title: GST: GPU-decodable supercompressed textures

abstract: Modern GPUs supporting compressed textures allow interactive application developers to save scarce GPU resources such as VRAM and bandwidth. Compressed textures use fixed compression ratios whose lossy representations are significantly poorer quality than traditional image compression formats such as JPEG. We present a new method in the class of supercompressed textures that provides an additional layer of compression to already compressed textures. Our texture representation is designed for endpoint compressed formats such as DXT and PVRTC and decoding on commodity GPUs. We apply our algorithm to commonly used formats by separating their representation into two parts that are processed independently and then entropy encoded. Our method preserves the CPU-GPU bandwidth during the decoding phase and exploits the parallelism of GPUs to provide up to 3X faster decode compared to prior texture supercompression algorithms. Along with the gains in decoding speed, our method maintains both the compression size and quality of current state of the art texture representations.


title: Spectral domain-transfer learning

abstract: Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world applications, however, we wish to make use of the labeled data from one domain (called in-domain) to classify the unlabeled data in a different domain (out-of-domain). This problem often happens when obtaining labeled data in one domain is difficult while there are plenty of labeled data from a related but different domain. In general, this is a transfer learning problem where we wish to classify the unlabeled data through the labeled data even though these data are not from the same domain. In this paper, we formulate this domain-transfer learning problem under a novel spectral classification framework, where the objective function is introduced to seek consistency between the in-domain supervision and the out-of-domain intrinsic structure. Through optimization of the cost function, the label information from the in-domain data is effectively transferred to help classify the unlabeled data from the out-of-domain. We conduct extensive experiments to evaluate our method and show that our algorithm achieves significant improvements on classification performance over many state-of-the-art algorithms.


title: A dual-band half-width microstrip leaky-wave antenna for beam scanning in the forward and backward directions

abstract: A microstrip leaky-wave antenna (LWA) loaded periodically with C-shaped slots is described. Uniform half-width (HW) microstrip LWAs (HW-MLWAs) operate in a single band and radiate a main beam that scans only in the forward direction. The proposed antenna operates in two frequency bands and the main beam can be steered in both the forward and backward directions by sweeping the frequency. The −10 dB reflection bandwidths of the antenna are 4.2 to 4.9 GHz and 8.23 to 8.8 GHz. The beam scanning ranges of the antenna are 22° to 59° and −76° to −18° when the frequency is swept from 4.2 to 5 GHz and 7.85 to 8.9 GHz, respectively.


title: Applying Image Analysis to Auto Insurance Triage: A Novel Application

abstract: For the auto insurance claims process, improvements in the First Notice of Loss and rapidity in the investigation and evaluation of claims could drive significant values by reducing loss adjustment expense. This paper proposes a novel application where advanced technologies in image analysis and pattern recognition are applied to automatically identify and characterize automobile damage. Success in this will allow some cases to proceed without human adjusters, while others to proceed more efficiently, thus ultimately shortening the time between the first Notice of Loss and the final payout. To investigate its feasibility, we built a prototype system which automatically identifies the damaged area(s) based on the comparison of before-and after-accident automobile images. Performance of the prototype system has been evaluated on images taken from forty scaled model cars under reasonably controlled environments, and encouraging results were obtained. It is our belief that, with the advancement of image analysis and pattern recognition technologies, the proposed idea could evolve into a very promising application area where the auto insurance industry could significantly benefit.


title: A certified type-preserving compiler from lambda calculus to assembly language

abstract: We present a certified compiler from the simply-typed lambda calculus to assembly language. The compiler is certified in the sense that it comes with a machine-checked proof of semantics preservation, performed with the Coq proof assistant. The compiler and the terms of its several intermediate languages are given dependent types that guarantee that only well-typed programs are representable. Thus, type preservation for each compiler pass follows without any significant "proofs" of the usual kind. Semantics preservation is proved based on denotational semantics assigned to the intermediate languages. We demonstrate how working with a type-preserving compiler enables type-directed proof search to discharge large parts of our proof obligations automatically.


title: Developing new machine learning ensembles for quality spine diagnosis

abstract: This research work adduces new hybrid machine learning ensembles for improving the performance of a computer aided diagnosis system integrated with multimethod assessment process and statistical process control, used for the spine diagnosis based on noninvasive panoramic radiographs. Novel methods are proposed for enhanced accurate classification. All the computations are performed considering steep error tolerance rate with statistical significance level of 5% as well as 1% and established the results with corrected t-tests. The kernel density estimator has been implemented to distinguish the affected patients against healthy ones. A new ensemble consisting of Bayesian network optimized by Tabu search algorithm as a classifier and Haar wavelets as the projection filter is used for relevant feature selection and attribute's ranking. The performance analysis of each method along with major findings is discussed using various evaluation metrics and concludes with propitious results. The results are compared to the existing SINPATCO platform that uses MLP, GRNN, and SVM. The optimization of machine learning algorithms is obtained using Design of Experiments scheme to achieve superior prediction accuracy. The highest classification accuracy obtained is 96.55% with sensitivity, specificity of 0.966 and 0.987 respectively. The objective is to enhance the software reliability and quality of spine disorder diagnosis using medical diagnostic system and reinforce the viability of precise treatment.


title: Clozapine-induced agranulocytosis

abstract: An epidemic of agranulocytosis and granulocytopenia occurred in 1975 in conjunction with clozapine treatment of mental patients in Finland. An attempt was made to assess the epidemiologic and genetic factors contributing to the adverse drug effect. The estimated incidence rate in Finland was 2.1/1000 patient-months. This figure could not be compared with rates from other countries because of the inexact nature of the figures reported so far. All 16 cases occurred in seven hospitals in southwestern Finland, whereas the overall hospital net use of the drug was geographically evenly distributed. The difference between the observed and the proportionally expected incidence of cases amongst the hospitals where clozapine was used was statistically significant. The average consumption of the drug did not differ between the hospitals where cases occurred and those where no definite cases could be diagnosed. Six-generation pedigree analyses failed to reveal significant parental consanguinity or genetic kinship between probands. Neither did the birth places of the ancestors of the probands disclose a typical isolate pattern. In conclusion, the cases appeared to be confined to a few hospitals in southwestern Finland. Although a genetic factor is not excluded, we found no evidence in support of a genetic mechanism.


title: Multi-agent-based reconfiguration for restoration of distribution systems with distributed generators

abstract: Restoration involves supplying the loads that lose power in the process of fault isolation. This process entails the development of a plan consisting of opening or closing of switches, the process called reconfiguration. Existing methods, based on centralized restoration schemes, which require a powerful central computer, may lead to a single point of failure. This paper proposes a decentralized scheme based on agents. The large-scale introduction of Distributed Generation (DG) in distribution systems has made it increasingly necessary to develop restoration schemes that consider DG. When a portion of the system isolated from the utility is fed by DGs, an island occurs. Load shedding may be the only option to maintain the island when conditions are so severe they require correction by restoration schemes. In this paper, a fast and efficient service restoration with load shedding method has been designed for land-based and ship systems, considering priority of customers. The load-shedding algorithm with the service restoration algorithm performs load shedding and restores the loads for systems with and without DG.


title: Interactive Visual Hull Refinement for Specular and Transparent Object Surface Reconstruction

abstract: In this paper we present a method of using standard multi-view images for 3D surface reconstruction of non-Lambertian objects. We extend the original visual hull concept to incorporate 3D cues presented by internal occluding contours, i.e., occluding contours that are inside the object's silhouettes. We discovered that these internal contours, which are results of convex parts on an object's surface, can lead to a tighter fit than the original visual hull. We formulated a new visual hull refinement scheme -- Locally Convex Carving that can completely reconstruct concavity caused by two or more intersecting convex surfaces. In addition we develop a novel approach for contour tracking given labeled contours in sparse key frames. It is designed specifically for highly specular or transparent objects, for which assumptions made in traditional contour detection/tracking methods, such as highest gradient and stationary texture edges, are no longer valid. It is formulated as an energy minimization function where several novel terms are developed to increase robustness. Based on the two core algorithms, we have developed an interactive system for 3D modeling. We have validated our system, both quantitatively and qualitatively, with four datasets of different object materials. Results show that we are able to generate visually pleasing models for very challenging cases.


LLVM: An Infrastructure for Multi-Stage Optimization


title: Bin-packing Algorithms for Periodic Task Scheduling

abstract: CAN (Controller Area Network) operation observes event triggering standards, it is widely applied in real-time distributional control system of the automobile electronic control system, satellite control system, medical equipment's electronic control system as well as the textile equipment and so on. As the extension of offline bin-packing problem, periodic task scheduling has many important applications in real-time distributed systems. For time triggered CAN control systems, two strategies to determine the basic cycle (BC) of TTCAN are presented. Next-fit algorithm, next-fit descending algorithm, best-fit algorithm and best-fit descending algorithm are proposed to construct periodic task scheduling tables. Time complexity and worst-case asymptotic performance ratio of these algorithms are analyzed, and the experimental results indicate that NFA algorithm is not worse than NFDA algorithm, BFA is also not worse than BFDA algorithm, four algorithms have the advantage over the typical one-dimensional bin-packing algorithm.


title: Image Descriptors Based on Curvature Histograms

abstract: Descriptors based on orientation histograms are widely used in computer vision. The spatial pooling involved in these representations provides important invariance properties, yet it is also responsible for the loss of important details. In this paper, we suggest a way to preserve the details described by the local curvature. We propose a descriptor that comprises the direction and magnitude of curvature and naturally expands classical orientation histograms like SIFT and HOG. We demonstrate the general benefit of the expansion exemplarily for image classification, object detection, and descriptor matching.


title: Traffic sign detection and recognition using OpenCV

abstract: The aim of the project is to detect and recognize traffic signs in video sequences recorded by an onboard vehicle camera. Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for automated intelligent driving vehicle or driver assistance systems. This paper presents a study to recognize traffic sign patterns using openCV technique. The images are extracted, detected and recognized by pre-processing with several image processing techniques, such as, threshold techniques, Gaussian filter, canny edge detection, Contour and Fit Ellipse. Then, the stages are performed to detect and recognize the traffic sign patterns. The system is trained and validated to find the best network architecture. The experimental results show the highly accurate classifications of traffic sign patterns with complex background images and the computational cost of the proposed method.


Lexical and Structural Ambiguity in Humorous Headlines


title: Puncturable Attribute-Based Encryption for Secure Data Delivery in Internet of Things

abstract: While the Internet of Things (IoT) is embraced as important tools for efficiency and productivity, it is becoming an increasingly attractive target for cybercriminals. This work represents the first endeavor to develop practical Puncturable Attribute Based Encryption schemes that are light-weight and applicable in IoTs. In the proposed scheme, the attribute-based encryption is adopted for fine grained access control. The secret keys are puncturable to revoke the decryption capability for selected messages, recipients, or time periods, thus protecting selected important messages even if the current key is compromised. In contrast to conventional forward encryption, a distinguishing merit of the proposed approach is that the recipients can update their keys by themselves without key re-issuing from the key distributor. It does not require frequent communications between IoT devices and the key distribution center, neither does it need deleting components to expunge existing keys to produce a new key. Moreover, we devise a novel approach which efficiently integrates attribute-based key and punctured keys such that the key size is roughly the same as that of the original attribute-based encryption. We prove the correctness of the proposed scheme and its security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. We also implement the proposed scheme on Raspberry Pi and observe that the computation efficiency of the proposed approach is comparable to the original attribute-based encryption. Both encryption and decryption can be completed within tens of milliseconds.


title: Human Intention Prediction in Human-Robot Collaborative Tasks

abstract: Enabling the robot to predict human intentions in human-robot collaborative hand-over tasks is a challenging but important issue to address. We develop a novel and effective teaching-learning-prediction (TLP) model for the robot to online learn from natural multi-modal human demonstrations during human-robot hand-overs and then predict human hand-over intentions using human wearable sensing information. The human could program the robot using partial demonstrations according to the updated tasks and his/her personal hand-over preferences, and the robot can online leverage its learned strategy to actively predict human hand-over intentions and assist the human in collaborative tasks. We evaluate the approach through experiments.


title: Adding semantics to internet of things

abstract: The development of Internet of Things IoT applications can be facilitated by encoding the meaning of the data in the messages sent by IoT nodes, but the constrained resources of these nodes challenge the common Semantic Web solutions for doing this. In this article, we examine enabling technologies for adding semantics to the IoT. Especially, we analyze data formats, which enable IoT applications consume semantic IoT data in a straightforward and general fashion, and evaluate resource usage of different alternatives with a sensor system. Our experiment illustrates encoding and decoding of different data formats and shows how big a difference a data format can make in energy consumption. Copyright © 2014 John Wiley & Sons, Ltd.


title: Translational Recommender Networks

abstract: Representing relationships as translations in vector space lives at the heart of many neural embedding models such as word embeddings and knowledge graph embeddings. In this work, we study the connections of this translational principle with collaborative filtering algorithms. We propose Translational Recommender Networks (\textsc{TransRec}), a new attentive neural architecture that utilizes the translational principle to model the relationships between user and item pairs. Our model employs a neural attention mechanism over a \emph{Latent Relational Attentive Memory} (LRAM) module to learn the latent relations between user-item pairs that best explains the interaction. By exploiting adaptive user-item specific translations in vector space, our model also alleviates the geometric inflexibility problem of other metric learning algorithms while enabling greater modeling capability and fine-grained fitting of users and items in vector space. The proposed architecture not only demonstrates the state-of-the-art performance across multiple recommendation benchmarks but also boasts of improved interpretability. Qualitative studies over the LRAM module shows evidence that our proposed model is able to infer and encode explicit sentiment, temporal and attribute information despite being only trained on implicit feedback. As such, this ascertains the ability of \textsc{TransRec} to uncover hidden relational structure within implicit datasets.


title: Sparsely Connected Convolutional Networks

abstract: Residual learning with skip connections permits training ultra-deep neural networks and obtains superb performance. Building in this direction, DenseNets proposed a dense connection structure where each layer is directly connected to all of its predecessors. The densely connected structure leads to better information flow and feature reuse. However, the overly dense skip connections also bring about the problems of potential risk of overfitting, parameter redundancy and large memory consumption. In this work, we analyze the feature aggregation patterns of ResNets and DenseNets under a uniform aggregation view framework. We show that both structures densely gather features from previous layers in the network but combine them in their respective ways: summation (ResNets) or concatenation (DenseNets). We compare the strengths and drawbacks of these two aggregation methods and analyze their potential effects on the networks' performance. Based on our analysis, we propose a new structure named SparseNets which achieves better performance with fewer parameters than DenseNets and ResNets.


title: SemRe-Rank: Incorporating Semantic Relatedness to Improve Automatic Term Extraction Using Personalized PageRank

abstract: Automatic Term Extraction deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that no existing methods can consistently outperforms others in all domains. This work adopts a different strategy towards this problem as we propose to 'enhance' existing ATE methods instead of 'replace' them. We introduce SemRe-Rank, a generic method based on the concept of incorporating semantic relatedness - an often overlooked venue - into an existing ATE method to further improve its performance. SemRe-Rank applies a personalized PageRank process to a semantic relatedness graph of words to compute their 'semantic importance' scores, which are then used to revise the scores of term candidates computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all methods and across all datasets. The best performing variants of SemRe-Rank have achieved, on some datasets, an improvement of 0.15 (on a scale of 0 ~ 1.0) in terms of the precision in the top ranked K term candidates, and an improvement of 0.28 in terms of overall F1.


title: Ad-versarial: Defeating Perceptual Ad-Blocking

abstract: Perceptual ad-blocking is a novel approach that uses visual cues to detect online advertisements. Compared to classical filter lists, perceptual ad-blocking is believed to be less prone to an arms race with web publishers and ad-networks. In this work we use techniques from adversarial machine learning to demonstrate that this may not be the case. We show that perceptual ad-blocking engenders a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks. We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's full visual-detection pipeline, that enable publishers or ad-networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. Our attacks exploit the unreasonably strong threat model that perceptual ad-blockers must survive. Finally, we evaluate a concrete set of attacks on an ad-blocker's internal ad-classifier by instantiating adversarial examples for visual systems in a real web-security context. For six ad-detection techniques, we create perturbed ads, ad-disclosures, and native web content that misleads perceptual ad-blocking with 100% success rates. For example, we demonstrate how a malicious user can upload adversarial content (e.g., a perturbed image in a Facebook post) that fools the ad-blocker into removing other users' non-ad content.


title: Less Provisioning: A Fine-grained Resource Scaling Engine for Long-running Services with Tail Latency Guarantees

abstract: Modern resource management frameworks guarantee low tail latency for long-running services using the resource over-provisioning method, resulting in serious waste of resource and increasing the service costs greatly. To reduce the over-provisioning cost, we present EFRA, an elastic and fine-grained resource allocator that enables much more efficient resource provisioning while guaranteeing the tail latency Service Level Objective (SLO). EFRA achieves this through the cooperation of three key components running on a containerized platform: The period detector identifies the period features of the workload through a convolution-based time series analysis. The resource reservation component estimates the just-right amount of resources based on the period analysis through a top-K based collaborative filtering approach. The online reprovisioning component dynamically adjusts the resources for further enforcing the tail latency SLO. Testbed experiments show that EFRA is able to increase the average resource utilization to 43%, and save up to 66% resources while guaranteeing the same tail latency objective.


title: A New Classification of Lip Zones to Customize Injectable Lip Augmentation

abstract: Objectives: To present a new classification of 15 anatomical lip zones used to direct placement of injectable fillers during lip augmentation and to evaluate the new classification's ability to customize lip contour and size. Methods: Study participants were consecutive patients presenting to a facial plastic and reconstructive surgery practice for injectable lip augmentation with a nonanimal-sourced stabilized hyaluronic acid (Restylane; Medicis Aesthetic Inc, Scottsdale, Arizona). A nonrandomized, prospective case series. Results: A total of 137 treatments were performed on lips of 66 patients. The mean (SD) satisfaction score was 4.5 (0.6) on an integral scale of 1 (dissatisfied) to 5 (most satisfied). The mean (SD) persistence until lips returned to preoperative appearance based on patient subjective evaluation was 4.9 (1.5) months. Patients were free of adverse effects. Conclusions: Using a new classification of lip anatomical zones to direct the injection of a nonanimal-sourced stabilized hyaluronic acid has increased my ability to better control lip shape and size in lip augmentation. This technique was met with high patient satisfaction and no adverse effects. Persistence of injected nonanimal-sourced stabilized hyaluronic acid was similar to that seen in other studies.


title: Characterizing Direct Monitoring Techniques in Software Systems

abstract: Monitoring is a consolidated practice to characterize the dependability behavior of a software system. A variety of techniques, such as event logging and operating system probes, are currently used to generate monitoring data for troubleshooting and failure analysis. In spite of the importance of monitoring, whose role can be essential in critical software systems, there is a lack of studies addressing the assessment and the comparison of the techniques aiming to monitor the occurrence of failures during operations. This paper proposes a method to characterize the monitoring techniques implemented in a software system. The method is based on a fault injection approach and allows measuring 1) precision and recall of a monitoring technique and 2) the dissimilarity of the data it generates upon failures. The method has been used in two critical software systems implementing event logging, assertion checking, and source code instrumentation techniques. We analyzed a total of 3 844 failures. With respect to our data, we observed that the effectiveness of a technique is strongly affected by the system and type of failure, and that the combination of different techniques is potentially beneficial to increase the overall failure reporting ability. More important, our analysis revealed a number of practical implications to be taken into account when developing a monitoring technique.


title: Correlation Based Classification of Complex PRI Modulation Types

abstract: Classification of pulse repetition interval (PRI) modulation type is an important task of electronic warfare systems. Correct classification of the PRI modulation of an emitter can lead to significant knowledge about the observed emitter and can facilitate an electronic warfare system with applying its functions. Several methods have been proposed in the literature for identifying standard PRI modulation types (random, stable, jittered, dwell \& switch, sliding, wobulated). The last two types, i.e. sliding and wobulated, are also called complex and the classification of their subtypes, i.e. sawtooth, triangle, sine, and saturated sine, remains unaddressed. This paper addresses the classification of complex PRI modulation subtypes. The proposed algorithm evaluates the correlation between a received signal and several ideal complex PRI modulated signals in order to detect such complex modulations. The performance of the proposed classification algorithm is demonstrated using simulated signals.


title: Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

abstract: Bilateral filters have wide spread use due to their edgepreserving properties. The common use case is to manually choose a parametric filter type, usually a Gaussian filter. In this paper, we will generalize the parametrization and in particular derive a gradient descent algorithm so the filter parameters can be learned from data. This derivation allows to learn high dimensional linear filters that operate in sparsely populated feature spaces. We build on the permutohedral lattice construction for efficient filtering. The ability to learn more general forms of high-dimensional filters can be used in several diverse applications. First, we demonstrate the use in applications where single filter applications are desired for runtime reasons. Further, we show how this algorithm can be used to learn the pairwise potentials in densely connected conditional random fields and apply these to different image segmentation tasks. Finally, we introduce layers of bilateral filters in CNNs and propose bilateral neural networks for the use of highdimensional sparse data. This view provides new ways to encode model structure into network architectures. A diverse set of experiments empirically validates the usage of general forms of filters.


Quantifying Product Favorability


MediConceptNet: An Affinity Score Based Medical Concept Network.


GPS and SMS-Based Child Tracking System Using Smart Phone


title: Characteristics Comparison of a Conventional and Modified Spoke-Type Ferrite Magnet Motor for Traction Drives of Low-Speed Electric Vehicles

abstract: Rare-earth magnets have been widely employed for traction drive motors of hybrid and electric vehicles (EVs) due to high energy density. Nevertheless, the magnets have critical problems such as unstable supply and high cost. Recently, in order to solve the problems, a variety of motors not using rare-earth materials have been reviewed, such as an induction motor, a switched reluctance motor, and a spoke-type ferrite magnet motor (FMM). In this paper, a modified spoke-type FMM with a novel rotor configuration was presented for a traction motor of low-speed EVs. The characteristics of the modified spoke-type FMM are numerically compared with a conventional spoke-type FMM at the same design condition. Finally, the modified spoke-type FMM is fabricated and tested in order to verify its performance and feasibility.


title: Information fusion to detect and classify pedestrians using invariant features

abstract: A novel approach to detect pedestrians and to classify them according to their moving direction and relative speed is presented in this paper. This work focuses on the recognition of pedestrian lateral movements, namely: walking and running in both directions, as well as no movement. The perception of the environment is performed through a lidar sensor and an infrared camera. Both sensor signals are fused to determine regions of interest in the video data. The classification of these regions is based on the extraction of 2D translation invariant features, which are constructed by integrating over the transformation group. Special polynomial kernel functions are defined in order to obtain a good separability between the classes. Support vector machine classifiers are used in different configurations to classify the invariants. The proposed approach was evaluated offline considering fixed sensors. Results obtained based on real traffic scenes demonstrate very good detection and classification rates.


title: Generating test data from state-based specifications

abstract: SUMMARY Although the majority of software testing in industry is conducted at the system level, most formal research has focused on the unit level. As a result, most system-level testing techniques are only described informally. This paper presents formal testing criteria for system level testing that are based on formal specifications of the software. Software testing can only be formalized and quantified when a solid basis for test generation can be defined. Formal specifications represent a significant opportunity for testing because they precisely describe what functions the software is supposed to provide in a form that can be automatically manipulated. This paper presents general criteria for generating test inputs from state-based specifications. The criteria include techniques for generating tests at several levels of abstraction for specifications (transition predicates, transitions, pairs of transitions and sequences of transitions). These techniques provide coverage criteria that are based on the specifications and are made up of several parts, including test prefixes that contain inputs necessary to put the software into the appropriate state for the test values. The test generation process includes several steps for transforming specifications to tests. These criteria have been applied to a case study to compare their ability to detect seeded faults. Copyright c � 2003 John Wiley & Sons, Ltd.


title: Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification

abstract: Purpose ::: Automatic segmentation of the retinal vasculature is a first step in computer-assisted diagnosis and treatment planning. The extraction of retinal vessels in pediatric retinal images is challenging because of comparatively wide arterioles with a light streak running longitudinally along the vessel’s center, the central vessel reflex. A new method for automatic segmentation was developed and tested.


title: Investigating the Problems of Prepaid Metering Systems in Nigeria.

abstract: The aim of this study is to investigate the problems and challenges faced by the prepaid metering system and efforts geared towards in resolving the identified issues using a humansurvey approach termed questionnaires to the EKEDC (Eko Electricity Distribution Company) officials and customers. Some of the identified challenges are absence of vending infrastructure, non-tripping of the contractor, the cost of acquiring the meter, and delays in receiving and installation of prepaid meters. The analysis of this study was based on the feedback mechanism and results received from the members of the public via questionnaire. The obtained results enabled this study to get to the far-reaching recommendations which are considered to be ideal for the successful implementation of the Prepaid Metering System. These include high quality meters, provision of electricity at all times, computerization of the Prepaid Metering System, introduction of prepayment metering country-wide and periodic training of electricity staff members and officials.


title: Online Word-of-Mouth (or Mouse): An Exploration of Its Antecedents and Consequences

abstract: This study developed an integrated model to explore the antecedents and consequences of online word-of-mouth in the context of music-related communication. Based on survey data from college students, online word-of-mouth was measured with two components: online opinion leadership and online opinion seeking. The results identified innovativeness, Internet usage, and Internet social connection as significant predictors of online word-of-mouth, and online forwarding and online chatting as behavioral consequences of online word-of-mouth. Contrary to the original hypothesis, music involvement was found not to be significantly related to online word-of-mouth. Theoretical implications of the findings and future research directions are discussed.


title: Methods for linking EHR notes to education materials

abstract: It has been shown that accessing the patients’ own electronic health records (EHR) can enhance their medical understanding and provide clinically relevant benefits. However, languages that are difficult for non-medical professionals to comprehend are prevalent in the EHR notes. The valuable and authoritative information contained in the EHR is thus less accessible to the patients, who ultimately stand to benefit the most from the information. To address this challenge, we are developing a system to retrieve EHR note-specific online consumer-oriented health education materials. We explored several query generation methods to convert long EHR notes to effective queries, including topic models and key concept identification. Our experiments show that queries using key concepts identified by a learning based model with pseudo-relevance feedback significantly outperform the baseline system of using the full text note.


title: Spacing effects in real‐world classroom vocabulary learning

abstract: Summary: As a primary goal, educators often strive to maximize the amount of information pupils remember. In the lab, psychologists havefoundefficient memory strategies forretainingschool-related materials.Onesuch strategyis thespacingeffect, a memory advantage that occurs when learning is distributed across time instead of crammed into a single study session. Spaced learning is not often explicitly utilized in actual classrooms, perhaps due to a paucity of research in applied settings and with school-aged children. The current study examined the spacing effect in real-world fifth-grade classrooms. We taught 39 children unfamiliar English words using both massed and spaced learning. Five weeks later, we tested vocabulary recall. One-week spacing produced superior long-term retention compared to massed learning. This finding demonstrates that the spacing effect can be generalized to vocabulary learning in applied settings and middle-school-aged children. Copyright # 2010 John Wiley & Sons, Ltd.


title: Problem Sets................................................................................................................................. 39

abstract: Many studies have been conducted to demonstrate the ability of hyperspectral data to discriminate plant dominant species. Most of them have employed the use of empirically based techniques, which are site specific, requires some initial training based on characteristics of known leaf and/or canopy spectra and therefore may not be extendable to operational use or adapted to changing or unknown land cover. In this paper we propose a physically based approach for separation of dominant forest type using hyperspectral data. The radiative transfer theory of canopy spectral invariants underlies the approach, which facilitates parameterization of the canopy reflectance in terms of the leaf spectral scattering and two spectrally invariant and structurally varying variables - recollision and directional escape probabilities. The methodology is based on the idea of retrieving spectrally invariant parameters from hyperspectral data first, and then relating their values to structural characteristics of three-dimensional canopy structure. Theoretical and empirical analyses of ground and airborne data acquired by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over two sites in New England, USA, suggest that the canopy spectral invariants convey information about canopy structure at both the macro- and micro-scales. The total escape probability (one minus recollision probability) varies as a power function with the exponent related to the number of nested hierarchical levels present in the pixel. Its base is a geometrical mean of the local total escape probabilities and accounts for the cumulative effect of canopy structure over a wide range of scales. The ratio of the directional to the total escape probability becomes independent of the number of hierarchical levels and is a function of the canopy structure at the macro-scale such as tree spatial distribution, crown shape and size, within-crown foliage density and ground cover. These properties allow for the natural separation of dominant forest classes based on the location of points on the total escape probability vs the ratio log-log plane.


title: Efficient Learning for Undirected Topic Models

abstract: Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient. This paper provides a novel estimator to speed up the learning based on Noise Contrastive Estimate, extended for documents of variant lengths and weighted inputs. Experiments on two benchmarks show that the new estimator achieves great learning efficiency and high accuracy on document retrieval and classification.


title: Cohesion Metrics for Predicting Maintainability of Service-Oriented Software

abstract: Although service-oriented computing (SOC) is a promising paradigm for developing enterprise software systems, existing research mostly assumes the existence of black box services with little attention given to the structural characteristics of the implementing software, potentially resulting in poor system maintainability. Whilst there has been some preliminary work examining coupling in a service-oriented context, there has to date been no such work on the structural property of cohesion. Consequently, this paper extends existing notions of cohesion in OO and procedural design in order to account for the unique characteristics of SOC, allowing the derivation of assumptions linking cohesion to the maintainability of service-oriented software. From these assumptions, a set of metrics are derived to quantify the degree of cohesion of service oriented design constructs. Such design level metrics are valuable because they allow the prediction of maintainability early in the SDLC.


title: Design and system performances of a dual-band 4-Port MIMO antenna for LTE applications

abstract: This paper presents a dual-band 4-port diversity antenna for LTE MIMO applications. The antenna element employs a dual-band design covering the 700 MHz band for LTE applications and the 2.4 GHz band for WLAN systems. The presented antenna was implemented on a laptop panel for evaluating the system performances of four-port MIMO configuration in a non-line of sight environment. Correlation coefficient and channel capacity are simulated and measured. Correlation between signals is found between 0.17 and 0.32. The calculated channel capacity from the measured results is about 90% of the ideal case. The experimental results suggest that the presented 4-port MIMO antenna is effective and suitable for the laptop LTE applications.


title: Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System

abstract: We propose a novel way to train ranking models, such as recommender systems, that are both effective and efficient. Knowledge distillation (KD) was shown to be successful in image recognition to achieve both effectiveness and efficiency. We propose a KD technique for learning to rank problems, called ranking distillation (RD). Specifically, we train a smaller student model to learn to rank documents/items from both the training data and the supervision of a larger teacher model. The student model achieves a similar ranking performance to that of the large teacher model, but its smaller model size makes the online inference more efficient. RD is flexible because it is orthogonal to the choices of ranking models for the teacher and student. We address the challenges of RD for ranking problems. The experiments on public data sets and state-of-the-art recommendation models showed that RD achieves its design purposes: the student model learnt with RD has less than an half size of the teacher model while achieving a ranking performance similar tothe teacher model and much better than the student model learnt without RD.


title: Design of phased array antenna for 5G mm-wave beamforming system

abstract: This paper presents 5G mm-wave phased-array antenna for 3D-hybrid beamforming. This uses MFC to steer beam for the elevation, and uses butler matrix network for the azimuth. In case of butler matrix network, this, using 180° ring hybrid coupler switch network, is proposed to get additional beam pattern and improved SRR in comparison with conventional structure. Also, it can be selected 15 of the azimuth beam pattern. When using the chip of proposed structure, it is possible to get variable kind of beam-forming over 1000. In addition, it is suitable 5G system or a satellite communication system that requires a beamforming.


Fractional Order Differentiator Based Filter For Edge Detection of Low Contrast Underwater Images


Fostering Intra- and Entrepreneurship in Engineering Students


An Overview of Cognitive Radio Networks


title: Bridging Text Visualization and Mining: A Task-Driven Survey

abstract: Visual text analytics has recently emerged as one of the most prominent topics in both academic research and the commercial world. To provide an overview of the relevant techniques and analysis tasks, as well as the relationships between them, we comprehensively analyzed 263 visualization papers and 4,346 mining papers published between 1992-2017 in two fields: visualization and text mining. From the analysis, we derived around 300 concepts (visualization techniques, mining techniques, and analysis tasks) and built a taxonomy for each type of concept. The co-occurrence relationships between the concepts were also extracted. Our research can be used as a stepping-stone for other researchers to 1) understand a common set of concepts used in this research topic; 2) facilitate the exploration of the relationships between visualization techniques, mining techniques, and analysis tasks; 3) understand the current practice in developing visual text analytics tools; 4) seek potential research opportunities by narrowing the gulf between visualization and mining techniques based on the analysis tasks; and 5) analyze other interdisciplinary research areas in a similar way. We have also contributed a web-based visualization tool for analyzing and understanding research trends and opportunities in visual text analytics.


title: Network Analysis Identifies Disease-Specific Pathways for Parkinson’s Disease

abstract: Neurodegenerative diseases are characterized by the progressive loss of specific neurons in selected regions of the central nervous system. The main clinical manifestation (movement disorders, cognitive impairment, and/or psychiatric disturbances) depends on the neuron population being primarily affected. Parkinson's disease is a common movement disorder, whose etiology remains mostly unknown. Progressive loss of dopaminergic neurons in the substantia nigra causes an impairment of the motor control. Some of the pathogenetic mechanisms causing the progressive deterioration of these neurons are not specific for Parkinson's disease but are shared by other neurodegenerative diseases, like Alzheimer's disease and amyotrophic lateral sclerosis. Here, we performed a meta-analysis of the literature of all the quantitative proteomic investigations of neuronal alterations in different models of Parkinson's disease, Alzheimer's disease, and amyotrophic lateral sclerosis to distinguish between general and Parkinson's disease-specific pattern of neurodegeneration. Then, we merged proteomics data with genetics information from the DisGeNET database. The comparison of gene and protein information allowed us to identify 25 proteins involved uniquely in Parkinson's disease and we verified the alteration of one of them, i.e., transaldolase 1 (TALDO1), in the substantia nigra of 5 patients. By using open-source bioinformatics tools, we identified the biological processes specifically affected in Parkinson's disease, i.e., proteolysis, mitochondrion organization, and mitophagy. Eventually, we highlighted four cellular component complexes mostly involved in the pathogenesis: the proteasome complex, the protein phosphatase 2A, the chaperonins CCT complex, and the complex III of the respiratory chain.


title: Human activity recognition with inertial sensors using a deep learning approach

abstract: Our focus in this research is on the use of deep learning approaches for human activity recognition (HAR) scenario, in which inputs are multichannel time series signals acquired from a set of body-worn inertial sensors and outputs are predefined human activities. Here, we present a feature learning method that deploys convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. The influence of various important hyper-parameters such as number of convolutional layers and kernel size on the performance of CNN was monitored. Experimental results indicate that CNNs achieved significant speed-up in computing and deciding the final class and marginal improvement in overall classification accuracy compared to the baseline models such as Support Vector Machines and Multi-layer perceptron networks.


title: Viewpoint selection for grasp detection

abstract: In grasp detection, the robot estimates the position and orientation of potential grasp configurations directly from sensor data. This paper explores the relationship between viewpoint and grasp detection performance. Specifically, we consider the scenario where the approximate position and orientation of a desired grasp is known in advance and we want to select a viewpoint that will enable a grasp detection algorithm to localize it more precisely and with higher confidence. Our main findings are that the right viewpoint can dramatically increase the number of detected grasps and the classification accuracy of the top-n detections. We use this insight to create a viewpoint selection algorithm and compare it against a random viewpoint selection strategy and a strategy that views the desired grasp head-on. We find that the head-on strategy and our proposed viewpoint selection strategy can improve grasp success rates on a real robot by 8% and 4%, respectively. Moreover, we find that the combination of the two methods can improve grasp success rates by as much as 12%.


title: Modeling ETL activities as graphs

abstract: Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the logical design of the ETL scenario of a data warehouse. Based on a formal logical model that includes the data stores, activities and their constituent parts, we model an ETL scenario as a graph, which we call the Architecture Graph. We model all the aforementioned entities as nodes and four different kinds of relationships (instance-of, part-of, regulator and provider relationships) as edges. In addition, we provide simple graph transformations that reduce the complexity of the graph. Finally, in order to support the engineering of the design and the evolution of the warehouse, we introduce specific importance metrics, namely dependence and responsibility, to measure the degree to which entities are bound to each other.


title: World-wide scale geotagged image dataset for automatic image annotation and reverse geotagging

abstract: In this paper, a dataset of geotagged photos on a world-wide scale is presented. The dataset contains a sample of more than 14 million geotagged photos crawled from Flickr with the corresponding metadata. To guarantee the spatial representativeness of the dataset, a crawling approach based on the small-world phenomena and the Flickr friendship's graph is applied. Furthermore, the noisiness of user-provided tags is reduced through an automatic tag cleaning approach. To enable efficient retrieval, photos in the dataset are indexed based on their location information using quad-tree data structure. The dataset can assists different applications, especially, search-based automatic image annotation and reverse geotagging.


title: Deformable Object Tracking with Gated Fusion

abstract: The tracking-by-detection framework receives growing attention through the integration with the convolutional neural networks (CNNs). Existing tracking-by-detection-based methods, however, fail to track objects with severe appearance variations. This is because the traditional convolutional operation is performed on fixed grids, and thus may not be able to find the correct response while the object is changing pose or under varying environmental conditions. In this paper, we propose a deformable convolution layer to enrich the target appearance representations in the tracking-by-detection framework. We aim to capture the target appearance variations via deformable convolution, which adaptively enhances its original features. In addition, we also propose a gated fusion scheme to control how the variations captured by the deformable convolution affect the original appearance. The enriched feature representation through deformable convolution facilitates the discrimination of the CNN classifier on the target object and background. The extensive experiments on the standard benchmarks show that the proposed tracker performs favorably against the state-of-the-art methods.


title: Simple Voice over IP (VoIP) Implementation

abstract: Voice over IP ( VoIP) technology has many advantages over the traditional Public Switched Telephone Networks. In our project, we realize a simple VoIP software, which mainly consists of a speech collection and playing back part, a speech CODEC and a networking API. For speech collection/playing part, we use the software (C++ class) and the hardware (sound card) to realize its functions. For the speech CODEC, we choose G.711 PCM µ-Law voice coder. For the transmission of voice conversation, we employ the socket communication API.


title: Characterising semantic relatedness using interpretable directions in conceptual spaces

abstract: Various applications, such as critique-based recommendation systems and analogical classifiers, rely on knowledge of how different entities relate. In this paper, we present a methodology for identifying such semantic relationships, by interpreting them as qualitative spatial relations in a conceptual space. In particular, we use multi-dimensional scaling to induce a conceptual space from a relevant text corpus and then identify directions that correspond to relative properties such as "more violent than" in an entirely unsupervised way. We also show how a variant of FOIL is able to learn natural categories from such qualitative representations, by simulating a fortiori inference, an important pattern of commonsense reasoning.


title: Sodium Benzoate, a Metabolite of Cinnamon and a Food Additive, Reduces Microglial and Astroglial Inflammatory Responses

abstract: Upon activation, microglia and astrocytes produce a number of proinflammatory molecules that participate in the pathophysiology of several neurodegenerative disorders. This study explores the anti-inflammatory property of cinnamon metabolite sodium benzoate (NaB) in microglia and astrocytes. NaB, but not sodium formate, was found to inhibit LPS-induced expression of inducible NO synthase (iNOS), proinflammatory cytokines (TNF-alpha and IL-1beta) and surface markers (CD11b, CD11c, and CD68) in mouse microglia. Similarly, NaB also inhibited fibrillar amyloid beta (Abeta)-, prion peptide-, double-stranded RNA (polyinosinic-polycytidylic acid)-, HIV-1 Tat-, 1-methyl-4-phenylpyridinium(+)-, IL-1beta-, and IL-12 p40(2)-induced microglial expression of iNOS. In addition to microglia, NaB also suppressed the expression of iNOS in mouse peritoneal macrophages and primary human astrocytes. Inhibition of NF-kappaB activation by NaB suggests that NaB exerts its anti-inflammatory effect through the inhibition of NF-kappaB. Although NaB reduced the level of cholesterol in vivo in mice, reversal of the inhibitory effect of NaB on iNOS expression, and NF-kappaB activation by hydroxymethylglutaryl-CoA, mevalonate, and farnesyl pyrophosphate, but not cholesterol and ubiquinone, suggests that depletion of intermediates, but not end products, of the mevalonate pathway is involved in the anti-inflammatory effect of NaB. Furthermore, we demonstrate that an inhibitor of p21(ras) farnesyl protein transferase suppressed the expression of iNOS, that activation of p21(ras) alone was sufficient to induce the expression of iNOS, and that NaB suppressed the activation of p21(ras) in microglia. These results highlight a novel anti-inflammatory role of NaB via modulation of the mevalonate pathway and p21(ras).


Understanding Semantic Impact of Source Code Changes: an Empirical Study


title: Data programming with DDLite: putting humans in a different part of the loop

abstract: Populating large-scale structured databases from unstructured sources is a critical and challenging task in data analytics. As automated feature engineering methods grow increasingly prevalent, constructing sufficiently large labeled training sets has become the primary hurdle in building machine learning information extraction systems. In light of this, we have taken a new approach called data programming [7]. Rather than hand-labeling data, in the data programming paradigm, users generate large amounts of noisy training labels by programmatically encoding domain heuristics as simple rules. Using this approach over more traditional distant supervision methods and fully supervised approaches using labeled data, we have been able to construct knowledge base systems more rapidly and with higher quality. Since the ability to quickly prototype, evaluate, and debug these rules is a key component of this paradigm, we introduce DDLite, an interactive development framework for data programming. This paper reports feedback collected from DDLite users across a diverse set of entity extraction tasks. We share observations from several DDLite hackathons in which 10 biomedical researchers prototyped information extraction pipelines for chemicals, diseases, and anatomical named entities. Initial results were promising, with the disease tagging team obtaining an F1 score within 10 points of the state-of-the-art in only a single day-long hackathon's work. Our key insights concern the challenges of writing diverse rule sets for generating labels, and exploring training data. These findings motivate several areas of active data programming research.


title: Context-awareness and the smart grid

abstract: New intelligent power grids (smart grids) will be an essential way of improving efficiency in power supply and power consumption, facilitating the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The delivery of efficiencies and advanced services in a smart grid will require both a comprehensive overlay communications network and flexible software platforms that can process data from a variety of sources, especially electronic sensor networks. Parallel developments in autonomic systems, pervasive computing and context-awareness (relating in particular to data fusion, context modelling, and semantic data) could provide key elements in the development of scalable smart grid data management systems and applications that utilise a multi-technology communications network. This paper describes: (1) the communications and data management requirements of the emerging smart grid, (2) state-of-the-art techniques and systems for context-awareness and (3) a future direction towards devising a context-aware middleware platform for the smart grid, as well as associated requirements and challenges.


title: Integrating real-time and batch processing in a polystore

abstract: This paper describes a stream processing engine called S-Store and its role in the BigDAWG polystore. Fundamentally, S-Store acts as a frontend processor that accepts input from multiple sources, and massages it into a form that has eliminated errors (data cleaning) and translates that input into a form that can be efficiently ingested into BigDAWG. S-Store also acts as an intelligent router that sends input tuples to the appropriate components of BigDAWG. All updates to S-Store's shared memory are done in a transactionally consistent (ACID) way, thereby eliminating new errors caused by non-synchronized reads and writes. The ability to migrate data from component to component of BigDAWG is crucial. We have described a migrator from S-Store to Postgres that we have implemented as a first proof of concept. We report some interesting results using this migrator that impact the evaluation of query plans.


title: All-Solid-State Repetitive Pulsed-Power Generator Using IGBT and Magnetic Compression Switches

abstract: By utilizing power semiconductor switches, especially high-voltage insulated gate bipolar transistors (IGBTs), as main switches, Marx modulators have demonstrated many advantages such as variable pulse length and pulse-repetition frequency, snubberless operation, and inherent redundancy. However, the relatively slow turn-on speed of the IGBT influences the pulse rise time of the Marx modulator. In this paper, a newly developed all-solid-state pulsed-power generator is proposed. This generator consists of a Marx modulator based on discrete IGBTs and a magnetic pulse-sharpening circuit, which is employed to compress the rising edge of the Marx output pulse. The experimental results are presented with a maximum voltage of 20 kV, a rise time of 200 ns, a pulse duration of 500 ns (full-width at half-maximum), and a repetition rate of 5 kHz on a 285- resistive load. The output power of the generator is 2.5 kW, and the average power in one pulse is 1 MW. The design methods of the IGBT drive circuits and the parameter calculation of the magnetic pulse-sharpening circuit are introduced in detail in this paper. The factors influencing the performance of the generator are also discussed.


A SOCIAL MEDIA ANALYTICS CAPABILITIY FRAMEWORK FOR FIRM’S COMPETITIVE ADVANTAGE


title: A Machine Learning Toolbox For Musician Computer Interaction

abstract: This paper presents the SARC EyesWeb Catalog, (SEC), a machine learning toolbox that has been specically developed for musician-computer interaction. The SEC features a large number of machine learning algorithms that can be used in real-time to recognise static postures, perform regression and classify multivariate temporal gestures. The algorithms within the toolbox have been designed to work with any N-dimensional signal and can be quickly trained with a small number of training examples. We also provide the motivation for the algorithms used for the recognition of musical gestures to achieve a low intra-personal generalisation error, as opposed to the inter-personal generalisation error that is more common in other areas of humancomputer interaction.


title: Computational design of walking automata

abstract: Creating mechanical automata that can walk in stable and pleasing manners is a challenging task that requires both skill and expertise. We propose to use computational design to offset the technical difficulties of this process. A simple drag-and-drop interface allows casual users to create personalized walking toys from a library of pre-defined template mechanisms. Provided with this input, our method leverages physical simulation and evolutionary optimization to refine the mechanical designs such that the resulting toys are able to walk. The optimization process is guided by an intuitive set of objectives that measure the quality of the walking motions. We demonstrate our approach on a set of simulated mechanical toys with different numbers of legs and various distinct gaits. Two fabricated prototypes showcase the feasibility of our designs.


title: Circuit-based interrogation of sleep control

abstract: Sleep is a fundamental biological process observed widely in the animal kingdom, but the neural circuits generating sleep remain poorly understood. Understanding the brain mechanisms controlling sleep requires the identification of key neurons in the control circuits and mapping of their synaptic connections. Technical innovations over the past decade have greatly facilitated dissection of the sleep circuits. This has set the stage for understanding how a variety of environmental and physiological factors influence sleep. The ability to initiate and terminate sleep on command will also help us to elucidate its functions within and beyond the brain.


title: Enriched Deep Recurrent Visual Attention Model for Multiple Object Recognition

abstract: We design an Enriched Deep Recurrent Visual Attention Model (EDRAM) — an improved attention-based architecture for multiple object recognition. The proposed model is a fully differentiable unit that can be optimized end-to-end by using Stochastic Gradient Descent (SGD). The Spatial Transformer (ST) was employed as visual attention mechanism which allows to learn the geometric transformation of objects within images. With the combination of the Spatial Transformer and the powerful recurrent architecture, the proposed EDRAM can localize and recognize objects simultaneously. EDRAM has been evaluated on two publicly available datasets including MNIST Cluttered (with 70K cluttered digits) and SVHN (with up to 250k real world images of house numbers). Experiments show that it obtains superior performance as compared with the state-of-the-art models.


title: A catheter malpositioned patient with pain and paresthesia

abstract: Introduction Central venous catheterization has become very common and it is a relatively inexpensive and easy method which can be used for several therapeutic and diagnostic purposes like measuring central venous pressure, fluid and blood transfusion, drug administration, hemodialysis and total parenteral nutrition (TPN). It is associated with many potential complications like other invasive procedures. Malposition as a complication is not a rare occurrence which usually occurs by misdirection of the catheter tip into the major tributaries of the superior vena cava (1). Chest x-ray has an important role in the detection of central venous catheter malposition (2) and it should be done to confirm the position of the catheter tip. In this case report we present a hemodialysis patient in which the catheter tip was malpositioned in the right subclavian vein.


title: Fourier tags: Smoothly degradable fiducial markers for use in human-robot interaction

abstract: In this paper we introduce the Fourier tag, a synthetic fiducial marker used to visually encode information and provide controllable positioning. The Fourier tag is a synthetic target akin to a bar-code that specifies multi-bit information which can be efficiently and robustly detected in an image. Moreover, the Fourier tag has the beneficial property that the bit string it encodes has variable length as a function of the distance between the camera and the target. This follows from the fact that the effective resolution decreases as an effect of perspective. This paper introduces the Fourier tag, describes its design, and illustrates its properties experimentally.


Data Warehouse Quality: A Review of the DWQ Project


title: Maximizing Cloud Provider Profit from Equilibrium Price Auctions

abstract: Auctioning constitutes a market-driven scheme for the allocation of cloud-based computing capacities. It is practically applied today in the context of Infrastructure as a Service offers, specifically, virtual machines. However, the maximization of auction profits poses a challenging task for the cloud provider, because it involves the concurrent determination of equilibrium prices and distribution of virtual machine instances to the underlying physical hosts in the data center. In the work at hand, we propose an optimal approach, based on linear programming, as well as a heuristic approach to tackle this Equilibrium Price Auction Allocation Problem (EPAAP). Through an evaluation based on realistic data, we show the practical applicability and benefits of our contributions. Specifically, we find that the heuristic approach reduces the average computation time to solve an EPAAP by more than 99.9%, but still maintains a favorable average solution quality of 96.7% in terms of cloud provider profit, compared to the optimal approach.


title: Sensitive attribute based non-homogeneous anonymization for privacy preserving data mining

abstract: Data mining is the process of extracting interesting patterns or knowledge from large amount of data. With the development of data mining technology, an increasing number of data can be mined out to reveal some potential information about the user, because of which privacy of the user may be violated easily. Privacy Preserving Data Mining (PPDM) is used to mine the potential valuable knowledge without revealing the personal information of the individuals. K-Anonymity is one of the Privacy Preserving model that aims at making the individual record be indistinguishable among a group records by using techniques of generalization and suppression. The existing approaches are based on homogeneous anonymization that anonymizes quasi attributes by choosing a single sensitive attribute. This approach causes high information loss and reduces the data utility. To overcome these issues in the existing system, sensitive attribute based non-homogeneous anonymization system is proposed. Based on the sensitive attribute, non-homogeneous anonymization technique (generalization and suppression) is applied to the identified quasi attributes and the non sensitive attributes are directly published. Thus the proposed system achieves high degree of data utility, reduces information loss and also achieves high degree of Data Integrity.


title: Developing mobile message security application using 3D Playfair Cipher algorithm

abstract: The theme of this research is to provide security for the messages of an Android phone that contains alphabets, numerals and special characters. This research overrules the functioning of Application Lock and secures the messages by encrypting through 3D-Playfair Cipher(4 × 4 × 4). 3D-Playfair works on trigraph and supports all of the 26 alphabets {A to Z}, the 10 digits {0 to 9} and the 28 basic special characters. 3D-Playfair increases the security by maximizing complexity. Using this application, all the messages will be displayed in the encrypted form on the mobile screen which can be decrypted upon the verification of valid user.


title: The Impact of User Rationality in Federated Clouds

abstract: With cloud computing, the long-envisioned dream of computing as utility is achieved. Many of the current standalone cloud providers offer resources and services using pay-per-use fixed pricing. Spot pricing, recently introduced in Amazon EC2, is more efficient by setting the resource price dynamically, based on demand. However, this pricing scheme, similar to the uniform price auction, is truthful only when supply can be adjusted, such as the case of a standalone cloud provider. In a federated cloud, where resources from multiple cloud providers are integrated to increase elasticity and reliability, rational users can have a greater impact. In this paper, we evaluate the impact of rationality in a federated cloud, by comparing the consumer welfare achieved by spot pricing, currently used in Amazon EC2, and a strategy-proof pricing scheme. We consider different EC2 regions as providers in a federated cloud, and use traces of spot prices to determine the consumer requests and the user welfare. Our results show that spot pricing is not suitable in a federated cloud, where rational users can increase their welfare by being untruthful.


Evaluation of Attention and Relaxation Levels of Archers in Shooting Process using Brain Wave Signal Analysis Algorithms


title: The value of competitive information in forecasting FMCG retail product sales and the variable selection problem:Working paper 2013:1

abstract: Sales forecasting at the UPC level is important for retailers to manage inventory. In this paper, we propose more effective methods to forecast retail UPC sales by incorporating competitive information including prices and promotions. The impact of these competitive marketing activities on the sales of the focal product has been extensively documented. However, competitive information has been surprisingly overlooked by previous studies in forecasting UPC sales, probably because of the high-dimensionality problem associated with the selection of variables. That is, each FMCG product category typically contains a large number of UPCs and is consequently associated with a large number of competitive explanatory variables. Under such a circumstance, time series models can easily become over-fitted and thus generate poor forecasting results. Our forecasting methods consist of two stages. At the first stage, we refine the competitive information. We identify the most relevant explanatory variables using variable selection methods, or alternatively, pool information across all variables using factor analysis to construct a small number of diffusion indexes. At the second stage, we specify the Autoregressive Distributed Lag (ADL) model following a general to specific modelling strategy with the identified most relevant competitive explanatory variables and the constructed diffusion indexes. We compare the forecasting performance of our proposed methods with the industrial practice method (benchmark model) and the ADL model specified exclusively with the price and promotion information of the focal product. The results show that our proposed methods generate substantially more accurate forecasts across a range of product categories.


title: ThemeDelta: Dynamic Segmentations over Temporal Topic Models

abstract: We present ThemeDelta, a visual analytics system for extracting and visualizing temporal trends, clustering, and reorganization in time-indexed textual datasets. ThemeDelta is supported by a dynamic temporal segmentation algorithm that integrates with topic modeling algorithms to identify change points where significant shifts in topics occur. This algorithm detects not only the clustering and associations of keywords in a time period, but also their convergence into topics (groups of keywords) that may later diverge into new groups. The visual representation of ThemeDelta uses sinuous, variable-width lines to show this evolution on a timeline, utilizing color for categories, and line width for keyword strength. We demonstrate how interaction with ThemeDelta helps capture the rise and fall of topics by analyzing archives of historical newspapers, of U.S. presidential campaign speeches, and of social messages collected through iNeighbors, a web-based social website. ThemeDelta is evaluated using a qualitative expert user study involving three researchers from rhetoric and history using the historical newspapers corpus.


title: Serious Vascular Complications after Nonsurgical Rhinoplasty: A Case Report

abstract: There has been an increased global demand for dermal filler injections in recent years. Although hyaluronic acid-based dermal fillers generally have a good safety profile, serious vascular complications have been reported. Here we present a typical case of skin necrosis following a nonsurgical rhinoplasty using hyaluronic acid filler. Despite various rescuing managements, unsightly superficial scars were left. It is critical for plastic surgeons and dermatologists to be familiar with the vascular anatomy and the staging of vascular complications. Any patients suspected to experience a vascular complication should receive early management under close monitoring. Meanwhile, the potentially devastating outcome caused by illegal practice calls for stricter regulations and law enforcement.


title: Pattern recognition by affine moment invariants

abstract: Abstract The paper deals with moment invariants, which are invariant under general affine transformation and may be used for recognition of affine-deformed objects. Our approach is based on the theory of algebraic invariants. The invariants from second- and third-order moments are derived and shown to be complete. The paper is a significant extension and generalization of recent works. Several numerical experiments dealing with pattern recognition by means of the affine moment invariants as the features are described.


A study of N-gram and Embedding Representations for Native Language Identification


title: Mathematical Formula Detection in Heterogeneous Document Images

abstract: This paper presents mathematical formula detection in heterogeneous document images that may contain figures, tables, text, and math formulas. We adopt the method originally proposed for sign detection in natural images to detect non-homogeneous regions and accordingly achieve text line detection and segmentation. Novel features based on centroid fluctuation information of non-homogeneous regions are proposed to more appropriately characterize both displayed formulas and embedded formulas. By comparing the proposed method with previous works, we demonstrate the effectiveness of the proposed features.


title: 3–3.6-GHz Wideband GaN Doherty Power Amplifier Exploiting Output Compensation Stages

abstract: We discuss the design, realization and experimental characterization of a GaN-based hybrid Doherty power amplifier for wideband operation in the 3-3.6-GHz frequency range. The design adopts a novel, simple approach based on wideband compensator networks. Second-harmonic tuning is exploited for the main amplifier at the upper limit of the frequency band, thus improving gain equalization over the amplifier bandwidth. The realized amplifier is based on a packaged GaN HEMT and shows, at 6 dB of output power back-off, a drain efficiency higher than 38% in the 3-3.6-GHz band, gain around 10 dB, and maximum power between 43 and 44 dBm, with saturated efficiency between 55% and 66%. With respect to the state of the art, we obtain, at a higher frequency, a wideband amplifier with similar performances in terms of bandwidth, output power, and efficiency, through a simpler approach. Moreover, the measured constant maximum output power of 20 W suggests that the power utilization factor of the 10-W (Class A) GaN HEMT is excellent over the amplifier band.


title: DBpedia-Based Entity Linking via Greedy Search and Adjusted Monte Carlo Random Walk

abstract: Facing a large amount of entities appearing on the web, entity linking has recently become useful. It assigns an entity from a resource to one name mention to help users grasp the meaning of this name mention. Unfortunately, many possible entities can be assigned to one name mention. Apparently, the usually co-occurring name mentions are related and can be considered together to determine their best assignments. This approach is called collective entity linking and is often conducted based on entity graph. However, traditional collective entity linking methods either consume much time due to the large scale of entity graph or obtain low accuracy due to simplifying graph. To improve both accuracy and efficiency, this article proposes a novel collective entity linking algorithm. It first constructs an entity graph by connecting any two related entities, and then a probability-based objective function is proposed on this graph to ensure the high accuracy of the linking result. Via this function, we convert entity linking to the process of finding the nodes with the highest PageRank Values. Greedy search and an adjusted Monte Carlo random walk are proposed to fulfill this work. Experimental results demonstrate that our algorithm performs much better than traditional linking methods.


title: PoseCut: Simultaneous Segmentation and 3D Pose Estimation of Humans Using Dynamic Graph-Cuts

abstract: We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Normally, when optimizing for pose, it is traditional to use some fixed set of features, e.g. edges or chamfer maps. In contrast, our novel approach consists of optimizing a cost function based on a Markov Random Field (MRF). This has the advantage that we can use all the information in the image: edges, background and foreground appearances, as well as the prior information on the shape and pose of the subject and combine them in a Bayesian framework. Previously, optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Note that although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any specified rigid, deformable or articulated object.


title: Dynamic detection of novice vs. skilled use without a task model

abstract: If applications were able to detect a user's expertise, then software could automatically adapt to better match exper-tise. Detecting expertise is difficult because a user's skill changes as the user interacts with an application and differs across applications. This means that expertise must be sensed dynamically, continuously, and unobtrusively so as not to burden the user. We present an approach to this prob-lem that can operate without a task model based on low-level mouse and menu data which can typically be sensed across applications at the operating systems level. We have implemented and trained a classifier that can detect "nov-ice" or "skilled" use of an image editing program, the GNU Image Manipulation Program (GIMP), at 91% accuracy, and tested it against real use. In particular, we developed and tested a prototype application that gives the user dy-namic application information that differs depending on her performance.


title: Drug discovery using induced pluripotent stem cell models of neurodegenerative and ocular diseases☆

abstract: The revolution of induced pluripotent stem cell (iPSC) technology provides a platform for development of cell therapy, disease modeling and drug discovery. Recent technological advances now allow us to reprogram a patient's somatic cells into induced pluripotent stem cells (iPSCs). Together with methods to differentiate these iPSCs into disease-relevant cell types, we are now able to model disease in vitro using iPSCs. Importantly, this represents a robust in vitro platform using patient-specific cells, providing opportunity for personalized precision medicine. Here we provide a review of advances using iPSC for drug development, and discuss the potential and limitations of iPSCs for drug discovery in neurodegenerative and ocular diseases. Emerging technologies that can facilitate the search for new drugs by assessment using in vitro disease models will also be discussed, including organoid differentiation, organ-on-chip, direct reprogramming and humanized animal models.


title: Training for intense exercise performance: high-intensity or high-volume training?

abstract: Performance in intense exercise events, such as Olympic rowing, swimming, kayak, track running and track cycling events, involves energy contribution from aerobic and anaerobic sources. As aerobic energy supply dominates the total energy requirements after 75 s of near maximal effort, and has the greatest potential for improvement with training, the majority of training for these events is generally aimed at increasing aerobic metabolic capacity. A shortterm period (six to eight sessions over 2–4 weeks) of highintensity interval training (consisting of repeated exercise bouts performed close to or well above the maximal oxygen uptake intensity, interspersed with low-intensity exercise or complete rest) can elicit increases in intense exercise performance of 2–4% in well-trained athletes. The influence of high-volume training is less discussed, but its importance should not be downplayed, as high-volume training also induces important metabolic adaptations. While the metabolic adaptations that occur with high-volume training and high-intensity training show considerable overlap, the molecular events that signal for these adaptations may be different. A polarized approach to training, whereby 75% of total training volume is performed at low intensities, and 10–15% is performed at very high intensities, has been suggested as an optimal training intensity distribution for elite athletes who perform intense exercise events.


title: IR meets NLP: On the Semantic Similarity between Subject-Verb-Object Phrases

abstract: Measuring the semantic similarity between phrases and sentences is an important task in natural language processing (NLP) and information retrieval (IR). We compare the quality of the distributional semantic NLP models against phrase-based semantic IR. The evaluation is based on the correlation between human judgements and model scores on a distributional phrase similarity task. We experiment with four NLP and two IR model variants. On the NLP side, models vary over normalization schemes and composition operators. On the IR side, models vary with respect to estimation of the probability of a term being in a document, namely P(t|d) where only term co-occurrence information is used and P(t|d, sim) which incorporates term distributional similarity. A mixture of the two methods is presented and evaluated. For both methods, word meanings are derived from large corpora of data: the BNC and ukWaC. One of the main findings is that grammatical distributional models give better scores than the IR models. This suggests that an IR model enriched with distributional linguistic information performs better in the long standing problem in IR of document retrieval where there is no direct symbolic relationship between query and document concepts.


title: Bismuth-based quadruple therapy for Helicobacter pylori- a single triple capsule plus lansoprazole

abstract: Background: ::: Recently a new ‘all in one’ single capsule with the three components of bismuth-based triple therapy became available in trials for treating Helicobacter pylori. ::: ::: Aim: ::: To investigate the efficacy and tolerability of this new capsule when combined with lansoprazole. ::: ::: Methods: ::: A total of 66 consecutive infected patients from a single centre received two single triple capsules four times daily and lansoprazole 30 mg b.d. for 7 days. Each capsule contained 60 mg of bismuth subcitrate, 125 mg of tetracycline and 125 mg of metronidazole. Endoscopy with biopsies for CLO-test, histology and culture from antrum and corpus was performed before and at least 5 weeks after treatment. ::: ::: Results: ::: The per protocol cure rate was 56/64 (88%, 95% CI: 79–95%); by intention-to-treat 56/65 (86%, 95% CI: 78–95%). The per protocol cure rate in metronidazole sensitive strains was 40/43 (93%, 95% CI: 85–100%); in resistant strains 5/9 (56%, 95% CI: 23–88%). There was one drop-out due to adverse events. ::: ::: Conclusions: ::: It is possible to combine the components of bismuth-based triple therapy into a single capsule. Based on the results it can be assumed that the capsule releases its content in the stomach. When combined with lansoprazole it reaches high cure rates, especially in metronidazole sensitive strains. This new approach simplifies bismuth-based anti-Helicobacter therapy.


title: X2Face: A network for controlling face generation by using images, audio, and pose codes

abstract: The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing. ::: We make the following three contributions. First, we introduce a network, X2Face, that can control a source face (specified by one or more frames) using another face in a driving frame to produce a generated frame with the identity of the source frame but the pose and expression of the face in the driving frame. Second, we propose a method for training the network fully self-supervised using a large collection of video data. Third, we show that the generation process can be driven by other modalities, such as audio or pose codes, without any further training of the network. ::: The generation results for driving a face with another face are compared to state-of-the-art self-supervised/supervised methods. We show that our approach is more robust than other methods, as it makes fewer assumptions about the input data. We also show examples of using our framework for video face editing.


title: Fluid-driven origami-inspired artificial muscles

abstract: Artificial muscles hold promise for safe and powerful actuation for myriad common machines and robots. However, the design, fabrication, and implementation of artificial muscles are often limited by their material costs, operating principle, scalability, and single-degree-of-freedom contractile actuation motions. Here we propose an architecture for fluid-driven origami-inspired artificial muscles. This concept requires only a compressible skeleton, a flexible skin, and a fluid medium. A mechanical model is developed to explain the interaction of the three components. A fabrication method is introduced to rapidly manufacture low-cost artificial muscles using various materials and at multiple scales. The artificial muscles can be programed to achieve multiaxial motions including contraction, bending, and torsion. These motions can be aggregated into systems with multiple degrees of freedom, which are able to produce controllable motions at different rates. Our artificial muscles can be driven by fluids at negative pressures (relative to ambient). This feature makes actuation safer than most other fluidic artificial muscles that operate with positive pressures. Experiments reveal that these muscles can contract over 90% of their initial lengths, generate stresses of ∼600 kPa, and produce peak power densities over 2 kW/kg—all equal to, or in excess of, natural muscle. This architecture for artificial muscles opens the door to rapid design and low-cost fabrication of actuation systems for numerous applications at multiple scales, ranging from miniature medical devices to wearable robotic exoskeletons to large deployable structures for space exploration.


The St Vincent Declaration. A long march for significant changes.


title: ONOS Intent Monitor and Reroute service: enabling plug&play routing logic

abstract: Software-Defined Networking (SDN) brought an unprecedented flexibility and programmability into computer networks. In order to simplify the management of an SDN network, several high-level languages have been proposed. The Intent Framework provided by Open Network Operating System (ONOS), for example, allows programmers to specify high-level policies without worrying about low-level device details, which are inferred by the controller. In addition, the Intent Framework ensures that the objective is met by transparently re-compiling the intents as a consequence of environment changes (e.g. link failures). In this work we extend the Intent Framework to make it able to both compile multiple intents together and to re-optimize their paths according to the network state based on flow statistics. We present Intent Monitor and Reroute service, a new ONOS module to optimize traffic forwarding of any ONOS applications based on intents, via an external plug&play routing logic with no modifications to ONOS applications at all. Finally we evaluate the benefits by enhancing the ONOS SDN-IP application with an adaptive Robust Traffic Engineering algorithm.


title: Emphatic Speech Prosody Prediction with Deep Lstm Networks

abstract: Controllable generation of emphasis in speech is desirable for expressive TTS systems utilized in various dialog applications. Usually such models remain voice-specific and the strength of emphasis can't be readily controlled. In this work we present a flexible emphatic prosody generation model based on Deep Recurrent Neural Networks (DRNN) for controllable word-level emphasis realization. The word emphasis DRNN model was trained on syllable-level piecewise linear prosodic trajectory parameters. A special data preprocessing technique was introduced to enable emphasis strength control, allowing to generate emphatic prosody trajectories of various strength. Additionally, we trained a DRNN model generating a sentence-level emphasis, i.e. producing whole sentences in forceful, decisive manner. Both models preserve quality and naturalness of the baseline TTS output.


title: Optimal Online Cyberbullying Detection

abstract: Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.


title: The Bayesian Network based program dependence graph and its application to fault localization

abstract: Abstract Fault localization is an important and expensive task in software debugging. Some probabilistic graphical models such as probabilistic program dependence graph (PPDG) have been used in fault localization. However, PPDG is insufficient to reason across nonadjacent nodes and only support making inference about local anomaly. In this paper, we propose a novel probabilistic graphical model called Bayesian Network based Program Dependence Graph (BNPDG) that has the excellent inference capability for reasoning across nonadjacent nodes. We focus on applying the BNPDG to fault localization. Compared with the PPDG, our BNPDG-based fault localization approach overcomes the reasoning limitation across nonadjacent nodes and provides more precise fault localization by taking its output nodes as the common conditions to calculate the conditional probability of each non-output node. The experimental results show that our BNPDG-based fault localization approach can significantly improve the effectiveness of fault localization.


title: Multi-target multi-object tracking, sensor fusion of radar and infrared

abstract: This paper presents algorithms and techniques for single-sensor tracking and multi-sensor fusion of infrared and radar data. The results show that fusing radar data with infrared data considerably increases detection range, reliability and accuracy of the object tracking. This is mandatory for further development of driver assistance systems. Using multiple model filtering for sensor fusion applications helps to capture the dynamics of maneuvering objects while still achieving smooth object tracking for not maneuvering objects. This is important when safety and comfort systems have to make use of the same sensor information. Comfort systems generally require smoothly filtered data whereas for safety systems it is crucial to capture maneuvers of other road users as fast as possible. Multiple model filtering and probabilistic data association techniques are presented and all presented algorithms are tested in real-time on standard PC systems.


Social Behavior in Organizational Studies


title: BoostClean: Automated Error Detection and Repair for Machine Learning

abstract: Predictive models based on machine learning can be highly sensitive to data error. Training data are often combined with a variety of different sources, each susceptible to different types of inconsistencies, and new data streams during prediction time, the model may encounter previously unseen inconsistencies. An important class of such inconsistencies is domain value violations that occur when an attribute value is outside of an allowed domain. We explore automatically detecting and repairing such violations by leveraging the often available clean test labels to determine whether a given detection and repair combination will improve model accuracy. We present BoostClean which automatically selects an ensemble of error detection and repair combinations using statistical boosting. BoostClean selects this ensemble from an extensible library that is pre-populated general detection functions, including a novel detector based on the Word2Vec deep learning model, which detects errors across a diverse set of domains. Our evaluation on a collection of 12 datasets from Kaggle, the UCI repository, real-world data analyses, and production datasets that show that Boost- Clean can increase absolute prediction accuracy by up to 9% over the best non-ensembled alternatives. Our optimizations including parallelism, materialization, and indexing techniques show a 22.2x end-to-end speedup on a 16-core machine.


title: Characterization of anthocyanins in caucasian blueberries (Vaccinium arctostaphylos L.) native to Turkey.

abstract: High-performance liquid chromatography (HPLC) combined with diode array (DAD) and electrospray ionization mass spectrometric (ESI-MS) detections were used to characterize anthocyanins in the berries of Vaccinium arctostaphylos L. The dark purple-black berries were collected from five Caucasian blueberry populations in northeastern Turkey. The HPLC-DAD profile consisted of 19 anthocyanin peaks, but HPLC-ESI-MS revealed fragment ion patterns of 26 anthocyanins. Delphinidin, cyanidin, petunidin, peonidin, and malvidin were all glycosylated with four different monosaccharide moieties (galactose, glucose, arabinose, and xylose) with the first two also conjugated with rhamnose. Furthermore, anthocyanidin disaccharides, tentatively identified as anthocyanidin sambubiosides, were characteristic for these berries. The mean content of the total anthocyanins was 1420 mg/100 g dry weight. The most predominant anthocyanidins were delphinidin (41%), petunidin (19%), and malvidin (19%). Glucose was the most typical (61%) sugar moiety. This study revealed that wild Caucasian blueberries contain an abundance of bioactive anthocyanins and thus are ideal for various functional food purposes.