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arXiv 2022年

作者： Li, Junyi Tang, Tianyi Gong, Zheng Yang, Lixin Yu, Zhuohao Chen, Zhipeng Wang, Jingyuan Zhao, Wayne Xin Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China Renmin University of China China Peng Cheng Laboratory China School of Computer Science and Engineering Beihang University China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Nowadays, pretrained language models (PLMs) have dominated the majority of NLP tasks. While, little research has been conducted on systematically evaluating the language abilities of PLMs. In this paper, we present a large-scale empirical study on genEral language ability evaluation of PLMs (ElitePLM). In our study, we design four evaluation dimensions, i.e., memory, comprehension, reasoning, and composition, to measure ten widely-used PLMs within five categories. Our empirical results demonstrate that: (1) PLMs with varying training objectives and strategies are good at different ability tests;(2) fine-tuning PLMs in downstream tasks is usually sensitive to the data size and distribution;(3) PLMs have excellent transferability between similar tasks. Moreover, the prediction results of PLMs in our experiments are released as an open resource for more deep and detailed analysis on the language abilities of PLMs. This paper can guide the future work to select, apply, and design PLMs for specific tasks. We have made all the details of experiments publicly available at https://***/RUCAIBox/ElitePLM. Copyright © 2022, The Authors. All rights reserved.

关键词： Computational linguistics

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Elementary symmetric partitions

arXiv

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arXiv 2024年

作者： Ballantine, Cristina Beck, George Merca, Mircea Sagan, Bruce E. Department of Mathematics and Computer Science College of the Holy Cross WorcesterMA01610 United States Department of Mathematics and Statistics Dalhousie University HalifaxNSB3H 4R2 Canada Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest BucharestRO-060042 Romania Academy of Romanian Scientists BucharestRO-050044 Romania Department of Mathematics Michigan State University East LansingMI48824 United States

Let ek(x1,..., x) be an elementary symmetric polynomial and let λ = (λ1,..., λ) be an integer partition. Define prek(λ) to be the partition whose parts are the summands in the evaluation ek(λ1,..., λ). The study of such partitions was initiated by Ballantine, Beck, and Merca who showed (among other things) that pre2 is injective as a map on binary partitions of n. In the present work we derive a host of identities involving the sequences which count the number of parts of a given value in the image of pre2. These include generating functions, explicit expressions, and formulas for forward differences. We generalize some of these to d-ary partitions and explore connections with color partitions. Our techniques include the use of generating functions and bijections on rooted partitions. We end with a list of conjectures and a direction for future *** Codes 05A17, 05E05 (Primary) 05A15, 05A19 (Secondary) © 2024, CC BY.

关键词： Integer linear programming

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Mexican violence displaces people, discourages international migration, and shrinks highway network connections

arXiv

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arXiv 2023年

作者： Coscia, Michele Gutiérrez-Romero, Roxana IT University of Copenhagen Computer Science Department Rued Langgaards Vej 7 Copenhagen2300 Denmark Center for International Development Harvard University CambridgeMA United States Policy and Quantitative Methods School of Business and Management Queen Mary University of London. Mile End campus LondonSE14 NS United Kingdom

This paper estimates the impact of violence on domestic and international migration in Mexico during 2005-2020. Unlike earlier studies, we use network analysis to quantify changes in the degree of interconnectedness among all municipalities in Mexico and with the US, breaking it down by origin-destination pairs. To identify the impact of violence on the changes of the migration network, we use instrumental variables. We exploit the exogenous variation in heroin street prices in the US, local prices of corn in Mexico, and differences in which regions received more Chinese immigrant communities during the 1930s, members of which introduced opium cultivation and drug smuggling networks in Mexico. These instruments help us identify the areas with a current presence of drug trafficking organizations and facing increases in violence. We show that municipalities that experience rises in homicide rates continue to attract immigrants within the country, but they face a larger rise in domestic emigration. The violence also discourages the return migration of Mexicans from the US and non-transit international emigration. Consistent with our findings, areas affected by violence also experience a long-term reduction in vehicle trips made in the highway network connecting them with the rest of the country. © 2023, CC BY-SA.

关键词： Costs

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Study of Manhattan’s consensus degrees through an extension based on the Uniform distribution

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Procedia computer science 2022年 199卷 643-649页

作者： J.M. Tapia F. Chiclana M.J. del Moral E. Herrera–Viedma Department of Quantitative Methods in Economic and Business University of Granada 18071 Granada Spain DIGITS Department of Informatics Faculty of Technology De Montfort University Leicester LE1 9BH UK University of Granada 18071 Granada Spain Department of Computer Science and A.I University of Granada 18071 Granada Spain

An important aspect to be considered in Group Decision Making problems is the study of consensus. Since in these problems it is desirable that the final decision is widely accepted, improving the consensus degree in a fair way is a very interesting task. This paper analyses the improvement in the consensus degrees -obtained by applying Manhattan distance- when the experts’ preferences are slightly modified using one of the properties of the Uniform distribution. We carry out an experimental study that shows the enhancement in different cases to which Uniform extension has been applied, with different number of both, experts and alternatives.

关键词： Consensus Group Decision Making Manhattan distance Uniform distribution

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On a Cyclometric Modification of the Chua System

On a Cyclometric Modification of the Chua System

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Bálint Nagy Antal Joós Attila Kovari Department of Mathematics Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Natural Sciences Kandó Kálmán Faculty of Electrical Engineering Institute of Electrophysics Obuda University Budapest Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eger Hungary Department of Natural Sciences Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary

Chaos is not only a unique chapter in the theory of dynamical systems but also a useful one with many applications in the field of communications. In this work a cyclometric modification of the well-known example of chaotic systems, the modified differential equations describing the behavior of the Chua-circuit is investigated. The results obtained can be applied in the encryption methods of the implementation of different communication channels like machine-machine or cognitive one.

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Breast cancer risk prediction in African women using Random Forest Classifier

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Cancer Treatment and Research Communications 2021年 28卷 100396-100396页

作者： Macaulay, Babafemi Oluropo Aribisala, Benjamin Segun Akande, Soji Alabi Akinnuwesi, Boluwaji Ade Olabanjo, Olusola Aanu Department of Computer Science Lagos State University Nigeria Department of Surgery Lagos State University Teaching Hospital Nigeria

Introduction: One of the most important steps in combating breast cancer is early and accurate diagnosis. Unfortunately, breast cancer is asymptomatic at the early stage, although some symptoms are presented at a later time, but at symptomatic stage treatment could be complicated or even become impossible thereby leading to death. Proper risk assessment is hence very important in reducing mortality. Some computational techniques have been developed for breast cancer risk assessment in the developed world, but such techniques do not work well in Africa because of the difference in risk profiles of African women e.g. later menarche, low drug abuse and low smoking rate. Aim: In this work, we propose a bespoke risk prediction model for African women using Random Forest Classifier (RFC) machine learning technique. methods: A total of 180 subjects were studied out of which 90 were confirmed cases of breast cancer and 90 were benign. Twenty-five risk factors were included, for example, smoking, alcohol intake, occupational hazards and age at menopause. Four approaches were empirically used in the feature selection, these are the use of Chi-Square, mutual information gain, Spearman correlation and the entire features. RFC algorithm was used to develop the prediction model. Results: We found that family history of breast cancer, dense breast, deliberate abortion, age at first child, fruit intake and regular exercise are predictors of breast cancer. The RFC model gave an accuracy of 91.67%, sensitivity of 87.10%, specificity of 96.55% and Area under curve (AUC) of 92% when all the risk factors were included in the model while an accuracy of 96.67%, sensitivity of 93.75%, specificity of 100% and AUC of 97% were obtained when correlation-selected features were included in the model. The Chi-Square selected features gave the best performance with 98.33% accuracy, 100% sensitivity, 96.55 specificity and 98% AUC. Mutual information gain selected feature gave the same results as Ch

关键词： African women Breast cancer Feature selection Machine learning Random forest Risk prediction

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Human Trajectory Prediction Using Stacked Temporal Convolutional Network

Human Trajectory Prediction Using Stacked Temporal Convoluti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yajie Wang Jinli Ma Zhao Pei Jun Li School of Information Engineering Shaanxi Xueqian Normal University Xi'an China School of Computer Science Shaanxi Normal University Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Xi'an China School of Computer Science Nanjing Normal University Nanjing China

ISBN: (纸本)9781665416597

Human trajectory prediction is essential for avoiding collisions in crowded environments in the navigation of autonomous driving system. In the past few years, human trajectory prediction based on deep learning methods has been extensively studied and significant progress has been made. However, both the social interactions among pedestrians and the intention of the pedestrians are unpredictable. They cause great difficulty in forecasting the future paths. Previous methods use recurrent architectures which will produce lots of inefficient parameters in training process and lead to the low accuracy. In this paper, we propose a novel method to predict human trajectory by using stacked temporal convolutional network. First, we use a kernel function to embed the social interactions among pedestrians with the adjacency matrix. Next, we represent a spatial temporal representation of all pedestrian in the scene. Then, we conduct a spatial temporal embedding for observed trajectories. Finally, the stacked temporal convolutional network receives the features extracted by spatial temporal network and predicts the future human trajectories. The proposed approach is a data-driven approach which combines the advantages of spatial temporal graph convolutional network and stacked temporal convolutional network. Experimental results on two public datasets demonstrate that our stacked temporal method has high data efficiency and outperforms the existing methods.

关键词： Deep learning Training Privacy Navigation Predictive models Feature extraction Trajectory

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Investigation of the effectiveness of applying ChatGPT in Dialogic teaching Using Electroencephalography

arXiv

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arXiv 2024年

作者： Zhang, Jiayue Liu, Yiheng Cai, Wenqi Wu, Lanlan Peng, Yali Yu, Jingjing Qi, Senqing Long, Taotao Ge, Bao School of Physics and Information Technology Shaanxi Normal University Xi’an China School of Psychology Shaanxi Normal University Xi’an China School of Computer Science Shaanxi Normal University Xi’an China Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi’an China Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China Key Laboratory of Modern Teaching Technology Ministry of Education School of Physics and Information Technology Shaanxi Normal University Xi’an China

In recent years, the rapid development of artificial intelligence technology, especially the emergence of large language models (LLMs) such as ChatGPT, has presented significant prospects for application in the field of education. LLMs possess the capability to interpret knowledge, answer questions, and consider context, thus providing support for dialogic teaching to students. Therefore, an examination of the capacity of LLMs to effectively fulfill instructional roles, thereby facilitating student learning akin to human educators within dialogic teaching scenarios, is an exceptionally valuable research topic. This research recruited 34 undergraduate students as participants, who were randomly divided into two groups. The experimental group engaged in dialogic teaching using ChatGPT, while the control group interacted with human teachers. Both groups learned the histogram equalization unit in the information-related course "Digital Image Processing". The research findings show comparable scores between the two groups on the retention test. However, students who engaged in dialogue with ChatGPT exhibited lower performance on the transfer test. Electroencephalography data revealed that students who interacted with ChatGPT exhibited higher levels of cognitive activity, suggesting that ChatGPT could help students establish a knowledge foundation and stimulate cognitive activity. However, its strengths on promoting students' knowledge application and creativity were insignificant. Based upon the research findings, it is evident that ChatGPT cannot fully excel in fulfilling teaching tasks in the dialogue teaching in information related courses. Combining ChatGPT with traditional human teachers might be a more ideal approach. The synergistic use of both can provide students with more comprehensive learning support, thus contributing to enhancing the quality of teaching. © 2024, CC BY.

关键词： Students

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Image Dehazing Algorithm Based on Particle Swarm Optimization for Sky Region Segmentation 21

Image Dehazing Algorithm Based on Particle Swarm Optimizatio...

引用

21st International Symposium on Distributed Computing and Applications for Business Engineering and science, DCABES 2022

作者： Zhou, Hao Yuan, Changjiu Pan, Hongbo Yang, Yue Wang, Ziyan Chen, Xiangyang College of Big Data and Artificial Intelligence Chizhou University Chizhou Anhui247000 China College of Big Data and Intelligent Engineering Chongqing College of International Business and Economics Chongqing401520 China College of Big Data and Artificial Intelligence Chizhou University Chizhou Anhui247100 China Anqing Medical College Basic Teaching Department AnQing Anhui246001 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781665454629

The well-known Dark Channel Prior (DCP) dehazing algorithm does not work well in the sky area, thus, we propose an image dehazing method for segmented sky regions based on Particle Swarm Optimization (PSO). First, we apply PSO to the Otsu segmentation method to accurately segment sky and non-sky regions in the hazy image. Second, the transmission of non-sky regions is derived by DCP. A transmission compensation method is proposed to obtain the transmission for the sky regions. The transmission is then refined by gradient-domain guided filtering. Finally, the restored haze-free image is obtained through the Atmospheric Scattering Model (ASM). Quantitative and qualitative experiments show that the proposed method has a better dehazing effect, especially for hazy images with the sky areas. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Multi-installment scheduling for coarse-grained divisible loads

Multi-installment scheduling for coarse-grained divisible lo...

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International Conference on Computational Intelligence and Security

作者： Kangjian Wu Yuan Li Xiaobo Song Xiaoli Wang School of Computer Science and Technology Xidian University Xi‘an China Basic Course Teaching Department Xi’ an Vocational and Technical College Xi'an China The 20th Research Institute of China Electronics Technology Group Corporation Xi'an China

The large-scale arbitrarily divisible load scheduling has been solved effectively by periodic multi-installment scheduling (PMIS), but there are relatively few studies on coarse-grained divisible loads. Therefore, we propose a coarse-grained multi-installment scheduling model in this paper, named CMIS. Two crucial issues were solved: First, an optimal load partition was derived through a strict mathematical derivation; Second, an optimal number of installments was obtained between the subtle upper and lower bounds we found. Finally, we compare the proposed model with an up-to-date model and the experimental results show that the proposed model outperforms the existing model in obtaining a shorter makespan.

关键词： Schedules Computational modeling Mathematical models Security Computational intelligence Load modeling

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