Reviewerly/exHarmony huggingface dataset:
Synthetic Image Classification Using ML

Machine learning approaches typically need large volumes of training data and often demand costly manual etiquette to achieve their maximum ability. The value of images produced solely by visual means is on the increase. The color histogram is commonly used for the image classification challenge as a significant color characteristic that indicates the material. Since synthetic representations are an essential means through which visual knowledge is collected and presented. It is increasingly critical that these images are accurately classified in sub-categories – such as symbols, diagrams, figures and tables, logos, etc. The proposed research work aims at classifying synthetic images into sub-categories. The essential features of images shall be extensively analyzed and processed. When the web begins, photographs are used to convey content and beautify, shape, and align. In particular, the work is directed to image recognition techniques to identify pictures that are of great interest. This work tries to classify synthetic images from different images using a machine learning approach.
Towards Group Activity Recognition and Analysis for Improving the Safety of Automated Vehicles

Exploiting IoT data collected via embedded sensors in mobile devices and things is useful in context-aware applications. Such context-awareness can also be useful for automated vehicles (AV s) which need to be aware of the surroundings for safety and efficiency reasons. Also, scaling up of Human Activity Recognition (HAR) to Group Activity Recognition (GAR) has at-tracted significant attention recently. One of the crucial elements of every context-aware system is obtaining context data from context providers (CPs) to be able to recognize a group activity (GA) or a situation among a group. In this paper, we extend our previous framework, called GARSAaaS+ (GARSA-as-a-Service+) to interact with external Context Providers to offer services for mobile Group Activity Recognition and Situation Analysis (GARSA) applications in relation to improving the safety for emerging AV s. Also, context-aware data caching in IoT-enabled applications in order to obtain context quickly is an open challenge - in this work, we propose an approach applying caching rules defined using GroupSense-L, which is a specification language for group activities. We demonstrate our proposed middleware via two scenarios to improve group and individual safety when an automated vehicle is involved. We also demonstrate the feasibility of our model and the expressiveness of our proposed model via a range of scenarios.
Using Artificial Intelligence to Capture Tabular Data in Mining Document at Scale

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An Automated Deep Learning Model for Detecting Sarcastic Comments

The concept of Natural Language Processing is immensely vast with a wide range of fields in which ideas can be explored and innovations can be developed. An algorithm based on deep learning is used to detect sarcasm in text in this paper. It is usually only possible to detect sarcasm through speech and very rarely through text. 1.3 million comments from Reddit were analyzed, of which half were sarcastic and half were not, and then various deep learning models were applied, such as standard neural networks, CNNs, and LSTM RNNs. The best performing model was LSTM-RNNs, followed by CNNs, and standard neural networks came last. With textual data, it is much harder to understand whether the other person is being sarcastic or not, it can only be understood by listening to their tone of voice or looking at their behaviour. The purpose of this paper is to demonstrate how to detect sarcasm in textual data using deep learning models.
Research on Recommendation Algorithm of Joint Light Graph Convolution Network and DropEdge

Overfitting in a deep neural network leads to low recommendation precision and high loss. To mitigate these issues in a deep neural network-based recommendation algorithm, we propose a recommendation algorithm, LG-DropEdge, joint light graph convolutional network, and the DropEdge. First, to reduce the cost of data storage and calculation, we initialize user and item embedding in the embedding layer of the algorithm. Then, to obtain high-order interaction relationships to optimize the embedding representation, we enrich the embedding by injecting high-order connectivity relationships in the convolutional layer. In the training phase, DropEdge is used to randomly discard connected relationships (interaction edges) to prevent overfitting. Finally, to reasonably aggregate the embedding results learned on all layers, the weighted average is expressed as the final embedding, so that users can make preferences in the item. We conduct experiments on three public datasets, using two performance indicators; namely, recall and NDCG, are used for evaluation. For the Gowalla dataset, compared with the optimal baseline method, recall@20 and ndcg@20 increased by 2.53% and 2.39%, respectively. For the Yelp2018 dataset, recall@20 and ndcg@20 increased by 6.17% and 5.58%, respectively. For the Amazon-book dataset, recall@20 and ndcg@20 increased by 4.82% and 4.67%, respectively. The results show that LG-DropEdge can not only reduce the degree of neural network overfitting but also improve the recommended results’ precision.
Improvement and Comparison of Traditional CNN and SVM Classification Based on Hog Descriptor in Pedestrian Detection

This paper first briefly introduces and compares several traditional convolutional neural networks. Next, it presents the Hog (Histogram of oriented gradient) Descriptor. Then, it mainly discusses and compares the differences between HOG + SVM algorithm and traditional convolutional neural network in the field of pedestrian detection. And finally, it describes how to improve the traditional HOG + SVM with Non- Maximum Supp and bootstrap method. Experiments show that the accuracy of the improved HOG + SVM algorithm is much higher than that of the traditional CNN, and the time required is not much
Application of Electric Power Mobile Work Attendance Based on Electronic Fence

With the rapid development of economic globalization, the business scope of enterprises is no longer limited by region and expands in a broader direction. Many enterprises develop their business to all parts of China and even abroad. Therefore, enterprises need a large number of staff to go out at any time or complete work tasks abroad for a long time. In order to further strengthen personnel management and ensure work quality and efficiency, this paper studies the attendance management method of enterprise employees going out. With the help of high-precision Beidou satellite positioning technology and electronic map, an electronic fence for employee mobile attendance is designed to realize the fine and standardized management of employee attendance.
A Generalized Net Model of the Prostate Gland’s Functioning

Over the last 20 years, many Generalized Net (GN) models of the ways of functioning of the different systems and organs in the human body and models related to the description of biomedical processes in living organisms have been constructed. In this paper, a GN model of the prostate gland’s functioning was developed, as a continuation of the previous research. The model provides the possibility to trace the logical relations of the interactions of the prostate gland and various individual organs in the human body. The model shows the possibility for the existence of currently unknown feedback loops.
Study of decoherence of a superposition of macroscopic quantum states by means the consideration of a multimode state of a Schrödinger cat

Quantum Schrodinger cat states are of great interest in quantum communications and quantum optics. These states are used in various scientific fields such as quantum computing, quantum error correction and high-precision measurements. The analysis of the Schrodinger cat states coherence is an important task for their complete practical application. Our developed approach makes it possible to estimate the coherence of the quantum Schrodinger cat state of arbitrary dimension, as well as to find the interference visibility of the state: an important optical characteristic. The obtained simple quantitative relationship between coherence and the Schmidt number, as well as the developed approach of reducing the multidimensional quantum cat state to a two-mode analog allow us to analyze macroscopic states formed by a large number of modes. Several explicit formulas for the reduced states that arise after measuring of some modes of the considered multimode system are obtained. The research results have significant application and can be used in the development of high-dimensional quantum information processing systems.
An Advanced JPEG Steganalysis Method with Balanced Depth and Width Based on Fractal Residual Network

Digital image steganalysis technology based on deep learning has made rapid development. The latest technology, SFNet based on fractal technology, exceeds the cutting-edge technology SRNet in performance. The disadvantage is that SFNet is not suitable for JPEG steganalysis. Due to the interference of compression noise in JPEG images, it is difficult for SFNet to extract weak stego signal by self-similarity extended network. In this paper, a JPEG steganalysis method based on fractal residual network, called FRNet, is proposed. This paper introduces the residual unit with a shortcut connection into the fractal structure, which helps the network effectively suppress the image content and generate the residual image with stego noise. Then, referring to the bottleneck block of ResNet, the deep feature extraction module is constructed to downsample the feature map and superimpose the weak stego signal between different channels of the convolution layer. Finally, the fractal residual module and depth feature extraction module are used to control the width and depth of the network to maximize the detection performance. Two adaptive steganography algorithms of J-UNIWARD and UERD are chosen to evaluate the performance. Experimental results show that the detection error of FRNet is 11.52% lower than J-XuNet, 10.12% lower than WangNet, and 2.54% lower than SRNet.
Educating for Children’s Health: Lessons Learned on Facilitating Media Literacy &amp; Food Marketing Programming

Food marketing is currently a multi-billion dollar industry. High levels of child-targeted food marketing, including on food packaging, suggests the need for media literacy skills to navigate persuasive techniques on food products. Evidence-based educational content on the topic of Media Literacy & Food Marketing (MLFM) was developed for children in Grades 3 to 9. This MLFM content has been taught to thousands of Canadian children across Canada, both in-person and virtually. This Practice Note highlights key strategies and lessons from implementing the program, and provides valuable insights into effective methods for empowering children's critical thinking around food promotion.
International Collaborative Research Center Criteria Assessment

The criteria including indicators from the high council for evaluation of research and higher education (HCERES) has been developed to evaluate the international collaborative research center. The criteria and indicators were a new finding based on case-studies from the HCERES. One set of the questionnaire are used as instruments for performance evaluation of the criteria and indicators of international collaborative research center. Thanks to the agreement of the international expert reviewers. The criteria for assessment of the international collaborative research centers have been investigated. Six criteria with forty-eight indicators for every aspect based on the function of the research centers are considered. It can be used to evaluate the centers positively to convince all staff, including executive person and others, to work together to bring their centers and countries step forward to a prosperous economic country.
Diversely-Supervised Visual Product Search

This article strives for a diversely supervised visual product search, where queries specify a diverse set of labels to search for. Where previous works have focused on representing attribute, instance, or category labels individually, we consider them together to create a diverse set of labels for visually describing products. We learn an embedding from the supervisory signal provided by every label to encode their interrelationships. Once trained, every label has a corresponding visual representation in the embedding space, which is an aggregation of selected items from the training set. At search time, composite query representations retrieve images that match a specific set of diverse labels. We form composite query representations by averaging over the aggregated representations of each diverse label in the specific set. For evaluation, we extend existing product datasets of cars and clothes with a diverse set of labels. Experiments show the benefits of our embedding for diversely supervised visual product search in seen and unseen product combinations and for discovering product design styles.
Model Evaluation

The model evaluation assesses the performance of models on new data. The procedure helps to select the optimal predictive model based on appropriate evaluation scores that reflect the model's quality. Evaluation scores differ for classification and regression. The former commonly uses area under the ROC curve and F1 scores, while the latter uses the mean average error and the mean squared error. In both cases, evaluation requires the use of k-fold cross-validation, or a similar sampling technique, to ensure the model is tested on a different set of data than it was trained on.
An Analysis of Image Compression Using Neural Network

Image compression belongs to the area of data compression because the image is itself made up of data and the task of compressing images has become vital in our current life. Because the scenario is that images are required to build more attractive contents, also in today’s world smartphones cover large fraction of internet traffic and are having low data bandwidth on average. Due to these factors and restrictions on bandwidth and other computing capabilities it has necessary for developers of websites/applications to reduce either size or resolution or both of an image to improve responsiveness of your websites/apps. For this purpose image compression is divided into two categories these are lossless image compression and lossy image compression. The requirement for lossless image compression is that during the decompression process the image data must be recovered without/with negligible loss in image quality while in lossy image compression certain amount or level of error is allowed in image data to achieve better compression ratios and performance. Neural Networks because of their good performance have been used to implement the task of image compression and there are multiple modified neural networks that are proposed to perform image compression tasks, however the consequent models are big in size, require high computational power and also best suited for fixed size compression rate and some of them are covered in this survey report.
The Similarities of Software Vulnerabilities for Interpreted Programming Languages

This short paper examines the similarities and differences of software vulnerabilities reported for interpreted programming languages. Based on a sample of vulnerabilities from four software repositories (Maven, npm, PyPI, and RubyGems), the Common Vulnerability Scoring System (CVSS) and the Common Weakness Enumeration (CWE) are used for comparing the vulnerabilities across the repositories. According to the results, (i) the severity of the vulnerabilities is similar across the repositories; the median CVSS v.3 base scores are around seven. Similarity can be observed also in terms of the weaknesses underneath the vulnerabilities. In particular, (ii) cross-site scripting and input validation have been the most typical weaknesses across all four repositories. The same applies to path-traversal bugs, unauthorized accesses, and resource management bugs. With these observations, the paper contributes to the recent active research on language-specific software repositories.
Stylized Text-to-Fashion Image Generation

In this paper, we focus on the problem of generating realistic stylized fashion images on the human body under a given text description. Existing stylized fashion image generation works mainly require additional style images as input and guidance. Very few works focus on the generation only in the context of textual description. And the fashion images generated by these works usually lack stylization and authenticity. There are two main challenges to this problem. Firstly, the shape of the generated garment should vary based on the text description (e.g., long sleeves, short sleeves, sleeveless) and the body shape and posture. Second, it is difficult to generate real textures directly from text descriptions. Although we can consider borrowing texture information from the auxiliary real clothing images, the misalignment of clothing image shapes can degrade performance. We address these changes by proposing a two-stage stylized text-to-fashion image generation framework. In the first stage, we predict the segmentation map for the generated clothing image based on the input text description and original body shape and posture. In the second stage, we focus on rendering colors and textures. In particular, we investigate a novel spatial-style loss that enables the generator to learn style distribution from text-related real images, even if shapes and postures are misaligned. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art methods quantitatively and qualitatively.
Quantum algorithm for the shortest superstring problem

In this paper, we consider the “Shortest Superstring Problem”(SSP) or the “Shortest Common Superstring Problem”(SCS). The problem is as follows. For a positive integer <i>n</i>, a sequence of n strings <i>S = (s<sup>1</sup>, . . . , s<sup>n</sup>)</i> is given. We should construct the shortest string <i>t</i> (we call it superstring) that contains each string from the given sequence as a substring. The problem is connected with the sequence assembly method for reconstructing a long DNA sequence from small fragments. We present a quantum algorithm with running time <i>O</i>∗(1.728<sup>n</sup>). Here <i>O</i>∗ notation does not consider polynomials of <i>n</i> and the length of <i>t</i>.
Graphical Tools for Visualization of Missing Data in Large Longitudinal Phenomena

Abstract The analysis of large quantities of longitudinal data requires quick decision tools to ensure data quality and to find useful patterns for analysis in exploratory stages. We propose algorithms based on ordering, sampling and grouping applied to lasagna plots, a special kind of matrix plot, which are heat maps created to visualize longitudinal studies. These algorithms can be applied to large data sets to find patterns of interest, monotone and intermittent, in the missing data with low computational cost compared to previous alternatives. Visualization with these algorithms addresses a trade‐off in visualization design: reducing visual clutter versus increasing the information content in a visualization. The method enables the visualization of missing data in a clear and concise way. We apply our techniques to four real‐world data sets of different origins and sizes that share analysis and visualization tasks and discuss the patterns found within them.
25 Years in Application Security: Looking Back, Looking Forward

The author looks at 25 years of industrial application security through a lens of a code review document released in 1996, examines the progress that’s been made and what those trends imply for the future.
Russian Universities Applicants’ Behavior Analysis in Web Communication Environment During the Pandemic

The contribution presents the results of an empirical study, the purpose of which was to reconstruct the actual route of a potential consumer of educational services of Russian universities during the pandemic, including the identification and analysis of problem areas of the web communication system “university-applicant”. Research methods: analysis, synthesis, induction, deduction, classification, modeling, survey. The main results of the study: 1) the actual route of the university applicant during the pandemic was reconstructed; 2) the following main channels of the route’s communicative environment were identified: the Internet and interpersonal communication; 3) the following priority web communication platforms of the route were identified and analyzed: USE calculators, official websites of universities; 4) the communicative problems of the official websites of Russian universities were identified, analyzed and systematized, which are consequences of violations of the requirements for the development of such basic parameters of the site as a friendly interface, communicative-effective content, communicative-effective graphical visualization of the site.
Entropy-Driven Stochastic Policy for Fast Federated Learning in Beyond 5G Edge-RAN

Scalability and sustainability are the corner stones to unleash the potential of beyond fifth-generation (B5G) ultra-dense networks that are expected to handle massive and heterogeneous services. This implies that the transport of the underlying raw monitoring data should be minimized across the network, and urges to bring the analysis functions closer to the data collection points. While federated learning (FL) is an efficient tool to implement such a decentralized strategy, real networks are generally characterized by time- and space-varying users distributions, traffic profiles, and channel conditions. This makes the data collected across different points non independent and identically distributed (non-IID), which is challenging for FL tasks. To cope with this issue, we first introduce a new a priori metric that we call dataset entropy, whose role is to capture the distribution, the quantity of information, the unbalanced structure and the &#x201C;non-IIDness&#x201D; of a dataset independently of the models. This entropy is calculated using a clustering scheme based on a similarity matrix defined over both the features and the supervised output spaces, and is targeting classification as well as regression tasks. The FL aggregation server then uses the reported dataset entropies to devise i) an entropy-based federated averaging scheme, and ii) a stochastic participant selection policy to significantly stabilize the training, minimize the convergence time, and reduce the corresponding computation cost. Numerical results are provided to illustrate all these advantages.