Socially-Sensitive Systems Design:Exploring Social Potential

In human society, individuals have long voluntarily organized themselves in groups, which embody, provide and/or facilitate a range of different social concepts, such as governance, justice, or mutual aid. These social groups vary in form, size, and permanence, but in different ways provide benefits to their members. In turn, members of these groups use their understanding and awareness of group expectations to help determine their own actions, to the benefit of themselves, each other, and the health of the group

PENGARUH KUALITAS PRODUK DAN HARGA TERHADAP KEPUASAN KONSUMEN PADA PT. MARGA TIRTA KENCANA (Survei Pada Penghuni Perumahan Permata Buah Batu 1)

ABSTRAK Penelitian ini bertujuan untuk mengetahui pengaruh Kualitas Produk dan Harga terhadap Kepuasan Konsumen pada PT. Marga Tirta Kencana (Survei Pada Perumahan Permata Buah Batu 1 Bandung). Rumusan masalah dalam penelitian ini adalah bagaimana tanggapan konsumen mengenai kualitas produk yang ditawarkan, bagaimana tanggapan konsumen mengenai harga yang ditawarkan, bagaimana kepuasan konsumen, dan seberapa besar pengaruh kualitas produk dan harga terhadap kepuasan konsumen di PT. Marga Tirta Kencana Bandung secara simultan dan parsial. Metode yang digunakan penulis dalam penelitian ini adalah penelitian deskriptif dan verifikatif dengan tehnik pengumpulan data dengan interview (wawancara), kuesioner (angket) dan observasi (pengamatan). Adapun ukuran populasinya 619 orang dengan sampel 87 orang. Sedangkan tehnik sampling yang digunakan untuk menghitung besarnya ukuran sampel dalam non-probability sampling. Sesuai dengan perhitungan statistik, Kualitas Produk berada dalam kategori baik dan Harga berada dalam kategori baik terhadap Kepuasan Konsumen pada PT. Marga Tirta Kencana yang berada dalam kategori puas. Kata Kunci : Kualitas Produk, Harga, Kepuasan Konsume

The cross-entropy method for continuous multi-extremal optimization

In recent years, the cross-entropy method has been successfully applied to a wide range of discrete optimization tasks. In this paper we consider the cross-entropy method in the context of continuous optimization. We demonstrate the effectiveness of the cross-entropy method for solving difficult continuous multi-extremal optimization problems, including those with non-linear constraints

Recognizing Interactions Between People from Video Sequences

his research study proposes a new approach to group activ- ity recognition which is fully automatic. The approach adopted is hierar- chical, starting with tracking and modelling local movement leading to the segmentation of moving regions. Interactions between moving regions are modelled using Kullback-Leibler (KL) divergence. Then the statistics of such movement interactions or as relative positions of moving regions is represented using kernel density estimation (KDE). The dynamics of such movement interactions and relative locations is modelled as well in a development of the approach. Eventually, the KDE representations are subsampled and considered as inputs of a support vector machines (SVM) classifier. The proposed approach does not require any interven- tion by an operato

learning and adaptation to detect changes and anomalies in high dimensional data

The problem of monitoring a datastream and detecting whether the data generating process changes from normal to novel and possibly anomalous conditions has relevant applications in many real scenarios, such as health monitoring and quality inspection of industrial processes. A general approach often adopted in the literature is to learn a model to describe normal data and detect as anomalous those data that do not conform to the learned model. However, several challenges have to be addressed to make this approach effective in real world scenarios, where acquired data are often characterized by high dimension and feature complex structures (such as signals and images). We address this problem from two perspectives corresponding to different modeling assumptions on the data-generating process. At first, we model data as realization of random vectors, as it is customary in the statistical literature. In this settings we focus on the change detection problem, where the goal is to detect whether the datastream permanently departs from normal conditions. We theoretically prove the intrinsic difficulty of this problem when the data dimension increases and propose a novel non-parametric and multivariate change-detection algorithm. In the second part, we focus on data having complex structure and we adopt dictionaries yielding sparse representations to model normal data. We propose novel algorithms to detect anomalies in such datastreams and to adapt the learned model when the process generating normal data changes

Statistics for Fission-Track Thermochronology

This chapter introduces statistical tools to extract geologically meaningful information from fission-track (FT) data using both the external detector and LA-ICP-MS methods. The spontaneous fission of 238U is a Poisson process resulting in large single-grain age uncertainties. To overcome this imprecision, it is nearly always necessary to analyse multiple grains per sample. The degree to which the analytical uncertainties can explain the observed scatter of the single-grain data can be visually assessed on a radial plot and objectively quantified by a chi-square test. For sufficiently low values of the chi-square statistic (or sufficiently high p values), the pooled age of all the grains gives a suitable description of the underlying ‘true’ age population. Samples may fail the chi-square test for several reasons. A first possibility is that the true age population does not consist of a single discrete age component, but is characterised by a continuous range of ages. In this case, a ‘random effects’ model can constrain the true age distribution using two parameters: the ‘central age’ and the ‘(over)dispersion’. A second reason why FT data sets might fail the chi-square test is if they are underlain by multimodal age distributions. Such distributions may consist of discrete age components, continuous age distributions, or a combination of the two. Formalised statistical tests such as chi-square can be useful in preventing overfitting of relatively small data sets. However, they should be used with caution when applied to large data sets (including length measurements) which generate sufficient statistical ‘power’ to reject any simple yet geologically plausible hypothesis

Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation

The computation of Gaussian orthant probabilities has been extensively studied for low-dimensional vectors. Here, we focus on the high-dimensional case and we present a two-step procedure relying on both deterministic and stochastic techniques. The proposed estimator relies indeed on splitting the probability into a low-dimensional term and a remainder. While the low-dimensional probability can be estimated by fast and accurate quadrature, the remainder requires Monte Carlo sampling. We further refine the estimation by using a novel asymmetric nested Monte Carlo (anMC) algorithm for the remainder and we highlight cases where this approximation brings substantial efficiency gains. The proposed methods are compared against state-of-the-art techniques in a numerical study, which also calls attention to the advantages and drawbacks of the procedure. Finally, the proposed method is applied to derive conservative estimates of excursion sets of expensive to evaluate deterministic functions under a Gaussian random field prior, without requiring a Markov assumption. Supplementary material for this article is available online