Reviewerly/exHarmony huggingface dataset:
Real time prediction of solar radiation of Indore region using machine learning algorithms

The most crucial data requirement for all solar energy researches is solar radiation. As solar radiation is the quantity which is dependent on time, the desired power output of any solar power plant is also dependent on time. The objective of this paper is to utilise machine learning models to estimate the solar radiations on daily data of Indore region (22.7196°N, 75.8577°E). The speed of wind, temperature, pressure, humidity along with solar radiation are used and applied in the process of prediction. The evaluation of the model is done in terms of prediction efficiency. In the work, boosted decision tree algorithm is used for the solar radiation prediction, which gives an accuracy of 96.9%. Also, the multiple linear regression algorithm is utilised in the work for the real time estimation of hourly solar radiations. The method gives an accuracy of 91.8%.
Racing Toward Tomorrow: ACRL's 9th National Conference

Racing Toward Tomorrow: ACRL's 9th National Conference. Preliminary program for the April 8-11, 1999 conference.
Root for a Phishing Page using Machine Learning

Phishing alludes of the mimicking of the first website. To infiltrate this sort of con,the correspondence claims will a chance to be starting with an official illustrative of a website alternately another institutional Furthermore starting with the place an individual need a probable benefits of the business with. (eg. PayPal,Amazon,UPS,Bank for america etc). It focuses those vunariblities Toward method for pop ups ,ads,fake login pages and so on. Web clients are pulled in Eventually Tom's perusing method for leveraging their trust on acquire their delicate data for example, such that usernames,passwords,account numbers or other data with open record on acquire loans or purchase all the merchandise through e-commerce locales. Upto 5% for clients appear on make lured under these attacks,so it might remain calm gainful for scammers-many about whom who send a large number for trick e-mails An day. In this system,we offer an answer with this issue Toward settling on those client mindful of such phishing exercises Eventually Tom's perusing identifying the trick joins Furthermore urls Toward utilizing the blending of the The majority powerful calculations for machine learning, Concerning illustration An result, we infer our paper with correctness from claiming 98.8% What's more mix from claiming 26 offers. The best algorithm being ,the logistic regression model.
"A Design of Novel Method for Classification of Waste Materials with its location using Deep Learning and Computer Vision for Smart Cities"

Waste management is the one of the main problems in all over the globe, presently waste materials are collected and sorted by hand, it is very time consuming and it also requires so much man power. Improper management of waste materials leads to hazards including environmental deterioration, soil contamination, water pollution, and air pollution. To solve this problem, there may be a requirement for an automatic method to aid to recognize the type of waste substances and it’s Position. Today’s technology is so sophisticated due of Artificial Intelligence and Machine Learning. These technologies may be utilized to address various real time issues, this article handles the fundamental challenge of detecting and separating the waste items like plastic, paper and metal with their location. In this article above stated issue is addressed using the Faster RCNN (Region Based Convolutional Neural Networks) model which is very much accurate compared to other algorithms like YOLO (You Look Only Once) and other similar algorithms. The model is trained on a custom dataset gathered on a mobile camera and pre-processed using Label-Img Tool. Data collected with different light conditions and in unique angles. The model is trained using Faster R-CNN identify objects and to obtain locations. This may assist individuals to keep their surroundings tidy and to become conscious of the garbage substances and to identify them. This paper has been precisely recognizing kind of items and places with higher accuracy.
Preface

chapter Share on Preface Authors: Laleh Jalali University of California, Irvine (UCI), Hitachi America Ltd. University of California, Irvine (UCI), Hitachi America Ltd.Search about this author , Ramesh Jain University of California, Irvine (UCI) University of California, Irvine (UCI)Search about this author Authors Info & Claims Event Mining for Explanatory ModelingMay 2021 https://doi.org/10.1145/3462257.3462258Published:20 May 2021Publication History 0citation5DownloadsMetricsTotal Citations0Total Downloads5Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Public policies for productive transformation in North Africa

This chapter examines government policies required for productive transformation in the countries of North Africa. These countries face structural constraints, which hinder international trade and the creation of quality jobs, both of which are necessary to reduce inequality. These challenges require changes to production and trade structures.
A Micro-view on Techniques for Knowledge Based Systems

Knowledge representation has been a topic of research and study from the time machine processing of data and information became popular. The attempt to mimic the human cognition using artificial intelligence techniques has been the goal of artificial intelligence systems. The crux of cognition is knowledge. Knowledge is an inference made from data and information that is acquired from a variety of sources. The ability to efficiently acquire information, process it and store it as a knowledge base for further retrieval and use makes the artificially intelligent system a cognitive system. The paper is a brief introduction to knowledge representation techniques and schemes
Density Based Emergency Vehicle Detection and Traffic Signal Controlling using Image Processing

In present day generation, the number of vehicles is increased because of increase in population. Hence in this paper, the design of vehicle detection and traffic control signal is implemented by using processing of image. The proposed system main intent is to acquire the images for the vehicle and control the traffic signal. Firstly an image is captures from the web camera, which is placed in traffic control area on the road. After this the traffic density is calculated from the obtained images. Basically, this image enhancement performs its operation in two forms they are operating phase and learning phase. Here the captured image as enhanced by using the image enhancement method. Hence the main advantage of this proposes system is that it processes the entire operation in simple way with high speed. The proposed system will capture the images of road areas in effective way. Hence the propose system has various features which will determine the color, width and many other. The proposed implemented system is mat lab software to prevent congestion of heavy traffic.
Topsoe opens new office in Indonesia to further support local customers

The Monte Carlo (MC) method is the most common technique used for uncertainty quantification, due to its simplicity and good statistical results. However, its computational cost is extremely high, and, in many cases, prohibitive. Fortunately, the MC algorithm is easily parallelizable, which allows its use in simulations where the computation of a single realization is very costly. This work presents a methodology for the parallelization of the MC method, in the context of cloud computing. This strategy is based on the MapReduce paradigm, and allows an efficient distribution of tasks in the cloud. This methodology is illustrated on a problem of structural dynamics that is subject to uncertainties. The results show that the technique is capable of producing good results concerning statistical moments of low order. It is shown that even a simple problem may require many realizations for convergence of histograms, which makes the cloud computing strategy very attractive (due to its high scalability capacity and low-cost). Additionally, the results regarding the time of processing and storage space usage allow one to qualify this new methodology as a solution for simulations that require a number of MC realizations beyond the standard.
Taking into account the HAWE architecture issue

Although they don't apply specifically to the paper and the method proposed by the authors, in my opinion the remarks posted by M.Ippolito will be very useful to the authors in order to refine their findings and address some specific use case for their data, eventually in future works.It is true, indeed, that the players in HAWES (I find more correct the collective definition of High Wind Altitude Energy Systems, rather than Airborne Wind Energy that recalls the systems that fly a turbine) have different architectures that rely on different wind profiles at different altitudes.Try to determine a priori the best
Empowering Avatars – The Impact of In-Game Presence on Everyday Functioning

AbstractThe status of the player within the game is still a subject of multidisciplinary research, and it changes along with videogame industry, the platforms, genres and particular titles. The most recognisable and interesting form of in-game presence are avatars: the visual (and sometimes auditory) representation of players. Avatars are said to create culture, influence human behaviour and change the way one performs as a player. Avatars, their look and agency, also influence players outside the game environment and be used therapeutically. Avatars can influence not only the gamers’ behaviour but can also empower them; solving in-game problems can help players transport causative feelings into their lives. This paper will focus on the results of an interdisciplinary questionnaire conducted among Polish gamers who experience various levels of social exclusion due to their gender, size, and/or socio-economic status. To compare, similar research was conducted among groups of males not threatened by either social exclusion nor discrimination. The study shows what aspects of avatars help with stress management and what mechanisms of avatar-gamer relations empower, enrich and influence behaviour.
Saving energy consumption for mixed workloads in cloud platforms

Virtualisation technology has been widely applied in cloud systems; however it also introduces many energy-efficiency losses especially when the I/O virtualisation mechanism is concerned. In this paper, we present an energy-efficiency enhanced virtual machine (VM) scheduling policy, namely share-reclaiming with collective I/O (SRC-I/O), with the aim to reduce the energy-efficiency losses caused by the I/O virtualisation. The SRC-I/O scheduler allows running VMs to reclaim extra CPU shares in certain conditions so as to increase the CPU utilisation. Meanwhile, SRC-I/O policy separates I/O-intensive VMs from CPU-intensive ones and schedules them in a batch manner, so as to reduce the context-switching costs of scheduling mixed workloads. Extensive experiments are conducted on various platforms by using different benchmarks to investigate the performance of the proposed policy. The results indicate that when the virtualisation platform is in presence of mixed workloads, SRC-I/O scheduler outperforms existing VM schedulers in terms of energy-efficiency and I/O responsiveness.
Correction

This article refers to:Self-organising map-based dynamic decision-making algorithm for heterogeneous wireless sensor network
Collective Information

Many challenging problems of scientific, technological, and societal significance require us to aggregate information supplied by multiple agents into a single piece of information of the same type—the collective information representing the stance of the group as a whole. Examples include expressive forms of voting and democratic decision making (where citizens supply information regarding their preferences), peer evaluation (where participants supply information in the form of assessments of their peers), and crowdsourcing (where volunteers supply information by annotating data). In this position paper, I outline the challenge of modelling, handling, and analysing all of these diverse instances of collective information using a common methodology. Addressing this challenge will facilitate a transfer of knowledge between different application domains, thereby enabling progress in all of them.
Penentuan Tes Kepribadian Calon Mahasiswa Berdasarkan Sidik Jari Menggunakan Minutie dan Support Vector Machine

Every human being is given its own uniqueness by an almighty god, one of which is a part of the body organs such as the fingerprint pattern of the hand, the fingerprint pattern of each human being determines personality, this can be known from many previous studies, which use fingerprints or someone's detection by the police to capture the perpetrators with the biometry approach in the form of footprint fingerprint records attached to other objects. Determination of a person's personality can be known through fingerprints, and also can adjust prospective students in choosing the study program correctly. Fingerprint student personality identification application provides convenience in determining the choice of prospective students of the study program. The minutie method and the Support Vector Machine algorithm are used in clustering personalities according to training data in the application. The minutie test on the fingerprint pattern shows 100% compatibility, with a precision input image source. SVM algorithm in testing reached 80,9% in grouping personality types accordingly.
Uslužna delatnost u oblasti obrazovanja i razvoj digitalnih veština

The paper discusses the problem of the quality of service activities in education in the context of the development of the information society in Serbia, as well as the challenges of the global crisis caused by the emergence of a pandemic caused by the new corona virus. The emergence and exceptional development of information and communication technologies has fundamentally changed all aspects of social life and conditioned the need for accelerated adaptation and transformation of all aspects of society in our countr,. at the same time representing the only way to overcome the current dramatic situation by ensuring the continuous development of services in education. As the Strategy for the Development of Digital Skills in the Republic of Serbia for the period from 2020 to 2024 was recently adopted, it is necessary to consider and undergo a critical analysis, both of the proposed solutions provided in this act and the possibility of their valid application. This paper is also dedicated to pointing out possible new approaches so that service activities in the extremely important field of higher education not only respond to the existing challenges of the new age, but also represent the development engine of our society as a whole.
Information seeking behaviour ofArab social science and humanities postgraduates: Adescriptive study

The purpose of this paper is to report the findings of a study of information-seeking behavior among a group of Arab postgraduate students in social science and humanities disciplines. The paper also explores information-seeking styles and examines how information seeking is affected by external factors. The study employed a qualitative approach to explore informationseeking behavior in the sample and the sources of information used to obtain scholarly information. A sample of 33 participants was interviewed to elucidate the information-seeking behavior of the Arabic language speakers. The analysis of the interviews revealed that the participants use different methods to find information on the internet. These methods vary from using search engines to using sites that provide pirated scholarly papers. The data showed that most of the sample students use search engines and databases provided by their universities, but they should be trained in research ethics to avoid unacceptable research practices. The results also indicate that searching in other languages represents a challenge for Arab postgraduates in the social sciences and humanities. This study was conducted with social science and humanities postgraduates as part of a series of studies aiming to explore Arab language speakers' scholarly practices. The information-seeking behavior of science disciplines may differ, as the teaching language is mainly in English. This study contributes to the field by expanding our understanding of how non-English language speakers seek scholarly information and what sources are used to obtain the scholarly papers.
IoT with Cloud Centric Vehicle Detection and Counting System for Smart Traffic Surveillance

The efficient management of road traffic is one primary facet of many, in smart cities. Traffic overcrowding can be managed successfully, if prior estimation of the number of vehicles that will pass though a crowded junction in a specific time is known. This paper introduces a methodology which targets vehicle extraction on videos covering vehicles. To resolve the problem of current vehicle detection such as the need of detection accuracy and slow speed, an improved YOLOv3 vehicle detection is utilized. The k-means clustering used to group the bounding box around the vehicle in training dataset. The method for calculation of loss with respect to the length and width of the bounding boxes was recovered through the implementation of the batch normalization process. Finally, to improve the feature extraction of the network the high repeated convolution layer are removed. The experiment results are carried out on the BIT-vehicle validation datasets which shows the improvement of mean Average Precision (mAP) could certainly reach 95.6%.
Full Issue

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 2 p. I-I Full Issue Full Issue First published: 01 April 2019 https://doi.org/10.1111/j.1467-6346.2019.08775.xAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume56, Issue2April 2019Pages I-I RelatedInformation
Info-Chunk Objects as New Behavior Representation for System-based Model of Product

Requirement Functional Logical Physical (RFLP) structure has emerged as one of the prominent approaches for modeling the multidisciplinary products.Information Content (IC) provides effective interaction between the human and multidisciplinary product model.Though it controls the RFLP level by the Multilevel Abstraction based Self-Adaptive Definition (MAAD) structure, it needs to be further enhanced in terms of Human-Computer Interaction (HCI), multidisciplinary product behavior representation and structured processing of interrelated engineering objects to obtain coordinated decisions.Therefore, this paper introduces the Object-Oriented Principle (OOP) concepts in the IC for behaviors representation of the multidisciplinary product where Info-Chunk is considered as an object.Here, Behavior Info-Chunk (BiC) and Context Info-Chunk (CxiC) objects are proposed in the MAAD structure to model the behavior of the multidisciplinary product.Further, the concepts of Info-Chunk objects are extended to Intelligent Property (IP) that uses Initiative Behavior Context and Action (IBCA) structure to handle the RFLP structure.Based on the communication between the MAAD and RFLP structure, an API (Application Programming Interface) called "InfoChunkLib" is proposed.It can generate the graphs to represent the behaviors of a multidisciplinary product model.The API is handled by the information content to represent the behavior information and store the results in a database.
Self-Similarity Sparse Representation for Image Restoration

Image restoration aims to restore an image from a degraded image. The degradation may occur during image acquisition or image transmission. Image degradation lowers the quality of the image. In this paper additive Gaussian noise is considered for degrading the original image. For restoring the image from degraded image the proposed method used both local and non-local similarity patterns. The restoration problem is modeled with regression model. Two regularization terms are considered for representing prior image information. One regularization term is for local patterns and other is for non-local similarity patterns. The additive local regularization term is used to restore the edges. The non-local regularization term works best for local smoothness and edge information will be lost. The proposed algorithm took a clean image of size 256x256 and added with Gaussian noise with different levels of noise levels. A self-adaptive dictionary is trained for a particular window of image with local and non-local patterns and stacked to three dimensional matrix. The patch size considered for training the dictionary is 10x10. For restoring each patch it searches best atoms form the trained dictionary. The efficiency of the algorithm is estimated by parameters mean square error, root mean square error, PSNR and FSIM. The algorithm is also tested for different images like cameraman, house, Barbara, Lena and parrot. The proposed algorithm is tested with conventional algorithms. .
Bespoke Anywhere

This paper reports on a project aimed to break away from the portability concerns of native DSP code between different platforms, thus freeing the instrument designer from the burden of porting new Digital Musical Instruments (DMIs) to different architectures.Bespoke Anywhere is a live modular style software DMI with an instance of the Audio Anywhere (AA) framework, that enables working with audio plugins that are compiled once and run anywhere.At the heart of Audio Anywhere is an audio engine whose Digital Signal Processing (DSP) components are written in Faust and deployed with Web Assembly (Wasm).We demonstrate Bespoke Anywhere as a hosting application, for live performance, and music production.We focus on an instance of AA using Faust for DSP, that is statically complied to portable Wasm, and Graphical User Interfaces (GUIs) described in JSON, both of which are loaded dynamically into our modified version of Bespoke.
Improving cloud computing services indexing based on B&lt;SUP align="right"&gt;Cloud&lt;/SUP&gt;-tree with users preferences

Wireless sensor networks and cloud computing are different but complementary. In a hand, the wireless nodes are resources limited and battery constrained. In the other hand, cloud computing is unlimited in terms of computing, storage, network and power resources. Integrating such different concepts results obviously some troubles; especially for WSN owners who want to pick up the most suitable cloud computing provider. In addition, we suppose that both of the clients (WSN owners) and services are heterogeneous, various and dissimilar. In this paper, we propose an indexation method of public IaaS virtual machines in an AVL-tree. For that, we employ a Z-order function to arrange services in the structure and make the research more efficient. Experiments prove the performance superiority of the proposed approach in comparison with similar works in the literature.