Reviewerly/exHarmony huggingface dataset:
A Non-Polynomial, Non-Sigmoidal, Bounded and Symmetric Activation Function for Feed – Forward Artificial Neural Networks

Feed-forward artificial neural networks are universal approximators of continuous functions. This property enables the use of these networks to solve learning tasks. Learning tasks in this paradigm are cast as function approximation problems. The universal approximation results for these networks require at least one hidden layer with non-linear nodes, and also require that the non-linearities be non-polynomial in nature. In this paper a non-polynomial and non-sigmoidal non-linear function is proposed as a suitable activation function for these networks. The usefulness of the proposed activation function is shown on 12 function approximation task. The obtained results demonstrate that the proposed activation function outperforms the logistic / log-sigmoid and the hyperbolic tangent activation functions.
Early Reviewers Prediction and Spammer Detection on E-Commerce Websites

Reviews which are posted online play a vital part in present world as most of the customer's purchase items through an e-commerce website. Reviews which are posted on websites at an early stage known as early reviews, even though their contribution is very small their opinions determine new product's success and failure. Most of the spam reviews are written to improve their profit and promote their products and defame other products. In this system, the concentration is mainly on early reviews of the products and the products categories ranking on e-commerce websites i.e., Amazon. The analysis of reviews of product defines ratings of early reviewers’ and helpfulness scores of them are probably influencing product promotion additionally this model is enhanced with ranking and spammer detection.
Extracting Information from Microblogs Posted During Natural Disasters

Social Networking websites plays an important role in our life. The usage of the above websites is in every domain of our lives and they have increasingly infused itself into daily life. In recent years, the social networking websites such as Twitter, Facebook, are used in natural disasters. Many features have been included in Twitter for fast responses in such kind of unexpected events. This paper is based on the experiments performed on the 2017 Microblog Track provided by Forum of Information Retrieval &amp; Evaluation. The Classification schemes are used with two predefined labels as need and availability. The various pre-processing and natural language processing techniques are applied before the training of the model. The experiments showed that the classification accuracy is increased when the term weight is modified by using the information gain method and using the SVM classifier. This system automatically annotated the FIRE-2015 dataset of microblog track with 97% accuracy.
Emotion-Driven Facial Animation for Chat-Bots

Facial animation is quickly becoming an important feature of virtual assistants and is gaining traction as the preferred communication technique between man and machine. In addition to lip movement, facial expressions such as those of the eyes and cheeks help in conveying the sentiment and context of what is being spoken. This paper aims to present a new methodology to create an emotionally expressive virtual AI that is capable of understanding the sentiment of the conversation and displaying emotions during conversations. To achieve this the system uses a generative chatbot and combines it with a 3D talking head that is animated parametrically. This work could be beneficial to virtual assistants and help facilitate more lifelike interactions, holding significance in environments that require the user to feel more comfortable with their interactions. The complexities lie in developing a domain specific chat-bot that will not only provide valuable replies but also recognize and display appropriate facial expressions while communicating.
KAYIP DEĞERLİ VERİ SETLERİNDE KÜMELEME UYGULAMALARI

Missing data is when one or more values cannot be obtained in the data sets. The purpose of cluster analysis is to provide summary information to the researcher by classifying the data according to their similarities and to reduce the number of data that is too much to less. In this study, the performances of the three clustering methods are compared using different missing data rates in eleven separate data sets consisting of numerical and nominal data. The correct clustering rates of the data were examined by decreasing the data at five percent, ten percent, fifteen percent, twenty percent, twenty five percent and thirty percent of the data sets completely and randomly. The methods whose working performance were tested using missing data are k-means, one of partitioned clustering methods and self-organizing maps, one of artificial neural network-based clustering methods - Self Organization Map (SOM) and linear vector segmentation model - Learning Vector Quantization (LVQ). According to the results of the analysis; it is observed that as the missing data rate increases, the correct cluster rate decreases. It was observed that the LVQ method performed better in four data sets with two sets of nominal and numerical data, while the SOM method performed better clustering in the other seven data sets consisting of numerical data.
Square wave excitability in optically injected quantum-dot lasers (Conference Presentation)

Optically injected quantum dot lasers display many unique nonlinear phenomena and, in particular, are excellent testbeds for excitable dynamics. We show that – in contrast to most systems – both Type I and Type II excitability can be found. We focus on the recent discovery of the Type II dynamics, which manifests as square wave excitability. The phenomenon arises via an optothermal instability that breaks a bistability and renders the system excitable. Numerical analysis of the system shows that the onset of the square wave regime has two potential origins: One via a bistability and the other via a canard explosion.
Digital disobedience

Unusual methods of online protest have sprung up recently, and it has become harder to tell what's real and what's not, says Annalee Newitz
Kursk libraries’ innovative practices and technologies for ecological education of population

Establishing of model ecolibrary within Kursk Centralized Library System is discussed. The innovative practices and technologies of Kursk municipal libraries are aimed at promoting ecological knowledge and educating ecological culture of Kursk population.
Improving Responsiveness Conversation of Thai Chatbot through Sentiment Analysis Classification Techniques

Nowadays, internet and social media are play and important role for the business and marketing. Especially, the social media marketing drives the businesses with fierce competition. if there is communication between a large number of customers, it is necessary to have the staff to coordinate thoroughly Resulting in higher expenses as well. Chatbot can be solve this problem by action like a human to deliver a suitable message for their customers. This paper proposes the techniques for analyzing the sentiments that coexist with chat messages or the conversations. Naïve Bayes, K-Nearest Neighbor, and Support Vector Machine techniques were used to classify the sentiments based on Cross-Industry Standard Process for Data Mining. As a result, the highest accuracy is produced by Support Vector Machine with value at 94.60% for improving the chatbot able to communicate effectively with sticker messages.
Detection and Control of Malicious activity and Digital Forensic in BYOD

Enterprises are focusing greatly on Bring your own Device strategy since 2009 when Intel Adopted Bring your Own Device phenomena for the employees and later on this became more popular, Since 2012 exponential growth of BYOD services in the corporate environment also observed for providing an alternate method of working environment using employee personal device, however due to increased security threats and malicious activities occurring in BYOD environment most of the corporate are facing major challenges in enabling BYOD program. Increased Cyber-attack fragmenting the business ecosystem and cyber security becomes business survival factor. Increased amount of cyber attack in BYOD environment has created a major road block in adoption of BYOD. Existing security models of BYOD implementation, tools and techniques does not match the pace of security landscape at which cyber threats are growing. Moreover existing detection and control techniques of malicious activities in BYOD environment are not sufficient for cyber forensic investigation post an attack. By creating a secured model of BYOD environment security risk can be reduced. During this research Two different approach was followed to mitigate these risks. First approach was reverse adoption of encryption technique used to protect corporate network from BYOD environment. In 2nd approach malicious activity detection and protection mechanism explored with cyber forensic readiness in BYOD environment. Significant positive result observed to protect the corporate network infrastructure from untrusted BYOD traffic using GetVpn in cluster deployment. 2nd phase of this research has resultant to build digital forensic readiness BYOD model. Building a cyber secured model of BYOD cluster deployment ecosystem has contributed to reduce the risk of cyber threat. And detection of malicious activities has contributed in building a cyber forensic BYOD infrastructure to provide cyber confidence BYOD services.
Integrating Social Media Marketing with AI through Predictive Examination

Social Media activities have a major shift in marketing of products and have become a universal tool for advertising products. Nowadays billions of people use social media for recreational purposes and the social media activities leave a digital footprint which makes it easy to analyze each online activity. But simply having a enormous amount of data is of no use unless one can read the past and predict the future. Ai can run through data, observe patterns, give individual high quality content to each customer. The project is implemented through U Methodology. This mode determines the prospect of a customer preferring a product in an online interaction through big data and predictive analysis. Involvement of artificial intelligence can reduce human error and inclination towards a certain product. The best product a customer needs is instantaneously displayed to the customer without any time lapse.
A hybrid approach to find cloned objects in copy move forged images

In this paper, we have proposed a new hybrid approach to detect the cloned object in images.In copy-move forgery, images are forged by making the clone of objects of the same image within the image.In our proposed work, we have integrated two states of art techniques named adhoc method and principle component analysis (PCA) based scale invariant feature transform (SIFT) method.In two step process, we have preprocessed the object image by the proposed dynamic block size technique based local contrast modification and contrast limited adaptive histogram equalisation (DBST-LCM CLAHE) up to desired PSNR and grey level and then after integrated adhoc, and PCA based SIFT algorithm to design a hybrid approach.Proposed method tested over CoMoFoD image database.Proposed method shows the better performance in comparison of the state of art techniques regarding false positive rate (FPR), true positive rate(TPR), and PSNR.
Multi-robot Collision Avoidance : improved on Voronoi-based method

This paper introduces the study of multi-robot coordinated collision avoidance and task collaboration in a multi-robot system considering the localization uncertainty. Use MATLAB simulation and use the Voronoi-based method to change the uncertainty of the robot’s position in a specific working environment and compare the performance of the robot and the effect of avoiding collisions. An effective solution is also considered for the deadlock problem in motion planning. It is feasible and secure to use the Voronoi-based method to avoid collisions and complete tasks under the localization uncertainty.
Zero-Sum Games with Two Players

In this chapter we study an extension of the projected subgradient method for zero-sum games with two players under the presence of computational errors. In our recent research we show that our algorithm generates a good approximate solution, if all the computational errors are bounded from above by a small positive constant. Moreover, if we know computational errors for our algorithm, we find out what an approximate solution can be obtained and how many iterates one needs for this. In this chapter we generalize these results for an extension of the projected subgradient method, when instead of the projection on a feasible set it is used a quasi-nonexpansive retraction on this set.
A New Single-Soldier Three-Phase Voltammeter with Intelligent Data Management

In this paper, a new single-soldier phase voltammeter with intelligent data management is developed. Based on the traditional voltammeter, data analysis function is added to realize the remote data upload and data download, cloud storage, data analysis and other functions. It has changed the asynchronous processing mode in the traditional overhaul and measurement process, which is first field testing and then data processing, first data display and then data transcription, so that data processing, field measurementand and test report can be carried out simultaneously. The results of this paper have a powerful data management function, very suitable for single operation, and can greatly improve the operation efficiency.
Artificial Neural Network Hyperparameter Effectiveness Determination And Optimization Algorithm

Machine learning models can contain many layers and branches. Each branch and layer, contain individual variables, know as hyperparameters, that require manual tuning. For instance, the genetic algorithm designed by Unit Amin [2] was designed to mimic the reproductive process of living organisms. The genetic algorithm and the Artificial Neural Network (ANN) training processes contain inherent randomness that reduces the replicability of results. Combined with the sheer magnitude of hyperparameter permutations, confidence that model has arrived at the best solution may be low. The algorithm designed for this thesis was designed to isolate portions of a complex ANN model and generate results showing the effect each hyperparameter has on the performance of the model as a whole. The results of this thesis show that the algorithm effectively generates data correlating model performance to hyperparameter selection. This is evident in section 3.1, and 3.2, where it is shown that using the sigmoid activation function with CNN layers, regardless of the number of filters, or hyperparameters used in the subsequent LSTM layers, produces superior RMSE scores. Section 3.2 also reveals that the model does not improve in performance as the number of CNN and LSTM layers are added to the model. Finally, the results in section 3.3 show that the rmsprop optimizer generates superior results regardless of the hyperparameters used in the rest of the model.
Shrinking gravity

The first law of thermodynamics is the conservation of energy. The first law of geology is that rocks fall downhill. Falling rocks gain energy. The energy must come from gravity. So why doesn't gravity get less every day?
Preface

ICTEC 2019 is the third international conference organized by Centre for Telecommunication Research and Innovation (CeTRI) together with Faculty of Electronic and Computer Engineering (FKEKK), Universiti Teknikal Malaysia Melaka (UTeM). This conference was organized in collaboration with Photonics Research Centre (PRC), Universiti Malaya (UM), Wireless Communication Centre (WCC), Universiti Teknologi Malaysia (UTM), Universitas Sriwijaya, Indonesia and Universiti Malaysia Sarawak (UNIMAS). ICTEC 2019 was successfully organized in the heart of the Historical City of Melaka on 22-24 October 2019 with the theme “The 4th Industrial Revolution: Recent and Future Trend in Telecommunication, Electronic &amp; Computer Engineering”. During the conference, a keynote speech entitled “The 4th Industrial Revolution: Disruptive Challenges to Higher Education” has been delivered by Prof. Ts. Ir. Dr Hj. Mohd Nasir Taib, Senior Professor at Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM).
Fault Diagnosis In Mixed-Mode Circuit By Using Artificial Neural Network Method

In these work it is said that artificial systems are connected to finding of calamitous imperfections in the advanced piece of a nonlinear blended mode circuit. The methodology is exhibited on the case of a moderately mind boggling sigma-delta modulator. A lot of shortcomings are chosen first. At that point, issue lexicon is made, by reproduction, utilizing the reaction of the loop path to an info incline flag. This spoken to type of a carry-into table. Counterfeit neural system is then prepared for displaying (retaining) the look-into table. The conclusion is carried out so the artificial neural network is energized by broken reactions so as to introduce the deficiency codes at its yield. There were no blunders in recognizing the shortcomings amid conclusion.
Challenges and opportunities for upscaling DRR good practices

The use at farm level of good agricultural practices for disaster risk reduction has vast potential to significantly reduce the impact of hazards on both agricultural assets and agricultural production. First by reviewing existing research, and then by presenting the results of this FAO study, the preceding chapters make a strong case for the benefits that farm-level DRR good practices can bring to farmers, fishers and livestock raisers whose livelihoods are recurrently threatened by weather and climate extremes. However, the full scale of potential benefits can be achieved only when suitable practices are systematically upscaled.
An Examination of Different Scheduling Algorithms in Cloud Computing

Rapid growth in the use of cloud base service comes up with so many challenges. Service providers of cloud base service looking for the best solution to these challenges. Users of service sends the request for a particular task so at a time number of request sends by different users for different task, this all task need to be complete in a particular time is one of the biggest challenges in cloud computing It is defined as a workflow scheduling. Parallel other key points are better utilization of resources, reduce make span time, energy-saving and many more consider as QoS parameters. Researchers conduct so many Qos parameter oriented research to make it efficient The Aim of this review paper is to provide systemic review and better understating of ongoing research on different approaches and service providers always looking for better solutions of challenges to maintain service level.
Brazil’s Information Ecosystem: What is Transparency’s Impact?

Does transparency make a difference in Brazil? The current essay discusses the determinants and health of Brazil’s information ecosystem; it advances arguments on how and why Brazil has tended to underinvest in information and transparency, and discusses postures the government ought to adopt as a means of moving forward.
Characterizing Acoustic Signals in the Buildup to a Magnet Quench

over time in some rampsbut not others. Moreover, there are occasional outlier events that are unusually strong, and thereappears to be no consistent threshold for a quench. We further uncover concerns about the validityof the acoustic data, including signicant variations in the relative strengths of an event in dierentsensors and a few inconsistencies between sensors concerning the arrival times of acoustic events. Weultimately conclude that, while the energy ratio does often increase over time, any further researchendeavors will require the data to be improved in many ways.
O conhecimento de leigos sobre os sinais e sintomas do infarto agudo do miocárdio: revisão integrativa / Laymen's knowledge about the signs and symptoms of acute myocardial infarction: an integrative review

Acute Myocardial Infarction is an ischemic disease with frequent presentation in the population, responsible for high mortality and expenditure in the health sector.In this sense, it is opportune for laypeople to know their signs and symptoms, in order to carry out the appropriate management of those affected by the disease.The aim of the study was to review, based on the scientific literature, the main signs and symptoms of Acute Myocardial Infarction and the level of recognition of these by the population.This is an integrative literature review carried out in the Medline, Lilacs, Science Direct and Embase databases, mediated by the descriptors: "Signs and Symptoms", "Emergency Identification" and "Myocardial Infarction", with their correspondents in English .From the searches, eight articles published between 2016 and 2021 were included in the review.In this context, it was possible to analyze the understanding of the signs and symptoms that are most associated with Acute Myocardial Infarction and how they manifest themselves, mainly, in relation to age and sex, as well as to list the main associated risk factors.Thus, there is a lack of identification of these signs by laypeople, which makes educational intervention necessary in order to expand this knowledge.
Hybrid Technique for Data Scrutiny using Artificial Intelligence

Today’s time is of artificial intelligence. We are trying to reduce the search space by some methods as uncertain data is also used in the system to solve problems. To manage our database we use hybrid techniques. Like Fuzzy expert system, fuzzy genetic system, in this paper we will see how can we use such system in solving an application.
Medical Image Classification Based On Normalized Coding Network with Multiscale Perception

Medical imaging classification is playing a vital role in identifying and diagnoses the diseases, which is very helpful to doctor. Conventional ways classify supported the form, color, and/or texture, most of tiny problematic areas haven’t shown in medical images, which meant less efficient classification and that has poor ability to identify disease. Advanced deep learning algorithms provide an efficient way to construct a finished model that can compute final classification labels with the raw pixels of medical images. These conventional algorithms are not sufficient for high resolution images due to small dataset size, advanced deep learning models suffer from very high computational costs and limitations in the channels and multilayers in the channels. To overcome these limitations, we proposed a new algorithm Normalized Coding Network with Multi-scale Perceptron (NCNMP), which combines high-level features and traditional features. The Architecture of the proposed model includes three stages. Training, retrieve, fuse. We examined the proposed algorithm on medical image dataset NIH2626. We got an overall image classification accuracy of 91.35, which are greater than the present methods.
Fuzzy Logic Based Path Planning for Industrial Robot

This article describes how industrial robots are generally used to perform different tasks in industries, such as pick and place, and many more operations in industries. Among these, pick and place is a very common and frequently used task. Path planning is the most important thing in order to make any process more economical. The main focus of the research is to design a fuzzy control system for path planning for industrial robots using artificial intelligence using fuzzy logic. For the analysis, ten different tasks are tested. For fuzzy logic systems, three membership functions are analyzed and compared to find the best result. From the research, it has been found that a Gaussian membership function gives more accurate result in comparison to the other two membership functions.