Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Consequences of a Channel Switch

Switching marketing channels is an expensive and sticky decision. While a number of theories suggest efficiency and strategic differences between channels, there is virtually no work on combining these ideas into an empirically workable methodology to assess the impact of a channel switch. In this study, we undertake to close this gap with an empirical study of the sports drink market, featuring competing producers and heterogeneous channels. We estimate demand and cost parameters for a number of alternative models of competitive interaction and use these estimates to study the switching of Gatorade from its extant (independent wholesaler) channel to the direct store delivery (DSD) channel belonging to Pepsi. Our initial results indicate the following: Pepsi should switch Gatorade to the DSD channel only if (i) the switch decreases Gatorade's manufacturing cost by at least 14%, or (ii) the switch increases the share of profit it can obtain by at least 13%, or (iii) the switch enhances demand by the equivalent of a price cut of 4.96¢ for a 32-ounces package. Absent these increases, Pepsi should not switch. Our methodology and results speak to both managers contemplating a channel switch and antitrust authorities faced with the task of evaluating the consequences of a change in vertical structure.
COTS-Based Systems Top 10 List

Presents a COTS-based system (CBS) software defect-reduction list as hypotheses, rather than results, that also serve as software challenges for enhancing our empirical understanding of CBSs. The hypotheses are: (1) more than 99% of all executing computer instructions come from COTS products (each instruction passed a market test for value); (2) more than half the features in large COTS software products go unused; (3) the average COTS software product undergoes a new release every 8-9 months, with active vendor support for only its latest three releases; (4) CBS development and post-deployment efforts can scale as high as the square of the number of independently developed COTS products targeted for integration; (5) CBS post-deployment costs exceed CBS development costs; (6) although glue-code development usually accounts for less than half the total CBS software development effort, the effort per line of glue code averages about three times the effort per line of developed applications code; (7) non-development costs, such as licensing fees, are significant, and projects must plan for and optimize them; (8) CBS assessment and tailoring efforts vary significantly by COTS product class (operating system, database management system, user interface, device driver, etc.); (9) personnel capability and experience remain the dominant factors influencing CBS development productivity; and (10) CBS is currently a high-risk activity, with effort and schedule overruns exceeding non-CBS software overruns, yet many systems have used COTS successfully for cost reduction and early delivery.
APPROCHES PSYCHOLOGIQUES ET DIDACTIQUES EN TECHNOLOGIE l'exemple du dessin technique

A la difference d'autres objets d'enseignement du champ technologique, le dessin technique beneficie d'une tradition de recherche deja longue. Cet article evoque d'abord les principales etapes de cette histoire qui ont conduit a concevoir l'activite de lecture du dessin comme resultant de la maitrise progressive et solidaire de trois champs conceptuels : geometrique, semiologique et technologique . Ensuite, au plan didactique, une approche psychologique des competences spatiales initiales des apprenants et une analyse fonctionnelle du code de representatio n analogique des formes et dimensions propre au dessin technique sont presentees comme pouvant contribuer a l'elaboration de savoirs a enseigner. A titre d'illustration, quelques propositions issues d'une progression destinee a une formation a la communication technique sont detaillees.
A survey of distributed denial of service attack

Information security deals with a large number of subjects like spoofed message detection, audio processing, video surveillance and cyber-attack detections. However the biggest threat for the homeland security is cyber-attacks. Distributed Denial of Service attack is one among them. Interconnected systems such as database server, web server, cloud computing servers etc., are now under threads from network attackers. Denial of service is common attack in the internet which causes problem for both the user and the service providers. Distributed attack sources can be used to enlarge the attack in case of Distributed Denial of Service so that the effect of the attack will be high. Distributed Denial of Service attacks aims at exhausting the communication and computational power of the network by flooding the packets through the network and making malicious traffic in the network. In order to be an effective service the DDoS attack must be detected and mitigated quickly before the legitimate user access the attacker's target. The group of systems that is used to perform the DoS attack is known as the botnets. This paper introduces the overview of the state of art in DDoS attack detection strategies.
Event detection and scene attraction by very simple contextual cues

We detect and arrange events in private photo archives by putting these photos into context. The problem is seen as a fully automated mining in one's personal life and behavior without actually recognizing the content of the photos. To this end, we build a contextual meaningful hierarchy of events. With the analysis of very simple cues of time, space and perceptual visual appearance we are refining and validating the event borders and their relation in an iterative way. Beginning with discriminating between routine and unusual events, we are able to robustly recognize the basic nature of an event. Further combination of the given cues efficiently gives a hierarchy of events that coincides with the given ground-truth at an F-measure of 0.83 for event detection and 0.70 for its hierarchical representation. We process the given task in a fully unsupervised and computationally inexpensive manner. Using standard clustering and machine learning techniques, sparse events in the collection would tend to be neglected by automated approaches. Opposed to these methods, the proposed approach is invariant to the distribution of the photo collection regarding the sparsity and denseness in time, space and visual appearance. This is improved by introducing a momentum of attraction measure for a meaningful representation of personal events.
A Machine Learning Teaching Aid MSc Development Project

The aim of this project was to develop an application to assist in teaching some of the basic concepts of machine learning. This is a subject area at the intersection of computer science and applied statistics. It offers powerful techniques for inferring information about huge collections of data, and algorithms from the field are constantly being applied to new problems in a diverse range of subject areas. Many of the algorithms involved are quite complicated from a mathematical standpoint, but lend themselves well to visualization. To this end, the application produced by the project implements simple two dimensional cases of a selection of these algorithms and allows users to explore them visually. In addition, it offers sophisticated features for generating data for use with these algorithms. The user evaluation carried out at the conclusion of the project showed a favourable reaction to the teaching aid software. Users with machine learning experience indicated that it represents a powerful didactic tool. It is designed to be modular and easily extensible, so that further algorithms can be added in the future, further increasing the usefulness of the program to students of machine learning.
Self-organisation or Selfcreation? From Social Physics to Realist Dynamics

The currently fashionable theory of self-organisation has its origins in statistical physics. Many believe that the underlying physics model, which is based on inanimate systems, can be employed to explain and predict the emergence of social structures, even of history itself. Some are even convinced that it will be possible to construct a social physics to displace the social sciences. The purpose of this article is to test those claims by reviewing some of the physical studies that have been made of human society, and its conclusion is that those claims cannot be substantiated. The underlying problem is that self-organisation is a one-dimensional theoretical concept that focuses exclusively upon supply-side interactions, from which order and complexity are said to ‘emerge’. But there is a better way. By systematic observation of living systems, both human and non-human, it has been possible to derive a general dynamic theory that embraces a more complex reality, involving a creative exchange between decision-making individuals and the changing needs of their society. I have called this interaction between the dynamic forces of demand and supply in living systems, the process of ‘strategic exchange’. And it is this strategic exchange that determines all other structural relationships in society, including the interaction between its constituent members. It is important in the social sciences, therefore, to move on from social physics to realist dynamics.
Simplicity is Better: Revisiting Single Kernel PPI Extraction

It has been known that a combination of multiple kernels and addition of various resources are the best options for improving effectiveness of kernel-based PPI extraction methods. These supplements, however, involve extensive kernel adaptation and feature selection processes, which attenuate the original benefits of the kernel methods. This paper shows that we are able to achieve the best performance among the state-of-the-art methods by using only a single kernel, convolution parse tree kernel. In-depth analyses of the kernel reveal that the keys to the improvement are the tree pruning method and consideration of tree kernel decay factors. It is noteworthy that we obtained the performance without having to use any additional features, kernels or corpora.
Scalable Indoor Localization via Mobile Crowdsourcing and Gaussian Process

Indoor localization using Received Signal Strength Indication (RSSI) fingerprinting has been extensively studied for decades. The positioning accuracy is highly dependent on the density of the signal database. In areas without calibration data, however, this algorithm breaks down. Building and updating a dense signal database is labor intensive, expensive, and even impossible in some areas. Researchers are continually searching for better algorithms to create and update dense databases more efficiently. In this paper, we propose a scalable indoor positioning algorithm that works both in surveyed and unsurveyed areas. We first propose Minimum Inverse Distance (MID) algorithm to build a virtual database with uniformly distributed virtual Reference Points (RP). The area covered by the virtual RPs can be larger than the surveyed area. A Local Gaussian Process (LGP) is then applied to estimate the virtual RPs’ RSSI values based on the crowdsourced training data. Finally, we improve the Bayesian algorithm to estimate the user’s location using the virtual database. All the parameters are optimized by simulations, and the new algorithm is tested on real-case scenarios. The results show that the new algorithm improves the accuracy by 25.5% in the surveyed area, with an average positioning error below 2.2 m for 80% of the cases. Moreover, the proposed algorithm can localize the users in the neighboring unsurveyed area.
Stability of enterocytes and certain enzymatic activities in suspensions of cells from the villous tip to the crypt of Lieberkühn of the mouse small intestine

A series of studies in this laboratory have focused on how an indigenous microbiota influences the activities of alkaline phosphatase, phosphodiesterase I, and thymidine kinase in the enterocytes of the upper small intestine of mice. To draw conclusions about the role of the microflora in determining levels of enzymatic activity, we found it necessary to develop a procedure by which cell suspensions could be obtained containing enterocytes isolated sequentially from the villous tip to the crypt of Lieberkühn. The procedure was modified from the one developed for rats by Weiser (J. Biol. Chem. 248:2536-2541, 1973), involved a minimum number of interfering factors (e.g., proteolytic enzymes and mechanical agitation), and worked reproducibly for mice. During development of the procedure, some variables affecting the assays of the enzymes known to be present in enterocytes were also explored. Rods to which were tied everted segments of gut were incubated in a series of tubes containing a solution of EDTA the concentration of which was changed from 1.5 to 5.0 mM, thus giving a greater yield of enterocytes at every step. The cells incubating in the chelating solution were most stable when 0.23 M sucrose was included in the EDTA solutions. Success in assaying enzymatic activities in the cell suspensions depended on (i) how the cells were isolated, (ii) the assay procedure for thymidine kinase, and (iii) whether cellular suspensions or extracts were assayed.
What Is Hip-Hop-Based Education Doing in Nice Fields Such as Early Childhood and Elementary Education?

Hip-Hop-Based Education (HHBE) has resulted in many positive educational outcomes, ranging from teaching academic skills to teaching critical reflection at secondary levels. Given what HHBE initiatives have accomplished, it is troubling that there is an absence of attention to these methods in education programs for elementary and early childhood educators. For that reason, I intend to use theories of sociocultural learning to examine how young urban children’s Hip Hop communities of practice influence their early learning and identities. Through personal narratives, this work theorizes young urban children’s Hip Hop identities by utilizing children’s situated learning activities. The goal of the work is to begin a dialogue for the application of HHBE in early childhood and elementary education pre-service teacher programs.
Component-based high fidelity interactive prototyping of post-WIMP interactions

In order to support interactive high-fidelity prototyping of post-WIMP user interactions, we propose a multi-fidelity design method based on a unifying component-based model and supported by an advanced tool suite, the OpenInterface Platform Workbench. Our approach strives for supporting a collaborative (programmer-designer) and user-centered design activity. The workbench architecture allows exploration of novel interaction techniques through seamless integration and adaptation of heterogeneous components, high-fidelity rapid prototyping, runtime evaluation and fine-tuning of designed systems. This paper illustrates through the iterative construction of a running example how OpenInterface allows the leverage of existing resources and fosters the creation of non-conventional interaction techniques.
Implementing a job change: the impact of employee focus of attention

Examines the relationship between focus of attention ‐ how much a person thinks about job and off‐job factors during work ‐ and people’s reactions to change. Survey respondents were 205 telecommunications repair technicians who were being trained to use a new computerized job assignment system. Results indicate that technicians with high job focus reacted more strongly to a job change over time, being less absent and more job involved, compared with low job focus technicians. High levels of off‐job focus were associated with increasing levels of withdrawal. Discusses focus of attention’s role in the facilitation of organizational changes.
Interracial Marriage Trends and Migration Patterns: An Analysis of LAFANS 2000

We hypothesized that interracial married couples will migrate more than intraracial married couples. We focus on three mechanisms (education, social support, and discrimination) that provide explanations for interracial couples to migrate more than interracial couples. The theoretical framework included the “Push-Pull” theory and perspective on social network ties and how they link to couples migration patterns. We used information gathered in a wave of the survey conducted in 2000 for the Los Angeles Family and Neighborhood Study. We used this survey and analyzed in a logistic regression model how the three mechanisms described above affect interracial married couples and migration patterns. The results provide a positive direction but did not provide a statistical significant due to small sample size. In conclusion, we find support from all three mechanisms for interracial marriage couples to have higher possibility for migration. Future implications should examine divorce rate and cohabitation to provide a higher migration pattern with interracial married couples.
Landscape Democracy, Three Sets of Values, and the Connoisseur Method

The European Landscape Convention has brought up the question of democracy in relation to landscape transformation, but without a clear definition of democracy. This paper conceptualises democracy in relation to three main sets of values related to self-determination, co-determination and respect for argument. It examines various methods that have been used to try to make landscape decisions more democratic. In the last part of the paper the connoisseur method is introduced. This method emphasises stakeholder participation in deliberative processes with a particular focus on place-based knowledge. It has been used in Sweden as a means of involving local stakeholders in the democratic process of defining goals and objectives of their landscapes. In the conclusion, this method is recommended despite its dependence on favourable conditions, particularly continuity and commitment, and a few suggestions about possible improvements are presented.
An Entropy-based Adaptive Genetic Algorithm Approach on Data Mining for Classification Problems

Genetic algorithm is one of the commonly used approaches on data mining. In this paper, we put forward a genetic algorithm approach for classification problems. Binary coding is adopted in which an individual in a population consists of a fixed number of rules that stand for a solution candidate. The evaluation function considers four important factors which are error rate, entropy measure, rule consistency and hole ratio, respectively. Adaptive asymmetric mutation is applied by the self-adaptation of mutation inversion probability from 1-0 (0-1). The generated rules are not disjoint but can overlap. The final conclusion for prediction is based on the voting of rules and the classifier gives all rules equal weight for their votes. Based on three databases, we compared our approach with several other traditional data mining techniques including decision trees, neural networks and naive bayes learning. The results show that our approach outperformed others on both the prediction accuracy and the standard deviation.
Aspects of 3-D Imaging by Classical Tomography for Dual Detector PEM

Images from dual detector positron emission mammography (PEM) systems are commonly reconstructed by backprojection methods of classical tomography. Characteristics of 3–D PEM images were investigated using analytic models and computer simulations, in particular depth resolution and the quality of images in the third dimension normal to the detectors. These modeling tools provide insight into the depth blurring observed in 3-D images from experimental line source and breast phantom studies acquired with detectors built using pixellated arrays of LGSO crystals. Approaches to improved 3-D breast imaging are discussed.
The Framing of Immigration

Framing is at the center of the recent immigration debate. Simply framing it as about “immigration” has shaped its politics, defining what count as “problems” and constraining the debate to a narrow set of issues. The language is telling. The linguistic framing is remarkable: frames for illegal immigrant, illegal alien, illegals, undocumented workers, undocumented immigrants, guest workers, temporary workers, amnesty, and border security. These linguistic expressions are anything but neutral. Each framing defines the problem in its own way, and hence constrains the solutions needed to address that problem. The purpose of this paper is twofold. First, we will analyze the framing used in the public debate. Second, we suggest some alternative framing to highlight important concerns left out of the current debate. Our point is to show that the relevant issues go far beyond what is being discussed, and that acceptance of the current framing impoverishes the discussion.
Reasoning in Expressive Gödel Description Logics

Fuzzy description logics (FDLs) are knowledge representation formalisms capable of dealing with imprecise knowledge by allowing intermediate membership degrees in the interpretation of concepts and roles. One option for dealing with these intermediate degrees is to use the so-called Gödel semantics, under which conjunction is interpreted by the minimum of the degrees. Despite its apparent simplicity, developing reasoning techniques for expressive FDLs under this semantics is a hard task. In this paper, we illustrate two algorithms for deciding consistency in (sublogics of) SROIQ under Gödel semantics.
Effective Crowd Expertise Modeling via Cross Domain Sparsity and Uncertainty Reduction

Characterizations of crowd expertise is vital to online applications where the crowd plays a central role, such as StackExchange for question-answering and LinkedIn as a workforce market. With accurately estimated worker expertise, new jobs can be assigned to the right workers more effectively and efficiently. Most existing methods solely rely on the sparse worker-job interactions, leading to poorly estimated expertise that does not generalize well to a large amount of unseen jobs. Though transfer learning can utilize external domains to mitigate the sparsity, the auxiliary domains can themselves suffer from incomplete information, leading to inferior performance. There is a lack of principled framework to handle the sparse and incomplete data to achieve better expertise modeling. Based on multitask learning, we propose a framework that uses the knowledge learned from one domain to gradually resolve the data sparsity or incompleteness problem in the other alternatively. Experimental results on several question-answering datasets demonstrate the effectiveness and convergence of the iterative framework.
From the Field: A Proposal for Educating Leaders for Social Justice

Purpose: The purpose of this article is to suggest the structure and content of an educational leadership program whose aim is to prepare principals for social justice work. Research Design: The authors have conceptualized foundational components for a comprehensive principalship program focused on social justice. They assert that educational leaders for social justice must have three goals at the forefront of their efforts: (a) They must raise the academic achievement of all the students in their school, that is, high test scores do matter; (b) they must prepare their students to live as critical citizens in society; and (c) both of these goals can only be achieved when leaders assign students to inclusive, heterogeneous classrooms that provide all students access to a rich and engaging curriculum. The components addressed for this social justice—oriented principalship preparation program include (a) how students should be selected for such a program and (b) an outline of the knowledge and content for educating social justice leaders. The importance of induction/praxis after students graduate from these programs is discussed. Conclusions: The aim of this article is to provoke a discussion in the field and spark faculty to engage in ongoing conversations and thinking about their own programs and to imagine new avenues for future research in this area. Faculty also can use these suggestions as a guide to assess their efforts and to bolster program quality, sensitive to the unique needs and schooling contexts of their particular students.