Reviewerly/exHarmony huggingface dataset:
Upper Bounds for Continuous-Time End-to-End Risks in Stochastic Robot Navigation

We present an analytical method to estimate the continuous-time collision probability of motion plans for autonomous agents with linear controlled Itô dynamics. Motion plans generated by planning algorithms cannot be perfectly executed by autonomous agents in reality due to the inherent uncertainties in the real world. Estimating end-to-end risk is crucial to characterize the safety of trajectories and plan risk optimal trajectories. In this paper, we derive upper bounds for the continuous-time risk in stochastic robot navigation using the properties of Brownian motion as well as Boole and Hunter's inequalities from probability theory. Using a ground robot navigation example, we numerically demonstrate that our method is considerably faster than the naïve Monte Carlo sampling method and the proposed bounds perform better than the discrete-time risk bounds.
A Numerical Measurement Method for Dynamic Granular Materials Based on Computer Vision

Granular materials are widespread in nature and human production, and their macro-mechanical behavior is significantly affected by granule movement. The development of computer vision has brought some new ideas for measuring the numerical information (including the amount of translation, the rotation angle, velocity, acceleration, etc.) of dynamic granular materials. In this paper, we propose a numerical measurement method for dynamic granular materials based on computer vision. Firstly, an improved video instance segmentation (VIS) network is introduced to perform end-to-end multi-task learning, and its temporal feature fusion module and tracking head with long-sequence external memory can improve the problems of poor video data quality and high similarity in appearance of granular materials, respectively. Secondly, the numerical information can be extracted through a series of post-processing steps. Finally, the effectiveness of the measurement method is verified by comparing the numerical measurement results with the real values. The experimental results indicate that our improved VIS obtains an average precision (AP) of 76.6, the relative errors and standard deviations are maintained at a low level, and this method can effectively be used to measure the numerical information of dynamic granular materials. This study provides an intelligent proposal for the task of measuring numerical information of dynamic granular materials, which is of great significance for studying the spatial distribution, motion mode and macro-mechanical behavior of granular materials.
Design Process of Extended Reality Educational Resources in Engineering: A Comparison of Three Cases in Latin American Universities

The Covid-19 pandemic demanded educators to explore new methods to provide their students with practical activities, despite the lockdown measures around the world. The use of Extended Reality (XR) technology, which includes Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), offers special tools to transform the learning experience in engineering education with immersive environments. This study presents a comparison of the process followed to design XR resources for engineering education applications at three different universities in Latin America. The motivation of this work is to share the design experience, as well as the lessons learned and recommendations for educators that are looking into adopting XR technology in their courses. Even though the group of professors worked together along the project, the design of the immersive experience and the XR resource was different for each university according to its specific context and needs. The design process comprises the resource conceptual definition, the technological development, and the final product with academic use. The three design processes were compared to identify lessons learned, best practices, and recommendations for future XR resources in engineering education. XR technologies have a great potential in engineering education closing the gap between the classroom and the real-life practice using the principles of educational innovation.
AIME: Watermarking AI Models by Leveraging Errors

The recent evolution of deep neural networks (DNNs) has made running complex data analytics tasks, which range from natural language processing, object detection to autonomous cars, artificial intelligence (AI) warfare, cloud, healthcare, industrial robots, and edge devices feasible. The benefits of AI are indisputable. However, there are several concerns regarding the security of the deployed AI models, such as reverse engineering and Intellectual Property (IP) piracy. Accumulating a sufficiently large amount of data - building, training, improvement, and model deployment require immense human and computational power, making the process expensive. Therefore, it is of utmost importance to protect the model against IP infringement. We propose AIME, a novel watermarking framework that captures model inaccuracy during the training phase and converts it into the owner-specific unique signature. The watermark is embedded within the class mispredictions of the DNN model. Watermark extraction is performed when the model is queried by an owner-specific sequence of key inputs, and the signature is decoded from the sequence of model predictions. AIME works with negligible watermark embedding runtime overhead while preserving the accurate functionality of the DNN. We have performed a comprehensive evaluation of AIME, which models on MNIST, Fashion-MNIST, and CIFAR-10 dataset and corroborated its effectiveness, robustness, and performance.
Ratio component-wise design method of fuzzy c-means clustering validity function

Fuzzy clustering is an important research field in pattern recognition, machine learning and image processing. The fuzzy C-means (FCM) clustering algorithm is one of the most common fuzzy clustering algorithms. However, it requires a given number of clusters in advance for accurate clustering of data sets, so it is necessary to put forward a better clustering validity index to verify the clustering results. This paper presents a ratio component-wise design method of clustering validity function based on FCM clustering method. By permutation and combination of six clustering validity components representing different meanings in the form of ratio, 49 different clustering validity functions are formed. Then, these functions are verified experimentally under six kinds of UCI data sets, and a clustering validity function with the simplest structure and the best classification effect is selected by comparison. Finally, this function is compared with seven traditional clustering validity functions on eight UCI data sets. The simulation results show that the proposed validity function can better verify the classification results and determine the optimal clustering number of different data sets.
Agile or Traditional Project Organization: A Quantitative Assessment of Decision Criteria among Firms in the DACH Region

The so-called agile approach is increasingly popular in the world of project management as a response to more dynamic and competitive environments.This study follows the question: What are the decisive criteria that result in the use of agile process models in practice?Therefore, a broad range of decision criteria is investigated, representing different reasons for firms to decide in favour or against the usage of agile process models, namely: Timesaving, increased efficiency, availability of qualified personnel, uniform terminology, project comparability and functions as a knowledge base.Most existing research on agile project management is of qualitative nature; this study uses a quantitative approach to assessing 51 firms and nine different industries within the DACH region.The collected data was analysed in a binary logistic regression model.Results reveal that time-saving positively predicts the use of agile process models, while high ratings in function as a knowledge base and project comparability predict the absence of agile process models.Therefore, practitioners are suggested to educate project and portfolio managers in the creation of hybrid environments and integration of agile process models in traditional project portfolios. Implications for Central European audience:The results of this study provide valuable insights into the selection of project management approaches across different industries in the DACH region.
Areal measurement of vibration modes of a hemispherical shell resonator by deflectometry

The hemispherical shell resonator (HSR) is the core and sensitive part of a hemispherical resonator gyro. The geometrical accuracy and vibration properties of HSR determine the navigation performance of the system. A lack of areal measurement methods of vibration modes limits investigation of the kinetic mechanism and improvement in navigation performance. Consequently, an areal measurement method is developed based on deflectometry. The blurry spots on the image plane reflected from the vibrating HSR are extracted, and the blurring trajectories are obtained by the Wiener deconvolution method. The vibrating amplitude distribution of a standing wave mode is transformed into the swing angle distribution of normal vectors. The parameters of the vibration mode are fitted by the Levenberg–Marquardt method. This method can find widespread applications in the areal inspection of vibration modes.
Integrating Augmented Reality in Learning Plants

Abstract Malaysia’s biodiversity forests are producing numerous species of plants. Books, picture walls, posters, and brochures often use to display these plants’ static images to promote these wilderness treasures. Though the use of these static images fits its purpose, they are less attractive in providing information. ARPlant prototype provides an interactive application that enhances rainforest plants’ information displayed using the mobile platform. We use the Mobile Application Development Lifecycle (MADLC) to design and develop ARPlant. We gauge potential users’ requirements and needs by conducting a survey. We then conduct the user testing using the System Usability Scale (SUS) score. The augmented reality in ARPlant allows users to learn by interactively exploring rainforest plants. Users can view the plants in a 3-dimensional environment.
A Secure and Efficient Framework for Multi-Round Data Trading Over the Internet of Artificially Intelligent Things

IoT data trading has greatly benefited the popularization of both the Internet of Things (IoT) and Artificial Intelligence of Things (AIoT). Current solutions mainly treat the dataset owned by each device as a commodity and focus on maintaining a data market. Meanwhile, inspired by recent achievements in distributed machine learning and federated learning, AIoT can actually realize its services by retaining the data locally on devices and only exchanging model parameters. However, existing IoT data trading methods fail to meet this secure and efficient process. Therefore, this article proposes a novel framework for data trading over AIoT. It allows the service provider to trade on deep learning model training instead of purchasing the full datasets. Key factors for the security issues and data pricing are addressed for the framework, including the design of the learning parameters and the comprehensive privacy concerns. Then a novel data trading strategy is proposed for the service provider to jointly maintain the model performance and the budget consumption. Evaluation results reveal the improvements on the model performance. Finally, some open problems in the design of data trading in AIoTs are discussed.
Introduction to a special issue on organic- and microbe-metal interactions in mineral systems

Graphical abstract caption Schematic diagrams summarizing key observations from papers of this special issue, which can be grouped into three main themes. Theme one : microbes in mineral exploration (1: the fungi and associated metals ( Bohu et al., 2022 ); 2: the gases generated by microbes during experimental weathering of ores ( Plet et al., 2021 )). Theme two : organic matter in ore deposits (1: Cu-sulfide and pyrobitumen in Jurassic stratabound Cu-(Ag) deposits in Chile ( Herazo et al., 2020 ); 2: gold precipitation triggered by oil droplets in Witwatersrand goldfields ( Fuchs et al., 2021 ); 3: U-bearing pyrite hosted by organic matter ( Cumberland et al., 2021 ); 4: hydrocarbons migrated from the host rock into the mineralised zone in the McArthur River Zn-Pb-Ag deposit ( Vinnichenko et al., 2021 ); 5: pyrite with metal-rich rim and metal-poor core in the unmineralized sedimentary rocks within the McArthur River Zn-Pb distal district ( Li et al., 2022 )); Theme three : microbes in bioremediation (1: Cu- and Zn-rich biofilms in sulfidic water ( Henne et al., 2021 ); 2: enhanced ferrugination of biocrusts ( Paz et al., 2021 )).
The Design of an Intelligent Chatbot with Natural Language Processing Capabilities to Support Learners

Abstract It would be ideal for the teacher to give individual attention to each student. However, this is infeasible as teachers only have limited time and they will be tired after long periods of consultation. Chatbots can be an effective tool to overcome this problem. A pilot was conducted on 11 Feb 2019 in which 27 students tried a chatbot prototype built by the author. The students compared the chatbot and a quiz website. The academic contents provided by the chatbot are exactly the same as the website. Over 80% of the students agreed that the chatbot is better than the quiz website in many aspects. However, about half of the students also indicated that the chatbot took them more time to complete the quiz than the quiz website. The author proposed using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to conduct more research into this area. The knowledge would allow more teachers to create chatbots in more disciplines of study so that students are more motivated to achieve the learning outcomes better.
Strategies for the Production of Molecular Animations

Molecular animations play an increasing role in scientific visualisation and science communication. They engage viewers through non-fictional, documentary type storytelling and aim at advancing the audience. Every scene of a molecular animation is to be designed to secure clarity. To achieve this, knowledge on design principles from various design fields is essential. The relevant principles help to draw attention, guide the eye, establish relationships, convey dynamics and/or trigger a reaction. The tools of general graphic design are used to compose a signature frame, those of cinematic storytelling and user interface design to choreograph the relative movement of characters and cameras. Clarity in a scientific visualisation is reached by simplification and abstraction where the choice of the adequate representation is of great importance. A large set of illustration styles is available to chose the appropriate detail level but they are constrained by the availability of experimental data. For a high-quality molecular animation, data from different sources can be integrated, even filling the structural gaps to show a complete picture of the native biological situation. For maintaining scientific authenticity it is good practice to mark use of artistic licence which ensures transparency and accountability. The design of motion requires knowledge from molecule kinetics and kinematics. With biological macromolecules, four types of motion are most relevant: thermal motion, small and large conformational changes and Brownian motion. The principles of dynamic realism should be respected as well as the circumstances given in the crowded cellular environment. Ultimately, consistent complexity is proposed as overarching principle for the production of molecular animations and should be achieved between communication objective and abstraction/simplification, audience expertise and scientific complexity, experiment and representation, characters and environment as well as structure and motion representation.
Probabilistic Node Selection for Federated Learning with Heterogeneous Data in Mobile Edge

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between global and local objectives, making the FL model slow to converge. In this paper, we proposed the Optimal Aggregation algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a Probabilistic Node Selection framework (FedPNS) to dynamically change the probability for each node to be selected based on the output of Optimal Aggregation. FedPNS can preferentially select nodes that propel faster model convergence. Experimental results demonstrate the effectiveness of FedPNS in accelerating the FL convergence rate, as compared to FedAvg with random node selection.
Cross-lingual transfer learning: A PARAFAC2 approach

The proposed framework addresses the problem of cross-lingual transfer learning resorting to Parallel Factor Analysis 2 (PARAFAC2). To avoid the need for multilingual parallel corpora, a pairwise setting is adopted where a PARAFAC2 model is fitted to documents written in English (source language) and a different target language. Firstly, an unsupervised PARAFAC2 model is fitted to parallel unlabelled corpora pairs to learn the latent relationship between the source and target language. The fitted model is used to create embeddings for a text classification task (document classification or authorship attribution). Subsequently, a logistic regression classifier is fitted to the training source language embeddings and tested on the training target language embeddings. Following the zero-shot setting, no labels are exploited for the target language documents. The proposed framework incorporates a self-learning process by utilizing the predicted labels as pseudo-labels to train a new, pseudo-supervised PARAFAC2 model, which aims to extract latent class-specific information while fusing language-specific information. Thorough evaluation is conducted on cross-lingual document classification and cross-lingual authorship attribution. Remarkably, the proposed framework achieves competitive results when compared to deep learning methods in cross-lingual transfer learning tasks.
Application Research for Fusion Model of Pseudolabel and Cross Network

Datasets usually suffer from supervised information missing and weak generalization ability in deep convolution neural network. In this paper, pseudolabel (PL) of Weakly Supervised Learning (WSL) was used to address the problem of supervised information missing, while Cross Network (CN) of Multitask Learning (MTL) was used to solve the problem of weak generalization ability in deep convolution neural network. In PL, the data of supervised information missing was predicted; thus, PL of the corresponding data was generated. In CN, PL data and labeled data were taken as two tasks to train together. Firstly, the labeled data was divided into training dataset and testing dataset, respectively, and image preprocessing was carried out. Secondly, the network was initialized and trained, and the model with high accuracy and good generalization was selected as the optimal model. Then, the optimal model was used to predict the unlabeled data and generate PL. Finally, the steps above were repeated several times to find a better optimal model. In the experiments of the fusion model of PL and CN, Facial Beauty Prediction was regarded as main task and the others as auxiliary tasks. Experimental results show that the model was suitable for multitask training of different tasks in different or similar datasets, and the accuracy of the main task of Facial Beauty Prediction reaches 64.76%, higher than the highest accuracy by conventional methods.
Interpretable Embedding and Visualization of Compressed Data

Traditional embedding methodologies, also known as dimensionality reduction techniques, assume the availability of exact pairwise distances between the high-dimensional objects that will be embedded in a lower dimensionality. In this article, we propose an embedding that overcomes this limitation and can operate on pairwise distances that are represented as a range of lower and upper bounds. Such bounds are typically estimated when objects are compressed in a lossy manner, so our approach is highly applicable in the case of big compressed datasets. Our methodology can preserve multiple aspects of the original data relationships: distances, correlations, and object scores/ranks, whereas existing techniques typically preserve only distances. Comparative experiments with prevalent embedding methodologies (ISOMAP, t-SNE, MDS, UMAP) illustrate that our approach can provide fidelitous preservation of multiple object relationships, even in the presence of inexact distance information. Our visualization method is also easily interpretable.
Reactivity and statefulness: Action-based sensors, plans, and necessary state

Typically to a roboticist, a plan is the outcome of other work, a synthesized object that realizes ends defined by some problem; plans qua plans are seldom treated as first-class objects of study. Plans designate functionality: a plan can be viewed as defining a robot’s behavior throughout its execution. This informs and reveals many other aspects of the robot’s design, including: necessary sensors and action choices, history, state, task structure, and how to define progress. Interrogating sets of plans helps in comprehending the ways in which differing executions influence the interrelationships between these various aspects. Revisiting Erdmann’s theory of action-based sensors, a classical approach for characterizing fundamental information requirements, we show how plans (in their role of designating behavior) influence sensing requirements. Using an algorithm for enumerating plans, we examine how some plans for which no action-based sensor exists can be transformed into sets of sensors through the identification and handling of features that preclude the existence of action-based sensors. We are not aware of those obstructing features having been previously identified. Action-based sensors may be treated as standalone reactive plans; we relate them to the set of all possible plans through a lattice structure. This lattice reveals a boundary between plans with action-based sensors and those without. Some plans, specifically those that are not reactive plans and require some notion of internal state, can never have associated action-based sensors. Even so, action-based sensors can serve as a framework to explore and interpret how such plans make use of state.
Single image haze removal using CNN based encoder-decoder architecture

Haze is a natural phenomenon, which severely obscure the visibility of the distant objects makes it difficult for both the autonomous and human driving vehicles to take appropriate decision and may cause an accident. An efficient and robust solution in removing haze will help in reducing accidents. Recently, many prior based dehazing algorithms were proposed and doing fairly good but computationally intensive. In this contribution, we have proposed a computationally efficient encoder-decoder model based on up sampling and down sampling was used in convolutional neural network (CNN). The model was trained to update weight matrix using different datasets such as RESIDE and FRIDA for getting model to experience wide variety of data. Maxpooling and dropout layers were used to get advantage in both computation and for better generalization on new data. Objective analysis has shown that the proposed architecture doing relatively better in terms of SSIM & PSNR compared with the recent methods.