Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilingually-Guided Monolingual Dependency Grammar Induction

This paper describes a novel strategy for automatic induction of a monolingual dependency grammar under the guidance of bilingually-projected dependency. By moderately leveraging the dependency information projected from the parsed counterpart language, and simultaneously mining the underlying syntactic structure of the language considered, it effectively integrates the advantages of bilingual projection and unsupervised induction, so as to induce a monolingual grammar much better than previous models only using bilingual projection or unsupervised induction. We induced dependency grammar for five different languages under the guidance of dependency information projected from the parsed English translation, experiments show that the bilinguallyguided method achieves a significant improvement of 28: 5% over the unsupervised baseline and 3: 0% over the best projection baseline on average.
Automatically Rigging Multi‐component Characters

Rigging an arbitrary 3D character by creating an animation skeleton is a time‐consuming process even for experienced animators. In this paper, we present an algorithm that automatically creates animation rigs for multi‐component 3D models, as they are typically found in online shape databases. Our algorithm takes as input a multi‐component model and an input animation skeleton with associated motion data. It then creates a target skeleton for the input model, calculates the rigid skinning weights, and a mapping between the joints of the target skeleton and the input animation skeleton. The automatic approach does not need additional semantic information, such as component labels or user‐provided correspondences, and succeeds on a wide range of models where the number of components is significantly different. It implicitly handles large scale and proportional differences between input and target skeletons and can deal with certain morphological differences, e.g., if input and target have different numbers of limbs. The output of our algorithm can be directly used in a retargeting system to create a plausible animated character.
Charging , Billing & Accounting in a multi-Operator and multi-Service Provider Environment

A near-term vision is the creation of an open market, where the mobile users will be able to access a plethora of value added services offered by a large number of independent content/service providers through a limited number of network providers. However, until this vision comes true there are several technical issues related to flexible and dynamic service provision that have to be tackled, such as charging, billing and accounting issues. In that context, we discuss the involved business players and we introduce a generic framework for Charging, Billing and Accounting contributing to flexible and dynamic service provision in an open marketplace.
Linking Perception, Cognition, and Action: Psychophysical Observations and Neural Network Modelling

It has been argued that perception, decision making, and movement planning are in reality tightly interwoven brain processes. However, how they are implemented in neural circuits is still a matter of debate. We tested human subjects in a temporal categorization task in which intervals had to be categorized as short or long. Subjects communicated their decision by moving a cursor into one of two possible targets, which appeared separated by different angles from trial to trial. Even though there was a 1 second-long delay between interval presentation and decision communication, categorization difficulty affected subjects’ performance, reaction (RT) and movement time (MT). In addition, reaction and movement times were also influenced by the distance between the targets. This implies that not only perceptual, but also movement-related considerations were incorporated into the decision process. Therefore, we searched for a model that could use categorization difficulty and target separation to describe subjects’ performance, RT, and MT. We developed a network consisting of two mutually inhibiting neural populations, each tuned to one of the possible categories and composed of an accumulation and a memory node. This network sequentially acquired interval information, maintained it in working memory and was then attracted to one of two possible states, corresponding to a categorical decision. It faithfully replicated subjects’ RT and MT as a function of categorization difficulty and target distance; it also replicated performance as a function of categorization difficulty. Furthermore, this model was used to make new predictions about the effect of untested durations, target distances and delay durations. To our knowledge, this is the first biologically plausible model that has been proposed to account for decision making and communication by integrating both sensory and motor planning information.
Doxorubicin-loaded poly (lactic-co-glycolic acid) nanoparticles coated with chitosan/alginate by layer by layer technology for antitumor applications

Natural polyelectrolyte multilayers of chitosan (CHI) and alginate (ALG) were alternately deposited on doxorubicin (DOX)-loaded poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) with layer by layer self-assembly to control drug release for antitumor activity. Numerous factors which influenced the multilayer growth on nano-colloidal particles were studied: polyelectrolyte concentration, NaCl concentration and temperature. Then the growth regime of the CHI/ALG multilayers was elucidated. The coated NPs were characterized by transmission electron microscopy, atomic force microscopy, X-ray diffraction and a zeta potential analyzer. In vitro studies demonstrated an undesirable initial burst release of DOX-loaded PLGA NPs (DOX-PLGA NPs), which was relieved from 55.12% to 5.78% through the use of the layer by layer technique. The release of DOX increased more than 40% as the pH of media decreased from 7.4 to 5.0. More importantly, DOX-PLGA (CHI/ALG)3 NPs had superior in vivo tumor inhibition rates at 83.17% and decreased toxicity, compared with DOX-PLGA NPs and DOX in solution. Thus, the presently formulated PLGA-polyelectrolyte NPs have strong potential applications for numerous controlled anticancer drug release applications.
The Use of Nonparametric Noise Models Extracted From Overlapping Subrecords for System Identification

In this paper, we study the asymptotic properties (consistency and asymptotic efficiency) of a frequency-domain errors-in-variables estimator using data extracted from overlapping subrecords. While the classical approach without overlap needs six consecutive periods, we show in this paper that, using overlapping subrecords, consistent estimators can be found with only two periods of the steady state response to a periodic excitation. This is done using overlapping subrecords. Moreover, the system identification procedure developed for data extracted from independent periods is shown to be valid for data extracted from overlapping subrecords. This allows the user to reduce the measurement time considerably without changing the identification procedure.
An exploration of the accentuation effect: errors in memory for voice fundamental frequency (F0) and speech rate

ABSTRACT The accentuation effect demonstrates how memory often reflects category typical representations rather than the specific features of learned items. The present study investigated the impact of manipulating fundamental frequency (F0) and speech rate (syllables per second) on immediate target matching performance (selecting a voice from a pair to match a previously heard target voice) for a range of synthesised voices. It was predicted that when participants were presented with high or low frequency target voices, voices even higher or lower in frequency would be selected. The same pattern was also predicted for speech rate. Inconsistent with the accentuation account, the results showed a general bias to select voices higher in frequency for high, moderate, and low frequency target voices. For speech rate, listeners selected voices faster in rate for slow rate target voices. Overall it seems doubtful that listeners rely solely on categorical information about voices during recognition.
Exploring the experiences of high-ability male dependents in Navy families

Through a qualitative interview study, the researcher investigated the experiences of high ability male dependents from Navy families and how these experiences were perceived to have influenced the male dependent’s social, emotional and cognitive development. Findings indicated that high ability male dependents did not perceive that the Navy lifestyle had negatively influenced their development. Participation in sports was credited as a very positive influence in helping male dependents make smooth transitions into new schools after relocations, as was a positive attitude toward change and challenge on the part of the Navy family. Themes include high expectations and goals for achievement, adapting to change, participation in sports, and pride in the Navy lifestyle. Recommendations for parents, teachers and counselors working with high ability males from military families are provided, and implications for future research involving military dependents are highlighted.
Automatic Zoom Level Prediction for Informal Location Descriptions

This paper is concerned with the automatic prediction of the zoom level at which to present a map with results for an informal location description. We propose the use of identifiability (relative uniqueness) and zoom level of each component geospatial expression (GE) in the location description, as a means of predicting the appropriate zoom level for the overall description. We apply a simple classification approach to zoom level prediction, and compare results using gold-standard and automatically-inferred GE information. We find the approach to have strong promise, including relative to the zoom level used in results from Google Maps for our location descriptions dataset.
A Taxonomy of Buffer Overflow Characteristics

Significant work on vulnerabilities focuses on buffer overflows, in which data exceeding the bounds of an array is loaded into the array. The loading continues past the array boundary, causing variables and state information located adjacent to the array to change. As the process is not programmed to check for these additional changes, the process acts incorrectly. The incorrect action often places the system in a nonsecure state. This work develops a taxonomy of buffer overflow vulnerabilities based upon characteristics, or preconditions that must hold for an exploitable buffer overflow to exist. We analyze several software and hardware countermeasures to validate the approach. We then discuss alternate approaches to ameliorating this vulnerability.
The Zarit Burden Interview: a new short version and screening version.

PURPOSE The purpose of the study was to develop a short and a screening version of the Zarit Burden Interview (ZBI) that would be suitable across diagnostic groups of cognitively impaired older adults, and that could be used for cross-sectional, longitudinal, and intervention studies. DESIGN AND METHODS We used data from 413 caregivers of cognitively impaired older adults referred to a memory clinic. We collected information on caregiver burden with the 22-item ZBI, and information about dependence in activities of daily living (ADLs) and the frequency of problem behaviors among care recipients. We used factor analysis and item-total correlations to reduce the number of items while taking into consideration diagnosis and change scores. RESULTS We produced a 12-item version (short) and a 4-item version (screening) of the ZBI. Correlations between the short and the full version ranged from 0.92 to 0.97, and from 0.83 to 0.93 for the screening version. Correlations between the three versions and ADL and problem behaviors were similar. We further investigated the behavior of the short version with a two-way analysis of variance and found that it produced identical results to the full version. IMPLICATIONS The short and screening versions of the ZBI produced results comparable to those of the full version. Reducing the number of items did not affect the properties of the ZBI, and it may lead to easier administration of the instrument.
GSK-3β Controls Osteogenesis through Regulating Runx2 Activity

Despite accumulated knowledge of various signalings regulating bone formation, the molecular network has not been clarified sufficiently to lead to clinical application. Here we show that heterozygous glycogen synthase kinase-3β (GSK-3β)-deficient mice displayed an increased bone formation due to an enhanced transcriptional activity of Runx2 by suppressing the inhibitory phosphorylation at a specific site. The cleidocranial dysplasia in heterozygous Runx2-deficient mice was significantly rescued by the genetic insufficiency of GSK-3β or the oral administration of lithium chloride, a selective inhibitor of GSK-3β. These results establish GSK-3β as a key attenuator of Runx2 activity in bone formation and as a potential molecular target for clinical treatment of bone catabolic disorders like cleidocranial dysplasia.
Understanding Online Knowledge Contribution in Social Learning Perspective

Online open knowledge sharing is the idea that the Internet can promote the aggregation and dissemination of useful knowledge between a potentially large number of people. Starting from the knowledge sharing idea, various types of online open knowledge sharing services have provided the central platform for users to interact with each other, share their knowledge, and even jointly create new knowledge. In this study, we derive two research questions: 1) what framework can better explain online knowledge contribution? and 2) what factors influence online knowledge contribution? The study draws on both social learning theory and the social model of knowledge creation to investigate the overall antecedents of knowledge contribution and to examine three facets, user-oriented, serviceoriented, and community-oriented knowledge contribution behaviour. In the study, we examine which knowledge sharing antecedents motivate people to contribute to knowledge sharing in the framework based on the social model of knowledge creation. We then verify each variable and hypothesis using a survey and the PLS analysis. This study uses social learning perspective to include all three aspects of knowledge sharing behaviour: personal, community-related, and service-related antecedents. With this new perspective, while previous studies have focused on personal cognitive factors in this area, this study examines the integrative influence of factors from social learning and social knowledge creation antecedents. In addition, our findings offer guidance and insights for knowledge sharing service practitioners and managers who are trying to encourage users’ contributions.
Energy Efficient Data Aggregation using Bitmask techniques for Wireless Sensor Networks

The rapid advancement of hardware technology has enabled the development of small, powerful, and inexpensive sensor nodes, which are capable of sensing, performing computation and wireless communication. This modernizes the deployment of wireless sensor network for monitoring some vicinity and collecting required information. However, sensor nodes have limited energy constraint which is a major challenge for such vision to become reality. We consider energy constrained wireless sensor network deployed over a region. The main job of such a network is to gather information from node and transmit it to sink node for further processing. So the aim of any data forwarding protocol is to conserve energy to maximize the network lifespan. Sensor nodes are capable of performing in-network aggregation of data coming from more than one source. In this paper we focused on energy consumption issue and aim to develop an energy efficient data aggregation protocol. To deliver energy efficiency we have considered a cluster-based wireless sensor network. This approach executes on each cluster independently and provides an energy efficient data aggregation in a cluster to maximize network lifetime for whole network.
Secure and energy-efficient data aggregation in Wireless Sensor Networks

Data aggregation in intermediate nodes (called aggregator nodes) is an effective approach for optimizing consumption of scarce resources like bandwidth and energy in Wireless Sensor Networks (WSNs). However, in-network processing poses a problem for the privacy of the sensor data since individual data of sensor nodes need to be known to the aggregator node before the aggregation process can be carried out. In applications of WSNs, privacy-preserving data aggregation has become an important requirement due to sensitive nature of the sensor data. Researchers have proposed a number of protocols and schemes for this purpose. He et al. (INFOCOM 2007) have proposed a protocol-called CPDA-for carrying out additive data aggregation in a privacy-preserving manner for application in WSNs. The scheme has been quite popular and well-known.
IRAS 20050+2720: ANATOMY OF A YOUNG STELLAR CLUSTER

IRAS 20050+2720 is young star-forming region at a distance of 700 pc without apparent high-mass stars. We present results of our multi-wavelength study of IRAS 20050+2720 which includes observations by Chandra and Spitzer, and Two Micron All Sky Survey and UBVRI photometry. In total, about 300 young stellar objects (YSOs) in different evolutionary stages are found. We characterize the distribution of YSOs in this region using a minimum spanning tree analysis. We newly identify a second cluster core, which consists mostly of class II objects, about 10′ from the center of the cloud. YSOs of earlier evolutionary stages are more clustered than more evolved objects. The X-ray luminosity function (XLF) of IRAS 20050+2720 is roughly lognormal, but steeper than the XLF of the more massive Orion Nebula complex. IRAS 20050+2720 shows a lower NH/AK ratio compared with the diffuse interstellar medium.
2 Knowledge Types in DSR and Their Truth Values

The central outcome of design science research (DSR) is prescriptive knowledge in the form of IT artifacts and recommendations. However, prescriptive knowledge is considered to have no truth value in itself. Given this assumption, the validity of DSR outcomes can only be assessed by means of descriptive knowledge to be obtained at the conclusion of a DSR process. This is reflected in the build-evaluate pattern of current DSR methodologies. Recognizing the emergent nature of IT artifacts this build-evaluate pattern, however, poses unfavorable implications regarding the achievement of rigor within a DSR project. While it is vital in DSR to prove the usefulness of an artifact a rigorous DSR process also requires justifying and validating the artifact design itself even before it has been put into use. This paper proposes three principles for evaluating DSR artifacts which not only address the evaluation of an artifact's usefulness but also the evaluation of design decisions made to build an artifact. In particular, it is argued that by following these principles the prescriptive knowledge produced in DSR can be considered to have a truth-like value.
Overview of sequential and parallel SAT solvers

This paper gives insight into techniques used by sequential as well as parallel SAT solver for solving Boolean Satisfiability Problem. It provides a detailed overview of evolution of SAT solvers thereby elaborating the successful techniques which resulted in higher performance for SAT solving and also in several cases, points out weaknesses involved in order to pave way for designing future performance efficient SAT solvers.
Modeling Infinite Topics on Social Behavior Data with Spatio-temporal Dependence

The problem of modeling topics on user behavior data in social networks has been widely studied in social marketing and social emotion analysis, where latent topic models are commonly used as the solutions. The user behavior data are highly related in time and space, which demands new latent topic models that consider both temporal and spatial distances. However, existing topic models either fail to model these two factors simultaneously, or cannot handle the high order dependence among user behaviors. In this paper we present a new nonparametric Bayesian model Time and Space Dependent Chinese Restaurant Processes (TSD-CRP for short). TSD-CRP can auto-select the number of topics and model high-order temporal and spatial dependence behind user behavior data. Empirical results on real-world data sets demonstrate the effectiveness of the proposed method.
Intersection of race, gender, and nationality in teaching about race and racism

Abstract Teaching about race and racism is important in social work education, yet at times it can present perplexing experiences for some faculty members. Using the concept of intersectionality, this paper presents a narrative on my experience of teaching race and racism as a Black, female professor from the Caribbean. Examples of my pedagogical responses during these experiences are presented.
Performance analysis of TCP/AQM under denial-of-service attacks

The interaction between TCP and various active queue management (AQM) algorithms has been extensively analyzed for the last few years. However, the analysis usually assumed that routers and TCP flows are not under any network attacks. In this paper, we investigate how the performance of TCP flows is affected by denial-of-service (DoS) attacks under the drop tail and various AQM schemes. In particular, we consider two types of DoS attacks-the traditional flooding-based DoS (FDDoS) attacks and the recently proposed pulsing DoS (PDoS) attacks. Both analytical and simulation results support that the PDoS attacks are more effective than the FDDoS attacks under the same average attack rate. Moreover, the drop tail surprisingly outperforms the RED-like AQMs when the router is under a PDoS attack, whereas the RED-like AQMs perform better under a severe FDDoS attack. On the other hand, the Adaptive Virtual Queue algorithm can retain a higher TCP throughput during PDoS attacks as compared with the RED-like AQMs.
Bearing-based distributed control and estimation of multi-agent systems

This paper studies the distributed control and estimation of multi-agent systems based on bearing information. In particular, we consider two problems: (i) the distributed control of bearing-constrained formations using relative position measurements and (ii) the distributed localization of sensor networks using bearing measurements. Both of the two problems are considered in arbitrary dimensional spaces. The analyses of the two problems rely on the recently developed bearing rigidity theory.We show that the two problems have the same mathematical formulation and can be solved by identical protocols. The proposed controller and estimator can globally solve the two problems without ambiguity. The results are supported with illustrative simulations.
A Supervised Semantic Parsing with Lexicon Extension and Syntactic Constraint

Existing semantic parsing research has steadily improved accuracy on a few domains and their corresponding meaning representations. In this paper, we present a novel supervised semantic parsing algorithm, which includes the lexicon extension and the syntactic supervision. This algorithm adopts a large-scale knowledge base from the open-domain Freebase to construct efficient, rich Combinatory Categorial Grammar (CCG) lexicon in order to supplement the inadequacy of its manually-annotated training dataset in the small closed-domain while allows for the syntactic supervision from the dependency-parsed sentences to penalize the ungrammatical semantic parses. Evaluations on both benchmark closed-domain datasets demonstrate that this approach learns highly accurate parser, whose parsing performance benefits greatly from the open-domain CCG lexicon and syntactic constraint.