Reviewerly/exHarmony huggingface dataset:
Motion-Based Activities Monitoring through Biometric Sensors Using Genetic Algorithm

Sensors and physical activity evaluation are quite limited for motionbased commercial devices.Sometimes the accelerometer of the smartwatch is utilized; walking is investigated.The combination can perform better in terms of sensors and that can be determined by sensors on both the smartwatch and phones, i.e., accelerometer and gyroscope.For biometric efficiency, some of the diverse activities of daily routine have been evaluated, also with biometric authentication.The result shows that using the different computing techniques in phones and watch for biometric can provide a suitable output based on the mentioned activities.This indicates that the high feasibility and results of continuous biometrics analysis in terms of average daily routine activities.In this research, the set of rules with the real-valued attributes are evolved with the use of a genetic algorithm.With the help of real value genes, the real value attributes cab be encoded, and presentation of new methods which are represents not to cares in the rules.The rule sets which help in maximizing the number of accurate classifications of inputs and supervise classifications are viewed as an optimization problem.The use of Pitt approach to the ML (Machine Learning) and Genetic based system that includes a resolution mechanism among rules that are competing within the same rule sets is utilized.This enhances the efficiency of the overall system, as shown in the research.
Survey on Graphical Password Authentication System

Authentication is a method performed by any system to identify whether the user is genuine or not. Currently, user authentication is one of the main challenges of information technology. The process of remembering alphanumeric passwords is very tedious. In most cases, the user will choose a short and easy to remember passwords, which makes the password weaker. As per psychological research, it is easy to learn images as compared to letters and numbers. This is one of the main reasons behind the growing use of graphical passwords. In this article, a solution is proposed that is almost perfect against shoulder attacks, hidden cameras, and spyware attacks and provides a more secure authentication system. The advantages and limitations of graphical password authentication with different techniques are discussed in this review paper. A road map is also provided in this survey for the future enhancement of various graphical authentication schemes.
Improvised Transformer Network for NER on Low Resource EnglishHindi Code-Mixed Language from Scratch

The use of Code-mixed languages in social media platforms is a common phenomenon, which poses a new set of challenges for understanding Code-mixed languages in the field of Natural Language Processing (NLP). The implementation of state-of-the-art (SOTA) algorithms is difficult due to the scarcity of available resources. In the work, we focus on one of the primary NLP tasks namely Named Entity Recognition (NER) for English-Hindi Code-mixed language. We propose an improvised Transformer network that learns word and character embedding from scratch and beneficial in processing low resource Code-mixed languages. We use the only available Twitter NER corpus and obtained a slight improvement over SOTA. The proposed transformer network is a general model and in the future can be useful for training low resource NLP tasks from scratch.
Denoising method of low illumination underwater motion image based on improved canny

In order to improve the efficiency of image denoising, this paper proposes a method of image denoising based on improved canny. The region and edge feature fusion method is used to segment the target roughly, and then the fusion threshold of image channel is segmented. According to the result of image segmentation, local intuitionistic fuzzy entropy is extracted on the basis of intuitionistic fuzzy, which is introduced into anisotropic diffusion model and diffusion function. The partial differential noise reduction model is improved and analyzed to obtain the classification information of pixels and realize the noise reduction of low illumination underwater moving image. The experimental results show that the method has a high SNR, and the highest value of structural similarity is 0.92, which proves that the structure similarity between the image after noise reduction and the original image is high, and the visual effect of noise reduction is good, which fully verifies the practical application of the method in image noise reduction.
Session-Based Recommendation with Graph Neural Networks for Repeat Consumption

Session-based recommendation aims to predict user actions only based on anonymous session sequence. The repeat consumption is a common phenomenon in the recommendation scenarios which the items will be clicked repeatedly many times. Existing recommended methods for session-based repeat consumption model a session as sequence and use Markov Chain (MC) or Recurrent Neural Network (RNNs) to generate the representations of items. Although achieved promising results, these models still have the deficiencies in obtaining the correct user vectors and complex transitions of items. On the other hand, the number of unclicked items is very large relative to the items that have been clicked in a session, that it is more difficult to mine information for unclicked items. In this paper, we proposed an improved model named GNN-RepeatNet based on RepeatNet to explicitly model the repeat consumptions in session-based recommendation, by utilizing the graph neural network and multi-layer self-attention. In GNN-RepeatNet, we get accurate item embedding and complex transitions of items via graph neural network (GNN). Then through a repeat-explore mechanism, we compute the probabilities of predicted items in repeat mode and deep-explore mode separately. In addition, the multi-layer self-attention networks is introduced to deeply explore the unclicked items in deep-explore model. Extensive experiments conducted on two real datasets show that GNN-RepeatNet can improve the performance compared to the state-of-the-art methods.
RETRACTED: Search query of English translation text based on embedded system and big data

Cross-Language Information Retrieval (CLIR) the purpose of another language (target language), a collection of documents written question from one language (source language).CLIR employees publish documents based on user queries, dictionary translation, machine translation methods, and promotion problems. Through various detection methods and translation, this word applies to single words, translation, transliteration of names, including transcription and translation and disambiguation. CLIR Recovery in Question Semantics Technology is the most appropriate) Translation to retrieve documents (dictionary related method) based on English Question Concentrations and Question translation. To the proposed model of translating Arabic to English, and provides the high accuracy Optical Character Recognition (OCR) errors in handling orthography, expanding outside and transliteration dubbed gives higher accuracy in resolving ambiguities. Thus, the single question expands with additional meanings and related words that improve significantly with semantic input. However, the documents related to the questions recover those cross language boundaries. The development of large data systems is completely different from the actual goal of small (traditional, structured) data system development. When the in-depth learning technology is developed, face some space and environmental barriers in different laboratory environments. To describe the requirements when running embedded applications on computers for deep learning.
Irradiance on the upper and lower modules of a two-high bifacial tracking system

We examine the illumination incident to each of the modules in a two-high bifacial tracking photovoltaic system. Experiments and simulations indicate that the upper module receives more diffuse sunlight, whereas either the upper or lower module can receive more direct sunlight depending on the configuration and conditions. We use a view-factor model to illustrate how the fraction of illumination received by the upper and lower modules depends on albedo, the sun's position in the sky, module tilt, and the fraction of direct sunlight. We also use ray tracing to illustrate how edge effects can significantly change the amount of sunlight reaching the upper and lower modules; these results underscore the complications of using measurements from small test facilities to predict the behavior of large PV fields.
Decision-making for 5x5 Very Small Size Soccer Teams

Due to the high dynamism of IEEE Very Small Size Soccer (VSSS) matches, the main differences between the teams are focused on their decision-making system, path planning methods, and control and positioning algorithms. A path planning algorithm that fits well in the context of IEEE VSSS differential robots is the univector field navigation. For the positioning of the robots the combination of Delaunay Triangulation and Gouraud Shading proves to be quite efficient. Regarding decision-making, behavior trees are a good approach, due to their modularity and easiness in changing player's actions. In addition, a system for changing player's roles is also important, in order to make a dynamic match possible. The techniques shown here are not novel in the context of robot soccer. In fact, this paper contribution lies in integrating them in a full working strategy for 5×5 VSSS, which ended up winning Latin American Robotics Competition (LARC) 2019.
Tracking Acid Generating Minerals and Trace Metal Spread from Mines using Hyperspectral Data: Case Studies from Northwest India

A flight campaign over sites in India using the National Aeronautics and Space Administration (NASA) Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) was undertaken in 2016. Among the sites overflown were abandoned and active mining sites, Ambaji and Zawar, in northwestern India in the states of Gujarat and Rajasthan. AVIRIS-NG data was examined for signs of acid-generating minerals that could contribute to acid mine drainage and for distinct minerals or changes in spectral absorption features that could be signs of contamination by trace metals released by mining activities. Processing approaches used included an expert system/spectral feature fitting programme, MICA (Material Identification and Classification Algorithm), that matches absorption features in image spectra to those in user-defined library spectra. Also used were low abundance material detection approaches. Over the Ambaji site, the acid-generating mineral jarosite was mapped with the MICA approach and relative abundance was mapped with a low probability detection algorithm. The Zawar mine has a better-defined drainage which was mapped with MICA. A larger number of phyllosilicate minerals were mapped in the Tiri River drainage from the Zawar mine than were observed in similar sized rivers in other Ambaji and Zawar AVIRIS-NG flightlines. The 2.2 μm absorption feature associated with the minerals mapped in these drainages was examined to determine band parameters associated with the absorption. It was determined that minerals mapped, including montmorillonite, muscovite, and illite had a wider scatter in band parameters, principally asymmetry, but to a lesser extent bandwidth, than similar exposures of these mineral classes in the other riverbeds in the AVIRIS-NG scenes. Also mapped in the Tiri River, using a spectral matched filter, were gypsum precipitates. Field samples of this precipitate had high trace metal abundances. The combination of mapping the precipitates, such as gypsum, and an association with distorted absorption features (due to trace metal adsorption) holds the prospect for using imaging spectrometer data to assess the spread of trace metals from mining sites.
Colloquial Arabic Tweets: Collection, Automatic Annotation, and Classification

In this paper, our goal is to explore the performance of our curated datasets of Arabic dialect tweets (dialects from 4 main regions: Gulf, Levant, North Africa, Egypt). There are two main datasets: The Twitter Arabic Dialect dataset and the Twitter Arabic Dialect Emoji (TADE) dataset. The automatic annotation of the tweets into the 4 selected dialects is achieved by using a manually prepared lexicon for each dialect. To validate the resulting corpus, we use traditional (shallow) and deep learning classifiers for the purpose of dialect classification using a modified version of the TADE dataset. We experiment with many sound shallow classifiers including Gradient Boosting, Logistic Regression, Nearest Centroid, Decision Tree, MultinomialNB, SVM, XGB, Random Forest, and AdaBoost. For the deep learning classifiers, we use MLP and CNN. We experiment with TFIDF and word embeddings for feature selections. We validate the usefulness of our dataset via utilization in the experiments. It will be made available to the research community after publication.
PuppetTalk: Conversation Between Glove Puppetry and Internet of Things

Glove Puppetry is popular in Chinese societies. Glove puppet shows have been performed in a traditional (manual) way for over 300 years. Due to the advances in information and communications technology (ICT), we can extend glove puppet art in a very different dimension. However, existing ICT solutions for puppet control have not demonstrated the time complexities for their control mechanisms nor how they can effectively control the puppets. This paper proposes PuppetTalk, an Internet of Things (IoT) application platform for puppet control. We allow multiple input IoT devices such as smart gloves to control a puppet robot, and smartphones can be used to control the sequences of actions on that robot at the same time. In particular, the PuppetTalk Graphical User Interface (GUI) allows users to quickly configure multiple distributed devices to control a puppet robot. Also, controlling puppet legs using a second glove and puppet movements with hand gestures in real time through smart phones are both innovations that have not appeared in the literature so far. When multiple input devices are used to control a puppet robot, their instructions must be received by the robot in a correct sequence. We have conducted measurements, simulation and analytic modeling to show that PuppetTalk can correctly control a puppet robot by multiple input devices with the probability higher than 99.7%.
Robust subspace clustering based on inter-cluster correlation reduction by low rank representation

Subspace clustering refers to clustering data points into their respective subspaces and finding a low-dimensional structure to fit each group of points. In subspace clustering, the inter-cluster correlation of data which is caused by noise such as illumination and background affects the performance of subspace clustering algorithms. To solve this problem, a new approach is proposed to detect the unusual data with strong inter-cluster correlation based on the representation matrix obtained from low-rank representation (LRR). Then a low-rank model was established by reducing the unusual part of data and subspace clustering is performed. In addition, in order to apply subspace clustering algorithm on unaligned data, the preprocessing is required to make the data aligned, and the preprocessed data is used in experiments. Experimental results on the face dataset and texture dataset show the efficiency of the proposed method. The experiment also indicates that with the signal ratio increasing, the inter-cluster correlation is becoming more obvious.
COWAR: An Android Based Mobile Application to Help Citizens and COVID-19 Warriors

Across the world, people are facing unforeseen challenges due to Coronavirus pandemic. Almost 213 countries are under the influence of the virus and the whole world population is uncertain about how long this pandemic will last. The only thing that can prevent its exponential growth apart from a vaccine is to be well aware of the situation and follow the necessary precautions. In this paper, we design and develop an android application to spread awareness and to help the people of the world amid this COVID-19. The COWAR app connects the people with the Doctors and the administration to come together and fight the pandemic. With this app, one can track the spread of the COVID-19 epidemic, check live statistics, check for symptoms, browse an interactive map, and also get prevention details. Mobile applications like these help to connect the authorities with people and provide the essential information and services needed in this pandemic. We have validated our app by distributing it with a large number of people and collecting the feedback on various features of the app.
RESIP Host Detection: Identification of Malicious Residential IP Proxy Flows

The number of residential proxies as a service has grown as an emerging gray-area business, in which a provider utilises (compromised) hosts inside residential networks in order to provide anonymity services, effectively hiding the IP addresses of their clients behind other, seemingly normal, Residential IP (RESIP) addresses. This paper investigates commercial RESIP proxy service providers and looks into their host recruitment practices, which are often suspicious or borderline legal. In turn, the paper proposes a detection mechanism for identifying RESIP proxy flows on compromised consumer electronic devices, whereby the proxy software may operate without the user's knowledge or explicit consent.
Saliency Prediction with External Knowledge

The last decades have seen great progress in saliency prediction, with the success of deep neural networks that are able to encode high-level semantics. Yet, while humans have the innate capability in leveraging their knowledge to decide where to look (e.g. people pay more attention to familiar faces such as celebrities), saliency prediction models have only been trained with large eye-tracking datasets. This work proposes to bridge this gap by explicitly incorporating external knowledge for saliency models as humans do. We develop networks that learn to highlight regions by incorporating prior knowledge of semantic relationships, be it general or domain-specific, depending on the task of interest. At the core of the method is a new Graph Semantic Saliency Network (GraSSNet) that constructs a graph that encodes semantic relationships learned from external knowledge. A Spatial Graph Attention Network is then developed to update saliency features based on the learned graph. Experiments show that the proposed model learns to predict saliency from the external knowledge and outperforms the state-of-the-art on four saliency benchmarks.
Paradigm and performance analysis of distributed frequent itemset mining algorithms based on Mapreduce

FrequentItemsetMining (FIM) is one of the most important data mining tasks and is the foundation of many data mining tasks. In Big Data era, centralized FIM algorithms cannot meet the needs of FIM for big data in terms of time and space, so Distributed Frequent Itemset Mining (DFIM) algorithms have been designed to meet the above challenges. In this paper, LocalGlobal and RedistributionMining which are two main paradigms of DFIM algorithm are discussed; Two algorithms of these paradigms on MapReduce named LG and RM are proposed while MapReduce is a popular distributed computing model, and also the related work is discussed. The experimental results show that the RM algorithm has better performance in terms of computation and scalability of sites, and can be used as the basis for designing the DFIM algorithm based on MapReduce. This paper also discusses the main ideas of improving the DFIM algorithms based on MapReduce.
Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty

Dataset bias is a problem in adversarial machine learning, especially in the evaluation of defenses. An adversarial attack or defense algorithm may show better results on the reported dataset than can be replicated on other datasets. Even when two algorithms are compared, their relative performance can vary depending on the dataset. Deep learning offers state-of-the-art solutions for image recognition, but deep models are vulnerable even to small perturbations. Research in this area focuses primarily on adversarial attacks and defense algorithms. In this paper, we report for the first time, a class of robust images that are both resilient to attacks and that recover better than random images under adversarial attacks using simple defense techniques. Thus, a test dataset with a high proportion of robust images gives a misleading impression about the performance of an adversarial attack or defense. We propose three metrics to determine the proportion of robust images in a dataset and provide scoring to determine the dataset bias. We also provide an ImageNet-R dataset of 15000+ robust images to facilitate further research on this intriguing phenomenon of image strength under attack. Our dataset, combined with the proposed metrics, is valuable for unbiased benchmarking of adversarial attack and defense algorithms.
IoT-Based Big Data Storage Systems in Cloud Computing

IoT-based applications have developed as a significant sector for designers and scientists, exemplifying the size and effect of information relevant issues to be resolved in voguish merchandise associations especially in dispersed computing. The paper presented here basically gives a utilitarian system that distinguishes the securing, the executives, managing and mining zones of large Internet of Things info, and distinct parallel functional subjects are individualized and portrayed as much as their pivotal qualities and abilities Wang and Chu (Data management for Internet of things: challenges and opportunities, pp. 1144–1151, 2013 [1]). At that point of ebb and flow look into Web of things, usance is examined, in addition, the complications and operations related with large IoT-based information are recognized. We additionally communiqué an investigation of basic IoT-based application productions and research proposals dependent on similar erudite and commerce distributions. At last, some open issues and challenges what is more, some run of the mill models are delivered under the prospective IoT-based research structure Hasan, E. Curry (Internet Technol. (TOIT), 14(1):2, 2014 [2]).
On Two Algorithms for Multi-Modality Image Registration Based on Gaussian Curvature and Application to Medical Images

Registration of multi-modal images is one of the challenging problems in image processing nowadays.In this paper, two novel non-rigid registration models are proposed for multi-modality images.In model 1, mutual information of the template and reference images is used as data fitting term with Gaussian curvature regularization.This approach may not give satisfactory results in noisy images or images having bias field.To overcome this drawback, model 2 is proposed which is based on normalized gradient of both template and reference images as a data fitting term instead of mutual information.To get best transformations, both the models are minimized by using Augmented Lagrangian Method.The proposed models can register multi-modality images without effecting edges and other important fine details and are also tested on various medical images like (T1-T2 MRI, PD weighted-T2 MRI) noisy and synthetic images.The proposed models are also tested on a well known free available Brainweb dataset, where they produced satisfactory results.From experimental results, it can be observed that normalized gradient field based model gives better results than mutual information based model.Comparison is done qualitatively and quantitatively through Jaccard Similarity Coefficient.