Reviewerly/exHarmony huggingface dataset:
Virtualised virtual field trips in environmental engineering higher education

Field trips are an established learning activity also in higher engineering education requiring considerable time efforts and high costs. Thus, we describe how 360° technology is used to create virtual field trips. Additionally, video conferencing software enables the virtual field trip participants to be at different locations. We describe a virtualised virtual field trip and its evaluation results in two undergraduate courses (N = 41). The measures used include pre- and post-test and a survey using standardised instruments on motivation, emotion, and usability. The results are to be seen as conducive to learning, although they are significantly lower in comparison to the corresponding values of a field trip. Overall, the results demonstrate the potential of virtualised virtual field trips using 360°VR rooms, which can not only be used in online education, but also in presence teaching in disciplines that use field trips as learning activities, such as environmental engineering.
Lecture Notes on Quantum Electrical Circuits

During the last 30 years, stimulated by the quest to build superconducting quantum processors, a theory of quantum electrical circuits has emerged and this theory goes under the name of circuit quantum electrodynamics or circuit-QED. The goal of the theory is to provide a quantum description of the most relevant degrees of freedom. The central objects to be derived and studied are the Lagrangian and the Hamiltonian governing these degrees of freedom. Central concepts in classical network theory such as impedance and scattering matrices can be used to obtain the Hamiltonian and Lagrangian description for the lossless (linear) part of the circuits. Methods of analysis, both classical and quantum, can also be developed for nonreciprocal circuits. These lecture notes aim at giving a pedagogical overview of this subject for theoretically-oriented Master or PhD students in physics and electrical engineering, as well as Master and PhD students who work on experimental superconducting quantum devices and wish to learn more theory.
ON THE ACCURACY OF YOLOV8-CNN REGARDING DETECTION OF HUMANS IN NADIR AERIAL IMAGES FOR SEARCH AND RESCUE APPLICATIONS

Abstract. The use of deep learning techniques especially in conjunction with convolutional neural networks (CNN) has attracted major attention of the remote sensing community. Main use cases are object detection, image classification and image segmentation. The paper will focus on object detection, specifically on detection of humans. In search and rescue applications it is common to map larger areas with downward facing cameras. However, there are many training data sets for CNNs showing oblique images which strongly differ from nadir aerial images used for real-time maps.To circumnavigate this issue, an unique data set was created. It solely contains nadir images at different ground sample distances (GSD) varying from one to five centimetres. Diversity of the training data is ensured through various flights using an unmanned aerial vehicle (UAV) at different locations. GSD dependency is valuable prior knowledge as it enhances the difficulty associated with human detection in aerial images. An image, depicting a human at one centimetre GSD contains much more information than the same human depicted in an image of three centimetres. That is one reason why networks trained on a variety of ground sample distances possibly struggle to detect humans reliably on a certain GSD. The unique data set consists of four subsets (divided by GSD). Each subset contains 1000 manually annotated humans, augmented by rotation and colour shift resulting in 12000 training samples used to train the new released YoloV8 CNN. The entire training and test process is unified to ensure comparable input conditions.
EDDAC: An Efficient and Decentralized Data Access Control Scheme With Attribute Privacy Preservation

The rapid development of the Internet of Things (IoT) has led to the generation and perception of large amounts of data from IoT devices. These data are outsourced to the cloud for flexible sharing and deep analytics, which can significantly enhance IoT applications. But this raises privacy concerns for IoT devices. Attribute-based encryption is applied to realize fine-grained access control, which offers data owners control capability over their outsourced data. However, the centralized infrastructure is susceptible to the single-point-of-failure problem and may not be suitable for highly distributed IoT applications due to high latency. To overcome this issue, blockchain technology is introduced to realize a distributed infrastructure and provide robust data services. Owing to the innate transparency characteristic of blockchain, challenges associated with privacy are amplified. Therefore, this paper presents an efficient and decentralized data access control scheme (EDDAC) that preserves attribute privacy. The scheme utilizes chameleon hash to implement attribute hiding, providing resistance against dictionary attacks. Additionally, it includes blockchain-based decryption tests that reduce the decryption overhead on clients through the application of inner-product predicate encryption. Furthermore, our scheme employs Shamir secret sharing to achieve decentralized authorization based on blockchain, thereby reducing the trust-building overhead on authorization nodes. Finally, we provide proof of the adaptive security of the proposed scheme and demonstrate its effectiveness and advantages through simulations and comparisons with existing literature.
Sketch2Stress: Sketching With Structural Stress Awareness

In the process of product design and digital fabrication, the structural analysis of a designed prototype is a fundamental and essential step. However, such a step is usually invisible or inaccessible to designers at the early sketching phase. This limits the user's ability to consider a shape's physical properties and structural soundness. To bridge this gap, we introduce a novel approach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sketch2Stress</i> that allows users to perform structural analysis of desired objects at the sketching stage. This method takes as input a 2D freehand sketch and one or multiple locations of user-assigned external forces. With the specially-designed two-branch generative-adversarial framework, it automatically predicts a normal map and a corresponding structural stress map distributed over the user-sketched underlying object. In this way, our method empowers designers to easily examine the stress sustained everywhere and identify potential problematic regions of their sketched object. Furthermore, combined with the predicted normal map, users are able to conduct a region- wise structural analysis efficiently by aggregating the stress effects of multiple forces in the same direction. Finally, we demonstrate the effectiveness and practicality of our system with extensive experiments and user studies.
Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference

Surgical context inference has recently garnered significant attention in robot-assisted surgery as it can facilitate workflow analysis, skill assessment, and error detection. However, runtime context inference is challenging since it requires timely and accurate detection of the interactions among the tools and objects in the surgical scene based on the segmentation of video data. On the other hand, existing state-of-the-art video segmentation methods are often biased against infrequent classes and fail to provide temporal consistency for segmented masks. This can negatively impact the context inference and accurate detection of critical states. In this study, we propose a solution to these challenges using a Space-Time Correspondence Network (STCN). STCN is a memory network that performs binary segmentation and minimizes the effects of class imbalance. The use of a memory bank in STCN allows for the utilization of past image and segmentation information, thereby ensuring consistency of the masks. Our experiments using the publicly-available JIGSAWS dataset demonstrate that STCN achieves superior segmentation performance for objects that are difficult to segment, such as needle and thread, and improves context inference compared to the state-of-the-art. We also demonstrate that segmentation and context inference can be performed at runtime without compromising performance.
Decentralised Multi-Robot Exploration Using Monte Carlo Tree Search

Autonomous robotic systems are useful in automating tasks such as inspection and surveying of unknown areas, where speed is often an important factor. In order to effectively reduce the time required to complete missions, an efficient exploration and coordination strategy is needed. In this spirit, this work proposes an approach based on the Monte Carlo Tree Search (MCTS) algorithm to guide robots during exploration missions. Our method first expands a search tree of possible actions from the robot's position towards unknown regions, and then selects the sequence of movements that best drive the exploration process forward with respect to a given reward function. The proposed approach, which is able to balance short- and long-term decision-making, is then extended to accommodate the presence of multiple robots, in a bid to push the efficiency of exploration further. Our method allows for the coordination of the robots' movements in a decentralized manner, relying on point-to-point communication. This results in an efficient strategy, which we refer to as Decentralized Monte Carlo Exploration (DMCE). The experimental results demonstrate that our pipeline outperforms a greedy exploration approach, as well as state-of-the-art planners, with up to 30% reduction in exploration times in a series of real-world maps.
F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving

Bird's Eye View (BEV) representations are tremendously useful for perception-related automated driving tasks. However, generating BEVs from surround-view fisheye camera images is challenging due to the strong distortions introduced by such wide-angle lenses. We take the first step in addressing this challenge and introduce a baseline, F2BEV, to generate discretized BEV height maps and BEV semantic segmentation maps from fisheye images. F2BEV consists of a distortion-aware spatial cross attention module for querying and consolidating spatial information from fisheye image features in a transformer-style architecture followed by a task-specific head. We evaluate single-task and multi-task variants of F2BEV on our synthetic FB-SSEM dataset, all of which generate better BEV height and segmentation maps (in terms of the IoU) than a state-of-the-art BEV generation method operating on undistorted fisheye images. We also demonstrate discretized height map generation from real-world fisheye images using F2BEV. Our dataset is publicly available at https://github.com/volvo-cars/FB-SSEM-dataset
Exploiting Spatio-Temporal Human-Object Relations Using Graph Neural Networks for Human Action Recognition and 3D Motion Forecasting

Human action recognition and motion forecasting is becoming increasingly successful, in particular with utilizing graphs. We aim to transfer this success into the context of industrial Human-Robot Collaboration (HRC), where humans work closely with robots and interact with workpieces in defined workspaces. For this purpose, it is necessary to use all the available information extractable in such a workspace and represent it with a natural structure, such as graphs, that can be used for learning. Since humans are the center of HRC, it is mandatory to construct the graph in a human-centered way and use real-world 3D information as well as object labels to represent their environment. Therefore, we present a novel Graph Neural Network (GNN) architecture which combines, human action recognition and motion forecasting for industrial HRC environments. We evaluate our method with two different and publicly available human action datasets, including one that is a particularly realistic representation of the industrial HRC, and compare the results with baseline methods for classifying the current human action and predicting the human motion. Our experiments show that our combined GNN approach improves the accuracy of action recognition compared to previous work, and significantly on the CoAx dataset by up to 20%. Further, our motion forecasting approach performs better than existing baselines, predicting human trajectories with a Final Displacement Error (FDE) of less than 10cm for a prediction horizon of 1s.
Algorithms for Selecting the Most Efficient Method for Solving Classification Problems

This research paper provides an exhaustive analysis of classification algorithms, a central concern in the field of intellectual data analytics. The paper aims to serve as a comprehensive resource for both academic researchers and industry practitioners by offering insights into the selection of the most appropriate classification algorithm tailored to specific analytical and computational needs. The study categorically divides the algorithms into four primary approaches: Probability-Based Methods, Decision Tree Methodologies, Nearest Neighbors Approaches, and Mathematical Function Techniques. Each category is further dissected to include a variety of algorithms, such as Naive Bayes, 1R, ID3, CART, KNN, and Support Vector Machines, among others, along with their numerous adaptations like selective Naive Bayes, GSVM, and TWSVMs.The paper not only lists these algorithms but also delves into their practical applications. For instance, Bayesian classifiers are commonly used in text classification tasks like spam filtering, while decision tree algorithms like CART and CHAID find frequent applications in healthcare for medical diagnosis. Nearest neighbor algorithms, particularly advanced versions like WKPDS, are effective in high-dimensional tasks such as image recognition. Mathematical function-based techniques like SVM and its adaptations are applied in complex scenarios ranging from financial forecasting to natural language processing.Moreover, the paper addresses the critical issue of model reliability by discussing the limitations of conventional data partitioning into training and test sets. It advocates for the use of the K-Fold Cross-Validation method as a more reliable alternative for assessing model performance. This method is particularly useful in scenarios where the test objects are closely related, or there is a lack of diverse learning instances, which could otherwise lead to significant classification errors and ambiguous outcomes.In summary, this paper offers a detailed overview of the landscape of classification algorithms, emphasizing their strengths, limitations, and areas of application. It also provides methodological recommendations for model testing, thereby serving as an invaluable guide for selecting the most effective classification algorithm based on both accuracy and computational efficiency.
KD-EKF: A Consistent Cooperative Localization Estimator Based on Kalman Decomposition

In this paper, we revisit the inconsistency problem of EKF-based cooperative localization (CL) from the perspective of system decomposition. By transforming the linearized system used by the standard EKF into its Kalman observable canonical form, the observable and unobservable components of the system are separated. Consequently, the factors causing the dimension reduction of the unobservable subspace are explicitly isolated in the state propagation and measurement Jacobians of the Kalman observable canonical form. Motivated by these insights, we propose a new CL algorithm called KD-EKF which aims to enhance consistency. The key idea behind the KD-EKF algorithm involves perform state estimation in the transformed coordinates so as to eliminate the influencing factors of observability in the Kalman observable canonical form. As a result, the KD-EKF algorithm ensures correct observability properties and consistency. We extensively verify the effectiveness of the KD-EKF algorithm through both Monte Carlo simulations and real-world experiments. The results demonstrate that the KD-EKF outperforms state-of-the-art algorithms in terms of accuracy and consistency.
Simultaneous Survey and Inspection with Autonomous Underwater Vehicles

As the future of autonomous underwater vehicle (AUV) deployments tends to multi-vehicle systems, new approaches in coordination and control are needed. In this work, we consider the problem of simultaneous survey and inspection where one vehicle dynamically discovers objects while another vehicle must inspect as many of the objects as possible over the course of the mission. This requires a fully autonomous inspection vehicle, and to this end, we present a planning approach which couples sampling-based motion planning with timed roadmap constraints as well as a real-time execution framework. The methods presented address the underlying challenges that arise during simultaneous survey and inspection using AUVs, namely those of communication constraints, safety of navigation constraints, and dynamically discovered tasks. Additionally, we present field results for the simultaneous survey and inspection mission using teamed AUVs.
Fuzzy linguistic forecasting of the results of the implementation of regional socio-economic projects

The article is devoted to the problem of improving the intellectual digital support for managing the implementation of regional socio-economic projects, which are an effective mechanism for the development of regions. Given the high level of uncertainty in the description of the subject area, it is proposed to use expert technologies to assess the degree of achievement of the intermediate and target indicators of the project. L. Zadeh's linguistic modeling apparatus was used as the basis for intellectual tools for representing and processing expert judgments. A linguistic indicative-temporal-territorial model of the project has been built in the form of a set of linguistic variables and a system of fuzzy production rules linking these variables. At the same time, temporal and territorial linguistic variables reflect the time and place of measurement of project indicators, the incomplete correspondence of which determines the degree of reliability of the measured values of indicators. The use of the constructed system of indicative-temporal-territorial fuzzy production rules makes it possible to link the intermediate values of the indicators with the target values of the project indicators and thereby predict the possible outcomes of its implementation. For this, a fuzzy logic inference procedure based on the use of the Mamdani algorithm has been developed. Preliminary results of using a research prototype of an intelligent expert system for analyzing the intermediate results of the project stages and predicting its results may indicate the effectiveness of the proposed approach.
The Efficacy of Intelligent Evaluation and Peer Review in Improving the English Writing Ability of Vocational College Students

The application of artificial intelligence has benefited vocational English writing courses but still lacks the capacity to dispose of contextual challenges. This study compared the effectiveness of two teaching methods in a vocational English writing course. A teaching method that combines intelligent evaluation and peer review was applied for the experimental group, while the control group adopted intelligent evaluation and conventional discussion. In this study, a quasi-experimental research method was used. Scores of students’ essays as well as the quality of the content were examined. The results exhibited that the experimental group showed greater improvement than the control group. Thus, the designed teaching method provides a valuable method for English writing courses in vocational education.
Analysis of the Benefits of SAP S/4 HANA Cloud IT/Is Investment in the Case of PT XYZ

PT XYZ is one of the largest telecommunication companies in Indonesia. In 2020, PT XYZ implemented a digital transformation to increase its excellence by investing in industry telecommunication and information systems (IT/IS) enterprise resource planning software called SAP (System Application and Processing) S/4 HANA Cloud. The study aims to analyze the benefits of these IT investments made in PT XYZ. The results are carried out using the Comparative Analysis of Financial Statements followed by Business Value and Financial Feasibility Analysis and the Cost Benefit Analysis (CBA) method between 2018 and 2021. It can be revealed that the SAP S/4 Hana Cloud investment benefits the company's operations, which are identified in the form of 5 sub-categories that fall into required categories based on Ranti's Generic IT/IS Business Value.
Penerapan Sistem Informasi Akutansi dan Kinerja Pegawai Pemerintah Agency Pembangunan Nasional di Kota Dili

Senior employees find it difficult to understand the basics of accounting in a system that is supposed to make employees' work easier so they don't work manually. The aim of this research is to determine the influence of the use of accounting information systems, understanding of accounting and the role of information technology on the performance of employees of the Dili National Development Agency as a sample of 100 observers. The research uses quantitative techniques with multiple linear regression analysis. The results of the research show that there is a positive influence on the use of accounting information systems, the role of information technology, the role of information technology on employee performance at the National Development Agency in Dili City.&#x0D; Keywords: Accounting Information Systems, Understanding of Accounting; Role of Information Technology; Performance
Transforming Sign Language into Text and Speech through Deep Learning Technologies

Objective: The goal of the proposed work is to leverage deep learning technologies to create an efficient and accurate system for transforming sign language into text and speech. People deliver their ideas, feelings, and experiences to others around them via their interactions with each other. The hand gesture plays a significant role since it reflects the user\'s thoughts more rapidly than other motions (head, face, eye, and body). For deaf-mute people with disabilities, this is still not the case. Sign language facilitates communication among deaf-mute individuals. An individual who is deaf-mute can communicate without the use of acoustic noises by using sign language. Methods: Convolutional neural networks (CNNs) are generally used to recognize and extract characteristics from sign language motions. These neural networks are employed to recognize and extract critical features from sign language gestures. These features are processed by natural language processing models for textual translation. Finally, neural text-to-speech (TTS) technology is used to translate the textual translations into synthesized speech, thereby bridging the communication gap for the Deaf community. To establish an inclusive and accessible communication system, this technique combines computer vision, natural language processing, and speech synthesis. Findings: The datasets used in this technique include hand gesture images, which contain different hand poses and expressions. It is used to train and assess the model. The experiment findings show an accuracy of 97.6% with a precision of 94.1%, a recall of 96.8%, and an F1-score of 95.9%. Novelty: This approach displays a cogent translation from text to speech and achieves an outstanding translation accuracy of 97.6% from sign language to text, producing a natural and understandable output. Keywords: Sign Language Translation, Deep Learning, Convolutional Neural Networks, Sequence­To­Sequence Models, Attention Mechanisms, Neural Text­To­Speech