Prediction of places of visit using tweets

We study the problem of predicting likely places of visit of users using their past tweets. What people write on their microblogs reflects their intent and desire relating to most of their common day interests. Taking this as a strong evidence, we hypothesize that tweets of the person can also be treated as source of strong indicator signals for predicting their places of visits. In this paper, we propose a novel approach for predicting place of visit within a given geospatial range considering the past tweets and the time of visit. These predictions can be used for generating places recommendation or for promotions. In this approach, we analyze use of various features that can be extracted from the historical tweets—for example, personality traits estimated from the past tweets and the actual words mentioned in the tweets. We performed extensive empirical experiments involving, real data derived from twitter timelines of 4600 persons with multi-label classification as predictive model. The performances of proposed approach outperform the four baselines with accuracy reaching 90 % for top five predictions. Based on our experimental study, we come up with general guidelines on building the prediction model in terms of the type of features extracted from historical tweets, window size of historical tweets and on the optimal radius of query around the place of visit at a given time.     

Automatic identification of relevant places from cellular network data

We present a methodology to automatically identify users’ relevant places from cellular network data.¹ In this work we used anonymized Call Detail Record (CDR) comprising information on where and when users access the cellular network. The key idea is to effectively cluster CDRs together and to weigh clusters to determine those associated to frequented places. The approach can identify users’ home and work locations as well as other places (e.g., associated to leisure and night life). We evaluated our approach threefold: (i) on the basis of groundtruth information coming from a fraction of users whose relevant places were known, (ii) by comparing the resulting number of inhabitants of a given city with the number of inhabitants as extracted by the national census. (iii) Via stability analysis to verify the consistency of the extracted results across multiple time periods. Results show the effectiveness of our approach with an average 90% precision and recall.     

Analyzing and handling local bias for calibrating parametric cost estimation models

ContextParametric cost estimation models need to be continuously calibrated and improved to assure more accurate software estimates and reflect changing software development contexts. Local calibration by tuning a subset of model parameters is a frequent practice when software organizations adopt parametric estimation models to increase model usability and accuracy. However, there is a lack of understanding about the cumulative effects of such local calibration practices on the evolution of general parametric models over time.ObjectiveThis study aims at quantitatively analyzing and effectively handling local bias associated with historical cross-company data, thus improves the usability of cross-company datasets for calibrating and maintaining parametric estimation models.MethodWe design and conduct three empirical studies to measure, analyze and address local bias in cross-company dataset, including: (1) defining a method for measuring the local bias associated with individual organization data subset in the overall dataset; (2) analyzing the impacts of local bias on the performance of an estimation model; (3) proposing a weighted sampling approach to handle local bias. The studies are conducted on the latest COCOMO II calibration dataset.ResultsOur results show that the local bias largely exists in cross company dataset, and the local bias negatively impacts the performance of parametric model. The local bias based weighted sampling technique helps reduce negative impacts of local bias on model performance.ConclusionLocal bias in cross-company data does harm model calibration and adds noisy factors to model maintenance. The proposed local bias measure offers a means to quantify degree of local bias associated with a cross-company dataset, and assess its influence on parametric model performance. The local bias based weighted sampling technique can be applied to trade-off and mitigate potential risk of significant local bias, which limits the usability of cross-company data for general parametric model calibration and maintenance.     

Facebook communities about nostalgic photos of Turkey: creative practices of remembering and representing the past

ABSTRACT

This article focuses on Facebook communities about nostalgic photos of Turkey to explore how citizenship is enacted through the participatory and collaborative use of social media to remember and represent the past. By sharing their personal photos, knowledge, testimonies, narratives and life stories, members of these communities actively and creatively use social media to generate new ways of remembering and representing the past, as well as improving its accessibility and visibility. Furthermore, through exchanging affectively and politically charged photos and conversations about the past, participants fashion nostalgia as a public feeling that becomes a source for affective political criticism of the present. This article addresses the participatory and collaborative creation of knowledge and memory of the past to discuss everyday creative citizenship practices facilitated by social media.     

Supporting children's online identity in international communities

Increasingly, adults and children socialise and communicate online. Children's safe and secure online communication with people from all over the world can increase their understanding of other cultures, which is an important goal in today's multicultural world. Our research studied such interactions between children from three countries, Hungary, Mexico, and the USA. The goals of our research were to study how children represent their identity online and what the implications are for the design of children's online communities. We used qualitative methods to derive a deep understanding of children's behaviours and motivations. Our results show that children exposed their true and complete identities online. They focused on sharing and learning about personal, ethnic, and gender identities via online media and largely ignored cultural identity. They learned about children from other countries and developed positive attitudes towards them. Based on our results we describe design guidelines for children's online identity tools.     

Geographic ‘Place’ and ‘Community Information’ Preferences

People dynamically structure social interactions and activities at various locations in their environments in specialized types of places such as the office, home, coffee shop, museum and school. They also imbue various locations with personal meaning, creating group ‘hangouts’ and personally meaningful ‘places’. Mobile location-aware community systems can potentially utilize the existence of such ‘places’ to support the management of social information and interaction. However, acting effectively on this potential requires an understanding of how: (1) places and place-types relate to people’s desire for place-related awareness of and communication with others; and (2) what information people are willing to provide about themselves to enable place-related communication and awareness. We present here the findings from two qualitative studies, a survey of 509 individuals in New York, and a study of how mobility traces can be used to find people’s important places in an exploration of these questions. These studies highlight how people value and are willing to routinely provide information such as ratings, comments, event records relevant to a place, and when appropriate their location to enable services. They also suggest how place and place-type data could be used in conjunction with other information regarding people and places so that systems can be deployed that respect users’ People-to-People-to-Places data sharing preferences. We conclude with a discussion on how ‘place’ data can best be utilized to enable services when the systems in question are supported by a sophisticated computerized user-community social-geographical model.     

EPISTEMOLOGY,SEMANTICS, AND SELF-REFERENCE

Abstract

The generally held view in semantics is that, for communication to take place, meanings must be shared, presuming a communality of experience between communicators. Recent advances in epistemology suggest that such communality is neither proven, nor credible. Alternative concepts of the mechanisms by which communication can take place have been developed and the properties of the relationships between such indications as can be observed and the distinctions they indicate have been explored. This paper considers the possibility of an ontogenetic theory of meaning, which, without requiring communality of experience, can nevertheless explain the possibility of communication.     

Place niche and its regional variability: Measuring spatial context patterns for points of interest with representation learning

In the built environment, places such as retail outlets and public sites are embedded in the spatial context formed by neighboring places. We define the sets of these symbiotic places in the proximity of a focal place as the place's “place niche”, which conceptually represents the features of the local environment. While current literature has focused on pairwise spatial colocation patterns, we represent the niche as an integrated feature for each type of place, and quantify the niches' variation across cities. Here, with point of interest (POI) data as an approximation of places in cities, we propose representation learning models to explore place niche patterns. The models generate two main outputs: first, distributed representations for place niche by POI category (e.g. Restaurant, Museum, Park) in a latent vector space, where close vectors represent similar niches; and second, conditional probabilities of POI appearance of each place type in the proximity of a focal POI. With a case study using Yelp data in four U.S. cities, we reveal spatial context patterns and find that some POI categories have more unique surroundings than others. We also demonstrate that niche patterns are strong indicators of the function of POI categories in Phoenix and Las Vegas, but not in Pittsburgh and Cleveland. Moreover, we find that niche patterns of more commercialized categories tend to have less regional variation than others, and the city-level niche-pattern changes for POI categories are generally similar only between certain city pairs. By exploring patterns for place niche, we not only produce geographical knowledge for business location choice and urban policymaking, but also demonstrate the potential and limitations of using spatial context patterns for GIScience tasks such as information retrieval and place recommendation.     

Understanding spaces as places: extending interaction design paradigms

This short paper focuses on some issues involved in the design of ubiquitous technologies and their integration within physical spaces. Current design methodologies and techniques do not explicitly consider the importance of gaining a full understanding of the human experience of space when designing technologies that will pervade and become an integral part of our physical environment. This paper is specifically focused on the importance of considering spaces as places, and of analysing those features of a place that are going to shape users interactions with technologically enhanced environments. It also examines the future implications that this approach may have on theoretical and methodological aspects of interaction design.

A user-adaptive city guide system with an unobtrusive navigation interface

In this paper, we describe an intelligent location-aware city guide system, which adapts to each user’s preferences, and uses an intuitive “metal detector” interface for navigation. Our system analyzes each user’s past location data history to estimate individual preferences, and allows users to find shops that match their tastes in the same way a metal detector would be used to detect metal objects. The procedure with which the system picks out shops that match each user’s preferences includes a newly developed place learning algorithm, which can efficiently find frequented places, complete with their proper names (e.g. “The Ueno Royal Museum”). We have conducted a series of evaluation tests at a popular shopping district inside Tokyo, and the results validate the effectiveness of our overall approach.     

Computing and visualizing popular places

Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures, analyzing professional sport matches or better understanding wildlife. A well-known collection of patterns which can occur for a subset of trajectories of moving objects exists. In this paper, we study the popular places pattern, that is, locations that are visited by many moving objects. We consider two criteria, strong and weak, to establish either the exact number of times that an object has visited a place during its complete trajectory or whether it has visited the place, or not. To solve the problem of reporting popular places, we introduce the popularity map. The popularity of a point is a measure of how many times the moving objects of a set have visited that point. The popularity map is the subdivision, into regions, of a plane where all the points have the same popularity. We propose different algorithms to efficiently compute and visualize popular places, the so-called popular regions and their schematization, by taking advantage of the parallel computing capabilities of the graphics processing units. Finally, we provide and discuss the experimental results obtained with the implementation of our algorithms.     

Connectionism and artificial intelligence: history and philosophical interpretation

Abstract

Hubert and Stuart Dreyfus have tried to place connectionism and artificial intelligence in a broader historical and intellectual context. This history associates connectionism with neuroscience, conceptual holism, and non-rationalism, and artificial intelligence with conceptual atomism, rationalism, and formal logic. The present paper argues that the Dreyfus account of connectionism and artificial intelligence is both historically and philosophically misleading.     

Globalisation, place and gender

Current processes linking different parts of the world together economically and culturally are referred to asglobalisation. Though this term has gained immense popularity within a short time, critics have argued that it is hard to find empirical evidence that the world is becoming one. A crucial question is thenhow to look for such evidence. In many studies of globalisation, the general view taken is that of global, meaning that one searches for a global overview, or outlook, which is situated at no specific place. The present paper argues for a shift of focus, reasoning that to understand what is global we have to start with the local. The experiences of the global take place in particular local places, and to study such processes of change we need to situate our study in such a way that we can study the relationships between the local and the global. The particular place where our study takes place is rural Malaysia. Changes related to industrialisation are often spoken of in rather deterministic terms, in the sense that the local population are victims of a global shift, whereas we argue for an alternative approach, analysing women workers as agents of change within their local community. Globalisation also mainly refers to external forces imposed on local actors, whereas we find the local to be imperative for the strategies of industrialists, as well as for the present processes of change.     

Examining teacher growth in professional learning groups for in‐service teachers of mathematics

Abstract

Teachers may face important challenges when encouraged to improve their mathematics teaching. Their personal beliefs, knowledge, confidence and personal intentions towards growth and change are all complex factors which may influence teachers’ capacity, and their decisions about personal change in their teaching. In this study, intermediate teachers and the conversations that took place during their monthly Professional Learning Group meetings over a one‐year period were examined in order to better understand issues teachers face in their growth and development as teachers of mathematics. We critically examine the notion and meaning of success to different stakeholders.     

Enhancing instructional outcomes with gamification: An empirical test of the Technology-Enhanced Training Effectiveness Model

BackgroundGamification, the use of game elements in non-game contexts, has become a popular technique to improve instructional outcomes in both organizational and educational contexts. In the organizational context, the Technology-Enhanced Training Effectiveness Model [TETEM] provides a framework to understand how technologies, like gamification, can effect change in various instructional outcomes. Specifically, application of TETEM suggests that gamification may not effect change in instructional outcomes when learner attitudes towards game-based learning and experience with video games are low.MethodIn this study, we test this model in the gamification context by assigning potential learners to read scenarios describing gamified instruction or traditional, PowerPoint instruction in a random order and assessing their training valence.ResultsOn average, participants anticipated greater value from gamified instruction, but as predicted by TETEM, this effect was moderated by both video game experience and attitudes towards game-based learning. Among potential learners with high experience and attitudes, gamification produces better outcomes than PowerPoint, but among potential learners with low experience and attitudes, gamification produces worse outcomes than PowerPoint.ImplicationsWe provide empirical support for TETEM and conclude that for gamification to be successful, the attitudes and experience of participants must be assessed and ensured before gamification is implemented.     

Don't underestimate the problems of user centredness in software development projectsthere are many!

Abstract

On the basis of a longitudinal field study of 29 commercial software development projects, the pros and cons of user centredness in software development were analysed. We looked at two concepts: user participation-an organizational device-involving a user representative in the team, and user orientation-a cognitive-emotional concept-which pertains to positive attitudes towards users. Both were found to be associated with project difficulties relating to process and product quality as well as overall project success. We suggest that the issue is no longer whether or not to involve users, but instead to develop a realistic understanding of the difficulties associated with user centredness.     

试析近现代中国设计艺术的发展历程

本文以中国现代设计艺术发展的三个阶段为主线,以世界现代设计史为暗线,阐述在世界设计史背景下中国现代设计艺术的发展状况。通过对同一时期国内不同地区设计艺术发展不平衡的分析,以及对同一时期中国与国际设计水平的比较,阐述了对待外来设计艺术不同态度产生的不同结果。借古鉴今,西体中用,回顾中国现代设计艺术的发展历程,理清思路,为探索新世纪中国现代设计艺术的发展道路提供某种启迪。     

Can video games change attitudes towards history? Results from a laboratory experiment measuring short- and long-term effects

This study investigates a video game's effects on implicit and explicit attitudes towards depicted historical events in the short- and long-term on a sample of 148 young adults. We used, as an intervention tool, a serious game Czechoslovakia 38–89: Borderlands that deals with the expulsion of the Sudeten Germans from the former Czechoslovakia after the WWII. Results showed more negative pretest-posttest explicit attitude changes towards the expulsion on a general level (d = −0.34) and a specific level (d = −0.53) compared to the control group. Over the long-term, group differences in attitude change remained significant for the specific level (d = −0.44), but not for general one (d = −0.16). Exploratory analysis on the item level indicated that especially attitudes towards the expulsion's (un)fairness were affected by the game. However, no significant changes were found in implicit attitudes in the experimental group. This study is the first of such scale to empirically investigate video games' effects on a society's historical awareness.