Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping

Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the proposed prototype and calibration method are evaluated by comparing the point clouds derived from the prototype with ground truth data collected by a terrestrial laser scanner (TLS). The overall analysis of the results shows that the proposed method achieves the seamless integration of multiple point clouds from three Kinect V2 cameras collected at 30 frames per second, resulting in low-cost, efficient, and high-coverage 3D color point cloud collection for indoor mapping applications.


Half-Mode Substrate Integrated Waveguide (HMSIW) Leaky-Wave Antenna for Millimeter-Wave Applications

A novel leaky-wave antenna is demonstrated and developed at Ka-band in this work based on the newly proposed half-mode substrate integrated waveguide (HWSIW). This antenna is accurately simulated by using a full-wave electromagnetic simulator and then fabricated through a single-layer printed circuit board (PCB) process. Wide bandwidth and a quasi-omnidirectional radiation pattern are obtained. The proposed antenna is therefore a good candidate for millimeter-wave applications. Measured results are in good agreement with simulated results.


"One of the greatest medical success stories:" Physicians and nurses' small stories about vaccine knowledge and anxieties.

In recent years, the Canadian province of Alberta experienced outbreaks of measles, mumps, pertussis, and influenza. Even so, the dominant cultural narrative maintains that vaccines are safe, effective, and necessary to maintain population health. Many vaccine supporters have expressed anxieties that stories contradicting this narrative have lowered herd immunity levels because they frighten the public into avoiding vaccination. As such, vaccine policies often emphasize educating parents and the public about the importance and safety of vaccination. These policies rely on health professionals to encourage vaccine uptake and assume that all professionals support vaccination. Health professionals, however, are socially positioned between vaccine experts (such as immunologists) and non-experts (the wider public). In this article, I discuss health professionals' anxieties about the potential risks associated with vaccination and with the limitations of Alberta's immunisation program. Specifically, I address the question: If medical knowledge overwhelmingly supports vaccination, then why do some professionals continue to question certain vaccines? To investigate this topic, I interviewed twenty-seven physicians and seven nurses. With stock images and small stories that interviewees shared about their vaccine anxieties, I challenge the common assumption that all health professionals support vaccines uncritically. All interviewees provided generic statements that supported vaccination and Alberta's immunisation program, but they expressed anxieties when I asked for details. I found that their anxieties reflected nuances that the culturally dominant vaccine narrative overlooks. Particularly, they critiqued the influence that pharmaceutical companies, the perceived newness of specific vaccines, and the limitations of medical knowledge and vaccine schedules.


Fuzzy Logic-Based Natural Language Processing and Its Application to Speech Recognition

In this paper we describe a fuzzy logic-based language processing method, which is applied to speech recognition. Our purpose is to create a system that can learn from a linguistic corpus the fuzzy semantic relations between the concepts represented by words and use such relations to process the word sequences generated by speech recognition systems. In particular, the system will be able to predict the words failed to be recognized by a speech recognition system. This will help to increase the accuracy of a speech recognition system. This will also serve as the first stage of deep semantic processing of speech recognition results by providing “semantic relatedness” between the recognized words. We report the fuzzy inference rule learning system, which we have developed and also report the experimental results based on the system. Key-words: Fuzzy logic, Natural Language Analysis, Speech Recognition, Corpus Linguistics.


KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor

MOTIVATION
KEGG PATHWAY is a service of Kyoto Encyclopedia of Genes and Genomes (KEGG), constructing manually curated pathway maps that represent current knowledge on biological networks in graph models. While valuable graph tools have been implemented in R/Bioconductor, to our knowledge there is currently no software package to parse and analyze KEGG pathways with graph theory.

RESULTS
We introduce the software package KEGGgraph in R and Bioconductor, an interface between KEGG pathways and graph models as well as a collection of tools for these graphs. Superior to existing approaches, KEGGgraph captures the pathway topology and allows further analysis or dissection of pathway graphs. We demonstrate the use of the package by the case study of analyzing human pancreatic cancer pathway.

AVAILABILITY
KEGGgraph is freely available at the Bioconductor web site (http://www.bioconductor.org). KGML files can be downloaded from KEGG FTP site (ftp://ftp.genome.jp/pub/kegg/xml).


Sentence Simplification by Monolingual Machine Translation

In this paper we describe a method for simplifying sentences using Phrase Based Machine Translation, augmented with a re-ranking heuristic based on dissimilarity, and trained on a monolingual parallel corpus. We compare our system to a word-substitution baseline and two state-of-the-art systems, all trained and tested on paired sentences from the English part of Wikipedia and Simple Wikipedia. Human test subjects judge the output of the different systems. Analysing the judgements shows that by relatively careful phrase-based paraphrasing our model achieves similar simplification results to state-of-the-art systems, while generating better formed output. We also argue that text readability metrics such as the Flesch-Kincaid grade level should be used with caution when evaluating the output of simplification systems.


Motivations and Methods for Text Simplification

Long and complicated sentences prove to be a stumbling block for current systems relying on NL input. These systems stand to gain from methods that syntactically simplify such sentences. To simplify a sentence, we need an idea of the structure of the sentence, to identify the components to be separated out. Obviously a parser could be used to obtain the complete structure of the sentence. However, full parsing is slow and prone to failure, especially on complex sentences. In this paper, we consider two alternatives to full parsing which could be used for simpli cation. The rst approach uses a Finite State Grammar (FSG) to produce noun and verb groups while the second uses a Supertagging model to produce dependency linkages. We discuss the impact of these two input representations on the simpli cation process. 1 Reasons for Text Simpli cation Long and complicated sentences prove to be a stumbling block for current systems which rely on natural language input. These systems stand to gain from methods that preprocess such sentences so as to make them simpler. Consider, for example, the following sentence: (1) The embattled Major government survived a crucial vote on coal pits closure as its last-minute concessions curbed the extent of Tory revolt over an issue that generated unusual heat in the House of Commons and brought the miners to London streets. Such sentences are not uncommon in newswire texts. Compare this with the multi-sentence version which has been manually simpli ed: (2) The embattled Major government survived a crucial vote on coal pits closure. Its last-minute concessions curbed the extent of On leave from the National Centre for Software Technology, Gulmohar Cross Road No. 9, Juhu, Bombay 400 049, India Tory revolt over the coal-mine issue. This issue generated unusual heat in the House of Commons. It also brought the miners to


Improving Text Simplification Language Modeling Using Unsimplified Text Data

In this paper we examine language modeling for text simplification. Unlike some text-to-text translation tasks, text simplification is a monolingual translation task allowing for text in both the input and output domain to be used for training the language model. We explore the relationship between normal English and simplified English and compare language models trained on varying amounts of text from each. We evaluate the models intrinsically with perplexity and extrinsically on the lexical simplification task from SemEval 2012. We find that a combined model using both simplified and normal English data achieves a 23% improvement in perplexity and a 24% improvement on the lexical simplification task over a model trained only on simple data. Post-hoc analysis shows that the additional unsimplified data provides better coverage for unseen and rare n-grams.


Video In Sentences Out

We present a system that produces sentential descriptions of video: who did what to whom, and where and how they did it. Action class is rendered as a verb, participant objects as noun phrases, properties of those objects as adjectival modifiers in those noun phrases, spatial relations between those participants as prepositional phrases, and characteristics of the event as prepositional-phrase adjuncts and adverbial modifiers. Extracting the information needed to render these linguistic entities requires an approach to event recognition that recovers object tracks, the trackto-role assignments, and changing body posture.


Multi-dimension semantic dictionary for online intelligence

The proposed online intelligence (OI) is to provide popular and well-formatted reports for direct decision support based on online data. Completeness, preciseness, conciseness and smartness are proposed as the basic technical indicators to evaluate an OI application. OI requires similar data processing flow and reporting tools with traditional business intelligence (BI). However, OI collects data basically from online other than provided by enterprise application systems. The online data is usually non-structural or semi-structural. Semantics techniques are required by OI to find and process online data into decision-making knowledge automatically in many cases. To this end, a multi-dimension semantic dictionary is proposed to divide online objects' semantics into four semantic aspects, i.e., information semantics, business semantics, application semantics and structural semantics. The four semantic aspects of the semantic dictionary are developed separately and interlinked as a multi-dimension semantic matrix to conduct matchmaking when find and process online data. In this work, the related concepts and implementation framework of the multi-dimension semantic dictionary are discussed and a real application for illegal drug tracing is provided to illustrate how OI make sense and semantics are developed.


Dead-time elimination method and current polarity detection circuit for three-phase PWM-controlled inverter

This paper will present a dead-time elimination scheme and the related current polarity detection circuit without separate power sources for three-phase inverters. The presented scheme includes the freewheeling current polarity detection circuit and the PWM control generator without dead time. It will be shown that the presented scheme eliminates the dead time of PWM control for inverter and therefore dramatically improving the output voltage loss and current distortion. Experimental results derived from an FPGA-based PWM-controlled inverter are shown for confirmation.


Single-image SVBRDF capture with a rendering-aware deep network

Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single picture of a flat surface lit by a hand-held flash. We achieve this goal by introducing several innovations on training data acquisition and network design. For training, we leverage a large dataset of artist-created, procedural SVBRDFs which we sample and render under multiple lighting directions. We further amplify the data by material mixing to cover a wide diversity of shading effects, which allows our network to work across many material classes. Motivated by the observation that distant regions of a material sample often offer complementary visual cues, we design a network that combines an encoder-decoder convolutional track for local feature extraction with a fully-connected track for global feature extraction and propagation. Many important material effects are view-dependent, and as such ambiguous when observed in a single image. We tackle this challenge by defining the loss as a differentiable SVBRDF similarity metric that compares the renderings of the predicted maps against renderings of the ground truth from several lighting and viewing directions. Combined together, these novel ingredients bring clear improvement over state of the art methods for single-shot capture of spatially varying BRDFs.


Attribute reduction in decision-theoretic rough set models

Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also the semantic measures such as decision-monotocity, cost and risk. The classification rules can be evaluated locally for each individual rule, or globally for a set of rules. Both the two types of classification rules can be generated from, and interpreted by, a decision-theoretic model, which is a probabilistic extension of the Pawlak rough set model. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as: decisionmonotocity, confidence, coverage, generality and cost. It is important to note that many of these properties can be truthfully reflected by a single measure c in the Pawlak rough set model. On the other hand, they need to be considered separately in probabilistic models. A straightforward extension of the c measure is unable to evaluate these properties. This study provides a new insight into the problem of attribute reduction. Crown Copyright 2008 Published by Elsevier Inc. All rights reserved.


Next-Term Student Performance Prediction: A Recommender Systems Approach

An enduring issue in higher education is student retention to successful graduation. National statistics indicate that most higher education institutions have four-year degree completion rates around 50%, or just half of their student populations. While there are prediction models which illuminate what factors assist with college student success, interventions that support course selections on a semester-to-semester basis have yet to be deeply understood. To further this goal, we develop a system to predict students’ grades in the courses they will enroll in during the next enrollment term by learning patterns from historical transcript data coupled with additional information about students, courses and the instructors teaching them. We explore a variety of classic and state-of-the-art techniques which have proven effective for recommendation tasks in the e-commerce domain. In our experiments, Factorization Machines (FM), Random Forests (RF), and the Personalized Linear Multiple Regression model achieve the lowest prediction error. Application of a novel feature selection technique is key to the predictive success and interpretability of the FM. By comparing feature importance across populations and across models, we uncover strong connections between instructor characteristics and student performance. We also discover key differences between transfer and non-transfer students. Ultimately we find that a hybrid FM-RF method can be used to accurately predict grades for both new and returning students taking both new and existing courses. Application of these techniques holds promise for student degree planning, instructor interventions, and personalized advising, all of which could improve retention and academic performance.


Semantics Extraction from Images

An overview of the state-of-the-art on semantics extraction from images is presented. In this survey, we present the relevant approaches in terms of content representation as well as in terms of knowledge representation. Knowledge can be represented in either implicit or explicit fashion while the image is represented in different levels, namely, low-level, intermediate and semantic level. For each combination of knowledge and image representation, a detailed discussion is addressed that leads to fruitful conclusions for the impact of each approach. 1 Semantics extraction basic pipeline Semantics extraction refers to digital data interpretation from a human point of view. In the case that the digital data correspond to images, this usually entails an appearance-based inference using color, texture and/or shape information along with a type of context inference (or representation) that can combine and transform these machine-extracted evidence into what we call a scene description. Following Biederman et al. [1] definitions of context in a visual scene, we can derive three types of context for real-world scene annotation problems: (i) semantic context which encodes the probability of a certain category to be present in a scene (e.g category “streets” has high probability to coexist with category “building” in the same scene ); (ii) spatial context which encodes the spatial relations of categories (e.g sky is usually above grass in a scene) and (iii) scale context which encodes the relative object size (category “human” is expected to occupy a small region in a scene which includes the“building” category). The research goals in semantics extraction are mostly a function of the granularity of the semantics in question. The goal could be the extraction of a single or multiple semantics of the entire image (e.g. indoor/outdoor setting), or the extraction of the semantics for different objects in an image. In the latter case, the semantics could be generic (e.g. a vehicle) or specific (e.g. a motorbike). Those goals make it clear that semantics extraction is not a new research area. Depending on the goal, the task of semantics extraction can be considered as a categorization, classification, recognition and understanding task that all share in common the effort for solving the semantic gap. As stated in [2], “Despite intensive recent research, the automatic establishment of a correspondence between


A Distributed Reinforcement Learning Scheme for Network Routing

In this paper we describe a self-adjusting algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used to keep accurate statistics at each node on which routing policies lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, this learning approach proves superior to a nonadaptive algorithm based on precomputed shortest paths. The authors would like to thank for their support the Bellcore Cognitive Science Research Group, the National Defense Science and Engineering Graduate fellowship program, and National Science Foundation Grant IRI-9214873. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the o cial policies, either expressed or implied, of Bellcore, the National Science Foundation or the U.S. Government.


VADI: GPU Virtualization for an Automotive Platform

Modern vehicles are evolving with more electronic components than ever before (In this paper, “vehicle” means “automotive vehicle.” It is also equal to “car.”) One notable example is graphical processing unit (GPU), which is a key component to implement a digital cluster. To implement the digital cluster that displays all the meters (such as speed and fuel gauge) together with infotainment services (such as navigator and browser), the GPU needs to be virtualized; however, GPU virtualization for the digital cluster has not been addressed yet. This paper presents a Virtualized Automotive DIsplay (VADI) system to virtualize a GPU and its attached display device. VADI manages two execution domains: one for the automotive control software and the other for the in-vehicle infotainment (IVI) software. Through GPU virtualization, VADI provides GPU rendering to both execution domains, and it simultaneously displays their images on a digital cluster. In addition, VADI isolates GPU from the IVI software in order to protect it from potential failures of the IVI software. We implement VADI with Vivante GC2000 GPU and perform experiments to ensure requirements of International Standard Organization (ISO) safety standards. The results show that VADI guarantees 30 frames per second (fps), which is the minimum frame rate for digital cluster mandated by ISO safety standards even with the failure of the IVI software. It also achieves 60 fps in a synthetic workload.


An Overview of Smart Home Environments: Architectures, Technologies and Applications

The aim of this research survey note is to provide a comprehensive overview of Smart Home Environments with a focus on their architectures and application areas, as well as utilized technologies, infrastructures, and standards. The main source of information was provided by past and existing research and development projects carried out in this area, while the main result of our survey is a classification of Smart Home Environments, as revealed by this projects’ survey.


Defending against malicious peripherals

Attacks on host computers by malicious peripherals are a growing problem. Inexpensive and powerful peripherals, which attach to plug-and-play buses, have made such attacks easy to mount. Making matters worse, commodity operating systems lack systematic defenses, and users are often not aware of the scope of the problem. We present Cinch, a pragmatic response to this threat. Cinch uses virtualization to place the hardware in a logically separate, untrusted machine, and includes an interposition layer between the untrusted machine and the protected one. This layer accepts or rejects interaction with devices and enforces security policies that are easily configured and extended by users. We show that Cinch integrates with existing OSes, enforces policies that thwart real world attacks, and has low overhead.


HADOOP BLOCK PLACEMENT POLICY FOR DIFFERENT FILE FORMATS

Now a day’s Peta-Bytes of data becomes the norm in industries. Handling, analyzing such big data is challenging task. Even frameworks like Hadoop (Open Source Implementation of MapReduce Paradigm) and NoSQL databases like Cassandra, HBase can be used to analyze and store such large data; heterogeneity of data is still an issue. Data centers usually have clusters formed using heterogeneous nodes. Ecosystem like Hadoop can be used to manage such types of cluster but it can not schedule jobs (Application) efficiently on this heterogeneous cluster when data is itself heterogeneous. Heterogeneity of data may be because of data format or because of the complexity. This paper is review of systems and algorithms for distributed management of huge size data, efficiency of these approaches.


Simultaneous estimation of food categories and calories with multi-task CNN

In this paper, we propose simultaneous estimation of food categories and calories for food photos. Since there exists strong correlation between food categories and calories in general, we expect that simultaneous training of both brings performance boosting compared to independent single training. To this end, we use a multitask CNN. In the experiments, we collected calorie-annotated recipe data from the online cooking recipe sites, and trained multi-task and single-task CNNs. As results, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs.


Bandit Structured Prediction for Learning from Partial Feedback in Statistical Machine Translation

We present an approach to structured prediction from bandit feedback, called Bandit Structured Prediction, where only the value of a task loss function at a single predicted point, instead of a correct structure, is observed in learning. We present an application to discriminative reranking in Statistical Machine Translation (SMT) where the learning algorithm only has access to a 1 − BLEU loss evaluation of a predicted translation instead of obtaining a gold standard reference translation. In our experiment bandit feedback is obtained by evaluating BLEU on reference translations without revealing them to the algorithm. This can be thought of as a simulation of interactive machine translation where an SMT system is personalized by a user who provides single point feedback to predicted translations. Our experiments show that our approach improves translation quality and is comparable to approaches that employ more informative feedback in learning.


Review of Machine Learning Algorithms in Differential Expression Analysis

In biological research machine learning algorithms are part of nearly every analytical process. They are used to identify new insights into biological phenomena, interpret data, provide molecular diagnosis for diseases and develop personalized medicine that will enable future treatments of diseases. In this paper we (1) illustrate the importance of machine learning in the analysis of large scale sequencing data, (2) present an illustrative standardized workflow of the analysis process, (3) perform a Differential Expression (DE) analysis of a publicly available RNA sequencing (RNASeq) data set to demonstrate the capabilities of various algorithms at each step of the workflow, and (4) show a machine learning solution in improving the computing time, storage requirements, and minimize utilization of computer memory in analyses of RNA-Seq datasets. The source code of the analysis pipeline and associated scripts are presented in the paper appendix to allow replication of experiments.


As Time Goes By: Comprehensive Tagging of Textual Phrases with Temporal Scopes

Temporal expressions (TempEx’s for short) are increasingly important in search, question answering, information extraction, and more. Techniques for identifying and normalizing explicit temporal expressions work well, but are not designed for and cannot cope with textual phrases that denote named events, such as “Clinton’s term as secretary of state”. This paper addresses the problem of detecting such temponyms, inferring their temporal scopes, and mapping them to events in a knowledge base if present there. We present methods for this kind of temponym resolution, using an entityand TempEx-oriented document model and the Yago knowledge base for distant supervision. We develop a family of Integer Linear Programs for jointly inferring temponym mappings to the timeline and knowledge base. This enriches the document representation and also extends the knowledge base by obtaining new alias names for events. Experiments with three different corpora demonstrate the viability of our methods.


Stakeholder participation for sustainable waste management

Inadequate environmental sanitation in many cities is a major cause of diseases and is a drain on the economy by way of lost workdays, cost of treatment and cleanup activities. Municipal authorities and policymakers need to act fast to address this issue. Sustainable waste management provides a comprehensive inter-disciplinary framework for addressing the problems of managing urban solid waste, in the resource constrained developing countries where quality of such services are poor and costs are high often with no effective means of recovering them. Upgrading the coverage of waste management and services and increasing their efficiency is a precondition for improving the environmental quality of cities. This paper highlights the fact that the involvement and participation of all the stakeholders such as the waste generators, waste processors, formal and informal agencies, non-governmental organisations and financing institutions is a key factor for the sustainable waste management. r 2005 Elsevier Ltd. All rights reserved.


HILOG: A Foundation for Higher-Order Logic Programming

D We describe a novel logic, called HiLog, and show that it provides a more suitable basis for logic programming than does traditional predicate logic. HiLog has a higher-order syntax and allows arbitrary terms to appear in places where predicates, functions, and atomic formulas occur in predicate calculus. But its semantics is first-order and admits a sound and complete proof procedure. Applications of HiLog are discussed, including DCG grammars, higher-order and modular logic programming, and deductive databases. a


GIST: A Model for Design and Management of Content and Interactivity of Customer-Centric Web Sites

Customer-centric Web-based systems, such as ecommerce Web sites, or sites that support customer relationship management (CRM) activities, are themselves information systems, but their design and maintenance need to follow vastly different approaches from the traditional systems lifecycle approach. Based on marketing frameworks that are applicable to the online world, and following design science principles, we develop a model to guide the design and the continuous management of such sites. The model makes extensive use of current technologies for tracking the customers and their behaviors, and combines elements of data mining and statistical analyses. A case study based on a financial services Web site is used to provide a preliminary validation and design evaluation of our approach. The case study showed considerable measured improvement in the effectiveness of the companys Web site. In addition, it also highlighted an important benefit of the our approach: the identification of previously unknown or unexpected segments of visitors. This finding can lead to promising new business opportunities.


18F-Fluciclovine PET/MRI for preoperative lymph node staging in high-risk prostate cancer patients

To investigate the diagnostic potential of simultaneous 18F-fluciclovine PET/MRI for pelvic lymph node (LN) staging in patients with high-risk prostate cancer. High-risk prostate cancer patients (n=28) underwent simultaneous 18F-fluciclovine PET/MRI prior to surgery. LNs were removed according to a predefined template of eight regions. PET and MR images were evaluated for presence of LN metastases according to these regions. Sensitivity/specificity for detection of LN metastases were calculated on patient and region basis. Sizes of LN metastases in regions with positive and negative imaging findings were compared with linear mixed models. Clinical parameters of PET-positive and -negative stage N1 patients were compared with the Mann-Whitney U test. Patient- and region-based sensitivity/specificity for detection of pelvic LN metastases was 40 %/87.5 % and 35 %/95.7 %, respectively, for MRI and 40 %/100 % and 30 %/100 %, respectively, for PET. LN metastases in true-positive regions were significantly larger than metastases in false-negative regions. PET-positive stage N1 patients had higher metastatic burden than PET-negative N1 patients. Simultaneous 18F-fluciclovine PET/MRI provides high specificity but low sensitivity for detection of LN metastases in high-risk prostate cancer patients. 18F-Fluciclovine PET/MRI scan positive for LN metastases indicates higher metastatic burden than negative scan. • 18 F-Fluciclovine PET/MRI has high specificity for detection of lymph node metastasis. • 18 F-Fluciclovine PET/MRI lacks sensitivity to replace ePLND. • 18 F-Fluciclovine PET/MRI may be used to aid surgery and select adjuvant therapy. • 18 F-Fluciclovine PET-positive patients have more extensive disease than PET-negative patients. • Size of metastatic lymph nodes is an important factor for detection.


Neuro-dynamic programming

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Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming

This paper extends previous work with Dyna a class of architectures for intelligent systems based on approximating dynamic program ming methods Dyna architectures integrate trial and error reinforcement learning and execution time planning into a single process operating alternately on the world and on a learned model of the world In this paper I present and show results for two Dyna archi tectures The Dyna PI architecture is based on dynamic programming s policy iteration method and can be related to existing AI ideas such as evaluation functions and uni versal plans reactive systems Using a nav igation task results are shown for a simple Dyna PI system that simultaneously learns by trial and error learns a world model and plans optimal routes using the evolving world model The Dyna Q architecture is based on Watkins s Q learning a new kind of rein forcement learning Dyna Q uses a less famil iar set of data structures than does Dyna PI but is arguably simpler to implement and use We show that Dyna Q architectures are easy to adapt for use in changing environments Introduction to Dyna How should a robot decide what to do The traditional answer in AI has been that it should deduce its best action in light of its current goals and world model i e that it should plan However it is now widely recognized that planning s usefulness is limited by its computational complexity and by its dependence on an accurate world model An alternative approach is to do the planning in advance and compile its result into a set of rapid reactions or situation action rules which are then used for real time decision making Yet a third approach is to learn a good set of reactions by trial and error this has the advantage of eliminating the dependence on a world model In this paper I brie y introduce Dyna a class of simple architectures integrating and permitting tradeo s among these three approaches Dyna architectures use machine learning algo rithms to approximate the conventional optimal con trol technique known as dynamic programming DP Bellman Ross DP itself is not a learn ing method but rather a computational method for determining optimal behavior given a complete model of the task to be solved It is very similar to state space search but di ers in that it is more incremental and never considers actual action sequences explicitly only single actions at a time This makes DP more amenable to incremental planning at execution time and also makes it more suitable for stochastic or in completely modeled environments as it need not con sider the extremely large number of sequences possi ble in an uncertain environment Learned world mod els are likely to be stochastic and uncertain making DP approaches particularly promising for learning sys tems Dyna architectures are those that learn a world model online while using approximations to DP to learn and plan optimal behavior Intuitively Dyna is based on the old idea that planning is like trial and error learning from hypothet ical experience Craik Dennett The theory of Dyna is based on the theory of DP e g Ross and on DP s relationship to reinforcement learning Watkins Barto Sutton Watkins to temporal di erence learning Sutton and to AI methods for planning and search Korf Werbos has previously argued for the general idea of building AI systems that approx imate dynamic programming and Whitehead and others Sutton Barto Sutton Pinette Rumelhart et al have presented results for the speci c idea of augmenting a reinforcement learning system with a world model used for planning Dyna PI Dyna by Approximating Policy Iteration I call the rst Dyna architecture Dyna PI because it is based on approximating a DP method known as pol icy iteration Howard The Dyna PI architec ture consists of four components interacting as shown in Figure The policy is simply the function formed by the current set of reactions it receives as input a description of the current state of the world and pro duces as output an action to be sent to the world The world represents the task to be solved prototypi cally it is the robot s external environment The world receives actions from the policy and produces a next state output and a reward output The overall task is de ned as maximizing the long term average reward per time step cf Russell The architecture also includes an explicit world model The world model is intended to mimic the one step input output behavior of the real world Finally the Dyna PI architecture in cludes an evaluation function that rapidly maps states to values much as the policy rapidly maps states to actions The evaluation function the policy and the world model are each updated by separate learning processes WORLD Action Reward (scalar) Heuristic Reward (scalar) State EVALUATION FUNCTION


Static hand gesture recognition using neural networks

This paper presents a novel technique for hand gesture recognition through human–computer interaction based on shape analysis. The main objective of this effort is to explore the utility of a neural network-based approach to the recognition of the hand gestures. A unique multi-layer perception of neural network is built for classification by using backpropagation learning algorithm. The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some predefined finite number of gesture classes. The proposed system presents a recognition algorithm to recognize a set of six specific static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, preprocessing, feature extraction, and classification. In preprocessing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the first method, the hand contour is used as a feature which treats scaling and translation of problems (in some cases). The complex moment algorithm is, however, used to describe the hand gesture and treat the rotation problem in addition to the scaling and translation. The algorithm used in a multi-layer neural network classifier which uses back-propagation learning algorithm. The results show that the first method has a performance of 70.83% recognition, while the second method, proposed in this article, has a better performance of 86.38% recognition rate.


Intrusion Detection Model Based On Particle Swarm Optimization and Support Vector Machine

Advance in information and communication technologies, force us to keep most of the information electronically, consequently, the security of information has become a fundamental issue. The traditional intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. However, normal operation often produces traffic that matches likely "attack signature", resulting in false alarms. One main drawback is the inability of detecting new attacks which do not have known signatures. In this paper particle swarm optimization (PSO) is used to implement a feature selection, and support vector machine (SVMs) with the one-versus-rest method serve as a fitness function of PSO for classification problems from the literature. Experimental result shows that our method allows us to recognize not only known attacks but also to detect suspicious activity that may be the result of a new, unknown attack. Our method simplifies features effectively and obtains a higher classification accuracy compared to other methods


Wireless Relay Communications with Unmanned Aerial Vehicles: Performance and Optimization

In this paper, we investigate a communication system in which unmanned aerial vehicles (UAVs) are used as relays between ground-based terminals and a network base station. We develop an algorithm for optimizing the performance of the ground-to-relay links through control of the UAV heading angle. To quantify link performance, we define the ergodic normalized transmission rate (ENTR) for the links between the ground nodes and the relay, and derive a closed-form expression for it in terms of the eigenvalues of the channel correlation matrix. We show that the ENTR can be approximated as a sinusoid with an offset that depends on the heading of the UAV. Using this observation, we develop a closed-form expression for the UAV heading that maximizes the uplink network data rate while keeping the rate of each individual link above a certain threshold. When the current UAV relay assignments cannot meet the minimum link requirements, we investigate the deployment and heading control problem for new UAV relays as they are added to the network, and propose a smart handoff algorithm that updates node and relay assignments as the topology of the network evolves.


Modeling Social Perception of Faces [Social Sciences]

The face is our primary source of visual information for identifying people and reading their emotional and mental states. With the exception of prosopagnosics (who are unable to recognize faces) and those suffering from such disorders of social cognition as autism, people are extremely adept at these two tasks. However, our cognitive powers in this regard come at the price of reading too much into the human face. The face is often treated as a window into a person's true nature. Given the agreement in social perception of faces, this paper discusses that it should be possible to model this perception.


Supporting Situationally Aware Cybersecurity Systems 30 th September 2015

In this report, we describe the Unified Cyber Security ontology (UCO) to support situational awareness in cyber security systems. The ontology is an effort to incorporate and integrate heterogeneous information available from different cyber security systems and most commonly used cyber security standards for information sharing and exchange. The ontology has also been mapped to a number of existing cyber security ontologies as well as concepts in the Linked Open Data cloud. Similar to DBpedia which serves as the core for Linked Open Data cloud, we envision UCO to serve as the core for the specialized cyber security Linked Open Data cloud which would evolve and grow with the passage of time with additional cybersecurity data sets as they become available. We also present a prototype system and concrete use-cases supported by the UCO ontology. To the best of our knowledge, this is the first cyber security ontology that has been mapped to general world ontologies to support broader and diverse security use-cases. We compare the resulting ontology with previous efforts, discuss its strengths and limitations, and describe potential future work directions.


Training Spiking ConvNets by STDP and Gradient Descent

This paper proposes a new method for training multi-layer spiking convolutional neural networks (CNNs). Training a multi-layer spiking network poses difficulties because the output spikes do not have derivatives and the commonly use backpropagation method for non-spiking networks is not easily applied. Our method uses a novel version of layered spike-timing- dependent plasticity (STDP) that incorporates supervised and unsupervised components. Our method starts with conventional learning methods and converts them to spatio-temporally local rules suited for spiking neural networks (SNNs). The training process uses two components for unsupervised feature extraction and supervised classification. The first component is a new STDP rule for spike-based representation learning which trains convolutional filters. The second introduces a new STDP-based supervised learning rule for spike pattern classification via an approximation to gradient descent. Stacking these components implements a novel spiking CNN of integrate-and-fire (IF) neurons with performances comparable with the state-of-the-art deep SNNs. The experimental results show the success of the proposed model for the MNIST handwritten digit classification. Our network architecture is the only high performance, spiking CNN which provides bio-inspired STDP rules in a hierarchy of feature extraction and classification in an entirely spike-based framework.


Practical Implementations of Arithmetic Coding 1

We provide a tutorial on arithmetic coding, showing how it provides nearly optimal data compression and how it can be matched with almost any probabilistic model. We indicate the main disadvantage of arithmetic coding, its slowness, and give the basis of a fast, space-e cient, approximate arithmetic coder with only minimal loss of compression e ciency. Our coder is based on the replacement of arithmetic by table lookups coupled with a new deterministic probability estimation scheme. Index terms : Data compression, arithmetic coding, adaptive modeling, analysis of algorithms, data structures, low precision arithmetic. A similar version of this paper appears in Image and Text Compression, James A. Storer, ed., Kluwer Academic Publishers, Norwell, MA, 1992, 85{112. A shortened version of this paper appears in the proceedings of the International Conference on Advances in Communication and Control (COMCON 3), Victoria, British Columbia, Canada, October 16{18, 1991. Support was provided in part by NASA Graduate Student Researchers Program grant NGT{ 50420 and by a National Science Foundation Presidential Young Investigators Award grant with matching funds from IBM. Additional support was provided by a Universities Space Research Association/CESDIS associate membership. Support was provided in part by National Science Foundation Presidential Young Investigator Award CCR{9047466 with matching funds from IBM, by NSF research grant CCR{9007851, by Army Research O ce grant DAAL03{91{G{0035, and by the O ce of Naval Research and the Defense Advanced Research Projects Agency under contract N00014{91{J{4052ARPA Order No. 8225. Additional support was provided by a Universities Space Research Association/CESDIS associate membership.


Emotional Branding and the Strategic Value of the Doppelgänger Brand Image

Emotional branding is widely heralded as a key to marketing success. However, little attention has been given to the risks posed by this strategy. This article argues that emotional-branding strategies are conducive to the emergence of a doppelgänger brand image, which is defined as a family of disparaging images and meanings about a brand that circulate throughout popular culture. This article’s thesis is that a doppelgänger brand image can undermine the perceived authenticity of an emotional-branding story and, thus, the identity value that the brand provides to consumers. The authors discuss how the tenets of emotional branding paradoxically encourage the formation and propagation of doppelgänger brand imagery. This article develops the counterintuitive proposition that rather than merely being a threat to be managed, a doppelgänger brand image can actually benefit a brand by providing early warning signs that an emotional-branding story is beginning to lose its cultural resonance. This article demonstrates these ideas through an analysis of the doppelgänger brand image that is beginning to haunt a paragon of emotional branding—Starbucks. The authors conclude with a discussion of how marketing managers can proactively use the insights gained by analyzing a doppelgänger brand image.


A longitudinal study of the social and emotional predictors and consequences of cyber and traditional bullying victimisation

Few longitudinal studies have investigated how cyberbullying interacts with traditional bullying among young people, who are increasingly using online environments to seek information, entertainment and to socialise. This study aimed to identify the associations between the relative contribution of cyberbullying victimisation and traditional bullying victimisation on social and emotional antecedents and outcomes among adolescents. Participants were a cohort of 1,504 adolescents from 16 Australian schools followed from age 13 to 15 years. Adolescents experiencing social and emotional difficulties were more likely to be cyberbullied and traditionally bullied, than traditionally bullied only. Those targeted in both ways experienced more harm and stayed away from school more often than those traditionally bullied only. These findings suggest a high coexistence of cyber and traditional bullying behaviours and their antecedents, and higher levels of harm from a combination of these behaviours for adolescents over time. Future research should engage students as co-researchers to enhance school and parent strategies to support adolescents experiencing difficulties, and to reduce the likelihood of both cyber and traditional bullying.


Seeing Stars When There Aren’t Many Stars: Graph-Based Semi-Supervised Learning For Sentiment Categorization

We present a graph-based semi-supervised learning algorithm to address the sentiment analysis task of rating inference. Given a set of documents (e.g., movie reviews) and accompanying ratings (e.g., “4 stars”), the task calls for inferring numerical ratings for unlabeled documents based on the perceived sentiment expressed by their text. In particular, we are interested in the situation where labeled data is scarce. We place this task in the semi-supervised setting and demonstrate that considering unlabeled reviews in the learning process can improve ratinginference performance. We do so by creating a graph on both labeled and unlabeled data to encode certain assumptions for this task. We then solve an optimization problem to obtain a smooth rating function over the whole graph. When only limited labeled data is available, this method achieves significantly better predictive accuracy over other methods that ignore the unlabeled examples during training.


CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction

Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for the goal of accurate and dense monocular reconstruction. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM, based on a scheme that privileges depth prediction in image locations where monocular SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa. We demonstrate the use of depth prediction to estimate the absolute scale of the reconstruction, hence overcoming one of the major limitations of monocular SLAM. Finally, we propose a framework to efficiently fuse semantic labels, obtained from a single frame, with dense SLAM, so to yield semantically coherent scene reconstruction from a single view. Evaluation results on two benchmark datasets show the robustness and accuracy of our approach.


Development of a one-axis actively regulated bearingless motor with a repulsive type passive magnetic bearing

This paper describes a novel one-axis actively regulated bearingless motor with a spinning top-shaped rotor, which is intended for a use as a cooling fan. This motor requires only one set of three-phase winding and thus one three-phase inverter to realize both rotation and magnetic suspension, and thus it is dubbed a single-drive bearingless motor. An axial position of the rotor is actively regulated and radial and tilting motions are passively stabilized by the passive magnetic bearing. Torque and axial suspension force are regulated by q-axis and d-axis currents, respectively. In this paper, the structure and principle, design, and test results of the proposed bearingless motor are presented.


Mutual information and minimum mean-square error in multiuser Gaussian channels

1. Introduction Due to the lack of explicit closed form expressions of the mutual information for binary inputs, which were provided only for the BPSK and QPSK for the single input single output (SISO) case, [1], [2], [3], it is of particular importance to address connections between information theory and estimation theory for the multiuser case. Connections between information theory and estimation theory dates back to the work of Duncan, in [4] who showed that for the continuous-time additive white Gaussian noise (AWGN) channel, the filtering minimum mean squared error (causal estimation) is twice the input-output mutual information for any underlying signal distribution. Recently, Guo, Shamai, and Verdu have illuminated intimate connections between information theory and estimation theory in a seminal paper, [1]. In particular, Guo et al. have shown that in the classical problem of information transmission through the conventional AWGN channel, the derivative of the mutual information with respect to the SNR is equal to the smoothing minimum mean squared error (noncausal estimation); a relationship that holds for scalar, vector, discrete-time and continuous-time channels regardless of the input statistics. There have been extensions of these results to the case of mismatched input distributions in the scalar Gaussian channel in [5] and [6]. However, the fundamental relation between the derivative of the mutual information and the MMSE, known as I-MMSE identity, and defined for point to point channels with any noise or input distributions in [1] is not anymore suitable for the multiuser case. Therefore, in this paper, we revisit the connections between the mutual information and the MMSE for the multiuser setup. We generalize the I-MMSE relation to the multiuser case. In particular, we prove that the derivative of the mutual information with respect to the signal to noise ratio (SNR) is equal to the minimum mean squared error (MMSE) plus a covariance induced due to the interference, quantified by a term with respect to the cross correlation of the users inputs' estimates, their channels, and their precoding matrices. Further, we capitalize on this unveiled multiuser I-MMSE relation to derive the components of the multiuser mutual information. In particular, we derive the derivative of the conditinal and non-conditional mutual information with respect to the SNR. Further extensions of this result allows a generalization of the relations of linear vector Gaussian channels in [7] to multiuser channels. In particular, [8], [9] generalize the I-MMSE relation to the per-user gradient …


Mersenne Twister A Pseudo-Random Number Generator

Pseudo random number generators have been widely used in numbers of applications, particularly simulation and cryptography. Among them is a Mersenne Twister. Introduced in 1998 by Makoto Matsumoto and Takuji Nishimura, it has been a highly preferred generator since it provides long period, high order of dimensional equidistribution, speed and reliability. In this paper, we will focus mainly on describing the theories and algorithm of the Mersenne Twister. We also briefly introduce its variants and discuss its limitations and improvements.


Evaluating WAP News Sites: The Webqual/m Approach

This paper reports on the evaluation of wireless Internet news sites using the WebQual/m instrument. From initial application in the domain of traditional Internet Web sites, the instrument has been adapted for sites delivered using the wireless application protocol (WAP). The WebQual approach is to assess the Web-site quality from the perspective of the 'voice of the customer’, an approach adopted in quality function deployment. The WebQual/m instrument is used to assess customer perceptions of information, site and useroriented qualities. In particular, the qualities of three UK-based WAP news sites are assessed via an online questionnaire. The results are reported and analysed and demonstrate considerable variations in the offerings of the news sites. The findings and their implications for mobile commerce are discussed and some conclusions and directions for further research are provided.


Phialemoniopsis endophytica sp. nov., a new species of endophytic fungi from Luffa cylindrica in Henan, China

During a survey of endophytic fungi in the cucurbit plants collected from Henan, China, a new species, Phialemoniopsis endophytica was isolated from the lower stem of Luffa cylindrica. It differs from other Phialemoniopsis species by its cylindrical to flask-shaped phialides, falcate conidia with blunt ends, ostiolate pycnidium-like conidiomata without marginal setae and ellipsoidal chlamydospores. Multi-locus (ITS, LSU, ACT, and TUB) phylogenetic analysis confirmed that P. endophytica is distinct from other species. A synopsis of the morphological characters of the new species is provided.


Learning user interaction models for predicting web search result preferences

Evaluating user preferences of web search results is crucial for search engine development, deployment, and maintenance. We present a real-world study of modeling the behavior of web search users to predict web search result preferences. Accurate modeling and interpretation of user behavior has important applications to ranking, click spam detection, web search personalization, and other tasks. Our key insight to improving robustness of interpreting implicit feedback is to model query-dependent deviations from the expected "noisy" user behavior. We show that our model of clickthrough interpretation improves prediction accuracy over state-of-the-art clickthrough methods. We generalize our approach to model user behavior beyond clickthrough, which results in higher preference prediction accuracy than models based on clickthrough information alone. We report results of a large-scale experimental evaluation that show substantial improvements over published implicit feedback interpretation methods.


Diffusion dynamics on multiplex networks

We study the time scales associated with diffusion processes that take place on multiplex networks, i.e., on a set of networks linked through interconnected layers. To this end, we propose the construction of a supra-laplacian matrix, which consists of a dimensional lifting of the laplacian matrix of each layer of the multiplex network. We use perturbative analysis to reveal analytically the structure of eigenvectors and eigenvalues of the complete network in terms of the spectral properties of the individual layers. The spectrum of the supra-laplacian allows us to understand the physics of diffusionlike processes on top of multiplex networks.


Gender Classification of Blog Authors: With Feature Engineering and Deep Learning using LSTM Networks

In this paper, we present two approaches to automatically classify the gender of blog authors: the first is a manual feature extraction based system incorporating two novel feature classes: variable length character sequence patterns and thirteen new word classes, along with an added class of surface features while the second is a first-ever application of a memory variant of Recurrent Neural Networks, i.e. Bidirectional Long Short Term Memory Networks (BLSTMs) on this task. We use two blog data sets to report our results: the first is a well-explored one used by the previous state-of-the-art model while the other is a 20 times larger corpus. For the first system, we use a voting of machine learning classifiers to obtain an improved accuracy with respect to the previous feature mining systems on the former data set. Using our second approach, we show that the accuracy obtained using such deep LSTMs is comparable to the current state-of-the-art deep learning system for the task of gender classification. Finally, we carry out a comparative study of performance of both the systems on the two data sets.


Decision Making Under Uncertainty in Visualisation

Decision making under uncertainty can lead to irrational behaviour; such errors are often being referred to as cognitive biases. Related work in this area has tended to focus on the human’s analytic and sensemaking processes. This paper puts forward a novel perspective on this, proposing that some cognitive biases can also occur in the process of viewing visualisations. Consequently, this source of error may have a negative impact on decision making. This paper presents examples of situations where cognitive biases in visualisation can occur and outlines a future user study to investigate the anchoring and adjustment cognitive biases in visualisation.


Detection of SQL injection and XSS attacks in three tier web applications

Web applications are used on a large scale worldwide, which handles sensitive personal data of users. With web application that maintains data ranging from as simple as telephone number to as important as bank account information, security is a prime point of concern. With hackers aimed to breakthrough this security using various attacks, we are focusing on SQL injection attacks and XSS attacks. SQL injection attack is very common attack that manipulates the data passing through web application to the database servers through web servers in such a way that it alters or reveals database contents. While Cross Site Scripting (XSS) attacks focuses more on view of the web application and tries to trick users that leads to security breach. We are considering three tier web applications with static and dynamic behavior, for security. Static and dynamic mapping model is created to detect anomalies in the class of SQL Injection and XSS attacks.


The reviewing of object files: Object-specific integration of information

A series of experiments explored a form of object-specific priming. In all experiments a preview field containing two or more letters is followed by a target letter that is to be named. The displays are designed to produce a perceptual interpretation of the target as a new state of an object that previously contained one of the primes. The link is produced in different experiments by a shared location, by a shared relative position in a moving pattern, or by successive appearance in the same moving frame. An object-specific advantage is consistently observed: naming is facilitated by a preview of the target, if (and in some cases only if) the two appearances are linked to the same object. The amount and the object specificity of the preview benefit are not affected by extending the preview duration to 1 s, or by extending the temporal gap between fields to 590 ms. The results are interpreted in terms of a reviewing process, which is triggered by the appearance of the target and retrieves just one of the previewed items. In the absence of an object link, the reviewing item is selected at random. We develop the concept of an object file as a temporary episodic representation, within which successive states of an object are linked and integrated.


On computable numbers, with an application to the Entscheidungsproblem

The "computable" numbers may be described briefly as the real numbers whose expressions as a decimal are calculable by finite means. Although the subject of this paper is ostensibly the computable numbers. it is almost equally easy to define and investigate computable functions of an integral variable or a real or computable variable, computable predicates, and so forth. The fundamental problems involved are, however, the same in each case, and I have chosen the computable numbers for explicit treatment as involving the least cumbrous technique. I hope shortly to give an account of the relations of the computable numbers, functions, and so forth to one another. This will include a development of the theory of functions of a real variable expressed in terms of computable numbers. According to my definition, a number is computable if its decimal can be written down by a machine.


No Free Lunch Theorems for Search

We show that all algorithms that search for an extremum of a cost function perform exactly the same when averaged over all possible cost functions In particular if algorithm A outperforms algorithm B on some cost functions then loosely speaking there must exist exactly as many other functions where B outperforms A Starting from this we analyze a number of the other a priori characteristics of the search problem like its geometry and its information theoretic aspects This analysis allows us to derive mathematical benchmarks for assessing a particular search algo rithm s performance We also investigate minimax aspects of the search problem the validity of using characteristics of a partial search over a cost function to predict future behavior of the search algorithm on that cost function and time varying cost functions We conclude with some discussion of the justi ability of biologically inspired search methods


Information Retrieval from Neurophysiological Signals

One of the ultimate goals of neuroscience is decoding someone’s intentions directly from his/her brain activities. In this thesis, we aim at pursuing this goal in different scenarios. Firstly, we show the possibility of creating a user-centric music/movie recommender system by employing neurophysiological signals. Regarding this, we employed a brain decoding paradigm in order to classify the features extracted from brain signals of participants watching movie/music video clips, into our target classes (two broad music genres and four broad movie genres). Our results provide a preliminary experimental evidence towards user-centric music/movie content retrieval by exploiting brain signals. Secondly, we addressed one of the main issue of the applications of brain decoding algorithms. Generally, the performance of such algorithms suffers from the constraint of having few and noisy samples, which is the case in most of the neuroimaging datasets. In order to overcome this limitation, we employed an adaptation paradigm in order to transfer knowledge from another domain (e.g. large-scale image domain) to the brain domain. We experimentally show that such adaptation procedure leads to improved results. We performed such adaptation pipeline on different tasks (i.e. object recognition and genre classification) using different neuroimaging modalities (i.e. fMRI, EEG, and MEG). Thirdly, we aimed at one of the fundamental goals in brain decoding which is reconstructing the external stimuli using only the brain features. Under this scenario, we show the possibility of regressing the stimuli spectrogram using time-frequency analysis of the brain signals. Finally, we conclude the thesis by summarizing our contributions and discussing the future directions and applications of our research.


An energy framework for the network simulator 3 (NS-3)

The Network Simulator-3 (ns-3) is rapidly developing into a flexible and easy-to-use tool suitable for wireless network simulation. Since energy consumption is a key issue for wireless devices, wireless network researchers often need to investigate the energy consumption at a battery powered node or in the overall network, while running network simulations. This requires the underlying simulator to support energy consumption and energy source modeling. Currently however, ns-3 does not provide any support for modeling energy consumption or energy sources. In this paper, we introduce an integrated energy framework for ns-3, with models for energy source as well as energy consumption. We present the design and implementation of the overall framework and the specific models therein. Further, we show how the proposed framework can be used in ns-3 to simulate energy-aware protocols in a wireless network.


Differentiating optic disc edema from optic nerve head drusen on optical coherence tomography.

OBJECTIVE
To assess optical coherence tomography in differentiating optic disc edema (ODE) due to papilledema and other optic neuropathies from optic nerve head drusen (ONHD).

METHODS
Optical coherence tomographic images from 60 subjects (20 with ODE, 20 with ONHD, and 20 control subjects) were assessed qualitatively and quantitatively. Qualitative criteria for ODE included an elevated optic nerve head with smooth internal contour and subretinal hyporeflective space (SHYPS) with recumbent "lazy V" pattern. Optic nerve head drusen displayed a "lumpy-bumpy" internal optic nerve contour and a rapid decline in SHYPS thickness. Quantitative comparisons included retinal nerve fiber layer and SHYPS thickness.

RESULTS
Optical coherence tomography differentiated ODE from ONHD qualitatively (sensitivity, 63%; specificity, 63%) and quantitatively (sensitivity, 80%; specificity, 90%). Respective differences in mean retinal nerve fiber layer thickness between ODE and ONHD were significant (P < .002) superiorly (206.8 vs 121.7 microm), nasally (176.3 vs 78.6 microm), inferiorly (247.2 vs 153.8 microm), and temporally (180.0 vs 85.5 microm). Respective differences in mean SHYPS thickness between ODE and ONHD were significant (P < .001) at radii of 0.75 mm (512.1 vs 274.4 microm), 1.5 mm (291.4 vs 103.0 microm), and 2.0 mm (145.5 vs 60.7 microm).

CONCLUSION
Optical coherence tomography can differentiate ODE from ONHD, particularly when the nasal retinal nerve fiber layer and SHYPS thickness at the 2.0-mm radius are greater than 86 microm and 127 microm, respectively.


Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme

Ball and plate system is the extension of traditional ball and beam system. In this paper, a trajectory planning and tracking problem of ball and plate system is put forward to proof-test diverse control schemes. Firstly, we derive the mathematical model of the ball and plate system in detail. Then a hierarchical fuzzy control scheme is proposed to deal with this problem. Our scheme is composed of three levels. The lowest level is a TS type fuzzy tacking controller; the middle level is a fuzzy supervision controller that takes actions in extreme situations; and the top level is a fuzzy planning controller that determines the desired trajectory. In order to optimize the ball’s moving trajectory, the genetic algorithm is introduced to adjust the output membership functions of the fuzzy planning controller. Simulation results have shown that the hierarchical fuzzy control scheme can control the ball from a point to another without hitting the obstacles and in the least time. c © 2003 Elsevier B.V. All rights reserved.


Anomaly detection and monitoring in Internet of Things communication

The Internet of Things (IoT) presents unique challenges in detecting anomaly and monitoring all connected devices in a network. Moreover, one of the objectives of anonymity in communication is to protect the data traffic of devices. The summary status visualization is indispensable to depict all devices/sensors that are most indicative of a pending failure and a predictive power/energy. Thus, this paper proposes a multi-platform monitoring and anomaly detection system that supports heterogeneous devices. The proposed system addresses the problems of: (i) how to monitor the network to prevent device failures and (ii) how to design a comprehensive feature for the early anomaly detection of IoT communication.


Fractional Order ControlA Tutorial

Many real dynamic systems are better characterized using a non-integer order dynamic model based on fractional calculus or, differentiation or integration of noninteger order. Traditional calculus is based on integer order differentiation and integration. The concept of fractional calculus has tremendous potential to change the way we see, model, and control the nature around us. Denying fractional derivatives is like saying that zero, fractional, or irrational numbers do not exist. In this paper, we offer a tutorial on fractional calculus in controls. Basic definitions of fractional calculus, fractional order dynamic systems and controls are presented first. Then, fractional order PID controllers are introduced which may make fractional order controllers ubiquitous in industry. Additionally, several typical known fractional order controllers are introduced and commented. Numerical methods for simulating fractional order systems are given in detail so that a beginner can get started quickly. Discretization techniques for fractional order operators are introduced in some details too. Both digital and analog realization methods of fractional order operators are introduced. Finally, remarks on future research efforts in fractional order control are given.


Machine Learning Approaches for Catchphrase Extraction in Legal Documents

The purpose of this research was to automatically extract catchphrases given a set of Legal documents. For this task, our focus was mainly on the Machine learning approaches: a comparative approach was used between the unsupervised and supervised approaches. The idea was to compare the different approaches to see which one of the two was comparatively better for automatic catchphrase extraction given a dataset of Legal documents. To perform this, two open source text mining software were used; one for the unsupervised approach while another one was used for the supervised approach. We then fine tuned some parameters for each tool before extracting catchphrases. The training dataset was used when fine tuning parameters in order to find optimal parameters that were then used for generating the final catchphrases. Different metrics were used to evaluate the results. We used the most common measures in Information Extraction which include Precision and Recall and the results from the two Machine learning approaches were compared. In general our results showed that the supervised approach performed far much better than the unsupervised approach.


Truthful incentives in crowdsourcing tasks using regret minimization mechanisms

What price should be offered to a worker for a task in an online labor market? How can one enable workers to express the amount they desire to receive for the task completion? Designing optimal pricing policies and determining the right monetary incentives is central to maximizing requester's utility and workers' profits. Yet, current crowdsourcing platforms only offer a limited capability to the requester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in inefficient use of the requester's budget or workers becoming disinterested in the task.
 In this paper, we address these questions and present mechanisms using the approach of regret minimization in online learning. We exploit a link between procurement auctions and multi-armed bandits to design mechanisms that are budget feasible, achieve near-optimal utility for the requester, are incentive compatible (truthful) for workers and make minimal assumptions about the distribution of workers' true costs. Our main contribution is a novel, no-regret posted price mechanism, BP-UCB, for budgeted procurement in stochastic online settings. We prove strong theoretical guarantees about our mechanism, and extensively evaluate it in simulations as well as on real data from the Mechanical Turk platform. Compared to the state of the art, our approach leads to a 180% increase in utility.


A hierarchical framework for evaluating simulation software

In simulation software selection problems, packages are evaluated either on their own merits or in comparison with other packages. In either method, a comprehensive list of criteria for evaluation of simulation software is essential for proper selection. Although various simulation software evaluation checklists do exist, there are dierences in the lists provided and the terminologies used. This paper presents a hierarchical framework for simulation software evaluation consisting of seven main groups and several subgroups. An explanation for each criterion is provided and an analysis of the usability of the proposed framework is further discussed. Ó 1999 Elsevier Science B.V. All rights reserved.


Applying Deep Learning to Enhance Momentum Trading Strategies in Stocks

We use an autoencoder composed of stacked restricted Boltzmann machines to extract features from the history of individual stock prices. Our model is able to discover an enhanced version of the momentum effect in stocks without extensive hand-engineering of input features and deliver an annualized return of 45.93% over the 1990-2009 test period versus 10.53% for basic momentum.


A New Method for Optimization of Dynamic Ride Sharing System

Dynamic ridesharing is a profitable way to reduce traffic and carbon emissions by providing an opportunity for a flexible and affordable service that utilizes vehicle seating space. Matching of ride seeker requests with the rides, distributed over the roads is a tedious work. While fulfilling the request of all passengers, the total travel distance of the trip may get increased. Therefore, this article proposes optimal dynamic ridesharing system which matches rides and requests in real time by satisfying multiple participant constraints (e.g. time bounds, availability of empty seat, maximum allowed deviation distance and minimized route ride) to minimize the total travel distance. To efficiently match ride givers and riders we are proposing a novel dynamic ride matching algorithm MRB (Minimal route bisearching algorithm) considering all above mentioned constraints. We demonstrate working of our algorithm by developing a prototype and evaluated our system on GPS (Global positioning system) trajectories of Lahore city dataset. Evaluated results are compared with existing algorithms which shows that our system significantly reduces the travel distance and computation cost in comparison with other recent ride searching methods to maximize efficiency.


On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks

Smartphones with Near-Field Communication (NFC) may emulate contactless smart cards, which has resulted in the deployment of various access control, transportation and payment services, such as Google Pay and Apple Pay. Like contactless cards, however, NFC-based smartphone transactions are susceptible to relay attacks, and ambient sensing has been suggested as a potential countermeasure. In this study, we empirically evaluate the suitability of ambient sensors as a proximity detection mechanism for smartphone-based transactions under EMV constraints. We underpin our study using sensing data collected from 17 sensors from an emulated relay attack test-bed to assess whether they can thwart such attacks effectively. Each sensor, where feasible, was used to record 350-400 legitimate and relay (illegitimate) contactless transactions at two different physical locations. Our analysis provides an empirical foundation upon which to determine the efficacy of ambient sensing for providing a strong anti-relay mechanism in security-sensitive applications. We demonstrate that no single, evaluated mobile ambient sensor is suitable for such critical applications under realistic deployment constraints.


Compact 4-element MIMO antenna with isolation enhancement for 4G LTE terminals

In this work, a wide-band, planar, printed inverted-F antenna (PIFA) is proposed with multiple-input-multiple-output (MIMO) antenna configuration. The MIMO antenna system consists of 4-elements operating at 2.1 GHz frequency band for 4G LTE applications. The proposed design is compact, low profile and suitable for wireless handheld devices. The MIMO antenna is fabricated on commercially available FR4 substrate with εr equal to 4.4. The dimensions of single element are 26×6 mm2 with board volume equal to 100×60×0.8 mm3. Isolation is improved by 5 dB in the proposed design using ground slots. Characteristics mode analysis (CMA) is used to analyze the behaviour of the antenna system.


LIMERIC: a linear message rate control algorithm for vehicular DSRC systems

Wireless vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g. a vehicle's speed and location, or a traffic signal's state and timing). The aggregate of these uncoordinated broadcasts will lead to channel congestion under dense traffic scenarios, with a resulting reduction in the effectiveness of the collision avoidance applications making use of the transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology for deployment. This paper defines a new congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This "perfect" convergence avoids the limit cycle behavior inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including: guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust stability under all conditions, convergence with asynchronous updates, and the implications of measurement noise for statistical properties of convergence. The paper illustrates key analytical results using MATLAB numerical results, and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high density scenarios.


Measuring cultural awareness in nursing students.

Recognizing the need for a valid and reliable way to measure outcomes of a program to promote multicultural awareness among nursing faculty and students, the authors developed a cultural awareness scale. In the first phase of the study, a scale consisting of 37 items was generated from a literature review on cultural awareness, sensitivity, and competence in nursing. A Cronbach's alpha reliability coefficient of .91 was obtained from a sample of 72 student nurses. In the second phase, the items were presented to a panel of experts in nursing and culture to determine content validity. A content validity index of .88 was calculated, and the total number of items on the scale was reduced to 36. The scale then was administered to 118 nursing students. Data from the two samples then were combined, and factor analysis was conducted to support construct validity. Cronbach's alpha for the combined samples was .82.


CORPORATE SOCIAL RESPONSIBILITY IN EMERGING MARKETS

Purpose: This paper analyses CSR initiatives in emerging markets from developed-countriesbased multinational companies (MNCs). Methods: The analysis is based on eight case studies with data collected through in-depth interviews with senior managers of the companies representing 85% of the Spanish foreign investments in Latin America. Results: The results show that CSR initiatives from these companies seem to be guided by Instrumental Theories as they use these initiatives as a strategic tool to achieve economic objectives, seek a positive relation between them and their financial performance, and use them to strengthen their reputation. Conclusions: The findings tend to indicate that Instrumental Theories of CSR seem to apply for Western MNCs operating in emerging markets.


Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm

Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets in high dimensional spaces. The PART algorithm is based on the assumptions that the model equations of PART (a large scale and singularly perturbed system of differential equations coupled with a reset mechanism) have quite regular computational performance. This paper provides a rigorous proof of these regular dynamics of the PART model when the signal functions are special step functions, and provides additional simulation results to illustrate the computational performance of PART.


OpenHSM: An Open Key Life Cycle Protocol for Public Key Infrastructure's Hardware Security Modules

The private keys used in a PKI are its most important asset. Protect these keys from unauthorised use or disclosure is essential to secure a PKI. Relying parties need assurances that the private key used to sign their certificates is controlled and managed following pre-defined statement policy. Hardware Security Modules (HSM) offer physical and logical protection and should be considered for any PKI deployment. The software that manages keys inside an HSM should control all life cycle of a private key. Normally this kind of equipment implements a embedded key management protocol and this protocols are not available to public scrutiny due to industrial interests. Other important issue is that HSMs are targeted in their development to the Bank industry and not to PKI, making some important PKI issues, like, strict key usage control and a secure auditing trail, play a secondary role. This paper presents an open protocol to securely manage private keys inside HSMs. The protocol is described, analysed and discussed.


Knowledge Begets Knowledge: Steps towards Assisted Knowledge Acquisition in Cyc

The Cyc project is predicated on the idea that, in order to be effective and flexible, computer software must have an understanding of the context in which its tasks are performed. We believe this context is what is known informally as “common sense.” Over the last twenty years, sufficient common sense knowledge has been entered into Cyc to allow it to more effectively and flexibly support an important task: increasing its own store of world knowledge. In this paper, we describe the Cyc knowledge base and inference system, enumerate the means that it provides for knowledge elicitation, including some means suitable for use by untrained or lightly trained volunteers, review some ways in which we expect to have Cyc assist in verifying and validating collected knowledge, and describe how we expect the knowledge acquisition process to accelerate in the future.


Trends in computer science research

Keywords in the ACM Digital Library and IEEE Xplore digital library and in NSF grants anticipate future CS research.


Spinal and supraspinal factors in human muscle fatigue.

Muscle fatigue is an exercise-induced reduction in maximal voluntary muscle force. It may arise not only because of peripheral changes at the level of the muscle, but also because the central nervous system fails to drive the motoneurons adequately. Evidence for "central" fatigue and the neural mechanisms underlying it are reviewed, together with its terminology and the methods used to reveal it. Much data suggest that voluntary activation of human motoneurons and muscle fibers is suboptimal and thus maximal voluntary force is commonly less than true maximal force. Hence, maximal voluntary strength can often be below true maximal muscle force. The technique of twitch interpolation has helped to reveal the changes in drive to motoneurons during fatigue. Voluntary activation usually diminishes during maximal voluntary isometric tasks, that is central fatigue develops, and motor unit firing rates decline. Transcranial magnetic stimulation over the motor cortex during fatiguing exercise has revealed focal changes in cortical excitability and inhibitability based on electromyographic (EMG) recordings, and a decline in supraspinal "drive" based on force recordings. Some of the changes in motor cortical behavior can be dissociated from the development of this "supraspinal" fatigue. Central changes also occur at a spinal level due to the altered input from muscle spindle, tendon organ, and group III and IV muscle afferents innervating the fatiguing muscle. Some intrinsic adaptive properties of the motoneurons help to minimize fatigue. A number of other central changes occur during fatigue and affect, for example, proprioception, tremor, and postural control. Human muscle fatigue does not simply reside in the muscle.


A Multiple Instance Learning Framework for Identifying Key Sentences and Detecting Events

State-of-the-art event encoding approaches rely on sentence or phrase level labeling, which are both time consuming and infeasible to extend to large scale text corpora and emerging domains. Using a multiple instance learning approach, we take advantage of the fact that while labels at the sentence level are difficult to obtain, they are relatively easy to gather at the document level. This enables us to view the problems of event detection and extraction in a unified manner. Using distributed representations of text, we develop a multiple instance formulation that simultaneously classifies news articles and extracts sentences indicative of events without any engineered features. We evaluate our model in its ability to detect news articles about civil unrest events (from Spanish text) across ten Latin American countries and identify the key sentences pertaining to these events. Our model, trained without annotated sentence labels, yields performance that is competitive with selected state-of-the-art models for event detection and sentence identification. Additionally, qualitative experimental results show that the extracted event-related sentences are informative and enhance various downstream applications such as article summarization, visualization, and event encoding.


LTE for vehicular networking: a survey

A wide variety of applications for road safety and traffic efficiency are intended to answer the urgent call for smarter, greener, and safer mobility. Although IEEE 802.11p is considered the de facto standard for on-the-road communications, stakeholders have recently started to investigate the usability of LTE to support vehicular applications. In this article, related work and running standardization activities are scanned and critically discussed; strengths and weaknesses of LTE as an enabling technology for vehicular communications are analyzed; and open issues and critical design choices are highlighted to serve as guidelines for future research in this hot topic.


3GPP LTE Versus IEEE 802.11p/WAVE: Which Technology is Able to Support Cooperative Vehicular Safety Applications?

The concept of vehicular ad-hoc networks enables the design of emergent automotive safety applications, which are based on the awareness among vehicles. Recently, a suite of 802.11p/WAVE protocols aimed at supporting car-to-car communications was approved by IEEE. Existing cellular infrastructure and, above all 3GPP LTE, is being considered as another communication technology appropriate for vehicular applications. This letter provides a theoretical framework which compares the basic patterns of both the technologies in the context of safety-of-life vehicular scenarios. We present mathematical models for the evaluation of the considered protocols in terms of successful beacon delivery probability.


A Survey on Device-to-Device Communication in Cellular Networks

Device-to-device (D2D) communications was initially proposed in cellular networks as a new paradigm for enhancing network performance. The emergence of new applications such as content distribution and location-aware advertisement introduced new user cases for D2D communications in cellular networks. The initial studies showed that D2D communications has advantages such as increased spectral efficiency and reduced communication delay. However, this communication mode introduces complications in terms of interference control overhead and protocols that are still open research problems. The feasibility of D2D communications in Long-Term Evolution Advanced is being studied by academia, industry, and standardization bodies. To date, there are more than 100 papers available on D2D communications in cellular networks, but there is no survey on this field. In this paper, we provide a taxonomy based on the D2D communicating spectrum and review the available literature extensively under the proposed taxonomy. Moreover, we provide new insights into the over-explored and under-explored areas that lead us to identify open research problems of D2D communications in cellular networks.


D2D for Intelligent Transportation Systems: A Feasibility Study

Intelligent transportation systems (ITS) are becoming a crucial component of our society, whereas reliable and efficient vehicular communications consist of a key enabler of a well-functioning ITS. To meet a wide variety of ITS application needs, vehicular-to-vehicular and vehicular-to-infrastructure communications have to be jointly considered, configured, and optimized. The effective and efficient coexistence and cooperation of the two give rise to a dynamic spectrum management problem. One recently emerged and rapidly adopted solution of a similar problem in cellular networks is the so-termed device-to-device (D2D) communications. Its potential in the vehicular scenarios with unique challenges, however, has not been thoroughly investigated to date. In this paper, we for the first time carry out a feasibility study of D2D for ITS based on both the features of D2D and the nature of vehicular networks. In addition to demonstrating the promising potential of this technology, we will also propose novel remedies necessary to make D2D technology practical as well as beneficial for ITS.


Vehicular communication systems: Enabling technologies, applications, and future outlook on intelligent transportation

Numerous technologies have been deployed to assist and manage transportation. But recent concerted efforts in academia and industry point to a paradigm shift in intelligent transportation systems. Vehicles will carry computing and communication platforms, and will have enhanced sensing capabilities. They will enable new versatile systems that enhance transportation safety and efficiency and will provide infotainment. This article surveys the state-of-the-art approaches, solutions, and technologies across a broad range of projects for vehicular communication systems.


Inferring Painting Style with Multi-Task Dictionary Learning

Recent advances in imaging and multimedia technologies have paved the way for automatic analysis of visual art. Despite notable attempts, extracting relevant patterns from paintings is still a challenging task. Different painters, born in different periods and places, have been influenced by different schools of arts. However, each individual artist also has a unique signature, which is hard to detect with algorithms and objective features. In this paper we propose a novel dictionary learning approach to automatically uncover the artistic style from paintings. Specifically, we present a multi-task learning algorithm to learn a style-specific dictionary representation. Intuitively, our approach, by automatically decoupling style-specific and artist-specific patterns, is expected to be more accurate for retrieval and recognition tasks than generic methods. To demonstrate the effectiveness of our approach, we introduce the DART dataset, containing more than 1.5K images of paintings representative of different styles. Our extensive experimental evaluation shows that our approach significantly outperforms state-of-the-art methods.