Data-Driven Approaches for Spatio-Temporal Analysis: A Survey of the State-of-the-Arts

With the advancement of telecommunications, sensor networks, crowd sourcing, and remote sensing technology in present days, there has been a tremendous growth in the volume of data having both spatial and temporal references. This huge volume of available spatio-temporal (ST) data along with the recent development of machine learning and computational intelligence techniques has incited the current research concerns in developing various data-driven models for extracting useful and interesting patterns, relationships, and knowledge embedded in such large ST datasets. In this survey, we provide a structured and systematic overview of the research on data-driven approaches for spatio-temporal data analysis. The focus is on outlining various state-of-the-art spatio-temporal data mining techniques, and their applications in various domains. We start with a brief overview of spatio-temporal data and various challenges in analyzing such data, and conclude by listing the current trends and future scopes of research in this multi-disciplinary area. Compared with other relevant surveys, this paper provides a comprehensive coverage of the techniques from both computational/methodological and application perspectives. We anticipate that the present survey will help in better understanding various directions in which research has been conducted to explore data-driven modeling for analyzing spatio-temporal data.

     

From computational intelligence to Web intelligence

The authors explore three topics in computational intelligence: machine translation, machine learning and user interface design and speculate on their effects on Web intelligence. Systems that can communicate naturally and learn from interactions will power Web intelligence's long term success. The large number of problems requiring Web-specific solutions demand a sustained and complementary effort to advance fundamental machine-learning research and incorporate a learning component into every Internet interaction. Traditional forms of machine translation either translate poorly, require resources that grow exponentially with the number of languages translated, or simplify language excessively. Recent success in statistical, nonlinguistic, and hybrid machine translation suggests that systems based on these technologies can achieve better results with a large annotated language corpus. Adapting existing computational intelligence solutions, when appropriate for Web intelligence applications, must incorporate a robust notion of learning that will scale to the Web, adapt to individual user requirements, and personalize interfaces.     

系统辨识算法的复杂性、收敛性及计算效率研究

实践中经常会遇到大型计算问题和优化问题, 使得求解问题算法的复杂性、计算量和计算精度等成为突出问题, 特别是大规模非线性多变量系统的辨识. 对此, 提出几个有趣的研究课题: 1) 利用信息滤波技术和多新息辨识理论研究能提高辨识精度的大规模系统辨识理论与方法; 2) 利用递阶辨识原理研究维数高、变量数目多、计算量小的多变量系统递阶辨识方法; 3) 利用鞅收敛理论建立非线性多变量系统辨识方法的收敛理论; 4) 利用并行计算与递阶计算技术提高辨识算法的计算效率, 以解决一类大规模非线性多变量系统的模型化问题.

     

Using artificial intelligence technology to fight COVID-19: a review

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.

     

The intelligent critic framework for advanced optimal control

The idea of optimization can be regarded as an important basis of many disciplines and hence is extremely useful for a large number of research fields, particularly for artificial-intelligence-based advanced control design. Due to the difficulty of solving optimal control problems for general nonlinear systems, it is necessary to establish a kind of novel learning strategies with intelligent components. Besides, the rapid development of computer and networked techniques promotes the research on optimal control within discrete-time domain. In this paper, the bases, the derivation, and recent progresses of critic intelligence for discrete-time advanced optimal control design are presented with an emphasis on the iterative framework. Among them, the so-called critic intelligence methodology is highlighted, which integrates learning approximators and the reinforcement formulation.

     

Analogical proportions

Analogy-making is at the core of human and artificial intelligence and creativity with applications to such diverse tasks as proving mathematical theorems and building mathematical theories, common sense reasoning, learning, language acquisition, and story telling. This paper introduces from first principles an abstract algebraic framework of analogical proportions of the form ‘a is to b what c is to d’ in the general setting of universal algebra. This enables us to compare mathematical objects possibly across different domains in a uniform way which is crucial for AI-systems. It turns out that our notion of analogical proportions has appealing mathematical properties. As we construct our model from first principles using only elementary concepts of universal algebra, and since our model questions some basic properties of analogical proportions presupposed in the literature, to convince the reader of the plausibility of our model we show that it can be naturally embedded into first-order logic via model-theoretic types and prove from that perspective that analogical proportions are compatible with structure-preserving mappings. This provides conceptual evidence for its applicability. In a broader sense, this paper is a first step towards a theory of analogical reasoning and learning systems with potential applications to fundamental AI-problems like common sense reasoning and computational learning and creativity.

     

FPGA acceleration analysis of LibSVM predictors based on high-level synthesis

Real-time artificial intelligence is the next frontier in data analysis and processing. Support vector machines (SVM) are well-known machine learning algorithms employed in many fields thanks to their high performance and reasonable memory cost. Although several libraries implement this algorithm, LibSVM stands out for its robust support, precision and flexibility. The main drawback is the amount of computational resources associated with nonlinear classification tasks. The development of hardware coprocessors to accelerate the most critical functions of this library is a must. The use of accelerated SVMs through field-programmable gate array (FPGA) coprocessors is challenging due to the computational complexity required. High-level synthesis (HLS) tools allow to generate fast solutions with reasonable accelerations. This work presents an analysis that provides to LibSVM users an a priori knowledge about the limits of their accelerated SVM predictors based on their training parameters, their required speedup and their available resources. The analysis also covers different data type representations (floating point and 16, 32 and 64 fixed point) and also can be used to estimate the better solution for LibSVM-based applications.

     

Topology optimization in OpenMDAO

Recently, topology optimization has drawn interest from both industry and academia as the ideal design method for additive manufacturing. Topology optimization, however, has a high entry barrier as it requires substantial expertise and development effort. The typical numerical methods for topology optimization are tightly coupled with the corresponding computational mechanics method such as a finite element method and the algorithms are intrusive, requiring an extensive understanding. This paper presents a modular paradigm for topology optimization using OpenMDAO, an open-source computational framework for multidisciplinary design optimization. This provides more accessible topology optimization algorithms that can be non-intrusively modified and easily understood, making them suitable as educational and research tools. This also opens up further opportunities to explore topology optimization for multidisciplinary design problems. Two widely used topology optimization methods—the density-based and level-set methods—are formulated in this modular paradigm. It is demonstrated that the modular paradigm enhances the flexibility of the architecture, which is essential for extensibility.

     

Adaptive-predictive game-playing programs

Abstract

predictive search (APS) is a new method by which systems can improve their search performance through experience. It is believed that the development of such methods is critical, as currently a tremendous number of computational results are potentially wasted by not integrating search and partial search results into the knowledge of a problem-solving system. In the APS model, pattern formation and associative recall are used to improve or replace search. In this paper, the theory, background, and motivations behind the model are presented and its application to two-player game-playing programs is discussed. In these programs, the system develops a knowledge base of patterns (boolean features) coupled with weights and, using pattern-oriented evaluation, performs only 1-ply search, yet competes respectably with programs that search more. The learning mechanism is a hybrid of machine learning and artificial intelligence techniques that have been successful in other settings. Specific examples and performance results are taken from the domains of Hexapawn, Tic-Tac-Toe, Pente, Othello, and chess.     

The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence

Artificial intelligence (AI) has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies.

     

PDP: Parallel Dynamic Programming

Deep reinforcement learning is a focus research area in artificial intelligence. The principle of optimality in dynamic programming is a key to the success of reinforcement learning methods. The principle of adaptive dynamic programming (ADP) is first presented instead of direct dynamic programming (DP), and the inherent relationship between ADP and deep reinforcement learning is developed. Next, analytics intelligence, as the necessary requirement, for the real reinforcement learning, is discussed. Finally, the principle of the parallel dynamic programming, which integrates dynamic programming and analytics intelligence, is presented as the future computational intelligence.      

Collaborative information environments to support knowledge construction by communities

Computer-based design environments for skilled domain workers have recently graduated from research prototypes to commercial products, supporting the learning of individual designers. Such systems do not, however, adequately support the collaborative nature of work or the evolution of knowledge within communities of practice. If innovation is to be supported within collaborative efforts, thesedomain-oriented design environments (DODEs) must be extended to becomecollaborative information environments (CIEs), capable of providing effective community memories for managing information and learning within constantly evolving collaborative contexts. In particular, CIEs must provide functionality that facilitates the construction of new knowledge and the shared understanding necessary to use this knowledge effectively within communities of practice.This paper reviews three stages of work on artificial (computer-based and Web-based) systems that augment the intelligence of people and organisations. NetSuite illustrates the DODE approach to supporting the work of individual designers with learning-on-demand. WebNet extends this model to CIEs that support collaborative learning by groups of designers. Finally, WebGuide shows how a computational perspectives mechanism for CIEs can support the construction of knowledge and of shared understanding within groups. According to recent theories of cognition, human intelligence is the product of tool use and of social mediations as well as of biological development; CIEs are designed to enhance this intelligence by providing computationally powerful tools that are supportive of social relations.     

Developing classroom‐focused research in technology education

Abstract

During the last 10 years, curriculum documents in Australia, the United Kingdom, the United States, Canada, Hong Kong, and New Zealand have emphasized the importance of students’ developing technological literacy. In utilizing research findings to consider future curriculum needs, there is the danger that the field may come to be understood in light of the research undertaken, not in light of what needs to be done. Past research has tended to focus on curriculum issues and the defining of the subject. If technology education is to advance as a curriculum area of worth and as a focus of research, then much more of our research effort must be on student and teacher learning in technology. This paper argues that classroom‐based research must become the focus of research over the next 10 years. While there is published research on what students do when involved in technological activities, we still lack significant research on students’ learning in technology and on ways in which this learning can be enhanced. Teacher and student conceptualization of technology is a complex issue and requires an understanding of the many factors that influence it. Classroom culture and student expectations appear to influence strongly the way in which students carry out their technological activities. Student learning in technology can be enhanced by effective formative interactions occurring between teacher and student and between student and student. Part of technology assessment should provide evidence of progression in learning, about which we currently know very little. This paper describes some fruitful areas of classroom‐based research that could inform technology curriculum development.     

Color image quantization using flower pollination algorithm

Flower pollination algorithm (FPA) is a swarm-based optimization technique that has attracted the attention of many researchers in several optimization fields due to its impressive characteristics. This paper proposes a new application for FPA in the field of image processing to solve the color quantization problem, which is use the mean square error is selected as the objective function of the optimization color quantization problem to be solved. By comparing with the K-means and other swarm intelligence techniques, the proposed FPA for Color Image Quantization algorithm is verified. Computational results show that the proposed method can generate a quantized image with low computational cost. Moreover, the quality of the image generated is better than that of the images obtained by six well-known color quantization methods.

     

THE B-21 CRYPTOGRAPH

ABSTRACT

The acquisition and exploitation of material found on the German cruiser Magdeburg played a major part in the early success at sea enjoyed by the Allied Powers in World War I. The Russian role in this effort, however, has been largely unknown or ignored, at least in English language publications. From the very beginning, the Russians played a major and on-going role, both in the cryptanalytic exploitation effort of the Magdeburg material and in the effective use of this communications intelligence by naval commanders of the Baltic Sea Fleet. The Russians continued to play an active role in this effort throughout the war.     

Next Generation University Library Information Systems Based on Cooperative Learning

An integrated library information system is a resource planning system for a library, used to track resources owned, bills paid, orders made, and patrons who have borrowed. In our research, we focused on university library information systems (ULISs). We identified an important research question regarding their main limitation in offering intelligent help to the students in their documentation/learning. We identified the importance of the endowment of ULISs with artificial intelligence. In this article, we analyzed different aspects related to the presence of computational intelligence in ULISs and intelligence of ULISs. Finally, we proposed a complex next generation ULIS based on a hybrid cooperative learning, being able to offer an intelligent help for personalized learning of students. We defined some novel paradigms in the context of a novel kind of cooperative hybrid personalized learning, such as learning role and sub-role; and learning intelligence level.     

Identifying invasive plant species using field spectroscopy in the VNIR region in successional systems of north-central Virginia

Remote sensing can potentially be used to monitor the extent and distribution of invasive species across landscapes and regions, thus aiding conservation efforts. We collected ground-level hyperspectral data of six exotic invasive plant species in abandoned agricultural fields at the Blandy Experimental Farm in northern Virginia to determine the degree to which species could be identified using visible and near-infrared wavelengths. The spectral profile from 350 to 1025 nm was used in support vector machine analysis to determine separability of these species. We used sensitivity analyses to determine which spectral regions were most influential to identifying species by removing 50 nm regions and comparing species identification to that using the full spectral profile. Ailanthus altissima, Carduus acanthoides, and Cirsium arvense had high ability to be identified (75%, 87.5%, and 75%, respectively). Galium verum had low ability to be identified (44.4%), perhaps due to high spectral contamination from soil. Celastrus orbiculatus and Rhamnus davurica had low ability to be identified (27.3% and 30.8%, respectively); however, they were often misclassified as each other, due to their physical overlap in the field. The sensitivity analysis revealed that the 350–399, 500–549, 700–749, and 900–949 nm regions were most useful for species identification, while 550–599 and 650–699 nm regions were detrimental, perhaps due to greater intraspecific variability than interspecific variability in these regions. These most influential regions for identification were similar to those found in other studies. Thus, it is possible to identify species using ground-level hyperspectral data.     

智能控制：从学习控制到平行控制

20世纪60年代,学习控制开启了人类探究复杂系统控制的新途径,基于人工智能技术的智能控制随之兴起.本文以智能控制为主线,阐述其由学习控制向平行控制发展的历程.本文首先介绍学习控制的基本思想,描述了智能机器的架构设计与运行机理.随着信息科技的进步,基于数据的计算智能方法随之出现.对此,本文进一步简述了基于计算智能的学习控制方法,并以自适应动态规划方法为切入点分析非线性动态系统自学习优化问题的求解过程.最后,针对工程复杂性与社会复杂性互相耦合的复杂系统控制问题,阐述了基于平行控制的学习与优化方法求解思路,分析其在求解复杂系统优化控制问题方面的优势.智能控制思想经历了学习控制、计算智能控制到平行控制的演化过程,可以看出平行控制是实现复杂系统知识自动化的有效方法.     

A learning system for communications network configuration

One of the major goals of artificial intelligence research is to create learning systems that are of practical use. To this end a great deal of effort has been expended in understanding and constructing learning mechanisms. This paper describes a learning system that is of practical use in the domain of communications network design and configuration. In constructing this system we have had to confront many of the issues for learning systems in poorly understood domains. Some important lessons for learning systems are presented. It is argued that in poorly understood domains a case-based learning approach is useful when implemented with careful regard for computational costs.     

Prediction of MHC II-binding peptides using rough set-based rule sets ensemble

Peptide binding to Major Histocompatibility Complex (MHC) is a prerequisite for any T cell-mediated immune response. Predicting which peptides can bind to a specific MHC molecule is indispensable to minimizing the number of peptides required to synthesize, to the development of vaccines and immunotherapy of cancer, and to aiding to understand the specificity of T-cell mediated immunity. At present, although predictions based on machine learning methods have good prediction performance, they cannot acquire understandable knowledge and prediction performance can be further improved. Thereupon, the Rule Sets ENsemble (RSEN) algorithm, which takes advantage of diverse attribute and attribute value reduction algorithms based on rough set (RS) theory, is proposed as the initial trial to acquire understandable rules along with enhancement of prediction performance. Finally, the RSEN is applied to predict the peptides that bind to HLA-DR4(B1^* 0401). Experimentation results show: (1) prepositional rules for predicting the peptides that bind to HLA-DR4 (B1^* 0401) are obtained; (2) compared with individual RS-based algorithms, the RSEN has a significant decrease (13%–38%) in prediction error rate; (3) compared with the Back-Propagation Neural Networks (BPNN), prediction error rate of the RSEN decreases by 4%–16%. The acquired rules have been applied to help experts make molecules modeling. An Zeng received the Ph.D. degree in computer applications technology from South China University of Technology in 2005. Nowadays she is a lecturer at the Faculty of Computer of Guangdong University of Technology. Her research interests are data mining, bioinformatics, neural networks, artificial intelligence, and computational immunology. In these areas she has published over 20 technical papers in various prestigious journals or conference proceedings. She is a member of the IEEE. Contact her at the Faculty of Computer, Guangdong Univ. of Technology, University Town, PanYu District, Guangzhou, 510006, P.R. China. Dan Pan received the Ph.D. degree in circuits and systems from South China University of Technology in 2001. He is a senior engineer in Guangdong Mobile Communication Co. Ltd at present. His research interests are data mining, machine learning, bioinformatics, and data warehousing, and applications of business modeling and software engineering to computer-aided business operations systems, especially in the telecom industry. In these areas he has published over 30 technical papers in refereed journals or conference proceedings. As a member of the International Association of Science and Technology for Development (IASTED) technical committee on artificial intelligence and expert systems, he served a number of conferences and publications. He is a member of the IEEE. Contact him at Guangdong Mobile Communication Co. Ltd., 208 Yuexiu South Rd., Guangzhou, 510100, P.R. China. Jian-bin He received the M.E. in computer science from South China University of Technology in 2002. He now is a data mining consultant at Teradata division of NCR (China), supporting telecom carriers to do data mining in data warehouses for market research. His research interests include statistical learning, semi-supervised learning, spectral clustering, multi-relational data mining and their application to social science. Contact him at NCR(China) Co. Ltd., Unit 2306, Tower B, Center Plaza, 161 Linhexi Road, Guangzhou, 510620, P.R. China.