Archive for the ‘mobile’ Category
To secure exclusive rights to the iPhone, [AT&T] acceded to Apple’s demand that the device come with a simple, flat-rate plan. But instead of selling only a few million as it expected, AT&T now has an estimated 12m-14m iPhones on its network. Its data traffic has grown by 5,000% in the past three years.
wireless networks are different from fixed ones. In most cases, the extent to which one subscriber uses a fixed connection has little impact on other customers, since each has a separate link to the internet. But the data-transfer capacity within a cell is shared between several handsets. If left unchecked, a small group of users can gobble up most of the bandwidth, as on AT&T’s network, where the top 3% of users consume 40% of it.
operators will have to introduce more stringent usage caps, demand a premium for better service or charge by usage.
The Economist’s article.
nice user study (CHI’09)
a mobile location sharing system. In our study, (n = 56), one group was given feedback in the form of a history of location requests, and a second group was given no feedback at all – feedback allays privacy concerns
Mobile phones offer more radical possibilities than ‘PC + internet’ in terms of bringing information into the real spatial environment, argues The City Project – which means architects and urban planners need to start engaging with the way space is experienced and manipulated through mobile software. Map-tagging and location-tracking could help planners to understand how space is used, reducing the tension between the ideal space of architecture and the real space of inhabitation.
So if the prophets of user-generated-everything need to learn that space matters, do those who dream of clean, Cartesian space also need to learn that use matters? No doubt – but to reduce location-aware software to a feedback channel from users to developers (in either sense), or to see it as another element in an architectural programme, would be to miss its truly radical potential, which would lie – if sufficiently open platforms could be developed – in enabling the unplanned, disorganised and ever-changing use of space, without architects.
1) The paper more or less ignores the effects of technical safeguards built into modern smartphones operating systems.
2) the paper mentions that the reason why there hasn’t been more mobile outbreaks is that no smartphone operating system is dominating enough. Then in the next paragraph it mentions that Symbian has, oh, 65% market share of all smartphones.
We model the mobility of mobile phone users to study the fundamental spreading patterns characterizing a mobile virus outbreak. We find that while Bluetooth viruses can reach all susceptible handsets with time, they spread slowly due to human mobility, offering ample opportunities to deploy antiviral software. In contrast, viruses utilizing multimedia messaging services could infect all users
in hours, but currently a phase transition on the underlying call graph limits them to only a small fraction of the susceptible users. These results explain the lack of a major mobile virus breakout so far and predict that once a mobile operating system´s market share reaches the phase transition point, viruses will pose a serious threat to mobile communications.
New technologies make it possible to run “web-like targeting in the real world. For example, a system developed by Singapore’s research agency lets advertising screens detect the genders of passers-by: it will soon be able to tell how old they are, too. IBM has worked on systems that can scan a crowd and estimate numbers, demographics, and where people are looking. And now that facial recognition has become a consumer technology it wouldn’t be difficult to install a series of ad screens that tracks individuals as they move through a subway system or mall, greeting them at each turn with a particular message or character. It’s hard to see how opt-out could be efficiently implemented for billboards on the street – at least without a new wave of devices that can communicate their owners’ preferences to nearby advertising systems.” More here.
The AcrossAir Nearest Subway App for the iPhone 3GS overlays nearest subway stops on the live image in front of you. You can hold the iPhone flat to see arrows pointing you to various subway stops or hold it up to see signs and distances for nearby stations. … They’re working on versions for London and Barcelona (more here)
“Nearest Tube” got Juniper’s first Gold Award for Mobile Apps. Meanwhile, the Silver Award was given to Tapulous, the developer behind the highly successful ‘Tap Tap Revenge’ and its recently published sequel, ‘Tap Tap Revenge 2’.
Daniele had briefly blogged here about this interesting paper, by Kurt Partridge and Bob Price, for which I will give a longer review. Some of the techniques used in this paper could be useful for further research and even its limitations are interesting subject of analysis.
Given that today’s Mobile Leisure Guide Systems need a big amount of user interaction (for configuration/preferences), this paper proposes to integrate current sensor data, models built from historical sensor data, and user studies, into a framework able to infer user high level activities, in order to improve recommendations and decrease the amount of user tasks.
Authors claim to address the problem of lack of situational user preferences by interpreting multidimensional contextual data using a categorical variable that represents high-level user activities like “EAT”, “SHOP”, “SEE”, “DO”, “READ”. A prediction is of course a probability distribution over the possible activity types.
Recommendations are provided through a thin client supported by a backend server. The following techniques are employed to produce a prediction:
- Static prior models
- PopulationPriorModel: based on time of the day, day of the week, and current weather, according to typical activities studies from the Japan Statistics Bureau.
- PlaceTimeModel: based on time and location, using hand-constructed data collected from a user study.
- UserCalendarModel: provides a likely activity based on the user’s appointment calendar.
- Learning models
- LearnedVisitModel: tries predicting the user’s intended activities from time of day, learning from observations of their contextual data history. A Bayesian network is employed to calculate the activity probability given location and time.
- LearnedInteractionModel: constructs a model of the user’s typical activities at specific times, by looking for patterns in the user’s interaction with his/her mobile device.
Activity inferences are made by combining the predictions from all the five models, using geometric combination of the probability distributions.
A query context module is fed to the activity prediction module to provide prediction data of the context in which the user may be interested. For example, the user could be at work when searching for a restaurant, but his/her actual context could be the area downtown in which he/she plans to go for dinner.
Authors carried out a user study, evaluating the capability of each model to provide accurate predictions. Eleven participants carried the device for two days, and were rewarded with cash discounts for leisure activities they engaged in while using the device. The Query Context Prediction module was not enabled because of the short duration. Results show high accuracy (62% for baseline=”always predict EAT”, 77% for PlaceTimeModel).
Some good and problematic issues with this paper
- the prediction techniques used are interesting and could be applied to other domains; moreover I think it’s useful to combine data from user studies and learning techniques as user profiling helps developers (and apps) to understand users in general - before applying this knowledge to a specific user
- the sample size makes the user study flawed: 11 participants carrying devices for 2 days approaches statistical insignificance; weekdays/weekends is the first issues that bumps into my mind, just to mention one
- offering cash discounts for leisure activities is presumably not the correct form of reward for this kind of study as it makes users more willing to engage in activities that require spending money over the free ones (e.g. EAT vs. SEE)
- authors admit they have mostly restaurants in their RS base, which I think is not taken in enough account when claiming high accuracy. Given that the baseline predictor has a 62% accuracy predicting always EAT, a deeper analysis would have made the paper more scientific
- one of the most interesting contribution of the paper is the definition of the query context module, which is unfortunately not employed in the study for usability reasons related to the its duration. Again, a better defined user study would have solved this problem. I question whether it’s worth carrying out user studies when resources are so limited that the statitistical significance becomes objectable. However, there is some attempt to discuss expected context vs. actual context which is potentially very interesting: e.g., a user wants to SHOP but the shops are closed, so he/she EATs. It would be interesting to discuss how a RS should react to such situations
- user-interaction issues: the goal of the presented system is to reduce user tasks on the mobile; yet, this is needed to tune the system and address its mistakes; yet, one of the predictors uses exactly user’s interaction with the mobile as a parameter. It looks like there is some confusion considering the role of user interaction in this kind of systems (imho, I think that a HCI approach could improve RS usability and, consequently, accuracy)
- the systems is not well suited to multi-purpose trips (e.g. one EATs whilst DOing, or alternatively SHOPs and EATs) and in this case predictions are mostly incorrect.
The primary goal of this project is to explore novel and effective ways to search geo-spatial data and leverage multi-lingual technologies within maps.
“Albert-László Barabási and colleagues monitored 100,000 users for 6 months and recorded the location of the cell phone tower that transmitted each call or text message. The team found that:
- most people stayed close to home, and a select few regularly took long trips. … regardless of how mobile they were, people returned over and over to a few top locations with similar probability. For example, two users would have roughly the same chance of being found in their third-favorite spots, whether it was the gym or the theater. These hangouts were often located near the path between their top two destinations–usually home and work
- most people traveled very short distances most of the time, while some traveled great distances.
- each individual’s data fit into the same mathematical model—a type of power law—that predicts the probability of finding a person in a certain location. That probability distribution is dependent on an individual’s average travel distance and decreases the further he or she roams. Human mobility and how we travel is so amazingly complex,” says Max Planck’s Brockmann. “What is very strange is that despite this complexity, all the traveling behavior can be accounted for by very simple mathematical laws.”
Potential applications: traffic forecasting and urban planning.
Reputation systems on mobile phones build “reputation scores” based on personal experiences and on other phones’ recommendations. One poorly-explored research question is when to use personal experiences and when to resort to recommendations. This paper in Biology Letter may suggest few future research directions:
Idea of : Any device can request a location proof from the infrastructure when it is within communication range; the recipient device can then transmit the proof obtained from the infrastructure to any application that wishes to verify the device’s location.
Applications: store discounts for loyal customers (frequent visitors), green commuting proof, location-restricted content delivery, reducing fraud on auction websites, and police investigations (alibis producing).
I don’t know how it works but it seems to be a nice mobile testbed. Anyone knows more?
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