Miracle-dz/UniFAR-FTUs huggingface dataset:
CFAR intrusion detection method based on support vector machine prediction

A novel constant false alarm rate (CFAR) intrusion detection method based on support vector machine (SVM) is proposed in this paper. By introducing the normal network traffic into an SVM neural network, the forthcoming traffic data can be predicted, therefore enhancing the detectability of network attacks. The CFAR threshold of the proposed detector is also derived in the paper theoretically. Computer simulations based on standard DARPA network intrusion data present that the proposed SVM prediction-based approach is superior to other standard intrusion detection method.
Radar emitter classification using self-organising Neural Network models

This paper presents a radar emitter identification and classification technique based on Fuzzy ART and ARTMAP Neural Networks. The radar emitterpsilas parameters of RF, PW, PRI, Direction of Arrival(DOA) etc., are taken as inputs for the networks. The network is trained with the available data of the emitter types. After training, the network is used to identify the emitter type by applying the parameters of the emitter as inputs to the neural network. A number of simulations are carried out and the simulated results show that the network quickly and accurately identify and classify the emitter types.
PORTFOLIO SHUFFLING AND TAX REFORM

This paper analyzes the response of households to the provision in the Tax Reform Act of 1986 that phased out the deductibility of interest paid on consumer debt The evidence suggests that the policy goals of the provision were frustrated because households shuffled their portfolios, substituting mortgage debt for consumer debt High-income homeowners appear to have shuffled more of their debt and thus increased their share of the benefits of the mortgage interest deduction. One reason for this difference in shuffling may be that highincome homeowners scored better on measures of financial sophistication, and better scores appear to predict greater shuffling. Policy options that would reduce the use of mortgage debt for nonhousing purchases are discussed.
A risk-driven security analysis for a bluetooth low energy based microdata ecosystem

This paper presents security requirements, risk survey, security objectives, and security controls of the Bluetooth Low Energy (BLE) based Catcher devices and the related Microdata Ecosystem of Ceruus company for a secure, energy efficient and scalable wireless content distribution. The system architecture was composed of the Mobile Cellular Network (MCN) based gateway/edge router device, such as Smart Phone, Catchers, and web based application servers. It was assumed that MCN based gateways communicate with application servers and surrounding Catcher devices. The analysis of the scenarios developed highlighted common aspects and led to security requirements, objectives, and controls that were used to define and develop the Catcher and MCN based router devices and guide the system architecture design of the Microdata Ecosystem.
Acting Globally but Thinking Locally ? The Influence of Local Communities on Organizations

We develop an institutional theory of how local communities continue to matter for organizations, and why community factors are particularly important in a global age. Since globalization has taken center stage in both practitioner and academic circles, research has shifted away from understanding effects of local factors. In this paper, our aim is to redirect theoretical and empirical attention back to understanding the determinants and importance of local influences. We review classical and contemporary research from organizational theory, sociology and economics that have focused on geographic influences on organizations. We adapt Scott’s (2001) influential three pillars model, including regulative, social-normative and cultural-cognitive features to conceptualize an overarching model of how communities influence organizations. We suggest that because organizations are simultaneously embedded in communities and organizational fields, by accounting for both of these different levels, researchers will better understand isomorphism and change dynamics. Our approach thus runs counter the idea that globalization is a homogeneity-producing process, and the view that society is moving from particularism to universalism. With globalization, not only has the local remained important, but in many ways local particularities have become more visible and salient, and so understanding these dynamics will be helpful for researchers addressing institutional isomorphism and change.
Maternal macho-1 is an intrinsic factor that makes cell response to the same FGF signal differ between mesenchyme and notochord induction in ascidian embryos

An extracellular signaling molecule acts on several types of cells, evoking characteristic and different responses depending on intrinsic factors in the signal-receiving cells. In ascidian embryos, notochord and mesenchyme are induced in the anterior and posterior margins, respectively, of the vegetal hemisphere by the same FGF signal emanating from endoderm precursors. The difference in the responsiveness depends on the inheritance of the posterior-vegetal egg cytoplasm. We show that macho-1, first identified as a localized muscle determinant, is also required for mesenchyme induction, and that it plays a role in making the cell response differ between notochord and mesenchyme induction. A zygotic event involving snail expression downstream of maternal macho-1 mediates the suppression of notochord induction in mesenchyme precursors.
Data-driven light field depth estimation using deep Convolutional Neural Networks

This paper presents a data-driven approach to estimate the object depths from light field data using Convolutional Neural Networks (CNN). By exploring the relationship between the epipolar-plane images (EPI) and the corresponding depth map, we propose an enhanced EPI feature that encodes the depth information of each physical point in the light field and obtains the disparity map of the whole scene in a supervised manner. This work covers two major contributions, namely the extraction of the enhanced EPI features and the light field depth estimation with CNN. The proposed features augment the depth information of the corresponding points in the light field, and then our CNN architecture differentiates them into different depth layers. Forward propagation step of the CNN model allows rapid recognition of the disparity map of the test light field data. In the experiments, we apply our method on the HCI (Heidelberg Col-laboratory for Image Processing) benchmark dataset and demonstrate that it is significantly faster than the state-of-the-art light field depth estimation approaches while achieving satisfactory performance.
Block transmission with linear frequency domain equalization for dispersive optical channels with direct detection

Block transmission with frequency-domain equalization (FDE) has been proven as an attractive alternative to orthogonal frequency division multiplex (OFDM) for radio frequency (RF) communication, especially at 60 GHz. In this paper, we show that FDE can be advantageously applied for optical transmission over dispersive channels as well, and in particular, in direct-detection systems. We review the optimal coefficients for zero forcing (ZF) and minimum mean square error (MMSE) equalization - both in the case of symbol-spaced and fractionally-spaced sampling, and present promising results for an illustrative visible-light communications (VLC) link based on non-return-to-zero (NRZ) on-off keying (OOK).
Deep Transform: Time-Domain Audio Error Correction via Probabilistic Re-Synthesis

In the process of recording, storage and transmission of time-domain audio signals, errors may be introduced that are difficult to correct in an unsupervised way. Here, we train a convolutional deep neural network to re-synthesize input time-domain speech signals at its output layer. We then use this abstract transformation, which we call a deep transform (DT), to perform probabilistic re-synthesis on further speech (of the same speaker) which has been degraded. Using the convolutive DT, we demonstrate the recovery of speech audio that has been subject to extreme degradation. This approach may be useful for correction of errors in communications devices.
A revised typology of opaque generalisations*

This paper is about opaque interactions between phonological processes in the two senses defined by Kiparsky (1971, 1973) and discussed in much recent work on the topic, most notably McCarthy (1999) : underapplication opacity, whereby a process appears to have failed to apply in expected contexts on the surface, and overapplication opacity, whereby a process appears to have applied in unexpected contexts on the surface. Specifically, I demonstrate that there are three distinct types of overapplication opacity in addition to the only case discussed and properly categorised as such in the literature, counterbleeding. The analysis of each type of opacity in terms of rule-based serialism and in terms of Optimality Theory is discussed, emphasising the strengths and weaknesses of the two frameworks in each case.
A Hybrid Multi-objective Evolutionary Algorithm and Its Application in Component-based Product Design

Component-based product design usually appears as a multi-objective optimization problem (MOP). Traditional methods solving MOPs are robust and have proven their effectiveness in handling many classes of optimization problems. However, such techniques can encounter difficulties such as getting trapped in local minima, increasing computational complexity, and not being applicable to certain classes of objective functions. Multi-Objective Evolutionary Algorithms (MOEAs) can overcome these disadvantages and have shown great potentials to solve MOPs. In this paper, an h-MOEA is proposed by employing effective strategies from evolutionary computation, which is suitable for solving the MOP in design optimization and can generate more diverse solutions in an accepted time span. Then, the effectiveness and correctness of h-MOEA is verified using several popular benchmark functions. Also, a prototype is developed and used in component-based product design optimization. Finally, the optimization results of a product design case are shown in detail.
The role of managed healthcare in the South African context: A systemic approach

Managed healthcare, in the context of this study, is defined as a series of management interventions in the healthcare supply chain, aimed at promoting appropriate and necessary care at a cost which is affordable to the consumer. It has evolved precisely because the healthcare supply chain is inefficient, too costly to be affordable to the majority of consumers in South Africa. Since managed healthcare represents a collection of interventions it is probable that these interventions will provoke responses which either support or counter the objectives of the intervening parties. The study focuses on trends which can be quantified in order to demonstrate this process, as well as on qualitative responses from role-players within the system, in order to explain behavioural changes that give rise to these trends. Managed healthcare is a management tool that is used in the private healthcare environment to manage the provision of care. In the past it was based on retrospective intervention and had cost containment as its main focus. Managed healthcare aims to address efficiency and quality of care but has yet to demonstrate measurable outcomes that would significantly impact on its WHO ratings. However, little attention is paid to the relationships and structure of the system within which care is provided and the dynamics that exist between and amongst role-players. The extent to which these relationships have an effect on the outcomes of the Private Healthcare Delivery System in South Africa is a relative unknown quantity. This article describes the research process which endeavours to develop a systemic approach to understand the current challenges in the PHDS better in order to optimise the outcome of it.
The School Breakfast Program: participation and impacts.

Participation in the School Breakfast Program is much less common than participation in the National School Lunch Program, even among children with access to both programs. This report examines the determinants of participation in the School Breakfast Program among third grade public school students, as well as the impacts of the program on food insecurity and children‘s risk of skipping breakfast. Data are from the Early Childhood Longitudinal Survey— Kindergarten Cohort and from the Wisconsin Schools Food Security Survey. The study found that students are more likely to participate when breakfast is served in the classroom, when time available for breakfast in school is longer, and when they come from lower income or time-constrained households. Children with access to the School Breakfast Program are more likely to eat breakfast in the morning and that program access may enhance food security among families at the margin of food insecurity. By Judi Bartfeld, Myoung Kim, Jeong Hee Ryu, and Hong-Min Ahn, University of Wisconsin – Madison The School Breakfast Program Participation and Impacts Contractor and Cooperator Report No. 54 July 2009 This study was conducted by the University of Michigan under a cooperative research contract with USDA’s Economic Research Service (ERS) Food and Nutrition Assistance Research Program (FANRP): contract number 58-5000-6-0111 (ERS project representative: Katerine Ralston). The views expressed are those of the authors and not necessarily those of ERS or USDA.
Model-assisted Weighting for Survey Data with Multiple Response Modes

Several large surveys administered by the Census allow responses in several difierent modes (mail, telephone, personal interview), in some cases | as in the American Community Survey (ACS) | after sub-sampling at some stages from that part of the sample which has not responded at any earlier stage. Analysis of the survey results usually proceeds by weighting responses roughly by inverse unconditional probability of response to the survey. But often, nonresponse in the difierent modes can be modelled meaningfully in terms of demographic and geographic variables (such as dwelling type and aggregated characteristics related to ethnicity and socieoeconomic status), and the results of such models derived from previous recent surveys could conceivably be used to improve the estimation of population totals and domain subtotals which is ordinarily the goal of large surveys. This paper applies the concepts of sample-survey theory to investigate the theoretical improvements possible assisted with both correctly and incorrectly specifled models, and illustrates the issues and improvements using data from the 1990 decennial census. In the setting of models no worse than those fltted to one state’s decennial-census data and applied to another similar and neighboring state, the mean-squared error is deflnitely improved by incorporating the model into sample-weighted estimators, through an adjustment factor which is constant across PSU’s.
Simple PID control algorithm adapted to deadband sampling

Deadband sampling is used to reduce the number of messages and CPU utilization in networked control systems. However, when applied in closed control loops, the deadband sampling should be treated in control algorithm to cope with such problems like largely irregular samples, timing of sensor and controller events, and limit cycles. A PID control algorithm for deadband sampling is proposed in this paper to overcome these problems by usage of simple heuristics. The algorithm is evaluated with the help of simulations in TrueTime Toolbox for Matlab/Simulink.
Quality of Service Abstractions for Software-defined Networks

Software-defined networking (SDN) provides a means of configuring the packet-forwarding behavior of a network from a logically-centralized controller. Expressive, high-level languages have emerged for expressing data-plane configurations, and new tools allow for verifying packet reachability properties in real time. But SDN largely ignores quality of service (QoS) primitives, such as queues, queuing disciplines, and rate limiters, leaving configuration of these elements to be performed out of band in an ad-hoc manner. Not only does this make QoS elements difficult to configure, it also leads to a “try it and see” approach to analysis and verification of QoS properties. We propose a new language for configuring SDNs with quality of service primitives. Our language comes equipped with a well-defined semantics drawn from the network calculus, which we believe will yield an equational theory for reasoning about network quality of service as well as decision procedures for verifying QoS properties.
AN OVERVIEW OF ADAPTIVE QUERY PROCESSING SYSTEMS

Traditional database query processors separate query optimization from query execution: query plans are chosen by query optimizers and sent to execution engines for processing, until all query results are completely computed. However, new applications such as data streams, large-scale and data integration applications, require query processors to adapt to unpredictable data characteristics and dynamic environments. Query optimization and query execution need to be interleaved to constantly find new plans and replace obsolete plans, as new information is discovered about the data being processed. The main challenges of adaptive query processing lie in ensuring correct results, eliminating duplicates and maintaining good performance. In this report we survey three different systems on adaptive query processing: Tukwila-99 [11], Tukwila-04 [12], and Cape-04 [20]. We then divide adaptive query processing into five stages: plan pre-optimization, plan monitoring, plan analysis, plan re-optimization and plan migration. In each of these stages, different approaches are examined, analyzed, and compared when applicable. Empirical evaluations of different systems are also discussed in this report.
Evolution of cortical activation during recovery from corticospinal tract infarction.

BACKGROUND AND PURPOSE Recovery from hemiparesis due to corticospinal tract infarction is well documented, but the mechanism of recovery is unknown. Functional MRI (fMRI) provides a means of identifying focal brain activity related to movement of a paretic hand. Although prior studies have suggested that supplementary motor regions in the ipsilesional and contralesional hemisphere play a role in recovery, little is known about the time course of cortical activation in these regions as recovery proceeds. METHODS Eight patients with first-ever corticospinal tract lacunes causing hemiparesis had serial fMRIs within the first few days after stroke and at 3 to 6 months. Six healthy subjects were used as controls. Statistically significant voxels during a finger-thumb opposition task were identified with an automated image processing program. An index of ipsilateral versus contralateral activity was used to compare relative contributions of the 2 hemispheres to motor function in the acute and chronic phases after stroke. RESULTS Controls showed expected activation in the contralateral sensorimotor cortex (SMC), premotor, and supplementary motor areas. Stroke patients differed from control patients in showing greater activation in the ipsilateral SMC, ipsilateral posterior parietal, and bilateral prefrontal regions. Compared with the nonparetic hand, the ratio of contralateral to ipsilateral SMC activity during movement of the paretic hand increased significantly over time as the paretic hand regained function. CONCLUSIONS The evolution of activation in the SMC from early contralesional activity to late ipsilesional activity suggests that a dynamic bihemispheric reorganization of motor networks occurs during recovery from hemiparesis.
Synthesis of branched-phosphodiester and mannose-centered fucosylated glycoclusters and their binding studies with Burkholderia ambifaria lectin (BambL)

Five fucosylated glycoclusters exhibiting 4, 6 or 8 residues were synthesised with two different spatial environments based on mannose-centered and branched-phosphodiester scaffolds. Their synthesis was performed in solution using phosphoramidite chemistry to generate phosphodiester linkages, combined with Cu(I)-catalyzed azide–alkyne cycloaddition (CuAAC). The multivalent ligands were evaluated for their ability to bind to Burkholderia ambifaria Lectin (BambL). Binding evaluation was performed through inhibition of hemagglutination (HIA), surface plasmon resonance (SPR) and isothermal titration microcalorimetry (ITC). All fucosylated glycoclusters exhibited a higher affinity to BambL than methyl α-L-fucoside. A dissociation constant of 43 nM was observed for the fucocluster exhibiting four residues with the branched-phosphodiester spatial environment corresponding to a 22-fold increase in comparison with methyl α-L-fucoside. These multivalent fucoclusters represent the first example of ligands of high affinity to BambL.
A defeasible logic for modelling policy-based intentions and motivational attitudes

In this paper we show how defeasible logic could formally account for the non-monotonic properties involved in motivational attitudes like intention and obligation. Usually, normal modal operators are used to represent such attitudes wherein classical logical consequence and the rule of necessitation comes into play, i.e., ? A/? A, that is from ? A derive ? A. This means that such formalisms are affected by the Logical Omniscience problem. We show that policy-based intentions exhibit non-monotonic behaviour which could be captured through a non-monotonic system like defeasible logic. To this end we outline a defeasible logic of intention that specifies how modalities can be introduced and manipulated in a non-monotonic setting without giving rise to the problem of logical omniscience. In a similar way we show how to add deontic modalities defeasibly and how to integrate them with other motivational attitudes like beliefs and goals. Finally we show that the basic aspect of the BOID architecture is captured by this extended framework.
On the Interpretation of Unemployment in Empirical Labour Supply Analysis

The purpose of this paper is to summarize a certain line of work on the interpretation of unemployment in the analysis of male labour supply behavior. Specifically, this work investigates whether the data support the null hypothesis that individuals experiencing unemployment are on a labour supply function, and if the data do not support this hypothesis, how might a researcher proceed in empirical work. The motivation for doing this is two fold. First, what unemployment represents is an intrinsically interesting question, and may have implications beyond labour supply analysis in terms of macroeconomic theory. Second, if unemployed workers are constrained in the sense that they are off their individual labour supply functions, standard labour supply estimation may involve a fundamental misspecification of the equation. However, it should be emphasized that the purpose of this paper is to survey one possible approach to this problem; the paper does not attempt to provide a general survey on labour supply estimation or on constraints in the labour market.