Reviewerly/exHarmony huggingface dataset:
PRESENT Algorithm and Authentication Protocol for a Secure Patient Monitoring System

Medical information is one of many confidential information that needs to be secured. Health information, such as medical records, should not be accessed and viewed by any unauthorized entities. PRESENT algorithm is a one way to protect the confidentiality of this medical information. This research presents a secure patient monitoring system using WMSN Authentication protocol to deliver authentication and PRESENT algorithm to provide confidentiality to the vital information of the patients. The test result show that the prototype is able to secure the patient&#x0027;s information, and the security testing fulfill the security standard needed by the system.
Performance Evaluation for Deterministic Six State Quantum Protocol (6DP) using Quantum

Quantum key distribution is a secure system that uses an encryption protocol using quantum mechanics to solve the problem of distributing keys between the sender and the receiver, and it is the most widely used in the field of quantum computing (QC). Various protocols are designed to ensure the security and integrity of information from any attempt by an eavesdropper who monitors the transmitted photons. Performance analysis is important in determining. The possibility of various QKD protocols when using effective simulations to analyze and evaluate the results. Therefore, in this paper Quantum entanglement OptiSystem is used which is commercial optical simulation application that widely used in telecommunications in modeling and simulating. This application also used to simulate the experimental components in the distribution of quantum keys. The results obtained in this paper show using quantum entanglement technology are optimize when get the sifted key and QBER across when using a pair of entangled photons. Simulations can help researchers to test their models before conducting actual experiments.
A Supply Chain Data Supervision System Based on Parent-Children Blockchain Structure

In the context of Industrial Internet logo analysis, this paper analyzes the feasibility and outstanding advantages of the blockchain technology applied to supply chain data supervision combining the pain spots of traditional supply chain management system and the technical superiority. Although blockchain technology has uprooted some deep-entrenched problems of supply chain data management system, it brings new issues to government supervision in the meanwhile. Upon the analysis of current development and the new problems of blockchain-based supply chain data management system, a new parent-children blockchain-based supply chain data supervision system is proposed, which targets to overcome the dilemma faced by the governmental regulation of supply chain. Firstly, with the characteristics of blockchain including decentralization, non-tampering and non-repudiation, the system can solve the problem puzzling the traditional database about untruthful and unreliable data, and has advantages in managing supply chain and realizing product traceability. The authenticity and reliability of data on the chain also make it easier for the government to investigate and affix the responsibility of vicious incidents. At the same time, the system adopts the parent-children chain structure and the storage mode combining on-chain and off-chain resources to overcome the contradiction between information disclosure requirements of the government and privacy protection requirements of enterprises, which can better meet the needs of various users. Moreover, the application of smart contracts can replace a large number of the manual work like repetitive data analysis, which can make analysis results more accurate and avoid human failure.
Prediction of Changed Faces with HSCNN

Convolutional Neural Networks (CNN) have been successfully employed in the field of image classification. However, CNN trained using images from several years ago may be unable to identify how such images have changed over time. Cross-age face recognition is, therefore, a substantial challenge. Several efforts have been made to resolve facial changes over time utilizing recurrent neural networks (RNN) with CNN. The structure of RNN contains hidden contextual information in a hidden state to transfer a state in the previous step to the next step. This paper proposes a novel model called Hidden State-CNN (HSCNN). This adds to CNN a convolution layer of the hidden state saved as a parameter in the previous step and requires no more computing resources than CNN. The previous CNN-RNN models perform CNN and RNN, separately and then merge the results. Therefore, their systems consume twice the memory resources and CPU time, compared with the HSCNN system, which works the same as CNN only. HSCNN consists of 3 types of models. All models load hidden state ht−1 from parameters of the previous step and save ht as a parameter for the next step. In addition, model-B adds ht−1 to x, which is the previous output. The summation of ht−1 and x is multiplied by weight W. In model-C the convolution layer has two weights: W1 and W2. Training HSCNN with faces of the previous step is for testing faces of the next step in the experiment. That is, HSCNN trained with past facial data is then used to verify future data. It has been found to exhibit 10 percent greater accuracy than traditional CNN with a celeb face database.
Random Number Generator Based on Discrete Cosine Transform Based Lossy Picture Compression

The The widespread use of digital data makes the security of this data important. Various cryptographic systems are used to ensure the security of this data. The most important part of these systems is random numbers. In this article, a random number generator based on the discrete cosine transform, which is the basis of image compression algorithms, is proposed. In this generator, the difference between the original image and the compressed image produced using the discrete cosine transform is used. The original picture is transferred to the frequency plane using the discrete cosine transform. It is then converted back to the space plane using the inverse discrete cosine transform. These transformations cause some losses as certain coefficients are taken into account. Raw random numbers were generated using the differences between the original image and the compressed image. Then, the possible weaknesses in the random numbers generated by passing these raw data through the hash function were fixed. The SHA-512 algorithm was used as the hash function. An important advantage of the developed system is that it can be easily produced using any digital data source. It has been shown by the analysis that the generated random numbers are safe.
Exploring the operation of a neural network using the example of handwritten digits recognition in an advanced informatics course using the python 3.8 programming language

The article discusses the possibility of including artificial intelligence topics in the informatics course at the level of secondary general education on the demonstration example of handwritten digits recognition using the Python 3.8 programming language and the TensorFlow package. A hands-on example of a classification problem using the classic MNIST (Modified National Institute of Standards and Technology) training example set is dealt with step-by-step.Most of the tools required for the work (language interpreter, basic libraries, and shell) are downloaded in the form of a single software distribution kit of Anaconda. The network is trained using different methods. When the model is trained, the optimization method, the loss function, and the metric for estimation are specified. Image processing by convolutional nets containing special layers, which "convolve" the image the way a bitmap filter does, is also demonstrated
Advancement of bitcoin enabling smart governance: A case study of innovating blockchain technology

This study can provide essential information about the advancement of Bitcoin Enabling Smart Governance. Blockchains are known as evolving technology that has brought attention from energy suppliers, start-ups and developers of technologies. The technology has brought several benefits to innovate new procedures. Blockchain technology ensures transparency and secured system for business solutions. In addition, it works for energy sector as well as state of the by reviewing present business perspectives along with the cases and literature. In bitcoin, the miner puts the data of transaction into the blocks, which are mined and transaction is achieved. On the contrary, it is important to develop proper procedure for bitcoin that becomes time consuming procedure. Cryptokitty is obtaining popularity in the world of gaming and bring a massive scope to co-cope with the research as well as study with the system like KYC in order to frame the process.
Efficient Person re-identification with Multi-Scale Feature Fusion

The task of Person Re-Identification (Re-ID) has attracted growing attention in recent years. Most State-Of-The-Art (SOTA) methods often employ the large-scale ResNet model as their backbone, which makes it tedious to explore various architecture modifications. In this study, we propose a small-sized Re-ID model with EfficientNet as its backbone, along with a novel feature pyramid branch for learning multi-scale features. Extensive experimental results show that the proposed network outperforms various SOTA methods with obvious margin on standard benchmark datasets such as Market1501, DukeMTMC-Re-ID, CUHK03, even though its mode only contains about 7.41M parameters.
Accurate Quaternion Polar Harmonic Transform for Color Image Analysis

Polar harmonic transforms (PHTs) have been applied in pattern recognition and image analysis. But the current computational framework of PHTs has two main demerits. First, some significant color information may be lost during color image processing in conventional methods because they are based on RGB decomposition or graying. Second, PHTs are influenced by geometric errors and numerical integration errors, which can be seen from image reconstruction errors. This paper presents a novel computational framework of quaternion polar harmonic transforms (QPHTs), namely, accurate QPHTs (AQPHTs). First, to holistically handle color images, quaternion-based PHTs are introduced by using the algebra of quaternions. Second, the Gaussian numerical integration is adopted for geometric and numerical error reduction. When compared with CNNs (convolutional neural networks)-based methods (i.e., VGG16) on the Oxford5K dataset, our AQPHT achieves better performance of scaling invariant representation. Moreover, when evaluated on standard image retrieval benchmarks, our AQPHT using smaller dimension of feature vector achieves comparable results with CNNs-based methods and outperforms the hand craft-based methods by 9.6% w.r.t mAP on the Holidays dataset.
Vision: A Noisy Picture or a Picker Wheel to Spin? Exploring Suitable Metaphors for Differentially Private Data Analyses

Personal data analyses, for instance, in the area of eHealth, can provide many benefits while posing privacy challenges at the same time. Applying differentially private mechanisms have become one of the dominant approaches for the protection of personal data in statistical analyses. Transparency of the privacy functionality of differentially private mechanisms can facilitate informed decision-making for using differentially private systems and understanding the privacy consequences of such decisions. However, differential privacy is a complex concept that makes it a challenge to explain the privacy functionality it comprises to lay users. Our research outlined in this vision paper aims to address this challenge in three phases by creating and analysing metaphors for conveying the functionality of differential privacy to lay data subjects who should decide about sharing their data in the context of differentially private data analysis. In this paper, we report the results of the first two phases of our study for extracting the metaphors and adapting and extending them based on two rounds of analytical evaluations and feedback from privacy experts. Further, we briefly discuss how, in the third phase, we want to move forward and empirically assess the resulted metaphors from previous steps by involving lay users.
Optimization-Based Robot Team Exploration Considering Attrition and Communication Constraints

Exploring robots may fail due to environmental hazards. Thus, robots need to account for the possibility of failure to plan the best exploration paths. Optimizing expected utility enables robots to find plans that balance achievable reward with the inherent risks of exploration. Moreover, when robots rendezvous and communicate to exchange observations, they increase the probability that at least one robot is able to return with the map. Optimal exploration is NP-hard, so we apply a constraint-based approach to enable highly-engineered solution techniques. We model exploration under the possibility of robot failure and communication constraints as an integer, linear program and a generalization of the Vehicle Routing Problem. Empirically, we show that for several scenarios, this formulation produces paths within 50% of a theoretical optimum and achieves twice as much reward as a baseline greedy approach.
Forecasting Trends in an Ambient Assisted Living Environment Using Deep Learning

Ambient Assisted Living (AAL) aims at assisting people in their Activities of Daily Living (ADL). We have seen an increased interest in their applicability to the rural seniors who are slowly losing their autonomy due to aging and chronic diseases. By deploying intelligent devices in the environment of an individual performing their ADLs, we can gather data in the form of a time series. One research venue is to seek to use forecasting techniques to discover trends and predict future trends that could be used to analyze the health of these individuals. With the recent advances in computational power new deep learning forecasting algorithms have been developed. In this paper, we compare a univariate one-dimensional CNN model and a LSTM model that performs multi-step forecasting for one week ahead. The novel dataset used comes from a set of activity and health related sensors deployed in a small apartment that uses our previously designed analytics architecture. We compare these to a forecasting baseline strategy. Both deep learning approaches increase the forecasting accuracy significantly.
Enhancing data security using global mutation based novel artificial immune network optimization in public cloud storage system

Abstract During the past few years, several investigations and efforts have undergone to achieve secure data in the cloud. On the other hand, few security problems that arise because of virtualization and multitenancies are communication, isolation, as well as resource management system. Practically, it becomes a troublesome task for the user to attain secure output. Therefore, in this article, a RSA cryptosystem using global mutation‐based novel artificial immune network optimization algorithm is proposed. Here, an optimal key generation is achieved using a global mutation based novel artificial immune network optimization (GM‐NAINO). In addition to this, the data integrity is achieved by enhancing secure data transmission. Finally, seven diverse test functions are employed in evaluating the efficiency of the proposed technique. The experimental analysis are performed and the evaluation results demonstrated that the performances of the proposed technique is better when comparison with other techniques.
FPGA Implementation of Mixed Robust Chaos-based Digital Color Image Watermarking

In this article, a hardware implementation of an invisible watermarking system has been proposed using Cyclone IV-E ’EP4CE115F29C7’ FPGA board of ALTERA company. Algorithms have been applied on digital color images using Discrete Wavelet Transform (DWT) for the decomposition and Rössler Chaotic Model (RCM) and Genesio-Tesi Chaotic Model (G-TCM) for the watermark encryption. The security of the implementation has been further increased by comparing image encryption using C-Eclipse software-generated best performing chaotic model outputs with VHDL in Quartus hardware generated Intellectual Property (IP) core ones. The experimental setup includes Video Graphics Array (VGA) monitor for different images display for the experimental validation. Implementation major performances of imperceptibility, robustness, resources consumption, and speed are analyzed and interpreted.
Blockchain-based Model Against selfish Attacks in Mobile ad hoc Networks

Over the past several decades, the importance of security has been increasing, in particular in the area of research addressing the Internet of Things (IoT) and, implicitly, ad hoc networks since they are part of it. Researchers have adopted blockchain as an interesting and trendy research topic in the wireless ad hoc environment. Due to its strong characteristics, such as consensus, immutability, finality and provenance, it&#x2019;s used not only as a secure data storage for critical data, but also as a platform that facilitates trustless data exchange between independent parties. However, the main challenge of applying blockchain in an ad hoc network is what kind of nodes should be involved in the validation process and how to adopt the heavy computational complexity of block validation in an appropriate way while maintaining its authentic characteristics. In this paper, we propose a new blockchain-based model against selfish attacks in ad hoc networks and optimize it in this context, to this end, we investigate the presence of MPR nodes to adapt and integrate the blockchain principle in MANETs and based on this we conduct a security analysis to evaluate our model by discussing the ability of our model to meet major security issues related to confidentiality, integrity, availability, non-repudiation, and vulnerability to attacks.
Smart Grievance Redressal System

Addressing the grievances of citizens is a tough task for an administration. Classifying and prioritizing the grievances will make the grievance redressal system more efficient. This paper proposes a software application to identify common issues by analysing multiple grievances, so as to provide the administration, with a view of the important issues that require immediate attention. The software application is developed using state of the art NLP technologies to implement text classification, keyword extraction and keyword clustering on the set of grievances.
Cryptography on Untrustworthy Cloud Storage for Healthcare Applications: A Performance Analysis

For cross-sectoral sharing of health data, cloud services provide benefits regarding the availability of relevant information in critical situations. Nevertheless, storing electronic health records in the cloud may impact patient’s privacy, since the cloud service provider might lack trustworthiness. Client-side cryptography mitigates the privacy risk by encrypting data in the user’s computational environment before transmitting them to the cloud. However, client-side cryptography raises performance concerns. This paper investigates Web Assembly and JavaScript solutions that enable client-side cryptography in web applications and compares their performance against server-side cryptography. We contextualize the study within two healthcare web applications: a prototype for patient record sharing during acute stroke care and an application for sharing data in sleep medicine treatment. Our results show that JavaScript and Web Assembly libraries add more time to encryption and decryption than server-side cryptography. However, due to the concurrency introduced by simultaneous users, the performance of server-side cryptography is worse than client-side, taking longer times to attend to the demand and requiring a larger infrastructure to be effective. Moreover, the JavaScript library asmCrypto and Web Assembly approaches perform better than other libraries and are feasible candidates for application development.
An Analytical Comparison of Approaches to Real-Time Object Detection to Handle Concurrent Surveillance Video Streams

In some environments, there is a need to process multiple real-time object detection algorithms concurrently, where each object detection algorithm receives a live stream from a camera. To cover a large scale of surveillance cameras on multiple live streams concurrently, we have to decrease the detection time per frame while performance does not make changes. Since there is no comprehensive study of known approaches to object detection in the camera surveillance environment, we conduct a state-of-the-art study to compare the approaches Single Shot MultiBox Detector (SSD), Region-Convolutional Neural Network (FASTER R-CNN), You Only Look Once version 3(YOLOv3), YOLOv4, and Scaled YOLOv4 in different Test servers. We use the multi-thread model that we proposed in our last work on multiple live streams to improve the efficiency of GPU and CPU system resources. The experiment results show that the Multi-thread model's detection algorithm is approximately 27%, 26%, and 22% faster than the original YOLOv3, YOLOv4, and Scaled YOLOv4 in all Test servers on video resolutions while there is no change inaccuracy in the detection process by detection algorithms. Furthermore, the multi-thread models are evaluated on four different servers with four GPU cards. The results show that the Analysis server RTX 4000 has the best result between provided Test servers.
Hardware-based Cryptographic Accelerator for Post Quantum Era

Emerging quantum computing brings many benefits in numerous applications and services as solving efficiently complex operations and problems. Nevertheless, quantum computing may also jeopardize current cryptography standard schemes by solving the problems used in these schemes. Therefore, new quantum-resistant cryptographic primitives and installations are required for the near future. In this work, we present a hardware-based cryptographic accelerator that provides resistance to quantum computer attacks. Directly in VHDL, we design and implement essential cryptographic functions which can be loaded to our accelerator equipped with the FPGA board. Our solution provides efficient encryption, signing, and hashing operations for securing the input data or network packets. Moreover, we create a high-level software demonstrator for setting and using these operations in the accelerator. The presented results prove that cryptography functions accelerated in our hardware-based solution are more efficient than software implementations. Therefore, our solution can provide high-speed and quantum-secure data processing.
An Approach to Design and Implement Parallel Web Crawler

Indefinite cyberspace crawling is an architecture for a parallel searching process occurred in various websites using internet. Web crawler is defined as a software or program which traverses the Web and downloads web documents in an automated manner. It’s depends on the type of questions asked by the user. The pertinence of Web Crawler in the field of web search of this architecture is to capably and successfully crawl the current set of publicly index able web pages to amplify the download rate on shrinking the parallelization. Now a days web grows is becoming extremely difficult to recover the substantial or significant portion of the Web using in a single method. There are many search engines like-Google, Bing, Yandex, Search Encrypt etcetera runs various threads in parallel to perform the above job, so that the download rate gets exploited. The requirement of web crawler is to gather and verify the data in order to store the huge amount of data in such a speed on the Web requires parallel computing. It is definite as a program or as a software that traverses the Website and downloads web documents in a systematic, robotic method. We are going to discuss about the relevance of the Web Crawler. The topic about web crawler is the option to do web search and a small examination on Web Crawler on different problematic fields or domains in web search as mention here.