional modified projectile path correction while the flight trajectory of ammunition needs to be accurately estimated. This algorithm can effectively solve the problem of accurately extracting trajectory landing point from the measured data of ground reconnaissance radar, and can greatly increase the concentration of the ammunition. A numerical example is given to illustrate the effectiveness of the proposed method.
Piping “personalized PageRank” Algorithm from the Analytic Hierarchy Process(AHP) – A Case study

The core objective of any educational institution is to make a student more productive in terms of economical activity. This is simply a ripple effect towards the country's economy. But, an extensive research work is done for ranking institutions by giving more importance for publications of research papers by the faculty members of the institutions. Everybody may not like to become a scientist. But on an average, all should survive economically. By applying one of the top 10 data mining algorithms, PageRank with the Analytic hierarchy process technique, we rank the institutions purely based on the output (alumni) instead of mixing input(students)-process(teaching-learning)-output model. Here, the candidates will arrive their own personalized ranking of institutions without depending on any national or international ranking agencies.
You Can’t Save all the Pandas: Impossibility Results for Privacy-Preserving Tracking

We consider the problem of target tracking whilst simultaneously preserving the target’s privacy as epitomized by the panda tracking scenario introduced by O’Kane at WAFR’08. The present paper reconsiders his formulation, with its elegant illustration of the utility of ignorance, and the tracking strategy he proposed, along with its completeness. We explore how the capabilities of the robot and panda affect the feasibility of tracking with a privacy stipulation, uncovering intrinsic limits, no matter the strategy employed. This paper begins with a one-dimensional setting and, putting the trivially infeasible problems aside, analyzes the strategy space as a function of problem parameters. We show that it is not possible to actively track the target as well as protect its privacy for every nontrivial pair of tracking and privacy stipulations. Secondly, feasibility is sensitive, in several cases, to the information available to the robot at the start — conditions we call initial I-state dependent cases. Quite naturally in the one-dimensional model, one may quantify sensing power by the number of perceptual (or output) classes available to the robot. The number of initial I-state dependent conditions does not decrease as the robot gains more sensing power and, further, the robot’s power to achieve privacy-preserving tracking is bounded, converging asymptotically with increasing sensing power. Finally, to relate some of the impossibility results in one dimension to their higher-dimensional counterparts, including the planar panda tracking problem studied by O’Kane, we establish a connection between tracking dimensionality and the sensing power of a one-dimensional robot.
Wikidata based Location Entity Linking

Online news reading has become general among people and suggesting relevant news articles to readers is a non-trivial task. News recommender systems (NRS) are built to provide appropriate stories to readers based on their interest. News articles usually contain mentions of persons, locations and other named entities which are excellent resources for making sense of readers' news interest. However, entity mentions are often ambiguous. It can make readers retrieve stories that are not relevant to them, impacting the performance of NRS. Entity linking (EL) is a task to extract mentions in documents, and then link them to their corresponding entities in a knowledge base (KB). This task is challenging due to name variations, high ambiguity of entity mentions and incompleteness of the KB. Several approaches have been proposed to tackle these challenges. However, current systems do not focus on improving the performance of EL on location entity mentions which are identified as far more informative entities in news article for user interest profiling. The goal of this paper is to present the design of location entity linking algorithms based on Wikidata KB. We propose new approaches to candidate entity generation and candidate entity ranking of the location EL task. We extensively evaluate the performance of our EL algorithms over a manually annotated AIDA-CoNLL testb news corpus. Experimental results show that our location EL method achieves top-1 precision of 95.58% which is much higher than the state-of-the-art results obtained on the same dataset by collective EL methods.
Constructing Lattice-Free Gradient Polyhedra in Dimension Two

Lattice-free gradient polyhedra are optimality certificates for mixed integer convex minimization models. We consider how to construct these polyhedra for unconstrained models with two integer variables. A classic result of Bell, Doignon, and Scarf states that a lattice-free gradient polyhedron exists with at most four facets. We show how to construct a sequence of (not necessarily lattice-free) gradient polyhedra, each of which has at most four facets, that finitely converges to a lattice-free gradient polyhedron. Each update requires constantly many gradient evaluations (By gradient evaluation, we refer to inner product evaluation using gradients. For our updates we require at most 18 gradient evaluations.). This update procedure imitates the gradient descent algorithm, and consequently, it yields a gradient descent type of algorithm for problems with two integer variables. An open question is to improve the convergence rates to obtain a minimizer or a lattice-free set.
The Diffusion of M-learning Innovation: The Case of Saudi Arabia

M-learning is a new innovation in the educational technology environment which has been received a lot of attention all over the world. M-learning is growing interest in higher education in the Arab world. Many studies have reported how students may adopt and what are the factors that drive m-learning adoption this new technology in their learning. However, the factors drive the adoption of m-learning in Saudi Arabia, in particular Taif University, remain relatively unexplored. Toward this end, a conceptual model based on Rogers' theory has been developed in this study. As a part of a larger research project, this paper also reports the development of survey instrument and validation. The survey was administrated randomly to 55 Saudi students at Taif University. This study was conducted with small number of students as a pilot study to test and evaluate instrument reliability before evaluating the model hypothesis over a large-scale sample.
Quality Assessment Techniques for Small Satellite Images

Recently, the occupancy of small satellite in space is increasing, and one potential application of this is satellite imaging. The difference in acquisition conditions of satellite images makes it susceptible to much degradation compared to normal images. The common degradations are motion blur, low resolution and noise due to low light conditions, rain, snow, etc. Since small satellites are planned to be launched in big numbers, the number of image data to be analyzed will be higher, and this calls for some automatic techniques for assessing the quality of acquired images. Even though perceptual image quality assessment is a much explored area, there is not much work done in the area of satellite image quality assessment. In this paper, we review different techniques used for satellite image quality assessment and suggest some techniques which can also be used. The first method that we propose extracts GIST feature from satellite images and then uses a shallow neural network to predict the quality of the image from these features. The second method uses a deep convolutional neural network (CNN) with a shallow fully connected neural network to predict satellite image quality.
Special Issue on Musical Instruments: Acoustics and Vibration

The sound characteristics of musical instruments have been constantly growing in importance [...]
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space

This paper proposes a novel deep reinforcement learning architecture that was inspired by previous tree structured architectures which were only useable in discrete action spaces. Policy Prediction Network offers a way to improve sample complexity and performance on continuous control problems in exchange for extra computation at training time but at no cost in computation at rollout time. Our approach integrates a mix between model-free and model-based reinforcement learning. Policy Prediction Network is the first to introduce implicit model-based learning to Policy Gradient algorithms for continuous action space and is made possible via the empirically justified clipping scheme. Our experiments are focused on the MuJoCo environments so that they can be compared with similar work done in this area.
An Educational Tool Aimed at Learning Metaheuristics

In this paper, we introduce an education tool for learning metaheuristic algorithms that allows displaying the convergence speed of the corresponding metaheuristic upon setting/changing the dependable parameters. This tool can be an educational assistant for beginners to learn metaheuristic in theoretical lectures as well as practical sessions. Implemented with Java, this tool provides a friendly GUI for setting the parameters and display the result from where the learner can see how the selected algorithm converges for a particular problem solution. Initially, this tool adopts only Crow Search, Jaya, and Sine Cosine algorithms. But more metaheuristics will be included in future research. However, this application is a useful tool that will help not only beginner learners but also the researchers. This paper also describes the proposed software tool and the mentioned metaheuristics in detail and provides future research work on it.
Motion Detection Method for Reducing Foreground Aperture Problem in Background Modelling

Motion detection plays as an important role in the implementation of video surveillance system and video analysis applications. The detection of moving foreground objects from the complex background scene is the first step in most of the computer vision based surveillance applications. In this paper, we present a new motion detection method using background modelling technique and moving average feature. For establishing the robust background model, we create the Gamma Mixture Model based on the Gamma distribution function. For handling the foreground aperture problem, we use the feature called moving average which can well recognize the alive silent objects. As post-processing, we handle the shadow removing process by generating the dynamic shadow threshold. The experimental results show that the proposed motion detection method can detect the accurate foreground object even though the foreground object appears as silent background objects in real-time environment.
Cognitive Work of Hypothesis Exploration During Anomaly Response

Four incidents from web-based software companies reveal important aspects of anomaly response processes when incidents arise in web operations, two of which are discussed in this article. One particular cognitive function examined in detail is hypothesis generation and exploration, given the impact of obscure automation on engineers’ development of coherent models of the systems they manage. Each case was analyzed using the techniques and concepts of cognitive systems engineering. The set of cases provides a window into the cognitive work "above the line" in incident management of complex web-operation systems.
“Tourism+ Yoga”Development Strategy Study from Perspective of Embodied Cognition

Questionnaires related to yoga groups in china are conducted in order to explore the tourist group of yoga consumption behavior towards the development trend of personalized and high-quality travel. Through analyzing yoga tourism consumption behavior characteristics and the related affecting embodied factors, this study has summarized the current problems existing in yoga tourism. Combining the attributes of "tourism +” and yoga, it is suggested that the yoga tourism market should play a supporting governmental role to establish professional organizations and create highquality yoga tourism projects that conform consumer intent. Promote industry standards and improve basic environment, establish professional institutions and strengthen publicity, create diversified yoga and travel experience,so as to improve the feasibility of further development of yoga tourism.
Image Preprocessing for Eye Pupil Tracking

The influence of image preprocessing methods on the quality of eye pupil tracking by video is investigated. The methods of blurring, sharping, contrasting and scaling are considered. An estimate of the signal-to-noise ratio for image recognition based on the signal spectrum is proposed. The results obtained make it possible to determine combinations of image preprocessing modes that give the best pupil detection accuracy and a sufficiently high frame processing speed.
CDCEP: Securing the Compressed Data using Certificateless Encryption in Public Clouds

User data must be secured from intruders and the channel must be safer too. Before, many organizations depend on local server/flash drives to store their data that may lead to data loss. Cloud storage secures data on remote servers and provides online access to the data. What if the key generator compromises with the intruders and share the keys? In this paper, an architecture is proposed that does not use any key generator to generate the key and any authority to certify it. The CDCEP makes use of LZ4 algorithm and RC4 algorithm to compress and encrypt the fragments of file respectively before uploading it to the cloud. The performance of the proposed system is analyzed with respect to uploading/downloading time and the amount of storage required in storing a file in the cloud. It is observed that the time taken and storage required is reduced as compared to the existing system mCL-PKE.
Semantic Web-Based Knowledge Extraction: Upper Ontology Guided Crime Knowledge Discovery

With the current trends and developments in the information technology domain, there is a high enthusiasm for using semantic Web technologies and decision analysis mechanisms to solve numerous recurring issues in societies. There are plenty of existing knowledge models available on the Internet, developed for solving various problems. But, the reusability aspects of those are almost very low, due to main barriers, such as complexities associated with schema understanding, technical barriers associated with querying and comprehension of semantic representations. This will hinder the reusability of existing knowledge models and also knowledge dissemination associated with new and existing knowledge models. These consequences are obstructing the opportunities of experiencing the advancements of semantic technologies to both technical and non-technical audiences. This research is focusing on proposing an architectural structure leading towards a framework, to resolve most of the above-listed technical barriers and open doors to wider audiences in experiencing the benefits of the semantic Web. The proposed architectural structure is a combination of an instructional upper ontology and multiples of decision support systems integrated to the endpoints of the upper ontology. Crime domain is selected for the proposal of the high-level architectural design, leading towards a framework, as crime escalation has been a crucial concern which needs timely attention to under control the further spread.
Hybrid Scalable Action Rule

Action Rule mining is a method to extract actionable pattern from datasets. Classification rules are those which helps predict the object's class, whereas Action Rules are actionable knowledge that provide suggestions on how an objects state or class can be changed to a more desirable state to benefit the user. In the internet era, digital data is wide spread and growing tremendously is such way that it is neccessary to develop systems that process the data in a much faster way. The literature of Action Rule mining involves two major frameworks; Rule-Based method: where extraction of Action Rules is dependent on the pre-processing step of classification rule discovery, and Object Based Method: extracts Action Rule directly from the database without the use of classification rules. Object based method extracts Action Rule in a apriori like method using frequent action sets. Since this method is iterative it takes longer time to process huge datasets. In this work we propose a novel hybrid approach to generate complete set of Action Rules by combining the Rule-Based and Object-Based methods. Our results show a significant improvement, where the existing algorithm does not span for the Twitter dataset. On the other hand the proposed hybrid approach completed execution and produces Action Rules in less than 500 seconds on a Cluster.
Method of Detecting Orientation of Red Blood Cells Based on Video Data

This article propose a methodology to estimate the orientation of red blood cells flowing in laboratory microfluidic devices. Inputs for this methodology is a video output from the laboratory experiment, with an assumption that cells have a moderate deformation in the microfluidic device. This methodology is based on a hypothesis, that we can identify the position and inclination of the cell if we know the dimensions of its 2D projection. We applied the methodology to cells from numerical simulations. We compared the exact values of extremal cell point coordinates, with the values obtained only with a restricted knowledge about the bounding box of the 2D projection of the cell. We identified the accuracy of estimating the information about the 3D position of the cell from the 2D projection data. We found a good agreement mainly for estimation of the 3rd dimension of the cell’s bounding box, when we know only the two dimensions of the bounding box of the 2D projection of the cell.
Hampshire Police Marine Unit provide lots of ideas for crime novels

Pauline Rowson helped to organise a talk by Hampshire Police Marine Unit at the Crime Writers Association annual conference in Southampton in April