Miracle-dz/UniFAR-FTUs huggingface dataset:
Ijca Special Issue on " Recent Trends in Image Processing and Pattern Recognition " Rtippr, 2010. Segmentation of Abnormal Region from Endoscopic Images Using Intelligent Scissors General Terms

The commonly found abnormalities in endoscopic images are cancer tumors, ulcers, bleeding due to internal injuries, etc. Several methods of segmentation are employed in recent past for proper segmentation of such images. Intelligent scissors is one of the tools for segmentation. Here the segmentation of endoscopic images is presented using the intelligent scissors. This method is used to segment the tumor, abnormal regions and cancerous growth in the human esophagus. Several methods implemented in the recent past have yielded good results. But this method is simpler. Here a seed point is selected then the cost matrix is constructed which gives the costs of the neighbouring points. Using Dijkstra " s algorithm, the nearest point falling in the same region is selected. The proposed method has shown encouraging results in segmenting the abnormal parts from esophegal endoscopic images. Image processing, medical image analysis.
PENGARUH KEPEMILIKAN MANAJERIAL DAN DEBT COVENANT TERHADAP KONSERVATISME AKUNTANSI

Penelitian ini bertujuan untuk mengetahui sejauh mana kepemilikan manajerial dan debt covenant berpengaruh terhadap tingkat konservatisme dalam laporan keuangan yang di laporkan oleh manajemen. Konservatisme merupakan variabel dependen dalam penelitian ini yang diukur dengan net asset measure. Variabel independen yang diteliti antara lain kepemilikan manajerial dan debt covenant. Populasi dalam penelitian ini adalah perusahaan manufaktur yang terdaftar di Bursa Efek Indonesia pada tahun 2010 - 2012. Analisis data dalam penelitian ini dilakukan dengan menggunakan analisis regresi linear berganda. Hasil penelitian ini menunjukan Kepemilikan manajerial berpengaruh positif signifikan terhadap konservatisme akuntansi. Debt covenant berpengaruh tidak signifikan terhadap konservatsime akuntansi. Kata Kunci: Konservatisme akuntansi, Kepemilikan manajerial, Debt Covenant
Information Technology and Business Alignments among Different Divisions : A Comparative Analysis of Japan and South Korea

This paper empirically investigates whether information technology (IT) strategies, business strategies, and divisions are aligned to meet overall business goals for Korean Small and medium-sized enterprises (SMEs), based on structure based Strategic Alignment Model, and make comparison with those of Japanese SMEs. Using 2,869 valid responses of Korean Human Capital Corporate Panel survey, a result of this study suggests that Korean human resources (HR) departments have a major influence over IT strategy, which is the same as Japanese SMEs, even though their management styles are quite different. As for IT strategy, it is not related to other departments at all for Korean SMEs. The Korean management seems to possess a great power over each division, such as Sales/Service, Research and Development/Technical Experts, HR, and Production. Keywords—IT-business alignment, structured based strategic alignment model, structural equation model, human resources department.
Persona: a portable tool for augmenting proactive applications with multimodal personalization support

User centric personalization plays an important role for the adoption of proactive applications. However, stipulating system support to facilitate personalization features in proactive applications generically is still an open issue. In this paper we have addressed this particular issue and presented Persona, a tool that enables adding personalization features in proactive applications in a generic manner. A key feature of Persona is portability that allows it to be injected in various pervasive middlewares as a plug-in. Consequently, existing proactive applications can easily be extended with Persona for personalization support. We have discussed the design and implementation rationale behind Persona and shown it's direct implications with two different middlewares and several proactive applications.
Polyrhythm in the Human Brain

Three complementary methods are used to analyze the dynamics of multivariate EEG data obtained from a human listening to a piece of music. The analysis yields parameters for a data sonification that conserves temporal and frequency relationships as well as wave intensities of the data. Multiple events taking place on different time scales are combined to a polyrhythmic display in real time.
Title : Decoupling functional mechanisms of adaptive encoding Authors :

In a natural setting, adaptive mechanisms constantly modul ate the encoding properties of sensory neurons in response to changes in the external environm ent. Recent experiments have revealed that adaptation affects both the spatiotemporal i ntegration properties and baseline membrane potential of sensory neurons. However, the precis e functional role of adaptation remains an open question, due in part to contradictory exper imental results. Here, we develop a framework to characterize adaptive encoding, including a cascade model with a time-varying receptive field (reflecting spatiotemporal integration pro perties) and offset (reflecting baseline membrane potential), and a recursive technique for trackin g changes in the model parameters during a single stimulus/response trial. Simulated and exp erimental responses from retinal neurons are used to track adaptive changes in receptive field structure and offset during nonstationary stimulation. Due to the nonlinear nature of spik ing neurons, the parameters of the receptive field and offset must be estimated simultaneously , or changes in the offset (or even in the statistical distribution of the stimulus) can mask, con fou d, or create the illusion of adaptive changes in the receptive field. Our analysis suggests that th ese confounding effects may be at the root of the inconsistency in the literature and shows tha t seemingly conflicting experimental results can be reconciled within our framework.
Performance problem diagnostics by systematic experimentation

Performance problems such as high response times in software applications have a significant effect on the customer's satisfaction. However, detecting performance problems is still a highly manual and cumbersome process requiring deep expertise in performance engineering. Uncovering performance problems and finding their root causes are two challenging problems which are not solved yet. Existing approaches either focus on certain types of performance problems, do not conduct root cause analysis or consider performance only under average load scenarios. In this PhD research proposal, we pursue the goal to support software engineers in uncovering performance problems and identifying their root causes. Based on a novel way of structuring the knowledge about performance problems, we propose an automatic, experimentation-based approach for diagnostics of performance problems. Utilizing monitoring data from operations, we aim at deriving performance tests which foster the detection of performance problems. Applying the methodology to software projects within SAP, we strive to ensure a profound evaluation of the proposed approach.
Milano Summer Particulate Matter (PM10) Triggers Lung Inflammation and Extra Pulmonary Adverse Events in Mice

Recent studies have suggested a link between particulate matter (PM) exposure and increased mortality and morbidity associated with pulmonary and cardiovascular diseases; accumulating evidences point to a new role for air pollution in CNS diseases. The purpose of our study is to investigate PM10sum effects on lungs and extra pulmonary tissues. Milano PM10sum has been intratracheally instilled into BALB/c mice. Broncho Alveolar Lavage fluid, lung parenchyma, heart and brain were screened for markers of inflammation (cell counts, cytokines, ET-1, HO-1, MPO, iNOS), cytotoxicity (LDH, ALP, Hsp70, Caspase8-p18, Caspase3-p17) for a putative pro-carcinogenic marker (Cyp1B1) and for TLR4 pathway activation. Brain was also investigated for CD68, TNF-α, GFAP. In blood, cell counts were performed while plasma was screened for endothelial activation (sP-selectin, ET-1) and for inflammation markers (TNF-α, MIP-2, IL-1β, MPO). Genes up-regulation (HMOX1, Cyp1B1, IL-1β, MIP-2, MPO) and miR-21 have been investigated in lungs and blood. Inflammation in the respiratory tract of PM10sum-treated mice has been confirmed in BALf and lung parenchyma by increased PMNs percentage, increased ET-1, MPO and cytokines levels. A systemic spreading of lung inflammation in PM10sum-treated mice has been related to the increased blood total cell count and neutrophils percentage, as well as to increased blood MPO. The blood-endothelium interface activation has been confirmed by significant increases of plasma ET-1 and sP-selectin. Furthermore PM10sum induced heart endothelial activation and PAHs metabolism, proved by increased ET-1 and Cyp1B1 levels. Moreover, PM10sum causes an increase in brain HO-1 and ET-1. These results state the translocation of inflammation mediators, ultrafine particles, LPS, metals associated to PM10sum, from lungs to bloodstream, thus triggering a systemic reaction, mainly involving heart and brain. Our results provided additional insight into the toxicity of PM10sum and could facilitate shedding light on mechanisms underlying the development of urban air pollution related diseases.
Detect Events on Noisy Textual Datasets

Social media, e.g. Weblog and Internet forum, generate rich historical textual datasets which record lots of valuable events. Automatic event detection tries to discover important and interesting events and their related documents. Existing solutions to event detection, however, are mostly proposed for high quality news stories and may not work well when they are applied to noisy social media datasets, where content quality varies drastically from informative to trivial or even spamming. In this paper, an event detection framework, which directly utilizes burst property of events to filter out noise, is proposed. Experimental results on real dataset from Tencent Internet forum, a popular forum in China, demonstrate the effectiveness of the proposed framework.
The Plausibility of a String Quartet Performance in Virtual Reality

We describe an experiment that explores the contribution of auditory and other features to the illusion of plausibility in a virtual environment that depicts the performance of a string quartet. ‘Plausibility’ refers to the component of presence that is the illusion that the perceived events in the virtual environment are really happening. The features studied were: Gaze (the musicians ignored the participant, the musicians sometimes looked towards and followed the participant's movements), Sound Spatialization (Mono, Stereo, Spatial), Auralization (no sound reflections, reflections corresponding to a room larger than the one perceived, reflections that exactly matched the virtual room), and Environment (no sound from outside of the room, birdsong and wind corresponding to the outside scene). We adopted the methodology based on color matching theory, where 20 participants were first able to assess their feeling of plausibility in the environment with each of the four features at their highest setting. Then five times participants started from a low setting on all features and were able to make transitions from one system configuration to another until they matched their original feeling of plausibility. From these transitions a Markov transition matrix was constructed, and also probabilities of a match conditional on feature configuration. The results show that Environment and Gaze were individually the most important factors influencing the level of plausibility. The highest probability transitions were to improve Environment and Gaze, and then Auralization and Spatialization. We present this work as both a contribution to the methodology of assessing presence without questionnaires, and showing how various aspects of a musical performance can influence plausibility.
Discrete Surface Modeling using Geometric Flows

We use various nonlinear geometric partial differential equations to efficiently solve several surface modelling problems, including surface blending, N -sided hole filling and free-form surface fitting. The nonlinear equations used include two second order flows (mean curvature flow and average mean curvature flow), one fourth order flow (surface diffusion flow) and a sixth order flow. These nonlinear equations are discretized based on discrete differential geometry operators. The proposed approach is simple, efficient and gives very desirable results, for a range of surface models, possibly having sharp creases and corners.
Time-Aware User Identification with Topic Models

Accounts are often shared by multiple users, eachof them having different item consumption and temporal habits. Identifying of the active user can lead to improvements ina variety of services by switching from account personalizedservices to user personalized services. To do so, we developa topic model extending the Latent Dirichlet Allocation usinga hidden variable representing the active user and assumingconsumption times to be generated by latent time topics. Wecreate a new dataset of composite accounts from real users totest the identification capabilities of our model. We show thatour model is able to learn temporal patterns from the whole setof accounts and infer the active user using both the consumptiontime and the consumed item.
A Model of Defensive Information Avoidance in Information Systems Use

�This paper looks at defensive information avoidance: the phenomenon that users avoid decision-relevant but incongruent information. Since only utilized information can contribute to better decision-making, the phenomenon of information avoidance is of great relevance to the information systems discipline. This paper contributes the first coherent conceptualization of information avoidance behavior by synthesizing scattered but related definitions. Building on this, it proposes a theoretical model and explains how encountering decision-relevant but incongruent information relates to different avoidance behaviors. In particular, it helps to understand yet unexplained IS use phenomena, where encountering incongruent information ‐ paradoxically ‐ leads to a higher information exposure but a decreased information absorption and use. The paper contributes methodologically by proposing a series of neurophysiological measures for testing the explanatory model. It derives and discusses two experimental paradigms from experimental psychology for testing the hypothesized effects.
Design of Microarray Experiments for Genetical Genomics Studies

Microarray experiments have been used recently in genetical genomics studies, as an additional tool to understand the genetic mechanisms governing variation in complex traits, such as for estimating heritabilities of mRNA transcript abundances, for mapping expression quantitative trait loci, and for inferring regulatory networks controlling gene expression. Several articles on the design of microarray experiments discuss situations in which treatment effects are assumed fixed and without any structure. In the case of two-color microarray platforms, several authors have studied reference and circular designs. Here, we discuss the optimal design of microarray experiments whose goals refer to specific genetic questions. Some examples are used to illustrate the choice of a design for comparing fixed, structured treatments, such as genotypic groups. Experiments targeting single genes or chromosomic regions (such as with transgene research) or multiple epistatic loci (such as within a selective phenotyping context) are discussed. In addition, microarray experiments in which treatments refer to families or to subjects (within family structures or complex pedigrees) are presented. In these cases treatments are more appropriately considered to be random effects, with specific covariance structures, in which the genetic goals relate to the estimation of genetic variances and the heritability of transcriptional abundances.
Competitive market-based allocation of consumer attention space

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the "consumer attention space", or banners. In our approach, each agent bids in an auction for the momentary attention of each consumer. Successive auctions allow agents to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. We demonstrate the feasibility of our system by an evolutionary simulation, and reflect on the advantages of this distributed market-based approach.
Evaluation of Flavorings-Related Lung Disease Risk at Six Microwave Popcorn Plants

Objective: After investigating fixed airways obstruction in butter flavoring-exposed workers at a microwave popcorn plant, we sought to further characterize lung disease risk from airborne butter-flavoring chemicals. Methods: We analyzed data from medical and environmental surveys at six microwave popcorn plants (including the index plant). Results: Respiratory symptom and airways obstruction prevalences were higher in oil and flavorings mixers with longer work histories and in packaging-area workers near nonisolated tanks of oil and flavorings. Workers were affected at five plants, one with mixing-area exposure to diacetyl (a butter-flavoring chemical with known respiratory toxicity potential) as low as 0.02 ppm. Conclusions: Microwave popcorn workers at many plants are at risk for flavoring-related lung disease. Peak exposures may be hazardous even when ventilation maintains low average exposures. Respiratory protection and engineering controls are necessary to protect workers.
Practical Approximate k Nearest Neighbor Queries with Location and Query Privacy

In mobile communication, spatial queries pose a serious threat to user location privacy because the location of a query may reveal sensitive information about the mobile user. In this paper, we study approximate k nearest neighbor (kNN) queries where the mobile user queries the location-based service (LBS) provider about approximate k nearest points of interest (POIs) on the basis of his current location. We propose a basic solution and a generic solution for the mobile user to preserve his location and query privacy in approximate kNN queries. The proposed solutions are mainly built on the Paillier public-key cryptosystem and can provide both location and query privacy. To preserve query privacy, our basic solution allows the mobile user to retrieve one type of POIs, for example, approximate k nearest car parks, without revealing to the LBS provider what type of points is retrieved. Our generic solution can be applied to multiple discrete type attributes of private location-based queries. Compared with existing solutions for kNN queries with location privacy, our solution is more efficient. Experiments have shown that our solution is practical for kNN queries.
Exploring the Relationship Between Child Maltreatment, Intimate Partner Violence Victimization, and Self-Injurious Thoughts and Behaviors

ABSTRACT Child maltreatment is considered a significant risk factor for young adult self-injury; however, the mechanisms that underlie this relationship are not yet understood. To elucidate this association, the mediating role of intimate partner violence victimization in the relationship between child maltreatment and self-injurious thoughts and behaviors was investigated. The sample consisted of 406 young adults (346 females; Mage = 19.87 years; SD = 1.72) involved in a couple relationship. Results of bootstrapping procedures demonstrated that intimate partner violence victimization mediated the relationship between child maltreatment and self-injurious behaviors but did not mediate the association between child maltreatment and self-injurious thoughts. Results suggest that young adults with a history of child maltreatment may be more likely to be exposed to IPV victimization, which, in turn, is associated with the use of self-injurious behaviors, but not the use of self-injurious thoughts. Findings highlight the utility of examining models that incorporate distal and proximal factors contributing to self-injury, and provide direction toward better understanding the relationship experiences of self-injuring young adults.
Rapid image binarization with morphological operators

In a grayscale image to be binarized, some pixels represent "foreground" text, to be converted to black-while others represent a shaded "background" region, to be converted to white. The difference between the two is largely one of scale. Morphological operators, the alternating sequential filter in particular, though an effective tool for extracting image background, are computationally expensive. We define reduced-complexity quick-open and quick-close morphological operators, which are employed to binarize grayscale images having complex backgrounds at modest computational cost.
RFID Data Management , Aggregation and Filtering

Radio Frequency Identification (RFID) technology enables a new era of business optimization. RFID provides capturing large volumes of data at high velocity and can be used for identifying, locating, tracking and monitoring physical objects without line of sight. However, despite these benefits, this technology poses many new challenges in the current data processing and management systems. RFID data are dynamically changing and time-dependent, RFID observations can contain redundant information, and the information volume generated by such a system can be enormous. Furthermore, low-level implicit RFID observations need to be transformed and aggregated into semantically meaningful data suitable for different enterprise applications. This paper aims to consider a system architecture for managing RFID data based on efficient analyzing, filtering and aggregation techniques for RFID data streams. The usage of these techniques in RFID middleware and by construction of the RFID warehouse is an important topic of our research.
Full-wave modeling of buried pipe detection with low-frequency ground-pentetrating radar

electromagnetic sensing of buried pipes with ground-penetrating radar (GPR) in realistic scenarios is presented. GPR uses resistively loaded wideband dipole antennas operating in the frequency band from 100 MHz to 1 GHz. To this end, a locally conformal finite-difference time-domain (FDTD) technique, useful to model losses in the soil, and the presence of ground-embedded inhomogeneities with arbitrary shape and material parameters, is adopted. Emphasis is put on the investigation of the antenna pair performance for different Tx–Rx separations and elevations over the ground, as well as on the analysis of scattering from buried dielectric and metallic pipes. Furthermore, a frequency-independent equivalent circuit suitable for CAD is provided to facilitate the design of the RF front-end of ground-penetrating radars, and the methodology to derive relevant circuital parameters is discussed in detail.
The Cornetto Database: Architecture and User-Scenarios

We outline the architecture of a semantic database for Dutch, developed in the Cornetto project, and its possible usage in language technology and information access applications. The database combines the Dutch part of EuroWordNet with the Referentie Bestand Nederlands. The resulting database is also aligned with the English WordNet and with a formal ontology. As such it represents a unique database with rich semantic and combinatoric information. There are many ways in which this knowledge can be exploited. In this paper we give an overview of possible application areas in order to inspire future research based on the Cornetto database.