Miracle-dz/UniFAR-FTUs huggingface dataset:
Inertial Sensor Error Reduction through Calibration and Sensor Fusion

This paper presents the comparison between cooperative and local Kalman Filters (KF) for estimating the absolute segment angle, under two calibration conditions. A simplified calibration, that can be replicated in most laboratories; and a complex calibration, similar to that applied by commercial vendors. The cooperative filters use information from either all inertial sensors attached to the body, Matricial KF; or use information from the inertial sensors and the potentiometers of an exoskeleton, Markovian KF. A one minute walking trial of a subject walking with a 6-DoF exoskeleton was used to assess the absolute segment angle of the trunk, thigh, shank, and foot. The results indicate that regardless of the segment and filter applied, the more complex calibration always results in a significantly better performance compared to the simplified calibration. The interaction between filter and calibration suggests that when the quality of the calibration is unknown the Markovian KF is recommended. Applying the complex calibration, the Matricial and Markovian KF perform similarly, with average RMSE below 1.22 degrees. Cooperative KFs perform better or at least equally good as Local KF, we therefore recommend to use cooperative KFs instead of local KFs for control or analysis of walking.
Reproducibility of functional MR imaging: preliminary results of prospective multi-institutional study performed by Biomedical Informatics Research Network.

PURPOSE To prospectively investigate the factors--including subject, brain hemisphere, study site, field strength, imaging unit vendor, imaging run, and examination visit--affecting the reproducibility of functional magnetic resonance (MR) imaging activations based on a repeated sensory-motor (SM) task. MATERIALS AND METHODS The institutional review boards of all participating sites approved this HIPAA-compliant study. All subjects gave informed consent. Functional MR imaging data were repeatedly acquired from five healthy men aged 20-29 years who performed the same SM task at 10 sites. Five 1.5-T MR imaging units, four 3.0-T units, and one 4.0-T unit were used. The subjects performed bilateral finger tapping on button boxes with a 3-Hz audio cue and a reversing checkerboard. In a block design, 15-second epochs of alternating baseline and tasks yielded 85 acquisitions per run. Functional MR images were acquired with block-design echo-planar or spiral gradient-echo sequences. Brain activation maps standardized in a unit-sphere for the left and right hemispheres of each subject were constructed. Areas under the receiver operating characteristic curve, intraclass correlation coefficients, multiple regression analysis, and paired Student t tests were used for statistical analyses. RESULTS Significant factors were subject (P < .005), k-space (P < .005), and field strength (P = .02) for sensitivity and subject (P = .03) and k-space (P = .05) for specificity. At 1.5-T MR imaging, mean sensitivities ranged from 7% to 32% and mean specificities were higher than 99%. At 3.0 T, mean sensitivities and specificities ranged from 42% to 85% and from 96% to 99%, respectively. At 4.0 T, mean sensitivities and specificities ranged from 41% to 73% and from 95% to 99%, respectively. Mean areas under the receiver operating characteristic curve (+/- their standard errors) were 0.77 +/- 0.05 at 1.5 T, 0.90 +/- 0.09 at 3.0 T, and 0.95 +/- 0.02 at 4.0 T, with significant differences between the 1.5- and 3.0-T examinations and between the 1.5- and 4.0-T examinations (P < .01 for both comparisons). Intraclass correlation coefficients ranged from 0.49 to 0.71. CONCLUSION MR imaging at 3.0- and 4.0-T yielded higher reproducibility across sites and significantly better results than 1.5-T imaging. The effects of subject, k-space, and field strength on examination reproducibility were significant.
Critical Assessment: A Student-Centered Approach to Assessment

The proposed session sought to engage participants in a discussion about building assessment practices with students, as opposed to for students. Critical pedagogy may provide one way to redefine this previously uni-directional practice (Keesing-Styles, 2003). This approach concentrates student experience at the center of assessment, causing a power shift in status quo assessment dynamics. Engaging students can reveal that assessments do not indicate the conclusion of learning, in turn helping students perceive learning as a lifelong process.The following proceedings present major ideas and questions which resulted from the aforementioned discussion, including theoretical uncertainty and barriers towards implementation. Suggestions for future research and practice are also proposed.
A Taxonomy of Knowledge Maps in Business Application

Knowledge Maps (K-Map), as a format of corporate taxonomies, can be used to increase the efficiency and benefits of organizational Knowledge Management (KM). Nevertheless, K-Map is often mentioned in the literature as an auxiliary concept in KM context such that very few business practice details are available. Moreover, existing descriptions and applications of K-Map are generally high-level and somewhat scattered, leading to a rather diverging and thus confusing impression. This research aims at clarifying the confusing concept of K-Map by forming a taxonomy of business applications based on an in-depth review of K-Map literature. Justification and application of the proposed taxonomy are provided. Suggestions and future work are also discussed alone with the analysis.
Americas Conference on Information Systems AMCIS 2008 Toronto

Is it possible to have too much information? This is a question that organizations are trying to address. In today’s information-driven world, organizations have more data than ever to store, access, and retrieve. But how do organizations transform this wealth of information it into a knowledge asset and a strategic advantage? Furthermore, these knowledge assets need to be accessed and retrieved by different parts of an organization. This paper looks at the challenges encountered by United States Northern Command (USNORTHCOM) to transform documents and content into knowledge that different groups within the organization could leverage. Based on the challenges experienced by USNORTHCOM, this paper proposes a model to assist organizations with the transfer of knowledge between groups.
Performance analysis of various distance measures for PCA based Face Recognition

Face Recognition (FR) is one the most active and widely investigated techniques in computer vision. It is used in a variety of problems like image and film processing, human-computer interaction, criminal identification and so on. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called `eigenfaces', which are essentially the principal components of the initial training set of face images. We perform FR using the popular dimensionality reduction technique, the Principal Component Analysis (PCA) and conduct extensive investigations on the ORL database to compare the three prominent distance classifiers: Euclidean, Mahalanobis and Cosine to determine which measure is more effective by stringently comparing them on the basis of the training set.
Aspect-Oriented Instrumentation for Capturing Task-Based Event Traces

User interface events provide valuable information about user behavior with respect to an application's user interface and therefore are considered as an important source of data for usability evaluation. Since usability evaluation is based on tasks that users perform, it is crucial to capture user interface events with sufficient information for task identification. However, how to make it possible is still an open question. We describe an approach to investigate the use of the aspect-oriented technique with assistance of localized instrumentation to capture task-based event traces. We also present a proof-of-concept tool to demonstrate its effectiveness and discuss its limitations.
Compact Representations of Markov Decision Processes and Their Application to Printer Management

Many planning problems can be framed as Markov decision processes (MDPs). In this paper we discuss situations where regularities in states and variables lead to compact MDPs, particularly when variables have many categories and strong interrelation. We develop techniques that generate optimal policies by exploiting regularities in MDPs. We illustrate these ideas with a real problem on management of printing clusters.
Divergent Evolution of (??)8-Barrel Enzymes

Abstract The (? [alpha)8-barrel is the most versatile and most frequently encountered fold among enzymes. It is an interesting question how the contemporary (? [alpha)8-barrels are evolutionarily related and by which mechanisms they evolved from more simple precursors. Comprehensive comparisons of amino acid sequences and threedimensional structures suggest that a large fraction of the known (? [alpha)8-barrels have divergently evolved from a common ancestor. The mutational interconversion of enzymatic activities of several (? [alpha)8-barrels further supports their common evolutionary origin. Moreover, the high structural similarity between the N and Cterminal (? [alpha)4 units of two (? [alpha)8-barrel enzymes from histidine biosynthesis indicates that the contemporary proteins evolved by tandem duplication and fusion of the gene of an ancestral halfbarrel precursor. In support of this hypothesis, recombinantly produced halfbarrels were shown to be folded, dimeric proteins.
Plasmon-induced transparency in metamaterials: Active near field coupling between bright superconducting and dark metallic mode resonators

Structured plasmonic metamaterial devices offer the design flexibility to be size scaled for operation across the electromagnetic spectrum and are extremely attractive for generating electromagnetically induced transparency and slow-light behaviors via coupling of bright and dark subwavelength resonators. Here, we experimentally demonstrate a thermally active superconductor-metal coupled resonator based hybrid terahertz metamaterial on a sapphire substrate that shows tunable transparency and slow light behavior as the metamaterial chip is cooled below the high-temperature superconducting phase transition temperature. This hybrid metamaterial opens up the avenues for designing micro-sized active circuitry with switching, modulation, and “slowing down terahertz light” capabilities.
The phase function for stellar acoustic oscillations — IV. Solar-like stars

ABSTRA C T In recent years there has been some progress towards detecting solar-like oscillations in stars. The goal of this challenging project is to analyse frequency spectra similar to that observed for the Sun in integrated light. In this context it is important to investigate what can be learned about the structure and evolution of the stars from such future observations. Here we concentrate on the structure of the upper layers, as reflected in the phase function. We show that it is possible to obtain this function from low-degree p modes, at least for stars on the main sequence. We analyse its dependence on several uncertainties in the structure of the uppermost layers. We also investigate a filtered phase function, which has properties that depend on the layers around the second helium ionization zone.
Analysis, simulation and implementation of space vector pulse width modulation inverter

This paper presents analysis, simulation and implementation of Space vector pulse width modulation (SVPWM) inverter. Principles of the SVPWM is introduced and analyzed. Modeling of the switching intervals and control signal generators are introduced. Implementation and Experimental results of SVPWM inverter are presented to realize the validity of the SVPWM technique.
Two-Phase Indoor Positioning Technique in Wireless Networking Environment

Positioning of real world objects (e.g., people) in indoor environment will facilitate location dependent or context-aware applications. Due to severe multi-path fading effect in indoor wireless environment, received signal strength indicator (RSSI) based indoor positioning systems usually require a great amount of human intervention for data measurement during the system initiation. This paper proposes a novel two-phase positioning technique that has been implemented and tested in real environment. Experiment results show that our method can significantly cut down the requirements on data acquisition and achieve satisfactory performance in terms of error distance.
Web Farm-inspired Computational Cluster in the Cloud

In this paper, we introduce a web farm-inspired framework for dynamic and concurrent computational processing in the cloud. We compare and contrast this with the Hadoop-cloud framework, discuss the main problems associated with our approach, and give suggestions on ways to overcome said challenges. To implement the web-inspired framework, we use Node.js - a lightweight, single threaded, server-side framework which uses asynchronous callbacks to allow non-dependent operations (parallel-like sections) to execute while waiting for I/O events such as "fetching a file" or "writing a file to disk. " We perform experiments to reveal two preliminary results that showcase the framework's functionality and scalability. One, for non-blocking operations, worker nodes which use Node.js servers are significantly faster than those which use traditional servers. In particular, a single Node.js is (on average) 2.11 times faster than one Ruby We brick server, and is (on average) 1.88 times faster than two Ruby We brick servers. Two, we find that increasing the number of worker nodes improves overall performance for blocking computational operations. As the number of worker nodes increase, the total execution time decreases exponentially and the number of requests per second increases linearly.
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Mutation testing is a powerful, but computationally expensive, technique for unit testing software. This expense has prevented mutation from becoming widely used in practical situations, but recent engineering advances have given us techniques and algorithms for signi cantly reducing the cost of mutation testing. These techniques include a new algorithmic execution technique called schema-based mutation, an approximation technique called weak mutation, a reduction technique called selective mutation, heuristics for detecting equivalent mutants, and algorithms for automatic test data generation. This paper reviews experimentation with these advances and outlines a design for a system that will approximate mutation, but in a way that will be accessible to everyday programmers. We envision a system to which a programmer can submit a program unit and get back a set of input/output pairs that are guaranteed to form an e ective test of the unit by being close to mutation adequate. We believe this system could be eÆcient enough to be adopted by leading-edge software developers. Full automation in unit testing has the potential to dramatically change the economic balance between testing and development, by reducing the cost of testing from the major part of the total development cost to a small fraction.
Vortex and half-vortex dynamics in a nonlinear spinor quantum fluid

Two-dimensional fluid of polaritons sheds light on quantum vortex dynamics. Vortices are archetypal objects that recur in the universe across the scale of complexity, from subatomic particles to galaxies and black holes. Their appearance is connected with spontaneous symmetry breaking and phase transitions. In Bose-Einstein condensates and superfluids, vortices are both point-like and quantized quasiparticles. We use a two-dimensional (2D) fluid of polaritons, bosonic particles constituted by hybrid photonic and electronic oscillations, to study quantum vortex dynamics. Polaritons benefit from easiness of wave function phase detection, a spinor nature sustaining half-integer vorticity, strong nonlinearity, and tuning of the background disorder. We can directly generate by resonant pulsed excitations a polariton condensate carrying either a full or half-integer vortex as initial condition and follow their coherent evolution using ultrafast imaging on the picosecond scale. The observations highlight a rich phenomenology, such as the spiraling of the half-vortex and the joint path of the twin charges of a full vortex, until the moment of their splitting. Furthermore, we observe the ordered branching into newly generated secondary couples, associated with the breaking of radial and azimuthal symmetries. This allows us to devise the interplay of nonlinearity and sample disorder in shaping the fluid and driving the vortex dynamics. In addition, our observations suggest that phase singularities may be seen as fundamental particles whose quantized events span from pair creation and recombination to 2D+t topological vortex strings.
Kea: Expression-level Sentiment Analysis from Twitter Data

This paper describes an expression-level sentiment detection system that participated in the subtask A of SemEval-2013 Task 2: Sentiment Analysis in Twitter. Our system uses a supervised approach to learn the features from the training data to classify expressions in new tweets as positive, negative or neutral. The proposed approach helps to understand the relevant features that contribute most in this classification task.
Real Options Application Framework for Shipbuilding Enterprises

This paper presents a real options framework to help shipbuilding business evaluating investment project properly and making a correct investment decision. In China, shipbuilding enterprises’ fixed assets investment evaluation methods used are almost based on the traditional DCF methods, nonetheless the DCF methods are have many defects. The paper begins with analyzed and summarized the advantages of real options, and then analyzed the options characteristic embedded in shipbuilding enterprises fixed assets investment. Furthermore, as an example, the paper applies the framework to the case of JD shipyard expansion investment. Its result suggests that real options indeed can reflect the value of flexibility and the framework can help management making investment decision.
The wisdom of social multimedia: using flickr for prediction and forecast

Social multimedia hosting and sharing websites, such as Flickr, Facebook, Youtube, Picasa, ImageShack and Photobucket, are increasingly popular around the globe. A major trend in the current studies on social multimedia is using the social media sites as a source of huge amount of labeled data for solving large scale computer science problems in computer vision, data mining and multimedia. In this paper, we take a new path to explore the global trends and sentiments that can be drawn by analyzing the sharing patterns of uploaded and downloaded social multimedia. In a sense, each time an image or video is uploaded or viewed, it constitutes an implicit vote for (or against) the subject of the image. This vote carries along with it a rich set of associated data including time and (often) location information. By aggregating such votes across millions of Internet users, we reveal the wisdom that is embedded in social multimedia sites for social science applications such as politics, economics, and marketing. We believe that our work opens a brand new arena for the multimedia research community with a potentially big impact on society and social sciences.
The perceptions of principals and math teachers in successful comprehensive 5A high schools on the role of the professional learning community

The Perceptions of Principals and Math Teachers in Successful Comprehensive 5A High Schools on the Role of the Professional Learning Community. (August 2011) Christie Branson Whitbeck, B.S., Missouri State University; M. Ed., University of Houston Chair of Advisory Committee: Dr. Linda Skrla As accountability for public schools continues to increase, educators are continually seeking the best practices in order to assure successful academic achievement, particularly in mathematics. This study focused on the popular reform movement of establishing professional learning communities (PLCs) within the schools, and combined it with a frame of reference from the math teaching context. The purpose of this study was to gain the perceptions of principals and math teachers in successful 5A high schools about the role of the professional learning communities in their schools. For several decades, educators have been trained and continue to be trained on the effective use of professional learning communities in all levels of education. Utilizing a PLC concept requires additional efforts by the school leadership and the teachers. By gaining insight into the perceptions of those in the field, the researcher was able to find common themes, analyze them, and develop recommendations for practice and further research. This qualitative research focused on reviews of literature beginning with early reform movements of forty years ago, which spawned the PLC movement to
Infanticide by a mother with untreated schizophrenia

Summary This case report describes a 30-year-old mother of four with a 6-year history of obvious paranoia and psychosis from a poor rural farming community in India. Her symptoms and social functioning deteriorated over time, but the family did not seek medical care until she killed her 3-month-old daughter while under the influence of command hallucinations. Subsequent treatment with antipsychotic medication resulted in control of her psychotic symptoms and greatly improved psychosocial functioning. This case is an example of one of the many negative consequences of a community’s failure to recognize and treat mental illnesses. The patient had severe symptoms that were obvious to all for 6 years prior to the infanticide, but the family’s lack of basic knowledge about mental illness, the lack of locally available mental health care, and the relatively high cost of care prevented family members from obtaining the treatment that almost certainly would have prevented the tragic death of her infant. Changing these three factors in poor rural communities of low- and middle-income countries is the challenge we must work together to address. Infanticide secondary to untreated mental illness is a glaring reminder of how urgent this task is.
Kinematic-aware random forest for static and dynamic action recognition from depth sequences

Existing work for action recognition from depth sequences focus on modeling the dynamics of various actions. In this paper, we tackle the problem of 24 hours-monitoring patient actions in a ward, where temporal movements can be significant or subtle, and their relative positions to scene layout and body postures become important cues, such as “lying on the bed”, “stretching an arm out of the bed”, “falling out of the bed”. To address this problem, we propose a kinematic-aware random forest method to maximize the discriminative power of depth image representation, which takes into account both spatio-temporal appearance by a pre-trained CNN and additional kinematic prior (e.g. layout, skeleton) information. We integrate the additional information in the split functions to guide the learning process, i.e. the spatio-temporal appearance feature selection, by directly measuring the uncertainty of kinematic information and closing the gap between posterior class distributions given the kinematic prior and the appearance features. This additional information is not required for test data, thus called “privileged information prior”. The learned relationship from the kinematic space to the appearance space helps maximize class separation of both static and dynamic actions. The inferred class label distribution is further refined by enforcing the consistency across frames. Experimental evaluations on our new dataset and Cornell Activity Dataset demonstrate that our method significantly outperforms various state-of-the-arts.