title: Lactic Acid Bacteria and Their Bacteriocins: Classification, Biosynthesis and Applications against Uropathogens: A Mini-Review

abstract: Several lactic acid bacteria (LAB) isolates from the Lactobacillus genera have been applied in food preservation, partly due to their antimicrobial properties. Their application in the control of human pathogens holds promise provided appropriate strains are scientifically chosen and a suitable mode of delivery is utilized. Urinary tract infection (UTI) is a global problem, affecting mainly diabetic patients and women. Many uropathogens are developing resistance to commonly used antibiotics. There is a need for more research on the ability of LAB to inhibit uropathogens, with a view to apply them in clinical settings, while adhering to strict selection guidelines in the choice of candidate LAB. While several studies have indicated the ability of LAB to elicit inhibitory activities against uropathogens in vitro, more in vivo and clinical trials are essential to validate the efficacy of LAB in the treatment and prevention of UTI. The emerging applications of LAB such as in adjuvant therapy, oral vaccine development, and as purveyors of bioprotective agents, are relevant in infection prevention and amelioration. Therefore, this review explores the potential of LAB isolates and their bacteriocins to control uropathogens, with a view to limit clinical use of antibiotics.


title: Development and Validation of a Surgical Workload Measure: The Surgery Task Load Index (SURG-TLX)

abstract: BACKGROUND ::: The purpose of the present study was to develop and validate a multidimensional, surgery-specific workload measure (the SURG-TLX), and to determine its utility in providing diagnostic information about the impact of various sources of stress on the perceived demands of trained surgical operators. As a wide range of stressors have been identified for surgeons in the operating room, the current approach of considering stress as a unidimensional construct may not only limit the degree to which underlying mechanisms may be understood but also the degree to which training interventions may be successfully matched to particular sources of stress. ::: ::: ::: METHODS ::: The dimensions of the SURG-TLX were based on two current multidimensional workload measures and developed via focus group discussion. The six dimensions were defined as mental demands, physical demands, temporal demands, task complexity, situational stress, and distractions. Thirty novices were trained on the Fundamentals of Laparoscopic Surgery (FLS) peg transfer task and then completed the task under various conditions designed to manipulate the degree and source of stress experienced: task novelty, physical fatigue, time pressure, evaluation apprehension, multitasking, and distraction. ::: ::: ::: RESULTS ::: The results were supportive of the discriminant sensitivity of the SURG-TLX to different sources of stress. The sub-factors loaded on the relevant stressors as hypothesized, although the evaluation pressure manipulation was not strong enough to cause a significant rise in situational stress. ::: ::: ::: CONCLUSIONS ::: The present study provides support for the validity of the SURG-TLX instrument and also highlights the importance of considering how different stressors may load surgeons. Implications for categorizing the difficulty of certain procedures, the implementation of new technology in the operating room (man-machine interface issues), and the targeting of stress training strategies to the sources of demand are discussed. Modifications to the scale to enhance clinical utility are also suggested.


title: The Relationship between Self-compassion and Other-focused Concern among College Undergraduates , Community Adults , and Practicing Meditators

abstract: The present study examined the link between self-compassion and concern for the well-being of others. Other-focused concern variables included compassion for humanity, empathetic concern, perspective taking, personal distress, altruism and forgiveness. Participants included 384 college undergraduates, 400 community adults, and 172 practicing meditators. Among all participant groups, higher levels of self-compassion were significantly linked to more perspective taking, less personal distress, and greater forgiveness. Self-compassion was linked to compassion for humanity, empathetic concern, and altruism among community adults and meditators but not college undergraduates. The strength of the association between self-compassion and other-focused concern also varied according to participant group and gender. The strongest links tended to be found among meditators, while women tended to show weaker associations than men.


title: Ethnobotanical uses of Brassicaceae in Sicily

abstract: The Brassicaceae are widely represented in Sicily: some of them have an ethnobotanical interest. In this frame, through interviews to representatives of small rural communities, we aimed to carry out an ethnobotanical survey on wild Brassica species in order to identify their traditional uses and compare the overall data with ethnobotanical literature about Sicily. The results showed that in the island sixty-four species, often linked only to dialect names, are used mainly for food. For example, the bitter back-taste given by some species (Brassica fruticulosa Cyr., B. nigra (L.) Koch, Bunias erucago L., Diplotaxis erucoides DC., D. tenuifolia (L.) DC., Nasturtium officinale (R.) Br., Raphanus raphanistrum L., Sinapis alba L., S. arvensis L., etc.) typifies many traditional dishes. Some others (Brassica rupestris Raf., B. oleracea L., Nasturtium officinale L., Raphanus raphanistrum L.) are used in popular medicine. Characteristic is the use of Matthiola incana L. in folklore manifestations during St. Joseph Day, when flowers are used to decorate horses and cover the unpleasant smell.


title: Logistic regression for data mining and high-dimensional classification

abstract: The focus of this thesis is fast and robust adaptations of logistic regression (LR) for data mining and high-dimensional classification problems. LR is well-understood and widely used in the statistics, machine learning, and data analysis communities. Its benefits include a firm statistical foundation and a probabilistic model useful for “explaining” the data. There is a perception that LR is slow, unstable, and unsuitable for large learning or classification tasks. Through fast approximate numerical methods, regularization to avoid numerical instability, and an efficient implementation we will show that LR can outperform modern algorithms like Support Vector Machines (SVM) on a variety of learning tasks. Our novel implementation, which uses a modified iteratively re-weighted least squares estimation procedure, can compute model parameters for sparse binary datasets with hundreds of thousands of rows and attributes, and millions or tens of millions of nonzero elements in just a few seconds. Our implementation also handles real-valued dense datasets of similar size.


title: A Mobile Health Intervention for Self-Management and Lifestyle Change for Persons With Type 2 Diabetes, Part 2: One-Year Results From the Norwegian Randomized Controlled Trial RENEWING HEALTH

abstract: Background: Self-management is crucial in the daily management of type 2 diabetes. It has been suggested that mHealth may be an important method for enhancing self-management when delivered in combination with health counseling. Objective: The objective of this study was to test whether the use of a mobile phone–based self-management system used for 1 year, with or without telephone health counseling by a diabetes specialist nurse for the first 4 months, could improve glycated hemoglobin A1c (HbA1c) level, self-management, and health-related quality of life compared with usual care. Methods: We conducted a 3-arm prospective randomized controlled trial involving 2 intervention groups and 1 control group. Eligible participants were persons with type 2 diabetes with an HbA 1c level ≥7.1% (≥54.1 mmol/mol) and aged ≥18 years. Both intervention groups received the mobile phone–based self-management system Few Touch Application (FTA). The FTA consisted of a blood glucose–measuring system with automatic wireless data transfer, diet manual, physical activity registration, and management of personal goals, all recorded and operated using a diabetes diary app on the mobile phone. In addition, one intervention group received health counseling based on behavior change theory and delivered by a diabetes specialist nurse for the first 4 months after randomization. All groups received usual care by their general practitioner. The primary outcome was HbA1c level. Secondary outcomes were self-management (heiQ), health-related quality of life (SF-36), depressive symptoms (CES-D), and lifestyle changes (dietary habits and physical activity). Data were analyzed using univariate methods (t test, ANOVA) and multivariate linear and logistic regression. Results: A total of 151 participants were randomized: 51 to the FTA group, 50 to the FTA-health counseling (FTA-HC) group, and 50 to the control group. Follow-up data after 1 year were available for 120 participants (79%). HbA1c level decreased in all groups, but did not differ between groups after 1 year. The mean change in the heiQ domain skills and technique acquisition was


title: Dynamic modeling of a VSC-HVDC converter

abstract: The aim of this paper is to present a nonlinear dynamic model for Voltage Source Converter-based HVDC (VSC-HVDC) links that can be used for dynamic studies. It includes the main physical elements and is controlled by PI controllers with antiwindup. A linear control model is derived for efficient tuning of the controllers of the nonlinear dynamic model. The nonlinear dynamic model is then tuned according to the performance of an ABB HVDC Light model.


title: Dual-arm framework for cooperative applications

abstract: This paper presents a framework schema that allows to bring simulation results obtained from different dual-arm robotic applications to executions in real environments. The framework uses the Robot Operating System (ROS) to communicate each component of the dual-arm, and the robotic software tool The Kautham Project for the computation of motion paths, inverse kinematics validations, collision checking and the graphical visualization of the simulated environment. The Anthropomorphic Dual Arm Robotic System (ADARS) was used in this work which is composed of two robotic arms of 6 degree of freedom, two anthropomorphic hands, and tactile sensors in the fingertips. The real execution of two different applications are presented to show the robustness of the framework. The resulting framework is general enough, allowing the reimplementation with minimum changes in any other dual-arm system.


title: A real-time curb detection and tracking method for UGVs by using a 3D-LIDAR sensor

abstract: Environment perception is essential for autonomous driving technology. The curb is a prominent feature of urban roads and therefore is a significant part of environment perception. In this paper, a real-time curb detection and tracking method is proposed for Unmanned Ground Vehicles (UGVs). The proposed curb detection algorithm uses the the surrounding environment data provided by a 3D-LIDAR sensor to extract the curb position based on its spatial features. The curb tracking algorithm is proposed to predict and update the curb position with respect to the current vehicle states in real time. The performance of the proposed method is verified through extensive experiments with a UGV driving on campus roads. The experimental results demonstrate the accuracy and robustness of the proposed method.


title: Self-Committee Approach for Image Restoration Problems using Convolutional Neural Network

abstract: There have been many discriminative learning methods using convolutional neural networks (CNN) for several image restoration problems, which learn the mapping function from a degraded input to the clean output. In this letter, we propose a self-committee method that can find enhanced restoration results from the multiple trial of a trained CNN with different but related inputs. Specifically, it is noted that the CNN sometimes finds different mapping functions when the input is transformed by a reversible transform and thus produces different but related outputs with the original. Hence averaging the outputs for several different transformed inputs can enhance the results as evidenced by the network committee methods. Unlike the conventional committee approaches that require several networks, the proposed method needs only a single network. Experimental results show that adding an additional transform as a committee always brings additional gain on image denoising and single image supre-resolution problems.


title: Strong Symmetry Defined By Twisting Modules, Applied to Quasi-Hereditary Algebras With Triangular Decomposition and Vanishing Radical Cube

abstract: For a finite dimensional algebra with triangular decomposition, a new kind of modules is defined, the twisting modules. Using the structure of these modules, for some algebras with vanishing radical cube the characteristic tilting module is described and its endomorphism ring is computed. This covers both Temperley–Lieb algebras and q-Schur algebras of finite representation type. Our basic tool is a new symmetry condition, stronger than the symmetry provided by the existence of a triangular decomposition in general.


PHYSICO-CHEMICAL EFFECTS OF SAND HARVESTING ON WATER QUALITY IN RIVER THWAKE MACHAKOS COUNTY, KENYA


title: Multi-view gait recognition using 3D convolutional neural networks

abstract: In this work we present a deep convolutional neural network using 3D convolutions for Gait Recognition in multiple views capturing spatio-temporal features. A special input format, consisting of the gray-scale image and optical flow enhance color invaranice. The approach is evaluated on three different datasets, including variances in clothing, walking speeds and the view angle. In contrast to most state-of-the-art Gait Recognition systems the used neural network is able to generalize gait features across multiple large view angle changes. The results show a comparable to better performance in comparison with previous approaches, especially for large view differences.


title: Reflections on Digital Literacy

abstract: Apparatus is disclosed herein which detects breaks in spinning yarns in ring spinning frames or the like and in particular senses vibrations inherent to a yarn guide disposed on a bobbin when the yarn guide comes into contact with the spinning yarns. The apparatus achieves quick inspection of whether breaks occur with the spinning yarns by the presence and absence of the vibrations, thereby monitoring joints in the spinning yarns and the operating state of the spinning machine.


Knowledge, attitude, and practices towards schistosomiasis among rural population in Yemen


title: CAST: Collaborative Agents for Simulating Teamwork

abstract: Psychological studies on teamwork have shown that an effective team often can anticipate information needs of teammates based on a shared mental model. Existing multi-agent models for teamwork are limited in their ability to support proactive information exchange among teammates. To address this issue, we have developed and implemented a multi-agent architecture called CAST that simulates teamwork and supports proactive information exchange in a dynamic environment. We present a formal model for proactive information exchange. Knowledge regarding the structure and process of a team is described in a language called MALLET. Beliefs about shared team processes and their states are represented using Petri Nets. Based on this model, CAST agents offer information proactively to those who might need it using an algorithm called DIARG. Empirical evaluations using a multi-agent synthetic testbed application indicate that CAST enhances the effectiveness of teamwork among agents without sacrificing a high cost for communications.


Treetables and Other Visualizations for Email Threads. Xerox PARC


title: Research Review: Gender identity in youth treatment paradigms and controversies

abstract: Background ::: Pediatric gender identity has gained increased attention over the past several years in the popular media, political arena, and medical literature. This article reviews terminology in this evolving field, traditional models of gender identity development and their limitations, epidemiology and natural history of cross-gender identification among children and adolescents, co-occurring conditions and behaviors, research into the biological and psychosocial determinants of cross-gender identification, and research into the options regarding and benefits of clinical approaches to gender incongruent youth. ::: ::: Methods ::: Based on a critical review of the extant literature, both theoretical and empirical, that addresses the issue of pediatric gender identity, the authors synthesized what is presently known and what is in need of further research in order to elucidate the developmental trajectory and clinical needs of gender diverse youth. ::: ::: Results ::: The field of pediatric gender identity has evolved substantially over the past several years. New research suggests that cross-gender identification is prevalent (approximately 1% of youth). These youth suffer disproportionately high rates of anxiety, depression, and suicidality. Although research into the etiology of cross-gender identification is limited, emerging data have shown that affirmative treatment protocols may improve the high rates of mental health difficulties seen among these patients. ::: ::: Conclusions ::: The field of pediatric gender identity has evolved dramatically. Emerging data suggest that these patients’ high rates of anxiety, depression, and suicidality appear to be improved with affirmative protocols, although future longitudinal data are needed.


title: A study of equivalent and stubborn mutation operators using human analysis of equivalence

abstract: Though mutation testing has been widely studied for more than thirty years, the prevalence and properties of equivalent mutants remain largely unknown. We report on the causes and prevalence of equivalent mutants and their relationship to stubborn mutants (those that remain undetected by a high quality test suite, yet are non-equivalent). Our results, based on manual analysis of 1,230 mutants from 18 programs, reveal a highly uneven distribution of equivalence and stubbornness. For example, the ABS class and half UOI class generate many equivalent and almost no stubborn mutants, while the LCR class generates many stubborn and few equivalent mutants. We conclude that previous test effectiveness studies based on fault seeding could be skewed, while developers of mutation testing tools should prioritise those operators that we found generate disproportionately many stubborn (and few equivalent) mutants.


title: Avoidance of overlearning characterises the spacing effect

abstract: The spacing of a fixed amount of study time across multiple sessions usually increases subsequent test performance—a finding known as the spacing effect. In the spacing experiment reported here, subjects completed multiple learning trials, and each included a study phase and a test. Once a subject achieved a perfect test, the remaining learning trials within that session comprised what is known as overlearning. The number of these overlearning trials was reduced when learning trials were spaced across multiple sessions rather than massed in a single session. In addition, the degree to which spacing reduced overlearning predicted the size of the spacing effect, which is consistent with the possibility that spacing increases subsequent recall by reducing the occurrence of overlearning. By this account, overlearning is an inefficient use of study time, and the efficacy of spacing depends at least partly on the degree to which it reduces the occurrence of overlearning.


title: Barriers to the Adoption of EHR Systems in the Kingdom of Saudi Arabia: An Exploratory Study Using a Systematic Literature Review

abstract: Objective: Electronic Health Records (EHRs) have become a key enabler to improving patient safety, improving healthcare quality, and increasing healthcare efficiency. Governments in various countries have moved beyond the local implementation of EHRs in different healthcare organizations to the national implementation and integration of EHRs. The Kingdom of Saudi Arabia (KSA) has lagged behind significantly in this regard, with only few hospitals have implemented the EHR. The purpose of this study is to identify barriers to the adoption of EHRs in the KSA using a systematic literature review. Methods: We searched for relevant articles using six search engines (PubMed, EBSCO Host, Web of Science, ACM, IEEE and Google Scholar). The search criteria focused on peer reviewed, empirical studies conducted in the KSA. The final set that met the inclusion criteria was twelve studies. The authors extracted, analyzed, summarized, and categorized empirical results related to EHR barriers in these studies. Results: After categorization and analysis, we identified the following twelve main barriers to EHR adoption: lack of computer experience by healthcare professionals (18%), lack of perceived usefulness by healthcare professionals (15%), lack of perceived ease of by healthcare professionals (15%), technical limitations of the software system (15%), lack of user support (9%), confidentiality concerns (9%), user resistance to change (6%), lack of quality in patients’ information (3%), lack of EHR standards (3%), uncertainty about EHR vendors (3%), hospital size (3%), and hospital’s level of care (3%). Conclusion: The findings of this study will be of great potential to policy makers and EHR vendors in the KSA. They can inform strategies to design systems and tailor implementation strategies toward factors that motivate adoption. A second important contribution of this study is that it provides evidence that the extant technology adoption theories like the Technology Acceptance Model (TAM) are not sufficient in explaining EHR adoption, as only 30% of identified barriers could be categorized according to TAM. There is a need for creating a new model for EHR adoption.


title: Chemical Sensing Using Sol-Gel Derived Planar Waveguides and Indicator Phases

abstract: A new optical sensing platform based on a combination of planar waveguiding and sol-gel processing technologies is described. The sensing element consists of two, submicrometer thick glass layers supported on an optically thick glass substrate; both layers were fabricated using a sol-gel coating method. The lower layer is a densified glass that functions as a planar integrated optical waveguide (IOW). The upper layer is an undensified glass of lower index doped with an optical indicator that is immobilized, yet remains sterically accessible to analytes that diffuse into the pore network. Formation of a complex between the analyte and indicator is detected via attenuated total reflection (ATR) of light guided in the IOW. Feasibility was evaluated by constructing IOW-ATR sensors for Pb{sup 2+} and pH, based on immobilized xylenol orange and bromocresol purple, respectively. The response of both sensors was sensitive and rapid, features that are difficult to achieve simultaneously in monolithic sol-gel glass sensors. In the IOW-ATR geometry, these features are realized simultaneously because the primary axes of light propagation and analyte diffusion are orthogonal. The overall approach is technically simple, inexpensive, and applicable to a wide variety of indicator chemistries. 48 refs., 8 figs.


title: 1.5 kW single phase CCM totem-pole PFC using 650V SiC cascodes

abstract: This paper presents benefits of using 650 V SiC cascodes to realize a 1.5 kW single phase, continuous conduction mode (CCM) totem-pole, bridgeless power factor correction (PFC) AC/DC converter switching at 100 kHz. By exploiting the excellent switching and conduction performance of SiC cascodes, the compatibility with a 12 V gate drive and synchronous rectification of the full-bridge, a very high peak efficiency of 98.6% is achieved for this AC/DC converter at high line (230 V) AC input with 400 V DC output. With correct digital control of the full-bridge gate signals the inductor current spike at zero-crossing is significantly reduced and a total harmonic distortion (THD) of 1.83% is achieved at high line near full load.


title: CARES: a ranking-oriented CADAL recommender system

abstract: A recommender system is useful for a digital library to suggest the books that are likely preferred by a user. Most recommender systems using collaborative filtering approaches leverage the explicit user ratings to make personalized recommendations. However, many users are reluctant to provide explicit ratings, so ratings-oriented recommender systems do not work well. In this paper, we present a recommender system for CADAL digital library, namely CARES, which makes recommendations using a ranking-oriented collaborative filtering approach based on users' access logs, avoiding the problem of the lack of user ratings. Our approach employs mean AP correlation coefficients for computing similarities among users' implicit preference models and a random walk based algorithm for generating a book ranking personalized for the individual. Experimental results on real access logs from the CADAL web site show the effectiveness of our system and the impact of different values of parameters on the recommendation performance.


title: Gaussian Process Training with Input Noise

abstract: In standard Gaussian Process regression input locations are assumed to be noise free. We present a simple yet effective GP model for training on input points corrupted by i.i.d. Gaussian noise. To make computations tractable we use a local linear expansion about each input point. This allows the input noise to be recast as output noise proportional to the squared gradient of the GP posterior mean. The input noise variances are inferred from the data as extra hyperparameters. They are trained alongside other hyperparameters by the usual method of maximisation of the marginal likelihood. Training uses an iterative scheme, which alternates between optimising the hyperparameters and calculating the posterior gradient. Analytic predictive moments can then be found for Gaussian distributed test points. We compare our model to others over a range of different regression problems and show that it improves over current methods.


title: Reduction of Walking Time in the Distribution Center of De Bijenkorf

abstract: In many distribution centers, there is a constant pressure to reduce the order throughput times. One such distribution center is the DC of De Bijenkorf, a retail organization in The Netherlands with 7 subsidiaries and a product assortment of about 300,000 SKUs (stock keeping units). The orders for the subsidiaries are picked manually in this warehouse, which is very labor intensive. Furthermore many shipments have to be finished at about the same time, which leads to peak loads in the picking process. The picking process is therefore a costly operation.


title: A colored Petri net model for the France Paris metro system

abstract: This paper describes how colored Petri nets can be used for modeling the Paris underground Metro system. This is one of the most complex metro systems in the world. In this paper, the metro system is analyzed and described. The method of analysis is outlined. A working model for three of the major lines was constructed using CPNs. The results and other issues that have to be tackled are explained.


title: Fine-Grained Resource Scaling in a Public Cloud: A Tenant's Perspective

abstract: Growing tenant workload needs and an increasingly competitive market will force cloud providers to operate their data centers at significantly higher utilization levels than seen today. We argue that a key enabler of such cloud ecosystems would be facilities for tenants to engage in fine-grained resource scaling in addition to those offered by current providers. The basic unit of resource scaling exposed by current cloud providers is the canonical interface of virtual machines (VMs) with relatively static resource capacities. This paper describes opportunities and challenges in augmenting this interface to also include fine-grained scaling of CPU and memory within an already procured VM. Qualitative arguments for why this would offer cost benefits for both the provider and its tenants are presented. We focus on the cost-effective operation of a tenant in such an environment via the design of a feedback controller. The efficacy of our ideas is illustrated by implementing a case study in a Memcached tenant workload. Our results are promising and point to an interesting and broad area for further research - e.g., with the real-world workload in our evaluation, up to 50% utility improvement can be achieved by just applying memory scaling, a further 66% improvement can be achieved by coordinating fine-grained CPU and memory scaling.


title: The Future of Creative Learning

abstract: Virtual reality has achieved an adequate level of development for it to be considered in innovative applications such as education, training, and research in higher education. Virtual reality offers both opportunities and challenges for the educational sector. One of the challenges of virtual reality technology is the costs associated which have been unaffordable for educational institutes. However, in recent years, computer hardware and software development has made it more feasible to incorporate virtual reality technology into future teaching strategies. ::: ::: Despite the cost challenges, educational benefits of implementing virtual reality remain compelling. This paper explains virtual reality principle and describes the interactive educational environment developed at WMG, the University of Warwick. It also discusses the benefits of using state-of-the-art 3D photorealistic interactive and immersive virtual environment for engineering undergraduates and postgraduate teaching, learning and training.


title: Combination of Multiple Searches

abstract: The TREC-2 project at Virginai Tech focused on methods for combining the evidence from multiple retrieval runs to improve performance over any single retrieval method. This paper describes one such method that has been shown to increase performance by combining the similarity values from five different retrieval runs using both vector space and P-norm extended boolean retrieval methods


title: The similarity metric

abstract: A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new "normalized information distance," based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the similarity metric . This theory forms the foundation for a new practical tool. To evidence generality and robustness, we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.


title: Are Slice-Based Cohesion Metrics Actually Useful in Effort-Aware Post-Release Fault-Proneness Prediction? An Empirical Study

abstract: Background . Slice-based cohesion metrics leverage program slices with respect to the output variables of a module to quantify the strength of functional relatedness of the elements within the module. Although slice-based cohesion metrics have been proposed for many years, few empirical studies have been conducted to examine their actual usefulness in predicting fault-proneness. Objective . We aim to provide an in-depth understanding of the ability of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction, i.e. their effectiveness in helping practitioners find post-release faults when taking into account the effort needed to test or inspect the code. Method . We use the most commonly used code and process metrics, including size, structural complexity, Halstead's software science, and code churn metrics, as the baseline metrics. First, we employ principal component analysis to analyze the relationships between slice-based cohesion metrics and the baseline metrics. Then, we use univariate prediction models to investigate the correlations between slice-based cohesion metrics and post-release fault-proneness. Finally, we build multivariate prediction models to examine the effectiveness of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction when used alone or used together with the baseline code and process metrics. Results . Based on open-source software systems, our results show that: 1) slice-based cohesion metrics are not redundant with respect to the baseline code and process metrics; 2) most slice-based cohesion metrics are significantly negatively related to post-release fault-proneness; 3) slice-based cohesion metrics in general do not outperform the baseline metrics when predicting post-release fault-proneness; and 4) when used with the baseline metrics together, however, slice-based cohesion metrics can produce a statistically significant and practically important improvement of the effectiveness in effort-aware post-release fault-proneness prediction. Conclusion . Slice-based cohesion metrics are complementary to the most commonly used code and process metrics and are of practical value in the context of effort-aware post-release fault-proneness prediction.


title: Community extraction for social networks

abstract: Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields and has diverse applications. Most community detection methods focus on partitioning the entire network into communities, with the expectation of many ties within communities and few ties between. However, many networks contain nodes that do not fit in with any of the communities, and forcing every node into a community can distort results. Here we propose a new framework that extracts one community at a time, allowing for arbitrary structure in the remainder of the network, which can include weakly connected nodes. The main idea is that the strength of a community should depend on ties between its members and ties to the outside world, but not on ties between nonmembers. The proposed extraction criterion has a natural probabilistic interpretation in a wide class of models and performs well on simulated and real networks. For the case of the block model, we establish asymptotic consistency of estimated node labels and propose a hypothesis test for determining the number of communities.


title: Notomelia and related neural tube defects in a baby born in Niger: case report and literature review

abstract: Notomelia associated with neural tube defects are rare diseases.A baby was born in Niger with multiple congenital embryonic malformations on the posterior midline. The most rostral malformation was an accessory limb (polymelia) at the level of the lumbar vertebrae composed of two long bones, a foot and three toes. Accessory male genitalia were present at the base of this malformed accessory limb which had no apparent motor or sensory innervation. The second malformation was a sacral vestigial appendage with an adjacent dermal sinus opening onto the posterior midline and extending internally to the dura through a defect of the vertebral arches. From the published literature and this particular case, we conclude that notomelia is a rare clinical sequela of a neural tube defect (NTD) and is correctly classified as a dysraphic appendage.The recent occurrence of three similar cases in the same ethnic group from Niger, three from consanguineous parents, suggests that genetic factors are likely to contribute significantly to the genesis of this syndrome, consistent with a recent report that mutation of the bovine NHLRC2 gene resulting in a V311A substitution at a highly conserved locus in the NHLRC2 protein is, when homozygous, causally associated with several forms of polymelia including notomelia, with heteropagus conjoined twinning and with other NTD-related embryonic malformations. Detailed genome-wide studies of children with dysraphic appendages are indicated.


title: Causal Networks: Semantics and Expressiveness

abstract: Dependency knowledge of the form"x is independent of y once z is known"invariably obeys the four graphoid axioms, examples include probabilistic and database dependencies. Often, such knowledge can be represented efficiently with graphical structures such as undirected graphs and directed acyclic graphs (DAGs). In this paper we show that the graphical criterion called d-separation is a sound rule for reading independencies from any DAG based on a causal input list drawn from a graphoid. The rule may be extended to cover DAGs that represent functional dependencies as well as conditional dependencies.


title: Measuring method complexity: UML versus BPMN

abstract: Graphical models are used to depict relevant aspects of real-world domains intended to be supported by an information system. Various approaches for modeling exist and approaches such as object-oriented and process-oriented modeling methods are in widespread use. These modeling methods differ in their expressive power as well as in their complexity of use, thereby leading to an important investment decision for organizations seeking to conduct modeling projects. In this paper, we used an established approach for evaluating the complexity of conceptual modeling methods and compared two important industry standards for modeling, Unified Modeling Language and Business Process Modeling Notation, based on their complexity. Our research finds that BPMN has very high levels of complexity when contrasted with UML.


title: Face Image Quality Assessment Based on Learning to Rank

abstract: Face image quality is an important factor affecting the accuracy of automatic face recognition. It is usually possible for practical recognition systems to capture multiple face images from each subject. Selecting face images with high quality for recognition is a promising stratagem for improving the system performance. We propose a learning to rank based framework for assessing the face image quality. The proposed method is simple and can adapt to different recognition methods. Experimental result demonstrates its effectiveness in improving the robustness of face detection and recognition.


title: Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates

abstract: Recent years have seen a surge of interest in stance classification in online debates. Oftentimes, however, it is important to determine not only the stance expressed by an author in her debate posts, but also the reasons behind her supporting or opposing the issue under debate. We therefore examine the new task of reason classification in this paper. Given the close interplay between stance classification and reason classification, we design computational models for examining how automatically computed stance information can be profitably exploited for reason classification. Experiments on our reason-annotated corpus of ideological debate posts from four domains demonstrate that sophisticated models of stances and reasons can indeed yield more accurate reason and stance classification results than their simpler counterparts.


title: Axonal excitability and motor propagation velocity of peripheral nerves in patients with acute vascular lesions of the brain.

abstract: From measurements of maximum and minimum motor nerve propagation velocity and neuronal excitability we conclude that there is a functional loss of motor units and distal nerve "dying back" in persons affected with unilateral acute cerebral vascular lesions. The study also suggests that transynaptic degeneration affects the lower motor neurone function on both sides.


A Distributed Online Learning Approach for Pattern Prediction over Movement Event Streams with Apache Flink


title: Fire flame detection in video sequences using multi-stage pattern recognition techniques

abstract: In this paper, we propose an effective technique that is used to automatically detect fire in video images. The proposed algorithm is composed of four stages: (1) an adaptive Gaussian mixture model to detect moving regions, (2) a fuzzy c-means (FCM) algorithm to segment the candidate fire regions from these moving regions based on the color of fire, (3) special parameters extracted based on the tempo-spatial characteristics of fire regions, and (4) a support vector machine (SVM) algorithm using these special parameters to distinguish between fire and non-fire. Experimental results indicate that the proposed method outperforms other fire detection algorithms, providing high reliability and a low false alarm rate.


title: Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction

abstract: Large-scale 3D reconstruction has recently received much attention from the computer vision community. Bundle adjustment is a key component of 3D reconstruction problems. However, traditional bundle adjustment algorithms require a considerable amount of memory and computational resources. In this paper, we present an extremely efficient, inherently out-of-core bundle adjustment algorithm. We decouple the original problem into several submaps that have their own local coordinate systems and can be optimized in parallel. A key contribution to our algorithm is making as much progress towards optimizing the global non-linear cost function as possible using the fragments of the reconstruction that are currently in core memory. This allows us to converge with very few global sweeps (often only two) through the entire reconstruction. We present experimental results on large-scale 3D reconstruction datasets, both synthetic and real.


title: Advances to Bayesian network inference for generating causal networks from observational biological data

abstract: Motivation: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. ::: ::: Results: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influence score for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. ::: ::: Availability: Source code and simulated data are available upon request. ::: ::: Supplementary information: http://www.jarvislab.net/Bioinformatics/BNAdvances/


title: A comparison of controllers for balancing two wheeled inverted pendulum robot

abstract: One of the challenging tasks concerning two wheeled inverted pendulum (TWIP) mobile robot is balancing its tilt to upright position, this is due to its inherently open loop instability. This paper presents an experimental comparison between model based controller and non-model based controllers in balancing the TWIP mobile robot. A Fuzzy Logic Controller (FLC) which is a non-model based controller and a Linear Quadratic Controller (LQR) which is a model-based controller, and the conventional controller, Proportional Integral Derivative (PID) were implemented and compared on a real time TWIP mobile robot. The FLC controller performance has given superior result as compared to LQR and PID in terms of speed response but consumes higher energy.


title: Applications of nanotechnology in water and wastewater treatment

abstract: Providing clean and affordable water to meet human needs is a grand challenge of the 21st century. Worldwide, water supply struggles to keep up with the fast growing demand, which is exacerbated by population growth, global climate change, and water quality deterioration. The need for technological innovation to enable integrated water management cannot be overstated. Nanotechnology holds great potential in advancing water and wastewater treatment to improve treatment efficiency as well as to augment water supply through safe use of unconventional water sources. Here we review recent development in nanotechnology for water and wastewater treatment. The discussion covers candidate nanomaterials, properties and mechanisms that enable the applications, advantages and limitations as compared to existing processes, and barriers and research needs for commercialization. By tracing these technological advances to the physicochemical properties of nanomaterials, the present review outlines the opportunities and limitations to further capitalize on these unique properties for sustainable water management.


title: Unsupervised Recognition of Literal and Non-Literal Use of Idiomatic Expressions

abstract: We propose an unsupervised method for distinguishing literal and non-literal usages of idiomatic expressions. Our method determines how well a literal interpretation is linked to the overall cohesive structure of the discourse. If strong links can be found, the expression is classified as literal, otherwise as idiomatic. We show that this method can help to tell apart literal and non-literal usages, even for idioms which occur in canonical form.


title: A robot kinematics simulation system based on OpenGL

abstract: Using Visual C++ and OpenGL, a simulation system for a specific type of robot is provided in this paper. It is a modular simulation system, which contains offline programming, robot assembly and teaching and playback modules. Part model of robot that constructed by other software is imported and assembled in the simulation system for simulation analysis. It can realize kinematics calculation, 3-dimensional animation simulation of robot and virtual teaching. It has friendly user interface and is easy to extend other useful functions.


title: ChromaGlasses: Computational Glasses for Compensating Colour Blindness

abstract: Prescription glasses are used by many people as a simple, and even fashionable way, to correct refractive problems of the eye. However, there are other visual impairments that cannot be treated with an optical lens in conventional glasses. In this work we present ChromaGlasses, Computational Glasses using optical head-mounted displays for compensating colour vision deficiency. Unlike prior work that required users to look at a screen in their visual periphery rather than at the environment directly, ChromaGlasses allow users to directly see the environment using a novel head-mounted displays design that analyzes the environment in real-time and changes the appearance of the environment with pixel precision to compensate the impairment of the user. In this work, we present first prototypes for ChromaGlasses and report on the results from several studies showing that ChromaGlasses are an effective method for managing colour blindness.


Data Mining Techniques For Diagnosis And Prognosis Of Breast Cancer


title: Detecting Cellular Fraud Using Adaptive Prototypes.

abstract: This paper discusses the current status of research on fraud detection undertaken a.s part of the European Commissionfunded ACTS ASPECT (Advanced Security for Personal Communications Technologies) project, by Royal Holloway University of London. Using a recurrent neural network technique, we uniformly distribute prototypes over Toll Tickets. sampled from the U.K. network operator, Vodafone. The prototypes, which continue to adapt to cater for seasonal or long term trends, are used to classify incoming Toll Tickets to form statistical behaviour proFdes covering both the short and long-term past. These behaviour profiles, maintained as probability distributions, comprise the input to a differential analysis utilising a measure known as the HeUinger distance[5] between them as an alarm criteria. Fine tuning the system to minimise the number of false alarms poses a significant task due to the low fraudulent/non fraudulent activity ratio. We benefit from using unsupervised learning in that no fraudulent examples ate requited for training. This is very relevant considering the currently secure nature of GSM where fraud scenarios, other than Subscription Fraud, have yet to manifest themselves. It is the aim of ASPECT to be prepared for the would-be fraudster for both GSM and UMTS,


title: Characterizing and predicting postpartum depression from shared facebook data

abstract: The birth of a child is a major milestone in the life of parents. We leverage Facebook data shared voluntarily by 165 new mothers as streams of evidence for characterizing their postnatal experiences. We consider multiple measures including activity, social capital, emotion, and linguistic style in participants' Facebook data in pre- and postnatal periods. Our study includes detecting and predicting onset of post-partum depression (PPD). The work complements recent work on detecting and predicting significant postpartum changes in behavior, language, and affect from Twitter data. In contrast to prior studies, we gain access to ground truth on postpartum experiences via self-reports and a common psychometric instrument used to evaluate PPD. We develop a series of statistical models to predict, from data available before childbirth, a mother's likelihood of PPD. We corroborate our quantitative findings through interviews with mothers experiencing PPD. We find that increased social isolation and lowered availability of social capital on Facebook, are the best predictors of PPD in mothers.


title: Multi-document Summarization by Graph Search and Matching

abstract: We describe a new method for summarizing similarities and differences in a pair of related documents using a graph representation for text. Concepts denoted by words, phrases, and proper names in the document are represented positionally as nodes in the graph along with edges corresponding to semantic relations between items. Given a perspective in terms of which the pair of documents is to be summarized, the algorithm first uses a spreading activation technique to discover, in each document, nodes semantically related to the topic. The activated graphs of each document are then matched to yield a graph corresponding to similarities and differences between the pair, which is rendered in natural language. An evaluation of these techniques has been carried out.


title: Person reidentification: What features are important

abstract: State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods.


title: A 79-GHz Planar Antenna Array Using Ceramic-Filled Cavity Resonators in LTCC

abstract: Design and measurement of a planar antenna array realized in low temperature co-fired ceramic (LTCC) are presented. The antenna elements are ceramic-filled cavity resonators fed by an integrated laminated waveguide network and a slot in each resonator. The antenna is designed to operate in the 79-GHz frequency range. The array shows good impedance matching and a stable radiation pattern within a bandwidth of approximately 5 GHz. The presented results of a first design show that the antenna is a promising candidate for 79-GHz automotive or related applications and ideally suited for LTCC integration.


title: Privacy-Preserving ID3 Data Mining over Encrypted Data in Outsourced Environments with Multiple Keys

abstract: The emergence and development of the Internet resulted in the generation of huge amounts of data, which are often distributed among different sites. Many organizations and companies attempted to mine the data with cloud computing. However, given the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. To process data mining, such as the ID3 algorithm, over encrypted data without the cloud decrypting the data is a very challenging task. In this paper, we consider a scenario where multiple parties with weak computational power need to run an ID3 algorithm on their databases jointly and outsource most of the computation of the protocol and databases to the cloud. In this scenario, each party can have the correct result calculated by the data from all of the parties with most of the computation outsourced to the cloud. For privacy, the data owned by each party should be kept confidential from both the other parties and the cloud. To ensure data privacy, we use an efficient homomorphic encryption scheme with multiple keys and design an Outsourced Secure Shared xlnx Protocol (OSSxlnx). We then propose a cloud-aided ID3 solution that is used to build an outsourced privacy-preserving ID3 data mining solution.


title: Comprehensive Analysis of Hierarchical Aggregation Functions Decision Trees, SVD, K-means Clustering, PCA and Rule Based AI Optimization in the Classification of Fuzzy based Epilepsy Risk Levels from EEG Signals

abstract: A comprehensive analysis for the performance of post classifiers such as Hierarchical Soft Decision Trees, Singular value decomposition(SVD), k-means clustering, Principal Component Analysis (PCA) and Rule based AI techniques in optimization of fuzzy outputs for the classification of epilepsy risk levels from EEG (Electroencephalogram) signals is presented in this paper. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Hierarchical Soft decision tree (post classifiers with max-min criteria) four types, SVD, K-means clustering, PCA and AI optimization are applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient's risk level. The efficacy of the above methods is compared and analyzed based on the bench mark parameters such as Performance Index (PI), and Quality Value (QV).


title: Malicious co-residency on the cloud: Attacks and defense

abstract: Attacker VMs try to co-reside with victim VMs on the same physical infrastructure as a precursor to launching attacks that target information leakage. VM migration is an effective countermeasure against attempts at malicious co-residency. In this paper, we first undertake an experimental study on Amazon EC2 to obtain an in-depth understanding of the side-channels an attacker can use to ascertain co-residency with a victim. Here, we identify a new set of stealthy side-channel attacks which, we show to be more effective than currently available attacks towards verifying co-residency. Based on the study, we develop a set of guidelines to determine under what conditions victim VM migrations should be triggered given performance costs in terms of bandwidth and downtime, that a user is willing to bear. Via extensive experiments on our private in-house cloud, we show that migrations using our guidelines can limit the fraction of the time that an attacker VM co-resides with a victim VM to about 1 % of the time with bandwidth costs of a few MB and downtimes of a few seconds, per day per VM migrated.


title: Automatic identification of application I/O signatures from noisy server-side traces

abstract: Competing workloads on a shared storage system cause I/O resource contention and application performance vagaries. This problem is already evident in today's HPC storage systems and is likely to become acute at exascale. We need more interaction between application I/O requirements and system software tools to help alleviate the I/O bottleneck, moving towards I/O-aware job scheduling. However, this requires rich techniques to capture application I/O characteristics, which remain evasive in production systems. ::: ::: Traditionally, I/O characteristics have been obtained using client-side tracing tools, with drawbacks such as non-trivial instrumentation/development costs, large trace traffic, and inconsistent adoption. We present a novel approach, I/O Signature Identifier (IOSI), to characterize the I/O behavior of data-intensive applications. IOSI extracts signatures from noisy, zero-overhead server-side I/O throughput logs that are already collected on today's supercomputers, without interfering with the compiling/execution of applications. We evaluated IOSI using the Spider storage system at Oak Ridge National Laboratory, the S3D turbulence application (running on 18,000 Titan nodes), and benchmark-based pseudo-applications. Through our experiments we confirmed that IOSI effectively extracts an application's I/O signature despite significant server-side noise. Compared to client-side tracing tools, IOSI is transparent, interface-agnostic, and incurs no overhead. Compared to alternative data alignment techniques (e.g., dynamic time warping), it offers higher signature accuracy and shorter processing time.


title: Wireless security through RF fingerprinting

abstract: The process of identifying radio transmitters by examining their unique transient characteristics at the beginning of transmission is called RF fingerprinting. The security of wireless networks can be enhanced by challenging a user to prove its identity if the fingerprint of a network device is unidentified or deemed to be a threat. This paper addresses the problem of identifying an individual node in a wireless network by means of its RF fingerprint. A complete identification system is presented, including data acquisition, transient detection, RF fingerprint extraction, and classification subsystems. The classification performance of the proposed system has been evaluated from experimental data. It is demonstrated that the RF fingerprinting technique can be used as an additional tool to enhance the security of wireless networks.


title: Twelve shades of grey: encountering urban colour in the street in British provincial towns, c. 1945–1970

abstract: This article examines the neglected sensory experience of visual physical colour in the city/town centre or what is now referred to as the Central Business District. It focuses on the post-war period when reconstruction, town planning, new architecture, novel materials and technologies, and investment were all transforming British city centres. The research uses film, photographs, planning documents, oral history and social media reminiscences to research the users’ experience of colour in the city centre streets. It argues that, although new materials in construction opened up the possibilities of bright, ‘non-natural’ colours in the urban built environment, the visual experience of colour was found mainly in the ephemera of everyday life. Furthermore, it argues that colour was an important component in constructing people's sense of place and belonging in the city.


title: Experimental studies on producer gas generation from wood waste in a downdraft biomass gasifier.

abstract: A process of conversion of solid carbonaceous fuel into combustible gas by partial combustion is known as gasification. The resulting gas, known as producer gas, is more versatile in its use than the original solid biomass. In the present study, a downdraft biomass gasifier is used to carry out the gasification experiments with the waste generated while making furniture in the carpentry section of the institute's workshop. Dalbergia sisoo, generally known as sesame wood or rose wood is mainly used in the furniture and wastage of the same is used as a biomass material in the present gasification studies. The effects of air flow rate and moisture content on biomass consumption rate and quality of the producer gas generated are studied by performing experiments. The performance of the biomass gasifier system is evaluated in terms of equivalence ratio, producer gas composition, calorific value of the producer gas, gas production rate, zone temperatures and cold gas efficiency. Material balance is carried out to examine the reliability of the results generated. The experimental results are compared with those reported in the literature.


title: Common mode implications of a dynamic latched comparator

abstract: A TGC-based dynamic comparator designed and simulated with HSPICE using 32/28 nm integrated CMOS PDK in SYNOPSYS environment in my recent work is further analyzed for common-mode implications to various factors like supply voltage, delay, power and dynamic range. The simulation results shows that the comparator topology is found to be well suited for the input common-mode range of 0.3 V to 0.8V and hence found to be sensitive to the differential input signal as weak as 0.5mV. The comparator is then also simulated over different supply voltage ranging from 1.2 V down to 0.3 V showing a huge power reduction with only a slight rise in delay from 60ps to 75ps approximately.


title: Social use of language in children with reactive attachment disorder and autism spectrum disorders

abstract: Children with a diagnosis of reactive attachment disorder (RAD) appear to show difficulties in social understanding. We aimed to compare the pragmatic language functioning of children with (RAD) and autism spectrum disorder (ASD). Assessments were made in three groups of children aged 5–8 years, with verbal IQ estimates in the normal range: 35 with a RAD diagnosis, 52 with an ASD diagnosis and 39 with typical development. The Children’s Communication Checklist (CCC) was used to compare their pragmatic language skills, and ADI-R algorithms were used to compare autistic symptomatology, according to parent report. According to the CCC, the RAD group demonstrated significant problems in their use of context, rapport and social relationships with a degree of severity equivalent to children in the ASD comparison group. More than 60% of the group with RAD met ADI-R clinical criteria on the Use of Language and Other Social Communication Skills subscale, 46% on the Reciprocal Social Interaction subscale, and 20% had significant repetitive and stereotyped behaviours. Children with RAD appear to be at least as impaired as children with ASD in certain domains of social relatedness, particularly in their pragmatic language skills.


title: A New Forecasting Framework for Bitcoin Price with LSTM

abstract: Long short-term memory (LSTM) networks are a state-of-the-art sequence learning in deep learning for time series forecasting. However, less study applied to financial time series forecasting especially in cryptocurrency prediction. Therefore, we propose a new forecasting framework with LSTM model to forecasting bitcoin daily price with two various LSTM models (conventional LSTM model and LSTM with AR(2) model). The performance of the proposed models are evaluated using daily bitcoin price data during 2018/1/1 to 2018/7/28 in total 208 records. The results confirmed the excellent forecasting accuracy of the proposed model with AR(2). The test mean squared error (MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) for bitcoin price prediction, respectively. The our proposed LSTM with AR(2) model outperformed than conventional LSTM model. The contribution of this study is providing a new forecasting framework for bitcoin price prediction can overcome and improve the problem of input variables selection in LSTM without strict assumptions of data assumption. The results revealed its possible applicability in various cryptocurrencies prediction, industry instances such as medical data or financial time-series data.


title: Support vector regression with chaos-based firefly algorithm for stock market price forecasting

abstract: Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE).


Determining the Type of Long Bone Fractures in X-Ray Images


title: Robustness of classifiers: from adversarial to random noise

abstract: Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to random noise. In this paper, we propose to study a \textit{semi-random} noise regime that generalizes both the random and worst-case noise regimes. We propose the first quantitative analysis of the robustness of nonlinear classifiers in this general noise regime. We establish precise theoretical bounds on the robustness of classifiers in this general regime, which depend on the curvature of the classifier's decision boundary. Our bounds confirm and quantify the empirical observations that classifiers satisfying curvature constraints are robust to random noise. Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes. We perform experiments and show that the derived bounds provide very accurate estimates when applied to various state-of-the-art deep neural networks and datasets. This result suggests bounds on the curvature of the classifiers' decision boundaries that we support experimentally, and more generally offers important insights onto the geometry of high dimensional classification problems.


title: Fragile Watermarking Based Proofs of Retrievability for Archival Cloud Data

abstract: Cloud storage is widely used to ease the storage burden of clients. Meanwhile it raises a basic issue in the security of outsourced data: whether the corruption can be detected and recovered? Focusing on this problem, most existing proofs of retrievability (POR) schemes relies on precomputed tokens or tags as well as code redundancy to achieve integrity verification and corruption recovery, which, however, results in extra storage cost for cloud storage and high computational overhead for clients. Fragile watermarking provides a new way to implement the scheme without such drawbacks. In this paper, we propose a novel fragile watermarking based public auditable POR scheme for archival cloud data, which is able to not only improve the efficiency of audit process but also ensure both privacy-preserving and replay attack resistance simultaneously. The simulation results validate both the correctness of our scheme in detecting and recovering data corruption and the large improvement in performance compared to traditional POR schemes.


title: Policy ratification

abstract: It is not sufficient to merely check the syntax of new policies before they are deployed in a system; policies need to be analyzed for their interactions with each other and with their local environment. That is, policies need to go through a ratification process. We believe policy ratification becomes an essential part of system management as the number of policies in the system increases and as the system administration becomes more decentralized. In this paper, we focus on the basic tasks involved in policy ratification. To a large degree, these basic tasks can be performed independent of policy model and language and require little domain-specific knowledge. We present algorithms from constraint, linear, and logic programming disciplines to help perform ratification tasks. We provide an algorithm to efficiently assign priorities to the policies based on relative policy preferences indicated by policy administrators. Finally, with an example, we show how these algorithms have been integrated with our policy system to provide feedback to a policy administrator regarding potential interactions of policies with each other and with their deployment environment.


title: Early experiments with neural diversity machines

abstract: The current paper introduces the concept of neural diversity machines (NDM) which, refers to hybrid artificial neural networks (HANN) with conditions on the minimum number of functions available to the network, amongst several other properties. The paper demonstrates how NDM networks can be optimized for solving different problems. The results demonstrate the feasibility of the approach and bolster some of the biological and computational arguments in favor of neural diversity. A substantial number of optimization experiments were conducted, generating a corresponding number of diverse neural architectures, which revealed several unexpected statistics, including the relative commonality of nodes combining inner-product and Gaussian functions. The paper confirms the advantages of HANN, demonstrates the potential of increasing the focus on neural diversity and hints at possible new neural computational strategies.


Chapter 2 – Knowledge of the Firm: Combinative Capabilities, and the Replication of Technology


title: A complex of recombination and repair genes in Haemophilus influenzae

abstract: Abstract Two transformation-defective strains of Haemophilias influenzae have been studied with respect to their ability (1) to be transformed (2) to repair DNA damage induced by ultraviolet light, X-rays and methyl methanesulfonate (3) to allow recombination of temperature-sensitive mutants of the H. influenzae phage HP1 c1 , and (4) to be induced after lysogenization with wild-type phage. One of the strains was obtained following exposure of wild-type cells to DNA from the other, originally selected as a u.v.-sensitive strain. These strains have been crossed by transformation with wild type and with each other, and repair and recombination in the transformants have been investigated. The results indicate that both strains contain a defect in their recombination mechanism, since phage recombination is not detectable in either of them. The recombination defects in the two strains are different from each other by the following criteria: (1) The residual transformation frequencies in the strains are different, about 10 −6 and 10 −7 that of the wild type. (2) The recombination defect in the original strain with the higher transformation frequency, which contains a mutation we have called rec 1 − , is inseparable from u.V., X-ray, and methyl methanesulfonate sensitivity and lack of inducibility following lysogenization. The derived strain, which is u.v. resistant and inducible, has an intermediate sensitivity to methyl methanesulfonate and slight sensitivity to X-rays. (3) The rec 1 + gene can be transferred into the original Rec 1 − strain, either with wild-type DNA or with DNA from the derived recombination-defective mutant; rec 1 − can also be transferred into the wild-type strain. We have been unable to transfer the derived Rec − defect into the wild type, nor have we succeeded in transforming the derived strain carrying this recombination defect to normal recombination, either with wild-type DNA or with DNA from the Rec − 1 mutant. In spite of the fact that one of the Rec − strains was obtained following transformation of wild-type cells with DNA from the other Rec − , the two Rec − strains can be crossed to produce a Rec + . To account for this anomalous result it is postulated that the original Rec − strain contains two mutations, rec − 1 and mex − , and that the derived strain is rec + 1 , mex − , the mex − mutation conferring intermediate sensitivity to methyl methanesulfonate and slight sensitivity to X-rays. It is further postulated that the derived Rec − strain contains a third altered gene, rec p 2 , which was obtained through the selection procedure used to isolate the strain from the wild-type culture exposed to rec 1 − mex − rec 2 + DNA, and that the combination mex − rec 2 p causes the Rec − phenotype.


Driver Behavior Analysis and Prediction Models : A Survey


Pathfinder - Full Text or Extending a Purely Relational XQuery Compiler with a Scoring Infrastructure for XQuery Full Text


title: Drizzle: Fast and Adaptable Stream Processing at Scale

abstract: Large scale streaming systems aim to provide high throughput and low latency. They are often used to run mission-critical applications, and must be available 24x7. Thus such systems need to adapt to failures and inherent changes in workloads, with minimal impact on latency and throughput. Unfortunately, existing solutions require operators to choose between achieving low latency during normal operation and incurring minimal impact during adaptation. Continuous operator streaming systems, such as Naiad and Flink, provide low latency during normal execution but incur high overheads during adaptation (e.g., recovery), while micro-batch systems, such as Spark Streaming and FlumeJava, adapt rapidly at the cost of high latency during normal operations. Our key observation is that while streaming workloads require millisecond-level processing, workload and cluster properties change less frequently. Based on this, we develop Drizzle, a system that decouples the processing interval from the coordination interval used for fault tolerance and adaptability. Our experiments on a 128 node EC2 cluster show that on the Yahoo Streaming Benchmark, Drizzle can achieve end-to-end record processing latencies of less than 100ms and can get 2-3x lower latency than Spark. Drizzle also exhibits better adaptability, and can recover from failures 4x faster than Flink while having up to 13x lower latency during recovery.


title: Congenital nasal pyriform aperture stenosis: A rare cause of nasal airway obstruction in a neonate

abstract: Congenital nasal pyriform aperture stenosis (CNPAS) is a rare cause of nasal airway obstruction that clinically mimics choanal atresia, but needs to be differentiated from the latter because of the widely divergent modes of management. We present a case of CNPAS, to highlight the importance of recognizing the classic signs of CNPAS on cross-sectional imaging.


title: An ASIC Front End for Planar High-Frequency Contactless Inductive Position Sensors

abstract: We describe an application-specific integrated circuit (ASIC) front end for readout and control of planar high-frequency contactless inductive position sensors that contain transmitter and receiver coils on a fixed printed circuit board and a moving passive resonant target. Such an inductive position sensor suffers from transmitter-to-receiver signal coupling, which can result in a phase-sensitive offset; hence, an error in the position measurement occurs. For the receiver front end, we consider two analog synchronous mixer demodulators, which we call mixer-1 and mixer-2, and analyze their ability to reject phase-sensitive offsets due to transmitter signal breakthrough. The mathematical analysis is validated with measured results from the fabricated ASIC in a 0.35-mum CMOS process technology. The ASIC front end contains the transmitter driver, the two receiver mixer variants, a frequency divider/shifter, and an amplifier low-pass filter. Measurements from five ASIC samples connected to the sensor show that, with a system gain of 320, the average output offset variation with phase difference from -99 to +117deg is more than 237 mV with mixer-1 compared to less than 7 mV with mixer-2.


title: Cross-Domain Visual Matching via Generalized Similarity Measure and Feature Learning

abstract: Cross-domain visual data matching is one of the fundamental problems in many real-world vision tasks, e.g., matching persons across ID photos and surveillance videos. Conventional approaches to this problem usually involves two steps: i) projecting samples from different domains into a common space, and ii) computing (dis-)similarity in this space based on a certain distance. In this paper, we present a novel pairwise similarity measure that advances existing models by i) expanding traditional linear projections into affine transformations and ii) fusing affine Mahalanobis distance and Cosine similarity by a data-driven combination. Moreover, we unify our similarity measure with feature representation learning via deep convolutional neural networks. Specifically, we incorporate the similarity measure matrix into the deep architecture, enabling an end-to-end way of model optimization. We extensively evaluate our generalized similarity model in several challenging cross-domain matching tasks: person re-identification under different views and face verification over different modalities (i.e., faces from still images and videos, older and younger faces, and sketch and photo portraits). The experimental results demonstrate superior performance of our model over other state-of-the-art methods.


title: A Highly Parallel Framework for HEVC Coding Unit Partitioning Tree Decision on Many-core Processors

abstract: High Efficiency Video Coding (HEVC) uses a very flexible tree structure to organize coding units, which leads to a superior coding efficiency compared with previous video coding standards. However, such a flexible coding unit tree structure also places a great challenge for encoders. In order to fully exploit the coding efficiency brought by this structure, huge amount of computational complexity is needed for an encoder to decide the optimal coding unit tree for each image block. One way to achieve this is to use parallel computing enabled by many-core processors. In this paper, we analyze the challenge to use many-core processors to make coding unit tree decision. Through in-depth understanding of the dependency among different coding units, we propose a parallel framework to decide coding unit trees. Experimental results show that, on the Tile64 platform, our proposed method achieves averagely more than 11 and 16 times speedup for 1920x1080 and 2560x1600 video sequences, respectively, without any coding efficiency degradation.


title: Antimicrobial peptides: Old Molecules with New Ideas

abstract: Almost 90 years have passed since Alexander Fleming discovered the antimicrobial activity of lysozyme, the first natural antibiotic isolated from our body. Since then, various types of molecules with antibiotic activity have been isolated from animals, insects, plants, and bacteria, and their use has revolutionized clinical medicine. So far, more than 1,200 types of peptides with antimicrobial activity have been isolated from various cells and tissues, and it appears that all living organisms use these antimicrobial peptides (AMPs) in their host defense. In the past decade, innate AMPs produced by mammals have been shown to be essential for the protection of skin and other organs. Their importance is because of their pleiotrophic functions to not only kill microbes but also control host physiological functions such as inflammation, angiogenesis, and wound healing. Recent advances in our understanding of the function of AMPs have associated their altered production with various human diseases such as psoriasis, atopic dermatitis, and rosacea. In this review, we summarize the history of AMP biology and provide an overview of recent research progress in this field.


title: Multimodal 2D+3D Facial Expression Recognition With Deep Fusion Convolutional Neural Network

abstract: This paper presents a novel and efficient deep fusion convolutional neural network (DF-CNN) for multimodal 2D+3D facial expression recognition (FER). DF-CNN comprises a feature extraction subnet, a feature fusion subnet, and a softmax layer. In particular, each textured three-dimensional (3D) face scan is represented as six types of 2D facial attribute maps (i.e., geometry map, three normal maps, curvature map, and texture map), all of which are jointly fed into DF-CNN for feature learning and fusion learning, resulting in a highly concentrated facial representation (32-dimensional). Expression prediction is performed by two ways: 1) learning linear support vector machine classifiers using the 32-dimensional fused deep features, or 2) directly performing softmax prediction using the six-dimensional expression probability vectors. Different from existing 3D FER methods, DF-CNN combines feature learning and fusion learning into a single end-to-end training framework. To demonstrate the effectiveness of DF-CNN, we conducted comprehensive experiments to compare the performance of DF-CNN with handcrafted features, pre-trained deep features, fine-tuned deep features, and state-of-the-art methods on three 3D face datasets (i.e., BU-3DFE Subset I, BU-3DFE Subset II, and Bosphorus Subset). In all cases, DF-CNN consistently achieved the best results. To the best of our knowledge, this is the first work of introducing deep CNN to 3D FER and deep learning-based feature-level fusion for multimodal 2D+3D FER.


title: RECONSTRUCTING THE GIANT: ON THE IMPORTANCE OF RIGOUR IN DOCUMENTING

abstract: Science is a cumulative endeavour as new knowledge is often created in the process of interpreting and combining existing knowledge. This is why literature reviews have long played a decisive role in scholarship. The quality of literature reviews is particularly determined by the literature search process. As Sir Isaac Newton eminently put it: If I can see further, it is because I am standing on the shoulders of giants. Drawing on this metaphor, the goal of writing a literature review is to reconstruct the giant of accumulated knowledge in a specific domain. And in doing so, a literature search represents the fundamental first step that makes up the giant's skeleton and largely determines its reconstruction in the subsequent literature analysis. In this paper, we argue that the process of searching the literature must be comprehensibly described. Only then can readers assess the exhaustiveness of the review and other scholars in the field can more confidently (re)use the results in their own research. We set out to explore the methodological rigour of literature review articles published in ten major information systems (IS) journals and show that many of these reviews do not thoroughly document the process of literature search. The results drawn from our analysis lead us to call for more rigour in documenting the literature search process and to present guidelines for crafting a literature review and search in the IS domain.


title: Valence, arousal and dominance in the EEG during game play

abstract: In this paper, we describe our investigation of traces of naturally occurring emotions in electrical brain signals, that can be used to build interfaces that respond to our emotional state. This study confirms a number of known affective correlates in a realistic, uncontrolled environment for the emotions of valence (or pleasure), arousal and dominance: (1) a significant decrease in frontal power in the theta range is found for increasingly positive valence, (2) a significant frontal increase in power in the alpha range is associated with increasing emotional arousal, (3) a significant right posterior power increase in the delta range correlates with increasing arousal and (4) asymmetry in power in the lower alpha bands correlates with self-reported valence. Furthermore, asymmetry in the higher alpha bands correlates with self-reported dominance. These last two effects provide a simple measure for subjective feelings of pleasure and feelings of control.


title: Requirements prioritization challenges in practice

abstract: Requirements prioritization is recognized as an important activity in product development. In this paper, we describe the current state of requirements prioritization practices in two case companies and present the practical challenges involved. Our study showed that requirements prioritization is an ambiguous concept and current practices in the companies are informal. Requirements prioritization requires complex context-specific decision-making and must be performed iteratively in many phases during development work. Practitioners are seeking more systematic ways to prioritize requirements but they find it difficult to pay attention to all the relevant factors that have an effect on priorities and explicitly to draw different stakeholder views together. In addition, practitioners need more information about real customer preferences.


title: www.elsevier.com/locate/jfranklin On the statistical analysis of Feigenbaum constants

abstract: Abstract We present statistical analysis of blocks in the binary expansions of Feigenbaum constants α and δ for the logistic map. The analysis is carried out on both 1016 and 3400 bit expansions. A χ2 test is applied for lumping data and a serial test is applied on gliding data. Contrary to a previous research by Karamanos and Kotsireas, our test results did not indicate any evidence to reject randomness of these constants. Additional 25 randomness tests also support the conjecture of randomness of these constants having transcendental character.


title: Financial time series forecasting with machine learning techniques: a survey

abstract: Stock index forecasting is vital for making informed investment decisions. This paper surveys recent literature in the domain of machine learning techniques and artificial intelligence used to forecast stock market movements. The publications are categorised according to the machine learning technique used, the forecasting timeframe, the input variables used, and the evaluation techniques employed. It is found that there is a consensus between researchers stressing the importance of stock index forecasting. Artificial Neural Networks (ANNs) are identified to be the dominant machine learning technique in this area. We conclude with possible future research directions.


title: Intercellular transfer mediated by tunneling nanotubes.

abstract: Animal cells have evolved different mechanisms to communicate with one another. In 2004, a new route of cell-to-cell communication mediated by tunneling nanotubes (TNT) was reported. These membranous cell bridges form de novo between cells and mediate the intercellular transfer of organelles, plasma membrane components and cytoplasmic molecules. The characterization of TNT-like bridges from several cell types revealed variations in the cytoskeletal composition as well as in the modality by which they interconnect cells, suggesting that different subclasses may exist. Furthermore, the growing number of cell types for which TNT-like structures were detected, supports the view that they represent a general mechanism for functional connectivity between cells, which could have important implications under physiological conditions.


title: Formation and psychometric evaluation of a health-related quality of life instrument for children living with HIV in India

abstract: In-depth interviews and focus group discussions were conducted to inform the development of an instrument to measure the health-related quality of life of children living with HIV. The QOL-CHAI instrument consists of four generic core scales of the “Pediatric Quality of Life Inventory” and two HIV-targeted scales—“symptoms” and “discrimination.” A piloting exercise involving groups of children living with HIV and HIV-negative children born to HIV-infected parents provided evidence for the acceptable psychometric properties and usability of the instrument. It is expected that the QOL-CHAI can serve well as a brief, standardized, and culturally appropriate instrument for assessing health-related quality of life of Indian children living with HIV.


title: Applicability of Big Data Techniques to Smart Cities Deployments

abstract: This paper presents the main foundations of big data applied to smart cities. A general Internet of Things based architecture is proposed to be applied to different smart cities applications. We describe two scenarios of big data analysis. One of them illustrates some services implemented in the smart campus of the University of Murcia. The second one is focused on a tram service scenario, where thousands of transit-card transactions should be processed. Results obtained from both scenarios show the potential of the applicability of this kind of techniques to provide profitable services of smart cities, such as the management of the energy consumption and comfort in smart buildings, and the detection of travel profiles in smart transport.


title: Coupled Triboelectric Nanogenerator Networks for Efficient Water Wave Energy Harvesting

abstract: Water wave energy is a promising clean energy source, which is abundant but hard to scavenge economically. Triboelectric nanogenerator (TENG) networks provide an effective approach toward massive harvesting of water wave energy in oceans. In this work, a coupling design in TENG networks for such purposes is reported. The charge output of the rationally linked units is over 10 times of that without linkage. TENG networks of three different connecting methods are fabricated and show better performance for the ones with flexible connections. The network is based on an optimized ball-shell structured TENG unit with high responsivity to small agitations. The dynamic behavior of single and multiple TENG units is also investigated comprehensively to fully understand their performance in water. The study shows that a rational design on the linkage among the units could be an effective strategy for TENG clusters to operate collaboratively for reaching a higher performance.


title: A comparison of ARMS-Plus and droplet digital PCR for detecting EGFR activating mutations in plasma

abstract: In this study, we introduce a novel amplification refractory mutation system (ARMS)-based assay, namely ARMS-Plus, for the detection of epidermal growth factor receptor (EGFR) mutations in plasma samples. We evaluated the performance of ARMS-Plus in comparison with droplet digital PCR (ddPCR) and assessed the significance of plasma EGFR mutations in predicting efficacy of EGFR-tyrosine kinase inhibitor (TKI) regimen. A total of 122 advanced non-small cell lung cancer (NSCLC) patients were enrolled in this study. The tumor tissue samples from these patients were evaluated by conventional ARMS PCR method to confirm their EGFR mutation status. For the 116 plasma samples analyzed by ARMS-Plus, the sensitivity, specificity, and concordance rate were 77.27% (34/44), 97.22% (70/72), and 89.66% (104/116; κ=0.77, P<0.0001), respectively. Among the 71 plasma samples analyzed by both ARMS-Plus and ddPCR, ARMS-Plus showed a higher sensitivity than ddPCR (83.33% versus 70.83%). The presence of EGFR activating mutations in plasma was not associated with the response to EGFR-TKI, although further validation with a larger cohort is required to confirm the correlation. Collectively, the performance of ARMS-Plus and ddPCR are comparable. ARMS-Plus could be a potential alternative to tissue genotyping for the detection of plasma EGFR mutations in NSCLC patients.


title: Introduction to differential power analysis

abstract: The power consumed by a circuit varies according to the activity of its individual transistors and other components. As a result, measurements of the power used by actual computers or microchips contain information about the operations being performed and the data being processed. Cryptographic designs have traditionally assumed that secrets are manipulated in environments that expose no information beyond the specified inputs and outputs. This paper examines how information leaked through power consumption and other side channels can be analyzed to extract secret keys from a wide range of devices. The attacks are practical, non-invasive, and highly effective—even against complex and noisy systems where cryptographic computations account for only a small fraction of the overall power consumption. We also introduce approaches for preventing DPA attacks and for building cryptosystems that remain secure even when implemented in hardware that leaks.


title: Explaining variations in client extra costs between software projects offshored to india

abstract: Gaining economic benefits from substantially lower labor costs has been reported as a major reason for offshoring labor-intensive information systems services to low-wage countries. However, if wage differences are so high, why is there such a high level of variation in the economic success between offshored IS projects? This study argues that offshore outsourcing involves a number of extra costs for the client organization that account for the economic failure of offshore projects. The objective is to disaggregate these extra costs into their constituent parts and to explain why they differ between offshored software projects. The focus is on software development and maintenance projects that are offshored to Indian vendors. A theoretical framework is developed a priori based on transaction cost economics (TCE) and the knowledge-based view of the firm, complemented by factors that acknowledge the specific offshore context. The framework is empirically explored using a multiple case study design including six offshored software projects in a large German financial service institution. The results of our analysis indicate that the client incurs post- contractual extra costs for four types of activities: (1) requirements specification and design, (2) knowledge transfer, (3) control, and (4) coordination. In projects that require a high level of client-specific knowledge about idiosyncratic business processes and software systems, these extra costs were found to be substantially higher than in projects where more general knowledge was needed. Notably, these costs most often arose independently from the threat of opportunistic behavior, challenging the predominant TCE logic of market failure. Rather, the client extra costs were particularly high in client-specific projects because the effort for managing the consequences of the knowledge asymmetries between client and vendor was particularly high in these projects. Prior experiences of the vendor with related client projects were found to reduce the level of extra costs but could not fully offset the increase in extra costs in highly client-specific projects. Moreover, cultural and geographic distance between client and vendor as well as personnel turnover were found to increase client extra costs. Slight evidence was found, however, that the cost-increasing impact of these factors was also leveraged in projects with a high level of required client-specific knowledge (moderator effect).


Novelist as Radical Pedagogue: George Bowering and Postmodern Reading Strategies.