Reviewerly/exHarmony huggingface dataset:
An intelligent unsupervised anomaly detection in videos using inception capsule auto encoder

ABSTRACTThe feature extraction model focuses on extracting the spatio-temporal features using an Inception-CAE. The extracted features have been enriched with essential data to achieve the abnormal events. In addition, the reconstruction error between the initial and the reconstructed video frames is estimated. Finally, the normality score is calculated and related to the threshold calculated using the CTOA. The proposed system is implemented in Python by initiating datasets from CUHK Avenue, UCSD Ped2 and LV. Performances are evaluated in terms of ROC, AUC, accuracy, loss, model size and time complexity. Then the performance of the proposed system is compared to recent existing techniques. The maximum AUC obtained by the proposed system is 99.1% for CUHK Avenue, 99% for UCSD Ped2 and 99.2% for LV datasets. Thus, the simulated outcomes clearly showed that the proposed system had achieved better performance against classical techniques for detecting the anomaly in videos.KEYWORDS: Threshold block differenceMax_D Shot TypeInception-CAEcoyote threshold optimization algorithmanomaly detection‌key frame selectionvideo shot segmentationunsupervised deep learning‌ Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article.Additional informationNotes on contributorsHarshadkumar S. ModiHarshadkumar S. Modi has received his Bachelor of Engineering degree in Computer Engineering from U.V. Patel College of Engineering, Hemachandracharya North Gujarat University, Gujarat, India in 2006, and Master of Engineering degree in Computer Engineering from Government Engineering College, Gandhinagar, Gujarat, India in 2018. Previously, he worked as an Assistant Professor with Information Technology Department, Ganpat University, Gujarat, India. He is currently working as a Lecturer with Computer Engineering Department, Government Polytechnic Gandhinagar, affiliated with Gujarat Technological University Gujarat, India and has a teaching experience of more than 15 years. His research interest includes Computer Vision, Deep learning applications, Crowd Behavior Analysis and Visual Tracking.Dhaval A. ParikhProf. (Dr.) Dhaval A Parikh has completed his bachelor degree from Gujarat University, Gujarat, India in 1991, master degree from Sardar Patel University, Gujarat, India in 2003 and PhD from C. U. Shah University, Gujarat, India in 2017. He has a teaching experience of more than 22 years. Currently he is working as a Professor and Head of the Department with Computer Engineering department, Government Engineering College, Gandhinagar, affiliated with Gujarat Technological University Gujarat, India.
An Effective Multi-Layer Attention Network for SAR Ship Detection

The use of deep learning-based techniques has improved the performance of synthetic aperture radar (SAR) image-based applications, such as ship detection. However, all existing methods have limited object detection performance under the conditions of varying ship sizes and complex background noise, to the best of our knowledge. In this paper, to solve both the multi-scale problem and the noisy background issues, we propose a multi-layer attention approach based on the thorough analysis of both location and semantic information. The solution works by exploring the richness of spatial information of the low-level feature maps generated by a backbone and the richness of semantic information of the high-level feature maps created by the same method. Additionally, we integrate an attention mechanism into the network to exclusively extract useful features from the input maps. Tests involving multiple SAR datasets show that our proposed solution enables significant improvements to the accuracy of ship detection regardless of vessel size and background complexity. Particularly for the widely-adopted High-Resolution SAR Images Dataset (HRSID), the new method provides a 1.3% improvement in the average precision for detection. The proposed new method can be potentially used in other feature-extraction-based classification, detection, and segmentation.
A novel hybrid meta‐heuristic‐oriented latency sensitive cloud object storage system

Summary Cloud providers must find out how to properly arrange data in a limited count of servers while ensuring latency assurances to reduce total storage expenses. Timeout is also important to consider because it has a substantial impact on response latency. The core aim of this task is to implement a new cloud object storage system strategy that handles challenges like “latency‐sensitive data allocation, latency‐sensitive data re‐allocation, and latency‐sensitive workload consolidation.” The main contribution here is that distributing the latency of the cloud object storage system allows for better data allocation, data reallocation, and workload consolidation. The primary aim is to use the fewest number of servers feasible to fulfill all requests while maintaining their latency requirements, lowering the overall data transmission cost. As a consequence, Whale Butterfly Optimization Method (WBOA) is a novel hybrid meta‐heuristic algorithm that solves NP‐hard problems by combining baseline advanced algorithms. The simulation outcomes reveal that the offered paradigm consistently provides the greatest outcomes regarding throughput utilization, lower latency, higher storage, and number of used nodes when compared to competing techniques.
Area Contrast Distribution Loss for Underwater Image Enhancement

In this paper, we aim to design a lightweight underwater image enhancement algorithm that can effectively solve the problem of color distortion and low contrast in underwater images. Recently, enhancement methods typically optimize a perceptual loss function, using high-level features extracted from pre-trained networks to train a feed-forward network for image enhancement tasks. This loss function measures the perceptual and semantic differences between images, but it is applied globally across the entire image and does not consider semantic information within the image, which limits the effectiveness of the perceptual loss. Therefore, we propose an area contrast distribution loss (ACDL), which trains a flow model to achieve real-time optimization of the difference between output and reference in training. Additionally, we propose a novel lightweight neural network. Because underwater image acquisition is difficult, our experiments have shown that our model training can use only half the amount of data and half the image size compared to Shallow-UWnet. The RepNet network reduces the parameter size by at least 48% compared to previous algorithms, and the inference time is 5 times faster than before. After incorporating ACDL, SSIM increased by 2.70% and PSNR increased by 9.72%.
An approach for modeling the operational requirements of FaaS applications for optimal deployment

FaaS can extend cloud capabilities to local edge devices. They enable composing applications into workflows and distributing their processes between the edge and the cloud. When deploying a FaaS application, the key issue is to define the desired operational requirements and find a configuration that satisfies them. Both problems are complex and subjective as they differ from one application to another depending on the application structure, run-time model, and optimization requirements. We address these issues using a general multi-criteria optimization approach based on a fuzzy analytical hierarchy process (AHP). Firstly, the specialists intuitively specify their fuzzy privacy in terms of data locality, cost, and performance requirements. Then, a Fuzzy AHP model is constructed to compare and select the optimal workflow configuration that satisfies the requirements. The work is evaluated using a real FaaS application, where AWS Cloud and AWS Greengrass present the cloud and the edge. We assessed the ability of the proposed approach to retrieve the optimal configurations for various scenarios and compared the results to one of the state-of-the-art approaches. Unlike existing hard-coded approaches, our approach is intuitive, modular, extensible, and addresses more DevOps requirements.
Unsupervised video segmentation for multi-view daily action recognition

Multi-layer representations have achieved outstanding performances in the complex daily action recognition. However, the fixed length of the sliding window (SW) leads to the segmented motion atoms incomplete or non-unique. To deal with this problem, we propose unsupervised video segmentation (UVS) for multi-view daily action recognition. Firstly, the average cosine similarity is designed to ensure the integrity of motion atoms. Secondly, we utilize the ordered combination of motion atoms to construct the table of multi-scale motion phrases in a top-down manner, instead of the fixed-scale traditional motion phrases. Finally, the experimental results based on the WVU dataset, the NTU RGB-D 120 dataset, and the N-UCLA dataset show that the proposed UVS method has state-of-the-art performance, compared with the classic methods such as IDT, MoFAP, JLMF, FGCN, and MVMLR.
Tactile internet of federated things: Toward fine-grained design of FL-based architecture to meet TIoT demands

The Tactile Internet of Things (TIoT) represents a special class of the Internet of Things (IoT) that has opened the door for a new generation of agile, highly dynamic, intelligent, and bandwidth-hungry applications in many vital domains such as medical, military, robotics, and entertainment. However, the TIoT’s high level of physical requirements constitute a challenge that hinders the technology’s full potential. The application of machine learning (ML) methods has become of high importance to facilitate the prolific deployment of TIoT devices and applications. Along with advances in 6G and networking-enabling technologies, federated learning (FL) can bridge this gap by boosting TIoT services due to its inherent and redundant distributed nature. To the best of our knowledge, no other work in the literature covers applications of FL in the TIoT. In this work, we identify the prospective approach with a final aim of finding an appropriate architecture for the TIoT based on FL as we survey, compare, and analyze recent literature. We conducted a comprehensive and in-depth study of FL design aspects relevant to the deployment of the TIoT and suggested categorization of these aspects into process, infrastructure, data, enabler, and defense classes. On the other hand, as we are seeking to define a role for FL in the TIoT, we identified services of ML in the IoT domain as a general case of the FL/TIoT special case. Moreover, the shortcomings of traditional ML in regard to the general IoT are highlighted. We find that FL can be replaced, especially in the case of the TIoT, to meet the high level of design constraints. Eventually, we draw conclusions and future directions of common challenges relevant to applying FL in the domain of the TIoT.
Blockchain based Enhancement of Digital Revolution in Financial Sector

The rapid advancement of newly developed technology has a substantial influence on the progress of the financial industry. Changes to the interactions between enterprises in the financial sector and the structural landscape of the sector are brought about as a result of digitalization, which leads to the creation of new business models, the transformation of value chains, new product delivery channels, and many more. Despite this, academics do not seem to have come to a consensus about the factors that are driving these strategic shifts or the directions in which the financial industry will likely expand in future. One way in which the development of financial technology might be defined as extremely inventive is through the introduction of alternative services and new businesses. Digital innovation is the operational equivalent of commonly used terms such as digital disruption and digital transformation. In the first case, digital innovation emphasizes strategic orientation by developing new products and business models using digital technologies, infrastructures, supply chain, and ecosystems. In the second case, digital innovation transforms traditional models to better address existing loyal customers by providing more usable in the digital ecosystems access channels, solutions, and product options.
A Comprehensive Survey of Automated Website Phishing Detection Techniques: A Perspective of Artificial Intelligence and Human Behaviors

Phishing is a web-based attack that applies social engineering techniques to take advantage of Internet users and obtain sensitive information. Mostly, the phishing attack works by creating a fake version of a real website interface to gain the trust of the user. Even though the phishing site looks similar or strikingly similar to the real website they imitate, user studies have demonstrated that user ignores browser-based indicator and frequently exploits the appearance of the website to judge the authenticity of the site, just as they use the appearance of the physical site to judge their authenticity. Several techniques are commonly used for the automated detection of phishing websites: URL-based detection, content-based detection, reputation-based detection, machine learning-based detection, and behavioural-based detection. These techniques can be used alone or in combination to improve the accuracy of phishing detection. This research focuses on the review of existing automated phishing detection techniques in websites. A taxonomy of automatic web phishing detection is presented and a systematic analysis of ongoing trends in web phishing detection techniques was performed. The study objective is to acknowledge the status of recent studies in automated web phishing detection and evaluate the performance. In addition, a brief introduction to the different types of phishing attacks and statistics is elaborated. The existing techniques are reviewed based on different aspects such as objectives, proposed methodology, merits, and demerits.
A High Level CNN-based App to Guide the People of Visually Diminished

For a human in order to lead a normal life five senses place a crucial role among them vision takes a most significant place. These days most people are facing problems to understand situations and difficulties around them due to blindness. They are not able to work independently. So, they want guidance from other people such as family members, friends etc. In addition, it will give the information about the direction of things and distance from the place where they are located. It notifies about the direction of the objects and the distance of the objects. So, our paper makes effort in enlarging the detection of objects through an application for blind people. To implement this essential component like camera, an audio device and an android application are required using all these components we developed an object detecting android application for detecting and identifying objects for blind people. To overcome this problem, we have proposed an android application to detect the object and provide an audio message. The main outcome of the paper is to detecting objects by using Android applications and to help vision challenging people in highly sophisticated and effective manner. This paper consists of different modules such as object detection and object localization along with distance calculation which is developed using OpenCV. This paper narrated using Convolution Neural Network Algorithm and it have given a tremendous result. The techniques adopted and implemented has leveraged an accuracy of 86.7%, whereas the data file contains more than 1100 brackets, a point-based denary approach using the supported features of service analysis is performed, The denary analysis represents the overall points of the systems in an surprising manner. It is observed that the system which is proposed has performed better than the existing systems that having total score of 9.6/10, where it is 8.5% more than the second-best.
Security Management Approaches Over the Cloud

Cloud computing is a new approach that is gaining popularity across practically all businesses. It refers to the on-demand accessibility of major computer resources, particularly those related to processing and data storage, without direct active user management. This is the era of cloud computing; most organizations are migrating to the cloud usage, computing purpose on these days. The cloud era has a huge number of services, through providers like Google Cloud Platform (GCP), Amazon Web Services (AWS), Azure, and many other Cloud Service Providers (CSP). Since most businesses are currently adopting cloud computing in some capacity, cloud security is essential. Because of the security, governance, and compliance concerns associated with storing material in the cloud, information, and communication technologies (IT) professionals are still wary of transferring more data and applications there. They fear that cyberattacks or inadvertent releases of highly confidential corporate data and intellectual property could expose them. Through this paper, we are going to take a few cloud service providers and illustrate why cloud security is good for storing data and how these cloud service providers overcome threats like malware, hackers, denial-of-service attacks, and unauthorized user access or use.
Identification of Deepfakes using Strategic Models and Architectures

Deepfake technology has been rapidly evolving and expanding in recent years. It has become increasingly easy to manipulate multimedia content, making it harder to detect what is real and what is manipulated. The research aims to explore how neural networks can be used to detect deepfake in multimedia, helping to protect users from potentially malicious and deceptive content. The aim is to explore what neural networks are, how they can be used to detect deepfakes and the potential implications of this technology. The research also aims to evaluate the advantages and disadvantages of using neural networks for deepfake detection. As the world of deepfake technology continues to evolve, this research will provide an overview of the latest developments in deepfake detection and their potential impact. The goal of this research is to use neural networks to detect deepfakes and to identify suspicious content to alert users. This could help protect users from being exposed to malicious content and help content producers ensure the integrity of their work. As deepfake technology continues to evolve, neural networks may become an essential tool for quickly and accurately detecting deepfakes in multimedia. The research explores topics like, CNN, 3D CNN, GATED RECURRENT UNIT and Architectures like Xception, VGG16, InceptionV3 and ResNet50V2. The outcomes are graphically represented and analyzed. the comparative stratification of the approach is done to analyze and detect deepfakes.
Towards Predictive Maintenance: an Edge-based Vibration Monitoring System in Industry 4.0

Due to the high acquisition and operation cost of industrial machinery, their cost-effectiveness is highly affected by the quality and continuity of their production. In this context, Predictive Maintenance emerges as a maintenance strategy aiming to maximize uptime by constantly monitoring a quantity related to the machine’s health, such as vibration patterns, in order to perform maintenance stops only when strictly required. The implementation of this strategy, however, faces multiple challenges. One of them is related to the design, installation, and operation of the required sensory systems, which are subjected to budget constraints and technical constraints such as sensor battery lifetime. Another challenge is given by the intelligence required for analyzing real-world data generated within uncontrolled industrial environments and producing a machinery health indicator from it. This paper illustrates a vibration monitoring system currently operating in a textile manufacturing machine; proposes a versatile Anomaly-Detection-based procedure towards analyzing unlabeled real-world data produced by such systems and extracting a machine health indicator from it, while considering energy consumption; and also proposes an extension to the system in order to adapt it to the connectivity requirements set by the current context of Industry 4.0.
High Security and Low Power AES Crypto Processor Security Algorithm for Image Encryption

Data encryption specifications include the Advanced Encryption Standard (AES) algorithm. This AES algorithm has been incorporated into both hardware and software, making it one of the most used forms of encryption technologies. A symmetric cryptography method with adequate security uses Field Programmable Gate Arrays (FPGA). The proposed design includes 8bit (1-byte) data route and 5 main blocks. The Key Register and State Register are the register banks to store text files, credential keys, and intermediate data. Shift Rows are added to the State Register to reduce an area. The Mix Column will be changed to an 8bit data route. has four internal registers, each of which can send and receive 8 bits, an 8-bit or 1-byte block that is designed for Mix Columns. Additionally, the key expansion and encryption phases employ shared optimized Sub-Bytes. To make different Sub-Bytes more efficient, we combine and streamline them. To cut down on power usage, the design makes use of the clock gating technique. AES architecture for a 128-bit image-based image encryption system is presented in this work. Verilog HDL, Model sim 6.4.c was used to implement this design in an FPGA XC3S 200 TQ-144. To synthesize Xilinx to tool is being used.
Blockchain based Lightweight and Secure Aggregation Scheme for Smart Farming

Modern farms are just the beginning of India’s extensive agricultural sector; nonetheless, misusing agricultural technology reduces crop production. Promoting the use of modern statistics in agriculture can help farmers deal with some challenges. A loss of production occurs when there is a lack of accurate information and communication. These are the topics that our article aims to cover. This Internet of Things (IoT) based software offers a smart tracking platform structure and is designed for an agricultural facility. With the primary goal of reducing water waste throughout the irrigation process, propose a fully intelligent agriculture system based on IoT. Develop a smart agricultural model based on the Internet of Things with the main objective of increasing crop yield by involving various parameters and by using blockchain. This study provides an in-depth understanding to support a contemporary, IoT-enabled blockchain framework to permit a consistent data exchange across many organizations. To provide a consistent record, transmission in the IoT is blockchain-assisted, and this study begin by expanding to the new authentication and key management schemes. Cloud servers use the encrypted transactions to analyze and detect intruders by using a unique deep-learning architecture. A thorough comparison shows the suggested approach offers higher safety and software features. The primary goal of this study is to use a blockchain-based methodology to ensure device safety.