Miracle-dz/UniFAR-FTUs huggingface dataset:
RvGIST: A Holistic Road Feature for Real-Time Road-Scene Understanding

Image-based road scene understanding is a critical issue for intelligent vehicles and autonomous mobile robots. It is challenging to deal with varying road conditions in a dynamic environment in real time. This paper presents an effective while simple approach to classify road types and locate the road-related elements through the analysis of a holistic visual road feature. The feature is abstracted from responses of Gabor-filter-set and grouped into super-pixel grids, consisting of road-scene textural context and dominant orientation distribution. From this feature we successively deduce the information of horizon line, road type and coarse locations of road surface, lanes, on-road obstacles and off-road regions. Experiments show that the proposed analyzing method based on the new holistic feature is beneficial to road estimation and vehicle detection in complex road scenes, achieving improvements in both accuracy and efficiency.
Statistical Matching of Discrete Data by Bayesian Networks

Statistical matching (also known as data fusion, data merging, or data integration) is the umbrella term for a collection of methods which serve to combine different data sources. The objective is to obtain joint information about variables which have not jointly been collected in one survey, but on two (or more) surveys with disjoint sets of observation units. Besides specific variables for the different data files, it is indispensable to have common variables which are observed in both data sets and on basis of which the matching can be performed. Several existing statistical matching approaches are based on the assumption of conditional independence of the specific variables given the common variables. Relying on the well-known fact that d-separation is related to conditional independence for a probability distribution which factorizes along a directed acyclic graph, we suggest to use probabilistic graphical models as a powerful tool for statistical matching. In this paper, we describe and discuss first attempts for statistical matching of discrete data by Bayesian networks. The approach is exemplarily applied to data collected within the scope of the German General Social Survey.
Application of wavelet transform in strip defect image processing

The basic idea, the definition and types of wavelet transform are introduced in this paper, then its properties and Mallat algorithm are analysed. Four aspects of the application of wavelet transform in image processing are proposed (image denoising, image compression, image overlaying and image enhancement based on wavelet transform). The characteristics of four kinds of application are analyzed and the corresponding matlab simulation experiments are made. The conclusion is that the image processing based on wavelet transform has the advantages of rapid speed, convenient processing, high ratio compression and good de-noising effect. Finally, the further development of wavelet transform in image processing is proposed in this paper.
The Attitudinal Entropy (AE) Framework as a General Theory of Individual Attitudes

Abstract This article introduces the Attitudinal Entropy (AE) framework, which builds on the Causal Attitude Network model that conceptualizes attitudes as Ising networks. The AE framework rests on three propositions. First, attitude inconsistency and instability are two related indications of attitudinal entropy, a measure of randomness derived from thermodynamics. Second, energy of attitude configurations serves as a local processing strategy to reduce the global entropy of attitude networks. Third, directing attention to and thinking about attitude objects reduces attitudinal entropy. We first discuss several determinants of attitudinal entropy reduction and show that several findings in the attitude literature, such as the mere thought effect on attitude polarization and the effects of heuristic versus systematic processing of arguments, follow from the AE framework. Second, we discuss the AE framework’s implications for ambivalence and cognitive dissonance.
Intrusion detection in computer networks by multiple classifier systems

The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools are currently available. Intrusion detection systems aim at detecting an intruder who eluded the "first line" protection. In the paper, a pattern recognition approach to network intrusion detection based on the multiple classifier systems paradigm is proposed. The potentialities of classifier combination for data fusion and some open issues are outlined.
Subchronic Treatment of Donepezil Rescues Impaired Social, Hyperactive, and Stereotypic Behavior in Valproic Acid-Induced Animal Model of Autism

Autism spectrum disorder (ASD) is a group of pervasive developmental disorders with core symptoms such as sociability deficit, language impairment, and repetitive/restricted behaviors. Although worldwide prevalence of ASD has been increased continuously, therapeutic agents to ameliorate the core symptoms especially social deficits, are very limited. In this study, we investigated therapeutic potential of donepezil for ASD using valproic acid-induced autistic animal model (VPA animal model). We found that prenatal exposure of valproic acid (VPA) induced dysregulation of cholinergic neuronal development, most notably the up-regulation of acetylcholinesterase (AChE) in the prefrontal cortex of affected rat and mouse offspring. Similarly, differentiating cortical neural progenitor cell in culture treated with VPA showed increased expression of AChE in vitro. Chromatin precipitation experiments revealed that acetylation of histone H3 bound to AChE promoter region was increased by VPA. In addition, other histone deacetyalse inhibitors (HDACIs) such as trichostatin A and sodium butyrate also increased the expression of AChE in differentiating neural progenitor cells suggesting the essential role of HDACIs in the regulation of AChE expression. For behavioral analysis, we injected PBS or donepezil (0.3 mg/kg) intraperitoneally to control and VPA mice once daily from postnatal day 14 all throughout the experiment. Subchronic treatment of donepezil improved sociability and prevented repetitive behavior and hyperactivity of VPA-treated mice offspring. Taken together, these results provide evidence that dysregulation of ACh system represented by the up-regulation of AChE may serve as an effective pharmacological therapeutic target against autistic behaviors in VPA animal model of ASD, which should be subjected for further investigation to verify the clinical relevance.
Compression and Approximate Matching

A population of sequences is called non-random if there is a statistical model and an associated compression algorithm that allows members of the population to be compressed, on average. Any available statistical model of population should be incorporated into algorithms for alignment of the sequences and doing so changes the rank order of possible alignments in general. The model should also be used in deciding if a resulting approximate match between two sequences is significant or not. It is shown how to do this for two plausible interpretations involving pairs of sequences that might or might not be related. Efficient alignment algorithms are described for quite general statistical models of sequences. The new alignment algorithms are more sensitive to what might be termed 'features' of the sequences. A natural significance test is shown to be rarely fooled by apparent similarities between two sequences that are merely typical of all or most members of the population, even unrelated members.
Performance Measurement of CRM in Financial Services

Customer Relationship Management (CRM), a management philosophy that focuses on the nurturing of customer relationships, emerged as a response to decreasing customer loyalty and increasing competition. Implementing CRM is expensive, as it demands high investment in organizational change activities and information technology (IT) and companies thus expect visible, measurable results. But companies not only have trouble assessing the results of their CRM initiatives but also with managing the performance of their CRM initiatives. The majority of financial services companies in Germany and Switzerland have, with varying success, conducted customer relationship management implementation projects. In this paper we analyze several case studies on CRM in Swiss and German banks with regards to performance measurement and related challenges. We aim to reveal the reasons for and consequences of common practices and shortcomings regarding CRM implementation. 1 Customer Relationship Management and Performance Measurement 1.1 CRM: strategy, processes and systems CRM is a management philosophy focusing on customer orientation. It emerged as an answer to decreasing customer loyalty in competitive markets, and was enabled by new technologies. Shaw and Reed (Shaw/Reed 1999, p.4) provide a comprehensive definition of CRM that captures its multiple aspects as well as deftly describing what CRM is about. Our work is in line with their definition of CRM as an interactive approach that achieves an optimum balance between corporate investments and the satisfaction of customer needs to generate maximum profit. It entails measuring inputs like marketing, sales, and service costs as well as outputs in terms of customer revenue and it entails acquiring, continuously updating and applying knowledge on customers to improve performance. This demands an integration of marketing, sales, and service activities and the implementation of appropriate systems to support customer knowledge acquisition, sharing, and the measurement of CRM effectiveness. Processes and information systems help to implement the strategic objectives of CRM. We therefore provide a brief overview of CRM processes and systems. CRM architecture (Geib et al. 2004) is comprised of CRM processes which have been identified through a comprehensive analysis of research literature from the areas of Relationship Marketing (e.g. Gummesson 1999), process-oriented CRM (e.g. Shaw/Reed 1999), and technology-oriented CRM (e.g. Swift 2000). We distinguish between different
Acid–Base Catalytic Effects on Reduction of Methanol in Hot Water

We have performed a number of quantum chemical simulations to examine the reduction process of methanol in hot water. Methanol is converted into a methane by capturing a hydrogen molecule and leaving a water molecule behind. The required energy for the reduction is too high to proceed in the gas phase. The energy barrier for the reduction of methanol is reduced by the catalytic effect of water molecules when we consider the reduction in aqueous solution. However, the calculated reduction rate is still much slower than that found experimentally. The ion product of water tends to increase in hot water, even though it eventually decreases at the high temperature of supercritical water. It is valuable to consider the acid–base catalytic effects on the reduction of methanol in hot water. The significant reduction of the energy barrier is accomplished by the acid–base catalytic effects due to hydronium or hydroxyde. Mean collision time between a hydronium and a methanol in hot water is shorter than the reduction time, during which a methanol is converted into a methane. The calculated reduction rate with the acid–base catalytic effects agrees well with that determined by laboratory experiments. The present study reveals a crucial role of the acid–base catalytic effects on reactions in hot water.
Decoupled Visual Servoing from a set of points imaged by an omnidirectional camera

This paper presents a hybrid decoupled vision-based control scheme valid for the entire class of central catadioptric sensors (including conventional perspective cameras). First, we consider the structure from motion problem using imaged 3D points. Geometrical relationships are exploited to enable a partial Euclidean reconstruction by decoupling the interaction between translation and rotation components of a homography matrix. The information extracted from the homography are then used to design a control law which allow us to fully decouple rotational motions from translational motions. Real time experimental results using an eye-to-hand robotic system with a paracatadioptric camera are presented and confirm the validity of our approach.
Enhanced Video Mosaicing using Camera Motion Properties

We propose a video mosaicing scheme which exploits the motion information, implicitly available in the video. The information about the camera motion is propagated to the homographies used for mosaicing. While some of the recent approaches make use of the information stemming from non-overlapping pairs of frames, the smoothness of the camera motion has gone largely under-capitalized. We present a technique which exploits this useful cue for refining homographies. Moreover, a generic framework which exploits the camera motion model, to relate homographies in a video, is also proposed. The analysis and results of the proposed algorithms demonstrate significant promise, in terms of accuracy and robustness.
Variables sociodemográficas relacionadas con el bienestar en personas con o sin discapacidad

The purpose of this cross-sectional study was to analyze whether socio-demographic variables: gender, age, marital status, educational level, income and working conditions are related to the well-being of people with and without disabilities in the city of Cuenca. Wellbeing levels were assessed in 1,064 people through the PERMA profile of Butler & Kern (2016) that was adapted for the present study. A descriptive and correlational statistical analysis was performed. The results showed high levels of well-being in the sample evaluated. As for the variables explored, higher levels of well-being were found in late adulthood, women showed a higher level of well-being than men, those with a partner had a higher level of well-being than those without one. Higher levels of wellbeing were found in people with higher education, higher income and job stability, there was no evidence of a relationship between well-being and the presence of disability. As a conclusion having greater economic income and job stability allows access to better social and hierarchical status, which results in higher levels of wellbeing. The majority of the participants registered high levels of wellbeing therefore it We concluded that the participants perceived that well-being is attainable. We suggest exploring the social variables related to sociodemographic variables since it seems that the conditions that generate networks of support, socialization and significant activities for the people raise the level of wellbeing.
Inter- and Intra-Hemispheric Fibers of Olfactory and Motor Areas inKallmann Syndrome with Defective Corpus Callosum

Kallmann syndrome (KS) is a form of hypogonadotropic hypogonadism with variable disturbances of olfaction, with some patients having complete anosmia. In this study, we report observations of cerebral white matter connectivity in a rare patient diagnosed with KS associated with a defective corpus callosum (DCC) (KS-DCC) using non-invasive tractography-based diffusion tensor imaging (DTI). In the partial CC, two genua remnants were observed. The KS-DCC patient had a normal life without cognitive impairment and had a normal intelligence quotient, manifesting only the two common symptoms hypogonadotropic hypogonadism and anosmia. Mirror movements were not observed. Compared with normal, DTI tractography of the olfactory related area in the KS-DCC patient revealed fewer inter-hemispheric fibers between right and left olfactory areas (including olfactory tubercle and anterior commissure). Overall, the KSDCC patient had higher numbers of intra-hemispheric fibers bilaterally and fewer inter-hemispheric connecting fibers. The higher numbers of intra-hemispheric fibers connected the right thalamus and right supplementary motor area (SMA) and the left thalamus and left SMA. The fewest number of fibers observed in the KS-DCC patient were those connecting right and left SMA. The Probst bundle together with increased intra-hemispheric fibers may indicate plastic rewiring of corpus callosum connections.
Discovery of Knowledge Using Association Rules in Wireless Sensor Epocs-a Survey

Data mining and knowledge discovery in huge data streams have recently involved in more applications used for decision making. Currently in wireless sensor networks, various mining techniques are used to discover knowledge on sensor data. Applying mining algorithm in wireless sensor data faces many challenges such as continuous arrival of sensor data, fast and huge data arrival, changes of mining results over time, online mining, data transformation, changing network topology, resource constraints and have emerged into various research problems. In Wireless Sensor Database, this paper presents a review on various approaches of association rule mining algorithms using various techniques forming sensor association rules generating frequent patterns to find upcoming sensor events or sensor fault detection or to estimate the missing sensor readings.
A novel seam measuring method of complex tight butt joint for laser welding

Seam measuring of complex tight butt joint without misalignment for laser welding is a challenging problem. The allowable seam gap width and misalignment in laser welding process are less than 0.1 mm for thin sheet around 2 mm thickness. Current measuring methods or sensors can not stably and accurately extract 3D seam position and seam gap width, due to narrow seam gap and too little misalignment of the butt joint. Furthermore, almost all sensors can not provide seam surface normal vector directly along with seam position and seam gap width. A novel seam measuring method, integrating the 2D machine vision and 3D structured light vision techniques, is proposed. By projecting a unique pattern of three structured light stripes onto the seam surface, 3D seam position, seam gap width and seam surface normal vector can be accurately acquired from a single image, using the 2D/3D vision information fusion approach. Experimental results demonstrate that the proposed seam measuring method is capable of extracting the integrated seam features of tight butt joint without misalignment stably and accurately.
Quantification of read species behavior within whole genome sequencing of cancer genomes for the stratification and visualization of genomic variation

The cancer genome is abnormal genome, and the ability to monitor its sequence had undergone a technological revolution. Yet prognosis and diagnosis remain an expert-based decision, with only limited abilities to provide machine-based decisions. We introduce a heterogeneity-based method for stratifying and visualizing whole-genome sequencing (WGS) reads. This method uses the heterogeneity within WGS reads to markedly reduce the dimensionality of next-generation sequencing data; it is available through the tool HiBS (Heterogeneity-Based Subclassification) that allows cancer sample classification. We validated HiBS using >200 WGS samples from nine different cancer types from The Cancer Genome Atlas (TCGA). With HiBS, we show progress with two WGS related issues: (i) differentiation between normal (NB) and tumor (TP) samples based solely on the information structure of their WGS data, and (ii) identification of specific regions of chromosomal amplification/deletion and their association with tumor stage. By comparing results to those obtained through available WGS analyses tools, we demonstrate some of the novelties obtained by the approach implemented in HiBS and also show nearly perfect normal/tumor classification, used to identify known and unknown chromosomal aberrations. Finally, the HiBS index has been associated with breast cancer tumor stage.
Fingerprintability of WebRTC

We examine WebRTC's suitability as a means of Internet censorship circumvention. WebRTC is a framework and suite of protocols for peer-to-peer communication between web browsers. We analyze the implementation differences in instantiations of WebRTC that make it possible to "fingerprint" implementations--potentially distinguishing circumvention-related uses from ordinary ones. This question is relevant to Snowflake, an upcoming circumvention system that uses WebRTC to turn web browsers into temporary peer-to-peer proxies. We conduct a manual analysis of WebRTC-using applications in order to map the space of distinguishing implementation features. We run a fingerprinting script on a day's worth of network traffic in order to quantify WebRTC's prevalence and diversity. Throughout, we find pitfalls that indicate that resisting fingerprinting in WebRTC is likely to be non-trivial.
Developing Small-Area Health and Exposure Data for the Use in Environmental Public Health Tracking

Author(s): Ortega Hinojosa, Alberto Manuel | Advisor(s): Jerrett, Michael | Abstract: The turn of the millennium has been accompanied by a rapid growth in data collection along with an increasing ability to store, manipulate and analyze it. In tandem with this development in technology and surveillance, there has been a growing understanding of the importance of the socio-physical environment on population behavior and human health. We capitalize on this progress to develop a methodology by which we develop two macro scale datasets, one national and one state-wide, to support the efforts of the Centers for Disease Control and Prevention's Environmental Public Health Tracking Network (EPHTN) in understanding two important health risks: smoking and obesity. Moreover, we use new geographic information science techniques, spatial statistics methodologies and machine learning algorithms to gain a better understanding of the relationship between spatial patterns in physical and socio-demographic characteristics and health risks. Specifically, we address three specific aims: (1) To use current data systems to develop national small-area predictions of adult smoking and obesity for the EPHTN and research; (2) To evaluate current data systems and spatial analysis tools available to describe the within-school environment and the school-neighborhood socio-physical characteristics of California's public schools thought to influence childhood obesity, and use these to develop a comprehensive multilevel dataset for the EPHTN and research; (3) To test the utility of the dataset developed in aim 2 by applying it to an analysis used to increase the understanding of the relationship between the school environment and childhood obesity by examining the relative importance of school attributes. This analysis determines that current data collection systems provide a valuable resource which we combine for the ease of future research use. We demonstrate that using the spatial structure and socio-demographic patterns of health risks, we are able to downscale adult smoking and obesity prevalence to the ZIP code and census tract levels for the conterminous United States, and develop five-year predictions for these for the four quinquennia in the 1991-2010 time period. Lastly, we confirm the utility of the school dataset and determine through the third aim that individual demographics and the social environment seem to be the predominant determinants of childhood obesity.
Correcting Syntactic Annotation Errors Using a Synchronous Tree Substitution Grammar

This paper proposes a method of correcting annotation errors in a treebank. By using a synchronous grammar, the method transforms parse trees containing annotation errors into the ones whose errors are corrected. The synchronous grammar is automatically induced from the treebank. We report an experimental result of applying our method to the Penn Treebank. The result demonstrates that our method corrects syntactic annotation errors with high precision.