Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Human Intelligence: Semi-automated Processing in Assuring Access to Digital Content

Need for standardization in the content production industry have led producers of popular authoring and publishing applications to adopt structured mark-up languages, such as XML, to implement their content file formats. As part of our effort to ensure long term access to such content, we need to consider properties of the mark-up schemas and devise methods to enable effective mapping among them. The methods may range from a fully automated mapping between two formats to semi-automated format transformation of individual artifacts through human intervention. The former is an ideal scenario and achievable when full specifications of the original and target formats are available and when the development of a full converter is economically feasible. However, a common lack of these resources creates challenges and requires exploration of alternative approaches. To that effect, we propose a concerted research effort in formal characterization of the mark-up languages and the programming languages that can be used to express transformations of content and structures described through document mark-ups. Among these, we anticipate an important role for methods akin to programming-by-example where the document transformation is learnt by observing user interaction with the contemporary applications as the user manually performs changes in the document. Based on observed examples, the program then generalizes the transformations for the single document and similar documents in the corpora. Such approaches that leverage human input and effectively infer the desired transformations are essential for both creating and testing automatic document converters in general and handling a long tail of structured document formats for which the converters are not available.
Spatial Coherence of Tropical Rainfall at the Regional Scale

This study examines the spatial coherence characteristics of daily station observations of rainfall in five tropical regions during the principal rainfall season(s): the Brazilian Nordeste, Senegal, Kenya, northwestern India, and northern Queensland. The rainfall networks include between 9 and 81 stations, and 29–70 seasons of observations. Seasonal-mean rainfall totals are decomposed in terms of daily rainfall frequency (i.e., the number of wet days) and mean intensity (i.e., the mean rainfall amount on wet days). Despite the diverse spatiotemporal sampling, orography, and land cover between regions, three general results emerge. 1) Interannual anomalies of rainfall frequency are usually the most spatially coherent variable, generally followed closely by the seasonal amount, with the daily mean intensity in a distant third place. In some cases, such as northwestern India, which is characterized by large daily rainfall amounts, the frequency of occurrence is much more coherent than the seasonal amount. 2) On daily time scales, the interstation correlations between amounts on wet days always fall to insignificant values beyond a distance of about 100 km. The spatial scale of daily rainfall occurrence is larger and more variable among the networks. 3) The regional-scale signal of the seasonal amount is primarily related to a systematic spatially coherent modulation of the frequency of occurrence.
Aggregators and Contextual Effects in Search Ad Markets

Allocation and pricing of ad slots in search ad markets is typically done through a Generalized Second Price (GSP) auction, which assumes that the click-through rate (CTR) of an ad displayed in a particular position depends only on the identity of the ad and the position it is displayed in. In particular, it is assumed that there are no contextual effects, where the CTR of an ad depends on the other ads displayed with it. We argue that such effects do exist. In particular, we discuss how the economics of ad aggregation may lead to ad aggregators imposing a negative contextual effect on the CTR of advertisers listed below them. We perform an experiment where we intervene in the auction to vary the position of selected aggregators relative to other advertisers. We then measure CTR on ads controlling for ad identity and position to quantify the magnitude of the contextual effect of being listed below an aggregator. In addition we describe the result of the intervention on search engine revenue, which is consistent with an increase in advertiser social welfare.
Study on improved grey integrated clustering method and its application

The literatures about general grey clustering methods and many improved methods focus on the clustering results based on the principle of the maximal element of a grey clustering coefficient vector. Such methods neglect the effect of other elements except maximal one of a grey clustering coefficient vector to the cluster result. Because grey integrated clustering method comprehensively considers the effect of all elements of a grey clustering coefficient vector to the cluster result, it is an important development to grey clustering techniques. But the way that the value area of integrated clustering coefficients was divided into equal intervals needs improvement. This paper proposes an improved grey integrated clustering method, with which the value interval of a grey integrated clustering coefficient for a grey class was divided by regarding k±0.5 as boundary points. It is proved that when the difference of clustering coefficients of ordinary grey clustering methods is more than 1−1/(s−1), there are the same clustering results for the improved grey integrated clustering and the ordinary grey clustering. At last, we illustrate the improved grey integrated clustering method with the evaluation to rural economic development of Henan Province.
Inferring and Learning Subgoal Sequences

Computational models for understanding human behavior are an important component for enabling robots to appropriately interact with people in shared environments. Recent techniques assume that behavior is goal-directed and infer the goals and future behaviors of people (Baker et al., 2006) to plan complementary robotic control (Ziebart et al., 2009; Kuderer et al., 2012) or learn imitative control policies (Verma & Rao, 2006; Michini et al., 2013).
Measuring Code Behavioral Similarity for Programming and Software Engineering Education

In recent years, online programming and software engineering education via information technology has gained a lot of popularity. Typically, popular courses often have hundreds or thousands of students but only a few course staff members. Tool automation is needed to maintain the quality of education. In this paper, we envision that the capability of quantifying behavioral similarity between programs is helpful for teaching and learning programming and software engineering, and propose three metrics that approximate the computation of behavioral similarity. Specifically, we leverage random testing and dynamic symbolic execution (DSE) to generate test inputs, and run programs on these test inputs to compute metric values of the behavioral similarity. We evaluate our metrics on three real-world data sets from the \pexforfun platform (which so far has accumulated more than 1.7 million game-play interactions). The results show that our metrics provide highly accurate approximation to the behavioral similarity. We also demonstrate a number of practical applications of our metrics including hint generation, progress indication, and automatic grading.
Completeness of cancer registration: a new method for routine use

We report a new method of estimating the completeness of cancer registration, in which the proportions of unregistered patients are derived from the time distributions of three probabilities, each of which can be directly estimated from the registry’s own data – the probabilities of survival, of registration of the cancer during the patient’s life, and of the mention of cancer on the death certificate of a cancer patient who dies. This method allows completeness to be assessed routinely by factors such as age, sex, geographical area and tumour type.
Performance Comparison between Profiles from Hidden Markov Models and from Pseudocount Method

Remote homolog detection is one of the main issues in bioinformatics, and the profile-profile comparison is one of the most effective approaches. Recently, we have proposed a novel profile, match-node profile, constructed via profile HMM [6]. To evaluate the performance of match-node profile, we construct the profile by two popular HMM packages, HMMER [2] and SAM [4], and compared the performance of match-node profile for the remote homolog detection with that of a representative profile-profile method, COMPASS [5], in which the profile is constructed by a psedocount method.
Topic-Sensitive Link-Ranking Approach for Academic Expert Recruiting

The problem of academic expert recruiting is concerned with finding the experts on a specified research field. It has many real-world applications and has recently attracted much attention. However, the existing methods are not versatile and entirely suit for the special requirements from academic area where the co-authorship and the citation relation play important roles in judging researcherspsila achievement. In this paper, we propose and develop a flexible data schema and a topic-sensitive co-pagerank algorithm for studying this problem. The main idea is measuring the authorspsila authorities with considering topics bias on the basis of their social networks and citation networks, and then, recommending expert candidates for the requests. To infer association between authors and topics, we derive a probability model on the basis of latent Dirichlet allocation (LDA) model. We further propose several techniques such as reasoning the interested topics of query, modeling author profile on the supporting documents to instruct the practices. Our experiments show that the proposed strategies are all effective to improve retrieval accuracy.
Adjusting output-limiter for stable haptic interaction with deformable objects

This paper presents a control method using an adjusting output-limiter for stable haptic rendering in a virtual environment. In a simulation of force-reflecting interaction with deformable objects in a virtual environment, a quick computation of the accurate impedance of deformable objects is rare. This is particularly true when physics-based models, such as tensor-mass models or mass-spring models, are used. The problem is aggravated if the simulation involves changes in the geometry and/or impedance of the deformation model, such as cutting or suturing. The proposed control method guarantees stable haptic interactions with deformable objects of unknown and/or varying impedance. The method is based on the time-domain passivity theorem and the two-port network model. The controller adjusts the maximum permissible force to guarantee the passivity of the haptic system at every sampling instant. The controller notes only the magnitude of the reflective force, and does not depend on properties of the employed force model. This allows the proposed control method applicable to haptic systems involving deformable objects with unknown, nonlinear, and/or time-varying impedance. Designs of the controllers are presented for impedance-type and admittance-type haptic systems. The method is also extended for multiple degrees-of-freedom.
The Effect of Structural Fund Spending on the Spanish Regions: An Assessment of the 1994-99 Objective 1 CSF

This Paper analyses the growth and employment effects of the 1994-99 Community Support Framework (CSF) for the Objective 1 Spanish regions, using a simple supply-side model estimated with a panel of regional data. The results suggest that the impact of the Structural Funds in Spain has been quite sizeable, adding around a percentage point to annual output growth in the average Objective 1 region, and 0.4 points to employment growth. Over the period 1994-2000, the Framework has resulted in the creation of over 300,000 new jobs and has eliminated 20% of the initial gap in income per capita between the assisted regions and the rest of the country.
Effects of Static or Oscillating Dietary Crude Protein Levels on Fermentation Dynamics of Beef Cattle Diets Using a Dual-Flow Continuous Culture System

The objective of this study was to evaluate the effects of increasing dietary crude protein (CP) levels and also comparing the effects of static versus oscillating dietary CP on ruminal nutrient digestibility, ruminal fermentation, nitrogen (N) metabolism, and microbial efficiency in beef cattle diets using a dual-flow continuous culture system. Eight fermenters (1,223 ± 21 mL) were used in a replicated 4 x 4 Latin square design with periods lasting 12 d each (8 d for adaptation and 4 d for sampling). Dietary treatments were: 1) 10% CP, 2) 12% CP, 3) 14% CP, and 4) 10 and 14% CP diets oscillating at 48-h intervals. Experimental diets consisted of 50% orchard hay and 50% concentrate. Fermenters were fed 72 g/d and solid and liquid dilution rates were adjusted to 5.5 and 11%/h, respectively. Data were analyzed using the MIXED procedure in SAS with α = 0.05. Apparent and true ruminal digestibilities of dry matter and organic matter were not affected (P > 0.05) by increasing dietary CP, nor by oscillating dietary CP. Total volatile fatty acids concentration and molar proportions of acetate, propionate, butyrate, valerate, iso-butyrate and iso-valerate were not affected (P > 0.05) by increasing or oscillating dietary CP. Ruminal NH3-N concentration increased linearly (P < 0.01) in response to increasing dietary CP. Total N, non-ammonia N, and rumen undegraded protein flows did not differ among treatments or between oscillating dietary CP and static 12% CP. Microbial N and NH3-N flows and microbial efficiency did not differ when comparing oscillating versus static CP (P > 0.05). However, there was a quadratic effect (P < 0.05) for these variables when dietary CP was increased. These results indicate that either ruminal microorganisms do not respond to oscillating CP levels or are capable of coping with 48-h periods of undernourishment.
Temperature prediction with FBG sensor based on relevance vector machine

With the wide application areas and increase industrial benefit of fiber bragg grating (FBG) sensors, the main concerns of how to build the realtime, efficient and stable monitoring system based on fiber sensing have been raised, which can provide early warning and decrease damage loss. Furthermore, the mass data include noises and uncertain information from the monitoring system. Evaluation of monitoring system dealing with noisy and uncertain data becomes the hot spot and difficulty. When FBG sensor network is employed to evaluate different structure health condition, the structural distortion and variation of stress are heavily associated with temperature, so it is essential to get higher temperature prediction accuracy using FBG sensors. Relevance vector machine is applied to regression calculation between wavelength of FBG and temperature in this paper, and the simulation results illustrate effectiveness of the presented method compared with least square method.
Star Prediction for Yelp Dataset

Yelp connects people to great local businesses. In this paper, we focus on the reviews for restaurants. We aim to predict the rating for a restaurant from previous information, such as the review text, the user’s review histories, as well as the restaurant’s statistic. We investigate the data set provided by Yelp Dataset Challenge round 5. In this project, we will predict the star(rating) of a review.Three machine learning algorithms are used, linear regression, random forest tree and latent factor model, combining with the sentiment analysis. After analyzed the performance of each models, the best model for predicting the ratings from reviews is the random forest tree algorithm. Also, we found sentiment features are very useful for rating prediction.
9 30 30 46 v 1 2 5 M ar 1 99 3 Large N Expansion for 4 − ǫ Dimensional Oriented Manifolds in Random Media

The equilibrium statistical mechanics of a d dimensional “oriented” manifold in an N + d dimensional random medium are analyzed in d = 4 − ǫ dimensions. For N = 1, this problem describes an interface pinned by impurities. For d = 1, the model becomes identical to the directed polymer in a random medium. Here, we generalize the functional renormalization group method used previously to study the interface problem, and extract the behavior in the double limit ǫ small and N large, finding non-analytic corrections in 1/N . For short-range disorder, the interface width scales as w ∼ L , with ζ = ǫ N+4 { 1 + 1 4e2 −( 2 ) (N+2) 2 N+4 [
Robust Data-Driven Inference in the Regression-Discontinuity Design

In this article, we introduce three commands to conduct robust data-driven statistical inference in regression-discontinuity (RD) designs. First, we present rdrobust, a command that implements the robust bias-corrected confidence intervals proposed in Calonico, Cattaneo, and Titiunik (2014d, Econometrica 82: 2295–2326) for average treatment effects at the cutoff in sharp RD, sharp kink RD, fuzzy RD, and fuzzy kink RD designs. This command also implements other conventional nonparametric RD treatment-effect point estimators and confidence intervals. Second, we describe the companion command rdbwselect, which implements several bandwidth selectors proposed in the RD literature. Following the results in Calonico, Cattaneo, and Titiunik (2014a, Working paper, University of Michigan), we also introduce rdplot, a command that implements several data-driven choices of the number of bins in evenly spaced and quantile-spaced partitions that are used to construct the RD plots usually encountered in empirical applications. A companion R package is described in Calonico, Cattaneo, and Titiunik (2014b, Working paper, University of Michigan).
Traffic Analysis of SSL Encrypted Web Browsing

The SSL protocol, an application-layer mechanism widely used for encrypted Web browsing, was not designed to address traffic analysis attacks. We investigate the threat to privacy posed by such attacks and consider possible defenses. We implement a prototype of a traffic analysis attack and employ it to identify the pages visited by users browsing a Web site. Numerical models and simulations are used to predict the effectiveness of traffic analysis on various sites, as well as the efficacy of several possible defenses. Our results show that an attack using simple techniques can identify the pages visited with very high accuracy, and suggest that defenses exist which may provide some degree of privacy protection in many cases.
Adaptive distributed monitoring with accuracy objectives

We present A-GAP, a novel protocol for continuous monitoring of network state variables, which aims at achieving a given monitoring accuracy with minimal overhead. Network state variables are computed from device counters using aggregation functions, such as SUM, AVERAGE and MAX. The accuracy objective is expressed as the average estimation error. A-GAP is decentralized and asynchronous to achieve robustness and scalability. It executes on an overlay that interconnects management processes on the devices. On this overlay, the protocol maintains a spanning tree and updates the network state variables through incremental aggregation. It dynamically configures local filters that control whether an update is sent towards the root of the tree. It reduces the overhead by attempting to minimize the maximum processing load over all management processes. We evaluate A-GAP through simulation using an ISP topology and real traces. The results show that we can effectively control the trade-off between accuracy and protocol overhead, that the overhead can be reduced significantly by allowing small errors, and that an accurate estimation of the error distribution can be provided in real-time.
On energy-based localization in wireless sensor networks

In this paper, the energy-based localization problem in wireless sensor networks is addressed. The maximum likelihood (ML) location estimation problem is a difficult optimization problem due to the non-convexity of the objective function, and an exact solution is difficult to obtain. An approximate solution to the ML problem is proposed, by relaxing the minimization problem into semidefinite programming form. Simulation results show that the proposed algorithm outperforms the existing solutions.
Chemistry of sprite discharges through ion-neutral reactions

We estimate the concentration changes, caused by streamer discharge in sprites, of ozone and related minor species as odd nitrogen (NO x ) and hydrogen (HO x ) families in the upper stratosphere and mesosphere. The streamer has an intense electric field and high electron density at its head, where a large number of chemically-radical ions and atoms are produced through electron impact on neutral molecules. After its propagation, densities of minor species can be perturbed through ion-neutral chemical reactions initiated by the relaxation of these radical products. We evaluate the production rates of ions and atoms using an electron kinetics model and by assuming that the electric field and electron density are in the head region. We calculate the density variations mainly for NO x , O x , and HO x species using a one-dimensional model of the neutral and ion composition of the middle atmosphere, including the effect of the sprite streamer. Results at the nighttime condition show that the densities of NO, O 3 , H, and OH increase suddenly through reactions triggered by the first atomic nitrogen and oxygen product, and electrons just after streamer initiation. It is shown that NO and NO 2 still remain for 1 h by a certain order of increase with their source-sink balance, predominantly around 60 km; for other species, increases in O 3 , OH, HO 2 , and H 2 O 2 still remain in the range of 40–70 km. From this affirmative result of long-time behavior previously not presented, we emphasize that sprites would have the power to impact local chemistry at night. We also discuss the consistency with previous theoretical and observational studies, along with future suggestions.
Analysis on wireless power transfer to moving devices based on array of resonators

We describe a wireless power transfer (WPT) system based on electromagnetic coupling of resonators in an array. The system can not only extend the effective range of a conventional resonant coupling based WPT system, but also provide power continuously to multiple moving devices, such as electric vehicles and robots. In this paper, the system is presented and analyzed with a transmission line model. The performance and characteristics of the system are studied and discussed.
Elementary block extraction for mobile image search

In this paper, we propose an original content-based image retrieval method using bag-of-words dedicated to building matching on mobile devices. In the literature, the repetitiveness of visual words in natural scenes, and especially in building images, has been demonstrated. Assuming images are composed of a set of elementary blocks, we represent them using only a few well-chosen features. In the context of image search on mobile devices, this allows to considerably reduce the size of the data to be sent to the server. This method has been experimented using SIFT descriptors on three well-known databases. Experimental results show that this method can outperform the standard bag-of-words approach while reducing the number of features used to represent images. Moreover, this general framework can be used in conjunction with any kind of descriptors and indexing methods.