Showing papers by "Helsinki Institute for Information Technology published in 2011"

A review of the use of psychophysiological methods in game research

Abstract: This article reviews the psychophysiological method in game research. Psychophysiological measurements provide an objective, continuous, real-time, noninvasive, precise and sensitive way to assess the game experience. However, the best results require controlled experiments with careful monitoring of variables, large enough sample sizes and expertise in electrical signal processing. We briefly explain the theory behind the method and present the most useful measures: electromyography (EMG), electrodermal activity (EDA), electroencephalography (EEG) and cardiac measures. We review previous studies that have used psychophysiological measures in game research and illustrate some future directions. Our article covers several research lines using the psychophysiological method in game studies, and offers a comprehensive list of references for those interested in the field.

Malware classification based on call graph clustering

Abstract: Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, enabling the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by human malware analysts. Experiments show that it is indeed possible to accurately detect malware families via call graph clustering. We anticipate that in the future, call graphs can be used to analyse the emergence of new malware families, and ultimately to automate implementation of generic detection schemes.

Correcting errors in short reads by multiple alignments

Abstract: Motivation: Current sequencing technologies produce a large number of erroneous reads. The sequencing errors present a major challenge in utilizing the data in de novo sequencing projects as assemblers have difficulties in dealing with errors. Results: We present Coral which corrects sequencing errors by forming multiple alignments. Unlike previous tools for error correction, Coral can utilize also bases distant from the error in the correction process because the whole read is present in the alignment. Coral is easily adjustable to reads produced by different sequencing technologies like Illumina Genome Analyzer and Roche/454 Life Sciences sequencing platforms because the sequencing error model can be defined by the user. We show that our method is able to reduce the error rate of reads more than previous methods. Availability: The source code of Coral is freely available at http://www.cs.helsinki.fi/u/lmsalmel/coral/. Contact: leena.salmela@cs.helsinki.fi

When does content float? Characterizing availability of anchored information in opportunistic content sharing

Abstract: We consider an opportunistic content sharing system designed to store and distribute local spatio-temporal “floating” information in uncoordinated P2P fashion relying solely on the mobile nodes passing through the area of interest, referred to as the anchor zone. Nodes within the anchor zone exchange the information in opportunistic manner, i.e., whenever two nodes come within each others' transmission range. Outside the anchor zone, the nodes are free to delete the information, since it is deemed relevant only for the nodes residing inside the anchor zone. Due to the random nature of the operation, there are no guarantees, e.g., for the information availability. By means of analytical models, we show that such a system, without any supporting infrastructure, can be a viable and surprisingly reliable option for content sharing as long as a certain criterion, referred to as the criticality condition, is met. The important quantity is the average number of encounters a randomly chosen node experiences during its sojourn time in the anchor zone, which again depends on the communication range and the mobility pattern. The theoretical studies are complemented with simulation experiments with various mobility models showing good agreement with the analytical results.

Energy-efficient trajectory tracking for mobile devices

Abstract: Emergent location-aware applications often require tracking trajectories of mobile devices over a long period of time. To be useful, the tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, when trajectory information needs to be sent to a remote server, on-device simplification of the trajectories is needed to reduce the amount of data transmission. While there has recently been a lot of work on energy-efficient position tracking, the energy-efficient tracking of trajectories has not been addressed in previous work. In this paper we propose a novel on-device sensor management strategy and a set of trajectory updating protocols which intelligently determine when to sample different sensors (accelerometer, compass and GPS) and when data should be simplified and sent to a remote server. The system is configurable with regards to accuracy requirements and provides a unified framework for both position and trajectory tracking. We demonstrate the effectiveness of our approach by emulation experiments on real world data sets collected from different modes of transportation (walking, running, biking and commuting by car) as well as by validating with a real-world deployment. The results demonstrate that our approach is able to provide considerable savings in the battery consumption compared to a state-of-the-art position tracking system while at the same time maintaining the accuracy of the resulting trajectory, i.e., support of specific accuracy requirements and different types of applications can be ensured.

Metabolic Regulation in Progression to Autoimmune Diabetes

Abstract: Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede β-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes.

Fast scaffolding with small independent mixed integer programs

Abstract: Motivation: Assembling genomes from short read data has become increasingly popular, but the problem remains computationally challenging especially for larger genomes. We study the scaffolding phase of sequence assembly where preassembled contigs are ordered based on mate pair data. Results: We present MIP Scaffolder that divides the scaffolding problem into smaller subproblems and solves these with mixed integer programming. The scaffolding problem can be represented as a graph and the biconnected components of this graph can be solved independently. We present a technique for restricting the size of these subproblems so that they can be solved accurately with mixed integer programming. We compare MIP Scaffolder to two state of the art methods, SOPRA and SSPACE. MIP Scaffolder is fast and produces better or as good scaffolds as its competitors on large genomes. Availability: The source code of MIP Scaffolder is freely available at http://www.cs.helsinki.fi/u/lmsalmel/mip-scaffolder/. Contact: leena.salmela@cs.helsinki.fi

Building Customer Relationship through Game Mechanics in Social Games

Abstract: This chapter examines mechanics of game design in social games that are used in building customer relationship. The developments in the game industry towards service orientation, and increased emphasis on social design, have resulted in overlap of game design and business design. This chapter examines the junction of these domains in contemporary social games, by studying how game design is used in pursuing business goals of the related business models. Several virtual worlds and social games are examined with the support of secondary data provided by experts in the field. The identified mechanics are then categorised and analysed in the context of business model literature on customer relationship building. The results provide several game mechanics that are located in the union of game design and business planning. Moreover, the results imply a new approach to game design in general by exemplifying how the traditional way of thinking about game design is no longer sufficient when the design of engaging mechanics needs to meet with business goals.

How do young people identify with online and offline peer groups? A comparison between UK, Spain and Japan

Abstract: Peer groups such as neighbourhoods and hobby circles are important sources of social identity for young people, but their viability is challenged by processes of urbanisation and labour mobility. In recent years, traditional peer groups have been joined by easily accessible computer-mediated groups, which have become an everyday part of life in many countries. In this article, we examine how young people identify with various online and offline peer groups. We compare online and offline identification experiences from the perspective of how socio-demographic position and individual sociability characteristics influence them, and examine how these identification processes differ between national contexts. Empirical analyses are conducted based on a survey of online community users from the UK, Spain and Japan (N=4299). It is found that participants identify as strongly with their online communities as they do with their own families, and stronger than with offline hobby groups. In the mature online societi...

Semi-supervised learning for WLAN positioning

Abstract: Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a "radio map" is constructed by modeling how the signal strength measurements vary according to the location. However, collecting a sufficient amount of location-tagged training data is a rather tedious and time consuming task, especially in indoor scenarios -- the main application area of WLAN positioning -- where GPS coverage is unavailable. To alleviate this problem, we present a semi-supervised manifold learning technique for building accurate radio maps from partially labeled data, where only a small portion of the signal strength measurements need to be tagged with the corresponding coordinates. The basic idea is to construct a non-linear projection that maps high-dimensional signal fingerprints onto a two-dimensional manifold, thereby dramatically reducing the need of location-tagged data. Our results from a deployment in a real-world experiment demonstrate the practical utility of the method.

An augmented reality interface to contextual information

Abstract: In this paper, we report on a prototype augmented reality (AR) platform for accessing abstract information in real-world pervasive computing environments. Using this platform, objects, people, and the environment serve as contextual channels to more information. The user’s interest with respect to the environment is inferred from eye movement patterns, speech, and other implicit feedback signals, and these data are used for information filtering. The results of proactive context-sensitive information retrieval are augmented onto the view of a handheld or head-mounted display or uttered as synthetic speech. The augmented information becomes part of the user’s context, and if the user shows interest in the AR content, the system detects this and provides progressively more information. In this paper, we describe the first use of the platform to develop a pilot application, Virtual Laboratory Guide, and early evaluation results of this application.

Bayesian Group Factor Analysis

Abstract: We introduce a factor analysis model that summarizes the dependencies between observed variable groups, instead of dependencies between individual variables as standard factor analysis does. A group may correspond to one view of the same set of objects, one of many data sets tied by co-occurrence, or a set of alternative variables collected from statistics tables to measure one property of interest. We show that by assuming group-wise sparse factors, active in a subset of the sets, the variation can be decomposed into factors explaining relationships between the sets and factors explaining away set-specific variation. We formulate the assumptions in a Bayesian model which provides the factors, and apply the model to two data analysis tasks, in neuroimaging and chemical systems biology.

From Space to Stage: How Interactive Screens Will Change Urban Life

Abstract: Framed digital displays will soon give way to walls and facades that creatively motivate individual and group interaction. A stage serves as an apt metaphor to explore the ways in which these ubiquitous screens can transform passive viewing into an involved performance.

A generative approach for image-based modeling of tumor growth

Abstract: Extensive imaging is routinely used in brain tumor patients to monitor the state of the disease and to evaluate therapeutic options. A large number of multi-modal and multi-temporal image volumes is acquired in standard clinical cases, requiring new approaches for comprehensive integration of information from different image sources and different time points. In this work we propose a joint generative model of tumor growth and of image observation that naturally handles multimodal and longitudinal data. We use the model for analyzing imaging data in patients with glioma. The tumor growth model is based on a reaction-diffusion framework. Model personalization relies only on a forward model for the growth process and on image likelihood. We take advantage of an adaptive sparse grid approximation for efficient inference via Markov Chain Monte Carlo sampling. The approach can be used for integrating information from different multi-modal imaging protocols and can easily be adapted to other tumor growth models.

What does it mean to be good at using a mobile device? An investigation of three levels of experience and skill

Abstract: An increasing number of computer users lack formal training in operating their devices. These daily users cannot be described as novices or experts within the predominant view of expertise. In order to describe and better understand this type of self-taught intermediate level of skill, 10 casual users of a high-end smartphone series were compared to 10 novices and 4 professionals (help desk personnel) in their learning histories, task performance, and cognitive outcomes. Our study suggests that this type of self-taught intermediate level of skill is device-specific. Experienced users (casual users and experts) exhibited superior performance for representative tasks. This is mainly attributable to faster navigation and better knowledge of interface terminology, not to deeper conceptual representation of the problems. Interviews suggest that this skill is the consequence of routine use and three recurring learning events: familiarization, following of media, and ad hoc problem-solving situations. We conclude by discussing why intermediate levels of skill deserve more attention in HCI research.

Mobile search and the cloud: The benefits of offloading

Abstract: This paper presents the benefits and drawbacks of mobile desktop search coupled with cloud-assisted operations, such as operation offloading, cloud storage, and cloud-assisted search. The energy trade-off when offloading a task is analyzed and measured in several different scenarios. An example case of offloading indexing is presented. The problem of cloud-assisted mobile desktop search is introduced and a possible solution outlined. This paper argues that the synergy between mobile platforms and cloud computing is under-utilized and should be explored further, particularly in the search and synchronization use case. Our measurements support offloading (parts of) search related tasks to a cloud service.

Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning

Abstract: Applications such as robot control and wireless communication require planning under uncertainty. Partially observable Markov decision processes (POMDPs) plan policies for single agents under uncertainty and their decentralized versions (DEC-POMDPs) find a policy for multiple agents. The policy in infinite-horizon POMDP and DEC-POMDP problems has been represented as finite state controllers (FSCs). We introduce a novel class of periodic FSCs, composed of layers connected only to the previous and next layer. Our periodic FSC method finds a deterministic finite-horizon policy and converts it to an initial periodic infinite-horizon policy. This policy is optimized by a new infinite-horizon algorithm to yield deterministic periodic policies, and by a new expectation maximization algorithm to yield stochastic periodic policies. Our method yields better results than earlier planning methods and can compute larger solutions than with regular FSCs.

Discriminative Learning of Bayesian Networks via Factorized Conditional Log-Likelihood

Abstract: We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (fCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that fCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.

Dimensionality Reduction for Data Visualization [Applications Corner]

Abstract: Dimensionality reduction is one of the basic operations in the toolbox of data analysts and designers of machine learning and pattern recognition systems. Given a large set of measured variables but few observations, an obvious idea is to reduce the degrees of freedom in the measurements by rep resenting them with a smaller set of more "condensed" variables. Another reason for reducing the dimensionality is to reduce computational load in further processing. A third reason is visualization.

"I'll press play, but I won't listen": profile work in a music-focused social network service

Abstract: We offer the concept of profile work to illustrate the effort people invest in their public profiles in social network services (SNSs). In our explorative study, we investigated profile work in Last.fm, an SNS that automatically publishes music listening information. We found that, instead of simply not publishing things they might rather keep private, users tend to change their music listening behavior in order to control their self-presentation. Four dimensions of profile work were identified, including detailed mechanisms to regulate one's profile. We suggest ways to support users' profile work in the context of automated sharing of behavior information.

A generative approach for image-based modeling of tumor growth

Abstract: 22nd International Conference, IPMI 2011, Kloster Irsee, Germany, July 3-8, 2011. Proceedings

High Density Lipoprotein structural changes and drug response in lipidomic profiles following the long-term fenofibrate therapy in the FIELD substudy

Abstract: In a recent FIELD study the fenofibrate therapy surprisingly failed to achieve significant benefit over placebo in the primary endpoint of coronary heart disease events. Increased levels of atherogenic homocysteine were observed in some patients assigned to fenofibrate therapy but the molecular mechanisms behind this are poorly understood. Herein we investigated HDL lipidomic profiles associated with fenofibrate treatment and the drug-induced Hcy levels in the FIELD substudy. We found that fenofibrate leads to complex HDL compositional changes including increased apoA-II, diminishment of lysophosphatidylcholines and increase of sphingomyelins. Ethanolamine plasmalogens were diminished only in a subgroup of fenofibrate-treated patients with elevated homocysteine levels. Finally we performed molecular dynamics simulations to qualitatively reconstitute HDL particles in silico. We found that increased number of apoA-II excludes neutral lipids from HDL surface and apoA-II is more deeply buried in the lipid matrix than apoA-I. In conclusion, a detailed molecular characterization of HDL may provide surrogates for predictors of drug response and thus help identify the patients who might benefit from fenofibrate treatment.

Conflict propagation and component recursion for canonical labeling

Abstract: The individualize and refine approach for computing automorphism groups and canonical forms of graphs is studied. Two new search space pruning techniques, conflict propagation based on recorded failure information and recursion over nonuniformly joined components, are presented. Experimental results show that the techniques can result in substantial decrease in both search space sizes and run times.

The number of Latin squares of order 11

Abstract: Constructive and nonconstructive techniques are employed to enumerate Latin squares and related objects. It is established that there are (i) 2036029552582883134196099 main classes of Latin squares of order 11; (ii) 6108088657705958932053657 isomorphism classes of one-factorizations of K 11,11 ; (iii) 12216177315369229261482540 isotopy classes of Latin squares of order 11; (iv) 1478157455158044452849321016 isomorphism classes of loops of order 11; and (v) 19464657391668924966791023043937578299025 isomorphism classes of quasigroups of order 11. The enumeration is constructive for the 1151666641 main classes with an autoparatopy group of order at least 3.

Everyday appropriations of information technology: A study of creative uses of digital cameras

Abstract: Repurposive appropriation is a creative everyday act in which a user invents a novel use for information technology (IT) and adopts it. This study is the first to address its prevalence and predictability in the consumer IT context. In all, 2,379 respondents filled in an online questionnaire on creative uses of digital cameras, such as using them as scanners, periscopes, and storage media. The data reveal that such creative uses are adopted by about half of the users, on average, across different demographic backgrounds. Discovery of a creative use on one's own is slightly more common than is learning it from others. Most users discover the creative uses either completely on their own or wholly through learning from others. Our regression model explains 34% of the variance in adoption of invented uses, with technology cognizance orientation, gender, exploration orientation, use frequency, and use tenure as the strongest predictors. These findings have implications for both design and marketing. © 2011 Wiley Periodicals, Inc.