Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Image Matching with Line Context

Some features are specially designed to cope with complex illumination variations [1, 2, 3, 4, 5]. These features are based on intensity orders rather than values so that their descriptors are invariant to non-linear intensity changes. However, one limitation of such features is that they are only able to handle monotonic illumination changes, as the examples shown in Figure 1. In this paper, we try to cope with this problem by proposing a new type of local feature, which describes the context information of neighboring line segments. Through extensive experiments and based on existing research, we found that the best pixel grouping level to handle large illumination variations is using curves or lines. Since curves require more sophisticated groupings and can be approximated by multiple line segments, we use line segments as the primitives of our feature descriptor. We call the proposed feature Line Context, since it is inspired by Shape Context. Line Context Detector. It is well known that edges are present at various scales. To detect edges at different scales we use multi-scale Canny edge detector with Gaussian derivatives at several pre-selected scales. Two phases are used to remove unstable edges. The first phase is to apply Laplacian operator. Those edges that do not attain a distinctive extremum over scales will be removed. The second phase is to remove those edges with homogeneous gradients, i.e. the points where the underlying curves have zero curvatures. We apply Harris matrix to achieve this purpose. Segments in Context. The edge pixels are linked to connected curves at different scales. These curves are then fitted by straight line segments. As shown in Figure 2-(a), several cases need to be considered for representing curves with line segments. One curve may be fitted by multiple segments like curve a. Two segments with small gap in between are merged into one larger segment no matter they are on the same scale (curve b and d) or different scales (curve f and g). In the latter case, the merged segment only exists in the lower scale (segment 8). Besides, all the segments in higher levels are also segments (segment 1, 2 and 3) or part of segments (segment 5 and 7) in lower levels. For each keypoint, we need to find line segments in its neighborhood, which is also called context of the feature. The line segments lying inside or partially inside the context are called context segments. The initial scale σ provides an estimate size of searching area. Let dpoint−set(v,S) be the shortest distance from point v to a set of points S. With keypoint k and all-segments {segi}, the segments set L in the context is defined as,
Dynamically Adjustable Approach through Obfuscation Type Recognition

The task of (monolingual) text alignment consists in finding similar text fragments between two given documents. It has applications in plagiarism detection, detection of text reuse, author identification, authoring aid, and information retrieval, to mention only a few. We describe our approach to the text alignment subtask of the plagiarism detection competition at PAN 2015. Our method relies on a sentence similarity measure based on a tf-idf-like weighting scheme and cosine and dice similarity measures. We used and extended our previous algorithm for clustering and introduced a new verbatim detection method and extended the decision making regarding which approach or output to use. We improve significantly the performance regarding our previous PAN 2014 approach and hence, our approach outperforms the best-performing system of the PAN 2014. Our system is available open source.
AN EFFICIENT SEMANTIC WEB SERVICES SELECTION MODEL USING CLUSTERING

Web Services are one of the fastest growing areas of information technology in recent years, also being a main motivating factor for internet computations in which, one of the services being, service discovery. Web service discovery is the process of finding appropriate services for the user defined tasks.Web Service clustering is a technique for efficiently facilitating service discovery. Most Web Service clustering approaches are based on suitable semantic similarity distance measure and a threshold. Threshold selection is essentially difficult and often leads to unsatisfactory accuracy. In this paper, a self-organizing based clustering algorithm called Taxonomy based clustering for taxonomically organizing semantic Web Service advertisements. A query matching method is also applied on these clusters to get more accurate and relevant results based for user requests. The system is tested and observed promising results both in terms of accuracy and performance.
Discourse, Consciousness, and Time: The Flow and Displacement of Conscious Experience in Speaking and Writing

In this text, the author demonstrates how the study of language and consciousness together can provide an unexpectedly broad understanding of the way the mind works. Relying on close analyses of conversational speech, as well as written fiction and non-fiction, he investigates both the flow of ideas through consciousness and the displacement of consciousness by way of memory and imagination. Chafe draws on several decades of research to demonstrate that understanding the nature of consciousness is essential to understanding many linguistic phenomena, such as pronouns, tense, clause structure and intonation, as well as stylistic usages, such as the historical present and the free indirect style. While the book focuses on English, there are also discussions of the North American Indian language, Seneca, and the music of Mozart and of the Seneca people.
Human skin color clustering for face detection

Computer vision is one of many areas that wants to understand the process of human functionality and copy that process with the intention to complement human life with intelligent machines. For better human-computer interaction it is necessary for the machine to see people. This can be achieved by employing face detection algorithms, like the one used in the installation "15 Seconds of Fame". This installation unites the areas of modern art and technology. Its algorithm is based on skin colour detection. One of the problems that this and similar algorithms have to deal with is sensitivity to the illumination conditions under which the input image is captured. Hence illumination sensitivity influences face detection results. One of the aspects from which we can observe illumination influence is the choice of proper colour space. Since some colour spaces are designed to eliminate the influence of illumination (brightness) when describing colour of an object, an idea of using such a colour space for skin-colour detection has been considered and some of the methods have been researched and tested.
Health warning messages on tobacco products: a review

Objective To review evidence on the impact of health warning messages on tobacco packages. Data sources Articles were identified through electronic databases of published articles, as well as relevant ‘grey’ literature using the following keywords: health warning, health message, health communication, label and labelling in conjunction with at least one of the following terms: smoking, tobacco, cigarette, product, package and pack. Study selection and data extraction: Relevant articles available prior to January 2011 were screened for six methodological criteria. A total of 94 original original articles met inclusion criteria, including 72 quantitative studies, 16 qualitative studies, 5 studies with both qualitative and qualitative components, and 1 review paper: Canada (n=35), USA (n=29) Australia (n=16), UK (n=13), The Netherlands (n=3), France (n=3), New Zealand (n=3), Mexico (n=3), Brazil (n=2), Belgium (n=1), other European countries (n=10), Norway (n=1), Malaysia (n=1) and China (n=1). Results The evidence indicates that the impact of health warnings depends upon their size and design: whereas obscure text-only warnings appear to have little impact, prominent health warnings on the face of packages serve as a prominent source of health information for smokers and non-smokers, can increase health knowledge and perceptions of risk and can promote smoking cessation. The evidence also indicates that comprehensive warnings are effective among youth and may help to prevent smoking initiation. Pictorial health warnings that elicit strong emotional reactions are significantly more effective. Conclusions Health warnings on packages are among the most direct and prominent means of communicating with smokers. Larger warnings with pictures are significantly more effective than smaller, text-only messages.
Optimizing LUT-Based RTM Inversion for Semiautomatic Mapping of Crop Biophysical Parameters from Sentinel-2 and -3 Data: Role of Cost Functions

Inversion of radiative transfer models (RTM) using a lookup-table (LUT) approach against satellite reflectance data can lead to concurrent retrievals of biophysical parameters such as leaf chlorophyll content (Chl) and leaf area index (LAI), but optimization strategies are not consolidated yet. ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity of old generation satellite sensors by providing superspectral images of high spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust, accurate, and operational retrieval methods. For three simulated Sentinel settings (S2-10 m: 4 bands, S2-20 m: 8 bands and S3-OLCI: 19 bands) various optimization strategies in LUT-based RTM inversion have been evaluated, being the role of i) added noise, ii) multiple best solutions, iii) combined parameters (Chl ×LAI), and iv) applied cost functions. By inverting the PROSAIL model and using data from the ESA-led field campaign SPARC (Barrax, Spain), it was demonstrated that introducing noise and opting for multiple best solutions in the inversion considerably improved retrievals. However, the widely used RMSE was not the best performing cost function. Three families of alternative cost functions were applied here: information measures, minimum contrast, and M-estimates. We found that so-called “Power divergence measure”, “Trigonometric”, and spectral measure with “Contrast function K(x) = -log(x) + x”, yielded more accurate results, although this also depended on the biophysical parameter. Particularly, when simultaneous retrieval of multiple biophysical parameters is the objective then “Contrast function K(x) = -log(x) + x” provided most consistent optimized estimates of leaf Chl, LAI and canopy Chl across the different Sentinel configurations (relative RMSE: 24-29 %).
Examining the Effect of Prenotification Postcards on Online Survey Response Rate in a University Graduate Sample

The Internet has become an increasingly popular avenue for data collection. Online surveys have many advantages, such as cutting the costs of printing and mailing surveys and reducing time for data collection and entry (Kaplow itz, Hadlock, and Levine 2004). However, online surveys have disadvantages (Schonlau, Fricker Jr., and Elliott 2002); most notable being the low response rates relative to other modes of data collection (Hoonakker and Carayon 200 9). With growing interest in the use of online surveys (Sue and Ritter 2011), research needs to clarify ways to increase response rates for this survey mode.
PCI versus CABG in patients with complex coronary artery disease: Time for reconciliation?

Final 5-year results of the Synergy between PCI with Taxus and Cardiac Surgery (SYNTAX) trial were recently presented at the Transcatheter Cardiovascular Therapeutics (TCT) and European Society of Thoracic Surgery (ESTS) meetings . SYNTAX is frequently quoted as “the most important percutaneous coronary intervention (PCI) vs. coronary artery bypass grafting (CABG) trial in multivessel and/or left main (LM) coronary artery disease (CAD) conducted to date”. However, the impact of its findings on day-to-day clinical practice remains surprisingly humble .
A passive method for estimating end-to-end TCP packet loss

This paper presents a passive end-to-end loss monitoring method, which relies on traffic monitoring at a core or ingress router interface. The monitoring node captures the packets of TCP connections generated by end-hosts. Based on the seen sequence number pattern the loss ratios are estimated for the two segments of end-to-end path divided by the monitor. This feature is especially useful if the monitoring node is placed at the border of an autonomous system, e.g., at the egress point of an ISP. When applied to mobile Internet access, packet losses on the radio interface can be distinguished from losses in the core network or in the public Internet. The packet loss estimation algorithm is rigorously validated using simulations and testbed measurements. Additionally, measurement results and an application example in a dial-up ISP are presented.
AbstFinder, a prototype abstraction finder for natural language text for use in requirements elicitation: design, methodology, and evaluation

In order to help solve the problems of requirements elicitation, this paper motivates and describes a new approach, based on traditional signal processing methods, for finding abstractions in natural language text. The design of AbstFinder, an implementation of the approach, and the evaluation of its effectiveness on an industrial-strength example are described. <<ETX>>
Microarray Analysis of Global Gene Expression in Mucoid Pseudomonas aeruginosa

ABSTRACT Pseudomonas aeruginosa is the dominant pathogen causing chronic respiratory infections in cystic fibrosis (CF). After an initial phase characterized by intermittent infections, a chronic colonization is established in CF upon the conversion of P. aeruginosa to the mucoid, exopolysaccharide alginate-overproducing phenotype. The emergence of mucoid P. aeruginosa in CF is associated with respiratory decline and poor prognosis. The switch to mucoidy in most CF isolates is caused by mutations in the mucA gene encoding an anti-sigma factor. The mutations in mucA result in the activation of the alternative sigma factor AlgU, the P. aeruginosa ortholog of Escherichia coli extreme stress sigma factor σE. Because of the global nature of the regulators of mucoidy, we have hypothesized that other genes, in addition to those specific for alginate production, must be induced upon conversion to mucoidy, and their production may contribute to the pathogenesis in CF. Here we applied microarray analysis to identify on the whole-genome scale those genes that are coinduced with the AlgU sigmulon upon conversion to mucoidy. Gene expression profiles of AlgU-dependent conversion to mucoidy revealed coinduction of a specific subset of known virulence determinants (the major protease elastase gene, alkaline metalloproteinase gene aprA, and the protease secretion factor genes aprE and aprF) or toxic factors (cyanide synthase) that may have implications for disease in CF. Analysis of promoter regions of the most highly induced genes (>40-fold, P ≤ 10−4) revealed a previously unrecognized, putative AlgU promoter upstream of the osmotically inducible gene osmE. This newly identified AlgU-dependent promoter of osmE was confirmed by mapping the mRNA 5′ end by primer extension. The recognition of genes induced in mucoid P. aeruginosa, other than those associated with alginate biosynthesis, reported here revealed the identity of previously unappreciated factors potentially contributing to the morbidity and mortality caused by mucoid P. aeruginosa in CF.
Automatic detection of Malaria infected RBCs from a focus stack of bright field microscope slide images

Malaria is a deadly infectious disease affecting red blood cells in humans due to the protozoan of type Plasmodium. In 2015, there is an estimated death toll of 438, 000 patients out of the total 214 million malaria cases reported world-wide. Thus, building an accurate automatic system for detecting the malarial cases is beneficial and has huge medical value. This paper addresses the detection of Plasmodium Falciparum infected RBCs from Leishman's stained microscope slide images. Unlike the traditional way of examining a single focused image to detect the parasite, we make use of a focus stack of images collected using a bright field microscope. Rather than the conventional way of extracting the specific features we opt for using Convolutional Neural Network that can directly operate on images bypassing the need for hand-engineered features. We work with image patches at the suspected parasite location there by avoiding the need for cell segmentation. We experiment, report and compare the detection rate received when only a single focused image is used and when operated on the focus stack of images. Altogether the proposed novel approach results in highly accurate malaria detection.
ROAL: A Randomly Ordered Activation and Layering Protocol for Ensuring K-Coverage in Wireless Sensor Networks

K-coverage in wireless sensor networks (WSNs) is defined as ensuring that every point in the area is monitored by at least K different sensor nodes. In this paper, we propose a new K-coverage algorithm for sensor networks, called randomly ordered activation and layering (ROAL), that solves the K-coverage problem in a small constant time in a distributed manner while providing simple and efficient dynamic reconfiguration for the WSNs. The simulation results show that the ROAL can guarantee K-coverage with the uncovered area less than 5% when a sufficient number of sensor nodes are provided, and that the lifetime of the sensor network is significantly extended by more than 400%.
Systems interface biology

The field of systems biology has attracted the attention of biologists, engineers, mathematicians, physicists, chemists and others in an endeavour to create systems-level understanding of complex biological networks. In particular, systems engineering methods are finding unique opportunities in characterizing the rich behaviour exhibited by biological systems. In the same manner, these new classes of biological problems are motivating novel developments in theoretical systems approaches. Hence, the interface between systems and biology is of mutual benefit to both disciplines.
The effect of multiple perpetrators on judgements of responsibility

This study questioned whether participants feel one person committing a crime alone is more, less, or equally responsible for his or her actions than two people committing a crime together; whether participants react differently to two equally culpable coconspirators than they do to a perpetrator who was coerced by a companion to commit the crime; and whether these factors affect the amount of responsibility attributed to the perpetrator or the length of sentence recommended. The study found that less responsibility was attributed to a coerced perpetrator; however, all perpetrators received sentences of similar lengths. Also, White respondents attributed more responsibility to the perpetrator than Black or ―other‖ respondents, and females gave longer sentences than males. It appears that the differing circumstances of the perpetrators and the backgrounds of the respondents are more influential factors than the number of perpetrators. Possible implications of these findings, methodological improvements, and suggestions for future research are discussed.
Object Manipulation Based on Memory and Observation

Object and tool manipulation are related to the ability to modify one’s environments and are common to many intelligent species. This ability is an important part of a complete account of intelligence and will likely be a cornerstone of machine intelligence as well. Moreover, a computational account of object and tool manipulation may provide insight into the mechanisms and processes that give rise to it in nature. Traditional approaches often consider a pipeline of segmentation, object recognition, and pose estimation to be an essential perception stage prior to motor activity. In this work, we propose an alternative approach to object recognition and pose estimation. In computer vision and robotics, object recognition is often defined as the process of labeling segments in an image or fitting a 3-D model to an observed point cloud. The object models used to accomplish these tasks usually include information about visual appearance and shape. However, what these object recognition systems provide is merely a label for each observed object. The sequence of actions that the robot should perform based on the object label are often manually defined. Without linking actions to object labels these models have limited utility to the robot. In this work, we propose an object model that is composed of a set of viewpoint-specific observations to capture how actions change observation of the object. In most robotics tasks that require manipulating known objects, pose estimation is often required before planning end effector trajectories. In the Willow Garage grasping pipeline [6], the iterative closest point algorithm is used to check how well a segmented point cloud matches to a stored mesh model. Precomputed grasp points associated with the model are then used to generate a valid motion trajectory. However, object pose estimation is often computationally expensive and inaccurate. In addition, there are many examples of tasks that do not require pose information. This paper proposes using the aspect transition graph object model that skips pose estimation and interacts with objects directly based on memory and observation.
Operating Performance of the Banking Industry: An Empirical Investigation of the South Eastern European Region

This paper examines the operating performance of the South Eastern European (SEE) banking industry over the period 1998-2003. To this end, we investigate the empirical relationship between operating expenses and bank, market and country specific characteristics. Operating performance is found to be positively related to loan quality and the asset size or the bankâ€™s market share, and negatively related to liquidity, the loan ratio and bank's age.
Effectiveness of botulinum toxin A in the treatment of spasticity of the lower extremities in adults - preliminary report.

BACKGROUND Botulinum toxin (BTX is currently a recognised treatment for local spasticity, especially in children with cerebral palsy. The following paper presents the early result of BTX treatment for adult patients with spastic paresis of the lower limbs. MATERIAL AND METHODS Twenty adult paraplegic patients (mean age 42 years) following cervical or thoracic SCI or suffering from MS, with moderate-to-severe spasticity in the lower extremities received BTX for the first time in life into the thigh adductor, knee flexor and foot flexor muscle groups. Results were evaluated using Modified Ashworth's Scale, Visual (Analogue) Scale for Pain Assessment, Modified Rivermead Mobility Index and Repty Functional Index prior to and three weeks after the administration of the toxin. RESULTS Improvement was observed in most patients, usually manifesting as reductions or resolution of pain. Mild side effects (low-grade fever and flu-like symptoms) occurred in only one patient. CONCLUSIONS Our study confirmed the efficacy and safety of BTX for focal lower limb spasticity in adults.