Query: Synthetic Social Media Data Generation

Relevant passages:
Social Turing Tests: Crowdsourcing Sybil Detection <=SEP=> As popular tools for spreading spam and malware, Sybils (or fake accounts) pose a serious threat to online communities such as Online Social Networks (OSNs). Today, sophisticated attackers are creating realistic Sybils that effec tively befriend legitimate users, rendering most automated Sybil detection techniques ineffective. In this paper, we explor e the feasibility of acrowdsourcedSybil detection system for OSNs. We conduct a large user study on the ability of humans to detect today’s Sybil accounts, using a large corpus of ground-truth Sybil accounts from the Facebook and Renren networks. We analyze detection accuracy by both “experts” and “turkers” under a variety of conditions, and find that while turkers vary significantly in their effective ness, experts consistently produce near-optimal results. We use these results to drive the design of a multi-tier crowdsourcing Sybil detection system. Using our user study data, we show that this system is scalable, and can be highly effective either as a standalone system or as a complementary technique to current tools.

Building Natural-Language Generation Systems <=SEP=> Reiter and Dale’s book is about natural language generation (NLG), and describes methods for building systems of this kind. It offers a comprehensive overview of the field of NLG, from a perspective that has rarely been described elsewhere. It is very valuable for people with little or even no knowledge about NLG particulars who intend to investigate applications where NLG technology might play a certain role, as well as for people who want to obtain an application-oriented overview of the field. In this sense, all communities intended to be served by the book—students, academics in related areas, and software developers—are well addressed. In addition, some sections provide material that is probably new even to researchers with considerable experience in the field, such as the discussions on corpus analysis and domain modeling. In the introduction, the field of NLG is briefly characterized from researchand application-oriented perspectives and is illustrated by screen shots produced by several systems. Then conditions for effective uses of this technology are elaborated and contrasted with conditions where other techniques are more appropriate, and methods for determining the intended functionality of a system to be built are discussed. The main sections of the book are devoted to the prototypical architecture of applicationoriented NLG systems and their major processing phases: document planning, microplanning, and surface realization. Each of these three phases is illustrated by a number of detailed examples, demonstrating the successive refinements of utterance specifications in the course of processing. In the final section, the embedding of natural language processing technology is discussed, featuring typography, combined uses with graphics and hypertext, and integration with speech. The methods are illustrated by a large number of examples—the book contains more than 120 figures on its 248 pages. At the end of each section, a number of useful references for further reading are related to the section topics. In the appendix, a table summarizing the 35 systems referred to in the book is given. In general, the presentations in the book are beneficial for their high degree of detail in documenting representations. Running examples are illustrated by intermediate representations at various stages of processing. They demonstrate very well the increasing degrees of precision in which utterances are specified and the associated commitments about possible text portions that express these specifications. Particularly valuable are the comparisons of alternative representations, ranging from skeletal propositions over lexicalized case frames to canned text, including a comparison of the consequences for the distribution of work over the system components involved and discussions of the associated pros and cons. These chapters provide a number of valuable hints, given the currently low degree of standardization in the field. Further important aspects addressed, which have been widely ignored in the literature so far,

Adversarially Learned Inference <=SEP=> We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.

Deep Neural Networks for Acoustic Modeling in Speech Recognition <=SEP=> Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input. An alternative way to evaluate the fit is to use a feedforward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks with many hidden layers, that are trained using new methods have been shown to outperform Gaussian mixture models on a variety of speech recognition benchmarks, sometimes by a large margin. This paper provides an overview of this progress and represents the shared views of four research groups who have had recent successes in using deep neural networks for acoustic modeling in speech recognition.

Practical Issues in Automatic Documentation Generation <=SEP=> PLANDoc, a system under joint development by Columbia and Bellcore, documents the activity of planning engineers as they study telephone routes. It takes as input a trace of the engineer’s interaction with a network planning tool and produces 1-2 page summary. In this paper, we describe the user needs analysis we performed and how it influenced the development of PLANDoc. In particular, we show how it pinpointed the need for a sublanguage specification, allowing us to identify input messages and to characterize the different sentence paraphrases for realizing them. We focus on the systematic use of conjunction in combination with paraphrase that we developed for PLANDoc, which allows for the generation of summaries that are both concise–avoiding repetition of similar information, and fluent– avoiding repetition of similar phrasing.

S3Email: A method for securing emails from service providers <=SEP=> We often send our confidential information such as passport, credit card, social security numbers over email without concern about the security of email services. Existing network security mechanisms provide adequate security from external malicious adversaries and eavesdroppers, but they don't guarantee that the email service providers (ESPs) wouldn't or can't access our email data themselves, which in some cases could be highly confidential. One of the ways to protect email data from ESPs is to use Pretty Good Privacy (PGP) that has many limitations including key storage problem and dependability on third party services, making it cumbersome to use in practice. In this paper, we present S3Email method that provides email security against ESPs. The proposed method uses a cryptographic secret sharing technique in a novel way and encrypts the email metadata, body and attachments before the email is sent. In the proposed solution, the email sender and receiver must have at least two email accounts on the existing ESPs, which is not unusual today. Experiments and analysis show that the S3Email method provides information theoretic security with minimal computational overhead.

Robust solutions for network design under transportation cost and demand uncertainty <=SEP=> In many applications, the network design problem (NDP) faces significant uncertainty in transportation costs and demand, as it can be difficult to estimate current (and future values) of these quantities. In this paper, we present a robust optimization-based formulation for the NDP under transportation cost and demand uncertainty. We show that solving an approximation to this robust formulation of the NDP can be done efficiently for a network with single origin and destination per commodity and general uncertainty in transportation costs and demand that are independent of each other. For a network with path constraints, we propose an efficient column generation procedure to solve the linear programming relaxation. We also present computational results that show that the approximate robust solution found provides significant savings in the worst case while incurring only minor sub-optimality for specific instances of the uncertainty. Journal of the Operational Research Society advance online publication, 7 February 2007 doi:10.1057/palgrave.jors.2602362

Design and Control of Concentric-Tube Robots <=SEP=> A novel approach toward construction of robots is based on a concentric combination of precurved elastic tubes. By rotation and extension of the tubes with respect to each other, their curvatures interact elastically to position and orient the robot's tip, as well as to control the robot's shape along its length. In this approach, the flexible tubes comprise both the links and the joints of the robot. Since the actuators attach to the tubes at their proximal ends, the robot itself forms a slender curve that is well suited for minimally invasive medical procedures. This paper demonstrates the potential of this technology. Design principles are presented and a general kinematic model incorporating tube bending and torsion is derived. Experimental demonstration of real-time position control using this model is also described.

Fractal Merkle Tree Representation and Traversal <=SEP=> We introduce a technique for traversal of Merkle trees, and propose an efficient algorithm that generates a sequence of leaves along with their associated authentication paths. For one choice of parameters, and a total of N leaves, our technique requires a worst-case computational effort of 2 log N/loglog N hash function evaluations per output, and a total storage capacity of less than 1.5 log N/loglog N hash values. This is a simultaneous improvement both in space and time complexity over any previously published algorithm.

Three-column fixation for complex tibial plateau fractures. <=SEP=> OBJECTIVES
1) To introduce a computed tomography-based "three-column fixation" concept; and 2) to evaluate clinical outcomes (by using a column-specific fixation technique) for complex tibial plateau fractures (Schatzker classification Types V and VI).


DESIGN
Prospective cohort study.


SETTING
Level 1 trauma center.


PATIENTS
Twenty-nine cases of complex tibial plateau fractures were included. Based on routine x-ray and computed tomography images, all the fractures were classified as a "three-column fracture," which means at least one separate fragment was found in lateral, medial, and posterior columns in the proximal tibia (Schatzker classification Types V and VI).


INTERVENTION
The patients were operated on in a "floating position" with a combined approach, an inverted L-shaped posterior approach combined with an anterior-lateral approach. All three columns of fractures were fixed.


OUTCOME MEASURES
Operative time, blood loss, quality of reduction and alignment, fracture healing, complications, and functional outcomes based on Hospital for Special Surgery score and lower-extremity measure were recorded.


RESULTS
All the cases were followed for average 27.3 months (range, 24-36 months). All the cases had satisfactory reduction except one case, which had a 4-mm stepoff at the anterior ridge of the tibial plateau postoperatively. No case of secondary articular depression was found. One case had secondary varus deformity, one case had secondary valgus deformity, and two cases of screw loosening occurred postoperatively. No revision surgery was performed. Two cases had culture-negative wound drainage. No infection was noted. The average radiographic bony union time and full weightbearing time were 13.1 weeks (range, 11-16 weeks) and 16.7 weeks (range, 12-24 weeks), respectively. The mean Short Form 36, Hospital for Special Surgery score, and lower-extremity measure at 24 months postoperatively were 89 (range, 80-98), 90 (range, 84-98), and 87 (range, 80-95), respectively. The average range of motion of the affected knee was 2.7° to 123.4° at 2 years after the operation.


CONCLUSION
Three-column fixation is a new fixation concept in treating complex tibial plateau fractures, which is especially useful for multiplanar fractures involving the posterior column. The combination of posterior and anterior-lateral approaches is a safe and effective way to have direct reduction and satisfactory fixation for such difficult tibial plateau fractures.

Data mining techniques for customer relationship management <=SEP=> Advancements in technology have made relationship marketing a reality in recent years. Technologies such as data warehousing, data mining, and campaign management software have made customer relationship management a new area where firms can gain a competitive advantage. Particularly through data mining—the extraction of hidden predictive information from large databases—organizations can identify valuable customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. The automated, future-oriented analyses made possible by data mining move beyond the analyses of past events typically provided by history-oriented tools such as decision support systems. Data mining tools answer business questions that in the past were too time-consuming to pursue. Yet, it is the answers to these questions make customer relationship management possible. Various techniques exist among data mining software, each with their own advantages and challenges for different types of applications. A particular dichotomy exists between neural networks and chi-square automated interaction detection (CHAID). While differing approaches abound in the realm of data mining, the use of some type of data mining is necessary to accomplish the goals of today’s customer relationship management philosophy.  2002 Elsevier Science Ltd. All rights reserved.

Word and Phrase Translation with word2vec <=SEP=> Word and phrase tables are key inputs to machine translations, but costly to produce. New unsupervised learning methods represent words and phrases in a high-dimensional vector space, and these monolingual embeddings have been shown to encode syntactic and semantic relationships between language elements. The information captured by these embeddings can be exploited for bilingual translation by learning a transformation matrix that allows matching relative positions across two monolingual vector spaces. This method aims to identify high-quality candidates for word and phrase translation more costeffectively from unlabeled data. This paper expands the scope of previous attempts of bilingual translation to four languages (English, German, Spanish, and French). It shows how to process the source data, train a neural network to learn the high-dimensional embeddings for individual languages and expands the framework for testing their quality beyond the English language. Furthermore, it shows how to learn bilingual transformation matrices and obtain candidates for word and phrase translation, and assess their quality.

Authenticated Append-only Skip Lists <=SEP=> In this work we describe, design and analyze the security of a tamper-evident, append-only data structure for maintaining secure data sequences in a loosely coupled distributed system where individual system components may be mutually distrust-ful. The resulting data structure, called an Authenticated Append-Only Skip List (AASL), allows its maintainers to produce one-way digests of the entire data sequence, which they can publish to others as a commitment on the contents and order of the sequence. The maintainer can produce efficiently succinct proofs that authenticate a particular datum in a particular position of the data sequence against a published digest. AASLs are secure against tampering even by malicious data structure maintain-ers. First, we show that a maintainer cannot " invent " and authenticate data elements for the AASL after he has committed to the structure. Second, he cannot equivocate by being able to prove conflicting facts about a particular position of the data sequence. This is the case even when the data sequence grows with time and its maintainer publishes successive commitments at times of his own choosing. AASLs can be invaluable in reasoning about the integrity of system logs maintained by untrusted components of a loosely-coupled distributed system .

High Sensitivity Flexible Capacitive Pressure Sensor Using Polydimethylsiloxane Elastomer Dielectric Layer Micro-Structured by 3-D Printed Mold <=SEP=> 3-D printing is used to fabricate mold for micro-structuring the polydimethylsiloxane (PDMS) elastomeric dielectric layer in capacitive flexible pressure sensors. It is shown that, despite of the limited resolution of the used commercial 3-D printer for producing the mold, the fabricated sensor with the micro-structured PDMS layers can achieve sensitivity higher than previous work using micro-fabricated silicon wafer molds. The devices also present very low detection limit, fast response/recovery speed, excellent durability and good tolerance to variations of ambient temperature and humidity, which enables to reliably monitor weak human physiological signals in real time. As an application example, the flexible pressure sensor is integrated in a wearable system to monitor wrist pulse.

Estimating Data Integration and Cleaning Effort <=SEP=> Data cleaning and data integration have been the topic of intensive research for at least the past thirty years, resulting in a multitude of specialized methods and integrated tool suites. All of them require at least some and in most cases significant human input in their configuration, during processing, and for evaluation. For managers (and for developers and scientists) it would be therefore of great value to be able to estimate the effort of cleaning and integrating some given data sets and to know the pitfalls of such an integration project in advance. This helps deciding about an integration project using cost/benefit analysis, budgeting a team with funds and manpower, and monitoring its progress. Further, knowledge of how well a data source fits into a given data ecosystem improves source selection. We present an extensible framework for the automatic effort estimation for mapping and cleaning activities in data integration projects with multiple sources. It comprises a set of measures and methods for estimating integration complexity and ultimately effort, taking into account heterogeneities of both schemas and instances and regarding both integration and cleaning operations. Experiments on two real-world scenarios show that our proposal is two to four times more accurate than a current approach in estimating the time duration of an integration process, and provides a meaningful breakdown of the integration problems as well as the required integration activities. 1. COMPLEXITY OF INTEGRATION AND CLEANING Data integration and data cleaning remain among the most human-work-intensive tasks in data management. Both require a clear understanding of the semantics of schema and data – a notoriously difficult task for machines. Despite much research and development of supporting tools and algorithms, state-of-the-art integration projects involve significant human resource cost. In fact, Gartner reports that 10% of all IT cost goes into enterprise software c ©2015, Copyright is with the authors. Published in Proc. 18th International Conference on Extending Database Technology (EDBT), March 23-27, 2015, Brussels, Belgium: ISBN 978-3-89318-067-7, on OpenProceedings.org. Distribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0. for data integration and data quality, and it is well recognized that most of those expenses are for human labor. Thus, when embarking on a data integration and cleaning project, it is useful and important to estimate in advance the effort and cost of the project and to find out which particular difficulties cause these. Such estimations help deciding whether to pursue the project in the first place, planning and scheduling the project using estimates about the duration of integration steps, budgeting in terms of cost or manpower, and finally monitoring the progress of the project. Cost estimates can also help integration service providers, IT consultants, and IT tool vendors to generate better price quotes for integration customers. Further, automatically generated knowledge of how well and how easy a data source fits into a given data ecosystem improves source selection. However, “project estimation for [. . . ] data integration projects is especially difficult, given the number of stakeholders involved across the organization as well as the unknowns of data complexity and quality.” [14]. Any integration project has several steps and tasks, including requirements analysis, selection of data sources, determining the appropriate target database, data transformation specifications, testing, deployment, and maintenance. In this paper, we focus on exploring the database-related steps of integration and cleaning and automatically estimate their effort. 1.1 Challenges There are simple approaches to estimate in isolation the complexity of individual mapping and cleaning tasks. For the mapping, evaluating its complexity can be done by counting the matchings (i.e., correspondences) among elements. For the cleaning problem, a natural solution is to measure its complexity by counting the number of constraints on the target schema. However, as several integration approaches have shown, the interactive nature of these two problems is particularly complex [5, 11, 13]. For example, a data exchange problem takes as input two relational schemas, a transformation between them (a mapping), a set of target constraints, and answers two questions: whether it is possible to compute a valid solution for a given setting and how. Interestingly, to have a solution, certain conditions must hold on the target constraints, and extending the setting to more complex languages or data models bring tighter restrictions on the class of tractable cases [6, 12]. In our work, the main challenge is to estimate complexity and effort in a setting that goes beyond these ad-hoc studies while satisfying four main requirements: Generality: We require independence from the language used to express the data transformation. Furthermore, real cases often fail the existence of solution tests considered in formal frameworks http://www.gartner.com/ technology/research/ it-spending-forecast/ http://www.gartner.com/newsroom/ id/2292815 (e.g., weak acyclicity condition [11]), but an automatic estimation is still desirable for them in practice. Completeness: Only a subset of the constraints that hold on the data are specified over the schema. In fact, business rules are commonly enforced at the application level and are not reflected in the metadata of the schemas, but should nevertheless be considered. Granularity: Details about the integration issues are crucial for consumption of the estimation. For a real understanding and proper planning, it is important to know which source and/or target attributes are cause of problems and how (e.g., phone attributes in source and target schema have different formats). Existing estimators do not reason over actual data structures and thus make no statements about the causes of integration effort. Configurability and extensibility: The actual effort depends on subjective factors like the capabilities of available tools and the desired quality of the output. Therefore, intuitive, yet rich configuration settings for the estimation process are crucial for its applicability. Moreover, users must be able to extend the range of problems covered by the framework. These challenges cannot be tackled with existing syntactical methods to test the existence of solutions, as they work only in specific settings (Generality), are restricted to declarative specifications over the schemas (Completeness), and do not provide details about the actual problems (Granularity). On the other hand, as systems that compute solutions require human interaction to finalize the process [8, 13], they cannot be used for estimation purpose and their availability is orthogonal to our problem (Configurability). 1.2 Approaching Effort Estimation Figure 1 presents our view on the problem of estimating the effort of data integration. The starting point is an integration scenario with a target database and one or more source databases. The right-hand side of the figure shows the actual integration process performed by an integration specialist, where the goal is to move all instances of the source databases into the target database. Typically, a set of integration tools are used by the specialist. These tools have access to the source and target and support her in the tasks. The process takes a certain effort, which can be measured, for instance as amount of work in hours or days or in a monetary unit. Our goal is to find that effort without actually performing the integration. Moreover, we want to find and present the problems that cause this effort. To this end, we developed a two-phase process as shown on the left-hand side of Figure 1. The first phase, the complexity assessment, reveals concrete integration challenges for the scenario. To address generality, these problems are exclusively determined by the source and target schemas and instances; if and how an integration practitioner deals with them is not addressed at this point. Thus, this first phase is independent of external parameters, such as the level of expertise of the specialist or the available integration tools. However, it is aided by the results of schema matching and data profiling tools, which analyze the participating databases and produce metadata about them (to achieve completeness). The output of the complexity assessment is a set of clearly defined problems, such as number of violations for a constraint or number of different value representations. This detailed breakdown of the problems achieves granularity and is useful for several tasks, even if not interpreted as an input to calculate actual effort. Examples of application are source selection [9], i.e., given a set of integration candidates, find the source with the best ‘fit’; and support for data visualization [7], i.e., highlight parts of the schemas that are hard to integrate. Data

Optical nano-imaging of gate-tunable graphene plasmons <=SEP=> The ability to manipulate optical fields and the energy flow of light is central to modern information and communication technologies, as well as quantum information processing schemes. However, because photons do not possess charge, a way of controlling them efficiently by electrical means has so far proved elusive. A promising way to achieve electric control of light could be through plasmon polaritons—coupled excitations of photons and charge carriers—in graphene. In this two-dimensional sheet of carbon atoms, it is expected that plasmon polaritons and their associated optical fields can readily be tuned electrically by varying the graphene carrier density. Although evidence of optical graphene plasmon resonances has recently been obtained spectroscopically, no experiments so far have directly resolved propagating plasmons in real space. Here we launch and detect propagating optical plasmons in tapered graphene nanostructures using near-field scattering microscopy with infrared excitation light. We provide real-space images of plasmon fields, and find that the extracted plasmon wavelength is very short—more than 40 times smaller than the wavelength of illumination. We exploit this strong optical field confinement to turn a graphene nanostructure into a tunable resonant plasmonic cavity with extremely small mode volume. The cavity resonance is controlled in situ by gating the graphene, and in particular, complete switching on and off of the plasmon modes is demonstrated, thus paving the way towards graphene-based optical transistors. This successful alliance between nanoelectronics and nano-optics enables the development of active subwavelength-scale optics and a plethora of nano-optoelectronic devices and functionalities, such as tunable metamaterials, nanoscale optical processing, and strongly enhanced light–matter interactions for quantum devices and biosensing applications.

Early Career Award. Clarifying the emotive functions of asymmetrical frontal cortical activity. <=SEP=> Asymmetrical activity over the frontal cortex has been implicated in the experience and expression of emotions and motivations. Explanations of the research have suggested that relatively greater left frontal activity is associated with positive affect and/or approach motivation, and that relatively greater right frontal activity is associated with negative affect and/or withdrawal motivation. In past research, affective valence and motivational direction were confounded, as only positive (negative) affects that were associated with approach (withdrawal) motivation were examined. Consequently, this research is unable to address whether asymmetrical frontal activity is associated with affective valence, motivational direction, or some combination of valence and motivation. In this article, I review research on the emotion of anger, a negative emotion often associated with approach motivation, that suggests that asymmetrical frontal cortical activity is due to motivational direction and not affective valence. Methodological and theoretical implications for the study of the frontal asymmetry specifically, and for emotion and motivation more generally, are discussed.

PV-OWL — Pharmacovigilance surveillance through semantic web-based platform for continuous and integrated monitoring of drug-related adverse effects in open data sources and social media <=SEP=> The recent EU regulation on Pharmacovigilance [Regulation (EU) 1235/2010, Directive 2010/84/EU] imposes both to Pharmaceutical companies and Public health agencies to maintain updated safety information of drugs, monitoring all available data sources. Here, we present our project aiming to develop a web platform for continuous monitoring of adverse effects of medicines (pharmacovigilance), by integrating information from public databases, scientific literature and social media. The project will start by scanning all available data sources concerning drug adverse events, both open (e.g., FAERS — FDA Adverse Event Reporting Systems, medical literature, social media, etc.) and proprietary data (e.g., discharge hospital records, drug prescription archives, electronic health records), that require agreement with respective data owners. Subsequent, pharmacovigilance experts will perform a semi-automatic mapping of codes identifying drugs and adverse events, to build the thesaurus of the web based platform. After these preliminary activities, signal generation and prioritization will be the core of the project. This task will result in risk confidence scores for each included data source and a comprehensive global score, indicating the possible association between a specific drug and an adverse event. The software framework MOMIS, an open source data integration system, will allow semi-automatic virtual integration of heterogeneous and distributed data sources. A web platform, based on MOMIS, able to merge many heterogeneous data sets concerning adverse events will be developed. The platform will be tested by external specialized subjects (clinical researchers, public or private employees in pharmacovigilance field). The project will provide a) an innovative way to link, for the first time in Italy, different databases to obtain novel safety indicators; b) a web platform for a fast and easy integration of all available data, useful to verify and validate hypothesis generated in signal detection. Finally, the development of the unified safety indicator (global risk score) will result in a compelling, easy-to-understand, visual format for a broad range of professional and not professional users like patients, regulatory authorities, clinicians, lawyers, human scientists.

Mundanely miraculous: the robot in healthcare <=SEP=> As both hero and villain, robots have played prominent roles in media such as films and books. Now, robots are no longer hidden away from the public conscious in fictive worlds or real-life factories. Robots are becoming a real part of our everyday encounters in environments such as healthcare settings. In this paper, we describe a discourse analysis of 60 YouTube videos that showcase robots in healthcare activities. Our narrative weaves three discourses that construct visions of the healthcare robot: (1) the miraculous robot as the robot that enhances patient care; (2) the mundane robot as the innocuous robot that integrates into the workflow seamlessly; and (3) the preternatural robot as the robot that is miraculous but never mundane. We propose several contrary visions to this dominant narrative of healthcare robots as a framework for future fieldwork that, we argue, should investigate the institutions of robotics.

An efficient deep neural networks training framework for robust face recognition <=SEP=> In recent years, the triplet loss-based deep neural networks (DNN) are widely used in the task of face recognition and achieve the state-of-the-art performance. However, the complexity of training the triplet loss-based DNN is significantly high due to the difficulty in generating high-quality training samples. In this paper, we propose a novel DNN training framework to accelerate the training process of the triplet loss-based DNN and meanwhile to improve the performance of face recognition. More specifically, the proposed framework contains two stages: 1) The DNN initialization. A deep architecture based on the softmax loss function is designed to initialize the DNN. 2) The adaptive fine-tuning. Based on the trained model, a set of high-quality triplet samples is generated and used to fine-tune the network, where an adaptive triplet loss function is introduced to improve the discriminative ability of DNN. Experimental results show that, the model obtained by the proposed DNN training framework achieves 97.3% accuracy on the LFW benchmark with low training complexity, which verifies the efficiency and effectiveness of the proposed framework.

Complexity in Phonological Treatment: Clinical Factors. <=SEP=> The construct of complexity has been advanced recently as a potentially contributing variable in the efficacy of treatment for children with functional phonological disorders. Thus far, complexity has been defined in terms of linguistic and psycholinguistic structure, articulatory phonetic variables, and conventional clinical factors. The focus of this paper is on clinical complexity as it influences the selection of target sounds for treatment, with three clinical factors reviewed: consistency of the error, normative age of acquisition, and number of errors to be treated. The collective findings suggest that treatment of seemingly more complex targets results in greater phonological gains. These results are integrated with converging evidence from other populations and language and learning domains.

Judgment of information quality and cognitive authority in the Web <=SEP=> In the Web, making judgments of information quality and authority is a difficult task for most users because overall, there is no quality control mechanism. This study examines the problem of the judgment of information quality and cognitive authority by observing people’s searching behavior in the Web. Its purpose is to understand the various factors that influence people’s judgment of quality and authority in the Web, and the effects of those judgments on selection behaviors. Fifteen scholars from diverse disciplines participated, and data were collected combining verbal protocols during the searches, search logs, and postsearch interviews. It was found that the subjects made two distinct kinds of judgment: predictive judgment, and evaluative judgment. The factors influencing each judgment of quality and authority were identified in terms of characteristics of information objects, characteristics of sources, knowledge, situation, ranking in search output, and general assumption. Implications for Web design that will effectively support people’s judgments of quality and authority are also discussed.

An Inventory of Preposition Relations <=SEP=> We describe an inventory of semantic relations that are expressed by prepositions. We define these relations by building on the word sense disambiguation task for prepositions and propose a mapping from preposition senses to the relation labels by collapsing semantically related senses across prepositions.

Fitting Tweedie ’ s Compound Poisson Model to Insurance Claims Data : Dispersion Modelling <=SEP=> We reconsider the problem of producing fair and accurate tariffs based on aggregated insurance data giving numbers of claims and total costs for the claims. Jørgensen and de Souza (Scand. Actuarial J., 1994) assumed Poisson arrival of claims and gamma distributed costs for individual claims. Jørgensen and de Souza (1994) directly modelled the risk or expected cost of claims per insured unit, μ say. They observed that the dependence of the likelihood function on μ is as for a linear exponential family, so that modelling similar to that of generalized linear models is possible. In this paper we observe that, when modelling the cost of insurance claims, it is generally necessary to model the dispersion of the costs as well as their mean. In order to model the dispersion we use the framework of double generalized linear models. Modelling the dispersion increases the precision of the estimated tariffs. The use of double generalized linear models also allows us to handle the case where only the total cost of claims and not the number of claims has been recorded.

Feeding State Modulates Behavioral Choice and Processing of Prey Stimuli in the Zebrafish Tectum <=SEP=> Animals use the sense of vision to scan their environment, respond to threats, and locate food sources. The neural computations underlying the selection of a particular behavior, such as escape or approach, require flexibility to balance potential costs and benefits for survival. For example, avoiding novel visual objects reduces predation risk but negatively affects foraging success. Zebrafish larvae approach small, moving objects ("prey") and avoid large, looming objects ("predators"). We found that this binary classification of objects by size is strongly influenced by feeding state. Hunger shifts behavioral decisions from avoidance to approach and recruits additional prey-responsive neurons in the tectum, the main visual processing center. Both behavior and tectal function are modulated by signals from the hypothalamic-pituitary-interrenal axis and the serotonergic system. Our study has revealed a neuroendocrine mechanism that modulates the perception of food and the willingness to take risks in foraging decisions.

inAir: measuring and visualizing indoor air quality <=SEP=> Good indoor air quality is a vital part of human health. Poor indoor air quality can contribute to the development of chronic respiratory diseases such as asthma, heart disease, and lung cancer. Complicating matters, poor air quality is extremely difficult for humans to detect through sight and smell alone and existing sensing equipment is designed to be used by and provide data for scientists rather than everyday citizens. We propose inAir, a tool for measuring, visualizing, and learning about indoor air quality. inAir provides historical and real-time visualizations of indoor air quality by measuring tiny hazardous airborne particles as small as 0.5 microns in size. Through user studies we demonstrate how inAir promotes greater awareness and motivates individual actions to improve indoor air quality.

Sub-sentencial Paraphrasing by Contextual Pivot Translation <=SEP=> The ability to generate or to recognize paraphrases is key to the vast majority of NLP applications. As correctly exploiting context during translation has been shown to be successful, using context information for paraphrasing could also lead to improved performance. In this article, we adopt the pivot approach based on parallel multilingual corpora proposed by (Bannard and Callison-Burch, 2005), which finds short paraphrases by finding appropriate pivot phrases in one or several auxiliary languages and back-translating these pivot phrases into the original language. We show how context can be exploited both when attempting to find pivot phrases, and when looking for the most appropriate paraphrase in the original subsentential “envelope”. This framework allows the use of paraphrasing units ranging from words to large sub-sentential fragments for which context information from the sentence can be successfully exploited. We report experiments on a text revision task, and show that in these experiments our contextual sub-sentential paraphrasing system outperforms a strong baseline system.

Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI) <=SEP=> Electroencephalogram (EEG) signals are frequently used in brain–computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by <inline-formula><tex-math notation="LaTeX">$10.02-19.77\%$</tex-math></inline-formula>, and at the same time increase the correlation to the true response speed by <inline-formula><tex-math notation="LaTeX">$19.39-86.47\%$</tex-math></inline-formula>.

Robust TiN HM process to overcome under etch issue for SAV scheme on 14nm node <=SEP=> For advance node such as 14nm technology and beyond, back end of line interconnect has implemented self-aligned via (SAV) schemes for better via-metal short process window [1]. A TiN metal hard mask (MHM) is used for the trench pattern definition, after which via lithography and partial via (PV) etch is performed where the TiN was opened. The via etch condition has very good selectivity so that via is formed in a self-aligned fashion by TiN HM [2]. It is indeed to have significant benefit of via to metal short [3]. However, one of the trade off in SAV scheme is the via under etch that whether or not via can well land on an opened oxide area during PV etch. To define the contact area between via resist hole and opened HM oxide as an effective area for via to open successfully. “Fig. 1” shows the effective area change during process variation due to Via alignment, Via photo resist CD variation (Via ADICD) and post hard mask etch CD variation (AMICD). “Fig. 2” shows the mechanism of this under etch failure mode and typical TEM image from 64nm metal pitch test vehicle. In this work, we try to enlarge the process window by an aggressive AMICD targeting in combining with a higher density TiN material to maintain profile.

Studying Bluetooth Malware Propagation: The BlueBag Project <=SEP=> Bluetooth worms currently pose relatively little danger compared to Internet scanning worms. The BlueBag project shows targeted attacks through Bluetooth malware using proof-of-concept codes and mobile devices