Hasil untuk "Instruments and machines"

Akshit Kurani, Pavan Doshi, Aarya Vakharia et al.

Yuan Wang, Norazmawati Md Sani, Jing Cai et al.

Background As aging populations continue to grow, smart home technologies—such as smart locks—have become increasingly essential to support older adults’ independent living. Long-term use remains a challenge, however, with most studies focusing on initial adoption rather than sustained engagement. Methods In this study, we examined the key factors related to older adults’ continuance intention toward smart locks, applying a socio-technical framework that integrated the Expectation-Confirmation Model of Information Systems (ECM-IS), the Task-Technology Fit (TTF) model, and external variables, including privacy and security, trust, and habit. We analyzed survey data from 422 Chinese participants aged 55 and older using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Matrix Analysis (IPMA). Results The model explained 71.6% of the variance in continuance intention (R2 = 0.716) and showed strong predictive relevance (Q2 = 0.623). Trust and perceived usefulness were positively related to continuance intention, followed by satisfaction. Task-technology fit and confirmation were significantly associated with perceived usefulness and satisfaction. Habit and privacy and security were not significant with respect to continuance intention. Conclusions These findings provide theoretical and practical insight for designing age-inclusive, trust-enhancing smart locks that better support older adults’ needs in post-adoption contexts.

Yuly Wu, Jiamou Liu, Libo Zhang

Partially Observable Markov Decision Processes (POMDPs) are fundamental to many real-world applications. Although reinforcement learning (RL) has shown success in fully observable domains, learning policies from traces in partially observable environments remains challenging due to non-Markovian observations. Inferring an automaton to handle the non-Markovianity is a proven effective approach, but faces two limitations: 1) existing automaton representations focus only on reward-based non-Markovianity, leading to unnatural problem formulations; 2) inference algorithms face enormous computational costs. For the first limitation, we introduce Transition Machines (TMs) to complement existing Reward Machines (RMs). To develop a unified inference algorithm for both automata types, we propose the Dual Behavior Mealy Machine (DBMM) that subsumes both TMs and RMs. We then introduce DB-RPNI, a passive automata learning algorithm that efficiently infers DBMMs while avoiding the costly reductions required by prior work. We further develop optimization techniques and identify sufficient conditions for inferring the minimal correct automata. Experimentally, our inference method achieves speedups of up to three orders of magnitude over SOTA baselines.

Semonti Banik, Sajal Chandra Banik, Sarker Safat Mahmud

ABSTRACT The essential factor in developing multi‐robot systems is the generation of an optimal path for task completion by multiple robots. To ensure effective path planning, this paper studies the recent publications and provides a detailed review of the path planning approaches to avoid collisions in uncertain environments. In this article, path‐planning approaches for multiple robots are categorized primarily into classical, heuristic, and artificial intelligence‐based methods. Among the heuristic approaches, bio‐inspired approaches are mostly employed to optimize the classical approaches to enhance their adaptability. The articles are analyzed based on static and dynamic scenarios, real‐time experiments, and simulations involving hybrid solutions. The increasing focus on using hybrid approaches in dynamic environments is found mostly in the papers employing heuristic and AI‐based approaches. In real‐time applications, AI‐based approaches are highly implemented in comparison to heuristic and classical approaches. Moreover, the findings from this review, highlighting the strengths and drawbacks of each algorithm, can help researchers select the appropriate approach to overcome the limitations in designing efficient multi‐robot systems.

T. V. Soumya, M. K. Sabu

Abstract The sequential three-way decision accepts additional information at each level and makes more accurate definite decisions with less uncertainty. This process can also be extended to two-way classification with the finer-grained information level. However, both the decision process cost and decision result cost of the model must be considered for optimal performance. The proposed model adapts the game-theoretic approach to deal with the trade-off between the decision process cost and the decision result cost, and thereby balance the number of levels of the model. The time complexity, information level, and feature importance contribute to the process cost while evaluation metrics stand for the result cost. The model starts with reliable initial results by using the most significant features at the first level itself and follows an objective function-based method to determine threshold pairs at each level, which avoids relying on domain experts. Furthermore, if the process cost outweighs the result cost, the number of levels is adjusted accordingly. Using the experimental datasets, instances are classified at each level at the optimal threshold pairs; therefore the trisection is obtained with the highest precision/recall value. The obtained results prove that the proposed model outperforms existing models in terms of precision, recall, and time complexity with balanced decision costs. In summary, the proposed model is cost-efficient, interpretable, termination-aware, and result-oriented, ensuring effective and practical decision-making.

Eduard Eiben, Tomohiro Koana, Magnus Wahlström

We introduce determinantal sieving, a new, remarkably powerful tool in the toolbox of algebraic FPT algorithms. Given a polynomial $P(X)$ on a set of variables $X=\{x_1,\ldots,x_n\}$ and a linear matroid $M=(X,\mathcal{I})$ of rank $k$, both over a field $\mathbb{F}$ of characteristic 2, in $2^k$ evaluations we can sieve for those terms in the monomial expansion of $P$ which are multilinear and whose support is a basis for $M$. Alternatively, using $2^k$ evaluations of $P$ we can sieve for those monomials whose odd support spans $M$. Applying this framework, we improve on a range of algebraic FPT algorithms, such as: 1. Solving $q$-Matroid Intersection in time $O^*(2^{(q-2)k})$ and $q$-Matroid Parity in time $O^*(2^{qk})$, improving on $O^*(4^{qk})$ over general fields (Brand and Pratt, ICALP 2021) 2. Long $(s,t)$-Path in $O^*(1.66^k)$ time, improving on $O^*(2^k)$, and Rank $k$ $(S,T)$-Linkage in so-called frameworks in $O^*(2^k)$ time, improving on $O^*(2^{|S|+O(k^2 \log(k+|\mathbb{F}|))})$ over general fields (Fomin et al., SODA 2023). 3. Many instances of the Diverse X paradigm, finding a collection of $r$ solutions to a problem with a minimum mutual distance of $d$ in time $O^*(2^{r(r-1)d/2})$, improving solutions for $k$-Distinct Branchings from time $2^{O(k \log k)}$ to $O^*(2^k)$ (Bang-Jensen et al., ESA 2021), and for Diverse Perfect Matchings from $O^*(2^{2^{O(rd)}})$ to $O^*(2^{r^2d/2})$ (Fomin et al., STACS 2021). Here, all matroids are assumed to be represented over fields of characteristic 2. Over general fields, we achieve similar results at the cost of using exponential space by working over the exterior algebra. For a class of arithmetic circuits we call strongly monotone, this is even achieved without any loss of running time. However, the odd support sieving result appears to be specific to working over characteristic 2.

Andrés A. Plazas Malagón, Chris Waters, Alex Broughton et al.

The Vera C. Rubin Legacy Survey of Space and Time (LSST) will conduct an unprecedented optical survey of the southern sky, imaging the entire available sky every few nights for 10 years. To achieve its ambitious science goals of probing dark energy and dark matter, mapping the Milky Way, and exploring the transient optical sky, the systematic errors in the LSST data must be exquisitely controlled. Instrument signature removal (ISR) is a critical early step in LSST data processing to remove inherent camera effects from the raw images and produce accurate representations of the incoming light. This paper describes the current state of the ISR pipelines implemented in the LSST Science Pipelines software. The key steps in ISR are outlined, and the process of generating and verifying the necessary calibration products to carry out ISR is also discussed. Finally, an overview is given of how the Rubin data management system utilizes a data Butler and calibration collections to organize datasets and match images to appropriate calibrations during processing. Precise ISR will be essential to realize the potential of LSST to revolutionize astrophysics.

Robbie Webbe, A. J. Young

The X-ray Timing Instrument as part of the Neutron Star Interior Composition Explorer has the potential to examine the time-domain properties of compact objects in regimes not explored by previous timing instruments, due to its combination of high effective area and timing resolution. We consider the effects of instrumental deadtime at a range of effective countrates in a series of observations of the X-ray binary GX 339-4 to determine what effect deadtime has on photometric and Fourier frequency-domain products. We find that there are no significant inconsistencies across the functional detectors in the instrument, and that in the regimes where instrumental deadtime is a limiting factor on observations that previous approaches to dealing with deadtime, as applied to RXTE and other detectors, are still appropriate, and that performing deadtime corrections to lightcurves before creating Fourier products are not necessary at the count rates considered in our analysis.

Filippo Bonchi, Elena Di Lavore, Mario Román

Effectful Mealy machines, which we introduce, are a generalization of Mealy machines with global effects determined by an effectful triple. We provide semantics of effectful Mealy machines in terms of both bisimilarity and traces: bisimilarity is characterized syntactically, via uniform feedback; traces are constructed coinductively in terms of streams. We prove that this framework characterizes standard causal processes and existing flavours of Mealy machine, bisimilarity, and trace equivalence. In the commutative case, we introduce a monoidal generalization of Raney's causal functions: monoidal causal processes.

Luigi Scorzato

In recent years, the question of the reliability of Machine Learning (ML) methods has acquired significant importance, and the analysis of the associated uncertainties has motivated a growing amount of research. However, most of these studies have applied standard error analysis to ML models, and in particular Deep Neural Network (DNN) models, which represent a rather significant departure from standard scientific modelling. It is therefore necessary to integrate the standard error analysis with a deeper epistemological analysis of the possible differences between DNN models and standard scientific modelling and the possible implications of these differences in the assessment of reliability. This article offers several contributions. First, it emphasises the ubiquitous role of model assumptions (both in ML and traditional Science) against the illusion of theory-free science. Secondly, model assumptions are analysed from the point of view of their (epistemic) complexity, which is shown to be language-independent. It is argued that the high epistemic complexity of DNN models hinders the estimate of their reliability and also their prospect of long-term progress. Some potential ways forward are suggested. Thirdly, this article identifies the close relation between a model's epistemic complexity and its interpretability, as introduced in the context of responsible AI. This clarifies in which sense, and to what extent, the lack of understanding of a model (black-box problem) impacts its interpretability in a way that is independent of individual skills. It also clarifies how interpretability is a precondition for assessing the reliability of any model, which cannot be based on statistical analysis alone. This article focuses on the comparison between traditional scientific models and DNN models. But, Random Forest and Logistic Regression models are also briefly considered.

Yue WANG, Na SU

Data classification and grading is the foundation for ensuring the safe circulation of data and promoting the release of data value.This paper focuses on the key task of government data classification and grading in digital reform.Using a theoretical case study method and based on publicly released plans by various provincial governments and ministries, the implementation of government data classification and grading in China is systematically sorted and quantitatively analyzed.This paper summarizes four key processes and five characteristics of the implementation of government data classification and classification in China.Based on the special complexity of the classification and grading of government data, this paper puts forward four problems corresponding solutions in the implementation of the classification and grading of government data in China, such as unclear overall target positioning, different classification and grading objects, separated classification and grading relations, and different security grading standards.Based on the practice of classification and grading government data of a national ministry, this paper verifies the scientificity and effectiveness of the solutions, and provides a reference for constructing a unified national government data classification and grading system.

Javier Osca, Jiri Vala

Stochastic Optical Quantum Circuit Simulator (SOQCS) is a C++ and Python library which offers a framework to define, simulate and study quantum linear optical circuits in presence of various imperfections typically encountered in experiments. Quantum circuits can be defined from basic components, including emitters, linear optical elements, delays and detectors. The imperfections come from partial distinguishability of photons, lossy propagation media, unbalanced beamsplitters and non-ideal emitters and detectors for example. SOQCS also provides various simulator cores and tools to analyze the output. Furthermore, the configuration of detectors also includes postselection. SOQCS is developed using a modular approach in which different modules are applied in an automated easy to use manner. Furthermore, the modular approach allows for further extensions of the SOQCS capabilities in future.

Yao He, Jing Yang, Shaobo Li et al.

Abstract Catastrophic forgetting in neural networks is a common problem, in which neural networks lose information from previous tasks after training on new tasks. Although adopting a regularization method that preferentially retains the parameters important to the previous task to avoid catastrophic forgetting has a positive effect; existing regularization methods cause the gradient to be near zero because the loss is at the local minimum. To solve this problem, we propose a new continuous learning method with Bayesian parameter updating and weight memory (CL-BPUWM). First, a parameter updating method based on the Bayes criterion is proposed to allow the neural network to gradually obtain new knowledge. The diagonal of the Fisher information matrix is then introduced to significantly minimize computation and increase parameter updating efficiency. Second, we suggest calculating the importance weight by observing how changes in each network parameter affect the model prediction output. In the process of model parameter updating, the Fisher information matrix and the sensitivity of the network are used as the quadratic penalty terms of the loss function. Finally, we apply dropout regularization to reduce model overfitting during training and to improve model generalizability. CL-BPUWM performs very well in continuous learning for classification tasks on CIFAR-100 dataset, CIFAR-10 dataset, and MNIST dataset. On CIFAR-100 dataset, it is 0.8%, 1.03% and 0.75% higher than the best performing regularization method (EWC) in three task partitions. On CIFAR-10 dataset, it is 2.25% higher than the regularization method (EWC) and 0.7% higher than the scaled method (GR). It is 0.66% higher than the regularization method (EWC) on the MNIST dataset. When the CL-BPUWM method was combined with the brain-inspired replay model under the CIFAR-100 and CIFAR-10 datasets, the classification accuracy was 2.35% and 5.38% higher than that of the baseline method, BI-R + SI.

Huifang Yu, Zhirui Lv

Ring signcryption with no group administrator satisfies the decentralization and blockchain anonymity. In this article, we construct new lattice-based ring signcryption scheme suitable for consortium blockchain (CB-LRSCS), in which the smart contract controls the process of signcryption and unsigncryption to make the system be fair and reliable. CB-LRSCS can protect the user privacy by reducing the connection between blockchain and user information, and it satisfies the reliability in ethereum environment. CB-LRSCS also has the characteristics of high efficiency, anti-quantum, anti-forgery, confidentiality and unconditional anonymity, and it can be applied in the electronic finance system.

Jessica Rodrigues Esteves, Jorge C. S. Cardoso, Berenice Santos Gonçalves

The growing popularity of immersive virtual reality (iVR) technologies has opened up new possibilities for learning English. In the literature, it is possible to find several studies focused on the design, development, and evaluation of immersive virtual reality applications. However, there are no studies that systematize design recommendations for immersive virtual reality applications for English learning. To fill this gap, we present a systematic review that aims to identify design recommendations for immersive virtual reality English learning applications. We searched the ACM Digital Library, ERIC, IEEE Xplore, Scopus, and Web of Science (1 January 2010 to April 2023) and found that 24 out of 847 articles met the inclusion criteria. We identified 18 categories of design considerations related to design and learning and a design process used to create iVR applications. We also identified existing trends related to universities, publications, devices, human senses, and development platforms. Finally, we addressed study limitations and future directions for designing iVR applications for English learning.

Luca Turchet, Johan Pauwels

This paper investigates to which extent state of the art machine learning methods are effective in classifying emotions in the context of individual musical instruments, and how their performances compare with musically trained and untrained listeners. To address these questions we created a novel dataset of 391 classical and acoustic guitar excerpts annotated along four emotions (aggressiveness, relaxation, happiness and sadness) with three emotion intensity levels (low, medium, high), according to the intended emotion of 30 professional guitarists acting as both composers and performers. A first experiment investigated listeners’ perception involving 8 professional guitarists and 8 non-musicians. Results showed that the emotions intended by a composer-performer are not always well recognized by listeners, and in general not with the same intensity. Listeners’ identification accuracy was proportional to the intensity with which an emotion was expressed. Emotions were better recognized by musicians than by listeners without musical background. Such differences between the two groups were found for different intensity levels of the intended emotions. A second experiment investigated machine listening performance based on a transfer learning method. To compare machine and human identification accuracies fairly, we derived a fifth, “ambivalent” category from the machine listening output categories (i.e., excerpts rated with more than one predominant emotion). Results showed that the machine perception of emotions matched or even exceeded musicians’ performance for all emotions except “relaxation”. The differences between the intended and human-perceived emotions, as well as those due to musical training, suggest that a device or application involving a music emotion recognition system should take into account the characteristics of the users (in particular their musical expertise) as well as their roles (e.g., composers, performers, listeners). For developers this translates into the use of datasets annotated by different categories of annotators, whose role and musical expertise will match the characteristics of the end users. Such results are particularly relevant to the creation of emotionally-aware smart musical instruments.

Timo Freiesleben, Gunnar König, Christoph Molnar et al.

To learn about real world phenomena, scientists have traditionally used models with clearly interpretable elements. However, modern machine learning (ML) models, while powerful predictors, lack this direct elementwise interpretability (e.g. neural network weights). Interpretable machine learning (IML) offers a solution by analyzing models holistically to derive interpretations. Yet, current IML research is focused on auditing ML models rather than leveraging them for scientific inference. Our work bridges this gap, presenting a framework for designing IML methods-termed 'property descriptors' -- that illuminate not just the model, but also the phenomenon it represents. We demonstrate that property descriptors, grounded in statistical learning theory, can effectively reveal relevant properties of the joint probability distribution of the observational data. We identify existing IML methods suited for scientific inference and provide a guide for developing new descriptors with quantified epistemic uncertainty. Our framework empowers scientists to harness ML models for inference, and provides directions for future IML research to support scientific understanding.

Bettina Eska, Jasmin Niess, Florian Müller

There is a recommended body posture and hand position for playing every musical instrument, allowing efficient and quick movements without blockage. Due to humans' limited cognitive capabilities, they struggle to concentrate on several things simultaneously and thus sometimes lose the correct position while playing their instrument. Incorrect positions when playing an instrument can lead to injuries and movement disorders in the long run. Previous work in HCI mainly focused on developing systems to assist in learning an instrument. However, the design space for posture correction when playing a musical instrument has not yet been explored. In this position paper, we present our vision of providing subtle vibrotactile or thermal feedback to guide the focus of attention back to the correct posture when playing a musical instrument. We discuss our concept with a focus on motion recognition and feedback modalities. Finally, we outline the next steps for future research.

ZHANGJicheng(张继成), RENShuai(任帅), LINLi(林立) et al.

针对注水开发的多层砂岩油藏分层动态分析难度大等问题，在常规井层开发指标计算基础上，结合动、静态劈分方法，综合考虑渗透率、孔隙度、地层系数、含水饱和度、位置系数、措施系数及注水量系数，提出了一种既可将油、水井作为统一整体，又可对小层、方向流动分量开发指标进行定量计算的体现渗流力学本质的方法。用大庆油田N2-O1井组的产液剖面资料进行验证。结果表明，所提方法的计算结果与测量结果吻合度较高，精度平均值达75.11%。用该方法计算开发指标，适用性强，能较真实地反映各小层、各方向的产液情况，对现场应用具有指导意义。

Chenhan Huang, Daijiao Shi

In order to study the driving effect of industrial product design, a method based on the application of 3D simulation technology in industrial product design technology was proposed. This method introduces the information about the change in industrial product design in industrial enterprises and analyzes the application of 3D simulation technology in industrial product design by taking DIALux, industrial robot, and resource information search system as examples. The results show that the application of 3D simulation system needs to be combined with industrial software, and the development of industrial software business mainly based on 3D simulation technology is emphasized so that the business revenue of enterprises increases from 709 million yuan in 2029 to 1.385 billion yuan in 2021, with a compound growth rate of 25.01%, which has achieved good economic benefits. 3D simulation technology plays an important role in promoting the development of industrial product design technology. It is necessary to actively promote the integration between 3D simulation technology and industrial software.