title: Test-cost-sensitive attribute reduction

abstract: In many data mining and machine learning applications, there are two objectives in the task of classification; one is decreasing the test cost, the other is improving the classification accuracy. Most existing research work focuses on the latter, with attribute reduction serving as an optional pre-processing stage to remove redundant attributes. In this paper, we point out that when tests must be undertaken in parallel, attribute reduction is mandatory in dealing with the former objective. With this in mind, we posit the minimal test cost reduct problem which constitutes a new, but more general, difficulty than the classical reduct problem. We also define three metrics to evaluate the performance of reduction algorithms from a statistical viewpoint. A framework for a heuristic algorithm is proposed to deal with the new problem; specifically, an information gain-based @l-weighted reduction algorithm is designed, where weights are decided by test costs and a non-positive exponent @l, which is the only parameter set by the user. The algorithm is tested with three representative test cost distributions on four UCI (University of California - Irvine) datasets. Experimental results show that there is a trade-off while setting @l, and a competition approach can improve the quality of the result significantly. This study suggests potential application areas and new research trends concerning attribute reduction.


title: A MultiBall Drive for Omni-Directional Mobility Prototyping a concept for a practical and agile omnidirectional mobility platform with the aid of 3 D printing Technology

abstract: In this paper, the concept of replacing the standard wheel, which is used by most ground vehicles today, by a spherical ball drive system is explored. The aim is to develop a practical and agile omnidirectional mobility platform which can especially be useful for applications related to mobile ground robotics. The paper discusses some of the advantages and drawbacks of using balls instead of wheels and the challenges involved in trying to implement a ball drive system. As a proof-of-concept a functional prototype vehicle that utilizes balls for mobility has been developed and successfully tested. 3D printing technology was utilized in the development of the prototype to fabricate some of the more intricate components of the design. Some of the lessons learned during the design, development, and testing of this prototype as well as some of the solutions implemented to tackle the challenges in developing a working prototype are also discussed. The paper shows that while there are numerous design challenges in developing a mobile platform driven by balls instead of wheels, once these challenges are overcome, the result can be a mobile platform with highly agile maneuvering capabilities. These maneuvering capabilities may especially be useful for simplifying navigation tasks and enhancing the mobility capabilities of mobile ground robots.


BERDS-BERkeley EneRgy Disaggregation Data Set


title: Happiness level prediction with sequential inputs via multiple regressions

abstract: This paper presents our solution submitted to the Emotion Recognition in the Wild (EmotiW 2016) group-level happiness intensity prediction sub-challenge. The objective of this sub-challenge is to predict the overall happiness level given an image of a group of people in a natural setting. We note that both the global setting and the faces of the individuals in the image influence the group-level happiness intensity of the image. Hence the challenge lies in building a solution that incorporates both these factors and also considers their right combination. Our proposed solution incorporates both these factors as a combination of global and local information. We use a convolutional neural network to extract discriminative face features, and a recurrent neural network to selectively memorize the important features to perform the group-level happiness prediction task. Experimental evaluations show promising performance improvements, resulting in Root Mean Square Error (RMSE) reduction of about 0.5 units on the test set compared to the baseline algorithm that uses only global information.


title: Stock market index prediction using deep neural network ensemble

abstract: In this paper, we put forward deep neural network ensemble to model and predict Chinese stock market index (including Shanghai composite index and SZSE component index), based on the input indices of recent days. A set of component networks are trained by historical data for this task, where Backpropagation and Adam algorithm are used to train each network efficiently. Bagging approach combines these component networks to generate ensemble, which reduces the generalization error. Indices of test examples are predicted with the model, and the trend predictions is calculated based on the predicted indices. Finally, relative errors between actual indices and predicted indices, as well as accuracy of trend predictions are computed to measure the performance of predictions. It turns out that the model proposed in this paper can partially model and predict the Chinese stock market. The accuracy of trend predictions of the daily barycenter, high, low are 71.34%, 74.15%, 74.15% respectively for Shanghai composite index and 75.95%, 73.95%, 72.34% respectively for SZSE component index. But the predictions on close are unsatisfactory.


title: Information Overload in Group Communication: From Conversation to Cacophony in the Twitch Chat

abstract: As social media replace traditional communication channels, we are often exposed to too much information to process. The presence of too many participants, for example, can turn online public spaces into noisy, overcrowded fora where no meaningful conversation can be held. Here, we analyse a large dataset of public chat logs from Twitch, a popular video-streaming platform, in order to examine how information overload affects online group communication. We measure structural and textual features of conversations such as user output, interaction and information content per message across a wide range of information loads. Our analysis reveals the existence of a transition from a conversational state to a cacophony-a state with lower per capita participation, more repetition and less information per message. This study provides a quantitative basis for further studies of the social effects of information overload, and may guide the design of more resilient online conversation systems.


title: Energy conservation in a smart home

abstract: In recent times, developments in home automation systems have given way to the construction of smarter infrastructure. Such systems offer an easy way for users to control devices in a building. Commercially, there are several technologies that are currently being implemented for home automation purposes namely Z-Wave [1], ZigBee [2] and X10 [3]. The proprietary Z-Wave technology has various advantages and hence is used in this experiment. Research in this field has extended the capabilities of the technology into areas such as remote monitoring and control, power management, tracking & security systems and disaster warning systems. Power management has been of particular interest as it allows for a greener future and is added advantage to users as a cost-cutting measure. This paper analyses the power consumption in a standard home taking into account commonly used appliances and other devices and shows the management capabilities of a home automation system as well as an estimation of the savings in terms of power consumed and cost. The aim of the study is to measure the energy conservation across 4 homes using a Z-Wave home automation system. It is found that there is an 18.70% decrease in energy consumption when the home automation system acts to manage the power consumption of the devices in the home.


Histopathology and the inflammatory response of European perch, Perca fluviatilis muscle infected with Eustrongylides sp. (Nematoda)


title: Hyaluronic Acid in Inflammation and Tissue Regeneration.

abstract: Abstract Hyaluronic acid (HA), the main component of extracellular matrix, is considered one of the key players in the tissue regeneration process. It has been proven to modulate via specific HA receptors, inflammation, cellular migration, and angiogenesis, which are the main phases of wound healing. Studies have revealed that most HA properties depend on its molecular size. High molecular weight HA displays anti-inflammatory and immunosuppressive properties, whereas low molecular weight HA is a potent proinflammatory molecule. In this review, the authors summarize the role of HA polymers of different molecular weight in tissue regeneration and provide a short overview of main cellular receptors involved in HA signaling. In addition, the role of HA in 2 major steps of wound healing is examined: inflammation and the angiogenesis process. Finally, the antioxidative properties of HA are discussed and its possible clinical implication presented.


title: QoE-Based Cross-Layer Optimization of Wireless Video with Unperceivable Temporal Video Quality Fluctuation

abstract: This paper proposes a novel approach for Quality of Experience (QoE) driven cross-layer optimization for wireless video transmission. We formulate the cross-layer optimization problem with a constraint on the temporal fluctuation of the video quality. Our objective is to minimize the temporal change of the video quality as perceivable quality fluctuations negatively affect the overall quality of experience. The proposed QoE scheme jointly optimizes the application layer and the lower layers of a wireless protocol stack. It allocates network resources and performs rate adaptation such that the fluctuations lie within the range of unperceivable changes. We determine corresponding perception thresholds via extensive subjective tests and evaluate the proposed scheme using an OPNET High Speed Downlink Packet Access (HSDPA) emulator. Our simulation results show that the proposed approach leads to a noticeable improvement of overall user satisfaction for the provided video delivery service when compared to state-of-the-art approaches.


title: LTE-advanced in 3GPP Rel -13/14: an evolution toward 5G

abstract: As the fourth generation (4G) LTE-Advanced network becomes a commercial success, technologies for beyond 4G and 5G are being actively investigated from the research perspective as well as from the standardization perspective. While 5G will integrate the latest technology breakthroughs to achieve the best possible performance, it is expected that LTE-Advanced will continue to evolve, as a part of 5G technologies, in a backward compatible manner to maximize the benefit from the massive economies of scale established around the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced ecosystem from Release 8 to Release 12. In this article we introduce a set of key technologies expected for 3GPP Release 13 and 14 with a focus on air interface aspects, as part of the continued evolution of LTE-Advanced and as a bridge from 4G to 5G.


title: Novel Applications of Machine Learning in Software Testing

abstract: Machine learning techniques have long been used for various purposes in software engineering. This paper provides a brief overview of the state of the art and reports on a number of novel applications I was involved with in the area of software testing. Reflecting on this personal experience, I draw lessons learned and argue that more research should be performed in that direction as machine learning has the potential to significantly help in addressing some of the long-standing software testing problems.


title: The Anti-Amyloid-β Monoclonal Antibody 4G8 Recognizes a Generic Sequence-Independent Epitope Associated with α-Synuclein and Islet Amyloid Polypeptide Amyloid Fibrils.

abstract: Recently we reported that several monoclonal antibodies that recognize linear segments of amyloid-β (Aβ) also recognize amyloid fibrils, but not monomers of unrelated sequences, indicating that recognition of a linear sequence segment is not a reliable indicator of sequence specificity. We asked whether any of the commonly used commercially available Aβ antibodies also recognize fibrils of unrelated sequence. Here we report that 4G8, which recognizes residues 18-23 of the Aβ sequence and is widely believed to be sequence-specific, also recognizes fibrils formed from α-synuclein and islet amyloid polypeptide (IAPP). The recognition of amyloid fibrils is aggregation-dependent because 4G8 does not recognize α-synuclein or IAPP monomer. 4G8 also stains fibrillar α-synuclein aggregates in human multiple system atrophy brain where it colocalizes with anti-α-synuclein monoclonal antibody LB509 immunoreactivity. We also found that LB509 recognizes Aβ fibrils, but not monomer, indicating that generic epitope-reactive antibodies are also produced in response to α-synuclein immunization. Taken together, our results indicate that generic fibril conformational epitope specificity may be a pervasive property among monoclonal antibodies raised against amyloid-forming antigens and that the specificity of their immunoreactivity should be rigorously established and otherwise interpreted with caution.


title: A CMOS High Speed Multi-Modulus Divider With Retiming for Jitter Suppression

abstract: A new asynchronous high speed multi-modulus divider (MMD) architecture is presented in this letter. This new architecture significantly reduces the delay of the critical path, which not only pushes to ultra-high speed operation, but also allows retiming techniques to suppress jitter accumulation from the divider chain simultaneously. A prototype in a 65 nm CMOS technology has demonstrated an improved speed over three times compared with a conventional MMD and a reduced phase noise about 8.4 dB due to a retiming scheme. To the authors' best knowledge, this MMD has demonstrated to date the highest operating frequency static MMD with retiming function in CMOS. Due to its static implementation, this MMD can operate from 19 GHz down to close to dc with programmable division ratios from 16 to 31. This MMD consumes 39.8 mW power and occupies 0.011 mm2 chip area.


title: On the stability of continuous-time T-S model

abstract: This paper proposes a new systematic approach to analyze the stability of continuous T-S fuzzy models in case of having 2×2 and symmetric state matrices. A sufficient stability criterion in terms of Lyapunov function candidate is introduced to investigate the global asymptotic stability of T-S models. This criterion is obtained from estimating the spectrum of Hermitian matrices and has the merit that selection of the common positive-definite matrix P is independent of the sub-diagonal entries of the state matrices. It means, for a set of fuzzy models having the same main diagonal state matrices in their subsystems, it suffices to apply the method once. Several examples are given to illustrate the effectiveness of the proposed method.


title: Mr.MAPP: Mixed Reality for MAnaging Phantom Pain

abstract: Phantom Limb Pain or simply, Phantom Pain is a severe chronic pain that is experienced as a vivid sensation of the pain in missing body part. Epidemiological studies obtained from a large samples indicate that the short-term incidence rate of the phantom limb pain is 72% [13], while long-term incidence rate (6 months after amputation) is 67%, [5, 13]. A wide spectrum of treatments developed for alleviating phantom limb pain includes the traditional mirror box therapy as well as recently developed virtual reality-based methods. Most of the virtual reality-based methods rely on 3D CAD models of the virtual limb, animating them using the motion data acquired either from patient's existing anatomical limb or myoelectric activity at patient's stump (of the amputated limb). Since motion activity is typically captured using body sensors (Electromyography, EMG, or inertial sensors), these methods are considered as invasive approaches. Further, in the case of virtual reality-based methods, the dependency on the pre-built 3D models degrades the immersive experience due to a mismatch in the skin color, clothes, artificial and rigid look and misalignment of the phantom limb. In this work, we propose a novel Mixed Reality based system for MAnaging Phantom Pain (Mr.MAPP), utilizing off-the-shelf RGB-D cameras such as Microsoft Kinect V2 to capture and generate a 3D model of the patient in real-time. An illusion of the virtual limb is crafted in real-time by mirroring the patient's symmetric anatomical limb in the captured data with the help of various computer vision and graphics techniques. Along with that, a phantom limb skeleton is also generated in real-time to enable interaction with virtual objects. We conducted a multi-pronged user Quality of Experience (QoE) study of Mr.MAPP employing various rendering displays such as 3D Television, and Head mounted displays (Oculus Rift, Samsung Gear VR). The user study involved two classes of users: (a) a big pool of Subject-Matter Experts (SMEs) that includes Physical Medicine and Rehab (PMR (b) healthy non-expert users. SMEs, as well as the healthy non-expert users provided a very positive feedback of Mr. MAPP indicating the potential value of Mr.MAPP for the phantom pain management.


title: A survey on face detection in the wild

abstract: We present a comprehensive and survey for face detection 'in-the-wild'.We critically describe the advances in the three main families of algorithms.We comment on the performance of the state-of-the-art in the current benchmarks.We outline future research avenues on the topic and beyond. Face detection is one of the most studied topics in computer vision literature, not only because of the challenging nature of face as an object, but also due to the countless applications that require the application of face detection as a first step. During the past 15years, tremendous progress has been made due to the availability of data in unconstrained capture conditions (so-called 'in-the-wild') through the Internet, the effort made by the community to develop publicly available benchmarks, as well as the progress in the development of robust computer vision algorithms. In this paper, we survey the recent advances in real-world face detection techniques, beginning with the seminal Viola-Jones face detector methodology. These techniques are roughly categorized into two general schemes: rigid templates, learned mainly via boosting based methods or by the application of deep neural networks, and deformable models that describe the face by its parts. Representative methods will be described in detail, along with a few additional successful methods that we briefly go through at the end. Finally, we survey the main databases used for the evaluation of face detection algorithms and recent benchmarking efforts, and discuss the future of face detection.


title: TASC: Combining Virtual Reality with Tangible and Embodied Interactions to Support Spatial Cognition

abstract: A growing body of empirical evidence from the cognitive sciences shows that physical experience can enhance cognition in areas that involve spatial thinking. At the same time, virtual environments provide opportunities to engage learners with novel spatial tasks that cannot be achieved in the real world. Yet combining virtual worlds with tangible interfaces to engage spatial cognition is still not a well-explored area. This paper describes the TASC (Tangibles for Augmenting Spatial Cognition) system, which combines movement tracking and tangible objects in order to create a strong sense of embodiment in a virtual environment for spatial puzzle solving, designed to engage perspective taking ability. We describe the motivation, design process, and development of TASC. We also report the results from our user study, showing the participants' positive experiences, linking to future research opportunities.


title: The support vector machine under test

abstract: Abstract Support vector machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation ( libsvm ) to 16 classification methods and 9 regression methods—all accessible through the software R —by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive.


title: Nutritional Recommendations for Water Polo

abstract: Water polo is an aquatic team sport that requires endurance, strength, power, swimming speed, agility, tactical awareness, and specific technical skills, including ball control. Unlike other team sports, few researchers have examined the nutritional habits of water polo athletes or potential dietary strategies that improve performance in water polo match play. Water polo players are typically well muscled, taller athletes; female players display higher levels of adiposity compared with their male counterparts. Positional differences exist: Center players are heavier and have higher body fat levels compared with perimeter players. Knowledge of the physical differences that exist among water polo players offers the advantage of player identification as well as individualizing nutrition strategies to optimize desired physique goals. Individual dietary counseling is warranted to ensure dietary adequacy, and in cases of physique manipulation. Performance in games and during quality workouts is likely to improv...


title: Markov jump linear system analysis of microgrid stability

abstract: In a typical microgrid, the power generation capacity is similar to the maximum total load. The low inertia of the system provides little margin for error in the power balance, both active and reactive, and requires rapid control response to load changes. In the present work, a microgrid is modeled as a Markov jump linear system (MJLS). An MJLS is a dynamic system with continuous states governed by one of a set of linear systems, and a continuous-time Markov process that determines which linear system is active. When the discrete state of the Markov process changes, there is a “jump” in the dynamics of the continuous states. In addition, the jump may be impulsive. The present work first explores impulsive MJLS stability. Conservative bounds on the expected value of the state are determined from a combination of the Markov process parameters, the dynamics of each linear system, and the magnitude of the impulses. Then the microgrid model is cast into the MJLS framework and stability analysis is performed. The conclusions are verified with detailed simulations.


title: Optimizing file systems for fast storage devices

abstract: Emerging high-performance storage devices have attractive features such as low latency and high throughput. This leads to a rapid increase in the demand for fast storage devices in cloud platforms, social network services, etc. However, there are few block-based file systems that are capable of utilizing superior characteristics of fast storage devices. In this paper, we find that the I/O strategy of modern operating systems prevents file systems from exploiting fast storage devices. To address this problem, we propose several optimization techniques for block-based file systems. Then, we apply our techniques to two well-known file systems and evaluate them with multiple benchmarks. The experimental results show that our optimized file systems achieve 32% on average and up to 54% better performance than existing file systems.


title: Embedded Software: Facts, Figures, and Future

abstract: Due to the complex system context of embedded-software applications, defects can cause life-threatening situations, delays can create huge costs, and insufficient productivity can impact entire economies. Providing better estimates, setting objectives, and identifying critical hot spots in embedded-software engineering requires adequate benchmarking data.


title: Power, politics, and MIS implementation

abstract: Theories of resistance to management information systems (MIS) are important because they guide the implementation strategies and tactics chosen by implementors. Three basic theories of the causes of resistance underlie many prescriptions and rules for MIS implementation. Simply stated, people resist MIS because of their own internal factors, because of poor system design, and because of the interaction of specific system design features with aspects of the organizational context of system use. These theories differ in their basic assumptions about systems, organizations, and resistance; they also differ in predictions that can be derived from them and in their implications for the implementation process. These differences are described and the task of evaluating the theories on the bases of the differences is begun. Data from a case study are used to illustrate the theories and to demonstrate the superiority, for implementors, of the interaction theory.


title: Customer segmentation and strategy development based on customer lifetime value: A case study

abstract: Abstract The more a marketing paradigm evolves, the more long-term relationship with customers gains its importance. CRM, a recent marketing paradigm, pursues long-term relationship with profitable customers. It can be a starting point of relationship management to understand and measure the true value of customers since marketing management as a whole is to be deployed toward the targeted customers and profitable customers, to foster customers' full profit potential. Corporate success depends on an organization's ability to build and maintain loyal and valued customer relationships. Therefore, it is essential to build refined strategies for customers based on their value. In this paper, we propose a framework for analyzing customer value and segmenting customers based on their value. After segmenting customers based on their value, strategies building according to customer segment will be illustrated through a case study on a wireless telecommunication company.


title: Preliminary report on the International Conference for the Development of Standards for the Treatment of Anorectal Malformations

abstract: Abstract Background Anorectal malformations (ARM) are common congenital anomalies seen throughout the world. Comparison of outcome data has been hindered because of confusion related to classification and asssessment systems. Methods The goals of the Krinkenbeck Conference on ARM was to develop standards for an International Classification of ARM based on a modification of fistula type and adding rare and regional variants, and design a system for comparable follow up studies. Results Lesions were classified into major clinical groups based on the fistula location (perineal, recto-urethral, recto-vesical, vestibular), cloacal lesions, those with no fistula and anal stenosis. Rare and regional variants included pouch colon, rectal atresia or stenosis, rectovaginal fistula, H-fistula and others. Groups would be analyzed according to the type of procedure performed stratified for confounding associated conditions such as sacral anomalies and tethered cord. A standard method for postoperative assessment of continence was determined. Conclusions A new International diagnostic classification system, operative groupings and a method of postoperative assessment of continence was developed by consensus of a large contingent of participants experienced in the management of patients with ARM. These methods should allow for a common standardization of diagnosis and comparing postoperative results.


title: Longer length improvement and more covert incision: a single-center, prospective study of two innovative surgical methods “one stitch” and “four stitch” for pediatric buried penis

abstract: BACKGROUND ::: To introduce the detailed procedures of two innovative surgical options for pediatric buried penis and prospectively compare their efficacy and safety. ::: ::: ::: METHODS ::: A single-center, non-randomized, prospective study was conducted at the Zhongnan Hospital of Wuhan University, where patients were operated on using the so-called "one stitch" (OS) or "four stitch" (FS) methods. The operation time, adverse events, and satisfaction were recorded for both groups. ::: ::: ::: RESULTS ::: Finally, 156 patients underwent the so-called OS (n = 65) or FS (n = 91) method, with a follow-up rate of 86.5% (135/156). During the perioperative period, the FS group spent much longer in surgery (P < 0.001), had more blood loss (P < 0.001), and took longer to recover from edema (P < 0.001) than the OS group. In contrast to the satisfaction after 12 months' follow up, both the objective length improvement (2.5 ± 0.6 vs 3.8 ± 0.5 cm, P < 0.001) and subjective satisfaction percent (86 vs 95%, P = 0.678) in the FS group were superior to those in the OS group. No significant differences were detected in postoperative infection, stenosis circle, scar hyperplasia, and relapse. ::: ::: ::: CONCLUSIONS ::: In conclusion, the two surgical options for pediatric buried penis are both safe and effective. The OS method has a simple procedure, so with shorter operation time and faster postoperative recovery; though the FS method with more complex procedure, patients can acquire a satisfactory improvement of penile length almost 4 cm and more covert incision at the midline of the scrotum. We primarily recommend the FS method for patients with moderate or severe buried cases; but for mild cases, we preferred the OS method.


title: Zero Shot Detection

abstract: As we move toward large-scale object detection, it is unrealistic to expect annotated training data, in the form of bounding box annotations around objects, for all object classes at sufficient scale; therefore, the methods capable of unseen object detection are required. We propose a novel zero-shot method based on training an end-to-end model that fuses semantic attribute prediction with visual features to propose object bounding boxes for seen and unseen classes. While we utilize semantic features during training, our method is agnostic to semantic information for unseen classes at test-time. Our method retains the efficiency and effectiveness of YOLOv2 for objects seen during training, while improving its performance for novel and unseen objects. The ability of the state-of-the-art detection methods to learn discriminative object features to reject background proposals also limits their performance for unseen objects. We posit that, to detect unseen objects, we must incorporate semantic information into the visual domain so that the learned visual features reflect this information and lead to improved recall rates for unseen objects. We test our method on PASCAL VOC and MS COCO dataset and observed significant improvements on the average precision of unseen classes.


title: Adoption of e‐commerce applications in SMEs

abstract: Purpose – The purpose of this paper is to examine the factors within the technology‐organization‐environment (TOE) framework that affect the decision to adopt electronic commerce (EC) and extent of EC adoption, as well as adoption and non‐adoption of different EC applications within small‐ and medium‐sized enterprises (SMEs).Design/methodology/approach – A questionnaire‐based survey was conducted to collect data from 235 managers or owners of manufacturing SMEs in Iran. The data were analyzed by employing factorial analysis and relevant hypotheses were derived and tested by multiple and logistic regression analysis.Findings – EC adoption within SMEs is affected by perceived relative advantage, perceived compatibility, CEO's innovativeness, information intensity, buyer/supplier pressure, support from technology vendors, and competition. Similarly, description on determinants of adoption and non‐adoption of different EC applications has been provided.Research limitations/implications – Cross‐sectional data ...


title: Real-time traffic sign recognition from video by class-specific discriminative features

abstract: In this paper we address the problem of traffic sign recognition. Novel image representation and discriminative feature selection algorithms are utilised in a traditional three-stage framework involving detection, tracking and recognition. The detector captures instances of equiangular polygons in the scene which is first appropriately filtered to extract the relevant colour information and establish the regions of interest. The tracker predicts the position and the scale of the detected sign candidate over time to reduce computation. The classifier compares a discrete-colour image of the observed sign with the model images with respect to the class-specific sets of discriminative local regions. They are learned off-line from the idealised template sign images, in accordance with the principle of one-vs-all dissimilarity maximisation. This dissimilarity is defined based on the so-called Colour Distance Transform which enables robust discrete-colour image comparisons. It is shown that compared to the well-established feature selection techniques, such as Principal Component Analysis or AdaBoost, our approach offers a more adequate description of signs and involves effortless training. Upon this description we have managed to build an efficient road sign recognition system which, based on a conventional nearest neighbour classifier and a simple temporal integration scheme, demonstrates a competitive performance in the experiments involving real traffic video.


title: Caching With Time-Varying Popularity Profiles: A Learning-Theoretic Perspective

abstract: Content caching at the small-cell base stations (sBSs) in a heterogeneous wireless network is considered. A cost function is proposed that captures the backhaul link load called the `offloading loss', which measures the fraction of the requested files that are not available in the sBS caches. As opposed to the previous approaches that consider time-invariant and perfectly known popularity profile, caching with non-stationary and statistically dependent popularity profiles (assumed unknown, and hence, estimated) is studied from a learning-theoretic perspective. A probably approximately correct result is derived, which presents a high probability bound on the offloading loss difference, i.e., the error between the estimated and the optimal offloading loss. The difference is a function of the Rademacher complexity, the $\beta-$mixing coefficient, the number of time slots, and a measure of discrepancy between the estimated and true popularity profiles. A cache update algorithm is proposed, and simulation results are presented to show its superiority over periodic updates. The performance analyses for Bernoulli and Poisson request models are also presented.


title: GPU-Based Research of Highly Efficient Ray Tracing

abstract: By further study of GPU architecture and GPU stream programming model. In this paper, uniform grid acceleration structure implements on the GPU stream programming model of the ray tracing. It has a lot of ray intersection calculations in the whole rendering process, reducing the efficiency of the whole scene rendering. Rendering without compromising the quality of the premise, put forward that use hardware accelerated Z-buffer to determine the main ray grating method to improve the efficiency of the main ray generation and its experimental verification.


Extending Traditional Telecom Churn Prediction Using Social Network Data


title: Dynamic symbolic execution for polymorphism

abstract: Symbolic execution is an important program analysis technique that provides auxiliary execution semantics to execute programs with symbolic rather than concrete values. There has been much recent interest in symbolic execution for automatic test case generation and security vulnerability detection, resulting in various tools being deployed in academia and industry. Nevertheless, (subtype or dynamic) polymorphism of object-oriented programs has been neglected: existing symbolic execution techniques can explore different targets of conditional branches but not different targets of method invocations. We address the problem of how this polymorphism can be expressed in a symbolic execution framework. We propose the notion of symbolic types, which make object types symbolic. With symbolic types,[ various targets of a method invocation can be explored systematically by mutating the type of the receiver object of the method during automatic test case generation. To the best of our knowledge, this is the first attempt to address polymorphism in symbolic execution. Mutation of method invocation targets is critical for effectively testing object-oriented programs, especially libraries. Our experimental results show that symbolic types are significantly more effective than existing symbolic execution techniques in achieving test coverage and finding bugs and security vulnerabilities in OpenJDK.


title: Interleukin-17 is a negative regulator of established allergic asthma

abstract: T helper (Th)17 cells producing interleukin (IL)-17 play a role in autoimmune and allergic inflammation. Here, we show that IL-23 induces IL-17 in the lung and IL-17 is required during antigen sensitization to develop allergic asthma, as shown in IL-17R-deficient mice. Since IL-17 expression increased further upon antigen challenge, we addressed its function in the effector phase. Most strikingly, neutralization of IL-17 augmented the allergic response in sensitized mice. Conversely, exogenous IL-17 reduced pulmonary eosinophil recruitment and bronchial hyperreactivity, demonstrating a novel regulatory role of IL-17. Mechanistically, IL-17 down modulated eosinophil-chemokine eotaxin (CCL11) and thymus- and activation-regulated chemokine/CCL17 (TARC) in lungs in vivo and ex vivo upon antigen restimulation. In vitro, IL-17 reduced TARC production in dendritic cells (DCs)-the major source of TARC-and antigen uptake by DCs and IL-5 and IL-13 production in regional lymph nodes. Furthermore, IL-17 is regulated in an IL-4-dependent manner since mice deficient for IL-4Ralpha signaling showed a marked increase in IL-17 concentration with inhibited eosinophil recruitment. Therefore, endogenous IL-17 is controlled by IL-4 and has a dual role. Although it is essential during antigen sensitization to establish allergic asthma, in sensitized mice IL-17 attenuates the allergic response by inhibiting DCs and chemokine synthesis.


Deep Learning For Video Saliency Detection.


title: A View on Fuzzy Systems for Big Data: Progress and Opportunities

abstract: AbstractCurrently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that allows at working with Big Data. By means of the MapReduce model and its different extensions, scalability can be successfully addressed, while maintaining a good fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining, those models based on fuzzy systems stand out for many applications. Among their advantages, we must stress the use of a representation close to the natural language. Additionally, they use an inference model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty. Despite the success of this type of systems, their migration to the Big Dat...


title: On the Anonymization of Sparse High-Dimensional Data

abstract: Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to enforce privacy-preserving paradigms, such as k- anonymity and lscr-diversity, while minimizing the information loss incurred in the anonymizing process (i.e. maximize data utility). However, existing techniques adopt an indexing- or clustering- based approach, and work well for fixed-schema data, with low dimensionality. Nevertheless, certain applications require privacy-preserving publishing of transaction data (or basket data), which involves hundreds or even thousands of dimensions, rendering existing methods unusable. We propose a novel anonymization method for sparse high-dimensional data. We employ a particular representation that captures the correlation in the underlying data, and facilitates the formation of anonymized groups with low information loss. We propose an efficient anonymization algorithm based on this representation. We show experimentally, using real-life datasets, that our method clearly outperforms existing state-of-the-art in terms of both data utility and computational overhead.


title: Detecting and Discriminating . . .

abstract: This paper aims to address the problem of anomaly detection and discrimination in complex behaviours, where anomalies are subtle and difficult to detect owing to the complex temporal dynamics and correlations among multiple objects' behaviours. Specifically, we decompose a complex behaviour pattern according to its temporal characteristics or spatial-temporal visual contexts. The decomposed behaviour is then modelled using a cascade of Dynamic Bayesian Networks (CasDBNs). In contrast to existing standalone models, the proposed behaviour decomposition and cascade modelling offers distinct advantage in simplicity for complex behaviour modelling. Importantly, the decomposition and cascade structure map naturally to the structure of complex behaviour, allowing for a more effective detection of subtle anomalies in surveillance videos. Comparative experiments using both indoor and outdoor data are carried out to demonstrate that, in addition to the novel capability of discriminating different types of anomalies, the proposed framework outperforms existing methods in detecting durational anomalies in complex behaviours and subtle anomalies that are difficult to detect when objects are viewed in isolation.


title: Improved Asymmetric Locality Sensitive Hashing (ALSH) for Maximum Inner Product Search (MIPS)

abstract: Recently it was shown that the problem of Maximum Inner Product Search (MIPS) is efficient and it admits provably sub-linear hashing algorithms. Asymmetric transformations before hashing were the key in solving MIPS which was otherwise hard. In the prior work, the authors use asymmetric transformations which convert the problem of approximate MIPS into the problem of approximate near neighbor search which can be efficiently solved using hashing. In this work, we provide a different transformation which converts the problem of approximate MIPS into the problem of approximate cosine similarity search which can be efficiently solved using signed random projections. Theoretical analysis show that the new scheme is significantly better than the original scheme for MIPS. Experimental evaluations strongly support the theoretical findings.


title: Denoising Prior Driven Deep Neural Network for Image Restoration

abstract: Deep neural networks (DNNs) have shown very promising results for various image restoration (IR) tasks. However, the design of network architectures remains a major challenging for achieving further improvements. While most existing DNN-based methods solve the IR problems by directly mapping low quality images to desirable high-quality images, the observation models characterizing the image degradation processes have been largely ignored. In this paper, we first propose a denoising-based IR algorithm, whose iterative steps can be computed efficiently. Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies. A convolutional neural network (CNN) based denoiser that can exploit the multi-scale redundancies of natural images is proposed. As such, the proposed network not only exploits the powerful denoising ability of DNNs, but also leverages the prior of the observation model. Through end-to-end training, both the denoisers and the BP modules can be jointly optimized. Experimental results on several IR tasks, e.g., image denoisig, super-resolution and deblurring show that the proposed method can lead to very competitive and often state-of-the-art results on several IR tasks, including image denoising, deblurring, and super-resolution.


Medication issues for people with early dementia


title: Critical success factor categories for big data: A preliminary analysis of the current academic landscape

abstract: Big Data (BD) is a popular talking point currently. Companies invest huge amounts of resources to manage and maintain Big Data with mixed results. Understanding the Critical Success Factors (CSFs) associated with the successful management and maintenance of BD can aid organisations to obtain higher returns on their investment. This study focused on the identification of Critical Success Factors (CSFs) categories published in academic journals and conference proceedings on the topic of Big Data. It adopted a qualitative approach to classify the factors into categories and proposes a high level, preliminary model of CSF categorization. The model postulates that CSFs can be classified in three main categories namely Organisational, People and Technology. These categories can be used in future studies to test for the applicability and utilisation of BD by organisations. Furthermore, the authors postulate that the CSFs for all three categories should prevail in order to achieve the maximum value and success of BD implementations. The study also indicated that further research is necessary to refine the classification by considering alternative frameworks such as BD capability frameworks.


title: Analyzing the Use of an Advance Booking Curve in Forecasting Hotel Reservations

abstract: ABSTRACTAlthough there is considerable interest in the advance bookings model as a forecasting method in the hotel industry, there has been little research analyzing the use of an advance booking curve in forecasting hotel reservations. The mainstream of advance booking models reviewed in the literature uses only the bookings-on-hand data on a certain day and ignores the previous booking data. This empirical study analyzes the entire booking data set for one year provided by the Hotel ICON in Hong Kong, and identifies the trends and patterns in the data. The analysis demonstrates the use of an advance booking curve in forecasting hotel reservations at property level.


title: Search Algorithms for Regression Test Case Prioritization

abstract: Regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the reexecution of all test cases during regression testing. In this situation, test case prioritization techniques aim to improve the effectiveness of regression testing by ordering the test cases so that the most beneficial are executed first. Previous work on regression test case prioritization has focused on greedy algorithms. However, it is known that these algorithms may produce suboptimal results because they may construct results that denote only local minima within the search space. By contrast, metaheuristic and evolutionary search algorithms aim to avoid such problems. This paper presents results from an empirical study of the application of several greedy, metaheuristic, and evolutionary search algorithms to six programs, ranging from 374 to 11,148 lines of code for three choices of fitness metric. The paper addresses the problems of choice of fitness metric, characterization of landscape modality, and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning greedy algorithms. They shed light on the nature of the regression testing search space, indicating that it is multimodal. The results also show that genetic algorithms perform well, although greedy approaches are surprisingly effective, given the multimodal nature of the landscape


title: Realization of a composite right/left-handed leaky-wave antenna with circular polarization

abstract: A leaky-wave antenna (LWA) with circular polarization based on the composite right/left-handed (CRLH) substrate integrated waveguide (SIW) is investigated and presented. Series interdigital capacitors have been introduced into the circuit by etching the slots on the waveguide surface achieving a CRLH functionality. Two symmetrical leaky traveling-wave transmission lines with orthogonal polarizations are placed side-by-side and excited with 90° phase difference generating a pure circular polarization mode. The main beam of this antenna can be steered continuously by varying the frequency while maintaining a low axial ratio (below 3 dB) within the main beam direction. The performance of this LWA is verified through both full-wave simulation and measurement of a fabricated prototype showing a good agreement.


title: Detecting adversarial advertisements in the wild

abstract: In a large online advertising system, adversaries may attempt to profit from the creation of low quality or harmful advertisements. In this paper, we present a large scale data mining effort that detects and blocks such adversarial advertisements for the benefit and safety of our users. Because both false positives and false negatives have high cost, our deployed system uses a tiered strategy combining automated and semi-automated methods to ensure reliable classification. We also employ strategies to address the challenges of learning from highly skewed data at scale, allocating the effort of human experts, leveraging domain expert knowledge, and independently assessing the effectiveness of our system.


title: Modeling, optimization, and experimental validation of a resonant piezo-optical ring sensor for enhanced active and passive structural health monitoring

abstract: A mechanical resonant piezo-optical ring sensor was studied, designed to selectively enhance the response of piezoelectric wafer active sensors (PWAS) and fiber Bragg grating (FBG) sensors. The frequency characteristics of the ring sensor were modeled through modal and harmonic analyses. The models were used to guide the experimentation, serving as a basis for comparison and implementation. Pitch-catch, resonance, and acoustic emission (AE) experiments were performed to compare the performance of the ring sensor to plate-mounted PWAS and FBG. Factors relating to optimal in-service implementation, particularly symmetric placement of FBG and PWAS, were investigated. It was found that the ring sensor was capable of amplifying an incoming Lamb wave signal. This was applied to AE experiments, where selective frequencies were amplified such that the time-domain signal had a larger amplitude response.


title: Multiple Player Tracking in Sports Video: A Dual-Mode Two-Way Bayesian Inference Approach With Progressive Observation Modeling

abstract: Multiple object tracking (MOT) is a very challenging task yet of fundamental importance for many practical applications. In this paper, we focus on the problem of tracking multiple players in sports video which is even more difficult due to the abrupt movements of players and their complex interactions. To handle the difficulties in this problem, we present a new MOT algorithm which contributes both in the observation modeling level and in the tracking strategy level. For the observation modeling, we develop a progressive observation modeling process that is able to provide strong tracking observations and greatly facilitate the tracking task. For the tracking strategy, we propose a dual-mode two-way Bayesian inference approach which dynamically switches between an offline general model and an online dedicated model to deal with single isolated object tracking and multiple occluded object tracking integrally by forward filtering and backward smoothing. Extensive experiments on different kinds of sports videos, including football, basketball, as well as hockey, demonstrate the effectiveness and efficiency of the proposed method.


title: Landmarking Manifolds with Gaussian Processes

abstract: We present an algorithm for finding landmarks along a manifold. These landmarks provide a small set of locations spaced out along the manifold such that they capture the low-dimensional nonlinear structure of the data embedded in the high-dimensional space. The approach does not select points directly from the dataset, but instead we optimize each landmark by moving along the continuous manifold space (as approximated by the data) according to the gradient of an objective function. We borrow ideas from active learning with Gaussian processes to define the objective, which has the property that a new landmark is "repelled" by those currently selected, allowing for exploration of the manifold. We derive a stochastic algorithm for learning with large datasets and show results on several datasets, including the Million Song Dataset and articles from the New York Times.


title: Joint VM placement and routing for data center traffic engineering

abstract: Today's data centers need efficient traffic management to improve resource utilization in their networks. In this work, we study a joint tenant (e.g., server or virtual machine) placement and routing problem to minimize traffic costs. These two complementary degrees of freedom—placement and routing—are mutually-dependent, however, are often optimized separately in today's data centers. Leveraging and expanding the technique of Markov approximation, we propose an efficient online algorithm in a dynamic environment under changing traffic loads. The algorithm requires a very small number of virtual machine migrations and is easy to implement in practice. Performance evaluation that employs the real data center traffic traces under a spectrum of elephant and mice flows, demonstrates a consistent and significant improvement over the benchmark achieved by common heuristics.


title: On the Suitability of Near-Infrared Systems for Next Generation Brain Computer Interfaces

abstract: A brain-computer interface (BCI) gives those suffering from neuromuscular impairments a means to interact and communicate with their surrounding environment. A BCI translates physiological signals, typically electrical, detected from the brain to control an output device. A significant problem with current BCIs is the lengthy training periods involved for proficient usage, which can often lead to frustration and anxiety on the part of the user. Ultimately this can lead to abandonment of the device. The primary reason for this is that relatively indirect measures of cognitive function, as can be gleaned from the electroencephalogram (EEG), are harnessed. A more suitable and usable interface would need to measure cognitive function more directly. In order to do this, new measurement modalities, signal acquisition and processing, and translation algorithms need to be addressed. In this paper, we propose a novel approach, using non-invasive near-infrared imaging technology to develop a user-friendly optical BCI. As an alternative to the traditional EEG-based devices, we have used practical non-invasive optical techniques to detect characteristic haemodynamic responses due to motor imagery and consequently created an accessible BCI that is simple to attach and requires little user training.


title: I said your name in an empty room: grieving and continuing bonds on facebook

abstract: In response to the death of a close friend or relative, bereaved individuals can use technology as part of the grieving process. We present a study that analyzes the messages of the friends and family of the deceased to their Facebook profile before and after their passing. Our analysis reveals that mourners use profiles as a way to maintain a continuing bond with the deceased, as well as a way to accomplish specific front stage bereavement communication, such as sharing memories, expressing sorrow and providing social support. These observations may improve the design of social networking technologies so that they remain useful, sensitive tools for the bereaved.


title: Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation

abstract: The performance of the level set segmentation is subject to appropriate initialization and optimal configuration of controlling parameters, which require substantial manual intervention. A new fuzzy level set algorithm is proposed in this paper to facilitate medical image segmentation. It is able to directly evolve from the initial segmentation by spatial fuzzy clustering. The controlling parameters of level set evolution are also estimated from the results of fuzzy clustering. Moreover the fuzzy level set algorithm is enhanced with locally regularized evolution. Such improvements facilitate level set manipulation and lead to more robust segmentation. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.


title: Executive functions and the self-regulation of eating behavior: A review

abstract: Abstract In order to pursue the long-term goal of losing weight, a dieter needs to resist the urge to eat appealing, tasty foods. Beside sufficient motivation to resist these foods, dieters also need the capacity for successful self-regulation, and this capacity is strongly related to executive functions. Executive function is an umbrella term encompassing a number of interrelated higher-order cognitive processes that allow people to take goal-directed action. In this review, we outline how basic facets of executive functioning (updating, inhibiting, and shifting) contribute to the successful self-regulation of eating behavior. Moreover, we identify aspects of the self-regulation of eating behavior that are still under-researched. We conclude by outlining the implications of the extant research for intervention strategies and the design of future research studies on the role of executive functions for the self-regulation of eating behavior.


title: End-to-End Learning for Image Burst Deblurring

abstract: We present a neural network model approach for multi-frame blind deconvolution. The discriminative approach adopts and combines two recent techniques for image deblurring into a single neural network architecture. Our proposed hybrid-architecture combines the explicit prediction of a deconvolution filter and non-trivial averaging of Fourier coefficients in the frequency domain. In order to make full use of the information contained in all images in one burst, the proposed network embeds smaller networks, which explicitly allow the model to transfer information between images in early layers. Our system is trained end-to-end using standard backpropagation on a set of artificially generated training examples, enabling competitive performance in multi-frame blind deconvolution, both with respect to quality and runtime.


title: Pedestrian Detection via Classification on Riemannian Manifolds

abstract: We present a new algorithm to detect pedestrian in still images utilizing covariance matrices as object descriptors. Since the descriptors do not form a vector space, well known machine learning techniques are not well suited to learn the classifiers. The space of d-dimensional nonsingular covariance matrices can be represented as a connected Riemannian manifold. The main contribution of the paper is a novel approach for classifying points lying on a connected Riemannian manifold using the geometry of the space. The algorithm is tested on INRIA and DaimlerChrysler pedestrian datasets where superior detection rates are observed over the previous approaches.


title: Humane and practical implications of using carbon dioxide mixed with oxygen for anesthesia or euthanasia of rats.

abstract: A series of studies was undertaken to determine whether CO2 can be used as a humane as well as practical agent for euthanasia or anesthesia of rats. Human volunteers rated the degree of discomfort associated with breathing 50 to 100% CO2 mixed with oxygen. Increasing concentrations of CO2 were judged as progressively more noxious, from "highly unpleasant" for 50% CO2 to "painful" for 100% CO2. The practical aspects of anesthesia and euthanasia with 50 to 100% CO2 were studied, using male Sprague Dawley rats. Time to anesthesia and death were inversely related to CO2 concentration, as were the frequency and severity of adverse reactions, including seizures and hemorrhaging from the nose. The severity of edema and hemorrhage, which were observed on histologic examination of the lungs of all rats euthanized with CO2, were greatest in the animals exposed to the lowest concentrations. There were no significant effects of CO2 concentration on time to recumbency or recovery, and there were no significant effects of precharging versus not precharging the chamber on any of the parameters studied. It was concluded that, although CO2 can be used in a humane manner, the concentrations that are least likely to cause pain and distress are associated with the longest times to anesthesia and death, highest incidence of unwanted side effects, and most severe histologic changes in the lungs. Acceptably humane and reasonably practical euthanasia or anesthesia can be achieved using a nonprecharged chamber and a low gas flow rate so that conscious animals are never exposed to CO2 concentrations > 70%.


title: Large-Scale Training of SVMs with Automata Kernels

abstract: This paper presents a novel application of automata algorithms to machine learning. It introduces the first optimization solution for support vector machines used with sequence kernels that is purely based on weighted automata and transducer algorithms, without requiring any specific solver. The algorithms presented apply to a family of kernels covering all those commonly used in text and speech processing or computational biology. We show that these algorithms have significantly better computational complexity than previous ones and report the results of large-scale experiments demonstrating a dramatic reduction of the training time, typically by several orders of magnitude.


The Pillars of the Italian Economy


title: Monte Carlo filtering using kernel embedding of distributions

abstract: Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.


title: Efficient many-core query execution in main memory column-stores

abstract: We use the full query set of the TPC-H Benchmark as a case study for the efficient implementation of decision support queries on main memory column-store databases. Instead of splitting a query into separate independent operators, we consider the query as a whole and translate the execution plan into a single function performing the query. This allows highly efficient CPU utilization, minimal materialization, and execution in a single pass over the data for most queries. The single pass is performed in parallel and scales near-linearly with the number of cores. The resulting query plans for most of the 22 queries are remarkably simple and are suited for automatic generation and fast compilation. Using a data-parallel, NUMA-aware many-core implementation with block summaries, inverted index data structures, and efficient aggregation algorithms, we achieve one to two orders of magnitude better performance than the current record holders of the TPC-H Benchmark.


title: Versatile and robust 3D walking with a simulated humanoid robot (Atlas): A model predictive control approach

abstract: In this paper, we propose a novel walking method for torque controlled robots. The method is able to produce a wide range of speeds without requiring off-line optimizations and re-tuning of parameters. We use a quadratic whole-body optimization method running online which generates joint torques, given desired Cartesian accelerations of center of mass and feet. Using a dynamics model of the robot inside this optimizer, we ensure both compliance and tracking, required for fast locomotion. We have designed a foot-step planner that uses a linear inverted pendulum as simplified robot internal model. This planner is formulated as a quadratic convex problem which optimizes future steps of the robot. Fast libraries help us performing these calculations online. With very few parameters to tune and no perception, our method shows notable robustness against strong external pushes, relatively large terrain variations, internal noises, model errors and also delayed communication.


title: A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design

abstract: The experimental design technique is used for the weight assignment.A mathematical model is constructed to help the DMs make reasonable decisions.Different MCDM evaluation methods are combined to solve the same MCDM problem.The top-ranked alternatives exactly match with those derived by the past researchers. Multiple criteria decision-making (MCDM) is a difficult task because the existing alternatives are frequently in conflict with each other. This study presents a hybrid MCDM method combining simple additive weighting (SAW), techniques for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA) techniques. A feature of this method is that it employs an experimental design technique to assign attribute weights and then combines different MCDM evaluation methods to construct the hybrid decision-making model. This model can guide a decision maker in making a reasonable judgment without requiring professional skills or extensive experience. The ranking results agreed upon by multiple MCDM methods are more trustworthy than those generated by a single MCDM method. The proposed method is illustrated in a practical application scenario involving an IC packaging company. Four additional numerical examples are provided to demonstrate the applicability of the proposed method. In all of the cases, the results obtained using the proposed method were highly similar to those derived by previous studies, thus proving the validity and capability of this method to solve real-life MCDM problems.


title: Identification and nomenclature of the genus Penicillium

abstract: Penicillium is a diverse genus occurring worldwide and its species play important roles as decomposers of organic materials and cause destructive rots in the food industry where they produce a wide range of mycotoxins. Other species are considered enzyme factories or are common indoor air allergens. Although DNA sequences are essential for robust identification of Penicillium species, there is currently no comprehensive, verified reference database for the genus. To coincide with the move to one fungus one name in the International Code of Nomenclature for algae, fungi and plants, the generic concept of Penicillium was re-defined to accommodate species from other genera, such as Chromocleista, Eladia, Eupenicillium, Torulomyces and Thysanophora, which together comprise a large monophyletic clade. As a result of this, and the many new species described in recent years, it was necessary to update the list of accepted species in Penicillium. The genus currently contains 354 accepted species, including new combinations for Aspergillus crystallinus, A. malodoratus and A. paradoxus, which belong to Penicillium section Paradoxa. To add to the taxonomic value of the list, we also provide information on each accepted species MycoBank number, living ex-type strains and provide GenBank accession numbers to ITS, β-tubulin, calmodulin and RPB2 sequences, thereby supplying a verified set of sequences for each species of the genus. In addition to the nomenclatural list, we recommend a standard working method for species descriptions and identifications to be adopted by laboratories working on this genus.


title: 4-points congruent sets for robust pairwise surface registration

abstract: We introduce 4PCS, a fast and robust alignment scheme for 3D point sets that uses wide bases, which are known to be resilient to noise and outliers. The algorithm allows registering raw noisy data, possibly contaminated with outliers, without pre-filtering or denoising the data. Further, the method significantly reduces the number of trials required to establish a reliable registration between the underlying surfaces in the presence of noise, without any assumptions about starting alignment. Our method is based on a novel technique to extract all coplanar 4-points sets from a 3D point set that are approximately congruent, under rigid transformation, to a given set of coplanar 4-points. This extraction procedure runs in roughly O(n2 + k) time, where n is the number of candidate points and k is the number of reported 4-points sets. In practice, when noise level is low and there is sufficient overlap, using local descriptors the time complexity reduces to O(n + k). We also propose an extension to handle similarity and affine transforms. Our technique achieves an order of magnitude asymptotic acceleration compared to common randomized alignment techniques. We demonstrate the robustness of our algorithm on several sets of multiple range scans with varying degree of noise, outliers, and extent of overlap.


title: An ankle-foot emulation system for the study of human walking biomechanics

abstract: Although below-knee prostheses have been commercially available for some time, today's devices are completely passive, and consequently, their mechanical properties remain fixed with walking speed and terrain. A lack of understanding of the ankle-foot biomechanics and the dynamic interaction between an amputee and a prosthesis is one of the main obstacles in the development of a biomimetic ankle-foot prosthesis. In this paper, we present a novel ankle-foot emulator system for the study of human walking biomechanics. The emulator system is comprised of a high performance, force-controllable, robotic ankle-foot worn by an amputee interfaced to a mobile computing unit secured around his waist. We show that the system is capable of mimicking normal ankle-foot walking behaviour. An initial pilot study supports the hypothesis that the emulator may provide a more natural gait than a conventional passive prosthesis


title: Problematic mobile phone use in adolescents: derivation of a short scale MPPUS-10

abstract: OBJECTIVES ::: Our aim was to derive a short version of the Mobile Phone Problem Use Scale (MPPUS) using data from 412 adolescents of the Swiss HERMES (Health Effects Related to Mobile phonE use in adolescentS) cohort. ::: ::: ::: METHODS ::: A German version of the original MPPUS consisting of 27 items was shortened by principal component analysis (PCA) using baseline data collected in 2012. For confirmation, the PCA was carried out again with follow-up data 1 year later. ::: ::: ::: RESULTS ::: PCA revealed four factors related to symptoms of addiction (Loss of Control, Withdrawal, Negative Life Consequences and Craving) and a fifth factor reflecting the social component of mobile phone use (Peer Dependence). The shortened scale (MPPUS-10) highly reflects the original MPPUS (Kendalls' Tau: 0.80 with 90% concordant pairs). Internal consistency of MPPUS-10 was good with Cronbach's alpha: 0.85. The results were confirmed using the follow-up data. ::: ::: ::: CONCLUSIONS ::: The MPPUS-10 is a suitable instrument for research in adolescents. It will help to further clarify the definition of problematic mobile phone use in adolescents and explore similarities and differences to other technological addictions.


title: Understanding ontological engineering

abstract: Ontological engineering has garnered increasing attention over the last few years, as researchers have recognized ontologies are not just for knowledge-based systems---all software needs models of the world, and hence can make use of ontologies at design time [1]. A recent survey of the field [4] suggests developers of practical AI systems may especially benefit from their use. This survey earmarked several application classes that benefit from using ontologies, including natural language processing, intelligent information retrieval (especially from the Internet), virtual organizations, and simulation and modeling.


title: iTILT and SmartVET: 2 EU Projects to Promote Effective Interactive Whiteboard Use in Language and Vocational Education

abstract: Although the interactive whiteboard (IWB) is becoming increasingly prevalent in classrooms throughout the more affluent parts of the world and research has shown how this tool can increase the effectiveness of teaching and even transform pedagogy (Kennewell & Beauchamp, 2007), research literature overviews (Higgins et al., 2007; Koenraad, 2008) also identify obstacles to the realisation of added value and to the uptake of this technology by teachers in some educational contexts and in language education in particular (Thomas & Cutrim Schmid, 2010; Cutrim Schmid & Whyte, 2012). One such obstacle is the lack of pedagogical quality and sustained support in teacher development and a desire to meet this need has motivated a number of European projects. This paper focuses on two such projects, funded via the Lifelong Learning Programme, which address teacher education and professional development with the IWB. Both based their approach on findings from IWB specific and general professional development research and on needs analysis, using related research instruments. a) iTILT (interactive Technologies In Language Teaching, 2011-2013) This project focuses on foreign language teaching in four educational sectors in six languages and seven European countries, and has produced an open educational resource (OER) at http://itilt.eu including over 250 video examples of classroom activities with teacher and learner commentary, as well as a training manual and sample teaching/training materials, plus a resource library. This paper discusses the process of developing quality OERs and how they can best be exploited in language teacher education (Whyte, Cutrim Schmid, van Hazebrouck Thompson & Oberhofer, 2013). b) SMARTVET (Supporting Continuous Professional Development of VET teachers in the use of Interactive Whiteboards, 2011-2013). SMARTVET addresses issues related to the IWB competence levels of (Irish) teachers in the vocational education and training (VET) sector. The project is developing materials to support a train-the-trainers approach based on the results of local needs analysis research and good practice as identified in international IWB training contexts and initiatives. * Contact author. Tel.: +31611534974 E-mail address: ton.koenraad@gmail.com EUROCALL 2013– 20 years of Eurocall: Learning from the Past, Looking to the Future 2 This paper focuses on the following results of both projects: the outcomes of and recommendations based on needs analysis the design and content of the training models and materials the available final and interim web-based resources reflections on the project-related processes and research results © 2013 EUROCALL All rights reserved


title: Intelligent Motion Control for Four-Wheeled Omnidirectional Mobile Robots Using Ant Colony Optimization

abstract: This paper presents an intelligent motion controller for four-wheeled omnidirectional mobile robots with four independent driving wheels equally spaced at 90 degrees from one another by using ant colony optimization (ACO). The optimal parameters of motion controller are obtained by minimizing the performance index using the metaheuristic ACO algorithm. These optimal parameters are used in the ACO motion controller to obtain better performance for four-wheeled omnidirectional mobile robots to achieve both trajectory tracking and stabilization. Simulation results are conducted to show the effectiveness and merit of the proposed ACO-based intelligent motion controller for four-wheeled omnidirectional mobile robots.


title: FAST: A fuzzy semantic sentence similarity measure

abstract: A problem in the field of semantic sentence similarity is the inability of sentence similarity measures to accurately represent perception based (fuzzy) words that are commonly used in natural language. This paper presents a new sentence similarity measure that attempts to solve this problem. The new measure, Fuzzy Algorithm for Similarity Testing (FAST) is an ontology based similarity measure that uses concepts of fuzzy and computing with words to allow for the accurate representation of fuzzy based words. Through human experimentation fuzzy sets were created for six categories of words based on their levels of association with particular concepts. These fuzzy sets were then defuzzified and the results used to create new ontological relations between the words. Using these relationships allows for the creation of a new ontology based semantic text similarity algorithm that is able to show the effect of fuzzy words on computing sentence similarity as well as the effect that fuzzy words have on non-fuzzy words within a sentence. Experiments on FAST were conducted using a new fuzzy dataset, the creation of which is described in this paper. The results of the evaluation showed that there was an improved level of correlation between FAST and human test results over two existing sentence similarity measures.


Prayas Occasional Report HT Energy Audit : The Crucial Starting Point for Curbing Revenue Loss 1 2002


title: Water Withdrawal Trends, Cost and Uses inOta, Ogun State, Nigeria

abstract: Water is an essential component ::: of life. Globally, there is a drawback ::: in its availability and this is ::: increasing and duplicating itself ::: thereby intensifying the struggle ::: for scarce water resources. This ::: study assessed the water resource ::: availability, access, water withdrawal ::: trends, and presents the outcome ::: of a survey of water use pattern ::: in a semi-urban settlement in ::: Southwest Nigeria. The study ::: employed the use of a detailed ::: questionnaire and oral interview ::: for the collection of data. The ::: water use pattern extends to ::: various water consumers such as ::: residential, institutional, commercial ::: and industrial. The collected data ::: were analyzed using descriptive ::: statistics. The results showed that ::: 70% of the settlement depend ::: on groundwater alone, while 30% ::: on surface water. The findings in ::: the study revealed that the rate ::: of patronizing commercial water ::: seller is very high compared to the ::: government approved tariff rate of ::: 66.7% price margin, and there is an ::: increasing demand on water from ::: the various settlements considered. ::: The outcome will enable better ::: investment and management ::: decisions in water withdrawal and ::: usage in the near future as this must ::: be based on estimates of future ::: needs and the available resource ::: potentials.


title: Students' use of web-based concept map testing and strategies for learning

abstract: The study reported in this paper developed and evaluated a web-based concept map testing system for science students. Thirty-eight Taiwanese high school students were involved and it was found that their performance on the system was not significantly related to their achievement as measured by traditional standard tests. Their views about the use of the system, in general, were positive. An analysis of students' future use of the system and their motivation and learning strategies revealed that those with more critical thinking metacognitive activities and an effort regulation management strategy showed more willingness to use the online testing system. Moreover, students with high test anxiety showed a preference to be tested through the system.


title: Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning

abstract: In this paper, we study the effective semi-supervised hashing method under the framework of regularized learning-based hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not only independent from the dimensionality of the original data space but also much smaller than the one using linear hash function. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which effectively corrects the errors by taking into account of all the previous learned bits holistically without incurring the extra computational overhead. Experimental results on the six benchmark data sets demonstrate that the presented method outperforms the state-of-the-art hashing algorithms at a large margin.


title: AN EFFICIENT SEARCH ALGORITHM FOR PARTIALLY ORDERED SETS

abstract: Consider the problem of membership query for a given partially ordered set. We devise a greedy algorithm which can produce near-optimal search strategies. Rigorous analysis has been given, which shows our algorithm can have fewer comparisons than the best known solution by at least a factor of 0.27 under random graph model. Experimental results have also been given, which suggest the advantage of the algorithm under other models.


title: A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition

abstract: Deep learning based approaches have been dominating the face recognition field due to the significant performance improvement they have provided on the challenging wild datasets. These approaches have been extensively tested on such unconstrained datasets, on the Labeled Faces in the Wild and YouTube Faces, to name a few. However, their capability to handle individual appearance variations caused by factors such as head pose, illumination, occlusion, and misalignment has not been thoroughly assessed till now. In this paper, we present a comprehensive study to evaluate the performance of deep learning based face representation under several conditions including the varying head pose angles, upper and lower face occlusion, changing illumination of different strengths, and misalignment due to erroneous facial feature localization. Two successful and publicly available deep learning models, namely VGG-Face and Lightened CNN have been utilized to extract face representations. The obtained results show that although deep learning provides a powerful representation for face recognition, it can still benefit from preprocessing, for example, for pose and illumination normalization in order to achieve better performance under various conditions. Particularly, if these variations are not included in the dataset used to train the deep learning model, the role of preprocessing becomes more crucial. Experimental results also show that deep learning based representation is robust to misalignment and can tolerate facial feature localization errors up to 10% of the interocular distance.


title: The Impact of Online Recommendations and Consumer Feedback on Sales

abstract: Quality uncertainty and high search costs for identifying relevant information from an ocean of information may prevent customers from making purchases. Recognizing potential negative impacts of this search cost for quality information and relevant information, firms began to invest in creating a virtual community that enables consumers to share their opinions and experiences to reduce quality uncertainty, and in developing recommendation systems that help customers identify goods in which they might have an interest. However, not much is known regarding the effectiveness of these efforts. In this paper, we empirically investigate the impacts of recommendations and consumer feedbacks on sales based on data gathered from Amazon.com. Our results indicate that more recommendations indeed improve sales at Amazon.com; however, consumer ratings are not found to be related to sales. On the other hand, number of consumer reviews is positively associated with sales. We also find that recommendations work better for less-popular books than for more-popular books. This is consistent with the search cost argument: a consumer’s search cost for less-popular books may be higher, and thus they may rely more on recommendations to locate a product of interest.


title: Incremental Learning in Deep Convolutional Neural Networks Using Partial Network Sharing

abstract: Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification. However, retraining these large networks to accommodate new, previously unseen data demands high computational time and energy requirements. Also, previously seen training samples may not be available at the time of retraining. We propose an efficient training methodology and incrementally growing DCNN to learn new tasks while sharing part of the base network. Our proposed methodology is inspired by transfer learning techniques, although it does not forget previously learned tasks. An updated network, for learning new set of classes, is formed using previously learned convolutional layers (shared from initial part of base network) with addition of few newly added convolutional kernels included in the later layers of the network. We employed a ‘clone-and-branch’ technique with calibration, which allows the network to learn new tasks (containing classes with similar features as old tasks) one after another without any performance loss in old tasks. We evaluated the proposed scheme on several recognition applications. The classification accuracy achieved by our approach is comparable to the regular incremental learning approach (where networks are updated with new training samples only, without any network sharing), while achieving energy efficiency, reduction in storage requirements, memory access and training time.


title: Visual Representation and Classification by Learning Group Sparse Deep Stacking Network

abstract: Deep stacking networks (DSNs) have been successfully applied in classification tasks. Its architecture builds upon blocks of simplified neural network modules (SNNM). The hidden units are assumed to be independent in the SNNM module. However, this assumption prevents SNNM from learning the local dependencies between hidden units to better capture the information in the input data for the classification task. In addition, the hidden representations of input data in each class can be expectantly split into a group in real-world classification applications. Therefore, we propose two kinds of group sparse SNNM modules by mixing $l_{1}$ -norm and $l_{2}$ -norm. The first module learns the local dependencies among hidden units by dividing them into non-overlapping groups. The second module splits the representations of samples in different classes into separate groups to cluster the samples in each class. A group sparse DSN (GS-DSN) is constructed by stacking the group sparse SNNM modules. Experimental results further verify that our GS-DSN model outperforms the relevant classification methods. Particularly, GS-DSN achieves the state-of-the-art performance (99.1%) on 15-Scene.


title: Interplay between Social Selection and Social Influence on Physical Proximity in Friendship Formation

abstract: People form social relationships based on certain characteristics they possess, called social selecti on. When people change their social behavior due to interact ion with others, social influence is at work. Current f riend recommendation systems are mainly based on common friends and similar profile characteristics, theref ore the system may recommend unknown people. Recent advances improve the quality of friend recommendations by using shared content and interactions such as co authored papers, patents, and comments, but neglect the physical interactions to associate how you may know that person. In this paper, the interplay between social selection and social influence on physical proximit y in friendship formation is quantified in a mobile loca tionbased social network deployed in an academic conference. Encounter is used to measure and record physical proximity between user pairs, then a frien d recommendation system is built that uses these enco unters to specify the reason why this person should be add ed as a friend. The cumulative average duration and average frequency of encounters are analyzed, before and af ter sending the friend request. The results present wea k social selection and strong social influence on physical proximity in friendship formation and the reasons a re explained. This work helps to improve the design implementation of friend recommendation in physical environments.


title: View Selection in Semantic Web Databases

abstract: We consider the setting of a Semantic Web database, containing both explicit data encoded in RDF triples, and implicit data, implied by the RDF semantics. Based on a query workload, we address the problem of selecting a set of views to be materialized in the database, minimizing a combination of query processing, view storage, and view maintenance costs. Starting from an existing relational view selection method, we devise new algorithms for recommending view sets, and show that they scale significantly beyond the existing relational ones when adapted to the RDF context. To account for implicit triples in query answers, we propose a novel RDF query reformulation algorithm and an innovative way of incorporating it into view selection in order to avoid a combinatorial explosion in the complexity of the selection process. The interest of our techniques is demonstrated through a set of experiments.


title: FACTORS AFFECTING INDIVIDUALS TO ADOPT MOBILE BANKING: EMPIRICAL EVIDENCE FROM THE UTAUT MODEL

abstract: ABSTRACT Fast advances in the wireless technology and the intensive penetration of cell phones have motivated banks to spend large budget on building mobile banking systems, but the adoption rate of mobile banking is still underused than expected. Therefore, research to enrich current knowledge about what affects individuals to use mobile banking is required. Consequently, this study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate what impacts people to adopt mobile banking. Through sampling 441 respondents, this study empirically concluded that individual intention to adopt mobile banking was significantly influenced by social influence, perceived financial cost, performance expectancy, and perceived credibility, in their order of influencing strength. The behavior was considerably affected by individual intention and facilitating conditions. As for moderating effects of gender and age, this study discovered that gender significantly moderated the effects of performance expectancy and perceived financial cost on behavioral intention, and the age considerably moderated the effects of facilitating conditions and perceived self-efficacy on actual adoption behavior. Keywords: mobile banking, UTAUT, wireless commerce, technology adoption 1. Introduction With the recently quick growth in the market of 3G smart mobile phones, the wireless service delivery channel becomes a promising alternative for firms to create commercial opportunities. However, despite many wireless commercial services increase quickly, the use of mobile banking service is much lower than expected [Cruz et al. 2010] and still underused [Huili & Chunfang 2011], and the market of mobile banking still remains very small in comparing to the whole banking transactions [Luarn & Lin 2005; Laukkanen 2007; Yang 2009]. That is, the widespread adoption and large usage of cell phones did not reflect on the adoption and usage of mobile banking, although mobile banking perhaps was the first commercial mobile service [Scornavacca & Hoehle 2007] and first introduced in the early 2000s through short messaging service and wireless access protocol [Dasgupta et al. 2010]. Both Internet banking and mobile banking are often considered as electronic banking [Suoranta & Mattila 2004; Laforet & Li 2005; Laukkanen 2007; Sripalawat et al. 2011], but Internet banking and mobile banking are two alternative channels for banks to deliver their services and for customers to acquire services [Scornavacca & Hoehle 2007]. That is, customers using Internet banking are through computers connected to Internet, while customers using mobile banking are through wireless devices [Riquelme & Rios 2010]. Concerning the difference between online banking and mobile banking contexts, customers considered mobility as the most valued feature of mobile banking [Suoranta & Mattila 2004] and the time-critical consumers considered the always-on functionality as the most important feature of mobile banking [Singh et al. 2010], while banking users considered that Internet banking took significant advantage in Usefulness and Purpose [Natarajan et al. 2010] and online banking was suggested as the cheapest delivery channel [Koenig-Lewis et al. 2010]. Considering the immense penetration of cell phones, Cruz et al. [2010] observed that banks has very large potential to offer mobile banking services to people living in remote villages where only few computers are connected to the Internet. Acknowledging the limitations of Internet banking as opposed to widespread mobile phone penetration, Dasgupta et al. [2011] suggested that the emerging mobile banking may give banks a good commercial opportunity providing their services to rural people who are unable to access the Internet. Hence, Dasgupta et al. [2011] pointed out that main customer segments of mobile and Internet banking were not necessarily the same, which might explain why Sadi et al. …


Fundamental Analysis: Combining the Search for Quality with the Search for Value


title: On the digital image additive white Gaussian noise estimation

abstract: Digital imaging is widely used in applications such as medical, biometrics, multimedia,…etc. In many cases, images are transmitted through Internet from one point to another. During image acquisition and transmission, factors such as moving objects, sensor quality, and channel interferences may result in additive noise. The presence of noise affects image quality. Image denoising process attempts to reconstruct a noiseless image and improve its quality. Denoising an image with additive white Gaussian noise (AWGN) is a challenging process. Parameters such as noise mean and variance provide noise characteristics of AWGN. This paper compares three different algorithm for noise estimations; ant colony optimization, fuzzy logic, and region merging. It is shown that region merging algorithm provides better results with less resources and minimum computation time.


title: AODV based Energy Efficient Routing Protocol for Maximum Lifetime in MANET

abstract: A mobile ad-hoc network which does not use a wired network and base station system is composed of a group of mobile and wireless nodes. There are various types of restrictions. The biggest restriction is the confined energy of the batteries. If the network is divided into more than two, and one of the nodes consumes all the energy, that node can no longer participate in the network. In recent years, much research has been under taken to not only improve the energy storage but also to lengthen the networks lifetime. In this paper, we propose that the enhanced AODV (Ad-hoc On-demand Distance Vector) routing protocol which is modified to improve the networks lifetime in MANET (Mobile Ad-hoc NETwork). One improvement for the AODV protocol is to maximize the networks lifetime by applying an Energy Mean Value algorithm which considerate node energy-aware. We know the effectiveness of enhanced AODV compared with existing AODV by using NS-2. ..


Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos


title: IRREGULARITIES IN THE DISTRIBUTION OF PRIMES AND TWIN PRIMES

abstract: The maxima and minima of 〈L(x)〉−π(x), 〈R(x)〉−π(x), and 〈L2(x)〉−π2(x) in various intervals up to x = 8×1010 are tabulated. Here π(x) and π2(x) are respectively the number of primes and twin primes not exceeding x, L(x) is the logarithmic integral, R(x) is Riemann’s approximation to π(x), and L2(x) is the Hardy-Littlewood approximation to π2(x). The computation of the sum of inverses of twin primes less than 8×1010 gives a probable value 1.9021604±5×10−7 for Brun’s constant. Comments Only the Abstract is given here. The full paper appeared as [1]. For a more recent evaluation of Brun’s constant, which incidentally resulted in the discovery of a bug in the Pentium floating- point divide, see [3]. Errata. On page 49, three lines from the bottom, “17” should be replaced by “16”, and “900” by “960”. On the same page, six lines from the bottom, “17” should be replaced by “16”. References [1] R. P. Brent, “Irregularities in the distribution of primes and twin primes”, Mathematics of Computation (Derrick H. Lehmer special issue) 29 (1975), 43–56. MR 50#1791, 51#5522; Zbl 295.10002. Errata: ibid 30 (1976), 198. MR 53#302. See also “Tables concerning irregularities in the distribution of primes and twin primes”, UMT 4, ibid 29 (1975), 331; and “Tables concerning irregularities in the distribution of primes and twin primes to 10”, UMT 21, ibid 30 (1976), 379. rpb024. [2] R. Sherman Lehman, “On the difference π(x)− li(x)”, Acta Arithmetica 11 (1966), 397–410. MR 34#2546. [3] T. R. Nicely, “Enumeration to 10 of the Twin Primes and Brun’s Constant”, Virginia Journal of Science 46 (1995), 195–204. For a review see review04.dvi.gz . Computer Centre, Australian National University, Canberra, Australia 1991 Mathematics Subject Classification. Primary 11-04, 11Y60, 11Y70; Secondary 11A41, 65A05, 65B05.