Reviewerly/exHarmony huggingface dataset:
Morpheus: An Adaptive DRAM Cache with Online Granularity Adjustment for Disaggregated Memory

Disaggregated memory introduces a cost-effective solution for improving the memory utilization rate of data centers, by sharing a distributed memory pool among several individual servers. However, latency penalty in the existing connection between a computing node and the memory pool introduces performance degradation due to frequent far memory accesses. Based on our observation, page caching in the local DRAM, despite its reductions in the number of far memory accesses, still faces severe data over-fetching problem.With a detailed analysis of far memory access traces collected via several representative real-world applications, we argue that exploiting the various page-specific preferences of caching granularity is the key point of solving the data over-fetching problem in the DRAM cache. Consequently, in this paper, we present that it is influential to enable 1) dynamic selection of caching granularity for each page to not only guarantee sufficient spatial localities compared to the conventional fine-grained cache lines but also avoid data over-fetching caused by the coarse-grained pages, as well as 2) adaptive adjustment of cache capacity during execution for each granularity to accommodate the varying proportion of pages with different granularity preferences. Specifically, we propose Morpheus, an adaptive DRAM cache architecture that determines an optimal page-specific caching granularity at run-time and dynamically adjusts capacity occupations of different caching granularities. Based on our modeling and evaluations within the DRAMSim3 simulator, Morpheus exhibits 1.17-1.34x performance speedup for a wide range of workloads against the state-of-the-art DRAM cache design.
Extracting Domain Models from Textual Requirements in the Era of Large Language Models

Requirements Engineering is a critical part of the software lifecycle, describing what a given piece of software will do (functional) and how it will do it (non-functional). Requirements documents are often textual, and it is up to software engineers to extract the relevant domain models from the text, which is an error-prone and time-consuming task. Considering the recent attention gained by Large Language Models (LLMs), we explored how they could support this task. This paper investigates how such models can be used to extract domain models from agile product backlogs and compare them to (i) a state-of-practice tool as well as (ii) a dedicated Natural Language Processing (NLP) approach, on top of a reference dataset of 22 products and 1, 679 user stories. Based on these results, this paper is a first step towards using LLMs and/or tailored NLP to support automated requirements engineering thanks to model extraction using artificial intelligence.
Assembly Line: A Tool for Collaborative Modeling of Ontologies in Public Administration

We introduce Assembly Line as a novel open-source web platform for collaborative development of ontologies, supporting change review process, and ontology publication. The platform is generic and domain independent by design, but it has been primarily used to curate a common Czech eGovernment ontology, as a consistent and coherent union of interlinked ontologies managed by individual offices. We illustrate the impact of the Assembly Line by giving an overview of developed ontologies in the Czech public administration domain, including the Registry of Rights and Obligations, and the Electronic Code of laws.
A strong composition theorem for junta complexity and the boosting of property testers

We prove a strong composition theorem for junta complexity and show how such theorems can be used to generically boost the performance of property testers.The $\varepsilon$-approximate junta complexity of a function f is the smallest integer r such that f is $\varepsilon$-close to a function that depends only on r variables. A strong composition theorem states that if f has large $\varepsilon$-approximate junta complexity, then $g \circ f$ has even larger $\varepsilon^{\prime}$-approximate junta complexity, even for $\varepsilon^{\prime} \gg \varepsilon$. We develop a fairly complete understanding of this behavior, proving that the junta complexity of $g \circ f$ is characterized by that of f along with the multivariate noise sensitivity of g. For the important case of symmetric functions g, we relate their multivariate noise sensitivity to the simpler and well-studied case of univariate noise sensitivity.We then show how strong composition theorems yield boosting algorithms for property testers: with a strong composition theorem for any class of functions, a large-distance tester for that class is immediately upgraded into one for small distances. Combining our contributions yields a booster for junta testers, and with it new implications for junta testing. This is the first boosting-type result in property testing, and we hope that the connection to composition theorems adds compelling motivation to the study of both topics.
Dynamic treewidth

We present a data structure that for a dynamic graph G that is updated by edge insertions and deletions, maintains a tree decomposition of G of width at most $6 k+5$ under the promise that the treewidth of G never grows above k. The amortized update time is $\mathcal{O}_{k}\left(2^{\sqrt{\log n} \log \log n}\right)$, where n is the vertex count of G and the $\mathcal{O}_{k}(\cdot)$ notation hides factors depending on k. In addition, we also obtain the dynamic variant of Courcelle’s Theorem: for any fixed property $\varphi$ expressible in the CMSO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> logic, the data structure can maintain whether G satisfies $\varphi$ within the same time complexity bounds. To a large extent, this answers a question posed by Bodlaender [WG 1993].
Towards Ontology Enabled Agent-Based Twinning for Cyber-Physical Systems

The twinning paradigm is a concept that embraces end-to-end amalgamation and bridging the physical and cyber worlds. Harnessing the power of twinning to cyber-physical systems offers several benefits and opportunities, that are encountered by multiple challenges. Data integration, knowledge representation, reasoning, and inferencing are crucial for providing dependable services to the twinned systems. To address these challenges, an ontology-enabled approach for modelling cyber-physical systems and their twins is proposed, which is incorporated with an intelligent agent-driven representation of the different elements of the system. Applied to a case study, this approach enables representing the knowledge of the system and its environment, capturing different aspects and integrating heterogeneous data from various sources. This allows performing reasoning and inferencing on the system to obtain valuable insights and information.
Model-Driven Optimization for Quantum Program Synthesis with MOMoT

In the realm of classical software engineering, model-driven optimization has been widely used for different problems such as (re)modularization of software systems. In this paper, we investigate how techniques from model-driven optimization can be applied in the context of quantum software engineering. In quantum computing, creating executable quantum programs is a highly non-trivial task which requires significant expert knowledge in quantum information theory and linear algebra. Although different approaches for automated quantum program synthesis exist—e.g., based on reinforcement learning and genetic programming—these approaches represent tailor-made solutions requiring dedicated encodings for quantum programs. This paper applies the existing model-driven optimization approach MOMoT to the problem of quantum program synthesis. We present the resulting platform for experimenting with quantum program synthesis and present a concrete demonstration for a well-known Quantum algorithm.
Obtaining Insights into the Interplay Between Systems and Software Engineering

The high-tech equipment industry is adopting approaches like Model-Based Systems Engineering (MBSE) and Scaled Agile Framework (SAFe) to improve their practices. Instantiations of these approaches do not necessarily align across disciplines. Given the importance of software aspects in systems, the interplay between software and other engineering disciplines should be carefully addressed. Decision makers are interested in mitigating any risks in the collaboration of specialists in different disciplines. This short positioning paper proposes an applied research approach to identify such risks for later mitigation. The approach is inspired by grounded theory (we constructed concepts from interviews) and systems thinking. Work still to do is to cross-check identified concepts with literature. This research is in progress and we welcome discussions and critical opinions.
Systematic Component-Oriented Language Reuse

Modern-day software is increasingly developed by domain experts. Domain-specific languages (DSLs) help them to bridge the conceptual gap between the problem and the solution domain. Engineering these languages is complex, as it requires comprehensive white-box knowledge of constituents of DSLs, i.e., syntax and semantics, and their composition and realization in respective technological spaces of language workbenches. For efficient language engineering reusing existing languages by composition is essential. For this purpose, various language composition and variability mechanisms have been developed. However, in the literature, there exists no approach enabling systematic language reuse holistically, i.e., including syntax and semantics, in a black-box fashion. We propose an approach for the systematic component-oriented reuse of DSLs (SCOLaR) that supports the integrated reuse of syntax, well-formedness rules, and semantics via black-box language components that are composable along their interface in a language family. We implemented a prototypical version of our solution with the MontiCore language workbench, that supports embedding and aggregation of grammar-based languages with translational semantics realized with code-generators. In the future, we plan to improve our implementation with the support of other language workbenches and to provide a comprehensive evaluation with a series of case studies. With our work, we aim to advance the field of software language engineering by providing a novel method for black-box language reuse capturing syntax and semantics definitions for textual external, translational DSLs. This paper outlines our approach by stating the problem, motivating our research question, and proposing our solution, our plan for evaluation, and presenting expected contributions.
Construction of New Finite Geometry Non-Binary LDPC Codes With Reduced Alphabets

The finite Euclidean and Projective geometry codes are the most known among One-Step Majority-Logic Decodable codes that are Low-Density Parity-Check codes as well. Unfortunately, these codes are rare, and we do not have a great diversity when designing communication systems with a fixed code-length and code rate. Regarding the non-binary Euclidian and projective geometric Codes, each code is restricted to a unique alphabet. To offer a bigger range of possible alphabets for each code, we propose new construction by redefining the incidence vector of the lines of Euclidean and projective geometries over finite fields. This approach leads to an enrichment of the class of EG and PG codes. An error performance study of some new codes has been carried out and shows good results.
Automatic System for Sequential Reconstruction from Image Sequences Acquired from A Camera by SfM-MVS

This paper describes an automatic system that performs three-dimensional (3D) modeling from image sequences acquired from a camera during a survey to understand the state of the environment sequentially. The developed system sequentially generates local 3D models from image sequences acquired from a camera by the Structure from Motion and Multi-View Stereo (SfM-MVS) and integrates the models. This system, consisting of a camera, a computer, and storage, handles streaming, image acquisition, 3D modeling, and integration processes. The system is designed to perform each process in parallel while performing image acquisition. Local 3D models are integrated based on the similarity of camera trajectories, which correspond to overlapped images in each sequence, estimated from SfM. Our experiments verified the operation of the developed system from the image acquisition to the integration of local 3D models in two cases. Consequently, we confirmed that an integrated model could be generated by automatically executing each process in the system through experiments.
A hybrid algorithm of UAV path planning for rescue in bushfire environments

This paper presents a novel hybrid algorithm aimed at optimizing the planning of forest fire rescue routes. The proposed method utilizes a hierarchical architecture to ensure safe navigation deployment, particularly in environments that are either unknown or only partially known. The observation layer effectively deploys a comprehensive set of feasible navigation points through the utilization of global path planning techniques. Subsequently, the execution layer takes charge of executing these navigational actions. To further enhance safety, the decision layer assesses whether the unmanned aerial vehicle (UAV) requires local obstacle avoidance strategies. Furthermore, an additional decision regarding replanning is incorporated into the decision layer, addressing the potential risks associated with dynamic avoidance approaches. This consideration effectively mitigates issues like being trapped in a perpetual loop or encountering path-finding challenges.
IGFNet: Illumination-Guided Fusion Network for Semantic Scene Understanding using RGB-Thermal Images

Semantic scene understanding is a fundamental task for autonomous driving. It serves as a build block for many downstream tasks. Under challenging illumination conditions, thermal images can provide complementary information for RGB images. Many multi-modal fusion networks have been proposed using RGB-Thermal data for semantic scene understanding. However, current state-of-the-art methods simply use networks to fuse features on multi-modality inexplicably, rather than designing a fusion method based on the intrinsic characteristics of RGB images and thermal images. To address this issue, we propose IGFNet, an illumination-guided fusion network for RGB-Thermal semantic scene understanding, which utilizes a weight mask generated by the illumination estimation module to weight the RGB and thermal feature maps at different stages. Experimental results show that our network outperforms the state-of-the-art methods on the MFNet dataset. Our code is available at: https://github.com/lab-sun/IGFNet.
An improved ORB-GMS image feature extraction and matching algorithm<sup>*</sup>

Feature point extraction and matching is the key technology in object detection and simultaneous localization and mapping (SLAM). Aiming at the problems such as easy redundancy of feature points extracted by traditional ORB algorithm, low matching accuracy of mainstream robust estimation algorithms and low real-time performance, an improved ORB-GMS image feature extraction and matching algorithm is proposed. Firstly, the algorithm uses the gray value of the image to calculate the adaptive extraction threshold of the feature points. Then the image pyramid is constructed according to the image size. The set number of total feature points to be extracted is evenly distributed to each layer image according to the area ratio; Extract feature points from each layer of the image pyramid, and count the extracted feature points from each layer. If the number of feature points extracted from each layer meets the set number of images from each layer, the extraction ends. Then the quadtree algorithm is used to homogenize the feature points. Finally, the network scoring model is optimized from 8 neighborhood to 4 neighborhood, which reduces the computing time. Experimental results show that the matching accuracy of the proposed algorithm is 14% higher than that of the original algorithm, and the running time is 12% lower.
Performance of BVC-based Obstacle Avoidance for a Quadrotor Relative to LiDAR Data Volume

Among the various collision avoidance algorithms available, those based on "buffered Voronoi cells (BVC)" have been successful in terms of performance. In this approach, the control input can be determined easily using only the relative positions of the quadrotors. However, this approach, when applied to quadrotors in an environment with stationary obstacles, causes occasional "deadlocks", wherein the quadrotors can not reach the goal. Moreover, it was assumed that each quadrotor could use the relative positions of the obstacles at any time; however, how to obtain this information using the conventional method was not discussed. In this study, we propose an algorithm to tackle deadlocks. Furthermore, to tackle wall-like obstacles that can not be handled by conventional methods, we propose using all point-cloud data obtained by LiDAR as Voronoi seeds. Additionally, because the calculation cost increases as the number of Voronoi seeds increases, we verify the relationship between the calculation time of the control input and the behavior of the quadrotor with respect to the number of used LiDAR data points.
Automatic Parking for a Surgical Robotic Patient-Side Cart Using Monocular Vision

A surgical robotic system used for minimally invasive surgery (MIS) usually includes a patient-side cart, exchangeable instruments and a few accessories. During the preoperative preparation, the patient-side cart is usually manually driven by a nurse to the desired location to connect to the trocar/sheath for docking. Aiming at improving the efficiency of manual docking, this paper, to the best of the authors’ knowledge, for the first time, proposes an automatic parking solution for the patient-side cart based on both a designed headband marker that is installed on the cart and a closed-loop pose controller that utilizes the pose feedback from the headband marker under an external monocular camera. The experimental results show that the average position and orientation estimation errors for the designed headband marker at a distance (from the camera to the marker) of 1.5 m ~ 6 m are 2.21 mm and 1.08°, respectively. The final position and orientation errors of the proposed closed-loop pose controller are 3.91 mm and 1.24°, respectively, which is considered sufficient for automatic parking of the patient-side cart.
Robust Adaptive Filter for Time-Varying Parameters Estimation in Integrated Navigation of Fixed-Wing Aerial Robot<sup>*</sup>

This paper focuses on the autonomous navigation problem of the fixed-wing aerial robot, of which the inertial measurement unit (IMU) has time-varying error characteristic parameters. A robust variational Bayesian adaptive Kalman filter (RVBAKF) is proposed to tackle the noise outliers in integrated navigation brought by sensor uncertainties. The proposed method formulates the joint probability distribution of the system state vector and the measurement noise covariance matrix. A fading factor matrix with strong tracking quality is introduced to enhance the prediction on the prior distribution of the process noise covariance. Then a weighted sliding window mechanism has been constructed to obtain the posterior distribution of the measurement noise outliers. Therefore, the proposed approach is impressive in approximating both the process and measurement noise. The RVBAKF algorithm is implemented in an integrated navigation system which is composed of the strapdown inertial navigation system (SINS) and the global navigation satellite system (GNSS). The integration framework based on RVBAKF is conducted on two typical verification scenarios, which is proven to be exceptional in coping with the time-varying process and measurement noise by comparing the average root mean square error in misalignment angle, velocity and position with the strong tracking filter and the variational Bayesian filter.
HaptiCharger: Robotic Charging of Electric Vehicles Based on Human Haptic Patterns

The growing demand for electric vehicles requires the development of automated car charging methods. At the moment, the process of charging an electric car is completely manual, and that requires physical effort to accomplish the task, which is not suitable for people with disabilities. Typically, the effort in the automation of the charging task research is focused on detecting the position and orientation of the socket, which resulted in a relatively high accuracy, ±5 mm, and ±10 degrees. However, this accuracy is not enough to complete the charging process. In this work, we focus on designing a novel methodology for robust robotic plug-in and plug-out based on human haptics to overcome the error in the orientation of the socket. Participants were invited to perform the charging task, and their cognitive capabilities were recognized by measuring the applied forces along with the movements of the charger. Eventually, an algorithm was developed based on the human's best strategies to be applied to a robotic arm.
Emotional Education in Vulnerable Contexts. An Emotion- and Art-Based Intervention in a Primary School

Objective. This study aimed to verify the effectiveness of an intervention based on Emotional Intelligence (EI) and the arts. Method. A didactic program was conducted to develop EI with 55 Primary Education students, ranging from 8 to 12 years old. The program focused on artistic and musical activities. The participants belonged to a school with high rates of marginality and social exclusion. The data were collected through a pretest, a post-test, and a deferred post-test, with the TMMS24-Questionnaire, to assess EI in three dimensions: attention, clarity, and emotional repair. Results. After data collection, the study analyzed the students’ performance in the emotional dimensions; it found significant improvements in most of the dimensions studied. Conclusions. The main results show the usefulness of these programs in improving the EI of students in disadvantaged environments.
PLV-IEKF: Consistent Visual-Inertial Odometry using Points, Lines, and Vanishing Points

In this paper, we propose an Invariant Extended Kalman Filter (IEKF) based Visual-Inertial Odometry (VIO) using multiple features in man-made environments. Conventional EKF-based VIO usually suffers from system inconsistency and angular drift that naturally occurs in feature-based methods. However, in man-made environments, notable structural regularities, such as lines and vanishing points, offer valuable cues for localization. To exploit these structural features effectively and maintain system consistency, we design a right invariant filter-based VIO scheme incorporating point, line, and vanishing point features. We demonstrate that the conventional additive error definition for point features can also preserve system consistency like the invariant error definition by proving a mathematically equivalent measurement model. And a similar conclusion is established for line features. Additionally, we conduct an invariant filter-based observability analysis proving that vanishing point measurement maintains unobservable directions naturally. Both simulation and real-world tests are conducted to validate our methods’ pose accuracy and consistency. The experimental results validate the competitive performance of our method, highlighting its ability to deliver accurate and consistent pose estimation in man-made environments.