Hasil untuk "General Works"

and P G Kevan, H. G. Baker

J. Flusser, T. Suk

S. P. Lin, R. Reitz

F. Berezin, A. Kirillov

David Weimer

This issue of the Bibliography includes items published from 2015 to 2018. The form of entries reflects the order and punctuation conventions of ISBD (M) : International Standard Bibliographic Description for Monographic Publications, rev. ed. (s.l. : International Federation of Library Associations and Institutions, 2002. — http://www.ifla.org/VII/s13/pubs/isbd_m0602.pdf), but with modifications to accommodate articles in journals and collective works. Some English translations or paraphrases of titles have been supplied. The abbreviation ‘ill.’ alone implies illustration(s) of a cartographic nature; where both ‘ill.’ and ‘maps’ occur together illustrations of a general and of a cartographic nature are included.

P. D. Costa, Jason Rhuggenaath, Yingqian Zhang et al.

Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions at a faster rate than previous state-of-the-art deep learning methods.

Riccardo Gervasi, L. Mastrogiacomo, F. Franceschini

Matteo Campanelli, D. Fiore, Anaïs Querol

M. Mowlaei, M. S. Abadeh, Hamidreza Keshavarz

Abstract Reviews expressed in e-commerce websites have formed an important source of information for both consumers and enterprises. Text sentiment analysis approaches aim to detect the sentiments of written reviews in order to achieve a better understanding of public opinion towards entities. Aspect-based sentiment analysis deals with capturing sentiments expressed towards each aspect of entities. A common approach in sentiment analysis problems is to take advantage of lexicons to generate features for classification of reviews. Existing aspect-based approaches fail to properly adapt general lexicons to the context of aspect-based datasets which results in reduced performance. To address this problem, this paper proposes extensions of two lexicon generation methods for aspect-based problems; one using statistical methods, and another using a genetic algorithm presented in our previous works. The aforementioned lexicons are then fused with prominent static lexicons to classify the aspects in reviews; this outperforms our previous works according to the t-test results (with a p-value of less than 0.001). Experimental results indicate that the proposed approach outperforms baseline methods in aspect-based polarity classification on Bing Liu's customer review datasets and improves precision, recall and F-measure by 6.0, 1.0, and 7.4 percentage points respectively.

D. Coady, M. Grosh, J. Hoddinott

E. Garfield

Anastasios K. Papazafeiropoulos, Cunhua Pan, P. Kourtessis et al.

Intelligent reflecting surface (IRS), consisting of low-cost passive elements, is a promising technology for improving the spectral and energy efficiency of the fifth-generation (5G) and beyond networks. It is also noteworthy that an IRS can shape the reflected signal propagation. Most works in IRS-assisted systems have ignored the impact of the inevitable residual hardware impairments (HWIs) at both the transceiver hardware and the IRS while any relevant works have addressed only simple scenarios, e.g., with single-antenna network nodes and/or without taking the randomness of phase noise at the IRS into account. In this work, we aim at filling up this gap by considering a general IRS-assisted multi-user (MU) multiple-input single-output (MISO) system with imperfect channel state information (CSI) and correlated Rayleigh fading. In parallel, we present a general computationally efficient methodology for IRS reflecting beamforming (RB) optimization. Specifically, we introduce an advantageous channel estimation (CE) method for such systems accounting for the HWIs. Moreover, we derive the uplink achievable spectral efficiency (SE) with maximal-ratio combining (MRC) receiver, displaying three significant advantages being: 1) its closed-form expression, 2) its dependence only on large-scale statistics, and 3) its low training overhead. Notably, by exploiting the first two benefits, we achieve to perform optimization with respect to the RB that can take place only per several coherence intervals, and thus, reduces significantly the computational cost compared to other methods based on instantaneous CSI which require frequent phase optimization. Among the insightful observations, we highlight that the unrealistic assumption of uncorrelated Rayleigh fading does not allow optimization of the SE, which makes the application of an IRS ineffective. Also, in the case that the phase drifts, describing the distortion of the phases in the RBM, are uniformly distributed, the presence of an IRS provides no advantage. The analytical results outperform previous works and are verified by Monte-Carlo (MC) simulations.

R. Blythe, M. Evans

We consider the general problem of determining the steady state of stochastic nonequilibrium systems such as those that have been used to model (among other things) biological transport and traffic flow. We begin with a broad overview of this class of driven-diffusive systems—which includes exclusion processes—focusing on interesting physical properties, such as shocks and phase transitions. We then turn our attention specifically to those models for which the exact distribution of microstates in the steady state can be expressed in a matrix-product form. In addition to a gentle introduction to this matrix-product approach, how it works and how it relates to similar constructions that arise in other physical contexts, we present a unified, pedagogical account of the various means by which the statistical mechanical calculations of macroscopic physical quantities are actually performed. We also review a number of more advanced topics, including nonequilibrium free-energy functionals, the classification of exclusion processes involving multiple particle species, existence proofs of a matrix-product state for a given model and more complicated variants of the matrix-product state that allow various types of parallel dynamics to be handled. We conclude with a brief discussion of open problems for future research.

Gustavo Guanuco, Juan Enriquez, Sandra Casas

API-First es un enfoque de desarrollo de software emergente que promueve situar a las APIs web en el centro del diseño de software, permitiendo que los equipos de desarrollo trabajen en ellas antes que en el resto de las aplicaciones. Diversos autores reportan que API-First no ha sido abordado por estudios académicos específicamente, aunque se encuentra muy instalado en la industria API web. En este trabajo se plantea describir la adopción de API-First, para ello se ejecuta un caso de estudio real a partir de una guía de pasos y actividades. La fuerte dependencia con las herramientas y tecnologías mostraron que fue necesario la realización de ajustes en la especificación original de las APIs, dejando en evidencia limitantes en la automatización de la implementación, como así también en la experiencia del uso de estas herramientas.

Ghosh Biyas, Singh Rajdeo, Sawant Madhuri

The integration of digital technology, particularly virtual reality, is proving to be a pivotal tool in preserving and sharing the rich cultural heritage found within Cave art sites. This article deliberates on the use and application of digital technology in the field of cave painting conservation that provides an authentic and effective method of Cave art preservation for the future. Employing advanced techniques of photogrammetry, terrestrial laser scanning, structured light scanning, and high-resolution photography can digitally preserve the intricate details of Cave art, while the utilization of humidity, temperature, and CO2 sensors may provide a comprehensive digital monitoring system to track the condition of Cave art over time. This paper deals with the evolving landscape of technologies and their application to safeguard Cave art from environmental degradation and anthropogenic factors. The digitization of cave art has the potential to accurately conserve a site if used in an effective manner while also allowing visitors to experience this art form in ways never before feasible. The application of advanced digital technology in cave art conservation is not merely a choice but a necessity, considering the impermanent nature of these paintings in their natural environment. This paper underscores the urgency and significance of leveraging digital tools to ensure the enduring legacy of Cave art, an example of how digital technologies, as they evolve, will play an increasingly essential role in the conservation and dissemination of our world’s extraordinary cultural heritage.

Luciano Bode

Xianghao Yu, Jun Zhang, M. Haenggi et al.

Millimeter wave (mm-wave) communications is considered a promising technology for 5G networks. Exploiting beamforming gains with large-scale antenna arrays to combat the increased path loss at mm-wave bands is one of the defining features. However, previous works on mm-wave network analysis usually adopted oversimplified antenna patterns for tractability, which can lead to significant deviation from the performance with actual antenna patterns. In this paper, using tools from stochastic geometry, we carry out a comprehensive investigation on the impact of directional antenna arrays in mm-wave networks. We first present a general and tractable framework for coverage analysis with arbitrary distributions for interference power and arbitrary antenna patterns. It is then applied to mm-wave ad hoc and cellular networks, where two sophisticated antenna patterns with desirable accuracy and analytical tractability are proposed to approximate the actual antenna pattern. Compared with previous works, the proposed approximate antenna patterns help to obtain more insights on the role of directional antenna arrays in mm-wave networks. In particular, it is shown that the coverage probabilities of both types of networks increase as a non-decreasing concave function with the antenna array size. The analytical results are verified to be effective and reliable through simulations, and numerical results also show that large-scale antenna arrays are required for satisfactory coverage in mm-wave networks.

Guili Ding, Guili Ding, Gaoyang Yan et al.

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather.

Fuad Mohammed Frieh, Hanan Khaled Ibrahim

Many researchers confirm that the time we live nowadays is witnessing conflicts and wars which made human being face psychological and biological stressors. Psychological literature suggested that such stressors could affect negatively our psychological and biological well-being. Therefore, so many studies were conducted investigating the negative impact of such stressors on personality and how our defense mechanisms could face such difficulties. The current study aims to identify the effectiveness of a counseling program in reducing anxiety disorders and strengthening the psychological immune system among students. To achieve the aims of the current study, the researchers adopted the experimental method and counseling program. The program was applied among a sample of (80) female students who got the highest scores on the Psychological Immune System Scale (PISS) and Taylor's Explicit Anxiety Scale which consists of 48 items. The group was divided into two groups (experimental and control group). Each group consisted of (40) students. After conducting the parity process in the variables of age, number of family members, birth order, economic and education level, the results showed that the counseling program was effective in increasing the effectiveness of psychological immunity and reducing the manifestations of anxiety among the experimental group. The recommendations and suggestions are discussed.

G. V. Stankevich

The article analyzes the features of the entrepreneurial legal personality of juvenile entrepreneurs and the mechanism of its formation and implementation. The author makes a proposal to indicate in the registration certificate of an entrepreneur that the person is under age. The author analyses particular features of the protection of the legal capacity of juvenile entrepreneurs in business activities.