Miracle-dz/UniFAR-FTUs huggingface dataset:
A Reconfigurable Microfluidics Platform for Microparticle Separation and Fluid Mixing

Microfluidics is an engineering tool used to control and manipulate fluid flows, with practical applications for lab-on-a-chip, point-of-care testing, and biological/medical research. However, microfluidic platforms typically lack the ability to create a fluidic duct, having an arbitrary flow path, and to change the path as needed without additional design and fabrication processes. To address this challenge, we present a simple yet effective approach for facile, on-demand reconfiguration of microfluidic channels using flexible polymer tubing. The tubing provides both a well-defined, cross-sectional geometry to allow reliable fluidic operation and excellent flexibility to achieve a high degree of freedom for reconfiguration of flow pathways. We demonstrate that microparticle separation and fluid mixing can be successfully implemented by reconfiguring the shape of the tubing. The tubing is coiled around a 3D-printed barrel to make a spiral microchannel with a constant curvature for inertial separation of microparticles. Multiple knots are also made in the tubing to create a highly tortuous flow path, which induces transverse secondary flows, Dean flows, and, thus, enhances the mixing of fluids. The reconfigurable microfluidics approach, with advantages including low-cost, simplicity, and ease of use, can serve as a promising complement to conventional microfabrication methods, which require complex fabrication processes with expensive equipment and lack a degree of freedom for reconfiguration.
Classification of Correlated Internet Traffic Flows

A critical problem for Internet traffic classification is how to obtain a high-performance statistical feature based classifier using a small set of training data. The solutions to this problem are essential to deal with the encrypted applications and the new emerging applications. In this paper, we propose a new Naive Bayes (NB) based classification scheme to tackle this problem, which utilizes two recent research findings, feature discretization and flow correlation. A new bag-of-flow (BoF) model is firstly introduced to describe the correlated flows and it leads to a new BoF-based traffic classification problem. We cast the BoF-based traffic classification as a specific classifier combination problem and theoretically analyze the classification benefit from flow aggregation. A number of combination methods are also formulated and used to aggregate the NB predictions of the correlated flows. Finally, we carry out a number of experiments on a large scale real-world network dataset. The experimental results show that the proposed scheme can achieve significantly higher classification accuracy and much faster classification speed with comparison to the state-of-the-art traffic classification methods.
Time management behaviors of single parent women educators at a two-year college

Case study methodology was utilized to examine and describe time management behavior of single parent women educators at a two-year college. A non­ probability, stratified sample of convenience (Snowball Sampling) where participants were referred by colleagues, friends, and other participants processed and determined final selection of three subjects for the case studies. The three participants selected for the study were single parent women, between the ages of 35 and 45, teaching full-time at a two-year college, with no more than two children living at home. The children were all teenagers attending either junior or senior high schools. Each participant had been married, was divorced, and was now a single parent for at least the past five years. The primary focus of the study examined and described how single parent women educators teaching full-time at a two-year college manage time, including variables that affect their time management, as well as, differences and/or similarities in time management behavior. As the study progressed a secondary question surfaced regarding the relationship of I.Q. level and time management behavior of single parent women educators at a two-year college. The study was conducted over two consecutive semesters, approximately nine months. Data were
A Probabilistic Approach to Modeling Socio-Behavioral Interactions

The vast growth and reach of internet and social media have led to a tremendous increase in socio-behavioral interaction content on the web. The ever-increasing number of online interactions have led to a growing interest to understand and interpret online communications to enhance user experience. This includes personalization, user retention, predicting user interests, and product recommendations. In this thesis, I address how to use machine learning methods to model sociobehavioral interactions and predict user behavior patterns in online networks. In the first part of this proposal, I focus on one such emerging online interaction platform—online courses (MOOCs). Structured data from these courses contain behavioral, and interaction data and provide opportunity to design machine learning methods for understanding user interaction. The data also contains unstructured data, such as natural language text from forum posts and other online discussions. I present a family of probabilistic models that I have developed for: 1) modeling student engagement, 2) predicting student completion and dropouts, 3) modeling student sentiment toward various course aspects (e.g., content vs. logistics), and 4) detecting coarse and fine-grained course aspects (e.g., grading, video, content) in online courses. These methods have the potential to improve student experience and focus limited instructor resources in ways that will have the most impact. In the second part of the proposal, I describe how I plan to extend the above-mentioned models to model socio-behavioral interactions at multiple scales in networks. I plan to test the effectiveness of this model via experimentation on different types of platforms such as MOOCs and professional networks (e.g., LinkedIn).
The Adaptive Evolution Database (TAED): a phylogeny based tool for comparative genomics

From 138 662 embryophyte (higher plant) and 348 142 chordate genes, 4216 embryophyte and 15 452 chordate gene families were generated. For each of these gene families, multiple sequence alignments, phylogenetic trees, ratios of non-synonymous to synonymous nucleotide substitution rates (Ka/Ks), mappings from gene trees to the NCBI taxonomy and structural links to solved three-dimensional protein structures in the Protein Data Bank (PDB) with Grantham-weighted mutational factors were all calculated. Of the ‘gene family trees’, 173 embryophyte and 505 chordate branches show Ka/Ks ≫ 1 and are candidates for functional adaptation. The calculated information is available both as a gene family database and as a phylogenetically indexed resource, called ‘The Adaptive Evolution Database’ (TAED), available at http://www.bioinfo. no/tools/TAED.
The Use of Comics to Allow Creativity and Promote Higher Order Thinking Skills

Abstract The study aimed to find out the effect of the use of comics on 11 th elective program graders of SMAN 2 Cianjur in their Narrative text understanding performance as well as their attitudes towards it. The results show that visual images in form of student-generated comic strips allow for creativity where students can express ideas by images combined with text, and promote higher order thinking skills (analyzing, evaluating, creating) through student-centered work. From its findings, the study concluded that comics is: 1) effective for constructive activities such as writing and speaking and 2) benefitted slow learners as they can take advantage of visual guide to practice target-language use. Keywords: Comics, Narrative, higher order thinking skills
Bacterioneuston Community Structure in the Southern Baltic Sea and Its Dependence on Meteorological Conditions

ABSTRACT The bacterial community in the sea surface microlayer (SML) (bacterioneuston) is exposed to unique physicochemical properties and stronger meteorological influences than the bacterial community in the underlying water (ULW) (bacterioplankton). Despite extensive research, however, the structuring factors of the bacterioneuston remain enigmatic. The aim of this study was to examine the effect of meteorological conditions on bacterioneuston and bacterioplankton community structures and to identify distinct, abundant, active bacterioneuston members. Nineteen bacterial assemblages from the SML and ULW of the southern Baltic Sea, sampled from 2006 to 2008, were compared. Single-strand conformation polymorphism (SSCP) fingerprints were analyzed to distinguish total (based on the 16S rRNA gene) and active (based on 16S rRNA) as well as nonattached and particle-attached bacterial assemblages. The nonattached communities of the SML and ULW were very similar overall (similarity: 47 to 99%; mean: 88%). As an exception, during low wind speeds and high radiation levels, the active bacterioneuston community increasingly differed from the active bacterioplankton community. In contrast, the particle-attached assemblages in the two compartments were generally less similar (similarity: 8 to 98%; mean: 62%), with a strong variability in the active communities that was solely related to wind speed. Both nonattached and particle-attached active members of the bacterioneuston, which were found exclusively in the SML, were related to environmental clones belonging to the Cyanobacteria, Bacteroidetes, and Alpha-, Beta-, and Gammaproteobacteria originally found in diverse habitats, but especially in water columns. These results suggest that bacterioneuston communities are strongly influenced by the ULW but that specific meteorological conditions favor the development of distinctive populations in the air-water interface.
TEST ANXIETY, COMPUTER SELF EFFICACY, AND PERCEIVED EASE OF USE DURING COMPUTER-BASED TEST ON FIRST-YEAR STUDENTS

This study aims to provide description of psychological factors such as test anxiety, computer self-efficacy, and perceived ease of use in completing computer-based tests (CBT) on first-year students. Such study is important because for first year students from different regions, not all of them have experience of CBT and there are possibilities of various psychological aspects such as CBT anxiety, Computer Self-Efficacy, and perceived ease of use affecting them in completing CBT. The subjects are 203 first-year students of Faculty of Psychology Universitas Airlangga. Scales used in this research include an adaptation of Computer Self-Efficacy scale by Compeau, DR, and Higgins, CA (1995), Test Anxiety Inventory (TAI) by Taylor and Diane (2002), Perceived Ease of Use scale by Davis (1989). The results showed that the student’s test anxiety test during CBT are categorized as 16.74% low, 76.26% moderate and 7% high. Regarding computer self-efficacy, about 28.57% students had low efficacy, 59.60% with mediocre efficacy and 11.82% had high efficacy. In term of perceived ease of use of the CBT, 2.46% perceived low ease, 34,48% perceived moderate degree of ease, while 63,05% perceived high ease of use.
Measure theory through dynamical eyes

These expository notes are a somewhat embellished version of two rather informal evening review sessions given by the second author at the 2008 Bedlewo summer school on "Dynamical Systems - Geometric Structures and Rigidity"; they provide a brief overview of some of the basics of measure theory and its applications to dynamics which are foundational to the various courses at the school.
Articulating Governance Mechanisms for Collective Learning

This paper seeks to shed light on the role that governance mechanisms at play within the firm have in organizational learning. Moreover, since different governance mechanisms co-exist within an organization, their alignments are of particular importance for achieving knowledge circulation within the organization. We here focus on knowing communities, seen as important locus for knowledge creation, and explore how the organization can take advantage of their cognitive activities. We argue in particular that such coordination can be realized at the governance level: formal rules set by the management provides a framework that can yoke the different norms sets followed by communities. Our proposals are confronted to a case study carried out in a large public organization.
Only connections: A critique of semantic networks.

This article examines theories that assume that semantic networks account for the mental representation of meaning. It assesses their similarities and divergencies, and argues that as a class of theories they remain too powerful to be refuted by empirical evidence. The theories are also confronted by a number of problematical semantic phenomena that arise because networks deal with the connections between concepts rather than with their connections to the world. The solution to these problems could be embodied in a new network system, but such a system would differ in both structure and function from current network theories.
Optimized segmentation techniques for arabic handwritten numeral character recognition

The segmentation of the Arabic words for recognition still remains a problem to rise for the variability of the writing styles. The adopted technique rests on the hybridization of several segmentation methods based on the various reference levels. The segmentation process led to the decomposition of the writing or words in elementary entities of morphology relatively simple. The choice of primitives of various types is essential in order to ensure an understanding description. The work presented in this paper reveals the approach of the technique used and proposes an algorithm which has been tested with a set of Arabic words by different writers ranging from poor to acceptable quality. A particular attention was fixed to segmentation techniques that gave suitable results. The initial experimental results are very encouraging and promising .
Assessing Social Presence In Asynchronous Text-based Computer Conferencing

Instructional media such as computer conferencing engender high levels of student-student and student-teacher interaction; therefore, they can support models of teaching and learning that are highly interactive and consonant with the communicative ideals of university education. This potential and the ubiquity of computer conferencing in higher education prompted three of the authors of the this article to develop a community of inquiry model that synthesizes pedagogical principles with the inherent instructional and access benefits of computer conferencing (Garrison, Anderson, & Archer, 2000). This article explicates one element of the model, social presence. Social presence is defined as the ability of learners to project themselves socially and affectively into a community of inquiry. A template for assessing social presence in computer conferencing is presented through content analysis of conferencing transcripts. To facilitate explication of the scheme and subsequent replication of this study, selections of coded transcripts are included, along with interrater reliability figures. The article concludes with a discussion of the implications and benefits of assessing social presence for instructors, conference moderators, and researchers. Les medias educatifs, tel le forum electronique, sont susceptibles d'engendrer un niveau eleve d'interaction entre les etudiants et entre le tuteur et chaque etudiant; par consequent, ils peuvent soutenir des modeles d'enseignement et d'apprentissage qui mettent en valeur l'interaction et qui sont compatibles avec les ideaux communicationnels d'une education universitaire. Ce potentiel, ainsi que l'ubiquite du forum asynchrone dans l'education superieure, ont mene trois des auteurs de cet article a developper un modele de communaute de recherche qui resume les principes pedagogiques applicables au forum electronique en incluant les benefices que l'enseignement peut en retirer et les avantages relatifs a l'accessibilite (Garrison, Anderson, & Archer, 2000). Cet article explique un element du modele : la presence sociale. On y definit la presence sociale comme la capacite des apprenants de se projeter sur le plan social et emotionnel dans une communaute de recherche. On y decrit un gabarit utilise pour evaluer la presence sociale lors d'une analyse de contenu de transcriptions de forums electroniques. Afin d'aider la comprehension des modalites d'evaluation et de faciliter la reproduction subsequente de cette etude, un echantillonnage de transcriptions codees ainsi que les baremes de fiabilite inter-evaluateurs sont fournis. L'article se termine par une discussion des consequences et des avantages de l'evaluation de la presence sociale du point de vue des formateurs, des animateurs de conference et des chercheurs.
Using Skeletonization and Shortest Skeleton Path Approach for Chinese Character Representation

This paper adopts skeletonization approach to represent Chinese character and uses the resulting written strokes for optimized matching. We use shortest path method that represented by end node pair and junction node pair in the character for matching. This strategy is improved from the original shortest skeleton path matching by introducing the junction node as an extra feature in order to be able to represent singularity, non-singularity and looping characteristics for Chinese characters. Our result shows an effective representation and matching of the character by skeletonization and shortest path method considering end nodes and junction nodes as matching strategy.
Change in Sleep Duration and Type 2 Diabetes: The Whitehall II Study

OBJECTIVE Evidence suggests that short and long sleep durations are associated with a higher risk of type 2 diabetes. Using successive data waves spanning >20 years, we examined whether a change in sleep duration is associated with incident diabetes. RESEARCH DESIGN AND METHODS Sleep duration was reported at the beginning and end of four 5-year cycles: 1985–1988 to 1991–1994 (n = 5,613), 1991–1994 to 1997–1999 (n = 4,193), 1997–1999 to 2002–2004 (n = 3,840), and 2002–2004 to 2007–2009 (n = 4,195). At each cycle, change in sleep duration was calculated for participants without diabetes. Incident diabetes at the end of the subsequent 5-year period was defined using 1) fasting glucose, 2) 75-g oral glucose tolerance test, and 3) glycated hemoglobin, in conjunction with diabetes medication and self-reported doctor diagnosis. RESULTS Compared with the reference group of persistent 7-h sleepers, an increase of ≥2 h sleep per night was associated with a higher risk of incident diabetes (odds ratio 1.65 [95% CI 1.15, 2.37]) in analyses adjusted for age, sex, employment grade, and ethnic group. This association was partially attenuated by adjustment for BMI and change in weight (1.50 [1.04, 2.16]). An increased risk of incident diabetes was also seen in persistent short sleepers (average ≤5.5 h/night; 1.35 [1.04, 1.76]), but this evidence weakened on adjustment for BMI and change in weight (1.25 [0.96, 1.63]). CONCLUSIONS This study suggests that individuals whose sleep duration increases are at an increased risk of type 2 diabetes. Greater weight and weight gain in this group partly explain the association.
An Outpost Vector placement evaluation of an incremental learning algorithm for Support Vector Machine

Outpost Vector model synthesizes new vectors at the boundary of two classes of data in order to increase the level of accuracy of classification. This paper presents a performance evaluation of four different placements of outpost vectors in an incremental learning algorithm for Support Vector Machine (SVM) on a non-complex problem. The algorithm generates outpost vectors from selected new samples, selected prior samples, both samples, or generates no outpost vector at all. After that, they are included in the final training set, as well as new samples and prior samples, based on the specified parameters. The experiments are conducted with a 2-dimension partition problem. The distribution of training and test samples is created in a limited location of a 2-dimension donut ring. There are two classes of data which are represented as 0 and 1. The context of the problem is assumed to shift 45 degrees in counterclockwise direction. Every consecutive partition is set to have different class of data. The experimental results show that the placement of outpost vectors generated from only selected new samples yields the highest level of accuracy of classification for both new data and old data. As a result, using samples from different part of the algorithm to generate outpost vectors affects the level of accuracy of classification.
Representation using the Weyl Transform

The Weyl transform is introduced as a powerful framework for representing measurement data. Transform coefficients are connected to the Walsh-Hadamard transform of multiscale autocorrelations, and different forms of dyadic periodicity in a signal are shown to appear as different features in its Weyl coefficients. A large group of multiscale transformations is shown to support very fast pooling since the Weyl coefficients are unique up to permutation and phase changes when the original signal is transformed by any element of this group. The effectiveness of the Weyl transform is demonstrated through the example of textured image classification.
Image Denoising via Residual Kurtosis Minimization

A new algorithm for the removal of additive uncorrelated Gaussian noise from a digital image is presented. The algorithm is based on a data driven methodology for the adaptive thresholding of wavelet coefficients. This methodology is derived from higher order statistics of the residual image, and requires no a priori estimate of the level of noise contamination of an image.
Object Oriented Video Coding Using Adaptive Wavelet Packet Decomposition

An object based rate control algorithm is proposed in this paper based on the wavelet packet transform. In particular, a precoding stage has been introduced for performing automatic foreground /background separation, especially in cases of videoconferences sequences. For this reason, a human face and body detector has been applied, followed by an adaptively trained neural network structure, which performs the object segmentation. Then, a wavelet packet transform (WPT) is proposed for coding each individual VOP. For each object, adaptive optimization is accomplished, taking into consideration its statistics per Group of Picture period so that total distortion per frame is minimized while the distortion of important objects, like foreground, is kept below a pre-selected constraint.
Finding the Cost-Optimal Path with Time Constraint over Time-Dependent Graphs

Shortest path query is an important problem and has been well studied in static graphs. However, in practice, the costs of edges in graphs always change over time. We call such graphs as time-dependent graphs. In this paper, we study how to find a cost-optimal path with time constraint in time-dependent graphs. Most existing works regarding the Time-Dependent Shortest Path (TDSP) problem focus on finding a shortest path with the minimum travel time. All these works are based on the following fact: the earliest arrival time at a vertex v can be derived from the earliest arrival time at v's neighbors. Unfortunately, this fact does not hold for our problem. In this paper, we propose a novel algorithm to compute a cost-optimal path with time constraint in time-dependent graphs. We show that the time and space complexities of our algorithm are O(kn log n + mk) and O((n + m)k) respectively. We confirm the effectiveness and efficiency of our algorithm through conducting experiments on real datasets with synthetic cost.