A cloud-based intelligent computing system for contextual exploration on personal sleep-tracking data using association rule mining

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.

A feedback-based algorithm forMotion analysis with application to object tracking

We present a motion detection algorithm which detects direction of motion at sufficient number of points and thus segregates the edge image into clusters of coherently moving points. Unlike most algorithms for motion analysis, we do not estimate magnitude of velocity vectors or obtain dense motion maps. The motivation is that motion direction information at a number of points seems to be sufficient to evoke perception of motion and hence should be useful in many image processing tasks requiring motion analysis. The algorithm essentially updates the motion at previous time using the current image frame as input in a dynamic fashion. One of the novel features of the algorithm is the use of some feedback mechanism for evidence segregation. This kind of motion analysis can identify regions in the image that are moving together coherently, and such information could be sufficient for many applications that utilize motion such as segmentation, compression, and tracking. We present an algorithm for tracking objects using our motion information to demonstrate the potential of this motion detection algorithm.

Kernel-based spatial-color modeling for fast moving object tracking

Visual tracking has been a challenging problem in computer vision over the decades. The applications of Visual Tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift (MS) tracker, which gained more attention recently, is known for tracking objects in a cluttered environment and its low computational complexity. The major problem encountered in histogram-based MS is its inability to track rapidly moving objects. In order to track fast moving objects, we propose a new robust mean-shift tracker that uses both spatial similarity measure and color histogram-based similarity measure. The inability of MS tracker to handle large displacements is circumvented by the spatial similarity-based tracking module, which lacks robustness to object's appearance change. The performance of the proposed tracker is better than the individual trackers for tracking fast-moving objects with better accuracy.

On the Average Case Communication Complexity for Detection in Sensor Networks

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space viewpoint is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces $\mathcal{S_I}$ and $\mathcal{S_C}$ and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating $\mathcal{S_I}$ and $\mathcal{S_C}$ is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. The average case CC of the relevant greater-than (GT) function is characterized within two bits. In the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm.

A reverse Stroop task with mouse tracking

In a reverse Stroop task, observers respond to the meaning of a color word irrespective of the color in which the word is printed—for example, the word red may be printed in the congruent color (red), an incongruent color (e.g., blue), or a neutral color (e.g., white). Although reading of color words in this task is often thought to be neither facilitated by congruent print colors nor interfered with incongruent print colors, this interference has been detected by using a response method that does not give any bias in favor of processing of word meanings or processing of print colors. On the other hand, evidence for the presence of facilitation in this task has been scarce, even though this facilitation is theoretically possible. By modifying the task such that participants respond to a stimulus color word by pointing to a corresponding response word on a computer screen with a mouse, the present study investigated the possibility that not only interference but also facilitation would take place in a reverse Stroop task. Importantly, in this study, participants’ responses were dynamically tracked by recording the entire trajectories of the mouse. Arguably, this method provided richer information about participants’ performance than traditional measures such as reaction time and accuracy, allowing for more detailed (and thus potentially more sensitive) investigation of facilitation and interference in the reverse Stroop task. These trajectories showed that the mouse’s approach toward correct response words was significantly delayed by incongruent print colors but not affected by congruent print colors, demonstrating that only interference, not facilitation, was present in the current task. Implications of these findings are discussed within a theoretical framework in which the strength of association between a task and its response method plays a critical role in determining how word meanings and print colors interact in reverse Stroop tasks.

ACE Research Vignette 050: What makes new migrant businesses grow? Connections or location?

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. In this vignette, Dr. Roxanne Zolin and Dr. Artemis Chang consider both the role of ethnic connections and location in an ethnic neighbourhood on new migrant business growth.

Measurements of the top-quark mass using charged particle tracking

We present three measurements of the top-quark mass in the lepton plus jets channel with approximately 1.9 fb-1 of integrated luminosity collected with the CDF II detector using quantities with minimal dependence on the jet energy scale. One measurement exploits the transverse decay length of b-tagged jets to determine a top-quark mass of 166.9+9.5-8.5 (stat) +/- 2.9 (syst) GeV/c2, and another the transverse momentum of electrons and muons from W-boson decays to determine a top-quark mass of 173.5+8.8-8.9 (stat) +/- 3.8 (syst) GeV/c2. These quantities are combined in a third, simultaneous mass measurement to determine a top-quark mass of 170.7 +/- 6.3 (stat) +/- 2.6 (syst) GeV/c2.

Alignment of the ALICE Inner Tracking System with cosmic-ray tracks

ALICE (A Large Ion Collider Experiment) is the LHC (Large Hadron Collider) experiment devoted to investigating the strongly interacting matter created in nucleus-nucleus collisions at the LHC energies. The ALICE ITS, Inner Tracking System, consists of six cylindrical layers of silicon detectors with three different technologies; in the outward direction: two layers of pixel detectors, two layers each of drift, and strip detectors. The number of parameters to be determined in the spatial alignment of the 2198 sensor modules of the ITS is about 13,000. The target alignment precision is well below 10 micron in some cases (pixels). The sources of alignment information include survey measurements, and the reconstructed tracks from cosmic rays and from proton-proton collisions. The main track-based alignment method uses the Millepede global approach. An iterative local method was developed and used as well. We present the results obtained for the ITS alignment using about 10^5 charged tracks from cosmic rays that have been collected during summer 2008, with the ALICE solenoidal magnet switched off.

Measurements of the top-quark mass using charged particle tracking

We present three measurements of the top-quark mass in the lepton plus jets channel with approximately 1.9 fb-1 of integrated luminosity collected with the CDF II detector using quantities with minimal dependence on the jet energy scale. One measurement exploits the transverse decay length of b-tagged jets to determine a top-quark mass of 166.9+9.5-8.5 (stat) +/- 2.9 (syst) GeV/c2, and another the transverse momentum of electrons and muons from W-boson decays to determine a top-quark mass of 173.5+8.8-8.9 (stat) +/- 3.8 (syst) GeV/c2. These quantities are combined in a third, simultaneous mass measurement to determine a top-quark mass of 170.7 +/- 6.3 (stat) +/- 2.6 (syst) GeV/c2.

Knowledge Transfer within Western Multinationals' Subsidiary Units in China and Finland - The Impact of Headquarter Control Mechanisms, Subsidiary Location and Social Capital (summary section only)

It is suggested that the ability and practices of how the multinational corporation (MNC) manages knowledge transfer among its geographically dispersed subsidiary units are crucial for the building and development of firm competitive advantage. However, cross-border transfer of valuable organizational knowledge is likely to be problematic and laborious, especially within diversified and differentiated MNCs. Using data collected from 164 western multinational companies’ subsidiary units located in China and Finland, this study aims to investigate cross-border knowledge transfer within the MNC. It explores a number of factors that influence the transfer of knowledge among units in the differentiated MNC. The study consists of five individual papers. Paper 1 investigates a range of organizational mechanisms that may positively influence a subsidiary’s propensity to undertake knowledge transfers to other parts of the corporation. Paper 2 explores the impact of subsidiary location on the motivational dispositions of knowledge receiving units to value and accept knowledge from subsidiaries located in economically less advanced countries. Paper 3 examines the influence of social capital variables on knowledge transfer in dyadic relationships between foreign-owned subsidiaries and their sister and patent units. Paper 4 provides some initial insights into potentially different effects of trust and shared vision in intra-organizational vs. inter-organizational relationships. Using a case study setting, Paper 5 explores means and mechanisms used in transferring human resource management practices to Western MNCs’ business units in China from a cultural perspective. The results of the study show that MNC management through choices regarding organizational controls can encourage and enhance corporate-internal knowledge transfer. It also finds evidence that more knowledge is transferred from subsidiaries located in an industrialized country (e.g., Finland) than subsidiaries located in a developing country (e.g., China). While the study has highlighted the importance of social capital in promoting knowledge transfer, it has also uncovered some new findings that the effect of trust and shared vision may be contingent upon different contexts. Finally, in Paper 5, a number of mechanisms used in transferring selected HRM practices and competences to the Chinese business units have been identified. The findings suggest that cultural differences should be taken into consideration in the choice and use of different transfer mechanisms.

Time and Location as Customer Perceived Value Drivers

Since the emergence of service marketing, the focus of service research has evolved. Currently the focus of research is shifting towards value co-created by the customer. Consequently, value creation is increasingly less fixed to a specific time or location controlled by the service provider. However, present service management models, although acknowledging customer participation and accessibility, have not considered the role of the empowered customer who may perform the service at various locations and time frames. The present study expands this scope and provides a framework for exploring customer perceived value from a temporal and spatial perspective. The framework is used to understand and analyse customer perceived value and to explore customer value profiles. It is proposed that customer perceived value can be conceptualised as a function of technical, functional, temporal and spatial value dimensions. These dimensions are suggested to have value-increasing and value-decreasing facets. This conceptualisation is empirically explored in an online banking context and it is shown that time and location are more important value dimensions relative to the technical and functional dimensions. The findings demonstrate that time and location are important not only in terms of having the possibility to choose when and where the service is performed. Customers also value an efficient and optimised use of time and a private and customised service location. The study demonstrates that time and location are not external elements that form the service context, but service value dimensions, in addition to the technical and functional dimensions. This thesis contributes to existing service management research through its framework for understanding temporal and spatial dimensions of perceived value. Practical implications of the study are that time and location need to be considered as service design elements in order to differentiate the service from other services and create additional value for customers. Also, because of increased customer control and the importance of time and location, it is increasingly relevant for service providers to provide a facilitating arena for customers to create value, rather than trying to control the value creation process. Kristina Heinonen is associated with CERS, the Center for Relationship Marketing and Service Management at the Swedish School of Economics and Business Administration

A new approach for fault location identification in transmission system using stability analysis and SVMs

This paper presents a new approach to the location of fault in the high voltage power transmission system using Support Vector Machines (SVMs). A knowledge base is developed using transient stability studies for apparent impedance swing trajectory in the R-X plane. SVM technique is applied to identify the fault location in the system. Results are presented on sample 3-power station, a 9-bus system illustrate the implementation of the proposed method.

Neural network approach for fault location in unbalanced distribution networks with limited measurements

This paper presents an Artificial Neural Network (ANN) approach for locating faults in distribution systems. Different from the traditional Fault Section Estimation methods, the proposed approach uses only limited measurements. Faults are located according to the impedances of their path using a Feed Forward Neural Networks (FFNN). Various practical situations in distribution systems, such as protective devices placed only at the substation, limited measurements available, various types of faults viz., three-phase, line (a, b, c) to ground, line to line (a-b, b-c, c-a) and line to line to ground (a-b-g, b-c-g, c-a-g) faults and a wide range of varying short circuit levels at substation, are considered for studies. A typical IEEE 34 bus practical distribution system with unbalanced loads and with three- and single- phase laterals and a 69 node test feeder with different configurations are considered for studies. The results presented show that the proposed approach of fault location gives close to accurate results in terms of the estimated fault location.

Locant: A nature inspired location service for ad hoc networks

An efficient location service is a prerequisite to any robust, effective and precise location information aided Mobile Ad Hoc Network (MANET) routing protocol. Locant, presented in this paper is a nature inspired location service which derives inspiration from the insect colony framework, and it is designed to work with a host of location information aided MANET routing protocols. Using an extensive set of simulation experiments, we have compared the performance of Locant with RLS, SLS and DLS, and found that it has comparable or better performance compared to the above three location services on most metrics and has the least overhead in terms of number of bytes transmitted per location query answered.