Miracle-dz/UniFAR-FTUs huggingface dataset:
VIDEO DETECTION OF DANGEROUS GOODS VEHICLES IN ROAD TUNNELS

We present a solution for the automatic detection and classification of dangerous goods on trucks. Dangerous goods are labeled by an orange dangerous goods plate and/or a dangerous goods symbol sign. The acquisition system consists of a camera and dedicated illumination setup. A computer vision system processes the images by localizing and reading the dangerous goods number. The proposed system can be installed on both ends of a tunnel, thus raising awareness of all dangerous goods currently within the tunnel. To demonstrate the system, we show qualitative and quantitative localization/recognition results on real world data.
Finding images of difficult entities in the long tail

While images of famous people and places are abundant on the Internet, they are much harder to retrieve for less popular entities such as notable computer scientists or regionally interesting churches. Querying the entity names in image search engines yields large candidate lists, but they often have low precision and unsatisfactory recall. In this paper, we propose a principled model for finding images of rare or ambiguous named entities. We propose a set of efficient, light-weight algorithms for identifying entity-specific keyphrases from a given textual description of the entity, which we then use to score candidate images based on the matches of keyphrases in the underlying Web pages. Our experiments show the high precision-recall quality of our approach.
Experience-based representation construction: learning from human and robot teachers

In this paper we address the problem of teaching robots to perform various tasks. We present a behavior-based approach that extends the capabilities of robots, allowing them to learn representations of complex tasks from their own experiences of interacting with a human, and to use the acquired knowledge to teach other robots in turn. A learner robot follows a human or robot teacher and maps its own observations of the environment to its internal behaviors, building at run-time a representation of the experienced task in the form of a behavior network. To enable this, we introduce an architecture that allows the representation and execution of complex and flexible sequences of behaviors and an online algorithm that builds the task representation from observations. We demonstrate our approach in a set of human(teacher)-robot(learner) and robot(teacher)-robot(learner) experiments, in which the robots learn representations for multiple tasks and are able to execute them even in environments with distractor objects that could hinder the learning and the execution process.
Systematic errors in future weak-lensing surveys: requirements and prospects for self-calibration

We study the impact of systematic errors on planned weak-lensing surveys and compute the requirements on their contributions so that they are not a dominant source of the cosmological parameter error budget. The generic types of error we consider are multiplicative and additive errors in measurements of shear, as well as photometric redshift errors. In general, more powerful surveys have stronger systematic requirements. For example, for a SuperNova/Acceleration Probe (SNAP)-type survey the multiplicative error in shear needs to be smaller than 1 per cent of the mean shear in any given redshift bin, while the centroids of photometric redshift bins need to be known to be better than 0.003. With about a factor of 2 degradation in cosmological parameter errors, future surveys can enter a self-calibration regime, where the mean systematic biases are self-consistently determined from the survey and only higher order moments of the systematics contribute. Interestingly, once the power-spectrum measurements are combined with the bispectrum, the self-calibration regime in the variation of the equation of state of dark energy w a is attained with only a 20‐30 per cent error degradation. Ke yw ords: cosmological parameters ‐ large-scale structure of Universe.
Optimally approximating exponential families

This article studies exponential families $\mathcal{E}$ on finite sets such that the information divergence $D(P\|\mathcal{E})$ of an arbitrary probability distribution from $\mathcal{E}$ is bounded by some constant $D>0$. A particular class of low-dimensional exponential families that have low values of $D$ can be obtained from partitions of the state space. The main results concern optimality properties of these partition exponential families. Exponential families where $D=\log(2)$ are studied in detail. This case is special, because if $D<\log(2)$, then $\mathcal{E}$ contains all probability measures with full support.
ECT-DATA FUSION BY THE INDEPENDENT COMPONENT ANALYSIS FOR NON-DESTRUCTIVE EVALUATION OF METALLIC SLABS

The aim of this paper is to present an application of ICA to non-destructive evaluation by unsupervised data-fusion, which aims at discovering the flaws affecting a metallic slab. The signals acquired through an eddy-currentprobe for nondestructive evaluation purposes are affected by strong noise and disturbances due to the mechanical system that the probe is mounted on. The availability of multiple measurements allows performing a linear data-fusion which returns independent latent signals, one of which represents the flawrelated signal recovered from the noisy mixture. A one-unit neural ICA system is employed to extract such latent signal.
High Performance, Energy Efficiency, and Scalability With GALS Chip Multiprocessors

Chip multiprocessors with globally asynchronous locally synchronous (GALS) clocking styles are promising candidates for processing computationally-intensive and energy-constrained workloads. The GALS methodology simplifies clock tree design, provides opportunities to use clock and voltage scaling jointly in system submodules to achieve high energy efficiencies, and can also result in easily scalable clocking systems. However, its use typically also introduces performance penalties due to additional communication latency between clock domains. We show that GALS chip multiprocessors (CMPs) with large inter-processor first-inputs-first-outputs (FIFOs) buffers can inherently hide much of the GALS performance penalty while executing applications that have been mapped with few communication loops. In fact, the penalty can be driven to zero with sufficiently large FIFOs and the removal of multiple-loop communication links. We present an example mesh-connected GALS chip multiprocessor and show it has a less than 1% performance (throughput) reduction on average compared to the corresponding synchronous system for many DSP workloads. Furthermore, adaptive clock and voltage scaling for each processor provides an approximately 40% power savings without any performance reduction. These results compare favorably with the GALS uniprocessor, which compared to the corresponding synchronous uniprocessor, has a reported greater than 10% performance (throughput) reduction and an energy savings of approximately 25% using dynamic clock and voltage scaling for many general purpose applications.
Hoverflow: exploring around-device interaction with IR distance sensors

By equipping a mobile device with distance sensing capabilities, we aim to expand the interaction possibilities of mobile and wearable devices beyond the confines of the physical device itself to include the space immediately around it. Our prototype, an Apple iPhone equipped with six IR distance sensors, allows for rich 3D input, comprising coarse movement-based hand gestures, as well as static position-based gestures. A demonstration application, HoverFlow, illustrates the use of coarse hand gestures for interaction with mobile applications. This type of interaction, which we call Around-Device Interaction (ADI) has the potential to help to solve occlusion problems on small-screen mobile devices and scales well to small device sizes.
Segmentation of wounds in the combined color-texture feature space

In this work we describe an application of the Support Vector Machine (SVM) classifier for the segmentation of wounds in color images. The SVM-based segmentation combines naturally a high dimensional space of image features into a single classification machine. Since particular choice of image features is crucial for the performance of SVM classifier, we investigate the efficiency of color- and texture-based features for the differentiation between skin and wound tissue. We find that color features provide better separation between these two tissues. However, incorporation of even a single textural feature improves an overall quality of the SVM classification. We test the impact of each color feature on the quality of wound segmentation and find optimal combination of these features which produces best segmentation result. We suggest a Histogram Sampling technique, which gives wider separation between wound and skin in the color space. Finally, we find a set of image features, which is typical for most types of wounds. When these features are used as an input to the SVM classifier, a fairly robust segmentation of different wound types is achieved. We evaluate the performance of SVM-based segmentation using ground-truth segmentation carried out by clinicians.
Adaptive Personalization : A Multi-Dimensional Approach to Boosting a Large Scale Mobile Music Portal

Although mobile commerce is a rapidly growing business, personalization has yet not the importance it should have. Bad usability of mobile devices and the enormous mass of data induces that the user gets frustrated and refuses the service. This is especially true in the context of mobile music distribution where additional, domain dependent problems arise. The approach we present in this paper tries to tackle the personalization problem as a whole with the aim to optimize the performance from the users point of view. We demonstrate how the combination of the results of several areas of research like music information retrieval, interaction design, etc. will lead to a better system.
The Peroxisomal 3-keto-acyl-CoA thiolase B Gene Expression Is under the Dual Control of PPARα and HNF4α in the Liver

PPARα and HNF4α are nuclear receptors that control gene transcription by direct binding to specific nucleotide sequences. Using transgenic mice deficient for either PPARα or HNF4α, we show that the expression of the peroxisomal 3-keto-acyl-CoA thiolase B (Thb) is under the dependence of these two transcription factors. Transactivation and gel shift experiments identified a novel PPAR response element within intron 3 of the Thb gene, by which PPARα but not HNF4α transactivates. Intriguingly, we found that HNF4α enhanced PPARα/RXRα transactivation from TB PPRE3 in a DNA-binding independent manner. Coimmunoprecipitation assays supported the hypothesis that HNF4α was physically interacting with RXRα. RT-PCR performed with RNA from liver-specific HNF4α-null mice confirmed the involvement of HNF4α in the PPARα-regulated induction of Thb by Wy14,643. Overall, we conclude that HNF4α enhances the PPARα-mediated activation of Thb gene expression in part through interaction with the obligate PPARα partner, RXRα.
Chaotic Quantized Feedback Stabilizers: The Scalar Case

In this paper we consider practical stabilization strategies of scalar linear systems by means of quantized feedback maps which use a minimal number of quantization levels. These stabilization schemes are based on the chaotic properties of piecewise affine maps and their perfor- mance can be analyzed in terms of the mean time needed to shrink the system from an initial interval into a fixed target interval. We show here that this entrance time grows linearly with respect to the contraction rate defined as the quotient of the length of the initial and target interval respectively. Estimations are obtained using denumerable Markov chains arguments. 1. Introduction and problem statement. Control problems where the in- formation flow between the plant and the controller is an important feature to be considered in the design, have become very popular in the last few years. See (1, 2, 5, 6, 7, 12, 13, 14) and the reference therein. Indeed, information flow be- comes important in situations where a channel with limited information rate has to be used between the plant and the controller or when the controller needs to be simple and is only allowed to process a limited number of information per time. We expect that an information flow constraint will in general degrade the performances of the feedback loop scheme and we also expect in general a trade-off between performances and the amount of information exchange allowed in the loop. The specific problem we consider in this paper is the stabilization problem for a scalar linear system
Metasample-Based Sparse Representation for Tumor Classification

A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l1-norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR-based method for tumor classification using gene expression data. A set of metasamples are extracted from the training samples, and then an input testing sample is represented as the linear combination of these metasamples by l1-regularized least square method. Classification is achieved by using a discriminating function defined on the representation coefficients. Since l1-norm minimization leads to a sparse solution, the proposed method is called metasample-based SR classification (MSRC). Extensive experiments on publicly available gene expression data sets show that MSRC is efficient for tumor classification, achieving higher accuracy than many existing representative schemes.
Toward a software product line for affective-driven self-adaptive systems

One expected characteristic in modern systems is self-adaptation, the capability of monitoring and reacting to changes into the environment. A particular case of self-adaptation is affective-driven self-adaptation. Affective-driven self-adaptation is about having consciousness of user's affects (emotions) and drive self-adaptation reacting to changes in those affects. Most of the previous work around self-adaptive systems deals with performance, resources, and error recovery as variables that trigger a system reaction. Moreover, most effort around affect recognition has been put towards offline analysis of affect, and to date only few applications exist that are able to infer user's affect in real-time and trigger self-adaptation mechanisms. In response to this deficit, this work proposes a software product line approach to jump-start the development of affect-driven self-adaptive systems by offering the definition of a domain-specific architecture, a set of components (organized as a framework), and guidelines to tailor those components. Study cases with systems for learning and gaming will confirm the capability of the software product line to provide desired functionalities and qualities.
A Kind of Precision Recommendation Method for Massive Public Digital Cultural Resources: A Preliminary Report

In this paper, we give a preliminary report regarding a precision recommendation method for massive public digital cultural resources, which help people find the resources they are really interested in. We classify the public digital cultural resources and propose a precision recommendation framework based on big data technology. Finally, we introduce a precision recommendation method for public digital cultural resources.
THE EVALUATION OF THE INTERNAL QUALITY OF LASER SCANNING STRIPS USING THE INTERACTIVE ORIENTATION METHOD AND POINT CLOUDS

The quality of laser scanning point clouds has beco me a topical research issue. The quality has been d termined by the sum of several error sources caused by various factors aff ecting accuracy. In this paper, it is proposed that overlapping laser strips are favourable for inspecting the quality of the point clouds. The internal quality of five almost complet e y overlapping strips from TopoSys Falcon was investigated using the interacti ve orientation method. The orientation was solved i n several small test sites located in different parts of the complete overlapp ing area. Each relative orientation between two las er point clouds revealed possible height or planimetric shifts at the examination are a. When this procedure was repeated in various loca tions within laser scanning strips, internal deviations of laser data strips be came visible. The comparison was done relatively. T herefore, no ground control points were used. As a result, the repeatability in heights was excellent, whereas the planimetric rep eatability, however, included more systematic and non-systematic errors. Interest ingly, the flight direction was the main error sour ce, and visible in the observed bias and random errors.
Experimental simulation of materials degradation of plasma-facing components using lasers

The damage and erosion of plasma-facing components (PFCs) due to extremely high heat loads and particle bombardment is a key issue for the nuclear fusion community. Currently high current ion and electron beams are used in laboratories for simulating the behaviour of PFC materials under ITER-like conditions. Our results indicate that high-power nanosecond lasers can be used for laboratory simulation of high heat flux PFC material degradation. We exposed tungsten (W) surfaces with repetitive laser pulses from a nanosecond laser with a power density ∼ a few GW cm−2. Emission spectroscopic analysis showed that plasma features at early times followed by intense particle emission at later times. Analysis of laser-exposed W surface demonstrated cracks and grain structures. Our results indicate that the typical particle emission features from laser-irradiated tungsten are consistent with high-power particle beam simulation results.
Full-Duplex Non-Orthogonal Multiple Access for Next Generation Wireless Systems

In this article, we study the combination of NOMA and full-duplex operation as a promising solution to improve the capacity of next-generation wireless systems. We study the application of full-duplex NOMA transmission in wireless cellular, relay and cognitive radio networks, and demonstrate achievable performance gains. It is shown that the effects of self-interference and inter-user interference due to full-duplex operation can be effectively mitigated by optimizing/enhancing the beamforming, power control, and link scheduling techniques. We also discuss research challenges and future directions so that full-duplex NOMA can be made practical in the near future.
Outsourcery: Simply extending home automation using plugins

HomeWizard B.V. is looking to expand the range of compatible TCP/IP devices for its home automation hub. To this end, the company wants to use third-party plugins that control these devices. To allow even people with little programming experience to make devices compatible with the HomeWizard hub, these plugins have to be easy to create. These demands pose an interesting challenge: to create a system that is flexible enough to control hundreds of different devices, while being simple enough to be used by people with little programming experience. During the research phase of the project, existing home automation solutions were studied, as well as home automation techniques. The clients current position in the market was also researched to get a better understanding of the clients needs. During the implementation of the product, scrum was applied to manage the project. Prototypes were created in sprints of two weeks. A final prototype, called Outsourcery, is delivered at the end of the project. Outsourcery is a flexible home automation solution, facilitating the use of plugins to extend compatibility with both devices and network protocols. Outsourcery has been tested using unit tests and manual testing. An acceptance test determined that the creation of plugins is relatively easy. Outsourcery is a good start towards a universal home automation solution. However, it has still some way to go before it reaches maturity. Some required steps have been detailed in the report. Furthermore, some recommendations are done regarding the development of a plugin developer platform and making said platform available to customers.
Wilsonian Approximated Renormalization Group for Matrix and Vector Models in 2<d<4

Wilson’s approximation scheme of RG recursion formula is applied to large-N vector and matrix models in dimensions 2 < d < 4 by making use of their exact solutions in zero dimension. Apparent nonuniversality is present in higher order terms of the ǫ-expansion under this approximation, and is shown to be removed in a certain limit to yield an exact exponent for vector models. Application to matrix models, considered previously by Ferretti, is then reexamined in this limit. It predicts critical exponents ν = 2/d and η = 2 − d/2 for the trΦ matrix model in 2 < d < 4. e-mail address: nishigaki@nbi.dk
An Iterative Estimator for Predicting the Heterogeneous Attribute Data Sets

The quality of the patterns which are the results of data mining is depends upon the quality of data supplied to it. Most of the real time databases which are the sources for data mining possess the deficiency in terms of completeness, correctness and consistency. Improving the quality of data in terms of completeness is a challenging task. Many methods were proposed for imputing the missing values for homogenous attributes. This paper proposes a mixed kernel function, which imputes the missing values for the mixed attributes (the independent attributes are heterogeneous). The mixed kernel function is an integrated unit which adopts the right method to impute the value for right attribute. For the categorical attribute, our kernel function first assigns the mode value and the iteration continues till the right (most probable) value gets converged and for the discrete attribute the mean value gets assigned and the iteration continues till the most probable value is reached. The mixed kernel function is tested with a sample database; it proves that it is performing well in terms of accuracy and iterations compared to linear kernel function.
Adaptive control of parallel DC-DC buck converters with uncertain parameters

Nonlinear backstepping adaptive controller is proposed for the design of parallel DC-DC buck converters with uncertainties of load and power disturbance. Global asymptotic stability of the system is proved by Lyapunov stability theory. The relationship between the control elements and circuit parameters is determined by simulation analysis. The simulation results demonstrate the effectiveness of the presented method.
Wireless TCP enhancements using multilevel ECN

Due to the increasing demand for mobility among the Internet users, there is a urgent requirement to identify and solve the deficiencies in the wireless domain. One such urgent problem is the poor performance of TCP over wireless links. TCP still being the only protocol used in the Internet for reliable transfers, the assumptions made by TCP in the wired domain are not valid in the wireless domain. To enhance the performace of the TCP in the wireless domain, we need to differentiate the 'congestion loss' from the 'wireless loss'. We find that the previous attempts in this direction make unjustified demands from the network or the accuracy of the schemes are inadequate. We are convinced that reliable transport is a end-to-end semantic and other networkcomp onents should not be burdened with this work. In this paper we propose a scheme called the 'Source-Centric Congestion Filtering', based on the MECN protocol, which tries to differentiate the losses based on the networkfeedback. Our simulations using the NS-2 simulator shows that our protocol has very less percentage of error and performs better than most of the other end-to-end TCP variants.