Reviewerly/exHarmony huggingface dataset:
5 Artificial intelligence in the modeling of chemical reactions kinetics

The work presents a selection of recent papers in the field of modeling chemical kinetics by the use of artificial intelligence methods. Due to the fact that kinetics of the chemical reaction is the key element of industrial reactor design and analysis, the work is focused on the presentation of the quality of modeling, the assembly of neural network systems and methods of training required to achieve acceptable results. The work covers a wide range of classes of chemical processes and modeling approaches presented by several authors. Because of the fact that the methods of neural networks training require huge amounts of data, many approaches proposed are intrinsically based on classical kinetics modeling like Monte Carlo methods, quantum ab initio models or classical Arrhenius-like approaches using mass balance rate equations. The work does not fully exhaust the area of artificial intelligence because of its very broad scope and very fast evolution, which has been greatly accelerated recently. However, it is a contribution to describing the current state of science in this field.
Challenges Block Chain Technology Using IOT for Improving Personal and Physical Safety - Review

Web of Things (IoT) is an interconnection of clever actual articles called things and People. IoT permits each “Thing” to associate and impart in this manner creating and sending a colossal measure of Data like a monster Information System. Because of the immense measure of Data dealing with by IoT gadgets it got fundamental to incorporate Cloud Computing, Machine Learning and Information Modelling into The IoT stage. The enormous development in the field of IoT is causing increment in Information and Communication Technology (ICT) business too. It is anticipated that before the finish of 2020 95% of the new items will have IoT as its canter. As can be seen there will be an expanding presence of IoT objects and thus their perceivability from the Internet and lawful admittance to assets is a subject of grave concern. IoT gives and empowers the formation of creative applications that improved the physical and individual existence of an individual, yet the absence of security and weakness of individuals may prompt basic issues like the wellbeing of our homes might be undermined, and Centralized organizations utilizing delicate information are consistently at a danger hacking. Block-chain innovation is increasing a ton of consideration from different associations and parcel of examination is being done as it gives extraordinary answers for the issues related with the traditional incorporated design of IoT. Since, there are so numerous IoT gadgets which are on the lookout for the upgrade of an individual's physical, mental development and improving the personal satisfaction by and large, a conveyed trust innovation, guaranteeing versatility, security, and unwavering quality, is the need of great importance for the development of IoT conditions. The joining of IoT and BC represents a ton of difficulties. The Objective of this exploration paper is to build up an incorporated IOT and Block-chain Application Environment to upgrade the individual and Physical existence of a being. Consequently, our center will be to build up a safe and safe climate for a Smart home. Our proposed design depends on progressive structure and disseminated foundation of Block-chain to keep up security and protection and make it appropriate for explicit prerequisites of IoT. Subsequently we have coordinated the mechanization of the home alongside the actual soundness of an individual in this manner guaranteeing wellbeing, accommodation and strength of an individual.
Development of a Novel Algorithm for Moving Object Tracking in Video Surveillance

Video surveillance is a process of analyzing video sequences. It involves analysis, interpretation of object behaviors, as well as object detection and tracking. Video processing plays an important role in the industry and computer vision such as online monitoring of assembly processes, video surveillance security system, medical treatment, robot navigation and military, etc. Detection and tracking of human objects is one of the important studies in improving the ability of the surveillance system. The aim of this research work is to measure and analyze the application of background subtraction method and block matching algorithm to trace object movements through video-based. This research applies background subtraction method to detect moving object, assisted with block matching algorithm which aims to get good results on objects that have been detected. Performance evaluation is carried out to determine the various parameters. In this paper author design and develop a novel algorithm for moving object tracking in video surveillance also compares and analyse existing algorithms for moving object tracking. Author main aim to design and develop an algorithm for moving object tracking to handle occlusion and complex object shapes.
Determining Trust Based Examination on Social Networks for Hotel Recommendation

The objective of this research work is to enhance the performance of a novel recommendation site for tavern by mining the data about all kinds of hotel in websites. When we are about to view the rating of a hotel given by other people, we can’t rely accurately on it as there are many fake reviews. So we can overcome the problem by viewing only the ratings given by our friends and friends of friends in our trusted network. TrustSVD algorithm considered for ensuring this. By considering both the implicit and explicit opinions of ratings the predictions are made. The proposed technique is used to merge with social trust information thus from that we can get the trusted network. Thus, by using the trusted network reviews we can avoid the fake reviews. In case of cold start and data sparsity problem the friends of friends list is considered.
Generating Thumbnail Images

PHP has an extensive range of functions designed to work with images. You’ve already met one of them, getimagesize(), in Chapter 5 . As well as providing useful information about an image’s dimensions, PHP can manipulate images by resizing or rotating them. It can also add text dynamically without affecting the original and can even create images on the fly.
Data Stream Clustering Algorithms: Challenges and Future Directions

In the fast growing world applications are generating data in enormous volumes called data streams. Data stream is imaginably large, continual, rapid flow of information and in data mining the important tool is called clustering, hence data stream clustering (DSC) can be said as active research area. Recent attention of data stream clustering is through the applications that contain large amounts of streaming data. Data stream clustering is used in many areas such as weather forecasting, financial transactions, website analysis, sensor network monitoring, e-business, telephone records and telecommunications. In case of data stream clustering most popularly used heuristic is K-means and other algorithms like K-medoids and the popular BIRCH are developed. The aim of the abstract is to review the developments and trends of data stream clustering methods and analyze typical DSC algorithms proposed in recent years, such as BIRCH, STREAM, DSTREAM and some more algorithms.
A Lossy Compression Algorithm for Phasor Measurement Units Data Based on Auto-encoder and Long Short Term Memory

Abstract With the wide deployment of phasor measurement units (PMU) in power system, the amount of data generated by various measurement devices is increasing, which brings new challenges to data transmission and storage. In this paper, a lossy data compression algorithm based on auto-encoder and Long Short-Term Memory (LSTM) is proposed for PMU data. Specifically, the auto-encoder is used to extract the features of the measurement data first, and then saved the feature vectors to achieve the effect of data compression. When need to decompress the measurement data, the compressed data is input into the decoder to achieve decompression. Compared with the traditional algorithms, the algorithm proposed in this paper can effectively reduce the reconstruction error. At the same time, it can be combined with the existing lossless compression algorithm to further improve the compression rate.
Arabic and Latin keyboard Choice in Lebanese Students’ Discussions on WhatsApp.

Written discourse in WhatsApp discussions has been addressed in several articles in Computer-Mediated Communication (CMC). The aim of this study is to determine Lebanese universities students’ choice between Arabic and Latin keyboards while typing their online messages in WhatsApp groups and try to determine variables that affect their keyboards’ choices. We joined 33 WhatsApp discussion groups from 7 major Lebanese universities and gathered 227,059 messages written by 1,112 multilingual students. The results showed that even though Arabic keyboard is not very popular amongst some Lebanese universities’ students, it is still present in WhatsApp groups’ discussions of students especially at some faculties of the official Lebanese University where courses are taught in Arabic language. The results showed also that Arabic is widely typed in Arabizi using a Latin keyboard. This seems to be the case in the majority of universities that took part in the study. In addition, students at universities with high tuition fees and those who are having their curriculum in foreign languages use Latin keyboard more than students at some faculties of the public university.
An improved adaptive cuckoo search algorithm based on the population feature and iteration information

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Lévy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.
Privacy and Security Aware Cloud Storage using Double Cryptography Method

Cloud computing, a remote technology that enlightens with several on-demand services. Cloud computing developed with a principle of sharing resources available as per the company needs. Prominent cloud services utilized today are networking, storage, servers, software’s, etc. Cloud computing's distributed nature and advanced features create a remarkable footprint in the IT field. It is fair to say without cloud technology the next technical era can't exist. However the cloud computing widely spaced, simultaneous some serious challenges still exists. As per the real-time scenario, there are several challenges still alive, but among which privacy and security are prominent to take concern. Among which the trustworthiness of third-party providers increases the attention on developing an enhanced cloud system. In this paper, we proposed an improved double encryption-decryption (IDED) method for a secure cloud system. The main intention of the proposed work is increasing the privacy and security for the cloud users. The principle applied to the proposed methodology is double encryption and double decryption. The encrypted files are decrypted to view the original data using the keys. These keys are available only to the authenticated users which ensures the system's privacy and security in all manner. The mechanism is processed under the query system of sending and response from the data authority
Eyeball Movement based Cursor Control using Raspberry Pi and OpenCV

In this study, a specific human computer interaction system using eyeball movement is presented. Conventionally, computer system uses mouse as one of the data input devices. But in this system, we use eyes instead of mouse which provides a unique way of operating the computer with the help of eyeball movements. The implementation work underlying this system for pupil identification uses raspberry pi board to control the cursor of the personal computer and moreover Eye Aspect Ratio technique is ascertained along with OpenCV to detect the pupil. This system tracks the eye movements of the user with an IP cam (Internet Protocol camera) and simulates the eye movements into mouse cursor movements on screen and also detects user’s eye staring on icon and will translate it into click operation on screen. The main aim of this system is to help the user to control the cursor without the use of hands and is of great use especially for the people with disability.
Direction Oriented Block based Inpainting using Morphological Operations

Inpainting is one of the wide growing area of image processing. Image inpainting is a technique to reconstruct the restored region using some background information and obtain the results very efficiently and effectively. The basic concept of image inpainting is to replace the unwanted object from the original image and to recover the image using some neighborhood pixels in an undetectable way. In this paper, we introduce an efficient algorithm for image inpainting i.e., Direction Oriented Block-Based using Morphological Operations approach. This algorithm gives better efficiency than the previously proposed algorithm. By this approach, we can inpaint large regions as well as recover small portions in an undetectable way. A more accurate patch will be found out by this proposed approach. The experimental results proved that our proposed work is more computationally efficient and effective compared to previous work.
Intelligent mobile robot navigation using a neuro-fuzzy approach

This paper introduces an intelligent navigation system allowing a car-like robot to attain its destination autonomously, intelligently and safely. Based on a neuro-fuzzy (FNN) approach, the applied technique permits the robot to avoid all encountered obstacles and seek for its target's location in a local manner referring to the concepts of learning and adaptation. It uses two fuzzy Artmap neural networks, a reinforcement trial and error neural network and a Mamdani fuzzy logic controller (FLC). Experimental results in the Generator of modules (GenoM) robotics architecture, in an unknown environment, shows the FNN effectiveness for the autonomous mobile robot Robucar.
Performance Analysis of Load Balancing Algorithms using Microsoft Windows Azure Cloud Platform

As we are living in technology dominating era, the role of cloud computing as one of the rapidly emerging technologies is highly indispensable. This cloud computing holds load distribution concept, which is usually addressed as a challenging phenomenon, to handle multiple requests which are coming from geographically differentiated locations. There are various load balancing mechanisms exits for the effective distribution of the work load. This load balancing mechanism takes the advent of the virtualization approach on resources to optimize the utilization of the resources. In virtualization, the creation of virtual machine and mapping the same to the suitable request makes the cloud computing a prominent technology. This paper work considers various load balancing mechanisms for the allocation of resources and also shows a comparative study on them with the help of an example scenario. These load balancing algorithms are applied on applications like real-time systems and prioritized medical field related data storage system. This research work is implemented in Windows Azure Platform as Cloud Operation System under load balancing component.
Dominant Lexicon Based Bi-LSTM for Emotion Prediction on a Text

User-generated content and opinionative data has become a massive source of information on World Wide Web in the past few decades. Through social media people can share more conveniently their opinions, views, feelings and attitude about a product, person or event at anytime and anywhere as daily basis. This ever-growing subjective data makes enormous amount of unstructured data in web. Analyzing emotion in this raw unstructured data gives a very fruitful information for any kind of decision making process taken by both government and industries. Sentiment or emotion analysis is a field of Natural Language Processing (NLP), is used to identify the emotion depicted (by) in the form of text. Computation of emotion and emotion intensity depicted by a text is a very difficult task. Feature extraction from the text for vector representation is a difficult step of emotion analysis because it defines the emotion accuracy of the prediction. In this paper, a selective lexicon based BI-LSTM technique has been proposed. This technique uses only the most affected lexicon and its features for final vector representation. This method is a combination of features collected from the convolutional Neural Network (CNN), Long Short Term Memory (Conv - LSTM) and Bidirectional Long Short Term Memory (BI-LSTM). As a result the proposed model Selective Lexicon Based BI-LSTM (SL + BI-LSTM) outperforms all the models with high accuracy.
Research on Mechanism and Challenges in Meta Search Engines

A Meta Search Engine (MSE) produces results gathered from other search engine (SE) on a given query. In brief MSEs have single interface corresponding to multiple searches. MSE employs their own algorithm to display search results. This paper reviews existing Meta Search Engines like Yippy, eTools.ch, Carrot2, qksearch and iBoogie commonly used for searching. This paper surveys and analysed the working of different result merging algorithms. Current research reviews MSE based on different approaches like clustering technique. Few MSEs are employing Neural networks for searching. Further it also discusses problem in existing MSEs.
Hand Gesture Recognition System in Smart Environment

The smart environment allows users to interact effortlessly with their immediate surroundings using embedded sensors, displays, and computing devices. This system will not only be useful for ordinary people with no disabilities but also for people with disabilities like vision disability, cognitive, psychological and etc. Hand Gesture Recognition opens up a lot of possibilities for IoT based projects. At the end of the project, the goal is to design a cost-effective and highly interactive system through which a user can control the electric home appliances through hand gestures. This is a wireless device that is used to manipulate various devices using finger movement. The proposed device incorporates a transmitter connected to the MEMS accelerometer which detects movement warnings and transmits signals wirelessly using the radio frequency radio module (Zig Bee). The gesture alert which is input to the system is transmitted and obtained wirelessly using a wi-fi radio frequency module (ZigBee). The instrument contains a triaxial accelerometer, a microprocessor, and a ZigBee wi-fi transmission module. Acceleration warnings measured by the triaxial accelerometer are sent to a receiver via the ZigBee Wi-Fi module.
An Insight of Script Text Extraction Performance using Machine Learning Techniques

With the evolution of huge amount of ancient and modern text available in digital format, it is ascertain to mine for researchers, government, tourist and travelers visiting all over the world. However, it is very challenging and costly. Further, it takes a lot of effort and time for script text mining. Therefore, the study investigates various techniques for script text mining viz supervised and unsupervised techniques. Firstly, the study presents a survey for various kinds of techniques adopted by the users for extraction of text from image. It also delivers information about gaps involved in the various approaches. Furthermore, it incorporate the quantitative comparisons based among the study of various approaches and techniques for text extraction as well as script level comparison. The result inferred on the basis of the script comparison indicates that, the accuracy level of ancient script was found to be 5% lesser than modern script. Again, furthermore comparison has been done on standalone and hybrid machine (Combination of CNN and KNN) / deep learning techniques. The accuracy has been found to be lower(4%) in case of standalone techniques.
NATECH hazard identification at national level for Seveso sites affected by floods and earthquakes

The intensity of disasters is on an upward trend and inhabited areas are expanding into various risk areas, threatened by natural, technological or complex multi-hazards.The present study focuses on the analysis of Seveso-type economic operators whose activity is regulated by Law 59/2016 on the control of major-accident hazards involving dangerous substances.In Romania, the natural hazards that can trigger technological accidents in these sites are largely represented by earthquakes and floods.The analysis presented in this study was performed for a recurrence period of 475 years for earthquakes and 500 years for floods.The results of the analysis highlighted the sites in Romania that present specific Natech risks.Also, the possible technological accidents that may occur as a result of the manifestation of natural hazards have been identified for each site while a more detailed analysis was performed for the selected processes based on qualitative criteria.Finally, a ranking of these sites for Natech risks is presented considering the two natural hazards.
Acknowledgement to Reviewers of Magnetochemistry in 2018

Rigorous peer-review is the corner-stone of high-quality academic publishing [...]
Automatic repair of pollution attacks in SDN topology network based on SOM algorithm

When using the current method to recover the vulnerabilities caused by pollution attacks in SDN network, there are some problems, such as low coverage, low accuracy of vulnerability detection results and low repair efficiency. To this end, an automatic repair method for topology pollution attacks in SDN network based on SOM algorithm is proposed. This paper analyses the security vulnerabilities in SDN architecture and SDN network, and extracts the attack flow characteristics of topology pollution attacks in SDN network. The extracted attack flow features are input into SOM algorithm to detect and identify vulnerabilities caused by pollution attacks. Combining the theory of vertex concavity and convexity and geometric triangle, the detected vulnerabilities are repaired to realise the automatic repair of topology pollution attacks in SDN network. The experimental results show that the proposed method has high coverage, high accuracy of vulnerability detection and high efficiency of vulnerability repair.