title: Characteristics of Pteroptyx Firefly Congregations in a Human Dominated Habitat

abstract: Riverbanks in Thailand are home to two fireflies, Pteroptyx malaccae and Pteroptyx valida, which form massive mixed-species mating congregations. In conservation, both represent flagship species that attract much public attention. However, their populations have become sparse. With the increasing spread of urbanization, more information is needed on whether and how the fireflies are able to adapt to such new environments. 24 population surveys were conducted across 13 months at a park surrounded by an urban area in Samut Prakan Province to record their pattern of occurrence. The results showed a year-round presence with high abundance around Jul-Oct, corresponding to local rainy season. On average, the park housed 7 ± 3 congregations with a total of 488 ± 274 displaying males (Mean ± SD, Max = 1281, Min = 111, N = 24). Congregation sizes ranged from 5 to 560 males with an average of 70 (N = 172) and the ratio of P. malaccae to P. valida around 3:1. The distribution pattern was dominated by a few very large and long-lasting congregations that contained most of the males in the area. Location-wise, these large congregations tended to occur at certain “hotspots”, which may aid in flash visibility. Theoretical comparison to hilltopping and other lekking insects was discussed. In terms of plant species, the fireflies most often utilized Sonneratia caseolaris as congregation platform which is consistent with many other areas. However, exotic ornamental plants such as Terminalia catappa were also frequently used here, especially during the rainy season. The behavioral adaptability may aid conservation efforts.


title: A Unified Data Model for Representing Multimedia, Timeline, and Simulation Data

abstract: This paper describes a unified data model that represents multimedia, timeline, and simulation data utilizing a single set of related data modeling constructs. A uniform model for multimedia types structures image, sound, video, and long text data in a consistent way, giving multimedia schemas and queries a degree of data independence even for these complex data types. Information that possesses an intrinsic temporal element can all be represented using a construct called a stream. Streams can be aggregated into parallel multistreams, thus providing a structure for viewing multiple sets of time-based information. The unified stream construct permits real-time measurements, numerical simulation data, and visualizations of that data to be aggregated and manipulated using the same set of operators. Prototypes based on the model have been implemented for two medical application domains: thoracic oncology and thermal ablation therapy of brain tumors. Sample schemas, queries, and screenshots from these domains are provided. Finally, a set of examples is included for an accompanying visual query language discussed in detail in another document.


title: Ridged Horn Antenna With Adjustable Metallic Grid Sidewalls and Cross-Shaped Back Cavity

abstract: This letter proposes a new ridged horn antenna with adjustable metallic grid sidewalls and a cross-shaped back cavity. An improved design of the coaxial-line-to-ridged-waveguide transition is introduced to improve the pattern behavior and return-loss performance of the horn antenna across the entire frequency band. The measurement results prove that there are no distortions at higher frequencies. The frequency range of this horn antenna is 0.8-20 GHz, and this design exhibits improved performance in terms of gain, VSWR, and radiation pattern.


title: Time series forecasting by evolving artificial neural networks with genetic algorithms, differential evolution and estimation of distribution algorithm

abstract: Time series forecasting is an important tool to support both individual and organizational decisions (e.g. planning production resources). In recent years, a large literature has evolved on the use of evolutionary artificial neural networks (EANN) in many forecasting applications. Evolving neural networks are particularly appealing because of their ability to model an unspecified nonlinear relationship between time series variables. In this work, two new approaches of a previous system, automatic design of artificial neural networks (ADANN) applied to forecast time series, are tackled. In ADANN, the automatic process to design artificial neural networks was carried out by a genetic algorithm (GA). This paper evaluates three methods to evolve neural networks architectures, one carried out with genetic algorithm, a second one carried out with differential evolution algorithm (DE) and the last one using estimation of distribution algorithms (EDA). A comparative study among these three methods with a set of referenced time series will be shown. In this paper, we also compare ADANN forecasting ability against a forecasting tool called Forecast Pro® (FP) software, using five benchmark time series. The object of this study is to try to improve the final forecasting getting an accurate system.


title: Modeling and querying data in NoSQL databases

abstract: Relational databases are providing storage for several decades now. However for today's interactive web and mobile applications the importance of flexibility and scalability in data model can not be over-stated. The term NoSQL broadly covers all non-relational databases that provide schema-less and scalable model. NoSQL databases which are also termed as Internetage databases are currently being used by Google, Amazon, Facebook and many other major organizations operating in the era of Web 2.0. Different classes of NoSQL databases namely key-value pair, document, column-oriented and graph databases enable programmers to model the data closer to the format as used in their application. In this paper, data modeling and query syntax of relational and some classes of NoSQL databases have been explained with the help of an case study of a news website like Slashdot.


title: Formal Concept Analysis and Tag Recommendations in Collaborative Tagging Systems

abstract: One of the most noticeable innovations that emerged with the advent of the Web 2.0 and the focal point of this publication are collaborative tagging systems. They allow users to annotate arbitrary resources with freely chosen keywords: so called tags. The tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for the user. The conceptual structure underlying collaborative tagging systems is called 'folksonomy'. In this book a new data mining task - the mining of all frequent tri-concepts - is presented, together with an efficient algorithm for discovering such implicit shared conceptualizations. Collaborative tagging systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource. Several recommendation algorithms on large-scale real-world datasets are compared: an adaptation of user-based Collaborative Filtering, a graph-based recommender and simpler methods based on tag co-occurences. The social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind, has been developed by the research group of the author. This publication introduces BibSonomy as an exemplary collaborative tagging system and gives an overview of its architecture and some of its features.


title: Hodgkin disease in the sinonasal region detected by FDG PET.

abstract: Hodgkin disease rarely involves the sinonasal region. The authors present such a case demonstrated by FDG PET imaging and confirmed by biopsy and subsequent pathologic examination.


title: SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control

abstract: In this work, we present an approach to deep visuomotor control using structured deep dynamics models. Our deep dynamics model, a variant of SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an encoder-decoder structure. Unlike prior work, our dynamics model is structured: given an input scene, our network explicitly learns to segment salient parts and predict their pose-embedding along with their motion modeled as a change in the pose space due to the applied actions. We train our model using a pair of point clouds separated by an action and show that given supervision only in the form of point-wise data associations between the frames our network is able to learn a meaningful segmentation of the scene along with consistent poses. We further show that our model can be used for closed-loop control directly in the learned low-dimensional pose space, where the actions are computed by minimizing error in the pose space using gradient-based methods, similar to traditional model-based control. We present results on controlling a Baxter robot from raw depth data in simulation and in the real world and compare against two baseline deep networks. Our method runs in real-time, achieves good prediction of scene dynamics and outperforms the baseline methods on multiple control runs. Video results can be found at: this https URL


title: The financial impact of health information exchange on emergency department care

abstract: Objective To examine the financial impact health information exchange (HIE) in emergency departments (EDs). Materials and Methods We studied all ED encounters over a 13-month period in which HIE data were accessed in all major emergency departments Memphis, Tennessee. HIE access encounter records were matched with similar encounter records without HIE access. Outcomes studied were ED-originated hospital admissions, admissions for observation, laboratory testing, head CT, body CT, ankle radiographs, chest radiographs, and echocardiograms. Our estimates employed generalized estimating equations for logistic regression models adjusted for admission type, length of stay, and Charlson co-morbidity index. Marginal probabilities were used to calculate changes in outcome variables and their financial consequences. Results HIE data were accessed in approximately 6.8% of ED visits across 12 EDs studied. In 11 EDs directly accessing HIE data only through a secure Web browser, access was associated with a decrease in hospital admissions (adjusted odds ratio (OR)¼0.27; p<0001). In a 12th ED relying more on print summaries, HIE access was associated with a decrease in hospital admissions (OR¼0.48; p<0001) and statistically significant decreases in head CT use, body CT use, and laboratory test ordering. Discussion Applied only to the study population, HIE access was associated with an annual cost savings of $1.9 million. Net of annual operating costs, HIE access reduced overall costs by $1.07 million. Hospital admission reductions accounted for 97.6% of total cost reductions. Conclusion Access to additional clinical data through HIE in emergency department settings is associated with net societal saving.


title: Efficient Design and Decoding of Polar Codes

abstract: Polar codes are shown to be instances of both generalized concatenated codes and multilevel codes. It is shown that the performance of a polar code can be improved by representing it as a multilevel code and applying the multistage decoding algorithm with maximum likelihood decoding of outer codes. Additional performance improvement is obtained by replacing polar outer codes with other ones with better error correction performance. In some cases this also results in complexity reduction. It is shown that Gaussian approximation for density evolution enables one to accurately predict the performance of polar codes and concatenated codes based on them.


title: Reviews Quantitative geography: perspectives on spatial data analysis by A S Fotheringham,

abstract: Establishing the Boundaries Spatial Data The Role of Geographic Information Systems Exploring Spatial Data Visually Local Analysis Point Pattern Analysis Spatial Regression and Geostatistical Models Statistical Inference for Spatial Data Spatial Modelling and the Evolution of Spatial Theory Challenges in Spatial Data Analysis


title: A Low-Cost and High-Gain 60-GHz Differential Phased Array Antenna in PCB Process

abstract: A broadband, low-cost, high-gain, multilayer differential antenna without air cavity is proposed for the 60-GHz phased array system. By using the standard low-cost printed circuit board process, a 60-GHz phased array antenna element and a 16-element phased array antenna prototype are implemented. To improve the bandwidth and antenna gain, a superstrate and a float rectangular ring are designed. The float ring can enhance the antenna vertical radiation, suppressing the antenna horizontal radiation and reducing the mutual coupling. Without any embedded air cavity, the proposed antenna is robust against the process variation, which is verified in the measurement. For the antenna element, the measured results demonstrate a peak gain of 8.9 dBi and a 10-dB return-loss bandwidth of more than 12 GHz, and the simulated radiation efficiency is above 90% at 60-GHz band. For the phased array antenna, two beam-fixed (0° and 30°) array antennas are tested to verify the beam scanning performance, which achieve gains of 16.3 and 13.6 dBi, respectively.


title: EEG classification of driver mental states by deep learning

abstract: Driver fatigue is attracting more and more attention, as it is the main cause of traffic accidents, which bring great harm to society and families. This paper proposes to use deep convolutional neural networks, and deep residual learning, to predict the mental states of drivers from electroencephalography (EEG) signals. Accordingly we have developed two mental state classification models called EEG-Conv and EEG-Conv-R. Tested on intra- and inter-subject, our results show that both models outperform the traditional LSTM- and SVM-based classifiers. Our major findings include (1) Both EEG-Conv and EEG-Conv-R yield very good classification performance for mental state prediction; (2) EEG-Conv-R is more suitable for inter-subject mental state prediction; (3) EEG-Conv-R converges more quickly than EEG-Conv. In summary, our proposed classifiers have better predictive power and are promising for application in practical brain-computer interaction .


title: Robust Deep Multi-modal Learning Based on Gated Information Fusion Network

abstract: The goal of multi-modal learning is to use complimentary information on the relevant task provided by the multiple modalities to achieve reliable and robust performance. Recently, deep learning has led significant improvement in multi-modal learning by allowing for the information fusion in the intermediate feature levels. This paper addresses a problem of designing robust deep multi-modal learning architecture in the presence of imperfect modalities. We introduce deep fusion architecture for object detection which processes each modality using the separate convolutional neural network (CNN) and constructs the joint feature map by combining the intermediate features from the CNNs. In order to facilitate the robustness to the degraded modalities, we employ the gated information fusion (GIF) network which weights the contribution from each modality according to the input feature maps to be fused. The weights are determined through the convolutional layers followed by a sigmoid function and trained along with the information fusion network in an end-to-end fashion. Our experiments show that the proposed GIF network offers the additional architectural flexibility to achieve robust performance in handling some degraded modalities, and show a significant performance improvement based on Single Shot Detector (SSD) for KITTI dataset using the proposed fusion network and data augmentation schemes.


title: Deep learning for self-driving cars: chances and challenges

abstract: Artificial Intelligence (AI) is revolutionizing the modern society. In the automotive industry, researchers and developers are actively pushing deep learning based approaches for autonomous driving. However, before a neural network finds its way into series production cars, it has to first undergo strict assessment concerning functional safety. The chances and challenges of incorporating deep learning for self-driving cars are presented in this paper.


title: A phase space model of Fourier ptychographic microscopy

abstract: A new computational imaging technique, termed Fourier ptychographic microscopy (FPM), uses a sequence of low-resolution images captured under varied illumination to iteratively converge upon a high-resolution complex sample estimate. Here, we propose a mathematical model of FPM that explicitly connects its operation to conventional ptychography, a common procedure applied to electron and X-ray diffractive imaging. Our mathematical framework demonstrates that under ideal illumination conditions, conventional ptychography and FPM both produce datasets that are mathematically linked by a linear transformation. We hope this finding encourages the future cross-pollination of ideas between two otherwise unconnected experimental imaging procedures. In addition, the coherence state of the illumination source used by each imaging platform is critical to successful operation, yet currently not well understood. We apply our mathematical framework to demonstrate that partial coherence uniquely alters both conventional ptychography's and FPM's captured data, but up to a certain threshold can still lead to accurate resolution-enhanced imaging through appropriate computational post-processing. We verify this theoretical finding through simulation and experiment.


title: A model of non-preferred hand mode switching

abstract: Effective mode-switching techniques provide users of tablet interfaces with access to a rich set of behaviors. While many researchers have studied the relative performance of mode-switching techniques in these interfaces, these metrics tell us little about the behavior of one technique in the absence of a competitor. Differing from past comparison-based research, this paper describes a temporal model of the behavior of a common mode switching technique, non-preferred hand mode switching. Using the Hick-Hyman Law, we claim that the asymptotic cost of adding additional non-preferred hand modes to an interface is a logarithmic function of the number of modes. We validate the model experimentally, and show a strong correlation between experimental data and values predicted by the model. Implications of this research for the design of mode-based interfaces are highlighted.


title: Cloud Computing and Grid Computing 360-Degree Compared

abstract: Cloud Computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for Cloud Computing and there seems to be no consensus on what a Cloud is. On the other hand, Cloud Computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established Grid Computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast Cloud Computing with Grid Computing from various angles and give insights into the essential characteristics of both.


title: Database abstractions: aggregation and generalization

abstract: Two kinds of abstraction that are fundamentally important in database design and usage are defined. Aggregation is an abstraction which turns a relationship between objects into an aggregate object. Generalization is an abstraction which turns a class of objects into a generic object. It is suggested that all objects (individual, aggregate, generic) should be given uniform treatment in models of the real world. A new data type, called generic, is developed as a primitive for defining such models. Models defined with this primitive are structured as a set of aggregation hierarchies intersecting with a set of generalization hierarchies. Abstract objects occur at the points of intersection. This high level structure provides a discipline for the organization of relational databases. In particular this discipline allows: (i) an important class of views to be integrated and maintained; (ii) stability of data and programs under certain evolutionary changes; (iii) easier understanding of complex models and more natural query formulation; (iv) a more systematic approach to database design; (v) more optimization to be performed at lower implementation levels. The generic type is formalized by a set of invariant properties. These properties should be satisfied by all relations in a database if abstractions are to be preserved. A triggering mechanism for automatically maintaining these invariants during update operations is proposed. A simple mapping of aggregation/generalization hierarchies onto owner-coupled set structures is given.


Neighborhood Disadvantage and Educational Opportunities


FRACTAL ANTENNAS : DESIGN , CHARACTERISTICS AND APPLICATION


title: Towards 3D Face Recognition in the Real: A Registration-Free Approach Using Fine-Grained Matching of 3D Keypoint Descriptors

abstract: Registration algorithms performed on point clouds or range images of face scans have been successfully used for automatic 3D face recognition under expression variations, but have rarely been investigated to solve pose changes and occlusions mainly since that the basic landmarks to initialize coarse alignment are not always available. Recently, local feature-based SIFT-like matching proves competent to handle all such variations without registration. In this paper, towards 3D face recognition for real-life biometric applications, we significantly extend the SIFT-like matching framework to mesh data and propose a novel approach using fine-grained matching of 3D keypoint descriptors. First, two principal curvature-based 3D keypoint detectors are provided, which can repeatedly identify complementary locations on a face scan where local curvatures are high. Then, a robust 3D local coordinate system is built at each keypoint, which allows extraction of pose-invariant features. Three keypoint descriptors, corresponding to three surface differential quantities, are designed, and their feature-level fusion is employed to comprehensively describe local shapes of detected keypoints. Finally, we propose a multi-task sparse representation based fine-grained matching algorithm, which accounts for the average reconstruction error of probe face descriptors sparsely represented by a large dictionary of gallery descriptors in identification. Our approach is evaluated on the Bosphorus database and achieves rank-one recognition rates of 96.56, 98.82, 91.14, and 99.21 % on the entire database, and the expression, pose, and occlusion subsets, respectively. To the best of our knowledge, these are the best results reported so far on this database. Additionally, good generalization ability is also exhibited by the experiments on the FRGC v2.0 database.


Phrasal translation and query expansion techniques for cross-language information retrieval


title: What are the core drivers in consumer adoption of NFC-based mobile payments?: A proposed research framework

abstract: The rapid evolution of mobile technologies and the increasing diffusion of smartphones have given significant opportunities for innovative companies to create new payment solutions and offer value-added services to their customers. Near Field Communication (NFC) mobile payment has been emerging as a noticeable phenomenon that can enable consumers to turn their smartphones into digital wallets. Although there has been a lot of coverage on consumer acceptance of mobile payments, there are only few researches providing guideline to interpret NFC-based mobile payments adoption. Taking into consideration of theoretical backgrounds of innovation diffusion and specific characteristics of NFC mobile payments, this study propose a research framework to provide a profound understanding of factors facilitating or impeding the adoption of NFC-based mobile payments among Taiwanese consumers. This study expects that intention to adopt NFC mobile payments is affected by product-related factors (perceived usefulness, perceived ease of use, compatibility, perceived security and privacy risk, trialability, perceived cost of use and additional value of NFC mobile payment), trust-based factors, personal-related factors (personal innovativeness in new technologies, absorptive capacity), and attractiveness of alternatives. This study will be able to advance literature on innovation adoption and facilitate technology marketers in NFC mobile payments. It provides a useful guideline to help researchers investigate of issues related to NFC mobile payments. It also bring some managerial implications by assisting relevant parties in NFC mobile payments ecosystem such as mobile network operators, card issuers, payment processing institutions, bank decision makers and merchants when devising their business strategies and marketing campaigns to fac


title: Autonomous Mobile Robot Localization and Navigation Using a Hierarchical Map Representation Primarily Guided by Vision

abstract: While impressive progress has recently been made with autonomous vehicles, both indoors and on streets, autonomous localization and navigation in less constrained and more dynamic environments, such as outdoor pedestrian and bicycle-friendly sites, remains a challenging problem. We describe a new approach that utilizes several visual perception modules-place recognition, landmark recognition, and road lane detection-supplemented by proximity cues from a planar laser range finder for obstacle avoidance. At the core of our system is a new hybrid topological/grid-occupancy map that integrates the outputs from all perceptual modules, despite different latencies and time scales. Our approach allows for real-time performance through a combination of fast but shallow processing modules that update the map's state while slower but more discriminating modules are still computing. We validated our system using a ground vehicle that autonomously traversed three outdoor routes several times, each 400 m or longer, on a university campus. The routes featured different road types, environmental hazards, moving pedestrians, and service vehicles. In total, the robot logged over 10 km of successful recorded experiments, driving within a median of 1.37 m laterally of the center of the road, and localizing within 0.97 m median longitudinally of its true location along the route.


title: Community detection in political Twitter networks using Nonnegative Matrix Factorization methods

abstract: Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection. We show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities. Word usage is the strongest indicator of users' political orientation among all content categories. Incorporating user-word matrix and word similarity regularizer provides the missing link in connectivity only methods which suffer from detection of artificially large number of clusters for Twitter networks.


title: Challenges and advances in computational docking: 2009 in review.

abstract: Docking is a computational technique that places a small molecule (ligand) in the binding site of its macromolecular target (receptor) and estimates its binding affinity. This review addresses methodological developments that have occurred in the docking field in 2009, with a particular focus on the more difficult, and sometimes controversial, aspects of this promising computational discipline. These developments aim to address the main challenges of docking: receptor representation (such aspects as structural waters, side chain protonation, and, most of all, flexibility (from side chain rotation to domain movement)), ligand representation (protonation, tautomerism and stereoisomerism, and the effect of input conformation), as well as accounting for solvation and entropy of binding. This review is strongly focused on docking advances in the context of drug design, specifically in virtual screening and fragment-based drug design.


title: A two-dimensional multilevel thresholding method for image segmentation

abstract: Graphical abstractDisplay Omitted HighlightsWe develop a two-dimensional multilevel thresholding approach.We compare the Rnyi and Tsallis entropies.We compare quantum genetic algorithm and differential evolution heuristics.We show that the proposed method is more appropriate for multimodal images.We demonstrate the effectiveness of the proposed method on noisy images. In this work, we develop a two-dimensional multilevel thresholding technique based on Rnyi and Tsallis entropies. The formulation of the proposed method gives rise to an NP-hard combinatorial optimization problem. In order to solve efficiently this problem, two leading evolutionary algorithms, namely the quantum genetic algorithm (QGA) and the differential evolution (DE) have been employed and compared. The effectiveness of both the proposed method and the optimizers was demonstrated on a sample of real-world and synthetic images showing different types of gray-level distributions. Moreover, the contribution of the two-dimensional histogram to the segmentation quality has been highlighted on some images corrupted by noise and containing shadow or reflection effects. Experimental results demonstrated, first, that DE is less time consuming than QGA which is slightly more efficient on complex problems. Second, the Rnyi and Tsallis entropies leads to similar image segmentation quality. Finally, we have shown that the proposed method is more appropriate than bilevel thresholding for multimodal and noisy images segmentation.


title: HPV vaccine acceptability among women in the Philippines.

abstract: AIM ::: To examine attitudes toward and acceptability of HPV vaccination among a community-based sample of women in the Philippines. ::: ::: ::: METHODS ::: Self-administered surveys were completed by 435 adult women. Intent to receive the HPV vaccine was assessed at low, moderate, and high vaccine price through responses on Likert scale items. The theory-grounded survey assessed attitudinal correlates, as well as sociodemographic, behavioral, and health-related characteristics. ::: ::: ::: RESULTS ::: Over half of the sample (54%) was accepting of HPV vaccination at the low price, but only 30% and 31% were accepting at the moderate and high price, respectively. Negative intent to receive the vaccine was significantly associated with women's indication that their mothers or partners were influential in their vaccination decisions. Perceived social support, access to transportation, perceived benefits of vaccination, perceived susceptibility to HPV, history of pap testing, and having been exposed to vaccine-promoting media were among factors independently associated with positive intent to receive the vaccine. ::: ::: ::: CONCLUSIONS ::: HPV vaccine acceptance among Filipina women is contingent on affordable pricing. A successful vaccine initiative in the region must minimize structural barriers, foster familial and social support for vaccination, incorporate HPV education, and work within cultural norms.


title: Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging

abstract: The aim of this study was to evaluate the accuracy of the spectro-optical, photochemical reflectance index (PRI) for quantifying the disease index (DI) of yellow rust (Biotroph Puccinia striiformis) in wheat (Triticum aestivum L.), and its applicability in the detection of the disease using hyperspectral imagery. Over two successive seasons, canopy reflectance spectra and disease index (DI) were measured five times during the growth of wheat plants (3 varieties) infected with varying amounts of yellow rust. Airborne hyperspectral images of the field site were also acquired in the second season. The PRI exhibited a significant, negative, linear, relationship with DI in the first season (r2 = 0.91, n = 64), which was insensitive to both variety and stage of crop development from Zadoks stage 3–9. Application of the PRI regression equation to measured spectral data in the second season yielded a coefficient of determination of r2 = 0.97 (n = 80). Application of the same PRI regression equation to airborne hyperspectral imagery in the second season also yielded a coefficient of determination of DI of r2 = 0.91 (n = 120). The results show clearly the potential of PRI for quantifying yellow rust levels in winter wheat, and as the basis for developing a proximal, or airborne/spaceborne imaging sensor of yellow rust in fields of winter wheat.


title: LIRS: an efficient low inter-reference recency set replacement policy to improve buffer cache performance

abstract: Although LRU replacement policy has been commonly used in the buffer cache management, it is well known for its inability to cope with access patterns with weak locality. Previous work, such as LRU-K and 2Q, attempts to enhance LRU capacity by making use of additional history information of previous block references other than only the recency information used in LRU. These algorithms greatly increase complexity and/or can not consistently provide performance improvement. Many recently proposed policies, such as UBM and SEQ, improve replacement performance by exploiting access regularities in references. They only address LRU problems on certain specific and well-defined cases such as access patterns like sequences and loops. Motivated by the limits of previous studies, we propose an efficient buffer cache replacement policy, called Low Inter-reference Recency Set (LIRS). LIRS effectively addresses the limits of LRU by using recency to evaluate Inter-Reference Recency (IRR) for making a replacement decision. This is in contrast to what LRU does: directly using recency to predict next reference timing. At the same time, LIRS almost retains the same simple assumption of LRU to predict future access behavior of blocks. Our objectives are to effectively address the limits of LRU for a general purpose, to retain the low overhead merit of LRU, and to outperform those replacement policies relying on the access regularity detections. Conducting simulations with a variety of traces and a wide range of cache sizes, we show that LIRS significantly outperforms LRU, and outperforms other existing replacement algorithms in most cases. Furthermore, we show that the additional cost for implementing LIRS is trivial in comparison with LRU.


title: Applying data mining in investigating money laundering crimes

abstract: In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.


title: Human Pose Estimation in Videos

abstract: In this paper, we present a method to estimate a sequence of human poses in unconstrained videos. In contrast to the commonly employed graph optimization framework, which is NP-hard and needs approximate solutions, we formulate this problem into a unified two stage tree-based optimization problem for which an efficient and exact solution exists. Although the proposed method finds an exact solution, it does not sacrifice the ability to model the spatial and temporal constraints between body parts in the video frames, indeed it even models the symmetric parts better than the existing methods. The proposed method is based on two main ideas: 'Abstraction' and 'Association' to enforce the intra-and inter-frame body part constraints respectively without inducing extra computational complexity to the polynomial time solution. Using the idea of 'Abstraction', a new concept of 'abstract body part' is introduced to model not only the tree based body part structure similar to existing methods, but also extra constraints between symmetric parts. Using the idea of 'Association', the optimal tracklets are generated for each abstract body part, in order to enforce the spatiotemporal constraints between body parts in adjacent frames. Finally, a sequence of the best poses is inferred from the abstract body part tracklets through the tree-based optimization. We evaluated the proposed method on three publicly available video based human pose estimation datasets, and obtained dramatically improved performance compared to the state-of-the-art methods.


title: Drumming through trauma: Music therapy with post-traumatic soldiers

abstract: Abstract Combat stress reaction is common among soldiers and can develop to a post-traumatic stress disorder (PTSD). This distressing condition embraces symptoms such as feelings of loneliness and isolation from society, intrusive memories, outbursts of anger and generalized feelings of helplessness. Drumming has been receiving considerable attention in music therapy. Only few references relate to such activity among those who suffer from PTSD, and even fewer relate to combat induced post-traumatic syndrome, none of them empirical. The current study presents music therapy group work with six soldiers diagnosed as suffering from combat or terror related PTSD. Data were collected from digital cameras which filmed the sessions, open-ended in-depth interviews, and a self-report of the therapist. Some reduction in PTSD symptoms was observed following drumming, especially increased sense of openness, togetherness, belonging, sharing, closeness, connectedness and intimacy, as well as achieving a non-intimidating access to traumatic memories, facilitating an outlet for rage and regaining a sense of self-control.


title: A latent mixed membership model for relational data

abstract: Modeling relational data is an important problem for modern data analysis and machine learning. In this paper we propose a Bayesian model that uses a hierarchy of probabilistic assumptions about the way objects interact with one another in order to learn latent groups, their typical interaction patterns, and the degree of membership of objects to groups. Our model explains the data using a small set of parameters that can be reliably estimated with an efficient inference algorithm. In our approach, the set of probabilistic assumptions may be tailored to a specific application domain in order to incorporate intuitions and/or semantics of interest. We demonstrate our methods on simulated data and we successfully apply our model to a data set of protein-to-protein interactions.


title: Security Vulnerabilities in Bluetooth Technology as Used in IoT

abstract: Bluetooth technology is a key component of wireless communications. It provides a low-energy and low-cost solution for short-range radio transmissions. Bluetooth, more specifically Bluetooth Low Energy (BLE) has become the predominant technology for connecting IoT (Internet of Things). It can be found in cell phones, headsets, speakers, printers, keyboards, automobiles, children’s toys, and medical devices, as well as many other devices. The technology can also be found in automated smart homes, to provide monitors and controls for lights, thermostats, door locks, appliances, security systems, and cameras. Bluetooth offers convenience and ease of use, but it lacks a centralized security infrastructure. As a result, it has serious security vulnerabilities, and the need for awareness of the security risks are increasing as the technology becomes more widespread. This paper presents an overview of Bluetooth technology in IoT including its security, vulnerabilities, threats, and risk mitigation solutions, as well as real-life examples of exploits. Our study highlights the importance of understanding attack risks and mitigation techniques involved with using Bluetooth technology on our devices. Real-life examples of recent Bluetooth exploits are presented. Several recommended security measures are discussed to secure Bluetooth communication.


title: Effect of Ring Fluorination on the Pharmacology of Hallucinogenic Tryptamines

abstract: A series of fluorinated analogues of the hallucinogenic tryptamines N,N-diethyltryptamine (DET), 4-hydroxy-N,N-dimethyltryptamine (4-OH-DMT, psilocin), and 5-methoxy-DMT was synthesized to investigate possible explanations for the inactivity of 6-fluoro-DET as a hallucinogen and to determine the effects of fluorination on the molecular recognition and activation of these compounds at serotonin receptor subtypes. The target compounds were evaluated using in vivo behavioral assays for hallucinogen-like and 5-HT(1A) agonist activity and in vitro radioligand competition assays for their affinity at 5-HT(2A), 5-HT(2C), and 5-HT(1A) receptor sites. Functional activity at the 5-HT(2A) receptor was determined for all compounds. In addition, for some compounds functional activity was determined at the 5-HT(1A) receptor. Hallucinogen-like activity, evaluated in the two-lever drug discrimination paradigm using LSD-trained rats, was attenuated or abolished for all of the fluorinated analogues. One of the tryptamines, 4-fluoro-5-methoxy-DMT (6), displayed high 5-HT(1A) agonist activity, with potency greater than that of the 5-HT(1A) agonist 8-OH-DPAT. The ED(50) of 6 in the two-lever drug discrimination paradigm using rats trained to discriminate the 5-HT(1A) agonist LY293284 was 0.17 micromol/kg, and the K(i) at [(3)H]8-OH-DPAT-labeled 5-HT(1A) receptors was 0.23 nM. The results indicate that fluorination of hallucinogenic tryptamines generally has little effect on 5-HT(2A/2C) receptor affinity or intrinsic activity. Affinity at the 5-HT(1A) receptor was reduced, however, in all but one example, and all of the compounds tested were full agonists but with reduced functional potency at this serotonin receptor subtype. The one notable exception was 4-fluoro-5-methoxy-DMT (6), which had markedly enhanced 5-HT(1A) receptor affinity and functional potency. Although it is generally considered that hallucinogenic activity results from 5-HT(2A) receptor activation, the present results suggest a possible role for involvement of the 5-HT(1A) receptor with tryptamines.


title: Fuzzy based obstacle avoidance system for autonomous mobile robot

abstract: The goal of this research was to develop a fuzzy obstacle avoidance system for an autonomous mobile robot using IR detection sensors. This paper presents implemented control architecture for behavior-based mobile robot. The mobile robot is able to interact with an unknown environment using a reactive strategy determined by sensory information. Current research in robotics aims to build autonomous and intelligent robots, which can plan its motion in a dynamic environment. Autonomous mobile robots are increasingly used in well structured environment such as warehouses, offices and industries. Fuzzy behavior able to make inferences is well suited for mobile robot navigation because of the uncertainty of the environment. A rule-based fuzzy controller with reactive behavior was implemented and tested on a two wheels mobile robot equipped with infrared sensors to perform collision-free navigation. The experimental results have shown that the proposed architecture provides an efficient and flexible solution for small wheeled mobile robots.


title: Design of a multi-band antenna for 4G wireless systems

abstract: In this paper, a multi-band antenna for 4G wireless systems is proposed. The proposed antenna consists of a modified planar inverted-F antenna with additional branch line for wide bandwidth and a folded monopole antenna. The antenna provides wide bandwidth for covering the hepta-band LTE/GSM/UMTS operation. The measured 6-dB return loss bandwidth was 169 MHz (793 MHz-962 MHz) at the low frequency band and 1030 MHz (1700 MHz-2730 MHz) at the high frequency band. The overall dimension of the proposed antenna is 55 mm × 110 mm × 5 mm.


title: Modeling and Processing Big Data of Power Transmission Grid Substation Using Neo4j

abstract: Abstract Data sizes in power transmission grid have increased rapidly, which results in challenges. These data are large in volume; they are generated fast and in different format, and come from various sources such as electrical substations. Traditional relational databases are inadequate in terms of response time and have impact on performance when applied to very large data sets, and also make this database difficult to evolve according to business needs. To address this shortcoming, the Big Data implementations are leveraging new technologies such as NoSQL data stores. This research paper aims and tries to improve this process by modeling and processing those data using Neo4j database, and presents modeling and processing the data of power transmission grid substation which has two power transformers, and then adding a new power transformer to simulate the evolving feature of Neo4j database according to the business needs.


title: Clusters of Driving Behavior From Observational Smartphone Data

abstract: Understanding driving behaviors is essential for improving safety and mobility of our transportation systems. Data is usually collected via simulator-based studies or naturalistic driving studies. Those techniques allow for understanding relations between demographics, road conditions and safety. On the other hand, they are very costly and time consuming. Thanks to the ubiquity of smartphones, we have an opportunity to substantially complement more traditional data collection techniques with data extracted from phone sensors, such as GPS, accelerometer gyroscope and camera. We developed statistical models that provided insight into driver behavior in the San Francisco metro area based on tens of thousands of driver logs. We used novel data sources to support our work. We used cell phone sensor data drawn from five hundred drivers in San Francisco to understand the speed of traffic across the city as well as the maneuvers of drivers in different areas. Specifically, we clustered drivers based on their driving behavior. We looked at driver norms by street and flagged driving behaviors that deviated from the norm.


title: MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition

abstract: This paper presents three different gesture recognition models which are capable of recognizing seven hand gestures, i.e., up, down, left, right, tick, circle, and cross, based on the input signals from MEMS 3-axes accelerometers. The accelerations of a hand in motion in three perpendicular directions are detected by three accelerometers respectively and transmitted to a PC via Bluetooth wireless protocol. An automatic gesture segmentation algorithm is developed to identify individual gestures in a sequence. To compress data and to minimize the influence of variations resulted from gestures made by different users, a basic feature based on sign sequence of gesture acceleration is extracted. This method reduces hundreds of data values of a single gesture to a gesture code of 8 numbers. Finally, the gesture is recognized by comparing the gesture code with the stored templates. Results based on 72 experiments, each containing a sequence of hand gestures (totaling 628 gestures), show that the best of the three models discussed in this paper achieves an overall recognition accuracy of 95.6%, with the correct recognition accuracy of each gesture ranging from 91% to 100%. We conclude that a recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.


title: Clostridial brain abscess after glioblastoma resection: case report and critical review of the literature

abstract: Abstract Clostridium perfringens is rare in neurosurgery. The source of clostridial brain abscess is usually a penetrating head injury. We report the case of a 57-year-old man who had parietal glioblastoma resection with local carmustine chemotherapy and who presented a clostridial brain abscess three weeks later. Progression was especially brutal, leading to patient's death in few hours. We discuss the etiology and progression of this case compared to the data reported in the literature.


title: An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram

abstract: In today’s digital era, there are large volumes of long-duration videos resulting from movies, documentaries, sports and surveillance cameras floating over internet and video databases (YouTube). Since manual processing of these videos are difficult, time-consuming and expensive, an automatic technique of abstracting these long-duration videos are very much desirable. In this backdrop, this paper presents a novel and efficient approach of video shot boundary detection and keyframe extraction, which subsequently leads to a summarized and compact video. The proposed method detects video shot boundaries by extracting the SIFT-point distribution histogram (SIFT-PDH) from the frames as a combination of local and global features. In the subsequent step, using the distance of SIFT-PDH of consecutive frames and an adaptive threshold video shot boundaries are detected. Further, the keyframes representing the salient content of each segmented shot are extracted using entropy-based singular values measure. Thus, the summarized video is then generated by combining the extracted keyframes. The experimental results show that our method can efficiently detect shot boundaries under both abrupt and gradual transitions, and even under different levels of illumination, motion effects and camera operations (zoom in, zoom out and camera rotation). With the proposed method, the computational complexity is comparatively less and video summarization is very compact.


title: Self- and peer-rated character strengths: How do they relate to satisfaction with life and orientations to happiness?

abstract: This paper addresses the question as to whether previously reported findings on a positive relation between character strengths, satisfaction with life, and orientations to happiness (OTH) can be replicated for peer ratings of character strengths. A sample of 334 Swiss adults completed questionnaires and collected informant ratings by 634 peers. Self- and peer ratings converged well and suggest that, primarily: the strengths of hope, zest, and curiosity – but also gratitude and love – play key roles in the interplay of strengths and satisfaction with life. Peer ratings of strengths also related positively with the endorsement of a pleasurable, engaged, and meaningful life. Further analyses show that the OTH predict satisfaction with life beyond self- and peer-rated character strengths. There, the engaged life (i.e. endorsement of flow) was most relevant. This study supports earlier findings that argue for an important role of character strengths when describing the well-being of a person.


title: Switching behavior of RF-LDMOS for class-F power amplifier in TCAD

abstract: Nowadays the characterization of RF devices under large signal is performed in TCAD by using computational load-pull (CLP) simulation technique. In this paper, we modified the CLP simulation technique further to study the switching response of RF-LDMOS transistor for high efficiency switching power amplifier (e.g. class-F). In class-F PA operation, we achieved 85 % power added efficiency (PAE) together with 1.0 W/mm RF power density on the basis of finite harmonics. By this CLP technique, we can study the switching response of intrinsic RF-devices directly under large signal operation prior to fabrication or non-linear model of RF-transistor without including any external lumped matching networks.


title: Language Control in the Bilingual Brain

abstract: How does the bilingual brain distinguish and control which language is in use? Previous functional imaging experiments have not been able to answer this question because proficient bilinguals activate the same brain regions irrespective of the language being tested. Here, we reveal that neuronal responses within the left caudate are sensitive to changes in the language or the meaning of words. By demonstrating this effect in populations of German-English and Japanese-English bilinguals, we suggest that the left caudate plays a universal role in monitoring and controlling the language in use.


Semantically Tractable Questions : Theory and Experiments


Scattering theory and spectral representations for general wave equations with short range perturbations


title: Where should people with dementia live? Using the views of service users to inform models of care

abstract: Accessible summary ::: We wanted to know what people with learning disabilities know about dementia. We asked service users what the word meant to them, if they knew anyone with dementia, and what it was like to share a house with someone who has dementia. We learnt that: ::: ::: •Service users can know a lot about dementia. ::: •Living with someone with dementia can be really hard – and staff do not always have enough time for everyone else. ::: •Services should think more about how dementia affects everyone – not just the person who has the dementia. ::: ::: ::: Summary ::: This paper reports on research that illuminates how people with a learning disability understand dementia and indicates the implications of these understandings for developing appropriate models of care. As this new policy and practice area struggles to provide appropriate and effective models of care for people with a learning disability and dementia, an awareness of service users’ understandings of dementia leads to a number of important insights. The wider research programme from which this paper is drawn aims to identify best practice in supporting people with learning disabilities to stay in their own homes as they grow older and develop dementia. A combination of focus groups, ethnography and individual interviews were conducted at eight sites across England. Participants were people with a learning disability who had dementia, and those who have lived with someone with dementia. The findings have the potential to improve support to this population as they grow older.


title: Development of “Souryu-IV” and “Souryu-V:” Serially connected crawler vehicles for in-rubble searching operations

abstract: The authors have developed Souryu-I, Souryu-II and Souryu-III, connected crawler vehicles that can travel in rubble. These machines were developed for the purpose of finding survivors trapped inside collapsed buildings. However, when conducting experiments in post-disaster environments with Souryu-III, mechanical and control limitations have been identified. This led the authors to develop novel crawler units using crawler tracks strengthened with metal, and develop two improved models, called Souryu-IV (composed of three double-sided crawler bodies, a joint driving unit, a blade-spring joint mechanism, and cameras) and Souryu-V (composed of mono-tread-crawler bodies, elastic-rod-joint mechanisms, and cameras). The authors then conducted basic motion experiments and teleoperated control experiments on off-road fields with Souryu-IV and Souryu-V. Their high performance in experiments of urban rescue operations was confirmed. However, several problems were identified during the driving experiments, and are reported in this paper, as well as a few possible solutions to solve them. This paper describes Souryu-IV and Souryu-V, their mechanisms, their cameras, and the experiments to test their mobile performance. © 2008 Wiley Periodicals, Inc.


title: Changes in the Ground Reaction Force, Lower-Limb Muscle Activity, and Joint Angles in Athletes with Unilateral Ankle Dorsiflexion Restriction During A Rebound-Jump Task

abstract: Background: This study compared differences between a control group and a group with unilateral ankle dorsiflexion restriction in the ground reaction force (GRF), angles of the lower limbs joints, and muscular activity during a rebound-jump task in athletes who continue to perform sports activities with unilateral ankle dorsiflexion restriction. Methods: The athletes were divided into the following two groups: The dorsiflexion group included those with a difference of ≥7° between bilateral ankle dorsiflexion angles (DF), and the control group included those with a difference of <7° between the two ankles (C). An ankle foot orthosis was attached to subjects in group C to apply a restriction on the right-angle dorsiflexion angle. The percentage of maximum voluntary contraction (%MVC) of the legs musculature, components of the GRF, and the hip and knee joint angles during the rebound-jump task were compared between groups DF and C. Results: Group DF showed increased %MVC of the quadriceps muscle, decreased upward component of the GRF, decreased hip flexion, and increased knee eversion angles. Conclusions: This study highlighted that athletes with ankle dorsiflexion restriction had significantly larger knee eversion angles in the rebound-jump task. The reduced hip flexion was likely caused by the restricted ankle dorsiflexion and compensated by the observed increase in quadriceps muscle activation when performing the jump.


title: Microstrip patch array antenna with inset fed and perturbation for a 3 GHz S-band coastal radar

abstract: Antenna is one of the most necessary parts in radar coastal application. Microstrip antena usually be chosen for that purpose because it is small, light, cheap, and easy to fabricate. But a conventional microstrip antenna is still inadequate to obtain higher gain and to minimize the beamwidth. So, in this research, for a 3 GHz S-Band coastal radar application, 4 modules of 2 × 6 patch array with inset fed and perturbation on each have been simulated to achieve the antenna specifications. A unidirectional radiation pattern has been chosen. By this improvement, the result gives antenna with gain = 17,6 dBi and return loss <-10 dB.


title: Novel Approach to Non-Invasive Blood Glucose Monitoring Based on Transmittance and Refraction of Visible Laser Light

abstract: Current blood glucose monitoring (BGM) techniques are invasive as they require a finger prick blood sample, a repetitively painful process that creates the risk of infection. BGM is essential to avoid complications arising due to abnormal blood glucose levels in diabetic patients. Laser light-based sensors have demonstrated a superior potential for BGM. Existing near-infrared (NIR)-based BGM techniques have shortcomings, such as the absorption of light in human tissue, higher signal-to-noise ratio, and lower accuracy, and these disadvantages have prevented NIR techniques from being employed for commercial BGM applications. A simple, compact, and cost-effective non-invasive device using visible red laser light of wavelength 650 nm for BGM (RL-BGM) is implemented in this paper. The RL-BGM monitoring device has three major technical advantages over NIR. Unlike NIR, red laser light has ~30 times better transmittance through human tissue. Furthermore, when compared with NIR, the refractive index of laser light is more sensitive to the variations in glucose level concentration resulting in faster response times ~7–10 s. Red laser light also demonstrates both higher linearity and accuracy for BGM. The designed RL-BGM device has been tested for both in vitro and in vivo cases and several experimental results have been generated to ensure the accuracy and precision of the proposed BGM sensor.


Is Tom Cruise Threatened? Using Netflix Prize Data to Examine the Long Tail of Electronic


title: The graph isomorphism disease

abstract: The graph isomorphism problem—to devise a good algorithm for determining if two graphs are isomorphic—is of considerable practical importance, and is also of theoretical interest due to its relationship to the concept of NP-completeness. No efficient (i.e., polynomial-bound) algorithm for graph isomorphism is known, and it has been conjectured that no such algorithm can exist. Many papers on the subject have appeared, but progress has been slight; in fact, the intractable nature of the problem and the way that many graph theorists have been led to devote much time to it, recall those aspects of the four-color conjecture which prompted Harary to rechristen it the “four-color disease.” This paper surveys the present state of the art of isomorphism testing, discusses its relationship to NP-completeness, and indicates some of the difficulties inherent in this particularly elusive and challenging problem. A comprehensive bibliography of papers relating to the graph isomorphism problem is given.


title: Leveraging disjoint communities for detecting overlapping community structure

abstract: Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network. In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure. We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.


title: Comparison of different color spaces for image segmentation using graph-cut

abstract: Graph-cut optimization has been successfully applied in many image segmentation tasks. Within this framework color information has been extensively used as a perceptual property of objects to segment the foreground object from background. There are different representations of color in digital images, each with special characteristics. Previous work on segmentation lacks a systematic study of which color space is better suited for image segmentation. This work applies the Graph Cut algorithm for image segmentation based on five different, widespread color spaces and evaluates their performance on public benchmark datasets. Most of the tested color spaces lead to similar results. Segmentations based on L*a*b* color space are of slightly higher or similar quality as all the other methods. In contrast, RGB-based segmentations are mostly worse than a segmentation based on any other tested color space.


title: Online Planning Algorithms for POMDPs

abstract: Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that state-of-the-art online heuristic search methods can handle large POMDP domains efficiently.


title: A multiscale retinex for bridging the gap between color images and the human observation of scenes

abstract: Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour.


title: Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messages

abstract: People share their opinions, stories, and reviews through online video sharing websites every day. The automatic analysis of these online opinion videos is bringing new or understudied research challenges to the field of computational linguistics and multimodal analysis. Among these challenges is the fundamental question of exploiting the dynamics between visual gestures and verbal messages to be able to better model sentiment. This article addresses this question in four ways: introducing the first multimodal dataset with opinion-level sentiment intensity annotations; studying the prototypical interaction patterns between facial gestures and spoken words when inferring sentiment intensity; proposing a new computational representation, called multimodal dictionary, based on a language-gesture study; and evaluating the authors' proposed approach in a speaker-independent paradigm for sentiment intensity prediction. The authors' study identifies four interaction types between facial gestures and verbal content: neutral, emphasizer, positive, and negative interactions. Experiments show statistically significant improvement when using multimodal dictionary representation over the conventional early fusion representation (that is, feature concatenation).


title: Simulation of vehicle ABS based on electromechanical brake system and road surface automatic identification

abstract: The designing of the ABS Controller based on electromechanical brake system and optimal slip ratio is studied. Firstly simplified wheel model and EMB controller model is given,then a corresponding ABS brake controller was designed. The fuzzy-pid ABS controller adopts optimal slip ratio based control scheme,we can achieve the optimal slip ratio from the automatic identification system. The ABS controller can gives a closed loop control of slip ratio S and brake press force F. The simulation model is founded by using simulink toolbox of Matalab. The result shows the road surface automatic identification system can identify the road surface correctly and ABS controller can keep the slip ratio close to the optimal slip ratio.


IoT based Monitoring and Control System for Home Automation


title: Automated semantic web service discovery with OWLS-MX

abstract: We present an approach to hybrid semantic Web service matching that complements logic based reasoning with approximate matching based on syntactic IR based similarity computations. The hybrid matchmaker, called OWLS-MX, applies this approach to services and requests specified in OWL-S. Experimental results of measuring performance and scalability of different variants of OWLS-MX show that under certain constraints logic based only approaches to OWL-S service I/O matching can be significantly outperformed by hybrid ones.


title: Security in Automotive Networks: Lightweight Authentication and Authorization

abstract: With the increasing amount of interconnections between vehicles, the attack surface of internal vehicle networks is rising steeply. Although these networks are shielded against external attacks, they often do not have any internal security to protect against malicious components or adversaries who can breach the network perimeter. To secure the in-vehicle network, all communicating components must be authenticated, and only authorized components should be allowed to send and receive messages. This is achieved through the use of an authentication framework. Cryptography is widely used to authenticate communicating parties and provide secure communication channels (e.g., Internet communication). However, the real-time performance requirements of in-vehicle networks restrict the types of cryptographic algorithms and protocols that may be used. In particular, asymmetric cryptography is computationally infeasible during vehicle operation. In this work, we address the challenges of designing authentication protocols for automotive systems. We present Lightweight Authentication for Secure Automotive Networks (LASAN), a full lifecycle authentication approach. We describe the core LASAN protocols and show how they protect the internal vehicle network while complying with the real-time constraints and low computational resources of this domain. By leveraging the fixed structure of automotive networks, we minimize bandwidth and computation requirements. Unlike previous work, we also explain how this framework can be integrated into all aspects of the automotive product lifecycle, including manufacturing, vehicle maintenance, and software updates. We evaluate LASAN in two different ways: First, we analyze the security properties of the protocols using established protocol verification techniques based on formal methods. Second, we evaluate the timing requirements of LASAN and compare these to other frameworks using a new highly modular discrete event simulator for in-vehicle networks, which we have developed for this evaluation.


title: Privacy Personas: Clustering Users via Attitudes and Behaviors toward Security Practices

abstract: A primary goal of research in usable security and privacy is to understand the differences and similarities between users. While past researchers have clustered users into different groups, past categories of users have proven to be poor predictors of end-user behaviors. In this paper, we perform an alternative clustering of users based on their behaviors. Through the analysis of data from surveys and interviews of participants, we identify five user clusters that emerge from end-user behaviors-Fundamentalists, Lazy Experts, Technicians, Amateurs and the Marginally Concerned. We examine the stability of our clusters through a survey-based study of an alternative sample, showing that clustering remains consistent. We conduct a small-scale design study to demonstrate the utility of our clusters in design. Finally, we argue that our clusters complement past work in understanding privacy choices, and that our categorization technique can aid in the design of new computer security technologies.


title: De-anonymization of Social Networks with Communities: When Quantifications Meet Algorithms

abstract: A crucial privacy-driven issue nowadays is re-identifying anonymized social networks by mapping them to correlated cross-domain auxiliary networks. Prior works are typically based on modeling social networks as random graphs representing users and their relations, and subsequently quantify the quality of mappings through cost functions that are proposed without sufficient rationale. Also, it remains unknown how to algorithmically meet the demand of such quantifications, i.e., to find the minimizer of the cost functions. We address those concerns in a more realistic social network modeling parameterized by community structures that can be leveraged as side information for de-anonymization. By Maximum A Posteriori (MAP) estimation, our first contribution is new and well justified cost functions, which, when minimized, enjoy superiority to previous ones in finding the correct mapping with the highest probability. The feasibility of the cost functions is then for the first time algorithmically characterized. While proving the general multiplicative inapproximability, we are able to propose two algorithms, which, respectively, enjoy an \epsilon-additive approximation and a conditional optimality in carrying out successful user re-identification. Our theoretical findings are empirically validated, with a notable dataset extracted from rare true cross-domain networks that reproduce genuine social network de-anonymization. Both theoretical and empirical observations also manifest the importance of community information in enhancing privacy inferencing.


title: A Four Russians algorithm for regular expression pattern matching

abstract: Given a regular expression <italic>R</italic> of length <italic>P</italic> and a word <italic>A</italic> of length <italic>N</italic>, the membership problem is to determine if <italic>A</italic> is in the language denoted by <italic>R</italic>. An <italic>O</italic>(<italic>PN</italic>/lg<italic>N</italic>) time algorithm is presented that is based on a lg<italic>N</italic> speedup of the standard <italic>O</italic>(<italic>PN</italic>) time simulation of <italic>R</italic>'s nonderministic finite automaton on <italic>A</italic> using a combination of the node-listing and “Four-Russians” paradigms. This result places a new worst-case upper bound on regular expression pattern matching. Moreover, in practice the method provides an implementation that is faster than existing software for small regular expressions.


title: Does Male Circumcision Affect Sexual Function, Sensitivity, or Satisfaction?—A Systematic Review

abstract: Abstract Introduction Circumcision of males is commonly carried out worldwide for reasons of health, medical need, esthetics, tradition, or religion. Whether circumcision impairs or improves male sexual function or pleasure is controversial. Aims The study aims to conduct a systematic review of the scientific literature. Methods A systematic review of published articles retrieved using keyword searches of the PubMed, EMBASE, and Cochrane databases was performed. Main Outcome Measures The main outcome measure is the assessment of findings in publications reporting original data relevant to the search terms and rating of quality of each study based on established criteria. Results Searches identified 2,675 publications describing the effects of male circumcision on aspects of male sexual function, sensitivity, sensation, or satisfaction. Of these, 36 met our inclusion criteria of containing original data. Those studies reported a total of 40,473 men, including 19,542 uncircumcised and 20,931 circumcised. Rated by the Scottish Intercollegiate Guidelines Network grading system, 2 were 1++ (high quality randomized controlled trials) and 34 were case-control or cohort studies (11 high quality: 2++; 10 well-conducted: 2+; 13 low quality: 2−). The 1++, 2++, and 2+ studies uniformly found that circumcision had no overall adverse effect on penile sensitivity, sexual arousal, sexual sensation, erectile function, premature ejaculation, ejaculatory latency, orgasm difficulties, sexual satisfaction, pleasure, or pain during penetration. Support for these conclusions was provided by a meta-analysis. Impairment in one or more parameters was reported in 10 of the 13 studies rated as 2−. These lower-quality studies contained flaws in study design (11), selection of cases and/or controls (5), statistical analysis (4), and/or data interpretation (6); five had multiple problems. Conclusion The highest-quality studies suggest that medical male circumcision has no adverse effect on sexual function, sensitivity, sexual sensation, or satisfaction. Morris BJ and Krieger JN. Does male circumcision affect sexual function, sensitivity, or satisfaction?—A systematic review. J Sex Med 2013;10:2644–2657.


Reduction of Work in Process Inventory and Production Lead Time in a Bearing Industry Using Value Stream Mapping Tool


title: Imirok: Real-time imitative robotic arm control for home robot applications

abstract: Training home robots to behave like human can help people with their daily chores and repetitive tasks. In this paper, we present Imirok, a system to remotely control robotic arms by user motion using low-cost, off-the-shelf mobile devices and webcam. The motion tracking algorithm detects user motion in real time, without classifier training or predefined action sets. Experimental results show that the system achieves 90% precision and recall rate on motion detection with blank background, and is robust under the change of cluttered background and user-to-camera distance.


title: Enhancing Source Camera Identification Based on Multiplicative Denoising Filter

abstract: Photo-response non-uniformity (PRNU) noise has been recognized as a unique fingerprint of digital cameras. However, how to extract precise PRNU noise from a given image is a main challenge. Previous literatures assume PRNU noise is a white Gaussian noise (WGN) and extracted by an additive denoising filter (ADF). However, experiments have demonstrated that the PRNU noise is a multiplicative noise. In this paper, an approach is proposed to extract more accurate PRNU noise through multiplicative denoising filter (MDF). Specifically, PRNU noise is modeled as a WGN multiplied by image content and extracted by proposed MDF based on linear minimum mean squared error (LMMSE) estimation. Experimental results demonstrate the proposed method outperforms, or at least performs comparably to, the state-of-the-art methods.


The Sonification Handbook


title: Titanium Silicide on Si:P With Precontact Amorphization Implantation Treatment: Contact Resistivity Approaching $1 \times 10^{-9}$ Ohm-cm2

abstract: In recent CMOS technology, extreme shrinking of contact area at source/drain regions raises serious concerns of high metal/semiconductor contact resistance. Confronting this problem, we introduce a precontact amorphization implantation plus Ti silicidation technique (PCAI + TiSi x ) and achieve ultralow contact resistivity ( $\rho _{c}$ ) of (1.3 – 1.5) $\times 10^{-9} ~\Omega \cdot \text {cm}^{2}$ on Si:P. This PCAI + TiSi x technique utilizes light amorphization (low-energy implantation), thin Ti and TiSi x film, and moderate thermal budget (500 °C –550 °C): these features are compatible with modern CMOS manufacturing. Moreover, the PCAI + TiSi x -induced $\rho _{c}$ reduction is proved universal on both n- and p-Si. With additional characterizations, we find that the silicidation-induced $\rho _{c}$ variation is not merely a Schottky barrier height tuning effect. The electrical and physical characterizations suggest that the low $\rho _{c}$ is strongly correlated with the formation of interfacial TiSi x crystallites between amorphous TiSi alloy and Si.


title: Extracting Three Dimensional Surface Model of Human Kidney from the Visible Human Data Set using Free Software

abstract: Three dimensional digital model of a representative human kidney is needed for a surgical simulator that is capable of simulating a laparoscopic surgery involving kidney. Buying a three dimensional computer model of a representative human kidney, or reconstructing a human kidney from an image sequence using commercial software, both involve (sometimes significant amount of) money. In this paper, author has shown that one can obtain a three dimensional surface model of human kidney by making use of images from the Visible Human Data Set and a few free software packages (ImageJ, ITK-SNAP, and MeshLab in particular). Images from the Visible Human Data Set, and the software packages used here, both do not cost anything. Hence, the practice of extracting the geometry of a representative human kidney for free, as illustrated in the present work, could be a free alternative to the use of expensive commercial software or to the purchase of a digital model.


title: Recognition of Information Leakage of Computer via Conducted Emanations on the Power Line

abstract: The security problem of screen image leakage from a display unit has become serious with the rapid speed of signal transmission technology. This paper presents a novel investigation on the characteristics of the power line compromising channel. Moreover, a measurement system has been actually developed for the leakage signal analyzing and image reconstruction. In order to overcome the degradation of reconstructed motion images and enhance the reconstructed image quality, a multi-image blind deconvolution method was proposed and test experiments were carried out to verify the effectiveness of the multi-image blind deconvolution algorithm based on the conducted signal from the power line.


The Effect Of Colors Of E-commerce Websites On Mood, Memorization And Buying Intention.


title: Machine Translation Approaches: Issues and Challenges

abstract: In the modern world, there is an increased need for language translations owing to the fact that language is an effective medium of communication. The demand for translation has become more in recent years due to increase in the exchange of information between various regions using different regional languages. Accessibility to web document in other languages, for instance, has been a concern for information Professionals. Machine translation (MT), a subfield under Artificial Intelligence, is the application of computers to the task of translating texts from one natural (human) language to another. Many approaches have been used in the recent times to develop an MT system. Each of these approaches has its own advantages and challenges. This paper takes a look at these approaches with the few of identifying their individual features, challenges and the best domain they are best suited to.


title: Mood Effects on Cognition: Affective Influences on the Content and Process of Information Processing and Behavior

abstract: Abstract How does affect influence the way people think, remember and process social information? Understanding the delicate interplay between feeling and thinking, affect and cognition has for long remained one of the greatest puzzles about human nature. This chapter reviews extensive research evidence for mood effects on both the content, and the process of cognition. After a brief introduction, theories of mood effects on cognition are reviewed. Next, research documenting the importance of affect congruence in the content of thinking is surveyed. Next we discuss the processing effects of affect, including recent empirical studies showing that affective states influence the quality of information processing. We conclude by discussing the theoretical and applied implications of this research, and the future prospects for this line of inquiry are considered.


Model comparison: A key challenge for transformation testing and version control in model driven software development


title: Crime Scene Reconstruction: Online Gold Farming Network Analysis

abstract: Many online games have their own ecosystems, where players can purchase in-game assets using game money. Players can obtain game money through active participation or “real money trading” through official channels: converting real money into game money. The unofficial market for real money trading gave rise to gold farming groups (GFGs), a phenomenon with serious impact in the cyber and real worlds. GFGs in massively multiplayer online role-playing games (MMORPGs) are some of the most interesting underground cyber economies because of the massive nature of the game. To detect GFGs, there have been various studies using behavioral traits. However, they can only detect gold farmers, not entire GFGs with internal hierarchies. Even worse, GFGs continuously develop techniques to hide, such as forming front organizations, concealing cyber-money, and changing trade patterns when online game service providers ban GFGs. In this paper, we analyze the characteristics of the ecosystem of a large-scale MMORPG, and devise a method for detecting GFGs. We build a graph that characterizes virtual economy transactions, and trace abnormal trades and activities. We derive features from the trading graph and physical networks used by GFGs to identify them in their entirety. Using their structure, we provide recommendations to defend effectively against GFGs while not affecting the existing virtual ecosystem.


title: The geometry of differential privacy: the sparse and approximate cases

abstract: In this work, we study trade-offs between accuracy and privacy in the context of linear queries over histograms. This is a rich class of queries that includes contingency tables and range queries, and has been a focus of a long line of work. For a set of $d$ linear queries over a database $x \in \R^N$, we seek to find the differentially private mechanism that has the minimum mean squared error. For pure differential privacy, an $O(\log^2 d)$ approximation to the optimal mechanism is known. Our first contribution is to give an $O(\log^2 d)$ approximation guarantee for the case of $(\eps,\delta)$-differential privacy. Our mechanism is simple, efficient and adds correlated Gaussian noise to the answers. We prove its approximation guarantee relative to the hereditary discrepancy lower bound of Muthukrishnan and Nikolov, using tools from convex geometry. We next consider this question in the case when the number of queries exceeds the number of individuals in the database, i.e. when $d>n \triangleq \|x\|_1$. It is known that better mechanisms exist in this setting. Our second main contribution is to give an $(\eps,\delta)$-differentially private mechanism which is optimal up to a $\polylog(d,N)$ factor for any given query set $A$ and any given upper bound $n$ on $\|x\|_1$. This approximation is achieved by coupling the Gaussian noise addition approach with a linear regression step. We give an analogous result for the $\eps$-differential privacy setting. We also improve on the mean squared error upper bound for answering counting queries on a database of size $n$ by Blum, Ligett, and Roth, and match the lower bound implied by the work of Dinur and Nissim up to logarithmic factors. The connection between hereditary discrepancy and the privacy mechanism enables us to derive the first polylogarithmic approximation to the hereditary discrepancy of a matrix $A$.


title: Bayesian inference for generalized linear mixed models of portfolio credit risk

abstract: Abstract The aims of this paper are threefold. First, we highlight the usefulness of generalized linear mixed models (GLMMs) in the modelling of portfolio credit default risk. The GLMM-setting allows for a flexible specification of the systematic portfolio risk in terms of observed fixed effects and unobserved random effects , in order to explain the phenomena of default dependence and time-inhomogeneity in historical default data. Second, we show that computational Bayesian techniques such as the Gibbs sampler can be successfully applied to fit models with serially correlated random effects, which are special instances of state space models . Third, we provide an empirical study using Standard and Poor's data on U.S. firms. A model incorporating rating category and sector effects, and a macroeconomic proxy variable for state-of-the-economy suggests the presence of a residual, cyclical, latent component in the systematic risk.


title: Using mobile phones for secure, distributed document processing in the developing world

abstract: Paper plays an essential role in many information ecologies in the developing world, but it can be inefficient and inflexible. We've developed an information services architecture that uses a smart phone equipped with a built-in digital camera to process augmented paper documents. The CAM document-processing framework exploits smart mobile phones' utility, usability, and growing ubiquity to link paper with modern information tools. CAM, so called because the phone's camera plays a key role in the user interface, is a three-tiered, document-based architecture for providing remote rural information services. The CAM framework comprises four components: CamForms, CamShell, CamBrowser, and CamServer.


title: A short form of the General Self-Efficacy Scale (GSE-6): Development, psychometric properties and validity in an intercultural non-clinical sample and a sample of patients at risk for heart failure

abstract: Objective: General self-efficacy has been found to be an influential variable related to the adaptation to stress and chronic illness, with the General Self-Efficacy (GSE) Scale by Jerusalem and Schwarzer being a reliable and valid instrument to assess this disposition. The aim of this study was to construct and test a short form of this scale to allow for a more economical assessment of the construct. ::: ::: Methods: The item characteristics of the original scale were assessed using an intercultural non-clinical sample (n=19,719). Six items with the highest coefficient of variation and good discrimination along the range of the trait were selected to build a short form of the instrument (GSE-6). Subsequently, the psychometric properties and the concurrent and predictive validity of the GSE-6 were tested in a longitudinal design with three measurements using a sample of patients with risk factors for heart failure (n=1,460). ::: ::: Results: Cronbach’s alpha for the GSE-6 was between .79 and .88. We found negative associations with symptoms of depression (–.35 and –.45), anxiety (–.35), and vital exhaustion (–.38) and positive associations with social support (.30), and mental health (.36). In addition, the GSE-6 score was positively associated with active problem-focused coping (.26) and distraction/self-encouragement (.25) and negatively associated with depressive coping (–.34). The baseline GSE-6 score predicted mental health and physical health after 28 months, even after controlling for the respective baseline score. The relative stability over twelve and 28 months was r=.50 and r=.60, respectively, while the mean self-efficacy score did not change over time. ::: ::: Conclusions: The six item short form of the GSE scale is a reliable and valid instrument that is useful for the economical assessment of general self-efficacy in large multivariate studies and for screening purposes.


Preserving Distributional Information in Dialogue Act Classification


title: Factors influencing online repurchase intention: The case of clothing customers in Thailand

abstract: The purpose of this study is to explore Thai online customers' repurchase intention towards clothing. This study integrated Delone and Mclean's e-commerce success model to predict customers' repurchase intention to purchase clothing on the Internet. The data was collected using convenience sampling method with a survey of the customers in Thailand who had experienced purchasing clothing online. The findings indicate that repurchase intention is mostly influenced by both online shopping satisfaction and online shopping trust. The relationships between Internet shopping value and online shopping satisfaction and online shopping trust are found to be significant as well. Components of website quality have differing effect on utilitarian and hedonic value. System quality and service quickness influences utilitarian value as well as the hedonic value. System accessibility and information timely positively influence utilitarian value while information variety and service receptiveness have a positive effect hedonic value.


title: Breast cancer statistics, 2017, racial disparity in mortality by state.

abstract: In this article, the American Cancer Society provides an overview of female breast cancer statistics in the United States, including data on incidence, mortality, survival, and screening. Approximately 252,710 new cases of invasive breast cancer and 40,610 breast cancer deaths are expected to occur among US women in 2017. From 2005 to 2014, overall breast cancer incidence rates increased among Asian/Pacific Islander (1.7% per year), non-Hispanic black (NHB) (0.4% per year), and Hispanic (0.3% per year) women but were stable in non-Hispanic white (NHW) and American Indian/Alaska Native (AI/AN) women. The increasing trends were driven by increases in hormone receptor-positive breast cancer, which increased among all racial/ethnic groups, whereas rates of hormone receptor-negative breast cancers decreased. From 1989 to 2015, breast cancer death rates decreased by 39%, which translates to 322,600 averted breast cancer deaths in the United States. During 2006 to 2015, death rates decreased in all racial/ethnic groups, including AI/ANs. However, NHB women continued to have higher breast cancer death rates than NHW women, with rates 39% higher (mortality rate ratio [MRR], 1.39; 95% confidence interval [CI], 1.35-1.43) in NHB women in 2015, although the disparity has ceased to widen since 2011. By state, excess death rates in black women ranged from 20% in Nevada (MRR, 1.20; 95% CI, 1.01-1.42) to 66% in Louisiana (MRR, 1.66; 95% CI, 1.54, 1.79). Notably, breast cancer death rates were not significantly different in NHB and NHW women in 7 states, perhaps reflecting an elimination of disparities and/or a lack of statistical power. Improving access to care for all populations could eliminate the racial disparity in breast cancer mortality and accelerate the reduction in deaths from this malignancy nationwide. CA Cancer J Clin 2017;67:439-448. © 2017 American Cancer Society.


title: Relative Building-Block Fitness and the Building Block Hypothesis

abstract: Abstract The building-block hypothesis states that the GA works well when short, low-order, highly-fit schemas recombine to form even more highly fit higher-order schemas. The ability to produce fitter and fitter partial solutions by combining building blocks is believed to be a primary source of the GA's search power, but the GA research community currently lacks precise and quantitative descriptions of how schema processing actually takes place during the typical evolution of a GA search. Another open problem is to characterize in detail the types of fitness landscapes for which crossover will be an effective operator. In this paper we first describe a class of fitness landscapes (the “Royal Road” functions) that we have designed to investigate these questions. We then present some unexpected experimental results concerning the GA's performance on simple instances of these landscapes, in which we vary the strength of reinforcement from “stepping stones”—fit intermediate-order schemas obtained by recombining fit low-order schemas. Finally, we compare the performance of the GA on these functions with that of three commonly used hill-climbing schemes, and find that one of them, “random-mutation hill-climbing”, significantly outperforms the GA on these functions.


title: A Survey of Text Summarization Extractive Techniques

abstract: Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and Abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An Abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented.