Miracle-dz/UniFAR-FTUs huggingface dataset:
Fear of burglary in the Honeydew police district

The research project investigated whether victims of housebreaking experienced motivational, cognitive and emotional deficits central to the Learned Helplessness phenomenon. In keeping with the Reformulated Learned Helplessness theory the attributional style of victims, were also assessed. The State-Trait Inventory developed by Spielberger, Gorsuch, Lushene, Vagg and Jacobs was administered to measure the anxiety levels of victims. Sub-goals served as illustration for the learned helplessness phenomenon. Threehundred victims, using probability sampling techniques, were interviewed by means of an interview schedule. Support was found for cognitive and some motivational deficits and a common range of emotions experienced by victims. The majority of victims exhibited a global attnbutional style. Burglary victims did not show appreciably higher trait and state scores means, except for females in the 19-39 age group, when compared to a psychological norm. Environmental factors did play a role in rendering homes vulnerable. Recommendations addressing the fear of housebreaking were made at a therapeutic and practical level. Key Terms Burglary; victims; learned helplessness; attributions; motivational deficits; cognitive deficits; response-outcome noncontingency; emotions; state anxiety; trait anxiety; environmental factors.
The analysis of multiple endpoints in clinical trials.

Treatment comparisons in randomized clinical trials usually involve several endpoints such that conventional significance testing can seriously inflate the overall Type I error rate. One option is to select a single primary endpoint for formal statistical inference, but this is not always feasible. Another approach is to apply Bonferroni correction (i.e., multiply each P-value by the total number of endpoints). Its conservatism for correlated endpoints is examined for multivariate normal data. A third approach is to derive an appropriate global test statistic and this paper explores one such test applicable to any set of asymptotically normal test statistics. Quantitative, binary, and survival endpoints are all considered within this general framework. Two examples are presented and the relative merits of the proposed strategies are discussed.
A Review of Privacy Essentials for Confidential Mobile Data Transactions

The increasingly rapid use of mobile devices for da ta transaction around the world has consequently le d to a new problem, and that is, how to engage in mobile d ata transactions while maintaining an acceptable le vel of data privacy and security. While most mobile devices engage in d ata transactions through a data cloud or a set of d ata servers, it is still possible to apply data confidentiality across data servers, and, as such, preserving privacy in any mo bile data transaction. Yet still, it is essential that a review of data privacy, data utility, the techniques, and methodolog ies employed in the data privacy process, is done, as the underlying data pr ivacy principles remain the same. In this paper, as a contribution, we present a review of data privacy essentials that ar e fundamental in delivering any appropriate analysi s and specific methodology implementation for various data privacy needs in mobile data transactions and computation.
Understanding user behavior in textual analysis: a thinking aloud approach for digital humanities research contexts

The exponential increase in availability of scientific papers, institutional reports or research monographies in digital contexts (i.e. in digital repositories, archives or social scientific networks) has led to the advancement of manual, semi-automatic or automatic-based methods to analyze these texts in the digital environment. These techniques cover a heterogeneous range, from manual expert analysis using computer methods (usually by annotation systems), to the application of natural language processing algorithms or discourse analysis techniques, which are able to identify cognitive relationships between text elements, e.g. causal structures or contrasts argumentations. This advancement is more evident in humanities research contexts, where most of the knowledge generated are expressed in textual formats. However, how the use of these techniques is affecting the analysis conducted by researchers in humanities' texts? Is it possible to measure the quality of the textual analysis? What kind of cognitive structures are identified in the text using these methods? This paper presents an empirical study conducted with humanities researchers, with the goal of obtaining a better understanding about how texts in digital contexts are analyzed by these professionals using semiautomatic discourse analysis techniques. The paper also proposes a method, based on Thinking Aloud protocols, in order to design experiments and evaluate software cognitive aspects, such as digital textual analysis, with humanities professionals. Finally, the paper discusses about how empirical studies and the Thinking Aloud method constitute a solid basis to better understand the relationship between expert textual analysis in humanities and it conducting using software methods.
CXCL17 and ICAM2 are associated with a potential anti-tumor immune response in early intraepithelial stages of human pancreatic carcinogenesis.

BACKGROUND & AIMS Anti-tumor immunity changes over the course of tumor progression; it is not clear how or when the developing tumor overcomes immune surveillance. Intraductal papillary mucinous neoplasm (IPMN) is an intraepithelial precursor lesion of pancreatic cancer that progresses from adenoma to carcinoma. We investigated when and how the human anti-tumor immune reaction changes during pancreatic tumor development. METHODS Using immunohistochemical analysis of cells isolated from patients with IPMN, the numbers of tumor-infiltrating lymphocytes and dendritic cells and the maturation state of dendritic cells in the regional lymph nodes were investigated during tumor progression. Gene expression profiles were compared among epithelial neoplastic cells at each stage of tumor development. Biological functions of the selected gene products were analyzed using syngeneic mouse models. RESULTS The anti-tumor immune reaction changed from an immune response to immune tolerance between the stages of intraductal papillary mucinous adenoma (IPMA) and intraductal papillary mucinous carcinoma (IPMC). Chemokine (C-X-C motif) ligand 17 (CXCL17) and intercellular adhesion molecule 2 (ICAM2) were involved in immune surveillance during tumor development-their expression levels were up-regulated exclusively in IPMA and disappeared from IPMC. CXCL17 and ICAM2 induced infiltration and accumulation of the tumor epithelial layer by immature myeloid dendritic cells. This was followed by a cellular immune reaction and ICAM2 simultaneously promoted the susceptibility of the tumor cells to cytotoxic T-cell-mediated cytolysis. These processes had a synergistic effect to increase the anti-tumor immune response. CONCLUSIONS Immune surveillance occurs during the early intraepithelial stages of human pancreatic carcinogenesis and is mediated by expression of CXCL17 and ICAM2.
Evaluating exploratory learning in LAVA

This paper reports on some initial experiences developing and evaluating a games-based approach to explorative learning. The approach adopted has particular relevance to subjects such as archaeology and geography that have scope for a significant fieldwork element, but which in practice are limited by the problems and costs associated with arranging real experience for potentially large numbers of students. A virtual simulation environment, LAVA, has been developed based on real world archaeological excavation data, with the aim of supporting exploratory learning and encouraging the development of an understanding of fieldwork techniques amongst undergraduate students within a classroom environment. LAVA seeks to make the processes involved in arranging and participating in an excavation engaging by use of a computer games approach, whereby a user, or group of users, is faced with a series of challenges with which they engage until such time as they have shown a certain level of competence within the virtual environment, at which point they can progress forward to the next level. We describe LAVA in outline, its initial deployment with a cohort of archaeology students, and the subsequent evaluation of the students' initial experiences with the system.
Idea Generation, Creativity, and Prototypicality

We explore the use of big data tools to shed new light on the idea generation process, automatically “read” ideas to identify promising ones, and help people be more creative. The literature suggests that creativity results from the optimal balance between novelty and familiarity, which can be measured based on the combinations of words in an idea. We build semantic networks where nodes represent word stems in a particular idea generation topic, and edge weights capture the degree of novelty versus familiarity of word stem combinations (i.e., the weight of an edge that connects two word stems measures their scaled co-occurrence in the relevant language). Each idea contains a set of word stems, which form a semantic subnetwork. The edge weight distribution in that subnetwork reflects how the idea balances novelty with familiarity. Based on the “beauty in averageness” effect, we hypothesize that ideas with semantic subnetworks that have a more prototypical edge weight distribution are judged as more creativ.
Why MCA? Nonlinear sparse coding with spike-and-slab prior for neurally plausible image encoding

Modelling natural images with sparse coding (SC) has faced two main challenges: flexibly representing varying pixel intensities and realistically representing low-level image components. This paper proposes a novel multiple-cause generative model of low-level image statistics that generalizes the standard SC model in two crucial points: (1) it uses a spike-and-slab prior distribution for a more realistic representation of component absence/intensity, and (2) the model uses the highly nonlinear combination rule of maximal causes analysis (MCA) instead of a linear combination. The major challenge is parameter optimization because a model with either (1) or (2) results in strongly multimodal posteriors. We show for the first time that a model combining both improvements can be trained efficiently while retaining the rich structure of the posteriors. We design an exact piece-wise Gibbs sampling method and combine this with a variational method based on preselection of latent dimensions. This combined training scheme tackles both analytical and computational intractability and enables application of the model to a large number of observed and hidden dimensions. Applying the model to image patches we study the optimal encoding of images by simple cells in V1 and compare the model's predictions with in vivo neural recordings. In contrast to standard SC, we find that the optimal prior favors asymmetric and bimodal activity of simple cells. Testing our model for consistency we find that the average posterior is approximately equal to the prior. Furthermore, we find that the model predicts a high percentage of globular receptive fields alongside Gabor-like fields. Similarly high percentages are observed in vivo. Our results thus argue in favor of improvements of the standard sparse coding model for simple cells by using flexible priors and nonlinear combinations.
Improving TCP Performance with Periodic Disconnections over Wireless Links

Since 1988, when the congestion control functions were first introduced, the performance of TCP has been greatly improved. However, TCP still suffers large performance degradation over wireless links due to characteristics specific to wireless environment that affect the behavior of TCP's congestion control and avoidance mechanisms. These mechanisms were designed and optimized for traditional wireline networks where packet losses are predominantly due to network congestion. In wireless networks, packet losses are mainly caused by the high bit error rate (BER) and hand-offs. Since TCP cannot differentiate packet losses caused by congestion from losses introduced by the wireless links, its performance degrades. M-TCP is a solution proposed to address the problems of TCP over wireless links with periodic disconnections. In this paper, we first give a brief introduction to M-TCP protocol. We then describe the OPNET implementation of MTCP and the simulation scenarios in a mixed wireline/wireless environment. We give performance comparisons between MTCP and TCP with and without presence of frequent disconnections in wireless links. Simulation results indicate that in the presence of frequent disconnections M-TCP outperforms TCP in terms of maintaining congestion window size, goodput, and sender size retransmission timer.
CHIVE - a program source visualisation framework

The CHIVE visualisation framework is an attempt to provide program comprehension and visualisation tool developers with a flexible means for creating 3-dimensional visualisations of hierarchies such as those that occur in program source. The CHIVE framework supports user definable datasets, multiple graph layouts and an environment in which users can interact with visualisations created from applying a graph layout to a dataset.
Epidemiology of Low Cholesterol Levels in Older Adults The Cardiovascular Health Study

BackgroundLow cholesterol levels have been associated with increased mortality from stroke, cancer, and other noncardiovascular diseases, but the reasons for this association remain unclear. Oneexplanation is that persons with low cholesterol levels have earlyoroccult disease that eventually leads to their deaths. Methods and ResultsThis possibility was explored in 2,091 men and 2,714 women 65-100 years old in the Cardiovascular Health Study, a multicenter observational study of risk factors for heart disease and stroke in older adults. Cholesterol levels <160 mg/dL were present in 11.6% of men and 3.7% of women and increased in prevalence with age. After adjustment for age, total cholesterol levels in this range were associated with a twofold increased prevalence of treated diabetes in men and women and with a twofold increased prevalence of cancer diagnosed in the preceding 5 years in women only. Low cholesterol was also associated with lower levels of hemoglobin, albumin, and factor VII, suggesting a link with hepatic synthetic function. On multivariate analysis, factors most strongly associated with low cholesterol levels in men and women were decreased factor VII levels, decreased albumin, and diabetes. ConclusionCross-sectional associations with low cholesterol levels differ by sex and suggest poorer health by some measures. The observed relations with treated diabetes and impaired hepatic synthetic function should be examined for risk of mortality in longitudinal data from this and other observational studies
Lack of Conceptual Cohesion of Methods: A new alternative to Lack Of Cohesion of Methods

While often defined in informal ways, class cohesion reflects important properties of modules in a software system. High cohesion for classes is one of the desirable properties in Object Oriented (OO) analysis as it supports program comprehension, testing, reusability, maintainability. Cohesion metrics have been used for quality assessment, fault prediction, software modularization etc. Existing approaches of class cohesion metrics are largely based on the structural information of the source code, such as attribute references in class methods. These cohesion metrics reflect particular interpretations of cohesion. However, only looking at structural aspect of cohesion is not sufficient for completely and accurately specifying class cohesion. So there is a need to pay attention on other aspects of cohesion like conceptual aspect. But only few conceptual metrics have been proposed till now. In our work, we have proposed a new set of cohesion metrics named LCCM (Lack of Conceptual Cohesion of Methods) metrics. These cohesion metrics are conceptual version of widely used LCOM (Lack of Cohesion of methods) metrics. LCOM metrics measure cohesion on structural information extracted entirely from the source code (e.g., attribute references in methods and method calls) that captures the degree to which the elements of a class belong together from a structural point of view. Proposed LCCM metrics use conceptual concerns embedded in source code entities for measuring class cohesion. These metrics are based on the analysis of latent topics embedded in comments and identifiers in source code. Latent Drichlet Allocation (LDA), a topic modeling tool is used for this purpose. These topics are used by proposed LCCM metrics to define similarity between methods of a class and on the basis of this similarity; proposed LCCM metrics define cohesion of the class. For the verification of proposed metrics, a case study on an open source java software system, called Rhino, is performed. The case study indicates that the novel cohesion metrics capture different aspects of class cohesion compared to the exiting cohesion metrics.
Aesthetic measure of alignment and regularity

To be effective as communications or sales tools, documents that are personalized and customized for each customer must be visually appealing and aesthetically pleasing. Producing perhaps millions of unique versions of essentially the same document not only presents challenges to the printing process but also disrupts the standard quality control procedures. The quality of the alignment in each document can easily distinguish professionally looking documents from amateur designs and some computer generated layouts. A multicomponent measure of document alignment and regularity, derived directly from designer knowledge, is developed and presented in computable form. The measure includes: edge quality, page connectivity, grid regularity and alignment statistics. It is clear that these components may have different levels of importance, relevance and acceptability for various document types and classes, thus the proposed measure should always be evaluated against the requirements of the desired class of documents.
Non-Orthogonal Multiple Access for Cooperative Multicast Millimeter Wave Wireless Networks

Millimeter wave (mmWave) wireless networks can operate in single-cell point-to-multipoint mode to provide local multicast services efficiently. In this paper, the performance of multicast mmWave wireless networks is studied, through stochastic geometry. Then, the use of power domain non-orthogonal multiple access (NOMA) for enhancing mmWave multicasting is also investigated. Furthermore, we study multicasting in two-tier mmWave heterogeneous networks, and propose a novel cooperative NOMA multicast scheme. Analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, the average number of served users, and the sum multicast rate are derived, in order to assess the performance of these schemes. Finally, we discuss the maximum sum multicast rates, by formulating them as optimization problems, and also develop efficient golden section search algorithms to solve them. The offered solutions reveal the impact of data transmission rate and power allocation on the sum multicast rate. Both analytical and numerical results demonstrate that NOMA can significantly improve the mmWave multicasting, while the proposed cooperative NOMA mmWave multicast scheme can further improve the NOMA mmWave multicasting.
Needed Computations Shortcutting Needed Steps

We define a compilation scheme for a constructor-based, strongly-sequential, graph rewriting system which shortcuts some needed steps. The object code is another constructor-based graph rewriting system. This system is normalizing for the original system when using an innermost strategy. Consequently, the object code can be easily implemented by eager functions in a variety of programming languages. We modify this object code in a way that avoids total or partial construction of the contracta of some needed steps of a computation. When computing normal forms in this way, both memory consumption and execution time are reduced compared to ordinary rewriting computations in the original system.
Dynamic models of brain imaging data and their Bayesian inversion

This work is about understanding the dynamics of neuronal systems, in particular with respect to brain connectivity. It addresses complex neuronal systems by looking at neuronal interactions and their causal relations. These systems are characterized using a generic approach to dynamical system analysis of brain signals - dynamic causal modelling (DCM). DCM is a technique for inferring directed connectivity among brain regions, which distinguishes between a neuronal and an observation level. DCM is a natural extension of the convolution models used in the standard analysis of neuroimaging data. This thesis develops biologically constrained and plausible models, informed by anatomic and physiological principles. Within this framework, it uses mathematical formalisms of neural mass, mean-field and ensemble dynamic causal models as generative models for observed neuronal activity. These models allow for the evaluation of intrinsic neuronal connections and high-order statistics of neuronal states, using Bayesian estimation and inference. Critically it employs Bayesian model selection (BMS) to discover the best among several equally plausible models. In the first part of this thesis, a two-state DCM for functional magnetic resonance imaging (fMRI) is described, where each region can model selective changes in both extrinsic and intrinsic connectivity. The second part is concerned with how the sigmoid activation function of neural-mass models (NMM) can be understood in terms of the variance or dispersion of neuronal states. The third part presents a mean-field model (MFM) for neuronal dynamics as observed with magneto- and electroencephalographic data (M/EEG). In the final part, the MFM is used as a generative model in a DCM for M/EEG and compared to the NMM using Bayesian model selection.
A Collaborative Filtering Recommendation Algorithm Based on Item Similarity of User Preference

The increasing users and items restrict the development of collaborative filtering recommendation systems. Then a series of problems, such as sparsity, cold start and scalability, come out. In this paper, we add user preference based on item genre, compute the similarity aimed at user preference. It can reduce the amount of data and improve the rapidity when computing similarity between items, and it can be more veracious and better recommendation quality. The experiment result shows that problems above can be solved with this approach.
Space complexity in propositional calculus

We study space complexity in the framework of propositional proofs. We consider a natural model analogous to Turing machines with a read-only input tape and such popular propositional proof systems as resolution, polynomial calculus, and Frege systems. We propose two different space measures, corresponding to the maximal number of bits, and clauses/monomials that need to be kept in the memory simultaneously. We prove a number of lower and upper bounds in these models, as well as some structural results concerning the clause space for resolution and Frege systems.