title: A Mathematical Framework for Superintelligent Machines

abstract: We describe a class calculus that is expressive enough to describe and improve its own learning process. It can design and debug programs that satisfy given input/output constraints, based on its ontology of previously learned programs. It can improve its own model of the world by checking the actual results of the actions of its robotic activators. For instance, it could check the black box of a car crash to determine if it was probably caused by electric failure, a stuck electronic gate, dark ice, or some other condition that it must add to its ontology in order to meet its sub-goal of preventing such crashes in the future. Class algebra basically defines the eval/eval-1 Galois connection between the residuated Boolean algebras of 1. equivalence classes and super/sub classes of class algebra type expressions, and 2. a residual Boolean algebra of biclique relationships. It distinguishes which formulas are equivalent, entailed, or unrelated, based on a simplification algorithm that may be thought of as producing a unique pair of Karnaugh maps that describe the rough sets of maximal bicliques of relations. Such maps divide the n-dimensional space of up to 2n-1 conjunctions of up to n propositions into clopen (i.e. a closed set of regions and their boundaries) causal sets. This class algebra is generalized to type-2 fuzzy class algebra by using relative frequencies as probabilities. It is also generalized to a class calculus involving assignments that change the states of programs. INDEX TERMS 4-valued Boolean Logic, Artificial Intelligence, causal sets, class algebra, consciousness, intelligent design, IS-A hierarchy, mathematical logic, meta-theory, pointless topological space, residuated lattices, rough sets, type-2 fuzzy sets


Deep Learning Image Classification Based on Neural Network Optimized SVM


title: Use of Renewable Energy Sources to Power Railroad Traffic Safety Installations

abstract: Railroad traffic safety installations must operate continuously and at nominal parameters. To reduce the energy consumption for installations between two consecutive train stations, we propose an alternative solution for their power supply. Our solution is considering supplying the electrical power from a photovoltaic system that is installed near the appliances box. The photovoltaic system together with the power supply system currently in place ensures the power supply redundancy for the consumers powered through the appliance box.


title: Multi-layer FSS for gain improvement of a wide-band stacked printed antenna

abstract: In this paper, an attempt has been made to improve the gain of a wide-band antenna using two layers of frequency-selective surfaces. It is seen that using two layers it is possible to achieve flat transmission characteristics over a reasonable bandwidth. However with an actual antenna, the gain improvement has at present been achieved over only a small band. It is expected that with further optimization and possibly increasing the number of layers, wide-band gain enhancement can be achieved.


title: Automatic fruit classification using random forest algorithm

abstract: The aim of this paper is to develop an effective classification approach based on Random Forest (RF) algorithm. Three fruits; i.e., apples, Strawberry, and oranges were analysed and several features were extracted based on the fruits' shape, colour characteristics as well as Scale Invariant Feature Transform (SIFT). A preprocessing stages using image processing to prepare the fruit images dataset to reduce their color index is presented. The fruit image features is then extracted. Finally, the fruit classification process is adopted using random forests (RF), which is a recently developed machine learning algorithm. A regular digital camera was used to acquire the images, and all manipulations were performed in a MATLAB environment. Experiments were tested and evaluated using a series of experiments with 178 fruit images. It shows that Random Forest (RF) based algorithm provides better accuracy compared to the other well know machine learning techniques such as K-Nearest Neighborhood (K-NN) and Support Vector Machine (SVM) algorithms. Moreover, the system is capable of automatically recognize the fruit name with a high degree of accuracy.


title: Economics and Identity

abstract: This paper considers how identity, a person's sense of self, affects economic outcomes. We incorporate the psychology and sociology of identity into an economic model of behavior. In the utility function we propose, identity is associated with different social categories and how people in these categories should behave. We then construct a simple game-theoretic model showing how identity can affect individual interactions. The paper adapts these models to gender discrimination in the workplace, the economics of poverty and social exclusion, and the household division of labor. In each case, the inclusion of identity substantively changes conclusions of previous economic analysis.


title: Semantic technologies for historical research: A survey

abstract: During the nineties of the last century, historians and computer scientists created together a research agenda around the life cycle of historical information. It comprised the tasks of creation, design, enrichment, editing, retrieval, analysis and presentation of historical information with help of information technology. They also identified a number of problems and challenges in this field, some of them closely related to semantics and meaning. In this survey paper we study the joint work of historians and computer scientists in the use of Semantic Web methods and technologies in historical research. We analyse to what extent these contributions help in solving the open problems in the agenda of historians, and we describe open challenges and possible lines of research pushing further a still young, but promising, historical Semantic Web.


title: Techniques of Glaucoma Detection From Color Fundus Images: A Review

abstract: Glaucoma is a generic name for a group of diseases which causes progressive optic neuropathy and vision loss due to degeneration of the optic nerves. Optic nerve cells act as transducer and convert light signal entered into the eye to electrical signal for visual processing in the brain. The main risk factors of glaucoma are elevated intraocular pressure exerted by aqueous humour, family history of glaucoma (hereditary) and diabetes. It causes damages to the eye, whether intraocular pressure is high, normal or below normal. It causes the peripheral vision loss. There are different types of glaucoma. Some glaucoma occurs suddenly. So, detection of glaucoma is essential for minimizing the vision loss. Increased cup area to disc area ratio is the significant change during glaucoma. Diagnosis of glaucoma is based on measurement of intraocular pressure by tonometry, visual field examination by perimetry and measurement of cup area to disc area ratio from the color fundus images. In this paper the different signal processing techniques are discussed for detection and classification of glaucoma.


title: A Survey of Current YouTube Video Characteristics

abstract: Given the impact of YouTube on Internet services and social networks, a healthy quantity of research has been conducted over the past few years. The majority of studies on traffic capture and evaluation were carried out prior to Google's acquisition of YouTube in 2007. Since then, there have been some changes made to the user policy and service infrastructure, including limits placed on video duration, file size, and resolution. This article depicts the latest YouTube traffic profiles and delivers updated and valuable information for future researchers. To obtain a detailed understanding of YouTube video characteristics, a customized Web spider was employed to crawl over a million YouTube videos. The study demonstrates consistency with previous research for major video streams while revealing that new categories of features have emerged within the YouTube service provision. Compared with traditional video repositories, YouTube exhibits many unique characteristics that could introduce novel challenges and opportunities for optimizing the performance of short video-sharing services.


title: Rapid Estimation of Camera Motion from Compressed Video with Application to Video Annotation

abstract: As digital video becomes more pervasive, efficient ways of searching and annotating video according to content will be increasingly important. Such tasks arise, for example, in the management of digital video libraries for content-based retrieval and browsing. We develop tools based on camera motion for analyzing and annotating a class of structured video using the low-level information available directly from MPEG-compressed video. In particular, we show that in certain structured settings, it is possible to obtain reliable estimates of camera motion by directly processing data easily obtained from the MPEG format. Working directly with the compressed video greatly reduces the processing time and enhances storage efficiency. As an illustration of this idea, we have developed a simple basketball annotation system which combines the low-level information extracted from an MPEG stream with the prior knowledge of basketball structure to provide high-level content analysis, annotation, and browsing for events such as wide-angle and close-up views, fast breaks, probable shots at the basket, etc. The methods used in this example should also be useful in the analysis of high-level content of structured video in other domains.


title: www.elsevier.com/locate/cad On visual similarity based 2D drawing retrieval

abstract: A large amount of 2D drawings have been produced in engineering fields. To reuse and share the available drawings efficiently, we propose two methods in this paper, namely 2.5D spherical harmonics transformation and 2D shape histogram, to retrieve 2D drawings by measuring their shape similarity. The first approach represents a drawing as a spherical function by transforming it from a 2D space into a 3D space. Then a fast spherical harmonics transformation is employed to get a rotation invariant descriptor. The second statistics-based approach represents the shape of a 2D drawing using a distance distribution between two randomly sampled points. To allow users to interactively emphasize certain local shapes that they are interested in, we have adopted a flexible sampling strategy by specifying a bias sampling density upon these local shapes. The two proposed methods have many valuable properties, including transform invariance, efficiency, and robustness. In addition, their insensitivity to noise allows for the user's causal input, thus supporting a freehand sketch-based retrieval user interface. Experiments show that a better performance can be achieved by combining them together using weights.


title: Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

abstract: This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning. The approach accomplishes the detection of the changed and unchanged areas by designing a deep neural network. The main guideline is to produce a change detection map directly from two images with the trained deep neural network. The method can omit the process of generating a difference image (DI) that shows difference degrees between multitemporal synthetic aperture radar images. Thus, it can avoid the effect of the DI on the change detection results. The learning algorithm for deep architectures includes unsupervised feature learning and supervised fine-tuning to complete classification. The unsupervised feature learning aims at learning the representation of the relationships between the two images. In addition, the supervised fine-tuning aims at learning the concepts of the changed and unchanged pixels. Experiments on real data sets and theoretical analysis indicate the advantages, feasibility, and potential of the proposed method. Moreover, based on the results achieved by various traditional algorithms, respectively, deep learning can further improve the detection performance.


title: Clinical Decision Support Systems: A Survey of NLP-Based Approaches from Unstructured Data

abstract: Clinical Decision Support on patients health outcomes can be performed from free text with Natural Language Processing techniques. However, it becomes a computational challenge due to the complexity of natural language. In recent years, several NLP-based approaches have been proposed to consider clinical decisions support. This paper presents a survey of Natural Language Processing approaches to support clinical decisions on patient health outcomes. The presented approaches are emphasized on the use of free text as input for diverse languages. An analysis of clinical decision support systems based on natural language processing in terms of their performance results is presented.


title: Pityriasis versicolor in the pediatric age group

abstract: BACKGROUND: Pityriasis versicolor (PV) is a mild chronic infection of the skin caused by Malassezia yeasts. Although it is primarily seen in adults, children are often affected in the tropics . METHODS: Over a period of 2 years, children (up to the age of 14 years) who were clinically and mycologically diagnosed as PV were included in the study. The clinical and epidemiological pattern in different age groups was noted. RESULTS: PV in this age group formed about 31% of the total cases of PV; 4.8% cases presented in infancy. The commonest site of involvement was the face in 39.9% of the cases. Most of the cases presented in summer months. CONCLUSIONS: PV is not an uncommon disease among children in the tropics. There is a sudden resurgence of cases in the hot monsoons and even infants are not spared.


title: Using hybrid data mining and machine learning clustering analysis to predict the turnover rate for technology professionals

abstract: Highlights? This research applies clustering analysis to predict trends in technology professional turnover. ? The turnover trend of technology professionals was examined in well-known Taiwanese companies. ? Based on cross-verification, the clustering accuracy rate was 92.7%. ? This study addressed problems related to the rapid loss of key human resources. ? This study should Q2 help organizations learn how to enhance competitiveness and efficiency. This study applies clustering analysis for data mining and machine learning to predict trends in technology professional turnover rates, including the hybrid artificial neural network and clustering analysis known as the self-organizing map (SOM). This hybrid clustering method was used to study the individual characteristics of turnover trend clusters. Using a transaction questionnaire, we studied the period of peak turnover, which occurs after the Chinese New Year, for individuals divided into various age groups. The turnover trend of technology professionals was examined in well-known Taiwanese companies. The results indicate that the high outstanding turnover trend circle was primarily caused by a lack of inner fidelity identification, leadership and management. Based on cross-verification, the clustering accuracy rate was 92.7%. This study addressed problems related to the rapid loss of key human resources and should help organizations learn how to enhance competitiveness and efficiency.


title: Video super-resolution via dynamic texture synthesis

abstract: This paper addresses the problem of hallucinating the missing high-resolution (HR) details of a low-resolution (LR) video while maintaining the temporal coherence of the hallucinated HR details by using dynamic texture synthesis (DTS). Most existing multi-frame-based video super-resolution (SR) methods suffer from the problem of limited reconstructed visual quality due to inaccurate sub-pixel motion estimation between frames in a LR video. To achieve high-quality reconstruction of HR details for a LR video, we propose a texture-synthesis-based video super-resolution method, in which a novel DTS scheme is proposed to render the reconstructed HR details in a time coherent way, so as to effectively address the temporal incoherence problem caused by traditional texture synthesis based image SR methods. To further reduce the complexity of the proposed method, our method only performs the DTS-based SR on a selected set of key-frames, while the HR details of the remaining non-key-frames are simply predicted using the bi-directional overlapped block motion compensation. Experimental results demonstrate that the proposed method achieves significant subjective and objective quality improvement over state-of-the-art video SR methods. Keywords—video super-resolution; video hallucination; dynamic texture synthesis; video upscaling; motion-compensated interpolation.


title: Do Users Focus on the Correct Cues to Differentiate Between Phishing and Genuine Emails?

abstract: This paper examines the cues that typically differentiate phishing emails from genuine emails. The research is conducted in two stages. In the first stage, we identify the cues that actually differentiate between phishing and genuine emails. These are the consistency and personalisation of the message, the perceived legitimacy of links and sender, and the presence of spelling or grammatical irregularities. In the second stage, we identify the cues that participants use to differentiate between phishing and genuine emails. This revealed that participants often use cues that are not good indicators of whether an email is phishing or genuine. This includes the presence of legal disclaimers, the quality of visual presentation, and the positive consequences emphasised in the email. This study has implications for education and training and provides a basis for the design and development of targeted and more relevant training and risk communication strategies.


title: Expression of common familial dyslipidemias in early childhood

abstract: Abstract Early identification of common familial dyslipidemias may prevent premature atherosclerotic disease. This study estimated the diagnostic values of few early childhood repeatedly deviant lipid samples by the knowledge of the parent's dyslipidemia. The first 7 years of age data of 353 children with their parents were evaluated from atherosclerosis risk-factor intervention study controls. Parents' low high-density-lipoprotein-cholesterol concentration (hypo-HDL-C), and high total cholesterol concentration-HDL-C (hyper-non-HDL-C) were defined. True hypo-HDL-C and hyper-non-HDL-C children were defined when their respective individual longitudinal means were beyond the appropriate lipid quintiles. Sensitivities, specificities, positive and negative predictive values of the early lipid samples were estimated with individual standard deviation models and bootstrap confidence. Hypo-HDL-C children proportions were 15.3% of all, and 20.9% of the children from the hypo-HDL-C parents ( p =0.26). Hyper-non-HDL-C children were 16.7% of all and 31.8% of the children from the hyper-non-HDL-C parents ( p =0.008). One early non-HDL-C sample in the highest quintile predicted 56% of the hyper-non-HDL-C children from healthy parents, but 83% of the hyper-non-HDL-C children from the hyper-non-HDL-C parents. Mean of three samples improved the latter prediction to 91%. This showed that if hypercholesterolemic parent's child expressed repeatedly hyper-non-HDL-C, it predicts true dyslipidemia of the child.


title: Deeply Learned Compositional Models for Human Pose Estimation

abstract: Compositional models represent patterns with hierarchies of meaningful parts and subparts. Their ability to characterize high-order relationships among body parts helps resolve low-level ambiguities in human pose estimation (HPE). However, prior compositional models make unrealistic assumptions on subpart-part relationships, making them incapable to characterize complex compositional patterns. Moreover, state spaces of their higher-level parts can be exponentially large, complicating both inference and learning. To address these issues, this paper introduces a novel framework, termed as Deeply Learned Compositional Model (DLCM), for HPE. It exploits deep neural networks to learn the compositionality of human bodies. This results in a novel network with a hierarchical compositional architecture and bottom-up/top-down inference stages. In addition, we propose a novel bone-based part representation. It not only compactly encodes orientations, scales and shapes of parts, but also avoids their potentially large state spaces. With significantly lower complexities, our approach outperforms state-of-the-art methods on three benchmark datasets.


title: Reversible Recurrent Neural Networks

abstract: Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which can be trained. Reversible RNNs---RNNs for which the hidden-to-hidden transition can be reversed---offer a path to reduce the memory requirements of training, as hidden states need not be stored and instead can be recomputed during backpropagation. We begin by arguing that perfectly reversible RNNs, which require no storage of the hidden activations, are fundamentally limited because they cannot forget information from their hidden state. We find that even if forgetting is allowed, the hidden-to-hidden transition can still be reversed by storing the forgotten information. Through efficient storage of this information, memory requirements of training RNNs are greatly reduced. Our method achieves roughly equivalent performance to traditional models while saving 5--15 times memory on language modeling and neural machine translation tasks.


title: Optimization of the Braking Strategy for an Emergency Braking System by the Application of Machine Learning

abstract: This paper explores a methodology whereby accident data is directly used to develop a braking strategy for an autonomous emergency braking system. Future vehicles will be equipped with additional technologies. Detailed information about accidents or critical situations can be recorded. If enough recorded data from critical situations are available, such data could be used to improve Active Safety Systems. In our approach, we do not model the behavior of pedestrians or drivers. The idea is to use the capability of machine learning to get the behaviors out of traffic data. Machine learning is used to derive the function design for an emergency braking system for pedestrians. Generated traffic scenarios are used to review the methodology. Random Forests and Neural Networks are used for the function designs and the learned function designs are compared with a reference implementation.


title: From a Micro–Macro Framework to a Micro–Meso–Macro Framework

abstract: At the beginning of the twenty-first century, the philosophy of engineering is becoming its own distinct branch of philosophy. The growing importance of philosophy of engineering cannot be overemphasized, since it has raised or will raise considerable and fundamental issues that challenge traditional ontology, methodology, and epistemology. Engineering is extremely complicated. Without initiating and advocating a new conceptual framework or paradigm, including a number of new categories, neither philosophers nor engineers could comprehend or demonstrate the essential characteristics of engineering. In particular, some social scientists pay significant attention to the relationship between micro (at the level of individuals) and macro (at the level of institutions or the social whole) issues, and as a result, a variety of micro–macro frameworks have advanced. There are four approaches for scientists to investigate social phenomena: micro-theory-based approach, macro-theory-based approach, micro–macro approach, and micro–meso–macro approach. As for engineering phenomena, scholars should focus on engineering facts, engineering acts, and engineering results, which comprise the three layers. A great number of perspectives contribute to a more complete and deeper understanding of engineering practice as a kind of multiple social construction assemblage. Engineering as a tangible architecture of social reality should be explained as a kind of multiple construction undertaken at micro, meso, and macro levels. The traditional micro–macro framework is obsolete. As such, it is time to establish a new kind of micro–meso–macro framework.


Approximating maximum independent set in bounded degree graphs


title: Towards Social Touch Intelligence: Developing a Robust System for Automatic Touch Recognition

abstract: Touch behavior is of great importance during social interaction. Automatic recognition of social touch is necessary to transfer the touch modality from interpersonal interaction to other areas such as Human-Robot Interaction (HRI). This paper describes a PhD research program on the automatic detection, classification and interpretation of touch in social interaction between humans and artifacts. Progress thus far includes the recording of a Corpus of Social Touch (CoST) consisting of pressure sensor data of 14 different touch gestures and first classification results. Classification of these 14 gestures resulted in an overall accuracy of 53% using Bayesian classifiers. Further work includes the enhancement of the gesture recognition, building an embodied system for real-time classification and testing this system in a possible application scenario.


title: A Stochastic Decoder for Neural Machine Translation

abstract: The process of translation is ambiguous, in that there are typically many valid translations for a given sentence. This gives rise to significant variation in parallel corpora, however, most current models of machine translation do not account for this variation, instead treating the problem as a deterministic process. To this end, we present a deep generative model of machine translation which incorporates a chain of latent variables, in order to account for local lexical and syntactic variation in parallel corpora. We provide an in-depth analysis of the pitfalls encountered in variational inference for training deep generative models. Experiments on several different language pairs demonstrate that the model consistently improves over strong baselines.


title: Extrinsically smooth direction fields

abstract: We consider the problem of finding a unit vector field (i.e., a direction field) over a domain that balances two competing objectives, smoothness and conformity to the shape of the domain. Common examples of this problem are finding normal directions along a curve and tangent directions over a surface. In a recent work, Jakob et al. observed that minimizing extrinsic variation of a tangent direction field on a surface achieves both objectives without the need for parameter-tuning or the use of additional constraints. Inspired by their empirical observations, we analyze the relation between extrinsic smoothness, intrinsic smoothness, and shape conformity in a continuous and general setting. Our analysis not only explains their observations but also suggests that an extrinsically smooth normal field along a curve can strike a similar balance between smoothness and shape-awareness. Our second contribution is offering extension of, justification for and improvement over the optimization framework of Jakob et al. In our experiments, we demonstrate the suitability of extrinsically smooth field in a variety of applications and compared with existing solutions. Graphical abstractDisplay Omitted HighlightsTheoretical analysis of extrinsically smooth direction field for different settings.Extrinsic variation can be decomposed into intrinsic variation and bending term.An effective optimization framework is provided.


title: Analysis of a very large AltaVista query log

abstract: In this paper we present an analysis of a 280 GB AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents approximately 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. Furthermore we present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques might not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such.


title: Strategic planning for management information systems

abstract: Planning for the information systems in an organization generally has not been closely related to the overall strategic planning processes through which the organization prepares for its future. An MIS strategic planning process is conceptualized and illustrated as one which links the organization's "strategy set" to an MIS "strategy set."


title: A Hybrid Deep Learning Architecture for Paraphrase Identification

abstract: The binary classification task of Paraphrase Identification (PI) is vital in the field of Natural Language Processing. The objective of this study is to propose an optimized Deep Learning architecture in combination with usage of word embedding technique for the classification of sentence pairs as paraphrases or not. For Paraphrase Identification task, this paper proposes a hybrid Deep Learning architecture aiming to capture as many features from the inputted sentences in natural language. The aim is to accurately classify whether the pair of sentences are paraphrases of each other or not. The importance of using an optimized word-embedding approach in combination with the proposed hybrid Deep Learning architecture is explained. This study also deals with the lack of the training data required to generate a robust Deep Learning model. The intention is to harness the memorizing power of Long Short Term Memory (LSTM) neural network and the feature extracting capability of Convolutional Neural Network (CNN) in combination with the optimized word-embedding approach which aims to capture wide-sentential contexts and word-order. The proposed model is compared with existing systems and it surpasses all the existing systems in the performance in terms of accuracy.


title: Web Data Extraction and Alignment Tools: A survey

abstract: Data extraction from the web pages is the process of analyzing and retrieving relevant data out of the data sources (usually unstructured or poorly structure) in a specific pattern for further processing, involves addition of metadata and data integration details for further process in the data workflow. This survey describes overview of the different web data extraction and data alignment techniques. Extraction techniques are DeLa, DEPTA, ViPER, and ViNT. Data alignment techniques are Pairwise QRR alignment, Holistic alignment, Nested structure processing. Query Result pages are generated by using Web database based on Users Query. The data from these query result pages should be automatically extracted which is very important for many applications, such as data integration, which are needed to cooperate with multiple web databases. New method is proposed for data extraction t that combines both tag and value similarity. It automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table. In which the data values from the same attribute are put into the same column. Data region identification method identify the noncontiguous QRRs that have the same parents according to their tag similarities. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs.


title: Multifractals: Theory and Applications

abstract: SECTION I: INTRODUCTION AND PRELIMINARIES MOTIVATION AND BACKGROUND Fractal Sets and Multifractal Measures Dynamical Systems Turbulence Rainfall Fields Earthquake Modelling Other Applications Concept of Multifractals Overview of Book THE MULTIFRACTAL FORMALISM Historical Development of Generalised Renyi Dimensions Generlised Renyi Lattice Dimensions Generalised Renyi Point Centred Dimensions Multifractal Spectrum and Formalism Review of Related Lattice Based Results Review of Related Point Centred Results THE MULTINOMIAL MEASURE Local Behaviour Global Averaging and Legendre Transforms Fractal Dimensions Point Centred Construction SECTION II: MULTIFRACTAL FORMALISM USING LARGE DEVIATIONS LATTICE BASED MULTIFRACTALS Large Deviation Formalism Uniform Spatial Sampling Measure A Family of Sampling Measures Hausdorff Dimensions POINT CENTERED MULTIFRACTALS Large Deviation Formalism A Family of Sampling Measures Hausdorff Dimensions Relationship Between Lattice and Point Centred Constructions MULTIPLICATIVE CASCADE PROCESSES Moran Cascades Processes Random Cascades Other Cascade Processes SECTION III: ESTIMATION OF THE RENYI DIMENSIONS INTERPOINT DISTANCES OF ORDER q AND INTRINSIC BIAS Boundary Effect Multiplicity of Boundaries Decomposition of FY(y) Differentiable Distribution ESTIMATION OF POINT CENTRED RENYI DIMENSIONS WITH q=2 Generalised Grassberger-Procaccia Algorithm Takens Estimator Hill Estimator Bootstrap Estimation Procedure Discussion and Examples EXTRINSIC SOURCES OF BIAS Imposed Boundary Effect Rounding Effect Effect of Noise APPLICATIONS OF DIMENSION ESTIMATION More on Estimation and Interpretation Spatial and Temporal Point Patterns Dynamical Systems Is a Process Stochastic or Deterministic? Stochastic Processes with Powerlaw Properties EARTHQUAKE ANALYSES Sources of Data Effects Causing Bias Results Comparison of Results and Conclusions APPENDICES Properties and Dimensions of Sets Large Deviations REFERENCES


title: A 5-GHz fully integrated full PMOS low-phase-noise LC VCO

abstract: A 5-GHz fully integrated, full PMOS, low-phase-noise and low-power differential voltage-controlled oscillator (VCO) is presented. This circuit is implemented in a 0.35-/spl mu/m four-metal BiCMOS SiGe process. At 2.7-V power supply voltage and a total power dissipation of only 13.5 mW, the proposed VCO features a worst case phase noise of -97 dBc/Hz and -117 dBc/Hz at 100 kHz and 1 MHz frequency offset, respectively. The oscillator is tuned from 5.13 to 5.68 GHz with a tuning voltage varying from 0 to 2.7 V.


title: Developing an Aerial Manipulator Prototype

abstract: This article focuses on the design, modeling, and control of an aerial manipulator prototype, i.e., an innovative configuration consisting of a miniature quadrotor helicopter endowed with a robotic manipulator. The overall system is designed to accomplish operations that require physical interaction with the surrounding environment while remaining airborne. To investigate the dynamical model of the aerial manipulator, a simple planar benchmark is used to analyze the interactions between the quadrotor, the robotic manipulator, and the environment. A control strategy for the planar system is designed to guarantee robustness in the presence or absence of contacts. Experiments on a real setup validate the control in the two different scenarios in which the aerial manipulator is either freely flying or physically interacting with the environment.


title: Mobile Tourist Guide Services with Software Agents

abstract: Applications for mobile devices have some restrictions because of the limited capabilities and heterogeneity of these devices. However, their communication capabilities allow the distribution of the application control and access to information in a network. If we also consider the changing environment when a user moves from one location to another, we should have software that is context-aware and able to adapt to new situations. Agent technology can support these requirements because of its distributed nature and the ability to combine flexible component architectures, some of them with planning and learning capabilities, which are appropriate for adapting to changing environments. In this paper we show one such application, a mobile tourist guide service, which has been built as a multi-agent system, where some agents are deliberative and combine the Beliefs-Desires-Intentions approach with learning capabilities of Case Base Reasoning techniques.


title: Implementation of a WiFi based plug and sense device for dedicated air pollution monitoring using IoT

abstract: The main objective of this system is to design and implement a Wi-Fi based plug and sense smart device for dedicated air pollution monitoring using Internet of Things simply called as IoT. This system designed on device to cloud architecture in IoT for monitoring air pollution precisely. Once the sensor node reads individual pollutants composition and location coordinates, Air quality index (AQI) will be calculated using linear segmented principle with greater Vancouver AQI table and Max operator aggregation method. Based on AQI value, corresponding LED will be actuated for indication and health impact with precaution steps messages will be displayed on the screen. All those data will be pushed to thingspeak cloud storage an open source application programming interface for IoT based devices. These pushed data along with date and time can be retrieved as a separate excel sheet for future analysis. Through thingview android app, real time pollution level with location can be visualized in terms of line graph. With the implementation of this low cost and small size smart device, alert can be given to people to wear anti-pollution mask and reroute path in transportation where there is high air pollution ensuring high reliability and consistency.


title: Analysis of quadratic dickson based envelope detectors for IoE sensor node applications

abstract: This paper presents a study of passive Dickson based envelope detectors operating in the quadratic small signal regime, specifically intended to be used in RF front end of sensing units of IoE sensor nodes. Critical parameters such as open-circuit voltage sensitivity (OCVS), charge time, input impedance, and output noise are studied and simplified circuit models are proposed to predict the behavior of the detector, resulting in practical design intuitions. There is strong agreement between model predictions, simulation results and measurements of 15 representative test structures that were fabricated in a 130 nm RF CMOS process.


title: ATM: Approximate Task Memoization in the Runtime System

abstract: Redundant computations appear during the execution of real programs. Multiple factors contribute to these unnecessary computations, such as repetitive inputs and patterns, calling functions with the same parameters or bad programming habits. Compilers minimize non useful code with static analysis. However, redundant execution might be dynamic and there are no current approaches to reduce these inefficiencies. Additionally, many algorithms can be computed with different levels of accuracy. Approximate computing exploits this fact to reduce execution time at the cost of slightly less accurate results. In this case, expert developers determine the desired tradeoff between performance and accuracy for each application. In this paper, we present Approximate Task Memoization (ATM), a novel approach in the runtime system that transparently exploits both dynamic redundancy and approximation at the task granularity of a parallel application. Memoization of previous task executions allows predicting the results of future tasks without having to execute them and without losing accuracy. To further increase performance improvements, the runtime system can memoize similar tasks, which leads to task approximate computing. By defining how to measure task similarity and correctness, we present an adaptive algorithm in the runtime system that automatically decides if task approximation is beneficial or not. When evaluated on a real 8-core processor with applications from different domains (financial analysis, stencil-computation, machine-learning and linear-algebra), ATM achieves a 1.4x average speedup when only applying memoization techniques. When adding task approximation, ATM achieves a 2.5x average speedup with an average 0.7% accuracy loss (maximum of 3.2%).


title: Business Intelligence and Analytics: From Big Data to Big Impact

abstract: Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework.


A First Step towards Eye State Prediction Using EEG


title: Cooperative Wiener-ICA for source localization and Separation by distributed microphone arrays

abstract: During the last decade, distributed microphone arrays have been proposed in order to increase accuracy and spatial coverage of speaker localization systems operating in large and reverberant rooms. In principle, the framework provided by a distributed microphone network can also be applied effectively when using Blind Source Separation (BSS). Separation is commonly performed by processing the signals sampled at closely spaced microphones in a single adaptation step, for example by means of Independent Component Analysis (ICA). When the microphone spacing or the distance between source and microphones increase, the separation performance reduces due to spatial aliasing effects and to a reduced spatial coherence at microphones. In this paper we propose a new method, here referred to as Cooperative Wiener ICA (CW-ICA), which is able to apply BSS to signals acquired by a network of distributed microphone arrays. Different ICA adaptations are applied to the signals recorded by each array and are interconnected in order to constrain each adaptation to converge to a solution related to the same physical interpretation. A preliminary analysis on a network of two arrays shows that the proposed method can be applied successfully to source separation and localization tasks.


title: Communication accommodation in text messages: exploring liking, power, and sex as predictors of textisms

abstract: ABSTRACTThis mixed-methods study applies Communication Accommodation Theory to explore how liking, power, and sex predict one’s likelihood for using textisms in digital interpersonal interactions. Textisms are digital cues that convey nonverbal meaning and emotion in text communication. The main experiment used a hypothetical texting scenario to manipulate textism amounts (none/many) and participant’s perceived power levels (low/equal/high) during texting interactions to examine the number of textisms participants used in subsequent responses in comparison to the number of textisms they viewed. Primary results show that (1) participants moderately converged to use similar amounts of textisms, and (2) those with low power who viewed many textisms were more likely to use textisms themselves during subsequent responses. Through the examination of adaption behaviors in text messaging, scholars can better understand the contexts in which users will include textisms to intentionally convey nonverbal meaning and...


title: Named-Entity Recognition from Greek and English Texts

abstract: Named-entity recognition (NER) involves the identification and classification of named entities in text. This is an important subtask in most language engineering applications, in particular information extraction, where different types of named entity are associated with specific roles in events. In this paper, we present a prototype NER system for Greek texts that we developed based on a NER system for English. Both systems are evaluated on corpora of the same domain and of similar size. The time-consuming process for the construction and update of domain-specific resources in both systems led us to examine a machine learning method for the automatic construction of such resources for a particular application in a specific language.


title: Zero-knowledge proofs of identity

abstract: In this paper we extend the notion of interactive proofs of assertions to interactive proofs of knowledge. This leads to the definition of unrestricted input zero-knowledge proofs of knowledge in which the prover demonstrates possession of knowledge without revealing any computational information whatsoever (not even the one bit revealed in zero-knowledge proofs of assertions). We show the relevance of these notions to identification schemes, in which parties prove their identity by demonstrating their knowledge rather than by proving the validity of assertions. We describe a novel scheme which is provably secure if factoring is difficult and whose practical implementations are about two orders of magnitude faster than RSA-based identification schemes. The advantages of thinking in terms of proofs of knowledge rather than proofs of assertions are demonstrated in two efficient variants of the scheme: unrestricted input zero-knowledge proofs of knowledge are used in the construction of a scheme which needs no directory; a version of the scheme based on parallel interactive proofs (which are not known to be zero knowledge) is proved secure by observing that the identification protocols are proofs of knowledge.


title: Toward Altmetric-Driven Research-Paper Recommender System Framework

abstract: The volume of literature and more particularly research-oriented publications is growing at an exponential rate, and better tools and methodologies are required to efficiently and effectively retrieve desired documents. The development of academic search engines, digital libraries and archives has led to better information filtering mechanisms that has resulted to improved search results. However, the state-of-the art research-paper recommender systems are still retrieving research articles without explicitly defining the domain of interest of the researchers. Also, a rich set of research output (research objects) and their associated metrics are also not being utilized in the process of searching, querying, retrieving and recommending articles. Consequently, a lot of irrelevant and unrelated information is being presented to the user. Then again, the use of citation counts to rank and recommend research-paper to users is still disputed. Recommendation metrics like citation counts, ratings in collaborative filtering, and keyword analysis' cannot be fully relied on as the only techniques through which similarity between documents can be computed, and this is because recommendations based on such metrics are not accurate and have lots of biasness. Henceforth, altmetric-based techniques and methodologies are expected to give better recommendations of research papers since the circumstances surrounding a research papers are taken into consideration. This paper proposes a research paper recommender system framework that utilizes paper ontology and Altmetric from research papers, to enhance the performance of research paper recommender systems.


title: Usage of SCRUM Practices within a Global Company

abstract: Global companies that experimented extensive waterfall phased plans are trying to improve their existing processes to expedite team engagement. SCRUM has become an acceptable path to follow for those companies because it comprises project management as part of its practices. SCRUM has been used with the objective of simplifying project control through simple processes, easy to update documentation and higher team iteration over exhaustive documentation. Instead of investing team effort on producing static documentation, SCRUM proposes to focus on team continuous improvement aiming to add value to business processes. The purpose of this industry report is to describe two projects that experimented SCRUM practices within a globally distributed company. This company has development centers across North America, South America and Asia. This report covers challenges faced by the project teams, strengths and practical recommendations of using SCRUM in a globally distributed environment.


title: Synthesis and bioactivity of some new 2-substituted-3,4-dihydro-3-(3'-chloro-4'-fluorophenyl)-2 H -[1,3,2]benzoxazaphosphorin 2-oxides

abstract: Synthesis, spectral analysis and bioactivity of the title compounds have been described. The structures of these compounds are confirmed by analytical and spectral (IR, 'H, 1 3 C, 3 1 P NMR and mass) data. They show significant antimicrobial and insecticidal activity. They promote growth of the phosphate solubilizing bacteria in the soil without any toxic effect on the host tissue.


title: Real-Time Detection and Classification of Road Lane Markings

abstract: This paper presents a method for detection and recognition of road lane markings using an uncalibrated onboard camera. Initially, lane boundaries are detected based on a linear parabolic model. Then, we build a simple model to represent pixels related to the pavement, and explore this model to estimate pixels related to lane markings. A set of features is computed based on the detected lane markings, and a cascade of binary classifiers is adopted to distinguish five types of markings: dashed, dashed-solid, solid-dashed, single-solid and double-solid. Experimental results show that the proposed method presents good classification results under a variety of situations (shadows, varying illumination, etc.).


title: What to do about non-standard (or non-canonical) language in NLP

abstract: Real world data differs radically from the benchmark corpora we use in natural language processing (NLP). As soon as we apply our technologies to the real world, performance drops. The reason for this problem is obvious: NLP models are trained on samples from a limited set of canonical varieties that are considered standard, most prominently English newswire. However, there are many dimensions, e.g., socio-demographics, language, genre, sentence type, etc. on which texts can differ from the standard. The solution is not obvious: we cannot control for all factors, and it is not clear how to best go beyond the current practice of training on homogeneous data from a single domain and language. ::: In this paper, I review the notion of canonicity, and how it shapes our community's approach to language. I argue for leveraging what I call fortuitous data, i.e., non-obvious data that is hitherto neglected, hidden in plain sight, or raw data that needs to be refined. If we embrace the variety of this heterogeneous data by combining it with proper algorithms, we will not only produce more robust models, but will also enable adaptive language technology capable of addressing natural language variation.


title: Does the Great Fire Wall cause self-censorship? The effects of perceived internet regulation and the justification of regulation

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to examine the impact of internet censorship, which is represented by the Great Fire Wall, on Chinese internet users’ self-censorship. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: A 3×2 factorial experiment (n=315) is designed. Different patterns of censorship (soft censorship, compared censorship, and hard censorship) and the justification of internet regulation are involved in the experiment as two factors. The dependent variable is self-censorship which is measured through the willingness to speak about sensitive issues and the behavior of refusing to sign petitions with true names. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The results show that perceived internet censorship significantly decreases the willingness to talk about sensitive issues and the likelihood of signing petitions with true names. The justification of censorship significantly decreases self-censorship on the behaviors of petition signing. Although there are different patterns of internet censorship that Chinese netizens may encounter, they do not differ from each other in causing different levels of self-censorship. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The subjects are college students who were born in the early 1990s, and the characteristics of this generation may influence the results of the experiment. The measurement of self-censorship could be refined. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The study contributes to the body of literature about internet regulation because it identifies a causal relationship between the government’s internet censorship system and ordinary people’s reaction to the regulation in an authoritarian regime. Unpacking different patterns of censorship and different dimensions of self-censorship depicts the complexity of censoring and being censored.


title: Hidden Markov models in computational biology: applications to protein modeling

abstract: Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PROFILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage over PROFILESEARCH in terms of lower rates of false negatives and false positives, even though the HMM is trained using only unaligned sequences, whereas PROFILESEARCH requires aligned training sequences. Our results suggest the presence of an EF-hand calcium binding motif in a highly conserved and evolutionarily preserved putative intracellular region of 155 residues in the alpha-1 subunit of L-type calcium channels which play an important role in excitation-contraction coupling. This region has been suggested to contain the functional domains that are typical or essential for all L-type calcium channels regardless of whether they couple to ryanodine receptors, conduct ions or both.


title: Using massively multiplayer online role playing games (MMORPGs) to support second language learning: Action research in the real and virtual world

abstract: Massively Multiplayer Online Role Playing Games (MMORPGs) create large virtual communities. Online gaming shows potential not just for entertaining, but also for education. The aim of this research project is to investigate the use of commercial MMORPGs to support second language teaching. MMORPGs offer a digital safe space in which students can communicate by using their target language with global players. This qualitative research based on ethnography and action research investigates the students’ experiences of language learning and performing while they play in the MMORPGs. Research was conducted in both the real and virtual worlds. In the real world the researcher observes the interaction with the MMORPGs by the students through actual discussion, and screen video captures while they are playing. In the virtual world, the researcher takes on the role of a character in the MMORPG enabling the researcher to get an inside point of view of the students and their own MMORPG characters. This latter approach also uses action research to allow the researcher to provide anonymous/private support to the students including in-game instruction, confidence building, and some support of language issues in a safe and friendly way. Using action research with MMORPGs in the real world facilitates a number of opportunities for learning and teaching including opportunities to practice language and individual and group experiences of communicating with other native/ second language speakers for the students. The researcher can also develop tutorial exercises and discussion for teaching plans based on the students’ experiences with the MMORPGs. The results from this research study demonstrate that MMORPGs offer a safe, fun, informal and effective learning space for supporting language teaching. Furthermore the use of MMORPGs help the students’ confidence in using their second language and provide additional benefits such as a better understanding of the culture and use of language in different contexts.


title: Software Maintenance Maturity Model ( SMmm ) : The software maintenance process model

abstract: SUMMARY We address the assessment and improvement of the software maintenance function by proposing improvements to the software maintenance standards and introducing a proposed maturity model for daily software maintenance activities: Software Maintenance Maturity Model (SM mm ). The software maintenance function suffers from a scarcity of management models to facilitate its evaluation, management, and continuous improvement. The SM mm addresses the unique activities of software maintenance while preserving a structure similar to that of the CMMi ©4 maturity model. It is designed to be used as a complement to this model. The SM mm is based on practitioners’ experience, international standards, and the seminal literature on software maintenance. We present the model’s purpose, scope, foundation, and architecture, followed by its initial validation. Copyright © 2004 John Wiley & Sons, Ltd.


title: Power divider based on grounded dielectric slab waveguide operating in E11x mode

abstract: A novel low-loss planar dielectric waveguide is proposed. It is based on a high-permittivity dielectric slab parallel to a metal ground. The guiding channel is limited at the sides by a number of air holes which are lowering the effective permittivity. A mode with the electric field primarily parallel to the ground plane is used, similar to the E11 x mode of an insulated image guide. A rather thick gap layer between the ground and the high-permittivity slab makes this mode to show the highest effective permittivity. The paper discusses the mode dispersion behaviour and presents measured characteristics of a power divider circuit operating at a frequency of about 8 GHz. Low leakage of about 14% is observed at the discontinuities forming the power divider. Using a compact dipole antenna structure, excitation efficiency of more than 90% is obtained.


title: Learning Invariances for Policy Generalization

abstract: While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unseen tasks. In this paper, we study a simple reinforcement learning problem and focus on learning policies that encode the proper invariances for generalization to different settings. We evaluate three potential methods for policy generalization: data augmentation, meta-learning and adversarial training. We find our data augmentation method to be effective, and study the potential of meta-learning and adversarial learning as alternative task-agnostic approaches. ::: Keywords: reinforcement learning, generalization, data augmentation, meta-learning, adversarial learning.


title: IoT Goes Nuclear: Creating a ZigBee Chain Reaction

abstract: Within the next few years, billions of IoT devices will densely populate our cities. In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will rapidly spread over large areas, provided that the density of compatible IoT devices exceeds a certain critical mass. In particular, we developed and verified such an infection using the popular Philips Hue smart lamps as a platform. The worm spreads by jumping directly from one lamp to its neighbors, using only their built-in ZigBee wireless connectivity and their physical proximity. The attack can start by plugging in a single infected bulb anywhere in the city, and then catastrophically spread everywhere within minutes. It enables the attacker to turn all the city lights on or off, to permanently brick them, or to exploit them in a massive DDOS attack. To demonstrate the risks involved, we use results from percolation theory to estimate the critical mass of installed devices for a typical city such as Paris whose area is about 105 square kilometers: The chain reaction will fizzle if there are fewer than about 15,000 randomly located smart lamps in the whole city, but will spread everywhere when the number exceeds this critical mass (which had almost certainly been surpassed already). To make such an attack possible, we had to find a way to remotely yank already installed lamps from their current networks, and to perform over-the-air firmware updates. We overcame the first problem by discovering and exploiting a major bug in the implementation of the Touchlink part of the ZigBee Light Link protocol, which is supposed to stop such attempts with a proximity test. To solve the second problem, we developed a new version of a side channel attack to extract the global AES-CCM key (for each device type) that Philips uses to encrypt and authenticate new firmware. We used only readily available equipment costing a few hundred dollars, and managed to find this key without seeing any actual updates. This demonstrates once again how difficult it is to get security right even for a large company that uses standard cryptographic techniques to protect a major product.


title: Collaborative Alert Ranking for Anomaly Detection

abstract: Given a large number of low-level heterogeneous categorical alerts from an anomaly detection system, how to characterize complex relationships between different alerts, filter out false positives, and deliver trustworthy rankings and suggestions to end users? This problem is motivated by and generalized from applications in enterprise security and attack scenario reconstruction. While existing techniques focus on either reconstructing abnormal scenarios or filtering out false positive alerts, it can be more advantageous to consider the two perspectives simultaneously in order to improve detection accuracy and better understand anomaly behaviors. In this paper, we propose CAR, a collaborative alerts ranking framework that exploits both temporal and content correlations from heterogeneous categorical alerts. CAR first builds a tree-based model to capture both short-term correlations and long-term dependencies in each alert sequence, which identifies abnormal action sequences. Then, an embedding-based model is employed to learn the content correlations between alerts via their heterogeneous categorical attributes. Finally, by incorporating both temporal and content dependencies into one optimization framework, CAR ranks both alerts and their corresponding alert patterns. Our experiments, using real-world enterprise monitoring data and real attacks launched by professional hackers, show that CAR can accurately identify true positive alerts and successfully reconstruct attack scenarios at the same time.


title: Gradient Regularization Improves Accuracy of Discriminative Models

abstract: Regularizing the gradient norm of the output of a neural network with respect to its inputs is a powerful technique, first proposed by Drucker & LeCun (1991) who named it Double Backpropagation. The idea has been independently rediscovered several times since then, most often with the goal of making models robust against adversarial sampling. This paper presents evidence that gradient regularization can consistently and significantly improve classification accuracy on vision tasks, especially when the amount of training data is small. We introduce our regularizers as members of a broader class of Jacobian-based regularizers, and compare them theoretically and empirically. ::: A straightforward objection against minimizing the gradient norm at the training points is that a locally optimal solution, where the model has small gradients at the training points, may possibly contain large changes at other regions. We demonstrate through experiments on real and synthetic tasks that stochastic gradient descent is unable to find these locally optimal but globally unproductive solutions. Instead, it is forced to find solutions that generalize well.


title: The aircraft ground routing problem: Analysis of industry punctuality indicators in a sustainable perspective

abstract: The ground routing problem consists in scheduling the movements of aircraft on the ground between runways and parking positions while respecting operational and safety requirements in the most efficient way. We present a Mixed Integer Programming (MIP) formulation for routing aircraft along a predetermined path. This formulation is generalized to allow several possible paths. Our model takes into account the classical performance indicators of the literature (the average taxi and completion times) but also the main punctuality indicators of the air traffic industry (the average delay and the on time performance). Then we investigate their relationship through experiments based on real data from Copenhagen Airport (CPH). We show that the industry punctuality indicators are in contradiction with the objective of reducing taxi times and therefore pollution emissions. We propose new indicators that are more sustainable, but also more relevant for stakeholders. We also show that alternate path cannot improve the performance indicators.


title: Applying Cognitive Linguistics to Pedagogical Grammar: The English Prepositions of

abstract: In this paper, we illustrate the merit of applying insights from Cognitive Linguistics to pedagogical grammar. We do so by examining English prepositions, long assumed to be one of the most difficult areas of acquisition for second language learners. The approach to the semantics of English prepositions we present is that developed in Evans and Tyler (2004a, b, In prep.) and Tyler and Evans (2001a, 2003). This account offers the following insights: 1) the concepts encoded by prepositions are image-schematic in nature and thus have an embodied basis. In other words, prepositions are not appropriately modelled as constituting linguistic propositions or semantic feature bundles (the received view in formal linguistics); 2) an English preposition encodes an abstract mental idealization of a spatial relation, derived from more specific spatial scenes. This forms the primary meaning component of a semantic network; 3) the idealized spatial relation also encodes a functional element, which derives from the way spatial relations are salient and relevant for human function and interaction with the physical environment; and 4) the additional senses in the semantic network have been extended in systematic, constrained ways. We discuss two key principles of extension: ways of viewing a spatial scene and experiential correlation. We demonstrate the usefulness of a Cognitive Linguistics approach by examining a few aspects of the lexicalization patterns exhibited by in and the four English prepositions of verticality, over, above, under and below. These prepositions provide good evidence that prepositional meanings are extended from the spatial to abstract domains in ways that are regular and constrained. We conclude that a Cognitive Linguistics approach to prepositions provides a more accurate, systematic account that, in turn, offers the basis for a more coherent, learnable presentation of this hitherto seemingly arbitrary aspect of English grammar.


title: Intent-Aware Semantic Query Annotation

abstract: Query understanding is a challenging task primarily due to the inherent ambiguity of natural language. A common strategy for improving the understanding of natural language queries is to annotate them with semantic information mined from a knowledge base. Nevertheless, queries with different intents may arguably benefit from specialized annotation strategies. For instance, some queries could be effectively annotated with a single entity or an entity attribute, others could be better represented by a list of entities of a single type or by entities of multiple distinct types, and others may be simply ambiguous. In this paper, we propose a framework for learning semantic query annotations suitable to the target intent of each individual query. Thorough experiments on a publicly available benchmark show that our proposed approach can significantly improve state-of-the-art intent-agnostic approaches based on Markov random fields and learning to rank. Our results further demonstrate the consistent effectiveness of our approach for queries of various target intents, lengths, and difficulty levels, as well as its robustness to noise in intent detection.


title: 1 3 A ug 2 01 8 A Reference Architecture for Datacenter Scheduling : Design , Validation , and Experiments [ Technical Report on the SC 18 homonym article ]

abstract: Datacenters act as cloud-infrastructure to stakeholders across industry, government, and academia. To meet growing demand yet operate efficiently, datacenter operators employ increasingly more sophisticated scheduling systems, mechanisms, and policies. Although many scheduling techniques already exist, relatively little research has gone into the abstraction of the scheduling process itself, hampering design, tuning, and comparison of existing techniques. In this work, we propose a reference architecture for datacenter schedulers. The architecture follows five design principles: components with clearly distinct responsibilities, grouping of related components where possible, separation of mechanism from policy, scheduling as complex workflow, and hierarchical multi-scheduler structure. To demonstrate the validity of the reference architecture, we map to it state-of-the-art datacenter schedulers. We find scheduler-stages are commonly underspecified in peer-reviewed publications. Through trace-based simulation and real-world experiments, we show underspecification of scheduler-stages can lead to significant variations in performance.


Neural Events Extraction from Movie Descriptions


title: Re-Scaling Patriotism: Competition And Urban Identity In Michael Bloomberg's New York

abstract: This article responds to Akhil Gupta's 1997 call for the exploration of spatialized commitment operating at scales be- sides that of the nation. It analyzes the attempts of the administra- tion of New York Mayor Michael Bloomberg to mobilize "urban patriotism" in support of development proposals for Manhattan's west side by portraying its opponents as insufficiently commit- ted to the city's future. Not just urban development policy was at stake in this debate, but the meaning of being a New Yorker. The urban identity that the administration and its allies espoused portrayed the city as a home for the ambitious, the innovative, the cosmopolitan, and above all the competitive, and was related to the local emergence of a new, globalized corporate elite. Other New Yorkers had different understandings of the city and their place in it, which ultimately doomed efforts to mobilize urban patriotism. The intersection of class-related identity and the fact of increased interurban competition created the conditions for urban identity to emerge as an issue. Finally, it is argued that urban identity must be distinguished from "boosterism," and the likelihood of urban patriotism being mobilized in other global cities operating in a competitive environment is appraised.


WIND TUNNEL MEASUREMENTS IN SKI JUMPERS AND SIMULATION OF THE JUMPS - THUNDER BAY, HILL K 90


title: Discovering personally meaningful places: An interactive clustering approach

abstract: The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, “home”, “work” or “Northwest Health Club”. It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices. Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than “points” are required to represent the full range of people's everyday places.


title: An Empirical Study of Face Recognition under Variations

abstract: Face recognition (FR) has recently made remark- able progress given the extraordinary capabilities of modern deep learning(DL) models. Though superior performance of DL based methods over human has been reported on benchmark dataset, it remains an open problem how those systems work in real-world condition with variations such as head pose, lighting, occlusion and image noises, in particular, in comparison to conventional FR approaches. It is hard to answer this question in a quantitative manner as current benchmark datasets are in lack of full range of variations. In this paper we propose a flexible approach to simulate face images under different variations with controllable degrees and based on the simulated dataset we quantitatively study how modern DL based and conventional FR methods perform. Based on the observations on a large number of synthesized face images, we draw several conclusions such as how head pose pitch and yaw variations will influence the FR systems and which part on the face is the most significant region, and in which situation conventional methods still show some advantages. The findings will not only be useful to assess current FR system in a quantitative manner but also shed light on future FR system design and data augmentation.


title: Differences between effects of psychological versus pharmacological treatments on functional and morphological brain alterations in anxiety disorders and major depressive disorder: A systematic review

abstract: The most prevalent mental disorders, anxiety and mood disorders, are associated with both functional and morphological brain changes that commonly involve the 'fear network' including the (medial) prefrontal cortex, hippocampus and amygdala. Patients suffering from anxiety disorders and major depressive disorder often show excessive amygdala and reduced prefrontal cortex functioning. It is, however, still unclear whether these brain abnormalities disappear or diminish following effective treatment. This review aims to compare the effects of psychotherapy and pharmacotherapy on functional and morphological brain measures in these disorders. Sixty-three studies were included, 30 investigating psychotherapy effects and 33 investigating pharmacotherapy effects. Despite methodological differences, results suggest a functional normalization of the 'fear network'. Pharmacotherapy particularly decreases over-activity of limbic structures (bottom-up effect) while psychotherapy tends to increase activity and recruitment of frontal areas (top-down effect), especially the anterior cingulate cortex. Additionally, pharmacotherapy, but not psychotherapy, has been associated with morphological changes, depending on the disorder. These findings suggest that both types of treatments normalize (functional) brain abnormalities each in specific ways. (C) 2011 Elsevier Ltd. All rights reserved


title: A Protection Strategy and Microprocessor-Based Relay for Low-Voltage Microgrids

abstract: One of the major challenges associated with microgrid protection is to devise an appropriate protection strategy that is effective in the grid-connected as well as islanded mode of operation. This paper proposes a protection strategy based on microprocessor-based relays for low-voltage microgrids. Further, the structure of a new relay enabling the proposed protection strategy is presented. One of the salient feature of the developed protection scheme is that it does not require communications or adaptive protective devices. Moreover, it is to a large extent independent of the fault current magnitude and the mode of operation. Transient time-domain simulation studies are conducted to demonstrate the effectiveness of the proposed protection strategy and its enabling relay, using the PSCAD/EMTDC software package.


title: Computational analysis of the safe zone for the antegrade lag screw in posterior column fixation with the anterior approach in acetabular fracture: A cadaveric study.

abstract: OBJECTIVE ::: The fluoroscopically-guided procedure of antegrade posterior lag screw in posterior column fixation through anterior approach is technique-dependent and requires an experienced surgeon. The purpose of this study was to establish the safe zone for the antegrade posterior lag screw by using computational analysis. ::: ::: ::: METHOD ::: The virtual three-dimensional model of 178 hemi-pelvises was created from the CT data (DICOM format) by using Mimics® program, and were used to measure the safe zone of antegrade lag screw fixation on the inner table of the iliac wing, and the largest diameter of cylindrical implant inside safe zone. The central point (point A) of the cylinder was assessed and was compared with the intersection point (point B) between the linea terminalis and the anterior border of the sacroiliac articulation. ::: ::: ::: RESULTS ::: The safe zone was triangular with an average area of 670.4mm2 (range, 374.8-1084.5mm2). The largest diameter of the cylinder was a mean 7.4mm (range, 5.0-10.0mm). When height was under 156.3cm, the diameter of the cylindrical implant was smaller than 7.0mm (p<0.001, regression coefficient=0.09). The linear distance between points A and B was 32.5mm (range, 19.2-49.3mm). Point A was far enough away from the well-positioned anterior column plate to prevent collision between the two. ::: ::: ::: CONCLUSION ::: The safe zone was shaped like a triangle, and was large enough for multiple screws. Considering the straight-line distance between points A and B, the central screw can be fixed without overlapping with the well-positioned anterior column plate at the point between holes 2 and 3.


title: Measuring consumer perception of social media marketing activities in e-commerce industry

abstract: This study developed a scale for perceived social media marketing activities (SMMA) of e-commerce.Employed a blend of qualitative and quantitative research to develop the scale.The study offers implications of perceived SMMA of e-commerce for academics and managers. The internet has changed the nature of shopping in the past two decades, which has supported the proliferation of e-commerce sites and thus shopping has shifted to e-shopping. Also, customers use social media to gain information on preferred products with the best price options, as social media provides shoppers a voice, and facilitate them to interact and share their opinion worldwide. Moreover, social media is extensively adopted platform for e-commerce. Although, social media marketing has achieved wide acceptance in business, especially in e-commerce, there is no scale in the extant literature to measure perceived social media marketing activities (SMMA) in an e-commerce context. Therefore, this study develops and validates a 15-item, five-dimensional scale for measuring perceived SMMA of e-commerce based on extant literature on e-commerce and social media marketing and five different studies conducted in this research. The scale revealed comprehensive psychometric characteristics as per the results from the diverse reliability and validity checks. It was revealed that perceived SMMA positively influenced purchase intention and brand equity; which endorses the nomological validity of the developed scale. The new scale provides both theoretical as well as managerial implications along with the avenues for forthcoming research.


title: Division of labor and working-class women's well-being across the transition to parenthood.

abstract: This study examines the degree to which the division of household and child-care tasks predicts working-class women's well-being across the transition to parenthood. Women completed questionnaires about the division of labor and their well-being before the birth of their first child and upon returning to work. Results showed that violated expectations regarding the division of child care were associated with increased distress postnatally, and there was some evidence that this relationship was moderated by gender ideology. Traditional women whose husbands did more child care than they expected them to do were more distressed. Work status also moderated the relationship between violated expectations and distress. The results suggest that the division of child care is more salient in predicting distress than the division of housework, for working-class women, at this time point.


title: Apoptosis in the nervous system

abstract: Neuronal apoptosis sculpts the developing brain and has a potentially important role in neurodegenerative diseases. The principal molecular components of the apoptosis programme in neurons include Apaf-1 (apoptotic protease-activating factor 1) and proteins of the Bcl-2 and caspase families. Neurotrophins regulate neuronal apoptosis through the action of critical protein kinase cascades, such as the phosphoinositide 3-kinase/Akt and mitogen-activated protein kinase pathways. Similar cell-death-signalling pathways might be activated in neurodegenerative diseases by abnormal protein structures, such as amyloid fibrils in Alzheimer's disease. Elucidation of the cell death machinery in neurons promises to provide multiple points of therapeutic intervention in neurodegenerative diseases.


Open Language Learning for Information Extraction


Customer relationship management technology


Efficient SAT Approach to Multi-Agent Path Finding Under the Sum of Costs Objective.


Consumers perception, purchase intention and actual purchase behavior of organic food products


title: A densitometric approach to web page segmentation

abstract: Web Page segmentation is a crucial step for many applications in Information Retrieval, such as text classification, de-duplication and full-text search. In this paper we describe a new approach to segment HTML pages, building on methods from Quantitative Linguistics and strategies borrowed from the area of Computer Vision. We utilize the notion of text-density as a measure to identify the individual text segments of a web page, reducing the problem to solving a 1D-partitioning task. The distribution of segment-level text density seems to follow a negative hypergeometric distribution, described by Frumkina's Law. Our extensive evaluation confirms the validity and quality of our approach and its applicability to the Web.


The Semantic Web" in Scientific American


title: Multi-level Access in Searchable Symmetric Encryption

abstract: Remote storage delivers a cost effective solution for data storage. If data is of a sensitive nature, it should be encrypted prior to outsourcing to ensure confidentiality; however, searching then becomes challenging. Searchable encryption is a well-studied solution to this problem. Many schemes only consider the scenario where users can search over the entirety of the encrypted data. In practice, sensitive data is likely to be classified according to an access control policy and different users should have different access rights. It is unlikely that all users have unrestricted access to the entire data set. Current schemes that consider multi-level access to searchable encryption are predominantly based on asymmetric primitives. We investigate symmetric solutions to multi-level access in searchable encryption where users have different access privileges to portions of the encrypted data and are not permitted to search over, or learn information about, data for which they are not authorised.


title: A method of driving fatigue detection based on eye location

abstract: For a driver fatigue monitoring system, one of the most important problems to solve is eye location. This paper presents a method of eye location based on AAM and a method of driver fatigue detection based on an improved method to calculate PERCOLS. AAM (Active Appearance Model) model is composited by the appearance model and fitting algorithm which is an efficient algorithm to extract facial feature points. First establish the AAM model, then use the fitting algorithm to detect the position of face and locate the position of eye, next calculate the improved physical PERCLOS which is used to measure the driver fatigue to determine the current state of driver is fatigue or not.


title: Relation Classification: CNN or RNN?

abstract: Convolutional neural networks (CNN) have delivered competitive performance on relation classification, without tedious feature engineering. A particular shortcoming of CNN, however, is that it is less powerful in modeling long-span relations. This paper presents a model based on recurrent neural networks (RNN) and compares the capabilities of CNN and RNN on the relation classification task. We conducted a thorough comparative study on two databases: one is the popular SemEval-2010 Task 8 dataset, and the other is the KBP37 dataset we designed based on MIML-RE [1], with the goal of learning and testing complex relations. The experimental results strongly indicate that even with a simple RNN structure, the model can deliver much better performance than CNN, particularly for long-span relations.


title: Literature survey of sarcasm detection

abstract: Sarcasm is a form of language in which individual convey their message in an implicit way i.e. the opposite of what is implied. Sarcasm detection is the task of predicting sarcasm in text. This is the crucial step in sentiment analysis due to inherently ambiguous nature of sarcasm. With this ambiguity, sarcasm detection has always been a difficult task, even for humans. Therefore sarcasm detection has gained importance in many Natural Language Processing applications. In this paper, we describe approaches, issues, challenges and future scopes in sarcasm detection.


title: A Comparative Survey of LPWA Networking

abstract: Motivated by the increasing variance of suggested Internet of Things (IoT) applications and the lack of suitability of current wireless technologies in scalable, long range deployments, a number of diverging Low Power Wide Area (LPWA) technologies have been developed. These technologies promise to enable a scalable high range network on cheap low power devices, facilitating the development of a ubiquitous IoT. This paper provides a definition of this new LPWA paradigm, presents a systematic approach to defined suitable use cases, and undertakes a detailed comparison of current LPWA standards, including the primary technologies, upcoming cellular options, and remaining proprietary solutions.


title: A generative model for simultaneous estimation of human body shape and pixel-level segmentation

abstract: This paper addresses pixel-level segmentation of a human body from a single image. The problem is formulated as a multi-region segmentation where the human body is constrained to be a collection of geometrically linked regions and the background is split into a small number of distinct zones. We solve this problem in a Bayesian framework for jointly estimating articulated body pose and the pixel-level segmentation of each body part. Using an image likelihood function that simultaneously generates and evaluates the image segmentation corresponding to a given pose, we robustly explore the posterior body shape distribution using a data-driven, coarse-to-fine Metropolis Hastings sampling scheme that includes a strongly data-driven proposal term.


title: Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running

abstract: Although human cognition often occurs during dynamic motor actions, most studies of human brain dynamics examine subjects in static seated or prone conditions. EEG signals have historically been considered to be too noise prone to allow recording of brain dynamics during human locomotion. Here we applied a channel-based artifact template regression procedure and a subsequent spatial filtering approach to remove gait-related movement artifact from EEG signals recorded during walking and running. We first used stride time warping to remove gait artifact from high-density EEG recorded during a visual oddball discrimination task performed while walking and running. Next, we applied infomax independent component analysis (ICA) to parse the channel-based noise reduced EEG signals into maximally independent components (ICs) and then performed component-based template regression. Applying channel-based or channel-based plus component-based artifact rejection significantly reduced EEG spectral power in the 1.5- to 8.5-Hz frequency range during walking and running. In walking conditions, gait-related artifact was insubstantial: event-related potentials (ERPs), which were nearly identical to visual oddball discrimination events while standing, were visible before and after applying noise reduction. In the running condition, gait-related artifact severely compromised the EEG signals: stable average ERP time-courses of IC processes were only detectable after artifact removal. These findings show that high-density EEG can be used to study brain dynamics during whole body movements and that mechanical artifact from rhythmic gait events may be minimized using a template regression procedure.


title: An Architecture for Action, Emotion, ans Social Behavior

abstract: The Oz project at Carnegie Mellon is studying the construction of artistically effective simulated worlds. Such worlds typically include several agents, which must exhibit broad behavior. To meet this need, we are developing an agent architecture, called Tok, that presently supports reactivity, goals, emotions, and social behavior. Here we briefly introduce the requirements of our application, summarize the Tok architecture, and describe a particular social agent we have constructed.


title: Application-aware data plane processing in SDN

abstract: A key benefit of Software Defined Networks is fine-grained management of network flows made possible by the execution of flow-specific actions based upon inspection and matching of various packet fields. However, current switches and protocols limit the inspected fields to layer 2-4 headers and hence any customized flow-handling that uses higher-layer information necessitates sending the packets to the controller. This is inefficient and slow, adding several switch-to-controller round-trip delays. This paper proposes an extended SDN architecture that enables fast customized packet-handling even when the information used is not restricted to L2-L4. We describe an implementation of this architecture that keeps most of the processing in the data plane and limits the need to send packets to the controller even when higher-layer information is used in packet-handling. We show how some popular applications can be implemented using this extended architecture and evaluate the performance of one such application using a prototype implementation on Open vSwitch. The results show that the proposed architecture has low overhead, good performance and can take advantage of a flexible scale-out design for application deployment.


title: Two-channel user authentication by using USB on Cloud

abstract: According to parallel computing technology, Cloud service is popular, and it is easy to use Cloud service at everywhere. Cloud means involving application systems that are executed within the cloud and operated via the internet enabled devices. Cloud computing does not rely on the use of cloud storage as it will be removed upon users download action. Clouds can be classified as public, private and hybrid. Cloud service comes up with Ubiquitous; Cloud service users can use their service at anywhere at any time. It is convenient. However, there is a tradeoff. If user’s username and password are compromised, user’s cloud system is in danger, and their confidential information will be in jeopardy. At anywhere and anytime with any device, Cloud user’s credential could be in jeopardy. Security concerns in Cloud play a major role. It is the biggest obstacle to developing in Cloud. However, Cloud is still popular and vulnerability for hacking because of one channel user authentication. Therefore, this research proposes two-channel user authentication by using USB to emphasise security.


title: Structural measures for multiplex networks

abstract: Many real-world complex systems consist of a set of elementary units connected by relationships of different kinds. All such systems are better described in terms of multiplex networks, where the links at each layer represent a different type of interaction between the same set of nodes, rather than in terms of (single-layer) networks. In this paper we present a general framework to describe and study multiplex networks, whose links are either unweighted or weighted. In particular we propose a series of measures to characterize the multiplexicity of the systems in terms of: i) basic node and link properties such as the node degree, and the edge overlap and reinforcement, ii) local properties such as the clustering coefficient and the transitivity, iii) global properties related to the navigability of the multiplex across the different layers. The measures we introduce are validated on a genuine multiplex data set of Indonesian terrorists, where information among 78 individuals are recorded with respect to mutual trust, common operations, exchanged communications and business relationships.


title: Estimation of Blood Pressure and Pulse Transit Time Using Your Smartphone

abstract: It is widely recognized today that there is an alarming rise of lifestyle-induced chronic diseases (e.g., type II diabetes) in our society. Therefore, a strong need exists for cost-effective and non-invasive devices that can measure blood pressure (BP) to monitor, diagnose and follow-up patients at risk, but also healthy population in general. One promising method for arterial BP estimation is to measure a surrogate marker of it, such as, Pulse Transit Time (PTT) and derive pressure values from it. However, current methods for measuring PTT require complex sensing and analysis circuitry and the related medical devices are expensive and inconvenient for the user to wear. In this paper, we present a new smartphone-based method to estimate PTT reliably and subsequently BP from the baseline sensors on smartphones. This new approach involves determining PTT by simultaneously measuring the time the blood leaves the heart, by recording the heart sound using the standard microphone of the phone and the time it reaches the finger, by measuring the pulse wave using the phone's camera. Moreover, we also describe algorithms that can be executed directly on current smartphones to obtain clean and robust heart sound signals and to extract the pulse wave characteristics using smartphones. We also present methods to ensure a synchronous capture of the waveforms, which is essential to obtain reliable PTT values with inexpensive sensors. Our experiments show that the computational overhead of the proposed two-phase processing method is minimum, with the ability to reliably measure the PTT values in a fully accurate (beat-to-beat) fashion using directly state-of-the-art smartphones as medical devices.