Miracle-dz/UniFAR-FTUs huggingface dataset:
The removal of blink and saccade artifact in EEG recordings by Independent Component Analysis

Pervasive electroencephalographic (EEG) artifacts are associated with eye movement. This study shows that Independent Component Analysis (ICA) using Joint Approximate Diagonalization of Eigenmatrice (JADE) algorithm can be applied to removing ocular artifacts in EEG. The nine channels of EEGs were recorded from three young healthy subjects with additional VEOG and HEOG channels in blinking and saccade conditions respectively. Seven EEG recordings were selected for artifacts removal tests with two EOG recordings indicating the possible interferences. Ocular artifacts for both conditions were successfully presented by an independent component, which was then eliminated to reconstruct the artifact-free EEGs. This study demonstrates that JADE algorithm can be an effective tool in correcting EOG interference with multichannel EEG recordings.
A study on the minimal number of particles for a simplified particle swarm optimization algorithm

Recent research on particle swarm optimization (PSO) emphasizes the need to simply this algorithm. This paper is a short study on finding the minimal number of particles in a simplified PSO. We have taken into consideration a social-only variant, Pedersen's simplified PSO, and tested it with four popular optimization benchmark functions in order to discover which is the minimal number of particles that can be used in most optimization problems.
Exploiting Indifference for Customization of Partial Order Skylines

Unlike numerical preferences, preferences on attribute values do not show an inherent total order, but skyline computation has to rely on partial orderings explicitly stated by the user. In such orders many object values are incomparable, hence skylines sizes become unpractical. However, the Pareto semantics can be modified to benefit from indifferences: skyline result sizes can be essentially reduced by allowing the user to declare some incomparable values as equally desirable. A major problem of adding such equivalences is that they may result in intransitivity of the aggregated Pareto order and thus efficient query processing is hampered. In this paper we analyze how far the strict Pareto semantics can be relaxed while always retaining transitivity of the induced Pareto aggregation. Extensive practical tests show that skyline sizes can indeed be reduced about two orders of magnitude when using the maximum possible relaxation still guaranteeing the consistency with all user preferences
Timed sequential therapy of acute leukemia with flavopiridol: in vitro model for a phase I clinical trial.

PURPOSE The survival of adults with acute leukemias remains unsatisfactory and requires new treatment approaches. Flavopiridol modulates cell cycle progression, inhibits transcription, and induces apoptosis. We designed an in vitro model of timed sequential therapy for acute leukemia to determine whether flavopiridol can: (a). trigger apoptosis in fresh acute leukemia; and (b). recruit surviving leukemic cells to a proliferative state, thereby priming such cells for the S-phase-related cytotoxicity of 1-beta-D-arabinofuranosylcytosine (ara-C). EXPERIMENTAL DESIGN Bone marrow cells from 20 adults with relapsed and refractory acute leukemias were enriched for blasts by Ficoll Hypaque sedimentation. Blasts were cultured on day 0 in flavopiridol 250 nM for 24 h, removed from flavopiridol for 24 h, and then cultured in ara-C 1 microM for an additional 72 h (F(250)A(1)). Apoptosis and cell cycle phase distribution were estimated from cells stained with propidium iodide. Cell survival was determined after the 72 h ara-C exposure by double cytofluorescence assay with fluorescein diacetate and propidium iodide. RESULTS Flavopiridol induced a 4.3-fold increase in apoptosis in human leukemia samples within the first 24 h of culture. Subsequent removal of flavopiridol led to a 1.7-fold increase in the proportion of cells in S phase by day 2. Mean survival in F(250)A(1) cultures after 72 h exposure to ara-C was 35.6% compared with flavopiridol alone (F(250)A(0), 56.1%; P = 0.0003) and ara-C alone (F(0)A(1), 65.2%; P < 0.00001). CONCLUSIONS Flavopiridol induces apoptosis in marrow blasts from patients with refractory acute leukemias. Furthermore, flavopiridol pretreatment increases the proapoptotic and cytotoxic effects of ara-C. The advantage of sequential FP(250)A(1) over either agent alone is seen for both acute myelogenous leukemia and acute lymphoblastic leukemia. These findings support a clinical trial of timed sequential therapy where flavopiridol is given for cytoreduction and subsequent priming of remaining leukemic cells for enhanced cycle-dependent drug cytotoxicity.
Traceability Across Refinement Steps in UML Modeling

Documenting the refinement relationship between layers allows developers to verify whether the code meets its specification or not, trace the impact of changes in the business goals and execute test assertions written in terms of abstract model’s vocabulary by translating them to the concrete model’s vocabulary. Refinement has been studied in many formal notations such as Z and B and in different contexts, but there is still a lack of formal definitions of refinement in semi-formal languages, such as the UML. The contribution of this article is to clarify the abstraction/refinement relationship between UML models, providing basis for tools supporting the refinement driven modeling process. We formally describe a number of refinement patterns and present PAMPERO, a tool integrated in the Eclipse environment, based on the formal definition of model refinement.
Document-Specific Keyphrase Extraction Using Sequential Patterns with Wildcards

Finding good keyphrases for a document is beneficial for many applications, such as text summarization, browsing, and indexing. In this paper, we propose a sequential pattern mining based document-specific keyphrase extraction method. Our key innovation is to use wildcards (or gap constraints) to help extract sequential patterns, where the flexible wildcard constraints within a pattern can capture semantic relationships between words. To achieve this goal, we regard each single document as a sequential dataset, and propose an efficient algorithm to mine sequential patterns with wildcard and one-off conditions that allows important keyphrases to be captured during the mining process. For each extracted keyphrase candidate, we use some statistical pattern features to characterize it. A supervised learning classifier is trained to identify keyphrases from a test document. Comparisons on keyphrase benchmark datasets confirm that our document-specific keyphrase extraction method is effective in improving the quality of extracted keyphrases.
Complete Web Security Testing Methods and Recommendations

This paper describes the main contents and major types of the current Web security testing to introduce several programmers' fallible aspects, and also gives some cases introduction to Web security testing problems. Finally this paper gives the security testing tools and the corresponding coping Web security testing strategy, hoping to improve the efficiency of Web security testing to further improve the level of Web security testing and to avoid causing damage to the enterprise. This paper makes a contribution to Web security testing not only from theoretical but also from practical fields. By using the testing tools and applying testing strategy can improve web security, which has great significance to software companies.
Combinations of covariance selections for graphical modelling

We explore the possibility of composing the results of a fixed number of Gaussian graphical model selections on some partially overlapping variables. This appears to be an useful approach in all the research areas where a large amount of data from different sources and types of experiments is available. Therefore the focus is in binding together information coming from heterogeneous studies to improve the understanding of a particular phe- nomenon of interest. The proposed approach relies on numerical results on artificial and real data.
Introducing Backstage - a digital backchannel for large class lectures

Purpose – This paper seeks to report on the conception of a novel digital backchannel, Backstage, dedicated to large classes, aiming at empowering not only the audience but also the speaker, at promoting the awareness of both audience and speaker, and at promoting an active participation of students in the lecture. In learning settings with a large and typically passive audience, the goal of Backstage is to foster active participation and facilitate collaboration akin to small‐learning groups.Design/methodology/approach – The authors present the concept of a novel kind of digital backchannel that supports different forms of inter‐student communication via short microblog messages, social evaluation, and ranking of messages by the audience. The backchannel further supports immediate concise feedback to the lecturer of selected and aggregated students' opinions, making it possible to strengthen the lecturer's awareness of students' difficulties.Findings – The concept is the outcome of a joint effort between.
Compositional Vector Space Models for Knowledge Base Inference

Traditional approaches to knowledge base completion have been based on symbolic representations. Lowdimensional vector embedding models proposed recently for this task are attractive since they generalize to possibly unlimited sets of relations. A significant drawback of previous embedding models for KB completion is that they merely support reasoning on individual relations (e.g., bornIn(X, Y ) ⇒ nationality(X, Y )). In this work, we develop models for KB completion that support chains of reasoning on paths of any length using compositional vector space models. We construct compositional vector representations for the paths in the KB graph from the semantic vector representations of the binary relations in that path and perform inference directly in the vector space. Unlike previous methods, our approach can generalize to paths that are unseen in training and, in a zero-shot setting, predict target relations without supervised training data for that relation.
An Effective QoS-aware Web Service Selection Approach

Service computing provides the capability of binding each service invocation in a composition to a service selected among the same function attributes but different QoS attributes to achieve a QoS goal. Hence, service selection approach plays a very important role in service composition. However existing approaches little considered the computation time. In this study, we propose a effective QoS-aware web service selection approach. This approach adopts a particle swarm optimization algorithm select the most service with users' QoS requirements Experimental results show that our approach can find best suitable services with low time cost in web service selection.
Longitudinal Analysis of New Information Types in Clinical Notes

It is increasingly recognized that redundant information in clinical notes within electronic health record (EHR) systems is ubiquitous, significant, and may negatively impact the secondary use of these notes for research and patient care. We investigated several automated methods to identify redundant versus relevant new information in clinical reports. These methods may provide a valuable approach to extract clinically pertinent information and further improve the accuracy of clinical information extraction systems. In this study, we used UMLS semantic types to extract several types of new information, including problems, medications, and laboratory information. Automatically identified new information highly correlated with manual reference standard annotations. Methods to identify different types of new information can potentially help to build up more robust information extraction systems for clinical researchers as well as aid clinicians and researchers in navigating clinical notes more effectively and quickly identify information pertaining to changes in health states.
The Role of Collaborative Reflection in Developing a Culture of Inquiry in a School-University Partnership: A U.S. Perspective.

This paper describes two cases that illustrate the role of collaborative reflection in developing a culture of inquiry and individual and organizational renewal in school-university partnerships. Three assertions underlie the study: that individual renewal leads to organizational renewal; that developing a culture of inquiry is a prerequisite to the kind of individual renewal that leads to organizational renewal; and that collaborative reflection is essential for developing and sustaining culture. The first section of the paper defines the terms culture of inquiry, collaborative reflection, individual renewal, and organizational renewal. The second section describes two cases in which educators engaged in collaborative reflection (one in a new graduate program on educational inquiry and the other in an established program that involved reading, observing schools, and discussing issues related to education in a democracy). The third section offers a model for individual and organizational renewal that includes the nine elements of collaborative reflection: building trust, making time, nurturing questions, forming groups, taking risks, being patient, giving gifts, accepting offerings, and recognizing results. (Contains 22 references.) (SM) Reproductions supplied by EDRS are the best that can be made from the original document. The Role of Collaborative Reflection in Developing a Culture of Inquiry in a School-University Partnership: A U.S. Perspective Russell T. Osguthorpe Professor of Pedagogy David 0. Mckay School of Education 4458 WSC, Brigham Young University Provo, UT 84602 Phone: 801-378-3674 Fax: 801-378-7467 E-mail: russ osguthorpe@byu.edu Abstract: This article describes two cases which illustrate the role of collaborative reflection in developing a culture of inquiry in a school-university partnership. Based upon experiences and observations from the cases, a model of individual and organizational renewal is described. The model includes the following nine elements of collaborative reflection which lead to a culture of inquiry: (1) build trust, (2) make time, (3) nurture questions, (4) form groups, (5) take risks, (6) be patient, (7) give gifts, (8) accept offerings, and (9) recognize results. This article describes two cases which illustrate the role of collaborative reflection in developing a culture of inquiry in a school-university partnership. Based upon experiences and observations from the cases, a model of individual and organizational renewal is described. The model includes the following nine elements of collaborative reflection which lead to a culture of inquiry: (1) build trust, (2) make time, (3) nurture questions, (4) form groups, (5) take risks, (6) be patient, (7) give gifts, (8) accept offerings, and (9) recognize results. Paper presented at the annual meeting of the American Educational Research Association, Montreal, Quebec, April 20, 1999 PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY
Price Formation and Intertemporal Arbitrage within a Low-Liquidity Framework: Empirical Evidence from European Natural Gas Markets

In this study, the informational efficiency of the European natural gas market is analyzed by empirically investigating price formation and arbitrage efficiency between spot and futures markets. Econometric approaches are specified that explicitly account for nonlinearities and the low liquidity-framework of the considered gas hubs. The empirical results reveal that price discovery takes place on the futures market, while the spot price subsequently follows the futures market price. Furthermore, there is empirical evidence of significant market frictions hampering intertemporal arbitrage. UK’s NBP seems to be the hub at which arbitrage opportunities are exhausted most efficiently, although there is convergence in the degree of intertemporal arbitrage efficiency over time at the hubs investigated.
Towards a light-weight message authentication mechanism tailored for Smart Grid communications

Smart Grid (SG) technology, which aims at bringing the world's aging electric grids into the 21st century by utilizing intelligent transmission and distributed networks, has been gaining momentum in recent years. Despite its attractive features, the SG technology remains vulnerable to some security threats, such as spoofing and man-in-the-middle attacks. In this paper, to address these potential security issues, we propose a light-weight and secure message authentication mechanism. The proposed mechanism is based on Diffie-Hellman key establishment protocol and hash-based message authentication code, which allows various smart meters at different points of the SG to make mutual authentication and achieve message authentication with low latency and few signal message exchanges. Detailed security analysis shows it can satisfy the desirable security requirements. In addition, extensive computer-based simulation also demonstrates its efficiency.
The Random Walk Hypothesis and Correlation in the Visegrad Countries Emerging Stock Markets

This paper examines the random walk hypothesis in the Visegrad Countries stock market as emerging stock markets. The results both from autocorrelation analysis and unit root tests imply that monthly stock price indices of the Visegrad Countries follow the random walk process. This means that the stock markets of all the Visegrad Countries are efficient in the weak form. By employing cointegration and causality tests, we investigate the long-run and short-run relationships among these markets of the Visegrad countries and interpret the findings in the context of capital market integration.
Supernova properties from shock breakout X-rays

We investigate the potential of the upcoming LOBSTER space observatory (due circa 2009) to detect soft-X-ray flashes from shock breakout in supernovae, primarily from Type II events. LOBSTER should discover many supernova (SN) breakout flashes, although the number is sensitive to the uncertain distribution of extragalactic gas columns. X-ray data will constrain the radii of the progenitor stars far more tightly than can be accomplished with optical observations of the SN light curve. We anticipate the appearance of blue supergiant explosions (SN 1987A analogues), which will uncover a population of these underluminous events. We consider also how the mass, explosion energy, and absorbing column can be constrained from X-ray observables alone and with the assistance of optically determined distances. These conclusions are drawn using known scaling relations to extrapolate, from previous numerical calculations, the LOBSTER response to explosions with a broad range of parameters. We comment on a small population of flashes with 0.2 < z < 0.8 that should exist as transient background events in XMM-Newton, Chandra, and ROSAT integrations.
Physiological and Biochemical Characterization of a Novel Nicotine-Degrading Bacterium Pseudomonas geniculata N1

Management of solid wastes with high nicotine content, such as those accumulated during tobacco manufacturing, poses a major challenge, which can be addressed by using bacteria such as Pseudomonas and Arthrobacter. In this study, a new species of Pseudomonas geniculata, namely strain N1, which is capable of efficiently degrading nicotine, was isolated and identified. The optimal growth conditions for strain N1 are a temperature of 30°C, and a pH 6.5, at a rotation rate of 120 rpm min−1 with 1 g l−1 nicotine as the sole source of carbon and nitrogen. Myosmine, cotinine, 6-hydroxynicotine, 6-hydroxy-N-methylmyosmine, and 6-hydroxy-pseudooxynicotine were detected as the five intermediates through gas chromatography-mass and liquid chromatography-mass analyses. The identified metabolites were different from those generated by Pseudomonas putida strains. The analysis also highlighted the bacterial metabolic diversity in relation to nicotine degradation by different Pseudomonas strains.
Playing for Improving the Reading Comprehension Skills of Primary School Poor Comprehenders

TERENCE is an FP7 ICT European project , highly multidisciplinary, that is developing an adaptive learning system for supporting poor comprehenders and their educators. Its learning material are stories and games, explicitly designed for classes of primary schools poor comprehenders, where classes were created via an extensive analysis of the context of use and user requirements. Its learning tasks are reading stories and playing with games. The games are specialised into smart games, which stimulate inference-making for story comprehension, and relaxing games, which stimulate visual perception and which train the interaction with devices (e.g., PC and tablet PC). The design of the reading and playing tasks is mainly based on the requirements resulting from the study of the context of use, which is made via field studies and expert-based inquiries. In this paper we focus on how we used the pedagogical underpinnings and the acquired requirements to design the games of the system.
BioConstructs-Methods for Bilo-Inspired and Bio-Fabricated Design . by Katia Zolotovsky

Acknowledgements Chapter 1: Introduction 1.1 1.2 1.3 1.4 1.5 BioConstructs Innovation in the use of computation and digital fabrication methods Collaboration with scientific community Bio-inspired design and biofabrication Thesis structure Chapter 2: The Polypterus study 2.
A Fast SVM Incremental Learning Algorithm Based on the Central Convex Hulls Algorithm

How to deal with the newly added training samples, and utilize the result of the previous training effectively to get better classification result fast are the main tasks of incremental learning. A fast SVM incremental learning algorithm based on the central convex hulls algorithm is proposed in this paper. To utilize the result of the previous training and retain the useful information in the training set effectively, the relationship between the KKT conditions of support vector machine and the distribution of the training samples is analyzed. To reduce the computational cost of the incremental learning, the current training sample set is pre-extracted from the geometric point of view, and the obtained between-class convex hull vectors are used as the training samples in the SVM incremental training. Experimental results with the standard dataset indicate the feasibility and effectiveness of the proposed algorithm.