Reviewerly/exHarmony huggingface dataset:
Food Ordering website “Cooked with care” developed using MERN stack

Recent de velopments in wireless technologies and the internet have led consumers to conduct their business digitally. The digital transactions made through these systems can be monitored and carried out efficiently. This article focuses on the need for and construction of a digital web application system, which will help us establish a modern means to carry out the food business for homemakers. The main aim of our website “Cooked with Care Using MERN Stack” is to provide a digital platform for homemakers and mess-owners to provide good and clean food to consumers such as students or employees. Cooked with care is a platform that will provide housewives and restaurant owners the means to sell their products and services online with customer feedback for the provided services. Homemakers and mess owners can use the ordering system to update menu presentations and set up the system in a wireless environment. This research work has utilised MERN as the tech stack, where M stands for “MongoDB,” our database for holding customer and management data. " E is an abbreviation for “ExpressJS,” which is utilised on our server. R is an abbreviation for “React,” which is a front-end programming language. N is an abbreviation for “NodeJS,” which refers to the complete programming environment. To save photos of menu items, the proposed work has leveraged AWS cloud storage. The resultant bills and invoices are generated by using the "react-to-print" library, and entries in an excel sheet using "react-csv." Some issues are considered together with storing and removing the images from the AWS database via the admin panel, but it has ultimately rectified the issue and were able to conduct CRUD operations on it. This paper discusses about the design and implementation of our website, which would make it possible to reduce food wastage without using any manual effort and manpower.
Utilization of DCT Coefficients for the Classification of Standard Datasets in Cloud/Edge Computing Environment

In this work we propose efficient deep neural networks for classification that are well suited to the edge computing and cloud computing environment. These environments inherently have to deal with bandwidth limitations and bounded computational resources. Our proposed methods tend to reduce the bandwidth requirements and reduce the computational costs for running these deep learning algorithms. We extensively utilized the Discrete Cosine Transforms (DCT) to exploit the redundancy in the image datasets. In this work we present a deep neural network that predicts the most significant DCT coefficients for an image and then employs these important DCT coefficients for classification purpose. This makes the deep neural network to achieve classification by processing much less input information. Broadly two approaches were used for classification purpose. In the first approach classification was done by employing the most significant DCT coefficients and in the second approach low resolution images, constructed from a limited number of DCT coefficients were utilized for classification purpose. The experiments were performed on well-known greyscale and RGB image datasets like FASHION MNIST, CIFAR-10 and CIFAR-100. VGG-16 architecture is mainly used for classification. The experiments showed promising results and the classification accuracies achieved were almost the same as that achieved by full resolution images.
Convolutional Neural Network Based Thermal Image Classification

Classification of Thermal Images has been extensively used for its significant applications in many fields. There are many problems with the visible spectrum like object shadows, clothes or the body of a human being matches the background and different lighting conditions. These limitations are overcome by using thermal imaging. Each and every object emits heat (Infrared energy) according to its temperature. Normally the hotter object emits more radiation than the colder one. As all objects have a mostly different temperature so thermal camera detects them and these objects get appear as distinct objects. In the start, thermal imaging was used by the military for detection, recognition, and identification of enemy personnel and equipment. Nowadays it is extensively used in the detection of face, self-driving car, detection of pedestrians and it also has application in the field of environmental work that is monitoring for energy conservation and pollution control. This research presents a novel study for the classification of thermal images using convolutional neural networks (CNN). The research focused on developing a framework that detects multiple thermal objects using CNN. Developed a framework based on deep learning Inception v3 model; work with thermal images that are captured by Seek Thermal and FLIR. For training and testing of the model two datasets are used that include three classes’ cat, car, and man. For the FLIR dataset the highest accuracy achieved is 98.91% and for Seek thermal dataset highest accuracy achieved is 100%. A comparison of the proposed framework with some other CNN models (DenseNet, MobileNet, and YOLOv4), with a customized CNN model and with a conventional model is also presented. The results of the proposed framework and comparison with other models prove that the proposed framework is effective for the classification of thermal images.
Networked Gardens: Remediating Local Nature Data Through the Internet of Things

Urban life is increasingly disconnected from nature. How might we design for greater awareness of the life around us? We present a study of an Internet of Things (IoT) device, the Ambient Birdhouse, and sensors to learn what kinds of interactions and media supported participants’ attention to local nature, and the sharing and exploration of nature data. Eight participants used the system and took part in individual interviews and group discussions. We found that diverse media and its reproductions and transformations (remediation), presented entry points for different kinds of users to engage with nature data. Notably, ambient acoustic media presented a means of both analytic sense-making and sensitizing users to nature. Patterns of sharing illuminated relational networks, as well as the affordances and challenges of different media. We contemplate how remediation impacts engagement with both nature and nature data and what this means for the design of IoT systems.
Comparative Analysis of Accuracy, Simplicity and Generality of Temperature-Based Global Solar Radiation Models: Application to the Solar Map of Asturias

The accuracy, complexity and generality of 13 temperature-based solar radiation models are compared using data measured during 2003–2016 at 21 weather stations in a large coastal area of northern Spain. The comparisons are based on dimensionless statistical indicators calculated for each model at each station, as well as on averages of errors calculated both for the group of eight stations located in the vicinity of the Principality of Asturias and for the set of all stations. Using site-calibrated coefficients, most models provide acceptable estimates, and no model outperforms the rest everywhere. The dispersion of the site-calibrated coefficients is analysed as a function of geographical variables, and general equations are obtained for each model, based on data from the group of eight stations. The results for the remaining stations allow the predictive capability of the models to be assessed in regions where radiometric measurements are not available. In general, models with a larger number of parameters perform worse, while a homogeneous single-parameter model achieves better results. Combined with GIS techniques, this model is used to update the Solar Map of Asturias, whose previous version was based on data from different time periods due to the scarcity of records at the time.
Analysis of image security by triple DES

We all know that in this growing world digital services usage has become more, the digital services are like communication over the internet, medical field, military imaging systems these need will need a high level of security. To correctly store and transmit digital photographs containing sensitive information, a security level is required. This is due to the rapid advancement of multimedia technologies, the internet, and cell phones. As an outcome, image encryption resolutions are compulsory to safeguard photos from such attacks. In this system, we employ Triple DES to hide photographs (Data Encryption Standard). This sort of encryption helps to keep both active and passive threats at bay.
Automated Category Tree Construction in E-Commerce

Category trees play a central role in many web applications, enabling browsing-style information access. Building trees that reflect users' dynamic interests is, however, a challenging task, carried out by taxonomists. This manual construction leads to outdated trees as it is hard to keep track of market trends. While taxonomists can identify candidate categories, i.e. sets of items with a shared label, most such categories cannot simultaneously exist in the tree, as platforms set a bound on the number of categories an item may belong to. To address this setting, we formalize the problem of constructing a tree where the categories are maximally similar to desirable candidate categories while satisfying combinatorial requirements and provide a model that captures practical considerations.
Heterogeneous Software Defect Prediction using Generative Models

It is widely known that one of the most helpful phases in SDLC is Software Defect Prediction (SDP). SDP can tell which modules have the highest chances to have bugs and companies can then devote suitable time in analysing those modules. SDP can be extremely helpful but it can be very difficult to predict buggy modules. Recently many studies have shown how in cases where enough data is not present, defect data from different projects can be used; this is called cross-project defect prediction (CPDP). However most of these studies require Homogeneous data for prediction i.e. our source dataset and target dataset must have the same parameters. Many times, especially for newer startups, previous data is not present for defect prediction, in such a case we want to be able to predict defects with data from other projects. We have trained and compared the performance of three models namely a GAN, Variational Autoencoder, and Adversarial Autoencoder for quickly transferring lessons learned about defect prediction among different datasets.
Reconnaissance for Penetration Testing Using Active Scanning of MITRE ATT&amp;CK

In the world of penetration testing, reconnaissance is the most important and first stage of securing an enterprise. Only through offensive exploitation by the pentesters can an enterprise be secured and patched up for vulnerabilities. Vulnerabilities that would be exploited by the hackers if left unpatched or solved. The MITRE ATT&CK framework lists out all the possible techniques and tactics to exploit the vulnerabilities in a machine. This ranges from reconnaissance to impact. Any kind of hacking starts with gathering information. The more the hackers manages to get the information about the enterprise like the infrastructure of machines, open ports, active services, directory structure, etc., the more chances the hackers could succeed in exploiting the vulnerabilities and hacking the system. The pentesters can safeguard an enterprise by not hiding most of the information and not accessible to the outside world. For this, the pentesters should think like hackers with malicious intent and perform offensive attacks. Only then, the pentesters can know where the vulnerabilities are and the correct measures to patch those vulnerabilities.
SparRL: Graph Sparsification via Deep Reinforcement Learning

Share on SparRL: Graph Sparsification via Deep Reinforcement Learning Author: Ryan Wickman University of Memphis, Memphis, TN, USA University of Memphis, Memphis, TN, USAView Profile Authors Info & Claims SIGMOD '22: Proceedings of the 2022 International Conference on Management of DataJune 2022 Pages 2521–2523https://doi.org/10.1145/3514221.3520254Online:11 June 2022Publication History 0citation34DownloadsMetricsTotal Citations0Total Downloads34Last 12 Months34Last 6 weeks4 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Scaling Equi-Joins

This paper proposes Adaptive-Multistage-Join (AM-Join) for scalable and fast equi-joins in distributed shared-nothing architectures. AM-Join utilizes (a) Tree-Join, a novel algorithm that scales well when the joined tables share hot keys, and (b) Broadcast-Join, the fastest-known when joining keys that are hot in only one table.
Application Value Evaluation of Blockchain Technology in Innovation and Entrepreneurship Information Platform for College Students

The current collegiate innovation and entrepreneurship information network works in a centralized manner, and there is a centralized trust dilemma. Malicious administrators can use their own rights to achieve public and private purposes. To solve this problem, a blockchain technology based on decentralization was introduced into the innovation as well as an entrepreneurship information platform for college students. It is critical to understand how to assess the usefulness of blockchain in innovation and entrepreneurship information platforms. This research mixes it with the current popular artificial intelligence trend and offers a neural network to assess the value of blockchain technology in terms of creativity and as an entrepreneurship information platform. The contents are as follows: (1) an application value evaluation method with an improved residual neural network is proposed. First, an improved data pooling layer is constructed by using three consecutive convolutional layers in series. The approach then has a significant feature learning ability by increasing the receptive field, thanks to an atrous residual block that combines atrous convolution and the residual block. Finally, the dropout method is introduced to avoid the negative impact of overfitting. (2) An application value evaluation method based on skip connection and residual network is proposed. With the inception module, this method creates a better data pooling layer and adds residual connections. The skip connection line is built in the residual block, which improves the residual block’s learning efficiency for feature information. The ordinary convolution in the residual block with a skip connection line is replaced with atrous convolution, and an atrous residual block with a skip connection line is designed. Finally, to construct a neural network, the two designed leftover blocks are connected end-to-end.
Relational Structure-Aware Knowledge Graph Representation in Complex Space

Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification.
Movie Recommendation System based on user’s search history using incremental clustering

In today’s world as the technology is advancing there has been a drastic increase in the number of websites, huge amounts of data have been generated and endless options emerged to choose from when it comes to movies. When there are several options available, one might be generally confused in making their choices. To avoid that, recommender systems are the ones that plays a major part in making recommendations of the movies to the users, by using some algorithms and techniques. These recommender systems are used for various purposes like, in recommending music, movies, products, research papers, youtube, temporal recommendation and so on. A recommender System gives suggestions based majorly on the user’s changing interest over time or the user searching patterns using some algorithms like Clustering, and suggests the movies based on the generated interests of the user in the course oftime
A registration-based stitching method for obtaining high-accuracy material removal distribution in the sub-aperture polishing process

In the research of rapidly developed deterministic sub-aperture polishing technologies, the analysis of material removal distribution is vital for promoting the precision, efficiency, and stability of the polishing process. To obtain a high-accuracy material removal distribution over a large-scale freeform surface with a high lateral resolution, sub-aperture stitching measurements are indispensable. Due to the limitation of traditional stitching methods, it remains a great challenge to obtain precise freeform surface topographies. This paper proposes a registration-based stitching method, which minimizes the negative impact of stitching error, to yield a material removal distribution with both high accuracy and fine lateral resolution. Firstly, to obtain an initial estimate of the rigid transformation for each sub-aperture, conventional stitching and registration based on the least-square stitching method and the robust iterative closest point algorithm are implemented. Secondly, a registration-based stitching model considering the invariance characteristic of the unpolished region is developed to cancel out the inconsistent stitching errors for obtaining a more precise material removal distribution. Finally, a polishing experiment has been conducted to validate the effectiveness of the proposed method. The registration-based stitching method and a commercial stitching program are utilized individually to obtain the material removal distribution. Comparison results show that the proposed method can largely improve the measuring accuracy of material removal distribution, which promotes the further study of the deterministic sub-aperture polishing process.
CPAS: A Copyright-Protected Autonomous Sharing Scheme by Blockchain and Smart Contract for BIM Data

Building Information Model (BIM) has been envisioned as one of the most promising technology for smart construction industry, e.g., industry 4.0 or industry digitalization. BIM data is of the most importance in the smart construction since the data can cover the whole life cycle of the building, which is somewhat also called digital twins. BIM data usually stored in cloud servers and finally BIM big data is formed. The BIM data is always required to be shared among a large number of contributors, and re-use of those data will greatly decrease the development delay. As BIM data usually encloses the intellectual property of contributors, the sharing of BIM data may be unwilling and thus difficult, especially in open data sharing over specific platforms or markets. To promote the re-use and replication of BIM data yet protect the intellectual property, we propose a blockchain and smart contract based scheme in this paper. The blockchain technology can encourage the sharing by awarding of tokens and trace the sharing behaviors. Smart contract can implement autonomous sharing with copyright registration, authentication, and verification. The experiment results justified that our proposed scheme is practical.
Spatio-temporal Analysis of Multi-agent Scheduling Behaviors on Fixed-track Networks

Multi-agent systems require coordination among the agents to solve a given task. For movement on fixed-track networks, traditional scheduling algorithms have dominated so far, but the interest in autonomous and intelligent agents is growing as they promise to react to unexpected and exceptional situations more robustly. In this paper, we study data from the Flatland 2020 NeurIPS Competition, where trains move through a virtual rail network. We developed a timeline-based visualization that provides an overview of all train movements in a simulated episode, clearly hinting at different phases, non-optimal routes, and issues such as deadlocks. This view is complemented with a map view and a graph view, interactively linked through highlighting and synchronous animation. Defining regions of interest in the map builds an analysis graph for detailed inspection. A comparison mode allows contrasting two different episodes regarding the same rail network across all views. We have conducted this application study in close collaboration with the Flatland community. Identified analysis goals stem from interviews with key persons of the community, while the approach itself was developed in two iterations based on feedback from experts with diverse backgrounds. This feedback, together with an analysis of the winning submissions from the competition, confirms that the initial analysis goals can be answered.
Explainable System for Suggestion Mining using attention

Deep Neural Network (DNN) models have shown remarkable results in Natural Language Processing(NLP) and Computer Vision(CV) applications. Convolutional Neural Networks (CNN), variants of Recurrent Neural Networks (RNN), and Transformer models are the architectures used for this. RNNs can capture the long-term dependencies but suffer from vanishing gradient problems. Long short-term memory networks (LSTM) and gated recurrent units (GRU) are introduced to overcome the limitations of traditional recurrent networks. Bi-directional information processing in combination with recurrent structure made to capture the past and future. All the components of the input sequence may not be relevant and essential in generating the target. An attention mechanism has been used to identify the crucial chunks from the input. Visualization of attention weights can describe the interpretability of model learning. Suggestion mining is treated as a text classification task to classify the given sentence into a suggestion or non-suggestion. This paper presents explainable systems using different RNNs and hybrid models for suggestion mining with and without attention components in the model. The experimental results on the standard datasets demonstrate that the local and global attention model outperforms. The heat map visualization on the input sentence also shows that the Bi-LSTM model with attention mechanism correctly captures the suggestive intent words.