Reviewerly/exHarmony huggingface dataset:
Modeling Heterogeneous IT Infrastructures: A Collaborative Component-Oriented Approach

The advent and growing sophistication of modern cloud-native architectures has brought into question the way we design IT infrastructures. As the architectures become more complex, modeling helps employees to better understand their environment and decision makers to better grasp the “big picture”. As the levels of abstraction multiply, modeling these infrastructures is becoming more difficult. This leads to incomplete, heterogeneous views difficult to reconcile. In this article, we present a collaborative approach focused on improving the accuracy of IT infrastructure modeling through the involvement of all stakeholders in the process. Our approach, applied in an incremental manner, is meant to increase confidence, accountability and knowledge of the infrastructure, by assigning responsibilities early in the process and leveraging the expertise of each stakeholder. It is suited for both a priori and a posteriori modeling at a low adoption cost, through adaptation of existing workflows and model reuse. Building collaborative models in such a way aims at bridging the gap between different areas of business expertise. As a result, we believe that our approach allows to perform analyses and use formal methods on larger scale models and cover wider technical domains.
Employing Thresholding and Template Rotation to Enhance the Effectiveness of Template Matching Programs

The most important first step in the majority of image analysis workflows is the localization of objects of interest. Typical picture segmentation techniques include thresholding and edge detection. Although such techniques work well for tagged objects, they are at their limit when samples have poor background contrast or when only a portion of bigger structures should be recognized. Furthermore, the creation of such pipelines necessitates extensive analysis process engineering and frequently yields case-specific solutions. This study describes how to create a two-stage OpenCV-based object counter with improved accuracy and performance by utilizing template rotation and thresholding approaches this negates certain limitation that other techniques such as edge detection has while also building on and improving the standard template matching algorithm.
A Selection Technique for Effective Utilization of Post-Quantum Cryptography in 5G Application and Beyond

In the quantum computing era, classical cryptography algorithms are expected to be unsafe. To provide a solution to the problem that may occur to this effect, Post-Quantum Cryptography (PQC) algorithms are being considered as replacements to classical algorithms. It is essential to consider the effect of using PQC algorithms on applications and services as many applications come with strict Quality of Service (QoS) requirements, especially in 5G and beyond 5G applications. Therefore, this paper proposes a technique for selecting a suitable PQC algorithm to achieve an effective utilization of PQC algorithms in applications/protocols. In this study, a rule-based selection technique is adopted. In particular, a PQC selection rule is generated based on an application/protocol traffic pattern/characteristics and the security requirements such as key/signature size, the strength of the PQC algorithm, time taken to share secret keys and so on. Thereafter, the process of selecting a PQC algorithm is presented. To select a suitable PQC algorithm, the use case in which an application traffic belongs is identified, and based on the traffic flow tag, a selection rule is applied. The performance of the proposed method is evaluated by experimenting with the PQC algorithms using the Transport Layer Security (TLS) handshake. The evaluation results show that the proposed PQC selection method is useful in selecting a suitable algorithm based on real application traffic and the QoS requirements.
Deep Learning Based Side-Channel Analysis for Lightweight Cipher PRESENT

The lightweight cipher is attracting attention as cryptographic technique for IoT devices. PRESENT, the target of this study, which is standardized in ISO/IEC29192-2 and it is one of the most popular lightweight ciphers. On the other hand, the risk of side-channel analysis (SCA) has been reported as a security issue for cryptographic circuits. Moreover, the deep learning based SCA (DL-SCA) has recently been reported as a more powerful analysis method. However, DL-SCA for PRESENT with hardware implementation has not been reported so far. Therefore, this study proposes DL-SCA for the lightweight cipher PRESENT. The experimental results using FPGA show that all round keys can be successfully estimated by using 300 waveforms, and that the proposed method can be analyzed with a smaller number of waveforms than the conventional SCA method CPA.
Artificial Intelligence-Based Smart Tele-Assisting Technology for First-Year Engineering Students

Engineering programs emphasize students career advancement by ensuring that engineering students gain technical and professional capabilities during their four-year study. In a traditional engineering laboratory, students "learn by doing", and laboratory equipment facilitates their discipline-specific knowledge acquisition. Unfortunately, there were significant educational uncertainties, such as COVID-19, which halted laboratory activities for an extended period, causing challenges for students to perform and obtain practical experiments on campus. To overcome these challenges, this research proposes and develops an Artificial Intelligence-based smart tele-assisting technology application to digitalize first-year engineering students practical experience by incorporating Augmented Reality (AR) and Machine Learning (ML) algorithms using the HoloLens 2. This application improves virtual procedural demonstrations and assists first-year engineering students in conducting practical activities remotely. This research also applies various machine learning algorithms to identify and classify different images of electronic components and detect the positions of each component on the breadboard (using the HoloLens 2). Based on a comparative analysis of machine learning algorithms, a hybrid CNN-SVM (Convolutional Neural Network - Support Vector Machine) model is developed and is observed that a hybrid model provides the highest average prediction accuracy compared to other machine learning algorithms. With the help of AR (HoloLens 2) and the hybrid CNN-SVM model, this research allows students to reduce component placement errors on a breadboard and increases students competencies, decision-making abilities, and technical skills to conduct simple laboratory practices remotely.
Machine Learning Based Power Analysis for Simon with Secure Mechanism

The IoT requires the implementation of cryptographic techniques to ensure the confidentiality, integrity, and availability of communicated data. Therefore, lightweight block cipher with small circuit size, low power consumption, and low latency are attracting attention. Simon is a typical lightweight block cipher with small circuit size and computational security. On the other hand, side-channel analysis, which steals secret keys by using side-channel information during hardware implementation, has been reported as a threat. Also, various countermeasures have been proposed. Recently, deep learning side-channel analysis has been proposed as a more advanced analysis using deep learning. Therefore, it is very important to evaluate the tamper resistance of new analysis methods as well as conventional method to ensure the safety of the IoT. This study proposes a deep learning side-channel attack on Simon with noise countermeasure. The proposed method analyzes the round key computed in the final round function under the known ciphertext attack for Simon with loop implementation. Experiments on a real device showed that most partial keys can be analyzed using 5,000 waveforms, and the effectiveness of the proposed method was evaluated.
Augmented Reality Campus Exploration Application Incorporating Equity, Diversity, and Inclusion

Augmented Reality (AR) is one of the most promising technologies with many practical applications, enhancing/augmenting the real world with virtual information/objects. With advances in AR technology and affordable AR hardware, AR has widely been used in training, classroom education, entertainment, tourism, public safety, retail, and many others. This paper presents an AR application that provides real-time information for campus exploration incorporating Equity, Diversity, and Inclusion (EDI). Many mobile AR applications have been developed for campus tours to help new students and visitors identify campus buildings and their services. However, from the User Experience (UX) design perspective, simply providing general information about each building may not be sufficient for students who need specific information based on their backgrounds and particular needs. The mobile AR application, TWUExplorAR, presented in this paper, incorporates the concept of EDI by overlaying the landmark information tailored to each user’s background and needs, thus improving UX. The paper outlines the background and related work, describes the development of the AR application, illustrates the incorporation of EDI in the application, summarizes the usability tests, and suggests future improvements.
A Lightweight Hybrid Framework for Real-Time Detection of Process Related Anomalies in Industrial Time Series Data Generated by Online Industrial IoT Sensors

Industrial Manufacturing plays an important role in the global economy, and estimates suggest that approximately 27 hours per month are lost in any major facility due to unplanned stoppages. The advent of Industrial IoT has seen manufacturing facilities deploy low-cost sensors with the hope of gaining improved visibility and thereby reducing unplanned machine stoppages and wastages. This paper introduces a lightweight, fast, easy-to-deploy framework that can be used for reliable and accurate identification of anomalies in real-time operational systems. The framework is holistic and includes data acquisition and data persistence modules to ensure that it can be deployed to a working production facility. The anomaly detection and contextualisation module is hybrid and uses statistical techniques and machine learning methods to provide fast responses while requiring minimal intervention. The framework was tested at a large metals manufacturing facility in New South Wales, Australia and the results show an accuracy of over 97% in anomaly detection.
Model for Verifying the Reliability of Candidate Data Based on Blockchain Technology

Big data is one of the most prominent technologies today and is applied in different areas of society. Big data is all information, collected from different sources and continuously updated over time. Therefore, big data has many challenges that make it difficult to apply in practice. One of the challenges that many researchers care about and find solutions to overcome is to ensure the reliability of the data. Meanwhile, blockchain technology emerges with a transparent, immutable, and secure data storage solution. Therefore, we focus on researching and developing a solution to verify the reliability of data based on blockchain technology. We analyze candidate data provided from multiple sources and find solutions to verify the reliability of this data to meet the urgent needs of employers looking for high quality human resources. Currently, on the internet, many systems provide an environment for information exchange between candidates and employers. However, it is difficult to verify the reliability of this information. Therefore, recruiters spend a lot of time checking the reliability of data provided by candidates. In this paper, we propose a model for verifying the reliability of candidate data based on blockchain technology (VRCD-BT). The system built according to this model can become an effective bridge between candidates and employers. Highly reliable candidate data creates many opportunities for candidates and employers to easily collaborate and grow. Thereby, contributing to the development of a sustainable economy operated by high-quality human resources.
Path Planning and Mapping of an Autonomous Agricultural Robot Using Robot Operating System (ROS) and Gazebo

Path planning and following rely deeply on mapping and object detection. In this paper, research into the application of these processes and the method to implement them are discussed and analyzed. Communication through robot operating system and MATLAB are utilized to visualize the data received and used for the programs that are run on robots. The Turtlebot serves as an educational tool with various sensors. A mapping and control method will be developed and examined to further the understanding of how each function of path planning and object detection runs and can be controlled.
Student Management Model Integrating E-Commerce Based on Blockchain Technology

Blockchain is one of the most prominent technologies today. With a combination of cryptography, distributed computing, consensus algorithms, and peer-to-peer networking, blockchain technology is a distributed network with strong security. This technology opens up a new opportunity to improve the performance of other sectors such as supply chain, finance, smart city, smart health, education, e-commerce, and more. For education, blockchain technology offers the same application potential as for other professions. E-commerce is no exception to the influence of this technology. Therefore, we design a student management model integrating e-commerce based on blockchain technology (SMM-EB). This model is maintained by tokens. At the same time, this model uses blockchain technology to store student information and distribute rewards in cryptocurrency. Students can use that currency in exchanges and transactions within the scope of a school or an education on the e-commerce platform. Our method makes the process of verifying student personal information and achievements quick and accurate. At the same time, this method also overcomes fraud in e-commerce. Thanks to blockchain technology, this model-based system ensures the transparency and integrity of student information and transactions on the e-commerce platform. Therefore, the system is more secure and efficient than the centralized system and does not depend on third-party management. The proposed architecture can be replicated with the benefits of current technology.
A Dataset and Multi-task Multi-view Approach for Question-Answering with the Dual Perspectives of Text and Knowledge

Question-answering (QA) systems are important tools for extracting information from large datasets and providing accurate and relevant answers to user queries. Two of the most widely studied and built QA systems are Natural Language Question Answering (NLQA) and Knowledge Graph Question Answering (KGQA). NLQA relies on sequence learning algorithms, which have limitations on the length of input they can handle, while KGQA relies on the Subject-Predicate-Object (SPO) tuple representation of data, which may not always be available in the knowledge graph. In this paper, we present a novel approach for addressing these challenges by utilizing the structural information from the Knowledge Graph (KG) and the semantic information from the Natural Language Context. Due to the lack of a dataset to enable this approach, we propose the creation of a multi-view dataset - MTL-QA, specifically designed for multi-task learning. We also present a multi-task learning approach to jointly train NLQA and KGQA models and demonstrate the effectiveness on the proposed MTL-QA dataset.
Smart Gloves — A tool to assist Individuals with Hearing difficulties

A sign language is a language that expresses meaning via physical gestures and body language rather than through the transmission of sound patterns. Using a variety of hand gestures, finger alignment and motion, arm or torso movements, and facial expressions may all help a speaker communicate their views clearly. There are many similarities between spoken and signed languages, but there are also some important differences that speak to why linguists still consider spoken and signed languages to be real languages. Currently, owing to contemporary technology, deaf people may communicate through hand gestures. With each movement of the hands, a different audio message is set up to play. The microcontroller then analysed the data from the accelerometer and flex sensor used to capture hand motions. In order to create recorded sound signals for speaker delivery, the technology makes use of the appropriate signals produced by the flex sensor, which tracks hand movements and detects deflections. There are many different Arduino Boards available right now, and they have many uses. Simply utilising an Arduino Mega 2560 is the greatest choice since it is the most cost-effective, provides faster computing, and can integrate and connect several sensors while using less power. An Arduino setup and a glove make up the bulk of the topical gadget. Four flex sensors are included in the data glove and are strategically placed. Hand signals may be translated instantly. It's possible to recognise all 26 letters just looking at where the fingers are.
Face Mask detection using Mask R-CNN to control the spread of Covid-19

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not.
Integrated AI Based Smart Wearable Assistive Device for Visually and Hearing-Impaired People

An Intelligent Robotics Device (IRD) is developed in order help the disabled people and elderly people with day-to-day activities. The system offers five main functions: obstacle detection and avoidance through bone conduction, live tracking, GPS navigation, GSM SOS alert system and AI based image and face recognition System. It works with a combination of ultrasonic detection and bone conduction, detecting obstacles and letting the user know about them. To assure safe mobility, the product offers some core features namely, GPS live tracking and GPS navigation. GPS live tracking feature is useful in case if any of the user's relative wants to continuously monitor the movements of the blind user. The GSM - SOS alert system is included in the product, which comes into handy when the user finds any exigencies while travelling with just a push of button, the SOS alert system will send customized help request SMS along with location link to their career. GPS navigation system sends alert vibrations regarding the directions to be taken for navigating from one place to another. Those alerts are conveyed to the user through bone conduction phones. Finally, AI-facial and image recognition feature is also included which helps the blind to distinguish between known person and unknown person. All the electronic components which support these features are assembled and embedded on a wearable vest for ease of access.
Blockchain Technology and Supply Chain Management in a Charity Donation System: A Literature Review

From the history India is being particularly susceptible to natural disasters because of its climatic characteristics. Nowadays, people are becoming very eager to contribute to society. During the disaster many people are struggling for their basic needs. We have many charitable organizations and NGO's they require funds to improve society. Donors are more interested in donating either funds or materials during the disaster. In India one of the major problems is civil supply tracking. Goods supplied by the donors can be taken away by strangers. This will affect the needy people as well as donors who have no trust in the system and to decrease the frauds, we came up with a new technology called Blockchain. In this paper we study about the system that makes the entire process more transparent and reliable.
Wavelet-based method for enhancing the visibility of hazy images using Color Attenuation Prior

Image processing may be used in the majority of computer vision applications, including those for remote sensing, aerial photography, and vehicle navigation. Images taken outside in bad weather could suffer from degradation. Therefore, it is essential for these applications to enhance the degraded images before processing them. The contrast and saturation that are lost due to scattering must be restored to improve the hazy image. To address this issue, a novel model is built for enhancing a single hazy image efficiently and effectively. Using multilevel wavelet transforms, the image is first decomposed into two frequency domains - the low-frequency domain and the high-frequency do-main. Later, it is dehazed only in the low-frequency domain using the Color Attenuation Prior(CAP). Soft-thresholding operation is carried out in the high-frequency domain to remove any residual noise. Additionally, texture detail in this domain can be improved by using the estimated transmission obtained during the dehazing of the low-frequency component of the image by CAP. Extensive tests were performed on images from the RESIDE and HazeRD datasets to assess the efficiency of our method. Results of tests show that our approach dehazes faster and enriches the hazy image more effectively than other existing dehazing techniques.
Image Denoising Using Feature Map Based Convolutional Neural Networks

Noise corruption is inexorable in image recognition and it may heavily agitate the optical features of the capturing image. Convolutional Neural Networks (CNNs) have acquired good contemplation in the research of denoising. They possess various approaches to removing noise from a degraded image and to reconstruct an image with high visual quality. Because of its stratified learning and self-operating feature extraction capabilities, CNN has become useful for Machine Vision tasks. Furthermore, these methods mostly train a specified model for every noise level and desire multiple methods for denoising images with various noise levels. Large feature sets result in producing a noise effect, which causes an over-fitting of the network. A new method of Feature Map Based Convolution Neural Network (FMBCNN) has been introduced for image denoising. The quantitative analysis is performed by calculating four metrics of MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio), SSIM (Structural Similarity Index), and Entropy. They put forward a building block for Convolutional Neural Networks that tends to increase channel interdependencies at nearly no cost of computation. The weights accompanying a kernel also called a mask, are tuned to choose features dynamically. The performance of classification, segmentation, and detection modules is heavily influenced by feature selection.