Reviewerly/exHarmony huggingface dataset:
Using Computer Vision to enhance Safety in a Post COVID World

The pandemic has being fought with passion over the world, and India has already in the critical phases. They must be proactive now more than ever, and governments all around the globe have realized the value of emerging technologies such as AI (Artificial Intelligence), ML (Machine Learning), and other similar technologies to combat the epidemic. Since wearing a mask and avoiding crowded places is the only option even a reliable vaccination is launched into the general market. Face mask detection can be a rejuvenating element in restoring normalcy to people’s life. In nations with huge populations, such as ours, real-time mask detection can also help with monitoring. This paper shows a variety of deep learning models for detecting whether or not a person is wearing a mask. Entry and exit access systems may be combined with such technology to assist reduce the transmission of the virus, not just for the Covid19 pandemic, but wherever and whenever facemasks are enforced to minimize any air-borne infections. This paper also shows a variety of deep learning models for social distancing monitoring.
Mapping Study of Critical Success Factor for Agile Software Project

Software development is a complicated process, but agile software development has become important for contemporary businesses over time. A description of the agile project’s critical success factors is defined and constructed based on previous literature. The preliminary list is then aggregated into a final list of five critical success factors using reliability analysis and confirmatory factors. The sources are ascertained and classified using the mapping study to gather the best material for the research objective before emerging with a list of critical success factors. The conclusions are collaborated by these findings. The findings are shown with a bubble chart and its conformity to the agile principles, finally the key components are addressed.
Compressed In-memory Graphs for Accelerating GPU-based Analytics

Processing large graphs has become an important irregular workload. We present Massively Parallel Log Graphs (MPLG) to accelerate GPU graph codes, including highly optimized codes. MPLG combines a compressed in-memory repre-sentation with low-overhead parallel decompression. This yields a speedup if the boost in memory performance due to the reduced footprint outweighs the overhead of the extra instructions to decompress the graph on the fly. However, achieving a sufficiently low overhead is difficult, especially on GPUs with their high-bandwidth memory. Prior work has only successfully employed similar ideas on CPUs, but those approaches exhibit limited parallelism, making them unsuitable for GPUs. On large real-world inputs, MPLG speeds up graph analytics by up to 67% on a Titan V GPU. Averaged over 15 graphs from several domains, it improves the performance of Rodinia's breadth-first search by 11.9%, Gardenia's connected components by 5.8%, and ECL's graph coloring by 5.0%.
Super-Resolution of Image and Video Using Deep Laplacian Pyramid Network

Convolutional neural networks are frequently used for super-resolution reconstruction of a single image. Existing methods require a large number of network parameters and significant runtime to compute loads in order to generate super resolution results with a high level of accuracy. Laplacian pyramid network is suggested to speed up super-resolution. The proposed network reconstructs the sub-band residuals of high-resolution images gradually at several pyramid levels. Unlike existing pre-processing methods that employ bi-cubic interpolation, the proposed method extracts features directly from low-resolution input images with little processing resources. The proposed model train the network using strong Char-bonnier loss functions to provide high-quality image reconstruction. In addition, recursive layers are employed to interchange parameters across and within pyramid levels, resulting in a large parameter reduction. Numerous quantitative and qualitative testing indicate that the proposed method outperforms other super resolution techniques in terms of image quality and processing time.
Development of a Low-cost, Portable Telepathology System for Smart Village using Foldscope

Telepathology is the practice of pathology at a distance accomplished mainly by transmission of digital media (images/videos of pathology slides) over telecommunication channels from a remote site (patient location) to a pathologist for diagnosis. In the concept of smart village, technology plays an essential role for overall development of the lives of village residents. The recent advancement of technologies has revolutionized the healthcare system with respect to accuracy, easy access, improved quality and reduced cost. In this study, a low-cost, portable telepathology system is prototyped to improve the healthcare services in smart villages. The developed system comprises of a Foldscope mounted with a mobile camera and a Raspberry Pi board. The images of the prepared slides can be captured using the foldscope and the camera and further processing will be done in the Pi board using image processing and machine learning techniques. Finally, the actual image along with initial diagnosis will be sent to the pathologist in another location for final report. As a proof of concept, classification of benign and malignant breast tumors has been done in this work. The microscopic images of histopathological slides of breast tumors (benign and malignant) are captured and the textural features are computed and fed to the classifier for classification. A high sensitivity (100%) and moderate specificity (88.89%) values demonstrate the effectiveness of the developed system. It has been observed that no false negative was identified in the test data. The developed telepathology system can be used to sustain health care services in smart villages as well.
Responder System in a Non-invasive Scenario Using Artificial Intelligence

In this era of civilized and technological world, everyone is becoming busy. Due to these diligent conditions, we are communicating through smartphones. However, there can be situations when our related people (Ex: Parents, Close Friends) call us, we might not be able to lift their phone call or sometimes we intentionally try not to answer, In such cases when we do not answer the call, the people on the other side might get panicked, distressed which can lead to some other problems. So in order to resolve this problem, we need to have a virtual assistant which can manage, when someone close to us calls us, so that the important person who is calling us, need not panic. Our work focuses on how the above mentioned ideal virtual assistant can be created using the 4.0 technologies such as Artificial Intelligence, IOT, Machine Learning etc and how the assistant can detect important incoming calls using learning algorithms and how it responds to the person who is calling to us. A system model is designed with respect to the parameters focused on the study.
Classification of UAVs using Time-Frequency Analysis of Remote Control Signals and CNN

Unmanned aerial vehicles (UAVs) have re-cently gained a significant interest in the research com-munity owing to their unrivaled commercial chances in wireless communications, search and rescue, surveillance, logistics, delivery, and intelligent agriculture. In safety-critical applications such as intrusions, identifying the type of drone enhances the countermeasures. This paper proposes classifying UAVs from radio frequency (RF) fingerprints using time-frequency transformation and con-volutional neural networks (CNN). The proposed method-ology involves RF fingerprints' wavelet synchrosqueezed transform (WSST) followed by a proposed lightweight CNN model. The methodology is verified on a data set containing fifteen different classes of drone's RF fingerprint. The proposed CNN model size, Raspberry Pi deployment feasibility, and accuracy are compared with the existing pre-trained state-of-art deep learning models. The proposed model achieves a testing accuracy of 99.09% at 387 kilobytes (KB) size and can run on Raspberry Pi in 25.54 milliseconds.
DGSM: A GPU-Based Subgraph Isomorphism framework with DFS exploration

Subgraph Isomorphism is a fundamental problem in graph analytics and it has been applied to many domains. It is well known that subgraph isomorphism is an NP-complete problem. Thus, it generally becomes bottle-neck of the applications to which it is applied. There has been a lot of efforts devoted to this problem in the past two decades. However, GPU-based subgraph isomorphism systems are relatively rare since the GPU memory is not big enough to hold all the instances during the matching process. Most current GPU subgraph isomorphism frameworks suffer from the limited GPU main memory and redundant computation. These issues restrict them on smaller patterns and graphs and limit their performance. To overcome these issues, we design a new GPU-based sub-graph isomorphism system named DGSM. Our system also efficiently utilize special architecture features to improve data parallelism and memory bandwidth for matching. We validate our techniques by comparing with two state-of-the-art systems, CPU-based DAF and GPU-based GSI. Our experimental results show that our system achieve 2 orders of magnitude faster than DAF and GSI on both labeled and unlabeled graph.
Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification

The Internet of Things (IoT) has gained attention for its rapid growth in the past few years. IoT devices such as temperature and humidity sensors and voice controllers are implemented widely, from household appliances to industrial machines. However, with the rapid growth and benefits IoT offers, we are exposed to various security vulnerabilities, such as data breaches and IoT-specific malware. Researchers are using IoT device identification as a solution for IoT security issues. IoT device identification helps network administrators identify network traffic into its originating devices. However, researchers often overlook an important issue in IoT device identification, which is accuracy degradation over time. Thus, this paper explores the severity of accuracy degradation in IoT device identification on different traffic representation approaches, which are flow, sub-flow, and packet. This paper utilizes a private, and the UNSW IoT Traffic Traces public dataset. Based on the experimental findings, the sub-flow-based approach recorded the best overall performance, with only 8% degradation in the private dataset and 1% degradation in the public dataset. Meanwhile, even though the packet-based approach only degraded 5% on the private dataset, it recorded up to an 11% accuracy decrease in the public dataset.
Diabetic monitoring system using Internet of Everything

A connection is established between the Internet and the real world. A proposition for an architectural framework that will monitor the health of a diabetic patient in both proactive and reactive monitoring behavior is implemented. Patients who fall into this category struggle to keep their glucose levels in a healthy range, which can put them at risk for developing diabetic coma, a state in which they become unconscious and are at risk of passing away if they are not treated. In the proposed method, a patient will have access to registries maintained by the health monitoring company. These registries will assist the patient in avoiding diabetic comas and unscheduled trips to the hospital. A fitness tracker and a device that monitors continuous glucose levels are both worn by him. The fitness tracker also monitors his level of exercise and his respiration. These devices will process and provide the data for the health monitoring company to take the appropriate action when the patient's state of health moves outside the normal range. Depending on the type of data transmission, either proactive or reactive, these devices will process and provide the data. In this paper, Cisco Packet Tracer is used to implement the proposed method
Metakey: A Novel and Seamless Passwordless Multifactor Authentication for Metaverse

The Metaverse offers a collection of virtual reality “worlds” for interaction, communication, teamwork, gaming, entertainment, and other purposes. The concept of the Meta-verse and its application encourage the development of human civilization across time. In contrast to communications via phone conversations or meetings, it removes the obstacles to distant communication and increases its dependability for everyone because each entity will be authenticated on a virtual platform. As a result, the spam callers or attackers stand out from the groups of permitted persons. Multilingual communication makes language barriers both locally and internationally more common. Additionally, the idea of digital assets is introduced. This research presents a novel authentication and identity management frame-work called MetaKey, which aims at securing one's digital assets and digital identity. The proposed system provides multi-factor, Passwordless authentication with the high security of biometric recognition and cryptographic algorithms.
Bibliometric Analysis of Global Scientific Literature on Robust Neural Network

The study aims to present a bibliographic review of publications from the Scopus database related to the robust neural network topic. As of 13th September 2022, this study managed to gather 16 articles from 2019-2023 based on the keywords of robust neural network used for the searching process. The three tools have been used to analyze the gathered Scopus database, which are Microsoft Excel, VOSviewer software and Harzing’s Publish and Perish software. This study reports the findings in terms of the current trend and the impact of publications of robust neural network studies. According to bibliometrics analysis, the number of publications has been increasing over time. This study focuses only on the Scopus database. For future research, other databases like PubMed, Lens, Dimensions, and Web of Science could be considered so the findings will be more meaningful and impactful. This study is the first article to do a bibliographic review related to the neural network.
YouHome System and Dataset: Making Your Home Know You Better

Recent research has actively investigated technolo-gies, such as artificial intelligence (AI), energy-efficient computing, and privacy protection, to bring us more advanced smart home experiences. However, existing work has failed to integrate these key technologies to provide a complete smart home solution to understand user behaviors, protect user pri-vacy, and provide personalized suggestions. In addition, there are not many available smart home datasets that can support thorough studies of user behaviors in household environments. To address these critical problems, we present YouHome: the first smart home framework for developing integrated smart systems with a strong emphasis on usability, scalability, user privacy, edge/cloud coordination, and a comprehensive activities-of-daily-living (ADL) dataset to assist the smart home research. YouHome includes a flexible framework with three levels of system abstraction to collect sensor data, recognize user behavior, and generate personalized suggestions. We also establish multi-stage approaches in this work to protect user privacy effectively from threats such as data leaks. Finally, we implement an end-to-end prototype system to demonstrate the efficacy of our proposed framework. The accompanying YouHome ADL dataset is the first annotated dataset with multiple sensing data types tailored for smart home research and applications, including video, illuminance, temperature, humidity, motion, and sound. It covers two household settings, 20 users, and 31 different activities. We have released our comprehensive dataset to enable numerous research opportunities in both smart home and machine learning communities. We also intend to open-source the framework in the future.
Resource Constrained Hardware Architecture for Training Deep Neural Networks at the Edge - FPGA Implementation

The problem of at the edge training of deep neural network (DNN) is being pursued by researchers extensively with several approaches that establish novelty at different levels, like algorithm, communication between networks, techniques involving data - bit width representation, etc. The work in this paper addresses one of the possibilities of training DNNs at the edge of the network by architecting the hardware needed to perform training at the edge with the objective of improving upon resource utilization and lowering power consumption. These objectives are key metrics by which application deployment on edge devices are evaluated. Further, this paper proposes a novel hardware architecture for both inference and training of DNNs with a Central and Pre-Fetch finite state machine (FSM) being designed to ensure efficient transfer of weights and biases from memory needed for computations pertaining to inference and training. The detailed hardware units designed for both inference and training have been illustrated with the resource utilization, power and timing reports for their implementation carried out on Xilinx ZCU104 embedded board using the Xilinx Vivado 2019.2 synthesis tool. The results prove, both the novelty and ability of the hardware architecture to be used for edge-based deployment of DNNs with learning.
Secure Peer to Peer Learning Using Auto Encoders

We need machine learning algorithms that can use clients' data to create tailored models while adhering to tight privacy rules because people are concerned about privacy as more and more personal devices are being connected to the internet. Here, we present a robust method with a provable convergence rate to solve the issue, as mentioned earlier, in a distributed (peer-to-peer) asynchronous setting. This paper introduces a generic framework that trains an autoencoder on each client and then transmits the encoded data to the remaining clients. In a single round of communication, each client compiles data from several clients and trains a deep-learning model on the encoded data. In this work, we detail the architecture and implementation of the proposed approach and assess its efficacy in light of its comparison to the best-in-class central training and federated learning (FL) algorithms. Compared to FL and central training algorithms, our experiments on various datasets reveal that our proposed technique yields comparable accuracy. Compared to FL, our results demonstrate that the suggested approach reduces the quantity of data transfer by over 95 %, saving a significant amount of bandwidth.
Towards Insightful Automated Dialog for Therapy through Top-down/Bottom-up Response Generation

Dialog-based procedures are important in the ther-apy, growth, and development of individuals. Response generation is a main component of any dialog system and producing insightful responses as part of a dialog remains challenging. This paper presents a response generation method that combines a fast and reactive top-down response generation flow with a slow and planned (e.g., reasoned) bottom-up flow. The two flows attempt to balance the speed and quality of response generation. Experiments present different types of generated responses.
Hybridization of Levy Flight and Chaotic Gravitational Search Algorithm for Image Segmentation

Image segmentation is an important step in image processing due to its enormous application potential in medical image analysis, data mining, and pattern recognition. In fact, image segmentation is a normalization procedure in which image is divided into individual pixels based on threshold values. In this work, Levy flight and Chaos theory based Gravitational Search Algorithm (LCGSA) has been employed for the image segmentation task. In LCGSA, the exploration is carried out by levy flight while exploitation is guaranteed by chaotic maps. Besides, Kapur's entropy method has been utilized to segment the sample image into various regions based on the pixel intensity values. To investigate the segmentation performance of LCGSA, we have used Couple and Pentagon images from the USC-SIPI database. The experimental and simulation analysis has been carried out thoroughly using convergence curves, segmented graphs, box plots, and histogram maps. The overall analysis of the experimental results indicated the efficient performance of LCGSA in terms of providing optimal values for the image thresholds and taking less computational overhead.
WEB TRAFFIC ANALYTICAL APPLICATION

The goal of Web Traffic Analyzer is to automate the current manual system with the aid of computerised hardware and comprehensive computer software, meeting their specifications, in order to retain their vital data and information for a longer period of time with simple access and manipulation.The necessary gear and software are readily available and simple to use.As previously mentioned, Web Traffic Analyzer can result in an error-free, secure, dependable, and quick management solution.Instead of focusing on record keeping, it can help the user focus on their other activities.As a result, it will aid organisations in making better use of their resources.The company can keep computerised records updated without making duplicate inputs.In order to access the knowledge, one does not need to be side-tracked by irrelevant information.The major goal of this paper is to categorize the approaches suitable for web traffic analysis application for reviews left by users and use this analysis for better understanding of web traffic.
Splicing forgery detection using image chrominance and GLCM features

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Bareq Raad, Mohammed Sahib Mahdi Altaei; Splicing forgery detection using image chrominance and GLCM features. AIP Conference Proceedings 2 February 2023; 2457 (1): 040001. https://doi.org/10.1063/5.0120155 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search