title: Universal OWL Axiom Enrichment for Large Knowledge Bases

text: The Semantic Web has seen a rise in the availability and usage of knowledge bases over the past years, in particular in the Linked Open Data initiative. Despite this growth, there is still a lack of knowledge bases that consist of high quality schema information and instance data adhering to this schema. Several knowledge bases only consist of schema information, while others are, to a large extent, a mere collection of facts without a clear structure. The combination of rich schema and instance data would allow powerful reasoning, consistency checking, and improved querying possibilities as well as provide more generic ways to interact with the underlying data. In this article, we present a light-weight method to enrich knowledge bases accessible via SPARQL endpoints with almost all types of OWL 2 axioms. This allows to semiautomatically create schemata, which we evaluate and discuss using DBpedia.


title: Role-Based Access Controls

text: While Mandatory Access Controls (MAC) are appropriate for multilevel secure military applications, Discretionary Access Controls (DAC) are often perceived as meeting the security processing needs of industry and civilian government. This paper argues that reliance on DAC as the principal method of access control is unfounded and inappropriate for many commercial and civilian government organizations. The paper describes a type of non-discretionary access control role-based access control (RBAC)  that is more central to the secure processing needs of non-military systems than DAC.


title: Leveraging the schema in latent factor models for knowledge graph completion

text: We focus on the problem of predicting missing links in large Knowledge Graphs (KGs), so to discover new facts. Over the last years, latent factor models for link prediction have been receiving an increasing interest: they achieve state-of-the-art accuracy in link prediction tasks, while scaling to very large KGs. However, KGs are often endowed with additional schema knowledge, describing entity classes, their sub-class relationships, and the domain and range of each predicate: the schema is actually not used by latent factor models proposed in the literature. In this work, we propose an unified method for leveraging additional schema knowledge in latent factor models, with the aim of learning more accurate link prediction models. Our experimental evaluations show the effectiveness of the proposed method on several KGs.


title: A new miniaturized antenna for ISM 433 MHz frequency band

text: A new miniaturized antenna for 433MHz frequency band is introduced for sensor networks. The antenna is a magnetic monopole loaded with a capacitor, which can significantly decrease the resonant frequency of the structure. It is designed to operate in the ISM 433MHz band on a small PCB (103mm⋆55mm) and has at least 3MHz bandwidth. Moreover, its radiation efficiency is suitable for the possible applications and its pattern is omnidirectionnal.


title: HeidelPlace: An Extensible Framework for Geoparsing

text: Geographic information extraction from textual data sources, called geoparsing, is a key task in text processing and central to subsequent spatial analysis approaches. Several geoparsers are available that support this task, each with its own (often limited or specialized) gazetteer and its own approaches to toponym detection and resolution. In this demonstration paper, we present HeidelPlace, an extensible framework in support of geoparsing. Key features of HeidelPlace include a generic gazetteer model that supports the integration of place information from different knowledge bases, and a pipeline approach that enables an effective combination of diverse modules tailored to specific geoparsing tasks. This makes HeidelPlace a valuable tool for testing and evaluating different gazetteer sources and geoparsing methods. In the demonstration, we show how to set up a geoparsing workflow with HeidelPlace and how it can be used to compare and consolidate the output of different geoparsing approaches.


title: Learning Recurrent Span Representations for Extractive Question Answering

text: The reading comprehension task, that asks questions about a given evidence document, is a central problem in natural language understanding. Recent formulations of this task have typically focused on answer selection from a set of candidates pre-defined manually or through the use of an external NLP pipeline. However, Rajpurkar et al. (2016) recently released the SQUAD dataset in which the answers can be arbitrary strings from the supplied text. In this paper, we focus on this answer extraction task, presenting a novel model architecture that efficiently builds fixed length representations of all spans in the evidence document with a recurrent network. We show that scoring explicit span representations significantly improves performance over other approaches that factor the prediction into separate predictions about words or start and end markers. Our approach improves upon the best published results of Wang & Jiang (2016) by 5% and decreases the error of Rajpurkar et al.’s baseline by > 50%.


title: Optimizing 2D Gabor Filters for Iris Recognition

text: The randomness and richness present in the iris texture make the 2D Gabor filter bank analysis a suitable technique to be used for iris recognition systems. To accurately characterize complex texture structures using 2D Gabor filters it is necessary to use multiple sets of parameters of this type of filters. This paper proposes a technique of optimizing multiple sets of 2D Gabor filter parameters to gradually enhance the accuracy of an iris recognition system. The proposed methodology is suitable to be applied on both near infrared and visible spectrum iris images. To illustrate the efficiency of the filter bank design technique, UBIRISv1 database was used for benchmarking.


title: Investigation of a new GaN AC/DC topology for battery charging application

text: Switching losses are reduced by gallium nitride (GaN) technology, which allows faster device switching speeds. At the same time, the weight and size of converters utilizing these devices are greatly reduced in several applications by using high frequency (HF) transformers. A new, low voltage high current, GaN-based AC/DC topology for battery charging applications is simulated, modeled, and investigated in this paper. The proposed topology is designed to operate at 1 MHz for 120Vac to 48Vdc/60Vdc conversion for 1.4 kW load. The integration of GaN totem-pole power factor correction rectifier (TP-PFC), high-frequency series-resonant converter (SRC), and current doubler rectifier (CDR) presents an opportunity to further increase the efficiency and compactness of this GaN battery charging system. In this work, a new GaN topology to combine the advantages of TP-PFC and SRC with CDR in a battery charging converter is investigated, simulated, and modelled. Loss analysis of the proposed topology is addressed.


title: BodyAvatar: creating freeform 3D avatars using first-person body gestures

text: BodyAvatar is a Kinect-based interactive system that allows users without professional skills to create freeform 3D avatars using body gestures. Unlike existing gesture-based 3D modeling tools, BodyAvatar centers around a first-person "you're the avatar" metaphor, where the user treats their own body as a physical proxy of the virtual avatar. Based on an intuitive body-centric mapping, the user performs gestures to their own body as if wanting to modify it, which in turn results in corresponding modifications to the avatar. BodyAvatar provides an intuitive, immersive, and playful creation experience for the user. We present a formative study that leads to the design of BodyAvatar, the system's interactions and underlying algorithms, and results from initial user trials.


title: Reduction in gravitational torques of an industrial robot equipped with 2 DOF passive counterbalance mechanisms

text: In most 6 DOF robot arms, considerable amounts of gravitational torques due to the robot's own weight are applied to pitch joints of the robot, which causes most arms to use high capacity motors and speed reducers. A spring-based counterbalance mechanism can compensate for this gravitational torque, thus leading to a significant reduction in the effect of gravity. However, a simple installation of counterbalance mechanisms at each pitch joint does not work properly because the gravitational torque at each joint is dependent also on the other joints. To achieve multi-DOF counterbalancing, we propose a parallelogram linkage combined with dual counterbalance mechanisms, each being composed of a slider-crank mechanism and springs. Simulations and experimental results showed that the counterbalance robot arm based on the proposed counterbalance mechanisms effectively reduced the torques required to support the robot mass, thus allowing the prospective use of much smaller motors and speed reducers than traditional industrial robots.


title: Simple Personalized Search Based on Long-Term Behavioral Signals

text: Extensive research has shown that content-based Web result ranking can be significantly improved by considering personal behavioral signals (such as past queries) and global behavioral signals (such as global click frequencies). In this work we present a new approach to incorporating click behavior into document ranking, using ideas of click models as well as learning to rank. We show that by training a click model with pairwise loss, as is done in ranking problems, our approach achieves personalized reranking performance comparable to the stateof-the-art while eliminating much of the complexity required by previous models. This contrasts with other approaches that rely on complex feature engineering.


title: Development of a Biomimetic Hand Exotendon Device (BiomHED) for Restoration of Functional Hand Movement Post-Stroke

text: Significant functional impairment of the hand is common among stroke survivors and restoration of hand function should be prioritized during post-stroke rehabilitation. The goal of this study was to develop a novel biomimetic device to assist patients in producing complex hand movements with a limited number of actuators. The Biomimetic Hand Exoskeleton Device (BiomHED) is actuated by exotendons that mimic the geometry of the major tendons of the hand. Ten unimpaired subjects and four chronic stroke survivors participated in experiments that tested the efficacy of the system. The exotendons reproduced distinct spatial joint coordination patterns similar to their target muscle-tendon units for both subject groups. In stroke survivors, the exotendon-produced joint angular displacements were smaller, but not significantly different, than those of unimpaired subjects (\mbi p = 0.15-0.84). Even with limited use of the BiomHED, the kinematic workspace of the index finger increased by 63%-1014% in stroke survivors. The device improved the kinematics of the tip-pinch task in stroke survivors and resulted in a significant reduction in the fingertip-thumb tip distance ( 17.9 ±15.3 mm). This device is expected to enable effective “task-oriented” training of the hand post-stroke.


title: Local-global ranking for facial expression intensity estimation

text: Facial action units provide an objective characterization of facial muscle movements. Automatic estimation of facial action unit intensities is a challenging problem given individual differences in neutral face appearances and the need to generalize across different pose, illumination and datasets. In this paper, we introduce the Local-Global Ranking method as a novel alternative to direct prediction of facial action unit intensities. Our method takes advantage of the additional information present in videos and image collections of the same person (e.g. a photo album). Instead of trying to estimate facial expression intensities independently for each image, our proposed method performs a two-stage ranking: a local pair-wise ranking followed by a global ranking. The local ranking is designed to be accurate and robust by making a simple 3-class comparison (higher, equal, or lower) between randomly sampled pairs of images. We use a Bayesian model to integrate all these pair-wise rankings and construct a global ranking. Our Local-Global Ranking method shows state-of-the-art performance on two publicly-available datasets. Our cross-dataset experiments also show better generalizability.


title: Development of humanoid robot HRP-3P

text: A development of humanoid robot HRP-3P, which is a humanoid robot HRP-3 prototype, is presented in this paper. HRP-3 is under development as the succession humanoid to HRP-2, which we developed in phase two of HRP (humanoid robotics project). One of futures of HRP-3P is that its main mechanical and structural components are designed for protection against dust and water. Another is that node controllers are developed for realization of distributed control system. Real-time communication on Ethernet is also newly developed for communication between main controller and node controllers. In this paper, mechanical features and electrical features are presented with specifications of HRP-3P


title: An Algebraic Fault Attack on the LED Block Cipher

text: In this paper we propose an attack on block ciphers where we combine techniques derived from algebraic and fault based cryptanalysis. The recently introduced block cipher LED serves us as a target for our attack. We show how to construct an algebraic representation of the encryption map and how to cast the side channel information gained from a fault injection into polynomial form. The resulting polynomial system is converted into a logical formula in conjunctive normal form and handed over to a SAT solver for reconstruction of the secret key. Following this approach we were able to mount a new, successful attack on the version of LED that uses a 64-bit secret key, requiring only a single fault injection.


title: The building blocks of economic complexity.

text: For Adam Smith, wealth was related to the division of labor. As people and firms specialize in different activities, economic efficiency increases, suggesting that development is associated with an increase in the number of individual activities and with the complexity that emerges from the interactions between them. Here we develop a view of economic growth and development that gives a central role to the complexity of a country's economy by interpreting trade data as a bipartite network in which countries are connected to the products they export, and show that it is possible to quantify the complexity of a country's economy by characterizing the structure of this network. Furthermore, we show that the measures of complexity we derive are correlated with a country's level of income, and that deviations from this relationship are predictive of future growth. This suggests that countries tend to converge to the level of income dictated by the complexity of their productive structures, indicating that development efforts should focus on generating the conditions that would allow complexity to emerge to generate sustained growth and prosperity.


title: 3D spatial pyramid: descriptors generation from point clouds for indoor scene classification

text: Traditionally, the indoor scene classification problem has been approached from a 2D image recognition point of view. In most visual scene classification systems, a descriptor for the input image is generated to obtain a suitable representation that includes features related to color, shape or spatial information. Techniques based on the use of a spatial pyramid have proven to be adequate to perform this step. In the past years, on the other hand, 3D sensors have become widely available, which allows to include new information sources to the framework previously described. In this work we rely on RGB-D data to extend the spatial pyramid approach, aimed at building descriptors that can lead to a more robust representation against changing lighting conditions. The proposed descriptors are evaluated on the RobotVision@ImageCLEF-2013 benchmark dataset, remarkably outperforming state-of-the-art 3D local and global descriptors.


title: Customer churn analysis : A case study on the telecommunication industry of Thailand

text: Customer churn creates a huge anxiety in highly competitive service sectors especially the telecommunications sector. The objective of this research was to develop a predictive churn model to predict the customers that will be to churn; this is the first step to construct a retention management plan. The dataset was extracted from the data warehouse of the mobile telecommunication company in Thailand. The system generated the customer list, to implement a retention campaign to manage the customers with tendency to leave the company. WEKA software was used to implement the followings techniques: C4.5 decision trees algorithm, the logistic regression algorithm and the neural network algorithm. The C4.5 algorithm of decision trees proved optimal among the models. The findings are unequivocally beneficial to industry and other partners.


title: A New Concave Hull Algorithm and Concaveness Measure for n-dimensional Datasets

text: Convex and concave hulls are useful concepts for a wide variety of application areas, such as pattern recognition, image processing, statistics, and classification tasks. Concave hull performs better than convex hull, but it is difficult to formulate and few algorithms are suggested. Especially, an n-dimensional concave hull is more difficult than a 2or 3dimensional one. In this paper, we propose a new concave hull algorithm for n-dimensional datasets. It is simple but creative. We show its application to dataset analysis. We also suggest a concaveness measure and a graph that captures geometric shape of an n-dimensional dataset. Proposed concave hull algorithm and concaveness measure/graph are implemented using java, and are posted to http://user.dankook.ac.kr/ ~bitl/dkuCH.


title: User Migration in Online Social Networks: A Case Study on Reddit During a Period of Community Unrest

text: Platforms like Reddit have attracted large and vibrant communities, but the individuals in those communities are free to migrate to other platforms at any time. History has borne this out with the mass migration from Slashdot to Digg. The underlying motivations of individuals who migrate between platforms, and the conditions that favor migration online are not well-understood. We examine Reddit during a period of community unrest affecting millions of users in the summer of 2015, and analyze large-scale changes in user behavior and migration patterns to Reddit-like alternative platforms. Using self-reported statements from user comments, surveys, and a computational analysis of the activity of users with accounts on multiple platforms, we identify the primary motivations driving user migration. While a notable number of Reddit users left for other platforms, we found that an important pull factor that enabled Reddit to retain users was its long tail of niche content. Other platforms may reach critical mass to support popular or “mainstream” topics, but Reddit’s large userbase provides a key advantage in supporting niche top-


title: Innovative technology-based interventions for autism spectrum disorders: a meta-analysis.

text: This article reports the results of a meta-analysis of technology-based intervention studies for children with autism spectrum disorders. We conducted a systematic review of research that used a pre-post design to assess innovative technology interventions, including computer programs, virtual reality, and robotics. The selected studies provided interventions via a desktop computer, interactive DVD, shared active surface, and virtual reality. None employed robotics. The results provide evidence for the overall effectiveness of technology-based training. The overall mean effect size for posttests of controlled studies of children with autism spectrum disorders who received technology-based interventions was significantly different from zero and approached the medium magnitude, d = 0.47 (confidence interval: 0.08-0.86). The influence of age and IQ was not significant. Differences in training procedures are discussed in the light of the negative correlation that was found between the intervention durations and the studies' effect sizes. The results of this meta-analysis provide support for the continuing development, evaluation, and clinical usage of technology-based intervention for individuals with autism spectrum disorders.


title: Social recommendation using Euclidean embedding

text: Traditional recommender systems assume that all the users are independent, and they usually face the cold start and data sparse problems. To alleviate these problems, social recommender systems use social relations as an additional input to improve recommendation accuracy. Social recommendation follows the intuition that people with social relationships share some kinds of preference towards items. Current social recommendation methods commonly apply the Matrix Factorization (MF) model to incorporate social information into the recommendation process. As an alternative model to MF, we propose a novel social recommendation approach based on Euclidean Embedding (SREE) in this paper. The idea is to embed users and items in a unified Euclidean space, where users are close to both their desired items and social friends. Experimental results conducted on two real-world data sets illustrate that our proposed approach outperforms the state-of-the-art methods in terms of recommendation accuracy.


title: The CMU Pose, Illumination, and Expression Database

text: In the Fall of 2000 we collected a database of over 40,000 facial images of 68 people. Using the CMU 3D Room we imaged each person across 13 different poses, under 43 different illumination conditions, and with 4 different expressions. We call this the CMU Pose, Illumination, and Expression (PIE) database. We describe the imaging hardware, the collection procedure, the organization of the images, several possible uses, and how to obtain the database.


title: Combining Models and Exemplars for Face Recognition : An Illuminating Example

text: We propose a modeland exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.


title: From Few to Many: Generative Models for Recognition Under Variable Pose and Illumination

text: Image variability due to changes in pose and illumination can seriously impair object recognition. This paper presents appearance-based methods which, unlike previous appearance-based approaches, require only a small set of training images to generate a rich representation that models this variability. Speci cally, from as few as three images of an object in xed pose seen under slightly varying but unknown lighting, a surface and an albedo map are reconstructed. These are then used to generate synthetic images with large variations in pose and illumination and thus build a representation useful for object recognition. Our methods have been tested within the domain of face recognition on a subset of the Yale Face Database B containing 4050 images of 10 faces seen under variable pose and illumination. This database was speci cally gathered for testing these generative methods. Their performance is shown to exceed that of popular existing methods.


title: Optical Flow Constraints on Deformable Models with Applications to Face Tracking

text: Optical flow provides a constraint on the motion of a deformable model. We derive and solve a dynamic system incorporating flow as a hard constraint, producing a model-based least-squares optical flow solution. Our solution also ensures the constraint remains satisfied when combined with edge information, which helps combat tracking error accumulation. Constraint enforcement can be relaxed using a Kalman filter, which permits controlled constraint violations based on the noise present in the optical flow information, and enables optical flow and edge information to be combined more robustly and efficiently. We apply this framework to the estimation of face shape and motion using a 3D deformable face model. This model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. We present experiments in extracting the shape and motion of a face from image sequences which validate the accuracy of the method. They also demonstrate that our treatment of optical flow as a hard constraint, as well as our use of a Kalman filter to reconcile these constraints with the uncertainty in the optical flow, are vital for improving the performance of our system.


title: Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

text: Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.


title: A New Word Language Model Evaluation Metric for Character Based Languages

text: Perplexity is a widely used measure to evaluate word prediction power of a word-based language model. It can be computed independently and has shown good correlation with word error rate (WER) in speech recognition. However, for character based languages, character error rate (CER) is commonly used instead of WER as the measure for speech recognition, although language model is still word based. Due to the fact that different word segmentation strategies may result in different word vocabulary for the same text corpus, in many cases, wordbased perplexity is incompetent to evaluate the combined effect of word segmentation and language model training to predict final CER. In this paper, a new word-based language model evaluation measure is proposed to account for the effect of word segmentation and the goal of predicting CER. Experiments were conducted on Chinese speech recognition. Compared to the traditional word-based perplexity, the new measure is more robust to word segmentation and shows much more consistent correlation with CER in a large vocabulary continuous Chinese speech recognition task.


title: Joint wavelet-based spectrum sensing and FBMC modulation for cognitive mmWave small cell networks

text: Millimetre-wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand-fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet-based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet-based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier-based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet-based system compared with its Fourier-based counterpart over the 60 GHz mmWave channel.


Model-Driven Game Development Addressing Architectural Diversity and Game Engine-Integration


title: The NumPy Array: A Structure for Efficient Numerical Computation

text: In the Python world, NumPy arrays are the standard representation for numerical data and enable efficient implementation of numerical computations in a high-level language. As this effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.


title: Survey of Event Correlation Techniques for Attack Detection in Early Warning Systems

text: In the context of early warning systems for detecting Internet worms and other attacks, event correlation techniques are needed for two reasons. First, network attack detection is usually based on distributed sensors, e.g. intrusion detection systems. During attacks but even in normal operation, the generated amount of events is hard to handle in order to evaluate the current attack situation for a larger network. Thus, the concept of event or alert correlation has been introduced. This survey was motivated by recent work on early warning systems. We summarize and clarify the typical terminology used in this context and present a requirement analysis from an early warning system’s point of view. In the main part of this survey, we summarize and classify event correlation techniques as described in the literature.


title: Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped

text: Robotics controllers often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. Simulation can aid in optimizing these controllers if parameters learned in simulation transfer to hardware. Unfortunately, this is often not the case in legged locomotion, necessitating learning directly on hardware. This motivates using data-efficient learning techniques like Bayesian Optimization (BO) to minimize collecting expensive data samples. BO is a black-box data-efficient optimization scheme, though its performance typically degrades in higher dimensions. We aim to overcome this problem by incorporating domain knowledge, with a focus on bipedal locomotion. In our previous work, we proposed a feature transformation that projected a 16-dimensional locomotion controller to a 1-dimensional space using knowledge of human walking. When optimizing a human-inspired neuromuscular controller in simulation, this feature transformation enhanced sample efficiency of BO over traditional BO with a Squared Exponential kernel. In this paper, we present a generalized feature transform applicable to non-humanoid robot morphologies and evaluate it on the ATRIAS bipedal robot, in both simulation and hardware. We present three different walking controllers and two are evaluated on the real robot. Our results show that this feature transform captures important aspects of walking and accelerates learning on hardware and simulation, as compared to traditional BO.


title: GENDER AND LEADERSHIP BEHAVIOR AMONG SENIOR PASTORS

text: Research findings have been equivocal as to the existence of gender difference in leadership across settings. However, some studies based on transformational leadership theory and employing the Multifactor Leadership Questionnaire to measure leadership behaviors have indicated a difference in the leadership styles of female and male leaders. This study sought to test whether there were gender differences in the use of transformational, transactional, and laissez-faire leadership behaviors by Senior Pastors in an Australian Christian Church. The study did not detect any significant gender differences in leadership behaviors. Gender and Leadership Behavior among Senior Pastors Gender and Leadership Leadership has been typically a male prerogative in most sectors of society, including the corporate, political, military, and church sectors (Eagly & Karau, 2002). However, over the last 30 years women have made steady progress in moving into leadership roles. In 1972, women held 17% of all management and professional positions in Fortune 500 Companies. By 2006, this number had grown to 50.3% (Hoyt, Simon, and Reid, 2009). Women typically tend to occupy lower and middle management ranks while men cluster around the most powerful positions at the top. Women managers still receive significantly less remuneration for their work, with female managers receiving 24 percent less pay than men performing the equivalent function (Haslam and Ryan, 2008). Nonetheless, despite continuing 138 Asian Journal of Pentecostal Studies 12:2 (2009) inequity, it is clear that women are gradually occupying an increasing number of management and leadership positions. Although women have increasingly gained access to supervisory and middle management positions, they remain quite rare as elite leaders. For example, in 2006 women represented 5.2% of top earners, 14.7% of board members, 7.9% of the highest earners, and less than 2% of CEOs in Fortune 500 Companies (Hoyt, Simon, and Reid, 2009). This phenomenon has been explained by use of the idea of a “glass ceiling” – an invisible barrier preventing the rise of women within leadership ranks (Haslam & Ryan, 2008). Eagly and Karau (2002) describe it as “a barrier of prejudice and discrimination that excludes women from higher leadership positions” (p. 573). It is evident in the lower number of women in leadership positions, and particularly in high-level leadership positions. Yukl (2006) observes that “In the complete absence of sex-based discrimination, the number of women in chief executive positions in business and government should be close to 50 percent” (p. 427). A variety of explanations have been offered for the existence of gender-based discrimination in the appointment of organizational leaders. These include: (1) gender stereotypes suggesting that men are more suited to leadership positions and that women are more suited to support roles (Yukl, 2006); (2) overt sexism in the workplace (Schwartz, 1971); (3) perceived incompatibilities between women’s abilities and the requirements of leadership (Arvey, 1979); (4) women’s competing responsibilities in the home (Schwartz, 1994); and (5) women’s fear of success (Horner, 1972). The explanations are not mutually exclusive and they may combine to create significant barriers to the advancement of women. Martell and DeSmet (2001) found that a contributing reason for the glass ceiling and the continued absence of women in the upper ranks of management is “the existence of gender-based stereotypes in the leadership domain” (p. 1227). Gender stereotypes are “categorical beliefs composed of the traits and behavioral characteristics assigned to women and men only on the basis of the group label” (Martell & DeSmet, 2001, p. 1223). Such stereotypes serve as a type of expectation regarding the likely abilities of group members and, if left unchallenged, can translate into discriminatory behavior. Eagly and Karau (2002) point out that a “potential for prejudice exists when social perceivers hold a stereotype about a social group that is incongruent with the attributes that are thought to be required for success in certain classes of social roles” (p. 574). Prejudice against women as leaders Fogarty, Gender and Leadership 139 “follows from the incongruity that many people perceive between the characteristics of women and the requirements of leader roles” (Eagly & Karau, 2002, p. 574). Gender and Leadership within Christian Churches Within Christian churches gender based discrimination has been reinforced by theological perspectives (Barron, 1990; Bridges, 1998; Franklin, 2008; Scholz, 2005). The case that women are forbidden by scripture and church tradition to assume leadership within the church has been made frequently (Barron, 1990). This exclusion has been predominantly based on two Pauline texts (1 Timothy 2:11-15 and 1 Corinthians 14:33-34) and a broader theological position which sees men and women as being ontologically equal but functionally different. Its basic logic is that “God designed women to be subordinate to men in role and function” and therefore “women should not operate in positions of authority over men” (Franklin, 2008, 14). For example, Piper and Grudem (1991) state that “we are persuaded that the Bible teaches that only men should be pastors and elders. That is, men should bear primary responsibility for Christlike leadership and teaching in the church. So it is unbiblical, we believe, and therefore detrimental, for women to assume this role” (p. 60-61). Complementary to this theological position is the suggestion that women do not have the capacity for effective church leadership (Bridges, 1998). Piper (1991) exemplifies this position when he writes: “At the heart of mature masculinity is a sense of benevolent responsibility to lead, provide for and protect women in ways appropriate to a man’s differing relationships” and “At the heart of mature femininity is a freeing disposition to affirm, receive, and nurture strength and leadership from worthy men in ways appropriate to a woman’s differing relationships’ (p. 35-36). The understanding portrayed is that men have the capacity to lead and that women do not. The assumption implicit within this understanding is that leadership does not involve affirming and nurturing behaviors.


title: A double-tail sense amplifier for low-voltage SRAM in 28nm technology

text: A double-tail sense amplifier (DTSA) is designed as a drop-in replacement for a conventional SRAM sense amplifier (SA), to enable a robust read operation at low voltages. A pre-amplification stage helps reduce the offset voltage of the sense amplifier by magnifying the input of the regeneration stage. The self-timed regenerative latch simplifies the timing logic so the DTSA can replace the SA with no area overhead. A test chip in 28nm technology achieves 56% error rate reduction at 0.44V. The proposed scheme achieves 50mV of VDDmin reduction compared to commercial SRAM with a faster timing option that demonstrates a smaller bitline swing.


title: Sampling cube: a framework for statistical olap over sampling data

text:
Sampling is a popular method of data collection when it is impossible or too costly to reach the entire population. For example, television show ratings in the United States are gathered from a sample of roughly 5,000 households. To use the results effectively, the samples are further partitioned in a multidimensional space based on multiple attribute values. This naturally leads to the desirability of OLAP (Online Analytical Processing) over sampling data. However, unlike traditional data, sampling data is inherently uncertain, i.e., not representing the full data in the population. Thus, it is desirable to return not only query results but also the confidence intervals indicating the reliability of the results. Moreover, a certain segment in a multidimensional space may contain none or too few samples. This requires some additional analysis to return trustable results.
 In this paper we propose a Sampling Cube framework, which efficiently calculates confidence intervals for any multidimensional query and uses the OLAP structure to group similar segments to increase sampling size when needed. Further, to handle high dimensional data, a Sampling Cube Shell method is proposed to effectively reduce the storage requirement while still preserving query result quality.


title: Diagnostic assessment of the borg MOEA for many-objective product family design problems

text: The recently introduced Borg multiobjective evolutionary algorithm (MOEA) framework features auto-adaptive search that tailors itself to effectively explore different problem spaces. A key auto-adaptive feature of the Borg MOEA is the dynamic allocation of search across a suite of recombination and mutation operators. This study explores the application of the Borg MOEA on a real-world product family design problem: the severely constrained, ten objective General Aviation Aircraft (GAA) problem. The GAA problem represents a promising benchmark problem that strongly highlights the importance of using auto-adaptive search to discover how to exploit multiple recombination strategies cooperatively. The auto-adaptive behavior of the Borg MOEA is rigorously compared against its ancestor algorithm, the ε-MOEA, by employing global sensitivity analysis across each algorithm's feasible parameter ranges. This study provides the first Sobol' sensitivity analysis to determine the individual and interactive parameter sensitivities of MOEAs on a real-world many-objective problem.


title: Foundations of RDF⋆ and SPARQL⋆ (An Alternative Approach to Statement-Level Metadata in RDF)

text: The standard approach to annotate statements in RDF with metadata has a number of shortcomings including data size blow-up and unnecessarily complicated queries. We propose an alternative approach that is based on nesting of RDF triples and of query patterns. The approach allows for a more compact representation of data and queries, and it is backwards compatible with the standard. In this paper we present the formal foundations of our proposal and of different approaches to implement it. More specifically, we formally capture the necessary extensions of the RDF data model and its query language SPARQL, and we define mappings based on which our extended notions can be converted back to ordinary RDF and SPARQL. Additionally, for such type of mappings we define two desirable properties, information preservation and query result equivalence, and we show that the introduced mappings possess these properties.


title: Learning to Decode for Future Success

text: We introduce a general strategy for improving neural sequence generation by incorporating knowledge about the future. Our decoder combines a standard sequence decoder with a ‘soothsayer’ prediction function Q that estimates the outcome in the future of generating a word in the present. Our model draws on the same intuitions as reinforcement learning, but is both simpler and higher performing, avoiding known problems with the use of reinforcement learning in tasks with enormous search spaces like sequence generation. We demonstrate our model by incorporating Q functions that incrementally predict what the future BLEU or ROUGE score of the completed sequence will be, its future length, and the backwards probability of the source given the future target sequence. Experimental results show that future prediction yields improved performance in abstractive summarization and conversational response generation and the state-of-the-art in machine translation, while also enabling the decoder to generate outputs that have specific properties.


title: Aspect extraction for opinion mining with a deep convolutional neural network

text: In this paper, we present the first deep learning approach to aspect extraction in opinion mining. Aspect extraction is a subtask of sentiment analysis that consists in identifying opinion targets in opinionated text, i.e., in detecting the specific aspects of a product or service the opinion holder is either praising or complaining about. We used a 7-layer deep convolutional neural network to tag each word in opinionated sentences as either aspect or non-aspect word. We also developed a set of linguistic patterns for the same purpose and combined them with the neural network. The resulting ensemble classifier, coupled with a word-embedding model for sentiment analysis, allowed our approach to obtain significantly better accuracy than state-of-the-art methods.


title: Design, fabrication and preliminary results of a novel anthropomorphic hand for humanoid robotics: RCH-1

text: Among social infrastructure technologies, robot technology (RT) is expected to play an important role in solving the problems of both decrease of birth rate and increase of elderly people in the 21st century, specially but not only in Japan. In order to achieve this objective, the new generation of personal robots should be capable of a natural communication with humans by expressing human-like emotion. In this sense, human hands play a fundamental role in communication, because they have grasping, sensing and emotional expression ability. This paper presents the recent results of the collaboration between the Takanishi Lab of Waseda University, Tokyo, Japan, and the Arts Lab of Scuola Superiore Sant'Anna, Pisa, Italy, and ROBOCASA. In this paper, the development of a novel anthropomorphic hand for humanoid robotics RCH-1 (ROBOCASA Hand No.1) and its integration into a humanoid robotic platform, named WE-4RII (Waseda Eye No.4 Refined II) is presented.


title: Educational System for the Holy Quran and Its Sciences for Blind and Handicapped People Based on Google Speech API

text: There is a great need to provide educational environments for blind and handicapped people. There are many Islamic websites and applications dedicated to the educational services for the Holy Quran and Its Sciences (Quran Recitations, the interpretations, etc.) on the Internet. Unfortunately, blind and handicapped people could not use these services. These people cannot use the keyboard and the mouse. In addition, the ability to read and write is essential to benefit from these services. In this paper, we present an educational environment that allows these people to take full advantage of the scientific materials. This is done through the interaction with the system using voice commands by speaking directly without the need to write or to use the mouse. Google Speech API is used for the universal speech recognition after a preprocessing and post processing phases to improve the accuracy. For blind people, responses of these commands will be played back through the audio device instead of displaying the text to the screen. The text will be displayed on the screen to help other people make use of the system.


title: Web 3.0 Emerging

text: While Web 3.0 technologies are difficult to define precisely, the outline of emerging applications has become clear over the past year. We can thus essentially view Web 3.0 as semantic Web technologies integrated into, or powering, large-scale Web applications. The base of Web 3.0 applications resides in the resource description framework (RDF) for providing a means to link data from multiple Web sites or databases. With the SPARQL query language, a SQL-like standard for querying RDF data, applications can use native graph-based RDF stores and extract RDF data from traditional databases.


title: Multiple Handshakes Security of TLS 1.3 Candidates

text: The Transport Layer Security (TLS) protocol is by far the most widely deployed protocol for securing communications and the Internet Engineering Task Force (IETF) is currently developing TLS 1.3 as the next-generation TLS protocol. The TLS standard features multiple modes of handshake protocols and supports many combinational running of successive TLS handshakes over multiple connections. Although each handshake mode is now well-understood in isolation, their composition in TLS 1.2 remains problematic, and yet it is critical to obtain practical security guarantees for TLS. In this paper, we present the first formal treatment of multiple handshakes protocols of TLS 1.3 candidates. First, we introduce a multi-level&stage security model, an adaptation of the Bellare-Rogaway authenticated key exchange model, covering all kinds of compositional interactions between different TLS handshake modes and providing reasonably strong security guarantees. Next, we prove that candidate handshakes of TLS 1.3 draft meet our strong notion of multiple handshakes security. Our results confirm the soundness of TLS 1.3 security protection design. Such a multi-level&stage approach is convenient for analyzing the compositional design of the candidates with different session modes, as they establish dependencies of multiple sessions. We also identify the triple handshake attack of Bhargavan et al. on TLS 1.2 within our multiple handshakes security model. We show generically that the proposed fixes (RFC 7627) for TLS 1.2 offer good protection against multiple handshakes attacks.


Improving Design of Systems Supporting Creativity-intensive Processes - A Cross-industry Focus Group Evaluation


title: Exploring Users' Attitudes and Intentions toward the Adoption of Cloud Computing in Saudi Arabia: an Empirical Investigation

text: Over the past few years, cloud computing has evolve d as one of the major advances in the field of Information Technology (IT) utilizing third-party s ervices. Therefore, trust in cloud vendors as well as the determination of potential risks, such as privacy a nd security issues, are crucial for ensuring the su ccessful adoption of an appropriate cloud. Prior research ha ve ddressed the technical aspects of cloud-based environments, such as cloud virtualization, scalabi lity and security. Nevertheless, it is argued that the biggest obstacle of cloud computing is not technolo gical, rather it is perceptual or attitudinal. The adoption of cloud computing has been central to several scho larly research areas, particularly user acceptance. This study presents an extended Technology Acceptance Mo del (TAM), which integrates Trust (TR), Anxiety (ANX) and Perceived Risk (PR), to investigate users ’ attitudes and intentions toward the adoption of c l ud computing. The proposed model was empirically exami ned using the Structural Equation Model (SEM) to analyze data gathered by a survey of both IT profes sionals and end users. The results herein suggested that trust, anxiety and PR can be successfully integrate d within the TAMs. Trust has demonstrated to have a strong positive influence on Perceived Ease of Use (PEOU), but it had no significant effects on Perceived Use fuln ss (PU). Both anxiety and PR were found to have signif icant negative effects on PEOU and PU. In addition, Behavioral Intention (BI) to use the cloud can be p r dicted by trust, attitudes and PU, as PR was show n t have no significant effect on BI. The proposed model, including PR, trust and anxiety , has been demonstrated to be a true predictor of user intentions, toward t he use of cloud computing, within the context of Sa udi Arabia.


title: Software-Defined LANs for Interconnected Smart Environment

text: In this paper, we propose a solution to delegate the control and the management of the network connecting the many devices of a smart environment to a software entity, while keeping end-users in control of what is happening in their networks. For this, we rely on the logical manipulation of all connected devices through device abstraction and network programmability. Applying Software Defined Networking (SDN) principles, we propose a software-based solution that we call Software-Defined LANs in order to interconnect devices of smart environments according to the services the users are requesting or expecting.We define the adequate virtualization framework based on Virtual Objects and Communities of Virtual Objects. Using these virtual entities, we apply the SDN architectural principles to define a generic architecture that can be applied to any smart environment. Then we describe a prototype implementing these concepts in the home networking context, through a scenario in which users of two different homes can easily interconnect two private but shareable DLNA devices in a dedicated video-delivery SD-LAN. Finally we provide a discussion of the benefits and challenges of our approach regarding the generalization of SDN principles, autonomic features, Internet of Things scalability, security and privacy aspects enabled by SD-LANs intrinsic properties.


title: Named entity recognition on Indonesian Twitter posts using long short-term memory networks

text: The task of Named-Entity Recognition (NER) can support the higher-level tasks such as question answering, text summarization, and information retrieval. This work views NER on Indonesian Twitter posts as a sequence labeling problem using supervised machine learning approach. The architecture used is Long Short-Term Memory Networks (LSTMs), with word embedding and POS tag as the model features. As the result, our model can give a performance with an F1 score of 77.08%.


title: Scrutinizing WPA2 Password Generating Algorithms in Wireless Routers

text: A wireless router is a networking device that enables a user to set up a wireless connection to the Internet. A router can offer a secure channel by cryptographic means which provides authenticity and confidentiality. Nowadays, almost all routers use a secure channel by default that is based on Wi-Fi Protected Access II (WPA2). This is a security protocol which is believed not to be susceptible to practical key recovery attacks. However, the passwords should have sufficient entropy to avert bruteforce attacks. In this paper, we compose a strategy on how to reverse-engineer embedded routers. Furthermore, we describe a procedure that can instantly gather a complete wireless authentication trace which enables an offline password recovery attack. Finally, we present a number of use cases where we identify extremely weak password generating algorithms in various routers which are massively deployed in The Netherlands. The algorithms are used to generate the default WPA2 password. Such a password is loaded during device initialization and hardware reset. Users that did not explicitly change their wireless password are most likely vulnerable to practical attacks which can recover their password within minutes. A stolen password allows an adversary to abuse someone else’s internet connection, for instance compromising the firewall, making a fraudulent transaction or performing other criminal activities. Together with the Dutch National Cyber Security Centre we have initiated a responsible disclosure procedure. However, since these routers are also used by many other companies in various countries, our findings seem to relate an international industry wide security issue.


title: A Simplification-Translation-Restoration Framework for Cross-Domain SMT Applications

text: Integration of domain specific knowledge into a general purpose statistical machine translation (SMT) system poses challenges due to insufficient bilingual corpora. In this paper we propose a simplification-translation-restoration (STR) framework for domain adaptation in SMT by simplifying domain specific segments of a text. For an in-domain text, we identify the critical segments and modify them to alleviate the data sparseness problem in the out-domain SMT system. After we receive the translation result, these critical segments are then restored according to the provided in-domain knowledge. We conduct experiments on an English-toChinese translation task in the medical domain and evaluate each step of the STR framework. The translation results show significant improvement of our approach over the out-domain and the naïve in-domain SMT systems. 用於跨領域統計式機器翻譯系統之簡化-翻譯-還原架構


title: A column approximate minimum degree ordering algorithm

text: Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero pattern of A, that limits the worst-case number of nonzeros in the factorization. The fill-in also depends on P, but Q is selected to reduce an upper bound on the fill-in for any subsequent choice of P. The choice of Q can have a dramatic impact on the number of nonzeros in L and U. One scheme for determining a good column ordering for A is to compute a symmetric ordering that reduces fill-in in the Cholesky factorization of ATA. A conventional minimum degree ordering algorithm would require the sparsity structure of ATA to be computed, which can be expensive both in terms of space and time since ATA may be much denser than A. An alternative is to compute Q directly from the sparsity structure of A; this strategy is used by MATLAB's COLMMD preordering algorithm. A new ordering algorithm, COLAMD, is presented. It is based on the same strategy but uses a better ordering heuristic. COLAMD is faster and computes better orderings, with fewer nonzeros in the factors of the matrix.


title: Simultaneous map building and localization for an autonomous mobile robot

text: In this paper, we discuss a significant open problem in mobile robotics: simultaneous map building and localization, which we define as long-term globally referenced position estimation without a priori information. This problem is difficult because of the following paradox: to move precisely, a mobile robot must have an accurate environment map; however, to build an accurate map, the mobile robot’s sensing locations must be known precisely. In this way, simultaneous map building and localization can be seen to present a question of “which came first, the chicken or the egg?” (The map or the motion?) When using ultrasonic sensing, to overcome this issue we equip the vehicle with multiple servo-mounted sonar sensors, to provide a means in which a subset of environment features can be precisely learned from the robot’s initial location and subsequently tracked to provide precise positioning.


title: Stiffness analysis for a 3-PUU parallel kinematic machine

text: This paper presents the stiffness characteristics of a three-prismatic-universal–universal (3-PUU) translational parallel kinematic machine (PKM). The stiffness matrix is derived intuitively based upon an alternative approach considering actuations and constraints, and the compliances subject to both actuators and legs are involved in the stiffness model. The stiffness performance of the manipulator is evaluated by utilizing the extremum stiffness values, and the influences of design parameters on the stiffness properties are presented, which will be valuable for the architecture design of a 3-PUU PKM. Moreover, the stiffness behavior of the PKM is investigated via the eigenscrew decomposition of the stiffness matrix, which provides a physical interpretation of the PKM stiffness and allows the identification of the stiffness center and compliant axis. 2007 Elsevier Ltd. All rights reserved.


title: Controllable Text Generation

text: Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible natural language sentences, whose attributes are dynamically controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders and holistic attribute discriminators for effective imposition of semantic structures. With differentiable approximation to discrete text samples, explicit constraints on independent attribute controls, and efficient collaborative learning of generator and discriminators, our model learns highly interpretable representations from even only word annotations, and produces realistic sentences with desired attributes. Quantitative evaluation validates the accuracy of sentence and attribute generation.


title: Large-scale visual sentiment ontology and detectors using adjective noun pairs

text: We address the challenge of sentiment analysis from visual content. In contrast to existing methods which infer sentiment or emotion directly from visual low-level features, we propose a novel approach based on understanding of the visual concepts that are strongly related to sentiments. Our key contribution is two-fold: first, we present a method built upon psychological theories and web mining to automatically construct a large-scale Visual Sentiment Ontology (VSO) consisting of more than 3,000 Adjective Noun Pairs (ANP). Second, we propose SentiBank, a novel visual concept detector library that can be used to detect the presence of 1,200 ANPs in an image. The VSO and SentiBank are distinct from existing work and will open a gate towards various applications enabled by automatic sentiment analysis. Experiments on detecting sentiment of image tweets demonstrate significant improvement in detection accuracy when comparing the proposed SentiBank based predictors with the text-based approaches. The effort also leads to a large publicly available resource consisting of a visual sentiment ontology, a large detector library, and the training/testing benchmark for visual sentiment analysis.


title: A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo

text: Nanoscale robots have potential as intelligent drug delivery systems that respond to molecular triggers. Using DNA origami we constructed an autonomous DNA robot programmed to transport payloads and present them specifically in tumors. Our nanorobot is functionalized on the outside with a DNA aptamer that binds nucleolin, a protein specifically expressed on tumor-associated endothelial cells, and the blood coagulation protease thrombin within its inner cavity. The nucleolin-targeting aptamer serves both as a targeting domain and as a molecular trigger for the mechanical opening of the DNA nanorobot. The thrombin inside is thus exposed and activates coagulation at the tumor site. Using tumor-bearing mouse models, we demonstrate that intravenously injected DNA nanorobots deliver thrombin specifically to tumor-associated blood vessels and induce intravascular thrombosis, resulting in tumor necrosis and inhibition of tumor growth. The nanorobot proved safe and immunologically inert in mice and Bama miniature pigs. Our data show that DNA nanorobots represent a promising strategy for precise drug delivery in cancer therapy.


title: 60-GHz Millimeter-Wave Life Detection System (MLDS) for Noncontact Human Vital-Signal Monitoring

text: A first reported experimental study of a 60 GHz millimeter-wave life detection system (MLDS) for noncontact human vital-signal monitoring is presented. This detection system is constructed by using V-band millimeter-wave waveguide components. A clutter canceller is implemented in the system with an adjustable attenuator and phase shifter. It performs clutter cancellation for the transmitting power leakage from the circulator and background reflection to enhance the detecting sensitivity of weak vital signals. The noncontact vital signal measurements have been conducted on a human subject in four different physical orientations from distances of 1 and 2 m. The time-domain and spectrum waveforms of the measured breathing and heartbeat are presented. This prototype system will be useful for the development of the 60-GHz CMOS MLDS detector chip design.


title: Working-memory capacity protects model-based learning from stress.

text: Accounts of decision-making have long posited the operation of separate, competing valuation systems in the control of choice behavior. Recent theoretical and experimental advances suggest that this classic distinction between habitual and goal-directed (or more generally, automatic and controlled) choice may arise from two computational strategies for reinforcement learning, called model-free and model-based learning. Popular neurocomputational accounts of reward processing emphasize the involvement of the dopaminergic system in model-free learning and prefrontal, central executive-dependent control systems in model-based choice. Here we hypothesized that the hypothalamic-pituitary-adrenal (HPA) axis stress response--believed to have detrimental effects on prefrontal cortex function--should selectively attenuate model-based contributions to behavior. To test this, we paired an acute stressor with a sequential decision-making task that affords distinguishing the relative contributions of the two learning strategies. We assessed baseline working-memory (WM) capacity and used salivary cortisol levels to measure HPA axis stress response. We found that stress response attenuates the contribution of model-based, but not model-free, contributions to behavior. Moreover, stress-induced behavioral changes were modulated by individual WM capacity, such that low-WM-capacity individuals were more susceptible to detrimental stress effects than high-WM-capacity individuals. These results enrich existing accounts of the interplay between acute stress, working memory, and prefrontal function and suggest that executive function may be protective against the deleterious effects of acute stress.


title: A Scoring Function for Learning Bayesian Networks based on Mutual Information and Conditional Independence Tests

text: We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.


title: Temporally Grounding Natural Sentence in Video

text: We introduce an effective and efficient method that grounds (i.e., localizes) natural sentences in long, untrimmed video sequences. Specifically, a novel Temporal GroundNet (TGN)1 is proposed to temporally capture the evolving fine-grained frame-by-word interactions between video and sentence. TGN sequentially scores a set of temporal candidates ended at each frame based on the exploited frameby-word interactions, and finally grounds the segment corresponding to the sentence. Unlike traditional methods treating the overlapping segments separately in a sliding window fashion, TGN aggregates the historical information and generates the final grounding result in one single pass. We extensively evaluate our proposed TGN on three public datasets with significant improvements over the stateof-the-arts. We further show the consistent effectiveness and efficiency of TGN through an ablation study and a runtime test.


title: Coping through emotional approach: scale construction and validation.

text: Four studies demonstrate the psychometric adequacy and validity of scales designed to assess coping through emotional approach. In separate undergraduate samples, exploratory and confirmatory factor analyses of dispositional (Study 1) and situational (Study 3) coping item sets yielded 2 distinct emotional approach coping factors: emotional processing (i.e., active attempts to acknowledge and understand emotions) and emotional expression. The 2 scales yielded high internal consistency and test-retest reliability, as well as convergent and discriminant validity. A study (Study 2) of young adults and their parents established the scales' interjudge reliabilities. Longitudinal (Study 3) and experimental (Study 4) research supported the predictive validity of the emotional approach coping scales with regard to adjustment to stressful encounters. Findings highlight the utility of functionalist theories of emotion as applied to coping theory.


title: Lévy flights and random searches

text: In this work we discuss some recent contributions to the random search problem. Our analysis includes superdiffusive Lévy processes and correlated random walks in several regimes of target site density, mobility and revisitability. We present results in the context of mean-field-like and closed-form average calculations, as well as numerical simulations. We then consider random searches performed in regular lattices and lattices with defects, and we discuss a necessary criterion for distinguishing true superdiffusion from correlated random walk processes. We invoke energy considerations in relation to critical survival states on the edge of extinction, and we analyze the emergence of Lévy behavior in deterministic search walks. Finally, we comment on the random search problem in the context of biological foraging. PACS numbers: 05.50.Fb, 05.40.−a


title: Jerk-bounded manipulator trajectory planning: design for real-time applications

text: An online method for obtaining smooth, jerk-bounded trajectories has been developed and implemented. Jerk limitation is important in industrial robot applications, since it results in improved path tracking and reduced wear on the robot. The method described herein uses a concatenation of fifth-order polynomials to provide a smooth trajectory between two way points. The trajectory approximates a linear segment with parabolic blends trajectory. A sine wave template is used to calculate the end conditions (control points) for ramps from zero acceleration to nonzero acceleration. Joining these control points with quintic polynomials results in a controlled quintic trajectory that does not oscillate, and is near time optimal for the jerk and acceleration limits specified. The method requires only the computation of the quintic control points, up to a maximum of eight points per trajectory way point. This provides hard bounds for online motion algorithm computation time. A method for blending these straight-line trajectories over a series of way points is also discussed. Simulations and experimental results on an industrial robot are presented.


title: Residual Reconstruction for Block-Based Compressed Sensing of Video

text: A simple block-based compressed-sensing reconstruction for still images is adapted to video. Incorporating reconstruction from a residual arising from motion estimation and compensation, the proposed technique alternatively reconstructs frames of the video sequence and their corresponding motion fields in an iterative fashion. Experimental results reveal that the proposed technique achieves significantly higher quality than a straightforward reconstruction that applies a still-image reconstruction independently frame by frame, a 3D reconstruction that exploits temporal correlation between frames merely in the form of a motion-agnostic 3D transform, and a similar, yet non-iterative, motion-compensated residual reconstruction.


title: Nonclassical Computation - A Dynamical Systems Perspective

text: In this chapter we investigate computation from a dynamical systems perspective. A dynamical system is described in terms of its abstract state space, the system’s current state within its state space, and a rule that determines its motion through its state space. In a classical computational system, that rule is given explicitly by the computer program; in a physical system, that rule is the underlying physical law governing the behaviour of the system. So a dynamical systems approach to computation allows us to take a unified view of computation in classical discrete systems and in systems performing non-classical computation. In particular, it gives a route to a computational interpretation of physical embodied systems exploiting the natural dynamics of their material substrates. We start with autonomous (closed) dynamical systems: those whose dynamics is not an explicit function of time, in particular, those with no inputs from an external environment. We begin with computationally conventional discrete systems, examining their computational abilities from a dynamical systems perspective. The aim here is both to introduce the necessary dynamical systems concepts, and to demonstrate how classical computation can be viewed from this perspective. We then move on to continuous dynamical systems, such as those inherent in the complex dynamics of matter, and show how these too can be interpreted computationally, and see how the material embodiment can give such computation “for free”, without the need to explicitly implement the dynamics. We next broaden the outlook to open (non-autonomous) dynamical systems, where the dynamics is a function of time, in the form of inputs from an external environment, and which may be in a closely coupled feedback loop with that environment. We finally look at constructive, or developmental, dynamical systems, where the structure of the state space is changing during the computation. This includes vari-


title: Energy-aware wireless microsensor networks

text: Self-configuring wireless sensor networks can be invaluable in many civil and military applications for collecting, processing, and disseminating wide ranges of complex environmental data. Because of this, they have attracted considerable research attention in the last few years. The WINS [1] and SmartDust [2] projects, for instance, aim to integrate sensing, computing, and wireless communication capabilities into a small form factor to enable low-cost production of these tiny nodes in large numbers. Several other groups are investigating efficient hardware/software system architectures, signal processing algorithms, and network protocols for wireless sensor networks [3]-[5]. Sensor nodes are battery driven and hence operate on an extremely frugal energy budget. Further, they must have a lifetime on the order of months to years, since battery replacement is not an option for networks with thousands of physically embedded nodes. In some cases, these networks may be required to operate solely on energy scavenged from the environment through seismic, photovoltaic, or thermal conversion. This transforms energy consumption into the most important factor that determines sensor node lifetime. Conventional low-power design techniques [6] and hardware architectures only provide point solutions which are insufficient for these highly energy-constrained systems. Energy optimization, in the case of sensor networks, is much more complex, since it involves not only reducing the energy consumption of a single sensor node but also maximizing the lifetime of an entire network. The network lifetime can be maximized


title: Differences in procrastination and motivation between undergraduate and graduate students

text:
Procrastination became increasingly prevalent among students in recent years. However, little research was found that directly compares academic procrastination across different academic grade levels.
  The
  present study used a self-regulated learning perspective to compare procrastination types and associated motivation between undergraduate and graduate students. Sixty-six undergraduate and sixty-eight graduate students responded to a packet of questionnaires concerning their experience in an educational psychology class. The results show that students’ beliefs about the usefulness of procrastination were a better predictor of academic procrastination than self-efficacy beliefs and achievement goal orientations. Student age was related to procrastination types. Among the undergraduate procrastinators, the younger students were more likely to engage in active procrastination while the older students tended to engage in passive procrastination. Implications and future research directions are discussed.


title: OpenPiton: An Open Source Manycore Research Framework

text: Industry is building larger, more complex, manycore processors on the back of strong institutional knowledge, but academic projects face difficulties in replicating that scale. To alleviate these difficulties and to develop and share knowledge, the community needs open architecture frameworks for simulation, synthesis, and software exploration which support extensibility, scalability, and configurability, alongside an established base of verification tools and supported software. In this paper we present OpenPiton, an open source framework for building scalable architecture research prototypes from 1 core to 500 million cores. OpenPiton is the world's first open source, general-purpose, multithreaded manycore processor and framework. OpenPiton leverages the industry hardened OpenSPARC T1 core with modifications and builds upon it with a scratch-built, scalable uncore creating a flexible, modern manycore design. In addition, OpenPiton provides synthesis and backend scripts for ASIC and FPGA to enable other researchers to bring their designs to implementation. OpenPiton provides a complete verification infrastructure of over 8000 tests, is supported by mature software tools, runs full-stack multiuser Debian Linux, and is written in industry standard Verilog. Multiple implementations of OpenPiton have been created including a taped-out 25-core implementation in IBM's 32nm process and multiple Xilinx FPGA prototypes.


title: Inertial properties of the human trunk of males determined from magnetic resonance imaging

text: The purpose of this study was to evaluate the segmental parameters of the human trunk of malesin vivo using magnetic resonance imaging (MRI). In addition, the efficacy of volumetric estimation and existing prediction formulas to produce segmental properties similar to those produced by MRI was evaluated. As opposed to finding one representative normal value for these parameters, a range of normal values was defined. For instance, the average trunk mass was 42.2%±3.5% (x±SD) of body mass, but values ranged from 35.8% to 48.0%. To account for segment parameters more accurately, specific anthropometric measures need to be considered in addition to overall measures of body height and mass. These specific measures included segment length, circumference, width, and depth. Studies reporting general percentages based on height and/or mass were found to be inadequate predictors of segmental parameters of the trunk compared with MRI estimates. Volumebased estimates, which assume a uniform density distribution within a segment, were found to correspond closely to MRI values except for the thorax. However, the use of density values reflective of the livingin vivo state would likely alleviate this disparity, thus indicating that the volumetric technique may be effective for deriving segmental parameters for large segments of the trunk. Future research should adopt noninvasive techniques such as MRI and/or volumetric estimation to enhance the predictability of segmental parameters of the body for specific population groups characterized by gender, developmental age, body type, and fitness level. Further efforts should be made to establish standardized boundary definitions for trunk segments to avoid unnecessary confusion, from which substantial errors may be introduced into biomechanical linked-segment analyses of human movement.


title: A New Dataset for Fine-Grained Citation Field Extraction

text: Citation field extraction entails segmenting a citation string into its constituent parts, such as title, authors, publisher and year. Despite the importance of this task, there is a lack of well-annotated citation data. This paper presents a new labeled dataset for citation extraction that, in comparison to the previous standard dataset, exceeds four-times more data, supplies detailed nested labels rather than coarse-grained flat labels, and is derived from four different academic fields rather than one. We describe our new dataset in detail, and provide baseline experimental results from a state-of-the-art extraction method.


title: Learning Feature Pyramids for Human Pose Estimation

text: Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multibranch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.


title: Treating patellar tendinopathy with Fascial Manipulation.

text: According to Fascial Manipulation theory, patellar tendon pain is often due to uncoordinated quadriceps contraction caused by anomalous fascial tension in the thigh. Therefore, the focus of treatment is not the patellar tendon itself, but involves localizing the cause of this incoordination, considered to be within the muscular fascia of the thigh region. Eighteen patients suffering from patellar tendon pain were treated with the Fascial Manipulation technique. Pain was assessed (in VAS) before (VAS 67.8/100) and after (VAS 26.5/100) treatment, plus a follow-up evaluation at 1 month (VAS 17.2/100). Results showed a substantial decrease in pain immediately after treatment (p<0.0001) and remained unchanged or improved in the short term. The results show that the patellar tendon may be only the zone of perceived pain and that interesting results can be obtained by treating the muscular fascia of the quadriceps muscle, whose alteration may cause motor incoordination and subsequent pathology.


title: Automatic question answering using the web: Beyond the Factoid

text: In this paper we describe and evaluate a Question Answering (QA) system that goes beyond answering factoid questions. Our approach to QA assumes no restrictions on the type of questions that are handled, and no assumption that the answers to be provided are factoids. We present an unsupervised approach for collecting question and answer pairs from FAQ pages, which we use to collect a corpus of 1 million question/answer pairs from FAQ pages available on the Web. This corpus is used to train various statistical models employed by our QA system: a statistical chunker used to transform a natural language-posed question into a phrase-based query to be submitted for exact match to an off-the-shelf search engine; an answer/question translation model, used to assess the likelihood that a proposed answer is indeed an answer to the posed question; and an answer language model, used to assess the likelihood that a proposed answer is a well-formed answer. We evaluate our QA system in a modular fashion, by comparing the performance of baseline algorithms against our proposed algorithms for various modules in our QA system. The evaluation shows that our system achieves reasonable performance in terms of answer accuracy for a large variety of complex, non-factoid questions.


title: A simulation-based software design framework for network-centric and parallel systems

text: In this paper we discuss a software design framework that is capable of realizing network-centricity and the rising multicore technology. Instead of producing static design documents in the form of UML diagrams, we propose automatic generation of a visual simulation model, which represents the target system design. We discuss a design environment that is responsible for the generation and execution of the simulation model.


title: Computational systems biology

text: To understand complex biological systems requires the integration of experimental and computational research — in other words a systems biology approach. Computational biology, through pragmatic modelling and theoretical exploration, provides a powerful foundation from which to address critical scientific questions head-on. The reviews in this Insight cover many different aspects of this energetic field, although all, in one way or another, illuminate the functioning of modular circuits, including their robustness, design and manipulation. Computational systems biology addresses questions fundamental to our understanding of life, yet progress here will lead to practical innovations in medicine, drug discovery and engineering.


title: A GPU implementation of inclusion-based points-to analysis

text:
Graphics Processing Units (GPUs) have emerged as powerful accelerators for many regular algorithms that operate on dense arrays and matrices. In contrast, we know relatively little about using GPUs to accelerate highly irregular algorithms that operate on pointer-based data structures such as graphs. For the most part, research has focused on GPU implementations of graph analysis algorithms that do not modify the structure of the graph, such as algorithms for breadth-first search and strongly-connected components.
 In this paper, we describe a high-performance GPU implementation of an important graph algorithm used in compilers such as gcc and LLVM: Andersen-style inclusion-based points-to analysis. This algorithm is challenging to parallelize effectively on GPUs because it makes extensive modifications to the structure of the underlying graph and performs relatively little computation. In spite of this, our program, when executed on a 14 Streaming Multiprocessor GPU, achieves an average speedup of 7x compared to a sequential CPU implementation and outperforms a parallel implementation of the same algorithm running on 16 CPU cores.
 Our implementation provides general insights into how to produce high-performance GPU implementations of graph algorithms, and it highlights key differences between optimizing parallel programs for multicore CPUs and for GPUs.


title: KD-tree acceleration structures for a GPU raytracer

text: Modern graphics hardware architectures excel at compute-intensive tasks such as ray-triangle intersection, making them attractive target platforms for raytracing. To date, most GPU-based raytracers have relied upon uniform grid acceleration structures. In contrast, the kd-tree has gained widespread use in CPU-based raytracers and is regarded as the best general-purpose acceleration structure. We demonstrate two kd-tree traversal algorithms suitable for GPU implementation and integrate them into a streaming raytracer. We show that for scenes with many objects at different scales, our kd-tree algorithms are up to 8 times faster than a uniform grid. In addition, we identify load balancing and input data recirculation as two fundamental sources of inefficiency when raytracing on current graphics hardware.


title: Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU

text: Recent advances in computing have led to an explosion in the amount of data being generated. Processing the ever-growing data in a timely manner has made throughput computing an important aspect for emerging applications. Our analysis of a set of important throughput computing kernels shows that there is an ample amount of parallelism in these kernels which makes them suitable for today's multi-core CPUs and GPUs. In the past few years there have been many studies claiming GPUs deliver substantial speedups (between 10X and 1000X) over multi-core CPUs on these kernels. To understand where such large performance difference comes from, we perform a rigorous performance analysis and find that after applying optimizations appropriate for both CPUs and GPUs the performance gap between an Nvidia GTX280 processor and the Intel Core i7-960 processor narrows to only 2.5x on average. In this paper, we discuss optimization techniques for both CPU and GPU, analyze what architecture features contributed to performance differences between the two architectures, and recommend a set of architectural features which provide significant improvement in architectural efficiency for throughput kernels.


title: Guaranteed-Quality Mesh Generation for Curved Surfaces

text: For several commonly-used solution techniques for partial differential equations, the first step is to divide the problem region into simply-shaped elements, creating a mesh. We present a technique for creating high-quality triangular meshes for regions on curved surfaces. This technique is an extension of previous methods we developed for regions in the plane. For both flat and curved surfaces, the resulting meshes are guaranteed to exhibit the following properties: (1) internal and external boundaries are respected, (2) element shapes are guaranteed—all elements are triangles with angles between 30 and 120 degrees (with the exception of badly shaped elements that may be required by the specified boundary), and (3) element density can be controlled, producing small elements in “interesting” areas and large elements elsewhere. An additional contribution of this paper is the development of a practical generalization of Delaunay triangulation to curved surfaces.


title: Bluetooth Based Home Automation and Security System Using ARM 9

text: -Today we are living in 21 century where automation is playing important role in human life. Home automation allows us to control household appliances like light, door, fan, AC etc. It also provides home security and emergency system to be activated. Home automation not only refers to reduce human efforts but also energy saving and time efficiency. The main objective of home automation and security is to help handicapped and old aged people who will enable them to control home appliances and alert them in critical situations. This paper put forwards the design of home automation and security system using ARM7 LPC2148 board. The design is based on a standalone embedded system board ARM7 LPC2148 at home. Home appliances are connected to the ARM7 and communication is established between the ARM7 and ARM9 with Bluetooth device. The home appliances are connected to the input / output ports of the embedded system board and their status is passed to the ARM7. We would develop an authentication to the system for authorized person to access home appliances. The device with low cost and scalable to less modification to the core is much important. It presents the design and implementation of automation system that can monitor and control home appliances via ARM9 S3C2440A board.


title: Player Profiling with Fallout 3

text: In previous research we concluded that a personality profile, based on the Five Factor Model, can be constructed from observations of a player’s behavior in a module that we designed for Neverwinter Nights (Lankveld et al. 2011a). In the present research, we investigate whether we can do the same thing in an actual modern commercial video game, in this case the game Fallout 3. We stored automatic observations on 36 participants who played the introductory stages of Fallout 3. We then correlated these observations with the participants’ personality profiles, expressed by values for five personality traits as measured by the standard NEO-FFI questionnaire. Our analysis shows correlations between all five personality traits and the game observations. These results validate and generalize the results from our previous research (Lankveld et al. 2011a). We may conclude that Fallout 3, and by extension other modern video games, allows players to express their personality, and can therefore be used to create person-


title: Future trends of electrical propulsion and implications to ship design

text: Over the last 10 years the electrical propulsion fleet grew three times faster than the world fleet. This paper provides an overlook of the main drivers supporting this impressive growth and explores supporting drivers for future growth. The paper also explores two emerging technologies in electrical propulsion and distribution and how these influence ship design: DC distribution and energy storage. with a considerable level of accuracy which led to development of Dynamic Positioning (DP). Since electric propulsion is the most suitable solution to cope with the power fluctuation and redundancy requirements from DP systems, the development of offshore segment was the biggest driver for the development of electrical propulsion, reaching more than 50% of the units in 2013. Figure 2 Distribution of electrical propulsion fleet Dredgers – Dredging equipment requires a considerable amount of power and consequently the auxiliary power tends to be quite high, very often higher than the propulsion power. Therefore, the merit of electric propulsion is to reduce the overall installed power on board. Cruise – The main driver for electrical propulsion is space saving and space optimization. By shifting into electrical propulsion it is possible to reduce the space occupied by main propulsion systems and above all to place the different equipment in a way that the spaces reserved for passengers are maximized in terms of volume and quality. Nevertheless, the redundancy levels offered by electrical propulsion and its ability to meet the regulations on this segment have also contributed significantly for the adoption of this technology. Survey vessels – These vessels are often used to tow underwater survey devices for different applications, e.g. hydrographic, seismic, etc. Therefore, the lower underwater noise offered by electrical propulsion is very attractive. In addition, the load requirements of the different operation modes can vary significantly which also favor electric propulsion. LNG carriers – When laden, LNG carriers use their own cargo as fuel which is released by the cargo containment system in a process known as boil-off. Therefore, the propulsion system is selected so that the boil-off gas can be used as fuel. Initially, most vessels were fitted with steam turbines connected to the propulsion shaft through a gearbox. However, the introduction of dual fuel engines led to the adoption of electric propulsion (Hansen et al. 2007). Icebreakers – Although traditional icebreakers use their own weight to break sheets of ice, it is quite often to have blocks of ice reaching the propellers. This causes a sudden drop of the propeller speed and consequently a sudden increase of torque which could easily stall the propulsion engine with mechanical drive. Electrical drives are able to vary the speed and the torque independently and therefore maintain the torque within the engine’s acceptable limits (Ådnanes, 2003).


title: Fast and Scalable Learning of Neuro-Symbolic Representations of Biomedical Knowledge

text: In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neurosymbolic representation learning, we show how to train fast (under 1 minute) log-linear neural embeddings of the entities. We utilize these representations as inputs for machine learning classifiers to enable important tasks such as biological link prediction. Classifiers are trained by concatenating learned entity embeddings to represent entity relations, and training classifiers on the concatenated embeddings to discern true relations from automatically generated negative examples. Our simple embedding methodology greatly improves on classification error compared to previously published state-of-the-art results, yielding a maximum increase of +0.28 F-measure and +0.22 ROC AUC scores for the most difficult biological link prediction problem. Finally, our embedding approach is orders of magnitude faster to train (≤ 1 minute vs. hours), much more economical in terms of embedding dimensions (d = 50 vs. d = 512), and naturally encodes the directionality of the asymmetric biological relations, that can be controlled by the order with which we concatenate the embeddings.


title: TensorLog: Deep Learning Meets Probabilistic DBs

text: We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neuralnetwork infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning frameworks to be used for tuning the parameters of a probabilistic logic. Experimental results show that TensorLog scales to problems involving hundreds of thousands of knowledge-base triples and tens of thousands of examples.