Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential Functionality of Right and Left Parietal Activity in Controlling a Motor Vehicle

Driving a motor vehicle is an inherently complex task that requires robust control to avoid catastrophic accidents. Drivers must maintain their vehicle in the middle of the travel lane to avoid high speed collisions with other traffic. Interestingly, while a vehicle’s lane deviation (LD) is critical, studies have demonstrated that heading error (HE) is one of the primary variables drivers use to determine a steering response, which directly controls the position of the vehicle in the lane. In this study, we examined how the brain represents the dichotomy between control/response parameters (heading, reaction time (RT), and steering wheel corrections) and task-critical parameters (LD). Specifically, we examined electroencephalography (EEG) alpha band power (8–13 Hz) from estimated sources in right and left parietal regions, and related this activity to four metrics of driving performance. Our results demonstrate differential task involvement between the two hemispheres: right parietal activity was most closely related to LD, whereas left parietal activity was most closely related to HE, RT and steering responses. Furthermore, HE, RT and steering wheel corrections increased over the duration of the experiment while LD did not. Collectively, our results suggest that the brain uses differential monitoring and control strategies in the right and left parietal regions to control a motor vehicle. Our results suggest that the regulation of this control changes over time while maintaining critical task performance. These results are interpreted in two complementary theoretical frameworks: the uncontrolled manifold and compensatory control theories. The central tenet of these frameworks permits performance variability in parameters (i.e., HE, RT and steering) so far as it does not interfere with critical task execution (i.e., LD). Our results extend the existing research by demonstrating potential neural substrates for this phenomenon which may serve as potential targets for brain-computer interfaces that predict poor driving performance.
An O(N²) Square Root Unscented Kalman Filter for Visual Simultaneous Localization and Mapping

This paper develops a square root unscented Kalman filter (SRUKF) for performing video-rate visual simultaneous localization and mapping (SLAM) using a single camera. The conventional UKF has been proposed previously for SLAM, improving the handling of nonlinearities compared with the more widely used extended Kalman filter (EKF). However, no account was taken of the comparative complexity of the algorithms: In SLAM, the UKF scales as O(N 3) in the state length, compared to the EKF's O(N 2), making it unsuitable for video-rate applications with other than unrealistically few scene points. Here, it is shown that the SRUKF provides the same results as the UKF to within machine accuracy and that it can be reposed with complexity O(N 2) for state estimation in visual SLAM. This paper presents results from video-rate experiments on live imagery. Trials using synthesized data show that the consistency of the SRUKF is routinely better than that of the EKF, but that its overall cost settles at an order of magnitude greater than the EKF for large scenes.
K-cluster based reconstruction for Compressive Sensing

In this paper, we extend the existing CS by including the prior knowledge of K-cluster valued intensities available for an image. In order to reduce the measurement numbers, we then propose in this paper K-cluster based reconstruction approach for Compressive Sensing (CS), by incorporating the K-means algorithm in recovery algorithm to model the prior of K-cluster valued intensities for digital images. Finally, the performance of conventional CS and K-cluster based CS is evaluated using some natural images and background subtraction images.
Predicting New Customers' Risk Type in the Credit Card Market

Recent studies in marketing have consistently shown that all customers are not equally profitable. In the credit card business, all customers are not equally risky. When a customer misses one payment on a credit card bill, a signal is sent to the credit card company. It is important for the card issuer to interpret the signal and to identify whether the customer is a low-risk one, who will eventually pay back the debt and contribute to the card issuer's profits by paying interest on the overdue balance, or a high-risk one, who will not pay back the debt. The issuer can then customize its policies to deal with these different consumer types. This article develops a dynamic model for debt repayment behavior of new customers in the credit card market that makes it possible to differentiate between low-risk, delinquent customers and high-risk customers. The authors apply the model to a data set of new consumers' monthly spending and repayment records.
Evaluation of motion tracking methods for therapeutic assistance in everyday living environments

In this paper we compare the performance of the Microsoft™ Kinect v2 with a high precision measurement system for therapeutic purposes. A precise and low cost motion tracking system is of importance for the development of therapeutic assistance in everyday living environments. Upcoming therapeutic assistance systems have to be affordable and able to analyze the motion of the inhabitant. Therefore, an evaluation of the low cost Kinect v2 sensor is necessary to examine the usability of the sensor for joint angle measurements. We will show that a median deviation up to 8.4° in comparison with the high precision measurement may be achieved. We will conclude that the Kinect v2 sensor offers an adequate opportunity to analyze therapeutic exercises in living environments.
Economics-Driven Resource Scalability on the Cloud

Virtualization of resources in cloud computing has enabled application developers to commission and recommission resources at will and on demand. This virtualization is a coin with two sides. On one hand, the flexibility in managing virtual resources has enabled application developers to more efficiently manage their costs; they can easily remove unnecessary resources or add resources temporarily when the demand suddenly increases. On the other hand, the volatility of such environment and the velocity with which changes can occur may have a greater impact on the economic position of a stakeholder and the business balance of the overall ecosystem. In this work, we recognise the business ecosystem of cloud computing as an economy of scale and explore the effect of this fact on decisions concerning scaling the infrastructure of web applications to account for fluctuations in demand. The goal is to reveal and formalize opportunities for economically optimal scaling decisions that take into account not only the cost of infrastructure, but also the revenue from service delivery and eventually the profit of the service provider.
Service-oriented testbed infrastructures and cross-domain federation for Future Internet research

This article outlines the necessity for federated service oriented testbeds in order to accommodate the requirements of Future Internet research. Extensive investigations have been carried out in the networking and grid computing area during the last decades. These activities have successfully instantiated a number of large scale collaborative network testbeds and network emulation testbeds. The article describes an architectural approach on how to design service oriented testbeds and federate them across the boundaries of administrative domains to provide testing and development platforms that serve the needs of Future Internet research not only on the networking layer but also in the service and control platform layers.
Nonlinear systems identification and control based on fuzzy-neural multi-model

The paper brings forward a hierarchical fuzzy-neural multi-model and Takagi-Sugeno(T -S) rules with recurrent neural for systems identification, adaptive control of complex nonlinear plants and states estimation. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considered-a two membership functions without overlapping and a three membership functions with overlapping. The simulation shows that good convergent results are obtained.
WPP, No. 103: Learning Phonetic Features from Waveforms

UCLA Working Papers in Phonetics, No. 103, 64-70 Learning phonetic features from waveforms Ying Lin (yinglin@ucla.edu) Abstract Unsupervised learning of broad phonetic classes by infants was simulated using a statistical mixture model. With the phonetic labels removed, hand-transcribed segments from the TIMIT database were used in model-based clustering to obtain data-driven classes. Simple Hidden Markov Models were chosen to be the components of the mixture, with Mel-Cepstral coeﬃcients as the front-end. The sound classes were found by iteratively partitioning the clusters. The results of running this algorithm on the TIMIT segments suggest that the partitions may be interpreted as gradient acoustic features, and that to some degree, the resulting clusters correspond to knowledge-based phonetic classes. Thus, the clusters may reﬂect the preliminary phonological categories formed during language learning in early childhood. Introduction An important change that occurs during early phonological development is that an infant changes from a “universal” perceiver to a language-speciﬁc one[7]. It is widely believed that one of the underlying mecha- nisms is the ability to learn sound prototypes from distributions of sounds[2]. Although unsupervised learning of sound prototypes has been simulated using connectionist models with artiﬁcial data, no model is yet avail- able that takes real speech signals as input. The current study is a ﬁrst step in building a computational model for the human-like learning of sub-lexical units from acoustic signals, using tools from Automatic Speech Recognition and statistical learning. Assuming for this ﬁrst step that a phone-level segmentation is given, we study the technical problem of using a statistical mixture model to cluster a set of unlabelled acoustic segments. To handle the challenge that acoustic segments are non-stationary and have variable durations, simple Hidden Markov Models (HMM) were chosen to be the components of the mixture, using Mel-cepstral coeﬃcients as the parameterized representation of speech. Method Mixture model The basic intuition behind mixture models is that the observed data may be generated by diﬀerent sources, each captured by a separate component model. The way to decide which component best accounts for the data is by comparing the data’s likelihood given each component. Under the mixture model assumption, the likelihood has the general form: p(d|M ) = p(m i ) · p(d|m i ) i=1,··· ,N where each m i is often a parametric model that serves as a mixture component of M , and p(m i ) is the prior probability over all components {m 1 , · · · , m N } of M . The intuitive interpretation of p(m i ) is the relative size of the subset of data that is attributed to m i , and p(d|m i ) corresponds to likelihood of the data given m i . Another important notion is the posterior probability of each mixture component, deﬁned as: p(m i |d) = p(m i )p(d|m i ) p(m i ) · p(d|m i ) i=1,··· ,N
NORGES TEKNISK-NATURVITENSKAPELIGE UNIVERSITET Modelling spatial variation in disease risk using Gaussian Markov random field proxies for Gaussian random fields

Analyses of spatial variation in disease risk based on area-level summaries of disease counts are most often based on the assumption that the relative risk is uniform across each region. Such approaches introduce an artificial piecewise-constant relative risk-surface with discontinuities at regional boundaries. A more natural approach is to assume that the spatial variation in risk can be represented by an underlying smooth relative risk-surface over the area of interest. This approach was taken by Kelsall and Wakefield (2002), who used an underlying Gaussian random field (GRF) to derive a multivariate log-Normal distribution of the risk at the regional level. The derivation rely on the approximation , which is frequently used in similar contexts in the geostatistics literature, but the different sizes and shapes of the regions typically found in disease mapping applications indicate that the validity of the approximation is questionable. We propose an approach to the modelling of a smoothly varying risk surface based on aggregated data avoiding this approximation. We also derive computationally efficient block MCMC-algorithms using a re-formulation of the geostatistical GRF model using Gaussian Markov random fields (GMRFs). We make extensive use of recent developments for GMRFs, including a method for fitting GMRFs to Gaussian random fields and computationally efficient algorithms for GMRFs based on numerical methods for sparse matrices. We demonstrate our approach on simulated data as well as a set of German oral cavity cancer mortality data from the period – , which have been previously analysed in the literature.
Tropical Cyclone Intensity Estimation in the North Atlantic Basin Using an Improved Deviation Angle Variance Technique

AbstractThis paper describes results from an improvement to the objective deviation angle variance technique to estimate the intensity of tropical cyclones from satellite infrared imagery in the North Atlantic basin. The technique quantifies the level of organization of the infrared cloud signature of a tropical cyclone as an indirect measurement of its maximum wind speed. The major change described here is to use the National Hurricane Center’s best-track database to constrain the technique. Results are shown for the 2004–10 North Atlantic hurricane seasons and include an overall root-mean-square intensity error of 12.9 kt (6.6 m s−1, where 1 kt = 0.514 m s−1) and annual root-mean-square intensity errors ranging from 10.3 to 14.1 kt. A direct comparison between the previous version and the one reported here shows root-mean-square intensity error improvements in all years with a best improvement in 2009 from 17.9 to 10.6 kt and an overall improvement from 14.8 to 12.9 kt. In addition, samples from the 7-y.
A Comparative Study of the Performances of the OLS and some GLS Estimators when Stochastic Regressors are both Collinear and Correlated with Error Terms

The Classical Linear Regression Model assumes that regressors are non – stochastic, independent and uncorrelated with the error terms. These assumptions are not always tenable especially where regressors are not often assumed fixed in repeated sampling. In this paper, with stochastic regressors, the performances of the Ordinary Least Square (OLS) and some Generalized Least Square (GLS) estimators are investigated and compared under various degree of non – validity of multicollinearity and correlation between regressor and error terms’ assumptions through Monte – Carlo studies at both low and high replications. The mean squared error criterion is used to examine and compare the estimators. Results show that the performances of the estimators improved with increased replication. The ML and MLGD (GLS) estimators compare favorably with the OLS estimator with low replication. However with increased replication, the OLS method is preferred among the estimators in estimating all the parameters of the model in all level of correlations.
The view from Husserl’s lectern : considerations on the role of phenomenology in cognitive semiotics.

The aim of this paper is to consider in what way semiotics, cognitive science, and phenomenology, which stem from different traditions that have only rarely been known to intermingle, can enter harmoniously into a common research paradigm, phenomenological cognitive semiotics, to which each one of them has a specific contribution to make, In the first part of the paper, the relationship between (traditional) cognitive science and semiotics is elucidated from different points of view. The second part is concerned with methodology in general, and with the phenomenological method in particular, contrasting the conceptions of the latter propounded by Husserl and Peirce. I then go on to discuss some onto-epistemological consequences of opting for the phenomenological method. Finally, I argue that phenomenological cognitive semiotics might play a central part in the renewal of the Enlightenment project.
Subjective experiments on influence of response timing in spoken dialogues

To verify the validity of analysis results relating to dialogue rhythm from earlier studies, we produced spoken dialogues based on analysis results relating to response timing and the other spoken dialogues, and performed subjective experiments to investigateparameters suchas the naturalness of the dialogue, the incongruity of the synthesized speech, and the ease of comprehension of the utterances. We used very short task-oriented four-turn dialogues using synthesized speech in Experiment 1, and approx. one-minute free-conversation dialogues in Experiment 2 using natural human speech and synthesized speech. As a result, we were able to show that a natural response timing exists for utterances, and that response timings that conform to the utterance contents are felt to be more natural, thus demonstrating the validity of the analysis results relating to dialogue rhythm. Index Terms: Speech analysis, Speech communication, Interactive systems, User interface human factors, User interfaces
Watermarking-based tamper detection and recovery algorithms for official documents

This paper proposes two watermarking-based algorithms for temper detection and recovery of the tampered regions, which can be applied to official documents, such as digitized passports and governmental registrations. In both algorithms, a halftone version of the original grayscale image is used as an approximated version of the host image (image digest), which is then embedded as a watermark sequence into the frequency domains of the host image. In the first algorithm, the Integer Wavelet Transform (IWT) is used for watermark embedding (WIA-IWT); while in the second one, the Discrete Cosine Transform (DCT) domain is used for this purpose (WIA-DCT). The experimental results demonstrate the robustness of both algorithms against content preserved modifications, such as JPEG compression, as well as recovery capability. Moreover, we employ a Multilayer Perceptron neural network (MLP) in inverse halftoning process to improve the recovered image quality, where the gray-scale of the modified area is estimated by the MLP using the extracted halftone image. The experimental results demonstrate the effectiveness of the proposed scheme.
Providing easier access to remote objects in client-server systems

The Java Environment for Distributed Invocation (JEDI) is efficient, dynamic and easier to use than alternative communication systems for distributed Java objects. Existing state-of-the-art mechanisms for remote method calls on Java objects, such as RMI (remote method invocation), require users to perform a complicated series of steps. Furthermore, the compiled static interfaces these systems use limit their functionality. This paper presents the design and implementation of JEDI's simpler approach utilizing dynamic proxies. We discuss a means of integrating JEDI with a publicly available CORBA object request broker (ORB), followed by the tests used to ensure the robustness of the JEDI system. Comparing this system's performance with that of other communication facilities such as UDP, TCP and RMI demonstrates the efficiency of JEDI. A calendar program illustrates the flexibility and usability tradeoffs of employing JEDI in distributed client-server applications.
FlurryDB: a dynamically scalable relational database with virtual machine cloning

Stateless services are easy to scale in the cloud since new replicas of these services can be created at any time and they operate completely independently of other instances. In contrast, scaling stateful services, such as a database system, can take minutes or even hours due to the need to present a consistent view of the system for users of the service. Currently, this problem is addressed by resource over-provisioning in anticipation of demand spikes. FlurryDB uses virtual machine cloning to improve resource utilization by drastically reducing the latency required to add a new replica. We also show that FlurryDB is capable of handling updates to resources in a fashion that preserves consistency across the cloning boundary.
A Transport-Layer Solution for Alleviating TCP Unfairness in a Wireless LAN Environment

Per-flow unfairness of TCP throughput in the IEEE 802.11 wireless LAN (WLAN) environment has been reported in past literature. A number of researchers have proposed various methods for alleviating the unfairness; most require modification of MAC protocols or queue management mechanisms in access points. However, the MAC protocols of access points are generally implemented at hardware level, so changing these protocols is costly. As the first contribution of this paper, we propose a transport-layer solution for alleviating unfairness among TCP flows, requiring a small modification to TCP congestion control mechanisms only on WLAN stations. In the past literature on fairness issues in the Internet flows, the performance of the proposed solutions for alleviating the unfairness has been evaluated separately from the network bandwidth utilization, meaning that they did not consider the trade-off relationships between fairness and bandwidth utilization. Therefore, as the second contribution of this paper, we introduce a novel performance metric for evaluating trade-off relationships between per-flow fairness and bandwidth utilization at the network bottleneck. We confirm the fundamental characteristics of the proposed method through simulation experiments and evaluate the performance of the proposed method through experiments in real WLAN environments. We show that the proposed method can achieve better a trade-off between fairness and bandwidth utilization, regardless of vendor implementations of wireless access points and wireless interface cards.
Rate-distortion optimization based skip mode early detection in H.264

Mode decision module is the computation intensive part in H.264. However, many macroblocks' best modes are skip mode, which can early terminate mode decision module. In this paper, a theoretical analysis is performed for rate-distortion optimization(RDO) algorithm for skip mode. As a result, an RDO based skip mode early detection algorithm is derived. Experimental results show that the proposed algorithm achieves major improvement of computation reduction in the range from 12.1% to 72.9% with averagely from 72.6% to 75.5% correct detection rate.