title: Tools and techniques for Network Forensics

abstract: Network forensics deals with the capture, recording and analysis of network events in order to discover evidential information about the source of security attacks in a court of law. This paper discusses the different tools and techniques available to conduct network forensics. Some of the tools discussed include: eMailTrackerPro – to identify the physical location of an email sender; Web Historian – to find the duration of each visit and the files uploaded and downloaded from the visited website; packet sniffers like Ethereal – to capture and analyze the data exchanged among the different computers in the network. The second half of the paper presents a survey of different IP traceback techniques like packet marking that help a forensic investigator to identify the true sources of the attacking IP packets. We also discuss the use of Honeypots and Honeynets that gather intelligence about the enemy and the tools and tactics of network intruders.


Flux: Revisiting Near Blocks for Proof-of-Work Blockchains


title: True Random Number Generator Embedded in Reconfigurable Hardware

abstract: This paper presents a new True Random Number Generator (TRNG) based on an analog Phase-Locked Loop (PLL) implemented in a digital Altera Field Programmable Logic Device (FPLD). Starting with an analysis of the one available on chip source of randomness - the PLL synthesized low jitter clock signal, a new simple and reliable method of true randomness extraction is proposed. Basic assumptions about statistical properties of jitter signal are confirmed by testing of mean value of the TRNG output signal. The quality of generated true random numbers is confirmed by passing standard NIST statistical tests. The described TRNG is tailored for embedded System-On-a-Programmable-Chip (SOPC) cryptographic applications and can provide a good quality true random bit-stream with throughput of several tens of kilobits per second. The possibility of including the proposed TRNG into a SOPC design significantly increases the system security of embedded cryptographic hardware.


title: Improved training of end-to-end attention models for speech recognition

abstract: Sequence-to-sequence attention-based models on subword units allow simple open-vocabulary end-to-end speech recognition. In this work, we show that such models can achieve competitive results on the Switchboard 300h and LibriSpeech 1000h tasks. In particular, we report the state-of-the-art word error rates (WER) of 3.54% on the dev-clean and 3.82% on the test-clean evaluation subsets of LibriSpeech. We introduce a new pretraining scheme by starting with a high time reduction factor and lowering it during training, which is crucial both for convergence and final performance. In some experiments, we also use an auxiliary CTC loss function to help the convergence. In addition, we train long short-term memory (LSTM) language models on subword units. By shallow fusion, we report up to 27% relative improvements in WER over the attention baseline without a language model.


title: Flexible, Wide-Area Storage for Distributed Systems with WheelFS

abstract: WheelFS is a wide-area distributed storage system intended to help multi-site applications share data and gain fault tolerance. WheelFS takes the form of a distributed file system with a familiar POSIX interface. Its design allows applications to adjust the tradeoff between prompt visibility of updates from other sites and the ability for sites to operate independently despite failures and long delays. WheelFS allows these adjustments via semantic cues, which provide application control over consistency, failure handling, and file and replica placement. ::: ::: WheelFS is implemented as a user-level file system and is deployed on PlanetLab and Emulab. Three applications (a distributed Web cache, an email service and large file distribution) demonstrate that WheelFS's file system interface simplifies construction of distributed applications by allowing reuse of existing software. These applications would perform poorly with the strict semantics implied by a traditional file system interface, but by providing cues to WheelFS they are able to achieve good performance. Measurements show that applications built on WheelFS deliver comparable performance to services such as CoralCDN and BitTorrent that use specialized wide-area storage systems.


title: Effects of pulse and DC charging on lithium iron phosphate (LiFePO4) batteries

abstract: Resonant converters which use a small DC bus capacitor to achieve high power factor are desirable for low cost Inductive Power Transfer (IPT) applications but produce amplitude modulated waveforms which are then present on any coupled load. The modulated coupled voltage produces pulse currents which could be used for battery charging purposes. In order to understand the effects of such pulse charging, two Lithium Iron Phosphate (LiFePO4) batteries underwent 2000 cycles of charge and discharging cycling utilizing both pulse and DC charging profiles. The cycling results show that such pulse charging is comparable to conventional DC charging and may be suitable for low cost battery charging applications without impacting battery life.


title: Model Predictive-Sliding Mode Control for Three-Phase Grid-Connected Converters

abstract: This paper focuses on the implementation of an extended model predictive-sliding mode control (EMP-SMC) for three-phase grid-connected converters (GcCs). The proposed control considers the GcC model in the dq synchronous reference frame to forecast possible future behavior of the grid current. After that, the applied voltage vector is selected so that a predefined cost function is minimized. This function is aimed to reduce the grid current ripples during steady-state operation. Compared to the conventional MPC, the proposed EMP-SMC algorithm uses 19 voltage vectors (7 real voltage vectors and 12 additional virtual voltage vectors) for the prediction process. Accordingly, lower grid current THD and lower switching losses are obtained. However, the increase of voltage vectors number will lead to higher computation time delay that may affect the control performances. To overcome this problem, the proposed control uses a sliding mode-based preselection step that limits the prediction process to only ten voltage vectors at the most and a table-based implementation process that reduces significantly the execution time of the whole control algorithm. Simulation and experimental results are presented in order to show performances and effectiveness of the proposed EMP-SMC algorithm.


title: Urban Gardeners’ Motivations in a Metropolitan City: The Case of Milan

abstract: Urban gardening (UG) as a component of urban agriculture (UA) has reached popularity during the last decades. This growing interest depends on several factors including the different functions that have been attributed to UG over the years, operating from the economic to the social, health and cultural levels. While multifunctionality of UG is well documented, only a few studies investigated individual gardeners’ motivations, which can be subjective and heavily affected by the local context in which it takes place. The paper aims to detect some peculiar features of Milan city gardeners, in order to highlight the motivations of their activity through an innovative and replicable approach based on multiple correspondence analysis (MCA) and hierarchical clustering analysis (HCA). The analysis has been applied to the Milan case study, in the North of Italy; the results suggest a great importance of the social component of UG, and trace some different gardeners’ profiles.


title: Hessian equations on compact non-negatively curved riemannian manifolds

abstract: On a compact connected riemannian manifold, we partly extend to hessian equations an existence result proved for Monge-Ampere equations. Non-negative curvature is required for a priori estimates.


title: Active coevolutionary learning of requirements specifications from examples

abstract: Within software engineering, requirements engineering starts from imprecise and vague user requirements descriptions and infers precise, formalized specifications. Techniques, such as interviewing by requirements engineers, are typically applied to identify the user's needs. We want to partially automate even this first step of requirements elicitation by methods of evolutionary computation. The idea is to enable users to specify their desired software by listing examples of behavioral descriptions. Users initially specify two lists of operation sequences, one with desired behaviors and one with forbidden behaviors. Then, we search for the appropriate formal software specification in the form of a deterministic finite automaton. We solve this problem known as grammatical inference with an active coevolutionary approach following Bongard and Lipson [2]. The coevolutionary process alternates between two phases: (A) additional training data is actively proposed by an evolutionary process and the user is interactively asked to label it; (B) appropriate automata are then evolved to solve this extended grammatical inference problem. Our approach leverages multi-objective evolution in both phases and outperforms the state-of-the-art technique [2] for input alphabet sizes of three and more, which are relevant to our problem domain of requirements specification.


Dynamic Identification of Manipulator : Comparison between CAD and Actual Parameters


title: Lean Reachability Tree for Unbounded Petri Nets

abstract: Elaborate efforts have been made to eliminate fake markings and refine ${\omega }$ -markings in the existing modified or improved Karp–Miller trees for various classes of unbounded Petri nets since the late 1980s. The main issues fundamentally are incurred due to the generation manners of the trees that prematurely introduce some potentially unbounded markings with ${\omega }$ symbols and keep their growth into new ones. Aiming at addressing them, this work presents a non-Karp–Miller tree called a lean reachability tree (LRT). First, a sufficient and necessary condition of the unbounded places and some reachability properties are established to reveal the features of unbounded nets. Then, we present an LRT generation algorithm with a sufficiently enabling condition (SEC). When generating a tree, SEC requires that the components of a covering node are not replaced by ${\omega }$ symbols, but continue to grow until any transition on an output path of an unbounded place has been branch-enabled at least once. In return, no fake marking is produced and no legal marking is lost during the tree generation. We prove that LRT can faithfully express by folding, instead of equivalently representing, the reachability set of an unbounded net. Also, some properties of LRT are examined and a sufficient condition of deadlock existence based on it is given. The case studies show that LRT outperforms the latest modified Karp–Miller trees in terms of size, expressiveness, and applicability. It can be applied to the analysis of the emerging discrete event systems with infinite states.


title: MTD assessment framework with cyber attack modeling

abstract: Moving Target Defense (MTD) has received significant focus in technical publications. The publications describe MTD approaches that periodically change some attribute of the computer network system. The attribute that is changed, in most cases, is one that an adversary attempts to gain knowledge of through reconnaissance and may use its knowledge of the attribute to exploit the system. The fundamental mechanism an MTD uses to secure the system is to change the system attributes such that the adversary never gains the knowledge and cannot execute an exploit prior to the attribute changing value. Thus, the MTD keeps the adversary from gaining the knowledge of attributes necessary to exploit the system. Most papers conduct theoretical analysis or basic simulations to assess the effectiveness of the MTD approach. More effective assessment of MTD approaches should include behavioral characteristics for both the defensive actor and the adversary; however, limited research exists on running actual attacks against an implemented system with the objective of determining the security benefits and total cost of deploying the MTD approach. This paper explores empirical assessment through experimentation of MTD approaches. The cyber-kill chain is used to characterize the actions of the adversary and identify what classes of attacks were successfully thwarted by the MTD approach and what classes of attacks could not be thwarted In this research paper, we identify the experiment environments and where experiment fidelity should be focused to evaluate the effectiveness of MTD approaches. Additionally, experimentation environments that support contemporary technologies used in MTD approaches, such as software defined networking (SDN), are also identified and discussed.


title: Ultra-stretchable and skin-mountable strain sensors using carbon nanotubes-Ecoflex nanocomposites.

abstract: Super-stretchable, skin-mountable, and ultra-soft strain sensors are presented by using carbon nanotube percolation network–silicone rubber nanocomposite thin films. The applicability of the strain sensors as epidermal electronic systems, in which mechanical compliance like human skin and high stretchability (e>100%) are required, has been explored. The sensitivity of the strain sensors can be tuned by the number density of the carbon nanotube percolation network. The strain sensors show excellent hysteresis performance at different strain levels and rates with high linearity and small drift. We found that the carbon nanotube–silicone rubber based strain sensors possess super-stretchability and high reliability for strains as large as 500%. The nanocomposite thin films exhibit high robustness and excellent resistance–strain dependency for over ∼1380% mechanical strain. Finally, we performed skin motion detection by mounting the strain sensors on different parts of the body. The maximum induced strain by the bending of the finger, wrist, and elbow was measured to be ∼ 42%, 45% and 63%, respectively. S Online supplementary data available from stacks.iop.org/NANO/26/375501/mmedia


title: The acceptance of computer technology by teachers in early childhood education

abstract: ABSTRACT This study investigated kindergarten teachers’ decision-making process regarding the acceptance of computer technology. We incorporated the Technology Acceptance Model framework, in addition to computer self-efficacy, subjective norm, and personal innovativeness in education technology as external variables. The data were obtained from 160 kindergarten teachers, from public kindergartens in Daejeon, South Korea. According to the results, subjective norm had the strongest effect on computer acceptance. In addition, perceived usefulness and computer self-efficacy had a direct effect on computer technology acceptance. On the other hand, perceived ease of use and personal innovativeness in education technology had an indirect effect on computer technology acceptance. The measures accounted for approximately 32% of the variance of intentions to use computers in kindergartens.


title: Optimizing Gabor Filter Design for Texture Edge Detection and Classification

abstract: An effective and efficient texture analysis method, based on a new criterion for designing Gabor filter sets, is proposed. The commonly used filter sets are usually designed for optimal signal representation. We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We propose an algorithm for deriving the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs in a given frequency range. The resulting filters differ significantly from the traditional ones. We test these maximal harmonic discrimination (MHD) filters in several texture analysis tasks: clustering, recognition, and edge detection. It turns out that the proposed filters perform much better than the traditional ones in these tasks. They can achieve performance similar to that of state-of-the-art, distribution based (texton) methods, while being simpler and more computationally efficient.


title: Detection of Xanthomonas axonopodis pv. manihotis, the causal agent of cassava bacterial blight diseases in cassava (Manihot esculenta) in Ghana by polymerase chain reaction

abstract: Xanthomonas axonopodis pv. manihotis (Xam) is the causal agent of cassava bacterial blight (CBB) disease. CBB is a major constraint to cassava cultivation in Ghana. In this study, a survey was conducted in eight regions of Ghana to assess the presence of CBB disease. Out of the eight regions visited, CBB, though at different prevalence, was observed in five regions. Cassava plants samples showing suspected bacterial blight symptoms were collected for analysis by Polymerase Chain Reaction (PCR). The results of the analysis showed that Ashanti region had the highest prevalence in percentage of CBB, which recorded (70%), followed by Volta region (60%); Brong Ahafo region (40%); Eastern region (40%) and Greater Accra region (20%). Morphological examination of the putative pathogen was carried out on Cefazolin trehalose agar (CTA) and Nutrient agar (NA) media. The isolates were subjected to conventional PCR using Xanthomonas genus specific primer, RST2/RST3, Xam specific Variable Number Tandem Repeat (VNTRs) loci, XaG1_67F/R and X-gumD primers, which produced 840, 446 and 402 bp, respectively. The isolates also tested positive with SYBR Green fluorescent dye, using Real-time PCR. The resulting PCR products were sequenced and analyzed using a BLASTn program, which revealed homology between 93 and 100% with several other Xam strains retrieved from GenBank nucleotide database. The pathogenicity test of the isolates on the susceptible Esam cassava variety produced symptoms typical of Xam and the pathogen was consistently re-isolated from the inoculated cassava plants and thereby satisfying the Koch’s postulates.


Profiling Without the Writer ’ s Text


title: ACCIDENT AVOIDANCE AND DETECTION ON HIGHWAYS

abstract: Technological approaches for detecting and monitoring fatigue levels of driver fatigue continue to emerge and many are now in the development, validation testing, or early implementation stages. Previous studies have reviewed available fatigue detection and prediction technologies and methodologies. As the name indicates this project is about advanced technologies in cars for making it more intelligent and interactive for avoiding accidents on roads. By using ARM7 this system becomes more efficient, reliable & effective. There are very less number of systems implemented on human behaviour detection in or with cars. In this paper, we describe a real-time online safety prototype that controls the vehicle speed under driver fatigue. The purpose of such a model is to advance a system to detect fatigue symptoms in drivers and control the speed of vehicle to avoid accidents. The main components of the system consist of number of real time sensors like gas, eye blink, alcohol, fuel, impact sensors and a software interface with GPS and Google Maps APIs for location.


title: Concreteness Fading in Mathematics and Science Instruction: a Systematic Review

abstract: A longstanding debate concerns the use of concrete versus abstract instructional materials, particularly in domains such as mathematics and science. Although decades of research have focused on the advantages and disadvantages of concrete and abstract materials considered independently, we argue for an approach that moves beyond this dichotomy and combines their advantages. Specifically, we recommend beginning with concrete materials and then explicitly and gradually fading to the more abstract. Theoretical benefits of this "con- creteness fading" technique for mathematics and science instruction include (1) helping learners interpret ambiguous or opaque abstract symbols in terms of well-understood concrete objects, (2) providing embodied perceptual and physical experiences that can ground abstract thinking, (3) enabling learners to build up a store of memorable images that can be used when abstract symbols lose meaning, and (4) guiding learners to strip away extraneous concrete properties and distill the generic, generalizable properties. In these ways, concreteness fading provides advantages that go beyond the sum of the benefits of concrete and abstract materials.


title: Sonar Image Processing for Underwater Object Detection Based on High Resolution System

abstract: This paper is concerned with the problem of recognition of objects laying on the sea-bed and presented on sonar images. Considering that high resolution sonar system provides acoustic images of high-quality, several researches have been interested in Synthetic Aperture Sonar (SAS) and Sides can sonar images for underwater objects. This work presents recent detection algorithms targeting their main specificity and innovations during the different steps of sonar image processing.


title: Deep reinforcement learning for page-wise recommendations

abstract: Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction between the system and its users is -- users are recommended a page of items and provide feedback; and then the system recommends a new page of items. To effectively capture such interaction for recommendations, we need to solve two key problems -- (1) how to update recommending strategy according to user's \textit{real-time feedback}, and 2) how to generate a page of items with proper display, which pose tremendous challenges to traditional recommender systems. In this paper, we study the problem of page-wise recommendations aiming to address aforementioned two challenges simultaneously. In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.


title: The Shared Bicycle and Its Network—Internet of Shared Bicycle (IoSB): A Review and Survey

abstract: With the expansion of Intelligent Transport Systems (ITS) in smart cities, the shared bicycle has developed quickly as a new green public transportation mode, and is changing the travel habits of citizens heavily across the world, especially in China. The purpose of the current paper is to provide an inclusive review and survey on shared bicycle besides its benefits, history, brands and comparisons. In addition, it proposes the concept of the Internet of Shared Bicycle (IoSB) for the first time, as far as we know, to find a feasible solution for those technical problems of the shared bicycle. The possible architecture of IoSB in our opinion is presented, as well as most of key IoT technologies, and their capabilities to merge into and apply to the different parts of IoSB are introduced. Meanwhile, some challenges and barriers to IoSB's implementation are expressed thoroughly too. As far as the advice for overcoming those barriers be concerned, the IoSB's potential aspects and applications in smart city with respect to technology development in the future provide another valuable further discussion in this paper.


title: Time domain Green's function for a semi-penetrable circular cone with longitudinal slots

abstract: The initial-boundary problem for finding the Green's function for a semi-penetrable cone with periodical longitudinal slots is considered. The solution method is a combination of the Meler-Fock integral transforms and the regularization. Analytical and numerical solutions are obtained.


A Flexible Framework for Secret Handshakes ( Multi-party Anonymous and Unobservable Authentication )


title: Usability of E-learning tools

abstract: The new challenge for designers and HCI researchers is to develop software tools for effective e-learning. Learner-Centered Design (LCD) provides guidelines to make new learning domains accessible in an educationally productive manner. A number of new issues have been raised because of the new "vehicle" for education. Effective e-learning systems should include sophisticated and advanced functions, yet their interface should hide their complexity, providing an easy and flexible interaction suited to catch students' interest. In particular, personalization and integration of learning paths and communication media should be provided.It is first necessary to dwell upon the difference between attributes for platforms (containers) and for educational modules provided by a platform (contents). In both cases, it is hard to go deeply into pedagogical issues of the provided knowledge content. This work is a first step towards identifying specific usability attributes for e-learning systems, capturing the peculiar features of this kind of applications. We report about a preliminary users study involving a group of e-students, observed during their interaction with an e-learning system in a real situation. We then propose to adapt to the e-learning domain the so called SUE (Systematic Usability Evaluation) inspection, providing evaluation patterns able to drive inspectors' activities in the evaluation of an e-learning tool.


title: Development, validation, and initial results of the Angioedema Activity Score

abstract: BACKGROUND ::: Recurrent angioedema (RecA) is a frequent clinical problem characterized by suddenly occurring cutaneous and/or mucosal swellings. Depending on their location, RecA may be painful, hindering, disfiguring, or even life-threatening. The assessment of disease activity in affected patients is important to guide treatment decisions. Currently, however, there is no standardized and validated outcome measure available to do so. ::: ::: ::: OBJECTIVE ::: To develop and validate the first specific patient-reported outcome instrument to assess disease activity in RecA patients, the Angioedema Activity Score (AAS). ::: ::: ::: METHODS ::: After a set of potential AAS items was developed, item evaluation and reduction were performed by means of impact analysis, factor analysis, regression analysis, and by checking for face validity. In addition, the items of the final AAS questionnaire were tested for their validity and reliability during a 12-week validation study. ::: ::: ::: RESULTS ::: In total, data from 110 and 80 RecA patients were used during the AAS item evaluation and validation phase, respectively. The resulting AAS consisted of five items and was found to have a one-dimensional structure and excellent internal consistency. It correlated well with other measures of disease activity and quality-of-life impairment, thus demonstrating its convergent validity. In addition, the known-groups validity and test-retest reliability of the AAS were found to be good. ::: ::: ::: CONCLUSIONS ::: The AAS is the first validated and reliable tool to determine disease activity in RecA patients, and it may serve as a valuable instrument in future clinical studies and routine patient care.


title: Maximizing submodular set function with connectivity constraint: Theory and application to networks

abstract: In this paper, we investigate the wireless network deployment problem, which seeks the best deployment of a given limited number of wireless routers. We found that many goals for network deployment, such as maximizing the number of covered users or areas, or the total throughput of the network, can be modelled with the submodular set function. Specifically, given a set of routers, the goal is to find a set of locations S, each of which is equipped with a router, such that S maximizes a predefined submodular set function. However, this deployment problem is more difficult than the traditional maximum submodular set function problem, e.g., the maximum coverage problem, because it requires all the deployed routers to form a connected network. In addition, deploying a router in different locations might consume different costs. To address these challenges, this paper introduces two approximation algorithms, one for homogeneous deployment cost scenarios and the other for heterogeneous deployment cost scenarios. Our simulations, using synthetic data and real traces of census in Taipei, show that the proposed algorithms achieve a better performance than other heuristics.


title: Relevance of Data Mining in Digital Library

abstract: Data mining involves significant process of identifying the extraction of hidden predictive information from vast array of databases and it is an authoritative new technology with potentiality to facilitate the Libraries and Information Centers to focus on the most important information in their data warehouses. It is a viable tool to predict future trends and behaviors in the field of library and information service for deducing proactive, knowledge-driven decisions. Mechanized, prospective analyses of data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data Mining, Process of Data Mining, Knowledge Discovery in Databases, DBMS, Data Mining Techniques etc. etc. have been discussed in this paper. Index Terms—Data mining, KDD, artificial neural Networks,


title: Empowering industrial research with shared biomedical vocabularies

abstract: The life science industries (including pharmaceuticals, agrochemicals and consumer goods) are exploring new business models for research and development that focus on external partnerships. In parallel, there is a desire to make better use of data obtained from sources such as human clinical samples to inform and support early research programmes. Success in both areas depends upon the successful integration of heterogeneous data from multiple providers and scientific domains, something that is already a major challenge within the industry. This issue is exacerbated by the absence of agreed standards that unambiguously identify the entities, processes and observations within experimental results. In this article we highlight the risks to future productivity that are associated with incomplete biological and chemical vocabularies and suggest a new model to address this long-standing issue.


title: child pornography and the internet: perpetuating a cycle of abuse

abstract: Thirteen men convicted of downloading child pornography were interviewed with a view to understanding how these men talked about the photographs and the function such talk played in their accounts. The interviews were informed by earlier work with defended subjects and were analyzed within a discursive framework. Quotations are used from the interviews to illustrate the analysis. Six principal discourses emerged within these accounts in relation to child pornography: sexual arousal; as collectibles; facilitating social relationships; as a way of avoiding real life; as therapy; and in relation to the Internet. These are discussed in the context of previous research. The analysis illustrates the important role that the Internet plays in increasing sexual arousal to child pornography and highlights individual differences in whether this serves as a substitute or as a blueprint for contact offenses. It also draws our attention to the important role that community plays in the Internet and how collecting facil...


title: Surface Stereo for Shallow Underwater Scenes

abstract: Imaging underwater scenes with a surface based stereo system allows for reconstruction where having an underwater system is unsafe or impractical. Refraction and other optical phenomena complicate the reconstruction process, and here we analyze these factors and propose techniques for mitigating problems. Existing techniques are of limited use in practical settings, or have ignored physical properties which complicate the reconstruction task. We demonstrate that physical properties can be used to aid in reconstruction and surface modeling using optical and thermal properties of water. In this work we analyze these properties and provide a grounded example with ecological and geophysical applications.


title: Information Theory in Ecology

abstract: The application of information theory (IT) to ecology has occurred along two separate lines: (1) it has been used to quantify the distribution of stocks and numbers of organisms; and (2) it has been employed to quantify the pattern of interactions of trophic processes. By and large, the first endeavor has resulted in relatively few insights into ecosystem dynamics and has generated much ambiguity and disappointment, so that most ecologists remain highly skeptical about the advisability of applying IT to ecology. By contrast, the second, and less well-known application has shed light on the possibility that ecosystem behavior is the most palpable example of a purely natural ‘infodynamics’ that transcends classical dynamics, but remains well within the realm of quantitative description.


title: Analyzing images' privacy for the modern web

abstract: Images are now one of the most common form of content shared in online user-contributed sites and social Web 2.0 applications. In this paper, we present an extensive study exploring privacy and sharing needs of users' uploaded images. We develop learning models to estimate adequate privacy settings for newly uploaded images, based on carefully selected image-specific features. We focus on a set of visual-content features and on tags. We identify the smallest set of features, that by themselves or combined together with others, can perform well in properly predicting the degree of sensitivity of users' images. We consider both the case of binary privacy settings (i.e. public, private), as well as the case of more complex privacy options, characterized by multiple sharing options. Our results show that with few carefully selected features, one may achieve extremely high accuracy, especially when high-quality tags are available.


title: Integrated Event Management: Event Correlation Using Dependency Graphs

abstract: Today’s fault management requires a sophisticated event management to condense events to meaningful fault reports. This severe practical need is addressed by event correlation which is an area of intense research in the scientific community and the industry. This paper introduces an approach for event correlation that uses a dependency graph to represent correlation knowledge. The benefit over existing approaches that are briefly classified here is that this approach is specifically well suited to instrument an existing management system for event correlation. It thereby deals with the complexity, dynamics and distribution of real–life managed systems. That is why it is considered to provide integrated event management. The basic idea is to connect the event correlator to a given management system and gain a dependency graph from it to model the functional dependencies within the managed system. The event correlator searches through the dependency graph to localize managed objects whose failure would explain a large number of management events received. The paper gives a short overview of existing approaches, introduces our approach and its application. It shows why dependency graphs are suitable, how they can be derived and finally presents the prototype developed so far.


title: An Adaptive Binarization Technique for Low Quality Historical Documents

abstract: Historical document collections are a valuable resource for human history. This paper proposes a novel digital image binarization scheme for low quality historical documents allowing further content exploitation in an efficient way. The proposed scheme consists of five distinct steps: a pre-processing procedure using a low-pass Wiener filter, a rough estimation of foreground regions using Niblack’s approach, a background surface calculation by interpolating neighboring background intensities, a thresholding by combining the calculated background surface with the original image and finally a post-processing step in order to improve the quality of text regions and preserve stroke connectivity. The proposed methodology works with great success even in cases of historical manuscripts with poor quality, shadows, nonuniform illumination, low contrast, large signal- dependent noise, smear and strain. After testing the proposed method on numerous low quality historical manuscripts, it has turned out that our methodology performs better compared to current state-of-the-art adaptive thresholding techniques.


title: Energy-Efficient Resource Allocation in NOMA Heterogeneous Networks

abstract: Non-orthogonal multiple access (NOMA) has attracted much recent attention owing to its capability for improving the system spectral efficiency in wireless communications. Deploying NOMA in heterogeneous network can satisfy users' explosive data traffic requirements, and NOMA will likely play an important role in the fifth-generation (5G) mobile communication networks. However, NOMA brings new technical challenges on resource allocation due to the mutual cross-tier interference in heterogeneous networks. In this article, to study the tradeoff between data rate performance and energy consumption in NOMA, we examine the problem of energy-efficient user scheduling and power optimization in 5G NOMA heterogeneous networks. The energy-efficient user scheduling and power allocation schemes are introduced for the downlink 5G NOMA heterogeneous network for perfect and imperfect channel state information (CSI) respectively. Simulation results show that the resource allocation schemes can significantly increase the energy efficiency of 5G NOMA heterogeneous network for both cases of perfect CSI and imperfect CSI.


title: Faster and Better Business Process Modeling with the IBM Pattern-based Process Model Accelerators

abstract: The IBM Pattern-based Process Model Accelerators add a set of patterns, transformations, refactoring operations, and a control-flow analysis feature to IBM WebSphere Business Modeler that make it easy for users to apply common best practices when modeling business processes. As a result, users create and edit higher-quality process models and they benefit from the automation of tedious editing tasks resulting in productivity gains and a more enjoyable user experience. This demo targets participants with interest on faster process modeling approaches leading to higher-quality process models.


title: Protection against pneumococcal infection by a ribosomal preparation.

abstract: A subcellular extract prepared from nonencapsulated Diplococcus pneumoniae type 3 protected mice against a subsequent challenge with virulent D. pneumoniae types 1, 2, 3, and 7. Potency ratios ranged from 25 (type 3) to >1,175 (type 1). The immunity induced in mice by this preparation was best obtained by intraperitoneal inoculation followed by intravenous challenge. Mice immunized in this manner were protected up to 12 weeks and could withstand a challenge of several hundred LD(50). The protection was destroyed by treatment of the preparation with ribonuclease and was decreased by treatment with protease. The preparation consisted of 60.5% ribonucleic acid, 29.1% protein, 6.3% deoxyribonucleic acid, and 4.0% hexose. Ultracentrifugation studies indicated that this extract had five constituents which are compatible with ribosomal material.


title: Factors Influencing Team Behaviors in Surgery: A Qualitative Study to Inform Teamwork Interventions

abstract: Background Surgical excellence demands teamwork. Poor team behaviors negatively affect team performance and are associated with adverse events and worse outcomes. Interventions to improve surgical teamwork focusing on frontline team members' nontechnical skills have proliferated but shown mixed results. Literature on teamwork in organizations suggests that team behaviors are also contingent on psychosocial, cultural, and organizational factors. This study examined factors influencing surgical team behaviors to inform more contextually sensitive and effective approaches to optimizing surgical teamwork. Methods This qualitative study of cardiac surgical teams in a large United States teaching hospital included 34 semistructured interviews. Thematic network analysis was used to examine perceptions of ideal teamwork and factors influencing team behaviors in the operating room. Results Perceptions of ideal teamwork were largely shared, but team members held discrepant views of which team and leadership behaviors enhanced or undermined teamwork. Other factors affecting team behaviors were related to the local organizational culture, including management of staff behavior, variable case demands, and team members' technical competence, and fitness of organizational structures and processes to support teamwork. These factors affected perceptions of what constituted optimal interpersonal and team behaviors in the operating room. Conclusions Team behaviors are contextually contingent and organizationally determined, and beliefs about optimal behaviors are not necessarily shared. Interventions to optimize surgical teamwork require establishing consensus regarding best practice, ability to adapt as circumstances require, and organizational commitment to addressing contextual factors that affect teams.


title: RDF2PT: Generating Brazilian Portuguese Texts from RDF Data

abstract: The generation of natural language from Resource Description Framework (RDF) data has recently gained significant attention due to the continuous growth of Linked Data. A number of these approaches generate natural language in languages other than English, however, no work has been proposed to generate Brazilian Portuguese texts out of RDF. We address this research gap by presenting RDF2PT, an approach that verbalizes RDF data to Brazilian Portuguese language. We evaluated RDF2PT in an open questionnaire with 44 native speakers divided into experts and non-experts. Our results suggest that RDF2PT is able to generate text which is similar to that generated by humans and can hence be easily understood.


title: Relevance search in heterogeneous networks

abstract: Conventional research on similarity search focuses on measuring the similarity between objects with the same type. However, in many real-world applications, we need to measure the relatedness between objects with different types. For example, in automatic expert profiling, people are interested in finding the most relevant objects to an expert, where the objects can be of various types, such as research areas, conferences and papers, etc. With the surge of study on heterogeneous networks, the relatedness measure on objects with different types becomes increasingly important. In this paper, we study the relevance search problem in heterogeneous networks, where the task is to measure the relatedness of heterogeneous objects (including objects with the same type or different types). We propose a novel measure, called HeteSim, with the following attributes: (1) a path-constrained measure: the relatedness of object pairs are defined based on the search path that connect two objects through following a sequence of node types; (2) a uniform measure: it can measure the relatedness of objects with the same or different types in a uniform framework; (3) a semi-metric measure: HeteSim has some good properties (e.g., self-maximum and symmetric), that are crucial to many tasks. Empirical studies show that HeteSim can effectively evaluate the relatedness of heterogeneous objects. Moreover, in the query and clustering tasks, it can achieve better performances than conventional measures.


title: Multifactor Customer Classification model for IP Transit product

abstract: Customer Classification has important role in Customer Relationship Management (CRM) and has been applied in many industries, such as retail and manufacturing. However, there is no single model purposely created only for telecommunication wholesale segment, especially IP Transit. This research develops a model for customer classification with consideration of all aspects of customer - company relationship. These aspects are customer value, customer loyalty, and customer risk. The main point is the suitability with real industry. To achieve this objective, we used real transactional data and appropriate method for processing data. Customer lifetime value analysis is done to measure customer value, while Artificial Neural Network is done for measuring customer loyalty, and also Ordinal Regression is done for measuring customer risk. The outputs from these three measurements become the input for clustering using K-Means. The optimal cluster is four clusters which can be retrieved from Elbow Rule on Ward's Method. From the value of distance to zero point, there is one customer in “Platinum” cluster, twenty two customers in “Gold” cluster, and ten customers in “Silver” and twenty three in “Bronze” clusters.


title: Pulse Wave Velocity and Digital Volume Pulse as Indirect Estimators of Blood Pressure: Pilot Study on Healthy Volunteers

abstract: The purpose of the study was to asses the potential use of pulse wave velocity (PWV) and digital volume pulse (DVP) as estimators of systolic (SBP) and diastolic (DPB) blood pressure. Single and multiple correlation studies were conducted, including biometric parameters and risk factors. Brachial-ankle PWV (baPWV) and DVP signals were obtained from a Pulse Trace PWV and Pulse Trace PCA (pulse contour analysis), respectively. The DVP (obtained by photoplethysmography), allowed stiffness (SI) and reflection indexes (RI) to be derived. The first study on 47 healthy volunteers showed that both SBP and DPB correlated significantly both with baPWV and SI. Multiple regression models of the baPWV and the waist-to-hip ratio (WHR) allowed SBP and DBP to be modeled with r = 0.838 and r = 0.673, respectively. SI results also employed WHR and modeled SBP and DBP with r = 0.852 and r = 0.663, respectively. RI did not correlate either with SBP or DBP. In order to avoid the use of ultrasound techniques to measure PWV, we then developed a custom-built system to measure PWV by photoplethysmography and validated it against the Pulse Trace. With the same equipment we conducted a second pilot study with ten healthy volunteers. The best SBP multiple regression model for SBP achieved r = 0.997 by considering the heart-finger PWV (hfPWV measured between R-wave and index finger), WHR and heart rate. Only WHR was significant in the DBP model. Our findings suggest that the hfPWV photoplethysmography signal could be a reliable estimator of approximate SBP and could be used, for example, to monitor cardiac patients during physical exercise sessions in cardiac rehabilitation.


title: Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images

abstract: Prompt and widely available diagnostics of breast cancer is crucial for the prognosis of patients. One of the diagnostic methods is the analysis of cytological material from the breast. This examination requires extensive knowledge and experience of the cytologist. Computer-aided diagnosis can speed up the diagnostic process and allow for large-scale screening. One of the largest challenges in the automatic analysis of cytological images is the segmentation of nuclei. In this study, four different clustering algorithms are tested and compared in the task of fast nuclei segmentation. K-means, fuzzy C-means, competitive learning neural networks and Gaussian mixture models were incorporated for clustering in the color space along with adaptive thresholding in grayscale. These methods were applied in a medical decision support system for breast cancer diagnosis, where the cases were classified as either benign or malignant. In the segmented nuclei, 42 morphological, topological and texture features were extracted. Then, these features were used in a classification procedure with three different classifiers. The system was tested for classification accuracy by means of microscopic images of fine needle breast biopsies. In cooperation with the Regional Hospital in Zielona Gora, 500 real case medical images from 50 patients were collected. The acquired classification accuracy was approximately 96-100%, which is very promising and shows that the presented method ensures accurate and objective data acquisition that could be used to facilitate breast cancer diagnosis.


title: Nonparametric Scene Parsing via Label Transfer

abstract: While there has been a lot of recent work on object recognition and image understanding, the focus has been on carefully establishing mathematical models for images, scenes, and objects. In this paper, we propose a novel, nonparametric approach for object recognition and scene parsing using a new technology we name label transfer. For an input image, our system first retrieves its nearest neighbors from a large database containing fully annotated images. Then, the system establishes dense correspondences between the input image and each of the nearest neighbors using the dense SIFT flow algorithm [28], which aligns two images based on local image structures. Finally, based on the dense scene correspondences obtained from SIFT flow, our system warps the existing annotations and integrates multiple cues in a Markov random field framework to segment and recognize the query image. Promising experimental results have been achieved by our nonparametric scene parsing system on challenging databases. Compared to existing object recognition approaches that require training classifiers or appearance models for each object category, our system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure.


title: Competence in providing mental health care: a grounded theory analysis of nurses' experiences.

abstract: OBJECTIVE ::: In view of the evidence that general nurses have difficulty in caring for patients experiencing mental health problems, the aim of this study was to explore and describe the subjective experience of nurses in providing care for this client group. ::: ::: ::: DESIGN ::: A grounded theory approach was used. The data were collected via semi-structured individual interviews and analysed using the constant comparative method. ::: ::: ::: SETTING ::: The study was conducted with nurses from general health care settings that provide medical and surgical care and treatment. ::: ::: ::: SUBJECTS ::: Four nurses who were completing their second year post graduation participated in the study. ::: ::: ::: MAIN OUTCOME MEASURE ::: The experiences of providing care for people experiencing a mental illness as described by participants. ::: ::: ::: RESULTS ::: The findings indicated the nurses were striving for competence in the provision of mental health care. They acknowledged the mental health needs of patients and their right to quality care. ::: ::: ::: CONCLUSIONS ::: This study supports the notion that general nurses lack confidence when caring for patients with mental health problems in medical and surgical settings. It also highlights a discrepancy between the holistic framework encouraged at undergraduate level and what is experienced in practice.


title: A Tutorial on Network Embeddings

abstract: Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of network embeddings by summarizing and categorizing recent advancements in this research field. We first discuss the desirable properties of network embeddings and briefly introduce the history of network embedding algorithms. Then, we discuss network embedding methods under different scenarios, such as supervised versus unsupervised learning, learning embeddings for homogeneous networks versus for heterogeneous networks, etc. We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.


title: Comparison Study on Critical Components in Composition Model for Phrase Representation

abstract: Phrase representation, an important step in many NLP tasks, involves representing phrases as continuous-valued vectors. This article presents detailed comparisons concerning the effects of word vectors, training data, and the composition and objective function used in a composition model for phrase representation. Specifically, we first discuss how the augmented word representations affect the performance of the composition model. Then, we investigate whether different types of training data influence the performance of the composition model and, if so, how they influence it. Finally, we evaluate combinations of different composition and objective functions and discuss the factors related to composition model performance. All evaluations were conducted in both English and Chinese. Our main findings are as follows: (1) The Additive model with semantic enhanced word vectors performs comparably to the state-of-the-art model; (2) The Additive model which updates augmented word vectors and the Matrix model with semantic enhanced word vectors systematically outperforms the state-of-the-art model in bigram and multi-word phrase similarity task, respectively; (3) Representing the high frequency phrases by estimating their surrounding contexts is a good training objective for bigram phrase similarity tasks; and (4) The performance gain of composition model with semantic enhanced word vectors is due to the composition function and the greater weight attached to important words. Previous works focus on the composition function; however, our findings indicate that other components in the composition model (especially word representation) make a critical difference in phrase representation.


title: Supramolecular shape memory hydrogels: a new bridge between stimuli-responsive polymers and supramolecular chemistry

abstract: Supramolecular shape memory hydrogels (SSMHs) refer to shape memory polymers, in which temporary shapes are stabilized by reversible crosslinks such as supramolecular interactions and dynamic covalent bonds. Following a brief introduction of the conventional shape memory polymers (SMPs), this tutorial review is focused to summarize the recent advancement in various reversible crosslinks employed to construct SSMHs (supramolecular interactions and dynamic covalent bonds) and different shape memory behaviors (dual and triple shape memory effects). In addition, current challenges and future perspectives in this field are also discussed to suggest a new developing direction.


title: Hedonism, Achievement, and Power: Universal values that characterize the Dark Triad

abstract: Using a sample of Swedes and Americans (N = 385), we attempted to understand the Dark Triad traits (i.e., Machiavellianism, narcissism, and psychopathy) in terms of universal social values. The Dark Triad traits correlated significantly with all 10 value types, forming a sinusoid pattern corresponding to the value model circumplex. In regression analyses, Machiavellianism and narcissism were positively associated with the values Achievement and Power, while psychopathy was positively associated with the values Hedonism, and Power. In addition, the Dark Triad traits explained significant variance over the Big Five traits in accounting for individual differences in social values. Differences between the Swedish and the US sample in the social value Achievement was mediated by the Dark Triad traits, as well as age. Given the unique complex of values accounted for by the Dark Triad traits compared to the Big Five traits, we argue that the former account for a system of self-enhancing ‘‘dark values’’, often hidden but constantly contributing in evaluations of others.


title: Searching an Encrypted Cloud Meets Blockchain: A Decentralized, Reliable and Fair Realization

abstract: Enabling search directly over encrypted data is a desirable technique to allow users to effectively utilize encrypted data outsourced to a remote server like cloud service provider. So far, most existing solutions focus on an honest-but-curious server, while security designs against a malicious server have not drawn enough attention. It is not until recently that a few works address the issue of verifiable designs that enable the data owner to verify the integrity of search results. Unfortunately, these verification mechanisms are highly dependent on the specific encrypted search index structures, and fail to support complex queries. There is a lack of a general verification mechanism that can be applied to all search schemes. Moreover, no effective countermeasures (e.g., punishing the cheater) are available when an unfaithful server is detected. In this work, we explore the potential of smart contract in Ethereum, an emerging blockchain-based decentralized technology that provides a new paradigm for trusted and transparent computing. By replacing the central server with a carefully-designed smart contract, we construct a decentralized privacy-preserving search scheme where the data owner can receive correct search results with assurance and without worrying about potential wrongdoings of a malicious server. To better support practical applications, we introduce fairness to our scheme by designing a new smart contract for a financially-fair search construction, in which every participant (especially in the multiuser setting) is treated equally and incentivized to conform to correct computations. In this way, an honest party can always gain what he deserves while a malicious one gets nothing. Finally, we implement a prototype of our construction and deploy it to a locally simulated network and an official Ethereum test network, respectively. The extensive experiments and evaluations demonstrate the practicability of our decentralized search scheme over encrypted data.


Support Vector Machines with Applications 1


title: Ecological resilience in urban ecosystems: Linking urban patterns to human and ecological functions

abstract: Urban ecosystems evolve over time and space as the outcome of dynamic interactions between socio-economic and biophysical processes operating over multiple scales. The ecological resilience of urban ecosystems—the degree to which they tolerate alteration before reorganizing around a new set of structures and processes—is influenced by these interactions. In cities and urbanizing areas fragmentation of natural habitats, simplification and homogenization of species composition, disruption of hydrological systems, and alteration of energy flow and nutrient cycling reduce cross-scale resilience, leaving systems increasingly vulnerable to shifts in system control and structure. Because varied urban development patterns affect the amount and interspersion of built and natural land cover, as well as the human demands on ecosystems differently, we argue that alternative urban patterns (i.e., urban form, land use distribution, and connectivity) generate varied effects on ecosystem dynamics and their ecological resilience. We build on urban economics, landscape ecology, population dynamics, and complex system science to propose a conceptual model and a set of hypotheses that explicitly link urban pattern to human and ecosystem functions in urban ecosystems. Drawing on preliminary results from an empirical study of the relationships between urban pattern and bird and aquatic macroinvertebrate diversity in the Puget Sound region, we propose that resilience in urban ecosystems is a function of the patterns of human activities and natural habitats that control and are controlled by both socio-economic and biophysical processes operating at various scales. We discuss the implications of this conceptual model for urban planning and design.


title: The Review and Analysis of Human Computer Interaction (HCI) Principles

abstract: The History of HCI is briefly reviewed together with three HCI models and structure including CSCW, CSCL and CSCR. It is shown that a number of authorities consider HCI to be a fragmented discipline with no agreed set of unifying design principles. An analysis of usability criteria based upon citation frequency of authors is performed in order to discover the eight most recognised HCI principles.


title: Saliency detection by multi-context deep learning

abstract: Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. This issue raises a serious problem for conventional approaches. In this paper, we tackle this problem by proposing a multi-context deep learning framework for salient object detection. We employ deep Convolutional Neural Networks to model saliency of objects in images. Global context and local context are both taken into account, and are jointly modeled in a unified multi-context deep learning framework. To provide a better initialization for training the deep neural networks, we investigate different pre-training strategies, and a task-specific pre-training scheme is designed to make the multi-context modeling suited for saliency detection. Furthermore, recently proposed contemporary deep models in the ImageNet Image Classification Challenge are tested, and their effectiveness in saliency detection are investigated. Our approach is extensively evaluated on five public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods.


title: Toward an EEG-Based Recognition of Music Liking Using Time-Frequency Analysis

abstract: Affective phenomena, as reflected through brain activity, could constitute an effective index for the detection of music preference. In this vein, this paper focuses on the discrimination between subjects' electroencephalogram (EEG) responses to self-assessed liked or disliked music, acquired during an experimental procedure, by evaluating different feature extraction approaches and classifiers to this end. Feature extraction is based on time-frequency (TF) analysis by implementing three TF techniques, i.e., spectrogram, Zhao-Atlas-Marks distribution and Hilbert-Huang spectrum (HHS). Feature estimation also accounts for physiological parameters that relate to EEG frequency bands, reference states, time intervals, and hemispheric asymmetries. Classification is performed by employing four classifiers, i.e., support vector machines, k-nearest neighbors (k-NN), quadratic and Mahalanobis distance-based discriminant analyses. According to the experimental results across nine subjects, best classification accuracy {86.52 (±0.76)%} was achieved using k-NN and HHS-based feature vectors ( FVs) representing a bilateral average activity, referred to a resting period, in β (13-30 Hz) and γ (30-49 Hz) bands. Activity in these bands may point to a connection between music preference and emotional arousal phenomena. Furthermore, HHS-based FVs were found to be robust against noise corruption. The outcomes of this study provide early evidence and pave the way for the development of a generalized brain computer interface for music preference recognition.


title: A Mobile Cloud Computing Model Using the Cloudlet Scheme for Big Data Applications

abstract: The wide spread of smart phones and their capabilities made them an important part of many people's life over the world. However, there are many challenges facing these devices such as: low computing power and fast energy drain from their batteries. One solution is to use mobile cloud computing services to run certain tasks at the cloud and returning back the results to the mobile device saving space and processing power. In this research, we introduce efficient Mobile Cloud Computing model based on the Cloudlet sheme. In our model, the mobile device don't need to communicate with the enterprise cloud server and instead contact the Cloudlet directly using cheaper technologies such as Wi-Fi, and no need for 3G/4G. Also, we propose a master-cloudlet management scheme to organize the communication between the cloudlets themselves. Our efficient mobile cloud computing model can be applied in many environments including universities and hospitals were big amounts of data is collected, stored and processed. The real implementation results show that our model out performs classical non-cloudlet mobile cloud computing models.


title: Telco Churn Prediction with Big Data

abstract: We show that telco big data can make churn prediction much more easier from the $3$V's perspectives: Volume, Variety, Velocity. Experimental results confirm that the prediction performance has been significantly improved by using a large volume of training data, a large variety of features from both business support systems (BSS) and operations support systems (OSS), and a high velocity of processing new coming data. We have deployed this churn prediction system in one of the biggest mobile operators in China. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having $0.96$ precision for the top $50000$ predicted churners in the list. Automatic matching retention campaigns with the targeted potential churners significantly boost their recharge rates, leading to a big business value.


title: Benchmark functions for the cec’2010 special session and competition on large-scale global optimization

abstract: Technology, the University of Science and Technology of China, Hefei, Anhui, China The Center of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science, the University of Birminham, Edgbaston, Birmingham B15 2TT, U.K. 3 School of EEE, Nanyang Technological University, Singapore, 639798 4 School of Computer Science & Software Engineering, the University of Western Australia, M002, 35 Stirling Highway, Crawley, Western Australia, 6009 Natural Computing Laboratory, Department of Computer Science, National Chiao Tung University, Taiwan


abstract: Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called manifold structure. This book shows how to exploit the special structure of such problems to develop efficient numerical algorithms. It places careful emphasis on both the numerical formulation of the algorithm and its differential geometric abstraction--illustrating how good algorithms draw equally from the insights of differential geometry, optimization, and numerical analysis. Two more theoretical chapters provide readers with the background in differential geometry necessary to algorithmic development. In the other chapters, several well-known optimization methods such as steepest descent and conjugate gradients are generalized to abstract manifolds. The book provides a generic development of each of these methods, building upon the material of the geometric chapters. It then guides readers through the calculations that turn these geometrically formulated methods into concrete numerical algorithms. The state-of-the-art algorithms given as examples are competitive with the best existing algorithms for a selection of eigenspace problems in numerical linear algebra. Optimization Algorithms on Matrix Manifolds offers techniques with broad applications in linear algebra, signal processing, data mining, computer vision, and statistical analysis. It can serve as a graduate-level textbook and will be of interest to applied mathematicians, engineers, and computer scientists.


title: Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome-wide associations

abstract: Genome-wide association studies (GWAS) using high-density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single-nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene-set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axes--SNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP-phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross-sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene-set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis-related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk.


title: Application of neural networks for software quality prediction using object-oriented metrics

abstract: The paper presents the application of neural networks in software quality estimation using object-oriented metrics. Quality estimation includes estimating reliability as well as maintainability of software. Reliability is typically measured as the number of defects. Maintenance effort can be measured as the number of lines changed per class. In this paper, two kinds of investigation are performed: predicting the number of defects in a class; and predicting the number of lines change per class. Two neural network models are used: they are Ward neural network; and General Regression neural network (GRNN). Object-oriented design metrics concerning inheritance related measures, complexity measures, cohesion measures, coupling measures and memory allocation measures are used as the independent variables. GRNN network model is found to predict more accurately than Ward network model.


title: An Improved DBSCAN Algorithm to Detect Stops in Individual Trajectories

abstract: With the increasing use of mobile GPS (global positioning system) devices, a large volume of trajectory data on users can be produced. In most existing work, trajectories are usually divided into a set of stops and moves. In trajectories, stops represent the most important and meaningful part of the trajectory; there are many data mining methods to extract these locations. DBSCAN (density-based spatial clustering of applications with noise) is a classical density-based algorithm used to find the high-density areas in space, and different derivative methods of this algorithm have been proposed to find the stops in trajectories. However, most of these methods required a manually-set threshold, such as the speed threshold, for each feature variable. In our research, we first defined our new concept of move ability. Second, by introducing the theory of data fields and by taking our new concept of move ability into consideration, we constructed a new, comprehensive, hybrid feature–based, density measurement method which considers temporal and spatial properties. Finally, an improved DBSCAN algorithm was proposed using our new density measurement method. In the Experimental Section, the effectiveness and efficiency of our method is validated against real datasets. When comparing our algorithm with the classical density-based clustering algorithms, our experimental results show the efficiency of the proposed method.


title: The Asthma-COPD Overlap Syndrome

abstract: Although in textbooks asthma and chronic obstructive pulmonary disease (COPD) are viewed as distinct disorders, there is increasing awareness that many patients have features of both. This article reviews the asthma–COPD overlap syndrome.


title: GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework

abstract: Abstract Although deep neural networks (DNNs) are being a revolutionary power to open up the AI era, the notoriously huge hardware overhead has challenged their applications. Recently, several binary and ternary networks, in which the costly multiply-accumulate operations can be replaced by accumulations or even binary logic operations, make the on-chip training of DNNs quite promising. Therefore there is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discrete. The second one is how to remove the full-precision hidden weights in the training phase to break the bottlenecks of memory/computation consumption. To address the first issue, we present a multi-step neuronal activation discretization method and a derivative approximation technique that enable the implementing the back propagation algorithm on discrete DNNs. While for the second issue, we propose a discrete state transition (DST) methodology to constrain the weights in a discrete space without saving the hidden weights. Through this way, we build a unified framework that subsumes the binary or ternary networks as its special cases, and under which a heuristic algorithm is provided at the website https://github.com/AcrossV/Gated-XNOR . More particularly, we find that when both the weights and activations become ternary values, the DNNs can be reduced to sparse binary networks, termed as gated XNOR networks (GXNOR-Nets) since only the event of non-zero weight and non-zero activation enables the control gate to start the XNOR logic operations in the original binary networks. This promises the event-driven hardware design for efficient mobile intelligence. We achieve advanced performance compared with state-of-the-art algorithms. Furthermore, the computational sparsity and the number of states in the discrete space can be flexibly modified to make it suitable for various hardware platforms.


title: High-Frequency Jet Ventilation in Nonintubated Patients

abstract: OBJECTIVES ::: High-frequency jet ventilation (HFJV) is a convenient method for providing ventilation during fiberoptic bronchoscopy. We describe an incipient approach of high-frequency jet ventilation via the working channel of a flexible bronchoscope for nonintubated patients who suffer from hypoxemia during bronchoscopy. The aim of this study was to test the efficacy of this incipient approach and determine the possible complications related to it. ::: ::: ::: MATERIALS AND METHODS ::: Sixteen patients who had oxygen saturation below 70% that did not resolve with nasal oxygen for 20 s during interventional bronchoscopy were included in the study. High-frequency jet ventilation was administrated via the working channel of a bronchoscope for 3 min. Arterial blood gas circumscriptions were compared before and after jet ventilation. ::: ::: ::: RESULTS ::: Oxygen saturation increased to >90% in all patients 30 s after jet ventilation. Mean arterial oxygen saturation pressure increased from 54.84 to 111.98 mmHg with jet ventilation (p=0.0001). Arterial carbon dioxide tension decreased after jet ventilation. The body mass index had no consequential effect on arterial carbon dioxide pressure after jet ventilation in our patients (p=0.1). Complications such as pneumothorax and working channel damage were not observed. ::: ::: ::: CONCLUSION ::: High-frequency jet ventilation via the working channel of the bronchoscope is a novel method that can provide optimal ventilation with minimal complications to nonintubated patients suffering from hypoxemia during bronchoscopy. This method also reduces the duration of bronchoscopy procedures.


title: Scalable product line configuration: A straw to break the camel's back

abstract: Software product lines are hard to configure. Techniques that work for medium sized product lines fail for much larger product lines such as the Linux kernel with 6000+ features. This paper presents simple heuristics that help the Indicator-Based Evolutionary Algorithm (IBEA) in finding sound and optimum configurations of very large variability models in the presence of competing objectives. We employ a combination of static and evolutionary learning of model structure, in addition to utilizing a pre-computed solution used as a "seed" in the midst of a randomly-generated initial population. The seed solution works like a single straw that is enough to break the camel's back -given that it is a feature-rich seed. We show promising results where we can find 30 sound solutions for configuring upward of 6000 features within 30 minutes.


title: Unsupervised Learning of Image Transformations

abstract: We describe a probabilistic model for learning rich, distributed representations of image transformations. The basic model is defined as a gated conditional random field that is trained to predict transformations of its inputs using a factorial set of latent variables. Inference in the model consists in extracting the transformation, given a pair of images, and can be performed exactly and efficiently. We show that, when trained on natural videos, the model develops domain specific motion features, in the form of fields of locally transformed edge filters. When trained on affine, or more general, transformations of still images, the model develops codes for these transformations, and can subsequently perform recognition tasks that are invariant under these transformations. It can also fantasize new transformations on previously unseen images. We describe several variations of the basic model and provide experimental results that demonstrate its applicability to a variety of tasks.


title: Modeling users' preferences and social links in Social Networking Services: a joint-evolving perspective

abstract: Researchers have long converged that the evolution of a Social Networking Service (SNS) platform is driven by the interplay between users' preferences (reflected in user-item consumption behavior) and the social network structure (reflected in user-user interaction behavior), with both kinds of users' behaviors change from time to time. However, traditional approaches either modeled these two kinds of behaviors in an isolated way or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of users' historical preferences and the dynamic social network structure affect the evolution of SNSs. Furthermore, can jointly modeling users' temporal behaviors in SNSs benefit both behavior prediction tasks? In this paper, we leverage the underlying social theories (i.e., social influence and the homophily effect) to investigate the interplay and evolution of SNSs. We propose a probabilistic approach to fuse these social theories for jointly modeling users' temporal behaviors in SNSs. Thus our proposed model has both the explanatory ability and predictive power. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.


title: CloudPack ⋆ Exploiting Workload Flexibility Through Rational Pricing

abstract: Infrastructure as a Service pricing models for resources are meant to reflect the operational costs and profit margins for providers to deliver virtualized resources to customers subject to an underlying Service Level Agreements (SLAs). While the operational costs incurred by providers are dynamic -- they vary over time depending on factors such as energy cost, cooling strategies, and aggregate demand -- the pricing models extended to customers are typically fixed -- they are static over time and independent of aggregate demand. This disconnect between the dynamic cost incurred by a provider and the fixed price paid by a customer results in an economically inefficient marketplace. In particular, it does not provide incentives for customers to express workload scheduling flexibilities that may benefit them as well as providers. In this paper, we utilize a dynamic pricing model to address this inefficiency and give customers the opportunity and incentive to take advantage of any flexibilities they may have regarding the provisioning of their workloads. We present CloudPack: a framework for workload colocation, which provides customers with the ability to formally express workload flexibilities using Directed Acyclic Graphs, optimizes the use of cloud resources to minimize total costs while allocating clients' workloads, and utilizes Shapley valuation to rationally -- and thus fairly in a game-theoretic sense -- attribute costs to the customers. Using extensive simulation, we show the practical utility of our CloudPack colocation framework and the efficacy of the resulting marketplace in terms of cost savings.


Information granulation and rough set approximation


title: Situated Learning in Computer Science Education

abstract: Sociocultural theories of learning such as Wenger and Lave's situated learning have been suggested as alternatives to cognitive theories of learning like constructivism. This article examines situated learning within the context of computer science (CS) education. Situated learning accurately describes some CS communities like open-source software development, but it is not directly applicable to other CS communities, especially those that deal with non-CS application areas. Nevertheless, situated learning can inform CS education by analyzing debates on curriculum and pedagogy within this framework. CS educators should closely examine professional CS communities of practice and design educational activities to model the actual activities of those communities.


title: A New Similarity Measure for Taxonomy Based on Edge Counting

abstract: This paper introduces a new similarity measure based on edge counting in a taxonomy like WorldNet or Ontology. Measurement of similarity between text segments or concepts is very useful for many applications like information retrieval, ontology matching, text mining, and question answering and so on. Several measures have been developed for measuring similarity between two concepts: out of these we see that the measure given by Wu and Palmer [1] is simple, and gives good performance. Our measure is based on their measure but strengthens it. Wu and Palmer [1] measure has a disadvantage that it does not consider how far the concepts are semantically. In our measure we include the shortest path between the concepts and the depth of whole taxonomy together with the distances used in Wu and Palmer [1]. Also the measure has following disadvantage i.e. in some situations, the similarity of two elements of an IS-A ontology contained in the neighborhood exceeds the similarity value of two elements contained in the same hierarchy. Our measure introduces a penalization factor for this case based upon shortest length between the concepts and depth of whole taxonomy.


title: Mobile Cloud-Computing-Based Healthcare Service by Noncontact ECG Monitoring

abstract: Noncontact electrocardiogram (ECG) measurement technique has gained popularity these days owing to its noninvasive features and convenience in daily life use. This paper presents mobile cloud computing for a healthcare system where a noncontact ECG measurement method is employed to capture biomedical signals from users. Healthcare service is provided to continuously collect biomedical signals from multiple locations. To observe and analyze the ECG signals in real time, a mobile device is used as a mobile monitoring terminal. In addition, a personalized healthcare assistant is installed on the mobile device; several healthcare features such as health status summaries, medication QR code scanning, and reminders are integrated into the mobile application. Health data are being synchronized into the healthcare cloud computing service (Web server system and Web server dataset) to ensure a seamless healthcare monitoring system and anytime and anywhere coverage of network connection is available. Together with a Web page application, medical data are easily accessed by medical professionals or family members. Web page performance evaluation was conducted to ensure minimal Web server latency. The system demonstrates better availability of off-site and up-to-the-minute patient data, which can help detect health problems early and keep elderly patients out of the emergency room, thus providing a better and more comprehensive healthcare cloud computing service.


title: Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

abstract: The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult @?@? control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.


title: A Review of Mental Leaps: Analogy in Creative Thought

abstract: A book review examining Mental Leaps: Analogy in Creative Thought, by Keith Holyoak and Paul Thagard, MIT Press, Cambridge, Massachusetts, 1994, 320 pp., ISBN 0-262-08233-0.


title: Coverage-Driven Verification - An approach to verify code for robots that directly interact with humans

abstract: Collaborative robots could transform several industries, such as manufacturing and healthcare, but they present a significant challenge to verification. The complex nature of their working environment neces- sitates testing in realistic detail under a broad range of circumstances. We propose the use of Coverage-Driven Verification (CDV) to meet this challenge. By automating the simulation-based testing process as far as possible, CDV provides an efficient route to coverage closure. We dis- cuss the need, practical considerations, and potential benefits of trans- ferring this approach from microelectronic design verification to the field of human-robot interaction. We demonstrate the validity and feasibility of the proposed approach by constructing a custom CDV testbench and applying it to the verification of an object handover task.


title: Cross-Cultural Case Study

abstract: This volume is one of a series of research annuals on qualitative research designed to take up issues and debates in this area that relate to methodology, the relationship between data collection and data analysis, the relationship between theory and method, and the implications of qualitative research for social policy and evaluation.


title: Robustness Analysis of Multi-attributes Decision Methods Based on Cloud Model

abstract: The approaches that assist people to ascertain appropriate multi-attributes decision methods become a basic issue that researcher must face and solve imminently in the field.A new quantificational subjective appropriate evaluation approach based on the cloud model is presented.The expected value and hyper entropy of subjective cloud are used to evaluate the reputation of methods,and the result of simulating experiments shows the validity and effectiveness of this approach.


title: Application of COBIT to Security Management in Information Systems Development

abstract: COBIT is a collection of good practices and processes for IT governance. It provides the effective measures, indicators and activities for enterprise. COBIT has also been applied to the other governance, e. g., software process, security governance, IT service management. However, since COBIT is too general-purpose, it requires deep expert knowledge for the implementation of each application. Although the guideline of security management is also published, its contents are abstract. Therefore, we examined the contents of COBIT and defined a framework which specializes in security engineering from the guideline. This paper presents the framework and its application to information systems development. The framework effectively utilizes the COBIT-based security management and solves various subjects of security in the development.


title: Beat the machine: Challenging workers to find the unknown unknowns

abstract: We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings, traditional methods for evaluating predictive models tend to miss rare-but-important errors--most importantly, rare cases for which the model is confident of its prediction (but wrong). In this paper we present a system that, in a game-like setting, asks humans to identify cases that will cause the predictive-model-based system to fail. Such techniques are valuable in discovering problematic cases that do not reveal themselves during the normal operation of the system, and may include cases that are rare but catastrophic. We describe the design of the system, including design iterations that did not quite work. In particular, the system incentivizes humans to provide examples that are difficult for the model to handle, by providing a reward proportional to the magnitude of the predictive model's error. The humans are asked to "Beat the Machine" and find cases where the automatic model ("the Machine") is wrong. Experiments show that the humans using Beat the Machine identify more errors than traditional techniques for discovering errors in from predictive models, and indeed, they identify many more errors where the machine is confident it is correct. Further, the cases the humans identify seem to be not simply outliers, but coherent areas missed completely by the model. Beat the machine identifies the "unknown unknowns".


title: A novel kernelized fuzzy c-means algorithm with application in medical image segmentation

abstract: Image segmentation plays a crucial role in many medical imaging applications. In this paper, we present a novel algorithm for fuzzy segmentation of magnetic resonance imaging (MRI) data. The algorithm is realized by modifying the objective function in the conventional fuzzy C-means (FCM) algorithm using a kernel-induced distance metric and a spatial penalty on the membership functions. Firstly, the original Euclidean distance in the FCM is replaced by a kernel-induced distance, and thus the corresponding algorithm is derived and called as the kernelized fuzzy C-means (KFCM) algorithm, which is shown to be more robust than FCM. Then a spatial penalty is added to the objective function in KFCM to compensate for the intensity inhomogeneities of MR image and to allow the labeling of a pixel to be influenced by its neighbors in the image. The penalty term acts as a regularizer and has a coefficient ranging from zero to one. Experimental results on both synthetic and real MR images show that the proposed algorithms have better performance when noise and other artifacts are present than the standard algorithms.


ACCELERATING READING ATTAINMENT: THE EFFECTIVENESS OF SYNTHETIC PHONICS


title: stimulation-induced temperature changes

abstract: There is a growing interest in the use of chronic deep brain stimulation (DBS) for the treatment of medically refractory movement disorders and other neurological and psychiatric conditions. Fundamental questions remain about the physiologic effects of DBS. Previous basic research studies have focused on the direct polarization of neuronal membranes by electrical stimulation. The goal of this paper is to provide information on the thermal effects of DBS using finite element models to investigate the magnitude and spatial distribution of DBS-induced temperature changes. The parameters investigated include stimulation waveform, lead selection, brain tissue electrical and thermal conductivities, blood perfusion, metabolic heat generation during the stimulation and lead thermal conductivity/heat dissipation through the electrode. Our results show that clinical DBS protocols will increase the temperature of surrounding tissue by up to 0.8 degrees C depending on stimulation/tissue parameters.


title: Adversarial Multi-Criteria Learning for Chinese Word Segmentation

abstract: Different linguistic perspectives causes many diverse segmentation criteria for Chinese word segmentation (CWS). Most existing methods focus on improve the performance for each single criterion. However, it is interesting to exploit these different criteria and mining their common underlying knowledge. In this paper, we propose adversarial multi-criteria learning for CWS by integrating shared knowledge from multiple heterogeneous segmentation criteria. Experiments on eight corpora with heterogeneous segmentation criteria show that the performance of each corpus obtains a significant improvement, compared to single-criterion learning. Source codes of this paper are available on Github.


title: Deep Learning Can Reverse Photon Migration for Diffuse Optical Tomography

abstract: Can artificial intelligence (AI) learn complicated non-linear physics? Here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains accurate 3D distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches to inverse problems, our deep network learns to invert the Lippmann-Schwinger integral equation which describes the essential physics of photon migration of diffuse near-infrared (NIR) photons in turbid media. As an example for clinical relevance, we applied the method to our prototype diffuse optical tomography (DOT). We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.


title: Unsupervised Word Sense Disambiguation Rivaling Supervised Methods

abstract: This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.


title: Turning Internet of Things(IoT) into Internet of Vulnerabilities (IoV) : IoT Botnets

abstract: Internet of Things (IoT) is the next big evolutionary step in the world of internet. The main intention behind the IoT is to enable safer living and risk mitigation on different levels of life. With the advent of IoT botnets, the view towards IoT devices has changed from enabler of enhanced living into Internet of vulnerabilities for cyber criminals. IoT botnets has exposed two different glaring issues, 1) A large number of IoT devices are accessible over public Internet. 2) Security (if considered at all) is often an afterthought in the architecture of many wide spread IoT devices. In this article, we briefly outline the anatomy of the IoT botnets and their basic mode of operations. Some of the major DDoS incidents using IoT botnets in recent times along with the corresponding exploited vulnerabilities will be discussed. We also provide remedies and recommendations to mitigate IoT related cyber risks and briefly illustrate the importance of cyber insurance in the modern connected world.


title: A novel compact ka-band high-rejection diplexer based on substrate integrated waveguide

abstract: In this paper, a compact diplexer using substrate integrated waveguide (SIW) resonators is introduced. The diplexer consists of two filters and a circulator. The diplexer channels are 4% and 5% relative bandwidth at 32GHz and 33GHz, respectively. The filters are based on SIW resonators, which have higher Q values. This paper also analyzed the factors which affect the Q value of resonators, and describes the designing procedure of Generalized Chebyshev filters, which used in this paper. The circulator also is on account of SIW. It uses inductive windows to increase bandwidth. At last, the paper gives the diplexer's simulation and measure results. And two results are similar with each other.