title: Investigation of security and QoS on SDN firewall using MAC filtering

abstract: Software Defined Networking (SDN) is the latest trend in the networking domain. In SDN, the control plane is decoupled from the data plane in network devices and controlled by the centralized controller using the OpenFlow Protocol. As the centralized controller does all the control functions, strong security support is mandatory. Firewall can be an effective means to protect the SDN controller from network security threats. The firewall rules can be predefined by using either the IP address or the MAC address filtering. In this paper, we propose a firewall implementation for SDN using the MAC filtering. Then, we compare the average packet delay performance for both IP filtering and MAC filtering firewall techniques. The results show that the security support (packet allow/deny) is pretty much the same in both types of firewall technique, whereas the MAC filtering firewall outperforms the IP filtering firewall for the delay performance analysis.


title: Functional relationships of phytoene synthase 1 alleles on chromosome 7A controlling flour colour variation in selected Australian wheat genotypes

abstract: Flour colour measured as a Commission Internationale de l’Eclairage (CIE) b* value is an important wheat quality attribute for a range of end-products, with genes and enzymes of the xanthophyll biosynthesis pathway providing potential sources of trait variation. In particular, the phytoene synthase 1 (Psy1) gene has been associated with quantitative trait loci (QTL) for flour b* colour variation. Several Psy1 alleles on chromosome 7A (Psy-A1) have been described, along with proposed mechanisms for influencing flour b* colour. This study sought to identify evolutionary relationships among known Psy-A1 alleles, to establish which Psy-A1 alleles are present in selected Australian wheat genotypes and establish their role in controlling variation for flour b* colour via QTL analysis. Phylogenetic analyses showed seven of eight known Psy-A1 alleles clustered with sequences from T. urartu, indicating the majority of alleles in Australian germplasm share a common evolutionary lineage. In this regard, Psy-A1a, Psy-A1c, Psy-A1e and Psy-A1p were common in Australian genotypes with flour b* colour ranging from white to yellow. In contrast Psy-A1s was found to be related to A. speltoides, indicating a possible A–B genome translocation during wheat polyploidisation. A new allele Psy-A1t (similar to Psy-A1s) was discovered in genotypes with yellow flour, with QTL analyses indicating Psy-A1t strongly influences flour b* colour in Australian germplasm. QTL LOD value maxima did not coincide with Psy-A1 gene locus in two of three populations and, therefore, Psy-A1a and Psy-A1p may not be involved in flour colour. Instead two other QTL were identified, one proximal and one distal to Psy-A1 in Australian wheat lines. Comparison of Psy-A1t and Psy-A1p predicted protein sequences suggests differences in putative sites for post-translational modification may influence enzyme activity and subsequent xanthophyll accumulation in the wheat endosperm. Psy-A1a and Psy-A1p were not involved in flour b* colour variation, indicating other genes control variation on chromosome 7A in some wheat genotypes.


title: Longitudinal growth changes in untreated subjects with Class II Division 1 malocclusion.

abstract: INTRODUCTION ::: The purpose of this longitudinal study was to compare the craniofacial growth changes in untreated subjects with Class II Division 1 malocclusion with those in subjects with normal (Class I) occlusion from the prepubertal through the postpubertal stages of development, as defined by a biological indicator of individual skeletal maturity (cervical vertebral maturation method). ::: ::: ::: METHODS ::: The Class II Division 1 sample consisted of 17 subjects (11 boys, 6 girls). The Class I sample also consisted of 17 subjects (13 boys, 4 girls). The lateral cephalograms of the subjects in both groups were analyzed at 6 consecutive stages of development, from CS1 through CS6. The statistical comparisons of the growth changes in the study groups were performed with Mann-Whitney U tests. ::: ::: ::: RESULTS ::: Craniofacial growth in subjects with untreated Class II malocclusion is essentially similar to that in untreated subjects with normal occlusion at all developmental intervals, with the exception of significantly smaller increases in mandibular length (P <0.001) at the growth spurt (interval CS3-CS4) and during the overall observation period (intervals CS1-CS6). ::: ::: ::: CONCLUSIONS ::: Class II dentoskeletal disharmony does not tend to self-correct with growth in association with worsening of the deficiency in total mandibular length and mandibular ramus height.


title: An architecture to automate performance tests on microservices

abstract: The microservices architecture provides a new approach to develop applications. As opposed to monolithic applications, in which the application comprises a single software artifact, an application based on the microservices architecture is composed by a set of services, each one designed to perform a single and well-defined task. These services allow the development team to decouple several parts of the application using different frameworks, languages and hardware for each part of the system. One of the drawbacks for adopting the microservices architecture to develop applications is testability. In a single application test boundaries can be more easily established and tend to be more stable as the application evolves, while with microservices we can have a set of hundreds of services that operate together and are prone to change more rapidly. Each one of these services needs to be tested and updated as the service changes. In addition, the different characteristics of these services such as languages, frameworks or the used infrastructure have to be considered in the testing phase. Performance tests are applied to assure that a particular software complies with a set of non-functional requirements such as throughput and response time. These metrics are important to ensure that business constraints are respected and to help finding performance bottlenecks. In this paper, we present a new approach to allow the performance tests to be executed in an automated way, with each microservice providing a test specification that is used to perform tests. Along with the architecture, we also provide a framework that implements some key concepts of this architecture. This framework is available as an open source project1.


title: Blizzard: fast, cloud-scale block storage for cloud-oblivious applications

abstract: Blizzard is a high-performance block store that exposes cloud storage to cloud-oblivious POSIX and Win32 applications. Blizzard connects clients and servers using a network with full-bisection bandwidth, allowing clients to access any remote disk as fast as if it were local. Using a novel striping scheme, Blizzard exposes high disk parallelism to both sequential and random workloads; also, by decoupling the durability and ordering requirements expressed by flush requests, Blizzard can commit writes out-of-order, providing high performance and crash consistency to applications that issue many small, random IOs. Blizzard's virtual disk drive, which clients mount like a normal physical one, provides maximum throughputs of 1200 MB/s, and can improve the performance of unmodified, cloud-oblivious applications by 2x-10x. Compared to EBS, a commercially available, state-of-the-art virtual drive for cloud applications, Blizzard can improve SQL server IOp rates by seven-fold while still providing crash consistency.


title: Twitter adoption, students’ perceptions, Big Five personality traits and learning outcome: Lessons learned from 3 case studies

abstract: Abstract This study examines the relationship of the Twitter’s use with the students’ learning outcome in a series of well-organised educational activities. Three studies were conducted in the context of two academic courses. In all three studies, the students who participated in the process received a higher laboratory grade than the students who did not participate. Students’ conscientiousness and openness to experience were related to their activity in one study. However, no relationship between the students’ personality traits and their grade was unveiled. Moreover, the students’ interventions in the activities are examined, as well as the differentiation in their attitudes towards social media use in learning. The implications of the results are discussed and a comparison with other studies is presented.


title: Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling

abstract: Minimizing job scheduling time is a fundamental issue in data center networks that has been extensively studied in recent years. The incoming jobs require different CPU and memory units, and span different number of time slots. The traditional solution is to design efficient heuristic algorithms with performance guarantee under certain assumptions. In this paper, we improve a recently proposed job scheduling algorithm using deep reinforcement learning and extend it to multiple server clusters. Our study reveals that deep reinforcement learning method has the potential to outperform traditional resource allocation algorithms in a variety of complicated environments.


title: Applying machine learning classifiers to dynamic Android malware detection at scale

abstract: The widespread adoption and contextually sensitive nature of smartphone devices has increased concerns over smartphone malware. Machine learning classifiers are a current method for detecting malicious applications on smartphone systems. This paper presents the evaluation of a number of existing classifiers, using a dataset containing thousands of real (i.e. not synthetic) applications. We also present our STREAM framework, which was developed to enable rapid large-scale validation of mobile malware machine learning classifiers.


title: Countering the effects of silicon aging on SRAM PUFs

abstract: Silicon aging, in particular NBTI, causes many PUFs to exhibit a natural tendency of growing less reliable over time. This is inconvenient or even unacceptable for in-the-field applications. In case of SRAM PUFs it is observed that the impact of NBTI aging depends on the data stored in the SRAM. In this work, we investigate the effects of data-dependent silicon aging on SRAM PUF reliability under a number of realistic scenarios. In an accelerated aging experiment on a 65nm CMOS SRAM PUF implementation it is observed that many scenarios cause a smaller reliability reduction than natural aging. Some scenarios even show anti-aging effects, i.e. they cause the SRAM PUF to grow more reliable over time. This is a significant improvement when using an SRAM PUF. Even more so because data-dependent (anti-)aging has a particularly low overhead, requiring neither any changes to the PUF circuit nor any pre-deployment effort.


title: Comparing Sources of Stress in College Student Athletes and Non-Athletes

abstract: The transition from high school to college can be stressful for any student (Hudd et al., 2000; Pritchard, Wilson, & Yamnitz, 2004), but recent evidence suggests that athletes may experience even greater levels of stress due to the dual demands of athletics and academics placed on them during their freshman year. Lazarus and Folkman (1996) have defined stress as the negative feeling that occurs when an individual feels unable to cope with the demands placed upon them by their environment. For the matriculating freshman student athlete, these demands may at times seem overwhelming.


Competitive Orientations and Men’s Acceptance of Cosmetic Surgery


title: Deep Manifold Traversal: Changing Labels with Convolutional Features

abstract: Many tasks in computer vision can be cast as a "label changing" problem, where the goal is to make a semantic change to the appearance of an image or some subject in an image in order to alter the class membership. Although successful task-specific methods have been developed for some label changing applications, to date no general purpose method exists. Motivated by this we propose deep manifold traversal, a method that addresses the problem in its most general form: it first approximates the manifold of natural images then morphs a test image along a traversal path away from a source class and towards a target class while staying near the manifold throughout. The resulting algorithm is surprisingly effective and versatile. It is completely data driven, requiring only an example set of images from the desired source and target domains. We demonstrate deep manifold traversal on highly diverse label changing tasks: changing an individual's appearance (age and hair color), changing the season of an outdoor image, and transforming a city skyline towards nighttime.


title: Describing human decisions in agent-based models - ODD + D, an extension of the ODD protocol

abstract: Representing human decisions is of fundamental importance in agent-based models. However, the rationale for choosing a particular human decision model is often not sufficiently empirically or theoretically substantiated in the model documentation. Furthermore, it is difficult to compare models because the model descriptions are often incomplete, not transparent and difficult to understand. Therefore, we expand and refine the 'ODD' (Overview, Design Concepts and Details) protocol to establish a standard for describing ABMs that includes human decision-making (ODD + D). Because the ODD protocol originates mainly from an ecological perspective, some adaptations are necessary to better capture human decision-making. We extended and rearranged the design concepts and related guiding questions to differentiate and describe decision-making, adaptation and learning of the agents in a comprehensive and clearly structured way. The ODD + D protocol also incorporates a section on 'Theoretical and Empirical Background' to encourage model designs and model assumptions that are more closely related to theory. The application of the ODD + D protocol is illustrated with a description of a social-ecological ABM on water use. Although the ODD + D protocol was developed on the basis of example implementations within the socio-ecological scientific community, we believe that the ODD + D protocol may prove helpful for describing ABMs in general when human decisions are included.


title: A simple and efficient eye detection method in color images

abstract: In this paper we propose a simple and efficient eye detection method for face detection tasks in color images. The algorithm first detects face regions in the image using a skin color model in the normalized RGB color space. Then, eye candidates are extracted within these regions. Finally, using the anthrophological characteristics of human eyes, the pairs of eye regions are selected. The proposed method is simple and fast, since it needs no template matching step for face verification. It is robust because it can deals with face rotation. Experimental results show the validity of our approach, a correct eye detection rate of 98.4% is achieved using a subset of the AR face database.


title: An Interactive Layout Tool for BPMN

abstract: Business processes are at the core of today’s businessworld. Most of the effort put into business processes in practice is either the task of designing a new process or the task of analyzing and improving an existing process. In both cases, visualizations of the process models support the user in achieving his objectives. In this paper, we present the first prototype of our tool “BPMN-Layouter”. Our supported notation language is the business process modeling notation (BPMN). The tool was implemented to integrate several concepts that we developed and adopted for the use in BPMN modeling. The integratedapproaches comprehend the tasks of finding an automatic layout for a BPMN model, increasing the readability of given visualizations and developing further existing BPMN models in an interactive fashion. Also, a method for automatic division of complex BPMN models is provided, too.


title: Footprints: A Visual Search Tool that Supports Discovery and Coverage Tracking

abstract: Searching a large document collection to learn about a broad subject involves the iterative process of figuring out what to ask, filtering the results, identifying useful documents, and deciding when one has covered enough material to stop searching. We are calling this activity "discoverage," discovery of relevant material and tracking coverage of that material. We built a visual analytic tool called Footprints that uses multiple coordinated visualizations to help users navigate through the discoverage process. To support discovery, Footprints displays topics extracted from documents that provide an overview of the search space and are used to construct searches visuospatially. Footprints allows users to triage their search results by assigning a status to each document (To Read, Read, Useful), and those status markings are shown on interactive histograms depicting the user's coverage through the documents across dates, sources, and topics. Coverage histograms help users notice biases in their search and fill any gaps in their analytic process. To create Footprints, we used a highly iterative, user-centered approach in which we conducted many evaluations during both the design and implementation stages and continually modified the design in response to feedback.


title: Advanced windowed interpolated FFT algorithms for harmonic analysis of electrical power system

abstract: Fast Fourier Transform (FFT) is used to obtain the Fourier spectrum of a periodic signal, but it is well known that special care must be taken to avoid severe distortions introduced by the sampling process. The major errors associated with this algorithm are spectral leakage and picket fence effect. This paper discusses the advancement in FFT algorithms for harmonic analysis in power system by applying windowed interpolation techniques. It is observed that the application of different WIFFT algorithms has different effects on the measuring accuracy. The advanced minimized sidelobe window (MSW) algorithm has better performance characteristics when compared to the conventional hanning and hamming windows adopted for harmonic analysis. By considering the practical harmonic data the hanning, hamming and MSW algorithms are validated using matlab programming. The simulation results demonstrate the effectiveness of hanning, hamming and MSW by considering the magnitude and phase angle error variations with respect to the harmonic order.


title: Fiducial marker indoor localization with Artificial Neural Network

abstract: A vision based positioning system could be categorized into two groups. One analyzes an environment's scenery by matching the inputs with imaginary database to find the optimum result. The other uses fiduciary markers. In proposed method, the system uses fiduciary markers with a capital alphabet in it. When the known size fiduciary marker is captured by a camera, by using homography transformation, the 6-DOF camera pose with respect to the marker's local coordinate can be calculated. To recognize the character in the marker, Artificial Neural Network (ANN) with back-propagation training method is used. 12 unique features of a character are defined and used as inputs of ANN. Since more than 95% recognition rate is achieved in testing phase, the Optical Character Recognition (OCR) with ANN could be used as a marker detection method. The localization experimental result with the fiduciary marker shows that the proposed method could be a solution for indoor localization.


title: Consumer finance: challenges for operational research

abstract: Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance.


Reducing the run-time complexity of Support Vector Machines


title: Applying Learning Algorithms to Music Generation

abstract: Several automated composition systems have arisen that generate or facilitate understanding of blues chord progressions, jazz improvisation, or classical pieces. Such systems often work by applying a set of rules explicitly provided to the system to determine what sequence of output values is appropriate. Others use pattern recognition and generation techniques such as Markov chains. We propose a system in which sliding window sequential learning techniques are used to generate rules that correspond to a training set of musical data. This approach has the dual advantages of greater generality than explicitly specifying rules to a system and the ability to apply effectively a wide variety of powerful existing learning algorithms. We present the design and implementation of the composition process. We also illustrate several reasonably successful samples of its output and discuss ways in which it can be improved with additional work.


title: EIO: Error Handling is Occasionally Correct

abstract: The reliability of file systems depends in part on how well they propagate errors. We develop a static analysis technique, EDP, that analyzes how file systems and storage device drivers propagate error codes. Running our EDP analysis on all file systems and 3 major storage device drivers in Linux 2.6, we find that errors are often incorrectly propagated; 1153 calls (13%) drop an error code without handling it. ::: ::: We perform a set of analyses to rank the robustness of each subsystem based on the completeness of its error propagation; we find that many popular file systems are less robust than other available choices. We confirm that write errors are neglected more often than read errors. We also find that many violations are not cornercase mistakes, but perhaps intentional choices. Finally, we show that inter-module calls play a part in incorrect error propagation, but that chained propagations do not. In conclusion, error propagation appears complex and hard to perform correctly in modern systems.


title: Digital Advertising Traffic Operation: Machine Learning for Process Discovery

abstract: In a Web Advertising Traffic Operation it's necessary to manage the day-to-day trafficking, pacing and optimization of digital and paid social campaigns. The data analyst on Traffic Operation can not only quickly provide answers but also speaks the language of the Process Manager and visually displays the discovered process problems. In order to solve a growing number of complaints in the customer service process, the weaknesses in the process itself must be identified and communicated to the department. With the help of Process Mining for the CRM data it is possible to identify unwanted loops and delays in the process. With this paper we propose a process discovery based on Machine Learning technique to automatically discover variations and detect at first glance what the problem is, and undertake corrective measures.


title: Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings

abstract: The placement of the complete baseband processing in a centralized pool results in high data rate requirement and inflexibility of the fronthaul network, which challenges the energy and cost effectiveness of the cloud radio access network (C-RAN). Recently, redesign of the C-RAN through functional split in the baseband processing chain has been proposed to overcome these challenges. This paper evaluates, by mathematical and simulation methods, different splits with respect to network level energy and cost efficiency having in the mind the expected quality of service. The proposed mathematical model quantifies the multiplexing gains and the trade-offs between centralization and decentralization concerning the cost of the pool, fronthaul network capacity and resource utilization. The event-based simulation captures the influence of the traffic load dynamics and traffic type variation on designing an efficient fronthaul network. Based on the obtained results, we derive a principle for fronthaul dimensioning based on the traffic profile. This principle allows for efficient radio access network with respect to multiplexing gains while achieving the expected users' quality of service.


title: Design of High Efficiency Circular Horn Feeds for Multibeam Reflector Applications

abstract: The above paper (Chan and Rao, 2008), has been read with considerable interest. We wish to point out that the design, including radiation and wide-bandwidth characteristics, of multiflared high-efficiency horns has already been published in an invention disclosure (Bhattacharyya and Sor, 2004) which has been in the public domain since June 8, 2006. The high-efficiency multiflared horn structure was first designed in 2003 at the company of the first author. Since then, the structure has been used as radiating elements for arrays and as reflector feeds in several communication satellite projects.


title: Kinematic Controllability and Decoupled Trajectory Planning for Underactuated Mechanical Systems

abstract: We introduce the notion of kinematic controllability for second-order underactuated mechanical systems. For systems satisfying this property, the problem of planning fast collision-free trajectories between zero velocity states can be decoupled into the computationally simpler problems of path planning for a kinematic system followed by time-optimal time scaling. While this approach is well known for fully actuated systems, until now there has been no way to apply it to underactuated dynamic systems. The results in this paper form the basis for efficient collision-free trajectory planning for a class of underactuated mechanical systems including manipulators and vehicles in space and underwater environments.


title: Variability in architectural simulations of multi-threaded workloads

abstract: Multi-threaded commercial workloads implement many important Internet services. Consequently, these workloads are increasingly used to evaluate the performance of uniprocessor and multiprocessor system designs. This paper identifies performance variability as a potentially major challenge for architectural simulation studies using these workloads. Variability refers to the differences between multiple estimates of a workload's performance. Time variability occurs when a workload exhibits different characteristics during different phases of a single run. Space variability occurs when small variations in timing cause runs starting from the same initial condition to follow widely different execution paths. Variability is a well-known phenomenon in real systems, but is nearly universally ignored in simulation experiments. In a central result of this paper we show that variability in multi-threaded commercial workloads can lead to incorrect architectural conclusions (e.g., 31% of the time in one experiment). We propose a methodology, based on multiple simulations and standard statistical techniques, to compensate for variability. Our methodology greatly reduces the probability of reaching incorrect conclusions, while enabling simulations to finish within reasonable time limits.


title: Graph Convolutional Networks for Text Classification

abstract: Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolution on regular grid, e.g., sequence) to classification. However, only a limited number of studies have explored the more flexible graph convolutional neural networks (convolution on non-grid, e.g., arbitrary graph) for the task. In this work, we propose to use graph convolutional networks for text classification. We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus. Our Text GCN is initialized with one-hot representation for word and document, it then jointly learns the embeddings for both words and documents, as supervised by the known class labels for documents. Our experimental results on multiple benchmark datasets demonstrate that a vanilla Text GCN without any external word embeddings or knowledge outperforms state-of-the-art methods for text classification. On the other hand, Text GCN also learns predictive word and document embeddings. In addition, experimental results show that the improvement of Text GCN over state-of-the-art comparison methods become more prominent as we lower the percentage of training data, suggesting the robustness of Text GCN to less training data in text classification.


title: Rapidly-Exploring Random Vines (RRV) for Motion Planning in Configuration Spaces with Narrow Passages

abstract: Classical RRT algorithm is blind to efficiently explore configuration space for expanding the tree through a narrow passage when solving a motion planning (MP) problem. Although there have been several attempts to deal with narrow passages which are based on a wide spectrum of assumptions and configuration setups, we solve this problem in rather general way. We use dominant eigenvectors of the configuration sets formed by properly sampling the space around the nearest node, to efficiently expand the tree around the obstacles and through narrow passages. Unlike classical RRT, our algorithm is aware of having the tree nodes in front of a narrow passage and in a narrow passage, which enables a proper tree expansion in a vine-like manner. A thorough comparison with RRT, RRT-connect, and DDRRT algorithm is provided by solving three different difficult MP problems. The results suggest a significant superiority the proposed Rapidly-exploring Random Vines (RRV) algorithm might have in configuration spaces with narrow passages.


title: Monophone-Based Background Modeling for Two-Stage On-Device Wake Word Detection

abstract: Accurate on-device wake word detection is crucial to products with far-field voice control such as the Amazon Echo. It is quite challenging to build a wake word system with both low False Reject Rate (FRR) and low False Alarm Rate (FAR) in real scenarios where there are various types of background speech, music or noise, especially when computational resources on the device is limited. In this paper, we introduce a two-stage wake word system based on Deep Neural Network (DNN) acoustic modeling, propose a new way to model the non-keyword background events using monophone-based units and present how richer information can be extracted from those monophone units for final wake word detection. Under the new system, we could get around 16% relative reduction in FRR when fixing the false alarm level, and about 37% relative reduction in FAR on the other hand if we maintain the miss rate. For the 2nd stage classifier itself, it is able to reduce the false alarm rate relatively by about 67% on top of 1st stage hypothesis with very few computational resources.


title: A Study of Consumers' Trust in Online Shopping between Pick-up Goods Behavior in the Convenience Stores

abstract: With the adoption of Internet, online shopping has provided a convenience way to purchase goods or services from anywhere at any time in recent years globally. Especially, convenience stores are available for consumers to pick up goods ordered from Internet shops in Taiwan. Therefore, convenience stores have become an important success factor for increasing a lot of profit in online shopping in Taiwan. Past researches have indicated that consumers dare not or are not willing to purchase goods in online shopping. The reasons include the problem of security and the lack of consumersi¦ trust. Thus, these problems constitute a key barrier to the use of online shopping as well as long-term commitment to the relationship building. Therefore, there is a need to build up consumersi¦ trust in order to overcome the influential factor in online shopping. In this paper, we use statistic analysis method by questionnaires to discuss the characteristics of pick-up goods in the convenience stores and illustrate the relationship among consumersi¦ trust in online shopping and pick-up goods behavior in convenience stores. In our experiment, questionnaire items are measured by Likert scale. In 227 valid questionnaires, 90% participants deeply believe that pick-up goods in the convenience stores can promote consumersi¦ trust in online shopping.


title: Mathematical existence results for the Doi-Edwards polymer model

abstract: In this paper, we present some mathematical results on the Doi-Edwards model describing the dynamics of flexible polymers in melts and concentrated solutions. This model, developed in the late 1970s, has been used and tested extensively in modeling and simulation of polymer flows. From a mathematical point of view, the Doi-Edwards model consists in a strong coupling between the Navier-Stokes equations and a highly nonlinear constitutive law. The aim of this article is to provide a rigorous proof of the well-posedness of the Doi-Edwards model, namely it has a unique regular solution. We also prove, which is generally much more difficult for flows of viscoelastic type, that the solution is global in time in the two dimensional case, without any restriction on the smallness of the data.


title: Coilgun Velocity Optimization With Current Switch Circuit

abstract: An inductive coil gun is a solenoid surrounding a cylindrical metallic armature (the projectile), both electromagnetically coupled. One or more capacitors discharge a stored voltage quickly through the coil, configuring a kind of RLC circuit, with the pulse-shaped current generating a magnetic field inside the coil that accelerates the projectile. Once the projectile crosses an equilibrium point inside the coil, the existing magnetic field pulls the armature back, therefore reducing the final muzzle velocity and decreasing the efficiency—defined as the ratio between the available capacitor electrostatic versus the slug kinetic energies. This paper describes a system that monitors the armature position inside the coil, with a laser beam, and automatically cuts the current when the projectile reaches the equilibrium point. The system, named current switch circuit, enables higher velocities using the same set of coilgun and projectiles, providing a higher efficiency.


title: Regression Test Selection Techniques: A Survey

abstract: Swarnendu Biswas and Rajib MallDept. of Computer Science and EngineeringIIT Kharagpur, India - 721302E-mail: {swarnendu, rajib}@cse.iitkgp.ernet.inManoranjan Satpathy and Srihari SukumaranGM India Science Lab, Bangalore, IndiaE-mail: {manoranjan.satpathy, srihari.sukumaran}@gm.comOverview paperKeywords: softwaremaintenance,regressiontesting,regressiontestselection,model-basedtesting,UML,softwarecom-ponents, embedded programsReceived: April 12, 2010Regression testing is an important and expensive activity that is undertaken every time a program is mod-iﬁed to ensure that the modiﬁcations do not introduce new bugs into previously validated code. An im-portant research problem, in this context, is the selection of a relevant subset of test cases from the initialtest suite that would minimize both the regression testing time and effort without sacriﬁcing the thorough-ness of regression testing. Researchers have proposed a number of regression test selection techniques fordifferent programming paradigms such as procedural, object-oriented, component-based, database, aspect,and web applications. In this paper, we review the important regression test selection techniques proposedfor various categories of programs and identify the emerging trends.Povzetek: Podan je pregled tehnik izbora testov za regresijsko testiranje programov.


title: Rational choice with status quo bias

abstract: Abstract Motivated by the empirical findings concerning the importance of one's current situation on her choice behavior, the main objective of this paper is to propose a rational choice theory that allows for the presence of a status quo bias, and that incorporates the standard choice theory as a special case. We follow a revealed preference approach, and obtain two nested models of rational choice that allow phenomena like the status quo bias and the endowment effect, and that are applicable in any choice situation to which the standard (static) choice model applies.


MODIFICATION OF STANDARD 8BL CHAIR FOR THE USE OF DOUBLE AMPUTEES


title: Kinematics and dynamics analysis of a 2-DOF spherical parallel robot

abstract: In this paper, the kinematic and dynamic analysis of a 2-degree-of-freedom spherical parallel manipulator is presented. The mobility and motion pattern of the manipulator are analyzed using screw theory, which resulted in obtaining the direct and inverse Jacobian matrices. Position, velocity and acceleration relations between different parts of the manipulator and the actuated joint angles are obtained, which are prerequisite for dynamic analysis. Since the manipulator, belongs to a class of parallel mechanism known as over-constrained mechanism, thus to obtain a dynamical model, a modification should be applied in its kinematic arrangement by preserving the performed motion pattern. Finally, accuracy of the dynamical model is verified by comparing the results obtained from the formulated model with results obtained from a SimMechanics model of the under study manipulator.


title: AuditoryMotor Interaction Revealed by fMRI: Speech, Music, and Working Memory in Area Spt

abstract: The concept of auditory-motor interaction pervades speech science research, yet the cortical systems supporting this interface have not been elucidated. Drawing on experimental designs used in recent work in sensory-motor integration in the cortical visual system, we used fMRI in an effort to identify human auditory regions with both sensory and motor response properties, analogous to single-unit responses in known visuomotor integration areas. The sensory phase of the task involved listening to speech (nonsense sentences) or music (novel piano melodies); the "motor" phase of the task involved covert rehearsal/humming of the auditory stimuli. A small set of areas in the superior temporal and temporal-parietal cortex responded both during the listening phase and the rehearsal/humming phase. A left lateralized region in the posterior Sylvian fissure at the parietal-temporal boundary, area Spt, showed particularly robust responses to both phases of the task. Frontal areas also showed combined auditory + rehearsal responsivity consistent with the claim that the posterior activations are part of a larger auditory-motor integration circuit. We hypothesize that this circuit plays an important role in speech development as part of the network that enables acoustic-phonetic input to guide the acquisition of language-specific articulatory-phonetic gestures; this circuit may play a role in analogous musical abilities. In the adult, this system continues to support aspects of speech production, and, we suggest, supports verbal working memory.


title: A Tangled Mass: The Android Root Certificate Stores

abstract: The security of today's Web rests in part on the set of X.509 certificate authorities trusted by each user's browser. Users generally do not themselves configure their browser's root store but instead rely upon decisions made by the suppliers of either the browsers or the devices upon which they run. In this work we explore the nature and implications of these trust decisions for Android users. Drawing upon datasets collected by Netalyzr for Android and ICSI's Certificate Notary, we characterize the certificate root store population present in mobile devices in the wild. Motivated by concerns that bloated root stores increase the attack surface of mobile users, we report on the interplay of certificate sets deployed by the device manufacturers, mobile operators, and the Android OS. We identify certificates installed exclusively by apps on rooted devices, thus breaking the audited and supervised root store model, and also discover use of TLS interception via HTTPS proxies employed by a market research company.


A COMPUTATIONAL ACCOUNT OF SYNTACTIC, SEMANTIC AND DISCOURSE PRINCIPLES FOR ANAPHORA RESOLUTION


title: Precision-Recall-Gain Curves: PR Analysis Done Right

abstract: Precision-Recall analysis abounds in applications of binary classification where true negatives do not add value and hence should not affect assessment of the classifier's performance. Perhaps inspired by the many advantages of receiver operating characteristic (ROC) curves and the area under such curves for accuracy-based performance assessment, many researchers have taken to report Precision-Recall (PR) curves and associated areas as performance metric. We demonstrate in this paper that this practice is fraught with difficulties, mainly because of incoherent scale assumptions - e.g., the area under a PR curve takes the arithmetic mean of precision values whereas the Fβ score applies the harmonic mean. We show how to fix this by plotting PR curves in a different coordinate system, and demonstrate that the new Precision-Recall-Gain curves inherit all key advantages of ROC curves. In particular, the area under Precision-Recall-Gain curves conveys an expected F1 score on a harmonic scale, and the convex hull of a Precision-Recall-Gain curve allows us to calibrate the classifier's scores so as to determine, for each operating point on the convex hull, the interval of β values for which the point optimises Fβ. We demonstrate experimentally that the area under traditional PR curves can easily favour models with lower expected F1 score than others, and so the use of Precision-Recall-Gain curves will result in better model selection.


Black Women Researchers’ Path to Breaking Silence: Three Scholars Reflect on Voicing Oppression, Self-reflexive Speech, and Talking Back to Elite Discourses


title: Design of a novel V-band coplanar waveguide low-pass filter based on defected ground structures

abstract: This paper presents a novel V-band coplanar waveguide low pass filter based on defected ground structure (DGS) cells. The proposed DGS cell provides low-pass characteristic whose cutoff frequency could be changed by tuning its dimensions. To realize a low pass filter with excellent rejection in the stop-band, several DGS cells with different sizes are cascaded together. Gallium arsenide is adopted to be the substrate of the low-pass filter with the cutoff frequency located in the V band. By optimization, a low-pass filter with the cutoff frequency of 60 GHz is designed, whose insertion loss in the pass band is less than 0.2 dB and the rejection in the stop-band is better than 25 dB from 65 GHz to 100 GHz.


title: Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest

abstract: The 2013 Data Fusion Contest organized by the Data Fusion Technical Committee (DFTC) of the IEEE Geoscience and Remote Sensing Society aimed at investigating the synergistic use of hyperspectral and Light Detection And Ranging (LiDAR) data. The data sets distributed to the participants during the Contest, a hyperspectral imagery and the corresponding LiDAR-derived digital surface model (DSM), were acquired by the NSF-funded Center for Airborne Laser Mapping over the University of Houston campus and its neighboring area in the summer of 2012. This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the fusion of spectral, spatial, and elevation information.


title: Why Complicate Things? Introducing Programming in High School Using Python

abstract: Deciding what to teach novices about programming and what programming language to use is a common topic for debate. Should an industry relevant programming language be taught, or should a language designed for teaching novices be used? Typically, these questions are raised at university level, but in this paper we address them from a high school perspective.We present a case study with a twofold goal: (1) examining how programming can be introduced at high school level, and (2) evaluating how suitable the programming language Python is to support both teachers and learners in this process. During the school year 2004/2005, an introductory programming course was given to four student groups in two different high schools. The students enjoyed programming and learnt to think in terms of re-use and interfaces. In addition, we found that many features of Python facilitated both teaching and learning (for instance, a simple and flexible syntax, immediate feedback, easy-to-use modules and strict requirements on proper indentation).Our findings support results from previous studies in that students have difficulties in dealing with abstract concepts - even though the syntax for implementing these is simple. In addition, compared to university students, high school students are young and have necessarily not yet developed the writing skills required for producing proper documentation. The course was designed to be well suited for high school students in general, but still all participants were boys. Since high schools should provide all-round learning to all students, we, as do all computer science teachers, face the challenge of making programming more appealing to girls.


title: Heat and gas transport properties in pelletized hydride–graphite-composites for hydrogen storage applications

abstract: Abstract Metal hydrides are suitable for the compact, efficient and safe storage of hydrogen. Considering hydride-based hydrogen storage tanks, the enhancement of the heat and gas transport properties of the hydride bed is crucial for increased (un-)loading dynamics of the tank. In this contribution, pelletized composites of different hydrogen storage materials (lithium amide, sodium alanate, magnesium hydride and transition metal hydride Hydralloy C5) with expanded natural graphite (ENG) are discussed. The materials were admixed with up to 25 wt.% ENG and compacted at compaction pressures up to 600 MPa. The resulting hydride-ENG pellets exhibit an increased effective thermal conductivity which can be tuned in a wide range. The pellets have an increased volumetric H 2 storage capacity compared to loose hydride powders. High gas permeability in radial direction and sufficient thermal conductivity (>10 W m −1 K −1 ) in combination with a stable pellet structure indicate a high potential to use suchlike prepared hydride-ENG composites for hydrogen storage applications with high loading dynamics.


title: CryoPause: A New Method to Immediately Initiate Experiments after Cryopreservation of Pluripotent Stem Cells

abstract: Human pluripotent stem cells (PSCs) provide an unlimited cell source for cell therapies and disease modeling. Despite their enormous power, technical aspects have hampered reproducibility. Here, we describe a modification of PSC workflows that eliminates a major variable for nearly all PSC experiments: the quality and quantity of the PSC starting material. Most labs continually passage PSCs and use small quantities after expansion, but the "just-in-time" nature of these experiments means that quality control rarely happens before use. Lack of quality control could compromise PSC quality, sterility, and genetic integrity, which creates a variable that might affect results. This method, called CryoPause, banks PSCs as single-use, cryopreserved vials that can be thawed and immediately used in experiments. Each CryoPause bank provides a consistent source of PSCs that can be pre-validated before use to reduce the possibility that high levels of spontaneous differentiation, contamination, or genetic integrity will compromise an experiment.


title: Drosophila as a lipotoxicity model organism--more than a promise?

abstract: Looking back over the century long research career of the fruit fly, Drosophila melanogaster has frequently been in the scientific spotlight with respect to fundamental discoveries in biology. The last decade witnessed the increasing importance of the fly as a human disease model but studies on energy homeostasis and lipometabolism remain in their infancy. This perspective, addressing readers largely unfamiliar with the Drosophila model system, aims to highlight the starting points for which the fly could be employed to gain a deeper understanding of lipotoxicity and possibly contribute to strategies for the identification of novel drug targets relevant to type 2 diabetes mellitus and the metabolic syndrome.


title: Adaptive beaconing for congestion and awareness control in vehicular networks

abstract: Cooperative vehicular networks require the exchange of positioning and basic status information between neighboring nodes to support higher layer protocols and applications, including active safety applications. The information exchange is based on the periodic transmission/reception of 1-hop broadcast messages on the so called control channel. The dynamic adaptation of the transmission parameters of such messages will be key for the reliable and efficient operation of the system. On one hand, congestion control protocols need to be applied to control the channel load, typically through the adaptation of the transmission parameters based on certain channel load metrics. On the other hand, awareness control protocols are also required to adequately support cooperative vehicular applications. Such protocols typically adapt the transmission parameters of periodic broadcast messages to ensure each vehicle's capacity to detect, and possibly communicate, with the relevant vehicles and infrastructure nodes present in its local neighborhood. To date, congestion and awareness control protocols have been normally designed and evaluated separately, although both will be required for the reliable and efficient operation of the system. To this aim, this paper proposes and evaluates INTERN, a new control protocol that integrates two congestion and awareness control processes. The simulation results obtained demonstrate that INTERN is able to satisfy the application's requirements of all vehicles, while effectively controlling the channel load.


Motivation reconsidered: the concept of competence


title: Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations.

abstract: We show that the gradient descent algorithm provides an implicit regularization effect in the learning of over-parameterized matrix factorization models and one-hidden-layer neural networks with quadratic activations. Concretely, we show that given $\tilde{O}(dr^{2})$ random linear measurements of a rank $r$ positive semidefinite matrix $X^{\star}$, we can recover $X^{\star}$ by parameterizing it by $UU^\top$ with $U\in \mathbb R^{d\times d}$ and minimizing the squared loss, even if $r \ll d$. We prove that starting from a small initialization, gradient descent recovers $X^{\star}$ in $\tilde{O}(\sqrt{r})$ iterations approximately. The results solve the conjecture of Gunasekar et al.'17 under the restricted isometry property. The technique can be applied to analyzing neural networks with one-hidden-layer quadratic activations with some technical modifications.


title: A novel type of activation function in artificial neural networks: Trained activation function

abstract: Abstract Determining optimal activation function in artificial neural networks is an important issue because it is directly linked with obtained success rates. But, unfortunately, there is not any way to determine them analytically, optimal activation function is generally determined by trials or tuning. This paper addresses, a simpler and a more effective approach to determine optimal activation function. In this approach, which can be called as trained activation function, an activation function was trained for each particular neuron by linear regression. This training process was done based on the training dataset, which consists the sums of inputs of each neuron in the hidden layer and desired outputs. By this way, a different activation function was generated for each neuron in the hidden layer. This approach was employed in random weight artificial neural network (RWN) and validated by 50 benchmark datasets. Achieved success rates by RWN that used trained activation functions were higher than obtained success rates by RWN that used traditional activation functions. Obtained results showed that proposed approach is a successful, simple and an effective way to determine optimal activation function instead of trials or tuning in both randomized single and multilayer ANNs.


title: The mirror system and its role in social cognition

abstract: Experiments in monkeys have shown that coding the goal of the motor acts is a fundamental property of the cortical motor system. In area F5, goal-coding motor neurons are also activated by observing motor acts done by others (the ‘classical’ mirror mechanism); in area F2 and area F1, some motor neurons are activated by the mere observation of goal-directed movements of a cursor displayed on a computer screen (a ‘mirror-like’ mechanism). Experiments in humans and monkeys have shown that the mirror mechanism enables the observer to understand the intention behind an observed motor act, in addition to the goal of it. Growing evidence shows that a deficit in the mirror mechanism underlies some aspects of autism.


title: Nonlinear Disturbance Observer-Based Control Design for a Robotic Exoskeleton Incorporating Fuzzy Approximation

abstract: To perform power augmentation tasks of a robotic exoskeleton, this paper utilizes fuzzy approximation and designed disturbance observers to compensate for the disturbance torques caused by unknown input saturation, fuzzy approximation errors, viscous friction, gravity, and payloads. The proposed adaptive fuzzy control with updated parameters' mechanism and additional torque inputs by using the disturbance observers are exerted into the robotic exoskeleton via feedforward loops to counteract to the disturbances. Through such an approach, the system does not need any requirement of built-in torque sensing units. In order to validate the proposed framework, extensive experiments are conducted on the upper limb exoskeleton using the state feedback and output feedback control to illustrate the performance of the proposed approaches.


title: Optimizing Constrained-Environment Redirected Walking Instructions Using Search Techniques

abstract: A goal of redirected walking (RDW) is to allow large virtual worlds to be explored within small tracking areas. Generalized steering algorithms, such as steer-to-center, simply move the user toward locations that are considered to be collision free in most cases. The algorithm developed here, FORCE, identifies collision-free paths by using a map of the tracking area's shape and obstacles, in addition to a multistep, probabilistic prediction of the user's virtual path through a known virtual environment. In the present implementation, the path predictions describe a user's possible movements through a virtual store with aisles. Based on both the user's physical and virtual location / orientation, a search-based optimization technique identifies the optimal steering instruction given the possible user paths. Path prediction uses the map of the virtual world; consequently, the search may propose steering instructions that put the user close to walls if the user's future actions eventually lead away from the wall. Results from both simulated and real users are presented. FORCE identifies collision-free paths in 55.0 percent of the starting conditions compared to 46.1 percent for generalized methods. When considering only the conditions that result in different outcomes, redirection based on FORCE produces collision-free path 94.5 percent of the time.


title: Skip Connections as Effective Symmetry-Breaking

abstract: Skip connections made the training of very deep neural networks possible and have become an indispendable component in a variety of neural architectures. A completely satisfactory explanation for their success remains elusive. Here, we present a novel explanation for the benefits of skip connections in training very deep neural networks. We argue that skip connections help break symmetries inherent in the loss landscapes of deep networks, leading to drastically simplified landscapes. In particular, skip connections between adjacent layers in a multilayer network break the permutation symmetry of nodes in a given layer, and the recently proposed DenseNet architecture, where each layer projects skip connections to every layer above it, also breaks the rescaling symmetry of connectivity matrices between different layers. This hypothesis is supported by evidence from a toy model with binary weights and from experiments with fully-connected networks suggesting (i) that skip connections do not necessarily improve training unless they help break symmetries and (ii) that alternative ways of breaking the symmetries also lead to significant performance improvements in training deep networks, hence there is nothing special about skip connections in this respect. We find, however, that skip connections confer additional benefits over and above symmetry-breaking, such as the ability to deal effectively with the vanishing gradients problem.


title: Deep learning for healthcare: review, opportunities and challenges.

abstract: Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability.


title: 50 mV-Input Batteryless Boost Converter for Thermal Energy Harvesting

abstract: A fully electrical startup boost converter for thermal energy harvesting is presented in this paper. The converter is implemented in a 65-nm bulk CMOS technology. With the proposed 3-stage stepping-up architecture, the minimum input voltage for startup is as low as 50 mV while the input voltage required for sustained power conversion is 30 mV. Due to the use of a zero-current-switching (ZCS) converter as the last stage and an automatic shutdown mechanism for the auxiliary converter, conversion efficiency up to 73% is achieved. By incorporating the boost converter and a thermoelectric generator (TEG), a miniaturized module is demonstrated for energy harvesting applications.


title: A Review of Ensemble Methods in Bioinformatics

abstract: Ensemble learning is an intensively studies technique in machine learning and pattern recognition. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures. The aim of this article is two-fold. First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based proteomics, gene-gene interaction identification from genome-wide association studies, and prediction of regulatory elements from DNA and protein sequences. Second, we try to identify and summarize future trends of ensemble methods in bioinformatics. Promising directions such as ensemble of support vector machine, meta-ensemble, and ensemble based feature selection are discussed.


title: Outline of a Theory of Intelligence

abstract: Intelligence is defined as that which produces successful behavior. Intelligence is assumed to result from natural selection. A model is proposed that integrates knowledge from research in both natural and artificial systems. The model consists of a hierarchical system architecture wherein: (1) control bandwidth decreases about an order of magnitude at each higher level, (2) perceptual resolution of spatial and temporal patterns contracts about an order-of-magnitude at each higher level, (3) goals expand in scope and planning horizons expand in space and time about an order-of-magnitude at each higher level, and (4) models of the world and memories of events expand their range in space and time by about an order-of-magnitude at each higher level. At each level, functional modules perform behavior generation (task decomposition planning and execution), world modeling, sensory processing, and value judgment. Sensory feedback control loops are closed at every level. >


title: A fully integrated battery-connected switched-capacitor 4:1 voltage regulator with 70% peak efficiency using bottom-plate charge recycling

abstract: This work presents a switched-capacitor (SC) DC-DC voltage regulator that converts a 3.7V battery voltage down to ~0.8V in order to power the `brain' SoC of a flapping-wing microrobotic bee. A cascade of two 2:1 SC converters offers high efficiency for a 4:1 conversion ratio. A charge recycling technique reduces the flying capacitor's bottom-plate parasitic loss by 50% and overall conversion efficiency reaches 70%. The output droop is less than 10% of the nominal output voltage for a worst-case 47mA load step.


title: Network-Assisted Outband D2D-Clustering in 5G Cellular Networks: Theory and Practice

abstract: We introduce a channel-opportunistic architecture that enhances the user experience in terms of throughput, fairness, and energy efficiency. Our proposed architecture leverages D2D communication and it is built on top of the forthcoming D2D features of 5G networks. In particular, we focus on outband D2D where cellular users are allowed to exploit both cellular (i.e., LTE-A) and WLAN (i.e., WiFi Direct) technologies to establish a D2D connection. In this architecture, cellular users form clusters, in which only the user with the best channel condition communicates with the base station on behalf of the entire cluster. Within the cluster, the unlicensed spectrum is utilized to relay traffic. In this article, we provide analytical models for the proposed system and study the impact of several payoff distribution methods commonly adopted in the literature on coalitional game theory. We then introduce an operator-controlled relay protocol based on the D2D features of LTE-A and WiFi Direct, and demonstrate the feasibility and the advantages of D2D-assisted cellular communication with our SDR prototype.


Implicit Learning : History & Applications


title: Object Oriented Concepts Databases And Applications

abstract: Thank you very much for downloading object oriented concepts databases and applications. Maybe you have knowledge that, people have look hundreds times for their favorite books like this object oriented concepts databases and applications, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their computer.


title: A Learning Framework for Age Rank Estimation Based on Face Images With Scattering Transform

abstract: This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age estimation based on face images. The proposed approach exploits relative-order information among the age labels for rank prediction. In our approach, the age rank is obtained by aggregating a series of binary classification results, where cost sensitivities among the labels are introduced to improve the aggregating performance. In addition, we give a theoretical analysis on designing the cost of individual binary classifier so that the misranking cost can be bounded by the total misclassification costs. An efficient descriptor, scattering transform, which scatters the Gabor coefficients and pooled with Gaussian smoothing in multiple layers, is evaluated for facial feature extraction. We show that this descriptor is a generalization of conventional bioinspired features and is more effective for face-based age inference. Experimental results demonstrate that our method outperforms the state-of-the-art age estimation approaches.


title: Goal and Preference Identification through natural language

abstract: Goal models allow efficient representation of stakeholder goals and alternative ways by which these can be satisfied. Preferences over goals in the goal model are then used to specify criteria for selecting alternatives that fit specific contexts, situations and strategies. Given such preferences, automated reasoning tools allow for efficient exploration of such alternatives. Nevertheless, to be amenable to such automated processing, goals and preferences need to be specified in a formal language, making automated processing inaccessible to the very bearers of goals and preferences, i.e., the stakeholders. We combine natural language processing techniques to allow specification of preferences through natural language statements. The natural language statement is first matched through regular expressions to distinguish between the preference component and the goal component. The former is then mapped to a preferential strength measure, while the latter is used to identify the relevant goal in the goal model through statistical semantic similarity techniques. The result constitutes a formal representation that can be used for alternatives analysis. In this way, stakeholders can access advanced goal reasoning techniques through simple natural language preference expressions, facilitating their decision making in various requirements analysis contexts. An experimental evaluation with human participants shows that the proposed system is of substantial precision and that a mapping from natural preferential verbalizations to predefined preferential strength labels is possible through sampling from crowds.


title: Blockchain application in food supply information security

abstract: With the increasingly serious problem of food safety in China, it directly or indirectly endangers people's health, quality of life and safety of life. The global economy, politics and society as a whole have a greater impact. As an effective means of product quality and safety management and control, many countries and regions have been researched, developed and operated of the traceability system. On the one hand, these technologies have not been able to achieve more accurate traceability, these results cannot be directly used in Chinese market. Therefore, the article introduces the concept of Blockchain technology, putting forward the application of Blockchain technology in information security of the food supply chain and comparing it with the traditional supply chain system.


title: Presence of hydrocarbon-degrading bacteria in the gills of mussel Mytilus galloprovincialis in a contaminated environment: a mesoscale simulation study

abstract: A number of previous studies have shown that the relationships of symbiosis existing between mussels and microorganisms are directly dependent on the environmental conditions. However, little is known about existing relationships between mussels and bacteria in hydrocarbon-impacted marine environments. The aim of this preliminary study is to investigate the presence of oil-degrading bacteria in the mussel Mytilus galloprovincialis during growth in polluted ecosystems. All the experiments were carried out in a mesocosm system designed to simulate chronic pollution and to enable direct exposure of mussels to chemicals. Quantitative (4′,6-diamidino-2-phenylindole, colony-forming units, Most Probable Number) analyses and screening (presence/absence) of metabolic functional genes were performed to analyse bacterial populations inside the gills of mussels exposed and not exposed to hydrocarbons. The data obtained show that the presence of hydrocarbons affected the abundance of bacteria inside the gills of speci...


title: The new eye of smart city: Novel citizen Sentiment Analysis in Twitter

abstract: Governments across the world are trying to move closer to their citizens for better smart city monitoring and governance. Twitter Sentiment Analysis is opening new opportunities to achieve it. In this paper, a methodological framework to collect, pre-process, analyse and map citizen sentiment from Twitter in helping the Governments monitor their citizens' moods is proposed based on the prior works. Multinomial Naive Bayes classifier is used to build a sentiment classifier, which employs a variety of features including a specific microblogging feature - emoji. Our proposed sentiment model outperforms the top system in the task of Sentiment Analysis in Twitter in SemEval-2013 in terms of averaged F scores. The novel feature emoji has proved to be useful for Sentiment Analysis in Twitter data in this work. We also apply our model to real-world tweets and present how Government agencies can track the fluctuation of citizens' moods using mapping techniques.


JAKARA: Developing Smart Dynamic Packaging Based Linked E-tourism Data


title: Comparison between first-order hold with zero-order hold in discretization of input-delay nonlinear systems

abstract: In large sampling period nonlinear systems, the Taylor series method was used to improve the performance of the controller. First-order hold (FOH) and zero-order hold (ZOH) are used respectively in the discretization of input time-delay systems. The sampled-data representation of FOH and ZOH are both described. The mathematical structure of new discretization schemes are proposed and characterized as useful methods of establishing concrete connections between numerical and system theoretic properties. The performances of the proposed discretization procedure are compared between FOH with ZOH. Various sampling rates and time-delay values are adopted to inspect the proposed method. The results demonstrate that the proposed discretization scheme can assure the system precision requirements even under the situation of a large sampling period. FOH and ZOH have their own advantages respectively for different input signals.


Hybrid Question Answering over Knowledge Base and Free Text


title: The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model

abstract: This study advances a detailed social-psychological model of climate change risk perceptions by combining and integrating cognitive, experiential, and socio-cultural factors. The conceptual model is tested empirically on a national sample (N = 808) of the UK population. Results indicate that the full climate change risk perception model (CCRPM) is able to explain nearly 70% of the variance in risk perception. Gender, political party, knowledge of the causes, impacts and responses to climate change, social norms, value orientations, affect and personal experience with extreme weather were all identified as significant predictors. Experiential and socio-cultural factors explained significantly more variance in risk perception than either cognitive or socio-demographic characteristics. Results also confirm that the factor analytic structure of climate change risk perceptions can be conceptualized along two key dimensions, namely: personal and societal risk judgments and that both dimensions have different psychological antecedents. Implications for theory and public risk communication are discussed.


title: Object representation and predictive action in infancy

abstract: Previous research has shown that 6-month-old infants extrapolate object motion on linear paths when they act predictively on fully visible moving objects but not when they observe partly occluded m ...


title: Growth and development of ovules of banana, plantain and enset (Musaceae)

abstract: Abstract Sterility has profound effects on the development of reproductive tissues in members of the Musaceae and limits genetic improvement required to deal with new diseases. Ovules of seeded diploid Musa acuminata (AA) and edible triploids (AAA) from the same cytogenetic group, plus edible triploids containing genomes of the M. balbisiana cytogenetic group (AAB, ABB) and the related genus Ensete sp. were studied to determine the effects of sterility on the growth and development of the ovule and its tissues. Specimens were collected from subtropical and Mediterranean environments in Australia. At anthesis, the ovules of triploid plants were 36% larger than the ovules of diploid plants. The diploid ovules ceased growth shortly after the inflorescence emerged from the pseudostem. In contrast, the triploid ovules continued to grow 7–10 days past anthesis, increasing in size by 70%. All ovules of diploid M. acuminata ssp. had an embryo sac at anthesis, against 97% for triploids. At anthesis the embryo sacs in diploid ovules occupied 2.7% of the nucellus compared with 1.5% in triploid ovules. The embryo sacs did not grow between bunch emergence and anthesis, once formed they maintained the same size. Many embryo sacs were not positioned correctly, flush with the nucellar cap. The diploid M. acuminata ssp. had 75% of embryo sacs correctly positioned against 10% in the edible triploids. The proportion of balbisiana genome (B) did not affect ovule or nucellus size or shape, or cell number across the nucellus. It increased the embryo sac presence 96–100% of ovules. The sterile edible triploid bananas have embryo sacs at anthesis but many are incorrectly positioned, which may contribute to their sterility. The balbisiana genome in the edible triploids was associated with a 2.4-fold increase in the number of correctly positioned embryo sacs and this may contribute to the increased fertility associated with the B genome.


title: Centroid-Based Summarization Of Multiple Documents: Sentence Extraction, Utility-Based Evaluation, And User Studies

abstract: We present a multi-document summarizer, called MEAD, which generates summaries using cluster centroids produced by a topic detection and tracking system. We also describe two new techniques, based on sentence utility and subsumption, which we have applied to the evaluation of both single and multiple document summaries. Finally, we describe two user studies that test our models of multi-document summarization.


title: Three Generative, Lexicalized Models For Statistical Parsing

abstract: In this paper we first propose a new statistical parsing model, which is a generative model of lexicalised context-free grammar. We then extend the model to include a probabilistic treatment of both subcategorisation and wh-movement. Results on Wall Street Journal text show that the parser performs at 88.1/87.5% constituent precision/recall, an average improvement of 2.3% over (Collins 96).


title: A security architecture for computational grids

abstract: State-of-the-art and emerging scientific applications require fast access to large quantities of data and commensurately fast computational resources. Both resources and data are often distributed in a wide-area network with components administered locally and independently. Computations may involve hundreds of processes that must be able to acquire resources dynamically and communicate efficiently. This paper analyzes the unique security requirements of large-scale distributed (grid) computing and develops a security policy and a corresponding security architecture. An implementation of the architecture within the Globus metacomputing toolkit is discussed.


title: Neuromorphic vision chips

abstract: The paper reviews the progress of neuromorphic vision chip research in decades. It focuses on two kinds of the neuromorphic vision chips: frame-driven (FD) and event-driven (ED) vision chips. The FD and ED vision chips are very different from each other in system architecture, image sensing, image information coding, image processing algorithm, design methodology. The vision chips can overcome serial data transmission and processing bottlenecks in traditional image processing systems. They can perform the high speed image capture and real-time image processing operations. This paper selects two typical chips from the two kinds of vision chips, respectively, and introduces their architectures, image sensing schemes, image processing processors and system operation. The FD neuromorphic reconfigurable vision chip comprises a high speed image sensor, a processing element array and self-organizing map neural network. The FD vision chip has the advantages in image resolution, static object detection, time-multiplex image processing, and chip area. The ED neuromorphic vision chip system is based on address-event-representation image sensor and event-driven multi-kernel convolution network. The ED vision chip has the advantages in fast sensing, low communication bandwidth, brain-like processing, and high energy efficiency. Finally, this paper discusses the architecture and the challenges of the future neuromorphic vision chip and indicates that the reconfigurable vision chip with left- and right-brain functions integrated in the three dimensional (3D) large-scale integrated circuit (LSI) technology becomes a trend of the research on the vision chip.


title: Bregman Alternating Direction Method of Multipliers

abstract: The mirror descent algorithm (MDA) generalizes gradient descent by using a Bregman divergence to replace squared Euclidean distance. In this paper, we similarly generalize the alternating direction method of multipliers (ADMM) to Bregman ADMM (BADMM), which allows the choice of different Bregman divergences to exploit the structure of problems. BADMM provides a unified framework for ADMM and its variants, including generalized ADMM, inexact ADMM and Bethe ADMM. ::: We establish the global convergence and the $O(1/T)$ iteration complexity for BADMM. In some cases, BADMM can be faster than ADMM by a factor of $O(n/\log(n))$. In solving the linear program of mass transportation problem, BADMM leads to massive parallelism and can easily run on GPU. BADMM is several times faster than highly optimized commercial software Gurobi.


title: Dependence, Correlation and Gaussianity in Independent Component Analysis

abstract: Independent component analysis (ICA) is the decomposition of a random vector in linear components which are "as independent as possible." Here, "independence" should be understood in its strong statistical sense: it goes beyond (second-order) decorrelation and thus involves the non-Gaussianity of the data. The ideal measure of independence is the "mutual information" and is known to be related to the entropy of the components when the search for components is restricted to uncorrelated components. This paper explores the connections between mutual information, entropy and non-Gaussianity in a larger framework, without resorting to a somewhat arbitrary decorrelation constraint. A key result is that the mutual information can be decomposed, under linear transforms, as the sum of two terms: one term expressing the decorrelation of the components and one expressing their non-Gaussianity.Our results extend the previous understanding of these connections and explain them in the light of information geometry. We also describe the "local geometry" of ICA by re-expressing all our results via a Gram-Charlier expansion by which all quantities of interest are obtained in terms of cumulants.


title: What We Talk About When We Talk About Game Aesthetics

abstract: Digital games are commonly described as phenomena that combine aesthetic, social and technological elements, yet our understanding of the aesthetic element of games and play is perhaps the least developed of all. All too often, an aesthetics perspective within game studies and design discourses is relegated to a marginal role, by conflating game aesthetics with graphics and “eye candy,” or by limiting aesthetic discussion to graphic style analysis or debates on the question “are games art?” Changing game technologies, as well as arguments from within philosophy, psychology, interaction design theory and cultural theory, call for us to examine the implicit and explicit assumptions we make when we write about aesthetics within game studies research, as a prelude to reclaiming a perspective that will allow us to better understand the way in which games function as sites for sensory and embodied play, creative activity and aesthetic experience.


title: A.I for Games with High Branching Factor

abstract: Monte Carlo tree search (MCTS) is a heuristic search method that is used to efficiently search decision trees. The method is particularly efficient in searching trees with a high branching factor. MCTS has a number of advantages over traditional tree search algorithms like simplicity, adaptability etc. This paper is a study of existing literature on different types of MCTS, specifically on using Genetic Algorithms with MCTS. It studies the advantages and disadvantages of this approach, and applies an enhanced variant to Gomoku, a board game with a high branching factor.


title: Steganography using B+trees and LSB

abstract: Data is the most valuable thing today from an individual to an organization because of it large size and useful information. But it is vulnerable and is more prone to attacks. Steganography hides selected files securely in a file system in such a way that without the corresponding access keys, an attacker would not be able to find their existence. Many steganography systems assure reliable data security over the internet. Still there are vulnerabilities which cause threat from attacker. B+trees are fast indexing method and increase performance. The aim of this paper is to introduce an efficient and strong approach for hiding secret message within image by using B+tree as a tool to compress the secret message, and an efficient steganographic algorithm for hiding text files in images for security. We have compared the proposed system with other existing Steganographic techniques. Our main focus is to maintain the quality of the image without distortion. However we have also tried to increase the embedding capacity of the hidden message in an image.


title: Single-Current-Sensor Control for PMSM Driven by Quasi-Z-Source Inverter

abstract: This paper proposes a new single-current-sensor (SCS) control method for a permanent-magnet synchronous motor (PMSM) driven by a quasi-Z-source inverter (qZSI). In this method, the sum of two branch currents in the inverter is sampled by an SCS twice for one control period, under actions of two different shoot-through voltage vectors. Then, the three-phase currents of the motor can be reconstructed by these two sample values and can be later used for the current closed-loop control of the PMSM. In this paper, the feasibility of the SCS control method is analyzed thoroughly and a corresponding modulation strategy is given for the qZSI. Compared with the traditional SCS control methods, the new method fully eliminates the measurement dead-zone problem without any additional compensation strategy. The operation range of the PMSM extends and utilization rate of the input voltage improves. In conclusion, the proposed method is easy and convenient to implement, which makes itself a promising application in electrical vehicles.


title: Techniques on text mining

abstract: Knowledge discovery from textual database refers generally to the process of extracting interesting or non-retrieval patterns or knowledge from unstructured text documents. This is also called as text data mining or knowledge discovery. It can be viewed as an extension of data mining or knowledge from structured databases. At present, the stored information is increasing tremendously day by day. This is unstructured form so we can not extract the needed information. Some data mining techniques are used to extract the useful information from text documents, such as classification, clustering, visualization and information extraction. Here framework of text mining with techniques is discussed as well as benefits and limitations of text mining have been discussed.


title: Retrieval-Based Neural Code Generation

abstract: In models to generate program source code from natural language, representing this code in a tree structure has been a common approach. However, existing methods often fail to generate complex code correctly due to a lack of ability to memorize large and complex structures. We introduce ReCode, a method based on subtree retrieval that makes it possible to explicitly reference existing code examples within a neural code generation model. First, we retrieve sentences that are similar to input sentences using a dynamic-programming-based sentence similarity scoring method. Next, we extract n-grams of action sequences that build the associated abstract syntax tree. Finally, we increase the probability of actions that cause the retrieved n-gram action subtree to be in the predicted code. We show that our approach improves the performance on two code generation tasks by up to +2.6 BLEU.


title: Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks

abstract: In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data. Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling. Our contributions are threefold: a) we present an efficient multi-scale approach to leverage both a large spatial context and the high resolution data, b) we investigate early and late fusion of Lidar and multispectral data, c) we validate our methods on two public datasets with state-of-the-art results. Our results indicate that late fusion make it possible to recover errors steaming from ambiguous data, while early fusion allows for better joint-feature learning but at the cost of higher sensitivity to missing data.


title: Why do teens spend real money in virtual worlds? A consumption values and developmental psychology perspective on virtual consumption

abstract: Spending real money on virtual goods and services is common among the young.We elucidate the customer value of virtual purchases in a social virtual world.We reflect the value against the developmental issues of adolescence.Premium user account, decoration, status, and boosted enjoyment motivate purchasing. Monetizing the users base is a major business challenge in social media, online social networking and online entertainment. Many of these services employ the freemium business model i.e. offer a free access to the service and try to generate revenue from selling premium user accounts with exclusive features or selling virtual items. However, the value users create by virtual purchasing is not well understood. Furthermore, young people constitute an important user segment for online social networking services that has not received proportionate attention in the literature. This study draws on Theory of Consumption Values and developmental psychology to elucidate the value teenage users create by spending money in a social virtual world (SVW) that employs the freemium business model in parallel with selling virtual items. The benefits of the premium user account, decoration, status, and boosted enjoyment of the user experience were the most common reasons for teenagers' purchasing within an SVW. From teenagers' developmental standpoint, virtual purchasing facilitates identity experiments and membership of peer groups. The results also reveal social hierarchies and discrimination among the users.


title: A 3.4 – 6.2 GHz Continuously tunable electrostatic MEMS resonator with quality factor of 460–530

abstract: In this paper we present the first MEMS electrostatically-tunable loaded-cavity resonator that simultaneously achieves a very high continuous tuning range of 6.2 GHz:3.4 GHz (1.8:1) and quality factor of 460–530 in a volume of 18×30×4 mm3 including the actuation scheme and biasing lines. The operating principle relies on tuning the capacitance of the loaded-cavity by controlling the gap between an electrostatically-actuated membrane and the cavity post underneath it. Particular attention is paid on the fabrication of the tuning mechanism in order to avoid a) quality factor degradation due to the biasing lines and b) hysteresis and creep issues. A single-crystal silicon membrane coated with a thin gold layer is the key to the success of the design.


title: Characteristic and sensitivity of Quantum Tunneling Composite (QTC) material for tactile device applications

abstract: This paper investigates the used of Quantum Tunneling Composite (QTC) Pill as a tactile sensor material for general tactile device application. The QTC Pill can be considered as soft, flexible material and has extraordinary electrical properties. It has capability of turning into a remarkable material for new era sensor. The piezoresistive experimental test of QTC Pill was performed to determine their resistivity characteristics and to evaluate the suitability of QTC Pills as tactile sensors. The experiments were conducted based on the sensitivity of the QTC Pill. The shows that, the QTC Pill was a perfect insulator as well as an extremely good conductor. This has been observed through resistance versus force (R vs F) investigation in which an exponential curve was obtained. This is coherent with the hypothesis of quantum tunneling charge transport within the polymer ‘assisted’ quantum tunneling. The hypothesis is further supported by comparing and contrasting current versus voltage (I vs V) curves. At intermediate pressure, QTC Pill exhibits a clearly not linear hysteric behaviour and current fluctuations. These characteristic features can be related to QTC Pill specific structure and electron quantum tunneling. Based on this investigation, QTC pill are suitable to be used as a material for general tactile device application and can be used as a guideline when designing a tactile sensor with this material.


title: A 15-Channel Digital Active Electrode System for Multi-Parameter Biopotential Measurement

abstract: This paper presents a digital active electrode (DAE) system for multi-parameter biopotential signal acquisition in portable and wearable devices. It is built around an IC that performs analog signal processing and digitization with the help of on-chip instrumentation amplifiers, a 12 bit ADC and a digital interface. Via a standard ${\rm I}^{{2}}{\rm C}$ bus, up to 16 digital active electrodes (15-channels) can be connected to a commercially available microcontroller, thus significantly reducing system complexity and cost. In addition, the DAE utilizes an innovative functionally DC-coupled amplifier to preserve input DC signal, while still achieving state-of-the-art performance: 60 nV/sqrt(Hz) input-referred noise and ${\pm} $ 350 mV electrode-offset tolerance. A common-mode feedforward scheme improves the CMRR of an AE pair from 40 dB to maximum 102 dB.


title: DepSpace: a byzantine fault-tolerant coordination service

abstract: The tuple space coordination model is one of the most interesting coordination models for open distributed systems due to its space and time decoupling and its synchronization power. Several works have tried to improve the dependability of tuple spaces through the use of replication for fault tolerance and access control for security. However, many practical applications in the Internet require both fault tolerance and security. This paper describes the design and implementation of DepSpace, a Byzantine fault-tolerant coordination service that provides a tuple space abstraction. The service offered by DepSpace is secure, reliable and available as long as less than a third of service replicas are faulty. Moreover, the content-addressable confidentiality scheme developed for DepSpace bridges the gap between Byzantine fault-tolerant replication and confidentiality of replicated data and can be used in other systems that store critical data.


title: An Efficient Global Point Cloud Descriptor for Object Recognition and Pose Estimation

abstract: This paper presents a global point cloud descriptor to be used for efficient object recognition and pose estimation. The proposed method is based on the estimation of a reference frame for the whole point cloud that represents an object instance, which is used for aligning it with the canonical coordinate system. After that, a descriptor is computed for the aligned point cloud based on how its 3D points are spatially distributed. Such descriptor is also extended with color distribution throughout the aligned point cloud. The global alignment transforms of matched point clouds are used for computing object pose. The proposed approach was evaluated with a publicly available dataset, showing that it outperforms major state of the art global descriptors regarding recognition rate and performance and that it allows precise pose estimation.


title: Depression and pain comorbidity: a literature review.

abstract: Because depression and painful symptoms commonly occur together, we conducted a literature review to determine the prevalence of both conditions and the effects of comorbidity on diagnosis, clinical outcomes, and treatment. The prevalences of pain in depressed cohorts and depression in pain cohorts are higher than when these conditions are individually examined. The presence of pain negatively affects the recognition and treatment of depression. When pain is moderate to severe, impairs function, and/or is refractory to treatment, it is associated with more depressive symptoms and worse depression outcomes (eg, lower quality of life, decreased work function, and increased health care utilization). Similarly, depression in patients with pain is associated with more pain complaints and greater impairment. Depression and pain share biological pathways and neurotransmitters, which has implications for the treatment of both concurrently. A model that incorporates assessment and treatment of depression and pain simultaneously is necessary for improved outcomes.


title: Computational modeling of chemo-bio-mechanical coupling: a systems-biology approach toward wound healing

abstract: Wound healing is a synchronized cascade of chemical, biological, and mechanical phenomena, which act in concert to restore the damaged tissue. An imbalance between these events can induce painful scarring. Despite intense efforts to decipher the mechanisms of wound healing, the role of mechanics remains poorly understood. Here, we establish a computational systems biology model to identify the chemical, biological, and mechanical mechanisms of scar formation. First, we introduce the generic problem of coupled chemo-bio-mechanics. Then, we introduce the model problem of wound healing in terms of a particular chemical signal, inflammation, a particular biological cell type, fibroblasts, and a particular mechanical model, isotropic hyperelasticity. We explore the cross-talk between chemical, biological, and mechanical signals and show that all three fields have a significant impact on scar formation. Our model is the first step toward rigorous multiscale, multifield modeling in wound healing. Our formulation...


Empathy in Medicine—A Neurobiological Perspective


title: Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

abstract: Because of their superior ability to preserve sequence information over time, Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with a more complex computational unit, have obtained strong results on a variety of sequence modeling tasks. The only underlying LSTM structure that has been explored so far is a linear chain. However, natural language exhibits syntactic properties that would naturally combine words to phrases. We introduce the Tree-LSTM, a generalization of LSTMs to tree-structured network topologies. Tree-LSTMs outperform all existing systems and strong LSTM baselines on two tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task 1) and sentiment classification (Stanford Sentiment Treebank).


Detailed methods and additional results accompanying “ Evolution of climate niches in European mammals ” by