Reviewerly/exHarmony huggingface dataset:
Patients Medical Record Monitoring Using IoT Based Biometrics Blockchain Security System

The Patient medical record monitoring using IoT based Biometrics and blockchain security system suggests an innovative strategy for patient monitoring of their health and disease that combines the Internet of Things (IoT) devices and blockchain technology through biometrics. The paper presents a proposed model of combine system architecture that uses IoT devices and biometrics system to collect patient health data and store it on hospital database and that data synchronized biometrics database which is used in system that is government data of Adhaar identification of patient data with a blockchain-based platform for secure and transparent sharing and analysis in the medical record of hospital and medical science. The proposed system architecture includes IoT devices such as smart wearable things such as smart watch, smart digital globs, digital face mask, smart goggles and sensors that collect patient health data and transmit it to a blockchain-based platform that is Ethereum node. The platform uses smart contracts to manage data access and sharing, ensuring that the patient data and information will be only shared to authorized person and parties. The platform also enables data analysis and machine learning algorithms to provide insights into patient health trends and enable early intervention in case of health issues.
Discriminative Feature Extraction Based on SWM for Playback Attack Detection

This paper proposes discriminative features for playback attack detection (PAD) based on statisticallyweighted magnitude spectrum (SWM). The proposed SWM method aims to magnify differences between playback and genuine speech through a discriminative feature, namely, statistically weighted magnitude spectrum constant-Q cepstrum coefficient (SWM-CQCC). The ASVspoof 2017 evaluation set is used to evaluate the proposed method. Our experimental results show that SWM-CQCC can outperforms most of all existing methods we are aware of including published implementations of I-Vector and VESAIFCC.
Automatic White Blood Cell Classification Using Convolutional Neural Network

Peripheral blood smear analysis is a commonly used technique for existing abnormalities in the health status of humans. Disorders in White Blood Cell (WBC) ratio imply the existence of diseases such as leukemia, lymphoma, anemia, and myelodysplastic syndrome (MDS). Deep learning techniques have gained promising results in many medical-related tasks such as dermatology, ophthalmology, and gastroenterology. In this paper, we developed a novel Convolutional Neural Network (CNN) architecture for leukocyte classification from microscopic peripheral blood cell images which help in the diagnosis of hematological disorders. To boost the performance of the proposed model and avoid overfitting phenomena we leveraged data augmentation techniques of rotation, flipping, width shift, and height shift. Unlike the conventional practice for designing a custom CNN model, we stacked homogenous layers and larger kernels in the early layers. To evaluate the performance of the proposed model, we have used classification metrics of accuracy F1- scores. The proposed model achieved an accuracy of 98.13% on the LISC dataset and then benchmarked against the state-of-the-art approaches.
Construction of Information Security System in Earthquake Industry

The author puts forward his own views on the development of informatization of earthquake prevention and disaster reduction at present and requirements of seismic industry information system, and how to do a good job of information security combined with hierarchical protection in the current environment. Taking Shandong Seismological Bureau as an example, through the research of security technology system and security management system and the use of IPSec VPN, UTM and other technologies to build a self-defense system and the overall deployment and implementation of the system, a set of information security system is finally established. Through the author's work in this field, it is found that it is of great practical significance to apply the research results of the security system to the information business of the whole earthquake industry. The research results and experience of this technology can be applied to the relevant units of the national seismic system, so that the information network system of the whole industry can be greatly improved in resource utilization and work efficiency.
Robots, Revolutionizing the Practice of Law: A Bright Future Ahead

The legal industry is on the cusp of a revolution, as robots and other automated technologies viz Artificial Intelligence, e-filing, web3.0, and blockchain technology are being used. Robots are being used to automate mundane and repetitive tasks, such as document review and data analysis. Web3.0 is making it easier for attorneys to interact with clients, as well as other attorneys, without having to be physically present. These technologies also make it easier for attorneys to access and share documents, as well as collaborate with others. This paper examines how robots are revolutionizing the legal landscape, discussing their potential to enhance efficiency, reduce costs, and increase accuracy. The paper also highlights the opportunities offered by these technologies and the challenges that must be addressed to realize their full potential. The current technologies used by courts in India are also discussed with its future scope.
CNN-Based Image Classification for Face-Spoof Detection Using AI and ML

With the rapid development of digital content identification in recent years, automatic image classification has become the most difficult task in computer vision. Compared to human vision, it is challenging for systems to comprehend and analyze images automatically. A few examinations have been finished to conquer issues in the existing grouping framework; however, the result was restricted exclusively to low even-out picture natives. However, these methods fail to classify images accurately. Our system employs a machine learning algorithm in this paper to produce the anticipated outcomes in computer visions. We achieved an experimental accuracy of 97.5% for grayscale image classification, demonstrating that our model can accurately classify images.
Electric Wheelchair for Physically Disabled Person

Wheelchairs can be moved manually, with the help of a companion, a joystick, or both. Patients with disabilities need alternate pushing or controlling techniques without the use of a wheelchair. Those who find it difficult or impossible to walk due to an illness, accident, or handicap use a wheelchair. Around 15% of individuals worldwide have a disability, according to the WHO Global Report on Disability. A wheelchair is also thought to be necessary for 131.8 million individuals, or 1.85% of the world's population. The whole body, including design, simulation, and manufacturing, has been used to create the suggested wheelchair. In essence, it is a three-wheeled electric wheelchair with a hub motor that is powered by a battery and is used to move the wheel. Frames and wheels are intended for persons who are unable to walk. The major goal of this project is to create an automated wheelchair that is affordable, simple to use, and useful. Many people with disabilities utilize hand-powered wheelchairs because they lack the physical stamina or hand-eye coordination to push themselves forward on a tricycle with their arms and hands alone. Whether they are from a rural or metropolitan region, any disabled person may afford this system.
Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing

Camera Image Signal Processing(ISP) pipelines can get appealing results in different image signal processing tasks. Nonetheless, the majority of these methods, including those employing an encoder-decoder deep architecture for the task, typically utilize a uniform filter applied consistently across the entire image. However, it is natural to view a camera image as heterogeneous, as the color intensity and the artificial noise are distributed vastly differently, even across the two-dimensional domain of a single image. Varied Moire ringing, motion blur, color-bleaching, or lens-based projection distortions can all potentially lead to a heterogeneous image artifact filtering problem. In this paper, we present a specific patch-based, local subspace deep neural network that improves Camera ISP to be robust to heterogeneous artifacts (especially image denoising). We call our three-fold deep-trained model the Patch Subspace Learning Autoencoder (PSL-AE) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Oursource code is publiclyavailable at https://github.com/CVC-Lab/Patch-Subspace-Learning-Autoencodd.. The PSL-AE model does not make assumptions regarding uniform levels of image distortion. Instead, it first encodes patches extracted from noisy a nd clean image pairs, with different artifact types or distortion levels, by contrastive learning. Then, the patches of each image are encoded into corresponding soft clusters within their suitable latent sub-space, utilizing a prior mixture model. Furthermore, the decoders undergo training in an unsupervised manner, specifically trained for the image patches present in each cluster. The experiments highlight the adaptability and efficacy through enhanced heterogeneous filtering, both from synthesized artifacts but also realistic SIDD image pairs.
Blockchain Security for the Internet of Things Enabled Business Services

The objective of this work is to discuss the case studies that answer the issues related to Enterprise/warehouse in the medical sector and respond to the anomaly related to health in households/medical care organizations. The case studies use industry-perspective Internet of Things hardware, technologies, platforms, and UIs. The work also introduces the three giants of Internet of Things Frameworks, Azure from Microsoft, AWS Internet of Things from Amazon, IBM which provides Internet of Things solutions with the most trusted deployments.
Virtual Private Network: A Study of its Various Aspects

The public internet infrastructure is utilized daily by billions of users for both personal and professional purposes. Many of these users transmit and receive sensitive information over the public network, with the expectation that non-functional attributes such as integrity, confidentiality, and privacy will be upheld. As information is an asset, it is crucial that it not be transmitted unprotected and unencrypted over the internet. The security of data is constantly evolving, providing benefits for legitimate users and data owners. Anonymity also protects users from detection. User also needs to extend the private network over the public network. Such facilities are provided by Virtual Private Network (VPN). It establishes a secure virtual tunnel between devices over the public network to encrypt the traffic and hide the users' identity to protect user and the generated traffic from miscreants. It extends the private network across the public WANs so that user can access private network globally. On the one hand, VPNs ensure the safety of the user and their transmitted data, however, on the other hand, certain issues may also arise. VPNs have been known to be misused for illegal and unethical activities. In this paper, the various aspects of VPN are studied and explored. The outreach of VPN in Government offices is also discussed to transform the modus operandi regarding the advancement and empowerment of digital operations. Additionally, the potential downside of VPN is also addressed.
Retrospective Study on Classical Homomorphic Encryption Algorithms

The field of cryptography is experiencing a new growth as a result of various homomorphic encryption techniques. It allows the user to run calculations directly on the encrypted text. The evolution of the homomorphic encryption scheme has not happened in one night, it took years of hard work by researchers. The paper studies various classical homomorphic encryption algorithms that served as a milestone in the journey of achieving a fully homomorphic encryption cryptographic model. The chronological survey of classical homomorphic encryption model is presented. A comparative analysis is done to analyze the homomorphic properties and ciphertext expansion ratio. The study has been done to clearly explain many classical homomorphic encryptions and their algorithms, making it simple for new researchers to comprehend the development of homomorphic encryption from the very first encryption algorithm, the RSA algorithm. It may help the latest researchers in the field of cryptography, to understand homomorphic encryption in-depth and its evolution.
Generating Synthetic Data for Better Prediction Modeling in Skill Demand Forecasting

The changing world of work brings with it massive changes. However, it is largely unclear what exactly these changes will look like in terms of the skills required. In order to close part of this knowledge gap, we collected, classified, and analyzed 12.66 million job advertisements published online in Germany from 2014 to 2020 in the fields of administration & customer care, human resources, marketing & sales, and information technology. We specifically focus on the ability to evaluate skills in higher-level clusters and how these develop in different industries and company sizes over time. We find major differences in the speed of adjustment between the professional fields. In addition, we cluster industries according to their demand and development over time for specific skills. This allows industries to be identified in which the demand for skills is much alike. Companies that are a part of a particular cluster might therefore specifically look for employees from other companies in the same cluster, as their overall skill requirements are similar. In addition, we train a Long Short-Term Memory (LSTM) predictor with synthetically generated data using a Time-Generative-Adversarial-Network (TimeGAN) on real validation and test data, which outperforms the model trained only with real data. For the training, time series of the demand of entire industries is aggregated into single time series, which further emphasizes the benefit of generating synthetic data in applications with a small subset of data.
IoT based Data Protection Technique for Security and Privacy Preserving in Cloud ERP

The use of cloud computing is a fundamental technology that enables the sharing of resources over the internet. The preservation of data confidentiality during storage is a crucial aspect in ensuring the protection of the security and privacy of information. The proposed algorithm has demonstrated an improvement in user-level authentication and usability. Thus, it could potentially benefit prominent business professionals when executed in real-time, the algorithm offers privacy protection. The challenges pertaining to security have been reviewed and analyzed in some of the extant research works that aim to preserve privacy. We measured the performance in term of encryption and decryption time. As a result, it takes less time to encrypt data than the previous suggested approach, which takes longer since it uses several keys to decode the data. Keywords-Cloud Computing, IoT, Security and Privacy, Cloud ERP
Big Data Privacy: A Survey Paper

Big Data (BD) has grown in popularity as a research subject in recent years. The likelihood of violating personal privacy also rises with the growth of big data. Distributed systems are used because BD calls for a lot of processing capacity and storage. These systems increase the danger of privacy violation because numerous parties are involved. Numerous PP techniques have been created to safeguard privacy at various stages of the BD life cycle, including data generation, storage, and processing. This paper aims to give a thorough overview of the privacy preservation mechanisms in BD and the difficulties of current mechanisms. In this paper, we present BD infrastructure and cutting-edge privacy in a different phase of the BD life cycle. Furthermore, a comparative analysis also provided for different PP techniques. This paper also discusses related works in terms of review and research. The paper is summed up with the conclusion and future work.
Multi-language Audio-Visual Content Generation based on Generative Adversarial Networks

In this paper, we introduce an approach for producing audio-visual content in multiple languages using only a facial image, a manuscript/audio, and a target language as inputs. The proposed technique allows the sound portion to be delivered as a previously recorded file for Audio to Audio conversion using Neural Machine Translators, or as a piece of text from which the audio can be generated by utilizing a pipeline to convert Text to Audio depending on the provided manuscript. Unlike previous approaches, we focus on creating more expressive, dynamic, and captivating audio-visual content by incorporating voice cloning, facial expressions, and head movements. We propose a method that operates by training advanced techniques such as LipGAN to generate lip-synced videos, LSTM-MLP to produce corresponding facial expressions and head movements by learning from filtered content specific to individual speakers, and CycleGAN to transfer voices in the desired language while preserving natural features as much as possible. Towards the end, we present a thorough evaluation of the proposed solution, both qualitatively and quantitatively, and compares it to other state of the art existing methods.
GPU Acceleration of Multi-Object Tracking with Motion Vector Interpolation and Affine Transformation

In recent studies of object detection and tracking, neural networks have been widely used, and their accuracy has improved. However, its computational complexity is very high and requires the use of high-end GPUs. In order to achieve realtime inference on edge devices, it is necessary to reduce the computational complexity of the network by scaling it down, but this leads to a loss of accuracy. To avoid this loss of accuracy, a method has been proposed in which object detection is performed using a neural network at regular intervals, and in the frames in between, the detected object positions are interpolated using motion prediction. In this research, we propose a method to improve the accuracy of interpolation even when the camera is moving by using an affine transformation used for image stabilization. We also show its realtime computation method on Jetson TX2, one of the lowest power embedded GPUs. The proposed method enables realtime processing of object detection using Yolov5s and tracking of the detected objects at the edge.
Mayalok: A Cyber-Deception Hardware Using Runtime Instruction Infusion

Rapid rise in malware attacks has added significant costs to cyber operations. As adversaries evolve, there is a growing need for fast, targeted defenses that effectively guard computer systems against these cyber-attacks. Cyber-deception is an increasingly adopted defense strategy with its ability to continually engage with adversaries and deploy counter-measures proactively by manipulating the malware program execution flow to non-useful states for the attacker. This paper introduces Mayalok, a novel hardware-based cyber-deception framework to combat malware through runtime instruction infusion. Mayalok employs hardware deception primitives to transparently insert or skip malware program instructions during runtime and deliver the attackers a deceptive view of the system state. We evaluate and demonstrate the deception efficacy of the Mayalok framework on malware samples representing various attack vectors: Ransomware, InfoStealers, Buffer overflow, and Side-channels.
Banking Industry's Transformation with Aid of AI Technology

The banking landscape is being significantly influenced by digitalization. AI becomes more deeply integrated into the industry, the banking industry must position itself to respond to the shifting business landscape. Traditional banking is transformed into digital banking when AI's distinctive characteristics are integrated into the banking industry. This article presents our viewpoint on the part artificial intelligence technology plays in assuring the transformation of the financial industry. As a result, we are able to draw the conclusion that the integration of AI into banking will result in growth, efficiency, and increased user convenience. Therefore, it will enable banks to innovate more quickly in the future, compete with digital natives in building deeper relationships with scaled customers, and achieve sustainable increases in valuations and profits in this new age. They will be better able to incorporate AI into their operations due to their increased focus on digital technologies and willingness to change.
SimPyler: A Compiler-Based Simulation Framework for Machine Learning Accelerators

Co-optimization of hardware and software in modern deep neural network (DNN) systems can be performed using design space exploration (DSE) tools. Leveraging estimation models to predict design decisions' impact on the system's performance allows a fast evaluation of up to billions of architectural choices. In this work, we propose SimPyler, an end-to-end framework for latency estimations of DNN workloads on machine learning (ML) accelerators. SimPyler represents DNN kernels as graphs executed on abstract accelerator models to simulate the system's latency. By generating the entire simulation infrastructure automated from the DNN operator description, the framework can flexibly adjust to changes at the hardware or algorithmic level, enabling the usage in DSE applications. A key enabler in this automation process is a machine-learning compiler. The framework is implemented in Python, using only open-source software. We demonstrate and validate the proposed methodology by modeling different single-core and multi-core hardware architectures and DNNs, comparable to state-of-the-art. Our experiments show we can estimate the end-to-end latency with an average error of 4.12%
Study on Informational and Intelligent of Vehicle Security Check and Audit Method

At present our country road traffic accident rate high all year round, motor vehicle safety detection becomes more and more important. Due to the limitation of manpower and material resources, the traditional vehicle detection technology can not meet the current requirements. Based on the above deficiencies, this paper designed and implemented an artificial intelligence vehicle safety detection platform, self-built vehicle detection image data set, and used a series of methods, such as YOLOv4, SAR, multidimensional convolution, attention mechanism, to automatically complete the detection of external hardware and internal information of motor vehicles. The experimental results show that our method has high speed and high precision, which can not only be used for motor vehicle detection, but also can be extended to real-time road safety detection in the future. It has high innovation, advanced nature and broad engineering application prospect.