Miracle-dz/UniFAR-FTUs huggingface dataset:
Shift to Cyber-I: Reexamining Personalized Pervasive Learning

Recently, there have been lots of researches on how to provide personalized services to different users according to their personal characters, needs, situations, contexts and so on. Such personalized services are often based on users' profiles provided by the users in the beginning, and users' records or experiences in using corresponding systems in certain periods. However, different systems or terminals collect user information independently and cannot share the user's personal information collected by these different systems/terminals. As a result, each system can only utilize the limited information collected by the system itself to provide services or recommendations, and it thus cannot meet what users' needs in varied situations across the different systems/terminals. Therefore, it is still an open issue on how to gather, share and utilize various kinds of personal information to effective personalized services in right time, right place and right means to users. In this paper, a case study for personalized pervasive learning is discussed as one typical application of the concept of Cyber-I, an individual's counterpart on the cyberspace. The Cyber-I aims to provide a better environment for users to obtain what they may really need on the Web, and it can be considered as an innovated possibility of web usage scenario in the near future. Our case study using pervasive devices (iPad, cell phone, and laptop) for learning issues can be regarded as the best practice to support the proposed Cyber-I.
CV-SDF: A synchronous data-flow model for real-time computer vision applications technical areas: Image analysis and real-time software

This paper presents a model for real-time computer vision applications based on the synchronous data flow (SDF) methodology. To accomplish the needs of computer vision applications (mainly access to pixel neighborhood and access to previous video frames) a new buffer concept called Structured Buffers replace the FIFO buffers used in the SDF model. The model minimizes the system's latency and memory consumption and allows an execution time analysis which is an essential prerequisite for real-time applications.
SOMGPU: An unsupervised pattern classifier on Graphical Processing Unit

Graphical processing units (GPUs) have been, lately used for general purpose tasks owing to their implicit parallel nature. One such task is that of pattern classification. Highly parallel tasks like these suffer from performance loss owing to the sequential nature of central processing unit (CPU). To match the image processing power of human brain even slightly, this problem beckons the utilization of enormous computational power and parallel environs of GPUs. Unless there is a task which can be parallelized to the required extent the gain obtained is lost owing to the overhead involved. Thus, it is equally important to understand some limitations of GPU before venturing in this direction and deal with it appropriately to obtain satisfactory results. Artificial neural networks (ANN) are found to be appropriate while dealing with pattern recognition problems. Kohonenpsilas self organizing map (SOM) has been used for classification out of other approaches for its implicit parallel nature, albeit with minor modifications to make it suit the parallel environment. nVIDIA GeForce 6150 Go with Microsoft Research Accelerator as the high level library has been chosen as the platform to provide this environment.
Energy-aware fast scheduling heuristics in heterogeneous computing systems

In heterogeneous computing systems it is crucial to schedule tasks in a manner that exploits the heterogeneity of the resources and applications to optimize systems performance. Moreover, the energy efficiency in these systems is of a great interest due to different concerns such as operational costs and environmental issues associated to carbon emissions. In this paper, we present a series of original low complexity energy efficient algorithms for scheduling. The main idea is to map a task to the machine that executes it fastest while the energy consumption is minimum. On the practical side, the set of experimental results showed that the proposed heuristics perform as efficiently as related approaches, demonstrating their applicability for the considered problem and its good scalability.
Executable Aspect Oriented Models for Improved Model Testing

Design validation is important for detecting errors early in the development life cycle. Testing the design is one significant means to achieve design validation. In this paper we introduce the KerTheme model. KerTheme provides a means symmetrically decomposing concern based executable class diagrams and concern test scenarios. KerTheme also facilitates simultaneous merging of these decomposed models into a coherent composite concern based executable class model and corresponding test scenarios. The KerTheme model allows us to investigate weather decomposed concern based executable class diagrams simplifies the definition of concern test scenarios. This will also allow us to investigate weather this approach ensures more rigorous testing of a complete system.
Phylogenetic Reconstruction of Orthology, Paralogy, and Conserved Synteny for Dog and Human

Accurate predictions of orthology and paralogy relationships are necessary to infer human molecular function from experiments in model organisms. Previous genome-scale approaches to predicting these relationships have been limited by their use of protein similarity and their failure to take into account multiple splicing events and gene prediction errors. We have developed PhyOP, a new phylogenetic orthology prediction pipeline based on synonymous rate estimates, which accurately predicts orthology and paralogy relationships for transcripts, genes, exons, or genomic segments between closely related genomes. We were able to identify orthologue relationships to human genes for 93% of all dog genes from Ensembl. Among 1:1 orthologues, the alignments covered a median of 97.4% of protein sequences, and 92% of orthologues shared essentially identical gene structures. PhyOP accurately recapitulated genomic maps of conserved synteny. Benchmarking against predictions from Ensembl and Inparanoid showed that PhyOP is more accurate, especially in its predictions of paralogy. Nearly half (46%) of PhyOP paralogy predictions are unique. Using PhyOP to investigate orthologues and paralogues in the human and dog genomes, we found that the human assembly contains 3-fold more gene duplications than the dog. Species-specific duplicate genes, or “in-paralogues,” are generally shorter and have fewer exons than 1:1 orthologues, which is consistent with selective constraints and mutation biases based on the sizes of duplicated genes. In-paralogues have experienced elevated amino acid and synonymous nucleotide substitution rates. Duplicates possess similar biological functions for either the dog or human lineages. Having accounted for 2,954 likely pseudogenes and gene fragments, and after separating 346 erroneously merged genes, we estimated that the human genome encodes a minimum of 19,700 protein-coding genes, similar to the gene count of nematode worms. PhyOP is a fast and robust approach to orthology prediction that will be applicable to whole genomes from multiple closely related species. PhyOP will be particularly useful in predicting orthology for mammalian genomes that have been incompletely sequenced, and for large families of rapidly duplicating genes.
What is Web 2.0? Ideas, technologies and implications for education

Within 15 years the web has grown from a group work tool for scientists at CERN into a global information space with more than a billion users. Currently, it is both returning to its roots as a read/write tool and also entering a new, more social and participatory stage. These trends have led to a feeling that the web is entering a ‘second phase’ – a new, ‘improved’ Web version 2.0. But how justified is this perception?
SYMMETRIC RANDOM WALKS ON HOMEO + (R)

Dedicated to John Milnor on his 80th anniversary We study symmetric random walks on finitely generated groups of orientation-preserving homeomorphisms of the real line. We establish an oscillation property for the induced Markov chain on the line that implies a weak form of recurrence. Except for a few special cases, which can be treated separately, we prove a property of “global stability at a finite distance”: roughly speaking, there exists a compact interval such that any two trajectories get closer and closer whenever one of them returns to the compact interval. The probabilistic techniques employed here lead to interesting results for the study of group actions on the line. For instance, we show that under a suitable change of the coordinates, the drift of every point becomes zero provided that the action is minimal. As a byproduct, we recover the fact that every finitely generated group of homeomorphisms of the real line is topologically conjugate to a group of (globally) Lipschitz homeomorphisms. Moreover, we show that such a conjugacy may be chosen in such a way that the displacement of each element is uniformly bounded. 1. Introduction. In this article, we study symmetric random walks on finitely generated groups of (orientation-preserving) homeomorphisms of the real line. The results presented here fit into the general framework of systems of iterated random functions [8]. However, besides the lack of compactness of
OPTIMAL CONTROL AND FLIGHT TRAJECTORY OPTIMIZATION APPLIED TO EVASION ANALYSIS

Optimal control and flight trajectory optimization applied to evasion analysis Finnish title: Optimisäätö ja lentoratojen optimointi sovellettuna väistöanalyysiin Number of pages: Abstract: This thesis deals with the numerical solution of optimal control problems arising from aircraft trajectory optimization and evasion analysis. The Introduction considers optimal control problems, solution methods for them, dynamic modeling of flight vehicles, and flight trajectory optimization in general. Encounter scenarios between an aircraft and a missile are considered. The missile pursues the aircraft, which in turn tries to avoid being hit. The methodology is based on dynamic modeling of the vehicles and optimal control theory. The missile is assumed to use a deterministic and known feedback guidance law while the controls and the resulting trajectory of the aircraft are subject to optimization. For the solution of the optimal control problem, a direct control parameterization method is applied. The first scenario considered is an edgame evasion where the objective of the optimal control problem is to maximize the minimum distance to the missile, i.e., the miss distance. For the aircraft, a point mass model extended to account for limited rotation rates is used, and a solution method is tailored to handle the kinematic rotation rate constraints and to deal with the different time constants of the dynamic models of the aircraft and the missile. The second scenario studies the maximal distance a missile can be launched from so that it still captures an optimally evading target. The problem is modeled as a bilevel dynamic optimization problem which is, by using optimality conditions, reformulated into a single level problem. A similar solution approach is applied as in the first study. For both scenarios numerical examples are computed and analyzed. Preface Optimal control, also referred to as dynamic optimization, is a widely used tool and framework for various types of analyses. Direct transcription methods have risen to be common approaches to numerically solve optimal control problems. What they lack in accuracy, they make up in larger region of convergence and less problem dependent solution procedure. Aerospace applications and related trajectory optimization problems are an important field on which optimal control is used. This work has been carried out at the Systems Analysis Laboratory of Helsinki University of Technology in conjuction with the Dynamics and Strategy of Flight project. I wish to thank Professor Raimo P. Hämäläinen, head of the laboratory, for the support and the opportunity to participate in …
How Does Our Motor System Determine Its Learning Rate?

Motor learning is driven by movement errors. The speed of learning can be quantified by the learning rate, which is the proportion of an error that is corrected for in the planning of the next movement. Previous studies have shown that the learning rate depends on the reliability of the error signal and on the uncertainty of the motor system’s own state. These dependences are in agreement with the predictions of the Kalman filter, which is a state estimator that can be used to determine the optimal learning rate for each movement such that the expected movement error is minimized. Here we test whether not only the average behaviour is optimal, as the previous studies showed, but if the learning rate is chosen optimally in every individual movement. Subjects made repeated movements to visual targets with their unseen hand. They received visual feedback about their endpoint error immediately after each movement. The reliability of these error-signals was varied across three conditions. The results are inconsistent with the predictions of the Kalman filter because correction for large errors in the beginning of a series of movements to a fixed target was not as fast as predicted and the learning rates for the extent and the direction of the movements did not differ in the way predicted by the Kalman filter. Instead, a simpler model that uses the same learning rate for all movements with the same error-signal reliability can explain the data. We conclude that our brain does not apply state estimation to determine the optimal planning correction for every individual movement, but it employs a simpler strategy of using a fixed learning rate for all movements with the same level of error-signal reliability.
Estimating the Returns to College Quality with Multiple Proxies for Quality

Existing studies of the effects of college quality on wages typically rely on a single proxy variable for college quality. This study questions the wisdom of using a single proxy given that it likely contains substantial measurement error. We consider four econometric approaches to the problem that involve the use of multiple proxies for college quality: factor analysis, instruments variables, a method recently proposed by Lubotsky and Wittenberg, and a GMM estimator. Our estimates suggest that the existing literature understates the wage effects of college quality and illustrate the value of using multiple proxies in this and other similar contexts.
Influence of Root Zone Calcium on Shoot Tip Necrosis and Apical Dominance of Potato Shoot: Simulation of This Disorder by Ethylene Glycol Tetra Acetic Acid and Prevention by Strontium

The important roles of calcium on plant growth and development including cell division and cell elongation is well documented. The purpose of the present study was to determine the impact of root zone calcium on the growth and health of potato apical meristem and on the maintenance of apical dominance. For this purpose, single-node potato cuttings (Solanum tuberosum L. cv. Dark Red Norland) were grown in sterilized modified Murashige and Skoog (MS) media containing varying concentrations of calcium (1 to 3000 mM). After 13 to 30 d of growth, plantlets were harvested and data for height of the main shoot and for the number of axillary shoots produced were recorded. Plantlets were ashed and tissue calcium concentration was determined. Shoot height was retarded with decreasing concentration of calcium in the media. Calcium deficiency induced shoot tip injury and loss of apical dominance. Tip injury was followed by the development of axillary shoots. The number of axillary shoots increased from one to 21 as calcium concentration in the media decreased from 3000 to 1 mM. At calcium concentration of 1500 mM or higher, there was a single main shoot with no axillary shoots. Addition of ethylene glycol tetra acetic acid (EGTA), a calcium chelator, to the media with 2720 mM calcium (sufficient calcium) resulted in the development of shoot injury and in the formation of axillary shoots. Calcium deficiency injury symptoms were prevented by the addition of a calcium analog, strontium, to MS media deficient in calcium. Strontium has been reported to strongly bind to plant cell walls and the inclusion of strontium prevented injury in shoots of plants grown on calcium-deficient media. These results suggest that strontium is able to mimic the role of calcium in the maintenance ofcell wall integrity andsupports previous studies that showed that calcium deficiency results from cell wall collapse of the subapical cells.
EventMinder: A Personal Calendar Assistant That Understands Events

Calendar applications do not understand calendar entries. This limitation prevents them from offering the range of assistance that can be provided by a human personal assistant. Understanding calendar entries is a difficult problem because it involves integrating many types of knowledge: commonsense knowledge, about common events and the particular instances in the world, and user knowledge about the individual’s preferences and goals. In this thesis, I present two models of event understanding: Romulus and Julius. Romulus addresses the problem of how missing information in a calendar entry can be filled in by having an event structure, goal knowledge, and past examples. This system is able to learn by observing the user, and constrains its inductive hypothesis by using knowledge about common goals specific to the event. Although this model is capable of representing some tasks, its structural assumptions limit the range of events that it can represent. Julius treats event understanding as a plan retrieval problem, and draws from the COMET plan library of 295 everyday plans to interpret the calendar entry. These plans are represented as a set of English activity phrases (i.e., “buy a cup of coffee”), and so the planning problem becomes a natural language understanding problem concerned with comprehending events. I show two techniques for retrieving plans: the first matches plans by their generalized predicate-argument structure, and the second retrieves plans by their goals. Goals are inferred by matching the plans against a database of 662 common goals, by computing the conceptual similarity between the goals and components of the plan. Combining the strengths of Romulus and Julius, I create a prototype of a personal assistant application, EventMinder, that is able to recognize users’ goals in order to propose relevant alternatives and provide useful recommendations. Thesis Supervisor: Henry Lieberman Research Scientist Program in Media Arts and Sciences EventMinder: A Personal Calendar Assistant That Understands Events
Towards Synthesis of Runtime Constraint Monitoring Code

DynaMICs is a system under development that manages integrity constraints to ensure the correct runtime behavior of programs. The intent of this approach is to facilitate the insertion of constraintchecking code into programs so that correctness properties are maintained throughout execution. Automation of the synthesis of constraint monitoring code and the insertion of constraint monitoring code is essential for the success of this approach. Possible approaches to this problem are discussed.
A Dialogical Narrative Analysis of Voice Hearers and Emotions

This thesis is the result of a process of Dialogical Narrative Analysis with twelve participants who hear voices that other people cannot hear. It uses the socio-narratology of Arthur Frank to examine their stories about their experiences in the context of the complex and conceptually contested fields of knowledge that are considered relevant to voice-hearing. The 12 voice-hearers present a wide range of ideas and explanatory frameworks about their experiences. Emotions are deliberately part of this thesis and many stories are as moving as they are complex. The author’s own experiences of these stories is also acknowledged and explored. Whereas much research about hearing voices is biological, psychological or occasionally sociological, this research has many aspects of political exploration and the field is understood as one of conflicting vested interests and ideologies. Many ethical considerations come to light, especially concerning forms of power and knowledge within mental health professions and services in the UK, but also within the Hearing Voices Movement, which is seen as a vital movement with a variety of possible histories. One possibility suggested by this research is that voice-hearers are able to tolerate, and benefit from, more dialogical approaches and that those that do may find more successful ways to live with their experiences than those who are subject to the strongly monological emplotments of much professionalised knowledge. Another strong theme is the great influence of neoliberal politics over individuals and notions of ‘health’.
MULTI-SEXUAL GENETIC ALGORITHM

another, so that it is not possible to improve on any of the objective functions without deteriorating at least one of the other objective functions. This is known as the concept of Pareto optimality (Rao, 1991). In multiobjective optimization, as opposed to single-objective optimization, there may not exist an unambigous optimal solution (global minimum or global maximum). Characteristic of the multiobjective optimization problems is a very large set of acceptable solutions that are superior to the rest of the solutions in the search space when all objectives are considered. At the same time they are not optimal from the point of any single objective. These solutions are known as Pareto-optimal solutions or nondominated solutions. The rest of the solutions are referred to as dominated solutions. Since none of the solutions in a Pareto-optimal set is absolutely better than any other, any one of them is an acceptable solution. The choice of one particular solution depends on the features of the problem and a number of problem-related factors. In this paper a new method for solving multicriteria optimization problems by Genetic Algorithms is proposed. Standard Genetic Algorithms use a population, where each individual has the same sex (or has no sex) and any two individuals can be crossed over. In the proposed Multisexual Genetic Algorithm (MSGA), individuals have an additional feature, their sex or gender and one individual from each sex is used in the recombination process. In our multicriteria optimization application there are as many sexes as optimization criteria and each individual is evaluated according to the optimization criterion related to its sex. Furthermore, a multi-parent crossover is applied to generate offspring of parents belonging to all different sexes, so the offspring represents intermediate solutions not totally optimal with respect to any single criterion. During the execution of the algorithm the set of nondominated solutions is updated and this set is presented as the output of MSGA at the end.