Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Analysis of Seller Profiles in Electronic Markets

In the last decade, there has been an explosion of online commercial activity enabled by World Wide Web (WWW). These days, many consumers are less attracted to online auctions, preferring to buy merchandise quickly using fixed price negotiations. This work is part of a research to characterize and analyze selling practices in a fixed-price e-market. An important part of this research is to evaluate qualitatively how the e-market changes through time, considering the seller profiles and their negotiations and the impact of them in terms of outcomes, such as price and transaction. We use real data from TodaOferta, a large Brazilian marketplace. In terms of transactions, we measure a variation from 10 to 15% of unsatisfaction, showing that TodaOferta has a considerable credibility. The retailers represent around 40 to 45% of e-market transactions share. There is also a significant participation of newcomers in the e-market. The proposed methodology and the results of this work can be applied in the design of new personalization and recommendation techniques for e-market traders.
A Network Lifetime Aware Cooperative MAC Scheme for 802.11b Wireless Networks

Cooperative communication techniques have earlier been applied to design of the IEEE 802.11 medium access control (MAC) and shown to perform better. High rate stations can help relay packets from low-rate stations resulting in better throughput for the entire network. However, this also involves additional energy costs on the part of the relay which can result in reducing the network lifetime. We propose a cooperative MAC protocol NetCoop with the objective of maximizing the network lifetime and achieving high throughput. Based on this design, we also propose a flexible strategy which allows cooperation to be achieved using more than one relay. We show that this can achieve at least as good throughput as that of single relay cooperation while maintaining a high network lifetime.
Learning Good Features to Track

Object tracking is an important task within the field of computer vision. Tracking accuracy depends mainly on finding good discriminative features to estimate the target location. In this paper, we introduce online feature learning in tracking and propose to learn good features to track generic objects using online convolutional neural networks (OCNN). OCNN has two feature mapping layers that are trained offline based on unlabeled data. In tracking, the collected positive and negative samples from the previously tracked frames are used to learn good features for a specific target. OCNN is also augmented with a classifier to provide a decision. We build a tracking system by combining OCNN and a color-based multi-appearance model. Our experimental results on publicly available video datasets show that the tracking system has superior performance when compared with several state of-the-art trackers.
A Survey On Different Image Encryption and Decryption Techniques

This paper focuses mainly on the different kinds of image encryption and decryption techniques. In addition focuses on image encryption techniques, As the use digital techniques for transmitting and storing images are increasing, it becomes an important issue that how to protect the confidentiality, integrity and authenticity of images. There are various techniques which are discovered from time to time to encrypt the images to make images more secure. This paper presents a survey of over 25 research papers dealing with image encryption techniques scrambled the pixels of the image and decrease the correlation among the pixels, so that we will get lower correlation among the pixel and get the encrypted image. In this paper a Survey of Different Image Encryption and encryption techniques that are existing is given. It additionally focuses on the functionality of Image encryption and decryption techniques. KeywordsImage Encryption, Image Decryption.
Optimal Pricing Strategy in the Case of Price Dispersion: New Evidence from the Tokyo Housing Market

In the wake of recent pronounced cycles in housing, substantial media and professional debate have focused on house price determination, and in particular, optimal seller pricing strategies. In this paper, we adopt a multistage search model, in which the home seller’s reservation price is determined by her opportunity cost, search cost, discount rate, and additional market parameters including the anticipated offer arrival rate and the offer price distribution. The optimal asking price is chosen so as to maximize the return from search. Theoretical results indicate that a greater dispersion in offer prices leads to higher reservation and optimal asking prices, which in turn result in a higher expected transaction price. Under the assumption that offer prices are normally distributed, a higher dispersion of offer prices also reduces time on the market for overpriced properties. A unique dataset from the Tokyo condominium re-sale market enables us to test those modeled hypotheses. Empirical results indicate that the standard deviation of transaction prices for each submarket, a proxy of offer price dispersion, is an important determinant of both pricing strategy and pricing outcomes. A one percentage point increase in the standard deviation of sub-market transaction prices results in a two-tenths of a percent increase in the initial asking price and in the final transaction price. Although overpriced properties stay on the market longer, increases in the dispersion of market prices enhance the probabilities of a successful transaction and/or an accelerated sale. Moreover, less well-informed sellers are more likely to list their properties at significantly higher prices and later reduce their list price. Those properties stay on the market longer and sell at about a three percent discount relative to the properties of better-informed sellers.
Screening for significant atherosclerotic renal artery stenosis with a regression model in patients undergoing transradial coronary angiography/intervention

ObjectiveEarly detection of atherosclerotic renal artery stenosis (ARAS) is clinically important with respect to blood pressure control, prevention of renal insufficiency, and even improving survival. We investigated whether the presence of significant ARAS (luminal diameter narrowing ≥70%) could be predicted using a logistic regression model before coronary angiography/intervention.MethodsInitially, we developed a logistic regression model for detecting significant ARAS based upon clinical and angiographic features and biochemical measurements in a cohort of 1 813 patients undergoing transfemoral coronary and renal angiography. This model was then prospectively applied to an additional 495 patients who received transradial renal angiography to ascertain its predictive accuracy for the presence of significant ARAS.ResultsMultivariate regression analysis revealed that older age (≥65 years), resistant hypertension, type 2 diabetes, creatinine clearance (Ccr) ≤60 ml/min, and multivessel coronary disease were independent predictors for significant ARAS. A logistic regression model for detecting ARAS by incorporating conventional risk factors and multivessel coronary disease was generated as: P/(1−P)=exp(−2.618+1.112[age≥65 years]+1.891[resistant hypertension]+0.453[type 2 diabetes]+0.587[Ccr≤60 ml/min]+2.254[multivessel coronary disease]). When this regression model was prospectively applied to the additional 495 patients undergoing transradial coronary and renal angiography, significant ARAS could be detected with a sensitivity of 81.2%, specificity of 88.9%, and positive and negative predictive accuracies of 53.8% and 96.7%, respectively.ConclusionsThe logistic regression model generated in this study may be useful for screening for significant ARAS in patients undergoing transradial coronary angiography/intervention.
Effect of 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) on Hormones of Energy Balance in a TCDD-Sensitive and a TCDD-Resistant Rat Strain

One of the hallmarks of the acute toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is a drastically reduced feed intake by an unknown mechanism. To further elucidate this wasting syndrome, we followed the effects of a single large dose (100 μg/kg) of TCDD on the serum levels of several energy balance-influencing hormones, clinical chemistry variables, and hepatic aryl hydrocarbon receptor (AHR) expression in two rat strains that differ widely in their TCDD sensitivities, for up to 10 days. TCDD affected most of the analytes in sensitive Long-Evans rats, while there were few alterations in the resistant Han/Wistar strain. However, analyses of feed-restricted unexposed Long-Evans rats indicated several of the perturbations to be secondary to energy deficiency. Notable increases in ghrelin and glucagon occurred in TCDD-treated Long-Evans rats alone, which links these hormones to the wasting syndrome. The newly found energy balance regulators, insulin-like growth factor 1 and fibroblast growth factor 21 (FGF-21), appeared to function in concert in body weight loss-induced metabolic state, and FGF-21 was putatively linked to increased lipolysis induced by TCDD. Finally, we demonstrate a reverse set of changes in the AHR protein and mRNA response to TCDD and feed restriction, suggesting that AHR might function also as a physiological regulator, possibly involved in the maintenance of energy balance.
A direct imaging algorithm for extended targets

We present a direct imaging algorithm for extended targets. The algorithm is based on a physical factorization of the response matrix of a transducer array. The multi-signal classification imaging function is used to visualize the results. A resolution and noise level-based thresholding strategy is developed for regularization. The algorithm is simple and efficient since no forward solver or iteration is needed. Multiple-frequency information improves both resolution and stability of the algorithm. Efficiency and robustness of the algorithm with respect to measurement noise and random medium fluctuations are demonstrated.
What makes end-user training successful? A mixed method study of a business process management system implementation

Under what conditions is end-user training (EUT) as part of the implementation of a business process management (BPM) system successful? This question is addressed in this paper. Based on the literature on EUT and implementation success, we first argue that user involvement with, and attitude towards, a BPM system, both have a conditional effect on the relationship between EUT and the implementation success of the system. Secondly, we investigated this expectation empirically, by measuring the practice of EUT as perceived by end-users. Using a mixed method approach, survey data was collected from 143 end-users of a BPM system in a large Dutch social insurance organisation, and by 49 additional semi-structured interviews. Regression analysis of the survey data shows that attitude variables indeed have a significant moderating influence on implementation success. In addition, the interviews revealed that specific attention must be paid to the arrangements for EUT when deploying BPM systems in this type of organisations. Arguments are given for a more comprehensive way of measuring and optimising EUT during the implementation of information systems/information technology in organisations.
Mining moving object trajectories in location-based services for spatio-temporal database update

Advances in wireless transmission and mobile technology applied to LBS (Location-based Services) flood us with amounts of moving objects data. Vast amounts of gathered data from position sensors of mobile phones, PDAs, or vehicles hide interesting and valuable knowledge and describe the behavior of moving objects. The correlation between temporal moving patterns of moving objects and geo-feature spatio-temporal attribute was ignored, and the value of spatio-temporal trajectory data was not fully exploited too. Urban expanding or frequent town plan change bring about a large amount of outdated or imprecise data in spatial database of LBS, and they cannot be updated timely and efficiently by manual processing. In this paper we introduce a data mining approach to movement pattern extraction of moving objects, build a model to describe the relationship between movement patterns of LBS mobile objects and their environment, and put up with a spatio-temporal database update strategy in LBS database based on trajectories spatiotemporal mining. Experimental evaluation reveals excellent performance of the proposed model and strategy. Our original contribution include formulation of model of interaction between trajectory and its environment, design of spatio-temporal database update strategy based on moving objects data mining, and the experimental application of spatio-temporal database update by mining moving objects trajectories.
Personal and social life of Israeli aging mothers to adult children with intellectual disabilities: differences related to family status and living arrangement

This study explores whether aging mothers living in two-parent families whose offspring with intellectual disabilities do better in respect to their undesired daily life events, level of social support and well-being scores than mothers of one-parent families and whether there is difference related to their living arrangement (living with their offspring at home or out-of-home)? Sample consists of 160 Israeli aging mothers of adult children with intellectual disabilities living at home or out-of-home. Core findings show that one-parent mothers do worse in respect to undesired life events than those living in two-parent ones. However, mothers of one-parent families whose adult children live at home report more engagement with family members than those of two-parent families whose children live out-of-home.
Ontology versioning on the Semantic Web

Ontologies are often seen as basic building blocks for the Semantic Web, as they provide a reusable piece of knowledge about a specific domain. However, those pieces of knowledge are not static, but evolve over time. Domain changes, adaptations to different tasks, or changes in the conceptualization require modifications of the ontology. The evolution of ontologies causes operability problems, which will hamper their effective reuse. A versioning mechanism might help to reduce those problems, as it will make the relations between different revisions of an ontology explicit. This paper will discuss the problem of ontology versioning. Inspired by the work done in database schema versioning and program interface versioning, it will also propose building blocks for the most important aspects of a versioning mechanism, i.e., ontology identification and change specification.
Distributed Hybrid Scheduling in Multi-Cloud Networks Using Conflict Graphs

Recent studies on cloud-radio access networks assume either signal-level or scheduling-level coordination. This paper considers a hybrid coordinated scheme as a means to benefit from both policies. Consider the downlink of a multi-cloud radio access network, where each cloud is connected to several base-stations (BSs) via high capacity links and, therefore, allows for joint signal processing within the cloud transmission. Across the multiple clouds, however, only scheduling-level coordination is permitted, as low levels of backhaul communication are feasible. The frame structure of every BS is composed of various time/frequency blocks, called power-zones (PZs), which are maintained at a fixed power level. This paper addresses the problem of maximizing a network-wide utility by associating users to clouds and scheduling them to the PZs, under the practical constraints that each user is scheduled to a single cloud at most, but possibly to many BSs within the cloud, and can be served by one or more distinct PZs within the BSs’ frame. This paper solves the problem using graph theory techniques by constructing the conflict graph. The considered scheduling problem is, then, shown to be equivalent to a maximum-weight independent set problem in the constructed graph, which can be solved using efficient techniques. This paper then proposes solving the problem using both optimal and heuristic algorithms that can be implemented in a distributed fashion across the network. The proposed distributed algorithms rely on the well-chosen structure of the constructed conflict graph utilized to solve the maximum-weight independent set problem. Simulation results suggest that the proposed optimal and heuristic hybrid scheduling strategies provide appreciable gain as compared with the scheduling-level coordinated networks, with a negligible degradation to signal-level coordination.
Local Model Architectures for NonlinearModelling and

Local Model Networks are learning systems which are able to model and control unknown nonlinear dynamic processes from their observed input-output behaviour. Simple, locally accurate models are used to represent a globally complex process. The framework supports the modelling process in real applications better than most artiicial neu-ral network architectures. This paper shows how their structure also allows them to more easily integrate knowledge, methods and a priori models from other paradigms such as fuzzy logic, system identiication and statistics. Algorithms for automatic parameter estimation and model structure identiication are given. Local Models intuitively lend themselves to the use of Local Controllers, where the global controller is composed of a combination of simple locally accurate control laws. A Local Controller Network (LCN) for controlling the lateral deviation of a car on a straight road is demonstrated.
Resegmentation of SWITCHBOARD

The SWITCHBOARD (SWB) corpus is one of the most important benchmarks for recognition tasks involving large vocabulary conversational speech (LVCSR). The high error rates on SWB are largely attributable to an acoustic model mismatch, the high frequency of poorly articulated monosyllabic words, and large variations in pronunciations. It is imperative to improve the quality of segmentations and transcriptions of the training data to achieve better acoustic modeling. By adapting existing acoustic models to only a small subset of such improved transcriptions, we have achieved a 2% absolute improvement in performance.
Curvature estimation for multilayer hinged structures with initial strains

Closed-form estimate of curvature for hinged multilayer structures with initial strains is developed. The finite element method is used for modeling of self-positioning microstructures. The geometrically nonlinear problem with large rotations and large displacements is solved using step procedure with node coordinate update. Finite element results for curvature of the hinged micromirror with variable width is compared to closed-form estimates.
Mesh-based global motion compensation for robust mosaicking and detection of moving objects in aerial surveillance

Global Motion Compensation is one of the key technologies for aerial image processing e.g. to detect moving objects on the ground or to generate a mosaick image of the observed area. For this task, it is necessary to estimate and compensate the motion of the pixels between the recorded frames evoked by the movement of the camera. As the camera is statically attached to a flying device such as a quadro-copter (also called Micro Air Vehicle, MAV) or a helicopter, the motion of the camera directly corresponds to the plane movements. For simplification, only a planar landscape model is used nowadays to describe the global motion of the scene. However, if objects like buildings or mountains are close to the camera, i.e. the MAV is at a low altitude, this simplification is not valid. Therefore we propose a more complex model by introducing a 2D mesh-based motion compensation technique, also known as image warping, to compensate the global motion. We show the benefits if used for mosaick creation by smaller artifacts due to perspective distortions and smaller drift problems. We also improve a moving object detection system to identify moving objects more reliably. Moreover, the proposed method is also more robust in case of lens distortions.
Introdução à gestão de custos em saúde

O exercicio da gestao de custos no Sistema Unico de Saude (SUS) tornou-seimperativo em virtude da necessidade de garantir maior eficiencia na aplicacao dosrecursos e sustentabilidade do sistema.Recentemente, o Ministerio da Saude, o Conselho Nacional de Secretarios deSaude (Conass) e o Conselho Nacional de Secretarias Municipais de Saude (Conasems)definiram que o desenvolvimento de metodologias, instrumentos e sistemas de informacaopara a apuracao de custos, que possibilitem estimar os recursos financeiros parao custeio global do SUS, e essencial ao aprimoramento da gestao interfederativa.Este reconhecimento revela o quanto a gestao de custos esta sendo consideradaestrategica para os gestores do SUS, assim como a essencialidade da elaboracao deferramentas, da formacao de pessoas e do desenvolvimento de cultura organizacionalque favoreca a sua implantacao.Nesse contexto, insere-se o Programa Nacional de Gestao de Custos (PNGC),um conjunto de acoes que visam promover a gestao de custos, no âmbito do SUS, pormeio da producao, difusao e aperfeicoamento de informacoes relevantes e pertinentesa custos, utilizadas como subsidio para a otimizacao do desempenho de servicos, unidades,regioes e redes de atencao em saude do SUS. (AU)
Who Are You? A Statistical Approach to Measuring User Authenticity

Passwords are used for user authentication by almost every Internet service today, despite a number of wellknown weaknesses. Numerous attempts to replace passwords have failed, in part because changing users’ behavior has proven to be difficult. One approach to strengthening password-based authentication without changing user experience is to classify login attempts into normal and suspicious activity based on a number of parameters such as source IP, geo-location, browser configuration, and time of day. For the suspicious attempts, the service can then require additional verification, e.g., by an additional phone-based authentication step. Systems working along these principles have been deployed by a number of Internet services but have never been studied publicly. In this work, we perform the first public evaluation of a classification system for user authentication. In particular: (i) We develop a statistical framework for identifying suspicious login attempts. (ii) We develop a fully functional prototype implementation that can be evaluated efficiently on large datasets. (iii) We validate our system on a sample of real-life login data from LinkedIn as well as simulated attacks, and demonstrate that a majority of attacks can be prevented by imposing additional verification steps on only a small fraction of users. (iv) We provide a systematic study of possible attackers against such a system, including attackers targeting the classifier itself.
Detecting and Solving Links Overlap to Make Structured P2P Network Topology-Aware

Mismatching between overlay and physical network in structured P2P system leads to overlap of links crossed on backbone network, thus it brings link waste and congestion. A method used for detecting and resolving the link overlap problem is proposed in this paper. It introduces TTL as the measurement of distance, and adjusts the routing table of node according to redirect message achieved from TTL calculation and comparison. This method is protocol- independent and easy to be built on any structured P2P system. It avoids the inaccuracy problem that the time latency or IP information method contained. Through illustration, it is proved to indeed decrease the number of links crossed in routing procedure, thus resolves the waste of link bandwidth and useless flow over the backbone network brought by mismatching problem.