title: Predicting and quantifying the technical debt in cloud software engineering

abstract: Identifying and managing effectively the Technical Debt has become an issue of great importance over recent years. In cloud marketplaces, where the cloud services can be leased, the difficulty to promptly predict and manage the Technical Debt has a significant impact. In this paper, we examine the Technical Debt, which stems from budget constraints during the software development process as well as the capacity of a cloud service. In this context, the budget and the cloud service selection decisions may introduce Technical Debt. Towards reaching a conclusion, two approaches are taken into consideration. Initially, a cost estimation approach is researched, which is related to implementing Software as a Service (SaaS) in the cloud for three scenarios aiming to predict the incurrence of the Technical Debt in the future. The Constructive Cost Model (COCOMO) is exploited, in order to estimate the implementation cost and define a range of secureness. In addition, a Technical Debt quantification approach is adopted, which is associated with leasing a cloud Software as a Service (SaaS), towards indicating the most appropriate cloud service to be selected.


title: Mathematical Model of Dengue Disease Transmission with Severe DHF Compartment

abstract: An SIR model for dengue disease transmission is discussed here. It is assumed that two viruses namely strain 1 and strain 2 cause the disease and long lasting immunity from infection caused by one virus may not be valid with respect to a secondary infection by the other virus. Our interest here is to derive and analyse the model taking into account the severe DHF compartment in the transmission model. The aim would be to find a control measure to reduce the DHF patients in the population, or to keep the number of patients at an acceptable level. Analysis of this model reveals that there are four equilibria, one of them is the disease-free, the other three equilibria correspond to the presence of single serotype respectively, and the coexistence of two serotypes. Stability analysis of each equilibria and their relations with type reproductive numbers are shown. We also discuss the ratio between total number of severe DHF compartment with respect to the total number of first infection compartment and the total number of secondary infection compartment, respectively. This ratio is needed for practical control measure in order to predict the “real” intensity of the endemic phenomena since only data of severe DHF compartment is available in the field. 2000 Mathematics Subject Classification: 92D30


Predicting Severity of Voice Disorder from DNN-HMM Acoustic Posteriors.


title: Probabilistic Text Structuring: Experiments With Sentence Ordering

abstract: Ordering information is a critical task for natural language generation applications. In this paper we propose an approach to information ordering that is particularly suited for text-to-text generation. We describe a model that learns constraints on sentence order from a corpus of domain-specific texts and an algorithm that yields the most likely order among several alternatives. We evaluate the automatically generated orderings against authored texts from our corpus and against human subjects that are asked to mimic the model's task. We also assess the appropriateness of such a model for multidocument summarization.


title: Accelerated Artificial Neural Networks on FPGA for fault detection in automotive systems

abstract: Modern vehicles are complex distributed systems with critical real-time electronic controls that have progressively replaced their mechanical/hydraulic counterparts, for performance and cost benefits. The harsh and varying vehicular environment can induce multiple errors in the computational/ communication path, with temporary or permanent effects, thus demanding the use of fault-tolerant schemes. Constraints in location, weight, and cost prevent the use of physical redundancy for critical systems in many cases, such as within an internal combustion engine. Alternatively, algorithmic techniques like artificial neural networks (ANNs) can be used to detect errors and apply corrective measures in computation. Though adaptability of ANNs presents advantages for fault-detection and fault-tolerance measures for critical sensors, implementation on automotive grade processors may not serve required hard deadlines and accuracy simultaneously. In this work, we present an ANN-based fault-tolerance system based on hybrid FPGAs and evaluate it using a diesel engine case study. We show that the hybrid platform outperforms an optimised software implementation on an automotive grade ARM Cortex M4 processor in terms of latency and power consumption, also providing better consolidation.


title: Combining Active Learning and Semi-Supervised Learning by Using Selective Label Spreading

abstract: In the literature, a number of methods have been proposed for semi-supervised learning. Recently, graph-based methods of semi-supervised learning have become popular because of their capability of handling large amounts of unlabeled data. However, the existing graph based semi-supervised learning algorithms do not optimize the process of selecting better labeled data. We have developed a new selective semi-supervised learning algorithm, called selective label spreading (SLS) by integrating the active learning model into the label spreading framework. SLS optimizes the process of selecting better labeled data in order to improve classification performance. We applied SLS to the well-known hand-written digits recognition data set and demonstrated that SLS can improve the classification performance. The selective label spreading scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection.


title: Symbolic Graph Reasoning Meets Convolutions

abstract: Beyond local convolution networks, we explore how to harness various external human knowledge for endowing the networks with the capability of semantic global reasoning. Rather than using separate graphical models (e.g. CRF) or constraints for modeling broader dependencies, we propose a new Symbolic Graph Reasoning (SGR) layer, which performs reasoning over a group of symbolic nodes whose outputs explicitly represent different properties of each semantic in a prior knowledge graph. To cooperate with local convolutions, each SGR is constituted by three modules: a) a primal local-to-semantic voting module where the features of all symbolic nodes are generated by voting from local representations; b) a graph reasoning module propagates information over knowledge graph to achieve global semantic coherency; c) a dual semantic-to-local mapping module learns new associations of the evolved symbolic nodes with local representations, and accordingly enhances local features. The SGR layer can be injected between any convolution layers and instantiated with distinct prior graphs. Extensive experiments show incorporating SGR significantly improves plain ConvNets on three semantic segmentation tasks and one image classification task. More analyses show the SGR layer learns shared symbolic representations for domains/datasets with the different label set given a universal knowledge graph, demonstrating its superior generalization capability.


title: Time-based language models

abstract: We explore the relationship between time and relevance using TREC ad-hoc queries. A type of query is identified that favors very recent documents. We propose a time-based language model approach to retrieval for these queries. We show how time can be incorporated into both query-likelihood models and relevance models. These models were used for experiments comparing time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.


title: True Online Temporal-Difference Learning

abstract: The temporal-difference methods TD($\lambda$) and Sarsa($\lambda$) form a core part of modern reinforcement learning. Their appeal comes from their good performance, low computational cost, and their simple interpretation, given by their forward view. Recently, new versions of these methods were introduced, called true online TD($\lambda$) and true online Sarsa($\lambda$), respectively (van Seijen & Sutton, 2014). These new versions maintain an exact equivalence with the forward view at all times, whereas the traditional versions only approximate it for small step-sizes. We hypothesize that these true online methods not only have better theoretical properties, but also dominate the regular methods empirically. In this article, we put this hypothesis to the test by performing an extensive empirical comparison. Specifically, we compare the performance of true online TD($\lambda$)/Sarsa($\lambda$) with regular TD($\lambda$)/Sarsa($\lambda$) on random MRPs, a real-world myoelectric prosthetic arm, and a domain from the Arcade Learning Environment. We use linear function approximation with tabular, binary, and non-binary features. Our results suggest that the true online methods indeed dominate the regular methods. Across all domains/representations the learning speed of the true online methods are often better, but never worse than that of the regular methods. An additional advantage is that no choice between traces has to be made for the true online methods. Besides the empirical results, we provide an in-depth analysis of the theory behind true online temporal-difference learning. In addition, we show that new true online temporal-difference methods can be derived by making changes to the online forward view and then rewriting the update equations.


STOIC : A database of dynamic and static faces expressing highly recognizable emotions


title: Deep spatial autoencoders for visuomotor learning

abstract: Reinforcement learning provides a powerful and flexible framework for automated acquisition of robotic motion skills. However, applying reinforcement learning requires a sufficiently detailed representation of the state, including the configuration of task-relevant objects. We present an approach that automates state-space construction by learning a state representation directly from camera images. Our method uses a deep spatial autoencoder to acquire a set of feature points that describe the environment for the current task, such as the positions of objects, and then learns a motion skill with these feature points using an efficient reinforcement learning method based on local linear models. The resulting controller reacts continuously to the learned feature points, allowing the robot to dynamically manipulate objects in the world with closed-loop control. We demonstrate our method with a PR2 robot on tasks that include pushing a free-standing toy block, picking up a bag of rice using a spatula, and hanging a loop of rope on a hook at various positions. In each task, our method automatically learns to track task-relevant objects and manipulate their configuration with the robot's arm.


title: Identifying similar code with program dependence graphs

abstract: We present an approach to identifying similar code in programs based on finding similar subgraphs in attributed directed graphs. This approach is used on program dependence graphs and therefore considers not only the syntactic structure of programs but also the data flow within (as an abstraction of the semantics). As a result, there is no tradeoff between precision and recall; our approach is very good in both. An evaluation of our prototype implementation shows that the approach is feasible and gives very good results despite the non polynomial complexity of the problem.


title: A 14-Bit, 1-ps resolution, two-step ring and 2D Vernier TDC in 130nm CMOS technology

abstract: This paper presents a time-to-digital (TDC) design with large detectable range and fine resolution, combining a ring TDC with a 2-dimentional (2D) Vernier TDC. The detectable range has been greatly increased to 14 bits with the ring structure. A 1-ps resolution was achieving with 2D Vernier architecture. Utilizing the 2nd order ΔΣ modulators (SDM) and a 2D spiral arbiter array, the proposed TDC greatly mitigates the quantization errors introduced by digitally controlled delay cells and the intrinsic arbiter line folding errors associated with the 2D array topology. The measured maximum DNL/INL are 0.41/0.79ps with ΔΣ linearization. A prototype TDC chip fabricated in 130nm CMOS technology achieves a conversion rate of 10 MS/s while consumes 2.4 mW power.


title: Wigner distributions and how they relate to the light field

abstract: In wave optics, the Wigner distribution and its Fourier dual, the ambiguity function, are important tools in optical system simulation and analysis. The light field fulfills a similar role in the computer graphics community. In this paper, we establish that the light field as it is used in computer graphics is equivalent to a smoothed Wigner distribution and that these are equivalent to the raw Wigner distribution under a geometric optics approximation. Using this insight, we then explore two recent contributions: Fourier slice photography in computer graphics and wavefront coding in optics, and we examine the similarity between explanations of them using Wigner distributions and explanations of them using light fields. Understanding this long-suspected equivalence may lead to additional insights and the productive exchange of ideas between the two fields.


title: Deep recurrent music writer: Memory-enhanced variational autoencoder-based musical score composition and an objective measure

abstract: In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of “interesting” outputs. Furthermore, and most importantly, none of the past attempts has focused on developing objective measures to evaluate the music composed, which would allow to evaluate the pieces composed against a predetermined standard as well as permitting to fine-tune models for better “performance” and music composition goals. In this work, we intend to advance state-of-the-art in this area by introducing and evaluating a new metric for an objective assessment of the quality of the generated pieces. We will use this measure to evaluate the outputs of a truly generative model based on Variational Autoencoders that we apply here to automated music composition. Using our metric, we demonstrate that our model can generate music pieces that follow general stylistic characteristics of a given composer or musical genre. Additionally, we use this measure to investigate the impact of various parameters and model architectures on the compositional process and output.


title: Rearranging the Familiar: Testing Compositional Generalization in Recurrent Networks

abstract: Systematic compositionality is the ability to recombine meaningful units with regular and predictable outcomes, and it's seen as key to humans' capacity for generalization in language. Recent work has studied systematic compositionality in modern seq2seq models using generalization to novel navigation instructions in a grounded environment as a probing tool, requiring models to quickly bootstrap the meaning of new words. We extend this framework here to settings where the model needs only to recombine well-trained functional words (such as"around"and"right") in novel contexts. Our findings confirm and strengthen the earlier ones: seq2seq models can be impressively good at generalizing to novel combinations of previously-seen input, but only when they receive extensive training on the specific pattern to be generalized (e.g., generalizing from many examples of"X around right"to"jump around right"), while failing when generalization requires novel application of compositional rules (e.g., inferring the meaning of"around right"from those of"right"and"around").


title: Preventing Poisoning Attacks On AI Based Threat Intelligence Systems

abstract: As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we use an ensembled semi-supervised approach to determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.


title: Understanding Pervasive Games through Gameplay Design Patterns

abstract: This paper reports on a cluster analysis of pervasive games through a bottom-up approach based upon 120 game examples. The basis for the clustering algorithm relies on the identification of pervasive gameplay design patterns for each game from a set of 75 possible patterns. The resulting hierarchy presents a view of the design space of pervasive games, and details of clusters and novel gameplay features are described. The paper concludes with a view over how the clusters relate to existing genres and models of pervasive games. Author Keywords Pervasive Games, Game Design, Gameplay Design Patterns


title: Artificial Intelligence for Vehicle-to-Everything: A Survey

abstract: Recently, the advancement in communications, intelligent transportation systems, and computational systems has opened up new opportunities for intelligent traffic safety, comfort, and efficiency solutions. Artificial intelligence (AI) has been widely used to optimize traditional data-driven approaches in different areas of the scientific research. Vehicle-to-everything (V2X) system together with AI can acquire the information from diverse sources, can expand the driver's perception, and can predict to avoid potential accidents, thus enhancing the comfort, safety, and efficiency of the driving. This paper presents a comprehensive survey of the research works that have utilized AI to address various research challenges in V2X systems. We have summarized the contribution of these research works and categorized them according to the application domains. Finally, we present open problems and research challenges that need to be addressed for realizing the full potential of AI to advance V2X systems.


Searching for exoplanets in the Kepler public data


title: Interface engineering of highly efficient perovskite solar cells

abstract: Advancing perovskite solar cell technologies toward their theoretical power conversion efficiency (PCE) requires delicate control over the carrier dynamics throughout the entire device. By controlling the formation of the perovskite layer and careful choices of other materials, we suppressed carrier recombination in the absorber, facilitated carrier injection into the carrier transport layers, and maintained good carrier extraction at the electrodes. When measured via reverse bias scan, cell PCE is typically boosted to 16.6% on average, with the highest efficiency of ~19.3% in a planar geometry without antireflective coating. The fabrication of our perovskite solar cells was conducted in air and from solution at low temperatures, which should simplify manufacturing of large-area perovskite devices that are inexpensive and perform at high levels.


abstract: Abstractive multi-document summarization aims at generating new sentences whose elements originate from different source sentence. It can be achieved via phrase selection and merging approach which aims at constructing new sentences by exploring syntactic units such as fine-grained noun and verb phrase. It can be also achieved by extracting semantic information from source sentence which uses the concept of Basic Semantic Unit (BSU) and semantic link network. Clustered semantic graph approach employs semantic role labeling and predicate argument structure to construct the summary. These approaches aim at generating efficient abstractive multi-document summarization. This paper presents the merits and demerits of the above methods in the context of abstractive text summarization.


title: How marketers can use celebrities to sell more effectively

abstract: The first part of this paper summarises the key points about the use of celebrities in advertising, sets this particular creative technique in context and demonstrates how significant its return on investment can be. In the second part the paper goes on to report a more detailed analysis of the ‘celebrity’ case histories among the winners in the IPA Effectiveness Awards, and how practitioners have applied celebrity use to brands to make exceptional impacts on profitability.


title: Harmonised digital forensic investigation process model

abstract: Digital forensics gained significant importance over the past decade, due to the increase in the number of information security incidents over this time period, but also due to the fact that our society is becoming more dependent on information technology. Performing a digital forensic investigation requires a standardised and formalised process to be followed. There is currently no international standard formalising the digital forensic investigation process, nor does a harmonised digital forensic investigation process exist that is acceptable in this field. This paper proposes a harmonised digital forensic investigation process model. The proposed model is an iterative and multi-tier model. The authors introduce the term “parallel actions”, defined as the principles which should be translated into actions within the digital forensic investigation process (i.e. principle that evidence's integrity must be preserved through the process and that chain of evidence must be preserved). The authors believe that the proposed model is comprehensive and that it harmonises existing state-of-the-art digital forensic investigation process models. Furthermore, we believe that the proposed model can lead to the standardisation of the digital forensic investigation process.


title: Interactive Visualization of Large Data Sets

abstract: Visualization provides a powerful means for data analysis. But to be practical, visual analytics tools must support smooth and flexible use of visualizations at a fast rate. This becomes increasingly onerous with the ever-increasing size of real-world datasets. First, large databases make interaction more difficult once query response time exceeds several seconds. Second, any attempt to show all data points will overload the visualization, resulting in chaos that will only confuse the user. Over the last few years, substantial effort has been put into addressing both of these issues and many innovative solutions have been proposed. Indeed, data visualization is a topic that is too large to be addressed in a single survey paper. Thus, we restrict our attention here to interactive visualization of large data sets. Our focus then is skewed in a natural way towards query processing problem—provided by an underlying database system—rather than to the actual data visualization problem.


Consciousness: here, there and everywhere?


title: Abstract KMPI: measuring knowledge management performance

abstract: This paper provides a new metric, knowledge management performance index (KMPI), for assessing the performance of a firm in its knowledge management (KM) at a point in time. Firms are assumed to have always been oriented toward accumulating and applying knowledge to create economic value and competitive advantage. We therefore suggest the need for a KMPI which we have defined as a logistic function having five components that can be used to determine the knowledge circulation process (KCP): knowledge creation, knowledge accumulation, knowledge sharing, knowledge utilization, and knowledge internalization. When KCP efficiency increases, KMPI will also expand, enabling firms to become knowledge-intensive. To prove KMPI's contribution, a questionnaire survey was conducted on 101 firms listed in the KOSDAQ market in Korea. We associated KMPI with three financial measures: stock price, price earnings ratio (PER), and R&D expenditure. Statistical results show that the proposed KMPI can represent KCP efficiency, while the three financial performance measures are also useful.


title: Staphylococcus pseudintermedius in the dog: taxonomy, diagnostics, ecology, epidemiology and pathogenicity.

abstract: The dog is the natural host of Staphylococcus pseudintermedius. Many research efforts are currently being undertaken to expand our knowledge and understanding of this important canine commensal and opportunistic pathogen. ::: ::: The objective of this review is to summarize the current knowledge of the species, including the latest research outcomes, with emphasis on taxonomy, diagnostics, ecology, epidemiology and pathogenicity. ::: ::: Despite the important taxonomic changes that have occurred over the past few years, the risk of misidentification in canine specimens is low and does not have serious consequences for clinical practice. Staphylococcus pseudintermedius carriage in the dog is more frequent and genetically heterogeneous compared with that of Staphylococcus aureus in man. It appears that these staphylococcal species have evolved separately through adaptation to their respective natural hosts and differ with regard to various aspects concerning ecology, population structure and evolution of antibiotic resistance. ::: ::: Further understanding of the ecology and epidemiology of S. pseudintermedius is hampered by the lack of a standard method for rapid and discriminatory typing and by the limited data available on longitudinal carriage and population structure of meticillin-susceptible strains. With regard to pathogenicity, it is only now that we are starting to explore the virulence potential of S. pseudintermedius based on genomic and proteomic approaches, and more research is needed to assess the importance of individual virulence factors and the possible existence of hypervirulent strains.


title: Information Retrieval with Verbose Queries

abstract: Recently, the focus of many novel search applications shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized tutorial on this topic. In this tutorial, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.


title: Recovery from aphasia as a function of language therapy in an early bilingual patient demonstrated by fMRI

abstract: Knowledge about the recovery of language functions in bilingual aphasic patients who suffer from left-hemispheric stroke is scarce. Here, we present the case of an early bilingual patient (German/French) with chronic aphasia. Functional magnetic resonance imaging (fMRI) was used to investigate neural correlates of language performance during an overt picture naming task in German and French (a) 32 months after stroke to assess differential recovery of both languages as a function of the preceding language therapy that was provided exclusively in German and (b) after additional short-term intensive (German) language training. At the first investigation behavioral performance confirmed selective recovery of German naming ability which was associated with increased functional brain activation compared to the French naming condition. Changes in behavioral performance and brain activation pattern as disclosed by fMRI after an additional experimental treatment were confined to the trained (German) language and indicate bilateral neuroplastic reorganization. No generalization to the untrained (French) language was observed. The present case results demonstrate use and/or training-dependent differential recovery of expressive language functions and an enhanced pattern of brain activation as a function of the rehabilitation efforts that were focussed exclusively on the patient’s German language abilities.


title: Prenatal sex hormone effects on child and adult sex-typed behavior: methods and findings

abstract: There is now good evidence that human sex-typed behavior is influenced by sex hormones that are present during prenatal development, confirming studies in other mammalian species. Most of the evidence comes from clinical populations, in which prenatal hormone exposure is atypical for a person's sex, but there is increasing evidence from the normal population for the importance of prenatal hormones. In this paper, we briefly review the evidence, focusing attention on the methods used to study behavioral effects of prenatal hormones. We discuss the promises and pitfalls of various types of studies, including those using clinical populations (concentrating on those most commonly studied, congenital adrenal hyperplasia, androgen insensitivity syndrome, ablatio penis, and cloacal exstrophy), direct measures of hormones in the general population (assayed through umbilical cord blood, amniotic fluid, and maternal serum during pregnancy), and indirect measures of hormones in the general population (inferred from intrauterine position and biomarkers such as otoacoustic emissions, finger length ratios, and dermatoglyphic asymmetries). We conclude with suggestions for interpreting and conducting studies of the behavioral effects of prenatal hormones.


title: Fast-Food and Full-Service Restaurant Consumption Among Children and Adolescents

abstract: OBJECTIVE ::: To examine the effect of fast-food and full-service restaurant consumption on total energy intake, dietary indicators, and beverage consumption. ::: ::: ::: DESIGN ::: Individual-level fixed-effects estimation based on 2 nonconsecutive 24-hour dietary recalls. ::: ::: ::: SETTING ::: Nationally representative data from the 2003-2004, 2005-2006, and 2007-2008 National Health and Nutrition Examination Survey. ::: ::: ::: PARTICIPANTS ::: Children aged 2 to 11 years (n = 4717) and adolescents aged 12 to 19 years (n = 4699). ::: ::: ::: MAIN OUTCOME MEASURES ::: Daily total energy intake in kilocalories; intake of grams of sugar, total fat, saturated fat, and protein and milligrams of sodium; and total grams of sugar-sweetened beverages, regular soda, and milk consumed. ::: ::: ::: RESULTS ::: Fast-food and full-service restaurant consumption, respectively, was associated with a net increase in daily total energy intake of 126.29 kcal and 160.49 kcal for children and 309.53 kcal and 267.30 kcal for adolescents and with higher intake of regular soda (73.77 g and 88.28 g for children and 163.67 g and 107.25 g for adolescents) and sugar-sweetened beverages generally. Fast-food consumption increased intake of total fat (7.03-14.36 g), saturated fat (1.99-4.64 g), and sugar (5.71-16.24 g) for both age groups and sodium (396.28 mg) and protein (7.94 g) for adolescents. Full-service restaurant consumption was associated with increases in all nutrients examined. Additional key findings were (1) adverse effects on diet were larger for lower-income children and adolescents and (2) among adolescents, increased soda intake was twice as large when fast food was consumed away from home than at home. ::: ::: ::: CONCLUSION ::: Fast-food and full-service restaurant consumption is associated with higher net total energy intake and poorer diet quality.


title: Depth matters: influence of depth cues on visual saliency

abstract: Most previous studies on visual saliency have only focused on static or dynamic 2D scenes. Since the human visual system has evolved predominantly in natural three dimensional environments, it is important to study whether and how depth information influences visual saliency. In this work, we first collect a large human eye fixation database compiled from a pool of 600 2D-vs-3D image pairs viewed by 80 subjects, where the depth information is directly provided by the Kinect camera and the eye tracking data are captured in both 2D and 3D free-viewing experiments. We then analyze the major discrepancies between 2D and 3D human fixation data of the same scenes, which are further abstracted and modeled as novel depth priors. Finally, we evaluate the performances of state-of-the-art saliency detection models over 3D images, and propose solutions to enhance their performances by integrating the depth priors.


title: FPGA virtualization with accelerators overcommitment for network function virtualization

abstract: Network Functions Virtualisation (NFV) is revolutionizing the telecom operators data centers as well as the way in which virtualization is applied to high performance/high availability scenarios. Hardware accelerators, and in particular FPGAs, are a very promising solution for NFV, both for their high performance, predictability, programmability and reconfigurability. Recently, consolidation is brought to its limit in this field with lightweight virtualization techniques, which require a high overcommitment of FPGA accelerators resources in order to cope with the needs of thousands of guests. The lack of such virtualization support, together with cloud integration and programmability issues, limits the deployment of FPGAs in NFV. This paper presents the Virtualization Manager (VirtManager), an FPGA IP block which enables accelerators overcommitment with direct guest access. This is a key feature to enable the acceleration of thousands of guests, as it is the case for lightweight VMs, containers and unikernels. This paper's main contribution is a novel architecture for FPGA virtualization with support for resources overcommitment, NFV management and orchestration. A performance analysis of the accelerators context switch mechanism is included to prove the feasibility of the proposed approach.


title: A.: Unsupervised Detection of Regions of Interest using Iterative Link Analysis

abstract: This paper proposes a fast and scalable alternating optimization technique to detect regions of interest (ROIs) in cluttered Web images without labels. The proposed approach discovers highly probable regions of object instances by iteratively repeating the following two functions: (1) choose the exemplar set (i.e. a small number of highly ranked reference ROIs) across the dataset and (2) refine the ROIs of each image with respect to the exemplar set. These two subproblems are formulated as ranking in two different similarity networks of ROI hypotheses by link analysis. The experiments with the PASCAL 06 dataset show that our unsupervised localization performance is better than one of state-of-the-art techniques and comparable to supervised methods. Also, we test the scalability of our approach with five objects in Flickr dataset consisting of more than 200K images.


title: ARDUINO UNO based packed U cell inverter for photovoltaic application

abstract: In the area of renewable energy uses, multilevel inverter still remain a subject matter for research and development to reduce its circuit complexity. Packed U Cell (PUC) multilevel inverter is a recent variant, extensively being used in photovoltaic applications as an alternative to meet the shortage in power demands focusing too much on decreasing the installation cost, increase system's efficiency, reliability and modularity. It is important to act to decrease this cost through designing a power conditioning circuit with lower cost. To meet this objective ARDUINO UNO is used in generating and providing pulse width modulated (PWM) signals to control the gates of Insulated Gate Bipolar Transistor (IGBT) that constitute the major building block of PUC multilevel inverter. MATLAB/Simulink is used in development of the photovoltaic system which includes PV modules, Maximum Power Point Tracking (MPPT) controller, DC-DC boost converter and PUC inverter. The simulation result validate the merits and performance of this proposed scheme.


title: Android Malware Detection using Large-scale Network Representation Learning

abstract: With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. Traditional methods such as signature-based ones cannot defend users from an increasing number of new types of malware or rapid malware behavior changes. In this paper, we propose a new Android malware detection approach based on deep learning and static analysis. Instead of using Application Programming Interfaces (APIs) only, we further analyze the source code of Android applications and create their higher-level graphical semantics, which makes it harder for attackers to evade detection. In particular, we use a call graph from method invocations in an Android application to represent the application, and further analyze method attributes to form a structured Program Representation Graph (PRG) with node attributes. Then, we use a graph convolutional network (GCN) to yield a graph representation of the application by embedding the entire graph into a dense vector, and classify whether it is a malware or not. To efficiently train such a graph convolutional network, we propose a batch training scheme that allows multiple heterogeneous graphs to be input as a batch. To the best of our knowledge, this is the first work to use graph representation learning for malware detection. We conduct extensive experiments from real-world sample collections and demonstrate that our developed system outperforms multiple other existing malware detection techniques.


title: Detecting Anomalies in Time Series Data via a Meta-Feature Based Approach

abstract: Anomaly detection of time series is an important topic that has been widely studied in many application areas. A number of computational methods were developed for this task in the past few years. However, the existing approaches still have many drawbacks when they were applied to specific questions. In this paper, we proposed a meta-feature-based anomaly detection approach (MFAD) to identify the abnormal states of a univariate or multivariate time series based on local dynamics. Differing from the traditional strategies of “sliding window” in anomaly detection, our method first defined six meta-features to statistically describe the local dynamics of a 1-D sequence with arbitrary length. Second, multivariate time series was converted to a new 1-D sequence, so that each of its segmented subsequence was represented as one sample with six meta-features. Finally, the anomaly detection of univariate/multivariate time series was implemented by identifying the outliers from the samples in a 6-D transformed space. In order to validate the effectiveness of MFAD, we applied our method on various univariate and multivariate time series datasets, including six well-known standard datasets (e.g. ECG and Air Quality) and eight real-world datasets in shield tunneling construction. The simulation results show that the proposed method MFAD not only identifies the local abnormal states in the original time series but also drastically reduces the computational complexity. In summary, the proposed method effectively identified the abnormal states of dynamical parameters in various application fields.


title: Generative Neural Machine Translation

abstract: We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence. GNMT achieves competitive BLEU scores on pure translation tasks, and is superior when there are missing words in the source sentence. We augment the model to facilitate multilingual translation and semi-supervised learning without adding parameters. This framework significantly reduces overfitting when there is limited paired data available, and is effective for translating between pairs of languages not seen during training.


title: A Survey of Mobile Crowdsensing Techniques: A Critical Component for the Internet of Things

abstract: Mobile crowdsensing serves as a critical building block for the emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy and storage), and may sacrifice the quality-of-service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing the redundant data, the data size and the load can be significantly reduced, thereby reducing resource cost, facilitating the timely delivery of unique, probably critical information and enhancing QoS. This paper presents a survey of existing works for the mobile crowdsensing strategies with emphasis on reducing the resource cost and achieving high QoS. We start by introducing the motivation for this survey, and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss the future research directions for mobile crowdsensing. The survey addresses a broad range of techniques, methods, models, systems and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in the prior works but also to discuss their applicability towards the IoT, and provide the guidance on the future research direction of mobile crowdsensing.


title: Chemi-net: a graph convolutional network for accurate drug property prediction

abstract: Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.


title: Multibiometric secure system based on deep learning

abstract: In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding. We present a feature-level fusion framework to generate a secure multibiometric template from each user's multiple biometrics. Two fusion architectures, fully connected architecture and bilinear architecture, are implemented to develop a robust multibiometric shared representation. The shared representation is used to generate a cancelable biometric template that involves the selection of a different set of reliable and discriminative features for each user. This cancelable template is a binary vector and is passed through an appropriate error-correcting decoder to find a closest codeword and this codeword is hashed to generate the final secure template. The efficacy of the proposed approach is shown using a multimodal database where we achieve state-of-the-art matching performance, along with cancelability and security.


title: Space-Time Alignment for Channel Estimation in Millimeter Wave Communication with Beam Sweeping

abstract: In millimeter wave (mmWave) communication systems with hybrid multiple-input multiple-output (MIMO) processors, it is often necessary to employ analog beam sweeping during pilot signal transmission/reception to increase the signal to noise ratio (SNR). In this paper, we develop a process, called the space-time (ST) alignment, for receiving pilot sequences for mmWave channel estimation from the signal received during beam sweeping. The spatial alignment removes the effect of beam sweeping on pilot arrival time (PATs). The process for locating the temporal windows on the received signal to obtain pilot-containing sequences for channel estimation is called the temporal alignment. Simulation results demonstrate that mmWave channels can be estimated successfully using the sequences obtained via the ST alignment.


Biogeographic Ancestry Is Associated with Higher Total Body Adiposity among African-American Females: The Boston Area Community Health Survey


title: Impact of online visual feedback on motor acquisition and retention when learning to reach in a force field

abstract: Abstract When subjects learn a novel motor task, several sources of feedback (proprioceptive, visual or auditory) contribute to the performance. Over the past few years, several studies have investigated the role of visual feedback in motor learning, yet evidence remains conflicting. The aim of this study was therefore to investigate the role of online visual feedback (VFb) on the acquisition and retention stages of motor learning associated with training in a reaching task. Thirty healthy subjects made ballistic reaching movements with their dominant arm toward two targets, on 2 consecutive days using a robotized exoskeleton (KINARM). They were randomly assigned to a group with (VFb) or without (NoVFb) VFb of index position during movement. On day 1, the task was performed before (baseline) and during the application of a velocity-dependent resistive force field (adaptation). To assess retention, participants repeated the task with the force field on day 2. Motor learning was characterized by: (1) the final endpoint error (movement accuracy) and (2) the initial angle (iANG) of deviation (motor planning). Even though both groups showed motor adaptation, the NoVFb-group exhibited slower learning and higher final endpoint error than the VFb-group. In some condition, subjects trained without visual feedback used more curved initial trajectories to anticipate for the perturbation. This observation suggests that learning to reach targets in a velocity-dependent resistive force field is possible even when feedback is limited. However, the absence of VFb leads to different strategies that were only apparent when reaching toward the most challenging target.


title: A two-stage algorithm for noisy and reverberant speech enhancement

abstract: In daily listening environments, speech is commonly corrupted by room reverberation and background noise. These distortions are detrimental to speech intelligibility and quality, and also severely degrade the performance of automatic speech and speaker recognition systems. In this paper, we propose a two-stage algorithm to deal with the confounding effects of noise and reverberation separately, where denoising and dereverberation are conducted sequentially using deep neural networks. In addition, we design a new objective function that incorporates clean phase information during training. As the objective function emphasizes more important time-frequency (T-F) units, better estimated magnitude is obtained during testing. By jointly training the two-stage model to optimize the proposed objective function, our algorithm improves objective metrics of speech intelligibility and quality significantly, and substantially outperforms one-stage enhancement baselines.


title: A survey of fault localization techniques in computer networks

abstract: Fault localization, a central aspect of network fault management, is a process of deducing the exact source of a failure from a set of observed failure indications. It has been a focus of research activity since the advent of modern communication systems, which produced numerous fault localization techniques. However, as communication systems evolved becoming more complex and offering new capabilities, the requirements imposed on fault localization techniques have changed as well. It is fair to say that despite this research effort, fault localization in complex communication systems remains an open research problem. This paper discusses the challenges of fault localization in complex communication systems and presents an overview of solutions proposed in the course of the last ten years, while discussing their advantages and shortcomings. The survey is followed by the presentation of potential directions for future research in this area.


title: Habits in everyday life: Thought, emotion, and action.

abstract: To illustrate the differing thoughts and emotions involved in guiding habitual and nonhabitual behavior, 2 diary studies were conducted in which participants provided hourly reports of their ongoing experiences. When participants were engaged in habitual behavior, defined as behavior that had been performed almost daily in stable contexts, they were likely to think about issues unrelated to their behavior, presumably because they did not have to consciously guide their actions. When engaged in nonhabitual behavior, or actions performed less often or in shifting contexts, participants' thoughts tended to correspond to their behavior, suggesting that thought was necessary to guide action. Furthermore, the self-regulatory benefits of habits were apparent in the lesser feelings of stress associated with habitual than nonhabitual behavior.


title: Detecting BGP configuration faults with static analysis

abstract: The Internet is composed of many independent autonomous systems (ASes) that exchange reachability information to destinations using the Border Gateway Protocol (BGP). Network operators in each AS configure BGP routers to control the routes that are learned, selected, and announced to other routers. Faults in BGP configuration can cause forwarding loops, packet loss, and unintended paths between hosts, each of which constitutes a failure of the Internet routing infrastructure. This paper describes the design and implementation of rcc, the router configuration checker, a tool that finds faults in BGP configurations using static analysis. rcc detects faults by checking constraints that are based on a high-level correctness specification. rcc detects two broad classes of faults: route validity faults, where routers may learn routes that do not correspond to usable paths, and path visibility faults, where routers may fail to learn routes for paths that exist in the network. rcc enables network operators to test and debug configurations before deploying them in an operational network, improving on the status quo where most faults are detected only during operation. rcc has been downloaded by more than sixty-five network operators to date, some of whom have shared their configurations with us. We analyze network-wide configurations from 17 different ASes to detect a wide variety of faults and use these findings to motivate improvements to the Internet routing infrastructure.


BARRIERS AND BENEFITS OF TOTAL QUALITY MANAGEMENT IN THE CONSTRUCTION INDUSTRY : EVIDENCE FROM TURKISH CONTRACTORS


title: A learning model for personalized adaptive cruise control

abstract: This paper develops a learning model for personalized adaptive cruise control that can learn from human demonstration online and mimic a human driver's driving strategies in the dynamic traffic environment. Under the framework of the proposed model, reinforcement learning is used to capture the human-desired driving strategy, and the proportion-integration-differentiation controller is adopted to convert the learning strategy to low-level control commands. The performance of the learning model is tested in the simulation environment built in a driving simulator using PreScan. Experimental results show that the learning model can duplicate human driving strategies with acceptable errors. Moreover, compared with the traditional adaptive cruise control, the proposed model can provide better driving comfort and smoothness in the dynamic situation.


title: Iterative Instance Segmentation

abstract: Existing methods for pixel-wise labelling tasks generally disregard the underlying structure of labellings, often leading to predictions that are visually implausible. While incorporating structure into the model should improve prediction quality, doing so is challenging - manually specifying the form of structural constraints may be impractical and inference often becomes intractable even if structural constraints are given. We sidestep this problem by reducing structured prediction to a sequence of unconstrained prediction problems and demonstrate that this approach is capable of automatically discovering priors on shape, contiguity of region predictions and smoothness of region contours from data without any a priori specification. On the instance segmentation task, this method outperforms the state-of-the-art, achieving a mean $\mathrm{AP}^{r}$ of 63.6% at 50% overlap and 43.3% at 70% overlap.


Facebook Acoustic Events Dataset


Friction-induced Vibration and Noise Research: the Status Quo and Its Prospect


title: Differential-Fed Patch Antenna Arrays With Low Cross Polarization and Wide Bandwidths

abstract: In this letter, two differential-fed Ku-band 2 $\,\times\,$ 2 patch antenna arrays with low cross polarization and wide bandwidths were developed using multilayer printed circuit board (PCB) technology. The patch elements in Design I were fed differentially, whereas the patches in Design II were single-ended. However, Design II is differential if the array is considered as a whole. The experimental demonstration revealed that Design I achieved a 10-dB impedance bandwidth of 15.3%. The measured gain was 10.41 dBi at 12.62 GHz with low cross polarization of $-$ 17.5 dB in the E-plane and $-$ 20.2 dB in the H-plane. Design II exhibited a 10-dB impedance bandwidth of 12.8%. The measured gain was 12.32 dBi at 13 GHz with cross polarization of $- $ 17.7 and $-$ 17 dB in the E-plane and H-plane, respectively. Design II achieves performance levels similar to that of Design I, but comprises a simple and compact feed network.


title: Nursing students’ immediate responses to distressed clients based on Orlando’s theory

abstract: BACKGROUND : Nowadays, problem solving skills, clinical decision making ability and making a proper relationship to clients are essential necessities for nursing graduates, however there are few studies which investigated nursing student’ responses to clients with problematic situations based on nursing theories addressing interactions. The purpose of this study was analyzing the nursing students’ immediate responses to distressed clients’ behaviors focusing on collaborative Orlando’s theory. METHODS : This exploratory study was assessed 60 nursing students’ responses to a simulated client’s questionnaire based on Orlando’s theory in 2008. All of the students were enrolling in bachelor degree of nursing. The data were analyzed by dimensional content analysis to specify the key categories according to concepts of Orlando’s theory. RESULTS : According to Orlando’s theory, students’ immediate responses to physical and mental problems of distressed clients were classified into 6 main categories: physical caring, uncertainty, assuring, recommending, asking information and explaining. The most frequent responses to clients’ behaviors were physical caring, assuring and recommending and most of the students were unable to respond to mental problems. CONCLUSIONS : Nursing students responded to clients’ needs automatically and they didn’t consider to patients’ authority in decision making. Medical diagnosis, physical caring and assuring were their main concerns and they were confused in responding to mental problems. Orlando’s theory emphasizes on nurse-client interactions and consider to nurses’ perceptions, thoughts, and feelings. It views to clients as a participant in caring, so teaching this theory can enhance students’ communication skills and improve quality of nursing care. KEY WORDS : Immediate responses, nursing students, Orlando’s theory, distressed clients.


title: Bandwidth Enhancement for a 60 GHz Substrate Integrated Waveguide Fed Cavity Array Antenna on LTCC

abstract: A substrate integrated waveguide fed cavity array antenna using multilayered low temperature co-fired ceramic technology is presented and designed at V-band (60 GHz). The 8 × 8 antenna array is designed with an enhanced bandwidth of 17.1% and a gain up to 22.1 dBi by reconfiguring radiating elements, feeding network, and the transition. The proposed array antenna also features the merits of compact size, stable performance, and high efficiency.


title: Optimal PID controller design for AVR system using particle swarm optimization algorithm

abstract: A proportional-integral-derivative (PID) controller is a generic feedback controller widely used in industrial control systems, process control, motor drive, and instrumentation. Despite the popularity, the tuning aspect of PID coefficients is a challenge for researchers and plant operators. In this paper Particle Swarm Optimization Algorithm is used to design the optimum PID controller parameters for a high order automatic voltage regulator (AVR). The proposed approach with new defined time-domain cost function, has a very easy implementation, stable convergence characteristic and ability of fast tuning of optimum PID controller parameters that requires fewer number of iterations. In order to evaluate the performance of the PSO-PID controller, the results are compared with the genetic algorithm (GA). The comparison shows the PSO-PID algorithm has more efficiency and robustness in improving the step response of an AVR system.


title: Building enriched document representations using aggregated anchor text

abstract: It is well known that anchor text plays a critical role in a variety of search tasks performed over hypertextual domains, including enterprise search, wiki search, and web search. It is common practice to enrich a document's standard textual representation with all of the anchor text associated with its incoming hyperlinks. However, this approach does not help match relevant pages with very few inlinks. In this paper, we propose a method for overcoming anchor text sparsity by enriching document representations with anchor text that has been aggregated across the hyperlink graph. This aggregation mechanism acts to smooth, or diffuse, anchor text within a domain. We rigorously evaluate our proposed approach on a large web search test collection. Our results show the approach significantly improves retrieval effectiveness, especially for longer, more difficult queries.


title: Reading Barcodes with Neural Networks

abstract: Barcodes are ubiquituous in modern society and they have had industrial application for decades. However, for noisy images modern methods can underperform. Poor lighting conditions, occlusions and ...


title: Cross-Validation and the Estimation of Conditional Probability Densities

abstract: Many practical problems, especially some connected with forecasting, require nonparametric estimation of conditional densities from mixed data. For example, given an explanatory data vector X for a prospective customer, with components that could include the customer's salary, occupation, age, sex, marital status, and address, a company might wish to estimate the density of the expenditure, Y, that could be made by that person, basing the inference on observations of (X, Y) for previous clients. Choosing appropriate smoothing parameters for this problem can be tricky, not in the least because plug-in rules take a particularly complex form in the case of mixed data. An obvious difficulty is that there exists no general formula for the optimal smoothing parameters. More insidiously, and more seriously, it can be difficult to determine which components of X are relevant to the problem of conditional inference. For example, if the jth component of X is independent of Y, then that component is irrelevant to es...


title: A SCADA data based anomaly detection method for wind turbines

abstract: In this paper, a data driven method for Wind Turbine system level anomaly detection is proposed. Supervisory control and data acquisition system (SCADA) data of a wind turbine is adopted and several parameters are selected based on physic knowledge and correlation coefficient analysis to build a normal behavior model. This model is based on Self-organizing map (SOM) which can project higher dimensional SCADA data into a two-dimension-map. After that, the Euclidean distance based indicator for system level anomalies is defined and a filter is created to screen out suspicious data points based on quantile function. Moreover, a failure data pattern based criterion is created for anomaly detection from system level. The method is tested with a two-month SCADA dataset with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method.


title: Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

abstract: Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel class. We address this issue and propose a data stream classification technique that integrates a novel class detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of concept-drift, when the underlying data distributions evolve in streams. In order to determine whether an instance belongs to a novel class, the classification model sometimes needs to wait for more test instances to discover similarities among those instances. A maximum allowable wait time Tc is imposed as a time constraint to classify a test instance. Furthermore, most existing stream classification approaches assume that the true label of a data point can be accessed immediately after the data point is classified. In reality, a time delay Tl is involved in obtaining the true label of a data point since manual labeling is time consuming. We show how to make fast and correct classification decisions under these constraints and apply them to real benchmark data. Comparison with state-of-the-art stream classification techniques prove the superiority of our approach.


title: Components for object detection and identification

abstract: We present a component-based system for object detection and identification. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity of their image features and their locations within the object image. The cluster centers build an initial set of component templates from which we select a subset for the final recognizer. The localization of the components is performed by normalized cross-correlation. Two types of components are used, gray value components and components consisting of the magnitudes of the gray value gradient.


Evaluation of String Searching Algorithms


title: A SIP-based medical event monitoring system

abstract: The medical industry is transitioning to Internet-based communication as the field of telemedicine broadens to include medical event monitoring systems. A medical event monitor generates different types of messages and alerts for healthcare providers, institutions and patients. We describe how the Session Initiation Protocol (SIP), a signaling protocol for Internet conferencing, telephony, presence, event notification and instant messaging, can be used to create a medical event monitoring system. SIP can work on a variety of devices; its adoption as the protocol of choice for third generation wireless networks allows for a robust and scalable environment that can easily extend across institutions. First, we describe the basics of SIP and how it can be used for event notifications. Secondly, we describe the use of medical logic modules (MLMs) in a clinical event monitoring system and we propose a SIP medical event monitoring system that can combine the use of MLMs, such as the Arden Syntax, with SIP event notification. We also propose an alternate method of event notification with the use of XML filters to deliver only relevant notifications. Finally, we discuss the different types of devices and wireless protocols that can be incorporated within the system, creating an integrated architecture.


title: Digital Filters for Fast Harmonic Sequence Component Separation of Unbalanced and Distorted Three-Phase Signals

abstract: In this paper, two methods for determining the fundamental frequency and harmonic positive- and negative-sequence components of three-phase signals are investigated. Many aspects of the space-vector discrete Fourier transform and generalized delayed signal cancellation (GDSC) such as response time for different possible implementations, frequency adaptation schemes, stability of recursive implementation, and rounding error effects are discussed. A new design procedure for GDSC transformations is presented. New indices for characterizing three-phase unbalanced and distorted signals are proposed. Simulations and experiments are included in order to verify the performances and illustrate the theoretical conclusions.


title: Determining anaerobic capacity in sporting activities.

abstract: Anaerobic capacity/anaerobically attributable power is an important parameter for athletic performance, not only for short high-intensity activities but also for breakaway efforts and end spurts during endurance events. Unlike aerobic capacity, anaerobic capacity cannot be easily quantified. The 3 most commonly used methodologies to quantify anaerobic capacity are the maximal accumulated oxygen deficit method, the critical power concept, and the gross efficiency method. This review describes these methods, evaluates if they result in similar estimates of anaerobic capacity, and highlights how anaerobic capacity is used during sporting activities. All 3 methods have their own strengths and weaknesses and result in more or less similar estimates of anaerobic capacity but cannot be used interchangeably. The method of choice depends on the research question or practical goal.


title: Superhuman Accuracy on the SNEMI3D Connectomics Challenge

abstract: For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images. Recent years have seen great improvements in accuracy, as evidenced by submissions to the SNEMI3D benchmark challenge. Here we report the first submission to surpass the estimate of human accuracy provided by the SNEMI3D leaderboard. A variant of 3D U-Net is trained on a primary task of predicting affinities between nearest neighbor voxels, and an auxiliary task of predicting long-range affinities. The training data is augmented by simulated image defects. The nearest neighbor affinities are used to create an oversegmentation, and then supervoxels are greedily agglomerated based on mean affinity. The resulting SNEMI3D score exceeds the estimate of human accuracy by a large margin. While one should be cautious about extrapolating from the SNEMI3D benchmark to real-world accuracy of large-scale neural circuit reconstruction, our result inspires optimism that the goal of full automation may be realizable in the future.


title: Method for Designing Scalable Microservice-based Application Systematically: A Case Study

abstract: Microservice is a new transformation of Service-Oriented Architecture (SOA) which is gaining momentum in both academic and industry. The success of microservice began when giant companies like Netflix used them as a service architecture for the purpose of serving customers. Monolithic architecture used by Netflix previously was no longer able to cope with business development and it is difficult to scale to meet user demands. Although Netflix has been successful with microservice architecture, there is no systematic method introduced to produce microservice. Academic studies related to microservice are still in the early stages and have not yet reached maturity. Microservice is seen to require a method that helps organizations to systematically design microservices and replicate the success achieved by Netflix. In forming a method for this systematic microservice then the methods for building an existing microservice are studied. Based on the Design Science Research method, two research artefacts have been produced. The first artefact is a systematic design of microservice that has four main steps. The second artefact is the instantiation by applying the proposed microservice design method to the case studies, namely, MyFlix. Next the evaluation is made on the new method produced by obtaining expert opinions through the process of demonstration and interviewing. The expert assessment results found that the proposed method was able to produce a systematic microservice design based on the six proposed principles and the four main steps. This method can also produce a complete feature microservice such as cohesive, loose coupling, distributed and decentralized that will contribute to the production of scalable system.


title: Creating Rapport with Virtual Agents

abstract: Recent research has established the potential for virtual characters to establish rapport with humans through simple contingent nonverbal behaviors. We hypothesized that the contingency, not just the frequency of positive feedback is crucial when it comes to creating rapport. The primary goal in this study was evaluative: can an agent generate behavior that engenders feelings of rapport in human speakers and how does this compare to human generated feedback? A secondary goal was to answer the question: Is contingency (as opposed to frequency) of agent feedback crucial when it comes to creating feelings of rapport? Results suggest that contingency matters when it comes to creating rapport and that agent generated behavior was as good as human listeners in creating rapport. A "virtual human listener" condition performed worse than other conditions.


title: Binary division algorithm and high speed deconvolution algorithm (Based on Ancient Indian Vedic Mathematics)

abstract: The performance of any processor solely depends upon its power, area and delay. In order to get an effective processor, its power, area and delay should be less. Division is always considered to be bulky and one of the most difficult operations in arithmetic and hence all the implementations of division algorithms in VLSI architecture have higher orders of time and space complexities. Vedic Mathematics on the other hand offers a new holistic approach to mathematics. Its range extends from the most concrete values of numerical computation to the most abstract aspects of the dynamics of intelligence. In this work we have implemented an optimized binary division architecture using sutras of Vedic Mathematics which are Nikhilam Sutra and Parvartya Sutra. This work discusses about these two algorithms of division and their application for calculating deconvolution. Both the algorithms have been implemented with improved results of time delay and are with fewer complexities. The proposed division algorithm is coded in Verilog, synthesized and simulated using Xilinx ISE design suit 14.2. Simulated results for proposed Vedic divider circuit shows a reduction in delay of 19% than the conventional method.


title: SASE: Complex Event Processing over Streams

abstract: RFID technology is gaining adoption on an increasing scale for tracking and monitoring purposes. Wide deployments of RFID devices will soon generate an unprecedented volume of data. Emerging applications require the RFID data to be filtered and correlated for complex pattern detection and transformed to events that provide meaningful, actionable information to end applications. In this work, we design and develop SASE, a com-plex event processing system that performs such data-information transformation over real-time streams. We design a complex event language for specifying application logic for such transformation, devise new query processing techniques to effi-ciently implement the language, and develop a comprehensive system that collects, cleans, and processes RFID data for deliv-ery of relevant, timely information as well as storing necessary data for future querying. We demonstrate an initial prototype of SASE through a real-world retail management scenario.


title: Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts

abstract: Automation-assisted reading (AAR) techniques have the potential to reduce errors and increase productivity in cervical cancer screening. The sensitivity of AAR relies heavily on automated segmentation of abnormal cervical cells, which is handled poorly by current segmentation algorithms. In this paper, a global and local scheme based on graph cut approach is proposed to segment cervical cells in images with a mix of healthy and abnormal cells. For cytoplasm segmentation, the multi-way graph cut is performed globally on the a* channel enhanced image, which can be effective when the image histogram presents a non-bimodal distribution. For segmentation of nuclei, especially when they are abnormal, we propose to use graph cut adaptively and locally, which allows the combination of intensity, texture, boundary and region information. Two concave points-based approaches are integrated to split the touching-nuclei. As part of an ongoing clinical trial, preliminary validation results obtained from 21 cervical cell images with non-ideal imaging condition and pathology show that our segmentation method achieved 93% accuracy for cytoplasm, and 88.4% F-measure for abnormal nuclei, outperforming state of the art methods in terms of accuracy. Our method has the potential to improve the sensitivity of AAR in screening for cervical cancer.


Sparse and low-rank matrix decomposition via alternating direction method


title: Deep Texture and Structure Aware Filtering Network for Image Smoothing

abstract: Image smoothing is a fundamental task in computer vision, that aims to retain salient structures and remove insignificant textures. In this paper, we aim to address the fundamental shortcomings of existing image smoothing methods, which cannot properly distinguish textures and structures with similar low-level appearance. While deep learning approaches have started to explore the preservation of structure through image smoothing, existing work does not yet properly address textures. To this end, we generate a large dataset by blending natural textures with clean structure-only images, and then build a texture prediction network (TPN) that predicts the location and magnitude of textures. We then combine the TPN with a semantic structure prediction network (SPN) so that the final texture and structure aware filtering network (TSAFN) is able to identify the textures to remove ("texture-awareness") and the structures to preserve ("structure-awareness"). The proposed model is easy to understand and implement, and shows excellent performance on real images in the wild as well as our generated dataset.


title: Daily Well-Being: The Role of Autonomy, Competence, and Relatedness

abstract: Emotional well-being is most typically studied in trait or trait-like terms, yet a growing literature indicates that daily (within-person) fluctuations in emotional well-being may be equally important. The present research explored the hypothesis that daily variations may be understood in terms of the degree to which three basic needs—autonomy, competence, and relatedness—are satisfied in daily activity. Hierarchical linear models were used to examine this hypothesis across 2 weeks of daily activity and well-being reports controlling for trait-level individual differences. Results strongly supported the hypothesis. The authors also examined the social activities that contribute to satisfaction of relatedness needs. The best predictors were meaningful talk and feeling understood and appreciated by interaction partners. Finally, the authors found systematic day-of-the-week variations in emotional well-being and need satisfaction. These results are discussed in terms of the importance of daily activities and...


title: Neural Network Vision for Robot Driving

abstract: Autonomous navigation is a difficult problem for traditional vision and robotic techniques, primarily because of the noise and variability associated with real world scenes. Autonomous navigation systems based on traditional image processing and pattern recognition techniques often perform well under certain conditions, but have problems with others. Part of the difficulty stems from the fact that the processing performed by these systems remains fixed across various environments.


WHITEPAPER – IP Streaming of MPEG-4 : Native RTP vs MPEG-2 Transport Stream


title: Adaptive fuzzy control of a class of MIMO nonlinear systems

abstract: This paper presents two indirect adaptive fuzzy control schemes for a class of uncertain continuous-time multi-input multi-output nonlinear dynamic systems. Within these schemes, fuzzy systems are employed to approximate the plant's unknown nonlinear functions and robustifying control terms are used to compensate for approximation errors. By using a regularized matrix inverse, a stable well-defined adaptive controller is firstly investigated. Then, in order to obtain an adaptive controller not depending upon any parameter initialization conditions and to relax the requirement of bounding parameter values, a second adaptive controller is proposed. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis so that, under appropriate assumptions, semi-global stability and asymptotic convergence to zero of tracking errors can be guaranteed. Simulations performed on a two-link robot manipulator illustrate the approach and exhibit its performance.


title: Symbol-Timing Recovery with Modified Gardner Detectors

abstract: For symbol timing recovery in digital receivers, a modified form of the Gardner detector is introduced. If applied a tracking loops for M-PSK modulated signals shaped as raised cosines, it is shown that the suggested synchronizer achieves a definitely better jitter performance (Gaussian and self-noise domain) than the original proposed by Gardner. In particular, this turns out to be true for baseband shapes with small roll-off factors as it is required for bandwidth-efficient systems


title: Multiple trait multiple interval mapping of quantitative trait loci from inbred line crosses

abstract: Background ::: Although many experiments have measurements on multiple traits, most studies performed the analysis of mapping of quantitative trait loci (QTL) for each trait separately using single trait analysis. Single trait analysis does not take advantage of possible genetic and environmental correlations between traits. In this paper, we propose a novel statistical method for multiple trait multiple interval mapping (MTMIM) of QTL for inbred line crosses. We also develop a novel score-based method for estimating genome-wide significance level of putative QTL effects suitable for the MTMIM model. The MTMIM method is implemented in the freely available and widely used Windows QTL Cartographer software.


title: Productive figurative communication: Conventional metaphors facilitate the comprehension of related novel metaphors

abstract: Abstract Three experiments explored whether conceptual mappings in conventional metaphors are productive, by testing whether the comprehension of novel metaphors was facilitated by first reading conceptually related conventional metaphors. The first experiment, a replication and extension of Keysar et al. [Keysar, B., Shen, Y., Glucksberg, S., Horton, W. (2000). Conventional language: How metaphorical is it? Journal of Memory and Language 43, 576–593] (Experiment 2), found no such facilitation; however, in the second experiment, upon re-designing and improving the stimulus materials, facilitation was demonstrated. In a final experiment, this facilitation was shown to be specific to the conceptual mappings involved. The authors argue that metaphor productivity provides a communicative advantage and that this may be sufficient to explain the clustering of metaphors into families noted by Lakoff and Johnson [Lakoff & Johnson, M. (1980a). The metaphorical structure of the human conceptual system. Cognitive Science 4, 195–208].


title: Mite infestations.

abstract: Mite infestations are important in dermatology because these may cause dermatologic diseases that range from papulosquamous eruptions to urticarial lesions to bullous eruptions and may spread infectious diseases. These clinical manifestations are important to recognize because mite-associated diseases may have systemic complications and may be confused with other dermatologic conditions. In treating mite infestations, oral antibiotics may be necessary. Prevention of infestation may be accomplished by pre-treating clothing with permethrin, using insect repellent N,N-Diethyl-meta-toluamide on clothing and skin, and treating animals infected with mites. This article will review etiology, clinical manifestation, and treatment of mite infestations.


title: A clustering method for efficient segmentation of 3D laser data

abstract: In this paper we present a novel method for the efficient segmentation of 3D laser range data. The proposed algorithm is based on a radially bounded nearest neighbor strategy and requires only two parameters. It yields deterministic, repeatable results and does not depend on any initialization procedure. The efficiency of the method is verified with synthetic and real 3D data.


title: Mouse trap: exploiting firmware updates in USB peripherals

abstract: Although many users are aware of the threats that malware pose, users are unaware that malware can infect peripheral devices. Many embedded devices support firmware update capabilities, yet they do not authenticate such updates; this allows adversaries to infect peripherals with malicious firmware. We present a case study of the Logitech G600 mouse, demonstrating attacks on networked systems which are also feasible against airgapped systems. ::: ::: If the target machine is air-gapped, we show that the Logitech G600 has enough space available to host an entire malware package inside its firmware. We also wrote a file transfer utility that transfers the malware from the mouse to the target machine. If the target is networked, the mouse can be used as a persistent threat that updates and reinstalls malware as desired. ::: ::: To mitigate these attacks, we implemented signature verification code which is essential to preventing malicious firmware from being installed on the mouse. We demonstrate that it is reasonable to include such signature verification code in the bootloader of the mouse.


title: Strategies for detecting fraudulent claims in the automobile insurance industry

abstract: Some property and casualty insurers use automated detection systems to help to decide whether or not to investigate claims suspected of fraud. Claim screening systems benefit from the coded experience of previously investigated claims. The embedded detection models typically consist of scoring devices relating fraud indicators to some measure of suspicion of fraud. In practice these scoring models often focus on minimizing the error rate rather than on the cost of (mis)classification. We show that focusing on cost is a profitable approach. We analyse the effects of taking into account information on damages and audit costs early on in the screening process. We discuss several scenarios using real-life data. The findings suggest that with claim amount information available at screening time detection rules can be accommodated to increase expected profits. Our results show the value of cost-sensitive claim fraud screening and provide guidance on how to render this strategy operational.


title: Ontology learning from text: Method for learning axioms

abstract: Ontologies provide a structural organizational knowledge, they support the exchange and sharing of information. Moreover, one of the main benefits of using ontologies is the ability to infer new knowledge that allows the development of more realistic applications. The need for overcoming the bottleneck, given in the knowledge acquisition by the manual construction of ontologies, has motivated studies on semi-automatic and automatic methods to build ontologies. One of the main sources of knowledge created by humans is given by text resources. The analysis and extraction of the elements of an ontology from texts is a very hard task. In this report we focus on present the method for learning axioms from text based on named entity recognition. In our proposal we exploit corpora with high occurrence of named entities that give information on the individuals in a specific domain knowledge expressed by the corpus. Given the set of identified named entities the axiomatic relations such as subClassOf, disjointWith, and equivalentClass were identified. For this purpose a named entity recognition tool was used and the linguistic context, where classes co-occur, was extracted. This report of activities corresponds to the thrid year of doctoral studies.


title: Heavy metal induced oxidative stress & its possible reversal by chelation therapy.

abstract: Exposure to heavy metals is a common phenomenon due to their environmental pervasiveness. Metal intoxication particularly neurotoxicity, genotoxicity, or carcinogenicity is widely known. This review summarizes our current understanding about the mechanism by which metalloids or heavy metals (particularly arsenic, lead, cadmium and mercury) induce their toxic effects. The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. The toxic manifestations of these metals are caused primarily due to imbalance between pro-oxidant and antioxidant homeostasis which is termed as oxidative stress. Besides these metals have high affinity for thiol groups containing enzymes and proteins, which are responsible for normal cellular defense mechanism. Long term exposure to these metals could lead to apoptosis. Signaling components affected by metals include growth factor receptors, G-proteins, MAP kinases and transcription factors. Chelation therapy with chelating agents like calcium disodium ethylenediamine tetra acetic acid (CaNa(2)EDTA), British Anti Lewisite (BAL), sodium 2,3-dimercaptopropane 1-sulfonate (DMPS), meso 2,3-dimercaptosuccinic acid (DMSA) etc., is considered to be the best known treatment against metal poisoning. Despite many years of research we are still far away from effective treatment against toxicity caused due to exposure to heavy metals/metalloids. The treatment with these chelating agents is compromised with number of serious side-effects. Studies show that supplementation of antioxidants along-with a chelating agent prove to be a better treatment regimen than monotherapy with chelating agents. This review attempts a comprehensive account of recent developments in the research on heavy metal poisoning particularly the role of oxidative stress/free radicals in the toxic manifestation, an update about the recent strategies for the treatment with chelating agents and a possible beneficial role of antioxidants supplementation to achieve the optimum effects. We have selected only arsenic, lead, mercury and cadmium for this article keeping in view current concerns and literature available.


title: Technical Reviews: A Behaviorally Motivated Program of Research

abstract: Software engineers use a number of different types of software development technical review (SDTR) for the purpose of detecting defects in software products. This paper applies the behavioral theory of group performance to explain the outcomes of software reviews. A program of empirical research is developed, including propositions to both explain review performance and identify ways of improving review performance based on the specific strengths of individuals and groups. Its contributions are to clarify our understanding of what drives defect detection performance in SDTRs and to set an agenda for future research. In identifying individuals' task expertise as the primary driver of review performance, the research program suggests specific points of leverage for substantially improving review performance. It points to the importance of understanding software reading expertise and implies the need for a reconsideration of existing approaches to managing reviews.


title: Shattering and Compressing Networks for Betweenness Centrality

abstract: The betweenness metric has always been intriguing and used in many analyses. Yet, it is one of the most computationally expensive kernels in graph mining. For that reason, making betweenness centrality computations faster is an important and well-studied problem. In this work, we propose the framework, BADIOS, which compresses a network and shatters it into pieces so that the centrality computation can be handled independently for each piece. Although BADIOS is designed and tuned for betweenness centrality, it can easily be adapted for other centrality metrics. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours.


title: Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W

abstract: Selective laser melting is a powder-based, additive-manufacturing process where a three-dimensional part is produced, layer by layer, by using a high-energy laser beam to fuse the metallic powder particles. A particular challenge in this process is the selection of appropriate process parameters that result in parts with desired properties. In this study, we describe an approach to selecting parameters for high-density (>99 %) parts using 316L stainless steel. Though there has been significant success in achieving near-full density for 316L parts, this work has been limited to laser powers 99 %, with the density reducing rapidly at high speeds due to insufficient melting, and less rapidly at low speeds due to the effect of voids created as the process enters keyhole mode.


title: A 8 Gbps 0.67mW 1 tap current integrating DFE in 40nm CMOS

abstract: This paper describes the implementation issues in designing a 1 tap current integrating half rate decision feedback equalizer. We introduce a new technique for designing an 8Gbps 1 tap half rate current integrating DFE in 40nm CMOS technology that draws 0.67mW from a 1.1V supply. The technique uses a delayed clock rather than speculation. This removes the speculation impact on power consumption and timing constraints especially in multi tap DFE.


title: Object Detection via Aspect Ratio and Context Aware Region-based Convolutional Networks

abstract: Jointly integrating aspect ratio and context has been extensively studied and shown performance improvement in traditional object detection systems such as the DPMs. It, however, has been largely ignored in deep neural network based detection systems. This paper presents a method of integrating a mixture of object models and region-based convolutional networks for accurate object detection. Each mixture component accounts for both object aspect ratio and multi-scale contextual information explicitly: (i) it exploits a mixture of tiling configurations in the RoI pooling to remedy the warping artifacts caused by a single type RoI pooling (e.g., with equally-sized 7 x 7 cells), and to respect the underlying object shapes more; (ii) it "looks from both the inside and the outside of a RoI" by incorporating contextual information at two scales: global context pooled from the whole image and local context pooled from the surrounding of a RoI. To facilitate accurate detection, this paper proposes a multi-stage detection scheme for integrating the mixture of object models, which utilizes the detection results of the model at the previous stage as the proposals for the current in both training and testing. The proposed method is called the aspect ratio and context aware region-based convolutional network (ARC-R-CNN). In experiments, ARC-R-CNN shows very competitive results with Faster R-CNN [41] and R-FCN [10] on two datasets: the PASCAL VOC and the Microsoft COCO. It obtains significantly better mAP performance using high IoU thresholds on both datasets.


title: Gonado-Somatic Index, Sex Ratio and Fecundity of Tilapia zilli in a Tropical Reservoir, South West Nigeria

abstract: Some aspects of reproductive biology of Tilapia zilli in Ero Reservoir were studied. Fish samples were c ollected from the fishermen between April and December, 2009 using gill netting and cast netting. 81 specimens collected were taken to the laboratory for gonado-somatic index, sex identifica tion and fecundity studies. There was no matured ov ary found for fecundity determination and the standard length in males ranged from 10.60-20.70 cm and 11.00-20.70 cm in females. The mean gonado-somatic index was 0.6 ± 1.0 and sex ratio was approximately 1:3 (males: female). Spawning was seasonal and more in the wet season through to September and seemed to be triggered off by either rainfall or l ow temperature. The analysis shows a high, positive and significant rel ationship between fish weight and standard length, and fish weight and weight of gonad (P< 0.05). On the other hand, there exist a weak and positive relationship between fis h weight and GSI, standard length and GSI, although significant at 0. 05 levels.


title: A MOS switched-capacitor instrumentation amplifier

abstract: Describes a precision switched-capacitor sampled-data instrumentation amplifier using NMOS polysilicon gate technology. It is intended for use as a sample-and-hold amplifier for low level signals in data acquisition systems. The use of double correlated sampling technique achieves high power supply rejection, low DC offset, and low 1/f noise voltage. Matched circuit components in a differential configuration minimize errors from switch channel charge injection. Very high common mode rejection (120 dB) is obtained by a new sampling technique which prevents the common mode signal from entering the amplifier. This amplifier achieves 1 mV typical input offset voltage, greater than 95 dB PSRR, 0.15 percent gain accuracy, 0.01 percent gain linearity, and an RMS input referred noise voltage of 30 /spl mu/V/input sample.


title: Design optimization of high breakdown voltage AlGaN-GaN power HEMT on an insulating substrate for R/sub ON/A-V/sub B/ tradeoff characteristics

abstract: High breakdown voltage AlGaN-GaN power high-electron mobility transistors (HEMTs) on an insulating substrate were designed for the power electronics application. The field plate structure was employed for high breakdown voltage. The field plate length, the insulator thickness and AlGaN layer doping concentration were design parameters for the breakdown voltage. The optimization of the contact length and contact resistivity reduction were effective to reduce the specific on-resistance. The tradeoff characteristics between the on-resistance and the breakdown voltage can be improved by the optimization of the above design parameters, and the on-resistance can be estimated to be about 0.6 m/spl Omega//spl middot/cm/sup 2/ for the breakdown voltage of 600 V. This on-resistance is almost the same as that for the device on a conductive substrate.


title: Large-scale brain networks and psychopathology: a unifying triple network model

abstract: The science of large-scale brain networks offers a powerful paradigm for investigating cognitive and affective dysfunction in psychiatric and neurological disorders. This review examines recent conceptual and methodological developments which are contributing to a paradigm shift in the study of psychopathology. I summarize methods for characterizing aberrant brain networks and demonstrate how network analysis provides novel insights into dysfunctional brain architecture. Deficits in access, engagement and disengagement of large-scale neurocognitive networks are shown to play a prominent role in several disorders including schizophrenia, depression, anxiety, dementia and autism. Synthesizing recent research, I propose a triple network model of aberrant saliency mapping and cognitive dysfunction in psychopathology, emphasizing the surprising parallels that are beginning to emerge across psychiatric and neurological disorders.