Miracle-dz/UniFAR-FTUs huggingface dataset:
Multirobot Coverage Using Observation-Based Cooperation with Backtracking

In cooperative robot teams, communications can speed up execution, reduce duplication, and prevent interference. Although many systems use explicit communications, persistent intra-team digital communications is not guaranteed. One approach to this challenge is to use implicit communication to infer state rather than using digital messages. We investigate using implicit communication in the form of observation to infer state to coordinate a robot team in a coverage task. We demonstrate how pruning and backtracking a search tree can improve multi-robot coverage. Experiments were conducted to compare team performance of a robot team using observation-based cooperation to one that uses explicit communications.
Are Industry Specialists More Efficient and Effective in Performing Analytical Procedures? A Multi-Stage Analysis

A substantial body of audit research has shown that auditor specialisation through training and experience in one main industry improves auditor judgements. However, because this research examines only one or two audit processes in isolation, it has not been possible to identify whether these improvements lead to gains over an entire task. Further, the comparison of auditors with very different industry specialisations has not allowed these studies to identify incremental gains of specialisation over task and domain experience within a specific industry. The purpose of this study is to examine incremental gains in auditor performance by specialist auditors over auditors with some industry experience (non-specialists) when both groups complete all stages of an analytical procedures task within the same industry. The results of a controlled experiment where both specialists and non-specialists had equivalent general and task-specific experience reveal that, while specialists do not exhibit superior performance in the initial hypothesis generation stage, differences do manifest in later stages. Compared to non-specialists, specialists had a more focused and efficient information search, were more able to generate the correct cause during the task, and identified the correct cause more often.
Forecasting Financial Time Series using Artificial Market Models

We discuss the theoretical machinery involved in predicting financial market movements using an artificial market model which has been trained on real financial data. This approach to market prediction - in particular, forecasting financial time-series by training a third-party or 'black box' game on the financial data itself - was discussed by Johnson et al in [10] and [13] and was based on some encouraging prelimary investigations of the dollar-yen exchange rate, various individual stocks, and stock market indices (see[12] for more details also). However, the initial attempts lacked a clear formal methodology. Here we present a detailed methodology, using optimization techniques to build an estimate of the strategy distribution across the multi-trader population. In contrast to earlier attempts, we are able to present a systematic method for identifying 'pockets of predictability' in real-world markets. We find that as each pocket closes up, the black-box system needs to be 'reset'- which is equivalent to saying that the current probability estimates of the strategy allocation across the multi-trader population are no longer accurate. Instead, new probability estimates need to be obtained by iterative updating, until a new 'pocket of predictability' emerges and reliable prediction can resume.
Comparative power analysis of CMOS & adiabatic logic gates

The present paper proposes the comparative power analysis of different logic gates using conventional CMOS Designs and adiabatic clock gating based designs. For achieving high performance, Adiabatic logic gates are designed using CPAL (complementary pass transistor adiabatic logic). The design of INVERTER, NAND and XOR has been simulated and verified. All the circuits have been simulated using 90nm technology. In comparison to conventional CMOS designs, adiabatic technology based designs consumes much less power and saves energy.
Has Emerging Asia Decoupled? An Analysis of Production and Trade Linkages Using the Asian International Input-Output Table

Due to the emergence of global production networks, trade statistics have became less accurate in describing the dependence of emerging Asia on external demand. This chapter analyses, using an update of the Asian International Input–Output (AIO) table, the interdependence of emerging Asian economies, the United States, the EU15, and Japan via trade and production linkages. According to the results, we do not find evidence of the decoupling of emerging Asia from the rest of the world. On the contrary, we find evidence on increasing trade integration, both globally and regionally. Nonetheless, our analysis indicates that emerging Asia's dependence on exports is only about one-third of its GDP, that is, well below the 50% exposure suggested by trade data. This finding can be explained by the high import content of exports in these economies, which is a result of the increasing segmentation of production across the region.
(RE) READING ENVIRONMENT IN A MARKET SOCIETY: THE SOCIO-SPATIALITY OF SOCIAL COSTS

The modern world faced a vital dilemma while market society inevitably merge to whole globe. The dominance of market in the modern industrial societies, resulting in an endless trend of growth, seems to have reached a turning point, and the transforming environment gives the impression of a vanishing ‘nature’. However, the cost of the economic growth, that was promoted as a gift to humankind, took more than the ‘nature’. Logically, it seems easy to decide what to do to stop the approaching catastrophe: that is, put an end to the dominance of market and economic reasoning. Yet, such a huge social evolution raises enormous difficulties at its realization.
Currency attack/defense with two-sided private information

A currency attack fails on its own when the speculator suffers from her financial problem. This paper extends the existing models and argues that the monetary authority? s willingness to peg and the speculator? s cost of attack are private information. Our model thus accounts for the duration of currency attack/defense, and more importantly, allows for failed attack. We employ an asymmetric war of attrition and gauge the time when the speculator stops attacking, or when the monetary authority de-pegs. Comparative static results throw light on the interest rate policy amidst the Exchange Rate Mechanism Crisis and the Asian Currency Crisis
A GENERALIZED THRESHOLD REGRESSION MODEL FOR ANALYZING NON-NORMAL NONLINEAR TIME SERIES : PLAGUE IN KAZAKHSTAN AS AN ILLUSTRATION

We introduce the (mixed-effect) Generalized Threshold Regression (GTR) model for piecewise linear stochastic regression with (possibly) non-normal time-series data. Specifically, it is assumed that the conditional probability distribution of the response variable belongs to the exponential family, and the conditional mean response is linked to some piecewise linear stochastic regression function. We study the specific case where the response variable equals zero in the lower regime. Some large-sample properties of a likelihood-based estimation scheme are derived. Our approach is motivated by the need for modeling nonlinearity in serially correlated epizootic events. Data coming from monitoring conducted in a natural plague focus in Kazakhstan are used to illustrate this model by obtaining biologically meaningful conclusions regarding the threshold relationship between prevalence of plague and some covariates including past abundance of great gerbils and other climatic variables.
Decomposing Corporate Bond Spreads

Sterling, dollar and euro-denominated corporate bond spreads narrowed substantially between late 2002 and mid-2007, but widened abruptly during the recent financial market turmoil. This article uses a structural credit risk model to examine the extent to which movements in spreads over the past decade have been driven by credit and non-credit related factors. Compensation for bearing non-credit related illiquidity risk appears to have been a particularly important driver of high-yield spreads, including during the recent financial market turmoil, but the compensation required for credit risk has also increased recently.
Neural Network Language Models for Candidate Scoring in Hybrid Multi-System Machine Translation

This paper presents the comparison of how using different neural network based language modeling tools for selecting the best candidate fragments affects the final output translation quality in a hybrid multi-system machine translation setup. Experiments were conducted by comparing perplexity and BLEU scores on common test cases using the same training data set. A 12-gram statistical language model was selected as a baseline to oppose three neural network based models of different characteristics. The models were integrated in a hybrid system that depends on the perplexity score of a sentence fragment to produce the best fitting translations. The results show a correlation between language model perplexity and BLEU scores as well as overall improvements in BLEU.
Representation theory of a Class of Hopf algebras

The representations of the pointed Hopf algebras $U$ and $\su$ are described, where $U$ and $\su$ can be regarded as deformations of the usual quantized enveloping algebras $U_q(\mathfrak{sl}(3))$ and the small quantum groups respectively. It is illustrated that these representations have a close connection with those of the quantized enveloping algebras $U_q(\mathfrak{sl}(2))$ and those of the half quantum groups of $\mathfrak{sl}(3)$.
Discriminant Subspaces of Some High Dimensional Pattern Classification Problems

In this paper, we report on an empirical study of several high dimensional classification problems and show that much of the discriminant information may lie in low dimensional subspaces. Feature subset selection is achieved either by forward selection or backward elimination from the full feature space with support vector machines (SVMs) as base classifiers. These "wrapper" methods are compared with a "filter" method of feature selection using information gain as discriminant criterion. Publicly available data sets in areas of text categorization, chemoinformatics, and gene expression analysis are used to illustrate the idea. We found that forward selection systematically outperforms backward elimination at low dimensions when applied to these problems. These observations are known anecdotally in the machine learning community, but here we provide empirical support on a wide range of problems in different domains.
The Study of Income Tax Complexity and Unintentional Non-Compliance: Research Method and Preliminary Findings

A study of taxpayer compliance literature leaves little doubt that there exists many gaps of knowledge and little consensus on appropriate methodologies in this field. Progress appears to have been hampered by the dominate pursuit of an over-arching theory of compliance behaviour, and by the use of methodologies with inherent weaknesses. This present study examines the impact of complexity upon compliance behaviour using a two-phase research method. Its contribution to the understanding of taxpayer compliance in in two respects. Firstly, it addresses only on, narrowly defined, aspect of compliance behaviour for a specific group or taxpayers, and uses a multi-phase research method, drawing upon the strengths of both the quantitative and qualitative paradigms.
Improved Utility-Based Cooperative Cognitive Radio Networks

As the demand on frequency spectrum continues to grow rapidly,the dynamic spectrum access by cognitive radio technologies becomes a hot research problem and a promising approach to increase the efficiency of spectrum utilization.In previous research, there is no inter-cooperation between primary and secondary users. So we wonder if we introduce a cooperative pattern, the performance of the whole network may be improved. In this paper,we suggest a new kind of cooperative pattern which on one hand,leveraging secondary users as the cooperative relays for the primary users transmission;similarly on the other hand, primary user can serve as secondary users relay, which can improve the whole rate of transmission.It has been showed in our work that to select a subset of the secondary users as relays properly, primary users data can be transferred in a shorter interval of time, which correspondingly lead to more spare time for secondary users data transferring, and also primary user may get more pay from the secondary users. Therefore,it is a win-win situation.Secondary users cooperating with primary transmissions have the right to decide their payment made for primary user in order to achieve a proportional access time to the wireless media and decide the strategy to transmit its data by cooperative transmission with primary user.Based on the system model, we analysis the network by Stackelberg game theory and find the unique Nash Equilibrium point.Then,we show that the framework we suggest is a improved one by our analytical results and simulation results.
Stretching Firm and Brand Reputation

I consider an adverse selection model of product quality. Consumers observe the performance of the firm's products, and product performance is positively related to the firm's (privately observed) quality level. If a firm is to launch a new product, should it use the same name as its base product (reputation stretching), or should it create a new name (and start a new reputation history)? I show that for a given level of past performance (reputation), firms stretch if and only if quality is sufficiently high. Stretching thus signals high quality.
Secure Dynamic SSE via Access Indistinguishable Storage

Cloud storage services such as Dropbox [1] and Google Drive [2] are becoming more and more popular. On the one hand, they provide users with mobility, scalability, and convenience. However, privacy issues arise when the storage becomes not fully controlled by users. Although modern encryption schemes are effective at protecting content of data, there are two drawbacks of the encryption-before-outsourcing approach: First, one kind of sensitive information, Access Pattern of the data is left unprotected. Moreover, encryption usually makes the data difficult to use. In this paper, we propose AIS (Access Indistinguishable Storage), the first client-side system that can partially conceal access pattern of the cloud storage in constant time. Besides data content, AIS can conceal information about the number of initial files, and length of each initial file. When it comes to the access phase after initiation, AIS can effectively conceal the behavior (read or write) and target file of the current access. Moreover, the existence and length of each file will remain confidential as long as there is no access after initiation. One application of AIS is SSE (Searchable Symmetric Encryption), which makes the encrypted data searchable. Based on AIS, we propose SBA (SSE Built on AIS). To the best of our knowledge, SBA is safer than any other SSE systems of the same complexity, and SBA is the first to conceal whether current keyword was queried before, the first to conceal whether current operation is an addition or deletion, and the first to support direct modification of files.
New approaches to operating system security extensibility

This dissertation proposes new approaches to commodity computer operating system (OS) access control extensibility that address historic problems with concurrency and technology transfer. Access control extensibility addresses a lack of consensus on operating system policy model at a time when security requirements are in flux: OS vendors, anti-virus companies, firewall manufacturers, smart phone developers, and application writers require new tools to express policies tailored to their needs. By proposing prin-cipled approaches to access control extensibility, this work allows OS security to be " designed in " yet remain flexible in the face of diverse and changing requirements. I begin by analysing system call interposition, a popular extension technology used in security research and products, and reveal fundamental and readily exploited con-currency vulnerabilities. Motivated by these failures, I propose two security extension models: the TrustedBSD Mandatory Access Control (MAC) Framework, a flexible kernel access control extension framework for the FreeBSD kernel, and Capsicum, practical capabilities for UNIX. The MAC Framework, a research project I began before starting my PhD, allows policy modules to dynamically extend the kernel access control policy. The framework allows policies to integrate tightly with kernel synchronisation, avoiding race conditions inherent to system call interposition, as well as offering reduced development and technology transfer costs for new security policies. Over two chapters, I explore the framework itself, and its transfer to and use in several products: the open source Free-BSD operating system, nCircle's enforcement appliances, and Apple's Mac OS X and iOS operating systems. Capsicum is a new application-centric capability security model extending POSIX. Capsicum targets application writers rather than system designers, reflecting a trend towards security-aware applications such as Google's Chromium web browser, that map distributed security policies into often inadequate local primitives. I compare Capsicum with other sandboxing techniques, demonstrating improved performance, programmability, and security. This dissertation makes original contributions to challenging research problems in security and operating system design. Portions of this research have already had a significant impact on industry practice. 3 4 Acknowledgements Writing this dissertation would not have been possible without the support and encouragement of my family (especially my parents), friends, mentors, and colleagues, to whom I offer my sincerest thanks and appreciation. Ross Anderson, my supervisor, deserves a special note of thanks: he has been supportive throughout my less than typical path through Cambridge's PhD programme, giving me space to pursue a variety of interests, many related to my PhD …
Copa 2014 FrameNet Brasil: a frame-based trilingual electronic dictionary for the Football World Cup

This paper presents the Copa 2014 FrameNet Brasil software (C-14/FN-Br): a frame-based trilingual electronic dictionary covering the domains of Football, Tourism and the World Cup. The dictionary relies on the infrastructure of FrameNet and is meant to be used by tourists, journalists and the staff involved in receiving foreign visitors. Vocabulary from the three domains is made available in English, Spanish and Brazilian Portuguese. Every lexical unit in the dictionary is described against an interlingual background frame.
Quality Approach of Web Documents by an Evaluation of Structure Relevance

In this paper we present a user approach to evaluate a commercial web site quality. Due to the difficulty to define a web site, the associated quality can be evaluated from different information. We chose to study the behavior of an observer during the exploration of the page and to convert it with a specific analysis of the document structure. In this paper we propose a definition of quality in relation with a retrieval information goal included designers' intentions and users’ need. The visual scanpath studied with an eye-tracker. We will explain and show the link between the visual quality and gaze movement obtained from oculometric data. Finally, the structure of document will show its strong connection with the web site visual quality.
Towards model integration and model-based decision support for environmental applications

We argue that scientific results from different fields that can help decision making about environmental problems should be delivered in the form of executable and combinable models. For this purpose, such models have to be stated in a coherent modeling framework. For their integration, it is not essential (and even unnecessary) that they share a mathematical formalism. More fundamentally, they have to be stated at a conceptual level in order to identify and relate the objects and quantities that occur in the various model fragments. In order to be truly compositional, the models have to be formulated as independent model fragments that represent elementary processes, and they have to be stated in a contextindependent way to enable their reuse for different purposes. Computer-based decision support should be based on such models and generic algorithms for drawing inferences from these models. The basic steps in such a model-based decision support system are situation assessment, which is the task of generating a model that is compliant with the observations provided. Based on such a situation assessment, the next step is therapy proposal, which amounts to generating a model that combines the model of the current situation and models of human interventions and is compliant with the goals to be achieved by the remedy. Figure 1 displays this basic architecture. These steps can be formalized as instances of model revision in logic and realized by consistencybased diagnosis techniques. As the modeling formalism, we propose what has been developed as process-oriented modeling in Artificial Intelligence. The elementary model fragments, called processes, contain an explicit representation of their preconditions, stated in conceptual terms by reference to objects, their existence, properties, and relations. Their effect part does not only relate quantities, but also create objects and relations. Such model fragments can be formalized as logical formulas of the form StructuralConditions ∧ QuantityConditions  StructuralEffects ∧ QuantityEffects. The logical foundation of this modeling formalism allows for the integration with the logic-based model revision algorithm. Since the effects of a process may imply (or negate) the preconditions of other proccesses, the composition of the model for a certain scenario based on a library of elementary processes can be performed by an automated reasoning process and is not dependent on modeling or domain experts. As a consequence, this approach promises multiple benefits, the major ones being • support for the integration of models from different research fields and sources, • re-use of model fragments in different contexts and for different purposes, • availability of expert knowledge (captured by the model library) for non-experts.