Reviewerly/exHarmony huggingface dataset:
Efficient Interactive Global Cellular Signal Strength Visualization

Cellular Signal Strength (CSS), defined as the signal power received by mobile phones, is an important aspect of geographic information flow analysis, because the density of such information can reflect the urbanization variables such as population, gross domestic product, built-up area, electric power consumption, etc. Despite the importance, the real-time analysis of global CSS distribution remains a challenging problem due to the large data scale. In this article, a Display-driven Computing (DisDC) technique is designed and applied to provide efficient large scale interactive CSS visualization, generating results by calculating the value of each pixel that directly for display. Specifically, we present an efficient CSS measurement algorithm, which introduces spatial indexes and a corresponding query strategy; besides, an optimized parallel computing architecture is proposed to ensure the ability of real-time visualization. Experiments show that our approach obviously outperforms traditional methods and is capable of handling more than 40 million base stations in real-time. Moreover, an online demonstration is provided at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MemoryMmy/CSSMap</uri> .
Amplification and Derandomization without Slowdown

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 5 January 2017Accepted: 20 July 2020Published online: 08 October 2020Keywordsderandomization, amplification, sketchingAMS Subject Headings68Q25, 68W20Publication DataISSN (print): 0097-5397ISSN (online): 1095-7111Publisher: Society for Industrial and Applied MathematicsCODEN: smjcat
Image Super-Resolution Based on Non-local Convolutional Neural Network

Super-resolution (SR) is a widely used image processing technology. Recently, deep learning theory has been introduced into image SR task and achieved a significant improvement. However, feature extraction by convolutional mask is often operated in image local blocks, few attentions are given to non-local. Our aim is to insert a non-local network module into deep learning framework to further improve the performance of SR network. We choose ResNet as backbone and SRGAN as benchmark. The non-local network module is added into ResNet and a new generator network based on SRGAN is designed. To our knowledge, it is the first time to introduce non-local module into SR convolutional neural network. With a full discussion about the new generator network structure, our experiment shows that the introduction of non-local network for high resolution image generation has a superior performance to local or global convolutional neural network in image SR tasks.
Cyber Security Resilience in Business Informatics: An Exploratory Paper

Although considerable effort is made to secure organisational infrastructures and to protect organizational assets, it is widely acknowledged that it is equally important to ensure that organisations need to define appropriate ways to harden their overall resilience including recovery from security incidents. In this exploratory paper we outline research challenges and we present the motivation and the foundations of a novel framework that is based on security resilience and capability modelling theory.
A Novel CNN Architecture for Real-Time Point Cloud Recognition in Road Environment

To ameliorate the problems of disorder, sparseness, and floating occur for 3D LiDAR point cloud in the road environment, we propose a novel deep CNN architecture for real-time point cloud features extraction. Specifically, we first code the 3D position of point cloud by the index of vertical and horizontal directions. In this way, the 3D point cloud can be converted into a multi-channel point feature map. Then, through multi-level features extraction and fusion of the point feature map, the semantic segmentation of the point cloud scene is finally realized. Comprehensive experiments and ablation studies on public available point cloud datasets demonstrate the superiority of our approach. More importantly, our approach has been successfully applied to the perception of the real-world self-driving system. The source code has been made public available at: https://github.com/Lab1028-19/A-Novel-CNN.
Curved beam stress analysis using a binary image of the cross‐section

Abstract Crane hooks, chain links, various rings, and many other curved structural members are widely used in mechanical engineering structures. The stress analysis procedure of this group of structural members includes two steps: definition of the cross‐section geometry properties and determination of stresses. The article presents a variation of the method for curved beam stress analysis where geometrical properties of the cross‐section are found by numerical processing of a binary image. This is suggested instead of the conventional procedure provided by many textbooks that includes decomposition of the cross‐section area into regular shapes and calculation of the area properties by analytical formulae. Having the complex shape of the cross‐section the conventional method becomes complicated and time‐consuming. The application of a numerical binary image is independent of the complexity of cross‐section, but image resolution may influence the results. The article presents procedures suitable for the alternative definition of the geometrical properties of curved beam cross‐section and gives an evaluation of how the image resolution affects the results. The rectangular and circular shape cross‐sections are analyzed in detail and stress analysis of an industrial crane hook with complex cross‐section shape is demonstrated in the case study. The results of the conventional and proposed methods are compared. The calculated stresses of the case study are also compared to finite element analysis results. The method is suggested as a convenient alternative for structural analysis students and engineers.
A comparison of OCMPM and OCSVM in motor and sensor fault detection for traction control system

Fault detection is critical to ensure the safe operation of high speed trains. One class support vector machine (OCSVM) and one class minimax probability machine (OCMPM) are two domain-based single class classification methods and commonly used for fault detection. This paper systematically analyzes their training and detecting complexity, principle of optimization and hyperparameter influence of both methods, and compares their performance on motor and sensor fault data from the simulated traction control system of the high speed train. It shows that OCMPM achieves higher fault detection rate than OCSVM given the same false alarm rate. But OCMPM is unfeasible used for real-time fault detection when the training dataset is large.
A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree

So far, most of the research on classification algorithms in machine learning has been focused only on improving the training speed and further improving the technical performance evaluation measures of the constructed models. There is no focus on improving the runtime efficiency of the classification phase which is much required in some critical applications. In this paper, we are considering the computation complexity of a decision tree’s classification phase as the major criterion. A novel approach has been proposed to predict the class label of an unseen instance using the decision tree in less time than the regular tree traversal method. In the proposed method, the constructed decision tree is represented in the form of arrays. Then, the process of finding the class label is carried out by performing the bitwise operations between the elements of the arrays and test instance. Empirical results on various UCI data sets proved that the proposed method outperforms the standard method and five other benchmark classifiers and its classification is at least four times faster than the regular method.
Use of Particle Systems to Create an Interactive Album

This paper gives a brief introduction to how to use Processing to make particle album and analyzes the basic image processing techniques used in this two-dimensional animation production.
A Proposed Model for Trust Management: Insights from a Simulation Study in the Context of Cloud Computing

In computing systems, one of the centric topics entails cloud computing. This dominance is attributed to the crucial role that the concept plays in the daily lives of individuals, especially in the wake of the increasing adoption of technology by individuals and organizations. Indeed, the motivation behind the establishment, adoption, and implementation of cloud computing has been attributed to the need to offer low-cost and quick consumer service provision, as well as data manipulation and storage. However, the cloud environment continues to face security threats, a trend that informs the need for further investigations and analyses that could provide room for new system improvements. The current simulation study presents a dynamic model for security management in a cloud computing environment, with the central parameter involving electronic trust. Imperatively the proposed study examines interactions between the data provider and the data owner, as well as the end user. Specifically, the proposed model is that which ensures that for authentication purposes and access permissions, there is a continuous update of trust values. From the results, the model is flexible relative to the provision of dynamic access control, a positive trend that points to its promising level of efficiency.
Interactive Mat an Innovative Implementation for the Rehabilitation of Disabled Children

Rehabilitation of children with disabilities is a major challenge. This project focuses on helping children suffering from neurological and motoric disabilities. It is very important to improve self-efficacy, activating sitting position, communication, and visual fixation. The aim of the study was to create a system that supports rehabilitation and responds to the needs of children. A rehabilitation system created for intellectually disabled persons has more requirements than devices for patients with musculoskeletal diseases. Based on all requirements, we created a new product: Interactive mat for children’s rehabilitation. The solution consists of four main elements: computer equipment with appropriate software, a light and rigid frame, an application with user instructions, and a therapeutic plan developed by an expert. The project has been realized and tested in association with Ośrodek Rewalidacyjno-Wychowawczy SANCJA in Poznań.
Research on High Speed and Low Power FPGA Implementation of LILLIPUT Cryptographic Algorithm

The computing capability of micro encryption devices has been a concerned problem by the people. This paper improves the implementation of LILLIPUT which was proposed in 2016. In this paper, we adopt the method of compromising area and power consumption. And with the utilization of parallel hardware processing and reduced excess hardware, the execution speed of the algorithm is extended with the minimal power consumption. Tested by experiments, the third-order LILLIPUT algorithm of parallel processing is 40% higher than that of the non-parallel processing, with only a small amount of area and power consumption used to significantly improve the computing performance of the micro-equipment.
FF-GAN: Feature Fusion GAN for Monocular Depth Estimation

Since the results of CNN methods for monocular depth estimation generally suffer the problem of visual dissatisfaction, we propose Feature Fusion GAN (FF-GAN) to address this issue. First, an end-to-end network based on encoder-decoder structure is proposed as the generator of FF-GAN, which can exploit the information of different scales. The encoder of our generator fuse features in different levels with a feature fusion module. The component which can obtain the information of multi-scale receptive field is the main part of the decoder of our generator. Second, in order to match the generator, the discriminator of FF-GAN is designed to efficiently learn the information of different scales by applying pyramid structure. Experiments on public datasets demonstrate the effectiveness of our generator and discriminator. Compared with the CNN methods, the results predicted by FF-GAN are significantly improved in terms of texture loss and edge blur while ensuring accuracy, and the visual effect is better.
Parametrization of 3D shapes using Superquadrics

A novel method for optimization of parameters associated in representing various multi-dimensional shapes using Superquadrics and Superellipses accordingly. Superquadrics is a set of adaptive three-dimensional shapes that are three-dimensional extensions of Super ellipses in two-dimensional models. Superquadrics become ideally capable for representing irregular shapes with simple mathematical effort. This method involves image segmentation and extraction of parameters governing the shapes. It is implemented in both two-dimensional and three-dimensional spaces using Superellipses and Superquadrics respectively. This process yields faster analysis and efficient data storage due to the reduction of total computations. The process of estimation is based on the optimization of nonlinear multivariable functions under certain constraints. This parametrization is considered for review and analysis, compared with the generic process of taking all the values of the image.
Usages of AI Technologies in Nepal’s Disaster Management

Artificial intelligence (AI) plays vital role in disaster management. AI not only can save lives of people during an emergency, it can help government to formulate strategies for increased safety and resiliency before a disaster strikes. In this paper the benefits of AI technologies in disaster response focusing to Nepal have been explored. This paper further discusses the role of AI technologies such as robotics, ontology and semantic web, and multi-agent systems in crisis response. AI is behind many of the recent advances in technologies for crisis response and management.
ANALISIS DESAIN FORMULIR KARTU PENGOBATAN PENCEGAHAN TB (TB 01 P) UNTUK KELENGKAPAN DATA DI PUSKESMAS TAHUN 2019

Tuberculosis is an infectious disease which becomes a global problem. The increase in TB case is caused by inadequate monitoring of treatment, unavailability of standardized recording and reporting system. This research was conducted to evaluate the completeness TB form design in providing more complete information. This research was administered in the form of a case study. Interviews were done to 3 nurses and 1 doctor and TB Form (TB.01P) was observed at Miroto Health Center. The completeness of 33 TB form filling (TB.01P) at the Malmahera Health Center was also evaluated. The obtained data were descriptively analyzed. The results showed the completeness of identification review reached 96%, recording review of 90%, reporting review of 64%,and 0% for authentication review. Incomplete information led to inaccurate TB control information. This incompleteness was associated toimproper design of the form, especially the unavailability of form filling instructions, names and signatures of officersin the anatomic aspect. In the aspect of content, lack of clinical data was also found. Regarding to the results of this research, revision in the design of TB Treatment and Prevention Card is needed in order to accommodate the data completeness of TB patients. This research also proposed a better design of TB Treatment and Prevention Card Keywords : Quantitative Completeness, Tuberculosis, Form Design, TB Treatment Card (TB.01P)
Hydra: Report and technical overview

The idea to let machines play games like Chess is quite old, with the Turk11 https://en.wikipedia.org/w/index.php?title=The_Turk&amp;oldid=968314029 being one of the first hints. With the invention of computers, this idea got a new boost, and soon the first chess programs were presented. It took about 50 years until it was clear that the machines are superior chess players, far beyond the playing strengths of any human players. In the 1970s, the era of efficient search engines started and in 2004, the chess program Hydra clearly crossed the 3000 Elo mark, ahead of all other machines and human players. In this paper, we present a review on the chess program Hydra, on a technical level as well as from a social perspective.
THE DEVELOPMENT OF ALGORITHMIC CULTURE AND PROGRAMMING CULTURE OF STUDENTS IN PROFILE CLASSES WHILE STUDING THE BASIS OF PROGRAMMING IN C++

The article presents the structure, content and results of approbation of the C++ programming course developed for the 10 th grade students of physics and mathematics profile and implemented as part of the academic subject “Informatics”. The aim of the course is to develop in the student not only knowledge and skills in programming, but also his algorithmic culture and programming culture as important qualities of a potential IT-specialist. This is facilitated by special control of educational process by the teacher, which consists in monitoring the activities of students in writing programs and timely correction of this activity. The assessment of the level of development of student algorithmic culture and programming culture relative to the basic level of their formation (when mastering the basics of algorithmization and programming in the 9 th grade) was carried out on the basis of a number of criteria presented in the article. The results of approbation showed that the specially organized teacher activity makes it possible to increase the level of algorithmic culture and programming culture of high school students when studying the basics of programming in C++.
A Privacy Preserved Scheme for Data Verification and Integrity Protocol

Remote data integrity is a crucial technology in the cloud environment .In Recent years, many have been focusing on providing data dynamics. Existing protocol /algorithm can support with the help of third party auditor. When the data is no longer under user’s direct control, the data security and integrity in cloud environment becomes obstacles. Integrity is a method for protecting the consistency of the put away data in cloud server and guaranteeing the innovation of the data put away in the cloud server innovation of the same. It implies that the data can be altered just by approved people, along these lines expanding the certification, confirmation and dependability of the cloud service provider.
Quantitative Characteristics of Human-Written Short Stories as a Metric for Automated Storytelling

Abstract Evaluating the extent to which computer-produced stories are structured like human-invented narratives can be an important component of the quality of a story plot. In this paper, we report on an empirical experiment in which human subjects have invented short plots in a constrained scenario. The stories were annotated according to features commonly found in existing automatic story generators. The annotation was designed to measure the proportion and relations of story components that should be used in automatic computational systems for matching human behaviour. Results suggest that there are relatively common patterns that can be used as input data for identifying similarity to human-invented stories in automatic storytelling systems. The found patterns are in line with narratological models, and the results provide numerical quantification and layout of story components. The proposed method of story analysis is tested over two additional sources, the ROCStories corpus and stories generated by automated storytellers, to illustrate the valuable insights that may be derived from them.
A quadratic form approach to Construction A of lattices over cyclic algebras

We propose a construction of lattices from (skew-) polynomial codes, by endowing quotients of some ideals in both number fields and cyclic algebras with a suitable trace form. We give criteria for unimodularity. This yields integral and unimodular lattices with a multiplicative structure. Examples are provided.
Multi-metric Joint Discrimination Network for Few-Shot Classification

Metric-based few-shot methods learn to recognize object categories from one or a few examples according to the distances between class features and query samples. The predicting labels of query samples are the same as those of the nearest class features. However, a single metric criterion can not model the distributions of different datasets very well. To get the more suitable metrics for a specified dataset, we propose a Multi-Metric Joint Discrimination Network (MMJDN) in this paper. Firstly, Deep Metric Module (DMM) is introduced to catch the complex relation between each pair of class features and query samples. Secondly, Adaptive Weights Module (AWM) is proposed to generate adaptive weights for different metric criteria. Our method is evaluated on three datasets: miniImageNet, Fewshot-Cifar100 (FC100) and Virus Texture Dataset (Virus15). The experimental results show that MMJDN provides positive performance for few-shot learning compared with some baselines.
Coronavirus disease 2019 pandemic: Strengthening the component of contact tracing for the containment of infection

<br>The coronavirus disease-2019 (COVID-19) pandemic, which was initially reported in the form of a cluster of pneumonia cases in the Wuhan city of China has raised serious concerns about the outbreak readiness across the world, including in the developed nations with the best quality health care delivery system. The initial facts depict that the primary mode of disease transmission is airborne, there is an indispensable need to revisit the infection control practices. Apart from the various strategies which have been proposed to strengthen the level of preparedness of a nation and also to contain the infection, contact tracing is an essential and integral component. The contacts should be advised to monitor their health for at least a period of 14 days after the last day of potential contact. In conclusion, contact tracing is a standard infection control strategy and is an important aspect in the interruption of the disease transmission in the ongoing COVID-19 outbreak. The need of the hour is to strengthen the same to ensure early detection of the COVID-19 suspects and interrupt the transmission of the disease.<br>
Book review “Teknik, Strategi, dan Pendekatan Pengajaran Bahasa Asing”

This is a review of a compilation book discussing foreign language teaching, published by Departemen Bahasa, Seni dan Manajemen Budaya (DBSMB) Sekolah Vokasi UGM. In learning foreign languages, we need appropriate teaching materials, teaching methods, and a learning assessment to achieve successful language learning. Therefore, this reference book is intended explicitly for SMA/SMK/MA teachers of a foreign language to gain information about the strategy, technique, and language learning approach. This compilation book is expected to provide some insights into the language learning process for language teachers in order to improve students' language skills.