Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel MAC scheme for solving the QoS parameter adjustment problem in IEEE 802.11e EDCA

We present a novel MAC scheme for solving the QoS parameter adjustment problem in IEEE 802.11e EDCA. The default values of the QoS parameters in EDCA, contention window (CW), arbitration Interframe Space (IFS) etc only yield good performance for few scenarios. We first propose a simple adaptation scheme, called a-EDCA, where the access point adapts the CWs based on the network conditions. The main contribution of this work is the second approach, called i-EDCA, where we make modifications to the backoff phase of EDCA and introduce a random IFS scheme. Simulation results show that both approaches result in stable capacity ratios between priority classes and very high channel utilization, compared to EDCA, when the number of stations is increased. Moreover, i-EDCA yields close to optimal channel utilization for a large number of scenarios without any need of adapting the parameters. In i-EDCA, the capacity for each class is directly proportional to its CW. Results also show that i-EDCA has improved fairness and prevents low priority classes to be starved under higher loads, a problem found in EDCA.
Motion-based segmentation by principal singular vector (PSV) clustering method

Motion-based segmentation has attracted a lot of attention. The task of identifying independent objects is called segmentation. Motion-based segmentation has a broad video application domain. An approach based on principal singular vectors (PSVs) of the image measurement matrix was proposed for separating independent moving objects in Kung and Yun-Ting Lin (1995). After applying SVD (singular value decomposition), feature blocks with different object-based motions tend to form separate clusters on the PSV space. Therefore, a frame can be divided into regions each with consistent motion. Our approach offers several additional features: (1) a multi-candidate feature tracker is adopted. (2) Multiple frames are utilized to facilitate motion-based separation. (3) We would like to achieve not only accurate motion estimation, but also the object regions should retain some neighborhood property (to save the bits for the coding boundary). For this, a neighborhood sensitivity parameter /spl delta/ is introduced. One application of motion-based segmentation is low-bit-rate video compression. In very low bit-rate video coding, only motion vectors of finite regions and the region boundary (coded in prediction error) need to be transmitted. Yet simulations yield quite respectable compensated frames.
Two-way Parallel Class Expression Learning

In machine learning, we often encounter datasets that can be described using simple rules and regular exception patterns describing situations where those rules do not apply. In this paper, we propose a two-way parallel class expression learning algorithm that is suitable for this kind of problem. This is a top-down renement-based class expression learning algorithm for Description Logic (DL). It is distinguished from similar DL learning algorithms in the way it uses the concepts generated by the renement operator. In our approach, we unify the computation of concepts describing positive and negative examples, but we maintain them separately, and combine them at the end. By doing so, we can avoid the use of negation in the renement without any loss of generality. Evaluation shows that our approach can reduce the search space signicantly, and therefore the learning time is reduced. Our implementation is based on the DL-Learner framework and we inherit the Parallel Class Expression Learning (ParCEL) algorithm design for parallelisation.
Association of the GCKR rs780094 polymorphism with metabolic traits including carotid intima-media thickness in Japanese community-dwelling men, but not in women

Abstract Background: The glucokinase regulator gene (GCKR) rs780094 has been shown to be strongly associated with some metabolic traits and atherosclerotic parameters, while the association between GCKR rs780094 and carotid intima-media thickness (CIMT) has not been fully investigated in the general population. The associations between the GCKR rs780094 genotype and metabolic traits including CIMT were examined in a Japanese community-dwelling population. Methods: A total of 2491 Japanese adults (907 men and 1584 women) who participated in a medical screening program for the general population from 29 to 94 years of age during 2008 to 2010 were enrolled. GCKR rs780094 was genotyped by the TaqMan polymerase chain reaction method, and associations with metabolic markers including CIMT were evaluated. Results: GCKR rs780094 AA genotype was significantly associated with higher TG (p<0.001 vs. GG), lower HDL-C (p=0.021 vs. GG), and lower HbA1c(p=0.023 vs. GG). The AA genotype showed significantly thinner CIMT (p=0.001 vs. GX). These associations were seen only in men. Conclusions: GCKR rs780094 was associated with TG, HDL-C, and HbA1c levels, as well as with CIMT in Japanese community-dwelling men, but not women.
Perceptions and realities: The main sources of information for non-Muslim Australians about the religion of Islam and Muslim people

In the context that Australian journalists and the Australian public are largely ignorant of Islam and Muslims and where racism and Islamophobia are historically rooted and persistent, this study reports on where Australians source their information about Islam and Muslims. In this study, we find that while a sample of Australian journalists believe overall that 80% of the Australian public relies on the mainstream news media as a primary source of information about the Islamic faith and its followers, this is a significant overestimation. This study identifies that the mainstream news media is a primary source of information about the religion of Islam and Muslim people for around half of Australia’s non-Muslim population (49.27% and 45.89%, respectively). Muslim people, books, social media and family and friends are also important primary sources of information. In identifying the main sources of information about Islam and Muslims in Australia, this study answers the question posed by social geographer Kevin Dunn more than a decade ago. By comparing perceptions with reality, we find the mainstream news media are not as significant a source of information for non-Muslim Australians about Islam and Muslims as our sample of journalists believe them to be. This opens up the space for policymakers to look for new and targeted ways (beyond improving the ways the news media report stories about Islam and Muslims) to normalise Islam as a religion of peace and further work to combat entrenched Islamophobia, key among them are Muslim people.
Pengaruh Pengetahuan Tentang Lingkungan, Sikap pada Lingkungan, dan Norma Subjektif terhadap Niat Pembelian Green Product

Purpose of this research to analyze the effect of knowledge about environment, attitude to the environment and subjective norms on purchase intention on green product. This study is a survey. The population is a consumer aged 17 years and over who know and understand about the green product in Surakarta. Sample of 100 people with convenience sampling data. Type data used quantitative and qualitative data. Source of data used primary data and secondary data. The technique of collecting data used questionnaires and literature. Data were analyzed used multiple linear regression. The results showed that the knowledge of the environment a significant effect on purchase intention green product. Attitude to the environment a significant effect on purchase intention green product. Subjective norms a significant effect on purchase intention green product. Attitude to the environment is the dominant variable effect on purchase intention green product. Keywords: knowledge about environment, attitude to the environment, subjective norms and purchase intention green product.
Design of signal-adapted biorthogonal filter banks

A method for the design of signal-adapted, M-channel biorthogonal filter banks of finite length is presented. The design problem is formulated as a constrained optimization problem and is solved by converting it into an iterative line-search problem through a first-order parameterization of the perfect reconstruction (PR) constraint. It is also shown for the two-channel case that if the analysis and synthesis lowpass filters are of different lengths, a refinement of the algorithm is possible that leads to a solution in a very small neighborhood of a local minimizer, which satisfies the PR constraint precisely.
Scalar field haloes as gravitational lenses

A non-topological soliton model with a repulsive scalar self-interaction of the Emden type provides a constant-density core, similarly as the empirical Burkert profile of dark matter (DM) haloes. As a further test, we derive the gravitational lens properties of our model, in particular, the demarcation curves between ‘weak’ and ‘strong’ lensing. Accordingly, strong lensing with typically three images is almost three times more probable for our solitonic model than for the Burkert fit. Moreover, some prospective consequences of a possible flattening of DM haloes are indicated.
Applying Feature Extraction for Classification Problems

With the wealth of image data that is now becoming increasingly accessible through the advent of the world wide web and the proliferation of cheap, high quality digital cameras it is becoming ever more desirable to be able to automatically classify images into appropriate categories such that intelligent agents and other such intelligent software might make better informed decisions regarding them without a need for excessive human intervention. However, as with most Artificial Intelligence (A.I.) methods it is seen as necessary to take small steps towards your goal. With this in mind a method is proposed here to represent localised features using disjoint sub-images taken from several datasets of retinal images for their eventual use in an incremental learning system. A tile-based localised adaptive threshold selection method was taken for vessel segmentation based on separate colour components. Arteriole-venous differentiation was made possible by using the composite of these components and high quality fundal images. Performance was evaluated on the DRIVE and STARE datasets achieving average specificity of 0.9379 and sensitivity of 0.5924.
The effect of apoE genotype and sex on ApoE plasma concentration is determined by dietary fat in healthy subjects

The interindividual variation in ApoE plasma concentration is considerable, mainly determined by apoE genotype and sex. However, a large amount of variability remains unexplained by these factors. We have evaluated whether the quantity and quality of dietary fat interacts with the apoE genotype and sex modifying ApoE plasma levels in young healthy subjects. Eighty-four volunteers (sixty-six apoE3/3, eight apoE4/3 and ten apoE3/2) were subjected to three dietary periods, each lasting 4 weeks. The first was a SFA-enriched diet (38 % fat and 20 % SFA), which was followed by a carbohydrate (CHO)-rich diet (30 % fat, < 10 % SFA and 55 % carbohydrate) or a MUFA-rich diet (38 % fat and 22 % MUFA) following a randomised crossover design. apoE2 carriers have the highest ApoE levels, whereas apoE4 individuals show the lowest concentration after the SFA, CHO and MUFA diets. Women had significantly higher ApoE concentration than men only after the consumption of the SFA diet. The SFA diet increased the ApoE plasma concentration when compared with the CHO- and MUFA-rich diets in women, but not in men. In women, but not in men, the shift from the SFA- to CHO- or MUFA-rich diets significantly decreased the ApoE concentration in apoE3/2 and apoE3/3 subjects, whereas no differences were observed in women with the apoE4/3 genotype. Sex and apoE genotype determine ApoE plasma levels; however, this effect is dependent on dietary fat.
Evaluating reward and risk for vertical selection

The aggregation of search results from heterogeneous verticals (news, videos, blogs, etc) has become an important consideration in search. When aiming to select suitable verticals, from which items are selected to be shown along with the standard "ten blue links", there exists the potential to both help (selecting relevant verticals) and harm (selecting irrelevant verticals) the existing result set. In this paper, we present an approach that considers both reward and risk within the task of vertical selection (VS). We propose a novel risk-aware VS evaluation metric that incorporates users' risk-levels and users' individual preference of verticals. Using the proposed metric, we present a detailed analysis of both reward and risk of current resource selection approaches within a multi-label classification framework. The results bring insights into the effectiveness and robustness of current vertical selection approaches.
Reducing CTL-live Model Checking to Semantic Entailment in First-Order Logic ( Version 1 )

The core of temporal logic model checking is the reachability problem, which is not expressible in first-order logic (FOL). Most model checking algorithms, both for finite and infinite Kripke structures, contain a loop that iterates to reach a fixed-point. As a result, reasoners with input languages no more expressive than FOL have been used iteratively for model checking rather than having the reasoner solve the problem completely by itself. In this article, we present a method for reducing model checking of finite and infinite Kripke structures that are expressed in FOL to entailment checking in FOL for a fragment of computational tree logic (CTL), which we call CTL-live. CTL-live includes all the CTL connectives that are expressible in the mu-calculus using the least fixed-point operator. These connectives are traditionally used to express liveness properties. This reduction allows us to consider model checking of CTL-live as a FOL theorem proving problem, and to use directly FOL reasoning techniques for model checking without the need of fixed-point operators, transitive-closure, or induction. We prove that CTL-live is maximal in the sense that model checking of CTL connectives that are not included in CTL-live is not reducible to semantic
The Development of Type Systems for Object-Oriented Languages

This paper, which is partly tutorial in nature, summarizes some basic research goals in the study and development of typed object-oriented programming languages. These include both immediate repairs to problems with existing languages and the long-term development of more flexible and expressive, yet type-safe, approaches to program organization and design. The technical part of the paper is a summary and comparison of three object models from the literature. We conclude by discussing approaches to selected research problems, including changes in the type of a method from super class to sub class and the use of types that give information about the implementations as well as the interfaces of objects. Such implementation types seem essential for adequate typing of binary operations on objects, for example.
Differential Prior Voluntary Disclosure Levels and Market Reaction to Interim Earnings Announcements *

This study examines the relationship between cross-sectional differences in prior voluntary disclosure levels and the market reaction to a forthcoming earnings announcement. Based on the Kim and Verrecchia (1991) framework, firms with higher levels of preannouncement voluntary disclosure are found to have less price and volume reaction about a forthcoming earnings announcement after controlling for other determinants such as firm size, level of unexpected earnings and the precision of the announced information. This suggests that increased voluntary disclosure by firms improves the market’s preannouncement information relative to a forthcoming announcement, resulting in less revision of expectations around the earnings announcement. However, the strength of the negative relationship between prior levels of voluntary disclosure and the market reaction appears to be decreasing as the amount of analyst following increases, suggesting that financial analysts’ information output and management’s voluntary disclosures are competing information sources for investors. Further analysis suggests that forward-looking disclosure appears more relevant to investors as compared to general business data disclosure and the management’s discussion and analysis of past operations.
How Nursing Students Perceive Neurofeedback Training as an Independent Nursing Intervention: Pilot Study Focusing on Attitude

The purpose of this study was to evaluate the practical feasibility of neurofeedback training (NFT) as an independent nursing intervention and to find out predictive factors of participatory practice in NFT. Data were collected from 250 nursing students using self-reports. The questionnaire was based on the Knowledge-Attitude-Practice (KAP) survey model using Delphi methodology with 3-round surveys. The standardized contents of scale were categorized into three domains, seven subgroups, and a total of 57 items. The data were analyzed using a t-test, ANOVA, a Scheffé test, Pearson's correlation coefficients, a regression analysis, and a Sobel test. Students' knowledge level was at 57%, a low score when compared to attitude and practice. Major barriers to NFT were associated with negative attitudes such as fear of side effects or complications, or using a computerized approach. The attitude toward NFT had a complete mediating effect on the relationship between knowledge and practice (R 2 =.43; Z=7.44, p<.001). In order for students to be able to apply NFT in the clinical field after graduation, a positive attitude should be cultivated in regular nursing school curriculum. We suggest that NFT can act as a positive nursing intervention if students ’ positive attitudes are increased. Future research on a variety of types of NFT is needed. It is the condensed version of the full text of this article.
Origin of volatiles in the main belt

We propose a scenario for the formation of the main belt in which asteroids incorporated icy particles formed in the outer solar nebula. We calculate the composition of icy planetesimals formed beyond a heliocentric distance of 5 au in the nebula by assuming that the abundances of all elements, in particular that of oxygen, are solar. As a result, we show that ices formed in the outer solar nebula are composed of a mix of clathrate hydrates, hydrates formed above 50 K and pure condensates produced at lower temperatures. We then consider the inward migration of solids initially produced in the outer solar nebula and show that a significant fraction may have drifted to the current position of the main belt without encountering temperature and pressure conditions high enough to vaporize the ices they contain. We propose that, through the detection and identification of initially buried ices revealed by recent impacts on the surfaces of asteroids, it could be possible to infer the thermodynamic conditions that were present within the solar nebula during the accretion of these bodies, and during the inward migration of icy planetesimals. We also investigate the potential influence that the incorporation of ices in asteroids may have on their porosities and densities. In particular, we show how the presence of ices reduces the value of the bulk density of a given body, and consequently modifies its macroporosity from that which would be expected from a given taxonomic type.
A Novel Framework of Software Reliability Evaluation with Software Reliability Growth Models and Software Metrics

This paper proposes a novel framework of software reliability growth models with software metrics. Our approach is to integrate a classical Poisson-regression-based fault prediction with non-homogeneous Poisson process based software reliability growth models. The remarkable feature of this approach is to handle time series data of fault detections and software metrics for a number of modules at the same time. In the paper, we present the modeling framework that combines Poisson-regression-based fault prediction and software reliability growth models, and also develop an efficient algorithm to estimate model parameters based on EM (expectation-maximization) algorithm. In numerical experiments, by comparing the proposed model with both Poisson-regression-based fault prediction and non-homogeneous Poisson process based software reliability growth models, we discuss the effectiveness of using time series data of fault detections and software metrics from both viewpoints of reliability estimation and fault prediction.
Increasing the milling accuracy for industrial robots using a piezo-actuated high-dynamic micro manipulator

The strong process forces arising during highspeed machining operations, combined with the limited stiffness of industrial robots, have hampered the usage of industrial robots in high-end milling tasks. However, since such manipulators may offer flexible and cost-effective machining solutions, a three-dimensional piezo-actuated compensation mechanism, which aims to compensate for the positioning errors of the robot, has earlier been developed. A prototype model-based control scheme for position control of the mechanism, utilizing LQG control, has been proposed. The main contribution of this paper is an experimental verification of the benefit of utilizing the online compensation scheme. We show that the milling accuracy achieved with the proposed compensation mechanism is increased up to three times compared to the uncompensated case.
Opinion Dynamics under Heterogeneous Bounds of Con(cid:12)dence for the Agents

A group of m agents is to (cid:12)nd a common agreement about a certain issue. An opinion is represented by a real number. We assume that each agent changes his opinion by repeated averaging over the opinions of his agents of con(cid:12)dence. An agent i should be an agent of con(cid:12)dence for agent j , if the opinion of agent i lies within the con(cid:12)dence interval of agent j , which is an interval with the opinion of j in the center. This iterated process is called opinion formation under bounded con(cid:12)dence. In this paper we model heterogenity of agents by admitting di(cid:11)erences in the sizes of the con(cid:12)dence intervals for each agent. We examine what happens if some of the agents have higher/lower bounds of con(cid:12)dence than the other agents. We focus on how the possibility of reaching a consensus among all agents changes through heterogenity. Interestingly there are both, positive and negative e(cid:11)ects. Simulations show that the positive e(cid:11)ects might be bigger. In addition, we present some mathematical results about the convergence of this kind of opinion formation.
Studying microarchitectural structures with object code reordering

Modern microprocessors have many microarchitectural features. Quantifying the performance impact of one feature such as dynamic branch prediction can be difficult. On one hand, a timing simulator can predict the difference in performance given two different implementations of the technique, but simulators can be quite inaccurate. On the other hand, real systems are very accurate representations of themselves, but often cannot be modified to study the impact of a new technique. We demonstrate how to develop a performance model for branch prediction using real systems based on object code reordering. By observing the behavior of the benchmarks over a range of branch prediction accuracies, we can estimate the impact of a new branch predictor by simulating only the predictor and not the rest of the microarchitecture. We also use the reordered object code to validate a reverse-engineered model for the Intel Core 2 branch predictor. We simulate several branch predictors using Pin and measure which hypothetical branch predictor has the highest correlation with the real one. This study in object code reorder points to way to future work on estimating the impact of other structures such as the instruction cache, the second-level cache, instruction decoders, indirect branch prediction, etc.