title: A simulation as a service cloud middleware

abstract: Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloud-based Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.


title: Interval type-2 fuzzy logic systems: theory and design

abstract: We present the theory and design of interval type-2 fuzzy logic systems (FLSs). We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs: one that is based on a general inference formula for them. We introduce the concept of upper and lower membership functions (MFs) and illustrate our efficient inference method for the case of Gaussian primary MFs. We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting when a nonstationary time-series is corrupted by additive noise where SNR is uncertain and demonstrate an improved performance over type-1 FLSs.


title: A Data Mining Approach to Generating Network Attack Graph for Intrusion Prediction

abstract: A network attack graph provides a global view of all possible sequences of exploits which an intruder may use to penetrate a system. Attack graphs can be generated by model checking techniques or intrusion alert correlation. In this paper we proposed a data mining approach to generating attack graphs. Through association rule mining, the algorithm generates multi-step attack patterns from historical intrusion alerts which comprise the attack graphs. The algorithm also calculates the predictability of each attack scenario in the attack graph which represents the probability for the corresponding attack scenario to be the precursor of future attacks. Then the real-time intrusion alerts can be correlated to attack scenarios and ranked by the predictability scores. The ranking result can help identify the appropriate evidence for intrusion prediction from a large volume of raw intrusion alerts. The approach is validated by DARPA 2000 and DARPA 1999 intrusion detection evaluation datasets.


title: Determinants of mobile consumers' perceived value of location-based advertising and user responses

abstract: This study examines factors affecting mobile consumers' perceived advertising value of latest location-based mobile advertising LBA and its relationship with user responses. Data was collected from a nationally representative web survey of 605 respondents who fit the demographic quotas of mobile consumers in Singapore. Partial least squares results show that privacy concerns have a stronger negative effect on perceived LBA value than perceived sacrifice, while perceived utility, utilisation of contextual information and trust positively predict perceived LBA value. Moreover, perceived LBA value increases consumers' responses to purchase advertised brands, followed by searching for brand information and passing LBA to others. Implications for mobile advertising theories and practices are discussed.


title: Spontaneous Splenic Rupture in Vascular Ehlers-Danlos Syndrome

abstract: Vascular Ehlers-Danlos Syndrome (VEDS) is a rare autosomal dominant collagen vascular disorder. Different from other Ehler-Danlos Syndrome subtypes, VEDS has poor prognosis due to severe fragility of connective tissues and association with life-threatening vascular and gastrointestinal complications. Spontaneous splenic rupture is a rare but hazardous complication related to this syndrome. To date, only 2 cases have been reported in the literature. Here we present another case of this uncommon complication, occurring in a 54-year-old woman in clinical follow-up for VEDS who presented with sudden onset of abdominal pain and hypotension.


title: Analytical analysis of the Pennes bioheat transfer equation with sinusoidal heat flux condition on skin surface

abstract: Abstract This study focuses on the effect of the temperature response of a semi-infinite biological tissue due to a sinusoidal heat flux at the skin. The Pennes bioheat transfer equation such as ρ t c t (∂ T /∂ t )+ W b c b ( T − T a )= k ∂ 2 T /∂ x 2 with the oscillatory heat flux boundary condition such as q (0, t )= q 0 e iωt was investigated. By using the Laplace transform, the analytical solution of the Pennes bioheat transfer equation with surface sinusoidal heating condition is found. This analytical expression is suitable for describing the transient temperature response of tissue for the whole time domain from the starting periodic oscillation to the final steady periodic oscillation. The results show that the temperature oscillation due to the sinusoidal heating on the skin surface is unstable in the initial period. Further, it is unavailable to predict the blood perfusion rate via the phase shifting between the surface heat flux and the surface temperature. Moreover, the lower frequency of sinusoidal heat flux on the skin surface induces a more sensitive phase shift response to the blood perfusion rate change, but extends the beginning time of sampling because of the avoidance of the unavailable first cyclic oscillation.


title: DepthCN: Vehicle detection using 3D-LIDAR and ConvNet

abstract: This paper addresses the problem of vehicle detection using Deep Convolutional Neural Network (ConvNet) and 3D-LIDAR data with application in advanced driver assistance systems and autonomous driving. A vehicle detection system based on the Hypothesis Generation (HG) and Verification (HV) paradigms is proposed. The data inputted to the system is a point cloud obtained from a 3D-LIDAR mounted on board an instrumented vehicle, which is transformed to a Dense-depth Map (DM). The proposed solution starts by removing ground points followed by point cloud segmentation. Then, segmented obstacles (object hypotheses) are projected onto the DM. Bounding boxes are fitted to the segmented objects as vehicle hypotheses (the HG step). Finally, the bounding boxes are used as inputs to a ConvNet to classify/verify the hypotheses of belonging to the category ‘vehicle’ (the HV step). In this paper, we present an evaluation of ConvNet using LIDAR-based DMs and also the impact of domain-specific data augmentation on vehicle detection performance. To train and to evaluate the proposed vehicle detection system, the KITTI Benchmark Suite was used.


title: Intent detection and semantic parsing for navigation dialogue language processing

abstract: Voice-based human-machine interface has become a prevalent feature for modern intelligent vehicles, especially in navigation and infotainment applications. Automatic Speech Recognition (ASR) converts spoken audio streams to plain texts, but a follow-up Natural Language Processing (NLP) sub-system is needed to understand the contextual meaning from the text and act. For the specific human-vehicle navigation dialogue application, the two major tasks include (1) intent detection — decide whether a sentence is navigation-related, and (2) semantic parsing — retrieve important information (e.g., point-of-interest destinations) from the words. To address these two tasks, this study proposes a Recurrent Neural Network (RNN) architecture, with the consideration of (1) one joint model vs. two separate models, (2) a context window approach vs. sequence-to-sequence translation approach, as well as (3) alternate model hyper-parameter selections. The experiment is conducted with both the benchmark ATIS dataset and the CU-Move in-vehicle dialogue corpus, and the result is compared against related state-of-the-art methods. Overall, the proposed solution on the CU-Move data achieves accuracies of 98.24% for intent detection and 99.60% for semantic parsing, outperforming other related methods.


title: Mitigating risk in ecommerce transactions: perceptions of information credibility and the role of user-generated ratings in product quality and purchase intention

abstract: Although extremely popular, electronic commerce environments often lack information that has traditionally served to ensure trust among exchange partners. Digital technologies, however, have created new forms of “electronic word-of-mouth,” which offer new potential for gathering credible information that guides consumer behaviors. We conducted a nationally representative survey and a focused experiment to assess how individuals perceive the credibility of online commercial information, particularly as compared to information available through more traditional channels, and to evaluate the specific aspects of ratings information that affect people’s attitudes toward ecommerce. Survey results show that consumers rely heavily on web-based information as compared to other channels, and that ratings information is critical in the evaluation of the credibility of online commercial information. Experimental results indicate that ratings are positively associated with perceptions of product quality and purchase intention, but that people attend to average product ratings, but not to the number of ratings or to the combination of the average and the number of ratings together. Thus suggests that in spite of valuing the web and ratings as sources of commercial information, people use ratings information suboptimally by potentially privileging small numbers of ratings that could be idiosyncratic. In addition, product quality is shown to mediate the relationship between user ratings and purchase intention. The practical and theoretical implications of these findings are considered for ecommerce scholars, consumers, and vendors.


title: EXAMINING A THEORY OF PLANNED BEHAVIOR (TPB) AND TECHNOLOGY ACCEPTANCE MODEL (TAM) IN INTERNETPURCHASING USING STRUCTURAL EQUATION MODELING

abstract: ABSTRACTInternet purchasing has been predicted to escalate with the increase of internet users around the globe. In line with the increase of users, it has been estimated that e-commerce spending would also amplify. In spite of the world internet potential, actual number of internet users who purchased online has declined. Thus, our study intends to investigate the drivers of internet purchasing based on the integration of theory of planned behavior (TPB) and technology acceptance model (TAM). By integrating TPB and TAM, this study examines the relationships between attitude, subjective norm, perceived behavior control, perceived usefulness and perceived ease of use toward intention and internet purchasing behavior. Data were collected from 304 university students via questionnaires. The analysis produced four structural models: hypothesized, re-specified, TPB competing and TAM competing models. It shows that hypothesized model created four significant direct impacts, re-specified model found three significant direct impacts, TPB competing model supported three direct impacts and TAM competing model supported four direct impacts. It seems that the direct impact of subjective norms on intention was consistently significant across three models namely, hypothesized, re-specified and TPB competing models. Conversely, the path from attitude to intention was consistently insignificant across the same three models. Other direct paths reveal inconsistent relationships between differing structural models. For mediating effects of intention on each hypothesized paths, we found two partial mediating effects of intention. The first effect was the partial mediating effects of intention on the relationship between attitude and behavior in TPB competing model. The second was the partial mediating effect of intention on the relationship between perceived usefulness and behavior in TAM. Mediating effects were not substantiated in hypothesized and revised model. Lastly, among the four structural models, revised model achieved the highest SMC (R^sup 2^), explaining 62.9% variance in internet purchasing behavior, followed by Theory of Planned Behavior (TPB) and Technology Acceptance model (TAM). According, hypothesized model obtained the lowest R^sup 2^ of 55% variance in internet purchasing behavior. The findings are discussed in the context of the internet purchasing behavior and intention in Malaysia.Keywords: Structural Equation Modeling, TPB, TAM, Internet Purchasing Behavior1. INTRODUCTION:Internet purchasing has been predicted to escalate with the increase of internet users around the globe. For example internet users worldwide has escalated from 655 million in 2002 to 941 million users in 2005 (Dholakia and Uusitalo, 2002). In Asia Pacific, it is predicted that there could be 242 million internet users in 2005 (Taylor, 2002). In line with the increase of users, it has been estimated that e-commerce spending could increase from USD 118 billion worldwide in 2001 to USD707 billion in 2005 (Wolverton, 2001). In spite of the world internet potential, actual number of internet users who purchased online has declined. Reasons cited were reluctance to shop on-line, mistrust and security issues (Taylor, 2002). However, there is limited empirical investigation to verify the causal antecedents of internet purchase behavior in Malaysia. The commonly used theories to explain internet purchase behavior are the Theory of Planned Behavior (TPB) and Technology acceptance model (TAM). Conversely, past studies have examined the predictors of internet purchasing using these theories separately, mostly conducted in Western countries and typically descriptive research in nature. Thus, our study intends to investigate the drivers of internet purchasing based on the integration of theory of planned behavior and technology acceptance model. This integration is plausible to increase the body of knowledge in this area as well as applying structural equation modeling (SEM) method of analysis. …


title: Steps Toward Deep Kernel Methods from Infinite Neural Networks

abstract: Contemporary deep neural networks exhibit impressive results on practical problems. These networks generalize well although their inherent capacity may extend significantly beyond the number of training examples. We analyze this behavior in the context of deep, infinite neural networks. We show that deep infinite layers are naturally aligned with Gaussian processes and kernel methods, and devise stochastic kernels that encode the information of these networks. We show that stability results apply despite the size, offering an explanation for their empirical success.


title: Instances and concepts in distributional space

abstract: The authors have received funding from DFG (SFB 732, project B9). and from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 715154; AMORE), as well as under the Marie Sklodowska-Curie grant agreement No 655577 (LOVe).


title: Scalable Recommendation with Poisson Factorization

abstract: We develop a Bayesian Poisson matrix factorization model for forming recommendations from sparse user behavior data. These data are large user/item matrices where each user has provided feedback on only a small subset of items, either explicitly (e.g., through star ratings) or implicitly (e.g., through views or purchases). In contrast to traditional matrix factorization approaches, Poisson factorization implicitly models each user's limited attention to consume items. Moreover, because of the mathematical form of the Poisson likelihood, the model needs only to explicitly consider the observed entries in the matrix, leading to both scalable computation and good predictive performance. We develop a variational inference algorithm for approximate posterior inference that scales up to massive data sets. This is an efficient algorithm that iterates over the observed entries and adjusts an approximate posterior over the user/item representations. We apply our method to large real-world user data containing users rating movies, users listening to songs, and users reading scientific papers. In all these settings, Bayesian Poisson factorization outperforms state-of-the-art matrix factorization methods.


title: Current trends in oral cancer: A review

abstract: Oral cancer remains one of the most common and challenging malignancies of the head and neck region. This review summarizes the incidence, prevalence and mortality rates associated with oral cancer. We also investigated the strategies for early diagnosis, the molecular pathway of carcinogenesis, and the current treatment modalities available for oral cancer.


title: Password Cracking Using Probabilistic Context-Free Grammars

abstract: Choosing the most effective word-mangling rules to use when performing a dictionary-based password cracking attack can be a difficult task. In this paper we discuss a new method that generates password structures in highest probability order. We first automatically create a probabilistic context-free grammar based upon a training set of previously disclosed passwords. This grammar then allows us to generate word-mangling rules, and from them, password guesses to be used in password cracking. We will also show that this approach seems to provide a more effective way to crack passwords as compared to traditional methods by testing our tools and techniques on real password sets. In one series of experiments, training on a set of disclosed passwords, our approach was able to crack 28% to 129% more passwords than John the Ripper, a publicly available standard password cracking program.


title: Impact of plant domestication on rhizosphere microbiome assembly and functions

abstract: The rhizosphere microbiome is pivotal for plant health and growth, providing defence against pests and diseases, facilitating nutrient acquisition and helping plants to withstand abiotic stresses. Plants can actively recruit members of the soil microbial community for positive feedbacks, but the underlying mechanisms and plant traits that drive microbiome assembly and functions are largely unknown. Domestication of plant species has substantially contributed to human civilization, but also caused a strong decrease in the genetic diversity of modern crop cultivars that may have affected the ability of plants to establish beneficial associations with rhizosphere microbes. Here, we review how plants shape the rhizosphere microbiome and how domestication may have impacted rhizosphere microbiome assembly and functions via habitat expansion and via changes in crop management practices, root exudation, root architecture, and plant litter quality. We also propose a “back to the roots” framework that comprises the exploration of the microbiome of indigenous plants and their native habitats for the identification of plant and microbial traits with the ultimate goal to reinstate beneficial associations that may have been undermined during plant domestication.


title: A Novel Scheme for Rapid Tracking of Maximum Power Point in Wind Energy Generation Systems

abstract: This paper presents a novel maximum power point (MPP) tracking (MPPT) algorithm for grid-connected wind energy generation systems (WEGS). This is a rapid tracking algorithm that uses the fact that the value of ?s,? an intermediate variable, especially defined for the purpose, remains constant ( =sMPP ) for a given WEGS at the MPP irrespective of the wind velocity. The value of sMPP is known in advance. The algorithm works in two stages. In the first stage, it uses large steps to quickly drive the operating point to lie within a narrow band with limits smax and smin. In the second stage, exact MPP is tracked using the ?perturb and observe? method. No extra hardware or measurements (sensors) are required compared to the existing algorithms. Hence, the cost is not increased. Application of the proposed algorithm to an example WEGS shows that the time taken by the system to reach MPP is much smaller compared to most of the existing algorithms. A prototype matrix converter has been developed for grid interfacing and the proposed MPPT scheme has been implemented in conjunction with Venturini and space-vector-modulation-based switching schemes. All the results of this study are presented.


title: Watching eyes on potential litter can reduce littering: evidence from two field experiments

abstract: Littering constitutes a major societal problem, and any simple intervention that reduces its prevalence would be widely beneficial. In previous research, we have found that displaying images of watching eyes in the environment makes people less likely to litter. Here, we investigate whether the watching eyes images can be transferred onto the potential items of litter themselves. In two field experiments on a university campus, we created an opportunity to litter by attaching leaflets that either did or did not feature an image of watching eyes to parked bicycles. In both experiments, the watching eyes leaflets were substantially less likely to be littered than control leaflets (odds ratios 0.22-0.32). We also found that people were less likely to litter when there other people in the immediate vicinity than when there were not (odds ratios 0.04-0.25) and, in one experiment but not the other, that eye leaflets only reduced littering when there no other people in the immediate vicinity. We suggest that designing cues of observation into packaging could be a simple but fruitful strategy for reducing littering.


title: Dispersionless phase discontinuities for controlling light propagation.

abstract: Ultrathin metasurfaces consisting of a monolayer of subwavelength plasmonic resonators are capable of generating local abrupt phase changes and can be used for controlling the wavefront of electromagnetic waves. The phase change occurs for transmitted or reflected wave components whose polarization is orthogonal to that of a linearly polarized (LP) incident wave. As the phase shift relies on the resonant features of the plasmonic structures, it is in general wavelength-dependent. Here, we investigate the interaction of circularly polarized (CP) light at an interface composed of a dipole antenna array to create spatially varying abrupt phase discontinuities. The phase discontinuity is dispersionless, that is, it solely depends on the orientation of dipole antennas, but not their spectral response and the wavelength of incident light. By arranging the antennas in an array with a constant phase gradient along the interface, the phenomenon of broadband anomalous refraction is observed ranging from visible to near-infrared wavelengths. We further design and experimentally demonstrate an ultrathin phase gradient interface to generate a broadband optical vortex beam based on the above principle.


title: X-band downlink antenna characterised by isoflux gain mask

abstract: This paper presents development and measurement results of a X-band isoflux downlink antenna. Isoflux antennas have to fulfil strict requirements concerning pattern mask, cross polarisation and backlobes. To satisfy these requirements and simultaneously providing circularly polarised waves, a compact polariser was matched and combined with a specific horn geometry. The antenna was manufactured, tested and delivered as flight model to enlarge time slots for transmitting big data between Earth observation satellites and ground stations. This paper discusses pros and cons of isoflux antennas and summarises the manufactured hardware as well as corresponding measurement results.


title: Joint Dictionary Learning for Multispectral Change Detection

abstract: Change detection is one of the most important applications of remote sensing technology. It is a challenging task due to the obvious variations in the radiometric value of spectral signature and the limited capability of utilizing spectral information. In this paper, an improved sparse coding method for change detection is proposed. The intuition of the proposed method is that unchanged pixels in different images can be well reconstructed by the joint dictionary, which corresponds to knowledge of unchanged pixels, while changed pixels cannot. First, a query image pair is projected onto the joint dictionary to constitute the knowledge of unchanged pixels. Then reconstruction error is obtained to discriminate between the changed and unchanged pixels in the different images. To select the proper thresholds for determining changed regions, an automatic threshold selection strategy is presented by minimizing the reconstruction errors of the changed pixels. Adequate experiments on multispectral data have been tested, and the experimental results compared with the state-of-the-art methods prove the superiority of the proposed method. Contributions of the proposed method can be summarized as follows: 1) joint dictionary learning is proposed to explore the intrinsic information of different images for change detection. In this case, change detection can be transformed as a sparse representation problem. To the authors' knowledge, few publications utilize joint learning dictionary in change detection; 2) an automatic threshold selection strategy is presented, which minimizes the reconstruction errors of the changed pixels without the prior assumption of the spectral signature. As a result, the threshold value provided by the proposed method can adapt to different data due to the characteristic of joint dictionary learning; and 3) the proposed method makes no prior assumption of the modeling and the handling of the spectral signature, which can be adapted to different data.


title: E-learning ecosystem based on service-oriented cloud computing architecture

abstract: Cloud computing refers to internet software as a service, hardware and servers in data centers, which run the applications. Cloud computing is highly scalable and provides virtualized resources as a subscription to users. Use of clouds in education provides great opportunity for students to improve achieved learning outcomes, and learners can gain significant advantages in the new learning environment. Cloud adaption can also help to significantly reduce cost of infrastructure, software and human resources. In this article service oriented cloud computing architecture is used to transfer E-learning into the cloud. These architectures cover challenges of e-learning such as scalability, application development, efficient use of resources, saving expense, and security.


title: Closed Forms for European Options in a Local Volatility Model

abstract: Because of its very general formulation, the local volatility model does not have an analytical solution for European options. In this article, we present a new methodology to derive closed form solutions for the price of any European options. The formula results from an asymptotic expansion, terms of which are Black-Scholes price and related Greeks. The accuracy of the formula depends on the payoff smooth- ness and it converges with very few terms.


title: The reverb challenge: A common evaluation framework for dereverberation and recognition of reverberant speech

abstract: Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel dereverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results.


title: Community Detection Using A Neighborhood Strength Driven Label Propagation Algorithm

abstract: Studies of community structure and evolution in large social networks require a fast and accurate algorithm for community detection. As the size of analyzed communities grows, complexity of the community detection algorithm needs to be kept close to linear. The Label Propagation Algorithm (LPA) has the benefits of nearly-linear running time and easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we propose new update rule and label propagation criterion in LPA to improve both its computational efficiency and the quality of communities that it detects. The speed is optimized by avoiding unnecessary updates performed by the original algorithm. This change reduces significantly (by order of magnitude for large networks) the number of iterations that the algorithm executes. We also evaluate our generalization of the LPA update rule that takes into account, with varying strength, connections to the neighborhood of a node considering a new label. Experiments on computer generated networks and a wide range of social networks show that our new rule improves the quality of the detected communities compared to those found by the original LPA. The benefit of considering positive neighborhood strength is pronounced especially on real-world networks containing sufficiently large fraction of nodes with high clustering coefficient.


title: Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow.

abstract: ObjectiveWe evaluated heart rate variability biofeedback as a method for increasing vagal baroreflex gain and improving pulmonary function among 54 healthy adults.MethodsWe compared 10 sessions of biofeedback training with an uninstructed control. Cognitive and physiological effects were measured in


Stochastic Video Prediction with Conditional Density Estimation


title: Detecting cyberbullying: query terms and techniques

abstract: In this paper we describe a close analysis of the language used in cyberbullying. We take as our corpus a collection of posts from Formspring.me. Formspring.me is a social networking site where users can ask questions of other users. It appeals primarily to teens and young adults and the cyberbullying content on the site is dense; between 7% and 14% of the posts we have analyzed contain cyberbullying content. The results presented in this article are two-fold. Our first experiments were designed to develop an understanding of both the specific words that are used by cyberbullies, and the context surrounding these words. We have identified the most commonly used cyberbullying terms, and have developed queries that can be used to detect cyberbullying content. Five of our queries achieve an average precision of 91.25% at rank 100. In our second set of experiments we extended this work by using a supervised machine learning approach for detecting cyberbullying. The machine learning experiments identify additional terms that are consistent with cyberbullying content, and identified an additional querying technique that was able to accurately assign scores to posts from Formspring.me. The posts with the highest scores are shown to have a high density of cyberbullying content.


title: Aligning IT To Business In Complex Multinational Corporations: The Case Of The U.N. Secretariat.

abstract: Business-IT strategic alignment grows in importance as organizations strive to link business and technology in light of the internationalization of their businesses. This positivistic research method uses a field survey design to examine (a) the role of knowledge management processes in the relationship between contextual factors and alignment in the U.N. Secretariat, and (b) the role of IT projects in the relationship between alignment and the performance and effectiveness of the U.N. Secretariat. Structural equation modeling techniques are conducted to analyze data collected through a sample of 166 IT managers and 97 business managers from 50 offices in the U.N. Secretariat. The measurement model exhibited a fairly good fit. The results of the study have at least four implications to leaders in the U.N. Secretariat and in multinational corporations (MNCs). A theoretical and practical perspective of business-IT strategic alignment in the U.N. Secretariat is provided. The study draws upon the strategic alignment model and the typology of MNCs to propose and test an IT strategic alignment model for MNCs (mSAM). The business-IT strategic alignment implementation model for MNCs (mSAIM) is the model for application proposed as the critical recommendation of the research study.


title: Information-Theoretically Secret Key Generation for Fading Wireless Channels

abstract: The multipath-rich wireless environment associated with typical wireless usage scenarios is characterized by a fading channel response that is time-varying, location-sensitive, and uniquely shared by a given transmitter-receiver pair. The complexity associated with a richly scattering environment implies that the short-term fading process is inherently hard to predict and best modeled stochastically, with rapid decorrelation properties in space, time, and frequency. In this paper, we demonstrate how the channel state between a wireless transmitter and receiver can be used as the basis for building practical secret key generation protocols between two entities. We begin by presenting a scheme based on level crossings of the fading process, which is well-suited for the Rayleigh and Rician fading models associated with a richly scattering environment. Our level crossing algorithm is simple, and incorporates a self-authenticating mechanism to prevent adversarial manipulation of message exchanges during the protocol. Since the level crossing algorithm is best suited for fading processes that exhibit symmetry in their underlying distribution, we present a second and more powerful approach that is suited for more general channel state distributions. This second approach is motivated by observations from quantizing jointly Gaussian processes, but exploits empirical measurements to set quantization boundaries and a heuristic log likelihood ratio estimate to achieve an improved secret key generation rate. We validate both proposed protocols through experimentations using a customized 802.11a platform, and show for the typical WiFi channel that reliable secret key establishment can be accomplished at rates on the order of 10 b/s.


title: DebugAdvisor: a recommender system for debugging

abstract: In large software development projects, when a programmer is assigned a bug to fix, she typically spends a lot of time searching (in an ad-hoc manner) for instances from the past where similar bugs have been debugged, analyzed and resolved. Systematic search tools that allow the programmer to express the context of the current bug, and search through diverse data repositories associated with large projects can greatly improve the productivity of debugging This paper presents the design, implementation and experience from such a search tool called D ebug A dvisor . The context of a bug includes all the information a programmer has about the bug, including natural language text, textual rendering of core dumps, debugger output etc. Our key insight is to allow the programmer to collate this entire context as a query to search for related information. Thus, D ebug A dvisor allows the programmer to search using a fat query, which could be kilobytes of structured and unstructured data describing the contextual information for the current bug. Information retrieval in the presence of fat queries and variegated data repositories, all of which contain a mix of structured and unstructured data is a challenging problem. We present novel ideas to solve this problem. We have deployed D ebug A dvisor to over 100 users inside Microsoft. In addition to standard metrics such as precision and recall, we present extensive qualitative and quantitative feedback from our users.


title: Data-driven kinodynamic RRT

abstract: We present a novel, data-driven kinodynamic motion planner. Our sampling-based planner is based on using a physics simulator as a black box to compute a trajectory considering dynamics, even when we cannot derive exact propagation functions. To improve its overall efficiency, we pre-compute a motion database containing different motions simulated with different controls and states defined in the local frame of a robot. We then use the motion database to efficiently estimate the simulated trajectory during iterations of our planner. When the planner requests the best control to reach a desired state from a query state, we retrieve nearby motions that are close to the query state and pick the motion that is closest to the desired state for the tree extension. To control accuracy of our planner with a high efficiency, we lazily validate retrieved motions. The pre-constructed motion database contains modular trajectories and thus can be reused for other test cases, where we have different composition of obstacles or different start/goal states.


title: X-band 1kW SSPA using 20-way Hybrid Radial combiner for accelerator

abstract: The X-band pulsed 1kW SSPA for XFEL has been designed and manufactured. Amplifier modules were combined by a 20-way Hybrid Radial combiner in order to reduce output loss. The manufactured 20-Way Hybrid Radial Combiner is capable of high output with a combining efficiency of 95%. The entire system is driven by Pulse and results in a Power Gain of 60dB with an Output Power of 1kW.


title: Enabling Wireless Power Transfer in Cellular Networks: Architecture, Modeling and Deployment

abstract: Microwave power transfer (MPT) delivers energy wirelessly from stations called power beacons (PBs) to mobile devices by microwave radiation. This provides mobiles practically infinite battery lives and eliminates the need of power cords and chargers. To enable MPT for mobile charging, this paper proposes a new network architecture that overlays an uplink cellular network with randomly deployed PBs for powering mobiles, called a hybrid network. The deployment of the hybrid network under an outage constraint on data links is investigated based on a stochastic-geometry model where single-antenna base stations (BSs) and PBs form independent homogeneous Poisson point processes (PPPs) and single-antenna mobiles are uniformly distributed in Voronoi cells generated by BSs. In this model, mobiles and PBs fix their transmission power at p and q, respectively; a PB either radiates isotropically, called isotropic MPT, or directs energy towards target mobiles by beamforming, called directed MPT. The model is applied to derive the tradeoffs between the network parameters including p, q, and the BS/PB densities under the outage constraint. First, consider the deployment of the cellular network. It is proved that the outage constraint is satisfied so long as the product the BS density decreases with increasing p following a power law where the exponent is proportional to the path-loss exponent. Next, consider the deployment of the hybrid network assuming infinite energy storage at mobiles. It is shown that for isotropic MPT, the product between q, the PB density, and the BS density raised to a power proportional to the path-loss exponent has to be above a given threshold so that PBs are sufficiently dense; for directed MPT, a similar result is obtained with the aforementioned product increased by the array gain. Last, similar results are derived for the case of mobiles having small energy storage.


title: A Random Walk around the City: New Venue Recommendation in Location-Based Social Networks

abstract: The popularity of location-based social networks available on mobile devices means that large, rich datasets that contain a mixture of behavioral (users visiting venues), social (links between users), and spatial (distances between venues) information are available for mobile location recommendation systems. However, these datasets greatly differ from those used in other online recommender systems, where users explicitly rate items: it remains unclear as to how they capture user preferences as well as how they can be leveraged for accurate recommendation. This paper seeks to bridge this gap with a three-fold contribution. First, we examine how venue discovery behavior characterizes the large check-in datasets from two different location-based social services, Foursquare and Go Walla: by using large-scale datasets containing both user check-ins and social ties, our analysis reveals that, across 11 cities, between 60% and 80% of users' visits are in venues that were not visited in the previous 30 days. We then show that, by making constraining assumptions about user mobility, state-of-the-art filtering algorithms, including latent space models, do not produce high quality recommendations. Finally, we propose a new model based on personalized random walks over a user-place graph that, by seamlessly combining social network and venue visit frequency data, obtains between 5 and 18% improvement over other models. Our results pave the way to a new approach for place recommendation in location-based social systems.


title: Row-less Universal Schema

abstract: Universal schema jointly embeds knowledge bases and textual patterns to reason about entities and relations for automatic knowledge base construction and information extraction. In the past, entity pairs and relations were represented as learned vectors with compatibility determined by a scoring function, limiting generalization to unseen text patterns and entities. Recently, ‘column-less’ versions of Universal Schema have used compositional pattern encoders to generalize to all text patterns. In this work we take the next step and propose a ‘row-less’ model of universal schema, removing explicit entity pair representations. Instead of learning vector representations for each entity pair in our training set, we treat an entity pair as a function of its relation types. In experimental results on the FB15k-237 benchmark we demonstrate that we can match the performance of a comparable model with explicit entity pair representations using a model of attention over relation types. We further demonstrate that the model performs with nearly the same accuracy on entity pairs never seen during training.


title: Parser Extraction of Triples in Unstructured Text

abstract: The web contains vast repositories of unstructured text. We investigate the opportunity for building a knowledge graph from these text sources. We generate a set of triples which can be used in knowledge gathering and integration. We define the architecture of a language compiler for processing subject-predicate-object triples using the OpenNLP parser. We implement a depth-first search traversal on the POS tagged syntactic tree appending predicate and object information. A parser enables higher precision and higher recall extractions of syntactic relationships across conjunction boundaries. We are able to extract 2-2.5 times the correct extractions of ReVerb. The extractions are used in a variety of semantic web applications and question answering. We verify extraction of 50,000 triples on the ClueWeb dataset.


title: Competitive Poaching in Sponsored Search Advertising and Strategic Impact on Traditional Advertising

abstract: Traditional advertising, such as TV and print advertising, primarily builds awareness of a firm's product among consumers, whereas sponsored search advertising on a search engine can target consumers closer to making a purchase because they reveal their interest by searching for a relevant keyword. Increased consumer targetability in sponsored search advertising induces a firm to “poach” a competing firm's consumers by directly advertising on the competing firm's keywords; in other words, the poaching firm tries to obtain more than its “fair share” of sales through sponsored search advertising by free riding on the market created by the firm being poached. Using a game theory model with firms of different advertising budgets, we study the phenomenon of poaching, its impact on how firms allocate their advertising budgets to traditional and sponsored search advertising, and the search engine's policy on poaching. We find that, as budget asymmetry increases, the smaller-budget firm poaches more on the keywords of the larger-budget firm. This may induce the larger-budget firm to allocate more of its budget to traditional advertising, which, in turn, hurts the search engine's advertising revenues. Therefore, paradoxically, even though poaching increases competition in sponsored search advertising, the search engine can benefit from limiting the extent of poaching. This explains why major search engines use “ad relevance” measures to handicap poaching on trademarked keywords.


Marula Sclerocarya birrea subsp. caffra dispersal by mammals: are squirrels seed predators or seed dispersers?


title: Question-Answering with Grammatically-Interpretable Representations

abstract: We introduce an architecture, the Tensor Product Recurrent Network (TPRN). In our application of TPRN, internal representations learned by end-to-end optimization in a deep neural network performing a textual question-answering (QA) task can be interpreted using basic concepts from linguistic theory. No performance penalty need be paid for this increased interpretability: the proposed model performs comparably to a state-of-the-art system on the SQuAD QA task. The internal representation which is interpreted is a Tensor Product Representation: for each input word, the model selects a symbol to encode the word, and a role in which to place the symbol, and binds the two together. The selection is via soft attention. The overall interpretation is built from interpretations of the symbols, as recruited by the trained model, and interpretations of the roles as used by the model. We find support for our initial hypothesis that symbols can be interpreted as lexical-semantic word meanings, while roles can be interpreted as approximations of grammatical roles (or categories) such as subject, wh-word, determiner, etc. Fine-grained analysis reveals specific correspondences between the learned roles and parts of speech as assigned by a standard tagger (Toutanova et al. 2003), and finds several discrepancies in the model's favor. In this sense, the model learns significant aspects of grammar, after having been exposed solely to linguistically unannotated text, questions, and answers: no prior linguistic knowledge is given to the model. What is given is the means to build representations using symbols and roles, with an inductive bias favoring use of these in an approximately discrete manner.


title: Generating default privacy policies for online social networks

abstract: Default privacy policies have a significant impact on the overall dynamics and success of online social networks, as users tend to keep their initial privacy policies. In this work-in-progress, we present a new method for suggesting privacy policies for new users by exploring knowledge of existing policies. The defaults generation process performs a collaborative analysis of the policies, finding personalized and representative suggestions. We show how the process can be extended to a wide range of domains, and present results based on 543 privacy policies obtained from a live location-based social network. Finally, we present a user interaction model that lets the user retain control over the default policies, allowing the user to make knowledgeable decisions regarding which default policy to take.


title: A Random Linear Network Coding Approach to Multicast

abstract: We present a distributed random linear network coding approach for transmission and compression of information in general multisource multicast networks. Network nodes independently and randomly select linear mappings from inputs onto output links over some field. We show that this achieves capacity with probability exponentially approaching 1 with the code length. We also demonstrate that random linear coding performs compression when necessary in a network, generalizing error exponents for linear Slepian-Wolf coding in a natural way. Benefits of this approach are decentralized operation and robustness to network changes or link failures. We show that this approach can take advantage of redundant network capacity for improved success probability and robustness. We illustrate some potential advantages of random linear network coding over routing in two examples of practical scenarios: distributed network operation and networks with dynamically varying connections. Our derivation of these results also yields a new bound on required field size for centralized network coding on general multicast networks


title: Interventions to reduce stress in university students: A review and meta-analysis

abstract: Abstract Background Recent research has revealed concerning rates of anxiety and depression among university students. Nevertheless, only a small percentage of these students receive treatment from university health services. Universities are thus challenged with instituting preventative programs that address student stress and reduce resultant anxiety and depression. Method A systematic review of the literature and meta-analysis was conducted to examine the effectiveness of interventions aimed at reducing stress in university students. Studies were eligible for inclusion if the assignment of study participants to experimental or control groups was by random allocation or parallel cohort design. Results Retrieved studies represented a variety of intervention approaches with students in a broad range of programs and disciplines. Twenty-four studies, involving 1431 students were included in the meta-analysis. Cognitive, behavioral and mindfulness interventions were associated with decreased symptoms of anxiety. Secondary outcomes included lower levels of depression and cortisol. Limitations Included studies were limited to those published in peer reviewed journals. These studies over-represent interventions with female students in Western countries. Studies on some types of interventions such as psycho-educational and arts based interventions did not have sufficient data for inclusion in the meta-analysis. Conclusion This review provides evidence that cognitive, behavioral, and mindfulness interventions are effective in reducing stress in university students. Universities are encouraged to make such programs widely available to students. In addition however, future work should focus on developing stress reduction programs that attract male students and address their needs.


title: An Ontology-Based Approach for Retrieving Information from Disparate Sectors in Government: The Patent System as an Exemplar

abstract: The ability to access patents and relevant patent-related information pertaining to a patented technology can fundamentally transform the patent system and its functioning and patent institutions such as the USPTO and the federal courts. This paper describes an ontology-based computational framework that can resolve some of difficult issues in retrieving patents and patent related information for the legal and justice system.


title: Realization of a Service Climbing Robot for Glass-wall Cleaning

abstract: Wall cleaning and maintenance of high-rise buildings is becoming one of the most appropriate fields for robotization due to its current low level of automation and lack of uniform building structure. The last decade has seen an increasing interest in developing and employing service robots for cleaning glass walls which is always very dangerous and strenuous in mid-air. In this paper, we propose a new kind of cleaning robot named sky cleaner which is totally actuated by pneumatic cylinders and sucked to the glass wall with vacuum suckers. The constraints for designing are discussed. After that, the drive method, principle of attachment, mechanical construction and unique aspects are presented in detail. A testing simulation shows that the robot can cross an obstacle safely and reliably when it moves from one column glass to another in the right-left direction. In the end, a summary of the main special features of the cleaning robot are given


title: Unsupervised Training Set Generation for Automatic Acquisition of Technical Terminology in Patents

abstract: NLP methods for automatic information access to rich technological knowledge sources like patents are of great value. One important resource for accessing this knowledge is the technical terminology of the patent domain. In this paper, we address the problem of automatic terminology acquisition (ATA), i.e., the problem of automatically identifying all technical terms in a document. We analyze technical terminology in patents and define the concept of technical term based on the analysis. We present a novel method for labeling large amounts of high-quality training data for ATA in an unsupervised fashion. We train two ATA methods on this training data, a term candidate classifier and a conditional random field (CRF), and investigate the utility of dierent types of features. Finally, we show that our method of automatically generating training data is eective and the two ATA methods successfully generalize, considerably increasing recall while preserving high precision relative to a state-of-the-art baseline.


title: Removing camera shake from a single photograph

abstract: Camera shake during exposure leads to objectionable image blur and ruins many photographs. Conventional blind deconvolution methods typically assume frequency-domain constraints on images, or overly simplified parametric forms for the motion path during camera shake. Real camera motions can follow convoluted paths, and a spatial domain prior can better maintain visually salient image characteristics. We introduce a method to remove the effects of camera shake from seriously blurred images. The method assumes a uniform camera blur over the image and negligible in-plane camera rotation. In order to estimate the blur from the camera shake, the user must specify an image region without saturation effects. We show results for a variety of digital photographs taken from personal photo collections.


title: Swallowing Preparation and Execution: Insights from a Delayed-Response Functional Magnetic Resonance Imaging (fMRI) Study

abstract: The present study sought to elucidate the functional contributions of sub-regions of the swallowing neural network in swallowing preparation and swallowing motor execution. Seven healthy volunteers participated in a delayed-response, go, no-go functional magnetic resonance imaging study involving four semi-randomly ordered activation tasks: (i) “prepare to swallow,” (ii) “voluntary saliva swallow,” (iii) “do not prepare to swallow,” and (iv) “do not swallow.” Results indicated that brain activation was significantly greater during swallowing preparation, than during swallowing execution, within the rostral and intermediate anterior cingulate cortex bilaterally, premotor cortex (left > right hemisphere), pericentral cortex (left > right hemisphere), and within several subcortical nuclei including the bilateral thalamus, caudate, and putamen. In contrast, activation within the bilateral insula and the left dorsolateral pericentral cortex was significantly greater in relation to swallowing execution, compared with swallowing preparation. Still other regions, including a more inferior ventrolateral pericentral area, and adjoining Brodmann area 43 bilaterally, and the supplementary motor area, were activated in relation to both swallowing preparation and execution. These findings support the view that the preparation, and subsequent execution, of swallowing are mediated by a cascading pattern of activity within the sub-regions of the bilateral swallowing neural network.


title: Shirtless and Dangerous: Quantifying Linguistic Signals of Gender Bias in an Online Fiction Writing Community

abstract: Imagine a princess asleep in a castle, waiting for her prince to slay the dragon and rescue her. Tales like the famous Sleeping Beauty clearly divide up gender roles. But what about more modern stories, borne of a generation increasingly aware of social constructs like sexism and racism? Do these stories tend to reinforce gender stereotypes, or counter them? In this paper, we present a technique that combines natural language processing with a crowdsourced lexicon of stereotypes to capture gender biases in fiction. We apply this technique across 1.8 billion words of fiction from the Wattpad online writing community, investigating gender representation in stories, how male and female characters behave and are described, and how authors' use of gender stereotypes is associated with the community's ratings. We find that male over-representation and traditional gender stereotypes (e.g., dominant men and submissive women) are common throughout nearly every genre in our corpus. However, only some of these stereotypes, like sexual or violent men, are associated with highly rated stories. Finally, despite women often being the target of negative stereotypes, female authors are equally likely to write such stereotypes as men.


title: Isolation in Docker through Layer Encryption

abstract: Containers are constantly gaining ground in the virtualization landscape as a lightweight and efficient alternative to hypervisor-based Virtual Machines, with Docker being the most successful representative. Docker relies on union-capable file systems, where any action performed to a base image is captured as a new file system layer. This strategy allows developers to easily pack applications into Docker image layers and distribute them via public registries. However, this image creation and distribution strategy does not protect sensitive data from malicious privileged users (e.g., registry administrator, cloud provider), since encryption is not natively supported. We propose and demonstrate a mechanism for secure Docker image manipulation throughout its life cycle: The creation, storage and usage of a Docker image is backed by a data-at-rest mechanism, which maintains sensitive data encrypted on disk and encrypts/decrypts them on-the-fly in order to preserve their confidentiality at all times, while the distribution and migration of images is enhanced with a mechanism that encrypts only specific layers of the file system that need to remain confidential and ensures that only legitimate key holders can decrypt them and reconstruct the original image. Through a rich interaction with our system the audience will experience first-hand how sensitive image data can be safely distributed and remain encrypted at the storage device throughout the container's lifetime, bearing only a marginal performance overhead.


title: Speech-Driven Facial Reenactment Using Conditional Generative Adversarial Networks

abstract: We present a novel approach to generating photo-realistic images of a face with accurate lip sync, given an audio input. By using a recurrent neural network, we achieved mouth landmarks based on audio features. We exploited the power of conditional generative adversarial networks to produce highly-realistic face conditioned on a set of landmarks. These two networks together are capable of producing a sequence of natural faces in sync with an input audio track.


title: Comparison of morphological characters in Irish and English populations of the acanthocephalan Pomphorhynchus laevis (Müller, 1776)

abstract: Pomphorhynchus laevis is believed on ecological evidence to exist as three strains in the British Isles. However, the strains have never been shown to be capable of being distinguished using morphological characters. A morphological comparison was made between a sample of P. laevis from Salmo trutta in L. Feeagh in the west of Ireland and a sample from Leuciscus cephalus in R. Culm in the south of England. The length and width of the trunk, neck, bulb, proboscis and hooks were measured. The number of hooks per row, the number of rows and the positions of the stoutest and longest hooks were also recorded. A Principal Components Analysis based on the morphological measurements confirmed the separation of the two populations and showed that two characters successfully identified the populations: the position of the stoutest hook and the ratio of numbers of anterior to posterior hooks.


Hybrid channel gain prioritized access‐aware cell association with interference mitigation in LTE‐Advanced HetNets


title: An improved ant algorithm with LDA-based representation for text document clustering

abstract: Document clustering can be applied in document organisation and browsing, document summarisation and classification. The identification of an appropriate representation for textual documents is extremely important for the performance of clustering or classification algorithms. Textual documents suffer from the high dimensionality and irrelevancy of text features. Besides, conventional clustering algorithms suffer from several shortcomings, such as slow convergence and sensitivity to the initial value. To tackle the problems of conventional clustering algorithms, metaheuristic algorithms are frequently applied to clustering. In this paper, an improved ant clustering algorithm is presented, where two novel heuristic methods are proposed to enhance the clustering quality of ant-based clustering. In addition, the latent Dirichlet allocation LDA is used to represent textual documents in a compact and efficient way. The clustering quality of the proposed ant clustering algorithm is compared to the conventional clustering algorithms using 25 text benchmarks in terms of F-measure values. The experimental results indicate that the proposed clustering scheme outperforms the compared conventional and metaheuristic clustering methods for textual documents.


title: Technology Timeline — Towards Life in 2020

abstract: No-one knows precisely what life will be like in 2020, but it is always fun guessing. If we know the development rates of many different technologies, we can anticipate many of the things that will be possible and when they are likely to happen. Considering the interactions between the many technologies and society along the way, we can also foresee many potential consequences on business and social life. Developing such scenarios allows us to plan with a much better picture of how life might look, realising that many things will still turn out differently in spite of our best efforts. ::: ::: It is in computer-based technology that we may see the greatest changes. By 2020, we will have synthetic intelligent life forms sharing our planet and they may even have legal rights. They will catch up with human intelligence before then in overall terms, though there will still be a few things left that only people can do. Most new knowledge will be developed by synthetic intelligence and we will have to accept that we just do not understand some of it, while accepting the resultant benefits. This will lead us into the care economy, where people gradually concentrate more on the human side of activity, as machines gradually take over both physical and mental work. A partnership between man and machine will make our work more productive and our play more enjoyable, augmenting the smallest spark of creativity with machine intelligence. Even entertainment will be within the machine domain, with today's crude computer game heroes and heroines evolving to a whole range of entertainers, even chat show hosts. It is even possible that some of our friends may be synthetic. Since many of our relationships will be net based, we will not even necessarily know which of our friends are synthetic.


Confidence intervals of discretized Euler-Maruyama approximate solutions of SDE's


title: Small and fast moving object detection and tracking in sports video sequences

abstract: We propose an algorithm for detection and tracking of small and fast moving objects, like a ping pong ball or a cricket ball, in sports video sequences. For detection, the proposed method uses only motion as a cue; moreover it does not use any texture information. Our method is able to detect the object with very low contrast and negligible texture content. Along with detection, we also propose a tracking algorithm using the multiple filter bank approach. Thus we provide a complete solution. The tracking algorithm is able to track maneuvering as well as non-maneuvering movements of the object without using any a priori information about the target dynamics


Local and global model interpretability via backward selection and clustering


title: Measuring diabetes self-care: a psychometric analysis of the Self-Care Inventory-Revised with adults.

abstract: OBJECTIVE ::: To examine psychometric properties of the Self-Care Inventory-revised (SCI-R), a self-report measure of perceived adherence to diabetes self-care recommendations, among adults with diabetes. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: We used three data sets of adult type 1 and type 2 diabetic patients to examine psychometric properties of the SCI-R. Principal component and factor analyses examined whether a general factor or common factors were present. Associations with measures of theoretically related concepts were examined to assess SCI-R concurrent and convergent validity. Internal reliability coefficients were calculated. Responsiveness was assessed using paired t tests, effect size, and Guyatt's statistic for type 1 patients who completed psychoeducation. ::: ::: ::: RESULTS ::: Principal component and factor analyses identified a general factor but no consistent common factors. Internal consistency of the SCI-R was alpha = 0.87. Correlation with a measure of frequency of diabetes self-care behaviors was r = 0.63, providing evidence for SCI-R concurrent validity. The SCI-R correlated with diabetes-related distress (r = -0.36), self-esteem (r = 0.25), self-efficacy (r = 0.47), depression (r = -0.22), anxiety (r = -0.24), and HbA(1c) (r = -0.37), supporting construct validity. Responsiveness analyses showed SCI-R scores improved with diabetes psychoeducation with a medium effect size of 0.62 and a Guyatt's statistic of 0.85. ::: ::: ::: CONCLUSIONS ::: The SCI-R is a brief, psychometrically sound measure of perceptions of adherence to recommended diabetes self-care behaviors of adults with type 1 or type 2 diabetes.


title: On-board dual-stereo-vision for autonomous quadrotor navigation

abstract: We present a quadrotor Micro Aerial Vehicle (MAV) capable of autonomous indoor navigation. The MAV is equipped with four cameras arranged in two stereo configurations. One camera pair is facing forward and serves as input for a reduced stereo SLAM system. The other camera pair is facing downwards and is used for ground plane detection and tracking. All processing, including sparse stereo matching, is run on-board in real-time and at high processing rates. We demonstrate the capabilities of this MAV design in several flight experiments. Our MAV is able to recover from pose estimation errors and can cope with processing failures for one camera pair. We show that by using two camera pairs instead of one, we are able to significantly increase navigation accuracy and robustness.


title: Reinforcement Learning by Value Gradients

abstract: The concept of the value-gradient is introduced and developed in the context of reinforcement learning. It is shown that by learning the value-gradients exploration or stochastic behaviour is no longer needed to find locally optimal trajectories. This is the main motivation for using value-gradients, and it is argued that learning value-gradients is the actual objective of any value-function learning algorithm for control problems. It is also argued that learning value-gradients is significantly more efficient than learning just the values, and this argument is supported in experiments by efficiency gains of several orders of magnitude, in several problem domains. Once value-gradients are introduced into learning, several analyses become possible. For example, a surprising equivalence between a value-gradient learning algorithm and a policy-gradient learning algorithm is proven, and this provides a robust convergence proof for control problems using a value function with a general function approximator.


title: Fitting human exposure data with the Johnson SB distribution

abstract: Exposure evaluations for epidemiological investigations and risk assessments mayrequire estimates of background concentrations and peak exposu res, as well as the population mean and variance. The SB distribution is a theoreticallyappealing probabilityfunction for characterizing ratios, and random variables bound byextremes, such as human exposures and environmental concentrations. However, fitting the parameters of this distribution with maximum likelihood methods is often problematic, and some alternative methods are examined here. Two methods based on percentiles, a quantile estimator, and a method-of-moments fitting procedure are explored. The quantile and method-of-moments procedures are based on new explicit expressions for the first four moments of this distribution. The fitting procedures are compared bysimulation, and with actual data sets consisting of measurements of human exposure to airborne contaminants. Journal of Exposure Science and Environmental Epidemiology (2006) 16, 56–62. doi:10.1038/sj.jea.7500437; published online 6 July2005


title: An Analysis of Lateral Crural Repositioning and Its Effect on Alar Rim Position

abstract: IMPORTANCE ::: Cephalically malpositioned lateral crura are a frequent cause of alar rim retraction during both primary and revision rhinoplasty. ::: ::: ::: OBJECTIVE ::: To demonstrate the efficacy of lateral crural repositioning as an isolated maneuver as and combined with adjunctive grafts for lowering the alar rim. ::: ::: ::: DESIGN, SETTING, AND PARTICIPANTS ::: From August through December 2014, we retrospectively reviewed the cases of 54 patients (102 hemi-noses) who had lateral crural repositioning performed by the same surgeon between 2007 and 2013. Only patients with standardized photographs taken preoperatively and at least 6 months postoperatively were included in the study. ::: ::: ::: INTERVENTIONS ::: All primary and revision cases were completed via an open rhinoplasty approach and had lateral crural repositioning performed. ::: ::: ::: MAIN OUTCOMES AND MEASURES ::: Preoperative and postoperative photographs were compared using Adobe Photoshop CS via a modified Gunter technique to measure the degree of alar rim retraction on lateral views. The change in angle was then calculated, and statistical analysis conducted using a paired t test. ::: ::: ::: RESULTS ::: A total of 54 patients (102 hemi-noses) met inclusion criteria; 42 (79%) were women, and the average patient age was 41.3 years. Forty-five (83%) of the cases were revision rhinoplasties, and the average time to obtaining postoperative photographs was 11.3 months. The mean (SD) anterior nostril apex angles preoperatively (31.3° [8.9°]) and postoperatively (24.5° [6.8°]) indicated a net decrease of 6.8° (P < .001). Excluding patients who had other rim-lowering grafts (alar rim and/or composite grafts), we saw a similar result comparing preoperative (31.1° [8.2°]) and postoperative (24.5° [6.7°]) angles, with a net decrease of 6.6° degrees (P < .001). When examining patients who underwent lateral crural repositioning alone compared with those who also had lateral crural strut grafts, we saw a decrease of 6.9° (P < .001) and 6.7° degrees (P < .001), respectively. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Repositioning of the lower lateral cartilages results in a statistically significant lowering of the alar rim. Our data suggest that when combined with lateral crural repositioning, the addition of adjunctive grafts does not add significantly to the rim-lowering effect. ::: ::: ::: LEVEL OF EVIDENCE ::: 3.


title: Exploring Representativeness and Informativeness for Active Learning

abstract: How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified best-versus-second-best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.


title: Vasopressin and Oxytocin in Stress

abstract: Though oxytocin and vasopressin are similar in structure and are produced in the same brain regions, they show specific responses under stress conditions. In humans, increases in peripheral blood vasopressin appear to be a consistent finding during many acute stress situations, while in rats, vasopressin secretion is unresponsive to several stimuli known to induce ACTH and catecholamine release. Even decreases in vasopressin levels during stress were described. In accordance with others, we observed enhanced vasopressin release in response to stress stimuli with an osmotic component such as hypertonic saline injection but also during exposure of rats to a warm environment. Immobilization stress which fails to induce vasopressin release was reported to increase hypothalamic vasopressin mRNA and plasma vasopressin levels in chronically adreno-demedullated rats. Unlike vasopressin, oxytocin may be considered a typical stress hormone responding to osmotic as well as other stress stimuli. We found that acute exposure of rats to immobilization stress resulted in an increase in oxytocin mRNA level. In addition, we have shown that magnocellular neurons of the paraventricular nucleus, but not the supraoptic nucleus, are essential for oxytocin release during immobilization stress. The release of posterior pituitary hormones represents an important component of the stress response.


title: Directional Analysis of Stochastic Gradient Descent via von Mises-Fisher Distributions in Deep learning

abstract: Although stochastic gradient descent (SGD) is a driving force behind the recent success of deep learning, our understanding of its dynamics in a high-dimensional parameter space is limited. In recent years, some researchers have used the stochasticity of minibatch gradients, or the signal-to-noise ratio, to better characterize the learning dynamics of SGD. Inspired from these work, we here analyze SGD from a geometrical perspective by inspecting the stochasticity of the norms and directions of minibatch gradients. We propose a model of the directional concentration for minibatch gradients through von Mises-Fisher (VMF) distribution, and show that the directional uniformity of minibatch gradients increases over the course of SGD. We empirically verify our result using deep convolutional networks and observe a higher correlation between the gradient stochasticity and the proposed directional uniformity than that against the gradient norm stochasticity, suggesting that the directional statistics of minibatch gradients is a major factor behind SGD.


title: Complete Gradient Clustering Algorithm for Features Analysis of X-Ray Images

abstract: Methods based on kernel density estimation have been successfully applied for various data mining tasks. Their natural interpretation together with suitable properties make them an attractive tool among others in clustering problems. In this paper, the Complete Gradient Clustering Algorithm has been used to investigate a real data set of grains. The wheat varieties, Kama, Rosa and Canadian, characterized by measurements of main grain geometric features obtained by X-ray technique, have been analyzed. The proposed algorithm is expected to be an effective tool for recognizing wheat varieties. A comparison between the clustering results obtained from this method and the classical k-means clustering algorithm shows positive practical features of the Complete Gradient Clustering Algorithm.


title: Automatic Modeling of Frequent User Behaviours in Intelligent Environments

abstract: Intelligent Environments depend on their capability to understand and anticipate user’s habits and needs. Therefore, learning user’s common behaviours becomes an important step towards allowing an environment to provide such personalized services. Due to the complexity of the entire learning system, this paper will focus on the automatic discovering of models of user’s behaviours. Discovering the models means to discover the order of such actions, representing user’s behaviours as sequences of actions.


title: Epithermal gold mineralization in Costa Rica, Cordillera de Tilarán — exploration geochemistry and genesis of gold deposits

abstract: *Corresponding author Epithermal gold mineralization in quartz veins forms part of a large ore belt extending in the NW–SE direction parallel to the Cordillera de Tilaran, Costa Rica. It is confined to Miocene–Pliocene andesites and basalts of the Aguacate Group volcanic arc. Gold-bearing quartz veins are related to faults and fractures of steep inclinations, accompanied by pronounced hydrothermal alteration. The key tectonic zones strike NW–SE but the majority of the ore veins are controlled by local extensional structures of Riedel shear type in the NE–SW, N–S to NNW–SSE directions. The brecciation, mylonitization and healing of deformed structures suggest that three main pulses of mineralization took place during the hydrothermal process. The gold is present as electrum (30 and 42 wt. % Ag) tiny inclusions up to 25 µm in size enclosed in quartz, pyrite and arsenopyrite. The other ore minerals are chalcopyrite, galena, sphalerite and marcasite and less abundant to scarce acanthite, pyrargyrite, greenockite, covellite, bornite and cassiterite. The principal elements exhibiting significant positive correlations with Au are Ag, Sb, As, Pb and Hg. Fluid inclusions of the H 2 O type were found in quartz and sphalerite from several Au-bearing occurrences. Temperatures of homogenization of fluid inclusions from several quartz generations and sphalerite vary generally between 150 and 290 °C; the salinity of the aqueous solution was very low, not exceeding 5 wt. % NaCl equiv. The age of the mineralization is estimated in the period between the intrusion of the Guacimal Pluton and effusions of the discordant volcanic Monteverde Formation, which is barren (i.e. between c. 6.0 and 2.1 Ma). Geochemical study indicated altogether 14 promising gold-bearing areas in the Montes del Aguacate and Cordillera de Tilaran, of which four can be recommended for further exploration.


title: Deep Learning for Time-Series Analysis

abstract: In many real-world application, e.g., speech recognition or sleep stage classification, data are captured over the course of time, constituting a Time-Series. Time-Series often contain temporal dependencies that cause two otherwise identical points of time to belong to different classes or predict different behavior. This characteristic generally increases the difficulty of analysing them. Existing techniques often depended on hand-crafted features that were expensive to create and required expert knowledge of the field. With the advent of Deep Learning new models of unsupervised learning of features for Time-series analysis and forecast have been developed. Such new developments are the topic of this paper: a review of the main Deep Learning techniques is presented, and some applications on Time-Series analysis are summaried. The results make it clear that Deep Learning has a lot to contribute to the field.


title: Saliency propagation from simple to difficult

abstract: Saliency propagation has been widely adopted for identifying the most attractive object in an image. The propagation sequence generated by existing saliency detection methods is governed by the spatial relationships of image regions, i.e., the saliency value is transmitted between two adjacent regions. However, for the inhomogeneous difficult adjacent regions, such a sequence may incur wrong propagations. In this paper, we attempt to manipulate the propagation sequence for optimizing the propagation quality. Intuitively, we postpone the propagations to difficult regions and meanwhile advance the propagations to less ambiguous simple regions. Inspired by the theoretical results in educational psychology, a novel propagation algorithm employing the teaching-to-learn and learning-to-teach strategies is proposed to explicitly improve the propagation quality. In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner. In the learning-to-teach step, the learner delivers its learning confidence to the teacher to assist the teacher to choose the subsequent simple regions. Due to the interactions between the teacher and learner, the uncertainty of original difficult regions is gradually reduced, yielding manifest salient objects with optimized background suppression. Extensive experimental results on benchmark saliency datasets demonstrate the superiority of the proposed algorithm over twelve representative saliency detectors.


title: Information retrieval in folksonomies: Search and ranking

abstract: Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, called FolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.


title: Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks

abstract: Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information.


title: A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN)

abstract: Dengue fever (DF) is an acute febrile viral disease frequently presented with headache, bone or joint and muscular pains, and rash. A significant percentage of DF patients develop a more severe form of disease, known as dengue haemorrhagic fever (DHF). DHF is the complication of DF. The main pathophysiology of DHF is the development of plasma leakage from the capillary, resulting in haemoconcentration, ascites, and pleural effusion that may lead to shock following defervescence of fever. Therefore, accurate prediction of the day of defervescence of fever is critical for clinician to decide on patient management strategy. To date, no known literature describes of any attempt to predict the day of defervescence of fever in DF patients. This paper describes a non-invasive prediction system for predicting the day of defervescence of fever in dengue patients using artificial neural network. The developed system bases its prediction solely on the clinical symptoms and signs and uses the multilayer feed-forward neural networks (MFNN). The results show that the proposed system is able to predict the day of defervescence in dengue patients with 90% prediction accuracy.


title: Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks

abstract: Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force a deep neural network (DNN) to provide adversary-selected outputs. Such attacks can seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles can be crashed, illicit or illegal content can bypass content filters, or biometric authentication systems can be manipulated to allow improper access. In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs. We analytically investigate the generalizability and robustness properties granted by the use of defensive distillation when training DNNs. We also empirically study the effectiveness of our defense mechanisms on two DNNs placed in adversarial settings. The study shows that defensive distillation can reduce effectiveness of sample creation from 95% to less than 0.5% on a studied DNN. Such dramatic gains can be explained by the fact that distillation leads gradients used in adversarial sample creation to be reduced by a factor of 10^30. We also find that distillation increases the average minimum number of features that need to be modified to create adversarial samples by about 800% on one of the DNNs we tested.


title: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Motion Detail Preserving Optical Flow Estimation

abstract: We discuss the cause of a severe optical flow estimation problem that fine motion structures cannot always be correctly reconstructed in the commonly employed multi-scale variational framework. Our major finding is that significant and abrupt displacement transition wrecks small-scale motion structures in the coarse-to-fine refinement. A novel optical flow estimation method is proposed in this paper to address this issue, which reduces the reliance of the flow estimates on their initial values propagated from the coarser level and enables recovering many motion details in each scale. The contribution of this paper also includes adaption of the objective function and development of a new optimization procedure. The effectiveness of our method is borne out by experiments for both large- and small-displacement optical flow estimation.


title: Paying attention to reading: The neurobiology of reading and dyslexia

abstract: Extraordinary progress in functional brain imaging, primarily advances in functional magnetic resonance imaging, now allows scientists to understand the neural systems serving reading and how these systems differ in dyslexic readers. Scientists now speak of the neural signature of dyslexia, a singular achievement that for the first time has made what was previously a hidden disability, now visible. Paralleling this achievement in understanding the neurobiology of dyslexia, progress in the identification and treatment of dyslexia now offers the hope of identifying children at risk for dyslexia at a very young age and providing evidence-based, effective interventions. Despite these advances, for many dyslexic readers, becoming a skilled, automatic reader remains elusive, in great part because though children with dyslexia can be taught to decode words, teaching children to read fluently and automatically represents the next frontier in research on dyslexia. We suggest that to break through this “fluency” barrier, investigators will need to reexamine the more than 20-year-old central dogma in reading research: the generation of the phonological code from print is modular, that is, automatic and not attention demanding, and not requiring any other cognitive process. Recent findings now present a competing view: other cognitive processes are involved in reading, particularly attentional mechanisms, and that disruption of these attentional mechanisms play a causal role in reading difficulties. Recognition of the role of attentional mechanisms in reading now offer potentially new strategies for interventions in dyslexia. In particular, the use of pharmacotherapeutic agents affecting attentional mechanisms not only may provide a window into the neurochemical mechanismsunderlyingdyslexiabutalsomayofferapotentialadjuncttreatmentforteachingdyslexicreaderstoreadfluently and automatically. Preliminary studies suggest that agents traditionally used to treat disorders of attention, particularly attention-deficit/hyperactivity disorder, may prove to be an effective adjunct to improving reading in dyslexic students.


title: Displaying shapes with various types of surfaces using visuo-haptic interaction

abstract: In this paper, we proposed a visuo-haptic system for displaying various shapes which have curve, edge, and inclined surfaces, using a simple transmutative physical device and the effect of visuo-haptic interaction. We aim to construct a perception-based shape display system to provide users with the sensation of touching virtual objects of varying shapes using only a simple mechanism. We have confirmed that the perception of each primitive shape such as curvature and angle could be modified by displacing a user's hand image on the monitor as if s/he were touching the visual shape while actually touching another shape. In this study, we constructed the method to merge these findings for displaying more various shapes, including angular ones. We built a transmutative device, which the user touches. The device does not undergo significant transformation, but its surface can be slightly bumped in and out, and displayed various shapes with various angles, length and curvature. The results of experimental trials confirmed that our method for displaying each primitive shape can also worked as designed when we combine these findings to display more complex objects using this device which transforms slightly.


Solving a multi-objective location routing problem for infectious waste disposal using hybrid goal programming and hybrid genetic algorithm


title: A dataset for evaluating intrusion detection systems in IEEE 802.11 wireless networks

abstract: Internet access by wireless networks has grownconsiderably in recent years. However, these networks are vulnerable to security problems, especially those related to denial of service attacks. Intrusion Detection Systems(IDS)are widely used to improve network security, but comparison among the several existing approaches is not a trivial task. This paperproposes building a datasetfor evaluating IDS in wireless environments. The data were captured in a real, operating network. We conducted tests using traditional IDS and achievedgreat results, which showed the effectiveness of our proposed approach.


title: How the motor system handles nouns: a behavioral study

abstract: It is an open question whether the motor system is involved during understanding of concrete nouns, as it is for concrete verbs. To clarify this issue, we carried out a behavioral experiment using a go-no go paradigm with an early and delayed go-signal delivery. Italian nouns referring to concrete objects (hand-related or foot-related) and abstract entities served as stimuli. Right-handed participants read the stimuli and responded when the presented word was concrete using the left or right hand. At the early go-signal, slower right-hand responses were found for hand-related nouns compared to foot-related nouns. The opposite pattern was found for the left hand. These findings demonstrate an early lateralized modulation of the motor system during noun processing, most likely crucial for noun comprehension.


title: System Design and Dynamic Walking of Humanoid Robot KHR-2

abstract: In this paper, we describe the mechanical design, system integration and dynamic walking of the humanoid, KHR-2 (KAIST Humanoid Robot– 2). KHR-2 has 41 DOFs in total, that allows it to imitate various human-like motions. To control all joint axes effectively, the distributed control architecture is used, which reduces computation burden on the main controller, and allows convenient system. A servo motor controller was used as the sub-controller, whereas a 3-axis force/torque sensor and an inertia sensor were used in the sensory system. The main controller attached on the back of KHR-2 communicates with the sub-controllers in real-time through CAN (Controller Area Network) protocol. Windows XP was used as the operation system, whereas RTX HAL extension commercial software was used to realize the real-time control capability in Windows environment. We define the walking pattern and describe several online controllers in each stage. Some of the experimental results of KHR-2 are also presented.


title: Preserving Privacy of Continuous High-dimensional Data with Minimax Filters

abstract: Preserving privacy of high-dimensional and continuous data such as images or biometric data is a challenging problem. This paper formulates this problem as a learning game between three parties: 1) data contributors using a lter to sanitize data samples, 2) a cooperative data aggregator learning a target task using the ltered samples, and 3) an adversary learning to identify contributors using the same ltered samples. Mini


title: Customer Purchasing Behavior Analysis as Alternatives for Supporting In-Store Green Marketing Decision-Making

abstract: Due to increasing concerns about environmental protection, the environmental sustainability of businesses has been widely considered in the manufacturing and supply chain context. Further, its adoption has been implemented in the retail industry for marketing field, including green product promotion. This study aimed to propose a customer purchasing behavior analysis as an alternative for supporting decision-making in order to promote green products in retail stores. Hence, right-on-target marketing strategies can be implemented appropriately. The study was carried out using shopping path data collected by radio frequency identification (RFID) from a large retail store in Seoul, South Korea. In addition, the store layout and its traffic were also analyzed. This method is expected to help experts providing appropriate decision alternatives. In addition, it can help retailers in order to increase product sales and achieve high levels of customer satisfaction.


Effect of PSA on diagnostic yield of 68Ga-PSMA PET in advanced prostate cancer: A systematic review and meta-analysis


title: The impact of switching costs on the decision to retain or replace IT outsourcing vendors

abstract: While the IT outsourcing market is growing, outsourcing vendors are being replaced more frequently by firms. Since replacing vendors can affect the stability and quality of the IT services a firm receives, it is important to understand the drivers behind the decision to replace/retain vendors. This paper examines the impact of switching costs on this decision. We classify the various examples of switching costs into three categories (relational, financial and procedural) and develop a model to explain their role in the decision to replace or retain a vendor. The model also includes possible moderators of the relationship between switching costs and the vendor replacement decision. This model will be evaluated through a series of case studies of firms who have made this decision, and the refined model will be tested with a survey of IT outsourcing managers.


title: Predicting benefit from anti-angiogenic agents in malignancy

abstract: A high probability of benefit is desirable to justify the choice of anti-angiogenic therapy from an ever-expanding list of expensive new anticancer agents. However, biomarkers of response to cytotoxic agents are not optimal for predicting benefit from anti-angiogenic drugs. This discussion will focus on both preclinical and clinical research to identify biomarkers for anti-angiogenic therapies that can inform dosing, early clinical benefit, initial drug choice, emerging resistance and second-line treatments.


Power System State Estimation-Theory and Implementations [Book Review]