title: Automatic Server Hang Bug Diagnosis: Feasible Reality or Pipe Dream?

abstract: It is notoriously difficult to diagnose server hang bugs as they often generate little diagnostic information and are difficult to reproduce offline. In this paper, we present a characteristic study of 177 real software hang bugs from 8 common open source server systems (i.e., Apache, Lighttpd, My SQL, Squid, HDFS, Hadoop Mapreduce, Tomcat, Cassandra). We identify three major root cause categories (i.e., Programmer errors, mishandled values, concurrency issues). We then describe two major problems (i.e., False positives and false negatives) while applying existing rule-based bug detection techniques to those bugs.


title: Crowdsourcing-based wifi fingerprint update for indoor localization

abstract: Researches on indoor localization become more and more popular because human spend more life time indoors than outdoors. Among all of the present indoor localization technologies, WiFi fingerprint localization is the most widely used. The method of fingerprint is based on matching the current received signal strength with fingerprints stored in the database to get user's position. This method can get high precision with the simple operation, but it's a labor-intensive work to acquire the fingerprint database which costs much time and human resources. In this paper, we proposed a crowdsourcing method to build an auto-update fingerprint database using the data fed back by numerous users. First we detect the user's step sequence using inertial sensors built in smartphones. Then we establish a Hidden Markov Model (HMM) and propose a Ratio-based Map Matching(RMM) algorithm to match the step sequence with the real path in the map. After the successful match, we bind each fingerprint collected during a walk to its corresponding position, so the auto-update fingerprint database is generated. We did some experiments in a teaching building to evaluate our proposed method, and the results show the accuracy achieved by the method is related to the length of the step sequence. If the step sequence is long enough, the database we generated is very close to the manual measuring results.


title: Privacy Preserving synonym based fuzzy multi-keyword ranked search over encrypted cloud data

abstract: Cloud Storage is currently one of the most widely used applications of cloud. As usage of cloud is increasing, critical and personal data is also being outsourced making it important to maintain confidentiality and integrity of this data. A basic way of protecting data is encrypting it before outsourcing, but the retrieval of required files from the encrypted cloud becomes a problem which requires searching over the encrypted data. Various schemes have been proposed to deal with this issue of searching over encrypted cloud data, however none of the existent schemes provide optimum user search experience resembling plaintext search. In this paper we propose Privacy Preserving Synonym Based Fuzzy Multi-Keyword Ranked Search Over Encrypted Cloud Data, a scheme which enhances user search experience to a paramount by providing both fuzzy and synonym based multi-keyword ranked search, thereby taking encrypted search experience closer to free text search engines. The scheme additionally improves upon index generation time and search time in comparison to existing schemes by utilizing a binary tree based dynamic index. Experimental results portray the effectiveness of this proposed scheme as it reduces the search time, i.e. the time for finding the desired documents, by 90% along with minimizing overhead of updating index when new files need to be uploaded (index generation time) as compared to the existing efficient indexing schemes in literature for a similar dataset. Optimization of search time together with index generation time has not been achieved to this extent before.


title: On the frequency control in high efficiency induction heating system with resonant inverter

abstract: This paper is focused on the possibilities of implementing an auto-adaptive control, without a phase-locked loop circuit, for the operating frequency in an induction heating system with single-phase voltage source inverter and resonant L-LC load. Based on the phasor diagram of the equivalent resonant load under resonance condition, three possibilities have been identified to express an error to be canceled in order to obtain the operation at resonance frequency. This error is proposed to be the input quantity of a PI frequency controller. To minimize the switching losses in the voltage source inverter, an increasing of the control frequency over the resonance value is achieved by increasing the component associated to the inductive current in the error expression. The Matlab/Simulink implementation and simulation results for the heating process of a seamless pipe confirm the theoretical findings. The results of the real-time implementation of the control algorithm on an experimental setup based on dSPACE DS1103 controller board illustrate its effectiveness.


title: Classification Using Association Rules: Weaknesses and Enhancements

abstract: Existing classification and rule learning algorithms in machine learning mainly use heuristic/greedy search to find a subset of regularities (e.g., a decision tree or a set of rules) in data for classification. In the past few years, extensive research was done in the database community on learning rules using exhaustive search under the name of association rule mining. The objective there is to find all rules in data that satisfy the user-specified minimum support and minimum confidence. Although the whole set of rules may not be used directly for accurate classification, effective and efficient classifiers have been built using the rules. This paper aims to improve such an exhaustive search based classification system CBA (Classification Based on Associations). The main strength of this system is that it is able to use the most accurate rules for classification. However, it also has weaknesses. This paper proposes two new techniques to deal with these weaknesses. This results in remarkably accurate classifiers. Experiments on a set of 34 benchmark datasets show that on average the new techniques reduce the error of CBA by 17% and is superior to CBA on 26 of the 34 datasets. They reduce the error of the decision tree classifier C4.5 by 19%, and improve performance on 29 datasets. Similar good results are also achieved against the existing classification systems, RIPPER, LB and a Naive-Bayes classifier.


title: Molecular dynamics simulation of elastic properties of multilayer MoS2 and graphene/MoS2 heterostructure

abstract: In this work the elastic properties of multilayer molybdenum disulfide (MoS 2 ) and graphene/MoS 2 heterostructure under uniaxial tensile and normal compressive strain is investigated. Molecular dynamic simulation is used to calculate the strain distribution and stress-strain relation of structures. The dependency of the stress-strain curve and elastic properties on the temperature, number of layers and stacking order of layers are considered. The results reveal and the elastic constants, yield strain and yield stress strongly depend on temperature and staking order of the MoS 2 layers. Furthermore, it can be concluded from the results that the Young's modulus and vertical elastic constant of the heterostructure is much larger than that of MoS 2 which is due to the higher stiffness and Young's modulus of graphene compared to MoS 2 .


title: Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data

abstract: Semantic technologies in enterprises have recently received increasing attention from both the research and industrial side. The concept of Linked Enterprise Data (LED) describes a framework to incorporate benefits of semantic technologies into enterprise IT environments. However, LED still remains an abstract idea lacking a point of origin, i.e., station zero from which it comes to existence. In this paper we argue and demonstrate that Enterprise Knowledge Graphs (EKGs) might be considered as an embodiment of LED lifting corporate information management to a semantic level which ultimately allows for real artificial intelligence applications. By EKG we refer to a semantic network of concepts, properties, individuals and links representing and referencing foundational and domain knowledge relevant for an enterprise. Although the concept of EKGs was not invented yesterday, both enterprise and semantic communities have not yet come up with a formal comprehensive framework for designing such graphs. In this paper we aim to join the dots between the expanding interest in EKGs expressed by those communities and the lack of blueprints for realizing the EKGs. A thorough study of the key design concepts provides a multi-dimensional aspects matrix from which an enterprise is able to choose specific features of the highest priority. We emphasize the importance of various data fusion approaches, e.g., unified and federated. In the extensive evaluation section we investigate the effect of the chosen approach on the EKG performance along several dimensions, e.g., basic reasoning and OWL entailment which account for machine understanding of the EKG data, and access control subsystem which is of the utmost importance in large enterprises.


title: A new recommender system framework for TV video

abstract: In recent years, recommender system has been one of the hottest topics in both industry and academia. The ability of providing personalized user experience has been a pivotal evaluation criterion to determine weather a company can be successful or not. In this paper, we fetch the first hand data from a Video Communication Co Ltd, and put forward a new recommender system framework for TV video. Based on Manhattan distance and considering programs popularity, we propose a new clustering algorithm, thus getting latent factors. According to similarity, we successfully map program to latent vector. Finally, we get the preference matrix by gradient descent method. Compared with SVDFeature on precision, recall and F1, it performs better.


title: Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition

abstract: Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos.


title: Transitioning to Physics-of-Failure as a Reliability Driver in Power Electronics

abstract: Power electronics has progressively gained an important status in power generation, distribution, and consumption. With more than 70% of electricity processed through power electronics, recent research endeavors to improve the reliability of power electronic systems to comply with more stringent constraints on cost, safety, and availability in various applications. This paper serves to give an overview of the major aspects of reliability in power electronics and to address the future trends in this multidisciplinary research direction. The ongoing paradigm shift in reliability research is presented first. Then, the three major aspects of power electronics reliability are discussed, respectively, which cover physics-of-failure analysis of critical power electronic components, state-of-the-art design for reliability process and robustness validation, and intelligent control and condition monitoring to achieve improved reliability under operation. Finally, the challenges and opportunities for achieving more reliable power electronic systems in the future are discussed.


title: Gender identity: A multidimensional analysis with implications for psychosocial adjustment.

abstract: This study examined the relations between components of gender identity and psychosocial adjustment. The aspects of gender identity assessed were (a) feelings of psychological compatibility with one's gender (i.e., feeling one is a typical member of one's sex and feeling content with one's biological sex), (b) feelings of pressure from parents, peers, and self for conformity to gender stereotypes, and (c) the sentiment that one's own sex is superior to the other (intergroup bias). Adjustment was assessed in terms of self-esteem and peer acceptance. Participants were 182 children in Grades 4 through 8. Felt gender compatibility (when operationalized as either self-perceived gender typicality or feelings of contentment with one's biological sex) was positively related to adjustment, whereas felt pressure and intergroup bias were negatively associated with adjustment. The results provide new insights into the role of gender identity in children's well-being, help identify sources of confusion in previous work, and suggest directions for future inquiry. Gender is a quintessential element of human identity. Given that one's gender assignment ordinarily is immutable and that many fundamental features of life—including the recreational, academic, occupational, and relationship activities that one is expected to pursue—are governed by gender, it seems likely that most people devote at least some time to reflecting on questions like these: How well do I fit with my gender category? Must I adhere to the stereotypes for my sex or am I free to explore cross-sex options? Is my sex superior or inferior to the other? We believe that beginning in childhood, people do ask these questions of themselves and, moreover, that their answers influence their psychosocial adjustment. The present study was designed to test these propositions. In our view, people's answers to the questions noted above represent components of their gender identity. We view gender identity as a multidimensional construct encompassing an individual's (a) knowledge of membership in a gender category, (b) felt compatibility with his or her gender group (i.e., self-perceptions of gender typicality as well as feelings of contentment with one's


title: Error patterns in MLC NAND flash memory: Measurement, characterization, and analysis

abstract: As NAND flash memory manufacturers scale down to smaller process technology nodes and store more bits per cell, reliability and endurance of flash memory reduce. Wear-leveling and error correction coding can improve both reliability and endurance, but finding effective algorithms requires a strong understanding of flash memory error patterns. To enable such understanding, we have designed and implemented a framework for fast and accurate characterization of flash memory throughout its lifetime. This paper examines the complex flash errors that occur at 30-40nm flash technologies. We demonstrate distinct error patterns, such as cycle-dependency, location-dependency and value-dependency, for various types of flash operations. We analyze the discovered error patterns and explain why they exist from a circuit and device standpoint. Our hope is that the understanding developed from this characterization serves as a building block for new error tolerance algorithms for flash memory.


title: 5G wareforms design for aeronautical communications

abstract: With the incoming of the new era of 5G mobile communications and Internet of Things (IoT), the aeronautical communications for aircraft, Air-to-Ground (A/G) communications and Air-to-Air (A/A) communications, confronts with new challenges of high safety, large transmission capacity, low latency, high elasticity, and synthetic service providing etc. In this paper, the new requirement and challenges of future aeronautical communications are reviewed. Moreover, the future aeronautical communications architecture is envisioned and the typical wareforms recommended for 5G are deeply analyzed. Finally, the transition from the current aeronautical datalinks is prospectively discussed.


title: Trajectory Outlier Detection: A Partition-and-Detect Framework

abstract: Outlier detection has been a popular data mining task. However, there is a lack of serious study on outlier detection for trajectory data. Even worse, an existing trajectory outlier detection algorithm has limited capability to detect outlying sub- trajectories. In this paper, we propose a novel partition-and-detect framework for trajectory outlier detection, which partitions a trajectory into a set of line segments, and then, detects outlying line segments for trajectory outliers. The primary advantage of this framework is to detect outlying sub-trajectories from a trajectory database. Based on this partition-and-detect framework, we develop a trajectory outlier detection algorithm TRAOD. Our algorithm consists of two phases: partitioning and detection. For the first phase, we propose a two-level trajectory partitioning strategy that ensures both high quality and high efficiency. For the second phase, we present a hybrid of the distance-based and density-based approaches. Experimental results demonstrate that TRAOD correctly detects outlying sub-trajectories from real trajectory data.


title: Cognitive Constraints and Island Effects

abstract: Competence-based theories of island effects play a central role in generative grammar, yet the graded nature of many syntactic islands has never been properly accounted for. Categorical syntactic accounts of island effects have persisted in spite of a wealth of data suggesting that island effects are not categorical in nature and that non-structural manipulations that leave island structures intact can radically alter judgments of island violations. We argue here, building on work by Deane, Kluender, and others, that processing factors have the potential to account for this otherwise unexplained variation in acceptability judgments.We report the results of self-paced reading experiments and controlled acceptability studies which explore the relationship between processing costs and judgments of acceptability. In each of the three self-paced reading studies, the data indicate that the processing cost of different types of island violations can be significantly reduced to a degree comparable to that of non-island filler-gap constructions by manipulating a single non-structural factor. Moreover, this reduction in processing cost is accompanied by significant improvements in acceptability. This evidence favors the hypothesis that island-violating constructions involve numerous processing pressures that aggregate to drive processing difficulty above a threshold so that a perception of unacceptability ensues. We examine the implications of these findings for the grammar of filler-gap dependencies.


title: Adaptive finite element methods for elliptic PDEs based on conforming centroidal Voronoi Delaunay triangulations

abstract: A new triangular mesh adaptivity algorithm for elliptic PDEs that combines a posteriori error estimation with centroidal Voronoi-Delaunay tessellations of domains in two dimensions is proposed and tested. The ability of the first ingredient to detect local regions of large error and the ability of the second ingredient to generate superior unstructured grids result in a mesh adaptivity algorithm that has several very desirable features, including the following. Errors are very well equidistributed over the triangles; at all levels of refinement, the triangles remain very well shaped, even if the grid size at any particular refinement level, when viewed globally, varies by several orders of magnitude; and the convergence rates achieved are the best obtainable using piecewise linear finite elements. This methodology can be easily extended to higher-order finite element approximations or mixed finite element formulations although only the linear approximation is considered in this paper.


Place-its: Location-based reminders on mobile phones


title: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition

abstract: This paper describes the winning algorithm we submitted to the recent NICE.I iris recognition contest. Efficient and robust segmentation of noisy iris images is one of the bottlenecks for non-cooperative iris recognition. To address this problem, a novel iris segmentation algorithm is proposed in this paper. After reflection removal, a clustering based coarse iris localization scheme is first performed to extract a rough position of the iris, as well as to identify non-iris regions such as eyelashes and eyebrows. A novel integrodifferential constellation is then constructed for the localization of pupillary and limbic boundaries, which not only accelerates the traditional integrodifferential operator but also enhances its global convergence. After that, a curvature model and a prediction model are learned to deal with eyelids and eyelashes, respectively. Extensive experiments on the challenging UBIRIS iris image databases demonstrate that encouraging accuracy is achieved by the proposed algorithm which is ranked the best performing algorithm in the recent open contest on iris recognition (the Noisy Iris Challenge Evaluation, NICE.I).


title: Dynamic graph metrics: Tutorial, toolbox, and tale

abstract: Abstract The central nervous system is composed of many individual units – from cells to areas – that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.


title: Vx32: Lightweight User-level Sandboxing on the x86

abstract: Code sandboxing is useful for many purposes, but most sandboxing techniques require kernel modifications, do not completely isolate guest code, or incur substantial performance costs. Vx32 is a multipurpose user-level sandbox that enables any application to load and safely execute one or more guest plug-ins, confining each guest to a system call API controlled by the host application and to a restricted memory region within the host's address space. Vx32 runs guest code efficiently on several widespread operating systems without kernel extensions or special privileges; it protects the host program from both reads and writes by its guests; and it allows the host to restrict the instruction set available to guests. The key to vx32's combination of portability, flexibility, and efficiency is its use of x86 segmentation hardware to sandbox the guest's data accesses, along with a lightweight instruction translator to sandbox guest instructions. ::: ::: We evaluate vx32 using microbenchmarks and whole system benchmarks, and we examine four applications based on vx32: an archival storage system, an extensible public-key infrastructure, an experimental user-level operating system running atop another host OS, and a Linux system call jail. The first three applications export custom APIs independent of the host OS to their guests, making their plug-ins binary-portable across host systems. Compute-intensive workloads for the first two applications exhibit between a 30% slowdown and a 30% speedup on vx32 relative to native execution; speedups result from vx32's instruction translator improving the cache locality of guest code. The experimental user-level operating system allows the use of the guest OS's applications alongside the host's native applications and runs faster than whole-system virtual machine monitors such as VMware and QEMU. The Linux system call jail incurs up to 80% overhead but requires no kernel modifications and is delegation-based, avoiding concurrency vulnerabilities present in other interposition mechanisms.


title: Update on the use of immunoglobulin in human disease: A review of evidence

abstract: Human immunoglobulin preparations for intravenous or subcutaneous administration are the cornerstone of treatment in patients with primary immunodeficiency diseases affecting the humoral immune system. Intravenous preparations have a number of important uses in the treatment of other diseases in humans as well, some for which acceptable treatment alternatives do not exist. We provide an update of the evidence-based guideline on immunoglobulin therapy, last published in 2006. Given the potential risks and inherent scarcity of human immunoglobulin, careful consideration of its indications and administration is warranted.


title: Employing patterns and layers for early-stage design and prototyping of cross-device user interfaces

abstract: Designing UIs that run across multiple devices is increasingly important. To address this, we have created a prototyping tool called Damask, which targets web UIs that run on PCs and mobile phones, and prompt-and-response style voice UIs. In Damask, designers sketch out their design for one device while using design patterns to specify higher-level concepts within their design. Damask's patterns include pre-built UI fragments that are already optimized for each device. Designers also use layers to specify which UI parts are common across devices and which are specific to one device. Damask uses the sketches and patterns to generate designs for the other devices, which the designers can refine. A study performed with 12 professional UI designers found that, in the early stages, designers using patterns and layers in Damask created cross-device UIs that are rated at least as good as those created without patterns and layers, without more time.


title: Discovery of activity patterns using topic models

abstract: In this work we propose a novel method to recognize daily routines as a probabilistic combination of activity patterns. The use of topic models enables the automatic discovery of such patterns in a user's daily routine. We report experimental results that show the ability of the approach to model and recognize daily routines without user annotation.


title: Effects of cyanobacteria decomposition on the remobilization and ecological risk of heavy metals in Taihu Lake

abstract: To investigate the relationship between cyanobacteria decomposition and the remobilization of heavy metals in Taihu Lake, the indoor simulation experiments were conducted. The areas of Taihu Lake that undergo harmful algal blooms mostly caused by excessive cyanobacteria have serious problems of heavy metal pollution. The results showed that cyanobacteria decomposition can release heavy metals into the water and change the total contents and chemical speciation of Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb in sediment due to the change of physical and chemical properties in overlying water and sediment. The decomposition rate of cyanobacteria with sediment was clearly faster than that without sediment, and decomposition changed the pH and dissolved oxygen (DO) concentrations in overlying water. The cyanobacteria decomposition reduced the oxidation-reduction potential (ORP), and increased organic matter (OM) and total organic carbon (TOC) in the surface sediment. According to ecological risk assessment, the cyanobacteria decomposition increased the degree of heavy metal pollution and the potential ecological risk in sediment.


title: Advanced Analytics for Train Delay Prediction Systems by Including Exogenous Weather Data

abstract: State-of-the-art train delay prediction systems neither exploit historical data about train movements, nor exogenous data about phenomena that can affect railway operations. They rely, instead, on static rules built by experts of the railway infrastructure based on classical univariate statistics. The purpose of this paper is to build a data-driven train delay prediction system that exploits the most recent analytics tools. The train delay prediction problem has been mapped into a multivariate regression problem and the performance of kernel methods, ensemble methods and feed-forward neural networks have been compared. Firstly, it is shown that it is possible to build a reliable and robust data-driven model based only on the historical data about the train movements. Additionally, the model can be further improved by including data coming from exogenous sources, in particular the weather information provided by national weather services. Results on real world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the current state-of-the-art train delay prediction systems. Moreover, the performed simulations show that the inclusion of weather data into the model has a significant positive impact on its performance.


title: SoK: Single Sign-On Security — An Evaluation of OpenID Connect

abstract: OpenID Connect is the OAuth 2.0-based replacement for OpenID 2.0 (OpenID) andone of the most important Single Sign-On (SSO) protocols used for delegatedauthentication. It is used by companies like Amazon, Google, Microsoft, andPayPal. In this paper, we systematically analyze well-known attacks on SSOprotocols and adapt these on OpenID Connect. Additionally, we introduce twonovel attacks on OpenID Connect, Identity Provider Confusion and MaliciousEndpoints Attack, abusing flaws in the current specification and breaking thesecurity goals of the protocol. In 2014 we communicated with the authors of theOpenID Connect specification about these attacks and helped to repair the issue(currently an RFC Draft). We categorize the described attacks into two classes: Single-Phase Attacksabusing a lack of a single security check and Cross-Phase Attacks requiring acomplex attack setup and manipulating multiple messages distributed across thewhole protocol workflow. We provide an evaluation of officially referencedOpenID Connect libraries and find 75% of them vulnerable to at least oneSingle-Phase Attack. All libraries are susceptible to Cross-Phase Attacks, which is not surprising since the attacks abuse a logic flaw in the protocoland not an implementation error. We reported the found vulnerabilities to thedevelopers and helped them to fix the issues. We address the existing problemsin a Practical Offensive Evaluation of Single Sign-On Services (PrOfESSOS). PrOfESSOS is our open source implementation for a fully automatedEvaluation-as-a-Service for SSO. PrOfESSOS introduces a generic approach toimprove the security of OpenID Connect implementations by system-aticallydetecting vulnerabilities. In collaboration with the IETF OAuth and OpenIDConnect working group, we integrate PrOfESSOS into the OpenID Connect certification process. PrOfESSOS is available at https://openid.sso-security.de.


title: Performance of IEEE 802.11 based WLAN devices under various jamming signals

abstract: IEEE 802.11 based WLAN systems have gained interest to be used in the military and public authority environments, where the radio conditions can be harsh due to intentional jamming. The radio environment can be difficult also in commercial and civilian deployments since the unlicensed frequency bands are crowded. To study these problems, we built a test bed with a controlled signal path to measure the effects of different interfering signals to WLAN communications. We use continuous wideband noise jamming as the point of comparison, and focus on studying the effect of pulsed jamming and frequency sweep jamming. In addition, we consider also medium access control (MAC) interference. Based on the results, WLAN systems do not seem to be sensitive to the tested short noise jamming pulses. Under longer pulses, the effects are seen, and long data frames are more vulnerable to jamming than short ones. In fact, even a small amount of long frames in a data stream can ruin the performance of the whole link. Under frequency sweep jamming, slow sweeps with narrowband jamming signals can be quite harmful to WLAN communications. The results of MAC jamming show significant variation in performance between the different devices: The clear channel assessment (CCA) mechanism of some devices can be jammed very easily by using WLAN-like jamming signals. As a side product, the study also revealed some countermeasures against jamming.


title: Measuring user satisfaction with data warehouses: an exploratory study

abstract: Data warehouses are users driven; that is, they allow end-users to be in control of the data. As user satisfaction is commonly acknowledged as the most useful measurement of system success, we identify the underlying factors of end-user satisfaction with data warehouses and develop an instrument to measure these factors. The study demonstrates that most of the items in classic end-user satisfaction measure are still valid in the data warehouse environment, and that end-user satisfaction with data warehouses depends heavily on the roles and performance of organizational information centers.


title: Therac 25: A New Medical Accelerator Concept

abstract: An electron linear accelerator system with several novel features has been developed for radiation therapy. The beam from a 25 cell S-band standing wave structure, operated in the ?/2 mode with on-axis couplers, is reflected in an achromatic isochronous magnet and reinjected into the accelerator. The second pass doubles the energy while conserving rf power and minimizing the overall length of the unit. The beam is then transported through an annular electron gun and bent into the collimator by an innovative two-element doubly achromatic doubly focusing 270° magnet which allows a significant reduction in unit height. The energy is reduced by adjusting the position of the reflecting magnet with respect to the accelerator. The system generates 5 Gy m2min-1 beams of 25 MV photons and 5 to 25 MeV electrons. Extensive use of tungsten shielding minimizes neutron leakage. The photon mode surface dose is reduced by a carefully optimized electron filter. An improved scanning system gives exceptionally low electron -mode photon contamination.


title: Topology-Aware Mapping Techniques for Heterogeneous HPC Systems: A Systematic Survey

abstract: At the present time, the modern platforms of high-performance computing (HPC) consists of heterogeneous computing devices which are connected through complex hierarchical networks. Moreover, it is moving towards the Exascale era and which makes the number of nodes to increase as well as the number of cores within a node to increase. As a consequence, the communication costs and the data movement are increasing. Given that, the efficient topology-aware process mapping has become vital to efficiently optimize the data locality management in order to improve the system performance and energy consumption. It will also decrease the communication cost of the processes by matching the application virtual topology (exploited by the system for assigning the processes to the physical processor) to the target underlying hardware architecture called physical topology. Additionally, improving the locality problem which is one of the most challenging issues faced by the current parallel applications. In this survey paper, we have studied various topology-aware mapping techniques and algorithms.


SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics


title: A Deep Learning Framework for Smart Street Cleaning

abstract: Conventional street cleaning methods include street sweepers going to various spots in the city and manually verifying if the street needs cleaning and taking action if required. However, this method is not optimized and demands a huge investment in terms of time and money. This paper introduces an automated framework which addresses street cleaning problem in a better way by making use of modern equipment with cameras and computational techniques to analyze, find and efficiently schedule clean-up crews for the areas requiring more attention. Deep learning-based neural network techniques can be used to achieve better accuracy and performance for object detection and classification than conventional machine learning algorithms for large volume of images. The proposed framework for street cleaning leverages the deep learning algorithm pipeline to analyze the street photographs and determines if the streets are dirty by detecting litter objects. The pipeline further determines the the degree to which the streets are littered by classifying the litter objects detected in earlier stages. The framework also provides information on the cleanliness status of the streets on a dashboard updated in real-time. Such framework can prove effective in reducing resource consumption and overall operational cost involved in street cleaning.


title: Incorporating partitioning and parallel plans into the SCOPE optimizer

abstract: Massive data analysis on large clusters presents new opportunities and challenges for query optimization. Data partitioning is crucial to performance in this environment. However, data repartitioning is a very expensive operation so minimizing the number of such operations can yield very significant performance improvements. A query optimizer for this environment must therefore be able to reason about data partitioning including its interaction with sorting and grouping. SCOPE is a SQL-like scripting language used at Microsoft for massive data analysis. A transformation-based optimizer is responsible for converting scripts into efficient execution plans for the Cosmos distributed computing platform. In this paper, we describe how reasoning about data partitioning is incorporated into the SCOPE optimizer. We show how relational operators affect partitioning, sorting and grouping properties and describe how the optimizer reasons about and exploits such properties to avoid unnecessary operations. In most optimizers, consideration of parallel plans is an afterthought done in a postprocessing step. Reasoning about partitioning enables the SCOPE optimizer to fully integrate consideration of parallel, serial and mixed plans into the cost-based optimization. The benefits are illustrated by showing the variety of plans enabled by our approach.


title: Real-Time Document Image Classification using Deep CNN and Extreme Learning Machines

abstract: This paper presents an approach for real-time training and testing for document image classification. In production environments, it is crucial to perform accurate and (time-)efficient training. Existing deep learning approaches for classifying documents do not meet these requirements, as they require much time for training and fine-tuning the deep architectures. Motivated from Computer Vision, we propose a two-stage approach. The first stage trains a deep network that works as feature extractor and in the second stage, Extreme Learning Machines (ELMs) are used for classification. The proposed approach outperforms all previously reported structural and deep learning based methods with a final accuracy of 83.24% on Tobacco-3482 dataset, leading to a relative error reduction of 25% when compared to a previous Convolutional Neural Network (CNN) based approach (DeepDocClassifier). More importantly, the training time of the ELM is only 1.176 seconds and the overall prediction time for 2,482 images is 3.066 seconds. As such, this novel approach makes deep learning-based document classification suitable for large-scale real-time applications.


The 2011 KIT QUAERO Speech-to-Text System for Russian


title: Towards Automated IoT Application Deployment by a Cloud-Based Approach

abstract: Internet of Things solutions are typically domain-specific, relying on heterogeneous hardware, communication protocols and data models. In such system environments, the deployment of IoT applications is very intricate. The application environments differ from one system to another and service management procedures are non-standardized, making it hard for solution providers to efficiently deploy and configure applications for a large number of users. This paper proposes to employ TOSCA-a new standard for cloud service management-to systematically specify the components and configurations of IoT applications. We will demonstrate that, by using TOSCA, application models can be reused, and deployment processes can be automated in heterogeneous IoT system environments.


A Data Filtering Method Based on Agglomerative Clustering


title: An under actuated robotic arm with adjustable stiffness shape memory polymer joints

abstract: Various robotic applications including surgical instruments, wearable robots and autonomous mobile robots are often constrained with strict design requirements on high degrees of freedom (DoF) and minimal volume and weight. An intuitive design to meet these contradictory requirements is to embed locking mechanism in under actuated robotic manipulators to direct the actuation from a single and remote source to drive different joints on demand. Mechanical clutches do serve such purposes but often are bulky and require auxiliary mechanism making it difficult to justify the high cost adding the additional DoF, especially in cm scale. Here, we introduce an under-actuated robotic arm with shape memory polymer (SMP) joints. Through controlling the temperature, the stiffness of the joints can be adjusted and selected joints will be activated while the rest are fixed in their position. The presented prototype can control the joints independently with a coupled actuation from two stepper motors. Since we have redundant DoFs in the arm, there can be more than one configuration to reach a given position. We use a probabilistic technique to determine the optimum configuration with the minimum number of active joints that can yield the desired posture. In this paper, we report on the performance of the proposed design for the hardware and the configuration planner.


title: The “Eye Avoidance” Hypothesis of Autism Face Processing

abstract: Although a growing body of research indicates that children with autism spectrum disorder (ASD) exhibit selective deficits in their ability to recognize facial identities and expressions, the source of their face impairment is, as yet, undetermined. In this paper, we consider three possible accounts of the autism face deficit: (1) the holistic hypothesis, (2) the local perceptual bias hypothesis and (3) the eye avoidance hypothesis. A review of the literature indicates that contrary to the holistic hypothesis, there is little evidence to suggest that individuals with autism do perceive faces holistically. The local perceptual bias account also fails to explain the selective advantage that ASD individuals demonstrate for objects and their selective disadvantage for faces. The eye avoidance hypothesis provides a plausible explanation of face recognition deficits where individuals with ASD avoid the eye region because it is perceived as socially threatening. Direct eye contact elicits a increased physiological response as indicated by heightened skin conductance and amygdala activity. For individuals with autism, avoiding the eyes is an adaptive strategy, however, this approach interferes with the ability to process facial cues of identity, expressions and intentions, exacerbating the social challenges for persons with ASD.


title: Activity recognition from acceleration data based on discrete consine transform and SVM

abstract: This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.


title: Primate Structures in Synthetic Dynamic Active Visual Saliency

abstract: We implement biologically plausible early vision processes on a distributed vision system centered around an active stereo vision mechanism. We develop visual saliency for active analysis of real, dynamic scenes. We optimise real-time performance by minimising network traffic and maximising CPU loads in the distributed synthetic vision system. We see that the structures and functional pathways of the synthetic system form an architecture broadly similar to that observed from neural correlates in primates. The correspondence to biology has emerged naively, as a result of performance optimisation, and not by directly modeling the known or hypothesized architecture and functional pathways of primate visual centers.


title: Mining sequences of temporal intervals

abstract: Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that at the same time: defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe an algorithm that employs the new support definition and demonstrate its performance on field data from the automotive business.


title: Enabling the Internet of Things

abstract: Merging the virtual World Wide Web with nearby physical devices that are part of the Internet of Things gives anyone with a mobile device and the appropriate authorization the power to monitor or control anything.


title: Detection of phishing URLs using machine learning techniques

abstract: Phishing costs Internet users billions of dollars per year. It refers to luring techniques used by identity thieves to fish for personal information in a pond of unsuspecting Internet users. Phishers use spoofed e-mail, phishing software to steal personal information and financial account details such as usernames and passwords. This paper deals with methods for detecting phishing Web sites by analyzing various features of benign and phishing URLs by Machine learning techniques. We discuss the methods used for detection of phishing Web sites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. The fine-tuned parameters are useful in selecting the apt machine learning algorithm for separating the phishing sites from benign sites.


title: Potent antagonistic action of synthetic analogues of APGWGNamide, an antagonist of molluscan neuropeptide APGWamide

abstract: Abstract Fifty-five kinds of analogues of APGWGNamide (Ala–Pro–Gly–Trp–Gly–Asn–NH2), which is an antagonist of molluscan neuropeptide APGWamide, were synthesized and their antagonistic activities were examined on two molluscan smooth muscles. Among all the analogues tested, on spontaneous contraction of the crop of the land snail, Euhadra congenita, APGWG( l -biphenylalanine, Bip)amide showed the most potent antagonistic activity and its potency was 50–100 times higher than that of APGWGNamide. Likewise, on phasic contraction of the anterior byssus retractor muscle (ABRM) of the sea mussel, Mytilus edulis, the effect of APGWG( d -homophenylalanine, dHfe) was the most potent and showed 5–10 times stronger activity than that of APGWGNamide. In the tolerance test to known exo- and endopeptidases or the crop tissue homogenate, APGWGNamide was not only easily degraded by a proline-specific endopeptidase but also by the homogenate. Two kinds of potent antagonists were thus developed: APGWG(Bip)amide and APGWG(dHfe)amide, which will be useful tools for investigation of the function of APGWamide in the snail and the mussel, respectively.


title: Cooperative self-organization of afferent and lateral connections in cortical maps

abstract: A self-organizing neural network model called LISSOM for the synergetic development of afferent and lateral connections in cortical feature maps is presented. The weight adaptation process is purely activity-dependent, unsupervised, and local. The afferent input weights self-organize into a topological map of the input space. At the same time, the lateral interconnection weights adapt, and a unique lateral interaction profile develops for each neuron. Weak lateral connections die off, leaving a pattern of connections that represents the significant long-term correlations of activity on the feature map. LISSOM demonstrates how self-organization can bootstrap based on input information only, without global supervision or predetermined lateral interaction. The model gives rise to a nontopographically organized lateral connectivity similar to that observed in the mammalian neocortex as illustrated by a LISSOM model of ocular dominance column formation in the primary visual cortex. In addition, LISSOM can potentially account for the development of multiple maps of different modalities on the same undifferentiated cortical architecture.


title: Siamese Generative Adversarial Privatizer for Biometric Data

abstract: State-of-the-art machine learning algorithms can be fooled by carefully crafted adversarial examples. As such, adversarial examples present a concrete problem in AI safety. In this work we turn the tables and ask the following question: can we harness the power of adversarial examples to prevent malicious adversaries from learning identifying information from data while allowing non-malicious entities to benefit from the utility of the same data? For instance, can we use adversarial examples to anonymize biometric dataset of faces while retaining usefulness of this data for other purposes, such as emotion recognition? To address this question, we propose a simple yet effective method, called Siamese Generative Adversarial Privatizer (SGAP), that exploits the properties of a Siamese neural network to find discriminative features that convey identifying information. When coupled with a generative model, our approach is able to correctly locate and disguise identifying information, while minimally reducing the utility of the privatized dataset. Extensive evaluation on a biometric dataset of fingerprints and cartoon faces confirms usefulness of our simple yet effective method.


title: Design Optimization of the Lines of the Bulbous Bow of a Hull Based on Parametric Modeling and Computational Fluid Dynamics Calculation

abstract: To reduce the ship wave-making resistance, the lines of the bulbous bow of a hull are optimized by an automatic optimization platform at the ship design stage. Parametric modeling was applied to the hull by using non-uniform rational basis spline (NURBS). The Rankine-source panel method was used to calculate the wave-making resistance. A hybrid optimization strategy was applied to achieve the optimization goal. A Ro-Ro ship was taken as an example to illustrate the optimization method adopted, with the objective to minimize the wave-making resistance. The optimization results show that wave-making resistance obviously reduces and the wave-shape of the near bow becomes gentle after the lines of the bulbous bow of the hull are optimized, which demonstrates the validity of the proposed optimization design strategy.


title: Research on Parallel Vulnerabilities Discovery Based on Open Source Database and Text Mining

abstract: For the question of information security vulnerabilities discovery, the parallel vulnerabilities discovery method is given based on the CAPEC, CWE, CVE and other open source database and text mining. Firstly, we can extract the association vulnerability CWE under the same attack mode, then from CWE associated with CVE based on open source database. That can help us to analyze the potential parallel relationship of the multiple vulnerabilities. Secondly, the vulnerability description information will be vectorized, so that the software system is able to intelligent processing to vulnerability data. That is different from the query based on keyword matching, analyzes the similarity between the multiple vulnerabilities according to the threshold from the training set, and computes the parallel relationship between the multiple vulnerabilities and discovery the parallel vulnerabilities. Finally, this method is correct and effective by the experimental verification and in practice. According to this method, we are able to repair other parallel vulnerabilities when finding a vulnerability is exploited. An advantage of our method is that is applied to network defense.


title: A fully integrated 802.11n radio with 24GHz harmonic LO generation for low-cost, low power, multi-standard systems

abstract: We report a novel harmonic LOG based direct conversion RF transceiver for 802.11n radio. This multi-comm transceiver consists of Bluetooth SoC and WiFi, it includes 19–24GHz VCO, integrated front-end including WiFi T/R and WiFi-BT switches. Fabricated in 90nm digital CMOS technology, this IC consumes 422/560mW (Rx/Tx 802.11n 300/150Mbps), 110mW in BT mode, with an area of approx 19mm2. A peak saturated power of 24/9dBm is achieved at antenna in WiFi/BT mode.


title: Visual tree detection for autonomous navigation in forest environment

abstract: This paper describes a classification based tree detection method for autonomous navigation of forest vehicles in forest environment. Fusion of color, and texture cues has been used to segment the image into tree trunk and background objects. The segmentation of images into tree trunk and background objects is a challenging task due to high variations of illumination, effect of different color shades, non-homogeneous bark texture, shadows and foreshortening. To accomplish this, the approach has been to find the best combinations of color, and texture descriptors, and classification techniques. An additional task has been to estimate the distance between forest vehicle and the base of segmented trees using monocular vision. A simple heuristic distance measurement method is proposed that is based on pixel height and a reference width. The performance of various color and texture operators, and accuracy of classifiers has been evaluated using cross validation techniques.


title: XML document clustering: techniques and challenges

abstract: The increasing availability of heterogeneous XML sources has raised a number of issues concerning how to represent and manage these semi-structured data. In recent years due to the importance of managing these resources and extracting knowledge from them, lots of methods have been proposed in order to represent and cluster them in different ways. Different similarity measures have been extended and also in some context semantic issues have been taken into account. In this context, we review different XML clustering methods with considering different representation methods such as tree based and vector based with use of different similarity measures. We also propose taxonomy for these proposed methods.


title: IOS Press Development of blind image deconvolution and its applications

abstract: This paper is a supplement and update to the reviews by Kundur and Hatzinakos (7,8) on blind image deconvolution. Most of the methods reviewed in (7,8) require that the PSF and the original image must be irreducible. However, this irreducibility assumption is not true in some important types of applications, such as when the PSF is Gaussian, which is a good model for many imaging systems. After a brief summary of existing blind deconvolution methods, we report the recent development in this field with an emphasis on Gaussian blind deconvolution and its clinical applications.


title: Tobler's First Law of Geography, Self Similarity, and Perlin Noise: A Large Scale Analysis of Gradient Distribution in Southern Utah with Application to Procedural Terrain Generation

abstract: A statistical analysis finds that in a 160,000 square kilometer region of southern Utah gradients appear to be exponentially distributed at resolutions from 5m up to 1km. A simple modification to the Perlin noise generator changing the gradient distribution in each octave to an exponential distribution results in realistic and interesting procedurally generated terrain. The inverse transform sampling method is used in the amortized noise algorithm to achieve an exponential distribution in each octave, resulting in the generation of infinite non-repeating terrain with the same characteristics.


title: Classification of massive mobile web log URLs for customer profiling & analytics

abstract: Many telecommunication companies today have actively started to transform the way they do business, going beyond communication infrastructure providers are repositioning themselves as data-driven service providers to create new revenue streams. In this paper, we present a novel industrial application where a scalable Big data approach combined with deep learning is used successfully to classify massive mobile web log data, to get new aggregated insights on customer web behaviors that could be applied to various industry verticals.


title: Where to Buy It: Matching Street Clothing Photos in Online Shops

abstract: In this paper, we define a new task, Exact Street to Shop, where our goal is to match a real-world example of a garment item to the same item in an online shop. This is an extremely challenging task due to visual differences between street photos (pictures of people wearing clothing in everyday uncontrolled settings) and online shop photos (pictures of clothing items on people, mannequins, or in isolation, captured by professionals in more controlled settings). We collect a new dataset for this application containing 404,683 shop photos collected from 25 different online retailers and 20,357 street photos, providing a total of 39,479 clothing item matches between street and shop photos. We develop three different methods for Exact Street to Shop retrieval, including two deep learning baseline methods, and a method to learn a similarity measure between the street and shop domains. Experiments demonstrate that our learned similarity significantly outperforms our baselines that use existing deep learning based representations.


title: Quantum Models of Cognition and Decision

abstract: Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.


title: Improved Split-Ring Resonator for Microfluidic Sensing

abstract: We present a method of making low-loss split-ring resonators (SRRs) for microfluidic sensing at microwave frequencies using silver-coated copper wire. We show that a simple geometric modification and the use of square cross-section wire give greater electric field confinement in the capacitive region of the resonant sensor. We use a combination of theoretical analysis, finite-element simulations, and empirical measurements to demonstrate the subsequent increases in the sensitivity of these SRRs for complex permittivity measurements of some common solvents.


title: Efficient people tracking in laser range data using a multi-hypothesis leg-tracker with adaptive occlusion probabilities

abstract: We present an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, and a multi-hypothesis data association strategy. People are defined as high-level tracks consisting of two legs that are found with little model knowledge. We extend the data association so that it explicitly handles track occlusions in addition to detections and deletions. Additionally, we adapt the corresponding probabilities in a situation-dependent fashion so as to reflect the fact that legs frequently occlude each other. Experimental results carried out with a mobile robot illustrate that our approach can robustly and efficiently track multiple people even in situations of high levels of occlusion.


title: Imaging the fetal central nervous system

abstract: The low prevalence of fetal central nervous system anomalies results in a restricted level of exposure and limited experience for most of the obstetricians involved in prenatal ultrasound. Sonographic guidelines for screening the fetal brain in a systematic way will probably increase the detection rate and enhance a correct referral to a tertiary care center, offering the patient a multidisciplinary approach of the condition. ::: ::: This paper aims to elaborate on prenatal sonographic and magnetic resonance imaging (MRI) diagnosis and outcome of various central nervous system malformations. Detailed neurosonographic investigation has become available through high resolution vaginal ultrasound probes and the development of a variety of 3D ultrasound modalities e.g. ultrasound tomographic imaging. In addition, fetal MRI is particularly helpful in the detection of gyration and neurulation anomalies and disorders of the gray and white matter.


title: A performance comparison of energy consumption for Mobile Ad Hoc Network routing protocols

abstract: The design of efficient routing protocols is a fundamental problem in a Mobile Ad-Hoc Network (MANET). Many different protocols have been proposed in the literature, each one based on different characteristics and properties. Some of these protocols have been studied and their performances have been evaluated in detail, focusing on aspects such as routing overhead latency and route length. We concentrate on the energy consumption issues of routing protocols. We present a performance comparison of the DSR, AODV, TORA and DSDV routing protocols with respect to energy consumption, evaluating how the different approaches and algorithms affect the energy usage in mobile devices.


title: Bekenstein-Hawking entropy from Criticality

abstract: Vacuum Einstein equations when projected on to a black hole horizon is analogous to the dynamics of fluids. In this work we address the question, whether certain properties of semi-classical black holes could be holographically mapped into properties of (2 + 1)-dimensional fluid living on the horizon. In particular, we focus on the statistical mechanical description of the horizon-fluid that leads to Bekenstein-Hawking entropy. Within the paradigm of Landau mean field theory and existence of a condensate at a critical temperature, we explicitly show that Bekenstein-Hawking entropy and other features of black hole thermodynamics can be recovered from the statistical modelling of the fluid. We also show that a negative cosmological constant acts like an external magnetic field that induces order in the system leading to the appearance of a tri-critical point in the phase diagram.


Dating the colonization of Sahul ( Pleistocene Australia – New Guinea ) : a review of recent research


title: Just Society as a Fair Game: John Rawls and Game Theory in the 1950s

abstract: I explore the influence of game theory on the political philosopher John Rawls as a way of analyzing the character of his democratic thought. Recent narratives bring Rawls closer to direct democracy as a result of game theory’s influence. I argue that game theory prompted creative, organic developments in Rawls’s political framework rather than shaping it. It prompted Rawls to emphasize autonomy and fairness, leading him to the analogy between a just society and a fair game. And it prompted thought experiments that analyzed our considered judgments. This was an idealized analysis unconnected to the vision of direct democracy.


title: A Time-based Cluster-Head Selection Algorithm for LEACH

abstract: A wireless sensor network consists of hundreds or thousands of small energy-limited sensors that are densely deployed in a large geographical region. It has been demonstrated that low-energy adaptive clustering hierarchy (LEACH) is an energy-efficient routing algorithm for wireless sensor networks (WSN). In this paper, we present a Time-based cluster-head selection algorithm for LEACH. We call this new protocol TB-LEACH. We state the principle of TB-LEACH and give the main flowchart and pseudo codes realizing TB-LEACH. We provide a comparison between our protocol and LEACH protocol. The implementation of this protocol is figured out by NS2. Simulation results show that our algorithm outperforms original LEACH by about 20% to 30% in terms of system lifetime.


title: Survey of neural transfer functions

abstract: The choice of transfer functions may strongly influence complexity and performance of neural networks. Although sigmoidal transfer functions are the most common there is no a priori reason why models based on such functions should always provide optimal decision borders. A large number of alternative transfer functions has been described in the literature. A taxonomy of activation and output functions is proposed, and advantages of various non-local and local neural transfer functions are discussed. Several less-known types of transfer functions and new combinations of activation/output functions are described. Universal transfer functions, parametrized to change from localized to delocalized type, are of greatest interest. Other types of neural transfer functions discussed here include functions with activations based on nonEuclidean distance measures, bicentral functions, formed from products or linear combinations of pairs of sigmoids, and extensions of such functions making rotations of localized decision borders in highly dimensional spaces practical. Nonlinear input preprocessing techniques are briefly described, offering an alternative way to change the shapes of decision borders.


title: Current and future prospects for CRISPR-based tools in bacteria

abstract: CRISPR-Cas systems have rapidly transitioned from intriguing prokaryotic defense systems to powerful and versatile biomolecular tools. This article reviews how these systems have been translated into technologies to manipulate bacterial genetics, physiology, and communities. Recent applications in bacteria have centered on multiplexed genome editing, programmable gene regulation, and sequence-specific antimicrobials, while future applications can build on advances in eukaryotes, the rich natural diversity of CRISPR-Cas systems, and the untapped potential of CRISPR-based DNA acquisition. Overall, these systems have formed the basis of an ever-expanding genetic toolbox and hold tremendous potential for our future understanding and engineering of the bacterial world.


title: Discriminative Multimetric Learning for Kinship Verification

abstract: In this paper, we propose a new discriminative multimetric learning method for kinship verification via facial image analysis. Given each face image, we first extract multiple features using different face descriptors to characterize face images from different aspects because different feature descriptors can provide complementary information. Then, we jointly learn multiple distance metrics with these extracted multiple features under which the probability of a pair of face image with a kinship relation having a smaller distance than that of the pair without a kinship relation is maximized, and the correlation of different features of the same face sample is maximized, simultaneously, so that complementary and discriminative information is exploited for verification. Experimental results on four face kinship data sets show the effectiveness of our proposed method over the existing single-metric and multimetric learning methods.


Cyber Security using Game theory


title: Feedback on household electricity consumption: a tool for saving energy?

abstract: Improved feedback on electricity consumption may provide a tool for customers to better control their consumption and ultimately save energy. This paper asks which kind of feedback is most successful. For this purpose, a psychological model is presented that illustrates how and why feedback works. Relevant features of feedback are identified that may determine its effectiveness: frequency, duration, content, breakdown, medium and way of presentation, comparisons, and combination with other instruments. The paper continues with an analysis of international experience in order to find empirical evidence for which kinds of feedback work best. In spite of considerable data restraints and research gaps, there is some indication that the most successful feedback combines the following features: it is given frequently and over a long time, provides an appliance-specific breakdown, is presented in a clear and appealing way, and uses computerized and interactive tools.


title: FPGA-based Real-time Optical Flow Algorithm Design and Implementation

abstract: Optical flow algorithms are difficult to apply to robotic vision applications in practice because of their extremely high computational and frame rate requirements. In most cases, traditional general purpose processors and sequentially executed software cannot compute optical flow in real time. In this paper, a tensor-based optical flow algorithm is developed and implemented using field programmable gate array (FPGA) technology. The resulting algorithm is significantly more accurate than previously published FPGA results and was specifically developed to be implemented using a pipelined hardware structure. The design can process 640 × 480 images at 64 fps, which is fast enough for most real-time robot navigation applications. This design has low resource requirements, making it easier to fit into small embedded systems. Error analysis on a synthetic image sequence is given to show its effectiveness. The algorithm is also tested on a real image sequence to show its robustness and limitations. The resulting limitations are analyzed and an improved scheme is then proposed. It is then shown that the performance of the design could be substantially improved with sufficient hardware resources.


title: Multiple annies : Feminist poststructural theory and the making of a teacher

abstract: Poststructuralist theories of subjectivity (1) reject the humanist (2) notion of a unified, fixed self that has a stable, essential core and instead propose the self as a site of disunity and conflict that is always in process and constructed within power relations (Britzman, 1994; Flax, 1990; Kondo, 1990; St. Pierre, 2000; Weedon, 1997). Furthermore, feminist poststructuralism posits that subjectivities shift among the discursive fields that create them and that the individual is "always the site of conflicting forms of subjectivity" (Weedon, 1997, p. 32). Weedon (1997) described this feminist concern with poststructural theory as a way to conceptualize multiple subject positions within varied discourses, (3) a way to give voice to constructed meaning and to rewrite personal experiences (p. 33). For Weedon, this opens up space for consciousness raising and resistance. The social structures and processes that shape our subjectivities are situated within discursive fields where language, social institutions, subjectivity, and power exist, intersect, and produce competing ways of giving meaning to and constructing subjectivity (de Lauretis, 1986; Kondo, 1990; Weedon, 1997). Reflecting certain values, competing discourses emerge within discursive fields, and the language and practices of these discourses give rise to an individual's conflicting subjectivities. In this way, the meaning of structures, as well as the subject positioned within, become sites of political struggle (Weedon, 1997). Embedded in the normative discourse of teacher education is the valorization of experience, and subsumed in this is the idea that learning to teach is a linear process in which a novice student becomes a teacher through the function of unproblematic experience. Britzman (1994) critiqued this discourse as too simplistic in its view of experience as following a preordained path, or a map, that excludes "an interrogation into how the dynamics of social activity and expression produce our understanding of experience" (p. 60). Britzman used poststructural theory to trouble the notion of a predetermined, unified teacher identity assumed to emerge if a novice assimilates and follows the already-organized, complete path of the student teaching experience. She claimed that learning to teach and shaping a teacher identity are struggles "to borrow, to negotiate, to claim ownership, and to take up that which seems already completed" (p. 54). Student teaching, in particular, provides a context in which novices must "speak and act as subjects from within a discursive field that they did not set up" (p. 61). In teacher education, the teacher/student, expert/novice binaries are laden with meaning, meaning constructed by those who are situated within the unstable relationships between power, knowledge, experience, and subjectivity. The normative discourse holds that those who have the most experience possess the most power and knowledge, and those who tout this discourse expect novice students to conform and fluidly take up an identity similar to that of their mentor, who is the master teacher. Other, competing discourses vie for the students' subject position, discourses constructed by the values and beliefs of those in power--mainly other teachers, university people, and administrative personnel who work with student teachers. Therefore, the discursive field of the student teaching experience offers multiple, conflicting subject positions for novice students. METHOD A feminist, poststructural deconstruction (4) of Annie, a young woman learning to teach illuminates how her subjectivities shifted among competing discourses within the discursive field of her student teaching experience. Annie is a 22-year-old, White, middle-class woman who was enrolled in an initial teacher certification program at a major southeastern university. She completed her student teaching practicum in English at a large, suburban high school during spring semester 2000. …


title: Predictive Maintenance by Electrical Signature Analysis to Induction Motors

abstract: Industries always try to increase the reliability of their productive process. In this context, predictive maintenance performs a fundamental role in order to reach high availability and reliability concerning their pieces of equipment. Predictive maintenance can be understood as the action on the equipment, system or installations based on the previous knowledge about the operation condition or performance, obtained by means of parameters previously determined (Bonaldi et al, 2007).


title: Giving Infants an Identity: Fingerprint Sensing and Recognition

abstract: There is a growing demand for biometrics-based recognition of children for a number of applications, particularly in developing countries where children do not have any form of identification. These applications include tracking child vaccination schedules, identifying missing children, preventing fraud in food subsidies, and preventing newborn baby swaps in hospitals. Our objective is to develop a fingerprint-based identification system for infants (age range: 0-12 months)1. Our ongoing research has addressed the following issues: (i) design of a compact, comfortable, high-resolution (>1,000 ppi) fingerprint reader; (ii) image enhancement algorithms to improve quality of infant fingerprint images; and (iii) collection of longitudinal infant fingerprint data to evaluate identification accuracy over time. This collaboration between Michigan State University, Dayalbagh Educational Institute, Saran Ashram Hospital, Agra, India and NEC Corporation, has demonstrated the feasibility of recognizing infants older than 4 weeks using fingerprints.


New Trends in the Diagnosis of Cystic Pancreatic Neoplasms ( CPNs ) : Is It the Time of Endoscopic Ultrasound ( EUS ) Guided Needle-Based Techniques ?


title: Understanding the consumer: Multi-modal market research on consumer attitudes in Germany towards lightweight furniture and lightweight materials in furniture design

abstract: The market study presented in this paper examines consumer attitudes in Germany toward lightweight furniture and the use of lightweight wood-based materials in furniture design. The multi-modal technique employed in the study is based on quantitative and qualitative methodologies used in social and economic research. A representative survey of furniture customers was conducted (n = 1,000), and the study included conjoint analysis. The survey was supplemented by expert interviews with furniture sales staff, focus group and observation of customers when they purchase furniture. The weight of furniture made primarily of wood-based materials is of secondary importance from the consumer perspective (in contrast to more relevant factors such as quality, price and design). The results of conjoint analysis reveal that for the attribute “weight” lightweight furniture materials are considered to be of at least comparable value. However, lightweight furniture is only accepted or preferred as long as there is no explicit indication that the product is lightweight furniture or features lightweight design. The majority of customers are skeptical whether furniture made of lightweight materials has the same quality as furniture made of heavy materials. Because furniture weight is less relevant to consumers, there is no indication of a need pull effect (consumer-driven) for lightweight furniture at the present time or in the near future. On the other hand, consumers can be expected to have few reservations if more lightweight furniture is placed on the market in the future. So consumers are unlikely to resist a technology push on the part of furniture manufacturers.


title: An Instructional Feedback Technique for Teaching Project Management Tools Aligned With PMBOK

abstract: Contribution: An approach is presented to provide contextualized feedback for students using a project management (PM) tool. This approach covers the ten PM knowledge areas, guiding students through the planning of software projects. Background: Because software PM is unfeasible without the support of a PM tool there is a growing demand that these tools be taught in the higher education computer courses. Hence, several approaches already have been proposed to improve this teaching, such as educational PM tools or simulators, which facilitate the application of specific PM techniques and the treatment of their results. Another approach to improve the teaching of PM tools is instructional feedback, but little information is as yet available on how to provide instructional feedback when teaching software PM. Intended Outcomes: Instructional feedback, essential to helping students to learn, is based on an evaluation of their actions and decisions. To take advantage of this instructional approach, this paper proposes an instructional feedback technique to be employed in an instructional unit (IU) teaching PM tools aligned with the project management body of knowledge. Application Design: This technique was evaluated through an experiment carried out in undergraduate computer courses. Control and experiential groups were exposed to the same IU, with only the experiential group using the instructional feedback technique. Findings: The proposed feedback technique contributes to student learning, meeting students’ need for better explanations to acquire a comprehensive understanding of PM theory and its practice.


title: Loss Investigation of Interior Permanent-Magnet Motors Considering Carrier Harmonics and Magnet Eddy Currents

abstract: In this paper, we investigate losses of interior permanent-magnet motors driven by pulsewidth-modulated inverters using 3D finite-element analysis, which can estimate the Eddy-current loss in the permanent magnet accurately. The calculated losses are compared with the measured results and the theoretical solution to verify the validity of the analysis. The variation of the magnet Eddy-current loss due to the division of the magnet is also investigated. It is clarified that the Eddy-current loss in the permanent magnet is mainly produced by the carrier harmonics of the inverter and that the axial length of the divided magnet should be smaller than the skin depth of the eddy currents produced by the major harmonics for the loss reduction.


title: Characterisation of Gliclazide-PEG 8000 Solid Dispersions

abstract: Purpose : The aim of the present study was to characterise gliclazide solid dispersions (SDs) prepared with polyethylene glycol (PEG) 8000 and compare them with SDs in PEG 6000. Methods : Gliclazide SDs containing varying concentrations of PEG 8000 were prepared using the fusion – solvent technique, and their phase solubility behavior and dissolution in 0.1N HCl were assessed at 37 o C. The physical state of, and gliclazide-PEG interactions in, SDs and physical mixtures prepared in ratios of 1:1, 1:2 and 1:5 (gliclazide: PEG 8000), respectively, were characterized by x-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy and differential scanning calorimetry (DSC). Results : The solubility of gliclazide increased with increasing amount of PEG 8000 in aqueous medium. Gibbs free energy ( �G o tr ) values were all negative, indicating the spontaneous nature of gliclazide solubilisation. Dissolution studies indicated a significant increase in the dissolution of gliclazide when dispersed in PEG 8000. FTIR analysis demonstrated the absence of well-defined gliclazide - PEG 8000 chemical interaction while DSC and XRD studies indicated the amorphous /microcrystalline state of gliclazide in the SDs. Conclusion : In both solid dispersions and physical mixture, PEG 8000 increases the solubility and dissolution rate of gliclazide. The increased dissolution rate of gliclazide may be due to the formation of microcrystals, increased wettability and dispersibility in systems containing PEG 8000.


GAMES AND SIMULATIONS AND THEIR RELATIONSHIPS TO LEARNING


Reflec tions on the Legitimacy of Mau Mau Rebellion 50 Years after Independence in Kenya


title: Genuine Knowledge and Conclusion Based on the Practice of Urban and Rural Ecological Corridor——The Design of Ecological and Green Corridors in Shanhaiguan District

abstract: The ecological corridor is derived from the foreign advanced concepts of developed countries, which protects the urban and rural mechanism and context, intensively utilizes land resources and conforms to the requirements of the construction of ecological urban and rural development. The design of Shanhaiguan District Cherry Vale greenway is based on the resources of local mountain, water, springs, and sea, using the greenway design means to design travel, relaxation, culture and other node elements, in which gathers many scattered resources along the corridor and preponderant landscape as dominant resources, not only protect the ecosystem of the natural environment, but also promote the urban and rural harmonious development of social economy. Keywords-local resources; ecological corridor; the way of ecological design; urban and rural industry ecosystem I. BACKGROUND OF THE RESEARCH ON THE URBAN AND RURAL ECOLOGICAL CORRIDORS Corridor is a concept in landscape ecology, it is different from linear or zonal landscape elements on both sides of the matrix, including roads, rivers, green belts, tree-lined belt are corridors in the urban and rural areas [1]. The definition of ecological corridor meaning protecting the species diversity, filtering pollutants, preventing soil erosion, windbreak and sand-fixation, controlling flood, etc, which provides the function of ecological service. Ecological corridor is mainly composed of vegetation, water, landscape elements, and it’s the same as "green corridor" in concept. Rural and urban ecological corridor is the integration of green space resources, which uses green ecological health corridor to the organic connection. Improving the rural and urban green land ecological benefit [2], economic benefit and social benefit, is one of the important choice of promoting scientific development. In today's rapid development of urban and rural integration, urban and rural problems are highlighted, for example: we have a fast progress of urbanization, and at the same time, urban rapid development do not give priority to respect the local ecological environment, which caused destruction of ecology of urban and rural habitats[3]. In this paper, the ecological corridors are the maintenance of urban and rural ecological bridge, to protect the mechanism of urban and rural areas and are also protect arable land, intensively utilize the land resources. II. SIGNIFICANCE OF THE ECOLOGICAL CORRIDOR Ecological corridor has many important functions: controlling the local regional environment, preventing pollution and soil erosion and renovating the function of ecosystem diversity [4]. Building a complete urban and rural road system, promoting the logistics links, prompting the country to support the city and urban to promote the countryside, and the economy, industry and culture to support each other. To make the urban and rural area become regional, scaled, systematic network industry pattern. It’s important for mining local natural resources, improving the industry chain, enhancing the quality of tourism, business services, and it’s the key link to form the urban and rural ecological circulation industry’s chain. III. EXPLORATION AND ANALYSIS OF THE ECOLOGICAL CORRIDOR CONSTRUCTION IN SHANHAIGUAN DISTRICT A. The present ecological and environmental situations in Shanhaiguan District under the urban-rural


title: Subspace video stabilization

abstract: We present a robust and efficient approach to video stabilization that achieves high-quality camera motion for a wide range of videos. In this article, we focus on the problem of transforming a set of input 2D motion trajectories so that they are both smooth and resemble visually plausible views of the imaged scene; our key insight is that we can achieve this goal by enforcing subspace constraints on feature trajectories while smoothing them. Our approach assembles tracked features in the video into a trajectory matrix, factors it into two low-rank matrices, and performs filtering or curve fitting in a low-dimensional linear space. In order to process long videos, we propose a moving factorization that is both efficient and streamable. Our experiments confirm that our approach can efficiently provide stabilization results comparable with prior 3D methods in cases where those methods succeed, but also provides smooth camera motions in cases where such approaches often fail, such as videos that lack parallax. The presented approach offers the first method that both achieves high-quality video stabilization and is practical enough for consumer applications.


title: A Configurable Transmitter Architecture for IEEE 802.11ac and 802.11ad Standards

abstract: IEEE 802.11ac (WiFi) and IEEE 802.11ad (60-GHz WiGig) are emerging gigabit-per-second standards providing complementary services but different nature of signals. The 802.11ac targets high-resolution and narrow-to-medium bandwidth channels, while 802.11ad aims to provide broadband communications with simple modulation schemes. This work proposes a single-physical-layer transmitter baseband architecture for both 11ac and 11ad standards. The core of the proposed transmitter is a configurable mixed-signal digital-to-analog converter (DAC), which has an embedded semidigital filtering tailored for four WiFi modes (20, 40, 80, and 160 MHz) and the 1.76-GHz bandwidth of the 60-GHz WiGig standard. The DAC operates on the oversampled WiFi and raw WiGig data at a common 3.52-GHz clock frequency. System-level simulations of the finite impulse response DAC-based architecture show that the requirements of the standards can be met with maximum hardware sharing and reduced area penalty.


title: Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation

abstract: Non-intrusive load monitoring (NILM) is the prevailing method used to monitor the energy profile of a domestic building and disaggregate the total power consumption into consumption signals by appliance. Whilst the most popular disaggregation algorithms are based on Hidden Markov Model solutions based on deep neural networks have attracted interest from researchers. The objective of this paper is to provide a comprehensive overview of the NILM method and present a comparative review of modern approaches. In this effort, many obstacles are identified. The plethora of metrics, the variety of datasets and the diversity of methodologies make an objective comparison almost impossible. An extensive analysis is made in order to scrutinize these problems. Possible solutions and improvements are suggested, while future research directions are discussed.


title: Oblivious Transfer with Adaptive Queries

abstract: We provide protocols for the following two-party problem: One party, the sender, has N values and the other party, the receiver, would like to learn k of them, deciding which ones in an adaptive manner (i.e. the ith value may depend on the first i-1 values). The sender does not want the receiver to obtain more than k values. This is a variant of the well known Oblivious Transfer (OT) problem and has applications in protecting privacy in various settings. ::: ::: We present efficient protocols for the problem that require an O(N) computation in the preprocessing stage and fixed computation (independent of k) for each new value the receiver obtains. The on-line computation involves roughly logN invocations of a 1-out-2 OT protocol. The protocols are based on a new primitive, sum consistent synthesizers.


title: Linearizability: a correctness condition for concurrent objects

abstract: A concurrent object is a data object shared by concurrent processes. Linearizability is a correctness condition for concurrent objects that exploits the semantics of abstract data types. It permits a high degree of concurrency, yet it permits programmers to specify and reason about concurrent objects using known techniques from the sequential domain. Linearizability provides the illusion that each operation applied by concurrent processes takes effect instantaneously at some point between its invocation and its response, implying that the meaning of a concurrent object's operations can be given by pre- and post-conditions. This paper defines linearizability, compares it to other correctness conditions, presents and demonstrates a method for proving the correctness of implementations, and shows how to reason about concurrent objects, given they are linearizable.


title: The Epsilon Object Language (EOL

abstract: Model-Driven Development requires model management languages and tools for supporting model operations such as editing, consistency checking, and transformation. At the core of these model management techniques is a set of facilities for model navigation and modification. A subset of the Object Constraint Language can be used for some of these tasks, but it has limitations as a general-purpose language to be used in a variety of model management tasks. We present the metamodel independent Epsilon Object Language (EOL) which builds on OCL. EOL can be used both as a standalone generic model management language or as infrastructure on which task-specific languages can be built. We describe how it has been used to construct a selection of languages, such as model merging, comparison, and text generation languages.


title: Optimal design methodology for LLC resonant converter

abstract: Although LLC resonant converter can achieve wide operation range with high efficiency, lack of design methodology makes it difficult to be implemented. In this paper, based on the theoretical analysis on the operation principles during normal condition and holdup time, the relationship between converter efficiency and operation range with different circuit parameters has be revealed. An optimal design methodology has been developed based on the revealed relationship. A 1MHz, 1kW LLC converter is designed to verify the proposed method.


The generation effect: Support for a two-factor theory.


title: A corpus for OCR research on mathematical expressions

abstract: This paper is concerned with research on OCR (optical character recognition) of printed mathematical expressions. Construction of a representative corpus of technical and scientific documents containing expressions is discussed. A statistical investigation of the corpus is presented, and usefulness of this analysis is demonstrated in the related research problems, namely, (i) identification and segmentation of expression zones from the rest of the document, (ii) recognition of expression symbols, (iii) interpretation of expression structures, and (iv) performance evaluation of a mathematical expression recognition system. Moreover, a groundtruthing format has been proposed to facilitate automatic evaluation of expression recognition techniques.


title: Similarity as the Basis for Children's Friendships: The Roles of Sociometric Status, Aggressive and Withdrawn Behavior, Academic Achievement and Demographic Characteristics

abstract: The purpose of the present study was to examine similarity in demographic, behavioral, academic and social attributes as descriptors and predictors of children's friendships. The characteristics of all possible pairs of unique classroom dyads (N = 4725) were used to predict reciprocated school, home and best friendship choices among 554 third (M = 9.38 years old) and fourth (M = 10.47 years old) graders. Peer reports of aggressive and withdrawn behavior and sociometric status, teacher reports of poverty, and archival reports of sex, race and academic achievement were obtained. The main finding was that as similarity increased, the likelihood of being friends also increased. Specifically, patterns of gender, race, poverty, aggression, withdrawn behavior, achievement and sociometric status between dyad members were descriptive and predictive of children's friendships.


title: Studies on cellular inhibitors of ribonuclease III. The levels of ribonucleases and ribonuclease inhibitor during the regeneration of rat liver

abstract: Abstract The specific activities of total alkaline RNAase, total acid RNAase and free RNAase inhibitor have been followed during the regeneration of rat liver induced by partial hepatectomy. A significant increase in the level of RNAase inhibitor occurred within 12 h of operation and a maximum increase (39%) was observed after 48h. A significant increase in the level of acid RNAase occurred between 28–48 h after operation and the level remained high until at least the 8th day after operation. No statistically significant changes in the level of alkaline RNAase were observed. The results are discussed in the light of the possible role that the RNAase-inhibitor system may play in the regulation of the RNA concentration and the growth of the cell.


title: Large-Scale Evolution of Image Classifiers

abstract: Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year. Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6% (95.6% for ensemble) and 77.0%, respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements.


PATTERN RECOGNITION