Miracle-dz/UniFAR-FTUs huggingface dataset:
Re: CAPTCHAs-Understanding CAPTCHA-Solving Services in an Economic Context

Reverse Turing tests, or CAPTCHA s, have become an ubiquitous defense used to protect open Web resources from being exploited at scale. An effective CAPTCHA resists existing mechanistic software solving, yet can be solved with high probability by a human being. In response, a robust solving ecosystem has emerged, re-selling both automated solving technology and real-time human labor to bypass these protections. Thus, CAPTCHA s can increasingly be understood and evaluated in purely economic terms; the market price of a solution vs the monetizable value of the asset being protected. We examine the market-side of this question in depth, analyzing the behavior and dynamics of CAPTCHA -solving service providers, their price performance, and the underlying labor markets driving this economy.
Automatic Comma Insertion of Lecture Transcripts Based on Multiple Annotations

To enhance readability and usability of speech recognition results, automatic punctuation is an essential process. In this paper, we address automatic comma prediction based on conditional random fields (CRF) using lexical, syntactic and pause information. Since there is large disagreement in comma insertion between humans, we model individual tendencies of punctuation using annotations given by multiple annotators, and combine these models by voting and interpolation frameworks. Experimental evaluations on real lecture speech demonstrated that the combination of individual punctuation models achieves higher prediction accuracy for commas agreed by all annotators and those given by individual annotators. Index Terms: Automatic punctuation, lecture speech, conditional random fields, multiple annotations
Portfolio Diversification Effects of Downside Risk

Risk managers use portfolios to diversify away the unpriced risk of individual securities. In this article we compare the benefits of portfolio diversification for downside risk in case returns are normally distributed with the case of fat-tailed distributed returns. The downside risk of a security is decomposed into a part which is attributable to the market risk, an idiosyncratic part, and a second independent factor. We show that the fat-tailed-based downside risk, measured as value-at-risk (VaR), should decline more rapidly than the normal-based VaR. This result is confirmed empirically.
Motivational Interviewing for Parent-child Health Interventions: A Systematic Review and Meta-Analysis.

PURPOSE Motivational interviewing (MI) is a patient-centered approach focusing on building intrinsic motivation for change. This paper presents a meta-analysis of parent-involved MI to improve pediatric health behavior and health outcomes. METHODS Study inclusion criteria: (1) examined modifiable pediatric health behaviors (< 18 years old); (2) used MI or motivational enhancement; (3) conducted a randomized controlled trial with a comparison group (non-MI control or active treatment group); (4) conducted the intervention with only a parent or both a parent and child; and (5) were written in English. Twenty-five studies (with 5,130 participants) were included and independently rated. Weighted mean effect sizes, using random-effects assumptions, were calculated. RESULTS Relative to comparison groups, MI was associated with significant improvements in health behaviors (e.g., oral health, diet, physical activity, reduced screen time, smoking cessation, reduced second hand smoke) and reduction in body mass index. Results suggest that MI may also outperform comparison groups in terms of dental caries, but more studies are needed. MI interventions were more successful at improving diets for Caucasians and when the intervention included more MI components. CONCLUSIONS Our findings provide support for providing motivational interviewing to parents and children to improve pediatric health behaviors.
Transporte difusivo em sólidos com forma arbitrária usando coordenadas generalizadas.

Este trabalho visa a estudar o fenomeno da difusao de calor ou de massa em solidos com forma arbitraria, apresentar a solucao numerica da equacao que descreve o fenomeno para solidos obtidos por extrusao e por revolucao de uma area geratriz. A equacao de difusao em regime transiente, escrita em coordenadas generalizadas, foi discretizada e resolvida numericamente, via metodo dos volumes finitos, com uma formulacao totalmente implicita, para condicoes de contorno de primeira, segunda e terceira especies, com propriedades constantes ou variaveis para o meio, incluindo variacoes dimensionais. O estudo para a geracao de uma malha estruturada bidimensional simplesmente conexa atraves da equacao de Poisson, via diferencas finitas e apresentado. Um software na plataforma Windows para a obtencao de contornos e geracao de malhas, bem como para a solucao da equacao de difusao em solidos com forma arbitraria foi desenvolvido e e apresentado. Para viabilizar o desenvolvimento do software foi criado um avaliador de expressoes (function parser) para a linguagem Fortran, que foi utilizada em todo o pacote computacional. Foram realizadas diversas simulacoes utilizando os codigos computacionais desenvolvidos, envolvendo um grande numero de situacoes fisicas de interesse, e todos os resultados obtidos foram teoricamente consistentes e coerentes com valores reportados na literatura. O pacote computacional desenvolvido foi aplicado na simulacao da cinetica de secagem de membranas cerâmicas na forma de tubos cilindricos, usados em processos de filtragem, nas seguintes condicoes experimentais: temperatura de 45 a 105 °C, teor de umidade inicial de 25 a 30% (bs) e umidade relativa do ar de 58%. Os resultados numericos foram comparados com os resultados experimentais obtidos nestas mesmas condicoes. Tal comparacao possibilitou determinar uma expressao para o coeficiente de difusao em funcao da razao de umidade e da temperatura do ar de secagem, e tambem o valor do coeficiente de transferencia convectiva de massa correspondente a cada temperatura estudada.
Bridging the gap between dual propagation and CNF-based QBF solving

Conjunctive Normal Form (CNF) representation as used by most modern Quantified Boolean Formula (QBF) solvers is simple and powerful when reasoning about conflicts, but is not efficient at dealing with solutions. To overcome this inefficiency a number of specialized non-CNF solvers were created. These solvers were shown to have great advantages. Unfortunately, non-CNF solvers cannot benefit from sophisticated CNF-based techniques developed over the years. This paper demonstrates how the power of non-CNF structure can be harvested without the need for specialized solvers; in fact, it is easily incorporated into most existing CNF-based QBF solvers using a pre-existing mechanism of cube learning. We demonstrate this using a state-of-the-art QBF solver DepQBF, and experimentally show the effectiveness of our approach.
Segmenting the Web document with document object model

We present a model about DOM-based Web document segmentation using the semistructure information of Web pages. This model builds DOM tree of the Web page by parsing HTML tags which organize structure of the Web page. By improving traditional plain text segmentation algorithms, we expand these algorithms to suit Web text segmentation. Then, with the boundaries between the nodes in the DOM tree, precision of segmentation results can be increased further.
A Flexible Strategy for Efficient Merging Maneuvers of Connected Automated Vehicles

Merging is a challenging task for automated vehicles. This paper proposes a strategy for connected automated vehicles (CAVs) to guide merging on-ramp vehicles efficiently while ensuring safe interactions with the mainline vehicles. Point-mass kinematic models are used to describe 2-D vehicle motion and receding horizon control is used to generate optimal trajectories of interacting vehicles. The strategy determines the optimal merging time instant for merging vehicles and acceleration of all involved vehicles to minimize deviation from the preceding vehicles' speed, deviation from preferred inter-vehicle gaps, accelerations, and the time spent merging. The strategy builds on a pre-determined order of vehicles passing the conflict zone but is not restricted to fixed merging points as previous research assumes. It resembles human-like behavior in the sense that on-ramp vehicles will accept smaller gaps when approaching the end of the acceleration lane. The performance of the strategy is demonstrated in simulations.
A Monte Carlo study of traditional and rank-based picked-points analyses

ABSTRACT In analysis of covariance with heteroscedastic slopes a picked-points analysis is often performed. Least-squares based picked-points analyses often lose efficiency (at times substantial) for nonnormal error distributions. Robust rank-based picked-points analyses are developed which are optimizable for heavy-tailed and/or skewed error distributions. The results of a Monte Carlo investigation of these analyses are presented. The situations include the normal model and models which violate it in one or several ways. Empirically the rank-based analyses appear to be valid over all these situations and more powerful than the least squares analysis for all the nonnormal models, while losing little efficiency at the normal model.
International Journal of Computer Mathematics

Extraction of lane markings is still a challenge due to the poor quality of lane markings and interferences from external circumstances. In this paper, we utilize line segments as low-level features to detect lane markings on structured road scenes. Our novel algorithm can be highlighted in four items as follows. Firstly, a road surface region is reasonably located using adaptive segmentation method, and then most of the interferences from the external circumstances are eliminated by Laplacian ﬁlter. Secondly, the line segments detected by the line segment detector are applied to represent the structural information of lanes. Thirdly, non-lane candidate markings are removed through orientation and vanishing point constraints. Finally,thelanesareaccuratelyextractedfromtheremainingcandidatelanemarking.Experimentalresults oncomplexstructuredroadscenariosinurbanstreetsareshownandtheeffectivenessandrobustnessofournovelalgorithmareunderpinnedbytheexperimentalresults.
Review of Apriori Based Algorithms on Map Reduce Framework

The Apriori algorithm that mines frequent itemsets is one of the most popular and widely used data mining algorithms. Now days many algorithms have been proposed on parallel and distributed platforms to enhance the performance of Apriori algorithm. They differ from each other on the basis of load balancing technique, memory system, data decomposition technique and data layout used to implement them. The problems with most of the distributed framework are overheads of managing distributed system and lack of high level parallel programming language. Also with grid computing there is always potential chances of node failures which cause multiple re-executions of tasks. These problems can be overcome by the MapReduce framework introduced by Google. MapReduce is an efficient, scalable and simplified programming model for large scale distributed data processing on a large cluster of commodity computers and also used in cloud computing. In this paper, we present the overview of parallel Apriori algorithm implemented on MapReduce framework. They are categorized on the basis of Map and Reduce functions used to implement them e.g. 1-phase vs. k-phase, I/O of Mapper, Combiner and Reducer, using functionality of Combiner inside Mapper etc. This survey discusses and analyzes the various implementations of Apriori on MapReduce framework on the basis of their distinguishing characteristics. Moreover, it also includes the advantages and limitations of MapReduce framework.
Discovering the Correlation between Stock Time Series and Financial News

It is always expected that a correlation exists between the movement of stock prices (technical analysis) and news sentiment (fundamental analysis). If we can determine such a correlation, further interesting research directions will certainly be generated. In this paper, a system prototype is proposed for investigating the correlation between stock prices and news sentiment. Our primary target market is Hong Kong and the system is customized for Chinese language. Different methods and the impacts of various design parameters are tested in the experiments.
Process variabilites and performances in a 90nm embedded SRAM

The migration of transistors in the very deep submicron region has allowed the integration of billions of transistors on a single chip. However, this relentless march has caused the rapid emergence of variability problems, which have adverse effects on the circuit's performance. This paper highlights the importance of taking into account process variability aspects in the design of an eSRAM for reducing the excessive design margin, introduced by the corner analysis method. We show that the sensitivity dispersions of the memory to process variations can be mitigated through the use of an appropriate dummy bit line driver (DBD). This component is in fact an essential element in a self-timed memory. We made use of the DBD in a 256kb SRAM in 90nm technology process.
DTN for WSN: FREAK, implementations and study

Sensor Networks can be envisaged to monitor mobile systems. Nevertheless, new challenges appear from mobility such as delay and connectivity disruptions. These challenges can be tackled by the Delay/Disruption Tolerant Networking architecture. However, implementing DTN mechanisms on sensors require to select the schemes which propose an efficient use of the memory. Our scheme, FREAK, aims at providing high performance with low-memory usage. This scheme relies on the mean frequency of past encounter with the base station. Transmissions are driven by this metric. The FREAK solution is keen because we assume that future can be predicted from the past events. The acknowledgements effects on performance is analysed. We run simulations using mobility quota-based mainstreammodels and real traces. FREAK is compared to several replication and quota-based mainstream DTN solutions and achieves quite better performance in realistic scenarios.
Multi-dimensional laser mode combs via active mode-locking

We present a realization and first experimental results of a conceptual d-dimensional laser mode lattices (mode hyper-combs). They are constructed from regular 1-dimensional combs by multi-frequency modulation in active mode-locking (AML). The hyper-comb, with near neighbor mode interaction and noise functioning as temperature, is mapped to interacting magnetic spin-lattices in the spherical-model, which is one of the few statistical-mechanics systems soluble in all dimensions. Such systems have in d>;2 dimensions, a phase-transition to a global mode-phase-ordered hyper-comb. Such lasers can have unique properties for generating short pulses compared to regular AML, by capturing very broad frequency bandwidths. Additionally, the hyper-combs can serve as a rare physical realization of the spherical-model in any dimension. We report on preliminary experimental results on the pulsation transition in "three-dimensional" AML.
Assessment of Prostate Cancer With Dynamic Contrast-Enhanced Computed Tomography Using an En Bloc Approach

ObjectivesThe aim of this study was to assess the performance of dynamic contrast-enhanced computed tomography of the prostate in patients with biopsy-proven prostate cancer. Material and MethodsA total of 46 male patients (median age, 65 years; range, 49–73 years) with biopsy-proven prostate cancer underwent an en bloc computed tomography perfusion (CTP) scan of the prostate before surgery. The perfusion parameters mean transit time (MTT), blood flow (BF), and blood volume (BV), as well as the microvessel density (MVD) of surgical specimens were determined. Differences in CTP parameters and MVD among postsurgical Gleason score (sGS) and postsurgical combined Gleason grade (sGG) groups were analyzed. Spearman correlation coefficients were determined between CTP parameters and presurgical biopsy-derived Gleason score (bGS), presurgical biopsy-derived combined Gleason grade (bGG), sGS, sGG, MVD, and pathological tumor stage. A linear regression analysis was carried out for exogenous variables BF, BV, MTT, bGS, and presurgical biopsy-derived combined Gleason grade and endogenous variables sGS, sGG, MVD, and T stage. A receiver operating characteristics analysis was performed to analyze the discriminating performance of CTP parameters and bGS between intermediate- and high-grade tumors. ResultsThe mean perfusion parameters within the prostate tissue were as follows: BF, 39.1 ± 13.4 mL/100 mL min−1; BV, 4.9 ± 2.4 mL/100 mL; and MTT, 8.9 ± 3.7 seconds. The mean MVD of the tumor tissue was 144.3 ± 55.6/mm2. Computed tomography perfusion parameters and MVD were significantly higher in patients with high-grade tumors compared with those with intermediate-grade tumors (P < 0.01 for BF, BV, and MVD). Only BV and MVD were significantly different among sGS and sGG groups. Moderate correlations were found between BF and sGS (0.38) and between BV and sGS (0.43). Linear relations of BV to sGS and to sGG were found. Blood volume (area under the curve, 0.86) was superior to bGS (area under the curve, 0.75) in discriminating high-grade from intermediate-grade tumors. ConclusionComputed tomography perfusion parameters derived by en bloc perfusion of the prostate are higher in high-grade tumors compared with intermediate-grade tumors. Blood flow and BV correlate with the definitive Gleason score. Blood volume predicts high-grade tumors better than does the Gleason score of biopsy specimens. Further studies are needed to determine a potential role for CTP in prostate cancer patients.
Pilot-aided coherent uplink for mobile satellite CDMA networks

A pilot-aided coherent uplink transmission scheme is proposed for mobile satellite communications at L-band. The scheme is designed to enhance link performance as compared to coded noncoherent M-ary orthogonal modulation, while maintaining small envelope fluctuations and low complexity. A rigorous analysis is performed for the proposed technique, when used in conjunction with a direct-sequence code-division multiple-access convolutionally encoded binary phase shift keying link, in a Rice-fading channel with satellite diversity. The pilot-aided scheme has been extensively simulated, accounting for finite interleaving in slow fading and nonideal carrier frequency recovery. The results show a significant performance gain with respect to noncoherent schemes designed for similar links, which goes up to 1.8 dB for 9.6 kb/s services with slow moving users exploiting dual-satellite diversity.
Quercetin improves the in vitro development of porcine oocytes by decreasing reactive oxygen species levels

Quercetin is a plant-derived flavonoid found in fruits or vegetables that has antioxidant properties and acts as a free radical scavenger. We investigated the effects of quercetin on porcine oocyte nuclear maturation and embryonic development after parthenogenetic activation. We then evaluated the antioxidant activities of quercetin by measuring reactive oxygen species (ROS) levels in matured oocytes. Immature oocytes were untreated or treated with 1, 10, and 50 µg/mL quercetin during in vitro maturation (IVM). Quercetin treatment did not improve oocyte nuclear maturation, but significantly higher blastocyst rates (p < 0.05) of parthenogenetically activated oocytes were achieved when the IVM medium was supplemented with an adequate concentration of quercetin (1 µg/mL). However, cleavage rates and blastocyst cell numbers were not affected. Oocytes treated with 1 or 10 µg/mL quercetin had significantly lower (p < 0.05) levels of ROS than the control and group treated with the highest concentration of quercetin (50 µg/mL). Moreover, this highest concentration was detrimental to oocyte nuclear maturation and blastocyst formation. Based on our findings, we concluded that exogenous quercetin reduces ROS levels during oocyte maturation and is beneficial for subsequent embryo development.
Unsupervised Visual Representation Learning for Indoor Scenes with a Siamese ConvNet and Graph Constraints

Indoor scene recognition has great significance for intelligent applications such as mobile robots, location-based services (LBS) and so on. Wherever we are or whatever we do, we are under a specific scene. The human brain can easily discern a scene with a quick glance. However, for a machine to achieve this purpose, on one hand, it often requires plenty of well-annotated data which is time-consuming and labor-intensive. On the other hand, it is hard to learn effective visual representations due to large intra-category variation and inter-categories similarity of indoor scenes. To solve these problems, in this paper, we adopted an unsupervised visual representation learning method which can learn from unlabeled data with a Siamese Convolutional Neural Network (Siamese ConvNet) and graph-based constraints. Specifically, we first mined relationships between unlabeled samples with a graph structure. And then, these relationships can be used as supervision for representation learning with a Siamese network. In this method, firstly, a k-NN graph would be constructed by taking each image as a node in the graph and its k nearest neighbors are linked to form the edges. Then, with this graph, cycle consistency and geodesic distance would be considered as criteria for positive and negative pairs mining respectively. In other words, by detecting cycles in the graph, images with large differences but in the same cycle can be considered as same category (positive pairs). By computing geodesic distance instead of Euclidean distance from one node to another, two nodes with large geodesic distance can be regarded as in different categories (negative pairs). After that, visual representations of indoor scenes can be learned by a Siamese network in an unsupervised manner with the mined pairs as inputs. In order to evaluate the proposed method, we tested it on two scene-centric datasets, MIT67 and Places365. Experiments with different number of categories have been conducted to excavate the potential of proposed method. The results demonstrated that semantic visual representations for indoor scenes can be learned in this unsupervised manner. In addition, with the learned visual representations, indoor scene recognition models trained with the learned representations and a few of labeled samples can achieve competitive performance compared to the state-of-the-art approaches.
DOMAIN‐INDEPENDENT TEMPORAL REASONING WITH RECURRING EVENTS

Numerous examples of temporal reasoning involve a process of abstraction from the number of times an event is to occur or the number of times events stand in a temporal relation. For example, scheduling a recurring event such as one's office hours may consider things like the relative temporal ordering of the office hours and a number of other events in a given work day. The number of times office hours will actually be held may be unknown, even irrelevant, at the time of scheduling them. The objective of this article is to formulate a domain‐independent framework for reasoning about recurring events and their relations. To achieve this end, we propose an ontology of recurrence based on the model‐theoretic structure underlying collective predication using plural noun phrases. We offer a calculus of binary temporal relations for temporal collections based on a well‐defined transformation of interval temporal relations into recurrence relations. Finally, we describe a reasoning framework based on manipulating knowledge stored in temporal relation networks, which is in turn a specialization of the CSP (constraint satisfaction problem) framework. The reasoner manipulates recurrence relations in the network to determine the network's consistency or to generate scenarios.