RoboCup: robot world cup

RoboCup is an attempt to foster intelligent robotics research by providing a standard problem where a wide range of technologies can be integrated and examined. The First Robot World Cup Soccer Games and Conferences (RoboCup-97) was held during IJCAI-97, Nagoya, with over 40 teams participating from throughout the world. RoboCup soccer is a task for a team of fast-moving robots in a dynamic, noisy environment. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor-fusion. This article describes technical challenges involved in RoboCup, its official rules, a report of RoboCup-97, and future perspectives     

Correction to: Attention-based multimodal contextual fusion for sentiment and emotion classification using bidirectional LSTM

Multimedia Tools and Applications - A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-10591-y     

融合语音情感词局部特征的语音情感识别方法

为有效利用语音情感词局部特征,提出了一种融合情感词局部特征与语音语句全局特征的语音情感识别方法。该方法依赖于语音情感词典的声学特征库,提取出语音语句中是否包含情感词及情感词密度等局部特征,并与全局声学特征进行融合,再通过机器学习算法建模和识别语音情感。对比实验结果表明,融合语音情感词局部特征与全局特征的语音情感识别方法能取得更好的效果,局部特征的引入能有效提高语音情感识别准确率。     

基于情感角色模型的文本情感分类方法

针对传统情感分类方法因情感项指向不明引发的误判和隐藏观点遗漏等问题,提出一种基于评价对象情感角色模型的文本情感分类方法.该方法首先识别文本中的潜在评价对象,通过局部语义分析对潜在评价对象所在语句进行情感标注,确定潜在评价对象所在语句的正负极性,并定义其情感角色;然后,改进特征权值计算方法,将情感角色对应的倾向值融入模型特征空间中;最后,通过特征聚合对特征空间实现模型降维.实验结果表明,所提方法与提取强主观性情感项作为特征的情感分类方法相比,分类准确率约提高3.2%,可有效改善文本情感分类效果.     

Using bidimensional regression to explore map lineage

This paper is concerned with the exploration of lineage in pre-twentieth century mapping taking Suffolk as an example. A non-parametric bidimensional regression (Tobler, 1994, Geographical Analysis, 26 186–212) is used to investigate patterns of distortion in seven maps of the county (Saxton: 1575, Jansson: 1646, Blome: 1673, Overton: 1713, Harrison: 1790, Rowe: 1831, and Wyld: 1891). For each map the locations of 50 towns and villages were digitised and are used to predict the position of the same 50 locations on 20C OS mapping. The residuals are used to interpolate a vector field for each map from which the OS grid may be warped to give a visual indication of distortion. Finally, a statistical comparison of the distortion is made using Akaike's Information Criterion (Akaike, 1973, In B. Petrov, & F. Csaki, 2nd Symposium on Information Theory (pp. 267–281), Budapest: Akademiai Kiado). The results suggest that pre-nineteenth century map makers were heavily dependent on the work of their predecessors.     

Introducing cluster heatmaps to explore city/firm interactions in world cities

This article demonstrates the added value of cluster heatmaps in urban and regional research. To this end, we apply this tool to two-mode network data on connections between world cities (mode 1) and global services firms (mode 2): we interpret structures and patterns in the location strategies of 175 globalized producer services firms across 707 world cities in 2016. To date, the analytical potential of such data has not yet been fully exploited because more standard visual-analytical tools fall short of capturing the full complexity of large datasets. Furthermore, the two-mode network data are typically projected into one-mode network data only focusing on cities. Cluster heatmaps are powerful tools because they condense large data matrices into information-thick and assumption-free visual summaries. We show how the algorithms underlying their construction are useful for exploratory data analysis and subsequent hypothesis-generation of the two-mode network data. The cluster heatmaps reveal which firms with a similar global presence co-produce similar types of world cities, and which sets of world cities mediate the location behavior of producer services firms by housing similar ecologies of firms. The heatmaps reveal subtle nuances in the position and role of cities and firms in different globalization arenas, and provide a comprehensive overview of lower-tier world cities in particular. We discuss notable nexuses, some of the technique's limitations, and avenues for further research.     

Using the variogram to explore imagery of two different spatial resolutions

The resolution of remotely sensed data is becoming increasingly fine, and there are now many sources of data with a pixel size of 1?m×1?m. This produces huge amounts of data that have to be stored, processed and transmitted. For environmental applications this resolution possibly provides far more data than are needed: data overload. This poses the question: how much is too much? We have explored two resolutions of data—20?m pixel SPOT data and 1?m?pixel Computerized Airborne Multispectral Imaging System (CAMIS) data from Fort A. P. Hill (Virginia, USA), using the variogram of geostatistics. For both we used the normalized difference vegetation index (NDVI). Three scales of spatial variation were identified in both the SPOT and 1?m data: there was some overlap at the intermediate spatial scales of about 150?m and of 500?m–600?m. We sub‐sampled the 1?m data and scales of variation of about 30?m and of 300?m were identified consistently until the separation between pixel centroids was 15?m (or 1 in 225?pixels). At this stage, spatial scales of about 100?m and 600?m were described, which suggested that only now was there a real difference in the amount of spatial information available from an environmental perspective. These latter were similar spatial scales to those identified from the SPOT image. We have also analysed 1?m CAMIS data from Fort Story (Virginia, USA) for comparison and the outcome is similar. From these analyses it seems that a pixel size of 20?m is adequate for many environmental applications, and that if more detail is required the higher resolution data could be sub‐sampled to a 10?m separation between pixel centroids without any serious loss of information. This reduces significantly the amount of data that needs to be stored, transmitted and analysed and has important implications for data compression.     

AGATHA: Using heuristic search to automate the construction of case law theories

In this paper we describe AGATHA, a program designed to automate the process of theory construction in case based domains. Given a seed case and a number of precedent cases, the program uses a set of argument moves to generate a search space for a dialogue between the parties to the dispute. Each move is associated with a set of theory constructors, and thus each point in the space can be associated with a theory intended to explain the seed case and the other cases in the domain. The space is large and so an heuristic search method is needed. This paper describes two methods based on A* and alpha/beta pruning and also a series of experiments designed to explore the appropriateness of different evaluation functions, the most useful precedents to use as seed cases and the quality of the resulting theories.     

A semi-supervised approach in detecting sentiment and emotion based on digital payment reviews

The Journal of Supercomputing - This paper investigates the sentiment and emotion of digital payment application consumers using a hybrid approach consisting of both supervised and unsupervised...     

EOSentiMiner: an opinion-aware system based on emotion ontology for sentiment analysis of Chinese online reviews

With the growing availability and popularity of online reviews, consumers' opinions towards certain products or services are generated and spread over the Internet; sentiment analysis thus arises in response to the requirement of opinion seekers. Most prior studies are concerned with statistics-based methods for sentiment classification. These methods, however, suffer from weak comprehension of text-based messages at semantic level, thus resulting in low accuracy. We propose an ontology-based opinion-aware framework – EOSentiMiner – to conduct sentiment analysis for Chinese online reviews from a semantic perspective. The emotion space model is employed to express emotions of reviews in the EOSentiMiner, where sentiment words are classified into two types: emotional words and evaluation words. Furthermore, the former contains eight emotional classes, and the latter is divided into two opinion evaluation classes. An emotion ontology model is then built based on HowNet to express emotion in a fuzzy way. Based on emotion ontology, we evaluate some factors possibly affecting sentiment classification including features of products (services), emotion polarity and intensity, degree words, negative words, rhetoric and punctuation. Finally, sentiment calculation based on emotion ontology is proposed from sentence level to document level. We conduct experiments by using the data from online reviews of cellphone and wedding photography. The result shows the EOSentiMiner outperforms baseline methods in term of accuracy. We also find that emotion expression forms and connection relationship vary across different domains of review corpora.     

Using conceptual models to explore business-ICT alignment in networked value constellations

In this paper, we introduce e ³ alignment for inter-organizational business-ICT alignment. With the e ³ alignment framework, we create alignment between organizations operating in an agile networked value constellation??which is a set of organizations who jointly satisfy a customer need??by (1) focusing on the interaction between the organizations in the constellation, (2) considering interaction from four different perspectives, and (3) utilizing conceptual modeling techniques to analyze and create alignment within and between the perspectives. By creating inter-organizational business-ICT alignment between the actors in the constellation, e ³ alignment ultimately contributes to a sustainable and profitable constellation. To actually create alignment, e ³ alignment iteratively takes three specific steps: (1) identification of alignment issues, (2) solution design, and (3) impact analysis. We illustrate our approach with cases from the Dutch aviation industry, Spanish electricity industry, and Dutch telecom industry.     

Using cognitive task analysis to explore issues in the procurement of intelligent decision support systems

Government statistics and various news reports suggest that upwards of half of all large-scale information technology (IT) development projects fail to meet expectations for facilitating cognitive work. Many of the failures point to the neglect of human-centering considerations during the development of sociotechnical systems. The groups of people who create IT themselves constitute a sociotechnical system. Therefore, laws of cognitive work apply to the cognitive work of IT development, and these laws include the “reductive tendency” for people to form simplified understandings when confronted with domains of dynamics and complexity. In this article, we report a study in which we “turned the tables” on IT systems development. Rather than using cognitive task analysis to study some work domain for which an envisioned IT system would be developed, we used cognitive task analysis to study the work domain of IT systems development itself. Through documentation analysis and critical decision method procedures, we sought to reveal specific challenges with regard to human-centering, and ways in which principles, methods, and tools of ergonomics (human factors, cognitive systems engineering) might help the developers of IT systems better address the human and social aspects of cognitive work. The findings highlight the outstanding challenges and barriers to the procurement and development of usable, useful, and understandable IT for sociotechnical systems. Challenges include the following: the need for better coordination mechanisms; the need to locate cognitive systems engineers, as advocates for workers, in key leadership roles; the need to reconceive concepts and methods of requirements and requirements specification; and the need for better negotiation of the trade-offs of cost/schedule considerations with human-centering considerations.

Avoiding the world model trap: An acting robot does not need to be so smart!

We propose several examples of robotics applications where interactions with the environment play an important role, and where no modelization of the robot or of the world is needed. We argue that, in general, this helps to simplify the complexity of the control system, and illustrate this claim through several examples. We propose different neural networks which allow clustering of scattered objects by several robots, learning of obstacle avoidance and learning of target retrieval.     

The newcastle connection or UNIXes of the world unite!

In this paper we describe a software subsystem that can be added to each of a set of physically interconnected UNIX or UNIX look-alike systems, so as to construct a distributed system which is functionally indistinguishable at both the user and the program level from a conventional single-processor UNIX system. The techniques used are applicable to a variety and multiplicity of both local and wide area networks, and enable all issues of inter-processor communication, network protocols, etc., to be hidden. A brief account is given of experience with such a distributed system, which is currently operational on a set of PDPlls connected by a Cambridge Ring. The final sections compare our scheme to various precursor schemes and discuss its potential relevance to other operating systems.     

基于人格理论的情感agent模型及应用

情感agent的模型设计是人工智能领域的一个重要内容,是计算机信息科学与生物学、心理学和社会学等不同领域内容相结合的一项研究.通过对心理学中人格和情感概念的研究和探讨,本文提出了基于人格理论的情感agent模型,并结合教育学的相关内容,构建了虚拟课堂教学环境,进而将此情感agent模型应用于该虚拟环境中,通过对模拟结果的观察分析来验证该情感agent模型的合理性.     

Riding the tide of sentiment change: sentiment analysis with evolving online reviews

The last decade has seen a rapid growth in the volume of online reviews. A great deal of research has been done in the area of opinion mining, aiming at analyzing the sentiments expressed in those reviews towards products and services. Most of the such work focuses on mining opinions from a collection of reviews posted during a particular period, and does not consider the change in sentiments when the collection of reviews evolve over time. In this paper, we fill in this gap, and study the problem of developing adaptive sentiment analysis models for online reviews. Given the success of latent semantic modeling techniques, we propose two adaptive methods to capture the evolving sentiments. As a case study, we also investigate the possibility of using the extracted adaptive patterns for sales prediction. Our proposal is evaluated on an IMDB dataset consisting of reviews of selected movies and their box office revenues. Experimental results show that the adaptive methods can capture sentiment changes arising from newly available reviews, which helps greatly improve the prediction accuracy.