Reviewerly/exHarmony huggingface dataset:
Theoretical analysis on applications aspects of smart materials preserving the security and privacy in medical big data and cloud

In the recent years the utilization of cloud to store and process the big data is increased rapidly. Security is the major concern faced by the big data in cloud environment. This paper proposed the authentication scheme for preserving the security and privacy of the big data in cloud environment. This paper considered the medical big data processing from the telemedicine system as the big data. The medical big data consists of huge volumes of medical records which are stores in the cloud. In the proposed mechanism follows mutual authentication and performs encryption operation between user and cloud environment. The proposed authentication mechanism has less computational load compared to the existing algorithms.
Statistical Grounding

chapter Share on Statistical Grounding Author: Ahmed Sabbir Arif Search about this author Authors Info & Claims Intelligent Computing for Interactive System Design: Statistics, Digital Signal Processing, and Machine Learning in PracticeFebruary 2021 Pages 59–99https://doi.org/10.1145/3447404.3447410Online:23 February 2021Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months10Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Discord Server Forensics: Analysis and Extraction of Digital Evidence

In recent years we can observe that digital forensics is being applied to a variety of domains as nearly any data can become valuable forensic evidence. The sheer scope of web-based investigations provides a vast amount of information. Due to a rapid increase in the number of cybercrimes the importance of application-specific forensics is greater than ever. Criminals use the application not only to communicate but also to facilitate crimes. It came to our attention that the gaming chat application Discord is one of them. Discord allows its users to send text messages as well as exchange image, video, and audio files. While Discord's community is not as large as that of the most popular messaging apps the stable growth of its userbase and recent incidents indicate that it is used by criminals. This paper presents our research into the digital forensic analysis of Discord client-side artefacts and presents experimental development of a tool for extraction, analysis, and presentation of the data from Discord application. The work then proposes a solution in form of a tool, `DiscFor', that can retrieve information from the application's local files and cache storage.
Facial Expression Recognition Using Patch-Based LBPS in an Unconstrained Environment

Facial expression recognition in the wild is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect on analyzing constrained frontal faces, they fail to perform well on partially occluded faces common in the wild. In this paper, an improved facial expression recognition technique, patch-based multiple local binary pattern (LBP) descriptor, comprises three and four patch LBPs [TPLBP, FPLBP]. The two-dimensional discrete cosine transform (DCT) was applied over the entire coded TPLBP and FPLBP face image as a feature extractor. The experiment results show that the proposed technique achieves a better recognition rate than state-of-the-art techniques. Oulu-CASIA dataset facial expression images have been evaluated using a support vector machine (SVM) classifier resulted in an accuracy of 92.1%.
A Joint Sentiment-Topic Model for Product Review Analysis of Electronic Goods

Online product review plays an important role in raising the voice of customers and their purchase decision. However, a conventional method in the area has largely focused on field research and surveys to acquire information about customer preferences. The already existing frameworks largely focus on supervised learning model techniques which is not feasible in all cases as it requires a labelled corpus which might be difficult to gather at times. This paper proposes a new framework for a review of topic modelling named joint sentiment topic model. The proposed research work extracts key features of online reviews and present a detailed analysis of different approaches and techniques, comparing the reliability of diverse systems among them. Also, this research work attempts to implement topic modelling in order to find how much a customer’s occupation and interests match a particular product and accordingly give weightage to their reviews. This idea is an effort to explore different approaches and techniques of topic modelling and provide valuable executive implications for electronic goods.
Performance Comparison for Sun-Tracking Mechanism Photovoltaic (PV) and Concentrated Photovoltaic (CPV) Solar Panels with Fixed System PV Panels in Jordan

The goal of this paper is to investigate performance enhancement of standard fixed photovoltaic (PV) solar systems when using single and dual sun tracking mechanisms, and compare it to concentrated photovoltaic (CPV) systems which inherently uses tracking systems. The study has two parts: experimental part and selected case study part. In the experimental analysis, characterization and performance comparison of four mounting types of standard PV systems are conducted. The PV panels are installed using either fixed mount, single axis (East-West tracking), single axis (NorthSouth tracking), or dual axis tracking. The analysis is done in Amman, Jordan. The data has been collected using data loggers every fifteen minutes, recorded then analyzed. The performance of these different mounting cases of PV system is compared to a CPV system installed in ASU. The results indicated that the Concentrated photovoltaic system utilized more electrical power than conventional photovoltaic. It produced 4.16% gain of power output compared with dual axes PV tracking system. The electrical power generation on the tracking surfaces were significantly greater than that on a fixed surface. There were increases of electrical power gain up to 37.72%, 24.05% and 16.56% for the two axes, east-west and north-south tracking, respectively, as compared with the fixed surface inclined 26̊ to the south in Amman, Jordan.
Applications of Machine Learning in Education

This research aims to explore how to enhance student engagement in higher education institutions using novel chatbots. This study's principal research methodology is design science research, which is executed in three iterations: personas elicitation, a survey and development of student engagement factor models (SEFMs), and chatbot interaction analysis. This chapter focuses on the first iteration, personas elicitation, which proposes a data-driven persona development method (DDPDM) that utilises machine learning, precisely a k-means clustering technique. Data analysis is conducted using two datasets. Eight personas are produced from the two data analyses. The pragmatic findings from this study make two contributions to the current literature. Firstly, the proposed DDPDM uses machine learning, specifically k-means clustering, to build data-driven personas. Secondly, the persona template is designed for university students, which supports the construction of data-driven personas. Future work will cover the second and third iterations.
Graph Representation Learning In A Contrastive Framework For Community Detection

Graph structured data has become very popular and useful recently. Many areas in science and technology are using graphs for modeling the phenomena they are dealing with (e.g., computer science, computational economics, biology, ...). Since the volume of data and its velocity of generation is increasing every day, using machine learning methods for analyzing this data has become necessary. For this purpose, we need to find a representation for our graph structured data that preserves topological information of the graph alongside the feature information of its nodes. Another challenge in incorporating machine learning methods as a graph data analyzer is to provide enough amount of labeled data for the model which may be hard to do in real-world applications. In this paper we present a graph neural network-based model for learning node representations that can be used efficiently in machine learning methods. The model learns representations in an unsupervised contrastive framework so that there is no need for labels to be present. Also, we test our model by measuring its performance in the task of community detection of graphs. Performance comparing on two citation graphs shows that our model has a better ability to learn representations that have a higher accuracy for community detection than other models in the field.
Animated mixed reality models for teaching solid state physics

Abstract The article deals with the preparation of animated content for mixed reality systems in solid state physics. The main obstacle to the mass adoption of these technologies is the complexity of model development. Here we have proposed one of the possible ways to simplify the creation of three-dimensional models for a course in solid state physics. In turn, the volumetric model can be supplemented with animated effects, which will provide greater interactivity and information content of the processes and phenomena under consideration. The proposed scheme is used to create models for the preparation of future physics teachers both in the process of teaching physics and directly for developing models. A survey was conducted among students about the complexity of developing such models. The results obtained testify in favor of the use of such methods both in teaching physics itself and in forming a base of models.
MyOcrTool: Visualization System for Generating Associative Images of Chinese Characters in Smart Devices

Majority of Chinese characters are pictographic characters with strong associative ability and when a character appears for Chinese readers, they usually associate with the objects, or actions related to the character immediately. Having this background, we propose a system to visualize the simplified Chinese characters, so that developing any skills of either reading or writing Chinese characters is not necessary. Considering the extensive use and application of mobile devices, automatic identification of Chinese characters and display of associative images are made possible in smart devices to facilitate quick overview of a Chinese text. This work is of practical significance considering the research and development of real-time Chinese text recognition, display of associative images and for such users who would like to visualize the text with only images. The proposed Chinese character recognition system and visualization tool is named as MyOcrTool and developed for Android platform. The application recognizes the Chinese characters through OCR engine, and uses the internal voice playback interface to realize the audio functions and display the visual images of Chinese characters in real-time.
Gradual Federated Learning Using Simulated Annealing

Federated learning is a machine learning framework that enables AI models training over a network of multiple user devices without revealing user data stored in the devices. Popularly used federated learning technique to enhance the learning performance of user devices is to globally evaluate the learning model at the server by averaging the locally trained models of the devices. This global model is then sent back to the user devices so that every device applies it in the next training iteration. However, this average-based model is not always better than the local update model of a user device. In this work, we put forth a new update strategy based on the simulated annealing (SA) algorithm, in which the user devices choose their training parameters between the global evaluation model and their local models probabilistically. The proposed technique, dubbed simulated annealing-based federated learning (SAFL), is effective in solving a wide class of federated learning problems. From numerical experiments, we demonstrate that SAFL outperforms the conventional approach on different benchmark datasets, achieving an accuracy improvement of 50% in a few iterations.
Mapping Images Over Elliptic Curve For Encryption

The emergence of Elliptic curve cryptography as a preferred cryptographic scheme using minimal computational resources is quite discernible. Its extension to the domain of image encryption using various mapping techniques has been analyzed in this paper. The efficiency of an encryption scheme based on ECC shall depend upon the appropriateness of mapping technique used to map pixels onto the Elliptic curve. Parameters like cost efficiency, Completeness, inversibility, and bandwidth used must be taken into consideration before designing any such mapping technique.
Learning graph representation by aggregating subgraphs via mutual information maximization

In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs. For this purpose, we propose a universal framework to generate subgraphs in an auto-regressive way and then using these subgraphs to guide the learning of graph representation by Graph Neural Networks. Under this framework, we can get a comprehensive understanding of the graph structure in a learnable way. And to fully capture enough information of original graphs, we design three information aggregators: \textbf{attribute-conv}, \textbf{layer-conv} and \textbf{subgraph-conv} to gather information from different aspects. And to achieve efficient and effective contrastive learning, a Head-Tail contrastive construction is proposed to provide abundant negative samples. Under all proposed components which can be generalized to any Graph Neural Networks, in the unsupervised case, we achieve new state-of-the-art results in several benchmarks. We also evaluate our model on semi-supervised learning tasks and make a fair comparison to state-of-the-art semi-supervised methods.
Routinggan: Routing Age Progression and Regression with Disentangled Learning

Although impressive results have been achieved for age progression and regression, there remain two major issues in generative adversarial networks (GANs)-based methods: 1) conditional GANs (cGANs)-based methods can learn various effects between any two age groups in a single model, but are insufficient to characterize some specific patterns due to completely shared convolutions filters; and 2) GANs-based methods can, by utilizing several models to learn effects independently, learn some specific patterns, however, they are cumbersome and require age label in advance. To address these deficiencies and have the best of both worlds, this paper introduces a dropout-like method based on GAN (RoutingGAN) to route different effects in a high-level semantic feature space. Specifically, we first disentangle the age-invariant features from the input face, and then gradually add the effects to the features by residual routers that assign the convolution filters to different age groups by dropping out the outputs of others. As a result, the proposed RoutingGAN can simultaneously learn various effects in a single model, with convolution filters being shared in part to learn some specific effects. Experimental results on two benchmarked datasets demonstrate superior performance over existing methods both qualitatively and quantitatively.
Development of mobile-based apps towards smart farming technology in Agro Innovation Park (Tagrinov) Management

Abstract As an effort to optimize the use of smart farming technology aimed at increasing efficiency in agriculture, a study was carried out at Agro-Innovation Park (Tagrinov), a place for education and learning of innovative home yard utilization concept. The smart farming applied in Tagrinov were irrigation control systems, pH and nutrition control systems of hydroponic plant, and temperature and humidity control in aeroponic systems. Those three monitoring and controlling systems were integrated in the mobile apps called Tagrinov 4.0. The deployment of control mechanism involved some systems, namely communication testing of android application using firebase server for irrigation; the Arduino Mega 2560 for pH level and nutrition control of hydroponic plant, and the Arduino Uno, DHT22 and DS18B20 sensors for temperature and humidity control of the aeroponic system. The test results showed some advantages of smart farming application in Tagrinov. Irrigation control system using internet network can be easily carried out by users. Arduino makes the system automatic, the pH sensor works as pH meter, the nutrients turn on and off when the pH value and nutrients are appropriate. The aeroponic sensor system read the temperature and humidity in the parent seed nursery, to activate the sprayer and makes the water become mist. All smart farming technologies were operated through mobile apps making these systems more efficient.
Semi-Supervised Learning Based Semantic Cross-Media Retrieval

With the advent of the era of big data, information has gradually changed from a single modal to a diversified form, such as image, text, video, audio, etc. With the growth of multimedia data, the key problem faced by cross-media retrieval technology is how to quickly retrieve multimedia data with different modalities of the same semantic. At present, many cross-media retrieval techniques use local annotated samples for training. In this way, the semantic information of the data cannot be fully utilized, and manual annotation is required, which is rather labor-intensive prone to errors and subjective viewing. Therefore, this paper proposes a Semi-Supervised learning based Semantic Cross-Media Retrieval (S3CMR) method aiming at the above problems. The main advantage of this method is to make full use of the relationship between the semantic information of the labeled samples and the unlabeled samples. Simultaneously, we integrate the linear regression term, correlation analysis term, and feature selection term into a joint cross-media learning framework. These terms interact with each other and embed more semantics in the shared subspace. Furthermore, an iterative method guaranteed with convergence is proposed to solve the formulated optimization problem. Experimental results on three publicly available datasets demonstrate that the proposed method outperforms eight state-of-the-art cross-media retrieval methods.
Strengthened 32‐bit AES implementation: Architectural error correction configuration with a new voting scheme

Digital data transmission is day by day more vulnerable to both malicious and natural faults. With an aim to assure reliability, security and privacy in communication, a low-cost fault resilient architecture for Advanced Encryption Standard (AES) is proposed. In order not to degrade the reliability of our AES architecture, the reliability of voter is very important, for which reason we have introduced a novel voting scheme include a majority voter (named TMR voter) and an error barrier element (named DMR voter). In this paper, a reliable and secure 32-bit data-path AES implementation based on our robust fault resilient approach is developed. We illustrate that the proposed architecture can tolerate up to triple-bit (byte) simultaneous faults at each pipeline stage’s logic and verify our claim through extensive error simulations. Error simulation results also show that our architecture achieves close to 100% fault-masking capability for multiple-bit (byte) faults. Finally, it is shown that the Application-Specific Integrated Circuit implementation of the fault-tolerant architectures using the composite field-based S-box, CFB-AES, and ROM-based S-box, RB-AES allows better area usage, throughput and fault resilience trade-off compared to their counterparts. So, it provides the most appropriate features to be used in highly-secure resource-constraint applications.
A Rule-Based Parsing for Bangla Grammar Pattern Detection

Rule-based parsing is the task that recognizes the structure of a sentence and assigns probable rules to parse the sentence. The structures can be assigned by context-free grammars that are employed to generate the accurate parse trees. We have developed formal grammars for Bangla language for various sentence structures. Since inherently these grammars are ambiguous, the CYK parsing algorithm is applied to verify the grammars. By checking the parse table, grammatical errors are detected if it has no entry for the terminal symbol. For recognizing the pattern of Bangla sentences, domain-specific context-free grammar (CFG) is developed based on the rules of Bangla grammar and applied to the domain. We have selected the air traffic information system (ATIS) as our selected domain. The efficiency of the proposed parsing scheme is shown using different kinds of Bangla sentences that show high accuracy within the domain.
Online Learning of Time-Varying Signals and Graphs

The aim of this paper is to propose a method for online learning of time-varying graphs from noisy observations of smooth graph signals collected over the vertices. Starting from an initial graph, and assuming that the topology can undergo the perturbation of a small percentage of edges over time, the method is able to track the graph evolution by exploiting a small perturbation analysis of the Laplacian matrix eigendecomposition, while assuming that the graph signal is bandlimited. The proposed method alternates between estimating the time-varying graph signal and recovering the dynamic graph topology. Numerical results corroborate the effectiveness of the proposed learning strategy in the joint online recovery of graph signal and topology.
Comparative Study of MongoDB vs Cassandra in big data analytics

In recent years, huge data is generating which needs large distributed databases to handle this massive amount of data. In general, relational databases were popularly used for many applications as it contains some rich set of features and transaction management capability. Yet, they are not efficient for storing and processing Big Data. Also, rapid growths happening in database models as these traditional relational databases are not capable to handle such vast data volumes. As Relational databases have several drawbacks, the requirement of novel technologies has emerged, that motivates developers migrate to Not Only SQL (NoSQL) databases. The key aim of this study is to make a review on capability of data storage and read/write performance in MongoDB vs Cassandra.
Object Detection in the DCT Domain: is Luminance the Solution?

Object detection in images has reached unprecedented performances. The state-of-the-art methods rely on deep architectures that extract salient features and predict bounding boxes enclosing the objects of interest. These methods essentially run on RGB images. However, the RGB images are often compressed by the acquisition devices for storage purpose and transfer efficiency. Hence, their decompression is required for object detectors. To gain in efficiency, this paper proposes to take advantage of the compressed representation of images to carry out object detection usable in constrained resources conditions. Specifically, we focus on JPEG images and propose a thorough analysis of detection architectures newly designed in regard of the peculiarities of the JPEG norm. This leads to a ×1.7 speed up in comparison with a standard RGB-based architecture, while only reducing the detection performance by 5.5%. Additionally, our empirical findings demonstrate that only part of the compressed JPEG information, namely the luminance component, may be required to match detection accuracy of the full input methods. Code is made available at: https://github.com/D3lt4lph4/jpeg_deep.