Miracle-dz/UniFAR-FTUs huggingface dataset:
Selecting the right statistical model for analysis of insect count data by using information theoretic measures

Abstract Researchers and regulatory agencies often make statistical inferences from insect count data using modelling approaches that assume homogeneous variance. Such models do not allow for formal appraisal of variability which in its different forms is the subject of interest in ecology. Therefore, the objectives of this paper were to (i) compare models suitable for handling variance heterogeneity and (ii) select optimal models to ensure valid statistical inferences from insect count data. The log-normal, standard Poisson, Poisson corrected for overdispersion, zero-inflated Poisson, the negative binomial distribution and zero-inflated negative binomial models were compared using six count datasets on foliage-dwelling insects and five families of soil-dwelling insects. Akaike's and Schwarz Bayesian information criteria were used for comparing the various models. Over 50% of the counts were zeros even in locally abundant species such as Ootheca bennigseni Weise, Mesoplatys ochroptera Stål and Diaecoderus spp. The Poisson model after correction for overdispersion and the standard negative binomial distribution model provided better description of the probability distribution of seven out of the 11 insects than the log-normal, standard Poisson, zero-inflated Poisson or zero-inflated negative binomial models. It is concluded that excess zeros and variance heterogeneity are common data phenomena in insect counts. If not properly modelled, these properties can invalidate the normal distribution assumptions resulting in biased estimation of ecological effects and jeopardizing the integrity of the scientific inferences. Therefore, it is recommended that statistical models appropriate for handling these data properties be selected using objective criteria to ensure efficient statistical inference.
Performance analysis and power allocation strategy in overlay cognitive networks

In this paper, we propose a power allocation strategy for overlay cognitive networks, where each node is equipped with single antenna. Owing to the overly strategy, the secondary user (SU) can help transmitting the primary user's (PU) data and meanwhile can convey itself own data with the superposition coding (SC). We first analyze the bit-error-rate (BER) of the PU and the SU Then, the power allocation strategy is devised by minimizing total power, providing that the BER of the PU and that of SU are guaranteed. Since the BER formulations are not convex, the optimization is difficult to conduct. We then propose two new tractable BER approximations for the PU and SU, which can sophisticatedly transfer the design into a convex problem. The solution can thus be obtained. Simulations verify that our power allocation design is validated for different channel environments.
Auth-Lite: Lightweight M2MAuthentication reinforcing DTLS for CoAP

The resource constrained characteristic of M2M systems has made secure channel establishment for sensor data communication a challenging task. While the proposed Constrained Application Protocol (CoAP) from Internet Engineering Task Force (IETF) enables lightweight application layer for sensor devices and gateways, lightweight security protocol on DTLS-secured CoAP (CoAPs) is still not optimally achieved. CoAPs proposes different modes of security starting from pre-shared key to certificate mode. This paper investigates the performance of the payload embedded novel authentication and key management mechanism proposed by the authors, which is robust in nature, compared to pre-shared key mode CoAPs, as well as lightweight. The proposed scheme can be integrated in future with pre-shared key mode of DTLS to reinforce DTLS for mutual authentication. We prove efficacy of our proposed scheme based on comparative analysis with pre-shared key mode of CoAPs.
Quadratic Gaussian source broadcast with individual bandwidth mismatches

We study the problem of broadcasting a Gaussian source over a Gaussian broadcast channel to two users with individual source-channel bandwidth mismatches, under a quadratic distortion measure. Specifically we study the tradeoff between the achievable distortion pairs between the two users. The case when the bandwidth-expansion factors of the two users are identical has been well studied in the literature and to our best knowledge remains an open problem. Surprisingly, when the bandwidth expansion factors are different, we characterize a range of values where both the users simultaneously attain their point-to-point optimal distortion. Furthermore in the high signal-to-noise ratio regime, this set includes nearly all points where the weaker user has the higher bandwidth expansion factor. In other cases, we propose an achievable tradeoff between the distortion pairs.
WFC3 TV3 Testing: IR Channel Nonlinearity Correction

Using data taken during WFC3's Thermal Vacuum 3 (TV3) testing campaign, we have calculated the correction coefficients necessary to remove non-linearity effects from IR detector (IR-4, FPA165) data. We find that a set of coefficients for each quadrant of the detector produces a better correction than calculating and applying a separate set of coefficients to each pixel.
Remote sensing monitoring system for maritime search and rescue

This paper presents a remote sensing monitoring system for maritime search and rescue (RS-MSR). This work is a part of our project—Vision Enhancement System for Maritime Search and Rescue. In this work, satellite remote sensing imageries of large scale water area are acquired to detect and locate for guiding the search operation. Some important data such as current and sea state are retrieved to help decision-making of the operation. A remote sensing monitoring system can, to some extent, overcome the shortcomings and inadequacies of human eyes. It can also improve the searching speed and accuracy, and is of significance in promoting rescue success rate and efficiency.
Calibration and stereo image correction for binocular vision system with divergent optical axes arrangement

A stereo image correction method is proposed to correct the vertical disparity (VD) and the horizontal disparity (HD) distortion of the images captured by binocular vision system with divergent optical axes arrangement. In this paper, we calibrate both intrinsic and structural parameters of the two cameras. Using the parameters, the vertical disparity and the horizontal disparity distortion was eliminated by an image stereo correction method using disparity consistency. The simulation results corroborate the robustness of the method under non-ideal image sampling conditions and changing view distance. The experimental results show that the proposed method eliminates both VD and distortion of HD.
Truthful Incentive Mechanism for Mobile Crowdsensing with Smart Consumer Devices

Smart consumer devices have become one of the fundamental communication and computing devices in people's everyday lives over the past decade. Their various sensors and wireless connectivity have paved the way for a new application area called mobile crowdsensing (MCS) where sensing services are provided by using the sensor outputs collected from smart consumer devices. MCS system's service quality heavily depends on the participation of smart device users who probably expect to be compensated in return for their participation. Therefore, MCS applications need incentive mechanisms to motivate such people into participating. In this work, we first defined a reverse auction based incentive mechanism for a representative MCS system. Then, we integrated the Vickrey-Clarke-Groves (VCG) mechanism into the initial incentive mechanism so that truthful bidding would become the dominant strategy in the resulting incentive mechanism. Finally, we conducted simulations of both incentive mechanisms in order to measure the fairness of service prices and the fairness of cumulative participant earnings using Jain's fairness index. We observed that both the fairness of service prices and the fairness of cumulative participant earnings were generally better in the derived incentive mechanism when the VCG mechanism was applicable. We also found that at least 70% of service requests had fair prices, while between 5% and 85% of participants had fair cumulative earnings in both incentive mechanisms.
Safety Requirements Specification and Verification for Railway Interlocking Systems

The integration of formal methods and requirements analysis increases the dependability of safety-critical systems. However it is still very difficult to obtain all of the safety requirements in practice, and formally construct the safety requirements model as well. In this paper, we propose an approach to capture safety requirements and formally describe them by classifying and developing safety requirements specification patterns. Our classification is a result of extracting safety properties from a variety of sources, such as interlocking tables and existing safety relevant functional requirements of railway interlocking system. They contain safety properties at analysis level and design level respectively. Furthermore, safety specification patterns based on the classification are used to formally describe and organize the safety requirements for formal verification. Finally, a tool called SRSV has been developed to enhance the process from deriving safety requirements to verifying. We applied it to the interlocking system at Mohe station in China, and the generated safety properties were then checked to hold by the verification tool.
A novel clustered multi-dictionary code compression method

Code compression is traditionally used to reduce the code size by compressing the codes with higher static frequency. A novel clustered multi-dictionary code compression method is proposed to effectively reduce the memory size which program code stored. We cluster the code set according to the repeat times frequency of different codes in it. Each cluster is compressed with different dictionary and the codeword of each cluster is different. We use shorter codeword to point to the more frequently occurred codes in cluster. By finding the best clusters, the entropy of clustered multi-dictionary code is high and the compression efficiency is the best. Theoretical proof and experimental results show that the compression effect of this method is significant. Code of MiBench benchmark compiled under ARM and MIPS instruction set architecture are compressed with this method and the best compression ratio is 50%.
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— Modulation techniques for multilevel converters can create distorted output voltages and currents if the DC link voltages are unbalanced. This situation can be avoided if the instantaneous DC voltage error is not taken into account in the modulation process. This paper proposes a feed-forward space vector modulation method for a single-phase multilevel cascade converter. Using this modulation technique, the modulated output voltage of the power converter always generates the reference determined by the controller even in worst case voltage unbalance conditions. In addition the possibility of optimizing the DC voltage ratio between the H-bridges of the power converter is introduced. Experimental results from a 5kVA prototype are presented in order to validate the proposed modulation technique.
Design and Evaluation of a Network Processor Accelerator f or Layer Seven Applications

We present a flexible accelerator designed for networking applications. The accelerator can be utilized efficiently by a variety of Network Processor designs. Many Network Processors employ hardware accelerators for implementing key domain-specific tasks. New applications require new tasks, such as pattern matching, to be performed on network packets in real-time. Using our proposed accelerator, we have implemented several such tasks and measured their performance. The accelerator achieves up to 25-fold improvement on the performance of pattern matching, and 12-fold improvement for tree lookup when compared to optimized software solutions. We present delay, area and power requirements of the accelerator for an ASIC design. We also present a reconfigurable design of the accelerator and present a comparison between these implementations. Since the accelerator can be used for different tasks, the hardware requirements are small compared to an accelerator group that implements the same set of tasks. We also present accurate analytic models to estimate the execution time of these networking tasks.
Numerical model of the sound field inside cars for the creation of virtual audible reconstructions

The research undertaken aims to the development of a new numerical predictive technique, capable of producing such a detailed simulation, so that it can be used for the creation of “virtual” sound samples, theoretically indistinguishable from binaural recordings inside a real car. This way, a proposed (not existing yet) sound system could be compared with the existing ones by direct listening tests. This paper describes the implementation of the numerical model, based on Boundary Elements for low frequencies and Pyramid Tracing for higher frequencies. The results of the two models are merged together, producing a binaural impulse response which can be convolved with the music signal. The performances of the system were evaluated by listening tests, in comparison with experimental measurements taken in a 1:5 scale model of a car compartment.
Measuring the Degree of Synonymy between Words Using Relational Similarity between Word Pairs as a Proxy

Two types of similarities between words have been studied in the natural language processing community: synonymy and relational similarity. A high degree of similarity exist between synonymous words. On the other hand, a high degree of relational similarity exists between analogous word pairs. We present and empirically test a hypothesis that links these two types of similarities. Specifically, we propose a method to measure the degree of synonymy between two words using relational similarity between word pairs as a proxy. Given two words, first, we represent the semantic relations that hold between those words using lexical patterns. We use a sequential pattern clustering algorithm to identify different lexical patterns that represent the same semantic relation. Second, we compute the degree of synonymy between two words using an inter-cluster covariance matrix. We compare the proposed method for measuring the degree of synonymy against previously proposed methods on the Miller-Charles dataset and the WordSimilarity-353 dataset. Our proposed method outperforms all existing Web-based similarity measures, achieving a statistically significant Pearson correlation coefficient of 0.867 on the Miller-Charles dataset. key words: synonymy, attributional similarity, relational similarity, Miller-Charles dataset, WordSimilarity-353 dataset
Single machine scheduling problem of minimizing maximum earliness and number of tardy jobs using a genetic algorithm

Single machine scheduling is of essential importance due to its practical applications and the fact that it reduces bottleneck in production lines and provides results that are useful for more complex scheduling environments. In this article, Single machine scheduling problem to minimize the number of tardy jobs and maximum earliness and no slack time is examined. The reason of applying these two criteria simultaneously is the tendency to use the JIT system that finds earlier or later production undesirable. In this study, a genetic algorithm has been proposed to solve the problem. A set of instance problems have been investigated and the results are compared with the results of genetic algorithm used in the research literature. Numerical experiments show better performance of proposed algorithm.
The PIA Project: Learning to Semantically Annotate Texts from an Ontology and XML-Instance Data

The development of the XML and RDF(S) standards offer a positive environment for machine learning to enable the automatic XML-annotation of texts that can encourage the extension of Semantic Web applications. After reviewing the current limitations of information extraction technology, specifically its lack of portability to new domains, we introduce the PIA project for automatically XML-annotating domain-based texts using example XML texts and an ontology for supervised training.
Constraining a System of Interacting Parameterizations through Multiple-Parameter Evaluation: Tracing a Compensating Error between Cloud Vertical Structure and Cloud Overlap

This study explores the opportunities created by subjecting a system of interacting fast-acting parameterizations to long-term single-column model evaluation against multiple independent measurements at a permanent meteorological site. It is argued that constraining the system at multiple key points facilitates the tracing and identification of compensating errors between individual parametric components. The extended time range of the evaluation helps to enhance the statistical significance and representativeness of the single-column model result, which facilitates the attribution of model behavior as diagnosed in a general circulation model to its subgrid parameterizations. At the same time, the high model transparency and computational efficiency typical of single-column modeling is preserved. The method is illustrated by investigating the impact of a model change in the Regional Atmospheric Climate Model (RACMO) on the representation of the coupled boundary layer–soil system at the Cabauw meteorological site in the Netherlands. A set of 12 relevant variables is defined that covers all involved processes, including cloud structure and amplitude, radiative transfer, the surface energy budget, and the thermodynamic state of the soil and various heights of the lower atmosphere. These variables are either routinely measured at the Cabauw site or are obtained from continuous large-eddy simulation at that site. This 12-point check proves effective in revealing the existence of a compensating error between cloud structure and radiative transfer, residing in the cloud overlap assumption. In this exercise, the application of conditional sampling proves a valuable tool in establishing which cloud regime exhibits the biggest impact.
Coherent State Measures and the Extended Dobinski relations

Conventional Bell and Stirling numbers arise naturally in the normal ordering of simple monomials in boson operators. By extending this process we obtain generalizations of these combinatorial numbers, defined as coherent state matrix elements of arbitrary monomials, as well as the associated Dobinski relations. These Bell-type numbers may be considered as power moments and give rise to positive measures which allow the explicit construction of new classes of coherent states.
A Comparison of State-of-The-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection

Several state-of-the-art binary classification techniques are experimentally evaluated in the context of expert automobile insurance claim fraud detection. The predictive power of logistic regression, C4.5 decision tree, k-nearest neighbor, Bayesian learning multilayer perceptron neural network, least-squares support vector machine, naive Bayes, and tree-augmented naive Bayes classification is contrasted. For most of these algorithm types, we report on several operationalizations using alternative hyperparameter or design choices. We compare these in terms of mean percentage correctly classified (PCC) and mean area under the receiver operating characteristic (AUROC) curve using a stratified, blocked, ten-fold cross-validation experiment. We also contrast algorithm type performance visually by means of the convex hull of the receiver operating characteristic (ROC) curves associated with the alternative operationalizations per algorithm type. The study is based on a data set of 1,399 personal injury protection claims from 1993 accidents collected by the Automobile Insurers Bureau of Massachusetts. To stay as close to real-life operating conditions as possible, we consider only predictors that are known relatively early in the life of a claim. Furthermore, based on the qualification of each available claim by both a verbal expert assessment of suspicion of fraud and a ten-point-scale expert suspicion score, we can compare classification for different target/class encoding schemes. Finally, we also investigate the added value of systematically collecting nonflag predictors for suspicion of fraud modeling purposes. From the observed results, we may state that: (1) independent of the target encoding scheme and the algorithm type, the inclusion of nonflag predictors allows us to significantly boost predictive performance; (2) for all the evaluated scenarios, the performance difference in terms of mean PCC and mean AUROC between many algorithm type operationalizations turns out to be rather small; visual comparison of the algorithm type ROC curve convex hulls also shows limited difference in performance over the range of operating conditions; (3) relatively simple and efficient techniques such as linear logistic regression and linear kernel least-squares support vector machine classification show excellent overall predictive capabilities, and (smoothed) naive Bayes also performs well; and (4) the C4.5 decision tree operationalization results are rather disappointing; none of the tree operationalizations are capable of attaining mean AUROC performance in line with the best. Visual inspection of the evaluated scenarios reveals that the C4.5 algorithm type ROC curve convex hull is often dominated in large part by most of the other algorithm type hulls.
The world as a hologram

According to ’t Hooft the combination of quantum mechanics and gravity requires the three‐dimensional world to be an image of data that can be stored on a two‐dimensional projection much like a holographic image. The two‐dimensional description only requires one discrete degree of freedom per Planck area and yet it is rich enough to describe all three‐dimensional phenomena. After outlining ’t Hooft’s proposal we give a preliminary informal description of how it may be implemented. One finds a basic requirement that particles must grow in size as their momenta are increased far above the Planck scale. The consequences for high‐energy particle collisions are described. The phenomenon of particle growth with momentum was previously discussed in the context of string theory and was related to information spreading near black hole horizons. The considerations of this paper indicate that the effect is much more rapid at all but the earliest times. In fact the rate of spreading is found to saturate the bound fro.
Post-Stratification with Optimized Effective Base : Linear and Nonlinear Ridge Regression Approach

Post-stratification, or sample balancing, or raking, is widely utilized in survey research to weighting a sample data to Census or other known population quotas. Cross-tables of counts are used in Deming-Stephan iterative proportional fitting to find the weights for adjusting data to known margins. A bi-criteria objective for finding weights with minimum variance yields a solution with maximum effective sample size. This model can be expressed as a ridge regression, which is applied to the original data, without its collapsing to cross-tables. Linear and nonlinear parameterization models are studied. The explicit regression solution allows to study the weighting analytically, that helps to interpret and improve the sample balance results.