Cinnamon for diabetes mellitus.

BACKGROUND
Diabetes mellitus is a chronic metabolic disorder that is associated with an increased risk of cardiovascular disease, retinopathy, nephropathy, neuropathy, sexual dysfunction and periodontal disease. Improvements in glycaemic control may help to reduce the risk of these complications. Several animal studies show that cinnamon may be effective in improving glycaemic control. While these effects have been explored in humans also, findings from these studies have not yet been systematically reviewed.

OBJECTIVES
To evaluate the effects of cinnamon in patients with diabetes mellitus.

SEARCH METHODS
Pertinent randomised controlled trials were identified through AARP Ageline, AMED, AMI, BioMed Central gateway, CAM on PubMed, CINAHL, Dissertations Abstracts International, EMBASE, Health Source Nursing/Academic edition, International Pharmaceutical Abstracts, MEDLINE, Natural medicines comprehensive database, The Cochrane Library and TRIP database. Clinical trial registers and the reference lists of included trials were searched also (all up to January 2012). Content experts and manufacturers of cinnamon extracts were also contacted.

SELECTION CRITERIA
All randomised controlled trials comparing the effects of orally administered monopreparations of cinnamon (Cinnamomum spp.) to placebo, active medication or no treatment in persons with either type 1 or type 2 diabetes mellitus.

DATA COLLECTION AND ANALYSIS
Two review authors independently selected trials, assessed risk of bias and trial quality, and extracted data. We contacted study authors for missing information.

MAIN RESULTS
Ten prospective, parallel-group design, randomised controlled trials, involving a total of 577 participants with type 1 and type 2 diabetes mellitus, were identified. Risk of bias was high or unclear in all but two trials, which were assessed as having moderate risk of bias. Risk of bias in some domains was high in 50% of trials. Oral monopreparations of cinnamon (predominantly Cinnamomum cassia) were administered at a mean dose of 2 g daily, for a period ranging from 4 to 16 weeks. The effect of cinnamon on fasting blood glucose level was inconclusive. No statistically significant difference in glycosylated haemoglobin A1c (HbA1c), serum insulin or postprandial glucose was found between cinnamon and control groups. There were insufficient data to pool results for insulin sensitivity. No trials reported health-related quality of life, morbidity, mortality or costs. Adverse reactions to oral cinnamon were infrequent and generally mild in nature.

AUTHORS' CONCLUSIONS
There is insufficient evidence to support the use of cinnamon for type 1 or type 2 diabetes mellitus. Further trials, which address the issues of allocation concealment and blinding, are now required. The inclusion of other important endpoints, such as health-related quality of life, diabetes complications and costs, is also needed.


A Context-aware Collaborative Filtering Approach for Urban Black Holes Detection

Urban black hole, as a traffic anomaly, has caused lots of catastrophic accidents in many big cities nowadays. Traditional methods only depend on the single source data (e.g., taxi trajectories) to design blackhole detection algorithm from one point of view, which is rather incomplete to describe the regional crowd flow. In this paper, we model the urban black holes in each region of New York City (NYC) at different time intervals with a 3-dimensional tensor by fusing cross-domain data sources. Supplementing the missing entries of the tensor through a context-aware tensor decomposition approach, we leverage the knowledge from geographical features, 311 complaint features and human mobility features to recover the blackhole situation throughout NYC. The information can facilitate local residents and officials' decision making. We evaluate our model with five datasets related to NYC, diagnosing the urban black holes that cannot be identified (or earlier than those detected) by a single dataset. Experimental results demonstrate the advantages beyond four baseline methods.


Optimum auto exposure based on high-dynamic-range histogram

This paper introduces a new approach for optimum auto exposure (AE) based on the high-dynamic-range histogram (HDH) of a scene. The exposure control is optimal in terms of recorded information of the scene. The advantages over AE based on mean values of images will be shown as well as a proof of concept implementation and results. Futhermore possible extensions to this approach are discussed. The consideration of the HDH for auto exposure enables new possibilities for auto exposure control for mutliple-slope cameras. An AE method for multiple-slope cameras is proposed which controls exposure as well as the transition curve parameters in order to record a maximum of information from the scene. It considers controllable dynamic range as well as corse quantization in bright image regions. Key–Words: Optimum Auto Exposure, High Dynamic Range (HDR), multiple-slope camera January 26, 2007


3HAN: A Deep Neural Network for Fake News Detection


A Middleware for Fast and Flexible Sensor Network Deployment

A key problem in current sensor network technology is the heterogeneity of the available software and hardware platforms which makes deployment and application development a tedious and time consuming task. To minimize the unnecessary and repetitive implementation of identical functionalities for different platforms, we present our Global Sensor Networks (GSN) middleware which supports the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN's central concept is the virtual sensor abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. In this demonstration, we specifically focus on the deployment aspects and allow users to dynamically reconfigure the running system, to add new sensor networks on the fly, and to monitor the effects of the changes via a graphical interface. The GSN implementation is available from http://globalsn.sourceforge.net/.


Discovery of Online Shopping Patterns Across Websites

• In the online world, customers can easily navigate to different online stores to make purchases • Market basket analysis is often used to discover associations among products for brick-and-mortar stores, but rarely for online shops • Defined to online shopping patterns and developed 2 novel methods to perform market basket analysis across websites • The methods presented in this paper can be only not only to online shopping application, but to other dimensions as well. Uyanga, Do young suk


Towards Literate Artificial Intelligence

Speaker: Mrinmaya Sachan Thesis Committee: Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth (University of Pennsylvania) Thesis Proposal Jan. 22, 2018 1:00pm 8102 GHC Link to draft document: http://www.cs.cmu.edu/~mrinmays/thesis_proposal.pdf Standardized tests are often used to test students as they progress in the formal education system. These tests are widely available and measurable with clear evaluation procedures and metrics. Hence, these can serve as good tests for AI. We propose approaches for solving some of these tests. We broadly categorize these tests into two categories: open domain question answering tests such as reading comprehensions and elementary school science tests, and closed domain question answering tests such as intermediate or advanced math and science tests. We present alignment based approach with multi-task learning for the former. For closed domain tests, we propose a parsing to programs approach which can be seen as a natural language interface to expert systems. We also describe approaches for question generation based on instructional material in both open domain as well as closed domain settings. Finally, we show that we can improve both the question answering and question generation models by learning them jointly. This mechanism also allows us to leverage cheap unlabelled data for learning the two models. Our work can potentially be applied for the social good in the education domain. We perform studies on human subjects who found our approaches useful as assistive tools in education.


Plasmodium vivax segmentation using modified fuzzy divergence

This paper aims at introducing a new approach to Plasmodium vivax (P. vivax) detection from Leishman stained thin blood film. This scheme follows retrospective study design protocol where patients were selected at random in the clinic. The scheme consists of two main stages - firstly artefacts reduction, and secondly fuzzy divergence based segmentation of P. vivax infected region(s) from erythrocytes. Here, malaria parasite segmentation is done using divergence based threshold selection. Fuzzy approach is chosen to minimize ambiguity inherent in the microscopic images. Divergence algorithm is derived from Cauchy membership function to overcome the drawbacks in comparison with other well known membership functions.


Acquiring linear subspaces for face recognition under variable lighting

Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition.


Local Decorrelation For Improved Pedestrian Detection

Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of the resulting decision boundary may not be well matched to the natural topology of the data. Given highly correlated data, decision trees with oblique (multiple feature) splits can be effective. Use of oblique splits, however, comes at considerable computational expense. Inspired by recent work on discriminative decorrelation of HOG features, we instead propose an efficient feature transform that removes correlations in local neighborhoods. The result is an overcomplete but locally decorrelated representation ideally suited for use with orthogonal decision trees. In fact, orthogonal trees with our locally decorrelated features outperform oblique trees trained over the original features at a fraction of the computational cost. The overall improvement in accuracy is dramatic: on the Caltech Pedestrian Dataset, we reduce false positives nearly tenfold over the previous state-of-the-art.


Filtered channel features for pedestrian detection

This paper starts from the observation that multiple top performing pedestrian detectors can be modelled by using an intermediate layer filtering low-level features in combination with a boosted decision forest. Based on this observation we propose a unifying framework and experimentally explore different filter families. We report extensive results enabling a systematic analysis. Using filtered channel features we obtain top performance on the challenging Caltech and KITTI datasets, while using only HOG+LUV as low-level features. When adding optical flow features we further improve detection quality and report the best known results on the Caltech dataset, reaching 93% recall at 1 FPPI.


Crossing Nets: Dual Generative Models with a Shared Latent Space for Hand Pose Estimation

State-of-the-art methods for 3D hand pose estimation from depth images require large amounts of annotated training data. We propose to model the statistical relationships of 3D hand poses and corresponding depth images using two deep generative models with a shared latent space. By design, our architecture allows for learning from unlabeled image data in a semi-supervised manner. Assuming a one-to-one mapping between a pose and a depth map, any given point in the shared latent space can be projected into both a hand pose and a corresponding depth map. Regressing the hand pose can then be done by learning a discriminator to estimate the posterior of the latent pose given some depth map. To improve generalization and to better exploit unlabeled depth maps, we jointly train a generator and a discriminator. At each iteration, the generator is updated with the back-propagated gradient from the discriminator to synthesize realistic depth maps of the articulated hand, while the discriminator benefits from an augmented training set of synthesized and unlabeled samples. The proposed discriminator network architecture is highly efficient and runs at 90ḞPS on the CPU with accuracies comparable or better than state-of-art on 3 publicly available benchmarks.


Evaluation of the Minstrel rate adaptation algorithm in IEEE 802.11g WLANs

Rate adaptation varies the transmission rate of a wireless sender to match the wireless channel conditions, in order to achieve the best possible performance. It is a key component of IEEE 802.11 wireless networks. Minstrel is a popular rate adaptation algorithm due to its efficiency and availability in commonly used wireless drivers. However, despite its popularity, little work has been done on evaluating the performance of Minstrel or comparing it to the performance of fixed rates. In this paper, we conduct an experimental study that compares the performance of Minstrel against fixed rates in an IEEE 802.11g testbed. The experiment results show that whilst Minstrel performs reasonably well in static wireless channel conditions, in some cases the algorithm has difficulty selecting the optimal data rate in the presence of dynamic channel conditions. In addition, Minstrel performs well when the channel condition improves from bad quality to good quality. However, Minstrel has trouble selecting the optimal rate when the channel condition deteriorates from good quality to bad quality.


FEATURE ATTRIBUTION AS FEATURE SELECTION

Feature attribution methods identify “relevant” features as an explanation of a complex machine learning model. Several feature attribution methods have been proposed; however, only a few studies have attempted to define the “relevance” of each feature mathematically. In this study, we formalize the feature attribution problem as a feature selection problem. In our proposed formalization, there arise two possible definitions of relevance. We name the feature attribution problems based on these two relevances as Exclusive Feature Selection (EFS) and Inclusive Feature Selection (IFS). We show that several existing feature attribution methods can be interpreted as approximation algorithms for EFS and IFS. Moreover, through exhaustive experiments, we show that IFS is better suited as the formalization for the feature attribution problem than EFS.


Android Malware Characterization Using Metadata and Machine Learning Techniques

Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android Markets are more relevant in detecting malware, such as the application developer and certificate issuer, and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.


Can machine learning explain human learning?

Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is exploited as a tool for analyzing data coming from experimental studies, but it has been recently applied to humans as if they were algorithms that learn from data. One example is the application of Rademacher Complexity, which measures the capacity of a learning machine, to human learning, which led to the formulation of Human Rademacher Complexity (HRC). In this line of research, we propose here a more powerful measure of complexity, the Human Algorithmic Stability (HAS), as a tool to better understand the learning process of humans. The experimental results from three different empirical studies, on more than 600 engineering students from the University of Genoa, showed that HAS (i) can be measured without the assumptions required by HRC, (ii) depends not only on the knowledge domain, as HRC, but also on the complexity of the problem, and (iii) can be exploited for better understanding of the human learning process. & 2016 Elsevier B.V. All rights reserved.


Children learning with a social robot

We used a social robot as a teaching assistant in a class for children's collaborative learning. In the class, a group of 6th graders learned together using Lego Mindstorms. The class consisted of seven lessons with Robovie, a social robot, followed by one lesson to test their individual achievement. Robovie managed the class and explained how to use Lego Mindstorms. In addition to such basic management behaviors for the class, we prepared social behaviors for building relationships with the children and encouraging them. The result shows that the social behavior encouraged children to work more in the first two lessons, but did not affect them in later lessons. On the other hand, social behavior contributed to building relationships and attaining better social acceptance.


Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection

In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic seizure detection is proposed. The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor (k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate using the top-ranked discriminative rules from the HKB. The data set is taken from a publicly available EEG database which aims to differentiate healthy subjects and subjects suffering from epilepsy diseases. Experimental results show the efficiency of our proposed system. The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes. 2011 Elsevier Ltd. All rights reserved.


Accelerator-Aware Pruning for Convolutional Neural Networks

Convolutional neural networks have shown tremendous performance in computer vision tasks, but their excessive amount of weights and operations prevent them from being adopted in embedded environments. One of the solutions involves pruning, where some unimportant weights are forced to be zero. Many pruning schemes have been proposed, but have focused mainly on the number of pruned weights. The previous pruning schemes hardly considered ASIC or FPGA accelerator architectures. When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems including internal buffer mis-alignment and load imbalance. This paper proposes a new pruning scheme that reflects accelerator architectures. In the proposed scheme, pruning is performed so that the same number of weights remain for each weight group corresponding to activations fetched simultaneously. In this way, the pruning scheme resolves the inefficiency problems. Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet. Furthermore, the proposed scheme demonstrated a comparable pruning ratio in slimmed networks that were already pruned channel-wisely. In addition to improving the efficiency of previous sparse accelerators, it will be also shown that the proposed pruning scheme can be used to reduce the logic complexity of sparse accelerators.


Hierarchical Attention Networks

We propose a novel attention network, which accurately attends to target objects of various scales and shapes in images through multiple stages. The proposed network enables multiple layers to estimate attention in a convolutional neural network (CNN). The hierarchical attention model gradually suppresses irrelevant regions in an input image using a progressive attentive process over multiple CNN layers. The attentive process in each layer determines whether to pass or suppress feature maps for use in the next convolution. We employ local contexts to estimate attention probability at each location since it is difficult to infer accurate attention by observing a feature vector from a single location only. The experiments on synthetic and real datasets show that the proposed attention network outperforms traditional attention methods in various attribute prediction tasks.


A persistence landscapes toolbox for topological statistics

Topological data analysis provides a multiscale description of the geometry and topology of quantitative data. The persistence landscape is a topological summary that can be easily combined with tools from statistics and machine learning. We give efficient algorithms for calculating persistence landscapes, their averages, and distances between such averages. We discuss an implementation of these algorithms and some related procedures. These are intended to facilitate the combination of statistics and machine learning with topological data analysis. We present an experiment showing that the low-dimensional persistence landscapes of points sampled from spheres (and boxes) of varying dimensions differ.


Anime Artist Analysis based on CNN

Artist recognition is not easy for human beings if the artworks are general and 1 share many similarities, but modern machine learning or deep learning models may 2 help people identify the artwork authors. In this project, we trained Convolutional 3 Neural Networks (CNN) identifying Japanese anime artists. Our dataset was 4 crawled from Pixiv, a well-known Japanese website containing lots of fine anime 5 artworks. Specifically, the dataset has 9 authors and each one has 200 paintings. 6 We trained different kinds of networks, from a very basic Conv2D net to more 7 advanced networks, in order to carry out the identification. It turned out that 8 traditional neural networks can in some way identify different authors, but the 9 accuracy could be improved to a better level by tuning hyperparameters or using 10 more advanced networks such as ResNet and VGGNet. 11


Wideband and Low-Profile H-Plane Ridged SIW Horn Antenna Mounted on a Large Conducting Plane

This communication presents a wide-band and low-profile H-plane horn antenna based on ridged substrate integrated waveguide (SIW) with a large conducting ground. The horn antenna is implemented in a single substrate with a thickness of 0.13 λ0 at the center frequency. Despite its low profile, the new H-plane horn antenna achieves a very wide bandwidth by employing an arc-shaped copper taper printed on the extended dielectric slab and a three-step ridged SIW transition. The ridged SIW is critical for widening the operation bandwidth and lowering the characteristic impedance so that an excellent impedance matching from the coaxial probe to the narrow SIW can be obtained over a wide frequency range. Measured VSWR of the fabricated horn antenna is below 2.5 from 6.6 GHz to 18 GHz. The antenna also exhibits stable radiation beam over the same frequency range. It is observed that measured results agree well with simulated ones.


Simurg: An Extendable Multilingual Corpus for Abstractive Single Document Summarization

Abstractive single document summarization is considered as a challenging problem in the field of artificial intelligence and natural language processing. Meanwhile and specifically in the last two years, several deep learning summarization approaches were proposed that once again attracted the attention of researchers to this field.
 It is a well-known issue that deep learning approaches do not work well with small amounts of data. With some exceptions, this is, unfortunately, the case for most of the datasets available for the summarization task. Besides this problem, it should be considered that phonetic, morphological, semantic and syntactic features of the language are constantly changing over the time and unfortunately most of the summarization corpora are constructed from old resources. Another problem is the language of the corpora. Not only in the summarization field, but also in other fields of natural language processing, most of the corpora are only available in English. In addition to the above problems, license terms, and fees of the corpora are obstacles that prevent many academics and specifically non-academics from accessing these data.
 This work describes an open source framework to create an extendable multilingual corpus for abstractive single document summarization that addresses the above-mentioned problems. We describe a tool consisted of a scalable crawler and a centralized key-value store database to construct a corpus of an arbitrary size using a news aggregator service.


Detecting Curve Text in the Wild: New Dataset and New Solution

Scene text detection has been made great progress in recent years. The detection manners are evolving from axisaligned rectangle to rotated rectangle and further to quadrangle. However, current datasets contain very little curve text, which can be widely observed in scene images such as signboard, product name and so on. To raise the concerns of reading curve text in the wild, in this paper, we construct a curve text dataset named CTW1500, which includes over 10k text annotations in 1,500 images (1000 for training and 500 for testing). Based on this dataset, we pioneering propose a polygon based curve text detector (CTD) which can directly detect curve text without empirical combination. Moreover, by seamlessly integrating the recurrent transverse and longitudinal offset connection (TLOC), the proposed method can be end-to-end trainable to learn the inherent connection among the position offsets. This allows the CTD to explore context information instead of predicting points independently, resulting in more smooth and accurate detection. We also propose two simple but effective post-processing methods named nonpolygon suppress (NPS) and polygonal non-maximum suppression (PNMS) to further improve the detection accuracy. Furthermore, the proposed approach in this paper is designed in an universal manner, which can also be trained with rectangular or quadrilateral bounding boxes without extra efforts. Experimental results on CTW-1500 demonstrate our method with only a light backbone can outperform state-of-the-art methods with a large margin. By evaluating only in the curve or non-curve subset, the CTD + TLOC can still achieve the best results. Code is available at https://github.com/Yuliang-Liu/Curve-Text-Detector.


Building and evaluating web corpora representing national varieties of English

Corpora are essential resources for language studies, as well as for training statistical natural language processing systems. Although very large English corpora have been built, only relatively small corpora are available for many varieties of English. National top-level domains (e.g., .au, .ca) could be exploited to automatically build web corpora, but it is unclear whether such corpora would reflect the corresponding national varieties of English; i.e., would a web corpus built from the .ca domain correspond to Canadian English? In this article we build web corpora from national top-level domains corresponding to countries in which English is widely spoken. We then carry out statistical analyses of these corpora in terms of keywords, measures of corpus comparison based on the Chi-square test and spelling variants, and the frequencies of words known to be marked in particular varieties of English. We find evidence that the web corpora indeed reflect the corresponding national varieties of English. We then demonstrate, through a case study on the analysis of Canadianisms, that these corpora could be valuable lexicographical resources.


Benefiting from Social Capital in Online Support Groups: An Empirical Study of Cancer Patients

With measures specific to the online cancer environment and data from an online survey of cancer patients, the current study finds support for the following model: asynchronous online communication --> social interaction --> social support --> positive health outcomes in terms of stress, depression, and coping. The findings suggest that the Internet can be a positive cyber venue for cancer patients as they confront illness, undergo treatment, and seek out support.


Contemporary reconstruction of the mandible.

Reconstruction of the mandible has evolved significantly over the last 40years. Early attempts were often disfiguring and wrought with complications but with the introduction of free tissue transfer of well vascularized bone in the 1970's there was a significant improvement in outcomes. In recent years the harvest, inset, and microvascular anatomosis have been refined to the point that success rates are reported as high as 99% throughout the literature. Focus has now shifted to optimizing functional and aesthetic outcomes after mandible reconstruction. This paper will be a review defect classification, goals of reconstruction, the various donor sites, dental rehabilitation, new advances, and persistent problems. Reconstruction of segmental mandibular defects after ablative surgery is best accomplished using free tissue transfer to restore mandibular continuity and function. Reestablishing occlusion and optimizing tongue mobility are important to post-operative oral function. Persistent problems in oro-mandibular reconstruction relate to the effects of radiation treatment on the native tissue and include xerostomia, dysgeusia, osteoradionecrosis and trismus. These problems continue to plague the oral cancer patient despite the significant advances that allow a far more complete functional restoration than could be accomplished a mere two decades ago.


Interactive Groundwater (IGW): An innovative digital laboratory for groundwater education and research

In this study, we present an award-wining software environment for groundwater education and research. The software functions as a ‘‘digital laboratory’’ in which students can freely explore: visually creating an aquifer of desired configurations and then immediately investigating and visualizing the groundwater system. Students learn by active exploration and interaction. The software allows introducing routinely research and complex problem-solving into the classroom. 2004 Wiley Periodicals, Inc. Comput Appl Eng Educ 11: 179 202, 2003; Published online in Wiley InterScience (www.interscience.wiley.com.); DOI 10.1002/cae.10052


Intelligent accident identification system using GPS , GSM modem

Recently technological and population development, the usage of vehicles are rapidly increasing and at the same time the occurrence accident is also increased. Hence, the value of human life is ignored. No one can prevent the accident, but can save their life by expediting the ambulance to the hospital in time. A new vivid scheme called Intelligent Transportation System (ITS) is introduced. The objective of this scheme is to minimize the delay caused by traffic congestion and to provide the smooth flow of emergency vehicles. The concept of this scheme is to green the traffic signal in the path of ambulance automatically with the help of RF module. So that the ambulance can reach the spot in time and human life can be saved and the accident location is identified sends the accident location immediately to the main server. The main server finds the nearest ambulance to the accident zone and sends the exact accident location to the emergency vehicle. The control unit monitors the ambulance and provides the shortest path to the ambulance at the same time it controls the traffic light according to the ambulance location and thus arriving at the hospital safely. This scheme is fully automated, thus it locates the accident spot accurately, controls the traffic lights, provide the shortest path to reach the location and to the hospital in time.


Development of vehicle tracking system using GPS and GSM modem

The ability to track vehicles is useful in many applications including security of personal vehicles, public transportation systems, fleet management and others. Furthermore, the number of vehicles on the road globally is also expected to increase rapidly. Therefore, the development of vehicle tracking system using the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) modem is undertaken with the aim of enabling users to locate their vehicles with ease and in a convenient manner. The system will provide users with the capability to track vehicle remotely through the mobile network. This paper presents the development of the vehicle tracking system's hardware prototype. Specifically, the system will utilize GPS to obtain a vehicle's coordinate and transmit it using GSM modem to the user's phone through the mobile network. The main hardware components of the system are u-blox NEO-6Q GPS receiver module, u-blox LEON-G100 GSM module and Arduino Uno microcontroller. The developed vehicle tracking system demonstrates the feasibility of near real-time tracking of vehicles and improved customizability, global operability and cost when compared to existing solutions.


The survey of GSM wireless communication system

In the past decade, wireless communications experienced an explosive growth period and became an integral part of modern society. The convenience and flexibility offered by mobile communications have made it one of the fastest growing areas of telecommunications. Mobile communication systems have experienced rapid growth in the number of users as well as the range of services provided during the last two decades. The Global System for Mobile communications, GSM, is a pan-European Mobile communication system in the 900 MHz band which was first introduced in the early years of this decade. An increasing demand for data transmission services over the GSM system has been driven by the wide use of the internet applications. In this thesis, we focus on the survey of GSM for wireless communication, which is one of the most widely deployed second generation wireless cellular systems in the world. While voice was the primary service provided early communication systems, current systems offer other transmission services. One of the most commonly used cellular systems is based on the Global System for Mobile communication, GSM, standard. We briefly introduce the GSM System configuration and major properties. It included five parts. It is about service and features, architecture of GSM system, channel and frame structure of GSM, GSM security features, data in the GSM System.


Adaptive postfiltering for quality enhancement of coded speech

AbstrucfAn adaptive postfiltering algorithm for enhancing the perceptual quality of coded speech is presented. The posffilter consists of a long-term postfilter section in cascade with a shortterm postfilter section and includes spectral tilt compensation and automatic gain control. The long-term section emphasizes pitch harmonics and attenuates the spectral valleys between pitch harmonics. The short-term section, on the other hand, emphasizes speech formants and attenuates the spectral valleys between formants. Both filter sections have poles and zeros. Unlike earlier postfilters that often introduced a substantial amount of muffling to the output speech, our postfilter significantly reduces this effect by minimizing the spectral tilt in its frequency response. As a result, this postlilter achieves noticeable noise reduction while introducing only minimal distortion in speech. The complexity of the postfilter is quite low. Variations of this postlilter are now being used in several national and international speech coding standards. This paper presents for the first time a complete description of our original postfiltering algorithm and the underlying ideas that motivated its development.


Developing a Web-Based Application using OWL and SWRL

Data integration is central in Web application development because these applications typically deal with a variety of information formats. Ontology-driven applications face the additional challenge of integrating these multiple formats with the information stored in ontologies. A number of mappings are required to reconcile the variety of formats to produce a coherent overall system. To address these mappings we have developed a number of open source tools that support transformations between some of the common formats encountered when developing an ontology-driven Web application. The Semantic Web Rule Language (SWRL) is a central building block in these tools. We describe these tools and illustrate their use in the development of a prototype Web-based application.


Styling with Attention to Details

Fashion as characterized by its nature, is driven by style. In this paper, we propose a method that takes into account the style information to complete a given set of selected fashion items with a complementary fashion item. Complementary items are those items that can be worn along with the selected items according to the style. Addressing this problem facilitates in automatically generating stylish fashion ensembles leading to a richer shopping experience for users. Recently, there has been a surge of online social websites where fashion enthusiasts post the outfit of the day and other users can like and comment on them. These posts contain a gold-mine of information about style. In this paper, we exploit these posts to train a deep neural network which captures style in an automated manner. We pose the problem of predicting complementary fashion items as a sequence to sequence problem where the input is the selected set of fashion items and the output is a complementary fashion item based on the style information learned by the model. We use the encoder decoder architecture to solve this problem of completing the set of fashion items. We evaluate the goodness of the proposed model through a variety of experiments. We empirically observe that our proposed model outperforms competitive baseline like apriori algorithm by ~28% in terms of accuracy for top-1 recommendation to complete the fashion ensemble. We also perform retrieval based experiments to understand the ability of the model to learn style and rank the complementary fashion items and find that using attention in our encoder decoder model helps in improving the mean reciprocal rank by ~24%. Qualitatively we find the complementary fashion items generated by our proposed model are richer than the apriori algorithm.


A Framework for Discovering Internal Financial Fraud Using Analytics

In today's knowledge based society, financial fraud has become a common phenomenon. Moreover, the growth in knowledge discovery in databases and fraud audit has made the detection of internal financial fraud a major area of research. On the other hand, auditors find it difficult to apply a majority of techniques in the fraud auditing process and to integrate their domain knowledge in this process. In this Paper a framework called "Knowledge-driven Internal Fraud Detection (KDIFD)" is proposed for detecting internal financial frauds. The framework suggests a process-based approach that considers both forensic auditor's tacit knowledge base and computer-based data analysis and mining techniques. The proposed framework can help auditor in discovering internal financial fraud more efficiently.


Modified image segmentation method based on region growing and region merging

Image segmentation is one of the basic concepts widely used in each and every fields of image processing. The entire process of the proposed work for image segmentation comprises of 3 phases: Threshold generation with dynamic Modified Region Growing phase (DMRG), texture feature generation phase and region merging phase. by dynamically changing two thresholds, the given input image can be performed as DMRG, in which the cuckoo search optimization algorithm helps to optimize the two thresholds in modified region growing. after obtaining the region growth segmented image, the edges are detected with edge detection algorithm. In the second phase, the texture feature is extracted using entropy based operation from the input image. In region merging phase, the results obtained from the texture feature generation phase is combined with the results of DMRG phase and similar regions are merged by using a distance comparison between regions. The proposed work is implemented using Mat lab platform with several medical images. the performance of the proposed work is evaluated using the metrics sensitivity, specificity and accuracy. the results show that this proposed work provides very good accuracy for the segmentation process in images.


Style Augmentation: Data Augmentation via Style Randomization

We introduce style augmentation, a new form of data augmentation based on random style transfer, for improving the robustness of convolutional neural networks (CNN) over both classification and regression based tasks. During training, our style augmentation randomizes texture, contrast and color, while preserving shape and semantic content. This is accomplished by adapting an arbitrary style transfer network to perform style randomization, by sampling input style embeddings from a multivariate normal distribution instead of inferring them from a style image. In addition to standard classification experiments, we investigate the effect of style augmentation (and data augmentation generally) on domain transfer tasks. We find that data augmentation significantly improves robustness to domain shift, and can be used as a simple, domain agnostic alternative to domain adaptation. Comparing style augmentation against a mix of seven traditional augmentation techniques, we find that it can be readily combined with them to improve network performance. We validate the efficacy of our technique with domain transfer experiments in classification and monocular depth estimation, illustrating consistent improvements in generalization.


Globally optimal toon tracking

The ability to identify objects or region correspondences between consecutive frames of a given hand-drawn animation sequence is an indispensable tool for automating animation modification tasks such as sequence-wide recoloring or shape-editing of a specific animated character. Existing correspondence identification methods heavily rely on appearance features, but these features alone are insufficient to reliably identify region correspondences when there exist occlusions or when two or more objects share similar appearances. To resolve the above problems, manual assistance is often required. In this paper, we propose a new correspondence identification method which considers both appearance features and motions of regions in a global manner. We formulate correspondence likelihoods between temporal region pairs as a network flow graph problem which can be solved by a well-established optimization algorithm. We have evaluated our method with various animation sequences and results show that our method consistently outperforms the state-of-the-art methods without any user guidance.


Aesthetic quality classification of photographs based on color harmony

Aesthetic quality classification plays an important role in how people organize large photo collections. In particular, color harmony is a key factor in the various aspects that determine the perceived quality of a photo, and it should be taken into account to improve the performance of automatic aesthetic quality classification. However, the existing models of color harmony take only simple color patterns into consideration–e.g., patches consisting of a few colors–and thus cannot be used to assess photos with complicated color arrangements. In this work, we tackle the challenging problem of evaluating the color harmony of photos with a particular focus on aesthetic quality classification. A key point is that a photograph can be seen as a collection of local regions with color variations that are relatively simple. This led us to develop a method for assessing the aesthetic quality of a photo based on the photo's color harmony. We term the method ‘bags-of-color-patterns.’ Results of experiments on a large photo collection with user-provided aesthetic quality scores show that our aesthetic quality classification method, which explicitly takes into account the color harmony of a photo, outperforms the existing methods. Results also show that the classification performance is improved by combining our color harmony feature with blur, edges, and saliency features that reflect the aesthetics of the photos.


Estimation of IMU and MARG orientation using a gradient descent algorithm

This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.


Mori-Zwanzig formalism as a practical computational tool.

An operational procedure is presented to compute explicitly the different terms in the generalized Langevin equation (GLE) for a few relevant variables obtained within Mori-Zwanzig formalism. The procedure amounts to introducing an artificial controlled parameter which can be tuned in such a way that the so-called projected dynamics becomes explicit and the GLE reduces to a Markovian equation. The projected dynamics can be realised in practice by introducing constraints, and it is shown that the Green-Kubo formulae computed with these dynamics do not suffer from the plateau problem. The methodology is illustrated in the example of star polymer molecules in a melt using their center of mass as relevant variables. Through this example, we show that not only the effective potentials, but also the friction forces and the noise play a very important role in the dynamics.


Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability

Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the number of samples. A number of kernel approximation methods have thus been proposed in the recent years, among which the random features method gains much popularity due to its simplicity and direct reduction of nonlinear problem to a linear one. Different random feature functions have since been proposed to approximate a variety of kernel functions. Among them the Random Binning (RB) feature, proposed in the first random-feature paper [21], has drawn much less attention than the Random Fourier (RF) feature proposed also in [21]. In this work, we observe that the RB features, with right choice of optimization solver, could be orders-of-magnitude more efficient than other random features and kernel approximation methods under the same requirement of accuracy. We thus propose the first analysis of RB from the perspective of optimization, which by interpreting RB as a Randomized Block Coordinate Descent in the infinite-dimensional space, gives a faster convergence rate compared to that of other random features. In particular, we show that by drawing R random grids with at least κ number of non-empty bins per grid in expectation, RB method achieves a convergence rate of O(1/κ R)), which not only sharpens its O(1/√R) rate from Monte Carlo analysis, but also shows a κ times speedup over other random features under the same analysis framework. In addition, we demonstrate another advantage of RB in the L1-regularized setting, where unlike other random features, a RB-based Coordinate Descent solver can be parallelized with guaranteed speedup proportional to κ. Our extensive experiments demonstrate the superior performance of the RB features over other random features and kernel approximation methods.


HHCART: An Oblique Decision Tree

Decision trees are a popular technique in statistical data classification. They recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. The basic Classification and Regression Tree (CART) algorithm partitions the feature space using axis parallel splits. When the true decision boundaries are not aligned with the feature axes, this approach can produce a complicated boundary structure. Oblique decision trees use oblique decision boundaries to potentially simplify the boundary structure. The major limitation of this approach is that the tree induction algorithm is computationally expensive. In this article we present a new decision tree algorithm, called HHCART. The method utilizes a series of Householder matrices to reflect the training data at each node during the tree construction. Each reflection is based on the directions of the eigenvectors from each classes’ covariance matrix. Considering axis parallel splits in the reflected training data provides an efficient way of finding oblique splits in the unreflected training data. Experimental results show that the accuracy and size of the HHCART trees are comparable with some benchmark methods in the literature. The appealing feature of HHCART is that it can handle both qualitative and quantitative features in the same oblique split.


Stillbirths: economic and psychosocial consequences

Despite the frequency of stillbirths, the subsequent implications are overlooked and underappreciated. We present findings from comprehensive, systematic literature reviews, and new analyses of published and unpublished data, to establish the effect of stillbirth on parents, families, health-care providers, and societies worldwide. Data for direct costs of this event are sparse but suggest that a stillbirth needs more resources than a livebirth, both in the perinatal period and in additional surveillance during subsequent pregnancies. Indirect and intangible costs of stillbirth are extensive and are usually met by families alone. This issue is particularly onerous for those with few resources. Negative effects, particularly on parental mental health, might be moderated by empathic attitudes of care providers and tailored interventions. The value of the baby, as well as the associated costs for parents, families, care providers, communities, and society, should be considered to prevent stillbirths and reduce associated morbidity.


Improved Text Extraction from PDF Documents for Large-Scale Natural Language Processing

The inability of reliable text extraction from arbitrary documents is often an obstacle for large scale NLP based on resources crawled from the Web. One of the largest problems in the conversion of PDF documents is the detection of the boundaries of common textual units such as paragraphs, sentences and words. PDF is a file format optimized for printing and encapsulates a complete description of the layout of a document including text, fonts, graphics and so on. This paper describes a tool for extracting texts from arbitrary PDF files for the support of largescale data-driven natural language processing. Our approach combines the benefits of several existing solutions for the conversion of PDF documents to plain text and adds a language-independent post-processing procedure that cleans the output for further linguistic processing. In particular, we use the PDF-rendering libraries pdfXtk, Apache Tika and Poppler in various configurations. From the output of these tools we recover proper boundaries using on-the-fly language models and languageindependent extraction heuristics. In our research, we looked especially at publications from the European Union, which constitute a valuable multilingual resource, for example, for training statistical machine translation models. We use our tool for the conversion of a large multilingual database crawled from the EU bookshop with the aim of building parallel corpora. Our experiments show that our conversion software is capable of fixing various common issues leading to cleaner data sets in the end.


Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News

While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting – in a totally automated way – a highcoverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way ‘crowd-sourced’ affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a naı̈ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.


Response and Habituation of the Human Amygdala during Visual Processing of Facial Expression

We measured amygdala activity in human volunteers during rapid visual presentations of fearful, happy, and neutral faces using functional magnetic resonance imaging (fMRI). The first experiment involved a fixed order of conditions both within and across runs, while the second one used a fully counterbalanced order in addition to a low level baseline of simple visual stimuli. In both experiments, the amygdala was preferentially activated in response to fearful versus neutral faces. In the counterbalanced experiment, the amygdala also responded preferentially to happy versus neutral faces, suggesting a possible generalized response to emotionally valenced stimuli. Rapid habituation effects were prominent in both experiments. Thus, the human amygdala responds preferentially to emotionally valenced faces and rapidly habituates to them.


Deception through telling the truth ? ! : Experimental evidence from individuals and teams

Informational asymmetries abound in economic decision making and often provide an incentive for deception through telling a lie or misrepresenting information. In this paper I use a cheap-talk sender-receiver experiment to show that telling the truth should be classified as deception too if the sender chooses the true message with the expectation that the receiver will not follow the sender’s (true) message. The experimental data reveal a large degree of ‘sophisticated’ deception through telling the truth. The robustness of my broader definition of deception is confirmed in an experimental treatment where teams make decisions. JEL-classification: C72, C91, D82


Metacognitive Beliefs About Procrastination : Development and Concurrent Validity of a Self-Report Questionnaire

This article describes the development of a questionnaire on metacognitive beliefs about procrastination. In Study 1 we performed a principal axis factor analysis that suggested a twofactor solution for the data obtained from the preliminary questionnaire. The factors identified were named positive and negative metacognitive beliefs about procrastination. The factor analysis reduced the questionnaire from 22 to 16 items, with each factor consisting of 8 items. In Study 2 we performed a confirmatory factor analysis that provided support for the twofactor solution suggested by the exploratory factor analysis. Both factors had adequate internal consistency. Concurrent validity was partially established through correlation analyses. These showed that positive metacognitive beliefs about procrastination were positively correlated with decisional procrastination, and that negative metacognitive beliefs about procrastination were positively correlated with both decisional and behavioral procrastination. The Metacognitive Beliefs About Procrastination Questionnaire may aid future research into procrastination and facilitate clinical assessment and case formulation.


One-Class Kernel Spectral Regression for Outlier Detection

The paper introduces a new efficient nonlinear oneclass classifier formulated as the Rayleigh quotient criterion optimisation. The method, operating in a reproducing kernel Hilbert subspace, minimises the scatter of target distribution along an optimal projection direction while at the same time keeping projections of positive observations distant from the mean of the negative class. We provide a graph embedding view of the problem which can then be solved efficiently using the spectral regression approach. In this sense, unlike previous similar methods which often require costly eigen-computations of dense matrices, the proposed approach casts the problem under consideration into a regression framework which is computationally more efficient. In particular, it is shown that the dominant complexity of the proposed method is the complexity of computing the kernel matrix. Additional appealing characteristics of the proposed one-class classifier are: 1-the ability to be trained in an incremental fashion (allowing for application in streaming data scenarios while also reducing the computational complexity in a non-streaming operation mode); 2-being unsupervised, but providing the option for refining the solution using negative training examples, when available; Last but not least, 3-the use of the kernel trick which facilitates a nonlinear mapping of the data into a high-dimensional feature space to seek better solutions. Extensive experiments conducted on several datasets verify the merits of the proposed approach in comparison with other alternatives.


Very greedy crossover in a genetic algorithm for the traveling salesman problem

In the traveling salesman problem, we are given a set of cities and the distances between them, and we seek a shortest tour that visits each city exactly once and returns to the starting city. Many researchers have described genetic algorithms for this problem, and they have often focused on the crossover operator, which builds offspring tours by combining two parental tours. Very greedy crossover extends several of these operators; as it builds a tour, it always appends the shortest parental edge to a city not yet visited, if there is such an edge. A steady-state genetic algorithm using this operator, mutation by inversion, and rank-based probabilities for both selection and deletion shows good results on a suite of flve test problems.


Phishing detection based Associative Classification data mining

Website phishing is considered one of the crucial security challenges for the online community due to the massive numbers of online transactions performed on a daily basis. Website phishing can be described as mimicking a trusted website to obtain sensitive information from online users such as usernames and passwords. Black lists, white lists and the utilisation of search methods are examples of solutions to minimise the risk of this problem. One intelligent approach based on data mining called Associative Classification (AC) seems a potential solution that may effectively detect phishing websites with high accuracy. According to experimental studies, AC often extracts classifiers containing simple ‘‘If-Then’’ rules with a high degree of predictive accuracy. In this paper, we investigate the problem of website phishing using a developed AC method called Multi-label Classifier based Associative Classification (MCAC) to seek its applicability to the phishing problem. We also want to identify features that distinguish phishing websites from legitimate ones. In addition, we survey intelligent approaches used to handle the phishing problem. Experimental results using real data collected from different sources show that AC particularly MCAC detects phishing websites with higher accuracy than other intelligent algorithms. Further, MCAC generates new hidden knowledge (rules) that other algorithms are unable to find and this has improved its classifiers predictive performance. 2014 Elsevier Ltd. All rights reserved.


Assessing the severity of phishing attacks: A hybrid data mining approach

Available online 19 August 2010


Why phishing works

To build systems shielding users from fraudulent (or phishing) websites, designers need to know which attack strategies work and why. This paper provides the first empirical evidence about which malicious strategies are successful at deceiving general users. We first analyzed a large set of captured phishing attacks and developed a set of hypotheses about why these strategies might work. We then assessed these hypotheses with a usability study in which 22 participants were shown 20 web sites and asked to determine which ones were fraudulent. We found that 23% of the participants did not look at browser-based cues such as the address bar, status bar and the security indicators, leading to incorrect choices 40% of the time. We also found that some visual deception attacks can fool even the most sophisticated users. These results illustrate that standard security indicators are not effective for a substantial fraction of users, and suggest that alternative approaches are needed.


Decision strategies and susceptibility to phishing

Phishing emails are semantic attacks that con people into divulging sensitive information using techniques to make the user believe that information is being requested by a legitimate source. In order to develop tools that will be effective in combating these schemes, we first must know how and why people fall for them. This study reports preliminary analysis of interviews with 20 non-expert computer users to reveal their strategies and understand their decisions when encountering possibly suspicious emails. One of the reasons that people may be vulnerable to phishing schemes is that awareness of the risks is not linked to perceived vulnerability or to useful strategies in identifying phishing emails. Rather, our data suggest that people can manage the risks that they are most familiar with, but don't appear to extrapolate to be wary of unfamiliar risks. We explore several strategies that people use, with varying degrees of success, in evaluating emails and in making sense of warnings offered by browsers attempting to help users navigate the web.


Wearable Real-Time Stereo Vision for the Visually Impaired

In this paper, a development of image processing, stereovision methodology and a sonification procedure for image sonification system for vision substitution are presented. The hardware part consists of a sunglass fitted with two mini cameras, laptop computer and stereo earphones. The image of the scene in front of blind people is captured by stereo cameras. The captured image is processed to enhance the important features in the scene in front of blind user. The image processing is designed to extract the objects from the image and the stereo vision method is applied to calculate the disparity which is required to determine the distance between the blind user and the objects. The processed image is mapped on to stereo sound for the blind’s understanding of the scene in front. Experimentations were conducted in the indoor environment and the proposed methodology is found to be effective for object identification, and thus the sound produced will assists the visually impaired for their collision free navigation.


NIMA: Neural Image Assessment

Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by data sets, such as AVA and TID2013. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment.


Convolutional neural networks and multimodal fusion for text aided image classification

With the exponential growth of web meta-data, exploiting multimodal online sources via standard search engine has become a trend in visual recognition as it effectively alleviates the shortage of training data. However, the web meta-data such as text data is usually not as cooperative as expected due to its unstructured nature. To address this problem, this paper investigates the numerical representation of web text data. We firstly adopt convolutional neural network (CNN) for web text modeling on top of word vectors. Combined with CNN for image, we present a multimodal fusion to maximize the discriminative power of visual and textual modality data for decision level and feature level simultaneously. Experimental results show that the proposed framework achieves significant improvement in large-scale image classification on Pascal VOC-2007 and VOC-2012 datasets.


Wireless Measurement of RFID IC Impedance

Accurate knowledge of the input impedance of a radio-frequency identification (RFID) integrated circuit (IC) at its wake-up power is valuable as it enables the design of a performance-optimized tag for a specific IC. However, since the IC impedance is power dependent, few methods exist to measure it without advanced equipment. We propose and demonstrate a wireless method, based on electromagnetic simulation and threshold power measurement, applicable to fully assembled RFID tags, to determine the mounted IC's input impedance in the absorbing state, including any parasitics arising from the packaging and the antenna-IC connection. The proposed method can be extended to measure the IC's input impedance in the modulating state as well.


Real-time High Performance Anomaly Detection over Data Streams: Grand Challenge

Real-time analytics over data streams are crucial for a wide range of use cases in industry and research. Today's sensor systems can produce high throughput data streams that have to be analyzed in real-time. One important analytic task is anomaly or outlier detection from the streaming data. In many industry applications, sensing devices produce a data stream that can be monitored to know the correct operation of industry devices and consequently avoid damages by triggering reactions in real-time.
 While anomaly detection is a well-studied topic in data mining, the real-time high-performance anomaly detection from big data streams require special studies and well-optimized implementation. This paper presents our implementation of a real-time anomaly detection system over data streams. We outline details of our two separate implementations using the Java and C++ programming languages, and provide technical details about the data processing pipelines. We report experimental results and describe performance tuning strategies.


MORE ON THE FIBONACCI SEQUENCE AND HESSENBERG MATRICES

Five new classes of Fibonacci-Hessenberg matrices are introduced. Further, we introduce the notion of two-dimensional Fibonacci arrays and show that three classes of previously known Fibonacci-Hessenberg matrices and their generalizations satisfy this property. Simple systems of linear equations are given whose solutions are Fibonacci fractions.


Municipal solid waste source-separated collection in China: A comparative analysis.

A pilot program focusing on municipal solid waste (MSW) source-separated collection was launched in eight major cities throughout China in 2000. Detailed investigations were carried out and a comprehensive system was constructed to evaluate the effects of the eight-year implementation in those cities. This paper provides an overview of different methods of collection, transportation, and treatment of MSW in the eight cities; as well as making a comparative analysis of MSW source-separated collection in China. Information about the quantity and composition of MSW shows that the characteristics of MSW are similar, which are low calorific value, high moisture content and high proportion of organisms. Differences which exist among the eight cities in municipal solid waste management (MSWM) are presented in this paper. Only Beijing and Shanghai demonstrated a relatively effective result in the implementation of MSW source-separated collection. While the six remaining cities result in poor performance. Considering the current status of MSWM, source-separated collection should be a key priority. Thus, a wider range of cities should participate in this program instead of merely the eight pilot cities. It is evident that an integrated MSWM system is urgently needed. Kitchen waste and recyclables are encouraged to be separated at the source. Stakeholders involved play an important role in MSWM, thus their responsibilities should be clearly identified. Improvement in legislation, coordination mechanisms and public education are problematic issues that need to be addressed.


Video classification with Densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off

The current state-of-the-art in video classification is based on Bag-of-Words using local visual descriptors. Most commonly these are histogram of oriented gradients (HOG), histogram of optical flow (HOF) and motion boundary histograms (MBH) descriptors. While such approach is very powerful for classification, it is also computationally expensive. This paper addresses the problem of computational efficiency. Specifically: (1) We propose several speed-ups for densely sampled HOG, HOF and MBH descriptors and release Matlab code; (2) We investigate the trade-off between accuracy and computational efficiency of descriptors in terms of frame sampling rate and type of Optical Flow method; (3) We investigate the trade-off between accuracy and computational efficiency for computing the feature vocabulary, using and comparing most of the commonly adopted vector quantization techniques: $$k$$ k -means, hierarchical $$k$$ k -means, Random Forests, Fisher Vectors and VLAD.


A Faster Scrabble Move Generation Algorithm

Appel and Jacobson1 presented a fast algorithm for generating every possible move in a given position in the game of Scrabble using a DAWG, a finite automaton derived from the trie of a large lexicon. This paper presents a faster algorithm that uses a GADDAG, a finite automaton that avoids the non-deterministic prefix generation of the DAWG algorithm by encoding a bidirectional path starting from each letter of each word in the lexicon. For a typical lexicon, the GADDAG is nearly five times larger than the DAWG, but generates moves more than twice as fast. This time/space trade-off is justified not only by the decreasing cost of computer memory, but also by the extensive use of move-generation in the analysis of board positions used by Gordon in the probabilistic search for the most appropriate play in a given position within realistic time constraints.


FraudFind: Financial fraud detection by analyzing human behavior

Financial fraud is commonly represented by the use of illegal practices where they can intervene from senior managers until payroll employees, becoming a crime punishable by law. There are many techniques developed to analyze, detect and prevent this behavior, being the most important the fraud triangle theory associated with the classic financial audit model. In order to perform this research, a survey of the related works in the existing literature was carried out, with the purpose of establishing our own framework. In this context, this paper presents FraudFind, a conceptual framework that allows to identify and outline a group of people inside an banking organization who commit fraud, supported by the fraud triangle theory. FraudFind works in the approach of continuous audit that will be in charge of collecting information of agents installed in user's equipment. It is based on semantic techniques applied through the collection of phrases typed by the users under study for later being transferred to a repository for later analysis. This proposal encourages to contribute with the field of cybersecurity, in the reduction of cases of financial fraud.


Social Brand Value and the Value Enhancing Role of Social Media Relationships for Brands

Due to the social media revolution and the emergence of communities, social networks, and user generated content portals, prevalent branding concepts need to catch up with this reality. Given the importance of social ties, social interactions and social identity in the new media environment, there is a need to account for a relationship measure in marketing and branding. Based on the concept of social capital we introduce the concept of social brand value, defined as the perceived value derived by exchange and interactions with other users of the brand within a `community. Within a qualitative study marketing experts were interviewed and highlighted the importance towards social media activities, but also indicated that they do not have a clear picture on how strategies should look like and how their success can be measured. A second quantitative study was conducted which demonstrates the influence the social brand value construct has for consumers brand evangelism and willingness to pay a price premium and hence the value contribution of the social brand value for consumers.


Information security incident management process

The modern requirements and the best practices in the field of Information Security (IS) Incident Management Process (ISIMP) are analyzed. "IS event" and "IS incident" terms, being used for ISIMP, have been defined. An approach to ISIMP development has been created. According to this approach ISIMP processes are described. As an example the «Vulnerabilities, IS events and incidents detection and notification» joint process is examined in detail.


Directly Addressable Variable-Length Codes

We introduce a symbol reordering technique that implicitly synchronizes variable-length codes, such that it is possible to directly access the i-th codeword without need of any sampling method. The technique is practical and has many applications to the representation of ordered sets, sparse bitmaps, partial sums, and compressed data structures for suffix trees, arrays, and inverted indexes, to name just a few. We show experimentally that the technique offers a competitive alternative to other data structures that handle this problem.


Deconstructing the Ladder Network Architecture

The manual labeling of data is and will remain a costly endeavor. For this reason, semi-supervised learning remains a topic of practical importance. The recently proposed Ladder Network is one such approach that has proven to be very successful. In addition to the supervised objective, the Ladder Network also adds an unsupervised objective corresponding to the reconstruction costs of a stack of denoising autoencoders. Although the empirical results are impressive, the Ladder Network has many components intertwined, whose contributions are not obvious in such a complex architecture. In order to help elucidate and disentangle the different ingredients in the Ladder Network recipe, this paper presents an extensive experimental investigation of variants of the Ladder Network in which we replace or remove individual components to gain more insight into their relative importance. We find that all of the components are necessary for achieving optimal performance, but they do not contribute equally. For semi-supervised tasks, we conclude that the most important contribution is made by the lateral connection, followed by the application of noise, and finally the choice of what we refer to as the ‘combinator function’ in the decoder path. We also find that as the number of labeled training examples increases, the lateral connections and reconstruction criterion become less important, with most of the improvement in generalization being due to the injection of noise in each layer. Furthermore, we present a new type of combinator function that outperforms the original design in both fullyand semi-supervised tasks, reducing record test error rates on Permutation-Invariant MNIST to 0.57% for the supervised setting, and to 0.97% and 1.0% for semisupervised settings with 1000 and 100 labeled examples respectively.


Using PMU Data to Increase Situational Awareness Final Project Report


Recent Advances in Indoor Localization: A Survey on Theoretical Approaches and Applications

The availability of location information has become a key factor in today’s communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the global positioning system (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular systems resides in the indoor environment and in scenarios with deep shadowing effects where the satellite or cellular signals are broken. In this paper, we survey different technologies and methodologies for indoor and outdoor localization with an emphasis on indoor methodologies and concepts. Additionally, we discuss in this review different localization-based applications, where the location information is critical to estimate. Finally, a comprehensive discussion of the challenges in terms of accuracy, cost, complexity, security, scalability, etc. is given. The aim of this survey is to provide a comprehensive overview of existing efforts as well as auspicious and anticipated dimensions for future work in indoor localization techniques and applications.


Speed control and electrical braking of axial flux BLDC motor

Axial flux brushless direct current motors (AFBLDC) are becoming popular in many applications including electrical vehicles because of their ability to meet the demand of high power density, high efficiency, wide speed range, robustness, low cost and less maintenance. In this paper, AFBLDC motor drive with single sided configuration having 24 stator poles and 32 permanent magnets on the rotor is proposed. It is driven by six pulse inverter that is fed from a single phase AC supply through controlled AC to DC converter. The speed control and braking methods are also proposed based on pulse width modulation technique. The overall scheme is simulated in MATLAB environment and tested under different operating conditions. A prototype of proposed AFBLDC motor drive is designed and fabricated. The control methods are implemented using DSC dsPIC33EP256MC202 digital signal controller. Tests are performed on this prototype to validate its performance at different speeds with and without braking mode. It is observed that the proposed scheme works effectively and can be used as wheel direct driven motor for electrical vehicle.


Visual Gesture Character String Recognition by Classification-Based Segmentation with Stroke Deletion

The recognition of character strings in visual gestures has many potential applications, yet the segmentation of characters is a great challenge since the pen lift information is not available. In this paper, we propose a visual gesture character string recognition method using the classification-based segmentation strategy. In addition to the character classifier and character geometry models used for evaluating candidate segmentation-recognition paths, we introduce deletion geometry models for deleting stroke segments that are likely to be ligatures. To perform experiments, we built a Kinect-based fingertip trajectory capturing system to collect gesture string data. Experiments of digit string recognition show that the deletion geometry models improve the string recognition accuracy significantly. The string-level correct rate is over 80%.


Automatic Identification of Word Translations from Unrelated English and German Corpora

Algorithms for the alignment of words in translated texts are w ell established. However, only recently, new approaches have been proposed to identify word translations f rom non-parallel or even unrelated texts. This task is more difficult, because most statistical clues useful in the processing of parallel texts can not be applied for non-parallel tex ts. For this reason, whereas for parallel texts in some studies up to 99% of the word alignments have been shown to be correct, the accuracy for non-parallel texts has been around 30% up to now. The current study, which is based on the assumption that there is a correlation between the patterns of word cooccurrences in corpora of different languages, makes a significant improvement to about 72% of word translations identified correctly.


Clustering on Multi-Layer Graphs via Subspace Analysis on Grassmann Manifolds

Relationships between entities in datasets are often of multiple nature, like geographical distance, social relationships, or common interests among people in a social network, for example. This information can naturally be modeled by a set of weighted and undirected graphs that form a global multi-layer graph, where the common vertex set represents the entities and the edges on different layers capture the similarities of the entities in term of the different modalities. In this paper, we address the problem of analyzing multi-layer graphs and propose methods for clustering the vertices by efficiently merging the information provided by the multiple modalities. To this end, we propose to combine the characteristics of individual graph layers using tools from subspace analysis on a Grassmann manifold. The resulting combination can then be viewed as a low dimensional representation of the original data which preserves the most important information from diverse relationships between entities. As an illustrative application of our framework, we use our algorithm in clustering methods and test its performance on several synthetic and real world datasets where it is shown to be superior to baseline schemes and competitive to state-of-the-art techniques. Our generic framework further extends to numerous analysis and learning problems that involve different types of information on graphs.


Solar powered unmanned aerial vehicle for continuous flight: Conceptual overview and optimization

An aircraft that is capable of continuous flight offers a new level of autonomous capacity for unmanned aerial vehicles. We present an overview of the components and concepts of a small scale unmanned aircraft that is capable of sustaining powered flight without a theoretical time limit. We then propose metrics that quantify the robustness of continuous flight achieved and optimization criteria to maximize these metrics. Finally, the criteria are applied to a fabricated and flight tested small scale high efficiency aircraft prototype to determine the optimal battery and photovoltaic array mass for robust continuous flight.


Aerial acoustic communications

This paper describes experiments in using audible sound as a means for wireless device communications. The direct application of standard modulation techniques to sound, without further improvements, results in sounds that are immediately perceived as digital communications and that are fairly aggressive and intrusive. We observe that some parameters of the modulation that have an impact in the data rate, the error probability and the computational overhead at the receiver also have a tremendous impact in the quality of the sound as perceived by humans. This paper focuses on how to vary those parameters in standard modulation techniques such as ASK, FSK and Spread-Spectrum to obtain communication systems in which the messages are musical and other familiar sounds, rather than modem sounds. A prototype called Digital Voices demonstrates the feasibility of this music-based communication technology. Our goal is to lay out the basis of sound design for aerial acoustic communications so that the presence of such communications, though noticeable, is not intrusive and can even be considered as part of musical compositions and sound tracks.


Basic local alignment search tool.

A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.


CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice.

The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.


Text-based adventures of the golovin AI agent

The domain of text-based adventure games has been recently established as a new challenge of creating the agent that is both able to understand natural language, and acts intelligently in text-described environments. In this paper, we present our approach to tackle the problem. Our agent, named Golovin, takes advantage of the limited game domain. We use genre-related corpora (including fantasy books and decompiled games) to create language models suitable to this domain. Moreover, we embed mechanisms that allow us to specify, and separately handle, important tasks as fighting opponents, managing inventory, and navigating on the game map. We validated usefulness of these mechanisms, measuring agent's performance on the set of 50 interactive fiction games. Finally, we show that our agent plays on a level comparable to the winner of the last year Text-Based Adventure AI Competition.


One for All: Towards Language Independent Named Entity Linking

Entity linking (EL) is the task of disambiguating mentions in text by associating them with entries in a predefined database of mentions (persons, organizations, etc). Most previous EL research has focused mainly on one language, English, with less attention being paid to other languages, such as Spanish or Chinese. In this paper, we introduce LIEL, a Language Independent Entity Linking system, which provides an EL framework which, once trained on one language, works remarkably well on a number of different languages without change. LIEL makes a joint global prediction over the entire document, employing a discriminative reranking framework with many domain and language-independent feature functions. Experiments on numerous benchmark datasets, show that the proposed system, once trained on one language, English, outperforms several state-of-the-art systems in English (by 4 points) and the trained model also works very well on Spanish (14 points better than a competitor system), demonstrating the viability of the approach.


An approach to fuzzy control of nonlinear systems: stability and design issues


Mining Professional's Data from LinkedIn

Social media has become very popular communication tool among internet users in the recent years. A large unstructured data is available for analysis on the social web. The data available on these sites have redundancies as users are free to enter the data according to their knowledge and interest. This data needs to be normalized before doing any analysis due to the presence of various redundancies in it. In this paper, LinkedIn data is extracted by using LinkedIn API and normalized by removing redundancies. Further, data is also normalized according to locations of LinkedIn connections using geo coordinates provided by Microsoft Bing. Then, clustering of this normalized data set is done according to job title, company names and geographic locations using Greedy, Hierarchical and K-Means clustering algorithms and clusters are visualized to have a better insight into them.


What do online behavioral advertising privacy disclosures communicate to users?

Online Behavioral Advertising (OBA), the practice of tailoring ads based on an individual's online activities, has led to privacy concerns. In an attempt to mitigate these privacy concerns, the online advertising industry has proposed the use of OBA disclosures: icons, accompanying taglines, and landing pages intended to inform users about OBA and provide opt-out options. We conducted a 1,505-participant online study to investigate Internet users' perceptions of OBA disclosures. The disclosures failed to clearly notify participants about OBA and inform them about their choices. Half of the participants remembered the ads they saw but only 12% correctly remembered the disclosure taglines attached to ads. When shown the disclosures again, the majority mistakenly believed that ads would pop up if they clicked on disclosures, and more participants incorrectly thought that clicking the disclosures would let them purchase advertisements than correctly understood that they could then opt out of OBA. "AdChoices", the most commonly used tagline, was particularly ineffective at communicating notice and choice. A majority of participants mistakenly believed that opting out would stop all online tracking, not just tailored ads. We dicuss challenges in crafting disclosures and provide suggestions for improvement.


Discrete Wigner Function Derivation of the Aaronson-Gottesman Tableau Algorithm

The Gottesman–Knill theorem established that stabilizer states and Clifford operations can be efficiently simulated classically. For qudits with odd dimension three and greater, stabilizer states and Clifford operations have been found to correspond to positive discrete Wigner functions and dynamics. We present a discrete Wigner function-based simulation algorithm for odd-d qudits that has the same time and space complexity as the Aaronson–Gottesman algorithm for qubits. We show that the efficiency of both algorithms is due to harmonic evolution in the symplectic structure of discrete phase space. The differences between the Wigner function algorithm for odd-d and the Aaronson–Gottesman algorithm for qubits are likely due only to the fact that the Weyl–Heisenberg group is not in SU(d) for d = 2 and that qubits exhibit state-independent contextuality. This may provide a guide for extending the discrete Wigner function approach to qubits.


LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification

The performance of scene classification relies heavily on the spatial and structural features that are extracted from high spatial resolution remote-sensing images. Existing approaches, however, are limited in adequately exploiting latent relationships between scene images. Aiming to decrease the distances between intraclass images and increase the distances between interclass images, we propose a latent relationship learning framework that integrates an adaptive graph with the constraints of the feature space and label propagation for high-resolution aerial image classification. To describe the latent relationships among scene images in the framework, we construct an adaptive graph that is embedded into the constrained joint space for features and labels. To remove redundant information and improve the computational efficiency, subspace learning is introduced to assist in the latent relationship learning. To address out-of-sample data, linear regression is adopted to project the semisupervised classification results onto a linear classifier. Learning efficiency is improved by minimizing the objective function via the linearized alternating direction method with an adaptive penalty. We test our method on three widely used aerial scene image data sets. The experimental results demonstrate the superior performance of our method over the state-of-the-art algorithms in aerial scene image classification.


Forming a story: the health benefits of narrative.

Writing about important personal experiences in an emotional way for as little as 15 minutes over the course of three days brings about improvements in mental and physical health. This finding has been replicated across age, gender, culture, social class, and personality type. Using a text-analysis computer program, it was discovered that those who benefit maximally from writing tend to use a high number of positive-emotion words, a moderate amount of negative-emotion words, and increase their use of cognitive words over the days of writing. These findings suggest that the formation of a narrative is critical and is an indicator of good mental and physical health. Ongoing studies suggest that writing serves the function of organizing complex emotional experiences. Implications for these findings for psychotherapy are briefly discussed.