Miracle-dz/UniFAR-FTUs huggingface dataset:
An optical artificial nose system for odor classifications based on LED arrays

Optical electronic nose (olfactory sensing) technologies have recently become a convenient technique to identify the quality of food and beverage products based on the odor classification. In this paper, we reported an optical-based electronic nose system consisting of thin-film sensing materials, array of light emitting diode (LED), photo-detector and pattern recognition program. The organic mixtures thin film gas sensor was prepared by spin coating of Zinc-2,9,16,23- tetra- tert-butyl-29H,31H-phthalocyanine (ZnTTBPc), Zinc-5,10,15,20-tetra-phenyl-21H,23H-porphyrin (ZnTPP) and manganese(III)-5,10,15,20-tetra-phenyl-21H,23H-porphyrin chloride (MnTPPCl) onto a clean glass substrate. The electronic nose system was developed by using the low-cost LED array as a light source. Then the light intensity that is transmitted through the organic thin film during the experiment was detected by the color light to frequency converter device (photo-detector). The ability of this system was tested by using volatile organic compound (VOCs) vapors such as methanol, ethanol, and isopropanol. Principal component analysis (PCA) has been used as the pattern recognition for this electronic nose system. The result confirms that the sensing layer that composed of the three types of organic compounds described the groups of chemical vapors by using the array of LED.
SemEval-2007 Task 16: Evaluation of Wide Coverage Knowledge Resources

This task tries to establish the relative quality of available semantic resources (derived by manual or automatic means). The quality of each large-scale knowledge resource is indirectly evaluated on a Word Sense Disambiguation task. In particular, we use Senseval-3 and SemEval-2007 English Lexical Sample tasks as evaluation bechmarks to evaluate the relative quality of each resource. Furthermore, trying to be as neutral as possible with respect the knowledge bases studied, we apply systematically the same disambiguation method to all the resources. A completely different behaviour is observed on both lexical data sets (Senseval-3 and SemEval-2007).
Link pattern prediction in opportunistic networks with kernel regression

Opportunistic networks (OppNets) have emerged as prospective network architecture due to the popularization of mobile devices and little monetary cost. Routing is the major concern in designing OppNets. The main obstacle for routing protocol design in OppNets is little knowledge of future link patterns, which leads to blind and unpredictable packet forwarding behavior. To achieve better packet delivery rate, OppNets have to retain and deliver multiple copies of a message, which consumes more devices' energy and causes a lackluster OppNets service. To this end, we aim at the prediction of future link patterns to explore mobile connectivity. In this paper, we propose PreKR-the kernel regression based estimation framework for link pattern prediction. We initially extract best features that can represent the network evolution. Then we models historical structural features by kernel regression with the output of link probability. Experimental results show that our method outperforms state-of-the-art prediction methods up to 25%. We also find that both reachability prediction and high degree nodes prediction reach more than 90% accuracy. In the end, we propose heterogeneous architecture for PreKR deployment and investigate two prospective OppNets applications to show how PreKR improve system performance.
Responses of Methanogenic and Methanotrophic Communities to Elevated Atmospheric CO2 and Temperature in a Paddy Field

Although climate change is predicted to affect methane (CH4) emissions in paddy soil, the dynamics of methanogens and methanotrophs in paddy fields under climate change have not yet been fully investigated. To address this issue, a multifactor climate change experiment was conducted in a Chinese paddy field using the following experimental treatments: (1) enrichment of atmospheric CO2 concentrations (500 ppm, CE), (2) canopy air warming (2°C above the ambient, WA), (3) combined CO2 enrichment and warming (CW), and (4) ambient conditions (CK). We analyzed the abundance of methanogens and methanotrophs, community structures, CH4 production and oxidation potentials, in situ CH4 emissions using real-time PCR, T-RFLP, and clone library techniques, as well as biochemical assays. Compared to the control under CE and CW treatments, CH4 production potential, methanogenic gene abundance and soil microbial biomass carbon significantly increased; the methanogenic community, however, remained stable. The canopy air warming treatment only had an effect on CH4 oxidation potential at the ripening stage. Phylogenic analysis indicated that methanogens in the rhizosphere were dominated by Methanosarcina, Methanocellales, Methanobacteriales, and Methanomicrobiales, while methanotrophic sequences were classified as Methylococcus, Methylocaldum, Methylomonas, Methylosarcina (Type I) and Methylocystis (Type II). However, the relative abundance of Methylococcus (Type I) decreased under CE and CW treatments and the relative abundance of Methylocystis (Type II) increased. The in situ CH4 fluxes indicated similar seasonal patterns between treatments; both CE and CW increased CH4 emissions. In conclusion results suggest that methanogens and methanotrophs respond differently to elevated atmospheric CO2 concentrations and warming, thus adding insights into the effects of simulated global climate change on CH4 emissions in paddy fields.
Using Crowdsourcing Techniques and Mobile Devices for Asphaltic Pavement Quality Recognition

Currently, many developing countries based their transportation infrastructure on roads. These roads qualities have a significant influence on how fast products are delivered and how much it costs. Due to the lack of low-cost solutions or negligence, roadways pavement quality inspections are usually set aside or hardly performed at some places. This fact leads to higher casualties rates, driving discomfort, vehicle damage, and money prejudice. A cheap and accurate way to solve this problem is the use of typical smartphones with accelerometers and GPS sensors to measure the impact inflicted on the car when driving over a poor quality road surface. This scrutiny helps to raise data that can be used by road maintenance staff. Most current systems tend to translate its output to an international standard called IRI (International Roughness Index) but, this practice demands high computer processing. Here is presented "RoadScan", a crowdsourcing Android application that determines pavements quality in a simple and lightweight way. Our approach allows the mobile device to be placed at any position in the car. Considered tests and results are promising, identifying in most cases road regions that have some deformity.
Analyzing Variation of Adaptive Game-Based Training with Event Sequence Alignment and Clustering

Adaptive Game-Based Training (AGBT) systems plan the content and ordering of learning opportunities to customize game-play, thereby addressing individual players’ learning needs. Determining the best combination of settings, rules, and algorithms to perform this intelligent tutoring is both complex and expensive, as it requires iteration over multiple experimental cycles so as to measure learning performance and compare it with pre/post testing. In this paper, we propose analyzing event sequence variations produced by the intelligent tutor as an indicator of the effect of changes on adaptive game-play experiences. We describe Event Sequence Alignment and Clustering (ESAC), an analytic method that characterizes variations in the selection and ordering of learning opportunities directly from play-test game logs (without pre/post testing). We present results of a post-hoc analysis of variation, over three experimental cycles, on a large-scale AGBT development effort. We conclude with a discussion of limitations, applications, and future work.
Inside the conductor's jacket: analysis, interpretation and musical synthesis of expressive gesture

We present the design and implementation of the Conductor's Jacket, a unique wearable device that measures physiological and gestural signals, together with the Gesture Construction, a musical software system that interprets these signals and applies them expressively in a musical context. Sixteen sensors have been incorporated into the Conductor's Jacket in such a way as to not encumber or interfere with the gestures of a working orchestra conductor. The Conductor's Jacket system gathers up to sixteen data channels reliably at rates of 3 kHz per channel, and also provides real-time graphical feedback. Unlike many gesture-sensing systems it not only gathers positional and accelerational data but also senses muscle tension from several locations on each arm. The Conductor's Jacket was used to gather conducting data from six subjects, three professional conductors and three students, during twelve hours of rehearsals and performances. Analyses of the data yielded thirty-five significant features that seem to reflect intuitive and natural gestural tendencies, including context-based hand switching, anticipatory 'flatlining' effects, and correlations between respiration and phrasing. The results indicate that muscle tension and respiration signals reflect several significant and expressive characteristics of a conductor's gestures. From these results we present nine hypotheses about human musical expression, including ideas about efficiency, intentionality, polyphony, signal-to-noise ratios, and musical flow state. Finally, this thesis describes the Gesture Construction, a musical software system that analyzes and performs music in realtime based on the performers gestures and breathing signals. A bank of software filters extracts several of the features that were found in the conductor study, including beat intensities and the alternation between arms. These features are then used to generate real-time expressive effects by shaping the beats, tempos, articulations, dynamics, and note lengths in a musical score. Thesis Supervisor: Tod Machover Title: Associate Professor of Music and Media Program in Media Arts and Sciences Thesis Supervisor: Rosalind W. Picard Title: Associate Professor of Media Arts and Sciences Program in Media Arts and Sciences
FLOW SHOP SCHEDULING PROBLEM WITH MISSING OPERATIONS: GENETIC ALGORITHM AND TABU SEARCH

Flow shop scheduling problem with missing operations is studied in this paper. Missing operations assumption refers to the fact that at least one job does not visit one machine in the production process. A mixed-binary integer programming model has been presented for this problem to minimize the makespan. The genetic algorithm (GA) and tabu search (TS) are used to deal with the optimization problem. According to computational experiments on data sets, it is suggested that GA is a more appropriate method to solve this problem. GA can reach good-quality solutions in short computational time, and can be used to solve large scale problems effectively.
Children’s Literature in Education

Children’s literature is a dynamic entity in its own right that offers its readers many avenues for pleasure, reflection and emotional engagement. As this essay argues, its place in education was established centuries ago, but this association continues today in ways that are both similar and different from its beginnings. The irony of children’s literature is that while it is ostensibly for children, it relies on adults for its existence. However, this reciprocal relationship between adult and child is also at the heart of education. Drawing on a range of scholars and children’s texts from Australia, Austria, Canada, China, Germany, Sweden, Switzerland, the United Kingdom, and the United States, this discussion canvasses some of the many ways in which children’s literature, and the research that it inspires, can be a productive and valuable asset to education in that its imaginative storytelling is the means by which it brings the world into the classroom and takes the classroom out into the world.
Accounting for changing overlap in variational image registration

Any similarity measure used for image registration depends in some way on the region Ω describing the overlap between the floating and reference images. In variational registration, where the Gâteaux derivative of the similarity measure drives the registration, most literature implicitly assumes that Ω remains constant. This assumption is valid if homogeneous Dirichlet or sliding boundary conditions are chosen for the displacement field; however, it is invalid if any other type of boundary conditions are chosen, or if the similarity measure is computed over some masked portion of the overlap region. This article illustrates how these more general situations of different boundary conditions and/or masked regions can be accommodated in variational registration by explicitly accounting for the varying Ω in the Gâteaux derivative of the similarity measure.
The Approach for Optimization in Watermark Signal of Relational Databases by using Genetic Algorithms

This paper studies a novel scheme of watermarking relational databases for copyright protection based genetic algorithms. In this algorithm, GA is used in watermark signal processes, and associated novel watermark insertion algorithm and detection algorithm are proposed. Thus, the watermark signal in this method is expected to be more meaningful and has closer correlative to the copyright holder. The experimental results verify that the proposed algorithm is feasible, effective and robust.
Induction of Terminological Cluster Trees

In this paper, we tackle the problem of clustering individual resources in the context of the Web of Data, that is characterized by a huge amount of data published in a standard data model with a well-defined semantics based on Web ontologies. In fact, clustering methods offer an effective solution to support a lot of complex related activities, such as ontology construction, debugging and evolution, taking into account the inherent incompleteness underlying the representation. Web ontologies already encode a hierarchical organization of the resources by means of the subsumption hierarchy of the classes, which may be expressed explicitly, with proper subsumption axioms, or it must be detected indirectly, by reasoning on the available axioms that define the classes (classification). However it frequently happens that such classes are sparsely populated as the hierarchy often reflect a view of the knowledge engineer prior to the actual introduction of assertions involving the individual resources. As a result, very general classes are often loosely populated, but this may happen also to specific subclasses, making it more difficult to check the types of a resource (instance checking), even through reasoning services. Among the large number of algorithms proposed in the Machine Learning literature, we propose a clustering method that is able to organize groups of resources hierarchically. Specifically, in this work, we introduce a conceptual clustering approach that combines a distance measure between individuals in a knowledge base in a divide-and-conquer solution that is intended to elicit ex post the underlying hierarchy based on the actual distributions of the instances.
Contextual Identities

As the number of international students studying at American universities continues to grow (Institute of International Education, 2014), campuses are increasingly becoming social spaces where the local, national, and international meet. Even though students’ identities may still be developing in college (Arnett, 2000) and their environment may influence their identity development (Erikson, 1968), little research has focused on the effects of this unique context on students’ identity formation. This study investigated the change in international and American student roommates’ ethnic and national identities over the course of one semester at three Midwestern universities. The qualitative results from semi-structured interviews with four undergraduate students suggest that these students were still grappling with their identities in different ways as they acted as discoverers, ambassadors, and negotiators and support a contextual approach to studying identity development in college students.
The crystal structure of a bacterial, bifunctional 5, 10 methylene‐tetrahydrofolate dehydrogenase/cyclohydrolase

The structure of a bifunctional 5, 10‐methylene‐tetrahydrofolate dehydrogenase/cyclohydrolase from Escherichia coli has been determined at 2.5 Å resolution in the absence of bound substrates and compared to the NADP‐bound structure of the homologous enzyme domains from a trifunctional human synthetase enzyme. Superposition of these structures allows the identification of a highly conserved cluster of basic residues that are appropriately positioned to serve as a binding site for the poly‐γ‐glutamyl tail of the tetrahydrofolate substrate. Modeling studies and molecular dynamic simulations of bound methylene‐tetrahydrofolate and NADP shows that this binding site would allow interaction of the nicotinamide and pterin rings in the dehydrogenase active site. Comparison of these enzymes also indicates differences between their active sites that might allow the development of inhibitors specific to the bacterial target.
The CASIA phrase-based statistical machine translation system for IWSLT 2007

This paper describes our phrase-based statistical machine translation system (CASIA) used in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT) 2007. In this year's evaluation, we participated in the open data track of clean text for the Chinese-to-English machine translation. Here, we mainly introduce the overview of the system, the primary modules, the key techniques, and the evaluation results.
Self-assembly technique for MEMS vertical comb electrostatic actuator

We report a very simple and reliable method for making Self-Assembled Vertical Comb (SAVC) drive actuators by using the standard SOI bulk micro-machining processes. The initial angular offset of the movable combs is prepared by using the process stiction which occurs during drying process after sacrificial release. The optical scanner integrated with SAVC has shown good electrostatic scanning performance of over 1.0-degree static-rotation angle with a typical drive voltage of 20V without any in-use electromechanical failure.
The paradox of overfitting

Model selection plays an important part in machine learning and in artificial intelligence in general. A central problem to model selection is overfitting. It can be measured as the generalization error on test samples. Minimizing the generalization error is not the only definition of a good model. Resemblance of the original function and minimum randomness deficiency are others. While we are not interested in resemblance of the original function we do want to know if minimum randomness deficiency and minimizing the generalization error can be combined. Kolmogorov complexity is a powerful mathematical tool. It has resulted in the theory of MDL for model selection. MDL selects a model that minimizes the combined complexity of model and data, known as the two-part code. It has been proven that MDL minimizes randomness deficiency [VV02]. To efficiently map the performance of MDL on as broad a selection of problems as possible we introduced our own application, the Statistical Data Viewer. While intended for the advanced scientist, it has an interface that is easy enough to use to allow uninitiated students to explore and comprehend the problems of model selection. Its interactive plots and sophisticated editor allow for a fast and efficient setup and execution of controlled experiments. All the relevant aspects of model selection are implemented. The problem space consists of two-dimensional regression problems and the models are polynomials. To objectively measure the generalization error we use i.i. d.test samples.
Connecting up Strategy: Are Senior Strategy Directors a Missing Link?

With companies being exhorted to become more strategically agile and internally connected, this article examines the role of the Senior Strategy Director, the executive tasked specifically with internal strategy. In particular, it explores what they do, what specific capabilities they deploy to enable effective contribution to the company, and in what ways they facilitate the connectedness of strategy. An analysis of multiple interviews over time with Senior Strategy Directors of large companies shows the vital and challenging role these executives play in both shaping, connecting up, and executing strategy. This article identifies the particular capabilities necessary for Senior Strategy Directors to perform their role and shows how it all depends upon their skilful deployment. These findings have significant implications for understanding unfolding micro-processes of strategy in large organizations, for assumptions about the skills and capabilities necessary to be an effective Senior Strategy Director, and for business schools in terms of the content and style of strategy courses they provide.
When and How Often Should Worked Examples be Given to Students ? New Results and a Summary of the Current State of Research

Our work explores the assistance dilemma: when should instruction provide or withhold assistance? In three separate but very similar studies, we have investigated whether worked examples, a high-assistance approach, studied in conjunction with tutored problems to be solved, a mid-level assistance approach, can lead to better learning. Contrary to prior results with untutored problem solving, a low-assistance approach, we found that worked examples alternating with isomorphic tutored problems did not produce more learning gains than tutored problems alone. On the other hand, the examples group across the three studies learned more efficiently than the tutored-alone group; the students spent 21% less time learning the same amount of material. Practically, if these results were to scale across a 20-week course, students could save 4 weeks of time – yet learn just as much. Scientifically, we provide an analysis of a key dimension of assistance: when and how often should problem solutions be given to students versus elicited from them? Our studies, in conjunction with past studies, suggest that on this exampleproblem dimension mid-level assistance may lead to better learning than either lower or higher level assistance. While representing a step toward resolving the assistance dilemma for this dimension, more studies are required to confirm that mid-level assistance is best and further analysis is needed to develop predictive theory for what combinations of assistance yield the most effective and efficient learning.
The role of working and short-term memory in predicting receptive vocabulary in monolingual and sequential bilingual children

ABSTRACT Previous studies showed that bilinguals tend to score lower on some language tests than monolinguals in each of their languages and outperform monolinguals on some cognitive tasks. We investigated whether short-term and working memory capacities that underlie language abilities differ between sequential bilinguals and monolinguals. We tested 4–6-year-old English monolinguals’ and English-Mandarin sequential bilingual’s receptive vocabulary, as well as predictors: (1) age, (2) non-verbal intelligence, (3) verbal short-term memory, (4) verbal working memory, and (5) visuo-spatial working memory. Mandarin monolinguals of the same age were also included and tested on vocabulary, verbal short-term memory, and visuo-spatial working memory. The bilinguals’ vocabulary scores were lower than the monolinguals’ in both languages, although much lower than the English monolinguals. Bilinguals’ vocabulary scores showed stronger correlations with working and short-term memory than the monolinguals’. These results suggest that learning another language might impose different memory processing demands than learning a native language.