Reviewerly/exHarmony huggingface dataset:
DNN-Based Map Deviation Detection in LiDAR Point Clouds

In this work we present a novel deep learning-based approach to detect and specify map deviations in erroneous or outdated high-definition (HD) maps using both sensor and map data as input to a deep neural network (DNN). We first present our proposed reference method for map deviation detection (MDD) utilizing a sensor-only DNN detecting traffic signs, traffic lights, and pole-like objects in LiDAR data, with deviations obtained by subsequently comparing detected objects and examined map. Second, we facilitate the object detection task by using the examined map as additional input to the network. Third, we employ a specialized MDD network to directly infer the correctness of the map input. Finally, we demonstrate the robustness of our approach for challenging scenes featuring occlusions and a reduced point density, e.g., due to heavy rain. Our code is available at https://github.com/Volkswagen/3dhd_devkit.
MarNASNets: Toward CNN Model Architectures Specific to Sensor-Based Human Activity Recognition

Deep learning (DL) models for sensor-based human activity recognition (HAR) are still in their nascent stages compared with image recognition. HAR’s inference is generally implemented on edge devices such as smartphones because of its secure privacy. However, lightweight DL models for HAR, while meeting the hardware limitations, are lacking. In this study, using the neural architecture search (NAS), we investigated an effective DL model architecture that can be used for inference on smartphones. We designed multiple search spaces for the type of convolution, the kernel size of the convolution process, the type of skip operation, the number of layers, and the number of output filters by Bayesian optimization. We propose models called mobile-aware convolutional neural network (CNN) for sensor-based HAR by NAS (MarNASNets). We constructed four MarNASNet networks, MarNASNet-A to -D, each with a different model size and a parameter search space of four patterns. Experimental results show that MarNASNets achieve the same accuracy as the existing CNN architectures with fewer parameters and are effective model architectures for on-device and sensor-based HAR. We also developed Activitybench, an iOS app, for measuring model performance on smartphones, and evaluated the on-device performance of each model. MarNASNets’ exploration achieved accuracy comparable to the existing CNN models with smaller model sizes. MarNASNet-C achieved accuracies of 92.60%, 94.52%, and 88.92% for Human Activity Sensing Consortium (HASC), UCI, and Wireless Sensor Data Mining (WISDM), respectively. Especially for HASC and UCI, MarNASNet-C achieved the highest accuracies despite the small model size. Their latency was also comparable to that of the existing CNN models, enabling real-time on-device inference.
New Results on Distributed Secret Sharing Protocols

A Distributed Secret Sharing Protocol (DSSP for short) allows a dealer to share multiple secrets among a set of users by storing the shares on storage nodes distributed over a public insecure network. Users can later download the shares from the nodes they can access, in order to reconstruct the secrets (each user is allowed to reconstruct exactly one secret). In this paper we propose three contributions: we first carefully analyze the security requirements for DSSPs and we show that if the shared secrets are statistically dependent, then no DSSP satisfying either the weak secrecy requirement or the perfect secrecy one can exist. Then, we propose two new definitions of security for DSSPs, which also take into account the statistical dependencies among the secrets. Afterwards, we consider DSSPs for a specific class of access structures, i.e., those which can be represented by a graph. In particular, we propose a protocol which can be used to share independent secrets having different sizes. The protocol can manage any kind of graph and generalizes previous protocols for secrets having the same sizes, while maintaining optimal storage requirements.
An IDE to Support the Development of Embedded Multi-Agent Systems

Embedded MAS development requires knowledge in different areas, such as agent-oriented programming, object-oriented programming, low-level programming, and basic electronics concepts. The literature has a consolidated Embedded MAS development architecture divided into four layers: Reasoning, Serial, Firmware, and Hardware. However, one of the main difficulties that MAS designers face is the need to use and configure different Integrated Development Environments (IDE) and make several integrations to embed the MAS. Even using all these technologies, embedding and monitoring the Embedded MAS is done using physical wired connections, making them limited and impracticable depending on the application. Therefore, this work aims to present an IDE to develop Embedded MAS that centralizes the entire development in a single IDE with all required integrations and configurations done. Moreover, the embedding and monitoring MAS of the IDE are done remotely without physical wired connections. Finally, aiming to show the IDE's applicability and functionalities, this work presents a case study set on a road junction with different Embedded MAS.
Localization and Navigation of ROS-Based Autonomous Robot in Hospital Environment

This work is part of a research project during the COVID-19 pandemic that aims to design and develop a mobile autonomous robot for hospitals. In practice, implementing a navigation program directly on a physical robot is both expensive and hazardous. The solution is to perform a simulation using ROS (Robot Operating System), which offers several advantages that make it an appealing option for testing and development. In an unknown hospital environment, this paper presents a simulation of the navigation process of the autonomous robot Turtlebot3 by employing the Simultaneous Localization and Mapping (SLAM) algorithm, specifically the GMapping method, utilizing the distributed software framework of ROS. In a known hospital environment, we utilize trajectory planning algorithms designed for deterministic models. However, considering the inherent uncertainty in the environment and the inevitable inaccuracies of the models, we integrate the Markov decision process (MDP) by applying the classical Q-Learning algorithm. Through these simulations, our aim is to test and refine the navigation algorithms to enhance the performance of our mobile robot. Ultimately, the proposed simulation approach contributes to the development of robotic solutions that can assist in performing various routine tasks remotely. This saves time for healthcare personnel and, most importantly, ensures their safety.
Control is Nothing Without Trust a First Look into Digital Identity Wallet Trends

In recent years, user-centric digital identity wallets have become increasingly available, aiming to give individuals direct control over their personal data. The EU proposal in the context of the recently revised eIDAS and its EU digital identity wallet reflects the high ambitions in this field at the EU level. However, to the best of our knowledge, no research review on the core technologies used to implement various features of these wallet solutions has been proposed so far. Our work addresses this issue providing a systematic analysis of a selection of available digital identity wallets on the market in terms of the technologies used to establish trust and controlled sharing. Building on this overview and help providers (including companies and European Member States) in charge of developing secure and trustworthy wallet-based infrastructures, we propose a classification of wallet solutions along two main dimensions (namely trust establishment and controlled credential sharing) with the goal of assisting designers to make informed design decisions and guarantee security, privacy, and trustworthiness.
Cognitive Assistant for Enhanced eHealth Monitoring

The use of cognitive assistants for elderly individuals can greatly benefit their everyday lives, particularly with medication management and household tasks. These assistants can send reminders for medication and even dispense pills, as well as help with tasks such as adjusting the thermostat and grocery shopping. Furthermore, this technology has the potential to improve the quality of life for elderly individuals and help them maintain their independence. This paper introduces a cognitive assistant designed for monitoring cardiorespiratory signals in remote healthcare settings, specifically for use in rural or underserved areas. The proposed tool utilizes a combination of mechanical, electronic, and computer components to enable analysis and pre-diagnosis of potential diseases.
A novel data quality framework for assessment of scientific lecture video indexing

Purpose The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video indexing. As lecture video indexing involves various steps, the proposed framework containing new dimensions, introduces new integrated approach for evaluating an indexing method or algorithm from the beginning to the end. Design/methodology/approach The emphasis in this study is on the fifth step of design science research methodology (DSRM), known as evaluation. That is, the methods that are developed in the field of lecture video indexing as an artifact, should be evaluated from different aspects. In this research, nine dimensions of data quality including accuracy, value-added, relevancy, completeness, appropriate amount of data, concise, consistency, interpretability and accessibility have been redefined based on previous studies and nominal group technique (NGT). Findings The proposed dimensions are implemented as new metrics to evaluate a newly developed lecture video indexing algorithm, LVTIA and numerical values have been obtained based on the proposed definitions for each dimension. In addition, the new dimensions are compared with each other in terms of various aspects. The comparison shows that each dimension that is used for assessing lecture video indexing, is able to reflect a different weakness or strength of an indexing method or algorithm. Originality/value Despite development of different methods for indexing lecture videos, the issue of data quality and its various dimensions have not been studied. Since data with low quality can affect the process of scientific lecture video indexing, the issue of data quality in this process requires special attention.
A new class of string transformations for compressed text indexing

Introduced about thirty years ago in the field of data compression, the Burrows-Wheeler Transform (BWT) is a string transformation that, besides being a booster of the performance of memoryless compressors, plays a fundamental role in the design of efficient self-indexing compressed data structures. Finding other string transformations with the same remarkable properties of BWT has been a challenge for many researchers for a long time. Among the known BWT variants, the only one that has been recently shown to be a valid alternative to BWT is the Alternating BWT (ABWT), an invertible string transformation introduced about ten years ago in connection with a generalization of Lyndon words. In this paper, we introduce a whole class of new string transformations, called local orderings-based transformations, which have all the “myriad virtues” of BWT. We show that this new family is a special case of a much larger class of transformations, based on context adaptive alphabet orderings, that includes BWT and ABWT. Although all transformations support pattern search, we show that, in the general case, the transformations within our larger class may take quadratic time for inversion and pattern search. As a further result, we show that the local orderings-based transformations can be used for the construction of the recently introduced r-index, which makes them suitable also for highly repetitive collections. In this context, we consider the problem of finding, for a given string, the BWT variant that minimizes the number of runs in the transformed string, and we provide an algorithm solving this problem in linear time.
Twin Pseudo-training for semi-supervised semantic segmentation

Recent advancements in semi-supervised semantic segmentation have demonstrated the effectiveness of utilizing pseudo labels supervision to mitigate the limitations of pixel-wise annotations. However, pseudo-labels generated using self-training techniques typically contain a significant amount of noise, which can impede the training process of the supervised model. In this study, we identify low- and high-level semantic errors as the two key factors that hinder the accuracy of pseudo labels. To fully exploit the potential of pseudo labels, we introduce a novel semi-supervised framework named Twin Pseudo-training (TPseudo), which employs a consistency and disagreement collaboration strategy. Specifically, we correct pseudo labels with a False-positive Filter (FPF) to reduce high-level semantic noise and refine low-level semantic biases using a Semantic Error Detector (SED). Lastly, we design a Self-Adaptive Weight (SAW) loss function based on a disagreement between two predictions to exploit each pixel of pseudo labels. Experimental results on the standard benchmarks PASCAL VOC2012 and Cityscapes demonstrate the efficacy of the proposed method.
The Privacy Issue of Counterfactual Explanations: Explanation Linkage Attacks

Black-box machine learning models are used in an increasing number of high-stakes domains, and this creates a growing need for Explainable AI (XAI). However, the use of XAI in machine learning introduces privacy risks, which currently remain largely unnoticed. Therefore, we explore the possibility of an explanation linkage attack , which can occur when deploying instance-based strategies to find counterfactual explanations. To counter such an attack, we propose k -anonymous counterfactual explanations and introduce pureness as a metric to evaluate the validity of these k -anonymous counterfactual explanations. Our results show that making the explanations, rather than the whole dataset, k -anonymous, is beneficial for the quality of the explanations.
Empirical study of privacy inference attack against deep reinforcement learning models

Most studies on privacy in machine learning have primarily focused on supervised learning, with little research on privacy concerns in reinforcement learning. However, our study has demonstrated that observation information can be extracted through trajectory analysis. In this paper, we propose a variable information inference attack targeting the observation space of policy models, which is categorised into two types: observed value inference attack and observed variable inference. Our algorithm has demonstrated a high success rate in privacy inference attacks for both types of observation information.
Reconstructing particles in jets using set transformer and hypergraph prediction networks

Abstract The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the input data. We deploy three separate set-to-set neural network architectures to reconstruct particles in events containing a single jet in a fully-simulated calorimeter. Performance is evaluated in terms of particle reconstruction quality, properties regression, and jet-level metrics. The results demonstrate that such a high-dimensional end-to-end approach succeeds in surpassing basic parametric approaches in disentangling individual neutral particles inside of jets and optimizing the use of complementary detector information. In particular, the performance comparison favors a novel architecture based on learning hypergraph structure, HGPflow , which benefits from a physically-interpretable approach to particle reconstruction.
Non-deterministic Approximation Operators: Ultimate Operators, Semi-equilibrium Semantics, and Aggregates

Abstract Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of non-monotonic logics. In recent work, AFT was generalized to non-deterministic operators, that is, operators whose range are sets of elements rather than single elements. In this paper, we make three further contributions to non-deterministic AFT: (1) we define and study ultimate approximations of non-deterministic operators, (2) we give an algebraic formulation of the semi-equilibrium semantics by Amendola et al ., and (3) we generalize the characterizations of disjunctive logic programs to disjunctive logic programs with aggregates.
Deep Learning Method for Evaluating Photovoltaic Potential of Rural Land Use Types

Rooftop photovoltaic (PV) power generation uses building roofs to generate electricity by laying PV panels. Rural rooftops are less shaded and have a regular shape, which is favorable for laying PV panels. However, because of the relative lack of information on buildings in rural areas, there are fewer methods to assess the utilization potential of PV on rural buildings, and most studies focus on urban buildings. In addition, in rural areas, concentrated ground-mounted PV plants can be built on wastelands, hillsides, and farmlands. To facilitate the overall planning and synergistic layout of rural PV utilization, we propose a new workflow to identify different types of surfaces (including building roofs, wastelands, water surfaces, etc.) by applying a deep learning approach to count the PV potential of different surfaces in rural areas. This method can be used to estimate the spatial distribution of rural PV development potential from publicly available satellite images. In this paper, 10 km2 of land in Wuhan is used as an example. The results show that the total PV potential in the study area could reach 198.02 GWh/year, including 4.69 GWh/year for BIPV, 159.91 GWh/year for FSPV, and 33.43 GWh/year for LSPV. Considering the development cost of different land types, several timespans (such as short-, medium-, and long-term) of PV development plans for rural areas can be considered. The method and results provide tools and data for the assessment of PV potential in rural areas and can be used as a reference for the development of village master plans and PV development plans.
Enhancing Kinematic Calibration Accuracy for Parallel Manipulators Based on Truncated Total Least-Square Regularization

Abstract This article proposes a novel kinematic calibration approach to address the ill-conditioned identification matrix problem and enhance the accuracy of parallel manipulators (PMs). The kinematic calibration approach applies the truncated total least-square (T-TLS) regularization method to the inverse kinematic calibration of PMs and does not have the unit inconsistency issue and configuration selection issue. The performance of the T-TLS method in the inverse kinematic calibration of PMs that have the ill-conditioned identification matrix problem is compared to that of classical regularization methods, including the truncated singular value decomposition (TSVD) method and Tikhonov method, in simulations. Then, the kinematic calibration approach is applied to a 3-PSS/S spherical PM prototype in the real world. For the 3-PSS/S spherical PM prototype, the kinematic calibration approach can reduce the absolute angular errors of the end-effector from over 1.0 deg before calibration to less than 0.1 deg after calibration.
Online short text clustering using infinite extensions of discrete mixture models

Abstract Short text clustering is one of the fundamental tasks in natural language processing. Different from traditional documents, short texts are ambiguous and sparse due to their short form and the lack of recurrence in word usage from one text to another, making it very challenging to apply conventional machine learning algorithms directly. In this article, we propose two novel approaches for short texts clustering: collapsed Gibbs sampling infinite generalized Dirichlet multinomial mixture model infinite GSGDMM) and collapsed Gibbs sampling infinite Beta‐Liouville multinomial mixture model (infinite GSBLMM). We adopt two flexible and practical priors to the multinomial distribution where in the first one the generalized Dirichlet distribution is integrated, while the second one is based on the Beta‐Liouville distribution. We evaluate the proposed approaches on two famous benchmark datasets, namely, Google News and Tweet. The experimental results demonstrate the effectiveness of our models compared to basic approaches that use Dirichlet priors. We further propose to improve the performance of our methods with an online clustering procedure. We also evaluate the performance of our methods for the outlier detection task, in which we achieve accurate results.
A point‐image fusion network for event‐based frame interpolation

Abstract Temporal information in event streams plays a critical role in event‐based video frame interpolation as it provides temporal context cues complementary to images. Most previous event‐based methods first transform the unstructured event data to structured data formats through voxelisation, and then employ advanced CNNs to extract temporal information. However, voxelisation inevitably leads to information loss, and processing the sparse voxels introduces severe computation redundancy. To address these limitations, this study proposes a point‐image fusion network (PIFNet). In our PIFNet, rich temporal information from the events can be directly extracted at the point level. Then, a fusion module is designed to fuse complementary cues from both points and images for frame interpolation. Extensive experiments on both synthetic and real datasets demonstrate that our PIFNet achieves state‐of‐the‐art performance with high efficiency.
Transformer Architecture-Based Transfer Learning for Politeness Prediction in Conversation

Politeness is an essential part of a conversation. Like verbal communication, politeness in textual conversation and social media posts is also stimulating. Therefore, the automatic detection of politeness is a significant and relevant problem. The existing literature generally employs classical machine learning-based models like naive Bayes and Support Vector-based trained models for politeness prediction. This paper exploits the state-of-the-art (SOTA) transformer architecture and transfer learning for respectability prediction. The proposed model employs the strengths of context-incorporating large language models, a feed-forward neural network, and an attention mechanism for representation learning of natural language requests. The trained representation is further classified using a softmax function into polite, impolite, and neutral classes. We evaluate the presented model employing two SOTA pre-trained large language models on two benchmark datasets. Our model outperformed the two SOTA and six baseline models, including two domain-specific transformer-based models using both the BERT and RoBERTa language models. The ablation investigation shows that the exclusion of the feed-forward layer displays the highest impact on the presented model. The analysis reveals the batch size and optimization algorithms as effective parameters affecting the model performance.