Reviewerly/exHarmony huggingface dataset:
Role of Grinding Spark Image Recognition on Enhancing the Smart Grinding Technology for Ti6Al4V Alloy

Research has shown that an intelligent grinding system (IGS) improves the reliability and maintenance of grinding operations, as it has the capabilities for real time monitoring and failure prediction. This research addresses the additional feature towards enhancing IGS, through Augmented Reality (AR) techniques. AR will provide a visual monitoring and failure prediction mechanism, as an overlay to the IGS. Thus, allowing the operator to view and compare the grinding process to a simulated model, in real time. This improves the on-line support offered to the operator and has the potential to greatly influence the down time, reliability, quality and inspection of grinding operations. The grinding of the Ti6Al4V alloy results in high temperatures at the grinding wheel-work interfaces and results in a thermal damage. Previous research conducted on IGS, found that operators generally use the acoustic emission (AE) signals and grinding force signatures as a check for characterizing the grinding process. Prediction of the grinding burn in real time offers better monitoring capabilities for mission critical components. In this study the grinding spark characteristics such as: color, spark area and quantity of spark lines was used as a measure for establishing the grinding process behavior. Spark images were used to assess the grinding processes in real time, with the use of a thermal imaging camera. To create the AR environment in the IGS system, a FLIR C5 thermal camera was used to capture the object’s depth and dimensions. Conclusively, the research investigations on AR and IGS systems, indicate that it could accurately track the grinding process and extract useful information regarding the failure mechanisms of the grinding wheel and workpiece and the system works in near real time.
Augmented Reality for Operators in Smart Manufacturing Environments: A Case Study Implementation

The fourth industrial revolution, or Industry 4.0, is gaining momentum globally and in various sectors. Promising smart and highly-connected digital and physical systems, the vision of Industry 4.0 relies on the effective integration of some key enabling technologies—amongst which is Augmented Reality (AR). AR is a digital medium that involves virtual objects and computer-generates elements that appear in one or more layers over the real-world environment. The integration of production data with customized AR experiences promises great benefits for operators in the manufacturing sector and beyond. This paper describes the case study implementation of an integrated AR experience for operators in a smart manufacturing environment. The case study uses a Microsoft Hololens II AR device, with Vuforia Studio software, to integrate an operator with the operation and control of a Fischertechnik Learning Factory 4.0 miniature manufacturing system. Within the case study, the implementation demonstrates an integrated AR experience capable of identifying components of the real manufacturing system using marker recognition and shape screening as well as visualizing multi-media data objects. Furthermore, an outlook of visualizing real-time IoT production data and sending operator commands via the AR system to affect the manufacturing system operation is given. In light of the case study results, the paper also discusses the possibilities and challenges for the deployment of AR in real manufacturing environments.
Research and Application of Multi-dimensional Virtual Simulation Packaging Based on AR Technology

In this study, AR technology was combined with product packaging to create a multi-objective recognition database and embed it into the community group buying mini program to improve the problem of single product packaging recognition and low functionality. Using Unity 3D engine and Vuforia to build AR scenes, realize the interconnection between virtual scenes and the real world, increase particle effects to enhance the fun of interaction, and intuitively understand the details of required products. The study combines the augmented reality technology and product packaging, from dimensional proceed with, put forward a new digital and recycled packaging new direction, to identify the personalized AR for specific population development, improve the functional sex of the packaging design, innovation of product packaging information display, realize the consumers and the depth of fusion products.
Human and Machine: Practicable Mechanisms for Measuring Performance in Partial Information Games

In this note, I introduce a new framework called n-person general-sum games with partial information, in which boundedly rational players have only limited information about the game---including actions, outcomes, and other players. For example, playing an actual game of chess is a game of partial information. To analyze these games, I introduce a set of new concepts and metrics for measuring the performance of players, with a focus on the interplay between human- and machine-based decision-making. Specifically, I introduce (i) gaming-proofness, which is a property of a mechanism that players cannot game from a practical perspective, and (ii) the Net Game Points (NGP) mechanism, which measures the success of a player's performance in a game, taking into account both the outcome of the game and the "mistakes" made during the game. The NGP mechanism provides a practicable way to assess game outcomes and can potentially be applied to a wide range of games, from poker and football to AI systems, organizations, and companies. To illustrate the concept, I apply the NGP mechanism to select chess games played between some of the world's top players, including the world champion.
Pig-Based Study of Crime Rates in Several Indian States

Big data is made up of a wide variety of data thatarrives quickly and in increasing volumes. The size of the data sets makes it impossible for conventional data processing algorithms to manage them. Big data solutions have become more popular as a result of Hadoop's high scalability, availability, and cluster environment mechanism, which enablesworking in a distributed manner. MapReduce, one of the majorcomponents of Hadoop, can handle unstructured data but demands significant programming skills. Because Apache Pig allows us to analyse data by simply running queries, customers are increasingly turning to this tool owing to high programming skill requirements. In this paper, we analyse the crime rates of India of different states from year 2001 to 2015. The crimes are categorized into 6 different categories i.e., Murder, Rape, Kidnapping, Foeticide, suicide and exposure. The motive of this paper is to analyse the pattern of crime in different states of India.
A Comparative Study of Feature Modelling Methods for Telugu Language Identification

Telugu is one of the most widely spoken language, with 82.7 million native speakers. Telugu is used predominantly in the Indian states of Telangana and Andhra Pradesh. This paper explains the design and implementation of a model that identifies whether the language spoken by the user is Telugu or from a multilingual dataset containing audio clips from Telugu, Malayalam, Gujarati, English, and Marathi of native Indian speakers. Five features, MFCC, Chroma CQT, Chroma STFT, Chroma CENS, Tonnetz-space, and combinations of these, are used for training the models. Five powerful deep learning architectures CNN, LSTM, CNN-LSTM, BiLSTM, and GRU are used in training over the features and different feature combinations. The performance of the models has been compared with state-of-the-art models for language identification. The proposed models yielded maximum accuracy of 94% for CNN with Chroma CQT+MFCC feature combination.
SR2 - Search and Rescue Robot for saving dangered civilians at Hazardous areas

Each year, thousands of human equivalents are killed by natural catastrophes that impact hundreds of millions of people. As a result, there is a strong need to upgrade the rescue system to one that is more advantageous and effective. With the invention and introduction of new technologies, robots have recently shown to be of considerable assistance in this industry. They are able to work in dangerous conditions and squeeze into tiny or awkward locations. They are also never weary. Furthermore, they've shown time and time again that they're better and quicker than humans at finding survivors in disaster zones, which not only saves lives but also enables the impacted community to start the path to recovery more quickly. The roles of airborne, terrestrial, and marine robotic systems in search and rescue (SAR) and salvage operations are being revolutionized by the quick advancements in technology. Robots and drones are used to communicate, gather data, carry supplies, and direct rescues in environments judged too dangerous for humans. Rescue robots are made to do a certain task that has been allocated to them, one that may be too dangerous for humans to perform. Another difficulty that these robots can handle is rescuing individuals from hazardous situations. The individuals from the afflicted areas are found by rescue robots. A robot that has been built specifically with human rescue in mind is a rescue robot. Mine accidents, urban catastrophes, hostage situations, and explosions are typical uses for rescue robots. Rescue robots may help these missions by reducing the number of persons needed, reducing tiredness, and providing access to places that wouldn't otherwise be accessible. With features including finding, mapping, clearing or shoring up debris, delivering supplies, providing medical care, and evacuating victims, rescue robots are currently being developed.
Self-driving robot cars using image processing and deep learning

In the field of image processing, image recognition is one of the major challenges for researchers in recent years. The goal of recognition is to detect and extract features in the images to classify samples into different layers. One problem of great interest in this field is self-driving robot cars using image processing. The aim of this research is to present a deep learning method to solve the path recognition matter for robots. A self-driving robot model will be built based on the convolutional neural network with the use of different layers to automatically extract the best features in the image. Experimentation procedure has been carried out and results with low error rates had been obtained
A quantum annealing approach to solve max-cover problem

The Max-Cover is a NP-hard problem. Therefore, the heuristic approach is suitable to solve this problem for large instances. Quantum annealing is a heuristic quantum optimization algorithm that can be used to handle the Max-Cover problem. Recent developments in quantum technology allows creating programmable quantum processors to implement the quantum annealing technique. In this article, we apply the quantum annealing approach to solve the Max-Cover problem. The experimental results show that this approach gives better results than Simulated Annealing in terms of both solution quality and annealing time.
Challenges for designing a multiband Ultra-Wide Band (UWB) Antenna with its Solutions

Technology known as multiband ultra-wideband (UWB) has the potential to provide a response to the rapid expansion of wireless communication thanks to its high data rate, large bandwidth, and greater resilience to interference from many paths. The spectral form of the pulses is altered as a result of the Multiband UWB antenna's function as a band pass filter. This gives it a unique character. As a consequence of this, the development of it is essential. UWB antennas are required to function throughout the entire FCC-designated UWB band (3.1-10.6 GHz ultra-wide bandwidth), have directional or omnidirectional radiation patterns, maintain constant gain and group delay throughout the entirety of the band, have exceptional radiation efficiency, and be compact. A number of different designs for UWB antennas are investigated, and after looking at the present status of fundamental approaches to UWB antennas, a unique class of UWB microstrip patch antennas is suggested as a solution.
Analyzing Impact of Technology-Facilitated Knowledge Creation on Student Satisfaction in Distance Learning Education

Although Covid-19 cases are continuing to decrease, the practice of online learning has become common in education. Many education institutions have online classes besides face-to-face classes. However, many research reported several problems in distance learning practice mainly because of the limitation of interactive conversation and learning activities and finally lead to an ineffective knowledge creation process. It is reported that in a distance learning setting during the pandemic, the motivation and participation rate decreased while the absence and dropout rates increased. Knowledge conversion is a term to reflect the acquisition of new know-how or tacit knowledge from the learning activities. Currently, various technology vendor keeps making innovations to solve this problem, and available technologies that can be utilized to support distance learning education are increasing. Therefore, this paper aims to know the impact of the knowledge creation process on student satisfaction and explain how to utilize technologies throughout the knowledge creation chain. Based on the statistical analysis it is found that the knowledge creation process that is represented using the SECI model simultaneously has a significant positive effect on student satisfaction. This means all phases in SECI (socialization, externalization, combination, and internalization) are simultaneously able to improve student satisfaction. However, partially, only socialization and internalization that has a significant positive impact on student satisfaction, which indicates these two factors partially could lead to better student satisfaction if it is planned and managed appropriately. This paper also constructs a matrix that helps education practitioners choose the right technology for each knowledge creation phase.
Utilizing Hyperledger Based Private Blockchain Technology to Secure Credit Card Payment System

A traditional credit card payment method involves using a third party to validate the transaction. Anyone using a credit card for online purchases exposes themselves to the risk of identity theft. Also, multiple costs are connected with a transaction using a payment gateway. These costs are typically concealed and unknown to customers. Many studies have been conducted on using public blockchain technology to secure e-commerce payment systems, e.g., Bitcoin and Ethereum. Unfortunately, the information and transactions stored in a public blockchain are open to anyone, destroying confidentiality. To address these inadequacies and decrease the danger of stolen credentials, we proposed a solution for protecting credit card transactions using Hyperledger Composer-based private blockchain technology. This eliminates the need for a third party to process a transaction and the hidden fees seen in the standard credit card payment system. Only the privileged participants can retrieve the credentials and transactions, which is ensured by the Access Control feature of Hyperledger. Moreover, a minimum of 3000 transactions per second can be executed in our proposed system, whereas the public blockchain has a throughput of only 2-3 TPS. These transactions are immutable as well as non-removable and the query feature, which is exclusive to Hyperledger, can be utilized for instantaneous retrieval of information from the ledger.
Semantic Heterogeneity Management between Weather Systems Using Ontology Mapping

Emergence of Information and Communication Technology (ICT) provides enormous volume of data over the WWW. Weather data one among them which is provided by different organizations with various formats and attributes. Weather data plays major role in various domains such as agriculture, forestry, disaster management. In order to effectively use the heterogeneous weather data, different processing techniques are used. This is useful for achieving interoperability and integration among similar systems. Our proposed system focuses on identifying the semantic and data heterogeneity between weather systems and resolve semantic incompatibilities by ontology mapping. The proposed technique is relied on background ontology which developed with all entities and attributes. The proposed algorithm OMFIM (Ontology Mapping For Information Management) performance is compared against state of the art algorithms S-match, COMA++ and Falcon-AO algorithms. Results show that the ontology matching of OMFIM is more optimum than the existing techniques and also applicable for the ontology with less terminological and schematic similarities.
An Empirical Framework for Identifying Sentiment from Multimodal Memes using Fusion Approach

Advances in social media platforms led to the widespread adoption of memes, making them a powerful communication tool on the internet. Memes’ visual aspect gives them a remarkable ability to influence users’ opinions. However, individuals misemploy this popularity to foment animosity. The spread of these hostile memes can have a detrimental effect on people, causing depression and suicidal thoughts. Therefore, stopping inappropriate memes from spreading on the internet is crucial. However, identifying memes is di cult due to their multimodal nature. This paper proposes a deep-learning-based framework to classify sentiment (into ‘positive’ or ‘negative’) from multimodal memes in Bengali. Due to the unavailability of standard corpora, a Bengali meme corpus consisting of 1671 memes is developed to perform the memes’ sentiment classification task. Five popular deep learning models (CNN, BiLSTM) and pre-trained models (VGG16, VGG19, InceptionV3) are investigated for textual and visual features. The framework is developed by combining visual and textual models. The comparative analysis confirms that the proposed model (BiLSTM + VGG19) achieved the highest f <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -score (0.68) compared to other multimodal methods.
BloodComm: A Peer-to-Peer Blockchain-based Community for Blood Donation Network

Blood transfusion is an integral part of the healthcare system that plays an important role in ensuring the quality of care for patients undergoing a variety of medical procedures and treatments. A large portion of this blood comes from voluntary donors. The existing blood donor management systems are unable to offer a reliable audit trail and traceability. Hence, there is a significant risk that patients may get transfusion of blood from unreliable sources. In this paper, we propose a system built on Ethereum with the goal of creating a decentralized, transparent, traceable, and secure network of blood donors. The platform uses smart contracts to facilitate peer-to-peer interactions. To encourage donors to donate blood more regularly, the system also offers rewards in the form of tokens. Our source code is available in a public Github repository <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Ensemble Segmentation of Nucleus Regions from Histopathological Images towards Breast Abnormality Detection

One of the most occurred cancers which cause death in women is breast cancer, contributing to 16% of all female cancers worldwide. Detection of the disease in the preliminary stage is the only way to treat the disease from any severity. Presence of the digital imaging modalities also allows the computerized diagnosis of a disease which overcomes the limitations of a human perception system. However, a pathologist who is knowledgeable and skilled is necessary for an appropriate diagnosis. Also, tissue sample analysis requires a lot of manual labor. Therefore, combining digital histopathology with computer-aided diagnostic (CAD) tools can assist in solving these issues. In this paper, we have proposed a hybrid framework of nucleus region segmentation from the histopathological images. The primary aim of the proposed framework is to ensemble information from multiple segmentations and, finally, fuse this information (in terms of intersection) to acquire the core and stable nucleus region(s). For this, we have ensemble the U-net model (with VGG-16 as the backbone network) with the fuzzy c-means algorithm for precise nucleus regions segmentation from the histopathological images. Experimental results reveal that the proposed framework performed better for cell nucleus segmentation with dice similarity index values of 0.8517 and 0.9357 using publicly available BreakHis and BreCaHAD, respectively.
An Evaluation of Transformer-Based Models in Personal Health Mention Detection

In public health surveillance, the identification of Personal Health Mentions (PHM) is an essential initial step. It involves examining a social media post that mentions an illness and determining whether the context of the post is about an actual person facing the illness or not. When attempting to determine how far a disease has spread, the monitoring of such public posts linked to healthcare is crucial, and numerous datasets have been produced to aid researchers in developing techniques to handle this. Unfortunately, social media posts tend to contain links, emojis, informal phrasing, sarcasm, etc., making them challenging to work with. To handle such issues and detect PHMs directly from social media posts, we propose a few transformer-based models and compare their performances. These models have not undergone a thorough evaluation in this domain, but are known to perform well on other language-related tasks. We trained the models on an imbalanced dataset produced by collecting a large number of public posts from Twitter. The empirical results show that we have achieved state-of-the-art performance on the dataset, with an average F1 score of 94.5% with the RoBERTa-based classifier. The code used in our experiments is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Surveillance in Maritime Scenario Using Deep Learning and Swarm Intelligence

Unmanned Aerial Vehicles (UAVs) have played a crucial role in supporting Search and Rescue (SAR) Operations due to their fast movement capabilities and flexibility. During a search and rescue operation scenario, the time constraint is a crucial parameter, so the required time to detect humans in distress with precision is also a vital part. Modern Deep learning algorithms like CNN also aid in these missions. However, most models and datasets available focus on search and rescue missions on the ground or land. UAV-based search and rescue operations in the Maritime Scenario remain a challenge. This study focused on using deep learning algorithms such as CNN to precisely detect a human in peril with a swarm of drones. At the same time, we emphasize using swarm intelligence algorithms such as Particle Swarm Algorithm (PSO) to effectively find a victim in the shortest time by exploring a massive area. The distinctiveness of this system is that it combines the model with the best accuracy to detect and the best swarm intelligence algorithm for finding targets in the quickest time possible, thus enhancing the surveillance mission. In this research, among VGG16, ResNet50V2, InceptionV3, Xception, and MobileNetV2 models, VGG16 produced an IoU (Intersection over Union) score of 0.62 with a Class Label accuracy of 99.15% and Bounding Box accuracy of 88.74% in CNN part. Along with that, among three different swarm intelligence algorithms, according to the simulation, Particle Swarm Optimization Algorithm took the minimum average time, which is 20.4 units, whereas the Grey Wolf Optimization algorithm and Bat Optimization Algorithm, respectively, took 65.6 and 73.8 units of time.