Parameterized Pattern Matching: Algorithms and Applications

The problem of finding sections of code that either are identical or are related by the systematic renaming of variables or constants can be modeled in terms of parameterized strings ( p-strings) and parameterized matches ( p-matches). P-strings are strings over two alphabets, one of which represents parameters. Two p-strings are a parameterized match ( p-match) if one p-string is obtained by renaming the parameters of the other by a one-to-one function. In this paper, we investigate parameterized pattern matching via parameterized suffix trees (p-suffix trees). We give two algorithms for constructing p-suffix trees: one (eager) that runs in linear time for fixed alphabets, and another that uses auxiliary data structures and runs in O(n log(n)) time for variable alphabets, where n is input length. We show that using a p-suffix tree for a pattern p-string P, it is possible to search for all p-matches of P within a text p-string T in space linear in |P| and time linear in |T | for fixed alphabets, or O( |T | log(min( |P|, _)) time and O( |P|) space for variable alphabets, where _ is the sum of the alphabet sizes. The simpler p-suffix tree construction algorithm eager has been implemented, and experiments show it to be practical. Since it runs faster than predicted by the above worst-case bound, we reanalyze the algorithm and show that eager runs in time O(min(t |S |+m(t, S) | t>0) log _)), where for an input p-string S, m(t, S) is the number of maximal p-matches of length at least t that occur within S, and _ is the sum of the alphabet sizes. Experiments with the author's program dup (B. Baker, in ``Comput. Sci. Statist.,'' Vol. 24, 1992) for finding all maximal p-matches within a p-string have found m(t, S) to be less than |S | in practice unless t is small. ] 1996 Academic Press, Inc.


Speech and Language Processing

CHAPTER 18 Lexicons for Sentiment and Affect Extraction " [W]e write, not with the fingers, but with the whole person. The nerve which controls the pen winds itself about every fibre of our being, threads the heart, pierces the liver. " Virginia Woolf, Orlando " She runs the gamut of emotions from A to B. " Dorothy Parker, reviewing Hepburn's performance in Little Women In this chapter we turn to tools for interpreting affective meaning, extending our affective study of sentiment analysis in Chapter 7. We use the word 'affective', following the tradition in affective computing (Picard, 1995) to mean emotion, sentiment, personality , mood, and attitudes. Affective meaning is closely related to subjectivity, subjectivity the study of a speaker or writer's evaluations, opinions, emotions, and speculations (Wiebe et al., 1999). How should affective meaning be defined? One influential typology of affec-tive states comes from Scherer (2000), who defines each class of affective states by factors like its cognition realization and time course: Emotion: Relatively brief episode of response to the evaluation of an external or internal event as being of major significance. Mood: Diffuse affect state, most pronounced as change in subjective feeling, of low intensity but relatively long duration, often without apparent cause. Interpersonal stance: Affective stance taken toward another person in a specific interaction, colouring the interpersonal exchange in that situation. Figure 18.1 The Scherer typology of affective states, with descriptions from Scherer (2000).


Programming with a Differentiable Forth Interpreter

Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.


Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.


TOLDI: An effective and robust approach for 3D local shape description

Feature description for the 3D local shape in the presence of noise, varying mesh resolutions, clutter and occlusion is a quite challenging task in 3D computer vision. This paper tackles the problem by proposing a new local reference frame (LRF) together with a novel triple orthogonal local depth images (TOLDI) representation, forming the TOLDI method for local shape description. Compared with previous methods, TOLDI manages to perform efficient, distinctive and robust description for the 3D local surface simultaneously under various feature matching contexts. The proposed LRF differs from many prior ones in its calculation of the z-axis and x-axis, the z-axis is calculated using the normal of the keypoint and the x-axis is computed by aggregating the weighted projection vectors of the radius neighbors. TOLDI feature descriptors are then obtained by concatenating three local depth images (LDI) captured from three orthogonal view planes in the LRF into feature vectors. The performance of our TOLDI approach is rigorously evaluated on several public datasets, which contain three major surface matching scenarios, namely shape retrieval, object recognition and 3D registration. Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of our method. Our method is also applied to aligning 3D object ∗Corresponding author Email addresses: jqyang@hust.edu.cn (Jiaqi Yang), hangfanzq@163.com (Qian Zhang), Yang Xiao@hust.edu.cn (Yang Xiao), zgcao@hust.edu.cn (Zhiguo Cao) Preprint submitted to Journal of LTEX Templates November 23, 2016 and indoor scene point clouds obtained by different devices (i.e., LiDAR and Kinect), the accurate outcomes further confirm the effectiveness of our method.


Synaptic patterning and the timescales of cortical dynamics

Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. The modular and hierarchical organization of this selectivity mirrors anatomical and physiological relations throughout the cortex and is likely determined by the regional electrochemical composition. Being consistently patterned and actively regulated, the expression of molecules involved in synaptic transmission constitutes the most significant source of laminar and regional variability. Due to their complex kinetics and adaptability, synapses form a natural primary candidate underlying this regional temporal selectivity. The ability of cortical networks to reflect the temporal structure of the sensory environment can thus be regulated by evolutionary and experience-dependent processes.


Consistent Generative Query Networks

Stochastic video prediction is usually framed as an extrapolation problem where the goal is to sample a sequence of consecutive future image frames conditioned on a sequence of observed past frames. For the most part, algorithms for this task generate future video frames sequentially in an autoregressive fashion, which is slow and requires the input and output to be consecutive. We introduce a model that overcomes these drawbacks – it learns to generate a global latent representation from an arbitrary set of frames within a video. This representation can then be used to simultaneously and efficiently sample any number of temporally consistent frames at arbitrary time-points in the video. We apply our model to synthetic video prediction tasks and achieve results that are comparable to state-of-the-art video prediction models. In addition, we demonstrate the flexibility of our model by applying it to 3D scene reconstruction where we condition on location instead of time. To the best of our knowledge, our model is the first to provide flexible and coherent prediction on stochastic video datasets, as well as consistent 3D scene samples. Please check the project website https://bit.ly/2jX7Vyu to view scene reconstructions and videos produced by our model.


Single-label and multi-label conceptor classifiers in pre-trained neural networks

Training large neural network models from scratch is not feasible due to over-fitting on small datasets and too much time consumed on large datasets. To address this, transfer learning, namely utilizing the feature extracting capacity learned by large models, becomes a hot spot in neural network community. At the classifying stage of pre-trained neural network model, either a linear SVM classifier or a Softmax classifier is employed and that is the only trained part of the whole model. In this paper, inspired by transfer learning, we propose a classifier based on conceptors called Multi-label Conceptor Classifier (MCC) to deal with multi-label classification in pre-trained neural networks. When no multi-label sample exists, MCC equates to Fast Conceptor Classifier, a fast single-label classifier proposed in our previous work, thus being applicable to single-label classification. Moreover, by introducing a random search algorithm, we further improve the performance of MCC on single-label datasets Caltech-101 and Caltech-256, where it achieves state-of-the-art results. Also, its evaluations with pre-trained rather than fine-tuning neural networks are investigated on multi-label dataset PASCAL VOC-2007, where it achieves comparable results.


A fuzzy logic-based text classification method for social media

A FUZZY LOGIC-BASED TEXT CLASSIFICATION METHOD FOR SOCIAL MEDIA


Exploiting the web of data in model-based recommender systems

The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and LinkedMDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -- both content-based and collaborative filtering ones.


Figurative language processing after traumatic brain injury in adults: A preliminary study

Figurative speech (e.g., proverb, irony, metaphor, and idiom) has been reported to be particularly sensitive to measurement of abstract thinking in patients who suffer from impaired abstraction and language abilities. Metaphor processing was investigated with fMRI in adults with moderate to severe post-acute traumatic brain injury (TBI) and healthy age-matched controls using a valence-judgment task. We hypothesized that TBI patients would display decreased activation of the left inferior frontal gyrus (LIFG), which is considered central to semantic memory retrieval and abstract thought, in comparison with healthy controls. We also predicted that decreased activation in TBI individuals would correlate with their behavioral response times. A whole-brain analysis across the two participant groups revealed that patients did not strongly engage frontal and temporal regions related to semantic processing for novel metaphor comprehension, whereas control participants exhibited more intensive and concentrated activation within frontal and temporal areas. A region of interest (ROI) analysis verified that the LIFG was underactivated in TBI patients compared to controls across all conditions. TBI patients' impaired abstraction of novel stimuli may stem from reduced prefrontal control of semantic memory as well as disrupted interconnectivity of prefrontal cortex with other regions.


High-density 80 K SNP array is a powerful tool for genotyping G. hirsutum accessions and genome analysis

High-throughput genotyping platforms play important roles in plant genomic studies. Cotton (Gossypium spp.) is the world’s important natural textile fiber and oil crop. Upland cotton accounts for more than 90% of the world’s cotton production, however, modern upland cotton cultivars have narrow genetic diversity. The amounts of genomic sequencing and re-sequencing data released make it possible to develop a high-quality single nucleotide polymorphism (SNP) array for intraspecific genotyping detection in cotton. Here we report a high-throughput CottonSNP80K array and its utilization in genotyping detection in different cotton accessions. 82,259 SNP markers were selected from the re-sequencing data of 100 cotton cultivars and used to produce the array on the Illumina Infinium platform. 77,774 SNP loci (94.55%) were successfully synthesized on the array. Of them, 77,252 (99.33%) had call rates of >95% in 352 cotton accessions and 59,502 (76.51%) were polymorphic loci. Application tests using 22 cotton accessions with parent/F1 combinations or with similar genetic backgrounds showed that CottonSNP80K array had high genotyping accuracy, good repeatability, and wide applicability. Phylogenetic analysis of 312 cotton cultivars and landraces with wide geographical distribution showed that they could be classified into ten groups, irrelevant of their origins. We found that the different landraces were clustered in different subgroups, indicating that these landraces were major contributors to the development of different breeding populations of modern G. hirsutum cultivars in China. We integrated a total of 54,588 SNPs (MAFs >0.05) associated with 10 salt stress traits into 288 G. hirsutum accessions for genome-wide association studies (GWAS), and eight significant SNPs associated with three salt stress traits were detected. We developed CottonSNP80K array with high polymorphism to distinguish upland cotton accessions. Diverse application tests indicated that the CottonSNP80K play important roles in germplasm genotyping, variety verification, functional genomics studies, and molecular breeding in cotton.


Comparison of different designs of cylindrical printed quadrifilar Helix antennas

INMARSAT allocated frequencies are 1.525 GHz to 1.559 GHz as downlink and 1.625 GHz to 1.6605 GHz as uplink GPS allocated frequencies 1.57542 GHz in L1 band. In this paper we design two types of Helix antenna as PQHA to operate in allocated frequencies for INMARSAT and GPS application. We used for their optimum design Electromagnetic simulations and were verified by fabrication and VSWR measurement.


Normalization at the field level: Fractional counting of citations

Van Raan et al. (2010) accepted our critique for the case of journal normalization (previously CPP/JCSm); CWTS has in the meantime adapted its procedures. However, a new indicator was proposed for field normalization (previously CPP/FCSm), called the “mean normalized citation score” (MNCS; cf. Lundberg, 2007). In our opinion, this latter change does not sufficiently resolve the problems. Since the new indicator is considered another “crown indicator,” it seems urgent to warn against and elaborate on these remaining problems. In addition to damaging evaluation processes at the level of individuals and institutions, the “crown indicator” is also used by CWTS for the “Leiden Rankings,” and flaws in it can therefore misguide policies at national levels. also


Caesar: A content router for high-speed forwarding on content names

Today, high-end routers forward hundreds of millions of packets per second by means of longest prefix match on forwarding tables with less than a million IP prefixes. Information-Centric Networking, a novel form of networking where content is requested by its name, poses a new challenge in the design of high-end routers: process at least the same amount of packets, assuming a forwarding table that contains hundreds of millions of content prefixes. In this work we design and preliminarily evaluate Caesar, the first content router that supports name-based forwarding at high speed. Caesar efficiently uses available processing and memory units in a high-end router to support forwarding tables containing a billion content prefixes with unlimited characters.


Ultra wideband antennas: past and present

Since the release by the Federal Communications Commission (FCC) of a bandwidth of 7.5GHz (from 3.1GHz to 10.6GHz) for ultra wideband (UWB) wireless communications, UWB is rapidly advancing as a high data rate wireless communication technology. As is the case in conventional wireless communication systems, an antenna also plays a very crucial role in UWB systems. However, there are more challenges in designing a UWB antenna than a narrow band one. A suitable UWB antenna should be capable of operating over an ultra wide bandwidth as allocated by the FCC. At the same time, satisfactory radiation properties over the entire frequency range are also necessary. This paper focuses on UWB planar printed circuit board (PCB) antenna design and analysis. Studies have been undertaken covering the areas of UWB fundamentals and antenna theory. Extensive investigations were also carried out on the development of UWB antennas from the past to present. First, the planar PCB antenna designs for UWB system is introduced and described. Next, the special design considerations for UWB antennas are summarized. State-of-the-art UWB antennas are also reviewed. Finally, a new concept for the design of a UWB antenna with a bandwidth ranging from 2GHz-11.3GHz is introduced, which satisfies the system requirements for S-DMB, WiBro, WLAN, CMMB and the entire UWB . KeywordsUltra wideband, planar PCB Antenna and bandwidth.


SfePy - Write Your Own FE Application

SfePy (Simple Finite Elements in Python) is a framework for solving various kinds of problems (mechanics, physics, biology, ...) described by partial differential equations in two or three space dimensions by the finite element method. The paper illustrates its use in an interactive environment or as a framework for building custom finite-element based solvers.


A New Framework for Computer Science and Engineering

T raditionally, computing studies has two partitions—science and engineering—that are separated by a line roughly at the computer architecture level. Above the line is computer science and below is computer engineering. Each of these two disciplines then breaks into subspecialties, such as the decomposition of computer science into theory, systems, and AI. Such an approach succeeds in dividing the field into affinity groups, but it tends to isolate its respective parts and the systems that come from them. This isolation, in turn, makes it difficult to incorporate either an integrative systems-oriented perspective or the interdisciplinary work that is rapidly becoming critical to computing studies’ future.1 A more effective organization for computer science and engineering (CS&E)—and one that helps point the way toward its promising future—requires an intrinsically interdisciplinary framework that combines academic and systems-oriented computing perspectives. My colleagues and I have been developing such a framework, which is already changing key segments of CS&E at the University of Southern California (USC). The framework reaggregates computer science and computer engineering and then repartitions the resulting single field into analysis and synthesis components.2-4 Analysis, the more academic of the two, focuses on the nature of computing and the relationships between computer science and other major scientific domains. The synthesis component builds up computer engineering as a more pragmatic systems-oriented activity through a hierarchy of related layers, each of which covers important interdisciplinary issues. The framework is based on the notion that science is foremost about dissecting and understanding, and engineering is mostly about envisioning and building. True, scientists also envision and build—integrated models, for example—but such activities are directed mostly at dissecting and understanding something. Likewise engineers also dissect and understand things, but primarily to envision and build. Extending these notions to computing studies, computer science is concerned with dissecting and understanding the nature of computing and its relationship to the other sciences, while computer engineering is concerned To realize the compelling future of computing studies, researchers and analysts must rethink the traditional way of partitioning and structuring computer science and engineering. A framework that USC is already partially implementing combines an explicitly interdisciplinary academic focus with a systems-oriented perspective. Paul S. Rosenbloom University of Southern California


Vision-based automated parking system

This paper describes an approach to overcome a situation of monitoring and managing a parking area using a vision based automated parking system. With the rapid increase of cars the need to find available parking space in the most efficient manner, to avoid traffic congestion in a parking area, is becoming a necessity in car park management. Current car park management is dependent on either human personnel keeping track of the available car park spaces or a sensor based system that monitors the availability of each car park space or the overall number of available car park spaces. In both situations, the information available was only the total number of car park spaces available and not the actual location available. In addition, the installation and maintenance cost of a sensor based system is dependent on the number of sensors used in a car park. This paper shows a vision based system that is able to detect and indicate the available parking spaces in a car park. The methods utilized to detect available car park spaces were based on coordinates to indicate the regions of interest and a car classifier. This paper shows that the initial work done here has an accuracy that ranges from 90% to 100% for a 4 space car park. The work done indicated that the application of a vision based car park management system would be able to detect and indicate the available car park spaces


Automatic Classification for Vietnamese News

This paper proposes an automatic framework to classify Vietnamese news from news sites on the Internet. In this proposed framework, the extracted main content of Vietnamese news is performed automatically by applying the improved performance extraction method from [1]. This information will be classified by using two machine learning methods: Support vector machine and naïve bayesian method. Our experiments implemented with Vietnamese news extracted from some sites showed that the proposed classification framework give acceptable results with a rather high accuracy, leading to applying it to real information systems.


Co-change Clusters: Extraction and Application on Assessing Software Modularity

The traditional modular structure defined by the package hierarchy suffers from the dominant decomposition problem and it is widely accepted that alternative forms of modularization are necessary to increase developer’s productivity. In this paper, we propose an alternative form to understand and assess package modularity based on co-change clusters, which are highly inter-related classes considering cochange relations. We evaluate how co-change clusters relate to the package decomposition of four real-world systems. The results show that the projection of co-change clusters to packages follows different patterns in each system. Therefore, we claim that modular views based on co-change clusters can improve developers’ understanding on how well-modularized are their systems, considering that modularity is the ability to confine changes and evolve components in parallel.


The Sensemaking-Coevolution-Implementation Theory of software design

Understanding software design practice is critical to understanding modern information systems development. New developments in empirical software engineering, information systems design science and the interdisciplinary design literature combined with recent advances in process theory and testability have created a situation ripe for innovation. Consequently, this paper utilizes these breakthroughs to formulate a process theory of software design practice: SensemakingCoevolution-Implementation Theory explains how complex software systems are created by collocated software development teams in organizations. It posits that an independent agent (design team) creates a software system by alternating between three activities: organizing their perceptions about the context, mutually refining their understandings of the context and design space, and manifesting their understanding of the design space in a technological artifact. This theory development paper defines and illustrates Sensemaking-Coevolution-Implementation Theory, grounds its concepts and relationships in existing literature, conceptually evaluates the theory and situates it in the broader context of information systems development.


Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.


Classification of watermelon leaf diseases using neural network analysis

This paper mainly discussed the process to classify Anthracnose and Downey Mildew, watermelon leaf diseases using neural network analysis. A few of infected leaf samples were collected and they were captured using a digital camera with specific calibration procedure under controlled environment. The classification on the watermelon's leaf diseases is based on color feature extraction from RGB color model where the RGB pixel color indices have been extracted from the identified Regions of Interest (ROI). The proposed automated classification model involved the process of diseases classification using Statistical Package for the Social Sciences (SPSS) and Neural Network Pattern Recognition Toolbox in MATLAB. Determinations in this work have shown that the type of leaf diseases achieved 75.9% of accuracy based on its RGB mean color component.


Human Interaction With Robot Swarms: A Survey

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human-swarm interaction (HSI) and identifies the core concepts needed to design a human-swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human-swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.


A k-mean clustering algorithm for mixed numeric and categorical data

Use of traditional k-mean type algorithm is limited to numeric data. This paper presents a clustering algorithm based on k-mean paradigm that works well for data with mixed numeric and categorical features. We propose new cost function and distance measure based on co-occurrence of values. The measures also take into account the significance of an attribute towards the clustering process. We present a modified description of cluster center to overcome the numeric data only limitation of k-mean algorithm and provide a better characterization of clusters. The performance of this algorithm has been studied on real world data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach. 2007 Elsevier B.V. All rights reserved.


Unsupervised Learning with Mixed Numeric and Nominal Data

ÐThis paper presents a Similarity-Based Agglomerative Clustering (SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure, proposed by Goodall for biological taxonomy [15], that gives greater weight to uncommon feature value matches in similarity computations and makes no assumptions of the underlying distributions of the feature values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a dendrogram and a simple distinctness heuristic is used to extract a partition of the data. The performance of SBAC has been studied on real and artificially generated data sets. Results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other clustering schemes illustrate the superior performance of this approach. Index TermsÐAgglomerative clustering, conceptual clustering, feature weighting, interpretation, knowledge discovery, mixed numeric and nominal data, similarity measures, 2 aggregation.


Chameleon: Hierarchical Clustering Using Dynamic Modeling

68 Computer C lustering is a discovery process in data mining. 1 It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. 1,2 These discovered clusters can help explain the characteristics of the underlying data distribution and serve as the foundation for other data mining and analysis techniques. Clustering is useful in characterizing customer groups based on purchasing patterns, categorizing Web documents, 3 grouping genes and proteins that have similar func-tionality, 4 grouping spatial locations prone to earthquakes based on seismological data, and so on. Most existing clustering algorithms find clusters that fit some static model. Although effective in some cases, these algorithms can break down—that is, cluster the data incorrectly—if the user doesn't select appropriate static-model parameters. Or sometimes the model cannot adequately capture the clusters' characteristics. Most of these algorithms break down when the data contains clusters of diverse shapes, densities , and sizes. Existing algorithms use a static model of the clusters and do not use information about the nature of individual clusters as they are merged. Furthermore, one set of schemes (the CURE algorithm and related schemes) ignores the information about the aggregate interconnectivity of items in two clusters. The other set of schemes (the Rock algorithm, group averaging method, and related schemes) ignores information about the closeness of two clusters as defined by the similarity of the closest items across two clusters. (For more information, see the " Limitations of Traditional Clustering Algorithms " sidebar.) By only considering either interconnectivity or close-ness, these algorithms can easily select and merge the wrong pair of clusters. For instance, an algorithm that focuses only on the closeness of two clusters will incorrectly merge the clusters in Figure 1a over those in Figure 1b. Similarly, an algorithm that focuses only on interconnectivity will, in Figure 2, incorrectly merge the dark-blue with the red cluster rather than the green one. Here, we assume that the aggregate interconnec-tivity between the items in the dark-blue and red clusters is greater than that of the dark-blue and green clusters. However, the border points of the dark-blue cluster are much closer to those of the green cluster than those of the red cluster. Chameleon is a new agglomerative hierarchical clustering algorithm that overcomes the limitations of existing clustering algorithms. Figure 3 (on page 70) provides an overview of the overall approach …


Cortical cores in network dynamics

Spontaneous brain activity at rest is spatially and temporally organized in networks of cortical and subcortical regions specialized for different functional domains. Even though brain networks were first studied individually through functional Magnetic Resonance Imaging, more recent studies focused on their dynamic 'integration'. Integration depends on two fundamental properties: the structural topology of brain networks and the dynamics of functional connectivity. In this scenario, cortical hub regions, that are central regions highly connected with other areas of the brain, play a fundamental role in serving as way stations for network traffic. In this review, we focus on the functional organization of a set of hub areas that we define as the 'dynamic core'. In the resting state, these regions dynamically interact with other regions of the brain linking multiple networks. First, we introduce and compare the statistical measures used for detecting hubs. Second, we discuss their identification based on different methods (functional Magnetic Resonance Imaging, Diffusion Weighted Imaging, Electro/Magneto Encephalography). Third, we show that the degree of interaction between these core regions and the rest of the brain varies over time, indicating that their centrality is not stationary. Moreover, alternating periods of strong and weak centrality of the core relate to periods of strong and weak global efficiency in the brain. These results indicate that information processing in the brain is not stable, but fluctuates and its temporal and spectral properties are discussed. In particular, the hypothesis of 'pulsed' information processing, discovered in the slow temporal scale, is explored for signals at higher temporal resolution.


Machine Learning Approach for Intrusion Detection on Cloud Virtual Machines

Development of the cloud computing in recent years is increasing rapidly and gained great success, its security issues have got more and more attention. Many challenges in cloud computation increase the threat of data and service availability. There is need of many security services in order to improve cloud security for users as well as providers. In this paper, we propose a Anomaly Intrusion Detection System using machine learning approach for virtual machines on cloud computing. Our proposal is feature selection over events from Virtual Machine Monitor to detect anomaly in parallel to training the system so it will learn new threats and update the model. The experiment has been carried out on NSL-KDD'99 datasets using Naïve Bayes Tree (NB Tree) Classifier and hybrid approach of NB Tree and Random Forest.


An Ontology for Secure Socio-Technical Systems

Security is often compromised by exploiting vulnerabilities in the interface between the organization and the information systems that support it. This reveals the necessity of modeling and analyzing information systems together with the organizational setting where they will operate. In this chapter we address this problem by presenting a modeling language tailored to analyze the problem of security at an organizational level. This language proposes a set of concepts founded on the notions of permission, delegation, and trust. The chapter also presents a semantics for these concepts, based on Datalog. A case study from the bank domain is employed to illustrate the proposed language.


Evaluating operating system vulnerability to memory errors

Reliability is of great concern to the scalability of extreme-scale systems. Of particular concern are soft errors in main memory, which are a leading cause of failures on current systems and are predicted to be the leading cause on future systems. While great effort has gone into designing algorithms and applications that can continue to make progress in the presence of these errors without restarting, the most critical software running on a node, the operating system (OS), is currently left relatively unprotected. OS resiliency is of particular importance because, though this software typically represents a small footprint of a compute node's physical memory, recent studies show more memory errors in this region of memory than the remainder of the system. In this paper, we investigate the soft error vulnerability of two operating systems used in current and future high-performance computing systems: Kitten, the lightweight kernel developed at Sandia National Laboratories, and CLE, a high-performance Linux-based operating system developed by Cray. For each of these platforms, we outline major structures and subsystems that are vulnerable to soft errors and describe methods that could be used to reconstruct damaged state. Our results show the Kitten lightweight operating system may be an easier target to harden against memory errors due to its smaller memory footprint, largely deterministic state, and simpler system structure.


Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Social interactions take place in environments that influence people’s behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a wavelet-based model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users’ textual content. We create the feature vector from the Discrete Wavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classification model using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: single theme and miscellaneous one.


Postmarketing Drug Safety Surveillance Using Publicly Available Health-Consumer-Contributed Content in Social Media

Postmarketing drug safety surveillance is important because many potential adverse drug reactions cannot be identified in the premarketing review process. It is reported that about 5% of hospital admissions are attributed to adverse drug reactions and many deaths are eventually caused, which is a serious concern in public health. Currently, drug safety detection relies heavily on voluntarily reporting system, electronic health records, or relevant databases. There is often a time delay before the reports are filed and only a small portion of adverse drug reactions experienced by health consumers are reported. Given the popularity of social media, many health social media sites are now available for health consumers to discuss any health-related issues, including adverse drug reactions they encounter. There is a large volume of health-consumer-contributed content available, but little effort has been made to harness this information for postmarketing drug safety surveillance to supplement the traditional approach. In this work, we propose the association rule mining approach to identify the association between a drug and an adverse drug reaction. We use the alerts posted by Food and Drug Administration as the gold standard to evaluate the effectiveness of our approach. The result shows that the performance of harnessing health-related social media content to detect adverse drug reaction is good and promising.


Single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp

A single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp is proposed. In the proposed converter, the operation principle is quite different from other boost bridgeless three-level converters. The charging stage is presented in the low side while the discharging stage in the high side and vice versa. As a result, nature voltage clamp is achieved. And the slow diodes are ‘shared’. They operate not only as rectifying diodes, but also as voltage clamping diodes. Besides, only single inductor is required and the device utilization factor is high. Besides, the common-mode(CM) noise is rather low. First, the operational principle are analyzed in detail. Then the performance evaluation and design consideration are presented. Finally, a 1kW experimental prototype is built. The result shows that the efficiency is above 94% at full load under 90V and the maximum efficiency is 98.4%.


WhatsApp network forensics: Decrypting and understanding the WhatsApp call signaling messages

WhatsApp is a widely adopted mobile messaging application with over 800 million users. Recently, a calling feature was added to the application and no comprehensive digital forensic analysis has been performed with regards to this feature at the time of writing this paper. In this work, we describe how we were able to decrypt the network traffic and obtain forensic artifacts that relate to this new calling feature which included the: a) WhatsApp phone numbers, b) WhatsApp server IPs, c) WhatsApp audio codec (Opus), d) WhatsApp call duration, and e) WhatsApp's call termination. We explain the methods and tools used to decrypt the traffic as well as thoroughly elaborate on our findings with respect to the WhatsApp signaling messages. Furthermore, we also provide the community with a tool that helps in the visualization of the WhatsApp protocol messages. © 2015 Elsevier Ltd. All rights reserved.


A survey of socially interactive robots

This paper reviews “socially interactive robots”: robots for which social human–robot interaction is important. We begin by discussing the context for socially interactive robots, emphasizing the relationship to other research fields and the different forms of “social robots”. We then present a taxonomy of design methods and system components used to build socially interactive robots. Finally, we describe the impact of these robots on humans and discuss open issues. An expanded version of this paper, which contains a survey and taxonomy of current applications, is available as a technical report [T. Fong, I. Nourbakhsh, K. Dautenhahn, A survey of socially interactive robots: concepts, design and applications, Technical Report No. CMU-RI-TR-02-29, Robotics Institute, Carnegie Mellon University, 2002]. © 2003 Elsevier Science B.V. All rights reserved.


Maximum Margin Projection Subspace Learning for Visual Data Analysis

Visual pattern recognition from images often involves dimensionality reduction as a key step to discover a lower dimensional image data representation and obtain a more manageable problem. Contrary to what is commonly practiced today in various recognition applications where dimensionality reduction and classification are independently treated, we propose a novel dimensionality reduction method appropriately combined with a classification algorithm. The proposed method called maximum margin projection pursuit, aims to identify a low dimensional projection subspace, where samples form classes that are better discriminated, i.e., are separated with maximum margin. The proposed method is an iterative alternate optimization algorithm that computes the maximum margin projections exploiting the separating hyperplanes obtained from training a support vector machine classifier in the identified low dimensional space. Experimental results on both artificial data, as well as, on popular databases for facial expression, face and object recognition verified the superiority of the proposed method against various state-of-the-art dimensionality reduction algorithms.


From Motion to Photons in 80 Microseconds: Towards Minimal Latency for Virtual and Augmented Reality

We describe an augmented reality, optical see-through display based on a DMD chip with an extremely fast (16 kHz) binary update rate. We combine the techniques of post-rendering 2-D offsets and just-in-time tracking updates with a novel modulation technique for turning binary pixels into perceived gray scale. These processing elements, implemented in an FPGA, are physically mounted along with the optical display elements in a head tracked rig through which users view synthetic imagery superimposed on their real environment. The combination of mechanical tracking at near-zero latency with reconfigurable display processing has given us a measured average of 80 μs of end-to-end latency (from head motion to change in photons from the display) and also a versatile test platform for extremely-low-latency display systems. We have used it to examine the trade-offs between image quality and cost (i.e. power and logical complexity) and have found that quality can be maintained with a fairly simple display modulation scheme.


A comprehensive study of hybrid neural network hidden Markov model for offline handwritten Chinese text recognition

This paper proposes an effective segmentation-free approach using a hybrid neural network hidden Markov model (NN-HMM) for offline handwritten Chinese text recognition (HCTR). In the general Bayesian framework, the handwritten Chinese text line is sequentially modeled by HMMs with each representing one character class, while the NN-based classifier is adopted to calculate the posterior probability of all HMM states. The key issues in feature extraction, character modeling, and language modeling are comprehensively investigated to show the effectiveness of NN-HMM framework for offline HCTR. First, a conventional deep neural network (DNN) architecture is studied with a well-designed feature extractor. As for the training procedure, the label refinement using forced alignment and the sequence training can yield significant gains on top of the frame-level cross-entropy criterion. Second, a deep convolutional neural network (DCNN) with automatically learned discriminative features demonstrates its superiority to DNN in the HMM framework. Moreover, to solve the challenging problem of distinguishing quite confusing classes due to the large vocabulary of Chinese characters, NN-based classifier should output 19900 HMM states as the classification units via a high-resolution modeling within each character. On the ICDAR 2013 competition task of CASIA-HWDB database, DNN-HMM yields a promising character error rate (CER) of 5.24% by making a good trade-off between the computational complexity and recognition accuracy. To the best of our knowledge, DCNN-HMM can achieve a best published CER of 3.53%.


Mobile robot programming using natural language

How will naive users program domestic robots? This paper describes the design of a practical system that uses natural language to teach a vision-based robot how to navigate in a miniature town. To enable unconstrained speech the robot is provided with a set of primitive procedures derived from a corpus of route instructions. When the user refers to a route that is not known to the robot, the system will learn it by combining primitives as instructed by the user. This paper describes the components of the Instruction Based Learning architecture and discusses issues of knowledge representation, the selection of primitives and the conversion of natural language into robot-understandable procedures.


Identifying Opposing Views in Online Discourse

Discourse in online media often takes the form of siloed discussions where unpopular views tend to get drowned out by the majority. This is especially true in platforms such as Reddit, where only the most popular opinions get visibility. In this work, we propose that this problem may be solved by building tools that surface opposing views and I advance a means for automatically identifying disagreeing views on Reddit.


An Active Pixel Sensor Fabricated Using CMOS / CCD Process Technology

Paul P. K. Lee, Russell C. Gee*, R. Michael Guidash, T-H. Lee, and Eric R. Fossum* Microelectronics Technology Division, Eastman Kodak Company 1669 Lake Avenue, Rochester, New York 14650-2008 Tel: (716) 477-2869, Fax: (716) 477-4947, Internet: ppklee@Kodak.COM *Jet Propulsion Laboratory, California Institute of Technology 4800 Oak Grove Drive, Pasadena, CA 91109 Abstract This paper describes the integration of the active pixel sensor (APS) architecture normally fabricated in conventional complementary metal oxide semiconductor (CMOS) technology with a pinned photodiode (PPD) device using a mixed process technology. This new technology allows mix and match of CMOS and high performance charged coupled device (CCD) modules. The PPD [1] becomes the photoactive element in an XYaddressable area array with each pixel containing active devices for the transfer, readout and reset functions. It is a standard photo-sensitive element, available in a high performance true two-phase CCD technology developed previously for CCD-based image sensors [2]. An n-well 2-μm CMOS technology was combined with the CCD process to provide the best features from both. A Design of Experiment approach was used with Technology Computer Aided Design (TCAD) tools to develop and optimize the new mixed process technology without sacrificing any CCD performance while minimizing impact to the CMOS device characteristics [3]. By replacing polysilicon photocapacitor or photogate in conventional APS with the pinned photodiode, deficiencies in poor blue response and high dark current are minimized [4].


Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior.

A set of criteria based upon biostatistical considerations for determining the interrater reliability of specific adaptive behavior items in a given setting was presented. The advantages and limitations of extant statistical assessment procedures were discussed. Also, a set of guidelines for differentiating type of adaptive behavior that are statistically reliable from those that are reliable in a clinical or practical sense was delineated. Data sets were presented throughout in order to illustrate the advantages of recommended statistical procedures over other available ones.


COID: A cluster–outlier iterative detection approach to multi-dimensional data analysis

Nowadays, most data mining algorithms focus on clustering methods alone. Also, there are a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat both clusters and outliers as concepts of the same importance in data analysis. In this paper, we present our continuous work on the cluster–outlier iterative detection algorithm (Shi in SubCOID: exploring cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace. Auburn, pp. 132–135, 2008; Shi and Zhang in Towards exploring interactive relationship between clusters and outliers in multi-dimensional data analysis. IEEE Computer Society. Tokyo, pp. 518–519, 2005) to detect the clusters and outliers in another perspective for noisy data sets. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields, such as pattern recognition, data clustering, and signal processing. Experimental results demonstrate the advantages of our approach.


Contextualized Sarcasm Detection on Twitter

Sarcasm requires some shared knowledge between speaker and audience; it is a profoundly contextual phenomenon. Most computational approaches to sarcasm detection, however, treat it as a purely linguistic matter, using information such as lexical cues and their corresponding sentiment as predictive features. We show that by including extra-linguistic information from the context of an utterance on Twitter – such as properties of the author, the audience and the immediate communicative environment – we are able to achieve gains in accuracy compared to purely linguistic features in the detection of this complex phenomenon, while also shedding light on features of interpersonal interaction that enable sarcasm in conversation.


Metamaterial advances for radar and communications

Metamaterial antennas have progressed considerably in the last few years. Kymeta expects to have its Ku-band metamaterial antenna commercially available in 2017. It is intended for use on vessels anywhere in the world for high speed internet using satellites. Echodyne and PARC, a Xerox Co., have developed a metamaterial array for radar and cell phone usage. The Army Research Laboratory in Adelphi MD has funded the development of a metamaterial 250–505 MHZ antenna with a λ/20 thickness. Complementing this a conventional tightly coupled dipole antenna (TCDA) has been developed which provides a 20∶1 bandwidth with a λ/40 thickness. Target cloaking has been demonstrated at microwaves using metamaterials. With cloaking the electromagnetic wave signal transmitted by a radar goes around the target making it invisible. It has also been demonstrated at L-band with a 50% bandwidth using fractals. Stealthing by absorption using a thin flexible and stretchable metamaterials sheet has been demonstrated to give 6 dB absorption over the band from 8–10 GHz, larger absorption over a narrower band. Using fractals material < 1 mm thick simulation gave a 10 dB reduction in backscatter from 2 to 20 GHz, ∼20 dB reduction from 9 to 15 GHz. Good results were obtained for large incidence angles and both polarizations. Using metamaterial one can focus 6× beyond diffraction limit at 0.38 µm (Moore's Law marches on); 40× diffraction limit, λ/80, at 375 MHz. Has been used in cell phones to provide antennas 5× smaller (1/10th λ) having 700 MHz–2.7 GHz bandwidth. Provided isolation between antennas having 2.5 cm separation equivalent to lm separation. Used for phased array wide angle impedance matching (WAIM). It has been found that n-doped graphene has a negative index of refraction.


Fast and flexible word searching on compressed text

We present a fast compression technique for natural language texts. The novelties are that (1) decompression of arbitrary portions of the text can be done very efficiently, (2) exact search for words and phrases can be done on the compressed text directly, using any known sequential pattern-matching algorithm, and (3) word-based approximate and extended search can also be done efficiently without any decoding. The compression scheme uses a semistatic word-based model and a Huffman code where the coding alphabet is byte-oriented rather than bit-oriented. We compress typical English texts to about 30% of their original size, against 40% and 35% for Compress and Gzip, respectively. Compression time is close to that of Compress and approximately half of the time of Gzip, and decompression time is lower than that of Gzip and one third of that of Compress. We present three algorithms to search the compressed text. They allow a large number of variations over the basic word and phrase search capability, such as sets of characters, arbitrary regular expressions, and approximate matching. Separators and stopwords can be discarded at search time without significantly increasing the cost. When searching for simple words, the experiments show that running our algorithms on a compressed text is twice as fast as running the best existing software on the uncompressed version of the same text. When searching complex or approximate patterns, our algorithms are up to 8 times faster than the search on uncompressed text. We also discuss the impact of our technique in inverted files pointing to logical blocks and argue for the possibility of keeping the text compressed all the time, decompressing only for displaying purposes.


Content-Aware Partial Compression for Big Textual Data Analysis Acceleration

Analysing text-based data has become increasingly important due to the importance of text from sources such as social media, web contents, web searches. The growing volume of such data creates challenges for data analysis including efficient and scalable algorithm, effective computing platforms and energy efficiency. Compression is a standard method for reducing data size but current standard compression algorithms are destructive to the organisation of data contents. This work introduces Content-aware, Partial Compression (CaPC) for text using a dictionary-based approach. We simply use shorter codes to replace strings while maintaining the original data format and structure, so that the compressed contents can be directly consumed by analytic platforms. We evaluate our approach with a set of real-world datasets and several classical MapReduce jobs on Hadoop. We also provide a supplementary utility library for Hadoop, hence, existing MapReduce programs can be used directly on the compressed datasets with little or no modification. In evaluation, we demonstrate that CaPC works well with a wide variety of data analysis scenarios, experimental results show ~30% average data size reduction, and up to ~32% performance increase on some I/O intensive jobs on an in-house Hadoop cluster. While the gains may seem modest, the point is that these gains are 'for free' and act as supplementary to all other optimizations.


An Unsupervised Aspect-Sentiment Model for Online Reviews

With the increase in popularity of online review sites comes a corresponding need for tools capable of extracting the information most important to the user from the plain text data. Due to the diversity in products and services being reviewed, supervised methods are often not practical. We present an unsupervised system for extracting aspects and determining sentiment in review text. The method is simple and flexible with regard to domain and language, and takes into account the influence of aspect on sentiment polarity, an issue largely ignored in previous literature. We demonstrate its effectiveness on both component tasks, where it achieves similar results to more complex semi-supervised methods that are restricted by their reliance on manual annotation and extensive knowledge sources.


Learning Attitudes and Attributes from Multi-aspect Reviews

Most online reviews consist of plain-text feedback together with a single numeric score. However, understanding the multiple `aspects' that contribute to users' ratings may help us to better understand their individual preferences. For example, a user's impression of an audio book presumably depends on aspects such as the story and the narrator, and knowing their opinions on these aspects may help us to recommend better products. In this paper, we build models for rating systems in which such dimensions are explicit, in the sense that users leave separate ratings for each aspect of a product. By introducing new corpora consisting of five million reviews, rated with between three and six aspects, we evaluate our models on three prediction tasks: First, we uncover which parts of a review discuss which of the rated aspects. Second, we summarize reviews by finding the sentences that best explain a user's rating. Finally, since aspect ratings are optional in many of the datasets we consider, we recover ratings that are missing from a user's evaluation. Our model matches state-of-the-art approaches on existing small-scale datasets, while scaling to the real-world datasets we introduce. Moreover, our model is able to `disentangle' content and sentiment words: we automatically learn content words that are indicative of a particular aspect as well as the aspect-specific sentiment words that are indicative of a particular rating.


Workload characterization of JVM languages

Being developed with a single language in mind, namely Java, the Java Virtual Machine (JVM) nowadays is targeted by numerous programming languages. Automatic memory management, Just-In-Time (JIT) compilation, and adaptive optimizations provided by the JVM make it an attractive target for different language implementations. Even though being targeted by so many languages, the JVM has been tuned with respect to characteristics of Java programs only – different heuristics for the garbage collector or compiler optimizations are focused more on Java programs. In this dissertation, we aim at contributing to the understanding of the workloads imposed on the JVM by both dynamically-typed and statically-typed JVM languages. We introduce a new set of dynamic metrics and an easy-to-use toolchain for collecting the latter. We apply our toolchain to applications written in six JVM languages – Java, Scala, Clojure, Jython, JRuby, and JavaScript. We identify differences and commonalities between the examined languages and discuss their implications. Moreover, we have a close look at one of the most efficient compiler optimizations – method inlining. We present the decision tree of the HotSpot JVM’s JIT compiler and analyze how well the JVM performs in inlining the workloads written in different JVM languages.


Elderly people at home: technological help in everyday activities

The aim of this paper is to understand to what extent elderly people are likely to accept a technological aid in performing everyday activities. In this perspective, the present research focused on elderly people's strategies in performing everyday activities at home, in order to understand in what domestic domains technology can be considered an acceptable help. We administered a questionnaire focusing on preferred strategies in carrying out common domestic tasks, and on attitudes towards new technologies and home modification to a sample of 123 elderly people living in Rome. Results show that the adoption of a strategy, including the introduction of technological devices, is highly problem-specific, while personal factors are relevant only in particular situations. With increasing age, people are more inclined to give up, and higher educational levels correspond to more frequent technological solutions.


Remote Sensing and Geospatial Technological Applications for Site-specific Management of Fruit and Nut Crops: A Review

Site-specific crop management (SSCM) is one facet of precision agriculture which is helping increase production with minimal input. It has enhanced the cost-benefit scenario in crop production. Even though the SSCM is very widely used in row crop agriculture like corn, wheat, rice, soybean, etc. it has very little application in cash crops like fruit and nut. The main goal of this review paper was to conduct a comprehensive review of advanced technologies, including geospatial technologies, used in site-specific management of fruit and nut crops. The review explores various remote sensing data from different platforms like satellite, LIDAR, aerial, and field imaging. The study analyzes the use of satellite sensors, such as Quickbird, Landsat, SPOT, and IRS imagery as well as hyperspectral narrow-band remote sensing data in study of fruit and nut crops in blueberry, citrus, peach, apple, etc. The study also explores other geospatial technologies such as GPS, GIS spatial modeling, advanced image processing techniques, and information technology for suitability study, orchard delineation, and classification accuracy assessment. The study also provides an example of a geospatial model developed in ArcGIS ModelBuilder to automate the blueberry production suitability analysis. The GIS spatial model is developed using various crop characteristics such as chilling hours, soil permeability, drainage, and pH, and land cover to determine the best sites for growing OPEN ACCESS Remote Sensing 2010, 2 1974 blueberry in Georgia, U.S. The study also provides a list of spectral reflectance curves developed for some fruit and nut crops, blueberry, crowberry, redblush citrus, orange, prickly pear, and peach. The study also explains these curves in detail to help researchers choose the image platform, sensor, and spectrum wavelength for various fruit and nut crops SSCM.


A biomimetic soft fingertip applicable to haptic feedback systems for texture identification

Humans recognize textures using the tactile data obtained from the human somatosensory system. Recognition of textures allows humans discriminate objects and materials. Moreover, by understanding the object's or material's texture, the human intuitively estimates roughness and the friction properties of the object or the material. This ability is necessary for object manipulative tasks. Likewise artificial haptic systems too, should have the ability to encode textures and feedback those data to haptic applications such as haptic displays. In this paper a biomimetic soft fingertip sensor that can be used in above haptic systems is introduced. The fingertip has the ability to detect force and vibration modalities. We propose three features calculated from the covariance signal of two adjacent accelerometers in the fingertip to use in texture identification. The covariance signal is transformed using Discrete Wavelet Transform (DWT) and the three features mentioned below are calculated. The mean and variance of the approximate signal, and the energies of the detailed signal are chosen as features. Then, the proposed features were validate by using those in an Artificial Neural Network (ANN) to classify seven wood samples. The results showed a 65% success rate in classifying wood samples and that the proposed features are acceptable to encode textures.


Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision

Neural networks have achieved state-ofthe-art performance on several structuredoutput prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural networkbased framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.


A benchmark for semantic image segmentation

Though quite a few image segmentation benchmark datasets have been constructed, there is no suitable benchmark for semantic image segmentation. In this paper, we construct a benchmark for such a purpose, where the ground-truths are generated by leveraging the existing fine granular ground-truths in Berkeley Segmentation Dataset (BSD) as well as using an interactive segmentation tool for new images. We also propose a percept-tree-based region merging strategy for dynamically adapting the ground-truth for evaluating test segmentation. Moreover, we propose a new evaluation metric that is easy to understand and compute, and does not require boundary matching. Experimental results show that, compared with BSD, the generated ground-truth dataset is more suitable for evaluating semantic image segmentation, and the conducted user study demonstrates that the proposed evaluation metric matches user ranking very well.


Multi-Feature Based Emotion Recognition for Video Clips

In this paper, we present our latest progress in Emotion Recognition techniques, which combines acoustic features and facial features in both non-temporal and temporal mode. This paper presents the details of our techniques used in the Audio-Video Emotion Recognition subtask in the 2018 Emotion Recognition in the Wild (EmotiW) Challenge. After the multimodal results fusion, our final accuracy in Acted Facial Expression in Wild (AFEW) test dataset achieves 61.87%, which is 1.53% higher than the best results last year. Such improvements prove the effectiveness of our methods.


Affective state detection via facial expression analysis within a human-computer interaction context

The advancement in technology indicates that there is an opportunity to enhance Human-Computer Interaction by way of affective state recognition. Affective state recognition is typically based on passive stimuli such as watching video clips, which does not reflect genuine interaction. This paper presents a study on affective state recognition using active stimuli, i.e. facial expressions of users when they attempt computerised tasks, particularly across typical usage of computer systems. A data collection experiment is presented for acquiring data from normal users whilst they interact with software, attempting to complete a set of predefined tasks. In addition, a hierarchical machine learning approach is presented for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customised encoding for users’ self-reported affective states. Consequently, the aim is to find the potential relationship between the facial expressions, as defined by Paul Ekman, and the self-reported emotional states specified by users using Russells Circumplex model, in relation to the actual feelings and affective states. The main findings of this study suggest that facial expressions cannot precisely reveal the actual feelings of users whilst interacting with common computerised tasks. Moreover, during active interaction tasks more variation occurs within the facial expressions of participants than occurs within passive interaction.


Cultural difference and adaptation of communication styles in computer-mediated group brainstorming

Supporting creativity via collaborative group brainstorming is a prevalent practice in organizations. Today's technology makes it easy for international and intercultural group members to brainstorm together remotely, but surprisingly little is known about how culture and medium shape the underlying brainstorming process. In a laboratory study, we examined the influences of individual cultural background (American versus Chinese), group cultural composition (same- versus mixed-culture groups), and communication medium (text-only versus video-enabled chatrooms) on group brainstorming conversations. Cultural differences and adaptation in conversational talkativeness and responsiveness were identified. The text-only medium reduced cultural differences in talkativeness. Working in a mixed-culture group led to cultural adaptation in the communication style of Chinese but not American participants. We discuss implications for international group brainstorming.


Gender identity disorder in twins: a review of the case report literature.

INTRODUCTION
The etiology of gender identity disorder (GID) remains largely unknown. In recent literature, increased attention has been attributed to possible biological factors in addition to psychological variables.

AIM
To review the current literature on case studies of twins concordant or discordant for GID.

METHODS
A systematic, comprehensive literature review.

RESULTS
Of 23 monozygotic female and male twins, nine (39.1%) were concordant for GID; in contrast, none of the 21 same-sex dizygotic female and male twins were concordant for GID, a statistically significant difference (P=0.005). Of the seven opposite-sex twins, all were discordant for GID.

CONCLUSIONS
These findings suggest a role for genetic factors in the development of GID.


Improving the Reliability of Series Resonant Inverters for Induction Heating Applications

This paper analyzes a high-power (100-kW) high-frequency (50-kHz) voltage-fed inverter with a series resonant load circuit for industrial induction heating applications which is characterized by a full-bridge inverter composed of isolated-gate bipolar transistors and a new power control based on phase-shift (PS) control. This power control circuit incorporates a load-adaptive variable-frequency controller and automated blanking time management in order to allow the inverter to work in zero-voltage switching for all output power levels and load conditions. An important improvement of the inverter reliability is achieved by choosing an appropriate and novel switching sequence for the PS inverter. The results are verified experimentally using a prototype for induction hardening applications. A comparative study between the proposed and standard PS power control will be made.


Applying PCA for Traffic Anomaly Detection: Problems and Solutions

Spatial Principal Component Analysis (PCA) has been proposed for network-wide anomaly detection. A recent work has shown that PCA is very sensitive to calibration settings, unfortunately, the authors did not provide further explanations for this observation. In this paper, we fill this gap and provide the reasoning behind the found discrepancies. First, we revisit PCA for anomaly detection and evaluate its performance on our data. We develop a slightly modified version of PCA that uses only data from a single router. Instead of correlating data across different spatial measurement points, we correlate the data across different metrics. With the help of the analyzed data, we explain the pitfalls of PCA and underline our argumentation with measurement results. We show that the main problems that make PCA difficult to apply are (i) the temporal correlation in the data; (ii) the non-stationarity of the data; and (iii) the difficulty about choosing the right number of components. Moreover, we propose a solution to deal with the most dominant problem, the temporal correlation in data. We find that when we consider temporal correlation, PCA detection results are significantly improved.


You Can Touch This: Eleven Years and 258218 Images of Objects

Touch has become a central input modality for a wide variety of interactive devices, most of our mobile devices are operated using touch. In addition to interacting with digital artifacts, people touch and interact with many other objects in their daily lives. We provide a unique photo dataset containing all touched objects over the last 11 years. All photos were contributed by Alberto Frigo, who was involved early on in the "Quantified Self" movement. He takes photos of every object he touches with his dominant hand. We analyzed the 258,218 images with respect to the types objects, their distribution, and related activities.


Business Intelligence and Analytics Education, and Program Development: A Unique Opportunity for the Information Systems Discipline

“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.


3D printing with polymers: Challenges among expanding options and opportunities.

OBJECTIVES
Additive manufacturing, which is more colloquially referred to as 3D printing, is quickly approaching mainstream adoption as a highly flexible processing technique that can be applied to plastic, metal, ceramic, concrete and other building materials. However, taking advantage of the tremendous versatility associated with in situ photopolymerization as well as the ability to select from a variety of preformed processible polymers, 3D printing predominantly targets the production of polymeric parts and models. The goal of this review is to connect the various additive manufacturing techniques with the monomeric and polymeric materials they use while highlighting emerging material-based developments.

METHODS
Modern additive manufacturing technology was introduced approximately three decades ago but this review compiles recent peer-reviewed literature reports to demonstrate the evolution underway with respect to the various building techniques that differ significantly in approach as well as the new variations in polymer-based materials being employed.

RESULTS
Recent growth of 3D printing has been dramatic and the ability of the various platform technologies to expand from rapid production prototypic models to the greater volume of readily customizable production of working parts is critical for continued high growth rates. This transition to working part production is highly dependent on adapting materials that deliver not only the requisite design accuracy but also the physical and mechanical properties necessary for the application.

SIGNIFICANCE
With the weighty distinction of being called the next industrial revolution, 3D printing technologies is already altering many industrial and academic operations including changing models for future healthcare delivery in medicine and dentistry.


Facebook groups as LMS: A case study

This paper describes a pilot study in using Facebook as an alternative to a learning management system (LMS). The paper reviews the current research on the use of Facebook in academia and analyzes the differences between a Facebook group and a regular LMS. The paper reports on a precedent-setting attempt to use a Facebook group as a course Web site, serving as a platform for delivering content and maintaining interactions among the students and between the students and the instructor. The paper presents findings from the students’ self-assessments and reflections on their experience. The students expressed satisfaction with learning in Facebook and willingness to continue using these groups in future courses.


Multi-resolution texture analysis for fingerprint based age-group estimation

In this paper the possibility of using digital fingerprints to estimate age-groups of human being, particularly children is investigated. To our knowledge, age-group estimation in humans, using digital fingerprints have not been addressed formally. Age-group estimation can be applied in many areas like on-line child protection, access control and customized internet services etc. Motivated by the fact that human digital fingerprint vary in texture as the person ages, a multi-resolution texture approach for automatic age-group estimation has been presented in this paper. Three standard classifiers were used to judge the accuracy of the proposed method. In the process of this research study, a novel method for digital fingerprint reference point generation was developed, which provides reference point for very poor quality images also. The proposed reference point generation method is compared with core-point method using FG-NET DB1 dataset. Experimental results proves that a digital fingerprint can be used to identify age-groups, particularly children. A classification accuracy of 80 percent was achieved for children below the age of 14 by using the aforesaid method.


Scaled Agile Framework: Presentation and real world example

This case focuses on the applicability of the Scaled Agile Framework (SAFe) founded by Dean Leffingwell. Modern organizations often work with agile software engineering teams using traditional single team-level methods like Scrum, but multiple teams and the program or portfolio level are not part of methods like Scrum. SAFe tries to apply agile methodologies to the whole organisation. The real world example focuses on a key element of SAFe, the Program Increment (PI) planning meeting and how it can improve multiple team collaboration.


Case Study On Social Engineering Techniques for Persuasion

T There are plenty of security software in market; each claiming the best, still we daily face problem of viruses and other malicious activities. If we know the basic working principal of such malware then we can very easily prevent most of them even without security software. Hackers and crackers are experts in psychology to manipulate people into giving them access or the information necessary to get access. This paper discusses the inner working of such attacks. Case study of Spyware is provided. In this case study, we got 100% success using social engineering techniques for deception on Linux operating system, which is considered as the most secure operating system. Few basic principal of defend, for the individual as well as for the organization, are discussed here, which will prevent most of such attack if followed.


Enhancing linear system theory curriculum with an inverted pendulum robot

The demands on both delivery methodology and content of control curriculum continue to evolve as more applications in our world incorporate control theory and the need for continuing education increases. Not only has the content evolved, but the application of social-behavioral science research has resulted in cooperative and active learning practices by faculty. In response to these shifts in education, an open-source inverted pendulum robot was used in a linear systems theory (LST) class taught as part of a professional master's program (PMP). The robot had to have a low cost, and enough capability to enable the students to explore and test the ideas presented in lecture and to engage in collaborative learning experiences. This paper discusses the robot, describes the key control theory experiments, and reviews the lessons learned from this experience.


Evaluating a tool for improving accessibility to charts and graphs

We discuss factors in the design and evaluation of natural language-driven assistive technologies that generate descriptions of, and allow interaction with, graphical representations of numerical data. In particular, we provide data in favor of 1) screen-reading technologies as a usable, useful, and cost-effective means of interacting with graphs. The data also show that by carrying out evaluation of Assistive Technologies on populations other than the target communities, certain subtleties of navigation and interaction may be lost or distorted.


Preventing Location-Based Identity Inference in Anonymous Spatial Queries

The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user identities. Nevertheless, the query contents may disclose the physical location of the user. In this paper, we present a framework for preventing location-based identity inference of users who issue spatial queries to location-based services. We propose transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source. Our methods optimize the entire process of anonymizing the requests and processing the transformed spatial queries. Extensive experimental studies suggest that the proposed techniques are applicable to real-life scenarios with numerous mobile users.


MobiHide: A Mobilea Peer-to-Peer System for Anonymous Location-Based Queries

Modern mobile phones and PDAs are equipped with positioning capabilities (e.g., GPS). Users can access public location-based services (e.g., Google Maps) and ask spatial queries. Although communication is encrypted, privacy and confidentiality remain major concerns, since the queries may disclose the location and identity of the user. Commonly, spatial K-anonymity is employed to hide the query initiator among a group of K users. However, existing work either fails to guarantee privacy, or exhibits unacceptably long response time. In this paper we propose MobiHide, a Peer-to-Peer system for anonymous location-based queries, which addresses these problems. MobiHide employs the Hilbert space-filling curve to map the 2-D locations of mobile users to 1-D space. The transformed locations are indexed by a Chord-based distributed hash table, which is formed by the mobile devices. The resulting Peer-to-Peer system is used to anonymize a query by mapping it to a random group of K users that are consecutive in the 1-D space. Compared to existing state-of-the-art, MobiHide does not provide theoretical anonymity guarantees for skewed query distributions. Nevertheless, it achieves strong anonymity in practice, and it eliminates system hotspots. Our experimental evaluation shows that MobiHide has good load balancing and fault tolerance properties, and is applicable to real-life scenarios with numerous mobile users.


Location Privacy in Mobile Systems: A Personalized Anonymization Model

This paper describes a personalized k-anonymity model for protecting location privacy against various privacy threats through location information sharing. Our model has two unique features. First, we provide a unified privacy personalization framework to support location k-anonymity for a wide range of users with context-sensitive personalized privacy requirements. This framework enables each mobile node to specify the minimum level of anonymity it desires as well as the maximum temporal and spatial resolutions it is willing to tolerate when requesting for k-anonymity preserving location-based services (LBSs). Second, we devise an efficient message perturbation engine which runs by the location protection broker on a trusted server and performs location anonymization on mobile users' LBS request messages, such as identity removal and spatio-temporal cloaking of location information. We develop a suite of scalable and yet efficient spatio-temporal cloaking algorithms, called CliqueCloak algorithms, to provide high quality personalized location k-anonymity, aiming at avoiding or reducing known location privacy threats before forwarding requests to LBS provider(s). The effectiveness of our CliqueCloak algorithms is studied under various conditions using realistic location data synthetically generated using real road maps and traffic volume data


User needs for location-aware mobile services

Mobile contexts of use vary a lot, and may even be continuously changing during use. The context is much more than location, but its other elements are still difficult to identify or measure. Location information is becoming an integral part of different mobile devices. Current mobile services can be enhanced with location-aware features, thus providing the user with a smooth transition towards context-aware services. Potential application fields can be found in areas such as travel information, shopping, entertainment, event information and different mobile professions. This paper studies location-aware mobile services from the user's point of view. The paper draws conclusions about key issues related to user needs, based on user interviews, laboratory and field evaluations with users, and expert evaluations of location-aware services. The user needs are presented under five main themes: topical and comprehensive contents, smooth user interaction, personal and user-generated contents, seamless service entities and privacy issues.


A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them

The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that “classical” flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.


Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling

Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.


Sign language recognition with recurrent neural network using human keypoint detection

We study the sign language recognition problem which is to translate the meaning of signs from visual input such as videos. It is well-known that many problems in the field of computer vision require a huge amount of dataset to train deep neural network models. We introduce the KETI sign language dataset which consists of 10,480 videos of high resolution and quality. Since different sign languages are used in different countries, the KETI sign language dataset can be the starting line for further research on the Korean sign language recognition.
 Using the sign language dataset, we develop a sign language recognition system by utilizing the human keypoints extracted from face, hand, and body parts. The extracted human keypoint vector is standardized by the mean and standard deviation of the keypoints and used as input to recurrent neural network (RNN). We show that our sign recognition system is robust even when the size of training data is not sufficient. Our system shows 89.5% classification accuracy for 100 sentences that can be used in emergency situations.


Enhancement of Images Using Histogram Processing Techniques

Image enhancement is a mean as the improvement of an image appearance by increasing dominance of some features or by decreasing ambiguity between different regions of the image. Image enhancement processes consist of a collection of techniques that seek to improve the visual appearance of an image or to convert the image to a form better suited for analysis by a human or machine. Many images such as medical images, remote sensing images, electron microscopy images and even real life photographic pictures, suffer from poor contrast. Therefore it is necessary to enhance the contrast.The purpose of image enhancement methods is to increase image visibility and details. Enhanced image provide clear image to eyes or assist feature extraction processing in computer vision system. Numerous enhancement methods have been proposed but the enhancement efficiency, computational requirements, noise amplification, user intervention, and application suitability are the common factors to be considered when choosing from these different methods for specific image processing application.


Insights into Alzheimer disease pathogenesis from studies in transgenic animal models

Alzheimer disease is the most common cause of dementia among the elderly, accounting for ~60-70% of all cases of dementia. The neuropathological hallmarks of Alzheimer disease are senile plaques (mainly containing p-amyloid peptide derived from amyloid precursor protein) and neurofibrillary tangles (containing hyperphosphorylated Tau protein), along with neuronal loss. At present there is no effective treatment for Alzheimer disease. Given the prevalence and poor prognosis of the disease, the development of animal models has been a research priority to understand pathogenic mechanisms and to test therapeutic strategies. Most cases of Alzheimer disease occur sporadically in people over 65 years old, and are not genetically inherited. Roughly 5% of patients with Alzheimer disease have familial Alzheimer disease--that is, related to a genetic predisposition, including mutations in the amyloid precursor protein, presenilin 1, and presenilin 2 genes. The discovery of genes for familial Alzheimer disease has allowed transgenic models to be generated through the overexpression of the amyloid precursor protein and/or presenilins harboring one or several mutations found in familial Alzheimer disease. Although none of these models fully replicates the human disease, they have provided valuable insights into disease mechanisms as well as opportunities to test therapeutic approaches. This review describes the main transgenic mouse models of Alzheimer disease which have been adopted in Alzheimer disease research, and discusses the insights into Alzheimer disease pathogenesis from studies in such models. In summary, the Alzheimer disease mouse models have been the key to understanding the roles of soluble b-amyloid oligomers in disease pathogenesis, as well as of the relationship between p-amyloid and Tau pathologies.


Centrality based Document Ranking

In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially, Lucene is used to retrieve top fifty documents that are relevant to the query and then our proposed approach is applied on these retrieved documents to rerank them. Experimental results show that our approach did not perform well as the documents retrieved by Lucene are not among the top 50 documents in the Gold Standard.


Proceso de Valoración para la Mejora de Procesos Software en Pequeñas Organizaciones

1 Grupo IDIS Facultad de Ingeniería Electrónica y Telecomunicaciones Universidad del Cauca Calle 5 No. 4 – 70. Popayán, Cauca, Colombia. fjpino@unicauca.edu.co. Web: http://www.unicauca.edu.co/idis/ 2 Grupo Alarcos Escuela Superior de Informática Universidad Castilla-La Mancha Paseo de la Universidad 4, Ciudad Real, España. {Felix.Garcia, Mario.Piattini}@uclm.es. Web: http://alarcos.inf-cr.uclm.es/


Exploiting Structure in Policy Construction

Markov decision processes (MDPs) have recently been appbed to the problem of modeling decisiontheoretic planning While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable We present an algorithm, called structured policy Iteration (SPI), that con structs optimal policies without explicit enumera tion of the state space The algorithm retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but ex ploits the variable and prepositional independencies reflected in a temporal Bayesian network represen tation of MDPs The principles behind SPI can be applied to any structured representation of stochas tic actions, pobcies and value functions, and the algorithm itself can be used in conjunction with re cent approximation methods