title: Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks

abstract: OBJECTIVE ::: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. ::: ::: ::: METHODS ::: The deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data are available, the proposed approach works as an image domain postprocessing algorithm. ::: ::: ::: RESULTS ::: Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. ::: ::: ::: CONCLUSION ::: Comparisons using single and multiple coil acquisition show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing CS methods. ::: ::: ::: SIGNIFICANCE ::: The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.


title: An End-to-end Neural Natural Language Interface for Databases

abstract: The ability to extract insights from new data sets is critical for decision making. Visual interactive tools play an important role in data exploration since they provide non-technical users with an effective way to visually compose queries and comprehend the results. Natural language has recently gained traction as an alternative query interface to databases with the potential to enable non-expert users to formulate complex questions and information needs efficiently and effectively. However, understanding natural language questions and translating them accurately to SQL is a challenging task, and thus Natural Language Interfaces for Databases (NLIDBs) have not yet made their way into practical tools and commercial products. In this paper, we present DBPal, a novel data exploration tool with a natural language interface. DBPal leverages recent advances in deep models to make query understanding more robust in the following ways: First, DBPal uses a deep model to translate natural language statements to SQL, making the translation process more robust to paraphrasing and other linguistic variations. Second, to support the users in phrasing questions without knowing the database schema and the query features, DBPal provides a learned auto-completion model that suggests partial query extensions to users during query formulation and thus helps to write complex queries.


title: Conceptual Metaphors: Ontology-Based Representation And Corpora Driven Mapping Principles

abstract: The goal of this paper is to integrate the Conceptual Mapping Model with an ontology-based knowledge representation (i.e. Suggested Upper Merged Ontology (SUMO)) in order to demonstrate that conceptual metaphor analysis can be restricted and eventually, automated. In particular, we will propose a corpora-based operational definition for Mapping Principles, which are explanations of why a conventional conceptual metaphor has a particular source-target domain pairing. This paper will examine 2000 random examples of 'economy' (jingji) in Mandarin Chinese and postulate Mapping Principles based frequency and delimited with SUMO.


title: DGW-canny: An improvised version of Canny edge detector

abstract: Edge detection is a field which has intrigued programmers since early 1970s. Since then, a good number of edge detection techniques have come up but the technique used by Canny [1] is very widely used. However, it has been observed that the results are not that efficient while dealing with alpha-numeric sections or geometrical figures or fine grain region in an image. To mitigate these limitations, we propose an improvised edge detection technique. The technique uses a Laplacian of Gaussian gradient with a new approach towards the thresholding section. The DGW-Canny framework has been experimented on a data set of images categorized on the basis of vectors proposed by us. The encouraging experimental results show that the DGW-Canny edge detector renders much better results compared to those of Canny. We have also performed experiments by varying parameters like nature and man-made component in an image. It is observed that varying the latter parameter adversely affects the edges of the image.


title: Fabrication of 5-GHz-band SAW filter with atomically-flat-surface AlN on sapphire

abstract: 5-GHz-band surface acoustic wave (SAW) filters for mobile communications were fabricated on an atomically-flat-surface (0001)aluminum nitride/(0001)sapphire (AlN/Al/sub 2/O/sub 3/) combination. The SAW devices were fabricated using electron beam lithography and a lift-off method. Atomically-flat-surface AIN films were used to reduce SAW propagation loss. The center frequency of the fabricated SAW filter was 5.18 GHz. SAW velocity was 5688 m/s at normalized thickness by wave number (kH) of 9.9. The effective coupling coefficient was 0.1% and temperature-coefficient of delay was 9 ppm//spl deg/C at kH of 5.9. The SAW propagation loss was 0.0053 dB at 5.18 GHz. The atomically-flat-surface (0001)AlN/(0001)Al/sub 2/O/sub 3/ combination is promising for use in 5-GHz-band SAW filters for application to mobile communications.


Intent Mining for the Good , Bad & Ugly Use of Social Web : Concepts , Methods , and Challenges


title: Design of low cost microstrip antenna arrays for mm-Wave applications

abstract: A planar, low profile, low cost microstrip patch array antenna is developed in the millimeter-wave band for large gain applications. A simple edge feeding is used for a sub-array of 16×1 elements, and finally a matching network or power divider/splitter is designed for a 32×16 elements with a gain of 27dBi for the design frequency of 79GHz and 26dBi at 80GHz. A method to reduce the slightly high “shoulder” radiation due to the feed network is introduced through a shielded feed.


title: E-Government Portals Maturity Models: A Best Practices' Coverage Perspective.

abstract: E-government is a field where oriented practice is considered crucial for its prosperity. Therefore, best practices are considered among the success factors of e-government portals. To this end, e-government maturity models can be used to provide guidance and guidelines to identify those best practices. After an extensive literature review, we have collected both; the e-government portals’ best practices and organized them according to their purposes in an e-Government Portals’ Best Practice Model (eGPBPM), and the set of 25 maturity models best practices in two separated previous published studies. The eGPBPM is composed of four best practice categories including: back-end, Web design, Web content and external. Moreover, each maturity model has several stages of maturity and each stage include a set of best practices used to rank the maturity of e-government portals. The goal of this paper is to identify the extent to which e-government maturity models are covering the best practices of the eGPBPM. To achieve this goal, a mapping between the maturity models’ best practices for each maturity stage and the best practices of the eGPBPM has been performed. Our findings show that although this set of maturity models are used in practice, they include only some of the e-government portals’ best practices and none of them have a full coverage of those best practices.


title: New Prototype of Hybrid 3D-Biometric Facial Recognition System

abstract: In the last decades, a lot of 3D face recognition techniques have been proposed. They can be divided into three parts, holistic matching techniques, feature-based techniques and hybrid techniques. In this paper, a hybrid technique is used, where, a prototype of a new hybrid face recognition technique depends on 3D face scan images are designed, simulated and implemented. Some geometric rules are used for analyzing and mapping the face. Image processing is used to get the two- dimensional values of predetermined and specific facial points, software programming is used to perform a three-dimensional coordinates of the predetermined points and to calculate several geometric parameter ratios and relations. Neural network technique is used for processing the calculated geometric parameters and then performing facial recognition. The new design is not affected by variant pose, illumination and expression and has high accurate level compared with the 2D analysis. Moreover, the proposed algorithm is of higher performance than latest's published biometric recognition algorithms in terms of cost, confidentiality of results, and availability of design tools.


title: The State of Solving Large Incomplete-Information Games, and Application to Poker

abstract: Game-theoretic solution concepts prescribe how rational parties should act, but to become operational the concepts need to be accompanied by algorithms. I will review the state of solving incomplete-information games. They encompass many practical problems such as auctions, negotiations, and security applications. I will discuss them in the context of how they have transformed computer poker. In short, game-theoretic reasoning now scales to many large problems, outperforms the alternatives on those problems, and in some games beats the best humans.


title: Anomaly detection in crowded scenes

abstract: A novel framework for anomaly detection in crowded scenes is presented. Three properties are identified as important for the design of a localized video representation suitable for anomaly detection in such scenes: 1) joint modeling of appearance and dynamics of the scene, and the abilities to detect 2) temporal, and 3) spatial abnormalities. The model for normal crowd behavior is based on mixtures of dynamic textures and outliers under this model are labeled as anomalies. Temporal anomalies are equated to events of low-probability, while spatial anomalies are handled using discriminant saliency. An experimental evaluation is conducted with a new dataset of crowded scenes, composed of 100 video sequences and five well defined abnormality categories. The proposed representation is shown to outperform various state of the art anomaly detection techniques.


title: Harmonic Networks: Deep Translation and Rotation Equivariance

abstract: Translating or rotating an input image should not affect the results of many computer vision tasks. Convolutional neural networks (CNNs) are already translation equivariant: input image translations produce proportionate feature map translations. This is not the case for rotations. Global rotation equivariance is typically sought through data augmentation, but patch-wise equivariance is more difficult. We present Harmonic Networks or H-Nets, a CNN exhibiting equivariance to patch-wise translation and 360-rotation. We achieve this by replacing regular CNN filters with circular harmonics, returning a maximal response and orientation for every receptive field patch. H-Nets use a rich, parameter-efficient and fixed computational complexity representation, and we show that deep feature maps within the network encode complicated rotational invariants. We demonstrate that our layers are general enough to be used in conjunction with the latest architectures and techniques, such as deep supervision and batch normalization. We also achieve state-of-the-art classification on rotated-MNIST, and competitive results on other benchmark challenges.


title: Characteristics of perovskite solar cells under low illuminance condition

abstract: In this study, we investigated the characteristics of perovskite solar cells under low illuminance condition for indoor application of perovskite solar cells. The open-circuit voltage remained around 70% at 0.1% illuminance of AM1.5. This result indicates the potential for indoor application of perovskite solar cells.


title: Optimal homogeneous networks with best possible synchronizability

abstract: A large number of real-world complex networks or their sub-networks possess excellent dynamical properties such as high dynamic synchronizability, optimal controllability, strong resistance to attacks, and fast information spreading capability, but existing network models are unable to well represent these intrinsic features and ubiquitous phenomena. This paper establishes an optimal homogeneous network model which can well describe at least one of such optimal dynamical behaviors — the best possible synchronizability.


title: Quartz-based vibrating MEMS fabricated using a wafer-bonding process with sealed cavities

abstract: Here we present the results of a wafer-level approach allowing the collective fabrication of gyroscope sensors based on quartz vibrating MEMS. More specifically, we focus on suspended quartz tuning fork microstructures of a desired thickness over controlled depth cavities. This approach is based on the bonding and thinning of 4-inch z-cut quartz wafer on pre-structured silicon wafer. InfraRed (IR) inspection shows a large bonded area (>98%) while structural characterizations of the thinned quartz layer indicate a crystal quality and mechanical properties equivalent to quartz bulk material. The obtained gyroscope exhibits a quality factor (Q) around 12300 at 86.6 kHz very close to quartz theoretical thermoelastic limit. This Quartz-On-Silicon (QOS) technology open the way to a new generation of highly integrated quartz devices.


title: Extending the Business Model Canvas: A Dynamic Perspective

abstract: When designing and assessing a business model, a more visual and practical ontology and framework is necessary. We show how an academic theory such as Business Model Ontology has evolved into the Business Model Canvas (BMC) that is used by practitioners around the world today. We draw lessons from usage and define three maturity level. We propose new concepts to help design the dynamic aspect of a business model. On the first level, the BMC supports novice users as they elicit their models; it also helps novices to build coherent models. On the second level, the BMC allows expert users to evaluate the interaction of business model elements by outlining the key threads in the business models' story. On the third level, master users are empowered to create multiple versions of their business models, allowing them to evaluate alternatives and retain the history of the business model's evolution. These new concepts for the BMC which can be supported by Computer-Aided Design tools provide a clearer picture of the business model as a strategic planning tool and are the basis for further research.


title: On Integrated Clustering and Outlier Detection

abstract: We model the joint clustering and outlier detection problem using an extension of the facility location formulation. The advantages of combining clustering and outlier selection include: (i) the resulting clusters tend to be compact and semantically coherent (ii) the clusters are more robust against data perturbations and (iii) the outliers are contextualised by the clusters and more interpretable. We provide a practical subgradient-based algorithm for the problem and also study the theoretical properties of algorithm in terms of approximation and convergence. Extensive evaluation on synthetic and real data sets attest to both the quality and scalability of our proposed method.


title: City, self, network: transnational migrants and online identity work

abstract: This paper uses qualitative interviews with 26 transnational migrants in New York City to analyze socio-technical practices related to online identity work. We focus specifically on the use of Facebook, where benefits included keeping in touch with friends and family abroad and documenting everyday urban life. At the same time, many participants also reported experiences of fatigue, socio-cultural tensions and concerns about maintaining a sense of personal privacy. These experiences highlight how transnational practices complicate context collapse, where the geographic dispersal of participants' personal networks renders visible conflicts of 'flattened' online networks. Our findings also suggest a kind of technology-enabled code-switching, where transnational migrants leverage social media to perform identities that alternate between communities, nationalities and geographies. This analysis informs HCI research on transnationalism and technological practices, as well as the complexities of online identity work in terms of shifting social and spatial contexts.


title: Mortality and health-related quality of life in prevalent dialysis patients: Comparison between 12-items and 36-items short-form health survey

abstract: To assess health- related quality of life (HRQOL) with SF-12 and SF-36 and compare their abilities to predict mortality in chronic dialysis patients, after adjusting for traditional risk factors. The Short-Form Health Survey (SF-36) with the embedded SF-12 was applied in 301 dialysis patients cross-sectionally. Physical and mental component summary (PCS-36, MCS-36, PCS-12, and MCS-12) scores were calculated. Clinical and demographic data were collected. Mortality (followed for up to 4.5 years) was analyzed with Kaplan Meier plots and Cox proportional hazards, after censoring for renal transplantation. Exclusion factors were observation time <2 months (n = 21) and missing component summary scores (n = 10 for SF-36; n = 28 for SF-12), thus 252 patient were included in the analyses. In 252 patients (60.2 ± 15.5 years, 65.9% males, dialysis vintage 9.0, IQR 5.0-23.0 months), mortality during follow-up was 33.7%.(85 deaths). Significant correlations were observed between PCS-36 and PCS-12 (ρ = 0.93, p < 0.001) and between MCS-36 and MCS-12 (ρ = 0.95, p < 0.001). Mortality rate was highest in patients in the lowest quartile of PCS-12 (χ2 = 15.3, p = 0.002) and PCS-36 (χ2 = 16.7, p = 0.001). MCS was not associated with mortality. Adjusted hazard ratios for mortality were 2.5 (95% CI 1.0-6.3, PCS-12) and 2.7 (1.1 – 6.4, PCS-36) for the lowest compared with the highest (“best perceived”) quartile of PCS. Compromised HRQOL is an independent predictor of poor outcome in dialysis patients. The SF-12 provided similar predictions of mortality as SF-36, and may serve as an applicable clinical tool because it requires less time to complete.


title: Interactions between contingency, organizational IT factors, and ERP success

abstract: Purpose – The purpose of this paper is to examine the impact of some organizational information technology (IT) factors (i.e. IT assets, employees' IT skills, IT resources, and satisfaction with legacy IT systems) and their interacting effects with two contingency factors (i.e. organization's size and structure) on enterprise resource planning (ERP) system success.Design/methodology/approach – Surveys were conducted in two European countries. Respondents came from diverse, private, and industrial organizations. Relevant hypotheses were developed and tested using a structural equation modeling technique.Findings – The analysis supported – partially or fully – six of the eight hypotheses formulated. For example, the data indicated strong positive relationships between IT assets and IT resources, on the one hand, and ERP success, on the other. Organization's size and structure were also found to be moderators in some of the relationships. Also, the analysis revealed that satisfaction with legacy IT systems i...


title: DATA MINING FOR IMBALANCED DATASETS: AN OVERVIEW

abstract: A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult “real-world” problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced and/or the costs of different errors vary markedly. In this Chapter, we discuss some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets.


AM backplane for AMOLED


JEDI: Joint Entity and Relation Detection using Type Inference


title: Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation

abstract: We propose Meta-Prod2vec, a novel method to compute item similarities for recommendation that leverages existing item metadata. Such scenarios are frequently encountered in applications such as content recommendation, ad targeting and web search. Our method leverages past user interactions with items and their attributes to compute low-dimensional embeddings of items. Specifically, the item metadata is injected into the model as side information to regularize the item embeddings. We show that the new item representations lead to better performance on recommendation tasks on an open music dataset.


3D PLANE-BASED MAPS SIMPLIFICATION FOR RGB-D SLAM SYSTEMS


title: Meal-assistance by Robot Suit HAL using detection of food position with camera

abstract: Meal-assistance is necessary for patients with upper limb paralysis caused by diseases such as cerebrovascular disorder and cervical cord damage. We have developed the upper limb type HAL (HAL-UL) to assist the arm movement during a meal for people with upper limb paralysis. HAL-UL achieves reaching movements according to pre-generated trajectories. Furthermore, for meal assistance it is required to provide the reaching movement for various locations, shapes, and amounts of food. Therefore, the purpose of this research is to achieve the detection the food position by HAL during meal-assistance using image processing and to verify its effectiveness. A camera is installed overhead to detect the image of the food on the table. Then the locations of the food pieces are detected using image processing. A microphone is installed as a command interface for the user to command the dish number. Finally, the optimal trajectory to the location is selected from pre-generated reaching trajectories. To determine the effectiveness of the proposed method, an eating experiment was performed. A subject wore the HAL-UL in front of a table, and 4 dishes with different types of food were prepared. Then, the image of the food was processed, the food locations were detected, and the optimal reaching trajectories were selected. According to the determined trajectories the HAL-UL provided eating movement support for the subject. Therefore, the effectiveness of the proposed method was determined and we confirmed the HAL-UL for meal-assistance may be used successfully for the actual meal-assistance.


title: Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech

abstract: Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech. We capitalize on the current breakthrough of Recurrent Neural Network (RNN) and seek to investigate its performance for emotion classification from noisy speech. We particularly focus on the recently proposed Gated Recurrent Unit (GRU), which is yet to be explored for emotion recognition from speech. Experiments conducted with speech compounded with eight different types of noises reveal that GRU incurs an 18.16% smaller run-time while performing quite comparably to the Long Short-Term Memory (LSTM), which is the most popular Recurrent Neural Network proposed to date. This result is promising for any embedded platform in general and will initiate further studies to utilize GRU to its full potential for emotion recognition on smartphones.


Neural Network based Performance Tuning in Distributed Database Environment


title: A short form of the metacognitions questionnaire: properties of the MCQ-30.

abstract: Abstract The metacognitions questionnaire (MCQ) measures individual differences in a selection of metacognitive beliefs, judgments and monitoring tendencies considered important in the metacognitive model of psychological disorders. The development and properties of a shortened 30-item version of the MCQ, the MCQ-30, are reported. Construct validity was evaluated by confirmatory and exploratory factor analysis. Overall, the fit indices suggested an acceptable fit to a five-factor model consistent with the original MCQ. Exploratory factor analysis supported a five-factor structure, which was almost identical to the original solution obtained in previous studies with the full MCQ. The five factors are cognitive confidence, positive beliefs about worry, cognitive self-consciousness, negative beliefs about uncontrollability of thoughts and danger, and beliefs about need to control thoughts. The MCQ-30 showed good internal consistency and convergent validity, and acceptable to good test–retest reliability. Positive relationships between metacognitions and measures of worry and obsessive–compulsive symptoms provided further support for the validity of the measure and the metacognitive theory of intrusive thoughts. The psychometric properties of MCQ-30 suggest that the instrument is a valuable addition to the assessment of metacognitions that has the advantage of being more economical to use compared with the original MCQ.


title: The qualitative research interview

abstract: Background Interviews are among the most familiar strategies for collecting qualitative data. The different qualitative interviewing strategies in common use emerged from diverse disciplinary perspectives resulting in a wide variation among interviewing approaches. Unlike the highly structured survey interviews and questionnaires used in epidemiology and most health services research, we examine less structured interview strategies in which the person interviewed is more a participant in meaning making than a conduit from which information is retrieved. ::: ::: Purpose In this article we briefly review the more common qualitative interview methods and then focus on the widely used individual face-to-face in-depth interview, which seeks to foster learning about individual experiences and perspectives on a given set of issues. We discuss methods for conducting in-depth interviews and consider relevant ethical issues with particular regard to the rights and protection of the participants.


title: A low-offset latched comparator using zero-static power dynamic offset cancellation technique

abstract: A low-offset latched comparator using new dynamic offset cancellation technique is proposed. The new technique achieves low offset voltage without pre-amplifier and quiescent current. Furthermore the overdrive voltage of the input transistor can be optimized to reduce the offset voltage of the comparator independent of the input common mode voltage. A prototype comparator has been fabricated in 90 nm 9M1P CMOS technology with 152 µm2. Experimental results show that the comparator achieves 3.8 mV offset at 1 sigma at 500 MHz operating, while dissipating 39 μW from a 1.2 V supply.


title: Detailed Surface Geometry and Albedo Recovery from RGB-D Video Under Natural Illumination

abstract: In this paper we present a novel approach for depth map enhancement from an RGB-D video sequence. The basic idea is to exploit the shading information in the color image. Instead of making assumption about surface albedo or controlled object motion and lighting, we use the lighting variations introduced by casual object movement. We are effectively calculating photometric stereo from a moving object under natural illuminations. The key technical challenge is to establish correspondences over the entire image set. We therefore develop a lighting insensitive robust pixel matching technique that out-performs optical flow method in presence of lighting variations. In addition we present an expectation-maximization framework to recover the surface normal and albedo simultaneously, without any regularization term. We have validated our method on both synthetic and real datasets to show its superior performance on both surface details recovery and intrinsic decomposition.


title: Ruthroff Transmission Line Transformers using Multilayer Technology

abstract: This paper presents Ruthroff type transmission line transformers for wideband impedance matching at microwave frequencies fabricated using both thin film microstrip (TFMS) operating up to approximately 30 GHz and multilayer MMIC processes operating up to approximately 60 GHz.


title: Composing Distributed Representations of Relational Patterns

abstract: Learning distributed representations for relation instances is a central technique in downstream NLP applications. In order to address semantic modeling of relational patterns, this paper constructs a new dataset that provides multiple similarity ratings for every pair of relational patterns on the existing dataset. In addition, we conduct a comparative study of different encoders including additive composition, RNN, LSTM, and GRU for composing distributed representations of relational patterns. We also present Gated Additive Composition, which is an enhancement of additive composition with the gating mechanism. Experiments show that the new dataset does not only enable detailed analyses of the different encoders, but also provides a gauge to predict successes of distributed representations of relational patterns in the relation classification task.


title: Semantic MEDLINE: An advanced information management application for biomedicine

abstract: To support more effective biomedical information management, Semantic MEDLINE integrates document retrieval, advanced natural language processing, automatic summarization and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected graph of semantic relations, with links to the original MEDLINE citations. The ability to connect salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Semantic MEDLINE can make an impact on biomedicine by supporting scientific discovery and the timely translation of insights from basic research into advances in clinical practice and patient care. Semantic MEDLINE is illustrated here with recent research on the clock genes.


title: An event-based platform for collaborative threats detection and monitoring

abstract: Organizations must protect their information systems from a variety of threats. Usually they employ isolated defenses such as firewalls, intrusion detection and fraud monitoring systems, without cooperating with the external world. Organizations belonging to the same markets (e.g., financial organizations, telco providers) typically suffer from the same cyber crimes. Sharing and correlating information could help them in early detecting those crimes and mitigating the damages. The paper discusses the Semantic Room (SR) abstraction which enables the development of collaborative event-based platforms, on the top of Internet, where data from different information systems are shared, in a controlled manner, and correlated to detect and timely react to coordinated Internet-based security threats (e.g., port scans, botnets) and frauds. In order to show the flexibility of the abstraction, the paper proposes the design, implementation and validation of two SRs: an SR that detects inter-domain port scan attacks and an SR that enables an online fraud monitoring over the Italian territory. In both cases, the SRs use real data traces for demonstrating the effectiveness of the proposed approach. In the first SR, high detection accuracy and small detection delays are achieved whereas in the second, new fraud evidence and investigation instruments are provided to law enforcement agencies.


title: Effect of Oven-Drying on the Recovery of Valuable Compounds from Ulva rigida, Gracilaria sp. and Fucus vesiculosus

abstract: The effect of oven-drying at 25, 40 and 60 °C was evaluated on three macroalgae of relevance in Europe, namely Ulva rigida, Gracilaria sp. and Fucus vesiculosus, with respect to quality aspects, including their potential to be exploited as a source of valuable compounds. Notably, as compared to freeze-drying, oven-drying at 25 °C promoted the extraction of chlorophylls and carotenoids from U. rigida, as well as those of phycoerythrin and chlorophyll a from Gracilaria sp., while 40 °C favored the recovery of fucoxanthin and pheophytin a from F. vesiculosus. On the other hand, the use of oven-drying had a negative impact on the extraction of phenolic compounds from this alga, also diminishing the antioxidant activity of the resulting extracts. Instead, the impact of oven-drying of raw material on the recovery of specific polysaccharides differed among the macroalgae. While the amounts of ulvans and fucoidans obtained from macroalgae dried at higher temperatures tended to be superior, the recovery of agar was not affected with the drying temperatures applied to Gracilaria sp. The overall results showed that oven-drying might serve as a good alternative to stabilize Ulva rigida, Gracilaria sp. and Fucus vesiculosus, especially if extraction of pigments and polysaccharides is aimed, thought the appropriate temperature applied must be adapted for each macroalgae.


title: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

abstract: In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients - manually annotated by up to four raters - and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all subregions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.


Bayesian networks for prediction, risk assessment and decision making in an inefficient Association Football gambling market.


title: Analysis of the potential for delay propagation in passenger airline networks

abstract: The paper analyzes the potential for delays to propagate in passenger airline networks. The aim is to better understand the relationship between the scheduling of aircraft and crew, and the operational performance of such schedules. In particular, when carriers decide how to schedule costly resources, the focus is primarily on achieving high levels of utilization. The resulting plans, however, often have little slack, limiting the schedule's ability to absorb disruption; instead, initial flight delays may propagate to delay subsequent flights as well. Understanding the relationship between planned schedules and delay propagation is a requisite precursor to developing tools for building more robust airline plans. This relationship is investigated using the flight data provided by two major US carriers, one traditional hub-and-spoke and one low-fare carrier operating a predominantly point-to-point network.


title: CPTSD is a distinct entity: comment on Resick et al. (2012).

abstract: The concept of complex posttraumatic stress disorder (CPTSD) is both conceptually and clinically useful. This distinct entity is highly prevalent, across different cultures, in survivors of prolonged, repeated trauma. Recognition of this entity as part of the spectrum of traumatic disorders would promote development of effective treatment.


title: Discovering Knowledge in Data: An Introduction to Data Mining

abstract: The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis.Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and VisualizationOffers extensive coverage of the R statistical programming languageContains 280 end-of-chapter exercisesIncludes a companion website with further resources for all readers, and Powerpoint slides, a solutions manual, and suggested projects for instructors who adopt the book


FRanCo - A Ground Truth Corpus for Fact Ranking Evaluation


title: * Author / title page Running head: Disgust and extreme prejudice Disgust is a factor in extreme prejudice

abstract: Understanding intergroup prejudice is a dominant research focus for social psychology. Prejudice is usually conceptualized as a continuum of positive/negative affect, but this has limitations. It neither reflects people's ability to maintain simultaneous positive and negative stereotypes of others nor explains extreme prejudice (bigotry). Some researchers have proposed multidimensional models of prejudice in which different negative emotions are evoked depending on the situation. Extending this to bigotry raises the question of which emotions are most relevant. Therefore, this study looked at ‘anti-group’ texts – writings which promote extreme intergroup hostility – and analysed the frequency of emotive language. Findings suggest that bigotry may be distinguished by high levels of disgust.


title: A cognitive and video-based approach for multinational License Plate Recognition

abstract: License Plate Recognition (LPR) is mainly regarded as a solved problem. However, robust solutions able to face real-world scenarios still need to be proposed. Country-specific systems are mostly, designed, which can (artificially) reach high-level recognition rates. This option, however, strictly limits their applicability. In this paper, we propose an approach that can deal with various national plates. There are three main areas of novelty. First, the Optical Character Recognition (OCR) is managed by a hybrid strategy, combining statistical and structural algorithms. Secondly, an efficient probabilistic edit distance is proposed for providing an explicit video-based LPR. Last but not least, cognitive loops are introduced at critical stages of the algorithm. These feedback steps take advantage of the context modeling to increase the overall system performances, and overcome the inextricable parameter settings of the low-level processing. The system performances have been tested in more than 1200 static images with difficult illumination conditions and complex backgrounds, as well as in six different videos containing 525 moving vehicles. The evaluations prove our system to be very competitive among the non-country specific approaches.


title: Autonomous locomotion of a miniature, untethered origami robot using hall effect sensor-based magnetic localization

abstract: Autonomous control of magnetically-actuated miniature robots enables greater versatility and complexity in function but has so far been a challenge to implement. In this paper, we present closed-loop position feedback control of a miniature origami robot utilizing its integrated magnet and an array of Hall effect sensors, enabling the robot's actuation, detection, and locomotion to be initiated from outside its body. An array of 33 Hall effect sensors arranged in repeated triangles cover a range of 60 mm by 75 mm, enabling position detection of the robot with average error of 0.995±0.520 mm. The robot's speed response to applied magnetic field was characterized, and a controller was designed to actuate the robot dependably. We demonstrate autonomous movement of the robot along preplanned paths and the viability of magnetic detection and actuation.


title: Deep Learning using Rectified Linear Units (ReLU)

abstract: We introduce the use of rectified linear units (ReLU) as the classification function in a deep neural network (DNN). Conventionally, ReLU is used as an activation function in DNNs, with Softmax function as their classification function. However, there have been several studies on using a classification function other than Softmax, and this study is an addition to those. We accomplish this by taking the activation of the penultimate layer $h_{n - 1}$ in a neural network, then multiply it by weight parameters $\theta$ to get the raw scores $o_{i}$. Afterwards, we threshold the raw scores $o_{i}$ by $0$, i.e. $f(o) = \max(0, o_{i})$, where $f(o)$ is the ReLU function. We provide class predictions $\hat{y}$ through argmax function, i.e. argmax $f(x)$.


title: Re-tiling polygonal surfaces

abstract: This paper presents an automatic method of creating surface models at several levels of detail from an original polygonal description of a given object. Representing models at various levels of detail is important for achieving high frame rates in interactive graphics applications and also for speeding-up the off-line rendering of complex scenes. Unfortunately, generating these levels of detail is a time-consuming task usually left to a human modeler. This paper shows how a new set of vertices can be distributed over the surface of a model and connected to one another to create a re-tiling of a surface that is faithful to both the geometry and the topology of the original surface. The main contributions of this paper are: 1) a robust method of connecting together new vertices over a surface, 2) a way of using an estimate of surface curvature to distribute more new vertices at regions of higher curvature and 3) a method of smoothly interpolating between models that represent the same object at different levels of detail. The key notion in the re-tiling procedure is the creation of an intermediate model called the mutual tessellation of a surface that contains both the vertices from the original model and the new points that are to become vertices in the re-tiled surface. The new model is then created by removing each original vertex and locally re-triangulating the surface in a way that matches the local connectedness of the initial surface. This technique for surface retessellation has been successfully applied to iso-surface models derived from volume data, Connolly surface molecular models and a tessellation of a minimal surface of interest to mathematicians.


title: Soft Pneumatic Actuator skin with embedded sensors

abstract: Soft Pneumatic Actuator skin (SPA-skin) is a novel concept of ultra-thin (< 1 mm) sensor embedded actuators with distributed actuation points that could cover soft bodies. This highly customizable and flexible SPA-skin is ideal for providing proprioceptive sensing by covering pre-existing structures and robots bodies. Having few limitation of the surface quality, dynamics, or shape, these mechanical attributes allow potential applications in autonomous flexible braille, active surface pattern reconfiguration, distributed actuation and sensing for tactile interface improvements. In this paper, the authors present a proof-of-concept SPA-skin. The mechanical parameters, design criteria, sensor selection, and actuator construction process are illustrated. Two control schemes, actuation mode and force sensing mode, are also demonstrated with the latest prototype.


title: A comparative study on RPL attacks and security solutions

abstract: Routing Protocol for Low Power and Lossy Networks (RPL) is the routing protocol for IoT and Wireless Sensor Networks. RPL is a lightweight protocol, having good routing functionality, but has basic security functionality. This may make RPL vulnerable to various attacks. Providing security to IoT networks is challenging, due to their constrained nature and connectivity to the unsecured internet. This survey presents the elaborated review on the security of Routing Protocol for Low Power and Lossy Networks (RPL). This survey is built upon the previous work on RPL security and adapts to the security issues and constraints specific to Internet of Things. An approach to classifying RPL attacks is made based on Confidentiality, Integrity, and Availability. Along with that, we surveyed existing solutions to attacks which are evaluated and given possible solutions (theoretically, from various literature) to the attacks which are not yet evaluated. We further conclude with open research challenges and future work needs to be done in order to secure RPL for Internet of Things (IoT).


title: Energy Efficient VM Scheduling for Cloud Data Centers: Exact Allocation and Migration Algorithms

abstract: This paper presents two exact algorithms for energy efficient scheduling of virtual machines (VMs) in cloud data centers. Modeling of energy aware allocation and consolidation to minimize overall energy consumption leads us to the combination of an optimal allocation algorithm with a consolidation algorithm relying on migration of VMs at service departures. The optimal allocation algorithm is solved as a bin packing problem with a minimum power consumption objective. It is compared with an energy aware best fit algorithm. The exact migration algorithm results from a linear and integer formulation of VM migration to adapt placement when resources are released. The proposed migration is general and goes beyond the current state of the art by minimizing both the number of migrations needed for consolidation and energy consumption in a single algorithm with a set of valid inequalities and conditions. Experimental results show the benefits of combining the allocation and migration algorithms and demonstrate their ability to achieve significant energy savings while maintaining feasible convergence times when compared with the best fit heuristic.


title: GrapH: Traffic-Aware Graph Processing

abstract: Distributed graph processing systems such as Pregel, PowerGraph, or GraphX gained popularity due to their superior performance of data analytics on graph-structured data. These systems employ partitioning algorithms to parallelize graph analytics while minimizing inter-partition communication. Recent partitioning algorithms, however, unrealistically assume a uniform and constant amount of data exchanged between graph vertices (i.e., uniform vertex traffic ) and homogeneous network costs between workers hosting the graph partitions. This leads to suboptimal partitioning decisions and inefficient graph processing. To this end, we developed GrapH, the first graph processing system using vertex-cut graph partitioning that considers both, diverse vertex traffic and heterogeneous network costs. The main idea is to avoid frequent communication over expensive network links using an adaptive edge migration strategy. Our evaluations show an improvement of 10 percent in graph processing latency and 60 percent in communication costs compared to state-of-the-art partitioning approaches.


title: Practical Bluetooth Traffic Sniffing: Systems and Privacy Implications

abstract: With the prevalence of personal Bluetooth devices, potential breach of user privacy has been an increasing concern. To date, sniffing Bluetooth traffic has been widely considered an extremely intricate task due to Bluetooth's indiscoverable mode, vendor-dependent adaptive hopping behavior, and the interference in the open 2.4 GHz band. In this paper, we present BlueEar -a practical Bluetooth traffic sniffer. BlueEar features a novel dual-radio architecture where two Bluetooth-compliant radios coordinate with each other on learning the hopping sequence of indiscoverable Bluetooth networks, predicting adaptive hopping behavior, and mitigating the impacts of RF interference. Experiment results show that BlueEar can maintain a packet capture rate higher than 90% consistently in real-world environments, where the target Bluetooth network exhibits diverse hopping behaviors in the presence of dynamic interference from coexisting Wi-Fi devices. In addition, we discuss the privacy implications of the BlueEar system, and present a practical countermeasure that effectively reduces the packet capture rate of the sniffer to 20%. The proposed countermeasure can be easily implemented on the Bluetooth master device while requiring no modification to slave devices like keyboards and headsets.


title: A digital library framework for biodiversity information systems

abstract: Biodiversity Information Systems (BISs) involve all kinds of heterogeneous data, which include ecological and geographical features. However, available information systems offer very limited support for managing these kinds of data in an integrated fashion. Furthermore, such systems do not fully support image content (e.g., photos of landscapes or living organisms) management, a requirement of many BIS end-users. In order to meet their needs, these users—e.g., biologists, environmental experts—often have to alternate between separate biodiversity and image information systems to combine information extracted from them. This hampers the addition of new data sources, as well as cooperation among scientists. The approach provided in this paper to meet these issues is based on taking advantage of advances in digital library innovations to integrate networked collections of heterogeneous data. It focuses on creating the basis for a next-generation BIS, combining new techniques of content-based image retrieval and database query processing mechanisms. This paper shows the use of this component-based architecture to support the creation of two tailored BIS systems dealing with fish specimen identification using search techniques. Experimental results suggest that this new approach improves the effectiveness of the fish identification process, when compared to the traditional key-based method.


title: FastQRE: Fast Query Reverse Engineering

abstract: We study the problem of Query Reverse Engineering (QRE), where given a database and an output table, the task is to find a simple project-join SQL query that generates that table when applied on the database. This problem is known for its efficiency challenge due to mainly two reasons. First, the problem has a very large search space and its various variants are known to be NP-hard. Second, executing even a single candidate SQL query can be very computationally expensive. In this work we propose a novel approach for solving the QRE problem efficiently. Our solution outperforms the existing state of the art by 2-3 orders of magnitude for complex queries, resolving those queries in seconds rather than days, thus making our approach more practical in real-life settings.


title: Blogosphere: research issues, tools, and applications

abstract: Weblogs, or Blogs, have facilitated people to express their thoughts, voice their opinions, and share their experiences and ideas. Individuals experience a sense of community, a feeling of belonging, a bonding that members matter to one another and their niche needs will be met through online interactions. Its open standards and low barrier to publication have transformed information consumers to producers. This has created a plethora of open-source intelligence, or "collective wisdom" that acts as the storehouse of over-whelming amounts of knowledge about the members, their environment and the symbiosis between them. Nonetheless, vast amounts of this knowledge still remain to be discovered and exploited in its suitable way. In this paper, we introduce various state-of-the-art research issues, review some key elements of research such as tools and methodologies in Blogosphere, and present a case study of identifying the influential bloggers in a community to exemplify the integration of some major aspects discussed in this paper. Towards the end, we also compare and contrast the blogosphere and social networks and the research therein.


title: Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects

abstract: We describe a model of visual processing in which feedback connections from a higher- to a lower- order visual cortical area carry predictions of lower-level neural activities, whereas the feedforward connections carry the residual errors between the predictions and the actual lower-level activities. When exposed to natural images, a hierarchical network of model neurons implementing such a model developed simple-cell-like receptive fields. A subset of neurons responsible for carrying the residual errors showed endstopping and other extra-classical receptive-field effects. These results suggest that rather than being exclusively feedforward phenomena, nonclassical surround effects in the visual cortex may also result from cortico-cortical feedback as a consequence of the visual system using an efficient hierarchical strategy for encoding natural images.


title: A holistic model of mining product aspects and associated sentiments from online reviews

abstract: Online product reviews are considered a significant information resource useful for both potential customers and product manufacturers. In order to extract the fundamental product aspects and their associated sentiments from those reviews of plain texts, aspect-based sentiment analysis has emerged and has been regarded as a promising technology. This paper proposes a novel model to realize aspect-based sentiment summarization in an integrative way: composing the system with consistently designed feature extraction and clustering, collocation orientation disambiguation, and sentence sentiment strength calculation. Collocations of product features and opinion words are initially extracted through pattern-based bootstrapping. A novel confidence estimation method considering two measurements, Prevalence and Reliability, is exploited to assess both patterns and features. The obtained features are further clustered into aspects. Each cluster is assigned a weight based on arithmetic means of feature similarities and confidences. The orientations of dynamic sentiment ambiguous adjectives (DSAAs) are then determined within opinion collocations. Finally, sentiment strengths of opinion clauses for each aspect are computed according to a set of fine-grained and stratified scoring formulae. Experimental results on a benchmark data set validates the effectiveness of the proposed model.


title: Accurate whole human genome sequencing using reversible terminator chemistry

abstract: DNA sequence information underpins genetic research, enabling discoveries of important biological or medical benefit. Sequencing projects have traditionally used long (400800 base pair) reads, but the existence of reference sequences for the human and many other genomes makes it possible to develop new, fast approaches to re-sequencing, whereby shorter reads are compared to a reference to identify intraspecies genetic variation. Here we report an approach that generates several billion bases of accurate nucleotide sequence per experiment at low cost. Single molecules of DNA are attached to a flat surface, amplified in situ and used as templates for synthetic sequencing with fluorescent reversible terminator deoxyribonucleotides. Images of the surface are analysed to generate high-quality sequence.We demonstrate application of this approach to human genome sequencing on flow-sorted X chromosomes and then scale the approach to determine the genome sequence of a male Yoruba from Ibadan, Nigeria. We build an accurate consensus sequence from.303 average depth of paired 35-base reads. We characterize four million single-nucleotide polymorphisms and four hundred thousand structural variants, many of which were previously unknown. Our approach is effective for accurate, rapid and economical whole-genome re-sequencing and many other biomedical applications.


title: Translation recognition and translation production: Comparing a new and an old tool in the study of bilingualism

abstract: This study explored the “translation-recognition” task. Each trial in this task presents a word pair, one word in L1, the second in L2. The participant has to decide whether or not the words within a pair are translations of one another. Performance is compared with that in translation production, where on each trial the participant has to come up with the translation of the presented word. The results of two experiments, one for 40 adult Dutch learners of English and the second for 80 people from the same population, suggest that translation recognition and translation production generally respond to the same manipulations. An exception to this pattern emerges when cognates and noncognates are focused on separately. With noncognate materials translation production from L1 to L2 shows a larger role of semantic variables than both translation production from L2 to L1 and translation recognition. Whether within the recognition task cognates and noncognates are presented mixed or blocked, and whether the nontranslation pairs consist of perceptually similar or dissimilar words, or both, do not affect the actual translation-retrieval process.


title: Gaussian Process Dynamical Models for Human Motion

abstract: We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motion capture data. A GPDM is a latent variable model. It comprises a low-dimensional latent space with associated dynamics, as well as a map from the latent space to an observation space. We marginalize out the model parameters in closed form by using Gaussian process priors for both the dynamical and the observation mappings. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach and compare four learning algorithms on human motion capture data, in which each pose is 50-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.


title: Dynamic sensor-based control of robots with visual feedback

abstract: Sensor-based robot control may be viewed as a hierarchical structure with multiple observers. Actuator, feature-based, and recognition observers provide the basis for multilevel feedback control at the actuator, sensor, and world coordinate frame levels, respectively. The analysis and design of feature-based control strategies to achieve consistent dynamic performance is addressed. For vision sensors, such an image-based visual servo control is shown to provide stable and consistent dynamic control within local regimes of the recognition observer. Simulation studies of two- and three-degree-of-freedom systems show the application of an adaptive control algorithm to overcome unknown and nonlinear relations in the feature to world space mapping.


title: Requirements engineering with viewpoints

abstract: The requirements engineering process involves a clear understanding of the requirements of the intended system. This includes the services required of the system, the system users, its environment and associated constraints. This process involves the capture, analysis and resolution of many ideas, perspectives and relationships at varying levels of detail. Requirements methods based on global reasoning appear to lack the expressive framework to adequately articulate this distributed requirementsknowledge structure. The paper describes the problems in trying to establish an adequate and stable set of requirements and proposes a viewpoint-oriented requirements definition (VORD) method as a means of tackling some of these problems. This method structures the requirements engineering process using viewpoints associated with sources of requirements. The paper describes VORD in the light of current viewpoint-oriented requirements approaches and shows how it improves on them. A simple example of a bank auto-teller system is used to demonstrate the method.


title: A power drive control for piezoelectric actuators

abstract: Today piezoelectric actuators are used in a growing number of applications. This article presents a new switching amplifier circuit for multilayer actuators, which allows a fast and precise driving as well as the recovery of energy from the actuator. Having modeled the power electronic analog circuit, its behavior could be well reproduced by a simulation. The design of a digital controller, which was considerably facilitated by this simulation, is presented. This controller and the analog switching amplifier as well as a model of the piezoelectric actuator are part of mixed analog digital simulation.


title: INTERPERSONAL RELATIONS AND GROUP PROCESSES Having Less , Giving More : The Influence of Social Class on Prosocial Behavior

abstract: Lower social class (or socioeconomic status) is associated with fewer resources, greater exposure to threat, and a reduced sense of personal control. Given these life circumstances, one might expect lower class individuals to engage in less prosocial behavior, prioritizing self-interest over the welfare of others. The authors hypothesized, by contrast, that lower class individuals orient to the welfare of others as a means to adapt to their more hostile environments and that this orientation gives rise to greater prosocial behavior. Across 4 studies, lower class individuals proved to be more generous (Study 1), charitable (Study 2), trusting (Study 3), and helpful (Study 4) compared with their upper class counterparts. Mediator and moderator data showed that lower class individuals acted in a more prosocial fashion because of a greater commitment to egalitarian values and feelings of compassion. Implications for social class, prosocial behavior, and economic inequality are discussed.


title: Pioneer: verifying code integrity and enforcing untampered code execution on legacy systems

abstract: We propose a primitive, called Pioneer, as a first step towards verifiable code execution on untrusted legacy hosts. Pioneer does not require any hardware support such as secure co-processors or CPU-architecture extensions. We implement Pioneer on an Intel Pentium IV Xeon processor. Pioneer can be used as a basic building block to build security systems. We demonstrate this by building a kernel rootkit detector.


title: Motor imagery effectiveness for mirror reversed movements.

abstract: Physical practice is known to enhance motor adaptation skills, which refer to the individual ability to compensate for environmental changes. So far, it is still unknown whether a similar effect can be observed following motor imagery (MI). Thirty-nine participants were tested during a joystick tracking task under both normal and mirror conditions (i.e., the inductive direction of the joystick was reversed), before and after a physical practice or MI training phase. Eye movements and electromyographic activity were recorded during MI. Motor performance was also evaluated after a 6 h interval during daytime. As compared to the control group, the results revealed that both MI and physical practice improved motor performance in the mirror condition, during the post-training test. Furthermore, the time to complete the task was further reduced after 6 hours, both in the normal and mirror conditions. These results demonstrate the effectiveness of MI for learning mirror-reversed movements, and for the consolidation process that follows motor adaptation.


title: Improved anomaly detection in surveillance videos based on a deep learning method

abstract: During the last few decades, surveillance cameras have been installed in different locations. Analysis of the information captured using these cameras can play effective roles in event prediction, online monitoring and goal-driven analysis applications including anomalies and intrusion detection. Nowadays, various Artificial Intelligence techniques have been used to detect anomalies, amongst them convolutional neural networks using deep learning techniques improved the detection accuracy significantly. The goal of this article is to propose a new method based on deep learning techniques for anomaly detection in video surveillance cameras. The proposed method has been evaluated in the UCSD dataset, and showed an increase in the accuracy of the anomaly detection.


title: Privacy and security in library RFID: issues, practices, and architectures

abstract: We expose privacy issues related to Radio Frequency Identification (RFID) in libraries, describe current deployments, and suggest novel architectures for library RFID. Libraries are a fast growing application of RFID; the technology promises to relieve repetitive strain injury, speed patron self-checkout, and make possible comprehensive inventory. Unlike supply-chain RFID, library RFID requires item-level tagging, thereby raising immediate patron privacy issues. Current conventional wisdom suggests that privacy risks are negligible unless an adversary has access to library databases. We show this is not the case. In addition, we identify private authentication as a key technical issue: how can a reader and tag that share a secret efficiently authenticate each other without revealing their identities to an adversary? Previous solutions to this problem require reader work linear in the number of tags. We give a general scheme for building private authentication with work logarithmic in the number of tags, given a scheme with linear work as a sub protocol. This scheme may be of independent interest beyond RFID applications. We also give a simple scheme that provides security against a passive eavesdropper using XOR alone, without pseudo-random functions or other heavy crypto operations.


title: Pretty good persuasion: a first step towards effective password security in the real world

abstract: In the past, research on password mechanisms has focussed almost entirely on technical issues. Only in recent years has the security research community acknowledged that user behavior plays a part in many security failures, and that policies alone may not be sufficient to ensure correct behavior. We argue that password mechanisms and their users form a socio-technical system, whose effectiveness relies strongly on users' willingness to make the extra effort that security-conscious behavior requires. In most organizations, users cannot be forced to comply; rather, they have to be persuaded to do so. Ultimately, the mechanisms themselves, policies, tutorials, training and the general discourse have to be designed with their persuasive power in mind. We present the results of a first study that can guide such persuasive efforts, and describe methods that can be used to persuade users to employ proper password practice.


title: Multi-agent path finding with kinematic constraints

abstract: Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as finite maximum velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST, a novel approach that makes use of a simple temporal network to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, takes their maximum translational and rotational velocities into account, provides a guaranteed safety distance between them, and exploits slack to absorb imperfect plan executions and avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.


title: CNN-based text image super-resolution tailored for OCR

abstract: Since low-resolution images may hamper the performance of optical character recognition (OCR), text image super-resolution (SR) has become an increasingly important problem in computer vision. Convolutional neural network (CNN) has been proposed for generic image SR as well as text image SR, but the previous works concern more on the objective quality (e.g. PSNR) rather than the OCR performance. In this paper, we propose a new loss function when training CNN for text image SR to facilitate OCR, and conduct model combination to further improve the performance. Also, we propose a simple yet effective image padding method to refine the image boundaries during SR. Experimental results show that we achieve an OCR accuracy of 78.10% on the ICDAR 2015 TextSR dataset, which is comparable with that of using the original high-resolution images (78.80%), and also exceeds the state-of-the-arts.


title: Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations

abstract: Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. However, they typically fall short of the performances of more complex models such as InferSent. Here, we generalize the concept of average word embeddings to power mean word embeddings. We show that the concatenation of different types of power mean word embeddings considerably closes the gap to state-of-the-art methods monolingually and substantially outperforms these more complex techniques cross-lingually. In addition, our proposed method outperforms different recently proposed baselines such as SIF and Sent2Vec by a solid margin, thus constituting a much harder-to-beat monolingual baseline. Our data and code are publicly available.


title: A Compromise Principle in Deep Monocular Depth Estimation

abstract: Monocular depth estimation, which plays a key role in understanding 3D scene geometry, is fundamentally an ill-posed problem. Existing methods based on deep convolutional neural networks (DCNNs) have examined this problem by learning convolutional networks to estimate continuous depth maps from monocular images. However, we find that training a network to predict a high spatial resolution continuous depth map often suffers from poor local solutions. In this paper, we hypothesize that achieving a compromise between spatial and depth resolutions can improve network training. Based on this "compromise principle", we propose a regression-classification cascaded network (RCCN), which consists of a regression branch predicting a low spatial resolution continuous depth map and a classification branch predicting a high spatial resolution discrete depth map. The two branches form a cascaded structure allowing the classification and regression branches to benefit from each other. By leveraging large-scale raw training datasets and some data augmentation strategies, our network achieves top or state-of-the-art results on the NYU Depth V2, KITTI, and Make3D benchmarks.


title: An Investigation into Neural Net Optimization via Hessian Eigenvalue Density

abstract: To understand the dynamics of optimization in deep neural networks, we develop a tool to study the evolution of the entire Hessian spectrum throughout the optimization process. Using this, we study a number of hypotheses concerning smoothness, curvature, and sharpness in the deep learning literature. We then thoroughly analyze a crucial structural feature of the spectra: in non-batch normalized networks, we observe the rapid appearance of large isolated eigenvalues in the spectrum, along with a surprising concentration of the gradient in the corresponding eigenspaces. In batch normalized networks, these two effects are almost absent. We characterize these effects, and explain how they affect optimization speed through both theory and experiments. As part of this work, we adapt advanced tools from numerical linear algebra that allow scalable and accurate estimation of the entire Hessian spectrum of ImageNet-scale neural networks; this technique may be of independent interest in other applications.


title: Enhancing JFLAP with automata construction problems and automated feedback

abstract: JFLAP is a powerful and popular educational software tool that allows students in Formal Languages and Automata courses to understand theoretical concepts in a visual and interactive manner. In this paper, we enhance the quality of JFLAP’s interactivity in two ways that are extremely useful for beginners learning to build finite automata: (1) our tool can automatically generate a rich variety of practice problems (and their solutions) and immediately evaluate student answers, and (2) we propose a novel way for students to obtain prompt and meaningful feedback on their solutions to problems taken from other sources such as assignments or textbooks. We believe that these enhancements can greatly improve the utility of JFLAP as an instructional resource. Keywords—Automatic problem generation and feedback; formal languages and automata; JFLAP


title: Listening to Musical Rhythms Recruits Motor Regions of the Brain

abstract: Perception and actions can be tightly coupled; but does a perceptual event dissociated from action processes still engage the motor system? We conducted 2 functional magnetic resonance imaging studies involving rhythm perception and production to address this question. In experiment 1, on each trial subjects 1st listened in anticipation of tapping, and then tapped along with musical rhythms. Recruitment of the supplementary motor area, midpremotor cortex (PMC), and cerebellum was observed during listen with anticipation. To test whether this activation was related to motor planning or rehearsal, in experiment 2 subjects naively listened to rhythms without foreknowledge that they would later tap along with them. Yet, the same motor regions were engaged despite no action--perception connection. In contrast, the ventral PMC was only recruited during action and action-coupled perceptual processes, whereas the dorsal part was only sensitive to the selection of actions based on higher-order rules of temporal organization. These functional dissociations shed light on the nature of action--perception processes and suggest an inherent link between auditory and motor systems in the context of rhythm.


Who Borrows? Political Inclusiveness and the Accumulation of Foreign Debt in Developing Societies


title: Sentiment analysis on Twitter with stock price and significant keyword correlation

abstract: Though uninteresting individually, Twitter messages, or tweets, can provide an accurate reflection of public sentiment on when taken in aggregation. In this paper, we primarily examine the effectiveness of various machine learning techniques on providing a positive or negative sentiment on a tweet corpus. Additionally, we apply extracted twitter sentiment to accomplish two tasks. We first look for a correlation between twitter sentiment and stock prices. Secondly, we determine which words in tweets correlate to changes in stock prices by doing a post analysis of price change and tweets. We accomplish this by mining tweets using Twitter’s search API and subsequently processing them for analysis. For the task of determining sentiment, we test the effectiveness of three machine learning techniques: Naive Bayes classification, Maximum Entropy classification, and Support Vector Machines. We discover that SVMs give the highest consistent accuracy through cross validation, but not by much. Additionally, we discuss various approaches in training these classifiers. We then apply our findings to on an intra-day market scale to find that there is very little direct correlation between stock prices and tweet sentiment on specifically an intra-day scale. Next, we improve on the keyword search approach by reverse correlating stock prices to individual words in tweets, finding, reasonably, that certain keywords are more correlated with changes in stock prices. Lastly, we discuss various challenges posed by looking at twitter for performing stock predictions.


title: Tumors of the nail unit. A review. Part I: acquired localized longitudinal melanonychia and erythronychia.

abstract: The article aims to be a guide to the interpretation of tumors specific to the nail, that is, tumors presenting peculiar histological features linked specifically to the nail unit. Therefore, the classical epithelial, fibroepithelial, and fibrous skin tumors occurring in the nail region are not analyzed. The interpretation of nail biopsies requires the identification and integration of the 2 main clinical modes of presentation of nail tumors, the acquired localized (monodactylous) longitudinal (ALL) band pattern, and the "masked" nail tumor. The ALL band pattern often allows the recognition of a nail tumor in its early phase of progression, with a limited differential diagnosis. The masked nail tumor mimics an inflammatory nail process, as a clinically misleading reactive benign lesion, which delays diagnosis with the subsequent development of partial nail loss and a locally destructive evolution. ALL band pattern appears as a longitudinal band starting at the matrix and extending to the tip of the nail plate. The band is usually single, rarely bifid. This clinical pattern can divided into 2 presentations. The generic term of ALL maculonychia could be proposed to define the macular aspect of the colored band of the nail plate. It encompasses 3 syndromes: longitudinal melanonychia, longitudinal erythronychia, and longitudinal leukonychia. ALL pachyonychia is a rare presentation. Pachyonychia indicates a localized thickening of the nail plate specific to the matrical nail tumor. In this group, there is differentiation toward cells of the nail matrix. The prototype tumor is the onychomatricoma, which present classically with a yellow (xantholeukonychia) band pattern. Recently, a new clinical band pattern has been described as longitudinal pachymelanonychia with 2 etiologies: pigmented onychomatricoma and onychocytic matricoma. The first part of this review delineate, in the first section, the distinctive microanatomical features of the nail unit and the second is dedicated to the most important pitfalls in pathological diagnosis of nail tumors because of nail surgery techniques. In the third section, the histopathology of ALL melanonychia and ALL erythronychia is discussed in a detailed description.


title: Arthroscopic stabilization of the shoulder for acute primary dislocations using a transglenoid suture technique.

abstract: Many studies report the results of arthroscopic stabilization for recurrent shoulder instability, with widely variable recurrence rates; however, there are very few reports of the use of these techniques in acute first-time dislocations. We report the clinical outcomes of 17 patients who had arthroscopic stabilization using a transglenoid suture technique for acute primary dislocation. The surgery took place between March 1992 and March 1994 and, to date, there has been one recurrent dislocation (6%) and no recurrent subluxation. There were no major complications, although a number of patients found the knot tied over the infraspinatus fascia to be uncomfortable until it resorbed. All patients examined had normal power and range of motion, and a clinically stable shoulder. All 16 patients without recurrence were satisfied with their result. Nine patients returned to sports at the same or higher level, including such vigorous contact sports as Australian Rules football and rugby. Three patients did not return to the same level of sporting activity because of lack of confidence in the shoulder or a fear of dislocation despite no clinical evidence of instability. Five patients reported a lack of confidence in the shoulder without clinical evidence of instability. We suggest that arthroscopic stabilization with transglenoid sutures or a suture anchor technique is a reasonable option for the athlete with an acute primary shoulder dislocation who wishes to return to sports.


title: Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithm

abstract: What is the best way to describe a user in a social network with just a few numbers? Mathematically, this is equivalent to assigning a vector representation to each node in a graph, a process called graph embedding. We propose a novel framework, GEM-D that unifies most of the past algorithms such as LapEigs, DeepWalk and node2vec. GEM-D achieves its goal by decomposing any graph embedding algorithm into three building blocks: node proximity function, warping function and loss function. Based on thorough analysis of GEM-D, we propose a novel algorithm, called UltimateWalk, which outperforms the most-recently proposed state-of-the-art DeepWalk and node2vec. The contributions of this work are: (1) The proposed framework, GEM-D unifies the past graph embedding algorithms and provides a general recipe of how to design a graph embedding; (2) the nonlinearlity in the warping function contributes significantly to the quality of embedding and the exponential function is empirically optimal; (3) the proposed algorithm, UltimateWalk is one-click (no user-defined parameters), scalable and has a closed-form solution.


title: Personality and Substance Use in Mexicans and Mexican-Americans

abstract: Within the theoretical framework of the five-factor model of personality, existing research identifies personality profiles significantly related to certain at-risk behaviors, such as substance use and poor health outcomes. Among Latinos, there is limited knowledge on whether personality traits are associated with substance use behaviors. This study uses a sample of Mexican (N = 323) and Mexicans American (N = 1,143) participants to examine the interplay of culture and personality differences in relation to substance use and alcohol dependence. Mexican Americans reported higher use of cocaine and marijuana, while Mexicans were more likely to report cigarette smoking. Lower Agreeableness and Conscientiousness were associated with a higher likelihood of substance use and alcohol dependence. Higher Extraversion was associated with higher likelihood of substance use, Openness with marijuana use, and for Neuroticism the associations differed across cultural groups and drug-type used. The study identified cross-cultural communalities and differences in personality traits related to drug use and dependence.


title: Classification of the harmonic structure in bird vocalization

abstract: The article is related to the development of techniques for automatic recognition of bird species by their sounds. It has been demonstrated earlier that a simple model of one time-varying sinusoid is very useful in classification and recognition of typical bird sounds. However, a large class of bird sounds are not pure sinusoids but have a clear harmonic spectrum structure. We introduce a way to classify bird syllables into four classes by their harmonic structure.


title: The role of users and customers in digital innovation: Insights from B2B manufacturing firms

abstract: We study three B2B manufacturing firms that deal with digital innovation.We identify the distinct roles of customers and users in guiding digital innovation.Customer knowledge provides short-term guidance for digital innovation processes.User knowledge provides long-term guidance for digital innovation processes.We identify 12 practices for leveraging customer and user knowledge for use in digital innovation. Diffusion of digital technologies into the manufacturing industry has created new opportunities for innovation that firms must address to remain competitive. We investigate the role of customer and user knowledge in the digital innovation processes of three global B2B manufacturing companies. We find that the B2B manufacturing industry's characteristics influence how users and customers may be leveraged. Customers making the purchasing decisions are considered for knowledge about short-term changes in market needs, while users working directly with the products provide long-term guidance for digital innovation. We identify practices for acquiring, distributing, and using customer and user knowledge for digital innovation.


title: Edge‐Optimized À‐Trous Wavelets for Local Contrast Enhancement with Robust Denoising

abstract: In this paper we extend the edge-avoiding a-trous wavelet transform for local contrast enhancement while avoiding common artifacts such as halos and gradient reversals. We show that this algorithm is a highly efficient and robust tool for image manipulation based on multi-scale decompositions. It can achieve comparable results to previous high-quality methods while being orders of magnitude faster and simpler to implement. Our method is much more robust than previously known fast methods by avoiding aliasing and ringing which is achieved by introducing a data-adaptive edge weight. Operating on multi-scale, our algorithm can directly include the BayesShrink method for denoising. For moderate noise levels our edge-optimized technique consistently improves separation of signal and noise.


title: Augmenting human senses to improve the user experience in cars: applying augmented reality and haptics approaches to reduce cognitive distances

abstract: Augmenting people's senses with computational support can improve the ability to perceive information and perform tasks. However, the impact of such augmentation may fluctuate according to user context, thereby impacting the quality of a user experience. In this paper, we present two systems that assess the in-situ effects of augmenting senses using Augmented Reality and Haptic technologies. We demonstrate that sensory augmentation systems can improve performance when users are multitasking; however, a hybrid assessment, including eye tracking and psycho-physiological measurement, reveals that the benefits and costs of such systems can differ depending on the demographics of a population with different cognitive capabilities. For elder adults, sensory augmentation improved perception for responding to local incidents in the physical space, but a richer intervention using sensory augmentation (visual, auditory, and haptic) strains cognitive load. For younger adults, additional modes for providing sensory information increased attentiveness for performing tasks, but can lead to overloading of already used sensory channels. Thus, sensory augmentation was more advantageous for improving global awareness for situated physical space, rather than responding to local incidents.


title: The effect of coffee consumption on blood pressure and the development of hypertension: a systematic review and meta-analysis.

abstract: CONTEXT ::: Coffee is one of the most widely consumed beverages worldwide and is known to acutely raise blood pressure (BP), but the effects of chronic consumption on BP is unclear. ::: ::: ::: OBJECTIVES ::: To conduct a systematic review and meta-analysis of available randomized controlled trials (RCTs) and cohort studies to assess the effect of chronic coffee consumption on BP and the development of hypertension. ::: ::: ::: DATA SOURCES ::: Ovid, MEDLINE (from 1948), EMBASE (from 1988), and all of Web of Science and Scopus. ::: ::: ::: STUDY SELECTION ::: RCTs and cohort studies of at least 1-week duration that assessed BP and/or the incidence of hypertension in coffee consumers compared with a control group that consumed less or no coffee. ::: ::: ::: DATA EXTRACTION ::: Two authors independently reviewed abstracts and full-text articles for inclusion. Data were abstracted using standardized forms. Risk of bias in the RCTs was examined using the method described in the Cochrane Handbook for Systematic Reviews of Interventions. Quality of the cohort studies were assessed using the Newcastle-Ottawa quality assessment scale for cohort studies. ::: ::: ::: DATA SYNTHESIS ::: Six hundred and ten articles were retrieved and a total of 15 (10 RCTs and five cohort studies) met inclusion criteria. Meta-analysis of RCTs demonstrated a pooled weighted difference in mean change in SBP of -0.55 mmHg [95% confidence interval (CI) -2.46 to 1.36) and DBP -0.45 mmHg (95% CI -1.52 to 0.61). Meta-analysis of the cohort studies demonstrated a pooled risk ratio for developing hypertension of 1.03 (95% CI 0.98-1.08). ::: ::: ::: CONCLUSION ::: Low-quality evidence did not show any statistically significant effect of coffee consumption on BP or the risk of hypertension. Given the quality of the currently available evidence, no recommendation can be made for or against coffee consumption as it relates to BP and hypertension.


title: Text recognition on PAN card using template matching method

abstract: Object recognition in computer vision refers to detecting a particular object in a video or an image. Computer vision being a branch of Machine Learning helps us to achieve a goal by making the machine automate the entire process through multiple algorithms and paradigms. PAN (Permanent Account Number) is a 10 character alpha-numeric code which is issued by Government of India to each of its citizen, especially those who are tax payee. The purpose of this research is to identify the PAN code and extract them so as to automate the process of retyping it manually. We achieve the above through text recognition process of template matching under Normalized Cross Correlation (NCC) method.


title: Gaussian Process Regression-Based Video Anomaly Detection and Localization With Hierarchical Feature Representation

abstract: This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.


title: Distributed Language Modeling For $N$-Best List Re-Ranking

abstract: In this paper we describe a novel distributed language model for N-best list re-ranking. The model is based on the client/server paradigm where each server hosts a portion of the data and provides information to the client. This model allows for using an arbitrarily large corpus in a very efficient way. It also provides a natural platform for relevance weighting and selection. We applied this model on a 2.97 billion-word corpus and re-ranked the N-best list from Hiero, a state-of-the-art phrase-based system. Using BLEU as a metric, the re-ranked translation achieves a relative improvement of 4.8%, significantly better than the model-best translation.


title: POI: Multiple Object Tracking with High Performance Detection and Appearance Feature

abstract: Detection and learning based appearance feature play the central role in data association based multiple object tracking (MOT), but most recent MOT works usually ignore them and only focus on the hand-crafted feature and association algorithms. In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting. We make our detection and appearance feature publicly available. In the following part, we first summarize the detection and appearance feature, and then introduce our tracker named Person of Interest (POI), which has both online and offline version.


Trading Data for Discounts: An Exploration of Unstructured Data Through Machine Learning in Wearable Technology


A virtual reality brainwave entrainment method for human augmentation applications


title: Current genetic methodologies in the identification of disaster victims and in forensic analysis

abstract: This review presents the basic problems and currently available molecular techniques used for genetic profiling in disaster victim identification (DVI). The environmental conditions of a mass disaster often result in severe fragmentation, decomposition and intermixing of the remains of victims. In such cases, traditional identification based on the anthropological and physical characteristics of the victims is frequently inconclusive. This is the reason why DNA profiling became the gold standard for victim identification in mass-casualty incidents (MCIs) or any forensic cases where human remains are highly fragmented and/or degraded beyond recognition. The review provides general information about the sources of genetic material for DNA profiling, the genetic markers routinely used during genetic profiling (STR markers, mtDNA and single-nucleotide polymorphisms [SNP]) and the basic statistical approaches used in DNA-based disaster victim identification. Automated technological platforms that allow the simultaneous analysis of a multitude of genetic markers used in genetic identification (oligonucleotide microarray techniques and next-generation sequencing) are also presented. Forensic and population databases containing information on human variability, routinely used for statistical analyses, are discussed. The final part of this review is focused on recent developments, which offer particularly promising tools for forensic applications (mRNA analysis, transcriptome variation in individuals/populations and genetic profiling of specific cells separated from mixtures).


title: Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research

abstract: Meaning has been called the "holy grail" of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences. The field of Artifical Intelligence (AI) is very much a part of that list: the development of sophisticated natural language semantics is a sine qua non for achieving a level of intelligence comparable to humans. Embodiment theories in cognitive science hold that human semantic representation depends on sensori-motor experience; the abundant evidence that human meaning representation is grounded in the perception of physical reality leads to the conclusion that meaning must depend on a fusion of multiple (perceptual) modalities. Despite this, AI research in general, and its subdisciplines such as computational linguistics and computer vision in particular, have focused primarily on tasks that involve a single modality. Here, we propose virtual embodiment as an alternative, long-term strategy for AI research that is multi-modal in nature and that allows for the kind of scalability required to develop the field coherently and incrementally, in an ethically responsible fashion.


title: Capacity Comparison Between MIMO-NOMA and MIMO-OMA With Multiple Users in a Cluster

abstract: In this paper, the performance of multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is investigated, when multiple users are grouped into a cluster. The superiority of MIMO-NOMA over MIMO-OMA in terms of both sum channel capacity and ergodic sum capacity is proved analytically. Furthermore, it is demonstrated that the more users are admitted to a cluster, the lower is the achieved sum rate, which illustrates the tradeoff between the sum rate and maximum number of admitted users. On this basis, a user admission scheme is proposed, which is optimal in terms of both sum rate and the number of admitted users when the signal-to-interference-plus-noise ratio thresholds of the users are equal. When these thresholds are different, the proposed scheme still achieves good performance in balancing both criteria. Moreover, under certain conditions, it maximizes the number of admitted users. In addition, the complexity of the proposed scheme is linear in the number of users per cluster. Simulation results verify the superiority of MIMO-NOMA over MIMO-OMA in terms of both sum rate and user fairness, as well as the effectiveness of the proposed user admission scheme.


title: Quasi-Cyclic LDPC Codes: Influence of Proto- and Tanner-Graph Structure on Minimum Hamming Distance Upper Bounds

abstract: Quasi-cyclic (QC) low-density parity-check (LDPC) codes are an important instance of proto-graph-based LDPC codes. In this paper we present upper bounds on the minimum Hamming distance of QC LDPC codes and study how these upper bounds depend on graph structure parameters (like variable degrees, check node degrees, girth) of the Tanner graph and of the underlying proto-graph. Moreover, for several classes of proto-graphs we present explicit QC LDPC code constructions that achieve (or come close to) the respective minimum Hamming distance upper bounds. Because of the tight algebraic connection between QC codes and convolutional codes, we can state similar results for the free Hamming distance of convolutional codes. In fact, some QC code statements are established by first proving the corresponding convolutional code statements and then using a result by Tanner that says that the minimum Hamming distance of a QC code is upper bounded by the free Hamming distance of the convolutional code that is obtained by"unwrapping"the QC code.