Reviewerly/exHarmony huggingface dataset:
Extractive Summarization of Telugu Text Using Modified Text Rank and Maximum Marginal Relevance

With the rapid growth of digital content, there is a need for an automatic text summarizer to provide short text from a long text document. Many research works have been presented for extractive text summarization (ETS). This article mainly focuses on the graph-based ETS approach for multiple Telugu text documents. A modified Text-Rank algorithm is employed with the noun and verb count of each sentence in the text as the initial score of each node. To get the optimal features, a novel feature selection algorithm called improved Flamingo Search Algorithm is proposed in this article. Though graph-based ETS is an important approach, the generated summaries are redundant. To reduce the redundancy in the generated summary, maximum marginal relevance is combined with the modified Text-Rank. Different word-embedding techniques such as Fast-Text, Word2vec, TF-IDF, and one-hot encoding are utilized to experiment with the proposed approach. The performance of the proposed text summarization approach is evaluated with BLEU and ROUGE in terms of F-measure, precision, and recall.
Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels.
Real-time distributed video analytics for privacy-aware person search

In this work, a novel distributed privacy-aware person search (PAPS) model has been proposed which circumvents the privacy risks. An intelligent IoT surveillance system has been designed to integrate the PAPS model for real-time distributed privacy-aware person search from surveillance videos. An important aspect of the intelligent surveillance system, particularly person search, is the visual feedback at the output, with ranked results of person images at the user-end. Therefore, even if edge processing is performed, there is still a need to store and transmit the cropped person images to the cloud server for displaying the results at the user-end. However, storing or transmission of videos/images to cloud-servers leads to privacy issues. The proposed PAPS model eliminates the need to store or transmit the images/videos while performing person search, thereby addressing the privacy concerns. The proposed system is easily scalable to incorporate more camera nodes to enhance the surveillance coverage as majority of the processing is performed at the edge servers, with a small amount of fog-processing. A very minimal amount of cloud-processing is performed only when a query is raised at the user-end. Only the processed and encoded data is transmitted across the edge, fog and the cloud servers, which protects privacy and significantly reduces bandwidth costs. Further, a new evaluation criterion, Person Capacity, has been proposed to evaluate the feasibility of an edge-based system to be deployed at crowded locations. The performance evaluation of our system, on our own video dataset, as well as the PRW, and CUHK-SYSU dataset for person search demonstrates that the proposed system achieves state-of-the-art or competitive performance while performing in real-time for practical scenarios.
CNN-RSVM: a hybrid approach for classification of poikilocytosis using convolutional neural network and radial kernel basis support vector machine

ABSTRACTThe most vital blood cells in the body are red blood cells (RBCs), which carry oxygen and nutrients to the tissues. Poikilocytosis refers to a change in the red blood cells’ normal physical shape and structure. The lack of oxygen and nutrients causes defective RBCs to suffer from health problems such as anaemia and stroke. This study proposes a hybrid CNN-SVM model for promptly and precisely classifying poikilocytosis abnormalities. The features of poikilocytosis were extracted using the proposed custom CNN (Convolutional Neural Network) model and classified using the Radial kernel basis SVM (Support Vector Machine) classifier. In comparison to the CNN model with a soft-max classifier and other benchmark work, the hybrid model produced better results. The overall accuracy of the algorithm is 98.21 %, which is higher than the traditional CNN (>5 %) and other benchmark methods. This hybrid CNN-SVM model is promising for the automatic classification of poikilocytosis abnormalities.KEYWORDS: PoikilocytosisSupport vector machineRed blood cells abnormalityConvolutional neural networkAnaemia Disclosure statementNo potential conflict of interest was reported by the authors.
An Ensemble Transfer Learning Model for Detecting Stego Images

As internet traffic grows daily, so does the need to protect it. Network security protects data from unauthorized access and ensures their confidentiality and integrity. Steganography is the practice and study of concealing communications by inserting them into seemingly unrelated data streams (cover media). Investigating and adapting machine learning models in digital image steganalysis is becoming more popular. It has been demonstrated that steganography techniques used within such a framework perform more securely than do techniques using hand-crafted pieces. This work was carried out to investigate and examine machine learning methods’ critical contributions and beneficial roles. Machine learning is a field of artificial intelligence (AI) that provides the ability to learn without being explicitly programmed. Steganalysis is considered a classification problem that can be addressed by employing machine learning techniques and recent deep learning tools. The proposed ensemble model had four models (convolution neural networks (CNNs), Inception, AlexNet, and Resnet50), and after evaluating each model, the system voted on the best model for detecting stego images. Since active steganalysis is a classification problem that may be solved using active deep learning tools and modern machine learning methods, this paper’s major goal was to analyze deep learning algorithms’ vital roles and main contributions. The evaluation shows how to successfully detect images that contain a steganography algorithm that hides data in images. Thus, it suggests which algorithms work best, which need improvement, and which are easier to identify.
Current Web Development Technologies: A Comparative Review

This paper highlights current web development technologies in IT industry and its measure. Developers of web-based applications are confronted with a bewildering array of available choice formats, languages, frameworks, and technical objects. They investigate, identify, and evaluate technology for creating web applications. They determine that although most of the web's connectivity issues have been resolved. The confusion of technology for web-based apps is that this area lacks a reliable model that is suited to it. A section-based library called ReactJS is intended to enhance clever user interfaces. It is the most well-known front-end JS library at present. In the Model View Regulator (M-V-C) scheme, it solidifies the view (V) layer. Facebook, Instagram, as well as local independent designers and organisations maintain it. The development of enormously complex electronic programmes that can modify their data without reviving the pages after them is the main focus of the answer. Better customer experiences and the advancement of web apps that are lightning fast and robust are its main goals. Similar to Angular JS, ReactJS can be planned with other JavaScript libraries or frameworks in MVC.
Fully Undistillable Quantum States Are Separable

Assume that Alice, Bob, and Charlie share a tripartite pure state <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \vert \psi _{ABC}\rangle $ </tex-math></inline-formula> . We prove that if Alice cannot distill entanglement with either Bob or Charlie using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ \vert \psi _{ABC}\rangle $ </tex-math></inline-formula> and local operations with any one of the following configurations for classical communication: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(A\to B, A\leftrightarrow C), (A\leftrightarrow B, A\to C)$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(A\leftrightarrow B, A\leftrightarrow C)$ </tex-math></inline-formula> , then the same is also true for the other two configurations. Moreover, this happens precisely when the state is such that both its reductions on systems <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AB$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$AC$ </tex-math></inline-formula> are separable, which is further equivalent to the reductions being PPT. This, in particular, implies that any NPT bipartite state is such that either the state itself or its complement is 2-way distillable. In proving these results, we first obtain an explicit lower bound on the 2-way distillable entanglement of low rank bipartite states. Furthermore, we show that even though not all low rank states are 1-way distillable, a randomly sampled low rank state will almost surely be 1-way distillable.
Empowerment of Women: A Critical Bibleometric Analysis in International Scenario towards Economic Developmenmt

Gender equality and sustainability require women's empowerment. Government institutions, NGOs, and international organizations have empowered women over time. Despite improvements, women still struggle with education, healthcare, income, and political representation. This Scopus-based report examines women's empowerment research. 476 2004–2023 studies were bibliometrically analyzed using VOSviewer software.The survey found that women's empowerment encompasses economic, political, social, and legal dimensions. Economic empowerment—women's access to resources, jobs, and entrepreneurship—has been the most studied topic. Political empowerment has gotten less emphasis than women's participation in decision-making and political institutions.The data also demonstrated a regional imbalance in women's empowerment studies. North America and Europe dominated the studies, whereas Africa and Asia were underrepresented. Researchers have used feminist, capacity, and social exchange theories to investigate women's empowerment.The study suggests greater research on women's empowerment, notably in political involvement, social empowerment, and legal empowerment. More studies in underrepresented locations are needed to understand how context affects women's empowerment. Policymakers and development practitioners must consider this study's evidence-based recommendations to empower women worldwide.
Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory

In this paper, we propose 2D dynamic visual servoing (Dynamic IBVS), where a quadrotor UAV tries to track a moving target using a single facing-down perspective camera. As an application, we propose the tracking of a car-type vehicle. In this case, data related to the altitude and the lateral angles have no importance for the visual system. Indeed, to perform the tracking, we only need to know the longitudinal displacements (along the x and y axes) and the orientation along the z-axis. However, those data are necessary for the quadrotor’s guidance problem. Thanks to the concept of differential flatness, we demonstrate that if we manage to extract the displacements according to the three axes and the orientation according to the yaw angle (the vertical axis) of the quadrotor, we can control all the other variables of the system. For this, we consider a camera equipped with a vertical stabilizer that keeps it in a vertical position during its movement (a gimbaled camera). Other specialized sensors measure information regarding altitude and lateral angles. In the case of classic 2D visual servoing, the elaboration of the kinematic torsor of the quadrotor in no way guarantees the physical realization of instructions, given that the quadrotor is an under-actuated system. Indeed, the setpoint has a dimension equal to six, while the quadrotor is controlled only by four inputs. In addition, the dynamics of a quadrotor are generally very fast, which requires a high-frequency control law. Furthermore, the complexity of the image processing stage can cause delays in motion control, which can lead to target loss. A new dynamic 2D visual servoing method (Dynamic IBVS) is proposed. This method makes it possible to generate in real time the necessary movements for the quadrotor in order to carry out the tracking of the target (vehicle) using a single point of this target as visual information. This point can represent the center of gravity of the target or any other part of it. A control by flatness has been proposed, which guarantees the controllability of the system and ensures the asymptotic convergence of the generated trajectory in the image plane. Numerical simulations are presented to show the effectiveness of the proposed control strategy.
SIGNed explanations: Unveiling relevant features by reducing bias

The rise of artificial intelligence (AI) is accompanied by a growing need for methods to explain the decisions of AI models. In the last decade, new explanation methods have been developed in the field of explainable AI (XAI) that enable the interpretation of predictions and provide additional information to human experts. In computer vision tasks, XAI methods are typically used to compute heatmaps to identify regions or pixels of an image that were particularly relevant to the output of the AI model. Heatmaps computed on deep neural networks often appear visually noisy due to the Shattered Gradients Problem. Therefore, several explanation methods use multiplication by the input values to increase contrast and reduce noise in heatmaps. However, this can induce a bias with the magnitude of the input values in the explanation. In this work, we proposed a novel technique called SIGN, which can be combined with existing XAI methods to prevent this bias. We implemented SIGN-based variants of the existing XAI methods Gradient × Input, SmoothGrad, DeconvNet, Guided Backpropagation, and Layer-wise Relevance Propagation (LRP), called Gradient × SIGN, SmoothGrad × SIGN, DeconvNet × SIGN, Guided Backpropagation × SIGN, and LRP-SIGN, and compared them to ten state-of-the-art XAI methods on MNIST, ImageNet, and MIT Places365 using different dense and convolutional model architectures. The SIGNed explanations were able to outperform the existing methods in all tested metrics. The code to reproduce the experiments is available from GitHub via https://github.com/nilsgumpfer/SIGN.
VIDGCN: Embracing input data diversity with a configurable graph convolutional network accelerator

Hardware accelerated inference is a promising solution for exploiting graph convolutional networks (GCN) in latency-sensitive applications. Existing accelerators overlook an important barrier to widespread adoption: the input data (i.e., weighted graphs) of GCN inference diverge from scale and sparsity, causing the accelerators optimized for an array of graphs to lose efficiency on other graphs. This paper presents a reconfigurable GCN inference accelerator, VIDGCN, that switches between all possible GCN inference computation schemes to realize timely inference for all input graphs. VIDGCN incorporates an analytical performance model and a reconfigurable hardware design. The performance model allows users to find the optimal computation scheme for any given input graph. The hardware design reuses all the computation units under all computation schemes, and only distributes the data to the units in different ways. Evaluation on seven real-world graphs shows that VIDGCN outperforms state of the art, SGCNAX, by 1.79×, and consistently yields the ideal amount of memory accesses.
Markov‐modulated marked Poisson processes for modeling disease dynamics based on medical claims data

We explore Markov-modulated marked Poisson processes (MMMPPs) as a natural framework for modeling patients' disease dynamics over time based on medical claims data. In claims data, observations do not only occur at random points in time but are also informative, that is, driven by unobserved disease levels, as poor health conditions usually lead to more frequent interactions with the health care system. Therefore, we model the observation process as a Markov-modulated Poisson process, where the rate of health care interactions is governed by a continuous-time Markov chain. Its states serve as proxies for the patients' latent disease levels and further determine the distribution of additional data collected at each observation time, the so-called marks. Overall, MMMPPs jointly model observations and their informative time points by comprising two state-dependent processes: the observation process (corresponding to the event times) and the mark process (corresponding to event-specific information), which both depend on the underlying states. The approach is illustrated using claims data from patients diagnosed with chronic obstructive pulmonary disease by modeling their drug use and the interval lengths between consecutive physician consultations. The results indicate that MMMPPs are able to detect distinct patterns of health care utilization related to disease processes and reveal interindividual differences in the state-switching dynamics.
Security Analysis of Web Open-Source Projects Based on Java and PHP

During website development, the selection of suitable computer language and reasonable use of relevant open-source projects is imperative. Although the two languages, PHP and Java, have been extensively investigated in this context, there are not many security test reports based on their open-source projects. In this article, we conducted separate security analyses on web-related open-source projects based on PHP and Java. To this end, different open-source frameworks and services are used to design websites used to test experimental attacks on 12 popular open-source filters available on GitHub, as well as investigate the use of Lightweight Directory Access Protocol (LDAP) in the Firefox browser environment. Using malicious payloads published by Open Web Application Security Project (OWASP) and others, Cross-site Scripting (XSS), Local File Inclusion (LFI), SQL injection, and LDAP injection are performed on the test targets. The experimental results reveal that although PHP-based open-source projects are more vulnerable to attacks than Java-based ones, there is significant room for improvement. Finally, a whitelist-based filtering scheme is proposed. This scheme filters the inline attributes of label elements so that the filter has an excellent detection rate of malicious payloads while having an excellent pass rate of benign payloads. Effective references and suggestions for web developers are also included to aid the selection of open-source web projects, and feasible solutions to improve filter performance are proposed.
PRIVACY AND SECURITY CONCERNS IN THE ADOPTION OF CLOUD COMPUTING TECHNOLOGY

In the field of computing, data security has always been a serious concern. It becomes considerably more severe in the cloud-computing technology environment because data is stored in several locations around the world. Users' biggest concerns about cloud technology are data privacy protection. Privacy and security protection are becoming more crucial for the future growth of cloud computing technology in government, industry, and business, even though numerous techniques on cloud computing themes have been researched in both academics and industries. In the cloud architecture, data security and privacy protection concern both hardware and software. This research will examine various security strategies, as well as problems from both software and hardware perspectives, for securing data in the cloud, to improve data security and privacy control in a reliable cloud environment. The article examines the most important security and privacy concerns surrounding cloud computing as they pertain to outsourcing elements of an organization's computing environment. It highlights areas of concern that require extra attention and offers the underlying information needed to make informed security decisions.
Multi-Scale Feature Selective Matching Network for Object Detection

Numerous deep learning-based object detection methods have achieved excellent performance. However, the performance on small-size object detection and positive and negative sample imbalance problems is not satisfactory. We propose a multi-scale feature selective matching network (MFSMNet) to improve the performance of small-size object detection and alleviate the positive and negative sample imbalance problems. First, we construct a multi-scale semantic enhancement module (MSEM) to compensate for the information loss of small-sized targets during down-sampling by obtaining richer semantic information from features at multiple scales. Then, we design the anchor selective matching (ASM) strategy to alleviate the training dominated by negative samples caused by the imbalance of positive and negative samples, which converts the offset values of the localization branch output in the detection head into localization scores and reduces negative samples by discarding low-quality anchors. Finally, a series of quantitative and qualitative experiments on the Microsoft COCO 2017 and PASCAL VOC 2007 + 2012 datasets show that our method is competitive compared to nine other representative methods. MFSMNet runs on a GeForce RTX 3090.