Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditing the Strategic Role of Operations

The literature relating to auditing the strategic role and contribution of operations has been dominated by methodologies attuned to the predominance of the environmental and competitive forces, and entry deterrence approaches to achieving and sustaining competitive advantage, broadly termed the outside-in perspective. However, tools suited to the resource-based and associated dynamic capability view to strategy formulation, deemed the inside-out perspective, are sparse. This paper makes a contribution to furthering understanding of the auditing of the strategic role and contribution of operations by conducting a review and critique of established ideas, practices and approaches from both strategy formulation perspectives. It argues that the reported methodologies reflect the traditional outside-in perspective to strategy formulation. It highlights the limitations of the available tools for an inside-out view and questions the suitability of the existing methods to the more recent inside-out emphasis, also a factor vital in circumstances where a firm typically is pursuing a combination or blend of the outside-in and inside-out approaches to strategy formulation. Finally, it presents the outline of an additional audit tool designed to address these limitations and describes next steps in future research.
Resource Usage Estimation of Data Stream Processing Workloads in Datacenter Clouds

Real-time computation of data streams over affordable virtualized infrastructure resources is an important form of data in motion processing architecture. However, processing such data streams while ensuring strict guarantees on quality of services is problematic due to: (i) uncertain stream arrival pattern; (ii) need of processing different types of continuous queries; and (iii) variable resource consumption behavior of continuous queries. Recent work has explored the use of statistical techniques for resource estimation of SQL queries and OLTP workloads. All these techniques approximate resource usage for each query as a single point value. However, in data stream processing workloads in which data flows through the graph of operators endlessly and poses performance and resource demand fluctuations, the single point resource estimation is inadequate. Because it is neither expressive enough nor does it capture the multi-modal nature of the target data. To this end, we present a novel technique which uses mixture density networks, a combined structure of neural networks and mixture models, to estimate the whole spectrum of resource usage as probability density functions. The proposed approach is a flexible and convenient means of modeling unknown distribution models. We have validated the models using both the linear road benchmark and the TPC-H, observing high accuracy under a number of error metrics: mean-square error, continuous ranked probability score, and negative log predictive density.
TEST REVIEW: Dean C. Delis, Edith Kaplan & Joel H. Kramer, Delis Kaplan Executive Function System (D-KEFS), The Psychological Corporation, San Antonio, TX, 2001. $415.00 (complete kit)

The Delis Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) is a comprehensive battery of nine individually administered tests that provides normative and qualitative data assessing higher level cognitive functions (reasoning, problem solving, planning, etc.). Executive functions can be defined as capacities that enable a person to engage successfully in independent, purposive, self-serving behavior (Lezak et al, 2005). Each test in the D-KEFS is designed to be a stand-alone measure of executive functioning, and there are no aggregate indexes, or composite scores. The battery offers one of the first psychometrically sound, nationally normed set of tests designed exclusively for the assessment of verbal and nonverbal executive functions in children, adolescents, and adults. Indeed, normative data are based on a sample of over 1,700 individuals ranging in age from 8 to 89 years. Neuropsychologists will be familiar with many of the tests in the D-KEFS, such as a version of a tower test and a trail-making test, yet the D-KEFS has greatly improved on earlier, psychometrically troubled versions of these tasks. A strength of the D-KEFS is that, in addition to overall performance scores for each of the nine tests, the D-KEFS provides process scores that offer insight into performance scores. Results from the D-KEFS can be used to assess the integrity of the frontal systems of the brain, and to determine how deficits in higher order thinking may impact an individual’s functioning. Individual performance can be used to develop coping strategies and rehabilitation programs tailored to an individual patient’s profile of strengths and weaknesses in executive functioning. The D-KEFS is a battery of cognitive assessment; as such, only a neuropsychologist with formal training and experience should administer and interpret the battery. ADMINISTRATION
Poisson allocations with bounded connected cells

Given a homogenous Poisson point process in the plane, we prove that it is possible to partition the plane into bounded connected cells of equal volume, in a translation-invariant way, with each point of the process contained in exactly one cell. Moreover, the diameter $D$ of the cell containing the origin satisfies the essentially optimal tail bound $P(D>r)
Obtaining extra coding gain for short codes by block Markov superposition transmission

In this paper, we present a new approach, called block Markov superposition transmission (BMST), to construct from short codes a class of convolutional codes with large constraint length. The BMST is very similar to superposition block Markov encoding (SBME), which has been widely used to prove multiuser coding theorems. We also present an iterative sliding-window decoding algorithm for the proposed transmission scheme. The extra coding gain obtained by BMST can be bounded in terms of the Markov order and with the help of the input-output weight enumerating function (IOWEF) of the BMST system, which can be computed from that of the short code by performing a trellis-based algorithm. Numerical results verify our analysis and show that an extra coding gain of 6.4 dB at bit-error rate (BER) 10-5 can be obtained by BMST of the [7, 4] Hamming code.
Decision table programming and reliability

The language in which programs are written influence their structure, style, reliability and verifiability. In this paper, we discuss the use of decision tables as a programming language, and related reliability questions. While both good and bad programs can be written in any language, it is suggested here that decision table programming is worth more serious consideration for general purpose computing.
Attachment Styles, Conflict Perception, and Adolescents' Strategies of Coping with Interpersonal Conflict

In this study, we examined the relationships between and among adolescents' attachment styles, conflict perceptions, and strategies for coping with conflicts with their peers. The study participants were 146 pupils at a junior high school who completed self-report questionnaires about their attachment styles (secure, anxious, or avoidant), conflict coping styles (avoiding, dominating, obliging, compromising, and integrating), and conflict perceptions (positive or negative), as well as social and academic status and the frequency with which they and their friends were involved in conflicts. We found strong, statistically significant correlations between attachment style, coping strategy, and conflict perception. Generally, participants whose secure attachment scores were higher reported that they held more positive attitudes toward conflict, used more cooperative strategies to cope with conflicts, and were involved in conflicts less often; they also seemed to be less obliging and more dominating in their coping strategies. Avoidant attachment adolescents in our study displayed more negative conflict perceptions and made greater use of dominating strategies. We also found that participants' conflict perceptions mediated the relationship between their attachment styles and coping styles. Because it is generally easier to change attitudes than it is to change attachment styles, which are more fixed, our findings suggest that changing adolescents' conflict perceptions, through school curricula, for example, may be an effective way to improve their ability to cope with conflict.
Reclaiming Stigma: Alternative Explorations of the Construct

Previously published research tends to rely on oversimplifications of Goffman’s (1963) work to define stigma–attributes=qualities devalued in particular situations. In descending order of typical approaches by Communication scholars, stigma is typically examined by looking at (a) if and then who is stigmatized, (b) how it affects that particular group of people, and (c) what can or should be done about it, with the latter technique inclining toward simplistic prescription of a ‘‘stop doing it’’ admonishment. Missing from this discussion is examination of the construct=ion of stigma. With few exceptions, the nuances of this construct–a subject of potentially great interest to communication scholars–are rarely explored. Certainly, the underlying importance of the development, valuing, and assignation of stigma drives much communication scholarship: terminology or label assignation arguments over GLBT participants (e.g., bears, grinders, dykes), health care (e.g., PLWA versus HIVþ), substance addiction (e.g., users versus abusers), relationship functions (e.g., healthy versus dysfunctional relationships), and relational identities (e.g., survivors versus victims). However, despite the advent of increasingly immediate forms of interpersonal and public communication, the use of labels, interpersonal behaviors, and complicated rhetorical constructions related to stigma have become more taken-for-granted by scholars using methods of social framing and influence. Rather than simplistically labeling a group as ‘‘stigmatized’’ and=or jumping to the assumption that this label is always negative, a more complex examination would search for the underlying mechanisms at play. This special issue of Communication Studies is a continuation of an ongoing set of panels from annual meetings of the
The effect of emotions in cross-cultural expatriate experiences

With increased recognition of the existence of emotions in everyday interpersonal interactions, the need for emotions in cross-cultural interactions has become more obvious. Emotions play an important role in cross-cultural expatriate interactions as these experiences are essentially social encounters. Yet, little research addresses the emotions felt and the emotional demands involved in the cross-cultural interaction. Research on expatriate experiences centres solely on the cross-cultural training, sensitivity, and adaptability of expatriates. What fails to be addressed, however, is the emotional aspect of this interaction. In this paper, we examine the cross-cultural expatriate experience, integrating both the cultural and emotional aspects of the interaction. In doing so, we aim to advance understanding of the expatriate experience and the potential reasons underlying expatriate success and failure.
PERFORMANCE EVALUATION OF LOGISTIC REGRESSION AND NEURAL NETWORK MODEL WITH FEATURE SELECTION METHODS AND SENSITIVITY ANALYSIS ON MEDICAL DATA MINING

Feature selection is an important topic of research in data mining. Feature selection is the process of choosing a small subset of features that optimally is necessary and sufficient to describe the target concept. Sensitivity analysis is the process to calculate changes in the output due to changes in the inputs. Logistic regression has become a widely used and accepted method to analyze binary or multi class variables, since it is a flexible tool that can predict the probability for the state of a dichotomous variable. Artificial neural network has been sufficiently applied to medical problems in recent years. In this research work an attempt has been made to evaluate the performance of logistic regression and neural network model with feature selection method and sensitivity analysis on publicly available medical datasets. The results have been conclusive about the effectiveness of feature selection and sensitivity analysis for neural network model (with and without hidden layer) and validate the hypothesis of the research. The classification accuracy is used to measure the performance of both logistic regression and neural network models. From the experimental results it is observed that the neural network model (without hidden layer) with feature selection (using backward elimination method) and sensitivity analysis gives more efficient result.
The RNA-Binding Protein HuR Promotes Cell Migration and Cell Invasion by Stabilizing the β-actin mRNA in a U-Rich-Element-Dependent Manner

ABSTRACT A high expression level of the β-actin protein is required for important biological mechanisms, such as maintaining cell shape, growth, and motility. Although the elevated cellular level of the β-actin protein is directly linked to the long half-life of its mRNA, the molecular mechanisms responsible for this effect are unknown. Here we show that the RNA-binding protein HuR stabilizes the β-actin mRNA by associating with a uridine-rich element within its 3′ untranslated region. Using RNA interference to knock down the expression of HuR in HeLa cells, we demonstrate that HuR plays an important role in the stabilization but not in the nuclear/cytoplasmic distribution of the β-actin mRNA. HuR depletion in HeLa cells alters key β-actin-based cytoskeleton functions, such as cell adhesion, migration, and invasion, and these defects correlate with a loss of the actin stress fiber network. Together our data establish that the posttranscriptional event involving HuR-mediated β-actin mRNA stabilization could be a part of the regulatory mechanisms responsible for maintaining cell integrity, which is a prerequisite for avoiding transformation and tumor formation.
Joint Estimation of Topics and Hashtag Relevance in Cross-Lingual Tweets

Twitter is a widely used platform for sharing news articles. An emerging trend in multi-lingual communities is to share non-English news articles using English tweets in order to spread the news to a wider audience. In general, the choice of relevant hashtags for such tweets depends on the topic of the non-English news article. In this paper, we address the problem of automatically detecting the relevance of the hashtags of such tweets. More specifically, we propose a generative model to jointly model the topics within an English tweet and those within the non-English news article shared from it to predict the relevance of the hashtags of the tweet. For conducting experiments, we compiled a collection of English tweets that share news articles in Bengali (a South Asian language). Our experiments on this dataset demonstrate that this joint estimation based approach using the topics from both the non-English news articles and the tweets proves to be more effective for relevance estimation than that of only using the topics of a tweet itself.
Immunomodulatory activity of butanol extract from Solanum lyratum in tumor-bearing mice

Solanum lyratum Thunb (Solanaceae) has been widely used for cancer as a folk remedy in Chinese traditional medicine. In this study, the main active fraction n-butanol extract from S. lyratum (BESL) was evaluated for the therapeutic efficacies on mice transplantable tumor and immunomodulatory potentials on the immune response in tumor-bearing mice. The effects of BESL on the growth of mouse transplantable S180 sarcoma, splenocyte proliferation, the activity of natural killer (NK) cells and cytotoxic T lymphocytes (CTL), production of cytokines from splenocytes, and serum antigen-specific antibody levels in S180-bearing mice were measured. BESL could not only significantly inhibit the growth of S180 sarcoma transplanted in mice, but also remarkably promote splenocytes proliferation, NK cell and CTL activity, interleukin-2 and interferon-γ production from splenocytes, and serum antigen-specific antibody levels in tumor-bearing mice (P < 0.05, P < 0.01, or P <0.001). The results suggested that BESL might exhibit antitumor activity by improving immune response, and it could act as antitumor agent with immunomodulatory activity. This study provided evidence to understand the therapeutic effects of S. lyratum for treatment of cancer and a natural product to further researches to be developed as a cancer chemopreventive agent.
Who Transfers More … and What? Crosslinguistic Influence in Relation to School Grade and Language Dominance in EFL

Cross-linguistic influence (CLI) is receiving increasing attention in multilingual learners (Cenoz et al., 2001). Research with bilingual learners has analysed CLI in relation to language dominance (see, for instance, Hulk & Müller, 2000; Yip & Stephen, 2000) and to language dominance and grade in school settings (Cenoz, 2003; Lasagabaster, 2003a; Sanz, 2000). The aim of this study is threefold: (a) to analyse the relationship between CLI and grade; (b) to investigate whether learners transfer more content words than function words; and (c) to analyse whether language dominance (in Catalan, Spanish or balanced bilingualism) moderates the relationship between CLI and grade. We examine CLI as measured through the use of borrowings and lexical inventions of EFL bilingual Catalan–Spanish learners (n=474) ranging from grades 5 to 12 in a writing task. The analyses of variance lead us to conclude that (a) learners at higher grades use fewer borrowings and lexical inventions; the differences are statistically significant for borrowings; (b) learners transfer similar proportions of content words and function words; (c) Spanishdominant learners behave differently from balanced bilinguals and Catalandominant learners both in the use of borrowings and lexical inventions. For borrowings, the differences between the groups are significant for grade.
Humane representation of thought: a trail map for the 21st century

New representations of thought -- written language, mathematical notation, information graphics, etc -- have been responsible for some of the most significant leaps in the progress of civilization, by expanding humanity's collectively-thinkable territory. But at debilitating cost. These representations, having been invented for static media such as paper, tap into a small subset of human capabilities and neglect the rest. Knowledge work means sitting at a desk, interpreting and manipulating symbols. The human body is reduced to an eye staring at tiny rectangles and fingers on a pen or keyboard. Like any severely unbalanced way of living, this is crippling to mind and body. But less obviously, and more importantly, it is enormously wasteful of the vast human potential. Human beings naturally have many powerful modes of thinking and understanding. Most are incompatible with static media. In a culture that has contorted itself around the limitations of marks on paper, these modes are undeveloped, unrecognized, or scorned. We are now seeing the start of a dynamic medium. To a large extent, people today are using this medium merely to emulate and extend static representations from the era of paper, and to further constrain the ways in which the human body can interact with external representations of thought. But the dynamic medium offers the opportunity to deliberately invent a humane and empowering form of knowledge work. We can design dynamic representations which draw on the entire range of human capabilities -- all senses, all forms of movement, all forms of understanding -- instead of straining a few and atrophying the rest. This talk suggests how each of the human activities in which thought is externalized (conversing, presenting, reading, writing, etc) can be redesigned around such representations.
Nonlinear Dynamical Systems as Movement Primitives

This paper explores the idea to create complex human-like movements from movement primitives based on nonlinear attractor dynamics. Each degree-of-freedom of a limb is assumed to have two independent abilities to create movement, one through a discrete dynamic system, and one through a rhythmic system. The discrete system creates point-to-point movements based on internal or external target specifications. The rhythmic system can add an additional oscillatory movement relative to the current position of the discrete system. In the present study, we develop appropriate dynamic systems that can realize the above model, motivate the particular choice of the systems from a biological and engineering point of view, and present simulation results of the performance of such movement primitives. The model was implemented for a drumming task on a humanoid robot.
Dating Violence and Sexual Assault Prevention With African American Middle Schoolers: Does Group Gender Composition Impact Dating Violence Attitudes?

A dating violence and sexual assault prevention program was presented to 396, predominately African American, middle schoolers in two inner city schools in the United States. In one school the program was offered with a same-gender group composition; in the other school, the same program was offered with mixed-gender group composition. A quasi-experimental design was used to assess how gender composition in programming influenced youths’ attitudes about dating violence. Study findings suggest that boys benefit most from participation in a same-gender program and girls benefit most from a mixed-gender program. Implications for prevention programming are discussed.
Robust Face Image Matching under Illumination Variations

Face image matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, a novel face image matching algorithm robust against illumination variations is proposed. The proposed image matching algorithm is motivated by the characteristics of high image gradient along the face contours. We define a new consistency measure as the inner product between two normalized gradient vectors at the corresponding locations in two images. The normalized gradient is obtained by dividing the computed gradient vector by the corresponding locally maximal gradient magnitude. Then we compute the average consistency measures for all pairs of the corresponding face contour pixels to be the robust matching measure between two face images. To alleviate the problem due to shadow and intensity saturation, we introduce an intensity weighting function for each individual consistency measure to form a weighted average of the consistency measure. This robust consistency measure is further extended to integrate multiple face images of the same person captured under different illumination conditions, thus making our robust face matching algorithm. Experimental results of applying the proposed face image matching algorithm on some well-known face datasets are given in comparison with some existing face recognition methods. The results show that the proposed algorithm consistently outperforms other methods and achieves higher than 93% recognition rate with three reference images for different datasets under different lighting conditions.