Understanding Stories with Large-Scale Common Sense

Story understanding systems need to be able to perform commonsense reasoning, specifically regarding characters’ goals and their associated actions. Some efforts have been made to form large-scale commonsense knowledge bases, but integrating that knowledge into story understanding systems remains a challenge. We have implemented the Aspire system, an application of large-scale commonsense knowledge to story understanding. Aspire extends Genesis, a rule-based story understanding system, with tens of thousands of goalrelated assertions from the commonsense semantic network ConceptNet. Aspire uses ConceptNet’s knowledge to infer plausible implicit character goals and story causal connections at a scale unprecedented in the space of story understanding. Genesis’s rule-based inference enables precise story analysis, while ConceptNet’s relatively inexact but widely applicable knowledge provides a significant breadth of coverage difficult to achieve solely using rules. Genesis uses Aspire’s inferences to answer questions about stories, and these answers were found to be plausible in a small study. Though we focus on Genesis and ConceptNet, demonstrating the value of supplementing precise reasoning systems with large-scale, scruffy commonsense knowledge is our primary contribution.


A comprehensive model of PMOS NBTI degradation

Negative bias temperature instability has become an important reliability concern for ultra-scaled Silicon IC technology with significant implications for both analog and digital circuit design. In this paper, we construct a comprehensive model for NBTI phenomena within the framework of the standard reaction–diffusion model. We demonstrate how to solve the reaction–diffusion equations in a way that emphasizes the physical aspects of the degradation process and allows easy generalization of the existing work. We also augment this basic reaction–diffusion model by including the temperature and field-dependence of the NBTI phenomena so that reliability projections can be made under arbitrary circuit operating conditions. 2004 Published by Elsevier Ltd.


Neural correlates of experimentally induced flow experiences

Flow refers to a positive, activity-associated, subjective experience under conditions of a perceived fit between skills and task demands. Using functional magnetic resonance perfusion imaging, we investigated the neural correlates of flow in a sample of 27 human subjects. Experimentally, in the flow condition participants worked on mental arithmetic tasks at challenging task difficulty which was automatically and continuously adjusted to individuals' skill level. Experimental settings of "boredom" and "overload" served as comparison conditions. The experience of flow was associated with relative increases in neural activity in the left anterior inferior frontal gyrus (IFG) and the left putamen. Relative decreases in neural activity were observed in the medial prefrontal cortex (MPFC) and the amygdala (AMY). Subjective ratings of the flow experience were significantly associated with changes in neural activity in the IFG, AMY, and, with trend towards significance, in the MPFC. We conclude that neural activity changes in these brain regions reflect psychological processes that map on the characteristic features of flow: coding of increased outcome probability (putamen), deeper sense of cognitive control (IFG), decreased self-referential processing (MPFC), and decreased negative arousal (AMY).


Discovering Structure in the Universe of Attribute Names

Recently, search engines have invested significant effort to answering entity–attribute queries from structured data, but have focused mostly on queries for frequent attributes. In parallel, several research efforts have demonstrated that there is a long tail of attributes, often thousands per class of entities, that are of interest to users. Researchers are beginning to leverage these new collections of attributes to expand the ontologies that power search engines and to recognize entity– attribute queries. Because of the sheer number of potential attributes, such tasks require us to impose some structure on this long and heavy tail of attributes. This paper introduces the problem of organizing the attributes by expressing the compositional structure of their names as a rule-based grammar. These rules offer a compact and rich semantic interpretation of multi-word attributes, while generalizing from the observed attributes to new unseen ones. The paper describes an unsupervised learning method to generate such a grammar automatically from a large set of attribute names. Experiments show that our method can discover a precise grammar over 100,000 attributes of Countries while providing a 40-fold compaction over the attribute names. Furthermore, our grammar enables us to increase the precision of attributes from 47% to more than 90% with only a minimal curation effort. Thus, our approach provides an efficient and scalable way to expand ontologies with attributes of user interest.


Fingerprint image enhancement using GWT and DMF

Fingerprint image enhancement is an essential preprocessing step in fingerprint recognition applications. In this paper we introduce an approach that extracts simultaneously orientation and frequency of local ridge in the fingerprint image by Gabor wavelet filter bank and use them in Gabor Filtering of image. Furthermore, we describes a robust approach to fingerprint image enhancement, which is based on integration of Gabor Filters and Directional Median Filter(DMF). In fact, Gaussian-distributed noises are reduced effectively by Gabor Filters and impulse noises by DMF. the proposed DMF not only can finish its original tasks, it can also join broken fingerprint ridges, fill out the holes of fingerprint images, smooth irregular ridges as well as remove some annoying small artifacts between ridges. Experimental results show our method to be superior to those described in the literature.


Combining haptic human-machine interaction with predictive path planning for lane-keeping and collision avoidance systems

This paper presents a first approach for a haptic human-machine interface combined with a novel lane-keeping and collision-avoidance assistance system approach, as well as the results of a first exploration study with human test drivers. The assistance system approach is based on a potential field predictive path planning algorithm that incorporates the drivers wishes commanded by the steering wheel angle, the brake pedal or throttle, and the intended maneuver. For the design of the haptic human-machine interface the assistance torque characteristic at the handwheel is shaped and the path planning parameters are held constant. In the exploration, both driving data as well as questionnaires are evaluated. The results show good acceptance for the lane-keeping assistance while the collision avoidance assistance needs to be improved.


Learning patterns of university student retention

Learning predictors for student retention is very difficult. After reviewing the literature, it is evident that there is considerable room for improvement in the current state of the art. As shown in this paper, improvements are possible if we (a) explore a wide range of learning methods; (b) take care when selecting attributes; (c) assess the efficacy of the learned theory not just by its median performance, but also by the variance in that performance; (d) study the delta of student factors between those who stay and those who are retained. Using these techniques, for the goal of predicting if students will remain for the first three years of an undergraduate degree, the following factors were found to be informative: family background and family’s social-economic status, high school GPA


Anonymizing sequential releases

An organization makes a new release as new information become available, releases a tailored view for each data request, releases sensitive information and identifying information separately. The availability of related releases sharpens the identification of individuals by a global quasi-identifier consisting of attributes from related releases. Since it is not an option to anonymize previously released data, the current release must be anonymized to ensure that a global quasi-identifier is not effective for identification. In this paper, we study the sequential anonymization problem under this assumption. A key question is how to anonymize the current release so that it cannot be linked to previous releases yet remains useful for its own release purpose. We introduce the lossy join, a negative property in relational database design, as a way to hide the join relationship among releases, and propose a scalable and practical solution.


Protecting Respondents' Identities in Microdata Release

ÐToday's globally networked society places great demand on the dissemination and sharing of information. While in the past released information was mostly in tabular and statistical form, many situations call today for the release of specific data (microdata). In order to protect the anonymity of the entities (called respondents) to which information refers, data holders often remove or encrypt explicit identifiers such as names, addresses, and phone numbers. Deidentifying data, however, provides no guarantee of anonymity. Released information often contains other data, such as race, birth date, sex, and ZIP code, that can be linked to publicly available information to reidentify respondents and inferring information that was not intended for disclosure. In this paper we address the problem of releasing microdata while safeguarding the anonymity of the respondents to which the data refer. The approach is based on the definition of k-anonymity. A table provides k-anonymity if attempts to link explicitly identifying information to its content map the information to at least k entities. We illustrate how k-anonymity can be provided without compromising the integrity (or truthfulness) of the information released by using generalization and suppression techniques. We introduce the concept of minimal generalization that captures the property of the release process not to distort the data more than needed to achieve k-anonymity, and present an algorithm for the computation of such a generalization. We also discuss possible preference policies to choose among different minimal


Generalizing Data to Provide Anonymity when Disclosing Information (Abstract)

The proliferation of information on the Internet and access to fast computers with large storage capacities has increased the volume of information collected and disseminated about individuals. The existence os these other data sources makes it much easier to re-identify individuals whose private information is released in data believed to be anonymous. At the same time, increasing demands are made on organizations to release individualized data rather than aggregate statistical information. Even when explicit identi ers, such as name and phone number, are removed or encrypted when releasing individualized data, other characteristic data, which we term quasi-identi ers, can exist which allow the data recipient to re-identify individuals to whom the data refer. In this paper, we provide a computational disclosure technique for releasing information from a private table such that the identity of any individual to whom the released data refer cannot be de nitively recognized. Our approach protects against linking to other data. It is based on the concepts of generalization, by which stored values can be replaced with semantically consistent and truthful but less precise alternatives, and of k-anonymity . A table is said to provide k-anonymity when the contained data do not allow the recipient to associate the released information to a set of individuals smaller than k. We introduce the notions of generalized table and of minimal generalization of a table with respect to a k-anonymity requirement. As an optimization problem, the objective is to minimally distort the data while providing adequate protection. We describe an algorithm that, given a table, e ciently computes a preferred minimal generalization to provide anonymity.


Bottom-Up Generalization : A Data Mining Solution to Privacy Protection

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. This paper investigates data mining as a technique for masking data; therefore, termed data mining based privacy protection. This approach incorporates partially the requirement of a targeted data mining task into the process of masking data so that essential structure is preserved in the masked data. The following privacy problem is considered in this paper: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. An iterative bottom-up generalization is adapted from data mining to generalize the data. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration.


M-invariance: towards privacy preserving re-publication of dynamic datasets

The previous literature of privacy preserving data publication has focused on performing "one-time" releases. Specifically, none of the existing solutions supports re-publication of the microdata, after it has been updated with insertions <u>and</u> deletions. This is a serious drawback, because currently a publisher cannot provide researchers with the most recent dataset continuously.
 This paper remedies the drawback. First, we reveal the characteristics of the re-publication problem that invalidate the conventional approaches leveraging k-anonymity and l-diversity. Based on rigorous theoretical analysis, we develop a new generalization principle m-invariance that effectively limits the risk of privacy disclosure in re-publication. We accompany the principle with an algorithm, which computes privacy-guarded relations that permit retrieval of accurate aggregate information about the original microdata. Our theoretical results are confirmed by extensive experiments with real data.


Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty

We report on results of a series of user studies on the perception of four visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of `sketchiness' as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency-ranging from child to adult strokes-where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness is a viable alternative for the visualization of uncertainty in lines and is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks.


An Architecture for Local Energy Generation, Distribution, and Sharing

The United States electricity grid faces significant problems resulting from fundamental design principles that limit its ability to handle the key energy challenges of the 21st century. We propose an innovative electric power architecture, rooted in lessons learned from the Internet and microgrids, which addresses these problems while interfacing gracefully into the current grid to allow for non-disruptive incremental adoption. Such a system, which we term a "Local" grid, is controlled by intelligent power switches (IPS), and can consist of loads, energy sources, and energy storage. The desired result of the proposed architecture is to produce a grid network designed for distributed renewable energy, prevalent energy storage, and stable autonomous systems. We will describe organizing principles of such a system that ensure well-behaved operation, such as requirements for communication and energy transfer protocols, regulation and control schemes, and market-based rules of operation.


A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences

In this paper, we propose a sequential neural encoder with latent structured description SNELSD for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory LSTM units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.


Advanced Gate Drive Unit With Closed-Loop $di_{{C}}/dt$  Control

This paper describes the design and the experimental investigation of a gate drive unit with closed-loop control of the collector current slope diC/dt for multichip insulated-gate bipolar transistors (IGBTs). Compared to a pure resistive gate drive, the proposed diC/dt control offers the ability to adjust the collector current slope freely which helps to find an optimized relation between switching losses and secure operation of the freewheeling diode for every type of IGBT. Based on the description of IGBT's switching behavior, the design and the realization of the gate drive are presented. The test setup and the comparison of switching tests with and without the proposed diC/dt control are discussed.


3-Dimensional Localization via RFID Tag Array

In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.


Inductive Learning of Answer Set Programs from Noisy Examples

In recent years, non-monotonic Inductive Logic Programming has received growing interest. Specifically, several new learning frameworks and algorithms have been introduced for learning under the answer set semantics, allowing the learning of common-sense knowledge involving defaults and exceptions, which are essential aspects of human reasoning. In this paper, we present a noise-tolerant generalisation of the learning from answer sets framework. We evaluate our ILASP3 system, both on synthetic and on real datasets, represented in the new framework. In particular, we show that on many of the datasets ILASP3 achieves a higher accuracy than other ILP systems that have previously been applied to the datasets, including a recently proposed differentiable learning framework.


On Formal and Cognitive Semantics for Semantic Computing

Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.


GENDER RECOGNITION FROM FACES USING BANDLET TRANSFORMATION

Gender Recognition is important in different commercial and law enforcement applications. In this paper we have proposed a gender recognition system through facial images. We have used a different technique that involves Bandlet Transform instead of previously used Wavelet Transform, which is a multi-resolution technique and more efficiently provides the edges of images, and then mean is combined to create the feature vectors of the images. To classify the images for gender, we have used fuzzy c mean clustering. Experimental results have shown that average 97.1% accuracy have been achieved using this technique when SUMS database was used and 93.3% was achieved when FERET database was used. Keywords----Bandlet, Gender Recognition, Fuzzy C-mean, Multi Resolution.


Evolution of the internal fixation of long bone fractures. The scientific basis of biological internal fixation: choosing a new balance between stability and biology.

The advent of 'biological internal fixation' is an important development in the surgical management of fractures. Locked nailing has demonstrated that flexible fixation without precise reduction results in reliable healing. While external fixators are mainly used today to provide temporary fixation in fractures after severe injury, the internal fixator offers flexible fixation, maintaining the advantages of the external fixator but allowing long-term treatment. The internal fixator resembles a plate but functions differently. It is based on pure splinting rather than compression. The resulting flexible stabilisation induces the formation of callus. With the use of locked threaded bolts, the application of the internal fixator foregoes the need of adaptation of the shape of the splint to that of the bone during surgery. Thus, it is possible to apply the internal fixator as a minimally invasive percutaneous osteosynthesis (MIPO). Minimal surgical trauma and flexible fixation allow prompt healing when the blood supply to bone is maintained or can be restored early. The scientific basis of the fixation and function of these new implants has been reviewed. The biomechanical aspects principally address the degree of instability which may be tolerated by fracture healing under different biological conditions. Fractures may heal spontaneously in spite of gross instability while minimal, even non-visible, instability may be deleterious for rigidly fixed small fracture gaps. The theory of strain offers an explanation for the maximum instability which will be tolerated and the minimal degree required for induction of callus formation. The biological aspects of damage to the blood supply, necrosis and temporary porosity explain the importance of avoiding extensive contact of the implant with bone. The phenomenon of bone loss and stress protection has a biological rather than a mechanical explanation. The same mechanism of necrosis-induced internal remodelling may explain the basic process of direct healing.


A Survey on Clustering Algorithms for Wireless Sensor Networks

A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.


80ºC, 50-Gb/s Directly Modulated InGaAlAs BH-DFB Lasers

Direct modulation at 50 Gb/s of 1.3-μm InGaAlAs DFB lasers operating at up to 80°C was experimentally demonstrated by using a ridge-shaped buried heterostructure.


Platform-as-a-Service (PaaS): The Next Hype of Cloud Computing

Cloud Computing is expected to become the driving force of information technology to revolutionize the future. Presently number of companies is trying to adopt this new technology either as service providers, enablers or vendors. In this way the cloud market is estimated be likely to emerge at a remarkable rate. Under the whole cloud umbrella, PaaS seems to have a relatively small market share. However, it is expected to offer much more as it is compared with its counterparts SaaS and IaaS. This paper is aimed to assess and analyze the future of PaaS technology. Year 2015 named as “the year of PaaS”. It means that PaaS technology has established strong roots and ready to hit the market with better technology services. This research will discuss future PaaS market trends, growth and business competitors. In the current dynamic era, several companies in the market are offering PaaS services. This research will also outline some of the top service providers (proprietary & open source) to discuss their current technology status and present a futuristic look into their services and business strategies. Analysis of the present and future PaaS technology infrastructure will also be a major discussion in this paper.


Hybrid CMOS/memristor circuits

This is a brief review of recent work on the prospective hybrid CMOS/memristor circuits. Such hybrids combine the flexibility, reliability and high functionality of the CMOS subsystem with very high density of nanoscale thin film resistance switching devices operating on different physical principles. Simulation and initial experimental results demonstrate that performance of CMOS/memristor circuits for several important applications is well beyond scaling limits of conventional VLSI paradigm.


Automatic Face Image Quality Prediction

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this work, we propose (and compare) two methods for automatic face image quality based on target face quality values from (i) human assessments of face image quality (matcherindependent), and (ii) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, LFW and IJB-A, which both contain facial variations with multiple quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the FNMR at 1% FMR by at least 13% for two face matchers (a COTS matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.


The Neural Architecture of the Language Comprehension Network: Converging Evidence from Lesion and Connectivity Analyses

While traditional models of language comprehension have focused on the left posterior temporal cortex as the neurological basis for language comprehension, lesion and functional imaging studies indicate the involvement of an extensive network of cortical regions. However, the full extent of this network and the white matter pathways that contribute to it remain to be characterized. In an earlier voxel-based lesion-symptom mapping analysis of data from aphasic patients (Dronkers et al., 2004), several brain regions in the left hemisphere were found to be critical for language comprehension: the left posterior middle temporal gyrus, the anterior part of Brodmann's area 22 in the superior temporal gyrus (anterior STG/BA22), the posterior superior temporal sulcus (STS) extending into Brodmann's area 39 (STS/BA39), the orbital part of the inferior frontal gyrus (BA47), and the middle frontal gyrus (BA46). Here, we investigated the white matter pathways associated with these regions using diffusion tensor imaging from healthy subjects. We also used resting-state functional magnetic resonance imaging data to assess the functional connectivity profiles of these regions. Fiber tractography and functional connectivity analyses indicated that the left MTG, anterior STG/BA22, STS/BA39, and BA47 are part of a richly interconnected network that extends to additional frontal, parietal, and temporal regions in the two hemispheres. The inferior occipito-frontal fasciculus, the arcuate fasciculus, and the middle and inferior longitudinal fasciculi, as well as transcallosal projections via the tapetum were found to be the most prominent white matter pathways bridging the regions important for language comprehension. The left MTG showed a particularly extensive structural and functional connectivity pattern which is consistent with the severity of the impairments associated with MTG lesions and which suggests a central role for this region in language comprehension.


Distribution of Eigenvalues for 2x2 MIMO Channel Capacity Based on Indoor Measurements

Based on the non line-of-sight (NLOS) indoor measurements performed in the corridors on the third floor of the Electronics and Telecommunications Research Institute (ETRI) building in Daejeon city, Republic of Korea, we investigated the distribution of the eigenvalues of HH*, where H denotes a 2×2 multiple-input multiple-output (MIMO) channel matrix. Using the observation that the distribution of the measured eigenvalues matches well with the Gamma distribution, we propose a model of eigenvalues as Gamma distributed random variables that prominently feature both transmitting and receiving correlations. Using the model with positive integer k_i, i=1, 2, which is the shape parameter of a Gamma distribution, we derive the closed-form ergodic capacity of the 2×2 MIMO channel. Validation results show that the proposed model enables the evaluation of the outage and ergodic capacities of the correlated 2×2 MIMO channel in the NLOS indoor corridor environment.


Speech recognition of Malayalam numbers

Digit speech recognition is important in many applications such as automatic data entry, PIN entry, voice dialing telephone, automated banking system, etc. This paper presents speaker independent speech recognition system for Malayalam digits. The system employs Mel frequency cepstrum coefficient (MFCC) as feature for signal processing and Hidden Markov model (HMM) for recognition. The system is trained with 21 male and female voices in the age group of 20 to 40 years and there was 98.5% word recognition accuracy (94.8% sentence recognition accuracy) on a test set of continuous digit recognition task.


Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval

We present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtopics of a query topic. In such a problem, the utility of a document in a ranking is dependent on other documents in the ranking, violating the assumption of independent relevance which is assumed in most traditional retrieval methods. Subtopic retrieval poses challenges for evaluating performance, as well as for developing effective algorithms. We propose a framework for evaluating subtopic retrieval which generalizes the traditional precision and recall metrics by accounting for intrinsic topic difficulty as well as redundancy in documents. We propose and systematically evaluate several methods for performing subtopic retrieval using statistical language models and a maximal marginal relevance (MMR) ranking strategy. A mixture model combined with query likelihood relevance ranking is shown to modestly outperform a baseline relevance ranking on a data set used in the TREC interactive track.


Kaleido: You Can Watch It But Cannot Record It

Recently a number of systems have been developed to implement and improve the visual communication over screen-camera links. In this paper we study an opposite problem: how to prevent unauthorized users from videotaping a video played on a screen, such as in a theater, while do not affect the viewing experience of legitimate audiences. We propose and develop a light-weight hardware-free system, called Kaleido, that ensures these properties by taking advantage of the limited disparities between the screen-eye channel and the screen-camera channel. Kaleido does not require any extra hardware and is purely based on re-encoding the original video frame into multiple frames used for displaying. We extensively test our system Kaleido using a variety of smartphone cameras. Our experiments confirm that Kaleido preserves the high-quality screen-eye channel while reducing the secondary screen-camera channel quality significantly.


A new design of XOR-XNOR gates for low power application

XOR and XNOR gate plays an important role in digital systems including arithmetic and encryption circuits. This paper proposes a combination of XOR-XNOR gate using 6-transistors for low power applications. Comparison between a best existing XOR-XNOR have been done by simulating the proposed and other design using 65nm CMOS technology in Cadence environment. The simulation results demonstrate the delay, power consumption and power-delay product (PDP) at different supply voltages ranging from 0.6V to 1.2V. The results show that the proposed design has lower power dissipation and has a full voltage swing.


Twenty-Five Years of Research on Women Farmers in Africa : Lessons and Implications for Agricultural Research Institutions with an Annotated Bibliography

.................................................................................................................... iv Acknowledgments ...................................................................................................... iv Introduction ............................................................................................................... 1 Labor ..........................................................................................................................2 Gender Division of Labor .................................................................................... 2 Household Labor Availability ............................................................................... 6 Agricultural Labor Markets .................................................................................. 8 Conclusions: Labor and Gender .......................................................................... 9 Land ........................................................................................................................ 10 Access to Land ................................................................................................... 10 Security of Land ................................................................................................ 11 Changing Access to Land ................................................................................... 11 Access to Other Inputs .............................................................................................. 12 Access to Credit ................................................................................................. 13 Access to Fertilizer ............................................................................................. 14 Access to Extension and Information ................................................................. 15 Access to Mechanization .................................................................................... 16 Gender Issues in Access to Inputs: Summary...................................................... 16 Outputs .................................................................................................................... 17 Household Decision-Making .................................................................................... 18 Cooperative Bargaining and Collective Models .................................................. 19 Noncooperative Bargaining Models ................................................................... 19 Conclusions .............................................................................................................. 21 References ................................................................................................................. 23 Annotated Bibliography ............................................................................................ 27


Sensor Based PUF IoT Authentication Model for a Smart Home with Private Blockchain

With ubiquitous adoption of connected sensors, actuators and smart devices are finding inroads into daily life. Internet of Things (IoT) authentication is rapidly transforming from classical cryptographic user-centric knowledge based approaches to device signature based automated methodologies to corroborate identity between claimant and a verifier. Physical Unclonable Function (PUF) based IoT authentication mechanisms are gaining widespread interest as users are required to access IoT devices in real time while also expecting execution of sensitive (even physical) IoT actions immediately. This paper, delineates combination of BlockChain and Sensor based PUF authentication mechanism for solving real-time but non-repudiable access to IoT devices in a Smart Home by utilizing a mining less consensus mechanism for the provision of immutable assurance to users' and IoT devices' transactions i.e. commands, status alerts, actions etc.


Defending the morality of violent video games

The effect of violent video games is among the most widely discussed topics in media studies, and for good reason. These games are immensely popular, but many seem morally objectionable. Critics attack them for a number of reasons ranging from their capacity to teach players weapons skills to their ability to directly cause violent actions. This essay shows that many of these criticisms are misguided. Theoretical and empirical arguments against violent video games often suffer from a number of significant shortcomings that make them ineffective. This essay argues that video games are defensible from the perspective of Kantian, Aristotelian, and utilitarian moral theories.


Education-specific Tag Recommendation in CQA Systems

Systems for Community Question Answering (CQA) are well-known on the open web (e.g. Stack Overflow or Quora). They have been recently adopted also for use in educational domain (mostly in MOOCs) to mediate communication between students and teachers. As students are only novices in topics they learn about, they may need various scaffoldings to achieve effective question answering. In this work, we focus specifically on automatic recommendation of tags classifying students' questions. We propose a novel method that can automatically analyze a text of a question and suggest appropriate tags to an asker. The method takes specifics of educational domain into consideration by a two-step recommendation process in which tags reflecting course structure are recommended at first and consequently supplemented with additional related tags.
 Evaluation of the method on data from CS50 MOOC at Stack Exchange platform showed that the proposed method achieved higher performance in comparison with a baseline method (tag recommendation without taking educational specifics into account).


Teaching a Machine to Read Maps With Deep Reinforcement Learning

The ability to use a 2D map to navigate a complex 3D environment is quite remarkable, and even difficult for many humans. Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community. In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before. Our system combines several state-of-theart methods such as A3C and incorporates novel elements such as a recurrent localization cell. Our agent learns to localize itself based on 3D first person images and an approximate orientation angle. The agent generalizes well to bigger mazes, showing that it learned useful localization and naviga-


Machine learning in automated text categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert labor power, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely, document representation, classifier construction, and classifier evaluation.


Discovering actionable patterns in event data

Applications such as those for systems management and intrusion detection employ an automated real-time operation system in which sensor data are collected and processed in real time. Although such a system effectively reduces the need for operation staff, it requires constructing and maintaining correlation rules. Currently, rule construction requires experts to identify problem patterns, a process that is timeconsuming and error-prone. In this paper, we propose reducing this burden by mining historical data that are readily available. Specifically, we first present efficient algorithms to mine three types of important patterns from historical event data: event bursts, periodic patterns, and mutually dependent patterns. We then discuss a framework for efficiently mining events that have multiple attributes. Last, we present Event Correlation Constructor—a tool that validates and extends correlation knowledge.


A tutorial on hidden Markov models and selected applications in speech recognition


Automatic Indexing Based on Bayesian Inference Networks

In this paper, a Bayesian inference network model for automatic indexing with index terms (descriptors) from a prescribed vocabulary is presented. It requires an indexing dictionary with rules mapping terms of the respective subject field onto descriptors and inverted lists for terms occuring in a set of documents of the subject field and descriptors manually assigned to these documents. The indexing dictionary can be derived automatically from a set of manually indexed documents. An application of the network model is described, followed by an indexing example and some experimental results about the indexing performance of the network model.


Beyond Market Baskets: Generalizing Association Rules to Correlations

One of the most well-studied problems in data mining is mining for association rules in market basket data. Association rules, whose significance is measured via support and confidence, are intended to identify rules of the type, “A customer purchasing item A often also purchases item B.” Motivated by the goal of generalizing beyond market baskets and the association rules used with them, we develop the notion of mining rules that identify correlations (generalizing associations), and we consider both the absence and presence of items as a basis for generating rules. We propose measuring significance of associations via the chi-squared test for correlation from classical statistics. This leads to a measure that is upward closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between correlated and uncorrelated itemsets in the lattice. We develop pruning strategies and devise an efficient algorithm for the resulting problem. We demonstrate its effectiveness by testing it on census data and finding term dependence in a corpus of text documents, as well as on synthetic data.


Measuring the Impact of Network Performance on Cloud-Based Speech Recognition Applications

Cloud-based speech recognition systems enhance Web surfing, transportation, health care, etc. For example, using voice commands helps drivers search the Internet without affecting traffic safety risks. User frustration with network traffic problems can affect the usability of these applications. The performance of these type of applications should be robust in difficult network conditions. We evaluate the performance of several client-server speech recognition applications, under various network conditions. We measure transcription delay and accuracy of each application under different packet loss and jitter values. Results of our study show that performance of client-server speech recognition systems is affected by jitter and packet loss; which commonly occur in WiFi and cellular networks.


Friction modeling in linear chemical-mechanical planarization

In this article, we develop an analytical model of the relationship between the wafer/pad friction and process configuration. We also provide experimental validation of this model for in situ process monitoring. CMP thus demonstrates that the knowledge and methodologies developed for friction modeling and control can be used to advance the understanding, monitoring, and control of semiconductor manufacturing processes. Meanwhile, relevant issues and challenges in real-time monitoring of CMP are presented as sources of future development.


Efficient, Safe, and Probably Approximately Complete Learning of Action Models

In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent’s actions is given. Instead of an action model, a set of successfully executed plans are given and the task is to generate a plan that is safe, i.e., guaranteed to achieve the goal without failing. To this end, we show how to learn a conservative model of the world in which actions are guaranteed to be applicable. This conservative model is then given to an off-the-shelf classical planner, resulting in a plan that is guaranteed to achieve the goal. However, this reduction from a model-free planning to a model-based planning is not complete: in some cases a plan will not be found even when such exists. We analyze the relation between the number of observed plans and the likelihood that our conservative approach will indeed fail to solve a solvable problem. Our analysis show that the number of trajectories needed scales gracefully.


Pressure characteristics at the stump/socket interface in transtibial amputees using an adaptive prosthetic foot.

BACKGROUND
The technological advances that have been made in developing highly functional prostheses are promising for very active patients but we do not yet know whether they cause an increase in biomechanical load along with possibly negative consequences for pressure conditions in the socket. Therefore, this study monitored the socket pressure at specific locations of the stump when using a microprocessor-controlled adaptive prosthetic ankle under different walking conditions.

METHODS
Twelve unilateral transtibial amputees between 43 and 59 years of age were provided with the Proprio-Foot (Ossur) and underwent an instrumented 3D gait analysis in level, stair, and incline walking, including synchronous data capturing of socket pressure. Peak pressures and pressure time integrals (PTI) at three different locations were compared for five walking conditions with and without using the device's ankle adaptation mode.

FINDINGS
Highest peak pressures of 2.4 k Pa/kg were found for incline ascent at the calf muscle as compared to 2.1 k Pa/kg in level walking with large inter-individual variance. In stair ascent a strong correlation was found between maximum knee moment and socket pressure. The most significant pressure changes relative to level walking were seen in ramp descent anteriorly towards the stump end, with PTI values being almost twice as high as those in level walking. Adapting the angle of the prosthesis on stairs and ramps modified the pressure data such that they were closer to those in level walking.

INTERPRETATION
Pressure at the stump depends on the knee moments involved in each walking condition. Adapting the prosthetic ankle angle is a valuable means of modifying joint kinetics and thereby the pressure distribution at the stump. However, large inter-individual differences in local pressures underline the importance of individual socket fitting.


Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results

The proliferation of Electronic Health Records (EHRs) challenges data miners to discover potential and previously unknown patterns from a large collection of medical data. One of the tasks that we address in this paper is to reveal previously unknown effects of drugs on laboratory test results. We propose a method that leverages drug information to find a meaningful list of drugs that have an effect on the laboratory result. We formulate the problem as a convex non smooth function and develop a proximal gradient method to optimize it. The model has been evaluated on two important use cases: lowering low-density lipoproteins and glycated hemoglobin test results. The experimental results provide evidence that the proposed method is more accurate than the state-of-the-art method, rediscover drugs that are known to lower the levels of laboratory test results, and most importantly, discover additional potential drugs that may also lower these levels.


A Survey of Vehicular Ad hoc Networks Routing Protocols

In recent years, the aspect of vehicular ad hoc network (VANET) is becoming an interesting research area; VANET is a mobile ad hoc network considered as a special case of mobile ad hoc network (MANET). Similar to MANET, VANET is characterized as autonomous and self-configured wireless network. However, VANET has very dynamic topology, large and variable network size, and constrained mobility; these characteristics led to the need for efficient routing and resource saving VANET protocols, to fit with different VANET environments. These differences render traditional MANET’s protocols unsuitable for VANET. The aim of this work is to give a survey of the VANETs routing mechanisms, this paper gives an overview of Vehicular ad hoc networks (VANETs) and the existing VANET routing protocols; mainly it focused on vehicle to vehicle (V2V) communication and protocols. The paper also represents the general outlines and goals of VANETs, investigates different routing schemes that have been developed for VANETs, as well as providing classifications of VANET routing protocols (focusing on two classification forms), and gives summarized comparisons between different classes in the context of their methodologies used, strengths, and limitations of each class scheme compared to other classes. Finally, it extracts the current trends and the challenges for efficient routing mechanisms in VANETs.


Predicting hospital admission at emergency department triage using machine learning

OBJECTIVE
To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.

METHODS
This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.

RESULTS
A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).

CONCLUSION
Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.


Stress sensitivity of fault seismicity : a comparison between limited-offset oblique and major strike-slip faults

We present a new three-dimensional inventory of the southern San Francisco Bay area faults and use it to calculate stress applied principally by the 1989 M=7.1 Loma Prieta earthquake, and to compare fault seismicity rates before and after 1989. The major high-angle right-lateral faults exhibit a different response to the stress change than do minor oblique (rightlateral/thrust) faults. Seismicity on oblique-slip faults in the southern Santa Clara Valley thrust belt increased where the faults were unclamped. The strong dependence of seismicity change on normal stress change implies a high coefficient of static friction. In contrast, we observe that faults with significant offset (> 50-100 km) behave differently; microseismicity on the Hayward fault diminished where right-lateral shear stress was reduced, and where it was unclamped by the Loma Prieta earthquake. We observe a similar response on the San Andreas fault zone in southern California after the Landers earthquake sequence. Additionally, the offshore San Gregorio fault shows a seismicity rate increase where right-lateral/oblique shear stress was increased by the Loma Prieta earthquake despite also being clamped by it. These responses are consistent with either a low coefficient of static friction or high pore fluid pressures within the fault zones. We can explain the different behavior of the two styles of faults if those with large cumulative offset become impermeable through gouge buildup; coseismically pressurized pore fluids could be trapped and negate imposed normal stress changes, whereas in more limited offset faults fluids could rapidly escape. The difference in behavior between minor and major faults may explain why frictional failure criteria that apply intermediate coefficients of static friction can be effective in describing the broad distributions of aftershocks that follow large earthquakes, since many of these events occur both inside and outside major fault zones.


Photoplethysmography and its detailed pulse waveform analysis for arterial stiffness

Arterial stiffness index is one of the biomechanical indices of vascular healthiness. These indexes are based on detailed pulse waveform analysis which is presented here. After photoplethysmographyic (PPG) pulse wave measurement, we decompose the pulse waveform for the estimation and determination of arterial elasticity. Firstly, it is electro-optically measured PPG signal and by electromechanical film (EMFi) measured signal that are analyzed and investigated by dividing each wave into five logarithmic normal function components. For both the PPG and EMFi waveform we can find very easily a good fit between the original and overlapped and summed wave components. Each wave component is assumed to resemble certain phenomenon in the arteries and certain indexes can be calculated for example based on the mutual timing of the components. Several studies have demonstrated that these kinds of indexes calculated based on actual biomechanical processed can predict future cardiovascular events. Many dynamic factors, e.g., arterial stiffness, depend on fixed structural features of the arterial wall. For more accurate description, arterial stiffness is estimated based on pulse wave decomposition analysis in the radial measured by EMFi and PPG method and tibial arterial walls measured by PPG method parallelly. Elucidation of the precise relationship between endothelial function and arterial stiffness can be done through biomechanics. However, arterial wall elasticity awaits still further biomechanical studies with clinical relations and the influence of arterial flexibility, resistance and ageing inside of the radial pulse waveform.


Iterated learning and the evolution of language

Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins.


Effective and efficient correlation analysis with application to Market Basket Analysis and Network Community Detection

Finding the most interesting correlations among items is essential for problems in many commercial, medical, and scientific domains. For example, what kinds of items should be recommended with regard to what has been purchased by a customer? How to arrange the store shelf in order to increase sales? How to partition the whole social network into several communities for successful advertising campaigns? Which set of individuals on a social network should we target to convince in order to trigger a large cascade of further adoptions? When conducting correlation analysis, traditional methods have both effectiveness and efficiency problems, which will be addressed in this dissertation. Here, we explore the effectiveness problem in three ways. First, we expand the set of desirable properties and study the property satisfaction for different correlation measures. Second, we study different techniques to adjust original correlation measure, and propose two new correlation measures: the Simplified χ with Continuity Correction and the Simplified χ with Support. Third, we study the upper and lower bounds of different measures and categorize them by the bound differences. Combining with the above three directions, we provide guidelines for users to choose the proper measure according to their situations. With the proper correlation measure, we start to solve the efficiency problem for a large dataset. Here, we propose a fully-correlated itemset (FCI) framework to decouple the correlation measure from the need for efficient search. By wrapping the desired measure in our FCI framework, we take advantage of the desired measure’s superiority in evaluating itemsets, eliminate itemsets with irrelevant items, and achieve good computational performance. In addition, we identify a 1-dimensional monotone property of the upper bound of any good correlation measure, and different 2-dimensional


Circularly polarized array antenna with corporate-feed network and series-feed elements

In this paper, corporate-feed circularly polarized microstrip array antennas are studied. The antenna element is a series-feed slot-coupled structure. Series feeding causes sequential rotation effect at the element level. Antenna elements are then used to form the subarray by applying sequential rotation to their feeding. Arrays having 4, 16, and 64 elements were made. The maximum achieved gains are 15.3, 21, and 25.4 dBic, respectively. All arrays have less than 15 dB return loss and 3 dB axial ratio from 10 to 13 GHz. The patterns are all quite symmetrical.


When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks

Social bots are automatic or semi-automatic computer programs that mimic humans and/or human behavior in online social networks. Social bots can attack users (targets) in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, we study data from the Social Bot Challenge 2011 an experiment conducted by the WebEcologyProject during 2011 in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter. Using this data, we aim to develop models to (i) identify susceptible users among a set of targets and (ii) predict users’ level of susceptibility. We explore the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Our results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users.


Speed Estimation and Abnormality Detection from Surveillance Cameras

Motivated by the increasing industry trends towards autonomous driving, vehicles, and transportation we focus on developing a traffic analysis framework for the automatic exploitation of a large pool of available data relative to traffic applications. We propose a cooperative detection and tracking algorithm for the retrieval of vehicle trajectories in video surveillance footage based on deep CNN features that is ultimately used for two separate traffic analysis modalities: (a) vehicle speed estimation based on a state of the art fully automatic camera calibration algorithm and (b) the detection of possibly abnormal events in the scene using robust optical flow descriptors of the detected vehicles and Fisher vector representations of spatiotemporal visual volumes. Finally we measure the performance of our proposed methods in the NVIDIA AI CITY challenge evaluation dataset.


Blind detection of photomontage using higher order statistics

We investigate the prospect of using bicoherence features for blind image splicing detection. Image splicing is an essential operation for digital photomontaging, which in turn is a technique for creating image forgery. We examine the properties of bicoherence features on a data set, which contains image blocks of diverse image properties. We then demonstrate the limitation of the baseline bicoherence features for image splicing detection. Our investigation has led to two suggestions for improving the performance of bicoherence features, i.e., estimating the bicoherence features of the authentic counterpart and incorporating features that characterize the variance of the feature performance. The features derived from the suggestions are evaluated with support vector machine (SVM) classification and is shown to improve the image splicing detection accuracy from 62% to about 70%.


Building a Question-Answering Chatbot using Forum Data in the Semantic Space

We build a conversational agent which knowledge base is an online forum for parents of autistic children. We collect about 35,000 threads totalling some 600,000 replies, and label 1% of them for usefulness using Amazon Mechanical Turk. We train a Random Forest Classifier using sent2vec features to label the remaining thread replies. Then, we use word2vec to match user queries conceptually with a thread, and then a reply with a predefined context window.


Time series forecasting of cyber attack intensity

Cyber attacks occur on a near daily basis and are becoming exponentially more common. While some research aims to detect the characteristics of an attack, little focus has been given to patterns of attacks in general. This paper aims to exploit temporal correlations between the number of attacks per day in order to predict future intensity of cyber incidents. Through analysis of attack data collected from Hackmageddon, correlation was found among reported attack volume in consecutive days. This paper presents a forecasting system that aims to predict the number of cyber attacks on a given day based only on a set of historical attack count data. Our system conducts ARIMA time series forecasting on all previously collected incidents to predict the expected number of attacks on a future date. Our tool is able to use only a subset of data relevant to a specific attack method. Prediction models are dynamically updated over time as new data is collected to improve accuracy. Our system outperforms naive forecasting methods by 14.1% when predicting attacks of any type, and up to 21.2% when forecasting attacks of a specific type. Our system also produces a model which more accurately predicts future cyber attack intensity behavior.


Semantics-Aware Machine Learning for Function Recognition in Binary Code

Function recognition in program binaries serves as the foundation for many binary instrumentation and analysis tasks. However, as binaries are usually stripped before distribution, function information is indeed absent in most binaries. By far, identifying functions in stripped binaries remains a challenge. Recent research work proposes to recognize functions in binary code through machine learning techniques. The recognition model, including typical function entry point patterns, is automatically constructed through learning. However, we observed that as previous work only leverages syntax-level features to train the model, binary obfuscation techniques can undermine the pre-learned models in real-world usage scenarios. In this paper, we propose FID, a semantics-based method to recognize functions in stripped binaries. We leverage symbolic execution to generate semantic information and learn the function recognition model through well-performing machine learning techniques.FID extracts semantic information from binary code and, therefore, is effectively adapted to different compilers and optimizations. Moreover, we also demonstrate that FID has high recognition accuracy on binaries transformed by widely-used obfuscation techniques. We evaluate FID with over four thousand test cases. Our evaluation shows that FID is comparable with previous work on normal binaries and it notably outperforms existing tools on obfuscated code.


eyond virtual museums : Experiencing immersive virtual reality in eal museums

Contemporary museums are much more than places devoted to the placement and the exhibition of collections and artworks; indeed, they are nowadays considered as a privileged means for communication and play a central role in making culture accessible to the mass audience. One of the keys to approach the general public is the use of new technologies and novel interaction paradigms. These means, which bring with them an undeniable appeal, allow curators to modulate the cultural proposal by structuring different courses for different user profiles. Immersive Virtual reality (VR) is probably one of the most appealing and potentially effective technologies to serve this purpose; nevertheless, it is still quite uncommon to find immersive installations in museums. Starting from our 10 years’ experience in this topic, and following an in-depth survey about these technologies and their use in cultural contexts, we propose a classification of VR installations, specifically oriented to cultural heritage applications, based on their features in terms of interaction and immersion. On the basis of this classification, aiming to provide a tool for framing VR systems which would hopefully suggest indications related to costs, usability and quality of the sensorial experience, we analyze a series of live examples of which we point out strengths and weak points. We then summarize the current state and the very next future, identifying the major issues that prevent these technologies from being actually widespread, and outline proposals for a more se of pervasive and effective u


Face Alignment With Deep Regression

In this paper, we present a deep regression approach for face alignment. The deep regressor is a neural network that consists of a global layer and multistage local layers. The global layer estimates the initial face shape from the whole image, while the following local layers iteratively update the shape with local image observations. Combining standard derivations and numerical approximations, we make all layers able to backpropagate error differentials, so that we can apply the standard backpropagation to jointly learn the parameters from all layers. We show that the resulting deep regressor gradually and evenly approaches the true facial landmarks stage by stage, avoiding the tendency that often occurs in the cascaded regression methods and deteriorates the overall performance: yielding early stage regressors with high alignment accuracy gains but later stage regressors with low alignment accuracy gains. Experimental results on standard benchmarks demonstrate that our approach brings significant improvements over previous cascaded regression algorithms.


A method to infer emotions from facial Action Units

We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.


Using phrases and document metadata to improve topic modeling of clinical reports

Probabilistic topic models provide an unsupervised method for analyzing unstructured text, which have the potential to be integrated into clinical automatic summarization systems. Clinical documents are accompanied by metadata in a patient's medical history and frequently contains multiword concepts that can be valuable for accurately interpreting the included text. While existing methods have attempted to address these problems individually, we present a unified model for free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts. In the proposed model, phrases are represented by chained n-grams and a Dirichlet hyper-parameter is weighted by both document-level and patient-level context. This method and three other Latent Dirichlet allocation models were fit to a large collection of clinical reports. Examples of resulting topics demonstrate the results of the new model and the quality of the representations are evaluated using empirical log likelihood. The proposed model was able to create informative prior probabilities based on patient and document information, and captured phrases that represented various clinical concepts. The representation using the proposed model had a significantly higher empirical log likelihood than the compared methods. Integrating document metadata and capturing phrases in clinical text greatly improves the topic representation of clinical documents. The resulting clinically informative topics may effectively serve as the basis for an automatic summarization system for clinical reports.


Faster Dynamic Programming for Markov Decision Processes

OF THESIS FASTER DYNAMIC PROGRAMMING FOR MARKOV DECISION PROCESSES Markov decision processes (MDPs) are a general framework used by Artificial Intelligence (AI) researchers to model decision theoretic planning problems. Solving real world MDPs has been a major and challenging research topic in the AI literature. This paper discusses two main groups of approaches in solving MDPs. The first group of approaches combines the strategies of heuristic search and dynamic programming to expedite the convergence process. The second makes use of graphical structures in MDPs to decrease the effort of classic dynamic programming algorithms. Two new algorithms proposed by the author, MBLAO* and TVI, are described here.


A Review of Technical Approaches to Realizing Near-Field Communication Mobile Payments

This article describes and compares four approaches to storing payment keys and executing payment applications on mobile phones via near-field communication at the point of sale. Even though the comparison hinges on security--specifically, how well the keys and payment application are protected against misuse--other criteria such as hardware requirements, availability, management complexity, and performance are also identified and discussed.


Exploiting geographic dependencies for real estate appraisal: a mutual perspective of ranking and clustering

It is traditionally a challenge for home buyers to understand, compare and contrast the investment values of real estates. While a number of estate appraisal methods have been developed to value real property, the performances of these methods have been limited by the traditional data sources for estate appraisal. However, with the development of new ways of collecting estate-related mobile data, there is a potential to leverage geographic dependencies of estates for enhancing estate appraisal. Indeed, the geographic dependencies of the value of an estate can be from the characteristics of its own neighborhood (individual), the values of its nearby estates (peer), and the prosperity of the affiliated latent business area (zone). To this end, in this paper, we propose a geographic method, named ClusRanking, for estate appraisal by leveraging the mutual enforcement of ranking and clustering power. ClusRanking is able to exploit geographic individual, peer, and zone dependencies in a probabilistic ranking model. Specifically, we first extract the geographic utility of estates from geography data, estimate the neighborhood popularity of estates by mining taxicab trajectory data, and model the influence of latent business areas via ClusRanking. Also, we use a linear model to fuse these three influential factors and predict estate investment values. Moreover, we simultaneously consider individual, peer and zone dependencies, and derive an estate-specific ranking likelihood as the objective function. Finally, we conduct a comprehensive evaluation with real-world estate related data, and the experimental results demonstrate the effectiveness of our method.


A Low-Bandwidth Network File System

Users rarely consider running network file systems over slow or wide-area networks, as the performance would be unacceptable and the bandwidth consumption too high. Nonetheless, efficient remote file access would often be desirable over such networks---particularly when high latency makes remote login sessions unresponsive. Rather than run interactive programs such as editors remotely, users could run the programs locally and manipulate remote files through the file system. To do so, however, would require a network file system that consumes less bandwidth than most current file systems.This paper presents LBFS, a network file system designed for low-bandwidth networks. LBFS exploits similarities between files or versions of the same file to save bandwidth. It avoids sending data over the network when the same data can already be found in the server's file system or the client's cache. Using this technique in conjunction with conventional compression and caching, LBFS consumes over an order of magnitude less bandwidth than traditional network file systems on common workloads.


Algorithms for Delta Compression and Remote File Synchronization

Delta compression and remote file synchronization techniques are concerned with efficient file transfer over a slow communication link in the case where the receiving party already has a similar file (or files). This problem arises naturally, e.g., when distributing updated versions of software over a network or synchronizing personal files between different accounts and devices. More generally, the problem is becoming increasingly common in many networkbased applications where files and content are widely replicated, frequently modified, and cut and reassembled in different contexts and packagings. In this chapter, we survey techniques, software tools, and applications for delta compression, remote file synchronization, and closely related problems. We first focus on delta compression, where the sender knows all the similar files that are held by the receiver. In the second part, we survey work on the related, but in many ways quite different, problem of remote file synchronization, where the sender does not have a copy of the files held by the receiver. Work supported by NSF CAREER Award NSF CCR-0093400 and by Intel Corporation.


Leases: An Efficient Fault-Tolerant Mechanism for Distributed File Cache Consistency

Caching introduces the overhead and complexity of ensuring consistency, reducing some of its performance benefits. In a distributed system, caching must deal with the additional complications of communication and host failures.
Leases are proposed as a time-based mechanism that provides efficient consistent access to cached data in distributed systems. Non-Byzantine failures affect performance, not correctness, with their effect minimized by short leases. An analytic model and an evaluation for file access in the V system show that leases of short duration provide good performance. The impact of leases on performance grows more significant in systems of larger scale and higher processor performance.


Implementing Fault-Tolerant Services Using the State Machine Approach: A Tutorial

The state machine approach is a general method for implementing fault-tolerant services in distributed systems. This paper reviews the approach and describes protocols for two different failure models—Byzantine and fail stop. Systems reconfiguration techniques for removing faulty components and integrating repaired components are also discussed.


Copy Detection Mechanisms for Digital Documents

In a digital library system, documents are available in digital form and therefore are more easily copied and their copyrights are more easily violated. This is a very serious problem, as it discourages owners of valuable information from sharing it with authorized users. There are two main philosophies for addressing this problem: prevention and detection. The former actually makes unauthorized use of documents difficult or impossible while the latter makes it easier to discover such activity.In this paper we propose a system for registering documents and then detecting copies, either complete copies or partial copies. We describe algorithms for such detection, and metrics required for evaluating detection mechanisms (covering accuracy, efficiency, and security). We also describe a working prototype, called COPS, describe implementation issues, and present experimental results that suggest the proper settings for copy detection parameters.


Formal Analysis of Graphical Security Models

Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. May not music be described as the mathematics of the sense, mathematics as music of the reason? The musician feels mathematics, the mathematician thinks music: music the dream, mathematics the working life. Summary The increasing usage of computer-based systems in almost every aspect of our daily life makes more and more dangerous the threat posed by potential attackers , and more and more rewarding a successful attack. Moreover, the complexity of these systems is also increasing, including physical devices, software components and human actors interacting with each other to form so-called socio-technical systems. The importance of socio-technical systems to modern societies requires verifying their security properties formally, while their inherent complexity makes manual analyses impracticable. Graphical models for security offer an unrivalled opportunity to describe socio-technical systems, for they allow to represent different aspects like human behaviour, computation and physical phenomena in an abstract yet uniform manner. Moreover, these models can be assigned a formal semantics, thereby allowing formal verification of their properties. Finally, their appealing graphical notations enable to communicate security concerns in an understandable way also to non-experts, often in charge of the decision making. This dissertation argues that automated techniques can be developed on graphical security models to evaluate qualitative and quantitative security properties of socio-technical systems and to synthesise optimal attack and defence strategies. In support to this claim we develop analysis techniques for widely-used graphical security models such as attack trees and attack-defence trees. Our analyses cope with the optimisation of multiple parameters of an attack and defence scenario. Improving on the literature, in case of conflicting parameters such as probability and cost we compute the set of optimal solutions …


Robust techniques for background subtraction in urban traffic video

Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow, and shadow cast by moving objects. Finally, its internal background model should react quickly to changes in background such as starting and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.


Face Recognition in a Meeting Room

In this paper, weinvestigaterecognition of humanfaces in a meetingroom. The major challenges of identifying humanfacesin this environmentincludelow quality of input images,poor illumination,unrestrictedheadposesand continuouslychangingfacial expressionsandocclusion.In order to addresstheseproblemswe proposea novel algorithm, DynamicSpaceWarping (DSW).Thebasic idea of the algorithm is to combinelocal features under certain spatial constraints. We compare DSWwith the eigenface approachondatacollectedfromvariousmeetings.Wehave testedboth front and profile face imagesand imageswith two stagesof occlusion.Theexperimentalresultsindicate thattheDSWapproachoutperformstheeigenfaceapproach in bothcases.


A Provably-Secure ECC-Based Authentication Scheme for Wireless Sensor Networks

A smart-card-based user authentication scheme for wireless sensor networks (in short, a SUA-WSN scheme) is designed to restrict access to the sensor data only to users who are in possession of both a smart card and the corresponding password. While a significant number of SUA-WSN schemes have been suggested in recent years, their intended security properties lack formal definitions and proofs in a widely-accepted model. One consequence is that SUA-WSN schemes insecure against various attacks have proliferated. In this paper, we devise a security model for the analysis of SUA-WSN schemes by extending the widely-accepted model of Bellare, Pointcheval and Rogaway (2000). Our model provides formal definitions of authenticated key exchange and user anonymity while capturing side-channel attacks, as well as other common attacks. We also propose a new SUA-WSN scheme based on elliptic curve cryptography (ECC), and prove its security properties in our extended model. To the best of our knowledge, our proposed scheme is the first SUA-WSN scheme that provably achieves both authenticated key exchange and user anonymity. Our scheme is also computationally competitive with other ECC-based (non-provably secure) schemes.


Laplacian Auto-Encoders: An explicit learning of nonlinear data manifold

A key factor contributing to the success of many auto-encoders based deep learning techniques is the implicit consideration of the underlying data manifold in their training criteria. In this paper, we aim to make this consideration more explicit by training auto-encoders completely from the manifold learning perspective. We propose a novel unsupervised manifold learning method termed Laplacian Auto-Encoders (LAEs). Starting from a general regularized function learning framework, LAE regularizes training of autoencoders so that the learned encoding function has the locality-preserving property for data points on the manifold. By exploiting the analog relation between the graph Laplacian and the Laplace–Beltrami operator on the continuous manifold, we derive discrete approximations of the firstand higher-order auto-encoder regularizers that can be applied in practical scenarios, where only data points sampled from the distribution on the manifold are available. Our proposed LAE has potentially better generalization capability, due to its explicit respect of the underlying data manifold. Extensive experiments on benchmark visual classification datasets show that LAE consistently outperforms alternative auto-encoders recently proposed in deep learning literature, especially when training samples are relatively scarce. & 2015 Elsevier B.V. All rights reserved.


Interactive surface design with interlocking elements

We present an interactive tool for designing physical surfaces made from flexible interlocking quadrilateral elements of a single size and shape. With the element shape fixed, the design task becomes one of finding a discrete structure---i.e., element connectivity and binary orientations---that leads to a desired geometry. In order to address this challenging problem of combinatorial geometry, we propose a forward modeling tool that allows the user to interactively explore the space of feasible designs. Paralleling principles from conventional modeling software, our approach leverages a library of base shapes that can be instantiated, combined, and extended using two fundamental operations: merging and extrusion. In order to assist the user in building the designs, we furthermore propose a method to automatically generate assembly instructions. We demonstrate the versatility of our method by creating a diverse set of digital and physical examples that can serve as personalized lamps or decorative items.


The Role of Instructional Design in Persuasion: A Comics Approach for Improving Cybersecurity

Although computer security technologies are the first line of defence to secure users, their success is dependent on individuals’ behaviour. It is therefore necessary to persuade users to practice good computer security. Our interview analysis of users’ conceptualization of security password guessing attacks, antivirus protection, and mobile online privacy shows that poor understanding of security threats influences users’ motivation and ability to practice safe behaviours. We designed and developed an online interactive comic series called Secure Comics based on instructional design principles to address this problem. An eye-tracking experiment suggests that the graphical and interactive components of the comics direct users’ attention and facilitate comprehension of the information. In our evaluations of Secure Comics, results from several user studies show that the comics improve understanding and motivate positive changes in security management behaviour. We discuss the implication of the findings to better understand the role of instructional design and persuasion in education technology.


SimRT: an automated framework to support regression testing for data races

Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.


PicToSeek: combining color and shape invariant features for image retrieval

We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-dimensional invariant feature set for discriminatory object retrieval. Experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. From the theoretical and experimental results it is concluded that object retrieval based on composite color and shape invariant features provides excellent retrieval accuracy. Object retrieval based on color invariants provides very high retrieval accuracy whereas object retrieval based entirely on shape invariants yields poor discriminative power. Furthermore, the image retrieval scheme is highly robust to partial occlusion, object clutter and a change in the object's pose. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.


Fake Colorized Image Detection

Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection, and image retouching detection are attracting significant attention from researchers. However, image editing techniques develop over time. An emerging image editing technique is colorization, in which grayscale images are colorized with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared with natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark, and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram-based fake colorized image detection and feature encoding-based fake colorized image detection. Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.


Assessing patient suitability for short-term cognitive therapy with an interpersonal focus

In the current study, the development and initial validation of the Suitability for Short-Term Cognitive Therapy (SSCT) interview procedure is reported. The SSCT is an interview and rating procedure designed to evaluate the potential appropriateness of patients for short-term cognitive therapy with an interpersonal focus. It consists of a 1-hour, semistructured interview, focused on eliciting information from the patient relevant to nine selection criteria. The procedures involved in the development of this scale are described in detail, and preliminary evidence suggesting that the selection criteria can be rated reliably is presented. In addition, data indicating that scores on the SSCT scale predict the outcome of short-term cognitive therapy on multiple dependent measures, including both therapist and patient perspectives, are reported. It is concluded that the SSCT is a potentially useful scale for identifying patients who may be suitable, or unsuitable, for the type of short-term cognitive therapy administered in the present study.


Visible Machine Learning for Biomedicine

A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology.


Automated Text Summarization In SUMMARIST

SUMMARIST is an attempt to create a robust automated text summarization system, based on the ‘equation’: summarization = topic identification + interpretation + generation. Each of these stages contains several independent modules, many of them trained on large corpora of text. We describe the system’s architecture and provide details of some of its modules.