Reviewerly/exHarmony huggingface dataset:
Structure-borne and Air-borne Sound Data for Condition Monitoring Applications

This paper provides a new machine learning dataset that contains labeled structure-borne and air-borne sound data for eight different operating conditions of a condition monitoring demonstrator. Our dataset is used to train and evaluate multiple classifiers in order to establish a baseline accuracy for classifiers on this dataset. It can be shown that both structure-borne and airborne sound data provide relevant information to train performant condition monitoring classifiers, which can be further increased by using a combination of both sound modalities.
VeloCity: Using Voice Assistants for Cyclists to Provide Traffic Reports

Cycling is on the rise as a relevant alternative to car-based mobility and even though there are mobile applications specifically designed for cyclists to support this development, many still face unresolved challenges in terms of safe user interaction with complex data while riding. We present the design, development, and evaluation of VeloCity - an application for reporting traffic incidents and structures relevant to cyclists. In a case study, we compared its' three input methods (touch, in-app speech recognition, the voice assistant of the operating system) to evaluate which attributes make for safe interaction while cycling. We found that participants prefer to use the voice assistant over the other modalities as it was least distracting due to its hands- and eyes-free interaction design. Furthermore, they chose short commands over conversational phrases. Based on our results, we present five guidelines for designing voice user interfaces for cyclists and argue for moving away from touch-based interfaces in this domain, which still make up most of the applied interaction techniques today.
An Explorative Dive into Decision Rights and Governance of Blockchain: A Literature Review and Empirical Study

Abstract Background: Blockchain technology and accompanying programmed protocols (smart contracts) offer disruptive opportunities for businesses, public institutions, society, and its citizens. However, blockchain is a relatively young research area: the number of publications available regarding blockchain did not begin to rise significantly until 2012, and certain fields of the blockchain domain remain to be explored. A similar situation exists with research into the governance of blockchain solutions focusing on decision rights: the limited number of theoretical and empirical contributions hinders the proper adoption of governance mechanisms in practice. Method: A mixed-method approach was conducted in which 1) a structured literature review, 2) semi-structured interviews, and 3) a focus group discussion were utilized to determine the current situation regarding decision rights in the context of blockchain governance. Results: The structured literature review resulted in a total of 23 relevant contributions. Those contributions were consolidated to serve as input for a total of twelve semi-structured interviews, and for a focus group session with five participants, who were not part of the interviewee pool. Using that approach, an overview of the concepts, relationships and mechanisms pertinent to decision rights was composed. Conclusions: Considered together, the results show that decision rights are often overlooked at the start of a blockchain project, where technical considerations are dominant in the discussion with stakeholders. However, research also points out that the longer it takes to address decision rights in a blockchain consortium, the more complex and costly it becomes to introduce governance mechanisms at a later stage. Another important conclusion is that consensus is currently lacking as to what constitutes blockchain governance and what part decision rights play in governance processes, in both theoretical and practical terms.
A Survey of Ontologies and Their Applications in e-Learning Environments

Ontology technology has been investigated in a wide range of areas and is currently being utilized in many fields. In the e-learning context, many studies have used ontology to address problems such as the interoperability in learning objects, modeling and enriching learning resources, and personalizing educational content recommendations. We systematically reviewed research on ontology for e-learning from 2008 to 2020. The review was guided by 3 research questions: “How is ontology used for knowledge modeling in the context of e-learning?”, “What are the design principles, building methods, scale, level of semantic richness, and evaluation of current educational ontologies?”, and “What are the various ontology-based applications for e-learning?” We classified current educational ontologies into 6 types and analyzed them by 5 measures: design methodology, building routine, scale of ontology, level of semantic richness, and ontology evaluation. Furthermore, we reviewed 4 types of ontology-based e-learning applications and systems. The observations obtained from this survey can benefit researchers in this area and help to guide future research.
Dataset Augmentation with Generated Novelties

As machine learning models take over an increasingly larger number of domains in our lives, their accuracy, fairness, transparency and adaptability become of greater importance. In the everchanging environments, the resulting ability of the models to perform accurately depends on whether they are able to handle novel, unpredicted and unforeseen instances, examples and classes or any other novel changes in the world of model operation, such as environmental, contextual, distributional changes. The proper handling of novelties sustains the model’s usefulness and adeptness in the long run. The efficiency of response to the encounter of novelties depends on the efforts that were invested at the model training, design and data collection stages. In this work, we propose a variety of approaches and methods which can be incorporated into the novelty generation techniques at the earliest stages of creating the machine learning dataset and the model to assure its robustness and reduce the bias. We revisit distinctions between novelties and anomalies to define a formal novelty generation framework that is domain-agnostic and budget efficient. Then we propose a video-specific use case and evaluate the result of the chosen methods on the video dataset. Our methods aim at making the machine learning solutions adaptable, responsible and show improvement in the accuracy and ability of the models to detect novelties.
Edit Like A Designer

Fashion editing has drawn increasing research interest with its extensive application prospect. Instead of directly manipulating the real fashion item image, it is more intuitive for designers to modify it via the design draft. In this paper, we model design workflows for a novel task of unaligned fashion editing, allowing the user to edit a fashion item through manipulating its corresponding design draft. The challenge lies in the large misalignment between the real fashion item and the design draft, which could severely degrade the quality of editing results. To address this issue, we propose an Unaligned Fashion Editing Network (UFE-Net). A coarsely rendered fashion item is firstly generated from the edited design draft via a translation module. With this as guidance, we align and manipulate the original unedited fashion item via a novel alignment-driven fashion editing module, and then optimize the details and shape via a reference-guided refinement module. Furthermore, a joint training strategy is introduced to exploit the synergy between the alignment and editing tasks. Our UFE-Net enables the edited fashion item to have semantically consistent geometric shape and realistic details to the edited draft in the edited region, as well as to keep the unedited region intact. Experiments demonstrate our superiority over the competing methods on unaligned fashion editing.
Skeleton-Based Sign Language Recognition with Attention-Enhanced Graph Convolutional Networks

The natural language processing of sign language is an important task in the field of artificial intelligence and information processing. In this paper, we propose an attention-enhanced graph convolutional networks (AEGCNs) for sign language recognition (SLR). First, there are four kinds of adaptive graphs for graph convolution and each graph topology can be either uniformly or individually learned based on the skeleton data in an end-to-end manner. In addition, we employ the spatial-temporal-channel attention mechanisms to give higher weight to the relative important joints, frames and features, and the higher-order connection with Chebychev polynomial approximation to enlarge the receptive field of graph convolution. Meanwhile, the information of both the joints and bones is simultaneously modeled in a framework, which further improves the representation of the movement about hand and finger. Finally, experiments on the DEVISIGN-D, DSL50 and ASL20 datasets show that the accuracies for top1 of three datasets reach 82.96%, 95.09% and 90.23% respectively and the accuracies for top5 of three datasets achieve 96.07%, 99.18% and 100% respectively. Compared with ST-GCN and BHOF, the accuracy of AEGCNs obtains significant improvements of +33.5% and +5.32% on ASL20 datasets, respectively, which demonstrates the effectiveness of our method on SLR.
Emotion Classification with Explicit and Implicit Syntactic Information

Emotion classification has become a hot research topic in natural language processing due to its wide application. Existing studies suffer from the error propagation problem when using the syntax information in emotion classification since the parser can not produce perfect syntax trees. To address this problem, we propose a new approach by comparing and combining different levels of syntactic information to make full use of syntactic information and alleviate the error propagation. First, we propose to use graph convolutional networks (GCN) to encode dependency trees, in which the probability matrix of all dependency arcs (edge-weighted graph) is treated as the GCN adjacent matrix. Next, we extract the dependency parser encoder hidden representations as the implicit syntactic representations, which can directly avoid the error propagation problem. Finally, we fuse the two different syntax-aware information and inject them into our baseline model as extra inputs. Further experimental results show that the explicit and implicit syntactic information can improve the performance of a BERT-based system which is much stronger than the baseline. In addition, we find that the syntactic knowledge that BERT can express is limited, and the syntactic information of our model brings more contributions, which makes our model consistently outperform the BERT on different sentence lengths.
AggNet for Self-supervised Monocular Depth Estimation: Go An Aggressive Step Furthe

Without appealing to exhaustive labeled data, self-supervised monocular depth estimation (MDE) plays a fundamental role in computer vision. Previous methods usually adopt a one-stage MDE network, which is insufficient to achieve high performance. In this paper, we dig deep into this task to propose an aggressive framework termed AggNet. The framework is based on a training-only progressive two-stage module to perform pseudo counter-surveillance as well as a simple yet effective dual-warp loss function between image pairs. In particular, we first propose a residual module, which follows the MDE network to learn a refined depth. The residual module takes both the initial depth generated from MDE and the initial color image as input to generate refined depth with residual depth learning. Then, the refined depth is leveraged to supervise the initial depth simultaneously during the training period. For inference, only the MDE network is retained to regress depth from a single image, which gains better performance without introducing extra computation. In addition to self-distillation loss, a simple yet effective dual-warp consistency loss is introduced to encourage the MDE network to keep depth consistency between stereo image pairs. Extensive experiments show that our AggNet achieves state-of-the-art performance on the KITTI and Make3D datasets.
Application and Teaching Exploration of Virtual Reality Technology in Art Appreciation

The combination of art class with VR (virtual reality) technology can realize the improvement of students' maximum subjective learning ability, and stimulate students' internal motivation to feel the pleasure and satisfaction of learning activities. At the same time, it can overcome the limitations of the teaching site, bring technical support for situational learning, visualize abstract problems in art teaching, and make theoretical thinking reliable. VR technology enriches art teaching content by enhancing the visualization and visualization of art works, and its authentic visual effect enables students to understand the three-dimensional dynamics and situational intention of sensory art works. It is not only a tool, but also an important carrier to inspire students to learn art initiative and explore art initiative. The application of VR technology in art teaching can avoid the shortcomings of traditional teaching, improve the quality and effectiveness of art education, and promote the development of education in the process of improving the effect and essence, which provides a new form for modern art teaching and realizes its qualitative leap.
Laparoscopic image enhancement based on distributed retinex optimization with refined information fusion

With intrinsic non-uniform illumination variations in laparoscopic images, such images often suffer from insufficient lighting and low visibility. This in turn may cause incorrect targeting, surgical risk, and extended operating time during laparoscopic surgery. Although various methods for nature image enhancement have been proposed in past decades, surgical laparoscopic image enhancement still needs improving due to issues including naturalness, blurred texture, color cast, and extensive computational time. To address these problems, this paper proposes a laparoscopic image enhancement method, based on distributed retinex optimization with refined information fusion. The proposed method minimizes a distributed optimization model with l2-norm regularization terms, resulting in a low computational complexity. By integrating the refined image information into the optimization process, our method significantly enhances dark regions while preserving naturalness and texture structures. Experimental results show that our optimization algorithm is more effective than conventional enhancement algorithms in terms of vision augmentation, performance index, and computation time.
Preliminary study of Augmented Reality based manufacturing for further integration of Quality Control 4.0 supported by metrology

Abstract Augmented Reality (AR) is a key technology enabling Industry 4.0, which enriches human perspectives by overlaying digital information onto the real world. The maturity of AR technology has grown recently. As processes in the automotive and aeronautic sectors require high quality and near-zero error rates to ensure the safety of end-users, AR can be implemented to facilitate workers with immersive interfaces to enhance productivity, accuracy and autonomy in the quality sector. In order to analyse whether there is a real and growing interest in the use of AR as assisting technology for manufacturing sector in general and quality control in particular, two specific research questions are defined. In addition, two well-known research databases (Scopus, Web of Science) are used for the paper selection phase in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to conduct a preliminary study and evaluate the current development of AR applications in manufacturing sector in order to answer the defined questions. It is found that while the development of AR technology has widely implemented to assign real-time information to several systems and processes in assembly and maintenance sectors, this tendency has only emerged in the quality sector over the last few years. However, AR-based quality control has proved its advantages in improving productivity, accuracy and precision of operators as well as benefits to manufacturing in terms of product and process quality control across different manufacturing phases.
A Logic for Binary Classifiers and Their Explanation

Recent years have witnessed a renewed interest in Boolean functions in explaining binary classifiers in the field of explainable AI (XAI). The standard approach to Boolean functions is based on propositional logic. We present a modal language of a ceteris paribus nature which supports reasoning about binary classifiers and their properties. We study a family of classifier models, axiomatize it and show completeness of our axiomatics. Moreover, we prove that satisfiability checking for our modal language relative to such a class of models is NP-complete. We leverage the language to formalize counterfactual conditional as well as a variety of notions of explanation including abductive, contrastive and counterfactual explanations, and biases. Finally, we present two extensions of our language: a dynamic extension by the notion of assignment enabling classifier change and an epistemic extension in which the classifier’s uncertainty about the actual input can be represented.
Probably Approximately Correct Vision-Based Planning using Motion Primitives

This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Probably Approximately Correct (PAC)-Bayes framework to obtain an upper bound on the expected cost of policies across all environments. Minimizing the PAC-Bayes upper bound thus trains policies that are accompanied by a certificate of performance on novel environments. The training pipeline we propose provides strong generalization guarantees for deep neural network policies by (a) obtaining a good prior distribution on the space of policies using Evolutionary Strategies (ES) followed by (b) formulating the PAC-Bayes optimization as an efficiently-solvable parametric convex optimization problem. We demonstrate the efficacy of our approach for producing strong generalization guarantees for learned vision-based motion planners through two simulated examples: (1) an Unmanned Aerial Vehicle (UAV) navigating obstacle fields with an onboard vision sensor, and (2) a dynamic quadrupedal robot traversing rough terrains with proprioceptive and exteroceptive sensors.
HYBRID MODEL FOR CONSTRUCTING INDIVIDUAL EDUCATIONAL TRAJECTORY BASED ON DIGITAL TWINS

The article focuses on building an individual educational trajectory of a university student. This concept is formalized in the form of a hybrid mathematical model. This model is based on a structural model of educational competence. In this case, the competence is a basic construct of the educational program, it acts as a prototype of the structural model of individualized educational content. The main source of semantic and control information is the digital twin sys-tem. The digital double of the educational program formalizes its structure and content in strict ac-cordance with the regulatory requirements of federal state educational standards. The digital twin of a student fixes and processes his digital tracks throughout his life. The digital twin of the labor market formalizes the structure of a certain segment of the labor market in the context of the re-quirements for professional and personal qualities of specialists. There has been substantiated the need for proactive management of restructuring and rebranding the professional educational pro-grams in accordance with the changes at the labor market. A significant contribution to the man-agement of these evolutionary processes is made by the dynamics of a student’s activity, which is reflected in his digital traces. Hybridization of these digital twins into the system of constructing individual educational trajectory allows the semantic marking and weighing of the educational situation and projected content. Based on a suspended educational content and taking into account the regulatory requirements of educational standards and additional requirements of the educational organization, optimization modeling of an individual educational trajectory in its structural and meaningful context is carried out. The study of the structural features of the constructed hybrid mathematical model made it possible to determine and substantiate its rational structure.
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This article introduces a novel model that restores color images degraded by blurring and mixed random-valued (RV) impulse and Gaussian noise. The model includes a data-fidelity term that consists of RV impulse noise and Gaussian noise components, and total variation regularization. Specifically, the data-fidelity term uses the l0- and l2-norms to represent the RV impulse and Gaussian noise components respectively. This results in an appropriate separation of these two noise components and thus enables simultaneous elimination of the mixed noise. Moreover, total variation regularization allows sufficient denoising in homogeneous regions while maintaining edges. To solve the nonconvex and nonsmooth model, we adopt a proximal alternating minimization approach and the alternating direction method of multipliers. These techniques contribute to an iterative algorithm with a convergence analysis provided. The experimental results demonstrate the effectiveness of the proposed model compared to other existing models, as measured by visual inspection and image quality assessments.
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions

Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time consuming and often requires manual labeling. To this end, we propose a simple, yet robust solution for learning to segment unknown objects grasped by a robot. Specifically, we exploit motion and temporal cues in RGB video sequences. Using optical flow estimation we first learn to predict segmentation masks of our given manipulator. Then, these annotations are used in combination with motion cues to automatically distinguish between background, manipulator and unknown, grasped object. In contrast to existing systems our approach is fully self-supervised and independent of precise camera calibration, 3D models or potentially imperfect depth data. We perform a thorough comparison with alternative baselines and approaches from literature. The object masks and views are shown to be suitable training data for segmentation networks that generalize to novel environments and also allow for watertight 3D reconstruction.