Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian decision making using neural fields

Bayesian statistics has become a popular framework in various fields of experimental psychology such as signal detection theory, speech recognition, cue integration and decision making. However, it is still an open question how the human brain actually incorporates this functionality. One assumption is that the activities of populations of neurons encode probability distributions. Indeed, it has been shown that probabilistic decoding of neuronal activities can be used to predict overt behaviour in rat [5] and monkey [1]. However, in order to make statistically optimal decisions it is not only necessary to represent probability distributions, but also to transform likelihoods into beliefs (via Bayes’ rule) and propagate beliefs between different levels of representation (via the marginalisation rule). Unlike biological neurons, Bayesian statistics does not involve any temporal dynamics. For example, Bayes’ rule will fail for temporally misaligned signals. There are several theoretical extensions like hidden Markov models and dynamic Bayesian networks that remedy this situation, but again it is unclear how such mechanisms could be incorporated in the human brain. Another approach is to implement decision making using neural fields, which have been applied successfully for explaining behavioural data [2, 6] and, in robotics, for dealing with complex uncertain environments [4]. Neural fields posses spatial structure and temporal dynamics similar to biological neural populations and, thus, provide temporal dynamics in a natural way. However, they do not necessarily incorporate Bayesian statistics. Here we show how two fundamental statistical laws (Bayes’ rule and the marginalisation rule) can be implemented using neural fields. The result is a truly dynamic framework capable of Bayesian decision making,
General Two-Party Oblivious Circuit Evaluation

A two-party oblivious circuit evaluation (2P- OCE) protocol is a way for a party Alice owns a secret x and another party Bob owns a secret y to compute the value of an agreed upon function f(x,y), where f can be computed by a polynomial sized Boolean circuit. This is done in such a way that Alice learns nothing about y and Bob learns nothing about x, except for what can be inferred from one's private input and the public value of f(x,y). This paper presents a general, correct, fair, honest, and maximum privacy 2P-OCE protocol that is based on a set of reductions to more simple cryptographic primitives. The protocol uses the primitives of one-out-of-two oblivious transfer (1-2-OT) and bit commitment (BC) as black boxes. Consequently, the protocol may be implemented with or without computational assumptions, depending on the type of 1-2-OT and BC used by participants.
Electronic Communications of the EASST Volume X ( 2010 ) Proceedings of the Fourth International Workshop on Graph-Based Tools ( GraBaTs 2010 ) Neighbourhood Abstraction in GROOVE — Tool Paper

In this paper we discuss the implementation of neighbourhood graph abstraction in the GROOVE tool set. Important classes of graph grammars may have unbounded state spaces and therefore cannot be verified with traditional model checking techniques. One way to address this problem is to perform graph abstraction, which allows us to generate a finite abstract state space that over-approximates the original one. In previous work we presented the theory of neighbourhood abstraction. In this paper, we present the implementation of this theory in GROOVE and illustrate its applicability with a case study that models a single-linked list.
Combining ab initio and density functional theories with semiempirical methods

For large reactive systems, the calculation of energies can be simplified by treating the active part with a high-level quantum mechanical (QM) (ab initio or density functional) approach and the environment with a less sophisticated semiempirical (SE) approach, as an improvement over the widely used hybrid quantum mechanical/molecular mechanical (QM/MM) methods. An example is the interaction between an active region of an enzyme and its immediate environment. One such method is the original “Our-own-N-layer Integrated molecular Orbital+Molecular Mechanics (ONIOM)” approach. In this paper, the interaction between the QM and SE region is described explicitly by two different schemes. In the iterative QM/SE schemes (QM/SE-I), the electrostatic interaction and polarization effects are introduced explicitly for both the QM and SE atoms by a self-consistent procedure based on either polarizable point charges or the electron density. In the noniterative QM/SE scheme, based on the ONIOM model (QM/SE-O), the excha.
Flexible Gesture Recognition for Immersive Virtual Environments

With powerful graphics hardware becoming affordable for everyone, there is an increasing tendency towards a new generation of user interfaces, with the focus shifting from traditional two-dimensional desktops to three-dimensional virtual environments. Therefore, there is a growing need for applicable immersive interaction metaphors to manipulate these environments. In our paper we propose a gesture recognition engine using an inexpensive data glove with integrated 6 DOF tracking. Despite of noisy input data from the glove, we are able to achieve reliable and flexible gesture recognition. New gestures can be trained easily and existing gestures can be individually adapted for different users
Tracking Groups of People in Presence of Occlusion

This paper addresses the problem of people group tracking in presence of occlusion as people form groups, interact within groups or leave groups. Foreground objects (a person or a group of people) from two consecutive frames are matched based on appearance (RGB histogram) and object location (2D region) similarity. While tracking, this method determines and handles some events such as objects merging and splitting using forward and backward matching matrices. The experimental results show that the proposed algorithm is efficient to track group of people in cluttered and complex environments even when total or partial occlusion occurs.
Multimodal Building of Monolingual Dictionaries for Machine Translation by Non-Expert Users

This paper explores a new approach to help non-expert users with no background in linguistics to add new words to a monolingual dictionary in a rule-based machine translation system. Our method aims at obtaining the correct paradigm which explains not only the particular surface form introduced by the user, but also the rest of inﬂected forms of the word. An initial set of potential paradigms is automatically obtained and then interactively reﬁned by the user with a novel graphical interface through active machine learning. We show the promising results of experiments performed with a Spanish monolingual dictionary.
ICA Based Automatic Segmentation of Dynamic ${\bf H}_{\bf 2}^{\bf 15}{\bf O}$ Cardiac PET Images

In this study, we applied an iterative independent component analysis (ICA) method for the separation of cardiac tissue components (myocardium, right, and left ventricle) from dynamic positron emission tomography (PET) images. Previous phantom and animal studies have shown that ICA separation extracts the cardiac structures accurately. Our goal in this study was to investigate the methodology with human studies. The ICA separated cardiac structures were used to calculate the myocardial perfusion in two different cases: 1) the regions of interest were drawn manually on the ICA separated component images and 2) the volumes of interest (VOI) were automatically segmented from the component images. For the whole myocardium, the perfusion values of 25 rest and six drug-induced stress studies obtained with these methods were compared to the values from the manually drawn regions of interest on differential images. The separation of the rest and stress studies using ICA-based methods was successful in all cases. The visualization of the cardiac structures from H 2 15 O PET studies was improved with the ICA separation. Also, the automatic segmentation of the VOI seemed to be feasible.
The -Version of the Cramér-von Mises Test for Two-Sample Comparisons in Microarray Data Analysis

Distribution-free statistical tests offer clear advantages in situations where the exact unadjusted -values are required as input for multiple testing procedures. Such situations prevail when testing for differential expression of genes in microarray studies. The Cramér-von Mises two-sample test, based on a certain -distance between two empirical distribution functions, is a distribution-free test that has proven itself as a good choice. A numerical algorithm is available for computing quantiles of the sampling distribution of the Cramér-von Mises test statistic in finite samples. However, the computation is very time- and space-consuming. An counterpart of the Cramér-von Mises test represents an appealing alternative. In this work, we present an efficient algorithm for computing exact quantiles of the -distance test statistic. The performance and power of the -distance test are compared with those of the Cramér-von Mises and two other classical tests, using both simulated data and a large set of microarray data on childhood leukemia. The -distance test appears to be nearly as powerful as its counterpart. The lower computational intensity of the -distance test allows computation of exact quantiles of the null distribution for larger sample sizes than is possible for the Cramér-von Mises test.
PAC-Bayesian inductive and transductive learning

We present here a PAC-Bayesian point of view on adaptive supervised classification. Using convex analysis, we show how to get local measures of the complexity of the classification model involving the relative entropy of posterior distributions with respect to Gibbs posterior measures. We discuss relative bounds, comparing two classification rules, to show how the margin assumption of Mammen and Tsybakov can be replaced with some empirical measure of the covariance structure of the classification model. We also show how to associate to any posterior distribution an {\em effective temperature} relating it to the Gibbs prior distribution with the same level of expected error rate, and how to estimate this effective temperature from data, resulting in an estimator whose expected error rate adaptively converges according to the best possible power of the sample size. Then we introduce a PAC-Bayesian point of view on transductive learning and use it to improve on known Vapnik's generalization bounds, extending them to the case when the sample is independent but not identically distributed. Eventually we review briefly the construction of Support Vector Machines and show how to derive generalization bounds for them, measuring the complexity either through the number of support vectors or through transductive or inductive margin estimates.
Soft switching three level inverter (S3L inverter)

The Soft Switching Three Level Inverter - abbreviated to S3L Inverter - was first introduced in 2011. It is a novel circuit topology for PWM inverters, whose areas of application include electrical drives and grid-tie inverters for photovoltaic installations and wind power plants, and for power supplies. It is of very simple design and therefore inexpensive. It is implemented completely as soft switching and hence offers very low switching losses. This means that its efficiency is very high, and it can achieve very high values for the switching frequency. This paper begins by describing how the device functions. It then goes on to discuss specialised methods of control. A variant of the S3L Inverter with a deactivatable snubber circuit is presented. A quantitative comparison between a well-known hard switching NPC 3-level inverter and the novel S3L Inverter using 90 kVA (three-phase) prototypes illustrates the advantages of the S3L inverter.
Rule-by-rule input significance analysis in fuzzy system modeling

Input or feature selection is one the most important steps of system modeling. Elimination of irrelevant variables can save time, money and can improve the precision of model that we are trying to discover. In fuzzy system modeling (FSM) approaches, input selection plays an important role too. The input selection algorithms that are under our investigation did not consider one crucial fact. An input variable may of may not be significant in a specific rule, not in overall system. In this paper, an input selection algorithm that takes this observation into account is proposed as an extension of the input selection algorithms found in the literature. The proposed algorithm is applied on a nonlinear function and successful results are achieved.
Melting of PP/PA6 Polymer Blends in Single Screw Extruders – An Experimental Study

A prototype modular single screw extruder fitted with a screw extracting device is used to monitor melting of an immiscible polymer blend (PP/PA6, with different weight ratios) in this widely used processing equipment. As anticipated, the phenomena observed are much more complex than those involved in extruding PP or PA6, when the well known Maddock/Tadmor mechanism is valid. Consequently a hybrid melting mechanism, involving Maddock/Tadmor and Dispersive melting sequences, is proposed.
NFC Attacks Analysis and Survey

The NFC (Near Field Communication) became a popular short distance wireless communication technology by providing many convenient services such as an easy payment in a store. It has fast connection ability between devices and provides certain secure communication. However, after studying NFC protocols deeply, we found six threats to protecting the confidential data and users' privacy are unsolved. This paper summaries four standards of NFC protocols, and presents the security issues and unsolved privacy threats in current application environment.
Leisure Time Activities as Cognitive Training: Can Cognitive Fitness Improve Well-Being of Older Adults?

Economic reports show that traditional economic variables are becoming insufficient in indicating nation’s progress and that self-reported well-being is emerging as an important indicator of nation’s prosperity and quality of life of its inhabitants. Since average life expectancy is consistently increasing all over the world, practitioners and scholars are increasingly confronted with the question of what can be done to improve the well-being of growing elderly population. An often highlighted characteristic of older adults is cognitive decline, and interventions aimed at improving or maintaining cognitive abilities of elderly are being extensively designed. In this chapter we will review studies indicating a link between cognition and well-being in aging and advocate (long-term) involvement in leisure activities as a form of cognitive training. Apart from its availability to a much wider audience than those participating in cognitive interventions, a key argument in favor of leisure activities is its unambiguous positive association with well-being.
SmallCAN - A Reliable, Low-Power and Low-Cost Distributed Embedded System for Energy Efﬁcient Building Automation

—In this paper we propose a new reliable, low- power and low-cost platform for a building automation system, BAS, which is suitable to be used as a single platform for all appliances of a building. Furthermore today’s platforms for building automation do not focus on low-energy consumption of the platform itself. The costs are also quite high and the installation has limited ﬂexibility and needs extensive planning. The proposed platform features low-power consumption, has a reliable wired structure which is ﬂexible, simply structured and can easily be extended. The low-power and low-cost approach targets the wide spreading of smart buildings to maximize global energy saving and to easily interface with a future smart grid. The SmallCAN platform has shown during evaluation in our demonstrator that it is able to fullﬁl all identiﬁed requirements of a modern building automation system.
Incident chronic kidney disease and the rate of kidney function decline in individuals with hypertension

Background. Little is known about the decline of kidney function in patients with normal kidney function at baseline. Our objectives were to (i) identify predictors of incident chronic kidney disease (CKD) and (ii) to estimate rate of decline in kidney function. Methods. The study used a retrospective cohort of adult patients in a hypertension registry in an inner-city health care delivery system in Denver, Colorado. The primary outcome was development of incident CKD, and the secondary outcome was rate of change of estimated glomerular filtration rate (eGFR) over time. Results. After a mean follow-up of 45 months, 429 (4.1%) of 10 420 patients with hypertension developed CKD. In multivariate models, factors that independently predicted incident CKD were baseline age [odds ratio (OR) 1.13 per 10 years, 95% confidence interval (CI), 1.03–1.24], baseline eGFR (OR 0.69 per 10 units, 95% CI 0.65–0.73), diabetes (OR 3.66, 95% CI 2.97–4.51) and vascular disease (OR 1.67, 95% CI 1.32–2.10). We found no independent association between age, gender or race/ethnicity and eGFR slope. In patients who did not have diabetes or vascular disease, eGFR declined at 1.5 mL/min/1.73 m2 per year. Diabetes at baseline was associated with an additional decline of 1.38 mL/min/1.73 m2. Conclusions. Diabetes was the strongest predictor of both incident CKD as well as eGFR slope. Rates of incident CKD or in decline of kidney function did not differ by race or ethnicity in this cohort.
A customized fastslam algorithm using scanning laser range finder in structured indoor environments

This paper presents a customized FastSLAM algorithm that takes both corners and line segments as map features to perform simultaneous localization and mapping. Unlike the conventional SLAM or FastSLAM algorithms, which only extract corner or point features and have a biased priority given to localization rather than mapping, the line segment features in this new algorithm naturally form a meaningful map of the indoor environment. In addition, using a variety of features is believed to result in a more robust performance. The proposed algorithm is tested based on measurements from a Hokuyo scanning laser range finder mounted on an quadrotor UAV developed by the UAV Research Team from the National University of Singapore.
Classroom and simulation team training: a randomized controlled trial.

OBJECTIVE To test the hypotheses that classroom and simulation-based crew resource management (CRM) training interventions improve teamwork attitudes and behaviours of participants and that classroom training combined with simulation-based training provide synergistic improvements. DESIGN A randomized controlled trial. SETTING Area Health Service in New South Wales, Australia. PARTICIPANTS A total of 157 doctors, nurses and midwives randomized into one of four groups, consisting of three intervention groups and a control group. INTERVENTION One-day CRM-based classroom course; one-day CRM style simulation-based training or classroom training followed by simulation-based training. MAIN OUTCOME MEASURES Pre- and post-test quantitative participant teamwork attitudes, and post-test quantitative trainee reactions, knowledge and behaviour. RESULTS Ninety-four doctors, nurses and midwives completed pre-intervention attitude questionnaires and 60 clinicians completed post-intervention assessments. No positive changes in teamwork attitudes were found associated with classroom or simulation training. Positive changes were found in knowledge (mean difference 1.50, 95% confidence interval (CI) 0.58-2.43, P = 0.002), self-assessed teamwork behaviour (mean difference 2.69, 95% CI 0.90-6.13, P = 0.009) and independently observed teamwork behaviour (mean difference 2.30, 95% CI 0.30-4.30, P = 0.027) when classroom-only trained group was compared with control; however, these changes were not found in the group that received classroom followed by simulation training. CONCLUSIONS Classroom-based training alone resulted in improvements in participant knowledge and observed teamwork behaviour. The study found no additional impact of simulation training.
Avoiding the Security Attacks by Hardening the Cloud

A type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications is Cloud Computing. Cloud computing is set of resources and services offered through the Internet. Cloud services are delivered from data centers located throughout the world. Cloud computing facilitates its consumers by providing virtual resources via internet. The rapid growth in field of "cloud computing" also increases severe security concerns. Security is needed not only for services provided over cloud but also for the cloud resources. Providing services over cloud implies providing secured services internally, which may be subjected to so many threats while providing over cloud, some of threats are old and can be handled and some may be new and unable to handle. Hence, instead of securing cloud and its services from threats a mechanism to prevent the threats is desired. Here, an attempt is made to propose the mechanism for hardening the cloud to minimize security attacks. User authentication is often needed in cloud environment as for the service provider to verify who can access the services. Hardening the cloud is done in 2 levels, one at the service provisioning level and the other at cloud resource level. All services (application, platform and infrastructure) are provided with the help of web services; a hacker can hack the SOAP (Simple Object Access Protocol) protocol of the cloud consumer while requesting the service and change the header information of the SOAP so that he can get the service on consumer id. Hence, every service that is provided over cloud needs to be authenticated. Therefore, an efficient authentication protocol is needed to avoid false service provisioning which is one of the problems that is concentrated in this paper. Introducing new Bio-Metric Authentication for accessing the cloud resources as well as services will definitely reduce the security risks. The authentication process is based on two credential information: (1) biometric feature vector of user and (2) the verification code.
An effective semantic search technique using ontology

In this paper, we present a semantic search technique considering the type of desired Web resources and the semantic relationships between the resources and the query keywords in the ontology. In order to effectively retrieve the most relevant top-k resources, we propose a novel ranking model. To do this, we devise a measure to determine the weight of the semantic relationship. In addition, we consider the number of meaningful semantic relationships between a resource and keywords, the coverage of keywords, and the distinguishability of keywords. Through experiments using real datasets, we observe that our ranking model provides more accurate semantic search results compared to existing ranking models.
Introduction. Neuroeconomics: the promise and the profit

Neuroeconomics investigates the neural mechanisms underlying decisions about rewarding or punishing outcomes (‘economic’ decisions). It combines the knowledge about the behavioural phenomena of economic decisions with the mechanistic explanatory power of neuroscience. Thus, it is about the neurobiological foundations of economic decision making. It is hoped that by ‘opening the box’ we can understand how decisions about gains and losses are directed by the brain of the individual decision maker. Perhaps we can even learn why some decisions are apparently paradoxical or pathological. The knowledge could be used to create situations that avoid suboptimal decisions and harm.