Miracle-dz/UniFAR-FTUs huggingface dataset:
Do Images of ‘Watching Eyes’ Induce Behaviour That Is More Pro-Social or More Normative? A Field Experiment on Littering

Displaying images of eyes causes people to behave more pro-socially in a variety of contexts. However, it is unclear whether eyes work by making people universally more pro-social, or by making them more likely to conform to local norms. If the latter, images of eyes could sometimes make people less pro-social if pro-social behaviour is not the local norm. To separate these hypotheses we conducted a field experiment in which we explored whether manipulating a local descriptive norm altered the eyes effect. We recorded litter dropping decisions on a university campus in a 2 x 2 design, comparing situations with and without litter already on the ground (a manipulation of the local descriptive norm) and with and without large signs displaying images of watching eyes. We additionally recorded the number of potential human observers in the vicinity at the time of each littering decision. We observed a norm effect: the presence of litter on the ground increased littering, replicating previous findings. We also found that images of watching eyes reduced littering, although contrary to previous findings this was only when there were larger numbers of people around. With regard to our central aim, we found no evidence that litter on the ground interacted non-additively with images of eyes to induce increased littering behaviour. Our data therefore support the hypothesis that images of eyes induce more pro-social behaviour, independent of local norms. This finding has positive implications for the application of eye images in combating anti-social behaviour.
Two-Dimensional Signal Localization Algorithm for Spectrum Sensing

The localization algorithm based on the double-thresholding (LAD) method was originally proposed for detecting and localizing narrowband (NB) signals with respect to the search bandwidth. Its weakness is that the localized signal is often split into several parts, especially when the signal-to-noise ratio (SNR) is low. This may lead to the illusion of unoccupied frequencies in the middle of the signals. In this paper, an extension of the LAD method, namely the two-dimensional LAD (2-D LAD), is proposed to solve that problem. In addition to offering low computational complexity, the proposed method is able to operate at lower SNR values than the original 1-D LAD method.
Estimating the sortedness of a data stream

The distance to monotonicity of a sequence is the minimum number of edit operations required to transform the sequence into an increasing order; this measure is complementary to the length of the longest increasing subsequence (LIS). We address the question of estimating these quantities in the one-pass data stream model and present the first sub-linear space algorithms for both problems. We first present O(√n)-space deterministic algorithms that approximate the distance to monotonicity and the LIS to within a factor that is arbitrarily close to 1. We also show a lower bound of Ω(n) on the space required by any randomized algorithm to compute the LIS (or alternatively the distance from monotonicity) exactly, demonstrating that approximation is necessary for sub-linear space computation; this bound improves upon the existing lower bound of Ω(√n) [LNVZ06]. Our main result is a randomized algorithm that uses only O(log2 n) space and approximates the distance to monotonicity to within a factor that is arbitrarily close to 4. In contrast, we believe that any significant reduction in the space complexity for approximating the length of the LIS is considerably hard. We conjecture that any deterministic (1 + ε) approximation algorithm for LIS requires Ω (√n) space, and as a step towards this conjecture, prove a space lower bound of Ω(√n) for a restricted yet natural class of deterministic algorithms.
Optimization of software cost estimation using harmony search algorithm

Accurate estimation of software costs is one of the key and most important activities in development of software projects. Uncertainty and intricacy of software systems has made it difficult to efficiently and effectively develop software and has led to software systems' tendency to new optimal techniques. Prediction of the required effort for developing software has benefited significant progression owing the application of Meta-heuristic optimization Algorithms such algorithms have the potential to be applied as credible and useful tools in software cost estimation. In this paper the COCOMO effort estimation method is optimized using Meta-heuristic harmony search Algorithm. Nasa dataset was used in order to test the results. The purpose of optimization methods in software efforts estimation is to decrease the Mean Magnitude of Relative Error which in this case led to almost 21% optimization.
EVT-based statistical characterization of aggregated Inter-Contact Time in opportunistic networks

The most common approach adopted in the literatures to study the aggregated ICT (Inter-Contact Time) is focused on a certain mobile wireless network, and then characterizing the ICT in this type of network. Tip to now, it still lacks a general approach to model the aggregated ICT effectively in different type of networks. This paper proposes a new approach to characterize the aggregated ICT based on EVT (extreme value theory), which uses the GPD (generalized Pareto distribution) as the unique asymptotic model of the tail distribution. Parameter estimation methods are discussed and applied to real mobility traces collected on different opportunistic networks, such as social pocket switched networks and VANET (vehicular ad hoc network). By performing extensive experiments using EVT-based model, the statistical characteristics of aggregated ICT are found to generally have a power-law behavior in the tail distribution in social opportunistic networks, while an exponential behavior in VANETs. According to the KS (Kolmogorov-Smirnov) test, it illustrates that EVT-based model performs better than other models in characterizing the tail behavior of real mobility traces. Our results thus provide fundamental guidelines on design a new mobility models in opportunistic networks, new routing protocols and their performance analysis.
Non-Canonical NF-κB Signaling Initiated by BAFF Influences B Cell Biology at Multiple Junctures

It has been more than a decade since it was recognized that the nuclear factor of kappa light polypeptide gene enhancer in B cells (NF-κB) transcription factor family was activated by two distinct pathways: the canonical pathway involving NF-κB1 and the non-canonical pathway involving NF-κB2. During this time a great deal of evidence has been amassed on the ligands and receptors that activate these pathways, the cytoplasmic adapter molecules involved in transducing the signals from receptors to nucleus, and the resulting physiological outcomes within body tissues. In contrast to NF-κB1 signaling, which can be activated by a wide variety of receptors, the NF-κB2 pathway is typically only activated by a subset of receptor and ligand pairs belonging to the tumor necrosis factor (TNF) family. Amongst these is B cell activating factor of the TNF family (BAFF) and its receptor BAFFR. Whilst BAFF is produced by many cell types throughout the body, BAFFR expression appears to be restricted to the hematopoietic lineage and B cells in particular. For this reason, the main physiological outcomes of BAFF mediated NF-κB2 activation are confined to B cells. Indeed BAFF mediated NF-κB2 signaling contributes to peripheral B cell survival and maturation as well as playing a role in antibody responses and long term maintenance plasma cells. Thus the importance BAFF and NF-κB2 permeates the entire B cell lifespan and impacts on this important component of the immune system in a variety of ways.
Distributed Power Control for Cellular MIMO Systems with Temporal and Spatial Filtering

This paper concerns the uplink in a cellular MIMO system employing CDMA. Specifically, the problem of joint power control and interference suppression is considered, where filtering is jointly done in both temporal and spatial domains. Iterative algorithms are developed that converge to the globally optimum solution; we then shown how they may be implemented in a distributed context utilizing only local measurements at each base station. Numerous linear filter structures are analyzed, each differing in the computation complexity of implementation. Soft dropping power control in the MIMO context is also investigated with the aim of increasing the number of users admitted to the wireless network and also the likelihood of having a feasible problem.
Towards the Unification of Critical Success Factors for ERP implementations

Abstract Despite the benefits that can be achieved from a successful ERP system implementation, there is already evidence of high failure risks in ERP implementation projects. Too often, project managers focus mainly on the technical and financial aspects of the implementation project, while neglecting or putting less effort on the nontechnical issues. Therefore, one of the major research issues in ERP systems today is the study of ERP implementation success. Some authors have shown that ERP implementation success definition and measurement depends on the points of view of the involved stakeholders. A typical approach used to define and measure ERP implementation success has been critical success factors approach.
The Dynamics of the Forward Interest Rate Curve with Stochastic String Shocks

This paper offers a new class of models of the term structure of interest rates. We allow each instantaneous forward rate to be driven by a different stochastic shock, constrained in such a way as to keep the forward rate curve continuous. We term the process followed by the shocks to the forward curve ``stochastic strings'', and construct them as the solution to stochastic partial differential equations, that allow us to offer a variety of interesting parametrizations. The models can produce, with parsimony, any sort of correlation pattern among forward rates of different maturities. This feature makes the models consistent with any panel dataset of bond prices, not requiring the addition of error terms in econometric models. Interest rate options can easily be priced by simulation. However, options can only be perfectly hedged by trading in bonds of all maturities available.
Why do people adopt, or reject, smartphone password managers?

People use weak passwords for a variety of reasons, the most prescient of these being memory load and inconvenience. The motivation to choose weak passwords is even more compelling on Smartphones because entering complex passwords is particularly time consuming and arduous on small devices. Many of the memory- and inconvenience-related issues can be ameliorated by using a password manager app. Such an app can generate, remember and automatically supply passwords to websites and other apps on the phone. Given this potential, it is unfortunate that these applications have not enjoyed widespread adoption. We carried out a study to find out why this was so, to investigate factors that impeded or encouraged password manager adoption. We found that a number of factors mediated during all three phases of adoption: searching, deciding and trialling. The study’s findings will help us to market these tools more effectively in order to encourage future adoption of password managers.
The Effect Of Immigration On Productivity: Evidence From U.S. States

Abstract In this paper we analyze the long-run impact of immigration on employment, productivity, and its skill bias. We use the existence of immigrant communities across U.S. states before 1960 and the distance from the Mexican border as instruments for immigration flows. We find no evidence that immigrants crowded out employment. At the same time, we find that immigration had a strong, positive association with total factor productivity and a negative association with the high skill bias of production technologies. The results are consistent with the idea that immigrants promoted efficient task specialization, thus increasing TFP, and also promoted the adoption of unskilled-efficient technologies.
Learning to Recommend Links using Graph Structure and Node Content

The link prediction problem for graphs is a binary classification task that estimates the presence or absence of a link between two nodes in the graph. Links absent from the training set, however, cannot be directly considered as the negative examples since they might be present links at test time. Finding a hard decision boundary for link prediction is thus unnatural. This paper formalizes the link prediction problem from the flexible perspective of preference learning: the goal is to learn a preference score between any two nodes---either observed in the network at training time or to appear only later in the test---by using the feature vectors of the nodes and the structure of the graph as side information. Our assumption is that the observed edges, and in general, shortest paths between nodes in the graph, can reinforce an existing similarity between the nodes feature vectors. We propose a model implemented by a simple neural network architecture and an objective function that can be optimized by stochastic gradient descent over appropriate triplets of nodes in the graph. Our first preliminary experiments in small undirected graphs show that our learning algorithm outperforms baselines in real networks and is able to learn the correct distance function in synthetic networks.
Neuropsychology and Neuroimaging Prefrontal Cortex and Long-Term Memory Encoding : An Integrative Review of Findings from

can be found at: The Neuroscientist Additional services and information for http://nro.sagepub. com/cgi/alerts Email Alerts: http://nro.sagepub. com/subscriptions Subscriptions: http://www.sagepub. com/journalsReprints.nav Reprints: http://www.sagepub. com/journalsPermissions.nav Permissions: http://nro.sagepub. com/cgi/content/refs/13/3/280 SAGE Journals Online and HighWire Press platforms): (this article cites 124 articles hosted on the Citations
COMPARATIVE STUDY OF READING COMPREHENSION BETWEEN STUDENTS WITH INTROVERT AND STUDENTS WITH EXTROVERT PERSONALITY AT SMA N 2 KALIANDA

The objective of this research was to find out whether there was a significant difference of students’ reading comprehension between introvert and extrovert personality. The population of the research was the students of the first grade of SMA N 2 Kalianda, Lampung Selatan in the academic year 2015/2016. The samples were class X.1 and class X.3. The total number of students in the sample class was 64 students. The research design used was ex post facto design. The result showed that there was a significant difference between introvert and extrovert students. The mean score of introvert was 71.96 and extrovert was 63.75. The writer used independent sample t-test formula in SPSS 16 to do calculation of the hypothesis testing with level of significant 0.05. The result of computation showed that t-value exceeded the t-table at the level of significance 0.005 (3052>2.009). Based on the result of the research stated above, it is proved that the students with introvert personality have better results in the reading comprehension test than those with extrovert personality. There was an implication for students’ personality in reading skill. Thus, teachers have to be able to provide the students with the most appropriate teaching technique, method or type of tasks based on their personality to give the same opportunities for introvert and extrovert students to be successful in reading. Keywords: reading comprehension, introversion, extroversion
Triadic Interaction between Social Cognitive Theory, Social Presence Theory, and Community of Inquiry: An Online Course Instructional Design Assessment Model Evaluating Students’ Perceptions of Social Presence, Collaborative Learning, Social Interaction, and Satisfaction with their Learning Experien

Community colleges have a mission of affording students open access to higher education. Student attrition in online courses at community colleges directly reduces student access to higher education and successful course and program completion and negatively impacts student success and institutional outcomes. From a social cognitive theory, social presence theory, and community of inquiry perspective, students’ perceptions of social presence, collaborative learning, social interaction, and satisfaction with their learning experience are critical components of student learning. This quasi-experimental study examines the impact of an instructional strategy, team-based learning, on students’ perceptions of social presence, collaborative learning, social interaction, and satisfaction with their learning experience in an online medical terminology course at a community college in Upstate New York. An online course instructional design assessment model emerged to assess students’ perceptions of social presence, collaborative learning, social interaction, and satisfaction with their learning environment through the intersection of social cognitive theory, social presence theory, and community of inquiry. Students’ perceptions of social presence, collaborative learning, social interaction, and satisfaction with their learning experience in an online course increased as a result of the student team-based instructional course design strategy. Recommendations support preparation of course designers, instructors, and implementation and evaluation of online courses. A circle of responsibility and success emerged in which student success is contingent on institutional commitment to research and best practices, course designer and instructor professional development, and student orientation and resources. Increasing higher education access and successful course and program completion is significant as a pathway out of poverty to improve social justice and equality. Recommendations include direct practice application, policy development, and future research. Document Type Dissertation Degree Name Doctor of Education (EdD) Department Executive Leadership First Supervisor C. Michael Robinson This dissertation is available at Fisher Digital Publications: https://fisherpub.sjfc. edu/education_etd/265 Subject Categories Education This dissertation is available at Fisher Digital Publications: https://fisherpub.sjfc. edu/education_etd/265 Triadic Interaction between Social Cognitive Theory, Social Presence Theory, and Community of Inquiry: An Online Course Instructional Design Assessment Model Evaluating Students’ Perceptions of Social Presence, Collaborative Learning, Social Interaction, and Satisfaction with their Learning Experience
Stellar Populations Found in the Central Kiloparsec of Four Luminous Compact Blue Galaxies at Intermediate z

We investigate the star formation history of the central regions of four luminous compact blue galaxies (LCBGs) at intermediate redshift using evolutionary population synthesis techniques. LCBGs are blue (B - V ≤ 0.6), compact (μB ≤ 21.0 mag arcsec-2) galaxies with absolute magnitudes MB brighter than -17.5. The LCBGs analyzed here are located at 0.436 ≤ z ≤ 0.525. They are among the most luminous (MB < -20.5), blue (B - V ≤ 0.4), and high surface brightness (μB ≤ 19.0 mag arcsec-2) of this population. The observational data used were obtained with the Hubble Space Telescope (HST) STIS, the WFPC2, and the first NICMOS camera. We have disentangled the stellar generations found in the central regions of the observed targets using a very simple model. This is one of the first times this has been done for compact galaxies at this redshift using HST data, and it provides a comparison benchmark for future work on this kind of galaxy using instruments with adaptive optics in 10 m class telescopes. We find evidence for multiple stellar populations. One of them is identified as the ionizing population, and the other corresponds to the underlying stellar generation. The estimated masses of the inferred stellar populations are compatible with the dynamical masses, which are typically (2-10) × 109 M⊙. Our models also indicate that the first episodes of star formation these LCBGs underwent happened between 5 and 7 Gyr ago. We compare the stellar populations found in LCBGs with the stellar populations present in bright, local H II galaxies, nearby spheroidal systems, and blue compact dwarf galaxies. It turns out that the underlying stellar populations of LCBGs are similar to yet bluer than those of local H II galaxies. It is also the case that the passive color evolution of the LCBGs could convert them into local spheroidal galaxies if no further episode of star formation takes place. Our results help to impose constraints on evolutionary scenarios for the population of LCBGs found commonly at intermediate redshifts.
Generalizing and Improving Weight Initialization

We propose a new weight initialization suited for arbitrary nonlinearities by generalizing previous weight initializations. The initialization corrects for the influence of dropout rates and an arbitrary nonlinearity's influence on variance through simple corrective scalars. Consequently, this initialization does not require computing mini-batch statistics nor weight pre-initialization. This simple method enables improved accuracy over previous initializations, and it allows for training highly regularized neural networks where previous initializations lead to poor convergence.
Abundance and Survival Rates of the Hawai’i Island Associated Spinner Dolphin (Stenella longirostris) Stock

Reliable population estimates are critical to implement effective management strategies. The Hawai’i Island spinner dolphin (Stenella longirostris) is a genetically distinct stock that displays a rigid daily behavioural pattern, foraging offshore at night and resting in sheltered bays during the day. Consequently, they are exposed to frequent human interactions and disturbance. We estimated population parameters of this spinner dolphin stock using a systematic sampling design and capture–recapture models. From September 2010 to August 2011, boat-based photo-identification surveys were undertaken monthly over 132 days (>1,150 hours of effort; >100,000 dorsal fin images) in the four main resting bays along the Kona Coast, Hawai’i Island. All images were graded according to photographic quality and distinctiveness. Over 32,000 images were included in the analyses, from which 607 distinctive individuals were catalogued and 214 were highly distinctive. Two independent estimates of the proportion of highly distinctive individuals in the population were not significantly different (p = 0.68). Individual heterogeneity and time variation in capture probabilities were strongly indicated for these data; therefore capture–recapture models allowing for these variations were used. The estimated annual apparent survival rate (product of true survival and permanent emigration) was 0.97 SE±0.05. Open and closed capture–recapture models for the highly distinctive individuals photographed at least once each month produced similar abundance estimates. An estimate of 221±4.3 SE highly distinctive spinner dolphins, resulted in a total abundance of 631±60.1 SE, (95% CI 524–761) spinner dolphins in the Hawai’i Island stock, which is lower than previous estimates. When this abundance estimate is considered alongside the rigid daily behavioural pattern, genetic distinctiveness, and the ease of human access to spinner dolphins in their preferred resting habitats, this Hawai’i Island stock is likely more vulnerable to negative impacts from human disturbance than previously believed.