Hasil untuk "General Works"

L. Goerigk, S. Grimme

J. Cosier, A. Glazer

Zhilin Zhang, Zhouyue Pi, Benyuan Liu

Heart rate monitoring using wrist-type photoplethysmographic signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this study, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/h showed that the average absolute error of heart rate estimation was 2.34 beat per minute, and the Pearson correlation between the estimates and the ground truth of heart rate was 0.992. This framework is of great values to wearable devices such as smartwatches which use PPG signals to monitor heart rate for fitness.

E. Lind, K. van den Bos

Xiaohe Dai, Zhiyuan Huang

The emoticon picture clarity in online service encounters has been overlooked in consumer research. Our study intends to investigate how emoticon picture clarity influences consumer service satisfaction. Across four experiments and a single-paper meta-analysis, we demonstrate that when service providers use clear rather blurred emoticon pictures to communicate with consumers, consumers will have higher service satisfaction (Study 1). This effect is attributed to the higher processing fluency induced by clear emoticon pictures, which in turn triggers greater satisfaction (Studies 2 and 3). Furthermore, this effect is weakened when consumers experience cognitive load (Study 4). These findings provide novel insights into consumers' biased evaluations of service providers and offer valuable guidance for marketers to enhance online shopping services through the strategic use of emoticon pictures.

Manuel Cuerno, Luis Guijarro, Rosa María Arnaldo Valdés et al.

Analyzing flight trajectory data sets poses challenges due to the intricate interconnections among various factors and the high dimensionality of the data. Topological Data Analysis (TDA) is a way of analyzing big data sets focusing on the topological features this data sets have as point clouds in some metric space. Techniques as the ones that TDA provides are suitable for dealing with high dimensionality and intricate interconnections. This paper introduces TDA and its tools and methods as a way to derive meaningful insights from ATM data. Our focus is on employing TDA to extract valuable information related to airports. Specifically, by utilizing persistence landscapes (a potent TDA tool) we generate footprints for each airport. These footprints, obtained by averaging over a specific time period, are based on the deviation of trajectories and delays. We apply this method to the set of Spanish' airports in the Summer Season of 2018. Remarkably, our results align with the established Spanish airport classification and raise intriguing questions for further exploration. This analysis serves as a proof of concept, showcasing the potential application of TDA in the ATM field. While previous works have outlined the general applicability of TDA in aviation, this paper marks the first comprehensive application of TDA to a substantial volume of ATM data. Finally, we present conclusions and guidelines to address future challenges in the ATM domain.

Mingxing Zhang, Junchang Chen, Shitong Zhang et al.

Crystalline porous materials such as covalent organic frame-works (COFs) are advanced materials to tackle challenges of catalysis and separation in industrial processes. Their synthetic routes often require elevated temperatures, closed systems with high pressure, and long reaction time which hampers their in-dustrial applications. Here, we use a traditionally unperceived strategy to assemble COFs by electron beam irradiation with controlled received dosage, contrasting sharply with the previ-ous observation that radiation damages the crystallinity of sol-ids. Such synthesis by electron beam irradiation can be achieved under ambient condition within minutes, and the pro-cess is amendable for large-scale productions. The intense and targeted energy input on reactants leads to new reaction path-ways that favor COFs formation with nearly quantitative yields. This strategy is applicable not only to known COFs, but also to new series of flexible COFs that are difficult to obtain using traditional methods.

Bulbul Ahamed, Mohammad Rashed Hasan Polas, Ahmed Imran Kabir et al.

The purpose of the study is to examine how cybersecurity knowledge, password security, and self-perception of skill affect cybersecurity awareness issues via the mediating lens of cybersecurity attitude among university students in Bangladesh. A sample of 430 university students from two public and three private universities provided the data in Dhaka, Bangladesh. An approach known as stratified random sampling was used in this cross-sectional study. The positivist approach was used, and a hypothetical statistical induction technique was used. The research constructs, which were adopted from earlier studies, were measured using scales that had undergone validation. Smart PLS-SEM 3.3.9 was used to quantitatively analyze the data. The results indicated a positive and significant association between cybersecurity knowledge and password security with cybersecurity awareness. No conventional association was found between self-perception of skills and cybersecurity awareness. Moreover, the data analysis confirmed that cybersecurity attitudemediates the relationship between cybersecurity knowledge, password security and self-perception of skills with cybersecurity awareness. This study implies that more effort needs to be put into informing the general people likely students about cybersecurity and ethical internet use. Furthermore, the main contribution of this study is to emphasize the need of raising cybersecurity awareness among students.

Qi Peng, Shuichiro Yokoyama, Kiyotomo Ichiki

A modification to the vis-viva equation that accounts for general relativistic effects is introduced to enhance the accuracy of predictions of orbital motion and precession. The updated equation reduces to the traditional vis-viva equation under Newtonian conditions and is a more accurate tool for astrodynamics than the traditional equation. Preliminary simulation results demonstrate the application potential of the modified vis-viva equation for more complex n-body systems. Spherical symmetry is assumed in this approach; however, this limitation could be removed in future research. This study is a pivotal step toward bridging classical and relativistic mechanics and thus makes an important contribution to the field of celestial dynamics.

A. Jajarmi, D. Baleanu

This paper deals with a general form of fractional optimal control problems involving the fractional derivative with singular or non‐singular kernel. The necessary conditions for the optimality of these problems are derived and a new numerical method is designed to solve these equations effectively. Simulation results indicate that the proposed method works well and provides satisfactory results with regard to accuracy and computational effort. Comparative results also verify that a particular case with Mittag‐Leffler kernel improves the performance of the controlled system in terms of the transient response compared to the other fractional‐ and integer‐order derivatives.

Wenbing Huang, Yu Rong, Tingyang Xu et al.

Increasing the depth of GCN, which is expected to permit more expressivity, is shown to incur performance detriment especially on node classification. The main cause of this lies in over-smoothing. The over-smoothing issue drives the output of GCN towards a space that contains limited distinguished information among nodes, leading to poor expressivity. Several works on refining the architecture of deep GCN have been proposed, but it is still unknown in theory whether or not these refinements are able to relieve over-smoothing. In this paper, we first theoretically analyze how general GCNs act with the increase in depth, including generic GCN, GCN with bias, ResGCN, and APPNP. We find that all these models are characterized by a universal process: all nodes converging to a cuboid. Upon this theorem, we propose DropEdge to alleviate over-smoothing by randomly removing a certain number of edges at each training epoch. Theoretically, DropEdge either reduces the convergence speed of over-smoothing or relieves the information loss caused by dimension collapse. Experimental evaluations on simulated dataset have visualized the difference in over-smoothing between different GCNs. Moreover, extensive experiments on several real benchmarks support that DropEdge consistently improves the performance on a variety of both shallow and deep GCNs.

Shunhua Jiang, Zhao Song, Omri Weinstein et al.

The fastest known LP solver for general (dense) linear programs is due to [Cohen, Lee and Song’19] and runs in O*(nω +n2.5−α/2 + n2+1/6) time. A number of follow-up works [Lee, Song and Zhang’19, Brand’20, Song and Yu’20] obtain the same complexity through different techniques, but none of them can go below n2+1/6, even if ω=2. This leaves a polynomial gap between the cost of solving linear systems (nω) and the cost of solving linear programs, and as such, improving the n2+1/6 term is crucial toward establishing an equivalence between these two fundamental problems. In this paper, we reduce the running time to O*(nω +n2.5−α/2 + n2+1/18) where ω and α are the fast matrix multiplication exponent and its dual. Hence, under the common belief that ω ≈ 2 and α ≈ 1, our LP solver runs in O*(n2.055) time instead of O*(n2.16).

Lemeng Wu, Bo Liu, P. Stone et al.

We propose firefly neural architecture descent, a general framework for progressively and dynamically growing neural networks to jointly optimize the networks' parameters and architectures. Our method works in a steepest descent fashion, which iteratively finds the best network within a functional neighborhood of the original network that includes a diverse set of candidate network structures. By using Taylor approximation, the optimal network structure in the neighborhood can be found with a greedy selection procedure. We show that firefly descent can flexibly grow networks both wider and deeper, and can be applied to learn accurate but resource-efficient neural architectures that avoid catastrophic forgetting in continual learning. Empirically, firefly descent achieves promising results on both neural architecture search and continual learning. In particular, on a challenging continual image classification task, it learns networks that are smaller in size but have higher average accuracy than those learned by the state-of-the-art methods.

Fitra Dharma, Mega Metalia, Sari Indah Oktanti Sembiring

Inseparable from managing regional fixed assets is the quality of information or data utilized by each unit. The quality of accounting information in government is heavily dependent on the leadership's commitment, the effectiveness of internal control, and the execution of good governance, according to various published works; nevertheless, this must be demonstrated further. Consequently, this study aims to investigate the link and size of the influence of these three elements on the quality of accounting information and their impact on the efficacy of local government fixed asset management. In Indonesia, 34 provincial governments, 416 district governments, and 98 city governments were surveyed for this quantitative research. This study included 529 participants. The research data was gathered using a questionnaire instrument that included in-person interviews—data analysis using the Structural Equation Modeling (SEM) method with Lisrel 8.8 statistical software. The explanation of research findings is both descriptive and causally explanatory. In general, local governments in Indonesia have excellent accounting data and management of fixed assets. In carrying out local government tasks, the local government has also built an effective internal control system and excellent governance. The study's findings demonstrate that the effectiveness of internal control and the function of good governance substantially impact the quality of accounting data. Similarly, leadership commitment, the importance of good governance, and the accuracy of accounting information substantially impact the success of fixed asset management. However, internal control efficacy does not significantly impact managing local governments' fixed assets

Joff P. N. Bradley, Mateo Belgrano (trad.)

Ari Nurfikri, Triana Karnadipa, Karin Amelia Safitri et al.

In submitting conference proceedings to <i>Proceedings</i>, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

María Gabriela Micheletti

Shruthi Gorantala, Rob Springer, Sean Purser-Haskell et al.

Fully homomorphic encryption (FHE) is an encryption scheme which enables computation on encrypted data without revealing the underlying data. While there have been many advances in the field of FHE, developing programs using FHE still requires expertise in cryptography. In this white paper, we present a fully homomorphic encryption transpiler that allows developers to convert high-level code (e.g., C++) that works on unencrypted data into high-level code that operates on encrypted data. Thus, our transpiler makes transformations possible on encrypted data. Our transpiler builds on Google's open-source XLS SDK (https://github.com/google/xls) and uses an off-the-shelf FHE library, TFHE (https://tfhe.github.io/tfhe/), to perform low-level FHE operations. The transpiler design is modular, which means the underlying FHE library as well as the high-level input and output languages can vary. This modularity will help accelerate FHE research by providing an easy way to compare arbitrary programs in different FHE schemes side-by-side. We hope this lays the groundwork for eventual easy adoption of FHE by software developers. As a proof-of-concept, we are releasing an experimental transpiler (https://github.com/google/fully-homomorphic-encryption/tree/main/transpiler) as open-source software.

Y. V. Tan, Jason A. Roy

Bayesian additive regression trees (BART) is a flexible prediction model/machine learning approach that has gained widespread popularity in recent years. As BART becomes more mainstream, there is an increased need for a paper that walks readers through the details of BART, from what it is to why it works. This tutorial is aimed at providing such a resource. In addition to explaining the different components of BART using simple examples, we also discuss a framework, the General BART model that unifies some of the recent BART extensions, including semiparametric models, correlated outcomes, and statistical matching problems in surveys, and models with weaker distributional assumptions. By showing how these models fit into a single framework, we hope to demonstrate a simple way of applying BART to research problems that go beyond the original independent continuous or binary outcomes framework.

V. Tarasov

For the first time, a general fractional calculus of arbitrary order was proposed by Yuri Luchko in 2021. In Luchko works, the proposed approaches to formulate this calculus are based either on the power of one Sonin kernel or the convolution of one Sonin kernel with the kernels of the integer-order integrals. To apply general fractional calculus, it is useful to have a wider range of operators, for example, by using the Laplace convolution of different types of kernels. In this paper, an extended formulation of the general fractional calculus of arbitrary order is proposed. Extension is achieved by using different types (subsets) of pairs of operator kernels in definitions general fractional integrals and derivatives. For this, the definition of the Luchko pair of kernels is somewhat broadened, which leads to the symmetry of the definition of the Luchko pair. The proposed set of kernel pairs are subsets of the Luchko set of kernel pairs. The fundamental theorems for the proposed general fractional derivatives and integrals are proved.