Miracle-dz/UniFAR-FTUs huggingface dataset:
Kids as informants: telling us what we didn't know or confirming what we knew already?

INTRODUCTION Joe: " So the pike comes down here and it says 'where's my dinner? ' . it grabs this one . 'yum yum! this is better than going to the fish and chip shop! ' " Alan: " And when everything is finished it says 'only the pike is left' . and the pike dies. and the pike says 'where did all the food go? ' . then the pike dissolves . or you just see it as bones " Figure 1 Joe and Alan, 9 years old, doing lo-fi prototyping This is a small extract from a prototyping design session where we asked pairs of 9 year olds to create a software game, using laminated cutouts , to teach some basic concepts in ecology to children younger than themselves. As evidenced by hours of videotape material collected, there is no stopping the likes of Joe and Alan once their imagination is fired up. But what are we to make of it all? Is this interaction anything more than kids being loveable? Should we really be using this kind of material to get insight into how to design better educational software? Moreover, how can a design team use such inputs? What criteria can they use to determine whether what children come up with is both pedagogically sound and fun? And why should such observations be of any value anyway? Aren't kids simply emulating what's already out there (cf Kafai, 1996)? Is it also not the case, that we already know – as designers and experts in educational technology –what we need to know? These questions may seem like just another version of a central concern when designing any kind of system or software-that of when, whether and how to involve users in the design process and what the actual benefits are of doing so. But in addition, the involvement of kids, rather than Scaife/Rogers 3 adults, in designing interactive software raises a number of quite specific considerations and it is these that we shall focus on in our chapter. We shall do this in the context of our own research, called the ECOi project 1 , where we have been developing novel interactive software for teaching 9 to 14 year-olds some of the basic concepts of ecology. Our overall research project has several interrelated, complex aims but what we wish to focus on here are the insights we gained into the many …
Feature Selection Based on Physicochemical Properties of Redefined N-term Region and C-term Regions for Predicting Disorder

The prediction of intrinsic disorder from amino acid sequence has been gaining increasing attention because these have come to be known as important regions for protein functions. The most common way of predicting disorder is based on binary classification with machine learning. Since amino acid composition has different propensities in the N-term, C-term, and internal regions, the accuracy of prediction increases by dividing training data into these three regions and predicting them separately. However, previous work has lacked discussion about a concrete definition of the N-term and C-term regions, and has only used the heuristic length from the terminal. Other previous work has shown that general physicochemical properties rather than specific amino acids are important factors contributing to disorder, and a reduced amino acid alphabet can maintain excellent precision in predicting disorder. In this paper, we redefine a suitable length and position for the N-term and C-term regions for predicting disorder. Moreover, we show that each region has different physicochemical properties, which are important factors contributing to disorder. We also suggest a region-specific-reduced set of amino acid and modified PSSM based on that for predicting disorder. We implemented our method and (1) compare it with the conventional division method, (2) compare our feature selection with all physicochemical features, on casp6 benchmark, PDB dataset, and DisProt. The result supports that the method of new data separation is effective, and indicates each region has different physicochemical properties that are important factors for predicting protein disorders.
Implementation of Business Intelligence to Increase the Effectiveness of Decision Making Process of Managers in Companies Providing Payment Services

The most important purpose of this research is implementation of business intelligence to increase the effectiveness of decision-making process of managers in service providing companies (Case Study: Saman Kish Electronic Payment Company). The importance and necessity of such research becomes clear according to criteria such as Development of knowledge about techniques to facilitate decision-making in business intelligence, strategic level of business intelligence, tactical level of business intelligence, operational level of business intelligence and quality of business intelligence implementation and absence of a system to provide advice for manager to for decision-making on matters related to business intelligence implementation. In the end, the sample size for this research consists of 30 available experts willing to cooperate who were selected using a combination of Purposive non-probability (judgment) sampling and snowball sampling. Data were collected using first set of measuring tools (tools to measure the effect of variables in order to increase the effectiveness of decision-making process of managers) and the second set of measuring tools (tools to validate “support system for decision making based on the principle of business intelligence implementation in order to increase the effectiveness of decision-making process of managers”). This fact that analyzing "business intelligence techniques to facilitate decision making" can make decision-making process of managers in Saman Kish Electronic Payment Company comprehensively effective is among the most important results of this study. In the end, it was determined that the final difference between the outputs of support system for decision making in this research which are BI+FDSS and average expert opinions has not been significant and has been calculated to be 0.06475 which means there is no significant relation between expert opinions and outputs of " BI+FDSS System". ”Techniques to facilitate decision-making in business intelligence”, ”strategic level of business intelligence”, ”tactical level of business intelligence”, ”operational level of business intelligence” and quality of business intelligence implementation” and “Companies providing payment services”
Class notes on Synchronous and Asynchronous Communication , Process Models and Monitoring Models

1 Synchronous and Asynchronous Message Passing System Models 1 1.1 Time and the Process Channel Model . 1 1.2 Synchronous Systems . 2 1.3 Asynchronous Systems . 6 1.4 Final Remarks on Synchronous and Asynchronous Systems . 7
Using a New Inter-rater Reliability Statistic

      "! #    $      %        &'(     (           %  ( (  &( '(  %   )% !    "'#   ")% *++#   $  (   $!  &,  & -   !  ( '          %' !      (   $      &'(  %  (   ' .        &/.      ( $  $  &'    (    $  %   0 &  ( !        .            (!  &&  % $    &'   $            %      %    &1      (               2     (   $ ((    (    $ &      %    $ 3,  $   & 3, (2     (     (  $ (        .    (  $    (  $ % $$(  &   (        ( $ 3, $   & (      2  %       (       '  .      &
CERN summer student program rapport: A better understanding of gas gain simulations in GEM detectors

2 Measurements 4 2.1 Setup . 4 2.2 Effective gas gain . 5 2.3 Orientation of the GEM . 8 2.4 Cross section milling and SEG of the GEM . 9 2.5 Charge up effects . 10
Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints

Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensional setting often fails due to the limited sample size. One approach to improving power is through meta-analyzing multiple studies which address the same scientific question. However, integrative analysis of high dimensional data from multiple studies is challenging in the presence of between-study heterogeneity. The challenge is even more pronounced with additional data sharing constraints under which only summary data can be shared across different sites. In this paper, we propose a novel data shielding integrative large–scale testing (DSILT) approach to signal detection allowing between-study heterogeneity and not requiring the sharing of individual level data. Assuming the underlying high dimensional regression models of the data differ across studies yet share similar support, the proposed method incorporates proper integrative estimation and debiasing procedures to construct test statistics for the overall effects of specific covariates. We also develop a multiple testing procedure to identify significant effects while controlling the false discovery rate (FDR) and false discovery proportion (FDP). Theoretical comparisons of the new testing procedure with the ideal individual–level meta–analysis (ILMA) approach and other distributed inference methods are investigated. Simulation studies demonstrate that the proposed testing procedure performs well in both controlling false discovery and attaining power. The new method is applied to a real example detecting interaction effects of the genetic variants for statins and obesity on the risk for type II diabetes.
Chemicals. Solutions. Equipment and General Procedures

I N T R O D U C T I O N . CHEMICALS AND SOLUTIONS . Chemicals . Solutions . (i) Buffer . (ii) Agarose . (iii) Staining solutions . (iv) Destaining solution . (v) Solution for protein fixation . (vi) Washing solutions . (vii) Antibodies .
Evidence for an extracellular l–amino acid oxidase in nitrogen-deprived Phaeodactylum tricornutum (Bacillariophyceae) and inhibition of enzyme activity by dissolved inorganic carbon

T.A. V. Rees and V.J. Allison. 2006. Evidence for an extracellular l–amino acid oxidase in nitrogen-deprived Phaeodactylum tricornutum (Bacillariophyceae) and inhibition of enzyme activity by dissolved inorganic carbon. Phycologia 45: 337–342. DOI: 10.2216/04-92.1 Nitrogen-deprived cells of the marine diatom Phaeodactylum tricornutum possessed extracellular l–amino acid oxidase (EC 1.4.3.2) activity. No enzyme activity was detectable in nitrogen-replete cells. When nitrogen-deprived cells were provided with 100 μM alanine, the amino acid was metabolised, with both pyruvate and H2O2 accumulating stoichiometrically in the medium; this accumulation was largely prevented if the cells were preincubated with the membrane-impermeable protease Proteinase K. Following the transfer of nitrogen-replete cells to nitrogen-free medium, development of enzyme activity lagged, with little or no enzyme activity detectable until 4 h after the onset of nitrogen deprivation. This lag was followed by rapid development of enzyme activity during the ensuing 20 h. In addition to alanine, methionine, methionine sulfoximine, leucine, glutamate and valine, at the same initial concentration of 100 μM, were exclusively metabolised by an amino acid oxidase. Histidine was partially metabolised by an amino acid oxidase, whereas arginine and lysine were transported into the cell with little or no metabolism by an amino acid oxidase. Neither serine nor proline was used. In general, the neutral nonpolar and acidic polar amino acids were used via an extracellular amino acid oxidase, whereas the basic polar amino acids were transported. l–Amino acid oxidase activity was inhibited in darkness and in light in the presence of the photosynthetic inhibitor DCMU (3-[3,4-dichlorophenyl]-1,1-dimethylurea). In addition, enzyme activity decreased on addition of bicarbonate to cells in light. Together these observations suggest that extracellular l–amino acid oxidase activity was inhibited by dissolved inorganic carbon.
Gene Expression Regulated by Abscisic Acid and its Relation to Stress Tolerance

INTRODUCTION . 1 1 3 ABA REGULATION OF GENE EXPRESSION . 114 Positive Regulation of Gene Expression 1 17 Negative Regulation of Gene Expression . 123 GENE PRODUCTS AND STRESS TOLERANCE . 124 Determination of Functions of ABA-Regulated Gene Products . 125 ABA-Regulated Genes and Cold Tolerance . 130 ABA-Regulated Genes and Desiccation Tolerance 133 CONCLUDING REMARKS 134
Leveraging topic feature for followee recommendation on Twitter network

With the fast growth of the Twitter network, users are overwhelmed by the huge amount of information, which is shared via the follower/followee social network, to overcome this problem, finding like-minded users becomes a very important task. Thus, a system to assist users in such a task is recommended. In this paper, we propose a followee recommendation system by leveraging the topic feature, for topic modeling, and the follower/followee topology, searching for similar users to recommend, based on topic similarities. To show the effectiveness of our approach, we evaluate it using a dataset ingathered from the Twitter platform. The experiment results indicate that our model outperforms the lexical-based [reference? ] approach and semantic-based approach [reference? ], achieving a recall value of more than 23% on recommending 10 followees, proving that dealing with users’ topics of interest in microblogging websites content is more efficient than semantic and lexical features.
Can an AI Algorithm Mitigate Racial Economic Inequality? An Analysis in the Context of Airbnb

We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasi-natural experiment. Among those who adopted the algorithm, the average nightly rate decreased by 5.7%, but average daily revenue increased by 8.6%. Before Airbnb introduced the algorithm, white hosts earned $12.16 more in daily revenue than Black hosts, controlling for observed characteristics of the hosts, properties, and locations. Conditional on its adoption, the revenue gap between white and Black hosts decreased by 71.3%. However, Black hosts were significantly less likely than white hosts to adopt the algorithm, so at the population level, the revenue gap increased after the introduction of the algorithm. We show that the algorithm’s price recommendations are not affected by the host’s race—but we argue that the algorithm’s race-blindness may lead to pricing that is sub- optimal, and more so for Black hosts than for white hosts. We also show that the algorithm’s effectiveness at mitigating the Airbnb revenue gap is limited by the low rate of algorithm adoption among Black hosts. We offer recommendations with which policy makers and Airbnb may advance smart-pricing algorithms in mitigating racial economic disparities.
Improved Trust Model to Enhance Availability in Private Cloud

In the process of cloud service selection, it is difficult for users to choose trusted, available, and reliable cloud services. A trust model is a perfect solution for this service selection problem. In cloud computing, data availability and reliability have always been major concerns. According to research, around $285 million is lost per year due to cloud service failures, with a 99.91 percent availability rate. Replication has long been used to improve the data availability of large-scale cloud storage systems where errors are anticipated. As compared to a small-scale environment, where each data node can have different capabilities and can only accept a limited number of requests, replica placement in cloud storage systems becomes more complicated. As a result, deciding where to keep replicas in the system to meet the availability criteria is an issue. To address above issue this paper proposes a trust model which helps in selecting appropriate node for replica placement. This trust model generates comprehensive trust value of the data center node based on dynamic trust value combined with QoS parameters. Simulation experiments show that the model can reflect the dynamic change of data center node subject trust, enhance the predictability of node selection, and effectively decreases the failure rate of node. Keywords—Trust; trust model; cloud; availability; reliability
Animals in Animal-Assisted Services: Are They Volunteers or Professionals?

Simple Summary Animal welfare is a growing concern in Animal-Assisted Services. Although studies have been conducted on stress signals and—to a lesser extent—positive emotions, no research has yet been conducted on the motivation of the integrated animal, to the best of our knowledge. Not all therapy animals are trained to assist. Are they volunteers or professionals? Volunteers have a higher degree of self-government and can quit when they are not motivated anymore. Professionals might, however, go the extra mile. Can we compare animal volunteers and professionals to their human equivalents? If so, this might help to structure discussions about animal welfare and motivation in interventions. Furthermore, it can provide better arguments for the career planning and career ending of these animals. Using animal-friendly interventions might influence motivation and, consequently, the moment at which efforts cease. Studying motivation is not easy, as it requires data on dopamine, the molecule involved in motivation, reward and repetition of behavior. However, the use of wearable techniques such as on site electroencephalograms (EEGs) for freely moving animals and non-invasive dopamine measurements is a developing and promising area of research. The translation of these data into context-ethograms—ethograms that show behavior in a context/intervention—can help handlers and therapists to understand the behavior of their therapy animal better and with less subjectivity. Abstract With the increasingly common practice of Animal-Assisted Services (AAS), whether in therapy, coaching, education, or volunteering programs, the concern over animal welfare has also risen. However, no standards have yet been established for good practices to ensure the animal’s mental health. This is largely due to the wide variety of roles played by animals in interventions and the lack of ‘job descriptions’ for the animal in diverse settings. Some professionals call their animal a ‘volunteer’, others mention that some directive guidance is given to the therapy animal, and some assistance animals are highly trained. Misunderstandings could be avoided if the integrated animal were to receive a justifiable label: volunteer or professional. Choosing either one comes with obligations for the owner, handler, or therapist. In this paper, we compare the roles of human volunteers and professionals to the roles of animals involved in therapy. We also demonstrate the obligations that come along with the decision to label animals as such either volunteers or professionals. Furthermore, we make a plea for animal-friendly interventions, whether in a volunteer position or as a professional, in order to stimulate the animal’s cooperation and motivation. Studying dopamine and translating the findings into context-ethograms can provide a way to judge behavior more objectively.
Improving scheduling by learning

Scheduling problems appear in many industrial problems with different facets and requirements of a solution. A solution is a schedule of a set of activities subject to constraints such as precedence relations and resource restrictions on the maximum number of concurrent activities. This dissertation presents new deductive techniques for precedence and cumulative resource constraints in constraint programming to improve solution processes of combinatorial scheduling problems, in particular, and combinatorial problems, in general. Due to their intractability, many schedulers either solve a simplified problem or are tailored to a specific problem, but are inflexible with respect to changes in the problem statement. Constraint Programming (Cp) offers a powerful framework for combinatorial problems which also tackles the demand of flexibility of changes in the problem statement due to a strict separation of problem modelling, search algorithms, and high-specialised deduction techniques. Moreover, recent advanced Cp solvers such as lazy clause generation (Lcg) additionally include sophisticated conflict learning technologies. Their efficiency depends, amongst other things, on reusable explanations formulated by deductive algorithms. Unit two variable per inequality (Utvpi) constraints are one of the largest integer constraint class that is solvable in polynomial time. These constraints can model precedence relations between activities. A new theoretical result about reasoning of Utvpi systems is shown, based on that, new incremental deductive algorithms are created for manipulating Utvpi constraints including satisfiability, implication, and explanation. These algorithms are asymptotically faster on sparse Utvpi systems than current algorithms. Cumulative constraints enforce resource restrictions in scheduling problems. We show how, by adding explanation to the cumulative constraint in an Lcg solver, we can solve resource-constrained project scheduling problems far faster than other comparable methods. Furthermore, a complete Lcg-based approach including cumulative constraints is developed for an industrial carpet cutting problem. This approach outperforms the current incomplete method on all industrial instances provided.