Reviewerly/exHarmony huggingface dataset:
Adapting Augmented Reality Systems to the users’ needs using Gamification and error solving methods

Animations of virtual items in AR support systems are typically predefined and lack interactions with dynamic physical environments. AR applications rarely consider users’ preferences and do not provide customized spontaneous support under unknown situations. This research focuses on developing adaptive, error-tolerant AR systems based on directed acyclic graphs and error resolving strategies. Using this approach, users will have more freedom of choice during AR supported work, which leads to more efficient workflows. Error correction methods based on CAD models and predefined process data create individual support possibilities. The framework is implemented in the Industry 4.0 model factory at FH Aachen.
RML2SHACL: RDF Generation Taking Shape

RDF graphs are often generated by mapping data in other (semi-)structured data formats to RDF. Such mapped graphs have a repetitive structure defined by (i) the mapping rules and (ii) the schema of the input sources. However, this information is not exploited beyond its original scope. SHACL was recently introduced to model constraints that RDF graphs should validate. SHACL shapes and their constraints are either manually defined or derived from ontologies or RDF graphs. We investigate a method to derive the shapes and their constraints from mapping rules, allowing the generation of the RDF graph and the corresponding shapes in one step. In this paper, we present RML2SHACL: an approach to generate SHACL shapes that validate RDF graphs defined by RML mapping rules. RML2SHACL relies on our proposed set of correspondences between RML and SHACL constructs. RML2SHACL covers a large variety of RML constructs, as proven by generating shapes for the RML test cases. A comparative analysis shows that shapes generated by RML2SHACL are similar to shapes generated by ontology-based tools, with a larger focus on data value-based constraints instead of schema-based constraints. We also found that RML2SHACL has a faster execution time than data-graph based approaches for data sizes of 90MB and higher.
Robustness Against Adversarial Attacks in Neural Networks Using Incremental Dissipativity

Adversarial examples can easily degrade the classification performance in neural networks. Empirical methods for promoting robustness to such examples have been proposed, but often lack both analytical insights and formal guarantees. Recently, some robustness certificates have appeared in the literature based on system theoretic notions. This letter proposes an incremental dissipativity-based robustness certificate for neural networks in the form of a linear matrix inequality for each layer. We also propose a sufficient spectral norm bound for this certificate which is scalable to neural networks with multiple layers. We demonstrate the improved performance against adversarial attacks on a feed-forward neural network trained on MNIST and an Alexnet trained using CIFAR-10.
SIGMAR: Ensuring Integrity and Authenticity of Maritime Systems using Digital Signatures

Distributed maritime bridge systems are customary standard equipment on today’s commercial shipping and cruising vessels. The exchange of nautical data, e.g., geographical positions, is usually implemented using multicast network communication without security measures, which poses serious risks to the authenticity and integrity of transmitted data. In this paper, we introduce digital SIGnatures for MARitime systems (SIGMAR), a low-cost solution to seamlessly retrofit authentication of nautical data based on asymmetric cryptography. Extending the existing IEC 61162-450 protocol makes it is possible to build a backward-compatible authentication mechanism that prevents common cyber attacks. The development was successfully accompanied by permanent investigations in a bridge simulation environment, including a maritime cyber attack generator. We demonstrate SIGMAR’s feasibility by introducing a proof-of-concept implementation on low-cost and low-resource hardware and present a performance analysis of our approach.
A non‐convex ternary variational decomposition and its application for image denoising

A non-convex ternary variational decomposition model is proposed in this study, which decomposes the image into three components including structure, texture and noise. In the model, a non-convex total variation (NTV) regulariser is utilised to model the structure component, and the weaker G and E spaces are used to model the texture and noise components, respectively. The proposed model provides a very sparse representation of the structure in total variation (TV) transform domain due to the use of non-convex regularisation and cleanly separates the texture and noise since two different weaker spaces are used to model these two components, respectively. In image denoising application, the proposed model can successfully remove noise while effectively preserving image edges and constructing textures. An alternating direction iteration algorithm combining with iteratively reweighted l1 algorithm, projection algorithm and wavelet soft threshold algorithm is introduced to effectively solve the proposed model. Numerical results validate the model and the algorithm for both synthetic and real images. Furthermore, compared with several state-of-the-art image variational restoration models, the proposed model yields the best performance in terms of the peak signal to noise ratio (PSNR) and the mean structural similarity index (SSIM).
Council Data Project: Software for Municipal Data Collection, Analysis, and Publication

Cities, counties, and states throughout the USA are bound by law to archive recordings of public meetings.Most local governments comply with these laws by posting documents, audio, or video recordings online.As there is no set standard for municipal data archives however, parsing and processing such data is typically time consuming and highly dependent on each municipality.Council Data Project (CDP) is a set of open-source tools that improve the accessibility of local government data by systematically collecting, transforming, and re-publishing this data to the web.The data re-published by CDP is packaged and presented within a searchable web application that vastly simplifies the process of finding specific information within the archived data.We envision this project being used by a variety of groups including civic technologists hoping to promote government transparency, researchers focused on public policy, natural language processing, machine learning, or information retrieval and discovery, and many others.
Development of e-learning content in educational program evaluation courses

Abstract This study aimed at developing learning products that can be used to overcome some of the problems that exist in the Education Program Evaluation course at the Masters level education, namely creating content that can be applied or applied to the e-learning platform of the Ganesha University of Education, so that learning activities can be assisted by the use of technology, and to find out the feasibility of the content that has been created. The method used in this research was the ADDIE model method which consists of Analyze, Design, Development, Implemenation, and Evaluation. The type of data in this study was quantitative data or data presented in the form of numbers. The instrument used in data collection was a questionnaire in the form of a questionnaire. The percentage result of the evaluation of the content expert test of the learning material was 98.07% or stated “Very Good”. The results of the assessment by instructional media experts, e-learning learning content obtained a percentage of 93.05%. which when converted states that the learning content applied to e-learning media was “Very Good”. The results of the target test assessment stated that the e-learning learning content was “Very Good” with a percentage of 93.2%. Based on the results of this assessment, the e-learning learning content is declared suitable for use.
LeQua@CLEF2022: Learning to Quantify

LeQua 2022 is a new lab for the evaluation of methods for "learning to quantify" in textual datasets, i.e., for training predictors of the relative frequencies of the classes of interest in sets of unlabelled textual documents. While these predictions could be easily achieved by first classifying all documents via a text classifier and then counting the numbers of documents assigned to the classes, a growing body of literature has shown this approach to be suboptimal, and has proposed better methods. The goal of this lab is to provide a setting for the comparative evaluation of methods for learning to quantify, both in the binary setting and in the single-label multiclass setting. For each such setting we provide data either in ready-made vector form or in raw document form.
What do (most of) our dispersion measures measure (most)? Dispersion?

Abstract This paper discusses the degree to which most of the most widely-used measures of dispersion in corpus linguistics are not particularly valid in the sense of actually measuring dispersion rather than some amalgam of a lot of frequency and a little dispersion. The paper demonstrates these issues on the basis of data from a variety of corpora. I then outline how to design a dispersion measure that only measures dispersion and show that (i) it indeed measures information that is different from frequency in an intuitive way and (ii) has a higher degree of predictive power of lexical decision times from the MALD database than nearly all other measures in nearly all corpora tested.
SAEP: A Surrounding-Aware Individual Emotion Prediction Model Combined with T-LSTM and Memory Attention Mechanism

The future emotion prediction of users on social media has been attracting increasing attention from academics. Previous studies on predicting future emotion have focused on the characteristics of individuals’ emotion changes; however, the role of the individual’s neighbors has not yet been thoroughly researched. To fill this gap, a surrounding-aware individual emotion prediction model (SAEP) based on a deep encoder–decoder architecture is proposed to predict individuals’ future emotions. In particular, two memory-based attention networks are constructed: The time-evolving attention network and the surrounding attention network to extract the features of the emotional changes of users and neighbors, respectively. Then, these features are incorporated into the emotion prediction task. In addition, a novel variant LSTM is introduced as the encoder of the proposed model, which can effectively extract complex patterns of users’ emotional changes from irregular time series. Extensive experimental results show that the proposed approach outperforms five alternative methods. The SAEP approach has improved by approximately 4.21–14.84% micro F1 on a dataset built from Twitter and 7.30–13.41% on a dataset built from Microblog. Further analyses validate the effectiveness of the proposed time-evolving context and surrounding context, as well as the factors that may affect the prediction results.
L-Diversity for Data Analysis: Data Swapping with Customized Clustering

Abstract Data anonymization should support the analysts who intend to use the anonymized data. Releasing datasets that contain personal information requires anonymization that balances privacy concerns while preserving the utility of the data. This work shows how choosing anonymization techniques with the data analyst requirements in mind improves effectiveness quantitatively, by minimizing the discrepancy between querying the original data versus the anonymized result, and qualitatively, by simplifying the workflow for querying the data.
Multi-UAV Coverage Path Planning for Gas Distribution Map Applications

Aerial coverage for Gas Distribution Map (GDM) using Unmanned Aerial Vehicles (UAVs) with olfaction capabilities has become a key for effectiveness and cost improvements. In this work, the main contribution is the application of Coverage Path Planning (CPP) approaches adapted to survey gas plumes using an autonomous fleet of UAVs. Indeed, the mission is carried out in four steps. First, a gas plume model was integrated to simulate the environment. Then, based on the sensor’s footprint model and the Voronoï diagram, an exact cellular decomposition method is proposed to generate and distribute measurement points or Points Of Interests (POIs) effectively. To take measurements from all these POIs, two planning approaches were proposed in the third step. One is based on the separation of missions between UAVs using the K-means algorithm principle and modeling each path as a Traveling Salesman Problem (TSP). While the second approach models the global mission for all UAVs as a Vehicle Routing Problem (VRP). The resolution of the two mathematical models is achieved using Genetic Algorithms (GAs) and Simulated Annealing (SA) algorithms, respectively. Eventually, the GDM is reconstituted based on triangulation-based cubic interpolation. The evaluation of the proposed simulation techniques based on comparative analyses shows their efficiency, speed and flexibility. The proposed methods take into account the UAV’s flight autonomy and the desired coverage quality, and they provide solutions for a variety of coverage applications, particularly for GDM.
Hardware-Assisted Security Monitoring Unit for Real-Time Ensuring Secure Instruction Execution and Data Processing in Embedded Systems

The hardware security of embedded systems is raising more and more concerns in numerous safety-critical applications, such as in the automotive, aerospace, avionic, and railway systems. Embedded systems are gaining popularity in these safety-sensitive sectors with high performance, low power, and great reliability, which are ideal control platforms for executing instruction operation and data processing. However, modern embedded systems are still exposing many potential hardware vulnerabilities to malicious attacks, including software-level and hardware-level attacks; these can cause program execution failure and confidential data leakage. For this reason, this paper presents a novel embedded system by integrating a hardware-assisted security monitoring unit (SMU), for achieving a reinforced system-on-chip (SoC) on ensuring program execution and data processing security. This architecture design was implemented and evaluated on a Xilinx Virtex-5 FPGA development board. Based on the evaluation of the SMU hardware implementation in terms of performance overhead, security capability, and resource consumption, the experimental results indicate that the SMU does not lead to a significant speed degradation to processor while executing different benchmarks, and its average performance overhead reduces to 2.18% on typical 8-KB I/D-Caches. Security capability evaluation confirms the monitoring effectiveness of SMU against both instruction and data tampering attacks. Meanwhile, the SoC satisfies a good balance between high-security and resource overhead.
Multimodal Data Integration for Interactive and Realistic Avatar Simulation in Augmented Reality

The affordance of Augmented Reality (AR) allows users to view the virtual objects along with real world simultaneously, which provides more realistic and immersive experiences to interact with them. In this paper, we propose to integrate data with different modalities (such as animation, audio, and user behavior data) to generate a realistic child avatar in the AR environment that is responsive to the user's behaviors. The proposed framework has three components: avatar interaction system, avatar action control system, and avatar display system. The avatar interaction system leverages user behavior data to allow for reasonable interactions of the avatar, the avatar action control system integrates actions and audio data to generate realistic avatar actions, and the avatar display system presents the avatar to the user via the AR interface. Furthermore, a child tantrum management training application is implemented based on the proposed system to allow users to experience child tantrum and learn how to respond and manage different child tantrum situations. The application based on our system can run in real-time with an average of 93.41 fps. Based on the qualitative evaluation, the simulated child avatar has shown to be realistic enough and is able to respond to the user's gaze instantly in the AR environment. The action, reaction, treatment, and mitigation of the child avatar behavior among different tantrum levels in the avatar action controller are accurately represented through the clinical evaluation experiences from pediatricians and trained psychologists.
Super-resolution of low-fidelity flow solutions via generative adversarial networks

While computational fluid dynamics (CFD) can solve a wide variety of fluid flow problems, accurate CFD simulations require significant computational resources and time. We propose a general method for super-resolution of low-fidelity flow simulations using deep learning. The approach is based on a conditional generative adversarial network (GAN) with inexpensive, low-fidelity solutions as inputs and high-fidelity simulations as outputs. The details, including the flexible structure, unique loss functions, and handling strategies, are thoroughly discussed, and the methodology is demonstrated using numerical simulations of incompressible flows. The distinction between low- and high-fidelity solutions is made in terms of discretization and physical modeling errors. Numerical experiments demonstrate that the approach is capable of accurately forecasting high-fidelity simulations.
Development of AI-based Recycling Bins Using MobileNet-SSD Networks

With the rapid development of industrial technologies, a lot of wastes have been produced in modern life. In order to efficiently recycle the wastes, this paper has designed and implemented an intelligent recycling bin using the MobileNet-SSD (Single-Shot Detector) networks. Experimental results indicate the feasibility and practicality of the implemented system, in which the accuracy reaches above 90% for the plastic bottles and metal cans.
Covert Timing Channels Detection Based on Auxiliary Classifier Generative Adversarial Network

Covert timing channels (CTCs) are defined as a mechanism that embeds covert information into network traffic. In a manner, information leakage caused by CTCs brings serious threat to network security. In recent years, detection of CTCs is a focus and a challenging task in the field of covert channel research. However, existing detection schemes based on statistical methods have poor performance in detecting multiple CTCs, and require so many inter-arrival times of packets that these schemes cannot detect CTCs in real time. In this paper, we propose a novel deep learning approach for CTCs detection, namely, covert timing channels detection based on auxiliary classifier generative adversarial network (CD-ACGAN). The network structure and loss function of CD-ACGAN are designed to be suitable for CTCs detection task. We first encode traffic flows into single-channel Gramian Angular Field (GAF) images. Then we use CD-ACGAN to learn features from GAF images and predict the classes of CTCs. Our experimental results show that our approach has high accuracy and strong robustness in detecting various CTCs.
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments

Geometric methods for solving open-world off-road navigation tasks, by learning occupancy and metric maps, provide good generalization but can be brittle in outdoor environments that violate their assumptions (e.g., tall grass). Learning-based methods can directly learn collision-free behavior from raw observations, but are difficult to integrate with standard geometry-based pipelines. This creates an unfortunate conflict -- either use learning and lose out on well-understood geometric navigational components, or do not use it, in favor of extensively hand-tuned geometry-based cost maps. In this work, we reject this dichotomy by designing the learning and non-learning-based components in a way such that they can be effectively combined in a self-supervised manner. Both components contribute to a planning criterion: the learned component contributes predicted traversability as rewards, while the geometric component contributes obstacle cost information. We instantiate and comparatively evaluate our system in both in-distribution and out-of-distribution environments, showing that this approach inherits complementary gains from the learned and geometric components and significantly outperforms either of them. Videos of our results are hosted at https://sites.google.com/view/hybrid-imitative-planning
Effect of Sense of Direction on Open Card Sorts for Websites

Card sorting is one widely used method to design website information architectures based on an understanding of their users' mental models. This paper studies the effect of participants' sense of direction on card sorting results. To this end, a two-phase study for an eshop was conducted. In the first phase, 40 participants rated their sense of direction on the standardized Santa Barbara Sense of Direction scale (SBSOD) and were then involved in an open card sort study. Then, we analyzed the card sort data following a median-split approach based on participants' self-reported SBSOD score. This resulted in two proposed information architectures for the eshop: one from the SBSOD-low participants and one from the SBSOD-high participants. In the second phase, we first developed two prototypes for the eshop, one for each proposed information architecture. Next, 30 participants were involved in a within-subjects user testing study. Results showed that users interacting with the eshop produced by the SBSOD-high participants were significantly more effective, more efficient and rated the tasks as easier compared to users interacting with the eshop produced by the SBSOD-low participants. No significant difference was observed for users' perceived usability.
A buffered online transfer learning algorithm with multi-layer network

Online transfer learning (OTL) has attracted much attention in recent years. It is designed to handle the transfer learning tasks, where the data of the target domain isn't available in advance but may arrive in an online manner, which may be a more realistic scenario in practice. However, there typically are two limitations of existing OTL algorithms. 1) Existing OTL algorithms are based on shallow online learning models (SOLMs), e.g., linear or kernel models. Due to this limitation of SOLMs they cannot effectively learn complex nonlinear functions in complicated application and the OTL algorithms based on SOLMs cannot either. 2) Existing algorithms only utilize the latest arrived instance to adjust the model. In this way, the previously arrived instances are not utilized. It may be better to utilize the previously arrived instances as well. In this paper, to overcome the abovementioned two limitations, a buffered online transfer learning (BOTL) algorithm is proposed. In the proposed BOTL algorithm, the learner is designed as a deep learning model, referred to as Online Hedge Neural Network (OHNN). In order to enable the OHNN to be effectively learned in an online manner, we propose a buffered online learning framework that utilizes several previously arrived instances to assist learning. Further, to enhance the performance of the OHNN, a model learned in the source domain is transferred to the target domain. The regret bound of the proposed BOTL algorithm is analyzed theoretically. Experimental results on realistic datasets illustrate that the proposed BOTL algorithm can achieve lower mistake rate than the algorithms compared.