Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.

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Friendship and mobility: user movement in location-based social networks

The Rise of the Creative Class

Contemporary Sociology 7(5) (September 1978):566—68. When the intellectual history of our times comes to be written, that peculiarly Left Bank mixture of Marxism and structuralism now in fashion will be among the most puzzlingofourideastoevaluate.Aliteral “archeology of knowledge” (the title of one of Foucault’s earlier books) will be required to sort out the valuable from the obvious rubbish. I suspect that in this exercise the iconographers of the present (like Barthes) will fare less well than those who have read the past. Of such “historians” (a description which does not really cover his method) Foucault is the most dazzlingly creative. Discipline and Punish (which, shamefully, has taken over two years to be translated into English) follows Madness and Civilization (1961) and The Birth of the Clinic (1971) as the next stage in Foucault’s massive project of tracing the genealogy of control institutions (asylums, teaching hospitals, prisons) and the human sciences symbiotically linked with them (psychiatry, clinical medicine, criminology, penology). His concern throughout is the relationship between power and knowledge, the articulation of each on the other. Here (as he makes explicit in an interview recently published in the English journal, Radical Philosophy) he opposes the humanist position that, once we gain power, we cease to know——it makes us blind—— and that only those who keep their distance from power, who are no way implicated in tyranny, can attain the truth. For Foucault, such forms of knowledge as psychiatry and criminology (with its “garrulous discourses” and “intermidable [sic] repetitions”) are directly related to the exercise of power. Power itself creates new objects of knowledge and accumulates new bodies of information. Thus to “liberate scientific research from the demands of monopoly capitalism” can only be a slogan. Placing such programmatic Big Issues on one side, though, a superficial first reading of the book mightstartatthelevelofitssubtitle, “The Birth of the Prison.” The key historical transition——at the end of the eighteenth century——is from punishment as torture, a public spectacle, to the more economically and politically discreet prison sentence. The body as the major target of penal repression disappears: within a few decades, the grisly spectacles of torture, dismemberment, exposure, amputation, and branding are over. Interest is transferred from the body to the mind; a coercive, solitary, and secret mode of punishment replaces one that was representative, scenic, and collective. Gone is the liturgy of torture and execution, where the triumph of the sovereign was symbolized in the processions, halts at crossroads, public readings of the sentence even after death, where the criminal’s corpse was exhibited or burnt. In its place comes a whole technology of subtle power. When punishment leaves the domain of more or less everyday perception and enters into abstract consciousness, it does not become less effective. But its effectiveness arises from its inevitability not its horrific theatrical intensity. The new power is not to punish less but to In Retrospect: 1978 29

https://journals.sagepub.com/doi/pdf/10.1177/0094306118815499a

Discipline and Punish: The Birth of the Prison:

Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

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Urban Computing: Concepts, Methodologies, and Applications

This paper examines the location traces of 489 users of a location sharing social network for relationships between the users' mobility patterns and structural properties of their underlying social network. We introduce a novel set of location-based features for analyzing the social context of a geographic region, including location entropy, which measures the diversity of unique visitors of a location. Using these features, we provide a model for predicting friendship between two users by analyzing their location trails. Our model achieves significant gains over simpler models based only on direct properties of the co-location histories, such as the number of co-locations. We also show a positive relationship between the entropy of the locations the user visits and the number of social ties that user has in the network. We discuss how the offline mobility of users can have implications for both researchers and designers of online social networks.

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Bridging the gap between physical location and online social networks

Personalization technologies offer powerful tools for enhancing the user experience in a wide variety of systems, but at the same time raise new privacy concerns. For example, systems that personalize advertisements according to the physical location of the user or according to the user's friends' search history, introduce new privacy risks that may discourage wide adoption of personalization technologies. This article analyzes the privacy risks associated with several current and prominent personalization trends, namely social-based personalization, behavioral profiling, and location-based personalization. We survey user attitudes towards privacy and personalization, as well as technologies that can help reduce privacy risks. We conclude with a discussion that frames risks and technical solutions in the intersection between personalization and privacy, as well as areas for further investigation. This frameworks can help designers and researchers to contextualize privacy challenges of solutions when designing personalization systems.

https://link.springer.com/content/pdf/10.1007%2Fs11257-011-9110-z.pdf

Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

The rapid adoption of location tracking and mobile social networking technologies raises significant privacy challenges. Today our understanding of people's location sharing privacy preferences remains very limited, including how these preferences are impacted by the type of location tracking device or the nature of the locations visited. To address this gap, we deployed Locaccino, a mobile location sharing system, in a four week long field study, where we examined the behavior of study participants (n=28) who shared their location with their acquaintances (n=373.) Our results show that users appear more comfortable sharing their presence at locations visited by a large and diverse set of people. Our study also indicates that people who visit a wider number of places tend to also be the subject of a greater number of requests for their locations. Over time these same people tend to also evolve more sophisticated privacy preferences, reflected by an increase in time- and location-based restrictions. We conclude by discussing the implications our findings.

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Empirical models of privacy in location sharing

Privacy by design (PbD) is a policy measure that guides software developers to apply inherent solutions to achieve better privacy protection. For PbD to be a viable option, it is important to understand developers' perceptions, interpretation and practices as to informational privacy (or data protection). To this end, we conducted in-depth interviews with 27 developers from different domains, who practice software design. Grounded analysis of the data revealed an interplay between several different forces affecting the way in which developers handle privacy concerns. Borrowing the schema of Social Cognitive Theory (SCT), we classified and analyzed the cognitive, organizational and behavioral factors that play a role in developers' privacy decision making. Our findings indicate that developers use the vocabulary of data security to approach privacy challenges, and that this vocabulary limits their perceptions of privacy mainly to third-party threats coming from outside of the organization; that organizational privacy climate is a powerful means for organizations to guide developers toward particular practices of privacy; and that software architectural patterns frame privacy solutions that are used throughout the development process, possibly explaining developers' preference of policy-based solutions to architectural solutions. Further, we show, through the use of the SCT schema for framing the findings of this study, how a theoretical model of the factors that influence developers' privacy practices can be conceptualized and used as a guide for future research toward effective implementation of PbD.

Privacy by designers: software developers' privacy mindset

Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

Eran Toch

Papers

Bridging the gap between physical location and online social networks

Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems

Empirical models of privacy in location sharing

Privacy by designers: software developers' privacy mindset

Analyzing large-scale human mobility data: a survey of machine learning methods and applications