Reviewerly/exHarmony huggingface dataset:
Security for Internet-of-Things Enabled E-Health using Blockchain and Artificial Intelligence: A Novel Integration Framework

This paper upon setting the background of ethical issues with electronic data in healthcare, discusses enabling technologies that ensure secured network in digital health. It reviews the unique features of e-health using Artificial Intelligence (AI), Internet-of-Things (IoT) and Blockchain (BC). The paper elaborates on areas in which one technology complements another by the integration of all three health technologies for smarter and secured application. As a result, we propose a novel framework for the actualization of the anticipated security of e-health data in AI, IoT and BC ecosystem and we conclude by positing that the integrated technology's security interface will be a robust and secured e-health services configuration that will lessen the security setbacks in the independent ones.
Class-Based Deep Non-Negative Matrix Factorization Algorithm for Facial Image Representation

Deep non-negative matrix factorizations (DNMF) are the promising multi-layer non-negativity feature extraction methods for image data representation and have been successfully applied to image data clustering and recognition tasks. However, most of the DNMF methods do not utilize the information of class labels which is more important for classification. Also, they have high computational complexity. To address the problems of DNMF methods, this paper proposes a novel class-based deep non-negative matrix factorization (CDNMF) approach. Under the non-negativity constraint, our CDNMF method recursively decomposes the basis matrix of each class and makes the basis vectors from different classes are orthogonal at the same layer. The CDNMF algorithm is shown to be convergent using the auxiliary function technique. Compared with the state-of-the-art deep NMF algorithms on face recognition, experimental results demonstrate that the proposed CDNMF algorithm has superior classification performance and higher computational efficiency.
Software Stack for Autonomous Vehicle: Motion planning

Shinde, Sonali Mane, Sunil B.An autonomous vehicle can operate itself without any human interaction through the capacity to sense its surroundings. It requires a navigation plan to travel avoiding collision and following all the traffic rules at the same time. Navigation issue is a computational issue to discover the succession of legitimate designs that move the object from source to destination. In the robotics field, this term is known as motion planning or path planning. There exist well-recognized methodologies for this problem; however, by applying some helpful heuristics, a better version of driving API can be designed. This study develops a complete software architecture needed for autonomous vehicles. It gives brief insights about available techniques for each module involved in motion planning and possible optimizations to achieve better results. The global planner uses a road network stored in open drive format to find the most eligible global path for ego vehicle. Whereas reactive local planner uses surrounding information to plan better paths avoiding static and dynamic obstacles. At last, the results are introduced in examination with existing methods to show improved measurements accomplished by this framework.
Hand Gesture Classification Using Grayscale Thermal Images and Convolutional Neural Network

We propose a convolutional neural network for classifying grayscale images of hand gestures in this paper. We look at ten different hand gestures collected from various people using a thermal camera for classification. The proposed model&#x2019;s performance in terms of classification accuracy and inference time is then compared to that of other benchmark models. Using extensive results, we show that the proposed model achieves higher classification accuracy while using a smaller model size. In terms of inference time, we show that the proposed model outperforms benchmark models.
Identification of Tamil Characters Using Deep Learning

Akashkumar, S. Dyaram, Atreya Niranjan Anand, M.Optical character recognition is a key field that attempts to convert printed text into a digital representation. In this digital era, more printed materials are available, and they are mostly unclassified. When dealing with large data, any unclassified data are useless. The study of categorizing digital messages into some intelligible structure is known as natural language processing (NLP). Also, the input should be in digital format. OCR mitigates the burden of translating printed text into digital text. Even though OCR has become popular for English scripts and many other foreign languages, there has been not much development in OCR for Indian regional languages like Tamil, Telugu, Malayalam, etc. The major reason is that the existing literature on regional language contains a large number of characters, and several methods are proposed to combine them in order to make words. Tamil has 247 characters, which include alphabets (12), consonants (18), and alphabets + consonants (216). The combination in character makes the language difficult in converting to digital format. This research work has introduced a method to label each class with a particular number by reducing the class length to 108. This makes it much easier to process due to less number of classes. The usage of Convolutional Neural Network (CNN) made the processing smoother. The proposed CNN architecture was self-made with various hit and trial methods, which will also be discussed in this paper.
Research on Entity Relation Extraction Based on BiLSTM-CRF Classical Probability Word Problems

Mathematical automatic problem solving is a very challenging task in the field of artificial intelligence. The key premise of problem-solving automatically is to understand the problem's meaning. For mathematical word problems with rich semantics, varied forms, and difficult to be understood by machines, this study focuses on solving the difficulty of overlapping entity relations recognition and multiple entity relation extractions across sentences, taking the word problem of the classical probability as the research object, an entity relations extraction method based on sequence annotation is proposed. The BiLSRM-CRF model is used to improve the effect of question comprehension. The experimental study found that, compared with the selected combination of different features based on the CRF model alone, the BiLSTM-CRF model can obtain the effect of the approximate CRF model at a superior cost, and improve the recognition effect of a few relations. Meanwhile, the accuracy of the overall problem understanding also gets improved.
An Intelligent Route Finder For UAE Desert Driver Based On A* Algorithm

Car driving in off-road areas and the deserts, in particular, is a challenging task. In such a harsh environment, drivers need to move while avoiding getting stuck in the sand or rolled upside down. In UAE, with its vast desert, this is a daily challenge for oil companies who operate in inland oil exploring, organizers of safari tours, and the border guards. This paper introduces an intelligent route finder for UAE desert driver based on A<sup>&#x002A;</sup> Algorithm (IRF) framework as a route guidance tool for off-road driving. The goal is to find the shortest and safest path to the destination. The IRF combines Artificial Intelligence (AI) pathfinding algorithms and Geographic Information Systems (GIS), such as the Digital Terrain Model (DTM) and Surface Type, to enhance pathfinding decisions. We model and combine this information to improve cost estimation in the A<sup>&#x002A;</sup> artificial intelligence searching algorithm. Drivers can use the developed software to predict the safest optimal route to help navigate more safely in the sandy desert and save time.
Comprehensive Comparison of Security Measurement Models

Security measurement models (SMMs) and their corresponding derived metrics form the main pillars of a systematic security measurement. Providing a desirable SMM is very challenging and has been investigated over the past two decades, so that numerous SMMs have been proposed and several surveys on SMMs have been performed. However, to the best of our knowledge, neither a systematic taxonomy nor a comprehensive comparison has yet been proposed for SMMs. This paper focuses on the comprehensive comparison of SMMs relying on a feature-based approach. The plurality and diversity of the compared SMMs enable us to deduce all the open issues.
The Application of VR Technology in Traditional Culture and Art Immersive Teaching for Chinese Children

Children's art education is closely related to the cultural environment. In consideration of the interconnection and interaction between education and culture, the impact of traditional culture on art education could not be underestimated. VR technology can break the limit of time and space to restore the history, and provide traditional culture and art education for children with vivid images. The advantages of VR technology in creating immersive teaching situation for children's traditional culture and art education can overcome limitations of teaching space and provide technical support for the creation of immersive learning situation. VR technology can realize the content dynamization and visualization of traditional culture in children's art education, stimulate students’ learning initiative, improve the quality and efficiency of Chinese children's traditional culture and art education, and provide feasibility for the formation of Chinese traditional culture in children's art education resources and the reform of teaching methods.
Study on Secure Communication of Internet of Vehicles Based on Identity-Based Cryptograph

Driven by the current computer technology and communication technology, traditional cars are equipped with sensors and communication equipment to realize the communication between cars and cars or between cars and infrastructure. It not only brings various information services to cars, but also improves road safety and traffic efficiency. However, as the security of Internet of Vehicles(IoVs) application service depends on the security of communication network, any malicious network attack will cause great harm to it. Therefore, it is of great significance to provide a reliable solution for the secure communication of vehicles. Our goal is to study a secure communication system between vehicle and infrastructure. In need of low bandwidth and low latency of the vehicle and infrastructure scenario, the communication between vehicle and infrastructure is convenient and safety. We propose a secure communication scheme for IoVs based on Identity-Based Cryptograph(IBC). It provides data encryption and access control between the vehicle and the system. The vehicle records contain sensitive information about the owner and the vehicle in the form of encryption. It not only solves the problem of unsafe communication between IoVs and infrastructure, but also realizes the safe communication of IoVs. Moreover, the crypto center and several key secrecy organizations are proposed to improve the trust of the key generation center and improve the insecurity of communication.
Quality-Aware Video Analytics in Edge Computing Environments

Edge computing has opened new doors for real-time video analytics applications due to its ability to offer significantly faster response times by processing videos near the source. However, the limited computing capabilities of edge devices can affect the quality of video analysis. While the existing literature has focused on minimizing latency, the quality aspect needs to be rigorously investigated to satisfy the high accuracy requirements of video analytics applications. This paper proposes a new quality-aware video analytics framework for edge networks. First, a real-time video analytics platform based on edge computing is designed and implemented. Then, the impacts of different factors on the quality and the overall latency of edge computing-based video analysis are investigated. Next, the trade-off between the video analysis quality and the latency, i.e., computation benefits at the edge vs. remote server, is studied. Finally, a quality-aware edge computing-based video analytics framework (QVAF) is designed to minimize the total latency while guaranteeing a required detection accuracy. Evaluation results show that QVAF could significantly improve response times across different detection accuracy requirements.
Forensic Event Reconstruction for Drones

Event reconstruction is one of the forensic processes used to determine and reconstruct event sequences of cyberse-curity attacks or incidents. In drone forensics, existing research focuses on artifact extraction and analysis. Therefore, in this paper, we propose an event reconstruction method for drones using a directed graph representation. The sentiment analysis technique is used to investigate any events of interest that have been represented as graph vertices. Experimental results on drone public datasets indicate that the proposed method can reconstruct events and show the suspicious activities of a drone to assist in an investigation.
Improved crow search algorithm based on arithmetic crossover—a novel metaheuristic technique for solving engineering optimization problems

The metaheuristic optimization techniques’ role is inevitable in solving real-world problems. Following the crow's intelligent trait, the classical crow search algorithm (CSA) was demonstrated. From the optimization point of view, it can be viewed that the crows are analogous to searchers and the environment is considered as the solution space. Every location in the solution space indicates a feasible solution. Also, the grade of food source is synonymous to the objective or fitness function and the optimum food source is the attainment of the global solution of the problem. The crows have good memory power and classical crow search optimization was proved to be efficient, generates promising outcomes, when compared with the classical optimization techniques. The performance of the genetic algorithm (GA) was improved by arithmetic crossover and the improved crow search optimization utilizes the arithmetic crossover operation of GA. The arithmetic crossover operation was incorporated in the position update of crows and formulated the hybrid crow optimization (CO) algorithm. The performance of the hybrid CSA was verified on benchmark functions and satisfactory results are produced while comparing with the classical crow search optimization. The hybrid CO technique was also coupled with support vector machine (SVM) for the classification of a brain tumor on MR brain images; effective results were achieved while comparing with SVM coupled with a GA, simulated annealing, and crow search optimization techniques. The hybrid crow search optimization was also validated on two design problems (three-bar truss and cantilever beam) and compared with the classical methods.
Classification of Indian Classical Dance Hand Gestures: A Dense SIFT Based Approach

Jisha Raj, R. Dharan, Smitha Sunil, T. T.Computer vision techniques can improve the understanding of classical dance gestures and also open up oppurtunities for automatically annotating videos. In this paper, we study Bharatanatyam, an Indian classical dance, which uses hand gestures(mudras), facial expressions, and whole body movements to communicate the intended meaning to the audience. Open datasets on hand gestures of Bharatanatyam are not presently available. An exhaustive Bharatanatyam mudra dataset consisting of 15,396 distinct single hand gesture images of 29 classes and 13,035 distinct double hand gesture images of 21 classes was created for this. This paper deals with performance comparison of machine learning algorithms like Support Vector Machines, Multilayer Perceptron, Decision Tree, and Random Forest based on SIFT and dense SIFT features extracted from the images of the dataset. Dense SIFT descriptors which are 128 dimensional were further dimensionally reduced to 64 and 32 using Principal Component Analysis (PCA) and their performance was also evaluated.
Making Matters: Samples and Documentation in Digital Craftsmanship

Digital fabrication machines enable the creation of a wide range of artifacts and materials. In the process of exploring the possibilities within these digital modes of creation, many samples are made. To encourage cross-pollination between different practices and strive for a community that shares more information, this research investigates the state of the art of sample making and documentation practices in the context of Digital Craftsmanship. Through in-depth interviews with designers with a focus on digital craftsmanship, in this paper we unpack some of the practices and challenges in material driven processes related to how to design, evaluate, document and archive material samples. We reflect on making practices, what forms of knowledge are produced through making and how the use of samples in different design processes can be supported through documentation.
Digitizing health data for public health protection in the context of European and international coordination

The health sector has long been affected by programs, actions, plans to digitize data and care processes with a view to better protecting individual health, as well as public health, resulting in a slow and uneven development of different and often incompatible national services.This paper aims to explore the grounds behind the urgency of turning the digital priority into concrete actions, as acknowledged by political leaders in the Rome Declaration, by explaining the capacity of digital tools to enhance healthcare management and the current obstacles.It considers the progressive extension of the EU institutions' scope of action during the pandemic, the related supporting financial strategies launched and some examples of digital contact tracing systems.It emerged that the pandemic highlighted the inadequacy of purely national policies and the advantages of leveraging the digital health data processing for governance, surveillance and response to cross-border and global threats.Considering what emerged during the pandemic and the solemn commitment of the world's major political leaders, the solution to the still existing technical and organizational interoperability issues will no longer be postponed.
SVM-RBF Kernel Learning Model for Activity Recognition in Smart Home

The world population is aging. Taiwan will have at least 20 percent of the population over 65 by 2026. Telemonitoring technology is one of the solutions used to assist elderly people live independently. We designed a SVM-RBF kernel learning model to classify activities of daily living and to analyze an individual&#x2019;s daily routines and habits, typically for the elderly who live alone. One of the CASAS smart home datasets was used to train and to retest the algorithm. A non-trained dataset was also used to validate the accuracy of the algorithm. Abnormal behaviors can be detected by compared with individual&#x2019;s daily activity pattern as baseline.
A Model Obfuscation Approach to IoT Security

Many emerging IoT systems adopt the model-based design and operations, where an appropriate model is first trained based on input/output sensory data streams and further used for prediction and estimation. We address a simple, yet fundamental question on securing such systems: Can we directly secure the trained model, rather than sensor data streams? We depart from data-oriented security schemes and design MORSE, which offers a model obfuscation to IoT applications. The core of our design is a novel data-dependent sampling scheme, which hides the dependency between input and output variables. The sampling leverages the dependency from the Bayesian network and enables efficient processing. MORSE further adopts a shuffling method to complement sampling. Consequently, the adversary cannot obtain the real model given the observed data, while an authentic MORSE user who is aware of the scheme can reconstruct the model. We analyze the security and convergence of MORSE and confirm its effectiveness via empirical evaluations.
Reversible Data Hiding for SMVQ Compressed Images Based on De-Clustering Rules

Vector quantization (VQ) is a popular digital image compression technique. Its resulting index table can be further compressed using the side match vector quantization (SMVQ). In this research, we propose a reversible data hiding scheme based on the de-clustering rules to embed secret data during SMVQ compression. Referring to differently assigned codebooks, the de-clustering rules are equally applicable to both compressible and uncompressible VQ indices. The proposed scheme can produce a camouflaged VQ index table with a high payload. Besides, our scheme is free from the indicator bit, which is required in the conventional SMVQ. Experimental results are compared with state-of-the-art methods.
A Survey on Semantic Segmentation of Aerial Images using Deep Learning Techniques

Semantic segmentation of remote sensing images has been utilized in an assortment of uses and has been a focal point of examination for a long time. With the achievement of profound learning strategies in PC vision, analysts have endeavored to move their better presentation than the field of distant detecting picture examination. In the field of computer vision, semantic segmentation is an interesting task. It is the method of assigning every pixel to a specific label. Aerial images have become more common in all fields of science as a result of technological advancements in recent years. Tables, maps, graphs, and other forms of crucial data for somewhat area could be attained, but these are insufficient to present a real-time analysis. As a result, an aerial picture seals in the gaps. To improve the quality of the images, they must go through several processing steps, Segmentation is one example of such processing. The foremost objective of image segmentation is to group the pixels of areas analogous to distinct planes, items, or usual portions of things in order to streamline or alter the illustration of an image into somewhat additional significant and easier to analyse. The availability of very high-resolution remote sensing images, combined with advances in deep learning methods, has moved the standard of image taxonomy left from pixel-based and object based approaches and toward deep learning-based semantic segmentation. In this paper, we presented a review of some cutting-edge Convolutional Neural Network (CNN) architectures such as AlexNet, GoogleNet, VGGNet, and ResNet that serve as the foundation for Semantic Segmentation, as well as a revision of numerous segmentation procedures applied to aerial images.
A single-frame spatial carrier phase-shifting method realized by minimizing the coefficient of variation

An advanced spatial carrier phase-shifting algorithm based on minimizing the coefficient of a variation method is proposed, which realizes the phase extraction of a single-frame spatial carrier fringe pattern. In our test, compared with the existing classical algorithms, the proposed method is proved to have the dual advantages of efficiency and accuracy. In addition, the main factors that may affect the performance of the method are analysed and discussed. The results show that the method proposed is a fast and accurate single-frame phase detection method, which can provide an effective solution for real-time phase demodulation.