Accounting decoupled : A case study of accounting regime change in a Malaysian company

Peer reviewedPreprin

The Advantage of Evidential Attributes in Social Networks

Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while others take use of both the graph structure and the node attributes. In real-world networks, there are many uncertain and noisy attributes in the graph. In this paper, we will present how we detect communities in graphs with uncertain attributes in the first step. The numerical, probabilistic as well as evidential attributes are generated according to the graph structure. In the second step, some noise will be added to the attributes. We perform experiments on graphs with different types of attributes and compare the detection results in terms of the Normalized Mutual Information (NMI) values. The experimental results show that the clustering with evidential attributes gives better results comparing to those with probabilistic and numerical attributes. This illustrates the advantages of evidential attributes.Comment: 20th International Conference on Information Fusion, Jul 2017, Xi'an, Chin

An experimental study of the buckling of complete spherical shells

Buckling of complete spherical shells to examine Tsien energy hypothesi

How can Francis Bacon help forensic science? The four idols of human biases

Much debate has focused on whether forensic science is indeed a science. This paper is not aimed at answering, or even trying to contribute to, this question. Rather, in this paper I try to find ways to improve forensic science by identifying potential vulnerabilities. To this end I use Francis Bacon's doctrine of idols which distinguishes between different types of human biases that may prevent scientific and objective inquiry. Bacon’s doctrine contains four sources for such biases: Idols Tribus (of the 'tribe'), Idols Specus (of the 'den'/'cave'), Idols Fori (of the 'market'), and Idols Theatre (of the 'theatre'). While his 400 year old doctrine does not, of course, perfectly match up with our current world view, it still provides a productive framework for examining and cataloguing some of the potential weaknesses and limitations in our current approach to forensic science

Tracking Uncertainty Propagation from Model to Formalization: Illustration on Trust Assessment

International audienceThis paper investigates the use of the URREF ontology to characterize and track uncertainties arising within the modeling and formalization phases. Estimation of trust in reported information, a real-world problem of interest to practitioners in the field of security, was adopted for illustration purposes. A functional model of trust was developed to describe the analysis of reported information, and it was implemented with belief functions. When assessing trust in reported information, the uncertainty arises not only from the quality of sources or information content, but also due to the inability of models to capture the complex chain of interactions leading to the final outcome and to constraints imposed by the representation formalism. A primary goal of this work is to separate known approximations, imperfections and inaccuracies from potential errors, while explicitly tracking the uncertainty from the modeling to the formalization phases. A secondary goal is to illustrate how criteria of the URREF ontology can offer a basis for analyzing performances of fusion systems at early stages, ahead of implementation. Ideally, since uncertainty analysis runs dynamically, it can use the existence or absence of observed states and processes inducing uncertainty to adjust the tradeoff between precision and performance of systems on-the-fly

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Deep Learning for Link Prediction in Dynamic Networks using Weak Estimators

Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization of traditional similarity metrics to inexpensively build an effective feature vector for a deep neural network. Weak estimators have been used in a variety of machine learning algorithms to improve model accuracy, owing to their capacity to estimate changing probabilities in dynamic systems. Experiments indicate that our approach results in increased prediction accuracy on several real-world dynamic networks

Markets and Growth

This paper studies key markets (financial, labor, natural resource, and product) to assess how they are facilitating or constraining growth. First, we draw on the body of existing theoretical and empirical literature to discuss the links between markets and growth. Second, we present four stylized scenarios of the process of growth, which summarize market infrastructure and efficient factor reallocation in response to shocks appear to be among the most important growth determinants. We highlight the relative lack of research on the relationship between labor markets and growth, as opposed to the relationship between human capital production and growth. Finally, we combine suggestions of Topel (1999) and Pritchett (2000) to argue that country-specific markets should be a principal focus of future research on growth. This paper provides a framework for such studies.http://deepblue.lib.umich.edu/bitstream/2027.42/39766/3/wp382.pd