Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time pedestrian learning-tracking with information fusion

This paper presents a novel and efficient object tracking method based on multi-sensor information fusion. Based on the popular detection-tracking framework, we consider the tracking process as 3 conditions and the fusion strategy can be adjusted in different conditions adaptively by designing a new online positive and negative sample classifier selecting method with the guidance of the depth information from the laser scanner. The results of our experiments show good robustness and performance when facing extreme cases such as the object rotation, long-period occlusion, and high similarity between object and background.
Discussions on applied mathematics in decision-making modeling with decision support systems and knowledge based systems

This paper tries to discuss some findings in mathematical decision-making modeling models with applications in business processes. We start by presenting some technological implications and implementations of decision-making models. After this we discuss some implementations realized by us and that consists in a neural network, a JAVA implementation of the decision-making model, an expert systems-shell implementation and an implementation with ontology and inference engine. The paper ends with usefull conclusions drawn for decision-making modeling activities.
Discriminative Boosting from Dictionary and Raw Text – A Novel Approach to Build A Chinese Word Segmenter

Chinese word segmentation (CWS) is a basic and important task for Chinese information processing. Standard approaches to CWS treat it as a sequence labelling task. Without manually annotated corpora, these approaches are ineffective. When a dictionary is available, dictionary maximum matching (DMM) is a good alternative. However, its performance is far from perfect due to the poor ability on out-of-vocabulary (OOV) words recognition. In this paper, we propose a novel approach that integrates the advantages of discriminative training and DMM, to build a high quality word segmenter with only a dictionary and a raw text. Experiments in CWS on different domains show that, compared with DMM, our approach brings significant improvements in both the news domain and the Chinese medicine patent domain, with error reductions of 21.50% and 13.66%, respectively. Furthermore, our approach achieves recall rate increments of OOV words by 42.54% and 23.72%, respectively in both domains.
Rules? Role Model? Relationship? The Impact of Parents on Their Children’s Problematic Mobile Phone Involvement

ABSTRACT Parents can influence their children’s problematic mobile phone involvement (PMPI) by engaging in parental mediation activities, such as restrictions or co-use, by being a role model, and by their general and domain-unspecific parenting style that facilitates more or less attachment security of the child. This study tested the associations between these different routes of influence—parental mediation of the mobile phone, parental PMPI, and secure child-parent attachment—with children’s PMPI. Data was acquired from a quota-sample survey with 500 children, between 8 and 14 years of age, and one of their parents. Results point to the importance of open and empathic parent-child-communication, as well as a positive relationship quality, and demonstrate the detrimental effects of parents’ own PMPI on children’s PMPI.
Acta Agrophysica, 2005, 6(1), 7-18 ORTHOGONAL SIGNAL CORRECTION TO PLS MODELLING IN APPLICATION TO SPECTRAL DATA

A typical task in chemometrics is to est ima e the linear relationship between two sets of variables, i.e. the set of spectra, X, and the concentrations of some sample constituents, Y. Among the classical regression methods, partial lea st squares (PLS) is one of the most commonly used tools. One of the complications which could ne gatively affect the interpretation of the PLS model is related to the systematic variation presen t in X that is unrelated with the variation in Y. This situation typically occurs when X variables re present the absorbance or reflectance measured at hundreds of wavelengths, and the measurements are p ossibly influenced by sources of different types of variation having nothing in common with th e information of interest. Orthogonal signal correction (OSC) is a recently proposed pre-process ing method that seems to be promising in this context. This approach determines and removes from spectral data X the part of information which is Y-orthogonal (i.e. not correlated with Y). The p ur ose of the present paper is to illustrate how th e technique works in application to near infrared (NI R) spectra of rapeseed meal. The results of PLS modelling for OSC pre-processed data have been comp ared with those of non-pre-processed as well as with those after multiplicative scatter correcti on (MSC). The main noticeable advantage of the OSC approach was the simplification of the calculat ed PLS models. It was also found that the combination of MSC with OSC may lead to improved pe rformance of the model.
Differential effects of experiential and problem-solving interventions in resolving marital conflict.

The present study compared the relative effectiveness of two interventions in the treatment of marital discord: a cognitive-behavioral intervention, teaching problemsolving skills, and an experiential intervention, focusing on emotional experiences underlying interaction patterns. Forty-five couples seeking therapy were randomly assigned to one of these treatments or to a wait-list control group. Each treatment was administered in eight sessions by six experienced therapists whose interventions were monitored and rated to ensure treatment fidelity. Results indicated that the perceived strength of the working alliance between couples and therapists and general therapist effectiveness were equivalent across treatment groups and that both treatment groups made significant gains over untreated controls on measures of goal attainment, marital adjustment, intimacy levels, and target complaint reduction. Furthermore, the effects of the emotionally focused treatment were superior to those of the problem-solving treatment on marital adjustment, intimacy, and target complaint level. At follow-up, marital adjustment scores in the emotionally focused group were still significantly higher than those in the problem-solving group.
A Practical Approach to Resolving Combination Ambiguity in Chinese Word Segmentation

In Chinese word segmentation task, combination ambiguity is one of challenges not being well settled. The main obstacle exists in the detection of ambiguous words in given texts and their proper segmentations. This paper puts forward a practical approach to automatically collecting ambiguous words and disambiguating based on maximum entropy principle. The experimental result reveals the approach of automatic collection ambiguous words can detect combination ambiguity effectively avoiding arduous manual work. As to the disambiguation based on maximum entropy, we investigate new features grounded on prior and contextual knowledge and achieve promising result
Towards Full Virtualization of Heterogeneous NoC-based Multicore Embedded Architectures

As the hardware complexity of embedded systems is increasing exponentially and flexibility is required to adapt them to a wide variety of applications, virtualization technology brings an effective and clean way of isolating applications from hardware. However, a virtualization-ready SoC platform must support the necessary extensions across the HW/SW stack: applications, programming model, hypervisor and hardware platform. In this work we present the main hardware extensions and architecture of a heterogeneous multicore embedded system supporting virtualization, which provides improved security and isolation among virtualized environments. At the same time, we detail the architecture of a hardware I/O management unit that enables virtualization and provides support for global coherent address space, flow isolation and security, resource utilization and runtime monitoring.
Strategic Properties of Peer-to-Peer Data Structures and Behaviour of Human Peers in Economic Experiments

Structured peer-to-peer networks manage large data sets. Each peer administers a part of the data. To answer a query, several peers must collaborate. However, experience with peer-to-peer file sharing systems shows that peers tend to abandon collaboration. Such behaviour is also expected in structured peer-to-peer systems. In the recent past, protocols for such systems to counter this kind of behaviour have been proposed. Our objective is to investigate how the behaviour of peers reacts to such protocols. We use idealized game theoretic models to analyze the strategic properties of structured peer-to-peer networks. The models predict cutoff strategies. I.e., if more than a certain percentage of their queries are answered, peers also answer queries in a stationary equilibrium. Another theoretical result is that free riding may occur in equilibrium. To check whether our theoretical predictions are in line with reality, we have implemented our model in an experimental laboratory and have analyzed the behaviour of human peers. These experiments support our theoretical results.
Design and implementation of a near maximum likelihood decoder for Cortex codes

Cortex codes are an emerging family among the rate-1/2 self-dual systematic linear block codes with good distance properties. This paper investigates the challenging issue of designing an efficient Maximum Likelihood (ML) decoder for Cortex codes. It first reviews a dedicated architecture that takes advantage of the particular structure of this code to simplify the decoding. Then, we propose a technique to improve the architecture by the generation of an optimal list of binary vectors. An optimal stopping criterion is also proposed. Simulation results show that the proposed architecture achieves an excellent performance/complexity trade-off for short Cortex codes. The proposed decoder architecture has been implemented on an FPGA device for the (24,12,8) Cortex code. This implementation supports an information throughput of 300 Mb/s. At a signal-to-noise ratio Eb/No=8 dB, the Bit Error Rate equals 2 × 10-10, which is close to the performance of the Maximum Likelihood decoder.
Varieties of Anticommutative n-ary Algebras

A fundamental problem in the theory of n-ary algebras is to determine the correct generalization of the Jacobi identity. This paper describes some computational results on this problem using representations of the symmetric group. It is well known that over a field of characteristic 0 any variety of n-ary algebras can be defined by multilinear identities. In the anticommutative case, it is shown that for
THE EFFECT OF MILD COGNITIVE IMPAIRMENT ON EEG TOPOGRAPHIC CHANGES AFTER ON-PUMP CORONARY ARTERY BYPASS GRAFTING

The purpose . Previously it was shown that on-pump coronary artery bypass grafting (CABG) induced cerebral ischemia and cognitive decline. The patients with mild cognitive impairment (MCI) could be a high-risk group of CABG-associated cognitive decline. Non-invasive neuromonitoring can provide information regarding subclinical symptoms and topography of cerebral ischemia. The aim of this study was to investigate the topographic changes in electroencephalogram (EEG) spectral power in coronary artery disease (CAD) patients with or without MCI before and after on-pump CABG. Methods : 62 males with CAD were divided into two groups according to their Mini-Mental State Examination: without MCI (n = 37) and with MCI (n = 25). Clinical factors were assessed, including the severity of coronary lesions (SYNTAX score), the left ventricular ejection fraction (LVEF). Eyes-closed rest EEG was recorded from 62 channels positioned according to the International 10–20 system. Spectral EEG power was calculated for frequencies from 0.1 to 50 Hz. Results : The patients with MCI had theta-1 (4–6 Hz) power increase 7-10 days after on-pump CABG only in frontal and fronto-central clusters of right and left hemispheres as compared to the preoperative values, whereas in the groups without MCI this effect was widespread over the brain. In the beta-1 band (13–20 Hz), CABG patients with MCI had power increase in the frontal, fronto-central and fronto-temporal regions of the brain cortex. Similar power changes in patients without MCI were observed only for occipital brain clusters. Conclusions : CAD patients with MCI have demonstrated EEG signs of cortical dysfunction focused on the frontal brain areas. Cortical dysfunction in this localization can be associated with the progression of cognitive deficits, causing loss social integration in CAD patients with MCI.
Enable++: a Second Generation Fpga Processor Enable++: a Second Generation Fpga Processor*

In the computing community field programmable processors are going to fill the niche for special pur-posecomputing devices. A typical example is ultra-fast pattern recognition in experimental partiele physics-a task for which we constructed two years ago Enable-I, an FPGA processor rather specialized for pattern recognition algorithms in I1-S domain, but also provided with modest features for coping with more general applications. This paper presents the follow-up modell Enable++, a 2nd generation FPGA processor that offers several substantial enhancements over the previous system for a wider range of applications: Enabfe++ . is structured into three different state-of-tlle-art modules for providing computing power, flexible and high-speed 1/0 communication and powerful intermodule communication with a raw bandwidth of 3.2 GByte/s by an active backplane. The technical realization of all three modules is guided by the maximum usage of field programmable logic. The actual demand of computing-and I/O-power can be satisified by the number of modules plugged into the crate. Enhanced features of Enable++ comprise the con-figurable processor topology provided by programmable crossbar switches. In combination with the 4-x 4-FPGA array and 12 MByte distributed RAM the Enable++ computing core offers a strongly increased and scal-able computing power. For building new applications the system offers a comfortable programming and de-bugging environment consistiilg of a compiler for the C-like hardware description language spC, a simula-tor and a source level debugger for hardware design. The goal in planning the hardware design environment for Enable++ from scrati:h is to transfer established methodologies in software design to the design of digital logic. Concerning pattern recognition tasks, we estimate that Enable++surpasses modern RISC processors by a factor of 100 to 1000.
EXTENSION OF THE LUGRE DYNAMIC TIRE FRICTION MODEL TO 2D MOTION

An extension of the LuGre dynamic friction model from longitudinal motion to longitudinal/lateral motion is developed. Applying this model to the motion of a tire we derive a model for tire-road contact forces and moments. A comparison of the steadystate behaviour of the dynamic model with existing static tire friction models is also presented. This comparison allows one to determine the values of the parameters for the new model. Introducing a set of mean states we reduce the order of the system and derive a model in lumped form which is useful for control purposes.
A Novel Method of P2P Hosts Detection Based on Flexible Neural Tree

It is estimated that 70 percent or more of broadband bandwidth is consumed by transporting music, games, video, and other content through P2P clients. In order to identify, and manage of P2P traffic, some port, payload and connection features based methods were proposed. But most of them focus on identifying P2P traffic based on rules and most of them can't identify P2P traffic with new features. None of the proposed methods can effectively help controlling and managing the P2P hosts. In this paper, we created a new P2P feature set, which include most of the proposed and new P2P features proposed by us. And a new anormaly detection method based on the feature set and flexible neural tree (FNT) is applied to detect P2P hosts. This approach is the first intelligent P2P hosts detection method. Experimental results show that the new method proposed by this paper has high detection performance and good expansion ability to add new P2P features. It can be easily applied to an on-line environment in our future planed work
Blind separation and localization of speeches in a meeting situation

The technique of blind source separation (BSS) has been well studied. In this paper, we apply the BSS technique, particularly based on independent component analysis (ICA), to a meeting situation. The goal is to enhance the spoken utterances and to estimate the location of each speaker by means of multiple microphones. The technique may help us to take the minutes of a meeting.
Fairness of MAC protocols: IEEE 1901 vs. 802.11

The MAC layer of the IEEE 1901 standard for power line communications employs a CSMA/CA method similar to, but more complex than, this of IEEE 802.11 for wireless communications. The differences between these protocols raise questions such as which one performs better and under what conditions. We study the fairness of the 1901 MAC protocol in single contention domain networks, where all stations hear each other. We examine fairness at the packet level: a MAC layer protocol is fair if all stations share equitably the medium during a fixed time interval. We focus on short-term fairness, that is, over short time intervals. Short-term fairness directly impacts end-user experience, because unfair protocols are susceptible to introduce substantial packet delays. We evaluate short-term fairness with two metrics: Jain's fairness index and the number of inter-transmissions. We present test-bed results of both protocols and compare them with simulations. Both simulation and test-bed results indicate that 802.11 is fairer in the short-term when the number of stations N is between 2 and 5. However, simulation results reveal that 1901 is fairer in the short-term for N ≥ 15. Importantly, our test-bed measurements indicate that 1901 unfairness can cause significant additional delay when N = 2. Finally, we confirm these results by showing analytically that 1901 is short-term unfair for N = 2.
Variational Bayes on Monte Carlo Steroids

Variational approaches are often used to approximate intractable posteriors or normalization constants in hierarchical latent variable models. While often effective in practice, it is known that the approximation error can be arbitrarily large. We propose a new class of bounds on the marginal log-likelihood of directed latent variable models. Our approach relies on random projections to simplify the posterior. In contrast to standard variational methods, our bounds are guaranteed to be tight with high probability. We provide a new approach for learning latent variable models based on optimizing our new bounds on the log-likelihood. We demonstrate empirical improvements on benchmark datasets in vision and language for sigmoid belief networks, where a neural network is used to approximate the posterior.
Rule-Based Classification of a Hyperspectral Image Using Mssc Hierarchical Segmentation

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. In this paper, we propose to use spectral-spatial classifiers at the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Then, a novel marker-based HSEG algorithm (that is called Multiple Spectral-Spatial Classifier-HSEG (MSSC-HSEG)) is applied, resulting in a segmentation map. The segmentation results are then used in a rule-based classification using spectral, geometric, textural, and contextual information. The experimental results, presented for a hyperspectral airborne image, demonstrate that the proposed approach yields accurate segmentation and classification maps, when compared to previously classification techniques.
Systemic Platelet-activating Factor Receptor Activation Augments Experimental Lung Tumor Growth and Metastasis

Pro-oxidative stressors including cigarette smoke (CS) generate novel lipids with platelet-activated factor-receptor (PAF-R) agonistic activity mediate systemic immunosuppression, one of the most recognized events in promoting carcinogenesis. Our previous studies have established that these oxidized-PAF-R-agonists augment murine B16F10 melanoma tumor growth in a PAF-R-dependent manner because of its effects on host immunity. As CS generates PAF-R agonists, the current studies sought to determine the impact of PAF-R agonists on lung cancer growth and metastasis. Using the murine Lewis Lung Carcinoma (LLC1) model, we demonstrate that treatment of C57BL/6 mice with a PAF-R agonist augments tumor growth and lung metastasis in a PAF-R-dependent manner as these findings were not seen in PAF-R-deficient mice. Importantly, this effect was because of host rather than tumor cells PAF-R dependent as LLC1 cells do not express functional PAF-R. These findings indicate that experimental lung cancer progression can be modulated by the PAF system.
Controlling the start of combustion on an HCCI diesel engine

In this paper we propose a control strategy to improve stability of the combustion of HCCI engines during sharp transients. This approach complements existing airpath and fuelpath controllers, and aims at accurately controlling the start of combustion (soc). For that purpose, injection time is adjusted based on a simple Knock Integral Model and real time intake manifold signals. Experimental results are presented, which stress the relevance of the approach.
Sleep architecture and NREM alterations in children and adolescents with Asperger syndrome.

STUDY OBJECTIVES To analyze sleep in children with Asperger syndrome (AS) by means of standard sleep questionnaires, to evaluate sleep architecture and NREM sleep alterations by means of cyclic alternating pattern (CAP) and to correlate objective sleep parameters with cognitive behavioral measures. DESIGN Cross-sectional study involving validated sleep questionnaires, neuropsychological scales, and PSG recording. SETTING Sleep medicine center. PARTICIPANTS Eight children with AS, 10 children with autism, and 12 healthy control children. INTERVENTIONS N/A. MEASUREMENTS AND RESULTS Children with AS had a higher prevalence of problems of initiating sleep and daytime sleepiness. Sleep architecture parameters showed minor differences between the 3 groups. CAP parameters showed an increased percentage of A1 and a decreased percentage of A2 subtypes in subjects with AS vs. controls. All A subtype indexes (number per hour of NREM sleep) were decreased, mostly in sleep stage 2 but not in SWS. With respect to children with autism, subjects with AS showed increased CAP rate in SWS and A1 percentage. In subjects with AS, verbal IQ had a significant positive correlation with total CAP rate and CAP rate in SWS and with global and SWS A1 index. The percentage of A2 negatively correlated with full scale IQ, verbal and performance IQ. CBCL total score correlated positively with CAP rate and A1 index while externalizing score correlated negatively with A3%. CONCLUSIONS This study shows peculiar CAP modifications in children with AS and represents an attempt to correlate the quantification of sleep EEG oscillations with the degree of mental ability/disability.