Reviewerly/exHarmony huggingface dataset:
Explainable Object Detection in the Field of Search and Rescue Robotics

State-of-the-art object detection in search and rescue robotics relies on CNNs, which reach excellent scores but lack explainability due to their “black box” characteristic. In such a domain, mission failure or misdetections can have drastic consequences. Therefore research should strive to increase the explainability regarding the CNNs’ classification strategies. In this paper, existing methods for object detection are applied and investigated in the context of search and rescue robotics in order to compose a fully explainable pipeline. Unlike existing object detection methods, the presented method is based on an exhaustive model investigation concerning post-hoc explainability. The method is applied to detecting handwheels of gate-valves, with a post-hoc analysis of the classification strategies learned by the object detector. In order to train and test the object detection model, a novel dataset is composed, including 2447 images, nine handwheel types, and 6696 annotations. Five CNN object detectors (R-CNN, Faster R-CNN, RetinaNet, YOLOv5s, and SSD) are compared based on mAP0.5 and mAP0.5:0.95. Two CNN object detectors’ classification strategies were investigated with the SpRAy method. Comparing mAP0.5, mAP0.5:0.95, and inference times reveals that YOLOv5s is the superior model across all categories. SpRAy analysis of R-CNN and YOLOv5s did not reveal any abnormal classification strategies, which indicates a well-balanced dataset. YOLOv5s appears to have learned different classification strategies for different handwheel types. Our handwheels dataset is available at: https://www.kaggle.com/hoenigpet/handwheels-for-gatevalves .
Deep Learning Prediction Models for the Detection of Cyber-Attacks on Image Sequences

With the introduction of Cyber Physical Systems and Industrial Internet of Things within Industry 4.0, vision systems, as indispensable element for robot cognition, become smart devices integrated into the control system using different communication links. In this control framework image streams are transferred between elements of distributed control system opening the possibility for various cyber-attacks that can cause changes in certain parts of images eventually triggering wrong decisions and negative consequences to the system performance. Timely detection of the attacks on communicated image streams is necessary to mitigate or completely avoid their negative effects. In this paper we propose a method for the prediction of the next image in the sequence which can be utilized for the development of anomaly-based cyber-attack detection mechanisms. For the model generation, we have explored the application of several deep learning architectures based on two-dimensional Convolutional Neural Networks and Convolutional Long Short-Term Memory Recurrent Neural Networks. Images obtained from the real-world experimental installation were utilized for model design. Our deep learning models proved to be effective in predicting the next frames according to the criteria of a discrepancy between pixels of the real and estimated images.
Optimal Camera Placement for Maximized Motion Capture Volume and Marker Visibility

In optical motion capture, camera placement strongly effects the dimensions of the measurable area and the accuracy of measured 3D marker positions calculated via triangulation. Aligning a given set of cameras by hand according to spatial conditions and measurement requirements relies on expertise and experience. This paper introduces an approach to find optimal camera poses for maximizing the measurement volume. It is based on a formulation for the visibility of individual points and considers the convergence angles between the cameras. In addition, a straightforward methodology to adjust the real cameras according to the optimization result is presented.
Copyright and Artificial Intelligence: Relevancy of Work Created in This Digital Era

With the speed of technological advancement, a new dimensional period is beginning on Earth, and artificial intelligence is one of them. Artificial Intelligence (AI) is one of the most significant innovations of this technologically driven world. It has the capacity to interfere with practically every element of human life. As there is a famous proverb that says that “everything comes with a cost,” so it is in the case of artificial intelligence. On one hand, it is changing every aspect of human life; on the other hand, it is also posing some important and relevant questions, especially related to the utility of this technology. And in relation to the utility of artificial intelligence, one of the fields is “Intellectual Property Rights,” especially in copyright, under which it poses the question of the copyrightability of artificial intelligence created works. However, courts have already considered whether a computer creating a replica can have the mental states essential for responsibility and they have often said “no” flat-out. But this study proposal challenges the idea that computers cannot have a legal mental state. Hence, as an alternative of extending “authorship” to bracket non-humans, the proposal reinterprets “employee” & “employer” in the “made for hire doctrine.” With this strategy, the current IP system may continue to support “science and useful arts” avoiding the need for a drawn-out or contentious revision of the laws and regulations.
Kisan-Kiosk For Rural Areas Farmer Guide

A Kisan-Kiosk is a computing Unit farmer's friendly machine which consist of both software and specialized hardware. Kiosk is an any time payment making machine(ATP) from which the public can access the bill payments, bill status, subsidies, loan status and also soil testing updates for farmer's. This machine in the past resembles the telephone booths, but as a future enhancement it is improvised to make the favour to the customers. The Kiosk machines are exorbitantly placed in the location having high transaction, business concerns like shops, malls, banks, so on and so forth purpose of bill payments includes water bill, electricity bill, phone bill, tax payment etc. Kiosk machine has embarked its glare in the tourist places, railway stations as a ticket vending machine
Automated Entrance Access Using Face Recognition

Now a days, confidentiality is a problem in any area. Researchers must thus use cutting-edge technologies to tackle these problems. In this research, we'll be taking pictures of people and comparing them to pictures from the library that has already been saved. The mechanism shall open the entrance if it verifies the authorized user. The demand for quick, precise facial identification systems that can identify attackers, prohibit unauthorized individuals from sensitive locations and reduce personal inaccuracy is always rising. Among the safest secure systems biometric analysis methods are utilized in a wide area of applications. The primary issue that defines the success of computerized facial detection in different scenarios is thought to be the speed and precision component. Multiple approaches utilizing multi-core computers have now been put forth. This offers the entire design and architecture and suggests an evaluation for an actual facial identification network with LBPH Algorithm while taking into account the current difficulty. This method separates the picture into bits and turns it from a colorful photo to a histogram. The pixels are then allocated in a grid, and the pictures are then saved in the memory. The suggested technique includes a mechanism for homeowners to open the entrance after recognition of an unidentified person. Once the system shows an image, the Micro controller initiates the motor driver unit, opens the door, and shuts it once more when power to that unit is switched off.
Deep Neural Network Based Anomaly Detection For Smart Surveillance System

Searching for anomalies in surveillance film is a practise that is becoming more and more popular. Anomaly detection is the procedure of spotting irregularities in data. When we talk about “abnormal data,” we mean data that drastically differs from its typical behaviour. Fraud detection, malfunction detection, and intrusion detection are just a few of the uses for anomaly detection. You Only Look Once is a method for quickly recognising objects (YOLO). It is an object identification system that is capable of quickly locating objects in images, real-time coverage, and video streams. An instant object recognition system is the YOLO Convolutional Neural Network (CNN). CNNs are classifier-based systems that can identify patterns in images and interpreting them as collections of well-organized data. Both the intersection over union (IOU) and mean average precision (mAP) values of YOLOv3 are quick and exact. This detection method is significantly slower than other detection methods that get comparable results. This study suggests utilising a modified YOLOv3 algorithm to track things automatically and notify people if anything seems off. The performance of the algorithm was also contrasted with that of other algorithms, including CNN and decision trees. Our system continuously records live video while abnormal things like fire and weapons are identified. High detection precision and quick processing are features of our technology.
Machine Learning For Detecting Credit Card Fraud

These days frauds related to credit cards are exponentially increasing as compared to earlier scenarios. Like every coin has two faces in a similar way where on one hand the introduction of credit cards has helped in the ease of online payment to make our lives easier, on the other hand, the same technology has increased the number of frauds. Fake identities and various technologies are used by the criminals or cyber attackers to trap the users. Henceforth, it has become essential to find a solution for all such frauds and abnormal activities so that money of the user can be protected at the time of transaction. In order to tackle all such problems, we can train our machines using Machine Learning Algorithms. This project has been designed to illustrate the analysis of the dataset taken from Kaggle and train our system accordingly so that any kind of abnormal activity during a transaction can be immediately detected. The issue involves examining previous credit card transactions using information from both the fraudulent ones and the zeroes that were legitimate Here, detecting 100% of fraudulent transactions while lowering erroneous fraud classifications is our key goal. Data sets are analyzed and pre-processed, and various anomaly detection techniques, like the Random Forest algorithm and Decision Tree Classifier, are used to get the Prompt Corrective Action modified Credit Card Transaction data, have been the main focuses of this procedure. The models are compared and evaluated based on training and testing accuracy. It has been found that the Decision tree classifier performed better on training accuracy i.e., 95% while random forest demonstrated better testing accuracy i.e., 94.11%.
Implementation of Handwritten Character Recognition using Quanvolutional Neural Network

Image classification is the process in which a computer can perform analysis on an image and identify the class in which that particular image falls under. In conventional classification models (Convolutional Neural Networks or CNN), data collection and processing takes a large amount of time in days or even weeks. CNN models also have less scalability and require a lot of resources. To overcome this problem, quantum computing can be used not just for enhancing the scalability but it also provides exponential speedups because they have the ability to perform massively parallel computations. The problem with the CNNs is that they need large numbers of parameters and run into an overfitting problem with small datasets. This work is an implementation of Quanvolutional Neural Networks (QNN), which is a quantum-hybrid model, to recognise English characters using EMNIST letters dataset for training. The results of this model are then compared to a CNN model in terms of accuracy and loss. The results show that the use of a quantum layer can significantly increase the accuracy as well as decrease the training time of a classification model.
Foodereum: A Blockchain-based Authenticated Solution for Food Supply Chain

Blockchain technology has evolved and been widely available over the past several years, revolutionizing a variety of industries, most notably the supply chain industry. The food supply chain currently in effect across different nations faces numerous challenges, including a lack of transparency and traceability of food items due to a centralized system, third-party interference during shipment, high transportation and storage costs, food waste due to rotting and contamination and poor accountability of entities involved. The decentralised nature of blockchain, as well as its ability to track ownership records and resist tampering, can be employed to tackle these critical challenges. This paper introduces Foodereum, a blockchain-based authenticated solution for food programmes that enables organisations to record, track and authenticate food products along the supply chain. The research also examines the industry's present issues followed by Foodereum's characteristics, a detailed analysis of gas consumption and future scope.
Artificial Gloves For Kinesics Communication To Articulate Dysfunction Individual Using ESP 32

Dactylology is an obscure branch of linguistics that can only be practised by those who no longer can communicate verbally. This paper tries to break through the communication barrier between normally functioning persons and those with varying degrees of impairment. This paper outlines the smart glove that persons with different types of impairments may wear to communicate with those around them using hand gestures that include performing specific movements. Intelligent gloves utilize flex sensors to identify various hand motions. In addition, the actions themselves are translated into the display of the written message and the sound output. The system is equipped with an ESP 32 microcontroller and an Arduino Nano, which work together to gather an adequate number of inputs for each command and then feed that information into the algorithm. Finally, the system's results are discussed, demonstrating that it can accurately convert hand motions into text that can be read in English and audio output in that language.
Creation of GIF dataset and implementation of a speech-to-sign language translator in Telugu

The paper proposes the design and implementation of a model that translates live voice or audio recordings of a native Indian regional language (Telugu) to text and further matches it to sign language animations from the pre-defined Graphics Interchange Format (GIF) dataset. The speech is converted to text using Google API. If the text matches with the words in GIF dataset, it generates the respective GIF. A GUI interface is also developed which displays ISL text alphabetically if the given input is outside the range of GIF dataset. Alongside, the performance of four powerful deep learning networks viz. RNN, LSTM, Bi-LSTM, and GRU, has been compared and trained over a Telugu multi-speaker speech dataset for speech recognition. The proposed models yielded accuracies of 51% for RNN, 68% for LSTM, 90% for Bi-LSTM and 88% for GRU. On evaluating the models, Google API-based speech to sign language conversion has been most promising and consistent with precision and recall scores of 94% each and a word error rate of 4.1%.
A strategic plan for developing eXtended reality tools to teach unit operations in chemical engineering: Defining needs, technology selection and project resources

In 2021, the PILOT PLANT at the Chemical and Biochemical Engineering department at the Technical University of Denmark (DTU.CBE) designed and developed a 360-VR product to support teaching and learning about chemical process unit operations. Given the scale and multidisciplinary nature of the project, a variety of methods and approaches were employed. Learning objectives for existing courses were analysed to establish product opportunities. SWOT and Matrix methods were applied to a range of eXtended Reality solutions to establish product fit. Further, project resources were measured to define constraints and opportunities. It was established that competencies that develop basic familiarity, operating and reporting skills are important in the undergraduate curriculum of chemical engineering students, whilst facilitating the development of many of these skills is possible using eXtended Reality solutions. In January 2022, four 360-VR unit operation experiments were fully commissioned for use by students and other stakeholders as part of the courses offered at the pilot-scale plant facilities of DTU.CBE. Given this experience, this work proposes a strategic plan for specifying and managing projects to develop eXtended Reality teaching tools for existing experiment-based courses.
Rail profile matching method based on stepped calibration plate and improved SURF algorithm

Abstract In order to avoid the calibration plane and laser plane do not coincide and the rail profile is difficult to match dynamically, a stepped calibration plate is made, and a rail profile matching method based on the improved SURF algorithm and random sample consensus algorithm (RANSAC) is proposed. Firstly, a stepped rail profile calibration plate is designed and fabricated, which can directly calibrate the laser plane, avoiding the problem that the calibration plane and the line laser plane do not coincide completely in practical application. In the calibration process, collect the optimal control points and calibrate the image by polynomial transformation and bilinear interpolation, then complete contour rough matching using SURF algorithm. During the SURF algorithm, construct the matching constraint of the center of the orbit waist to delete part of the false matches. Finally, the improved RANSAC algorithm is used to filter the wrong matches, achieving precise contour matching. The experimental results show that compared with traditional contour matching methods, the improved contour matching algorithm has higher accuracy and stronger robustness.
Multi-Branch CNN GRU with attention mechanism for human action recognition

Abstract For recognition of human actions, deep neural networks have been widely used in recent years including convolutional neural networks. They have gained very much popularity due to their effectiveness in feature representation over traditional approaches. But at the same time, deep learning networks faced some challenges such as the requirement of a sufficient amount of labelled data which are rarely available and the non-availability of computationally effective resources for deep neural networks. To overcome these challenges, multi-head deep learning architecture by combining Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) with attention mechanism is proposed in this research for recognition of human actions. Three lightweight CNN heads GRU are utilized, and attention mechanisms are introduced in each head for the effective representation of important features and suppression of other undesired features. Three benchmark datasets PAMAP2, UCI-HAR and WISDM are taken for experimentation on proposed model. The performance of this architecture outperformed other models in terms of achieved accuracy, F-1 score and computational efficiency. The proposed approach obtained accuracy of 99.23%, 94.19% and 98.65% on WISDM, UCI-HAR and PAMAP2 datasets.
Memory Errors and Memory Safety: A Look at Java and Rust

We consider memory errors and memory safety in the cases of the Java and Rust programming languages. We also give a view of how type safety fits in.
Understanding the Impact of Low Personalization on Customers’ Prior Negative Experience with Virtual Conversational Agents: A Conceptual Framework

Virtual conversational agents (VCAs) powered by artificial intelligence (AI) have attracted great interest from many research scholars. Its diverse applications are widely acknowledged, and the ability to deliver personalized responses has become a key for increased service quality provided by this AI-powered tool. Building on the perspective of learning from experience theory, this research explores the prior negative experience and presents a conceptual framework to understand its effects on customers’ avoidance behavior with VCAs. Low personalization is expected as the main driver of perceived low informativeness, low credibility, low enjoyment, violation of shared language and information overload, leading to avoidance behavior and switching intention of customers. In addition, time pressure is believed to moderate the links between perceived low informativeness, violation of shared language and information overload as negative prior experiences and switching intention. The findings offer a novel way to understand customer behavior with VCAs under avoidance rather than approach perspective. Important theoretical and managerial implications are provided.
Comparison of QSVM with Other Machine Learning Algorithms on EEG Signals

In the Brain-Computer Interfaces where Electroencephalogram (EEG) signals are used, fast and accurate analysis of data is one of the important steps. For this aim, various machine learning methods are being developed. In this study, we compared the performance of the Quantum Powered Support Vector Machine (QSVM) with classical machine learning algorithms for classifying EEG data. Analyzes were performed in the quantum simulation environment and due to the inadequacies in the quantum environment, the number of samples in the data set was reduced by using Principal Component Analysis. The maximum performance of QSVM was found to be 99.47% in the experiments. When all the results in the analyzes are examined, it is seen that the QSVM has achieved good performance. By removing the limitations of quantum computers and making changes in the hyperparameters of quantum-based algorithms, the use of quantum-based machine learning methods in the classification of EEG data can provide better results in terms of accuracy and runtime.
Exploring the Potential of Quantum-Based Machine Learning: A Comparative Study of QSVM and Classical Machine Learning Algorithms

Research using innovative, rapidly evolving quantum-based systems is becoming increasingly important. The acceleration provided by the real use of a few quantum computers in the world constitutes very important steps for the computer world. Quantum-based systems are also frequently used in data science and machine learning. In this study, Quantum Support Vector Machine (QSVM), a quantum-based machine learning algorithm, is compared with classical machine learning algorithms. IBM - Studies on quantum computer simulation have been carried out. The data dimension is reduced by first applying Principal Component Analysis (PCA) to the dataset. Afterwards, analyzes were made and the results were presented comparatively. The highest performance of the QSVM algorithm was obtained at 92.3%. Compared to classical machine learning algorithms, the performance of the QSVM algorithm is slightly lower. It is thought that higher performance and speed will be achieved in QSVM by eliminating limitations such as limited qubit resources and system restrictions. It is clear that the results found are promising for quantum-based machine learning algorithms.