Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and Analysis of Cloud Radio Access Networks Using Mat\'ern Hard-core Point Processes

In this study, the authors investigate the Gallager’s error exponents of dual-hop amplify-and-forward systems over generalised η-μ and κ-μ fading channels, two versatile channel models which encompass a number of popular fading channels such as Rayleigh, Rician, Nakagami-m, Hoyt and one-sided Gaussian fading channels. The authors present new analytical expressions for the probability density function of the end-to-end signal-to-noise-ratio (SNR) of the system. These analytical expressions are then applied to analyse the system performance through the study of Gallager’s exponents, which are classical tight bounds of error exponents and present the tradeoff between practical information rate and the reliability of communication. Two types of Gallager’s exponents, namely, random coding error exponent and expurgated error exponent, are studied. Based on the newly derived analytical expressions, the authors provide an efficient method to compute the required codeword length to achieve a predefined upper bound of error probability. In addition, the analytical expressions are derived for the cutoff rate and ergodic capacity of the system. Moreover, simplified expressions are presented at the high SNR regime. All the analytical results are verified via Monte–Carlo simulations.
The untrusted computer problem and camera based authentication using optical character recognition

The use of a public computer requires an implied trust in that computer to carry out the user's requests correctly. When attempting to perform a secure transaction using the computer, there should be no trust at all placed in the computer. This thesis presents the problem of using an untrusted computer, proposes a number of possible solutions to the problem, and discusses the implementation of a specific solution that entails verification of the screen content using optical character recognition techniques. Thesis Supervisor: Srinivas Devadas Title: Professor of Electrical Engineering and Computer Science
Complex Signals

Wavelet transforms enable us to represent signals with a high degree of scarcity. Wavelet thresholding is a signal estimation technique that exploits the capabilities of wavelet transform for signal denoising. The aim of this paper is to study various thresholding techniques such as Sure Shrink, Visu Shrink and Bayes Shrink and determine the best one for image denoising. This paper presents an overview of various threshold methods for image denoising. Wavelet transform based denoising techniques are of greater interest because of their performance over Fourier and other spatial domain techniques. Selection of optimal threshold is crucial since threshold value governs the performance of denoising algorithms. Hence it is required to tune the threshold parameter for better PSNR values. In this paper, we present various wavelet based shrinkage methods for optimal threshold selection for noise removal.
Modelling the Wireless Propagation Channel: A simulation approach with MATLAB

A practical tool for propagation channel modeling with MATLAB simulations. Many books on wireless propagation channel provide a highly theoretical coverage, which for some interested readers, may be difficult to follow. This book takes a very practical approach by introducing the theory in each chapter first, and then carrying out simulations showing how exactly put the theory into practice. The resulting plots are analyzed and commented for clarity, and conclusions are drawn and explained from the obtained results. Key features include: A unique approach to propagation channel modeling with accompanying MATLAB simulations to demonstrate the theory in practice Contains step by step commentary and analysis of the obtained simulation results in order to provide a comprehensive and structured learning tool Covers a wide range of topics including shadowing effects, coverage and interference, Multipath Narrowband channel, Multipath Wideband channel, propagation in micro and pico-cells, the land mobile satellite (LMS) channel, the directional Multipath channel and MIMO and propagation effects in fixed radio links (terrestrial and satellite) The book comes with an accompanying website that contains the MATLAB simulations and allows readers to try them out themselves Well suited for lab-use, as reference and as a self-learning tool both for advanced students and professionals Modeling the Wireless Propagation Channel: A simulation approach with MATLAB will be best suited for postgraduate (Masters and PhD) students and practicing engineers in telecommunications and electrical engineering fields, who are seeking to familiarise themselves with the topic without too many formulas. The book will also be of interest to network engineers, system engineers and researchers
XML and information retrieval: a SIGIR 2000 workshop

XML - the eXtensible Markup Language has recently emerged as a new standard for data representation and exchange on the Interact. It is believed that it will become a universal format for data exchange on the Web and that in the near future we will find vast amounts of documents in XML format on the Web. As a result, it has become crucial to address the question of how large collections of XML documents can be sorted and retrieved efficiently and effectively.To date, most work on storing, indexing, querying, and searching documents in XML has stemmed from the database community's work on semi-structured data. An alternative approach, that has received less attention to date, is to view XML documents as a collection of text documents with additional tags and relations between these tags. IR techniques have traditionally been applied to search large sets of textual data and should thus be extended to encode the structure and semantics inherent in XML documents. Integrating IR and XML search techniques will enable more sophisticated search on the structure as well as the content of these documents, while leveraging the success of IR techniques in document similarity ranking and keyword search.The SIGIR workshop on XML and information retrieval was held July 28th, in Athens Greece. The goal of the workshop was to bring together researchers and practitioners interested in XML and IR to discuss and define the most relevant topics in the relation between these two technologies, present recent results, and propose future directions for research. The topics for discussion included:&bull; How to extend IR technologies to search XML documents&bull; How to integrate XML structure in IR indexing structures&bull; How to query XML documents both on content and structure&bull; How to introduce the semantics inherent in XML into the search process&bull; How to adopt database indexing techniques in an IR frameworkThe opening session of the workshop consisted of a survey of search engines for XML documents. This was followed by three technical sessions: query languages, retrieval algorithms, and IR systems for XML documents. The final talk of the day, "Searching Annotated Language Resources in XML", by Nancy Ide was given from the perspective of potential users of XML search systems and opened many topics for discussion. The workshop was concluded with a panel discussion where the panelists outlined their vision of the future of XML search.
Opportunities for Volunteered Geographic Information Use in Spatial Planning

This chapter highlights two types of georeferenced User-Generated Content (geo-UGC) that show considerable potential for fruitful usage in spatial planning in practice: Volunteered Geographic Information (VGI) and Social Media Geographic Information (SMGI). By describing selected case studies, the chapter illustrates how geo-UGC can be used at different stages of spatial planning processes, supporting a more pluralist understanding of places, fostering the collaboration between decision-makers and contributing to a more participatory practice in spatial planning. The Geodesign approach is used as the framework for underpinning the discussion. Selected case studies developed by the authors are presented showing how geo-UGC can be beneficial for building knowledge on current urban and territorial dynamics, for identifying possible alternative futures and for finding agreement on preferable future developments. In all the selected cases, large numbers of users were involved in collecting volunteered content. The findings are also interpreted within the Smart Cities paradigm, where participation is an essential factor for building successful smart communities.
Local Cuts and Two-Period Convex Hull Closures for Big-Bucket Lot-Sizing Problems

Despite the significant attention they have drawn, big-bucket lot-sizing problems remain notoriously difficult to solve. Previous literature contained results (computational and theoretical) indicating that what makes these problems difficult are the embedded single-machine, single-level, multiperiod submodels. We therefore consider the simplest such submodel, a multi-item, two-period capacitated relaxation. We propose a methodology that can approximate the convex hulls of all such possible relaxations by generating violated valid inequalities. To generate such inequalities, we separate two-period projections of fractional linear programming solutions from the convex hulls of the two-period closure we study. The convex hull representation of the two-period closure is generated dynamically using column generation. Contrary to regular column generation, our method is an outer approximation and can therefore be used efficiently in a regular branch-and-bound procedure. We present computational results that il.
DISCUSSION PAPERS IN STATISTICS AND ECONOMETRICS

We study the quantitative eects of German income tax reform proposals in a general equilibrium model with elastic labor supply. Agents are heterogenous with regard to their productivity and their assets. Two tax reform proposals are considered: i) a flat- rate tax and ii) a consumption tax. Total government expenditures are assumed to remain constant. The model is calibrated with regard to the German economy in 1996. In particular, the endogenous labor income distribution as computed from our model is equal to the empirical labor income distribution in Germany. As our first main result, both reform proposals are shown to have only negligible eects on labor income distribution. Second, both tax reform proposals results in a strong increase of both aggregate employment and aggregate savings. And third, both reform proposals imply significant steady-state welfare gains equivalent to a rise of total consumption of 7.3% and 14.9%, respectively.
Forest Resources of the United States, 2007

Forest resource statistics from the 2000 Resources Planning Act (RPA) Assessment were updated to provide current information on the Nation's forests. Resource tables present estimates of forest area, volume, mortality, growth, removals, and timber products output in various ways, such as by ownership, region, or State. Current resource data and trends are analyzed and placed within the context of changes since 1953. Additional analyses look at the resource from an ecological, health, and productivity perspective. A mini-atlas of map plates includes national displays of forest type, ownership, biomass, fragmentation, and other key spatial elements. An interactive RPA Data Wiz CD is also included to provide user access to the resource data.
ZU Scholars ZU Scholars

The last few years have revealed that social bots in social networks have become more sophisticated in design as they adapt their features to avoid detection systems. The deceptive nature of bots to mimic human users is due to the advancement of artificial intelligence and chatbots, where these bots learn and adjust very quickly. Therefore, finding the optimal features needed to detect them is an area for further investigation. In this paper, we propose a hybrid feature selection (FS) method to evaluate profile metadata features to find these optimal features, which are evaluated using random forest, naïve Bayes, support vector machines, and neural networks. We found that the cross-validation attribute evaluation performance was the best when compared to other FS methods. Our results show that the random forest classifier with six optimal features achieved the best score of 94.3% for the area under the curve. The results maintained overall 89% accuracy, 83.8% precision, and 83.3% recall for the bot class. We found that using four features: favorites_count , verified , statuses_count , and average_tweets_per_day, achieves good performance metrics for bot detection (84.1% precision, 81.2% recall).
Improving the Performance of Speculatively Parallel Applications on the Hydra CMP

Hydra is a chip multiprocessor (CMP) with integrated support for thread-level speculation. Thread-level speculation provides a way to parallelize sequential programs without the need for data dependence analysis or synchronization. This makes it possible to paral-lelize applications for which static memory dependence analysis is difficult or impossible. While performance of the baseline Hydra system on applications with medium to large grain parallelism is good, the performance on integer applications with fine-grained parallelism is unimpressive. In this paper, we describe a collection of software and hardware techniques for improving speculation performance over the baseline speculative Hydra CMP. These techniques focus on reducing the overheads associated with speculation and improving the speculation behavior of the applications using code restructuring. When these techniques are applied to a set of eleven integer, multimedia and floating-point benchmarks, significant performance improvements result. In particular, the overall performance of the integer benchmarks is improved by seventy-five percent.
A Generic Personal Assistant Agent Model for Support in Demanding Tasks * (extended abstract)

Human task performance varies over time, depending on the personal characteristics of the human, the task and the environment. High effectiveness and efficiency levels are of particular importance when tasks of an organisation are demanding and consequences of performance impairment are high. Furthermore, as a longer term aim, the human should remain healthy during the processes of task execution. We propose a personal agent that provides automated adaptive assistance to ensure proper task execution. In the past, a variety of intelligent personal assistants have been proposed to support humans during the execution of tasks (e.g. [1]). Such personal assistants usually include models that represent the state of the human and his or her tasks at particular time points (e.g., a model for the cognitive load evaluation of the human), which can be used to determine when intervention is needed. However, these assistants usually consider only some aspects of human behaviour and the tasks. As a result, a specific personal assistant may react only to particular events and states of the environment. To provide a more proper form of assistance it may be important that a larger set of models is available from which an appropriate instance can be chosen depending on circumstances. Thus, the assistant will be able to adapt its behaviour to the circumstances. This paper presents a generic design for a multi-agent system architecture for adaptive personal support during task execution. Besides being adaptive to the task, the proposed personal assistant tries to keep the operator in-the-loop, in the sense that it only intervenes when a possible problem is detected. As automated systems may reduce situation awareness (the ‘picture’ of the environment) and increase cognitive load (e.g. [2]), such an adaptive assistant provides better human support than non-adaptive systems. To realize its adaptiveness, the personal assistant agent has two modes of functioning: the selfmaintenance mode and the monitoring and guidance mode. In the self-maintenance mode the personal assistant agent is able to reason about the model specifications required to perform its tasks and to achieve its goals. Some of the agent’s goals are related to the quality of the task execution, others concern the operator’s well-being. The personal assistant agent
Online Single Trial Ern Detection As An Interaction Aid In HCI Applications

Error-related negativity (ERN) is a form of an Event Related Potential signal which can be triggered in the brain when a user either makes a mistake or the application behaves differently from the expectation of the user. The first step to harness the benefits of ERN in HCI applications is to detect the patterns in real-time on a single trial basis. In this paper we present our initial results in detecting ERN. Using a logistic regression technique, we have achieved a 70% recognition rate of erroneous and correct single trials. We then explored several designs, e.g. using ERN to help a user in the moment of bad decision in a map navigation task. Through multiple designs and careful user testing, we aim to identify guidelines and design principles that can help HCI researchers to include ERN as an interaction aid in their applications applied right after its occurrence. There is also little knowledge about designing guidelines for ERN in HCI. This paper will investigate these problems further. The use of Error Related Negativity Nowadays, both disabled and able-users can benefit from BCI systems as an alternative communication method with the outside world. However, even a well designed BCI system cannot guarantee 100% of success in interpreting the users' intended goals. This is because of the misunderstandings between users and machines that may arise on either side [1]. These misunderstandings may reduce the user's performance and irritate them while interacting. This problem can be overcome by using ERN to predict and then correct those mismatches. From examples of using ERN in the P300 speller [7] and imaginary movements [4], it can be concluded that ERN may be used as an integrated error detection and correction module. This module runs in parallel and in an open feedback loop with the user interface. Consequently, HCI systems have the ability to cross validate the result or predict intuitively the user's intended goals. Single trial ERN detection – Why and How? Like other ERPs, ERN was discovered using the conventional averaging method [3]. This method provides an easier way to achieve a clear pattern as noise is reduced by the procedure. However, in HCI, ERN will be the most useful when it is used immediately after it is detected. Hence, there must be a technique to recognize this pattern when capturing EEG which is known as the online detection mechanism. One solution that was tried was caching and averaging few patterns in order to retrieve a reasonably clear ERN pattern. Nevertheless, as ERN is a manifestation of error awareness, it may take a long time to have collected enough signals. The caching procedure may delay the correction progress as well. Therefore, it is crucial to detect this pattern using only single trial in real-time as closely as possible to the user's moment of decision. This can be done by applying a moving time window through continuous EEG signals then pass them to a feature extractor and classifier. The output will indicate whether there is an ERN pattern or not.
Classification and Characterization of Landscapes in the Territory of Adilabad District, Telangana, Deccan Region, India

We are in the Anthropocene and recent studies have revealed that conventional land management strategies diminished natural landscapes and created anthropogenic ones. The classification and characterization of landscapes, therefore, provide a mechanism for quantifying anthropogenic changes to the landscape systems in the Anthropocene. This study was aimed at classifying and characterizing landscapes in the territory of Adilabad District, Telangana, Deccan region, India, which was selected as the area of case study. It was a multi-method approach, where ecogeographic and environmental data were gathered using user/visual-based approach, while socio-economic data were gathered through direct observations, interviews, focus group discussions, questionnaires and literature surveys. The data were analyzed using descriptive and inferential statistics. The study was undertaken by National Bureau of Plant Genetic Resources, Regional Station, Rajendranagar in collaboration with Vegetable Research Station, Rajendranagar during 2010-2012. In the District, a very wide range of land use types was observed. In the last 60 years, the District’s landscape has undergone a remarkable transformation and fragmentation as a consequence of anthropogenic activities like deforestation, agriculture expansion, intensive agriculture, transport infrastructure, urbanization, trade, tourism and recreation. The results revealed that there is a growing body of evidence supporting the hypothesis that the geographical space, which is a mosaic of natural and anthropogenic landscapes (semi-natural and artificial landscapes), is limited in the District. Hills, valleys, forests, rivers, streams, waterfalls and lakes were certain natural landscapes with little or no human impact found in the District. Traditional land management strategies including burning and clearing practiced by indigenous tribal communities, the native inhabitants of the area, have diminished natural landscapes and created semi-natural and artificial landscapes. Complex interactions between humans and nature resulted in a rich amalgam of anthropogenic landscapes and new ecosystems and habitats were created in urban, rural and tribal areas. The artificial landscapes are classified into three main categories: urban, rural and tribal landscapes based on the dominant human societies inhabiting them in the District. Our study identifies the same importance to all types of landscapes: thus natural, semi-natural, tribal, rural, urban landscapes are the key factors in cultural identity of the District. Our work on landscape analysis as a tool for the strategic assessment of the case study area enabled us to use user/visual-based approach based on the assessment of the visual fields of the District.
Evaluating Multiple Aspects of Coherence in Student Essays

Criterion SM Online Essay Evaluation Service includes a capability that labels sentences in student writing with essay-based discourse elements (e.g., thesis statements). We describe a new system that enhances Criterion ’s capability, by evaluating multiple aspects of coherence in essays. This system identiﬁes features of sentences based on semantic similarity measures and discourse structure. A support vector machine uses these features to capture breakdowns in coherence due to relatedness to the essay question and relatedness between discourse elements. Intra-sentential quality is evaluated with rule-based heuristics. Results indicate that the system yields higher performance than a baseline on all three aspects.
BLOWING THE WHISTLE ON TROUBLED

D espite advances in software engineering, project failure temains a critical challenge for the software development communicy. According to the Standish Group's 1998 survey, only 26% of such projects wete delivered on time, on budget, and with promised functionality, wasting billions of dollars annually; 46% wete completed over budget and behind schedule, with fewer functions and features than orig nally specified, and could therefore be classified as runaway projects [10]. In 1994, a sur-^ vey by consulting company KPMG of 120 organizations in the UK found 62% of them had experienced a runaway project [7]. Today, you can fdl your shelves with books covering numerous examples of runaway software projects [3].