title: Transferring an automated test generation tool to practice: from pex to fakes and code digger

abstract: Producing industry impacts has been an important, yet challenging task for the research community. In this paper, we report experiences on successful technology transfer of Pex and its relatives (tools derived from or associated with Pex) from Microsoft Research and lessons learned from more than eight years of research efforts by the Pex team in collaboration with academia. Moles, a tool associated with Pex, was shipped as Fakes with Visual Studio since August 2012, benefiting a huge user base of Visual Studio around the world. The number of download counts of Pex and its lightweight version called Code Digger has reached tens of thousands within one or two years. Pex4Fun (derived from Pex), an educational gaming website released since June 2010, has achieved high educational impacts, reflected by the number of clicks of the "Ask Pex!" button (indicating the attempts made by users to solve games in Pex4Fun) as over 1.5 million till July 2014. Evolved from Pex4Fun, the Code Hunt website has been used in a very large programming competition. In this paper, we discuss the technology background, tool overview, impacts, project timeline, and lessons learned from the project. We hope that our reported experiences can inspire more high-impact technology-transfer research from the research community.


title: Reactive path deformation for nonholonomic mobile robots

abstract: This paper presents a novel and generic approach of path optimization for nonholonomic systems. The approach is applied to the problem of reactive navigation for nonholonomic mobile robots in highly cluttered environments. This is a collision-free initial path being given for a robot, and obstacles detected while following this path can make it in collision. The current path is iteratively deformed in order to get away from obstacles and satisfy the nonholonomic constraints. The core idea of the approach is to perturb the input functions of the system along the current path in order to modify this path, making an optimization criterion decrease.


title: InfoGather: entity augmentation and attribute discovery by holistic matching with web tables

abstract: The Web contains a vast corpus of HTML tables, specifically entity attribute tables. We present three core operations, namely entity augmentation by attribute name, entity augmentation by example and attribute discovery, that are useful for "information gathering" tasks (e.g., researching for products or stocks). We propose to use web table corpus to perform them automatically. We require the operations to have high precision and coverage, have fast (ideally interactive) response times and be applicable to any arbitrary domain of entities. The naive approach that attempts to directly match the user input with the web tables suffers from poor precision and coverage. Our key insight is that we can achieve much higher precision and coverage by considering indirectly matching tables in addition to the directly matching ones. The challenge is to be robust to spuriously matched tables: we address it by developing a holistic matching framework based on topic sensitive pagerank and an augmentation framework that aggregates predictions from multiple matched tables. We propose a novel architecture that leverages preprocessing in MapReduce to achieve extremely fast response times at query time. Our experiments on real-life datasets and 573M web tables show that our approach has (i) significantly higher precision and coverage and (ii) four orders of magnitude faster response times compared with the state-of-the-art approach.


Unified inverse depth parametrization for monocular SLAM


title: Analysis of LCLC resonant converters for high-voltage high-frequency applications

abstract: This paper proposes a novel LCLC full-bridge resonant converter with ZCS and ZVS for high-voltage high-frequency applications. In this operating model, both of ZCS and ZVS can be achieved and the switching losses can be reduced to a minimum. The proposed converter adopts the parasitic parameters of the step-up voltage transformer and one other serial capacitor is needed to form the resonant tank. Compared with other resonant converters, where only ZCS or ZVS can be achieved, the proposed working model can increase conversion efficiency with soft-switching features. Finally, A 40 V input, 4800 V output and 300 W prototype with 500 kHz switching frequency was built to verify the benefits of the proposed converter. The achieved efficiency of the converter peaked at 96.7%.


title: A 1-1-1-1 MASH Delta-Sigma Modulator With Dynamic Comparator-Based OTAs

abstract: A dynamic comparator-based OTA is introduced as a replacement for a conventional OTA. It performs charge transfer in a switched-capacitor circuit by repeatedly evaluating the polarity of the differential input using a dynamic clocked comparator and injecting current pulses at the output to move the input voltage toward zero. The amplitude of the current pulse is reduced each time the input voltage crosses zero to provide fast but accurate settling of the output voltage. Dynamic comparator-based OTAs are applied to the design of a 1-1-1-1 MASH delta-sigma modulator. The 65-nm CMOS prototype achieves a 70.4 dB peak SNDR over a 2.5-MHz bandwidth while consuming 3.73 mW from a 1.2-V supply. The 276-fJ/conv-step FoM represents a four times improvement over previously-reported delta-sigma modulators using zero-crossing-based circuits or comparator-based switched capacitors. Because of the dynamic operation of the OTAs and discrete-time delta-sigma modulator architecture, both bandwidth and power consumption linearly scale with the sampling frequency without any reconfiguration of the modulator.


title: Transcriptome Analysis for Cytoprotective Actions of Rebamipide against Indomethacin-Induced Gastric Mucosal Injury in Rats

abstract: We have reported that rebamipide, a gastroprotective drug, suppresses indomethacin-induced gastric mucosal injury in humans and rats. However, the mechanisms of the cytoprotective actions of rebamipide have not been fully addressed. In the present study, we determined mRNA expression profile of the gastric mucosa treated with indomethacin in rats, and investigated the cytoprotective effects of rebamipide against indomethacin-induced injury with a high-density oligonucleotide array (Rat Toxicology U34 GeneChip array). Gastric epithelial cells were obtained by laser-assisted microdissection. Data analysis was performed with a GeneChip Operating Software, GeneSpring software 7.0, and Ingenuity Pathway Analysis. Among 1,031 probes, the expression of 160 probes (15.5%) showed at least 2.0-fold up-regulation (158 probes) and down-regulation (2 probes) 2 h after indomethacin administration in comparison with the vehicle-treated rats. The pathway analysis of the up-regulated 123 probes identified the network with a highly significant score, which consisted of known clusters of cell death, cancer, and endocrine system disorders. We succeeded in listing 10 genes that were up-regulated by the treatment with indomethacin and that were down-regulated by rebamipide, including growth arrest and DNA damage-induced 45α. In conclusion, we demonstrated that cell death, especially apoptosis, pathway is involved in the pathogenesis of indomethacin-induced gastric mucosal injury, and that inhibition of apoptosis-related genes is possibly important for the cytoprotective effect of rebamipide against this injury.


title: Efficient Discovery of Ontology Functional Dependencies

abstract: Poor data quality has become a pervasive issue due to the increasing complexity and size of modern datasets. Constraint based data cleaning techniques rely on integrity constraints as a benchmark to identify and correct errors. Data values that do not satisfy the given set of constraints are flagged as dirty, and data updates are made to re-align the data and the constraints. However, many errors often require user input to resolve due to domain expertise defining specific terminology and relationships. For example, in pharmaceuticals, 'Advil' \emph{is-a} brand name for 'ibuprofen' that can be captured in a pharmaceutical ontology. While functional dependencies (FDs) have traditionally been used in existing data cleaning solutions to model syntactic equivalence, they are not able to model broader relationships (e.g., is-a) defined by an ontology. In this paper, we take a first step towards extending the set of data quality constraints used in data cleaning by defining and discovering \emph{Ontology Functional Dependencies} (OFDs). We lay out theoretical and practical foundations for OFDs, including a set of sound and complete axioms, and a linear inference procedure. We then develop effective algorithms for discovering OFDs, and a set of optimizations that efficiently prune the search space. Our experimental evaluation using real data show the scalability and accuracy of our algorithms.


title: Deep Learning Based Recognition of Meltdown in Autistic Kids

abstract: Children with autism often experience sudden meltdowns which not only makes the moment tough for the caretakers/parents but also make the children hurt themselves physically. Studies have discovered that children with autistic spectrum disorder exhibit certain actions through which we can anticipate mutilating meltdowns in them. The objective of our project is to build a system that can recognize such kind of actions using deep learning techniques thereby, notifying the caretakers/parents so that they can get the situation under control in lesser time. Using deep learning RCNNs, we can train the system faster yet reliable because unlike all the machine learning algorithms, deep learning algorithms are more efficient and have more scope into future. We have trained a classifier on images that are gathered from videos and reliable internet sources with most predictive gestures, through which we can detect the meltdowns more precisely. We have trained a model that validated the accuracy by ~93% which is accompanied by a loss/train classifier with a minimal 0.4% loss. Functional testing was done through feeding the deep neural network with chosen actions performed by five individuals that resulted in an accuracy of ~92% in all cases, which can assure the real-time usage of the system.


title: Design Optimization With Multiphysics Analysis on External Rotor Permanent Magnet-Assisted Synchronous Reluctance Motors

abstract: Multiphase permanent magnet-assisted synchronous reluctance motor (PMa-SynRM) is proposed as one of the optimal machine designs for vehicular applications such as electric vehicles due to their fault tolerant operation capability. However, optimization of the multiphase PMa-SynRMs for in-wheel applications in EVs and aircrafts require more research efforts to increase their power density with the size constraint. In addition, optimization of the machine design with multiphysics structural and thermal analysis has not been intensively discussed in the literature. In this paper, an optimal design procedure which includes multiphysics analysis to design the multiphase external rotor PMa-SynRMs is presented. In specific, a five-phase external rotor PMa-SynRM with neodymium-based magnets has been proposed as a solution to produce higher power density compared to the conventional internal rotor PMa-SynRMs. Multiphysics analysis is included in the optimization procedure to determine the effects due to rotational forces and heat flow on the proposed optimal five-phase external rotor PMa-SynRM design. Detailed electromagnetic finite element simulations are carried out to depict the performance characteristics. A 3.8-kW prototype is fabricated and the experimental results are compared with same volume 3-kW five-phase internal rotor PMa-SynRM.


Clustergrammer, a web-based heatmap visualization and analysis tool for high-dimensional biological data


title: DTMC : An Actionable e-Customer Lifetime Value Model Based on Markov Chains and Decision Trees

abstract: We describe a model for estimating the customer lifetime value (CLV) of customers in an e-commerce environment. The model is explained and experiments are performed on real-life data from a large Dutch Internet retailer. Our method results in CLV estimates that have similar accuracy to estimates generated by the commonly used model, while keeping the number of customer segments much lower,and thus more 'actionable'.


Bayesian Computation via Markov chain Monte Carlo


title: Efficient RDF Storage and Retrieval in Jena2

abstract: RDF and related Semantic Web technologies have been the recent focus of much research activity. This work has led to new specifications for RDF and OWL. However, efficient implementations of these standards are needed to realize the vision of a world-wide semantic Web. In particular, implementations that scale to large, enterprise-class data sets are required. Jena2 is the second generation of Jena, a leading semantic web programmers' toolkit. This paper describes the persistence subsystem of Jena2 which is intended to support large datasets. This paper describes its features, the changes from Jena1, relevant details of the implementation and performance tuning issues. Query optimization for RDF is identified as a promising area for future research.


title: GODA: A goal-oriented requirements engineering framework for runtime dependability analysis

abstract: Abstract Context: Many modern software systems must deal with changes and uncertainty. Traditional dependability requirements engineering is not equipped for this since it assumes that the context in which a system operates be stable and deterministic, which often leads to failures and recurrent corrective maintenance. The Contextual Goal Model (CGM), a requirements model that proposes the idea of context-dependent goal fulfillment, mitigates the problem by relating alternative strategies for achieving goals to the space of context changes. Additionally, the Runtime Goal Model (RGM) adds behavioral constraints to the fulfillment of goals that may be checked against system execution traces. Objective: This paper proposes GODA (Goal-Oriented Dependability Analysis) and its supporting framework as concrete means for reasoning about the dependability requirements of systems that operate in dynamic contexts. Method: GODA blends the power of CGM, RGM and probabilistic model checking to provide a formal requirements specification and verification solution. At design time, it can help with design and implementation decisions; at runtime it helps the system self-adapt by analyzing the different alternatives and selecting the one with the highest probability for the system to be dependable. GODA is integrated into TAO4ME, a state-of-the-art tool for goal modeling and analysis. Results: GODA has been evaluated against feasibility and scalability on Mobee: a real-life software system that allows people to share live and updated information about public transportation via mobile devices, and on larger goal models. GODA can verify, at runtime, up to two thousand leaf-tasks in less than 35ms, and requires less than 240 KB of memory. Conclusion: Presented results show GODA’s design-time and runtime verification capabilities, even under limited computational resources, and the scalability of the proposed solution.


title: MEMTUNE: Dynamic Memory Management for In-Memory Data Analytic Platforms

abstract: Memory is a crucial resource for big data processing frameworks such as Spark and M3R, where the memory is used both for computation and for caching intermediate storage data. Consequently, optimizing memory is the key to extracting high performance. The extant approach is to statically split thememory for computation and caching based on workload profiling. This approach is unable to capture the varying workload characteristics and dynamic memory demands. Another factor that affects caching efficiency is the choice of data placement and eviction policy. The extant LRU policy is oblivious of task scheduling information from the analytic frameworks, and thus can lead to lost optimization opportunities. In this paper, we address the above issues by designing MEMTUNE, a dynamic memory manager for in-memory data analytics. MEMTUNE dynamically tunes computation/caching memory partitions at runtime based on workload memory demand and in-memory data cache needs. Moreover, if needed, the scheduling information from the analytic framework isleveraged to evict data that will not be needed in the near future. Finally, MEMTUNE also supports task-level data prefetching with a configurable window size to more effectively overlap computation with I/O. Our experiments show that MEMTUNE improves memory utilization, yields an overall performance gain of up to 46%, and achieves cache hit ratio of up to 41% compared to standard Spark.


title: Semantically Documenting Virtual Reconstruction: Building a Path to Knowledge Provenance

abstract: Abstract. The outcomes of virtual reconstructions of archaeological monuments are not just images for aesthetic consumption but rather present a scholarly argument and decision making process. They are based on complex chains of reasoning grounded in primary and secondary evidence that enable a historically probable whole to be reconstructed from the partial remains left in the archaeological record. This paper will explore the possibilities for documenting and storing in an information system the phases of the reasoning, decision and procedures that a modeler, with the support of an archaeologist, uses during the virtual reconstruction process and how they can be linked to the reconstruction output. The goal is to present a documentation model such that the foundations of evidence for the reconstructed elements, and the reasoning around them, are made not only explicit and interrogable but also can be updated, extended and reused by other researchers in future work. Using as a case-study the reconstruction of a kitchen in a Roman domus in Grand, we will examine the necessary documentation requirements, and the capacity to express it using semantic technologies. For our study we adopt the CIDOC-CRM ontological model, and its extensions CRMinf, CRMBa and CRMgeo as a starting point for modelling the arguments and relations.


title: An image analysis toolbox for high-throughput C. elegans assays

abstract: We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available via the open-source CellProfiler project and enables objective scoring of whole-animal high-throughput image-based assays of C. elegans for the study of diverse biological pathways relevant to human disease.


title: Interactive analysis of event data using space-time cube

abstract: In exploratory data analysis, the choice of tools depends on the data to be analyzed and the analysis tasks, i.e. the questions to be answered. The same applies to design of new analysis tools. In this paper, we consider a particular type of data: data that describe transient events having spatial and temporal references, such as earthquakes, traffic incidents, or observations of rare plants or animals. We focus on the task of detecting spatio-temporal patterns in event occurrences. We demonstrate the insufficiency of the existing techniques and approaches to event exploration and substantiate the need in a new exploratory tool. The technique of space-time cube, which has been earlier proposed for the visualization of movement in geographical space, possesses the required properties. However, it must be implemented so as to allow particular interactive manipulations: changing the viewing perspective, temporal focusing, and dynamic linking with a map display through simultaneous highlighting of corresponding symbols. We describe our implementation of the space-time cube technique and demonstrate by an example how it can be used for detecting spatio-temporal clusters of events.


title: A comparative study on content-based music genre classification

abstract: Content-based music genre classification is a fundamental component of music information retrieval systems and has been gaining importance and enjoying a growing amount of attention with the emergence of digital music on the Internet. Currently little work has been done on automatic music genre classification, and in addition, the reported classification accuracies are relatively low. This paper proposes a new feature extraction method for music genre classification, DWCHs. DWCHs stands for Daubechies Wavelet Coefficient Histograms. DWCHs capture the local and global information of music signals simultaneously by computing histograms on their Daubechies wavelet coefficients. Effectiveness of this new feature and of previously studied features are compared using various machine learning classification algorithms, including Support Vector Machines and Linear Discriminant Analysis. It is demonstrated that the use of DWCHs significantly improves the accuracy of music genre classification.


title: A Survey on Approaches for Interoperability and Portability of Cloud Computing Services

abstract: Over the recent years, the rapid development of Cloud Computing has driven to a large market of cloud ::: ::: services that offer infrastructure, platforms and software to everyday users. Yet, due to the lack of common ::: ::: accepted standards, cloud service providers use different technologies and offer their clients services that are ::: ::: operated by a variety of proprietary APIs. The lack of standardization results in numerous heterogeneities ::: ::: (e.g., heterogeneous service descriptions, message level naming conflicts, data representation conflicts etc.) ::: ::: making the interoperation, collaboration and portability of services a very complex task. In this work we ::: ::: focus on the problems of interoperability and portability in Cloud Computing, we address their differences ::: ::: and we discuss some of the latest research work in this area. We evaluate and point out relationships between ::: ::: the identified solutions. Finally we present a use case scenario of the FI-STAR project, that aims to bridge the ::: ::: gap of healthcare provision in cloud computing. We demonstrate the architecture of the project and we discuss ::: ::: possible interoperability and portability issues.


title: Implementation of ZigBee-GSM based home security monitoring and remote control system

abstract: Home security and control is one of the basic needs of mankind from early days. But today it has to be updated with the rapidly changing technology to ensure vast coverage, remote control, reliability, and real time operation. Deploying wireless technologies for security and control in home automation systems offers attractive benefits along with user friendly interface. In this paper, implementation of a novel security and control system for home automation is presented. The proposed system consists of a control console interfaced with different sensors using ZigBee. Suspected activities are conveyed to remote user through SMS (Short Message Service) or Call using GSM (Global System for Mobile communication) technology. Upon reply, the remote user can control his premises again through GSM-ZigBee combination. Besides, traditional burglar alarm enhances security in case of no acknowledgment from remote user. This system offers a low cost, low power consumption and user friendly way of a reliable portable monitoring and control of the secured environment. Using the concept of serial communication and mobile phone AT-commands (Attention Telephone/Terminal commands), the software is programmed using C-language. The design has been implemented in the hardware using ZigBee EM357 module, Atmega128 MCU (microcontroller unit) and Sony Ericsson T290i mobile phone set.


title: Incorporating Tweet Relationships into Topic Derivation

abstract: With its rapid users growth, Twitter has become an essential source of information about what events are happening in the world. It is critical to have the ability to derive the topics from Twitter messages (tweets), that is, to determine and characterize the main topics of the Twitter messages (tweets). However, tweets are very short in nature and therefore the frequency of term co-occurrences is very low. The sparsity in the relationship between tweets and terms leads to a poor characterization of the topics when only the content of the tweets is used. In this paper, we exploit the relationships between tweets and propose intLDA, a variant of Latent Dirichlet Allocation (LDA) that goes beyond content and directly incorporates the relationship between tweets. We have conducted experiments on a Twitter dataset and evaluated the performance in terms of both topic coherence and tweet-topic accuracy. Our experiments show that intLDA outperforms methods that do not use relationship information.


title: Makings of the 4 × 4 space-time block code of choice

abstract: We present a new 4×4 Hadamard-precoded Quasi-Orthogonal Space-Time Block Code (QO-STBC) that enables reliability-based prioritized symbol detection using linear filters. Approximate block-error-rate analysis is carried out and used to optimize the code rotation angles. For benchmarking purposes, the maximum-likelihood (ML) code detector is also derived with complexity of the order of joint two-real-symbol decoding. Numerical and simulation studies compare linear filter decoding against ML decoding.


title: Overcoming the Brittleness Bottleneck using Wikipedia : Enhancing Text Categorization with Encyclopedic Knowledge

abstract: When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle--they traditionally represent documents as bags of words, and are restricted to learning from individual word occurrences in the (necessarily limited) training set. For instance, given the sentence "Wal-Mart supply chain goes real time", how can a text categorization system know that Wal-Mart manages its stock with RFID technology? And having read that "Ciprofloxacin belongs to the quinolones group", how on earth can a machine know that the drug mentioned is an antibiotic produced by Bayer? In this paper we present algorithms that can do just that. We propose to enrich document representation through automatic use of a vast compendium of human knowledge--an encyclopedia. We apply machine learning techniques to Wikipedia, the largest encyclopedia to date, which surpasses in scope many conventional encyclopedias and provides a cornucopia of world knowledge. Each Wikipedia article represents a concept, and documents to be categorized are represented in the rich feature space of words and relevant Wikipedia concepts. Empirical results confirm that this knowledge-intensive representation brings text categorization to a qualitatively new level of performance across a diverse collection of datasets.


title: BCube: a high performance, server-centric network architecture for modular data centers

abstract: This paper presents BCube, a new network architecture specifically designed for shipping-container based, modular data centers. At the core of the BCube architecture is its server-centric network structure, where servers with multiple network ports connect to multiple layers of COTS (commodity off-the-shelf) mini-switches. Servers act as not only end hosts, but also relay nodes for each other. BCube supports various bandwidth-intensive applications by speeding-up one-to-one, one-to-several, and one-to-all traffic patterns, and by providing high network capacity for all-to-all traffic. BCube exhibits graceful performance degradation as the server and/or switch failure rate increases. This property is of special importance for shipping-container data centers, since once the container is sealed and operational, it becomes very difficult to repair or replace its components. Our implementation experiences show that BCube can be seamlessly integrated with the TCP/IP protocol stack and BCube packet forwarding can be efficiently implemented in both hardware and software. Experiments in our testbed demonstrate that BCube is fault tolerant and load balancing and it significantly accelerates representative bandwidth-intensive applications.


title: Boosted Multifeature Learning for Cross-Domain Transfer

abstract: Conventional learning algorithm assumes that the training data and test data share a common distribution. However, this assumption will greatly hinder the practical application of the learned model for cross-domain data analysis in multimedia. To deal with this issue, transfer learning based technology should be adopted. As a typical version of transfer learning, domain adaption has been extensively studied recently due to its theoretical value and practical interest. In this article, we propose a boosted multifeature learning (BMFL) approach to iteratively learn multiple representations within a boosting procedure for unsupervised domain adaption. The proposed BMFL method has a number of properties. (1) It reuses all instances with different weights assigned by the previous boosting iteration and avoids discarding labeled instances as in conventional methods. (2) It models the instance weight distribution effectively by considering the classification error and the domain similarity, which facilitates learning new feature representation to correct the previously misclassified instances. (3) It learns multiple different feature representations to effectively bridge the source and target domains. We evaluate the BMFL by comparing its performance on three applications: image classification, sentiment classification and spam filtering. Extensive experimental results demonstrate that the proposed BMFL algorithm performs favorably against state-of-the-art domain adaption methods.


title: Noiseprint: A CNN-Based Camera Model Fingerprint

abstract: Forensic analyses of digital images rely heavily on the traces of in-camera and out-camera processes left on the acquired images. Such traces represent a sort of camera fingerprint. If one is able to recover them, by suppressing the high-level scene content and other disturbances, a number of forensic tasks can be easily accomplished. A notable example is the PRNU pattern, which can be regarded as a device fingerprint, and has received great attention in multimedia forensics. In this paper, we propose a method to extract a camera model fingerprint, called noiseprint, where the scene content is largely suppressed and model-related artifacts are enhanced. This is obtained by means of a Siamese network, which is trained with pairs of image patches coming from the same (label +1) or different (label −1) cameras. Although the noiseprints can be used for a large variety of forensic tasks, in this paper we focus on image forgery localization. Experiments on several datasets widespread in the forensic community show noiseprint-based methods to provide state-of-the-art performance.


title: Security Authentication System Using Encrypted Channel on UAV Network

abstract: Today, UAVs are used in various fields such as monitor, search and rescue missions as well as military. UAVs are useful in various fields, but security has always been a major concern. Additionally, UAV carries sensitive information related to people's privacy and it causes critical problems if the data is exploited by malicious attackers. There were many attempts to solve security problems of UAVs but there is no typical way to protect UAVs from cyber attacks. In this research, we focus on a solution of hijacking network channel or physical hardware by anonymous attackers on commercial UAVs. This paper proposed maintaining control of UAV in hijacking problem with an additional encrypted communication channel, authentication algorithm and DoS attack through Raspberry Pi and shown high applicability on the commercial UAVs.


title: Security of sharded NoSQL databases: A comparative analysis

abstract: NoSQL databases are easy to scale-out because of their flexible schema and support for BASE (Basically Available, Soft State and Eventually Consistent) properties. The process of scaling-out in most of these databases is supported by sharding which is considered as the key feature in providing faster reads and writes to the database. However, securing the data sharded over various servers is a challenging problem because of the data being distributedly processed and transmitted over the unsecured network. Though, extensive research has been performed on NoSQL sharding mechanisms but no specific criterion has been defined to analyze the security of sharded architecture. This paper proposes an assessment criterion comprising various security features for the analysis of sharded NoSQL databases. It presents a detailed view of the security features offered by NoSQL databases and analyzes them with respect to proposed assessment criteria. The presented analysis helps various organizations in the selection of appropriate and reliable database in accordance with their preferences and security requirements.


title: Using hypervisor to provide data secrecy for user applications on a per-page basis

abstract: Hypervisors are increasingly utilized in modern computer systems, ranging from PCs to web servers and data centers. Aside from server applications, hypervisors are also becoming a popular target for implementing many security systems, since they provide a small and easy-to-secure trusted computing base. This paper presents a novel way of using hypervisors to protect application data privacy even when the underlying operating system is not trustable. Each page in virtual address space is rendered to user applications according to the security context the application is running in. The hypervisor encrypts and decrypts each memory page requested depending on the application's access permission to the page. The main result of this system is the complete removal of the operating system from the trust base for user applications' data privacy. To reduce the runtime overhead of the system, two optimization techniques are employed. We use page-frame replication to reduce the number ofcryptographic operations by keeping decrypted versions of a page frame. We also employ lazy synchronization to minimize overhead due to an update to one of the replicated page frame. Our system is implemented and evaluated by modifying the Xen hypervisor, showing that it increases the application execution time only by 3% for CPU and memory-intensive workloads.


title: End-to-end performance isolation through virtual datacenters

abstract: The lack of performance isolation in multi-tenant datacenters at appliances like middleboxes and storage servers results in volatile application performance. To insulate tenants, we propose giving them the abstraction of a dedicated virtual datacenter (VDC). VDCs encapsulate end-to-end throughput guarantees--specified in a new metric based on virtual request cost--that hold across distributed appliances and the intervening network. ::: ::: We present Pulsar, a system that offers tenants their own VDCs. Pulsar comprises a logically centralized controller that uses new mechanisms to estimate tenants' demands and appliance capacities, and allocates datacenter resources based on flexible policies. These allocations are enforced at end-host hypervisors through multi-resource token buckets that ensure tenants with changing workloads cannot affect others. Pulsar's design does not require changes to applications, guest OSes, or appliances. Through a prototype deployed across 113 VMs, three appliances, and a 40 Gbps network, we show that Pulsar enforces tenants' VDCs while imposing overheads of less than 2% at the data and control plane.


title: Perceptions of the role of the man in family planning, during pregnancy and childbirth : A qualitative study with fifteen Nepali men

abstract: Introduction: In Nepal, by tradition, family life and marriage are generally controlled by patriarchal norms, sanctions, values and gender differences. Women in Nepal have limited possibilities to ...


title: Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds

abstract: The advent of Cloud computing as a new model of service provisioning in distributed systems encourages researchers to investigate its benefits and drawbacks on executing scientific applications such as workflows. One of the most challenging problems in Clouds is workflow scheduling, i.e., the problem of satisfying the QoS requirements of the user as well as minimizing the cost of workflow execution. We have previously designed and analyzed a two-phase scheduling algorithm for utility Grids, called Partial Critical Paths (PCP), which aims to minimize the cost of workflow execution while meeting a user-defined deadline. However, we believe Clouds are different from utility Grids in three ways: on-demand resource provisioning, homogeneous networks, and the pay-as-you-go pricing model. In this paper, we adapt the PCP algorithm for the Cloud environment and propose two workflow scheduling algorithms: a one-phase algorithm which is called IaaS Cloud Partial Critical Paths (IC-PCP), and a two-phase algorithm which is called IaaS Cloud Partial Critical Paths with Deadline Distribution (IC-PCPD2). Both algorithms have a polynomial time complexity which make them suitable options for scheduling large workflows. The simulation results show that both algorithms have a promising performance, with IC-PCP performing better than IC-PCPD2 in most cases. Highlights? We propose two workflow scheduling algorithms for IaaS Clouds. ? The algorithms aim to minimize the workflow execution cost while meeting a deadline. ? The pricing model of the Clouds is considered which is based on a time interval. ? The algorithms are compared with a list heuristic through simulation. ? The experiments show the promising performance of both algorithms.


title: LSVC2017: Large-Scale Video Classification Challenge

abstract: Recognizing visual contents in unconstrained videos has become a very important problem for many applications, such as Web video search and recommendation, smart advertising, robotics, etc. This workshop and challenge aims at exploring new challenges and approaches for large-scale video classification with large number of classes from open source videos in a realistic setting, based upon an extension of Fudan-Columbia Video Dataset (FCVID). This newly collected dataset contains over 8000 hours of video data from YouTube and Flicker, annotated into 500 categories. We hope this dataset can stimulate innovative research on this challenging and important problem.


title: The effect of AlN interlayer thicknesses on scattering processes in lattice-matched AlInN/GaN two-dimensional electron gas heterostructures

abstract: The scattering mechanisms governing the transport properties of high mobility AlInN/AlN/GaN two-dimensional electron gas (2DEG) heterostructures with various AIN spacer layer thicknesses from zero to 2 nm were presented. The major scattering processes including acoustic and optical phonons, ionized impurity, interface roughness, dislocation and alloy disorder were applied to the temperature-dependent mobility data. It was found that scattering due mainly to alloy disorder limits the electron mobility for samples having spacer layer thicknesses up to 0.3 nm. On the other hand, alloy scattering is greatly reduced as the AlN spacer layer thickness increases further, and hence the combination of acoustic, optical and interface roughness become operative with different degrees of effectiveness over different temperature ranges. The room-temperature electron mobility was observed to increase gradually as the AlN spacer layer increases. A peak electron mobility of 1630 cm2 V−1 s−1 was realized for the sample consisting of a 1 nm AlN spacer layer. Then, the electron mobility decreased for the sample with 2 nm AlN. Moreover, the measured 2DEG densities were also compared with the theoretical predictions, which include both piezoelectric and spontaneous polarization components existing at AlN/GaN interfaces. The experimental sheet carrier densities for all AlInN/AlN/GaN HEMT structures were found to be in excellent agreement with the theoretical predictions when the parasitic (unintentional) GaN layer deposited between AlN and AlInN was taken into account. From these analyses, 1 nm AlN spacer layer thickness is found to be the optimum thickness required for high electron mobility and hence low sheet resistance once the sheet carrier density is increased to the theoretically expected value for the sample without unintentional GaN layer.


title: Hydrodynamic analysis of a rim-driven thruster based on RANS method

abstract: Rim-driven thruster is gaining wide attention in applications such as underwater vehicles and low-speed vessels. Literature on the influence of rotating rim and the rim-fixed blades on performance is scanty. In this paper, a rim-driven propeller was modeled with its hydrodynamic performance numerically simulated by a commercial Reynolds-averaged Navier-Stokes equation solver. The influence of rim on the wake field and friction loss was studied by simulations using rims of different lengths. The results show a long rim will increase friction torque slightly and will induce a circumferential velocity to the local flow, which means at blade tip region the relative tangential velocity of inflow to the blade section reduces and the pitch angle increases. Analysis of the pressure contours indicates the rim-driven propeller is easier to cause cavitation problem if directly modified from Ka-series. In addition, a following blade thickness study shows the thin blade has an efficiency advantage over the thick one.


title: Topic labeled text classification: a weakly supervised approach

abstract: Supervised text classifiers require extensive human expertise and labeling efforts. In this paper, we propose a weakly supervised text classification algorithm based on the labeling of Latent Dirichlet Allocation (LDA) topics. Our algorithm is based on the generative property of LDA. In our algorithm, we ask an annotator to assign one or more class labels to each topic, based on its most probable words. We classify a document based on its posterior topic proportions and the class labels of the topics. We also enhance our approach by incorporating domain knowledge in the form of labeled words. We evaluate our approach on four real world text classification datasets. The results show that our approach is more accurate in comparison to semi-supervised techniques from previous work. A central contribution of this work is an approach that delivers effectiveness comparable to the state-of-the-art supervised techniques in hard-to-classify domains, with very low overheads in terms of manual knowledge engineering.


title: Seizure Detection Algorithms Based on Analysis of EEG and ECG Signals: a Survey

abstract: Epilepsy is a chronic disorder of the CNS that predisposes individuals to recurrent seizures. Computerized seizure detection algorithms will enable alerting systems that may decrease the harm of the seizures. This paper attempts to provide a comprehensive survey of different types of seizure detection algorithms and their potential role in diagnostic and therapeutic applications. Major recent algorithms use electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect the seizure onset and seizure event. In these algorithms, various features are extracted from the EEG signal alone or in concert with the ECG signal until the patients are classified into two classes, seizure and non-seizure. We identify three major categories for seizure detectors; EEG-based seizure-event detectors, EEG based seizure-onset detectors, and EEG/ECG-based seizure-onset detectors. In addition, some other related issues, such as dataset and evaluation measures, are also discussed. Finally, the performance of algorithms is evaluated, and their capabilities and limitations are described.


title: Robust lane detection in urban environments

abstract: Most of the lane marking detection algorithms reported in the literature are suitable for highway scenarios. This paper presents a novel clustered particle filter based approach to lane detection, which is suitable for urban streets in normal traffic conditions. Furthermore, a quality measure for the detection is calculated as a measure of reliability. The core of this approach is the usage of weak models, i.e. the avoidance of strong assumptions about the road geometry. Experiments were carried out in Sydney urban areas with a vehicle mounted laser range scanner and a ccd camera. Through experimentations, we have shown that a clustered particle filter can be used to efficiently extract lane markings.


title: Stock market prediction by using artificial neural network

abstract: A neural networks based model have been used in predicting of the stock market. One of the methods, as an intelligent data mining, is artificial neural network (ANN). In this paper represents how to predict a NASDAQ's stock value using ANNs with a given input parameters of share market. We used real exchange rate value of NASDAQ Stock Market index. This paper makes use generalized feed forward networks. The network was trained using input data of stock market price in between 2012 and 2013. It shows a good performance for NASDAQ stock market prediction.


title: Model for Predicting the Effect of Nitrogen Intake, Body Mass, and Dietary Calcium and Phosphorus on Manure Nitrogen Content

abstract: ABSTRACT THIS study describes a method of predicting voided manure constituents based on animal mass and diet. Male laboratory rats used to test the model were grown from weaning to puberty. Nitrogen excreted followed the consumption pattern very closely. Prediction equations using mass and dietary nitrogen, phosphorus and calcium were more accurate for predicting fecal, urinary and body nitrogen than relationships developed from mass only. Nitrogen in the feces, urine and body can be estimated accurately when constituents of the diet are known.


title: MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

abstract: In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains 10M images in version 1, is the largest publicly available one in the world.


Low Power Fully Differential Folded Cascode OTA with CMFB Circuit


title: Simulation of Phasor Measurement Unit (PMU) using labview

abstract: With the advent of Phasor Measurement Unit in Power System a new dimension has been given to power system monitoring and analysis. In this paper simulation of Nonrecursive and recursive DFT phasor estimation algorithm is done using Labview. The algorithm developed in Labview can be used for real time estimation of phasor by integrating with labview compliant data acquisition system like NI ELVIS kit.


Maximum Likelihood Estimation of Logistic Regression Models : Theory and Implementation


Human motion and emotion parameterization


Spiral Slot Patch Antenna and Circular Disc Monopole


Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks


title: High-Isolation, Low Cross-Polarization, Dual- Polarization, Hybrid Feed Microstrip Patch Array Antenna for MPAR Application

abstract: This paper presents a high-isolation, low cross-polarization dual-polarized patch antenna for multifunction phased array radar applications. Its hybrid feed design has been implemented, and the vertical and horizontal polarizations are excited by a balanced-probe feed and a slot-coupled feed, respectively. Simulations and measurements have demonstrated an input isolation of 45 and 43 dB between the horizontal and vertical ports, respectively. For further improvement in the cross-polarization level, the image feed method is also implemented, and a $2\times 2$ -element array made up of designed elements with image configuration has been fabricated. The simulated and measured S-parameter and radiation patterns of the horizontal and vertical polarizations of the designed $2\times 2$ -element array are presented and the measured cross-polarization level of less than −37 dB is achieved. To examine the performance of the designed element in an array, a $3\times 3$ -element array of designed $2\times 2$ -element subarray is fabricated and tested. In the $6\times 6$ -element measurements, −35.4 and −36 dB cross-polarization levels for horizontal and vertical polarizations are achieved, respectively. Also, using the measured embedded element patterns, the cross-polarization level lower than −36 dB for scan angles up to 45° is achieved.


title: Software-only video production switcher for the Internet MBone

abstract: In this paper, we describe the design and implementation of a prototype software video production switcher, vps, that improves the quality of the content of MBone broadcasts. vps is modeled after the broadcast television industry's studio production switcher. It provides special effects processing to incorporate audience discussions, add titles and other information, and integrate stored videos into the presentation. vps is structured to work with other MBone conferencing tools. The ultimate goal is to automate the production of MBone broadcasts.


title: Theory of Deep Learning III: explaining the non-overfitting puzzle

abstract: A main puzzle of deep networks revolves around the absence of overfitting despite large overparametrization and despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamics associated to gradient descent minimization of nonlinear networks is topologically equivalent, near the asymptotically stable minima of the empirical error, to linear gradient system in a quadratic potential with a degenerate (for square loss) or almost degenerate (for logistic or crossentropy loss) Hessian. The proposition depends on the qualitative theory of dynamical systems and is supported by numerical results. Our main propositions extend to deep nonlinear networks two properties of gradient descent for linear networks, that have been recently established (1) to be key to their generalization properties: 1. Gradient descent enforces a form of implicit regularization controlled by the number of iterations, and asymptotically converges to the minimum norm solution for appropriate initial conditions of gradient descent. This implies that there is usually an optimum early stopping that avoids overfitting of the loss. This property, valid for the square loss and many other loss functions, is relevant especially for regression. 2. For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for "low noise" datasets. This property holds for loss functions such as the logistic and cross-entropy loss independently of the initial conditions. The robustness to overparametrization has suggestive implications for the robustness of the architecture of deep convolutional networks with respect to the curse of dimensionality.


Chapter 17 PARETO-FRONT EXPLOITATION IN SYMBOLIC REGRESSION


title: Robot Task Planning using Semantic Maps

abstract: Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, such as labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation and localization), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowledge, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrates hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in two ways: extending the capabilities of the planner by reasoning about semantic information, and improving the planning efficiency in large domains. We show several experiments that demonstrate the effectiveness of our solutions in a domain involving robot navigation in a domestic environment.


title: Mental animation: Inferring motion from static displays of mechanical systems

abstract: Reaction-time and eye-fixation data are analyzed to investigate how people infer the kinematics of simple mechanical systems (pulley systems) from diagrams showing their static configuration. It is proposed that this mental animation process involves decomposing the representation of a pulley system into smaller units corresponding to the machine components and animating these components in a sequence corresponding to the causal sequence of events in the machine's operation. Although it is possible for people to make inferences against the chain of causality in the machine, these inferences are more difficult, and people have a preference for inferences in the direction of causality. The mental animation process reflects both capacity limitations and limitations of mechanical knowledge. Language: en


title: Scientific Evaluation of Charles Dickens

abstract: Abstract I report the results of the test, where the takers had to tell the prose of Charles Dickens from that of Edward Bulwer-Lytton, who is considered by many to be the worst writer in history of letters. The average score is about 50%, which is on the level of random guessing. This suggests that the quality of Dickens’s prose is the same as of that of Bulwer-Lytton.


title: Communication mechanism between scientists and entrepreneurs

abstract: Technology transfer has been examined on more macro levels in former studies. However, communication between academia and industry on a micro level still needs to be studied. An effective communication mechanism is of key importance in technology transfer. In this study the author interviews top scientists and company owners from science parks or R&D centers in China to examine the communication strategies in searching for a cooperator from academia, their R&D cooperation, the process in evaluating a potential commercialization and communication during production. The paper gives policy implications on how to support academia-industrial cooperation and gives suggestions for how to create a more effective communication.


title: Using social media to quantify nature-based tourism and recreation

abstract: Scientists have traditionally studied recreation in nature by conducting surveys at entrances to major attractions such as national parks. This method is expensive and provides limited spatial and temporal coverage. A new source of information is available from online social media websites such as flickr. Here, we test whether this source of “big data” can be used to approximate visitation rates. We use the locations of photographs in flickr to estimate visitation rates at 836 recreational sites around the world, and use information from the profiles of the photographers to derive travelers' origins. We compare these estimates to empirical data at each site and conclude that the crowd-sourced information can indeed serve as a reliable proxy for empirical visitation rates. This new approach offers opportunities to understand which elements of nature attract people to locations around the globe, and whether changes in ecosystems will alter visitation rates.


title: A planning system for robot construction tasks

abstract: This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and the use of extra blocks as temporary supports and counterweights in the course of the construction. ::: ::: BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces. ::: ::: Suggestions are included for the extension of BUILD-like systems to other domains. Also discussed are the merits of BUILD's implementation language, conniver, for this type of problem solving.


title: Presence of two tumor necrosis factor (tnf)-α homologs on different chromosomes of zebrafish (Danio rerio) and medaka (Oryzias latipes).

abstract: Two or more isoforms of several cytokines including tumor necrosis factors (tnfs) have been reported from teleost fish. Although zebrafish (Danio rerio) and medaka (Oryzias latipes) possess two tnf-α genes, their genomic location and existence are yet to be described and confirmed. Therefore, we conducted in silico identification, synteny analysis of tnf-α and tnf-n from both the fish with that of human TNF/lymphotoxin loci and their expression analysis in zebrafish. We identified two homologs of tnf-α (named as tnf-α1 and tnf-α2) and a tnf-n gene from zebrafish and medaka. Genomic location of these genes was found to be as: tnf-α1, and tnf-n and tnf-α2 genes on zebrafish chromosome 19 and 15 and medaka chromosome 11 and 16, respectively. Several features such as existence of TNF family signature, conservation of genes in TNF loci with human chromosome, phylogenetic clustering and amino acid similarity with other teleost TNFs confirmed their identity as tnf-α and tnf-n. There were a constitutive expression of all three genes in different tissues, and an increased expression of tnf-α1 and -α2 and a varied expression of tnf-n ligand in zebrafish head kidney cells induced with 20 μg mL(-1) LPS in vitro. Our results suggest the presence of two tnf-α homologs on different chromosomes of zebrafish and medaka and correlate this incidence arising from the fish whole genome duplication event.


Object-oriented software for industrial robots


title: Chapter 6 THE BIG FIVE CAREER THEORIES

abstract: Career guidance and counselling in the western world, most notably in the United States (USA), has developed a comprehensive system of theories and intervention strategies in its more than 100 years of history. It began in the years of Frank Parson as a trait-factor approach in the early twentieth century (Betz, Fitzgerald, & Hill, 1989; Zunker, 2002), and slowly evolved to become a rather mature discipline today in the twenty-first century with a strong theoretical and empirical base, with the potential to further develop into a more “global” discipline in the years ahead. Indeed, vocational and career related issues are salient across different cultures and nationalities (Hesketh & Rounds, 1995; Leung, 2004). In an age of economic globalisation, all individuals are affected by an array of work related concerns, some of these concerns are unique to certain cultures, but others are common to many cultural groups. The search for life purposes and meanings, the journey to actualise oneself through various life and workrelated roles, and the efforts by nations to deal with problems of employment and unemployment, are examples of universal issues that seem to affect many individuals from diverse cultures. Under the theme of career development, there are experiences, concerns, and issues that we could share, explore, and discussed at a global stage (Richardson, 1993; Lips-Wiersma & McMorland, 2006). The development of career guidance and development into a global discipline requires a set of theoretical frameworks with universal validity and applications, as well as culture-specific models that could be used to explain career development issues and phenomenon at a local level. The focus of this chapter is on the five theories of career development that have guided career guidance and counselling practice and research in the past few decades in the USA as well as internationally. These five theories are (a) Theory of Work-Adjustment, (b) Holland’s Theory of Vocational Personalities in Work Environment, (c) the Self-concept Theory of Career Development formulated by Super and more recently by Savickas, (d) Gottfredson’s Theory of Circumscription and Compromise, and (e) Social Cognitive Career Theory. Given that the “big-five” theoretical models were developed by scholars in the USA, most of the existing reviews and summaries covering


title: Effect of Iyengar yoga therapy for chronic low back pain

abstract: Low back pain is a significant public health problem and one of the most commonly reported reasons for the use of Complementary Alternative Medicine. A randomized control trial was conducted in subjects with non-specific chronic low back pain comparing Iyengar yoga therapy to an educational control group. Both programs were 16 weeks long. Subjects were primarily self-referred and screened by primary care physicians for study of inclusion/exclusion criteria. The primary outcome for the study was functional disability. Secondary outcomes including present pain intensity, pain medication usage, pain-related attitudes and behaviors, and spinal range of motion were measured before and after the interventions. Subjects had low back pain for 11.2G1.54 years and 48% used pain medication. Overall, subjects presented with less pain and lower functional disability than subjects in other published intervention studies for chronic low back pain. Of the 60 subjects enrolled, 42 (70%) completed the study. Multivariate analyses of outcomes in the categories of medical, functional, psychological and behavioral factors indicated that significant differences between groups existed in functional and medical outcomes but not for the psychological or behavioral outcomes. Univariate analyses of medical and functional outcomes revealed significant reductions in pain intensity (64%), functional disability (77%) and pain medication usage (88%) in the yoga group at the post and 3-month follow-up assessments. These preliminary data indicate that the majority of self-referred persons with mild chronic low back pain will comply to and report improvement on medical and functional pain-related outcomes from Iyengar yoga therapy. q 2005 International Association for the Study of Pain. Published by Elsevier B.V. All rights reserved.


title: Accurate fact harvesting from natural language text in wikipedia with Lector

abstract: Many approaches have been introduced recently to automatically create or augment Knowledge Graphs (KGs) with facts extracted from Wikipedia, particularly its structured components like the infoboxes. Although these structures are valuable, they represent only a fraction of the actual information expressed in the articles. In this work, we quantify the number of highly accurate facts that can be harvested with high precision from the text of Wikipedia articles using information extraction techniques bootstrapped from the entities and relations already in a KG. Our experimental evaluation, which uses Freebase as reference KG, reveals we can augment several relations in the domain of people by more than 10%, with facts whose accuracy are over 95%. Moreover, the vast majority of these facts are missing from the infoboxes, YAGO and DBpedia.


title: Engineering Route Planning Algorithms

abstract: Algorithms for route planning in transportation networks have recently undergone a rapid development, leading to methods that are up to three million times faster than Dijkstra's algorithm. We give an overview of the techniques enabling this development and point out frontiers of ongoing research on more challenging variants of the problem that include dynamically changing networks, time-dependent routing, and flexible objective functions.


title: Facial beauty and fractal geometry

abstract: What is it that makes a face beautiful? Average faces obtained by photographic (1-4) or digital (5) blending are judged attractive (1-5) but not optimally attractive (6,7) - digital exaggerations of deviations from average face blends can lead to higher attractiveness ratings(7). My novel approach to face design, however, does not involve blending at all. Instead, the image of a female face with high ratings is composed from a fractal geometry based on rotated squares and powers of 2. The corresponding geometric rules are more specific than those previously used by artists such as Leonardo and D\"{u}rer. They yield a short algorithmic description of all facial characteristics, many of which are compactly encodable with the help of simple feature detectors similar to those observed in mammalian brains. This suggests that a face''s beauty correlates with simplicity relative to the subjective observer''s way of encoding it.


title: Particle RRT for Path Planning with Uncertainty

abstract: This paper describes a new extension to the rapidly-exploring random tree (RRT) path planning algorithm. The particle RRT algorithm explicitly considers uncertainty in its domain, similar to the operation of a particle filter. Each extension to the search tree is treated as a stochastic process and is simulated multiple times. The behavior of the robot can be characterized based on the specified uncertainty in the environment, and guarantees can be made as to the performance under this uncertainty. Extensions to the search tree, and therefore entire paths, may be chosen based on the expected probability of successful execution. The benefit of this algorithm is demonstrated in the simulation of a rover operating in rough terrain with unknown coefficients of friction


title: Distant Supervision for Relation Extraction Using Tree Kernels

abstract: In this paper we define a simple Relation Extraction system based on SVMs using tree kernels and employing a weakly supervised approach, known as Distant Supervision (DS). Our method uses the simple one-versus-all strategy to handle overlapping relations, i.e., defined on the same pair of entities. The DS data is defined over the New York Times corpus by means of Freebase as an external knowledge base, which indicates the relations of some of the entities of the NYT text. Our experiments show that our simple approach performs well in this domain with respect to the current state of the art.


title: Desiccation diagnosis in lumbar discs from clinical MRI with a probabilistic model

abstract: Lumbar intervertebral disc diseases are among the main causes of lower back pain (LBP). Desiccation is a common disease resulting from various reasons and ultimately most people are affected by desiccation at some age. We propose a probabilistic model that incorporates intervertebral disc appearance and contextual information for automating the diagnosis of lumbar disc desiccation. We utilize a Gibbs distribution for processing localized lumbar intervertebral discs' appearance and contextual information. We use 55 clinical T2-weighted MRI for lumbar area and achieve over 96% accuracy on a cross validation experiment.


title: Loss Distribution Estimation, External Data and Model Averaging

abstract: This paper will discuss a proposed method for the estimation of loss distribution using information from a combination of internally derived data and data from external sources. The relevant context for this analysis is the estimation of operational loss distributions used in the calculation of capital adequacy. We present a robust, easy-to-implement approach that draws on Bayesian inferential methods. The principal intuition behind the method is to let the data itself determine how they should be incorporated into the loss distribution. This approach avoids the pitfalls of managerial choice on data weighting and cut-off selection and allows for the estimation of a single loss distribution.


title: Intelligent parking assist

abstract: In this paper a new parking guiding and information system is described. The system assists the user to find the most suitable parking space based on his/her preferences and learned behavior. The system takes into account parameters such as driver's parking duration, arrival time, destination, type preference, cost preference, driving time, and walking distance as well as time-varying parking rules and pricing. Moreover, a prediction algorithm is proposed to forecast the parking availability for different parking locations for different times of the day based on the real-time parking information, and previous parking availability/occupancy data. A novel server structure is used to implement the system. Intelligent parking assist system reduces the searching time for parking spots in urban environments, and consequently leads to a reduction in air pollutions and traffic congestion. On-street parking meters, off-street parking garages, as well as free parking spaces are considered in our system.


title: Unsupervised Learning of Sequence Representations by Autoencoders

abstract: Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples. In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error. Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies. Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, which results in a higher-quality representation. The learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information. Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style). One promising application of the ISA model is that it can be readily used in the semi-supervised learning scenario, in which a large amount of unlabeled data is leveraged to extract high-quality sequence representations and thus to improve the performance of the subsequent supervised learning tasks on limited labeled data.


title: A dynamic description logic for representation and reasoning about actions

abstract: We present a dynamic description logic for representation and reasoning about actions, with an approach that embrace actions into the description logic ALCO@. With this logic, description logic concepts can be used for describing the state of the world, and the preconditions and effects of atomic actions; Complex actions can be modeled with the help of standard action operators, such as the test, sequence, choice, and iteration operators; And both atomic actions and complex actions can be used as modal operators to construct formulas. We develop a terminable and correct algorithm for checking the satisfiability of formulas. Based on the algorithm, many reasoning tasks on actions are effectively carried out, including the realizability, executability, projection and planning problems.


title: Assamese WordNet based Quality Enhancement of Bilingual Machine Translation System

abstract: Machine Translation is a task to translate the text from a source language to a target language in an automatic manner. Here, we describe a system that translate the English language to Assamese language text which is based on Phrase based statistical translation technique. To overcome the translation problem related with highly open word class like Proper Noun or the Out Of Vocabulary words we develop a transliteration system which is also embedded with our translation system. We enhance the translation output by replacing words with their most appropriate synonymous word for that particular context with the help of Assamese WordNet Synset. This Machine Translation system outcomes with a reasonable translation output when analyzed by linguist for Assamese language which is a less computationally aware language among the Indian languages.


title: Thesaurus-based named entity recognition system for detecting spatio-temporal crime events in Spanish language from Twitter

abstract: Social networks offer an invaluable amount of data from which useful information can be obtained on the major issues in society, among which crime stands out. Research about information extraction of criminal events in Social Networks has been done primarily in English language, while in Spanish, the problem has not been addressed. This paper propose a system for extracting spatio-temporally tagged tweets about crime events in Spanish language. In order to do so, it uses a thesaurus of criminality terms and a NER (named entity recognition) system to process the tweets and extract the relevant information. The NER system is based on the implementation OSU Twitter NLP Tools, which has been enhanced for Spanish language. Our results indicate an improved performance in relation to the most relevant tools such as Standford NER and OSU Twitter NLP Tools, achieving 80.95% precision, 59.65% recall and 68.69% F-measure. The end result shows the crime information broken down by place, date and crime committed through a webservice.


title: Parametric instabilities of parallel propagating incoherent Alfvén waves in a finite ion beta plasma

abstract: Large amplitude, low-frequency Alfven waves constitute one of the most essential elements of magnetohydrodynamic (MHD) turbulence in the fast solar wind. Due to small collisionless dissipation rates, the waves can propagate long distances and efficiently convey such macroscopic quantities as momentum, energy, and helicity. Since loading of such quantities is completed when the waves damp away, it is important to examine how the waves can dissipate in the solar wind. Among various possible dissipation processes of the Alfven waves, parametric instabilities have been believed to be important. In this paper, we numerically discuss the parametric instabilities of coherent/incoherent Alfven waves in a finite ion beta plasma using a one-dimensional hybrid (superparticle ions plus an electron massless fluid) simulation, in order to explain local production of sunward propagating Alfven waves, as suggested by Helios/Ulysses observation results. Parameter studies clarify the dependence of parametric instabilities ...


title: Professional freedom: A grounded theory on the use of music analysis in psychodynamic music therapy

abstract: ABSTRACTAlthough music is the fundamental element of music therapy, music analysis methods are a particularly under-researched area. This study investigates how and when psychodynamically orientated music therapists employ the analysis of musical material in both clinical work and research. Constructivist grounded theory was employed in the collection and analysis of the data. Eight participants, all highly experienced in psychodynamic music therapy, were recruited using referral sampling. In-depth interviews focused on therapists’ experiences of working with different client groups, and the applicability of different assessment methods. Strauss and Corbin’s coding paradigm was used to determine causal and intervening conditions, action strategies and the consequences of music analysis. Professional freedom (a tension between creative forces and professional responsibilities) emerged as the most important factor influencing the method, application and frequency of music analysis. Therapists used either ex...


title: Ka-band high-gain mesh deployable reflector antenna enabling the first radar in a CubeSat: RainCube

abstract: This paper presents a novel mesh deployable Ka-band antenna design folding in a 1.5U (10×10×15cm3) stowage volume suitable for 6U (10×20×30cm3) class CubeSats. Detailed results, considering all aspects of the deployable mesh reflector antenna, show that 42.6 dBi gain and 52% aperture efficiency is achievable at 35.75GHz. The mechanical deployment mechanism is also described as it constraints and drive the RF choices. The mesh prototype antenna has been fabricated and measurement results show excellent agreement with simulations.


title: Biomedical event trigger detection based on bidirectional LSTM and CRF

abstract: Trigger detection plays a key role in the extraction of biomedical events, so it will influence the results of biomedical events extraction directly. The traditional biomedical event trigger recognition method is based on artificial design features and construct feature vectors; Not only does it consume great amounts of manpower, it also lacks system generalization ability. Most of methods of trigger detection are based on the convolutional neural network that identify each word in the text, and regard it as a multi-classification task. However for the multi-word composed of the trigger, there is no useful recognition effect. In this paper, we will use the IBO format and consider the trigger detection as a task of sequence annotation, a solution that improves the recognition accuracy of multi-word triggers by bidirectional LSTM and CRF.


title: The Additive Value of Multimodal Features for Predicting Engagement, Frustration, and Learning during Tutoring

abstract: Detecting learning-centered affective states is difficult, yet crucial for adapting most effectively to users. Within tutoring in particular, the combined context of student task actions and tutorial dialogue shape the student's affective experience. As we move toward detecting affect, we may also supplement the task and dialogue streams with rich sensor data. In a study of introductory computer programming tutoring, human tutors communicated with students through a text-based interface. Automated approaches were leveraged to annotate dialogue, task actions, facial movements, postural positions, and hand-to-face gestures. These dialogue, nonverbal behavior, and task action input streams were then used to predict retrospective student self-reports of engagement and frustration, as well as pretest/posttest learning gains. The results show that the combined set of multimodal features is most predictive, indicating an additive effect. Additionally, the findings demonstrate that the role of nonverbal behavior may depend on the dialogue and task context in which it occurs. This line of research identifies contextual and behavioral cues that may be leveraged in future adaptive multimodal systems.


title: On benchmarking camera calibration and multi-view stereo for high resolution imagery

abstract: In this paper we want to start the discussion on whether image based 3D modelling techniques can possibly be used to replace LIDAR systems for outdoor 3D data acquisition. Two main issues have to be addressed in this context: (i) camera calibration (internal and external) and (ii) dense multi-view stereo. To investigate both, we have acquired test data from outdoor scenes both with LIDAR and cameras. Using the LIDAR data as reference we estimated the ground-truth for several scenes. Evaluation sets are prepared to evaluate different aspects of 3D model building. These are: (i) pose estimation and multi-view stereo with known internal camera parameters; (ii) camera calibration and multi-view stereo with the raw images as the only input and (iii) multi-view stereo.


A Novel Framework to Recognize Complex Human Activity


title: FinFET Device Circuit Co-design Issues: Impact of Circuit Parameters on Delay

abstract: Fin FET circuits' performance suffers from high level of parasitics at short channel lengths despite their excellent intrinsic behavior. The effective values of a Fin FET logic gate's input and parasitic capacitances (Cin, Cp) depend strongly on its terminal voltages due to strong gate controlled modulation of carrier densities in the low doped part of the extension region which shields gate-extension fringing field capacitance. Therefore, for developing a systematic circuit design methodology with best performance, it is very important to have a quantitative understanding of Fin FET device parasitics, their dependence on circuit parameters and thereby impact on circuit delay. In this paper, we investigate the impact of this strong dependence of capacitances and its relationship with delay of a multi-stage logic circuit. For this, we study the influence of circuit parameters such as input and output transition times, inverter size (no. Of fins), load capacitance etc. On transient behavior of Fin FET inverter chains. We explain that the trend of change in inverter chain delay with respect to driver-load ratio (NF1/NF2) and load (CL) can be predicted if this phenomenon is considered.


title: Requirements capture as a cooperative activity

abstract: There is an interesting recognition that software development is not merely a mathematical or technological challenge, but a complex social process. The social process at the earliest stages of software development, i.e., scoping and capturing requirements, is examined. Some of the problems that can arise when insufficient attention is paid to the social process are considered together with alternative team structures. A cooperative requirements capture method called user skills task match (USTM) is presented, in which the social process is explicitly managed through use of a human facilitator and which provides a structured approach to the management of the requirements capture task. The cooperative approach is illustrated using a case study from the electricity distribution industry within the UK. >


title: Recommenders everywhere:: the WikiLens community-maintained recommender system

abstract: Suppose you have a passion for items of a certain type, and you wish to start a recommender system around those items. You want a system like Amazon or Epinions, but for cookie recipes, local theater, or microbrew beer. How can you set up your recommender system without assembling complicated algorithms, large software infrastructure, a large community of contributors, or even a full catalog of items? WikiLens is open source software that enables anyone, anywhere to start a community-maintained recommender around any type of item. We introduce five principles for community-maintained recommenders that address the two key issues: (1) community contribution of items and associated information; and (2) finding items of interest. Since all recommender communities start small, we look at feasibility and utility in the small world, one with few users, few items, few ratings. We describe the features of WikiLens, which are based on our principles, and give lessons learned from two years of experience running wikilens.org.


title: Data-to-Text Generation Improves Decision-Making Under Uncertainty

abstract: Decision-making is often dependent on uncertain data, e.g. data associated with confidence scores or probabilities. This article presents a comparison of different information presentations for uncertain data and, for the first time, measures their effects on human decision-making, in the domain of weather forecast generation. We use a game-based setup to evaluate the different systems. We show that the use of Natural Language Generation (NLG) enhances decision-making under uncertainty, compared to state-of-the-art graphicalbased representation methods. In a task-based study with 442 adults, we found that presentations using NLG led to 24% better decision-making on average than the graphical presentations, and to 44% better decision-making when NLG is combined with graphics. We also show that women achieve significantly better results when presented with NLG output (an 87% increase on average compared to graphical presentations). Finally, we present a further analysis of demographic data and its impact on decision-making, and we discuss implications for future NLG systems.


A Review on Networking and Multiplayer Computer Games


title: DOI: 10.1080/00926230390154826 Validation of the Female Sexual Function Index (FSFI) in Women with Female Orgasmic Disorder and in Women with Hypoactive Sexual Desire Disorder

abstract: Department of Psychology, University of Texas at Austin, Austin, Texas, USA The Female Sexual Functioning Index (FSFI; Rosen et al., 2000) is a self-report measure of sexual functioning that has been validated on a clinically diagnosed sample of women with female sexual arousal disorder. The present investigation extended the validation of the FSFI to include women with a primary clinical diagnosis of female orgasmic disorder (FOD; n = 71) or hypoactive sexual desire disorder (HSDD; n = 44). Internal consistency and divergent validity of the FSFI were within the acceptable range for these populations of women. Significant differences between women with FOD and controls and between women with HSDD and controls were noted for each of the FSFI domain and total scores.


Sales promotion by wholesalers affects general practitioners’ prescription behaviours in Japan


title: Nonlinear estimation using cubature quadrature points

abstract: In this paper, a novel method based on spherical radial cubature and Gauss Laguerre quadrature rule has been proposed for nonlinear state estimation problem. The proposed method is named as cubature quadrature Kalman filter (CQKF). The performance of CQKF is demonstrated with a severely nonlinear single dimensional problem. Preliminary result suggests that the proposed filter has potential to outperform conventional extended Kalman filter (EKF) and more advanced cubature Kalman filter (CKF). Due to computational efficiency and enhanced accuracy compared to standard nonlinear estimators, the projected algorithm may find place in on-board real life applications.


title: Effect of Non-linear Deep Architecture in Sequence Labeling

abstract: If we compare the widely used Conditional Random Fields (CRF) with newly proposed “deep architecture” sequence models (Collobert et al., 2011), there are two things changing: from linear architecture to non-linear, and from discrete feature representation to distributional. It is unclear, however, what utility nonlinearity offers in conventional featurebased models. In this study, we show the close connection between CRF and “sequence model” neural nets, and present an empirical investigation to compare their performance on two sequence labeling tasks ‐ Named Entity Recognition and Syntactic Chunking. Our results suggest that non-linear models are highly effective in low-dimensional distributional spaces. Somewhat surprisingly, we find that a nonlinear architecture offers no benefits in a high-dimensional discrete feature space.


title: The enrichment of BPMN business process model with SBVR business vocabulary and rules

abstract: Aspects of business process models' integration with structured business vocabularies & rules are analyzed in this paper. Despite the fact that business process (BP) modeling has its long-lasting traditions in various areas of application, this discipline remains in the constant process of improvement and issue-solving. The paper deals with one of such issues, namely, the existing gap between BP modeling and specification of business vocabularies & rules. In not dealt appropriately, this may lead to some issues while developing, reading and interpreting business models themselves, also to miscommunication issues within and among the organizations dependent on such models, and so on. The gap could be diminished by the means of integration of BP models with business vocabularies & rules; the paper presents some argumentation to back such statements. Later, basic principles of the approach for BPMN BP model integration with SBVR business vocabulary & rules are presented and briefly described in this paper.


title: Determination of rotor temperature for an interior permanent magnet synchronous machine using a precise flux observer

abstract: In this paper an enhanced method to determine the rotor temperature of permanent magnet synchronous machines during dynamic operations is presented. The approach is based on a fundamental wave flux observer and is therefore independent of coolant conditions to a large extend. This contribution continues the work presented earlier, where the temperature observer was introduced for steady state operation in exemplary operating points. Hence, an extension to dynamic operation in a wide speed and torque range is proposed. Consequently, the focus now lies on a discrete-time machine modelling as well as on the parameter identification. Measurement results prove the satisfying performance of the observer.


title: CTSiM: A Computational Thinking Environment for Learning Science through Simulation and Modeling

abstract: Computational thinking (CT) draws on fundamental computer science concepts to formulate and solve problems, design systems, and understand human behavior. CT practices (e.g., problem representation, abstraction, decomposition, simulation, verification, and prediction) are also central to the development of expertise in a variety of STEM disciplines. Exploiting this synergy between CT and STEM disciplines, we have developed CTSiM, a cross-domain, scaffolded, visual-programming and agent-based learning environment for middle school science. We present and justify the CTSiM architecture and its implementation. To identify challenges and scaffolding needs in learning with CTSiM, we present a case study describing the challenges that a highand a low-achieving student faced while working on kinematics and ecology units using CTSiM. Decreases in the number of challenges for both students over sequences of related activities illustrate the combined effectiveness of our approach. Further, the specific challenges and scaffolds identified suggest the design of an adaptive scaffolding framework to help students develop a synergistic understanding of CT and science concepts.


title: The daylight blocking optical stereo see-through HMD

abstract: In this paper we present an innovative daylight blocking optical stereo see-through HMD. Its outstanding capability is to pixelwise block incident daylight before super-imposing virtual content on the real scene. By doing so the device allows to seamlessly mix real and virtual content without the well-known effects of other optical see-through displays where real content will always shine through virtual content. To our knowledge related work on daylight blocking displays is very limited and we present the first commercial HMD available.


title: Detecting Highly Overlapping Communities with Model-Based Overlapping Seed Expansion

abstract: As research into community finding in social networks progresses, there is a need for algorithms capable of detecting overlapping community structure. Many algorithms have been proposed in recent years that are capable of assigning each node to more than a single community. The performance of these algorithms tends to degrade when the ground-truth contains a more highly overlapping community structure, with nodes assigned to more than two communities. Such highly overlapping structure is likely to exist in many social networks, such as Facebook friendship networks. In this paper we present a scalable algorithm, MOSES, based on a statistical model of community structure, which is capable of detecting highly overlapping community structure, especially when there is variance in the number of communities each node is in. In evaluation on synthetic data MOSES is found to be superior to existing algorithms, especially at high levels of overlap. We demonstrate MOSES on real social network data by analyzing the networks of friendship links between students of five US universities.


title: Why phishing works

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


title: All syntax errors are not equal

abstract: Identifying and correcting syntax errors is a challenge all novice programmers confront. As educators, the more we understand about the nature of these errors and how students respond to them, the more effective our teaching can be. It is well known that just a few types of errors are far more frequently encountered by students learning to program than most. In this paper, we examine how long students spend resolving the most common syntax errors, and discover that certain types of errors are not solved any more quickly by the higher ability students. Moreover, we note that these errors consume a large amount of student time, suggesting that targeted teaching interventions may yield a significant payoff in terms of increasing student productivity.


title: Efficient support of dynamic inheritance for class- and prototype-based languages

abstract: Highlights? We define language-neutral dynamic inheritance for both class- and prototype-based object models. ? The semantics has been included as part of a production JIT-compiler virtual machine. ? The resulting virtual machine has been evaluated to provide significant runtime performance benefits with low memory consumption. ? Our platform supports both dynamically and statically typed languages, allowing a direct interoperation between them. ? Backward compatibility with existing. Net applications is maintained. Dynamically typed languages are becoming increasingly popular for different software development scenarios where runtime adaptability is important. Therefore, existing class-based platforms such as Java and .Net have been gradually incorporating dynamic features to support the execution of these languages. The implementations of dynamic languages on these platforms commonly generate an extra layer of software over the virtual machine, which reproduces the reflective prototype-based object model provided by most dynamic languages. Simulating this model frequently involves a runtime performance penalty, and makes the interoperation between class- and prototype-based languages difficult.Instead of simulating the reflective model of dynamic languages, our approach has been to extend the object-model of an efficient class-based virtual machine with prototype-based semantics, so that it can directly support both kinds of languages. Consequently, we obtain the runtime performance improvement of using the virtual machine JIT compiler, while a direct interoperation between languages compiled to our platform is also possible. In this paper, we formalize dynamic inheritance for both class- and prototype-based languages, and implement it as an extension of an efficient virtual machine that performs JIT compilation. We also present an extensive evaluation of the runtime performance and memory consumption of the programming language implementations that provide dynamic inheritance, including ours.


title: Algorithm for computer control of a digital plotter

abstract: An algorithm is given for computer control of a digital plotter. ::: ::: The algorithm may be programmed without multiplication or division instructions and is efficient with respect to speed of execution and memory utilization.


title: Understanding Tables in Context Using Standard NLP Toolkits

abstract: Tabular information in text documents contains a wealth of information, and so tables are a natural candidate for information extraction. There are many cues buried in both a table and its surrounding text that allow us to understand the meaning of the data in a table. We study how natural-language tools, such as part-of-speech tagging, dependency paths, and named-entity recognition, can be used to improve the quality of relation extraction from tables. In three domains we show that (1) a model that performs joint probabilistic inference across tabular and natural language features achieves an F1 score that is twice as high as either a puretable or pure-text system, and (2) using only shallower features or non-joint inference results in lower quality.