Reviewerly/exHarmony huggingface dataset:
Least Square Parallel Extreme Learning Machine for Modeling NO<sub>x</sub> Emission of a 300MW Circulating Fluidized Bed Boiler

It is very important to establish an accurate combustion characteristics model of a boiler to reduce NOx emission. In this paper, a novel least square parallel extreme learning machine (LSPELM) is firstly proposed, all of whose weights and thresholds are determined by using least square method twice. Then, LSPELM is applied to 11 classical regression problems to test the validity. The experimental results show that, compared with other methods, LSPELM with a few hidden neurons can achieve good generalization and stability. Next, using Moore-Penrose generalized inverse theory and Woodbury formula, an online learning way of LSPELM (OLSPELM) based on sample increment is also proposed. If the samples of the current time are the same as those of the last time, the weights and thresholds of OLSPELM remain unchanged and are not updated. Only when the input samples of two times are different, can the weights and thresholds of OLSPELM be updated adaptively. Finally, LSPELM and OLSPELM are employed to successfully establish offline and online models of NOx emission concentration for a 300WM circulating fluidized bed boiler. The simulation results also show that LSPELM and OLSPELM have better nonlinear generalization ability and stability performance than some other state-of-the-art models. So, the proposed LSPELM and OLSPELM have good application value.
Gesture-based control for People Persons with Severe Motor Dysfunction by AAGI

We have researched gesture interfaces for persons with motor dysfunction who cannot use normal interface switches. For this purpose, we have collected data on various types of gestures to develop a contactless, non-constraining interface for operating PCs and home electrical appliances by utilizing a commercially available RGB-D camera. We describe the gesture menu and the remote-control device for controlling them.
Suitable Sites for CSP Power Plants Installation in Algeria

The objective of this study is to identify sites that are potentially viable for the contracted solar power installing in Algeria using multi criteria decision-making approach (MCDA) and geographical mapping models (Geographical Information System GIS). A variety of renewable energy applications has been undertaken using these technics. Coupling MCDA and GIS will provide a narrowing down for the optimal site selection for the contracted solar power installing. This endeavor will be beneficial to explore the possibility of commissioning such energy systems. Algeria is endowed with various types of renewable energies. Selecting a site for optimal production and generation of photovoltaic electricity without harming environmental, social, political and economic factors that may ensue conflicting objectives is based on a typical and complex process requiring careful studying. Summing up, the aim of this study is to design set of criteria to evaluate the appropriate sites of CSP for Algeria, obtain the first model of suitable area to install CSP and selecting the criteria of unsuitable area for installing CSP. The results highlighted the importance of electricity grid, direct normal irradiation and water resource. In addition, the first model showed that southern region are the most suitable regions for investors in the field of solar energy. Tamanrasset was found appropriate area to install solar power plants.
Open Geodata Reuse: Towards Natural Language Interfaces to Web APIs

Open Government datasets have been flooding the Web recently, and Application Programming Interfaces (APIs) are key to the development of services on top of these datasets. An issue of current APIs worldwide is that their learnability is limited. This work has explored the potential of querying APIs using natural language terms to mitigate that issue. A user study with 20 participants has demonstrated that a natural-language-based, along with an order-agnostic approach to API design can produce easily learnable APIs for both novice and experienced API users. These insights can pave the way for a paradigm change on Web-API design for open geodata retrieval and beyond.
Analyzing Social Media Opinions Using Data Analytics

In recent years, Twitter has become one of the most prevalent social media websites, in which people in the Arab world try to express their feelings and opinions about several political, social and economic events in their daily lives. In this project, we collected Arabic tweets about a certain event that happened in 2018. We preprocessed and analyzed the data to extract the key information. We used several algorithms to construct the classification model, including Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (K-NN) and Ensemble Classifiers (ex: Bagging, Voting) that combined these algorithms. The results showed that the Voting classifier with SVM in Python gives the best performance, in terms of accuracy, precision, and recall, as compared with the other algorithms.
Graph Embedding with Personalized Context Distribution

Graph representation learning embeds graph nodes in a low-dimensional latent space, which allows for mathematical operations on nodes using low-dimensional vectors for downstream tasks, such as link prediction, node classification, and recommendation. Traditional graph embedding methods rely on hyper-parameters to capture the rich variation hidden in the structure of real-world graphs. In many applications, it may not be computationally feasible to search for optimal hyper-parameters. In this work, built on WatchYourStep which a graph embedding method leveraging graph attention, we propose a method that utilizes node-personalized context attention to capture the local variation in a graph structure. Specifically, we replace the shared context distribution among nodes with learnable personalized context distribution for each node. We evaluate our model on seven real-world graphs and show that our method outperforms the state-of-the-art baselines on both link prediction and node classification tasks. We further analyze the learned node context distribution to provide insights into its connection to graph structural properties.
Cultural Diversity and Performance of Cameroonian Companies

The objective of this study is to highlight the effects of ethnic, linguistic, and religious diversity on business sustainability in Cameroon. To achieve this objective, the authors used data from a survey conducted in the cities of Douala and Yaoundé by the Laboratory for Research in Fundamental and Applied Economics (LAREFA) of the University of Dschang. Using a censored Tobit model, the following results were obtained: (1) Ethnic diversity and linguistic diversity each have a positive effect on the sustainability of Cameroonian companies. However, when the linguistic diversity index is too high, its effect becomes significantly negative. (2) Whatever its level, religious diversity has a negative effect on the sustainability of Cameroonian companies.
Attention-Based Siamese Region Proposals Network for Visual Tracking

In this paper, we propose a multi-scale visual tracking algorithm based on attention mechanism to solve the problem that the appearance characteristic model of region proposals network has weak ability to distinguish foreground and semantic background. The method introduces attention mechanism on the basis of region proposals network to realize the self-adaptive salient characteristic expression. The attention mechanism is essentially realized by convolutional neural network. The feature optimization mainly includes spatial attention selection and channel attention selection. Specifically, the spatial attention convolutional neural network is used to learn the planar weights to enhance the foreground and suppress the interference background. The channel attention convolutional neural network is used to learn dimensional weights and discard redundant noisy feature maps to simplify appearance characteristic expression. In addition, spatial and channel attention network respectively deal with high-level and low-level features according to their structural differences to focus on the similarity appearance characteristic and semantic classification characteristic. The experimental results illustrate the outstanding performance compared with several state-of-the-art visual tracking methods on the challenging video sequences.
Web Objectionable Video Recognition Based on Deep Multi-Instance Learning With Representative Prototypes Selection

To protect underage people from accessing objectionable videos in the Internet, an effective objectionable video recognition algorithm is necessary for web filtering. Recently, the multi-instance learning has been introduced for objectionable video recognition and achieves impressive results. However, hand-crafted features as well as redundant and noisy frames in objectionable videos become an intractable problem that inevitably degrades the recognition performance. In this paper, we propose a novel representative prototype selection algorithm embedding deep multi-instance representation learning. In the proposed method, an improved convolutional neural network is designed for multimodal multi-instance feature learning and a self-expressive dictionary learning model based on sparse and low rank constraint is designed to select the representative prototypes from each subspace of instances. Then the bag-level feature is constructed via mapping the bag to the selected prototypes. Experiments on three objectionable video sets show the effectiveness of our method for objectionable video recognition.
KIDSTAR: Augmented Reality to Measure Imagination Using Dynamic Visualization

Imagination is the action of forming new ideas, based on our mental knowledge, which we collect through our life, especially in childhood. For the last decades, reading retained to be the best tool to enhance children’s imagination. Our proposed solution—KIDSTAR uses augmented reality technology to enhance children’s reading experience and to measure the imagination patterns from children’s interaction. KIDSTAR uses the interaction and feedback benefits of the technology, retaining the original reading experience using physical books. KIDSTAR accommodates children with augmented reality tools, to illustrate and interact with their imaginary books’ scene. The visual tools will enable children to color, scale, and locate virtual objects to draw their imagination into reality. Using the visual content created by children, KIDSTAR will be able to track their decisions, choices, and thinking patterns. Moreover, KIDSTAR will allow children to create imaginary hero, which will be a measurement tool for their behavioral patterns through time. The innovative contribution of KIDSTAR is the merging of augmented reality dynamic visualization with analytical feedback. This merging converts the reading experience into a two-way creative channel and a unique platform for children to visualize their imagination. Applying behavior analysis can export beneficial reports such as user behavior patterns, parent alerts. We believe KIDSTAR is a potential tool for children’s psychiatric treatment—Autism, violence, creativity, and education potentiality.
Blockchain Technology Applications in Government

Blockchain in government services has a wide range of application areas for various departments from legal document storage and management to healthcare benefits. Decentralized nature opens opportunities for a government that can achieve operations 24/7 and cut back office operations. A Blockchain system for government offers a potential solution to legacy pain points and establishes a secure storage for citizens and businesses. It also reduces intensive paper processes, excessive managing costs, and potential corruption and abuse. Instead, it greatly increases security of digital services and more importantly trust in the government. This chapter provides a detailed review of Blockchain solutions for three major government services: land registry, GDPR, and healthcare. For each service, a state-of-the-art is given, followed with the technical details of use cases and potential improvements for current problems.
Comparison of Distance Function to Performance of K-Medoids Algorithm for Clustering

The clustering task aims to assign a cluster for each observation data in such a way that observations data within each cluster are more homogeneous to one another than with those in the other groups. Its wide applications in many research fields have motivated many researchers to propose a plethora of clustering algorithms. K-medoids are a prominent clustering algorithm as an improvement of the predecessor, K-Means algorithm. Despite its widely used and less sensitive to noises and outliers, the performance of K-medoids clustering algorithm is affected by the distance function. This paper presents experimentation findings to compare the performance of K-medoids clustering algorithm using Euclidean, Manhattan and Chebyshev distance functions. In this study the K-medoids algorithm was tested using the village status dataset from Gorontalo Province, Indonesia. Execution time and Davies Bouldin Index were used as performance metrics of the clustering algorithm. Experiment results showed that methods of Manhattan distance and Euclidean distance with the Index Davies value of 0.050.
Automatic Detection of Lung Cancer Identification using ENNPSO Classification

The main source of lung cancer is to gasping the tobacco smoke regularly, which affects around 90% of lung cancers. Cancer cells are to be carried to and from the lungs within the blood or lymph fluid ambience the lung tissue. Early diagnosis and treatment can save life. In this, the image processing techniques have been utilized to identify the lung cancer. Initially, the CT scan image is pre-processed for removing the unwanted signals and smoothing them by employing Improved Kaun Filter (IKF). Subsequently, the preprocessed image is portioned by an Active contour method to get exactness of segmented results. Next, specific features are extracted to raise the anticipated accuracy. At last, the tumour has been categorized by Elman Neural Network (ENN) and weights are optimized with PSO and compared the accuracy results with SVM, RBFN and ANFIS.
E-REA Symmetric Key Cryptographic Technique

As the internet evolves and computer networks grow bigger and bigger every day, network security has become one of the most unsafe factors for the companies towards digital computations. Security is a major concern in any kind of data communication. As technology evolves we tend to lose privacy indirectly. The process is automated but what about the data, where it goes how it is safeguarded still remains unanswered. By securing the web, one can decrease the chance of confidentiality spoofing, identity or information theft by the malicious or an intruder in the system, and factors that lead to the exploitation of resources. The information in transit includes our user credentials on different domains to the most private documents. So, privacy is the biggest challenge to enterprises that are victims of their effects. To transmit the data more securely, various cryptographic algorithms are used. These algorithms strive hard to avoid active and passive attacks. Though various cryptographic processes are being used to ensure the security of data, security in terms of various technicalities of the technique used such as key size plays a vital role in ensuring security. We propose a new and efficient cryptographic algorithm to secure the data from malicious attacks and provide data security during data transmission. This is the symmetric key encryption algorithm which uses same key for both encryption and decryption. The proposed algorithm is the enhancement of the Reverse encryption algorithm. The runtime estimation of the results obtained ensures that the algorithm is efficient and can be used for secure file transfer.
A Framework For Captioning The Human Interactions

Caption generation is an emerging Artificial Intelligent challenge where a content description has resulted in a given input. Captioning involves the Computer Vision methodologies for the identification of content from input images and language modeling techniques for processing the text. The objective of Video Captioning is to generate a natural language sentence relevant to the content of the input video clips. In this paper, a deep learning-based encoder-decoder model has been used to result in effective video captions for human actions. The Caption Generative model takes video as input and generates a caption for the interactive actions performed by a human. This model comprises of two stages. The first stage (Encoder) performs extraction of the features using the Inception V3 model in Convolution Neural Network (CNN), and the second stage (Decoder) uses Long Short Term Memory (LSTM) a sequence modeling neural network is used for generating the captions. SBU Interaction dataset is used to evaluate the framework dealt in with this paper. Metrics such as accuracy, recall, precision, and F-score are measured to demonstrate the performance of the model. Bilingual Evaluation Understudy (BLEU) Score is also calculated for evaluating the generated captions.
Real Time Conversion of Sign Language using Deep Learning for Programming Basics

Sign Languages are languages that use the visual- manual modality to convey meaning. It serves as a medium of communication between speech-impaired people and normal people. They are full-fledged natural languages with their own grammar, lexicon and classifications such as American, Indian, Chinese and so on. However, due to the non-universality of sign languages, there is a huge communication gap between the speech or hearing-impaired people and others. Hence, they also suffer from access to basic education. In this work, a low-latency real-time sign language recognition application is developed for detecting and processing gestures performed from the Indian Sign Language (ISL) dictionary using a Convolutional Neural Network model, and identify the words that are being communicated. We focus our application on words from a specific domain, i.e., basic programming keywords, through a custom-made dataset containing 500 different images of the gesture corresponding to each word. Our application strives to detect both static and dynamic gestures performed by the user, and generate Python- like syntax for various constructs such as if and loop, with a minimal response time of less than 0.1s. This can potentially make inroads into educating disabled people in the field of programming.
A Novel and Simple Mathematical Transform Improves the Perfomance of Lernmatrix in Pattern Classification

The Lernmatrix is a classic associative memory model. The Lernmatrix is capable of executing the pattern classification task, but its performance is not competitive when compared to state-of-the-art classifiers. The main contribution of this paper consists of the proposal of a simple mathematical transform, whose application eliminates the subtractive alterations between patterns. As a consequence, the Lernmatrix performance is significantly improved. To perform the experiments, we selected 20 datasets that are challenging for any classifier, as they exhibit class imbalance. The effectiveness of our proposal was compared against seven supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, logistic function, support vector machines, and neural networks). By choosing balanced accuracy as a performance measure, our proposal obtained the best results in 10 datasets. The elimination of subtractive alterations makes the new model competitive against the best classifiers, and sometimes beats them. After applying the Friedman test and the Holm post hoc test, we can conclude that within a 95% confidence, our proposal competes successfully with the most effective classifiers of the state of the art.
Model and method of chunk processing of payload for HTTP authorization protocols

The existing HTTP authorizations methods, which guarantee the authentication of the payload were analyzed. The most implementations of modern existing servers and their extensions are optimized for a small payload size. A new method of authorization for HTTP protocols for requests with large payload was proposed. In order to apply the chunking approach, HTTP and HTTP/2 protocols were decomposed to states for blocking and non-blocking mode. The method was described and techniques of its implementation for HTTP and HTTP/2 protocols were developed. The new method was applied to OAuth 1.0a original and our modified version. The analysis of the newly developed method showed greater efficiency than the standard approach. As a result, the new method enhanced the web services to verify requests with large payload size and not prone to denial of service attacks.
DWT, DCT and PBFO based Approach for Biometric Image Security

Concealment of biometric features widely attempted in the domain of watermarking of digital data. This draft is the representation of such an example in which fusion of iris (host image) and palm print image (watermark image) has been undergone into a robust and reliable watermarking scheme leaving unobjectionable distortion. These two biometric are logically fused by using the combination DWT (Discrete wavelet transform), DCT (Discrete cosine transform) and PBFO (Parallel bacterial foraging optimization). These 3 techniques are processed for the embedding of palm print image into the iris scan biometrics. The palm-print watermark preserves the ownership of host iris. A combination of both will be used for complete authentication. The quality check of the watermarked image and the watermark (after extraction) has been done by using standard approaches such as PSNR, NCC, BER, and MSE. The results say that the property of the iris image is unchanged after embedding the palm print in it and the image quality of palm print after the extraction process is also contented even after applying attacks. The rate of imperceptibility of host image after embedding is not only qualifying the standards but also conquer among the previous algorithm. The approach of this paper is to realize the concept of dual authentication which enhances the security of user's media.
Adopting Jaya Algorithm for Team Formation Problem

This paper presents a simple and mighty metaheuristic algorithm, Jaya, which is applied to solve the team formation (TF) problem and it is a very fundamental problem in many databases and expert collaboration networks or web applications. The Jaya does not need any distinctive parameters that require comprehensive tuning, which is usually troublesome and inefficient. Among several optimization methods, Jaya is chosen for TFP because of its simplicity and it always avoids the worst solutions and moving towards the global best solution. This victorious nature makes Jaya Algorithm more powerful and significant as compared to any other contemporary optimization algorithms. To evaluate the efficiency of the Jaya Algorithm (JA) against another metaheuristic algorithm, Sine-Cosine Algorithm (SCA), both algorithms are tested and assessed for the TF problem solution using an ACM dataset containing experts and their skills. The experimental results validate the improved performance of the optimization solutions and the potential of JA with fast convergence for solving TF problems which are better than SCA.