Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Refinement of a Semiotic Information Quality Framework

This paper presents the refinement of a semiotic information quality framework based on the analysis of practitioner and academic focus group feedback. The initial information quality framework was developed to address the question of what criteria should be used to define and assess quality. Semiotic theory, the philosophical theory of signs, provided a theoretical foundation for defining the framework structure (i.e. quality categories and their criteria) and for integrating objective, product-based and subjective, service-based quality perspectives, thus addressing problems observed in other proposed quality schemes with respect to scope and inconsistency. Following on from the initial theoretical work, empirical methods were used to refine the framework, especially the subjective components related to consumer quality perceptions. Specifically, focus group feedback served to (1) further clarify the scope and boundaries of the research and (2) to identify inter-dependencies, ambiguities, and gaps in the initial set of quality criteria.
Development of Detection System for the Bed Accident Using Ultrasonic Wave Sensor

This paper proposes a sensing system for detecting the accident at a space between bed fences. The number of accidents of elderly people on the bed has increased rapidly. Especially, there are a lot of fatal accidents that relate to the bed fence. For instance, some elderly people choked to death because their necks were caught in a space between the bed fences. To prevent such accidents, the full care of the elderly person by the caretakers is necessary. However, such care for a long time becomes a big burden for them. Therefore automated monitoring system for elderly person on the bed is required. Now there is no existing research related to the monitoring system for detecting bed fence accidents. Therefore, we develop a detection system which is able to monitor an elderly person on a bed.
A Reinforcement Learning Model of Precommitment in Decision Making

Addiction and many other disorders are linked to impulsivity, where a suboptimal choice is preferred when it is immediately available. One solution to impulsivity is precommitment: constraining one's future to avoid being offered a suboptimal choice. A form of impulsivity can be measured experimentally by offering a choice between a smaller reward delivered sooner and a larger reward delivered later. Impulsive subjects are more likely to select the smaller-sooner choice; however, when offered an option to precommit, even impulsive subjects can precommit to the larger-later choice. To precommit or not is a decision between two conditions: (A) the original choice (smaller-sooner vs. larger-later), and (B) a new condition with only larger-later available. It has been observed that precommitment appears as a consequence of the preference reversal inherent in non-exponential delay-discounting. Here we show that most models of hyperbolic discounting cannot precommit, but a distributed model of hyperbolic discounting does precommit. Using this model, we find (1) faster discounters may be more or less likely than slow discounters to precommit, depending on the precommitment delay, (2) for a constant smaller-sooner vs. larger-later preference, a higher ratio of larger reward to smaller reward increases the probability of precommitment, and (3) precommitment is highly sensitive to the shape of the discount curve. These predictions imply that manipulations that alter the discount curve, such as diet or context, may qualitatively affect precommitment.
Assessing Different Bayesian Neural Network Models for Militarized Interstate Dispute

This article develops and compares two Bayesian neural network models, a more restrictive Bayesian framework using Gaussian approximation and a less restrictive one using a hybrid version of Markov Chain Monte Carlo method (HMC), for the prediction of militarized interstate disputes (MIDs). In addition, to compare and analyze different Bayesian models for international conflict, the authors introduce a new measurement to interpret the relative influence of the model variables on the MIDs. The results indicate that the Gaussian approximation and HMC models are not statistically different in their performance. However HMC correctly recognized a marginally higher number of militarized disputes whose classification is important for policy purpose. On the variable effect, both models indicate similar patter of influences, where the two key liberal variables, democracy and economic interdependence, produce a strong dynamic feedback loop among each other, which greatly increases or decreases the probability of MIDs.
Object imperatives!

The phrase 'paradigm shift' has largely been used to describe a dichotomy between imperative and object oriented styles of development [2]. However the changes in the nature of software products, and hence of the processes used to produce them, have been much more extensive. This richer view of the nature of computing is inherent within the experiences of students entering higher education and is at variance with the implicit model enshrined within academic computing curricula. A direct implication of this is that radical changes may be needed in computing curricula; but there are many principled and pragmatic objections.
Digital Repository @ Iowa State University

In nearly one decade of Twitter’s being it has witnessed an ever growing user base from various realms of the world, one of them being politics. In the political domain, Twitter is used as a vital tool for communication purposes, running effective e-campaigns, and mining and affecting public opinions to name a few. We study the problem of automatically detecting whether a tweet posted by a state’s Senate’s twitter handle in the US has a reference to policy agenda(s). Such a capability can help detect the policy agendas that a state focuses on and also capture the inception of ideas leading to framing of bill/law. Furthermore, analyzing the spatial and temporal dynamics of tweets carrying policy agendas can facilitate study of policy diffusion among states, and help in comprehending the changing aspects of states learning policy-making from each other. Currently, no study has been carried out that analyzes Twitter data to detect whether or not a tweet refers to a policy agenda. We present our analysis on 122,965 tweets collected from verified Twitter handles of the US state’s upper house – Senate. We present our high-level analysis on (a) how much Twitter has penetrated into state politics and (b) how states use the medium differently in terms of the messages they broadcast. Our proposed approach aims to automate classification of a tweet based on having a reference to policy agenda (Has Agenda) or not (No Agenda). We accomplish this by leveraging existing text classification methodology and achieve a recall of 89.1% and precision of 77.2% for the “Has Agenda” class. We investigate several machine learning algorithms to determine the best performing one for our binary classification problem. We conclude that support vector machine using linear kernel was the most efficient algorithm to use for our dataset. Lastly, we propose a set of hand-crafted features that together with feature selection and stemming improved our classifier’s performance. Prior to including these features the classifier was developed using, basic preprocessing techniques, and term occurrence (for feature extraction). An overall improvement of 5.187 % at a significance level of 𝛼 = 0.05 was achieved.
Three‐Dimensional In Situ Stress‐Field Simulations of Shale Gas Formations: A Case Study of the 5th Member of the Xujiahe Formation in the Xinchang Gas Field, West Sichuan Depression

This work established a geological model for the 5th member of the Xujiahe Formation (X5 member) in the Xinchang gas field of the West Sichuan Depression based on the lithological, structural and depositional properties, as well as logging and well completion data and drill‐core observations. Rock mechanical parameters were calculated according to rock mechanic experiments and rock mechanic interpretations from logging data. We also calculated the magnitudes and orientations of the in situ stresses based on acoustic emission tests, differential strain tests, fracturing behaviour and logging interpretations as well as anisotropy logging tests, borehole‐breakout measurements and well‐log data. Additionally, the present stress field of the X5 member was simulated using finite element numerical (FEM) simulation methods. The numerical simulation results indicate that the distributions of lithology and fractures are key factors that influence the present stress field. The stress field in the study area is discontinuous as a result of fractures and faults in the central and eastern areas. Stress is concentrated at the end sections and bends of faults, but dissipates with distance away from both sides of the faults. A longitudinal profile clearly demonstrates the zonality and continuity of the stress field and an increase with depth. The differential stress distribution is relatively uniform; however, large deviations occur in fracture zones.
3 D Motion Segmentation of Articulated Rigid Bodies based on RGB-D Data-Supplementary Material

In the present work, the data matrix, as in Eq. (1), depends on the number of frames F of the sequence of RGB-D images, since X ∈ R3F×N . In the general case where the optimization program (2) must be solved, each frame induces three constraints that must be satisfied. Thus, it is natural to assume that the complexity and processing time of the algorithm increase with the number of frames to be considered. However, experimentally that was not observed, as shown in Fig. 3 and Table 3, where the number of frames considered was 2F (i.e. double the original number of frames F , which corresponds to watching the sequence forward and then backward). As illustrated in Fig. 3, the proposed method’s time required to process a sequence of F or 2F frames is identical. The average processing time per point is also similar, as presented in Table 3 (e.g. for F frames the average processing time per point is 1.32 msec, whereas for 2F frames it is 1.37 msec). The reason for this is related to which method is used to solve the sparse subspace optimization problem (2). Since the problem is convex, one could use any generic convex solver. However, as suggested in [1], an Alternating Direction Method of Multipliers (ADMM) was adopted for an efficient implementation of the proposed problem. In the present work, since we assume complete point trajectories (i.e. without the E term, since we are not considering outliers), Eq. (2) becomes
Fetch: A Personalised Information Retrieval Tool

Due to both the size and growth of the internet, new tools are needed to assist with the finding and extraction of very specific resources relevant to a user's task. Previously, the definition of relevance has been related to the matching between documents and query terms but recently the emphasis is shifting towards a more personalised model based on the relevance of a particular resource for one specific user. In this paper, we introduce our system, Fetch, which adopts this concept within an information-seeking environment specifically designed to provide users with means to describe a long-term multifaceted information need. By taking advantage of the way in which users bundle together groups of documents representing a particular topic, query languages as we know them can be taken to a higher and more useful level of abstraction. The agent personalises the search experience by using this information to formulate queries with the aim of returning documents relevant to the user's information need. In this paper we report on both qualitative and quantitative aspects of system use based on information collected in the pilot evaluation.
BIOMEDICAL ONTOLOGIES

Ontology design is an important aspect of medical informatics, and reusability is a key issue that is determined by the level of c ompatibility among ontology concepts and among the theories of the biomedical d omain they convey. In this article, we examine OpenGALEN, the UMLS Semant ic Network, SNOMED CT, the Foundational Model of Anatomy, and t he MENELAS ontology as well as descriptions of the biomedical domain in two general ontologies, OpenCyc and WordNet. Using the represen tatio of Blood in each system, we examine issues in compatibility among th ese ontologies. The presence of additional knowledge is also illustrate d and some issues in creating and aligning biomedical ontologies are dis cus ed.
The Graphical User Interface on the Home Machineon the AVS kernel , we can con gure a network at runtime by graphically connecting anddisconnecting the data ows between remote compute nodes and the Graph Viewer .

Financial modeling represents a promising industry application of high performance computing. In previous work, parallel stock option pricing models were developed for the Connection Machine-2 and DECmpp-12000. These parallel model run approximately two orders of magnitude faster than sequential models on high-speed workstations. To further develop this application, a portable, workstation based, interactive visualization environment was developed for a heterogeneous computing environment. Application Visualization System (AVS) was used to integrate massively parallel processing , workstation based visualization, an interactive system control, and distributed I/O modules. A preliminary performance analysis of this distributed model is discussed.
Tomographic Node Placement Strategies and the Impact of the Routing Model

Fault-tolerant computer networks rely on mechanisms supporting the fast detection of link failures. Tomographic techniques can be used to implement such mechanisms at low cost: it is often sufficient to deploy a small number of tomography nodes exchanging probe messages along paths between them and detect link failures based on these messages. Our paper studies a practically relevant aspect of network tomography: the impact of the routing model. While the relevance of the routing model on path diversity and hence tomography cost is obvious and well-known on an anecdotal level, we lack an analytical framework to quantify the influence of different routing models (such as destination-based routing) exists. This paper fills this gap and introduces a formal model for asymmetric network tomography and a taxonomy of path routing models. This facilitates algorithmic reasoning about tomographic placement problems and quantifying the difference between routing models. In particular, we provide optimal and near-optimal algorithms to deploy a minimal number of asymmetric and symmetric tomography nodes for basic network topologies (modelled as graphs) under different routing model classes. Interestingly, we find that in many cases routing according to a more restrictive routing model gives better results: compared to a more general routing model, computing a good placement is algorithmically more tractable and does not entail high monitoring costs, a desirable trade-off in practice.
UNIVERSITY OF NAIROBI SCHOOL OF COMPUTING AND INFORMATICS MODI C FOR PREDICTING INI PROBABILITY OF EVENT OCCURRENCE USING LOGISTIC REGRESSION: CASE I OR A KENYAN COMMERCIAL BANK BY CIIRISGONE

Credit Scoring has been a key undertaking of lenders over the last few decades. However, the most common use of credit scoring has been as a credit application appraisal tool rather than as a credit monitoring tool for existing credit holdings. This study, in the literature, investigated the different sets of sophisticated and classical credit scoring techniques used in the areas of classification and prediction o f customers defaulting on credit repayments. The main aim of this study was to build a behavioral credit scoring model from a provided dataset using Logistic Regression. The study also aimed to use data mining concepts to develop a prototype to automate the modeling process and calibrate predictions so as to formulate score cards. Our review of literature indicated the non-existence of an overall superior method for credit scoring since modeling objectives always differ and are thus suited for diverse methods. Model validation and assessments methods, variable selection and interpretation of validation results were investigated. The results of the study indicated the possibility of use of validation methods from related fields, the formulation of model deployment frameworks and formulation of a guide to trade-ofT between model hit-rate and false alarms. Transformation variables were also found to often offer better predictive power over raw data variables whilst the Gini-Index was shown to be a good indicator o f model over-fitting. Finally, this study suggests possible future research endeavors in the effort to eliminate the requirements of statistical knowledge in the Credit Scoring process.
Further intensification of deep convection in the Labrador Sea in 2016

There has been a progressive deepening of winter convection in the Labrador Sea since 2012, with the individual profile maximum depth exceeding 1800 m since 2014 and reaching 2100 m in 2016. This increase, during repeated positive phases of the winter North Atlantic Oscillation (NAO), resembles that during the formation of the record depth (2500 m) Labrador Sea Water (LSW) class in 1987–1994, attributed to repeated positive NAO forcing having provided critical preconditioning. The 2012–2016 LSW class is one of the deepest and most persistent ever observed (back to 1938). Year‐round observations from profiling Argo floats since 2002 complemented by annual surveys are providing novel information on the seasonal‐to‐decadal evolution of LSW, such as its variable density, the recent multiyear preconditioning, and its 2016 density being the highest since the mid‐1990s. These findings should help international observation programs and numerical model studies investigating LSW influences on the subpolar North Atlantic and Atlantic Meridional Overturning Circulation.
Semantic-aware blocking for entity resolution

In this work we propose a semantic-aware blocking framework for entity resolution (ER). The proposed framework is built using locality-sensitive hashing (LSH) techniques to efficiently unify both textual and semantic features into an ER blocking process. In order to understand how similarity metrics may affect the effectiveness of ER blocking, we study the robustness of similarity metrics and their properties in terms of LSH families. We further discuss how the semantic similarity of records can be captured, measured, and integrated with LSH techniques over multiple similarity spaces. We have evaluated our proposed framework over two real-world data sets, and compared it with the state-of-the-art blocking techniques. The experimental study shows that using a combination of semantic features and textual features can considerably improve the quality of blocking. Due to the probabilistic nature of LSH, this semantic-aware blocking framework also enables us to build fast and reliable blocking for performing entity resolution tasks in a large-scale data environment.
On -kernel Construction 2 Some -kernel Concepts 2.1 Address Spaces Map a a a a a a a a a a Disk ? ? ? 2.2 Threads and Ipc Supervising Ipc

15th ACM Symposium on Operating System Principles (SOSP) December 3-6, Copper Mountain Resort, Colorado From a software-technology point of view, the-kernel concept is superior to large integrated kernels. On the other hand, it is widely believed that (a)-kernel based systems are inherently ineecient and (b) they are not suuciently exible. Contradictory to this belief, we show and support by documentary evidence that ineeciency and innexibility of current-kernels is not inherited from the basic idea but mostly from overloading the kernel and/or from improper implementation. Based on functional reasons, we describe some concepts which must be implemented by a-kernel and illustrate their exibility. Then, we analyze the performance critical points. We show what performance is achievable, that the eeciency is suucient with respect to macro-kernels and why some published contradictory measurements are not evident. Furthermore, we describe some implementation techniques and illustrate why-kernels are inherently not portable, although they improve portability of the whole system. 1 Rationale-kernel based systems have been built long before the term itself was introduced, e.g. by Brinch Hansen 1970] and Wulf et al. 1974]. Traditionally, the word`kernel' is used to denote the part of the operating system that is mandatory and common to all other software. The basic work for personal or classroom use is granted without fee provided that copies are not made or distributed for proot or commercial advantage and that new copies bear this notice and the full citation on the rst page. Copyrights for components of this WORK owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior speciic permission and/or a fee. idea of the-kernel approach is to minimize this part, i.e. to implement outside the kernel whatever possible. The software technological advantages of this approach are obvious: (a) A clear-kernel interface enforces a more modular system structure. 1 (b) Servers can use the mechanisms provided by the-kernel like any other user program. Server malfunction is as isolated as any other user program's malfunction. (c) The system is more exible and tailorable. Diier-ent strategies and APIs, implemented by diierent servers, can coexist in the system. Although much eeort has been invested in-kernel construction, the approach is not (yet) generally accepted. This is due to the fact that most existing-kernels do not perform suuciently well. Lack of ee-ciency also heavily restricts exibility, since …
Computational Fluid mechanics and heat transfer - Second edition

Keywords: CFD ; numerique ; transfert de chaleur ; ecoulement ; mecanique des : fluides ; mathematiques ; modelisation ; simulation ; equations : differentielles Note: 1e edition: MEF84.4, livre disparu, rachete en 2003 Reference Record created on 2005-11-18, modified on 2016-08-08
A Posteriori Integration of University CAPE Software Developments

This contribution deals with the mutual integration of existing CAPE software products developed at different universities in Germany, Denmark, and Italy. After the motivation MOSAIC is presented as the bridge building the connection between the modelling tool ICAS-MoT and the numerical processing tool BzzMath. In the main part a case-study is presented, including descriptions of the benefits, challenges, implementation, and application. This paper is completed with aspects of current research activities and a conclusion summarizing the results.
A forward-backward Kalman for the estimation of time-variant channels in OFDM

OFDM combines the advantages of high achievable rates and relatively easy implementation. However, for proper recovery of the input, the OFDM receiver needs accurate channel information. In this paper, we propose an expectation-maximization (EM) algorithm for joint channel and data recovery. The algorithm makes use of the rich structure of the underlying communication problem-a structure induced by the data and channel constraints. These constraints include pilots, the cyclic prefix, and the finite alphabet constraints on the data, and sparsity, finite delay spread, and the statistical properties of the channel (frequency and time correlation). The algorithm boils down to a forward-backward (FB) Kalman filter. We also suggest a suboptimal modification that is able to track the channel and recover the data with no latency.