Miracle-dz/UniFAR-FTUs huggingface dataset:
Storing data on RFID tags: A standards-based approach

The potential of Radio Frequency Identification (RFID) for increasing supply chain efficiency has been repeatedly stressed by practitioners and researchers alike. The cross-company usage of RFID applications can only work if the collaborating companies agree on the syntax and semantic used. EPCglobal, an international industry consortium, has specified a stack of specifications that enable a standardized identifier to be stored on the RFID tag and all object related data to be kept on the network. Such a standardized concept does not yet exist to store object related data on RFID tags. To minimize the coordination effort as well as the emerging interoperability or integration problems and, therefore, also guarantee wide-spread adoption of the data-on-tag approach, it is advisable to build on existing standards for the storage of data on RFID tags. In this paper, we recommend applying the ISO 13584 standard for parts libraries (PLIB). We conceptualize how to use ISO 13584 to store data on RFID tags and use a case study on a kitchen furniture manufacturer, which uses RFID tagged components in a cross-company application with its suppliers, to develop a scenario for the storage of data on RFID tags.
Detrimental effect of post Status Epilepticus treatment with ROCK inhibitor Y-27632 in a pilocarpine model of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common type of epilepsy in adults where 20–30% of the patients are refractory to currently available anti-epileptic drugs. The RhoA/Rho-kinase signaling pathway activation has been involved in inflammatory responses, neurite outgrowth and neuronal death under pathological conditions such as epileptic insults. Acute preventive administration of ROCK inhibitor has been reported to have beneficial outcomes in Status Epilepticus (SE) epilepsy. In the present study, we evaluate the effect of chronic post SE treatment with the ROCK inhibitor Y-27632 in a rat pilocarpine model of TLE. We used chronic i.p. injections of Y-27632 for 5 days in 6 week old control rats or rats subjected to pilocarpine treatment as a model of TLE. Surprisingly, our findings demonstrate that a systemic administration of Y-27632 in pilocarpine-treated rats increases neuronal death in the CA3 region and ectopic recurrent mossy fiber sprouting (rMFS) in the dentate gyrus of the hippocampal formation. Interestingly, we found that chronic treatment with Y-27632 exacerbates the down-regulation and pathological distribution of the K+-Cl− cotransporter KCC2, thus providing a putative mechanism for post SE induced neuronal death. The involvement of astrogliosis in this mechanism appears to be intricate as ROCK inhibition reduces reactive astrogliosis in pilocarpine rats. Conversely, in control rats, chronic Y-27632 treatment increases astrogliosis. Together, our findings suggest that Y-27632 has a detrimental effect when chronically used post SE in a rat pilocarpine model of TLE.
Systemic small-player market exclusion in an east African context

ABSTRACT Producer–consumers in Kenya and Uganda face challenges in meeting their subsistence goals. They face a paradoxical inclusion and exclusion from the contemporary market system that the solutions proposed in the agriculture development, subsistence markets and BOP perspectives cannot address because of faults in the pervasive marketing ecosystem perspective. In this article, we go beyond the traditional discussion of producer–consumers’ market access, to include upstream as well as downstream challenges. We introduce the concept of integration gaps to markets as a counterpoint to typical measures of market access. We show the integration gap is a systematic neocolonialist exclusion of producer–consumers that international aid agencies exhorting greater market engagement may worsen. We offer suggestions for improvement from a marketing ecosystem perspective.
Real-Time Task Assignment in Heterogeneous Distributed Systems with Rechargeable Batteries

Real-time systems are one of the fields of computing where major benefits are expected from the increasing availability of multiprocessor technology. Heterogeneous computing environments, which utilize different high-performance machines interconnected via a high speed communication system, are well suited to the large, computation intensive, real-time or non-real-time applications. Nowadays, many of the systems in these environments are powered by rechargeable batteries. Scheduling real-time tasks on these rechargeable systems is an important issue which has been studied in the literatures. In this paper, we explore the task assignment problem on heterogeneous distributed system with rechargeable batteries. Our techniques to solve the problem are based on four heuristics, namely Minimum Schedule Length (MSL), min-min schedule length (MmSL), genetic algorithm (GA), and ant colony optimization (ACO). While the modifications of the MSL, MmSL and GA approaches from their original implementation are somewhat straight-forward, we design a novel structure using ACO. The performance comparisons of these four techniques are performed and the results are discussed. This paper not only gives a suggestion on which heuristic is best suited for the specific problem, but also provides a new direction to solve similar problems.
CFDC: a flash-aware replacement policy for database buffer management

Flash disks are becoming an important alternative to conventional magnetic disks. Although accessed through the same interface by applications, flash disks have some distinguished characteristics that make it necessary to reconsider the design of the software to leverage their performance potential. This paper addresses this problem at the buffer management layer of database systems and proposes a flash-aware replacement policy that significantly improves and outperforms one of the previous proposals in this area.
CoMon: cooperative ambience monitoring platform with continuity and benefit awareness

Mobile applications that sense continuously, such as location monitoring, are emerging. Despite their usefulness, their adoption in real-world deployment situations has been extremely slow. Many smartphone users are turned away by the drastic battery drain caused by continuous sensing and processing. Also, the extractable contexts from the phone are quite limited due to its position and sensing modalities. In this paper, we propose CoMon, a novel cooperative ambience monitoring platform, which newly addresses the energy problem through opportunistic cooperation among nearby mobile users. To maximize the benefit of cooperation, we develop two key techniques, (1) continuity-aware cooperator detection and (2) benefit-aware negotiation. The former employs heuristics to detect cooperators who will remain in the vicinity for a long period of time, while the latter automatically devises a cooperation plan that provides mutual benefit to cooperators, while considering running applications, available devices, and user policies. Through continuity- and benefit-aware operation, CoMon enables applications to monitor the environment at much lower energy consumption. We implement and deploy a CoMon prototype and show that it provides significant benefit for mobile sensing applications.
Metaphysical realism and thought

MR-P It is (metaphysically) possible that some empirical truths are not know able through even ideal human inquiry (performed under ideal conditions, including ideal cognitive conditions for the inquirer). In defense of MR-P, it might be noted that nothing in the nature of the empirical world, or of the cognitive processes through which humans (aided by scientific technology) aim to acquire knowledge of that world, guaran tees that all empirical truths are knowable. Many philosophers, of various ideological stripes, express an inclination to endorse some version of metaphysical realism entail ing MR-P. Consider next a position often designated as 'content externalism. ' This is a thesis re garding the individuation of the attitudes, to the effect that
Integrating Plot, Character and Natural Language Processing in the Interactive Drama Façade

Facade is an artificial intelligence-based art/research experiment in electronic narrative - an attempt to move beyond traditional branching or hy- per-linked narrative to create a fully-realized, one-act interactive drama. We are completing a three year collaboration to engineer a novel architecture that inte- grates emotional, interactive character behavior, drama-managed plot and shal- low natural language processing. In this architecture, authors can organize hierarchies of reactive behaviors and natural-language discourse contexts into entities called story beats, achieving rich, robust local interaction; a drama manager globally sequences beats chosen from a large pool in order to make story tension rise and fall to match an Aristotelian arc. Within this architecture we are building a dramatically interesting, real-time 3D virtual world inhabited by computer-controlled characters, in which the user experiences a theatrical drama from a first-person perspective. Facade will be publicly released as a free download in 2003.
SecuRank: Starving Permission-Hungry Apps Using Contextual Permission Analysis

Competition among app developers has caused app stores to be permeated with many groups of general-purpose apps that are functionally-similar. Examples are the many flashlight or alarm clock apps to choose from. Within groups of functionally-similar apps, however, permission usage by individual apps sometimes varies widely. Although (run-time) permission warnings inform users of the sensitive access required by apps, many users continue to ignore these warnings due to conditioning or a lack of understanding. Thus, users may inadvertently expose themselves to additional privacy and security risks by installing a more permission-hungry app when there was a functionally-similar alternative that used less permissions. We study the variation in permission usage across 50,000 Google Play Store search results for 2500 searches each yielding a group of 20 functionally-similar apps. Using fine-grained contextual analysis of permission usage within groups of apps, we identified over 3400 (potentially) over-privileged apps, approximately 7% of the studied dataset. We implement our contextual permission analysis framework as a tool, called SecuRank, and release it to the general public in the form of an Android app and website. SecuRank allows users to audit their list of installed apps to determine whether any of them can be replaced with a functionally-similar alternative that requires less sensitive access to their device. By running SecuRank on the entire Google Play Store, we discovered that up to 50% of apps can be replaced with preferable alternative, with free apps and very popular apps more likely to have such alternatives.
Incorporating adaptive discretization into genetic programming for data classification

Genetic programming (GP) for data classification using decision trees has been successful in creating models which obtain high classification accuracies. When categorical data is used GP is able to directly use decision trees to create models, however when the data contains continuous attributes discretization is required as a pre-processing step prior to learning. There has been no attempt to incorporate the discretization mechanism into the GP algorithm and this serves as the rationale for this paper. This paper proposes an adaptive discretization method for inclusion into the GP algorithm by randomly creating intervals during the execution of the algorithm through the use of a new genetic operator. This proposed approach was tested on five data sets and serves as an initial attempt at dynamically altering the intervals of GP decision trees while simultaneously searching for an optimal solution during the learning phase. The proposed method performs well when compared to other non-GP adaptive methods.
Improved Teaching Learning Based Optimization ( ITLBO ) Algorithm For Solving Optimal Reactive Power Dispatch Problem

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in a power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer setting are taken as the optimization variables. This paper introduces a new search model Teaching-Learning-Based Optimization (TLBO), it is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces . This paper presents an, improved version of TLBO algorithm, called the improved Teaching-Learning-Based Optimization (ITLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms reported those before in literature. Results show that TLBO is more efficient than others for solution of single-objective ORPD problem.
A field interpolated texture mapping algorithm for skeletal implicit surfaces

Addresses the problem of texture mapping implicit surfaces. Recent work in providing 2D mappings for implicit surfaces maps points from an auxiliary parametric (support) surface to the implicit surface. In this work, we extend this method by observing that all commonly used skeletal elements are easily parameterized and can be used as their own support surfaces. In areas of a surface defined by the combination of multiple skeletal primitives, we compute a mapping through relations found with the zero-level surface of a primitive by using the gradient field.
Learning maximum likelihood semi-naive Bayesian network classifier

We propose a technique to construct a sub-optimal semi-naive Bayesian network when given a bound on the maximum number of variables that can be combined into a node. We theoretically show that our approach has less computation cost when compared with the traditional semi-naive Bayesian network. At the same time, we can obtain a resulting sub-optimal structure according to the maximum likelihood criterion. We conduct a series of experiments to evaluate our approach. The results show our approach is encouraging and promising.
Characterizing business intelligence tasks, use and users in the workplace

This paper investigates business intelligence (BI) tasks, use and users in aworkplace setting. The study reports on a mixed methods study of users in three different typesof organisations employing BI. 1052 respondents answered a survey and 15 individual and 3group interviews were conducted to elaborate on the survey results. The study finds that themajority of public BI users are employees, and fewer managers and students, that are handlinga variety of tasks. Although they can experience challenges learning and using the BI system,they are still satisfied with it from different perspectives.
The Effect of Using Cube Connected Cycle for Improving Locality Awareness in Peer-to-Peer Networks

Today, peer-to-peer networks are become more popular among internet users and more than millions peers share high volume of data from anywhere. Sending and receiving these data increase the network traffics terribly. By improving the performance of these networks not only the efficiency of these networks increase but also the overall performance of the internet augments. To achieve high performance and resilience to failures, a peer can make connections with those other peers that are geographically closer to it. There are many solutions that are suggested for locality problem. In this paper, we suggest a Cube Connected Cycle as an overlay network to improve locality with higher performance.
A Method for Quantifying Consistency in Animal Distributions Using Survey Data

The degree of consistency with which groups of animals use the landscape is determined by a variety of ecological processes that influence their movements and patterns of habitat use. We developed a technique termed Distributional Consistency that uses survey data of unmarked individuals to quantify temporal consistency in their spatial distribution, while accounting for changes in population size. Distributional consistency is quantified by comparing the observed distribution patterns to all theoretically possible distribution patterns of observed individuals, leading to a proportional score between 0 and 1, reflecting increasingly consistent use of sites within a region. The technique can be applied to survey data for any taxa across a range of spatial and temporal scales. We suggest ways in which distributional consistency could provide inferences about the dispersal and habitat decisions of individuals, and the scales at which these decisions operate. Distributional consistency integrates spatial and temporal processes to quantify an important characteristic of different habitats and their use by populations, which in turn will be particularly useful in complimenting and interpreting other ecological measures such as population density and stability. The technique can be applied to many existing data sets to investigate and evaluate a range of important ecological questions using simple survey data.
Unsupervised classification using evolutionary strategies approach and the Xie and Beni criterion

The kmeans algorithm is an unsupervised classification algorithm. It has some drawbacks, the number of classes has to be known a priori, the initialization phase and the local optimums. We present in this paper an improvement based on evolutionary strategies and on the Xie and Beni criterion in order to get around these three difficulties. We design a new evolutionist kmeans algorithm. We suggest a new mutation operator that allows the algorithm to avoid local solutions and to converge to the global solution in a small computation time. We have optimized the Xie and Beni criterion by evolutionary strategies for the optimal choice of the number of classes. The proposed method is validated on several simulation examples. The experimental results obtained show the rapid convergence and the good performances of this new approach.
Power allocation strategy for target localization in distributed MIMO radar systems without previous position estimation

Distributed MIMO radar systems have demonstrated to offer enhanced target localization capabilities. Reasonable power allocation strategies are designed to motivate a maximal system potential. However, the existing power allocation strategies strongly depend on the accuracy of previous position estimation, which restricts the real-time applications. In this paper, we extend the existing works to a more general case of unknown previous position information. Since the Cramer-Rao lower bound is known to provide a tight lower bound on the error of any unbiased estimator, it is derived and then employed as the optimization metric. The proposed power allocation strategy is ultimately formulated as a minimax problem to minimize the maximum probability of localization estimation errors over the whole area. Numerical results demonstrate its superior performance.
Developmental trajectories of adolescent risky drinking: predictors from the Drug Education in Victoria Schools (DEVS) study

ABSTRACT With alcohol misuse one of the leading causes of disability among young Australians, determination of potential predictors of risky drinking trajectories of young people is crucial. This study aimed to identify risky drinking trajectories from early to mid-adolescence and to determine if membership of a harm minimization intervention, alcohol knowledge, attitudes towards alcohol and prevalence of alcohol harms would predict trajectory group membership. Longitudinal data from 1746 students were used to identify alcohol consumption trajectory groups for both intervention and control students. Higher baseline knowledge predicted a higher, increasing, consumption trajectory for controls, whereas, safer attitudes at baseline were not associated with a higher, increasing trajectory. All other alcohol harms at baseline were strongly associated with higher consumption trajectories. The intervention group had fewer increasing trajectories and a lower level of consumption at the end of the programme, suggesting the drug education programme reduced the number of students who substantially increased their consumption over time, while at the same time reducing their level of consumption in relative terms. The consistency of better intervention student outcomes across all trajectories provides evidence that the drug education programme was influential with all types of student drinkers and is suitable for universal delivery.
Sensing the 'Health State' of our Society

Mobile phones are a pervasive platform for opportunistic sensing of behaviors and opinions. We show that location and communication sensors can be used to model individual symptoms, long-term health outcomes, and diffusion of opinions in society. For individuals, phone-based features can be used to predict changes in health, such as common colds, influenza, and stress, and automatically identify symptomatic days. For longer-term health outcomes such as obesity, we find that weight changes of participants are correlated with exposure to peers who gained weight in the same period, which is in direct contrast to currently accepted theories of social contagion. Finally, as a proxy for understanding health education we examine change in political opinions during the 2008 US presidential election campaign. We discover dynamic patterns of homophily and use topic models (Latent Dirchlet Allocation) to understand the link between specific behaviors and changes in
Hansenula polymorpha Pex1p and Pex6p are peroxisome‐associated AAA proteins that functionally and physically interact

We have cloned the Hansenula polymorpha PEX1 and PEX6 genes by functional complementation of the corresponding peroxisome‐deficient (pex) mutants. The gene products, HpPex1p and HpPex6p, are ATPases which both belong to the AAA protein family. Cells deleted for either gene (Δpex1 or Δpex6) were characterized by the presence of small peroxisomal remnants which contained peroxisomal membrane proteins and minor amounts of matrix proteins. The bulk of the matrix proteins, however, resided in the cytosol. In cell fractionation studies HpPex1p and HpPex6p co‐sedimented with the peroxisomal membrane protein HpPex3p in both wild‐type cells and in Δpex4, Δpex8 or Δpex14 cells. Both proteins are loosely membrane‐bound and face the cytosol. Furthermore, HpPex1p and HpPex6p physically and functionally interact in vivo. Overexpression of PEX6 resulted in defects in peroxisomal matrix protein import. By contrast, overexpression of PEX1 was not detrimental to the cells. Interestingly, co‐overproduction of HpPex1p rescued the protein import defect caused by HpPex6p overproduction. Overproduced HpPex1p and HpPex6p remained predominantly membrane‐bound, but only partially co‐localized with the peroxisomal membrane protein HpPex3p. Our data indicate that HpPex1p and HpPex6p function in a protein complex associated with the peroxisomal membrane and that overproduced, mislocalized HpPex6p prevents HpPex1p from reaching its site of activity. Copyright © 1999 John Wiley & Sons, Ltd.