Query: Knowledge Graph Identification

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Overview of the TAC 2010 Knowledge Base Population Track <=SEP=> In this paper we give an overview of the Knowledge Base Population (KBP) track at TAC 2010. The main goal of KBP is to promote research in discovering facts about entities and expanding a structured knowledge base with this information. A large source collection of newswire and web documents is provided for systems to discover information. Attributes (a.k.a. “slots”) derived from Wikipedia infoboxes are used to create the reference knowledge base (KB). KBP2010 includes the following four tasks: (1) Regular Entity Linking, where names must be aligned to entities in the KB; (2) Optional Entity linking, without using Wikipedia texts; (3) Regular Slot Filling, which requires a system to automatically discover the attributes of specified entities from the source document collection and use them to expand the KB; (4) Surprise Slot Filling, which requires a system to return answers regarding new slot types within a short time period. KBP2010 has attracted many participants (over 45 teams registered for KBP 2010 (not including the RTEKBP Validation Pilot task), among which 23 teams submitted results). In this paper we provide an overview of the task definition and annotation challenges associated with KBP2010. Then we summarize the evaluation results and discuss the lessons that we have learned based on detailed analysis.

Probabilistic Similarity Logic <=SEP=> Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect–the need to reason about similarities–which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domainspecific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.

A short introduction to probabilistic soft logic <=SEP=> Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This paper provides an overview of the PSL language and its techniques for inference and weight learning. An implementation of PSL is available at http://psl.umiacs.umd.edu/.

Yago: a core of semantic knowledge <=SEP=> 

Second-order cone programming <=SEP=> Second-order cone programming (SOCP) problems are convex optimization problems in which a linear function is minimized over the intersection of an affine linear manifold with the Cartesian product of second-order (Lorentz) cones. Linear programs, convex quadratic programs and quadratically constrained convex quadratic programs can all be formulated as SOCP problems, as can many other problems that do not fall into these three categories. These latter problems model applications from a broad range of fields from engineering, control and finance to robust optimization and combinatorial optimization. On the other hand semidefinite programming (SDP)—that is the optimization problem over the intersection of an affine set and the cone of positive semidefinite matrices—includes SOCP as a special case. Therefore, SOCP falls between linear (LP) and quadratic (QP) programming and SDP. Like LP, QP and SDP problems, SOCP problems can be solved in polynomial time by interior point methods. The computational effort per iteration required by these methods to solve SOCP problems is greater than that required to solve LP and QP problems but less than that required to solve SDP’s of similar size and structure. Because the set of feasible solutions for an SOCP problem is not polyhedral as it is for LP and QP problems, it is not readily apparent how to develop a simplex or simplex-like method for SOCP. While SOCP problems can be solved as SDP problems, doing so is not advisable both on numerical grounds and computational complexity concerns. For instance, many of the problems presented in the survey paper of Vandenberghe and Boyd [VB96] as examples of SDPs can in fact be formulated as SOCPs and should be solved as such. In §2, 3 below we give SOCP formulations for four of these examples: the convex quadratically constrained quadratic programming (QCQP) problem, problems involving fractional quadratic functions such as those that arise in structural optimization, logarithmic Tchebychev approximation and the

Whitening Black-Box Neural Networks <=SEP=> Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks – we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.

Development of a superior frontal–intraparietal network for visuo-spatial working memory <=SEP=> Working memory capacity increases throughout childhood and adolescence, which is important for the development of a wide range of cognitive abilities, including complex reasoning. The spatial-span task, in which subjects retain information about the order and position of a number of objects, is a sensitive task to measure development of spatial working memory. This review considers results from previous neuroimaging studies investigating the neural correlates of this development. Older children and adolescents, with higher capacity, have been found to have higher brain activity in the intraparietal cortex and in the posterior part of the superior frontal sulcus, during the performance of working memory tasks. The structural maturation of white matter has been investigated by diffusion tensor magnetic resonance imaging (DTI). This has revealed several regions in the frontal lobes in which white matter maturation is correlated with the development of working memory. Among these is a superior fronto-parietal white matter region, located close to the grey matter regions that are implicated in the development of working memory. Furthermore, the degree of white matter maturation is positively correlated with the degree of cortical activation in the frontal and parietal regions. This suggests that during childhood and adolescence, there is development of networks related to specific cognitive functions, such as visuo-spatial working memory. These networks not only consist of cortical areas but also the white matter tracts connecting them. For visuo-spatial working memory, this network could consist of the superior frontal and intraparietal cortex.

Inferring Query Performance Using Pre-retrieval Predictors <=SEP=> The prediction of query performance is an interesting and important issue in Information Retrieval (IR). Current predictors involve the use of relevance scores, which are time-consuming to compute. Therefore, current predictors are not very suitable for practical applications. In this paper, we study a set of predictors of query performance, which can be generated prior to the retrieval process. The linear and non-parametric correlations of the predictors with query performance are thoroughly assessed on the TREC disk4 and disk5 (minus CR) collections. According to the results, some of the proposed predictors have significant correlation with query performance, showing that these predictors can be useful to infer query performance in practical applications.

Nobel Lecture: The Economic Way of Looking at Behavior <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Foot Step Based Person Identification Using Histogram Similarity and Wavelet Decomposition <=SEP=> Research in person identification and authentication has attracted significant attention from the researchers and scientists. This paper presents a biometric user authentication based on a person's foot step. The advantage of this recognition method over other types of biometrics is that it enables unobtrusive user authentication where other types of biometrics are not available. Firstly the ground reaction force data was extracted using force plate to gather ground reaction force for individuals. Later we utilized the discrete wavelet transform to de-noise the experimental data and in the final step, histograms were used to identify different person's foot step. The experimental results show improvements in identification accuracies compared to previously reported work.

Pharmaceutical cold chain management: Platform based on a distributed ledger <=SEP=> In the process of distributing pharmaceutical products multiple stages are involved until products are delivered to patient. A reliable way for validating and verifying that products had been maintained within a licensed range is required. The paper presents a solution for pharmaceutical cold chain management using distributed ledger technologies. An application framework is proposed for shipment tracking which will deliver information to all stakeholders during the distribution phase of pharmaceutical products. The solution is based on Hyperledger Sawtooth distributed ledger framework, which has been extended with a custom transactions family and a sensors gateway for automatically collecting data from temperature tracking devices. The focus of the paper is to describe the data model and how entities of the system communicate.

Fiat-Shamir with Aborts: Applications to Lattice and Factoring-Based Signatures <=SEP=> We demonstrate how the framework that is used for creating efficient number-theoretic ID and signature schemes can be transferred into the setting of lattices. This results in constructions of the most efficient to-date identification and signature schemes with security based on the worst-case hardness of problems in ideal lattices. In particular, our ID scheme has communication complexity of around 65, 000 bits and the length of the signatures produced by our signature scheme is about 50, 000 bits. All prior lattice-based identification schemes required on the order of millions of bits to be transferred, while all previous lattice-based signature schemes were either stateful, too inefficient, or produced signatures whose lengths were also on the order of millions of bits. The security of our identification scheme is based on the hardness of finding the approximate shortest vector to within a factor of Õ(n) in the standard model, while the security of the signature scheme is based on the same assumption in the random oracle model. Our protocols are very efficient, with all operations requiring Õ(n) time. We also show that the technique for constructing our lattice-based schemes can be used to improve certain number-theoretic schemes. In particular, we are able to shorten the length of the signatures that are produced by Girault’s factoring-based digital signature scheme ([10,11,31]).

Arabic Optical Character Recognition Software : A Review 1 <=SEP=> This paper provides a thorough evaluation of a set of six important Arabic OCR systems available in the market; namely: Abbyy FineReader, Leadtools, Readiris, Sakhr, Tesseract and NovoVerus. We test the OCR systems using a randomly selected images from the well known Arabic Printed Text Image database (250 images from the APTI database) and using a set of 8 images from an Arabic book. The APTI database contains 45.313.600 of both decomposable and non-decomposable word images. In the evaluation, we conduct two tests. The first test is based on usual metrics used in the literature. In the second test, we provide a novel measure for Arabic language, which can be used for other non-Latin languages.

What to expect from teleconferencing. <=SEP=> Like other marvels of the electronic age, teleconferencing has been both oversold and underused. Though it has many potential uses, what managers know, or think they know, about it is generally based on misconceptions. Rather than relying only on vendors of teleconferencing, potential purchasers should first decide what their communication needs are, then choose the system that suits them best. These authors explain the new teleconferencing options and give guidelines for adapting them to a particular company.

Synthesizing tests for detecting atomicity violations <=SEP=> Using thread-safe libraries can help programmers avoid the complexities of multithreading. However, designing libraries that guarantee thread-safety can be challenging. Detecting and eliminating atomicity violations when methods in the libraries are invoked concurrently is vital in building reliable client applications that use the libraries. While there are dynamic analyses to detect atomicity violations, these techniques are critically dependent on effective multithreaded tests. Unfortunately, designing such tests is non-trivial. In this paper, we design a novel and scalable approach for synthesizing multithreaded tests that help detect atomicity violations. The input to the approach is the implementation of the library and a sequential seed testsuite that invokes every method in the library with random parameters. We analyze the execution of the sequential tests, generate variable lock dependencies and construct a set of three accesses which when interleaved suitably in a multithreaded execution can cause an atomicity violation. Subsequently, we identify pairs of method invocations that correspond to these accesses and invoke them concurrently from distinct threads with appropriate objects to help expose atomicity violations. We have incorporated these ideas in our tool, named Intruder, and applied it on multiple open-source Java multithreaded libraries. Intruder is able to synthesize 40 multithreaded tests across nine classes in less than two minutes to detect 79 harmful atomicity violations, including previously unknown violations in thread-safe classes. We also demonstrate the effectiveness of Intruder by comparing the results with other approaches designed for synthesizing multithreaded tests.

Parallel Processing of Dynamic Continuous Queries over Streaming Data Flows <=SEP=> More and more real-time applications need to handle dynamic continuous queries over streaming data of high density. Conventional data and query indexing approaches generally do not apply for excessive costs in either maintenance or space. Aiming at these problems, this study first proposes a new indexing structure by fusing an adaptive cell and KDB-tree, namely CKDB-tree. A cell-tree indexing approach has been developed on the basis of the CKDB-tree that supports dynamic continuous queries. The approach significantly reduces the space costs and scales well with the increasing data size. Towards providing a scalable solution to filtering massive steaming data, this study has explored the feasibility to utilize the contemporary general-purpose computing on the graphics processing unit (GPGPU). The CKDB-tree-based approach has been extended to operate on both the CPU (host) and the GPU (device). The GPGPU-aided approach performs query indexing on the host while perform streaming data filtering on the device in a massively parallel manner. The two heterogeneous tasks execute in parallel and the latency of streaming data transfer between the host and the device is hidden. The experimental results indicate that (1) CKDB-tree can reduce the space cost comparing to the cell-based indexing structure by 60 percent on average, (2) the approach upon the CKDB-tree outperforms the traditional counterparts upon the KDB-tree by 66, 75 and 79 percent in average for uniform, skewed and hyper-skewed data in terms of update costs, and (3) the GPGPU-aided approach greatly improves the approach upon the CKDB-tree with the support of only a single Kepler GPU, and it provides real-time filtering of streaming data with 2.5M data tuples per second. The massively parallel computing technology exhibits great potentials in streaming data monitoring.

(DE)$^2$CO: Deep Depth Colorization <=SEP=> The ability to classify objects is fundamental for robots. Besides knowledge about their visual appearance, captured by the RGB channel, robots heavily need also depth information to make sense of the world. While the use of deep networks on RGB robot images has benefited from the plethora of results obtained on databases like ImageNet, using convnets on depth images requires mapping them into three-dimensional channels. This transfer learning procedure makes them processable by pretrained deep architectures. Current mappings are based on heuristic assumptions over preprocessing steps and on what depth properties should be most preserved, resulting often in cumbersome data visualizations, and in suboptimal performance in terms of generality and recognition results. Here, we take an alternative route and we attempt instead to learn an optimal colorization mapping for any given pretrained architecture, using as training data a reference RGB-D database. We propose a deep network architecture, exploiting the residual paradigm, that learns how to map depth data to three channel images. A qualitative analysis of the images obtained with this approach clearly indicates that learning the optimal mapping preserves the richness of depth information better than current hand-crafted approaches. Experiments on the Washington, JHUIT-50 and BigBIRD public benchmark databases, using CaffeNet, VGG-16, GoogleNet, and ResNet50 clearly showcase the power of our approach, with gains in performance of up to 16% compared to state of the art competitors on the depth channel only, leading to top performances when dealing with RGB-D data.

GEOSTATISTICS : PAST , PRESENT , AND FUTURE <=SEP=> Geostatistical analyses were first developed in the 1950's as a result of interest in areal or block averages for ore reserves in the mining industry. Today, variogram estimation and spatial prediction (kriging) span all sciences where data exhibit spatial correlation. Theoretical properties of the spatial process are addressed under the distribution-free and likelihood-based perspectives. Strengths and weaknesses of these two dominant methodologies for modeling spatial correlation and predicting responses at unsampled sites and areal units are explored. Examples of hot spot detection and areal prediction show the flexibility of the Bayesian paradigm. Current bottlenecks and future directions in the field of geostatistics are discussed.

iPinYou Global RTB Bidding Algorithm Competition Dataset <=SEP=> RTB (Real Time Bidding) is one of the most exciting developments in computational advertising in recent years. It drives transparency and efficiency in the display advertising ecosystem and facilitates the healthy growth of the display advertising industry. It enables advertisers to deliver the right message to the right person at the right time, publishers to better monetize their content by leveraging their website audience, and consumers to view relevant information through personalized ads. However, researchers in computational advertising area have been suffering from lack of publicly available datasets. iPinYou organizes a three-season global RTB algorithm competition in 2013. For each season, there is offline stage and online stage. On the offline stage, iPinYou releases a dataset for model training and reserves a dataset for testing. The dataset includes logs of ad biddings, impressions, clicks, and final conversions. After the whole competition ends, iPinYou organizes and releases all these three datasets for public use. These datasets can support experiments of some important research problems such as bid optimization and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. In this paper, we give descriptions of these datasets to further boost the interests of computational advertising research community using this dataset.

Towards Extracting Faithful and Descriptive Representations of Latent Variable Models <=SEP=> Methods that use latent representations of data, such as matrix and tensor factorization or deep neural methods, are becoming increasingly popular for applications such as knowledge base population and recommendation systems. These approaches have been shown to be very robust and scalable but, in contrast to more symbolic approaches, lack interpretability. This makes debugging such models difficult, and might result in users not trusting the predictions of such systems. To overcome this issue we propose to extract an interpretable proxy model from a predictive latent variable model. We use a socalled pedagogical method, where we query our predictive model to obtain observations needed for learning a descriptive model. We describe two families of (presumably more) descriptive models, simple logic rules and Bayesian networks, and show how members of these families provide descriptive representations of matrix factorization models. Preliminary experiments on knowledge extraction from text indicate that even though Bayesian networks may be more faithful to a matrix factorization model than the logic rules, the latter are possibly more useful for interpretation and debugging.

A design of an ICN architecture within the framework of SDN <=SEP=> The core design principle of Information-Centric Networking (ICN) is in the name based routing that enables users to ask for a data object by its name and makes the network deliver it to users from a nearby cache if available. Software-Defined Networking (SDN) lowers the cost and complexity of network management by decoupling architecture from infrastructure, which promises the continuous evolution of the network architecture in a flexible manner. The synergy between ICN supporting efficient data dissemination as the norm and SDN providing flexible management framework enables the combination to be a fully controllable framework for efficient data dissemination. In this paper, we propose a design of an ICN architecture within the framework of SDN.

The Tri-Wheel: A novel wheel-leg mobility concept <=SEP=> The Tri-Wheel is a novel wheel-leg locomotion concept inspired by work with first responders. Through its two modes of operation-Driving Mode and Tumbling Mode- this mechanism is able to both drive quickly on smooth surfaces at roughly 1.7 times desired speed and climb objects as tall as 67% of the diameter of the mechanism. The unique gearing configuration that facilitates these dual capabilities is described, and testing quantifies that this nonprecision gearing system is roughly 81% efficient in a worst-case scenario of loading. This work introduces the Tri-Wheel concept and provides preliminary testing to validate its predicted operating characteristics.

Crowdsourcing Information Systems – A Systems Theory Perspective <=SEP=> Crowdsourcing harnesses the potential of large and open networks of people. It is a relatively new phenomenon and attracted substantial interest in practice. Related research, however, lacks a theoretical foundation. We propose a systemtheoretical perspective on crowdsourcing systems to address this gap and illustrate its applicability by using it to classify crowdsourcing systems. By deriving two principal dimensions from theory, we identify four fundamental types of crowdsourcing systems that help to distinguish important features of such systems. We analyse their respective characteristics and discuss implications and requirements for various aspects related to the design of such systems. Our results demonstrate that systems theory can inform the study of crowdsourcing systems. The identified system types and the implications on their design may prove useful for researchers to frame future studies and for practitioners to identify the right crowdsourcing systems for a particular purpose.

A multistage dataflow implementation of a Deep Convolutional Neural Network based on FPGA for high-speed object recognition <=SEP=> Deep Neural Networks (DNNs) have progressed significantly in recent years. Novel DNN methods allow tasks such as image and speech recognition to be conducted easily and efficiently, compared with previous methods that needed to search for valid feature values or algorithms. However, DNN computations typically consume a significant amount of time and high-performance computing resources. To facilitate high-speed object recognition, this article introduces a Deep Convolutional Neural Network (DCNN) accelerator based on a field-programmable gate array (FPGA). Our hardware takes full advantage of the characteristics of convolutional calculation; this allowed us to implement all DCNN layers, from image input to classification, in a single chip. In particular, the dateflow from input to classification is uninterrupted and paralleled. As a result, our implementation achieved a speed of 409.62 giga-operations per second (GOPS), which is approximately twice as fast as the latest reported result. Furthermore, we used the same architecture to implement a Recurrent Convolutional Neural Network (RCNN), which can, in theory, provide better recognition accuracy.

Hierarchical data-driven vehicle dispatch and ride-sharing <=SEP=> Modern transportation system suffers from increasing passenger demand, limited vehicle supply and inefficient mobility service. Towards building an intelligent transportation system to address these issues, we propose a hierarchical framework to implement strategies that is capable of allocating vehicles to serve passengers in different locations based on limited supply. In the higher hierarchy, we optimize idle mileage induced by rebalancing vehicles across regions using receding horizon control towards current and predicted future requests. In addition, we design a dispatch strategy that is robust against passenger demand and vehicle mobility pattern uncertainties. In the lower hierarchy, within each region, pick-up and drop-off schedules for real-time requests are obtained for each vehicle by solving mixed-integer linear programs (MILP). The objective of the MILP is to minimize total mileage delay due to ride-sharing while serving as many requests as possible. We illustrate the validity of our framework via numerical simulations on taxi trip data from New York City.

Brain tumor segmentation from MRI data sets using region growing approach <=SEP=> Brain segmentation is an important part of medical image processing. Most commonly, it aims at locating various lesions and pathologies inside the human brain. In this paper, a new brain segmentation algorithm is proposed. The method is seeded region growing approach which was developed to segment the area of the brain affected by a tumor. The proposed algorithm was described. Results of testing the developed method on real MRI data set are presented and discussed.

MIMIC-CXR: A large publicly available database of labeled chest radiographs <=SEP=> Chest radiography is an extremely powerful imaging modality, allowing for a detailed inspection of a patient’s thorax, but requiring specialized training for proper interpretation. With the advent of high performance general purpose computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. However, a key challenge in the development of these techniques is the lack of sufficient data. Here we describe MIMIC-CXR, a large dataset of 371,920 chest x-rays associated with 227,943 imaging studies sourced from the Beth Israel Deaconess Medical Center between 2011 2016. Each imaging study can pertain to one or more images, but most often are associated with two images: a frontal view and a lateral view. Images are provided with 14 labels derived from a natural language processing tool applied to the corresponding free-text radiology reports. All images have been de-identified to protect patient privacy. The dataset is made freely available to facilitate and encourage a wide range of research in medical computer vision.

Eliminative Materialism and the Propositional Attitudes <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Wearable Ultrasonic Sensor Using Double-Layer PVDF Films for Monitoring Tissue Motion <=SEP=> Monitoring of the physical properties of the tissues provides valuable information for the clinical diagnosis and evaluation. However, one of the challenges of an ultrasonic method for continuous monitoring of a tissue motion using a conventional clinical ultrasonic image system could be motion artifacts due to the weight and size of its handheld ultrasonic probe employed. A wearable ultrasonic sensor, made of a polyvinylidene fluoride (PVDF) polymer piezoelectric film, may be able to reduce the motion artifacts due to its lightweight and flexible properties. However, the PVDF ultrasonic sensor has a relatively weak transmitting acoustic signal strength which causes poor signal-to-noise ratio of the ultrasonic signal acquired in pulse-echo measurements, particularly for the signal reflected from deeper tissue. This paper investigated an improvement of the ultrasonic performance of the WUS using double-layer PVDF films. The sensor was constructed using two 52-μm thick PVDF films. The developed double-layer WUS showed the 1.7 times greater ultrasonic signal amplitude compared to the WUS made of a single-layer PVDF having the equivalent PVDF film thickness. Thus, the developed double-layer PVDF WUS improved the depth of the ultrasonic penetration into the tissue. The developed WUS successfully demonstrated to monitor the contractions of biceps muscles in an upper arm. In addition, a cardiac tissue motion was clearly observed in the M-mode measurement corresponding with the cardiac cycles obtained from the ECG measurement.

Cerebral bases of subliminal speech priming <=SEP=> While the neural correlates of unconscious perception and subliminal priming have been largely studied for visual stimuli, little is known about their counterparts in the auditory modality. Here we used a subliminal speech priming method in combination with fMRI to investigate which regions of the cerebral network for language can respond in the absence of awareness. Participants performed a lexical decision task on target items preceded by subliminal primes, which were either phonetically identical or different from the target. Moreover, the prime and target could be spoken by the same speaker or by two different speakers. Word repetition reduced the activity in the insula and in the left superior temporal gyrus. Although the priming effect on reaction times was independent of voice manipulation, neural repetition suppression was modulated by speaker change in the superior temporal gyrus while the insula showed voice-independent priming. These results provide neuroimaging evidence of subliminal priming for spoken words and inform us on the first, unconscious stages of speech perception.