Reviewerly/exHarmony huggingface dataset:
A Push Model of Advertisement Classification Matching Based on Machine Learning

Abstract With the leap-forward development of Internet technology, it is more effective for marketing to meet personalized needs based on massive data. In this paper, through the screening of advertisements and user feature vectors, combining factor decomposition machine and neural network, a multi-label model is built, and through training data, a static “advertisement-user” classification matching push model is obtained. On the basis of the static model, three sub-models are respectively established for comprehensive evaluation to realize the “user-advertisement” household matching push. At the same time, the recommendation model is updated in real time to establish the “channel user-video advertisement” classification matching push update model, and the static model is dynamic to build the classification matching push model. Combined with data set verification, it is concluded that the model is true and effective and can realize personalized recommendation.
Research on the Design of Primary School Mathematics Teaching Activities Based on Augmented Reality Technology

With the rapid development of information technology, new forms of education continue to emerge. Augmented reality as a new type of technical means to provide educators with new ways of teaching. This paper takes the firstgrade mathematics curriculum as the research orientation and designs a teaching model based on augmented reality technology. First, we combed the basic definition of Augmented Reality and its advantages, and designed the corresponding teaching system through analytical learning theory. Secondly, it expounded the specific interaction mode of Augmented Reality technology in mathematics teaching. Finally, we designed a course to further explain the potential feasibility of augmented reality-based mathematics curriculum system design, and conducted some user research and related discussions. Hoping to provide some reference for the theoretical research and practical application of augmented reality technology in the future.
Artificial Intelligence Based Intelligent Social Humanoid Robot – Ajit 2.0

A humanoid robot may be defined as a robot resembling the human body and capable of interacting with external world and exhibit basic motions such as hand gestures and biped or wheeled locomotion. The present trends in robotics has gained importance for development of autonomous robots capable of responding real-time scenarios in manufacturing and production areas. The domestic robots such Poppy developed by Poppy project and NAO developed by SoftBank Robotics have proved to be good human companion in performing basic human chores. The presented research focuses on development of indoor humanoid robot capable of understanding human emotions accepting voice commands and responding with simple hand gestures and indoor locomotion.
Simulation and Modelling in Fire Safety

Actual and future concerns in fire safety in buildings and infrastructure are challenging. Modern technologies provide rapid development in area of fire safety, especially in education, training, and fire-engineering. Modelling as a tool in fire-engineering provides possibility to design specific fire scenarios and investigate fire spread, smoke movement or evacuation of occupants from buildings. Development of emerging technologies and software provides higher possibility to apply these models with interactions of augmented and virtual reality. Augmented reality and virtual reality expand effectivity of training and preparedness of first (fire wardens) and second (firefighters) responders. Limitations such as financial demands, scale and scenarios of practical training of first and second responders are much lower than in virtual reality. These technologies provide great opportunities in preparedness to crisis in a safety way with significantly limited budget. Some of these systems are already developed and applied in safety and security area e.g. XVR (firefighting, medical service).
Towards a preventive maintenance approach for multi-agent applications

Software preventive maintenance is an important software activity which consists to include modifications and updations in order to prevent future serious issues of the software. In the context of multi-agent systems, such activity is completely omitted. The inherent specificities of multi-agent sy stems (e.g., autonomy, pro-activity, reactivity, adaptability, etc.) make their maintenance difficult task to achieve. We propose, in this paper, a conditional preventive maintenance approach for multi-agent applications. The proposed approach is based on MAS quality measurements and uses Aspect-Oriented programming. The proposed approach consists of three major steps, (i) measuring two quality metrics (autonomy and sociability) of the running application in a dynamic and continuous way by using AspectJ code and comparing them with minimum thresholds previously defined by the designer, (ii) warning the maintainer in case of detection of abnormal regression of the MAS quality, and (iii) intervention of the maintainer for preserving the application quality and thus avoid potential damage. The proposed approach is supported by a software tool we developed called PMMAS (Preventive Maintenance of Multi-Agent Systems). The approach and the associated tool are illustrated using a concrete case study.
Statistical analysis ANOVA and MANOVA of irradiation and temperature database for photovoltaic systems

Photovoltaic solar energy has been exploited as a solution to meet the growing demand for electricity through a renewable source of energy. Optimized simulations of photovoltaic systems require the use of accurate solarimetric and climate data. This paper aims to compare the difference in solar radiation and temperature data provided by the NASA SSE and Meteonorm databases with the data measured by a real solarimetric station installed in Campinas, Brazil. Analyzes were performed using statistical methods such as analysis of variance from ANOVA and MANOVA. The analyzes present the hypothesis of equality with the significance of 95 % for each case is not rejected. Hypothesis rejection results occur in MANOVA analysis for temperature and irradiation data with daily data. The paper concludes that NASA SSE data is closest to the actual data.
Applications and use Cases of Multilevel Granularity for Network Traffic Classification

Network traffic classification is a fundamental process in network management and security. It allows network administrators to classify traffic based on various levels of classification granularity such as the source type or application. Existing literature focuses on analyzing the entire network traffic classification process with emphasis on the classification techniques. However, besides classification techniques, the literature lacks coverage on classification granularity, which deserves proper attention due to its increasing application in modern networks. Understanding the various levels of classification granularity and their use cases allow for more optimized traffic classification. As such, this paper aims to explore the different levels of classification granularity and their use cases. We studied papers published between 2013 and 2019 in order to investigate the different levels of granularity and use cases in the literature. As a result, this paper groups the classification granularity into a systematic multilevel taxonomy to assist in attaining a deeper understanding of their applications. Finally, to motivate future research, we elaborated on the current challenges and future directions for network traffic classification.
Design and Implementation of an Autopilot for an UAV

Based on a theoretical model, a flight control system (autopilot) is designed for an UAV. The mathematical basics were analysed and transfered into a closed software system supported by Matlab/Simulink and by practical flight tests. Therefore, essential part of the project are the necessary hardware design of the control electronics and the connected sensor subsystems. The most important sensors will be introduced. The hardware based on the ARM Cortex M4 architecture. For safety reasons each hardware node has its own CPU. In order to maximum flexibility in the application all control electronics are designed as expandable systems of hardware modules. The Futaba SBUS.2 is used for communication between sensors, actuators and autopilot. The flight tests of the entire system are carried out in different flight configurations, i.e. several airframes were tested for optimizing and adapting for different operational scenarios.
A Deep Neural Network for Anomaly Detection and Forecasting for Multivariate Time Series in Smart City

In the progress of constructing a smart city, large amounts of univariate and multivariate times series data is generated by complex real-world systems and internet of things(IoT) with sensors such as wearable devices. Abnormal status in univariate and multivariate time series are necessary to be identified by abnormal detection methods. Time series forecasting in univariate and multivariate which refers to detect the different patterns in the input time series is significant for managers. However, building a system for anomaly detection and forecasting is challenging. On the one hand, the temporal dependency is required to capture in time series data, on the other hand, inter-correlations in different pairs of time series data are so important for the system that the system needs to encode inter-correlations. In this work, we propose an Attention based Convolutional Recurrent Encoder-Decoder (ACRED), which is effective to address anomaly detection and forecasting problems in time series. The studies based on a Secure Water Treatment tested (SWaT) dataset suggest that ACRED can outperform popular deep recurrent neural network methods.
Cluster Quantity Distinguished by Geometric Angle Measurement

As an unsupervised learning algorithm applied in artificial intelligence, clustering algorithm has long time existed the problem of clustering quantity and position recognition. Aiming at the problem of clustering quantity recognition and point classification, a new clustering quantity recognition algorithm based on geometric angle measurement (QGAM) is proposed. The algorithm measures the quantity of clusters by geometric angle scanning and determines the boundaries of clusters by radius density measurement. The algorithm finds temporary cluster by searching the strongest density direction and location, repeats the process for the remaining points until all temporary clusters are found, and then identifies the farthest temporary cluster as the right one. Then the state of the remaining points is reset to initial state, and the same process is repeated until all clusters are found. The results show that QGAM is an effective method to identify the quantity of clusters.
An improved artificial bee colony algorithm based on elite search strategy with segmentation application on robot vision system

Summary Aiming at accelerating the convergence speed and enhancing relative poor local search ability of the traditional artificial bee colony algorithm (ABC), this article introduces an ABC with a new elite search strategy. First, we propose a strategy of recording individuals with high performance. Then bees have more chances to learn from a real elite. In the onlooked bee phase, its updating equation is changed for having more opportunities to search in a valuable area. Furthermore, for saving the value of function evaluations, a new learning equation for the best onlooked bee is proposed. The image segmentation of a robot binocular stereo vision system is a key problem in mechanical robot vision system, but the computation time limits its application. The experimental results show that the proposed algorithm achieves better performance on 10 benchmark functions and the image segmentation problem of mechanical robot in comparison with several other state of the art algorithms.
Text Extraction from videos using MapReduce

Due to recent outbursts in the number of point and shoot devices. The data generated in the form of raw video has also increased exponentially. On YouTube alone, 300 hours of video alone is added every minute. This brings up the need of coming up with a long term solutions to tackle such behemoth tasks. The solution should be flexible, robust with ability to scale up and down dynamically as and when required. Needless to say, such system have a high cost and it is also of utmost importance to consider the cost factor and feasibility. Hadoop is one such framework which met all our requirements. It provides facility to distribute a big and complex task to low as well as high end computers to achieve a common goal. Hadoop is a highly scalable storage platform, because it can store and distribute very large data sets across hundreds of inexpensive servers that operate in parallel. Another advantage of using a Hadoop cluster is that it is fault tolerance as copies of the data are made and can be used in case of failure of a node. etc.
A video conversion technique to image mosaic

Videomosaic is a very important research axis in the field of computer vision. In this article, we will attempt to give details about the most important steps in the process of creating a wider view of the field of vision based on a video scene. This algorithm will be applied in the real-time conversion of videos to mosaic images.
FUTURE TEACHER’S EMOTIONAL INTELLIGENCE IN SPIRITUALLY-MORAL DIMENSION

The study focuses on the problem of a new generation teacher education and training in epoch of technogenic civilization, which is characterized by further scientific and technological progress, on one hand, and, by spiritual degradation in society caused by the material benefits for society, on other hand. Moreover, “psychological” crisis, connected with the general “mental illness” of the society is becoming global. “Bringing mind to emotions” is the leading idea of the theory of emotional intelligence of American scientists, who see it as a panacea in the contemporary society. The theory of emotional intelligence, which is widely recognized in the world, is attracting the attention of Ukrainian scientists, in particular, in higher education. Ukrainian scientists interpret the emotional intelligence of the teacher as a personal and professional quality that provides professional acmeological growth, productive, professional, humanistic oriented activity, effective interpersonal communication, stress-protective function. It is concluded, that these criteria should guide the professional training and education of new generation teachers.
Hadoop MapReduce's InputSplit based Indexing for Join Query Processing

Join queries are amongst the most used form of queries, where records from two or more tables or files are retrieved in order to have a comprehensive, comparable and contrasted view of certain data. However, the processing of the join queries come with higher overhead since all the tables or files involved in the process have to be considered. It can easily be imagined how much the overhead could become when data contained in such tables/files is big data. The use of indexing on Hadoop and its abstractions have resulted in improved performance when processing queries. However, even with the use of some of the indexing approaches, the processing of join query indicates higher overhead, except when the amount of data to processed is reduced by the indexing techniques before the query processing even get started. One indexing technique that ensures this, is the InputSplit based index. This paper showcases how InputSplit based indexing can be implemented in Hadoop MapReduce as well the experimental results of running a join query using such index. The results show at least 50% reduction in runtime when compared to both normal Hadoop MapReduce and Clustered Index based on blockIds query processing approaches.
Author Gender Identification from Arabic Youtube Comments

In this paper, we present a machine learning based approach to gender identification from Arabic text in Youtube comments. The proposed methodology handles dialectal Arabic, addresses the limitations of state-of-the art approaches and achieves an accuracy of 92% and an average precision of 98%.
Performance Analysis of Transmitter Identification Based on I/Q Imbalance Estimation

Performance of a transmitter identification method based on I/Q imbalance estimation is analyzed for different modulation types in this work. This analysis is considered on the basis of the constellation structure of proper and improper modulation signals. The effect of impropriety of complex signals on the features obtained from I/Q imbalance is analyzed. Simulation results show that transmitters can be identified with a high accuracy for proper communication signals even at low SNR, whereas the identification performance degrades for improper communications signals, especially at low SNR.
Reliable Services from Service Providers Based on the Ratings of IoT Devices Using Blockchain

The communication between the Internet of Things (IoTs) devices is not secure and reliable. A large number of security risks are involved. The existing security mechanisms are not easy to manage because they require extra resources and thus, increase the overall cost of the system. The IoT devices are resource-limited devices and they do not perform many computation. The services requested from the IoT devices may be malicious and may have severe consequences when they are not being tackled by any efficient security mechanism. The reliability of the services is an important aspect of the IoT network. Blockchain provides a solution that is secure and effective in terms of reliability and cost. We have proposed a system model through which we protect the IoT devices from unreliable services. These services are provided by cloud service providers. The proposed system allows IoT devices to know about the ratings of the service providers before requesting the service. Smart contracts are introduced to store ratings of the service providers into the blockchain. IoT devices invoke the smart contract to acquire ratings of the service providers by providing service codes. Performance analysis and the experimental results show that the proposed model protects the IoT devices from unreliable services in a reasonable time and affordable cost.
Automatic Sign Language Manual Parameter Recognition (I): Survey

A system that can automatically translate between South African Sign Language (SASL) and English could prove to be of immense benefit to a largely impoverished Deaf community in South Africa. A major component of such a system is the ability to automatically analyse and extract sign language semantic information from SASL video, ahead of translation into English. This paper surveys past work on the automatic recognition and transcription of sign language semantic information from monocular video. Research has shown that four fundamental parameters—called sign language manual parameters—convey meaning in a sign language conversation, namely, the hand shape, orientation, location, and motion. The extraction and transcription of sign language semantic information thus amounts to recognising and transcribing these four parameters. All currently existing systems are able to recognise only one or two of these manual parameters simultaneously. Also, all currently existing systems that provide a feasible accuracy and processing speed assume that the hand(s) will be orientated in only a single frontal-upright orientation, whereas many other hand orientations are used in sign languages. There are currently no systems with a feasible accuracy and processing speed that attempt to recognise the orientation of the hand. Finally, there have also never been any endeavours to process raw video input into transcribed sign language. This paper reviews ongoing work in simultaneous real-time recognition of all four sign language manual parameters and their representation in a sign language transcription notation.
A FPGA Implementation of a Dual-Axis Solar Tracking System

This paper aims at the efficient development and implementation of a dual-axis solar tracking system by using Field Programmable Gate Array (FPGA). Fuzzy logic has been suggested for this purpose. Here, the focused is lay down on Multi-Input, Multi-Output (MIMO) nature of tracking system and the issue of the interaction between them, which may cause instability, oscillation or lack of tracking and bias. It is shown how to develop fuzzy rules for removing or minimizing interactions. Specific requirements for the solar tracker and their implementation are also discussed. The rules and the above features were then encoded in VHSIC (Very High-Speed Integrated Circuit) High Definition Language (VHDL) for synthesizing the FPGA based controller. Results illustrate a successful implementation specially in development countries in Africa, where ample solar energy available nearly every day.
Hexagon-Based Generalized Voronoi Diagrams Generation for Path Planning of Intelligent Agents

Grid-based Generalized Voronoi Diagrams (GVDs) are widely used to represent the surrounding environment of intelligent agents in the fields of robotics, computer games, and military simulations, which improve the efficiency of path planning of intelligent agents. Current studies mainly focus on square-grid-based GVD construction approaches, and little attention has been paid to constructing GVDs from hexagonal grids. In this paper, an algorithm named hexagon-based crystal growth (HCG) is presented to extract GVDs from hexagonal grids. In addition, two thinning patterns for obtaining one-cell-wide GVDs from rough hexagon-based GVDs are proposed. On the basis of the principles of a leading square-grid-based algorithm named Brushfire, a hexagon-based Brushfire algorithm is realized. A comparison of the HCG and the hexagon-based Brushfire algorithm shows that HCG is much more efficient. Further, the usefulness of hexagon-based GVDs for the path planning tasks of intelligent agents is demonstrated using several representative simulation experiments.
Ethical Guidelines for SARS-CoV-2 Digital Tracking and Tracing Systems

The World Health Organisation declared COVID-19 a global pandemic on 11th March 2020, recognising that the underlying SARS-CoV-2 has caused the greatest global crisis since World War II. In this chapter, we present a framework to evaluate whether and to what extent the use of digital systems that track and/or trace potentially infected individuals is not only legal but also ethical.
Storage as a Service (STaaS) Security Challenges and Solutions in Cloud Computing Environment: An Evaluation Review

Cloud Computing is an emerging technology which aims to provide efficient access to remote and distributed resources with the help of cloud services providers such as Amazon Googles Application, Microsoft Azure. Storage as a Service (STaaS) is among the most important and popular kind of services being investigated in the Cloud computing community to serve both private users and business enterprise with remote and scalable data storage that can be charged per customer usage. Providing safety, privacy and availability for the data that is hosted in cloud are the major concerns for users and businesses. This survey presents the data storage security concerns that arise due to the nature of cloud computing like multi-tenancy and virtualization, in addition to the details of the recent solutions presented in the literature to overcome the security issues in cloud storage.