title: Software Defined Wireless Networks: A Survey of Issues and Solutions

abstract: Wireless networks such as mobile networks, with their inflexible and expensive network infrastructure, are facing various challenges in efficiently handling the exponentially growing traffic demands of users. Hence, mobile network operators (MNOs) are looking forward to novel networking paradigms which could simplify the task of network management and control and allow faster deployment of newer solutions on top of existing hardware by software updates. Software defined networking (SDN) is a revolutionary technology which makes networks more agile and flexible by separation of data plane and control plane tasks. SDN is playing a key role in deploying mobile network services on network function virtualization (NFV) infrastructure for elastic and flexible deployment of core network services e.g., virtualization of evolved packet core in LTE to efficiently handle huge control signal overhead and traffic demands from machine-to-machine and internet of things devices. Besides, NFV and SDN are offering scalable, flexible and adaptable network service chaining platforms as a replacement for inflexible middleboxes. Mobile edge computing (MEC) is a new evolving platform which brings together IT services and cloud computing for offering end-users network aware services and solutions. In this survey, we enumerate numerous issues and challenges in designing SDN based wireless networks and review various SDN based seminal solutions for 4G/5G. Finally, this survey presents the role of SDN, NFV and MEC in designing 5G networks.


title: A Region Based Convolutional Network for Tumor Detection and Classification in Breast Mammography

abstract: This paper addresses the problem of detection and classification of tumors in breast mammograms. We introduce a novel system that integrates several modules including a breast segmentation module and a fibroglandular tissue segmentation module into a modified cascaded region-based convolutional network. The method is evaluated on a large multi-center clinical dataset and compared to ground truth annotated by expert radiologists. Preliminary experimental results show the high accuracy and efficiency obtained by the suggested network structure. As the volume and complexity of data in healthcare continues to accelerate generalizing such an approach may have a profound impact on patient care in many applications.


title: Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

abstract: Abstract According to previous reports, information could be leaked from GPU memory; however, the security implications of such a threat were mostly over-looked, because only limited information could be indirectly extracted through side-channel attacks. In this paper, we propose a novel algorithm for recovering raw data directly from the GPU memory residues of many popular applications such as Google Chrome and Adobe PDF reader. Our algorithm enables harvesting highly sensitive information including credit card numbers and email contents from GPU memory residues. Evaluation results also indicate that nearly all GPU-accelerated applications are vulnerable to such attacks, and adversaries can launch attacks without requiring any special privileges both on traditional multi-user operating systems, and emerging cloud computing scenarios.


title: oro.open.ac.uk Cohere: Towards Web 2.0 Argumentation

abstract: Students, researchers and professional analysts lack effective tools to make personal and collective sense of problems while working in distributed teams. Central to this work is the process of sharing---and contesting---interpretations via different forms of argument. How does the “Web 2.0” paradigm challenge us to deliver useful, usable tools for online argumentation? This paper reviews the current state of the art in Web Argumentation, describes key features of the Web 2.0 orientation, and identifies some of the tensions that must be negotiated in bringing these worlds together. It then describes how these design principles are interpreted in Cohere, a web tool for social bookmarking, idea-linking, and argument visualization.


title: DeepProduct: Mobile Product Search With Portable Deep Features

abstract: Features extracted by deep networks have been popular in many visual search tasks. This article studies deep network structures and training schemes for mobile visual search. The goal is to learn an effective yet portable feature representation that is suitable for bridging the domain gap between mobile user photos and (mostly) professionally taken product images while keeping the computational cost acceptable for mobile-based applications. The technical contributions are twofold. First, we propose an alternative of the contrastive loss popularly used for training deep Siamese networks, namely robust contrastive loss, where we relax the penalty on some positive and negative pairs to alleviate overfitting. Second, a simple multitask fine-tuning scheme is leveraged to train the network, which not only utilizes knowledge from the provided training photo pairs but also harnesses additional information from the large ImageNet dataset to regularize the fine-tuning process. Extensive experiments on challenging real-world datasets demonstrate that both the robust contrastive loss and the multitask fine-tuning scheme are effective, leading to very promising results with a time cost suitable for mobile product search scenarios.


title: Character-level Chinese-English Translation through ASCII Encoding

abstract: Character-level Neural Machine Translation (NMT) models have recently achieved impressive results on many language pairs. They mainly do well for Indo-European language pairs, where the languages share the same writing system. However, for translating between Chinese and English, the gap between the two different writing systems poses a major challenge because of a lack of systematic correspondence between the individual linguistic units. In this paper, we enable character-level NMT for Chinese, by breaking down Chinese characters into linguistic units similar to that of Indo-European languages. We use the Wubi encoding scheme, which preserves the original shape and semantic information of the characters, while also being reversible. We show promising results from training Wubi-based models on the character- and subword-level with recurrent as well as convolutional models.


title: Cross: Efficient Low-rank Tensor Completion

abstract: The completion of tensors, or high-order arrays, attracts significant attention in recent research. Current literature on tensor completion primarily focuses on recovery from a set of uniformly randomly measured entries, and the required number of measurements to achieve recovery is not guaranteed to be optimal. In addition, the implementation of some previous methods are NP-hard. In this article, we propose a framework for low-rank tensor completion via a novel tensor measurement scheme we name Cross. The proposed procedure is efficient and easy to implement. In particular, we show that a third order tensor of Tucker rank-$(r_1, r_2, r_3)$ in $p_1$-by-$p_2$-by-$p_3$ dimensional space can be recovered from as few as $r_1r_2r_3 + r_1(p_1-r_1) + r_2(p_2-r_2) + r_3(p_3-r_3)$ noiseless measurements, which matches the sample complexity lower-bound. In the case of noisy measurements, we also develop a theoretical upper bound and the matching minimax lower bound for recovery error over certain classes of low-rank tensors for the proposed procedure. The results can be further extended to fourth or higher-order tensors. Simulation studies show that the method performs well under a variety of settings. Finally, the procedure is illustrated through a real dataset in neuroimaging.


title: Multiple Imputation for Missing Data: Concepts and New Development (Version 9.0)

abstract: Multiple imputation provides a useful strategy for dealing with data sets with missing values. Instead of filling in a single value for each missing value, Rubin’s (1987) multiple imputation procedure replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. These multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results from different imputed data sets is essentially the same. This results in valid statistical inferences that properly reflect the uncertainty due to missing values. This paper reviews methods for analyzing missing data, including basic concepts and applications of multiple imputation techniques. The paper also presents new SAS R procedures for creating multiple imputations for incomplete multivariate data and for analyzing results from multiply imputed data sets. These procedures are still under development and will be available in experimental form in Release 8.1 of the SAS System. Introduction Most SAS statistical procedures exclude observations with any missing variable values from the analysis. These observations are called incomplete cases. While using only complete cases has its simplicity, you lose information in the incomplete cases. This approach also ignores the possible systematic difference between the complete cases and incomplete cases, and the resulting inference may not be applicable to the population of all cases, especially with a smaller number of complete cases. Some SAS procedures use all the available cases in an analysis, that is, cases with available information. For example, PROC CORR estimates a variable mean by using all cases with nonmissing values on this variable, ignoring the possible missing values in other variables. PROC CORR also estimates a correlation by using all cases with nonmissing values for this pair of variables. This may make better use of the available data, but the resulting correlation matrix may not be positive definite. Another strategy is simple imputation, in which you substitute a value for each missing value. Standard statistical procedures for complete data analysis can then be used with the filled-in data set. For example, each missing value can be imputed from the variable mean of the complete cases, or it can be imputed from the mean conditional on observed values of other variables. This approach treats missing values as if they were known in the complete-data analyses. Single imputation does not reflect the uncertainty about the predictions of the unknown missing values, and the resulting estimated variances of the parameter estimates will be biased toward zero. Instead of filling in a single value for each missing value, a multiple imputation procedure (Rubin 1987) replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. The multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results from different data sets is essentially the same. Multiple imputation does not attempt to estimate each missing value through simulated values but rather to represent a random sample of the missing values. This process results in valid statistical inferences that properly reflect the uncertainty due to missing values; for example, valid confidence intervals for parameters. Multiple imputation inference involves three distinct phases: The missing data are filled in m times to generate m complete data sets. The m complete data sets are analyzed by using standard procedures. The results from the m complete data sets are combined for the inference. A new SAS/STAT R procedure, PROC MI, is a multiple imputation procedure that creates multiply imputed data sets for incomplete p-dimensional multivariate data. It uses methods that incorporate appropriate variability across the m imputations. Once the m complete data sets are analyzed by using standard procedures, another new procedure, PROC MIANALYZE, can be used to generate valid statistical inferences about these parameters by combining results from the m complete data sets. Statistics and Data Analysis


title: Deep Reinforcement Learning for Optimal Control of Space Heating

abstract: Classical methods to control heating systems are often marred by suboptimal performance, inability to adapt to dynamic conditions and unreasonable assumptions e.g. existence of building models. This paper presents a novel deep reinforcement learning algorithm which can control space heating in buildings in a computationally efficient manner, and benchmarks it against other known techniques. The proposed algorithm outperforms rule based control by between 5-10% in a simulation environment for a number of price signals. We conclude that, while not optimal, the proposed algorithm offers additional practical advantages such as faster computation times and increased robustness to non-stationarities in building dynamics.


title: Nine Ways to Reduce Cognitive Load in Multimedia Learning

abstract: First, we propose a theory of multimedia learning based on the assumptions that humans possess separate systems for processing pictorial and verbal material (dual-channel assumption), each channel is limited in the amount of material that can be processed at one time (limited-capacity assumption), and meaningful learning involves cognitive processing including building connections between pictorial and verbal representations (active-processing assumption). Second, based on the cognitive theory of multimedia learning, we examine the concept of cognitive overload in which the learner's intended cognitive processing exceeds the learner's available cognitive capacity. Third, we examine five overload scenarios. For each overload scenario, we offer one or two theory-based suggestions for reducing cognitive load, and we summarize our research results aimed at testing the effectiveness of each suggestion. Overall, our analysis shows that cognitive load is a central consideration in the design of multimedia instru...


title: Nonlinear decoding of a complex movie from the mammalian retina

abstract: Retinal circuitry transforms spatiotemporal patterns of light into spiking activity of ganglion cells, which provide the sole visual input to the brain. Recent advances have led to a detailed characterization of retinal activity and stimulus encoding by large neural populations. The inverse problem of decoding, where the stimulus is reconstructed from spikes, has received less attention, in particular for complex input movies that should be reconstructed "pixel-by-pixel". We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small discs executing mutually-avoiding random motions. We constructed nonlinear (kernelized) decoders that improved significantly over linear decoding results, mostly due to their ability to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous or network activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. Our results suggest a general paradigm in which downstream neural circuitry could discriminate between spontaneous and stimulus-driven activity on the basis of higher-order statistical structure intrinsic to the incoming spike trains.


title: Zoanthids (Anthozoa, Hexacorallia) from the Great Barrier Reef and Torres Strait, Australia: systematics, evolution and a key to species

abstract: Zoanthid taxonomy is currently in a state of chaos, with many described species very few of which can be reliably identified. As part of a genetically based, objective reappraisal of the number of northern Australian species, a total of 355 zoanthid specimens were collected from 19 localities in the Great Barrier Reef and Torres Strait during 1992–1994. Specimens, initially assigned to one of ten morphological or ecological forms, were subjected to allozyme electrophoretic analysis. Analysis of genetic data revealed only seven discrete (i.e., non-interbreeding) groups in the family Zoanthidae. These groups, which are delimited by fixed gene differences, are considered species under a biological species concept. Some species show considerable morphological variation and have broad environmental tolerances. We provide the first key to Great Barrier Reef zoanthid species based on our results and observations. Species can be differentiated in the field on the basis of gross colony morphology, sand encrusting habit, polyp form and habitat. Genetic data are also used to generate a phylogenetic hypothesis of relationships among the Zoanthidae, which is compared to previous morphologically based systems. Division of the group on the basis of mesenterial arrangement appears justified, but nematocyst data appear less valuable in phylogenetic studies of the group than has been suggested previously.


title: Interactive Collision Detection for Deformable Models Using Streaming AABBs

abstract: We present an interactive and accurate collision detection algorithm for deformable, polygonal objects based on the streaming computational model. Our algorithm can detect all possible pairwise primitive-level intersections between two severely deforming models at highly interactive rates. In our streaming computational model, we consider a set of axis aligned bounding boxes (AABBs) that bound each of the given deformable objects as an input stream and perform massively-parallel pairwise, overlapping tests onto the incoming streams. As a result, we are able to prevent performance stalls in the streaming pipeline that can be caused by expensive indexing mechanism required by bounding volume hierarchy-based streaming algorithms. At runtime, as the underlying models deform overtime, we employ a novel, streaming algorithm to update the geometric changes in the AABB streams. Moreover, in order to get only the computed result (i.e., collision results between AABBs) without reading back the entire output streams, we propose a streaming en/decoding strategy that can be performed in a hierarchical fashion. After determining overlapped AABBs, we perform a primitive-level (e.g., triangle) intersection checking on a serial computational model such as CPUs. We implemented the entire pipeline of our algorithm using off-the-shelf graphics processors (GPUs), such as nVIDIA GeForce 7800 GTX, for streaming computations, and Intel Dual Core 3.4G processors for serial computations. We benchmarked our algorithm with different models of varying complexities, ranging from 15K up to 50K triangles, under various deformation motions, and the timings were obtained as 30~100 FPS depending on the complexity of models and their relative configurations. Finally, we made comparisons with a well-known GPU-based collision detection algorithm, CULLIDE and observed about three times performance improvement over the earlier approach. We also made comparisons with a SW-based AABB culling algorithm and observed about two times improvement


title: Application of robots using pneumatic artificial rubber muscles for operating construction machines

abstract: In the restoration work from disasters, the remote control of construction machine is required to ensure the worker's safety. However, conventional remote-controlled construction machine is larger than ordinary ones and limited in types and numbers, so there is a problem that the transportation to the destructed sites takes time and is troublesome. Therefore, the development of the remote-control system using a robot, which can be easily installed in ordinary construction machine, is desired. We have been developing the pneumatic humanoid type robot arm, which can be installed in any models of construction machine, and realizing remote control of construction machine with the aim of easy installation and portability. In consideration of portability, the lightweight pneumatic artificial rubber muscle (PARM) was adopted as the actuator. In this research, we realized the remote control of the construction machine using the pneumatic robot that has 6 degree of freedom using the fiber knitted type PARM. We experimented in order to measure the static characteristic of the fiber knitted type PARM. Then we did experiment on the remote control of the construction machine. Experimental results show the developed system is availability in remote control of a construction machine.


title: Risk-sensitive inverse reinforcement learning via semi- and non-parametric methods

abstract: The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive IRL in order to explicitly account for a human's risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk-neutral to worst-case. We propose efficient non-parametric algorithms based on linear programming and semi-parametric algorithms based on maximum likelihood for inferring a human's underlying risk measure and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with ten human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk-averse to risk-neutral in a data-efficient manner. Moreover, comparisons of the Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively, especially in scenarios where catastrophic outcomes such as collisions can occur.


title: Classifiers Unclassified: An Efficient Approach to Revealing IP Traffic Classification Rules

abstract: A variety of network management practices, from bandwidth management to zero-rating, use policies that apply selectively to different categories of Internet traffic (e.g., video, P2P, VoIP). These policies are implemented by middleboxes that must, in real time, assign traffic to a category using a classifier. Despite their important implications for network management, billing, and net neutrality, little is known about classifier implementations because middlebox vendors use proprietary, closed-source hardware and software. In this paper, we develop a general, efficient methodology for revealing classifiers' matching rules without needing to explore all permutations of flow sizes and contents in our testbed environment. We then use it to explore implementations of two other carrier-grade middleboxes (one of which is currently deployed in T-Mobile). Using packet traces from more than 1,000,000 requests from 300 users, we find that all the devices we test use simple keyword-based matching rules on the first two packets of HTTP/S traffic and small fractions of payload contents instead of stateful matching rules during an entire flow. Our analysis shows that different vendors use different matching rules, but all generally focus on a small number of HTTP, TLS, or content headers. Last, we explore the potential for misclassification based on observed matching rules and discuss implications for subversion and net neutrality violations.


title: A face antispoofing database with diverse attacks

abstract: Face antispoofing has now attracted intensive attention, aiming to assure the reliability of face biometrics. We notice that currently most of face antispoofing databases focus on data with little variations, which may limit the generalization performance of trained models since potential attacks in real world are probably more complex. In this paper we release a face antispoofing database which covers a diverse range of potential attack variations. Specifically, the database contains 50 genuine subjects, and fake faces are made from the high quality records of the genuine faces. Three imaging qualities are considered, namely the low quality, normal quality and high quality. Three fake face attacks are implemented, which include warped photo attack, cut photo attack and video attack. Therefore each subject contains 12 videos (3 genuine and 9 fake), and the final database contains 600 video clips. Test protocol is provided, which consists of 7 scenarios for a thorough evaluation from all possible aspects. A baseline algorithm is also given for comparison, which explores the high frequency information in the facial region to determine the liveness. We hope such a database can serve as an evaluation platform for future researches in the literature.


title: Synergetic Artificial Intelligence and Social Robotics

abstract: The fundamentals of synergetic artificial intelligence and its relationships with swarm intelligence are considered. Basic classifications of agents and multi-agent systems are presented, the comparison between intelligent and reactive agents is made. Different synergy sources for conventional group intelligence and swarm intelligence are elicited. The concepts of swarms, swarm intelligence and swarm robotics are discussed. The principles and models of swarm tasks distribution via local interactions are formulated. The results of experimental investigation of pack-hunting task are analyzed.


title: Ask, and shall you receive?: Understanding Desire Fulfillment in Natural Language Text

abstract: The ability to comprehend wishes or desires and their fulfillment is important to Natural Language Understanding. This paper introduces the task of identifying if a desire expressed by a subject in a given short piece of text was fulfilled. We propose various unstructured and structured models that capture fulfillment cues such as the subject's emotional state and actions. Our experiments with two different datasets demonstrate the importance of understanding the narrative and discourse structure to address this task.


title: An Assessment of Intrinsic and Extrinsic Motivation on Task Performance in Crowdsourcing Markets

abstract: Crowdsourced labor markets represent a powerful new paradigm for accomplishing work. Understanding the motivating factors that lead to high quality work could have significant benefits. However, researchers have so far found that motivating factors such as increased monetary reward generally increase workers’ willingness to accept a task or the speed at which a task is completed, but do not improve the quality of the work. We hypothesize that factors that increase the intrinsic motivation of a task – such as framing a task as helping others – may succeed in improving output quality where extrinsic motivators such as increased pay do not. In this paper we present an experiment testing this hypothesis along with a novel experimental design that enables controlled experimentation with intrinsic and extrinsic motivators in Amazon’s Mechanical Turk, a popular crowdsourcing task market. Results suggest that intrinsic motivation can indeed improve the quality of workers’ output, confirming our hypothesis. Furthermore, we find a synergistic interaction between intrinsic and extrinsic motivators that runs contrary to previous literature suggesting “crowding out” effects. Our results have significant practical and theoretical implications for crowd work.


title: The Effect of Perceived Novelty on the Adoption of Information Technology Innovations: A Risk/Reward Perspective

abstract: A fundamental characteristic of any innovation is its novelty, the newness or freshness of the innovation in the eyes of the adopter. Past research has often considered novelty to be inherent to an information technology (IT) innovation, yet it is also likely that perceptions of novelty differ widely across individuals. Nevertheless, the role that the novelty of an IT innovation plays in adoption is not well understood. The primary goal of this research effort is to frame the perceived novelty of an IT innovation as a salient affective belief in the nomological network related to adoption. Further, we examine how perceived novelty influences the way individuals reconcile their perceptions of risk versus reward when considering the adoption of an IT innovation. Two empirical studies with 424 and 138 participants, respectively, examine the effect of perceived novelty on IT innovations from a risk/reward perspective. Results indicate that perceived novelty is a salient affective belief that plays a significant role in the adoption of IT innovations. Implications for both theory and organizational decision making are examined.


title: A simple space vector PWM scheme to operate a three-level NPC inverter at high modulation index including over-modulation region, with neutral point balancing

abstract: Multilevel inverters are being increasingly used for high-power medium-voltage applications. Three-level neutral point clamped (NPC) topology is most widely used topology of multilevel inverters. However, at higher modulation index, particularly in over-modulation region, the neutral point fluctuation deteriorates the performance of the inverter. Furthermore, operating the inverter at lower modulation index implies that it is operated at lower voltages only, and the installed DC link capacity is not fully utilized. This paper proposes a simple space vector PWM scheme for operating a three-level NPC inverter at higher modulation indices including over-modulation region, with neutral point balancing. Experimental results are provided.


title: Knowledgeable Explanations for Recommender Systems

abstract: Recommender Systems (RS) serve online customers in identifying those items from a variety of choices that best match their needs and preferences. In this context explanations summarize the reasons why a specific item is proposed and strongly increase the users' trust in the system's results. In this paper we propose a framework for generating knowledgeable explanations that exploits domain knowledge to transparently argue why a recommended item matches the user's preferences. Furthermore, results of an online experiment on a real-world platform show that users' perception of the usability of a recommender system is positively influenced by knowledgeable explanations and that consequently users' experience in interacting with the system, their intention to use it repeatedly as well as their commitment to recommend it to others are increased.


title: Visual-Motor Skills Performance on the Beery- VMI: A Study of Canadian Kindergarten Children

abstract: Abstract The Beery VMI is one of the standardized assessment tools most widely used by occupational therapists toassess visual-motor integration. Currently, no specific norms exist for Canadian children. This study wasdeveloped to assess whether kindergarten children in Canada compared similarly to the norms established inthe U.S. sample of the Beery VMI in order to validate its use with Canadian children. Possible genderdifferences were also examined. The Beery VMI was administered to 151 kindergarten children, aged 5 to 6years, at the end of the school year. The data collection took place in seven schools, where the participantswere individually assessed. T-tests were used to compare the mean standard scores of the Canadian sample tothose provided in the Beery VMI, as well as gender differences. Overall, the study sample showed a similarscore (p= .997) compared to the U.S. norms, as well as a significant gender difference; girls obtained highermean scores than boys (p= .003). These findings support the use of the Beery VMI reference norms tointerpret performance results obtained by Canadian kindergarten children. However, the significant genderdifference observed should be addressed in future studies.


MovieGEN : A Movie Recommendation System


title: Free Will in Scientific Psychology

abstract: Some actions are freer than others, and the difference is palpably important in terms of inner process, subjective perception, and social consequences. Psychology can study the difference between freer and less free actions without making dubious metaphysical commitments. Human evolution seems to have created a relatively new, more complex form of action control that corresponds to popular notions of free will. It is marked by self-control and rational choice, both of which are highly adaptive, especially for functioning within culture. The processes that create these forms of free will may be biologically costly and therefore are only used occasionally, so that people are likely to remain only incompletely self-disciplined, virtuous, and rational.


title: Novel low cost compact phased array antenna for millimeter-wave 3D beam scanning applications

abstract: A novel 3D scanning antenna is presented for 5G mobile wireless communication applications. The proposed antenna employs 8 phase shifters for a 16-element phased array. It provides a 3D scanning window of Phi from 165° to 200° and from 245 ° to 270 ° while Theta can go up to 20 °. The size of the antenna including the feeding network is 2.5cm × 2.5cm. The Antenna gain is 16.5dB at 30GHz, and the gain variation is less than 1dB over the scanning window.


title: Extracting Social Network and Character Categorization From Bengali Literature

abstract: Literature network analysis is an emerging area in the computational research domain. Literature network is a type of social network with various distinct features. The analysis explores significance of human behavior and complex social relationships. The story consists of some characters and creates an interconnected social system. Each character of the literature represents a node and the edge between any two nodes offered the interaction between them. An annotation and a novel character categorization method are developed to extract interactive social network from the Bengali drama. We analyze Raktakarabi and Muktodhara , two renowned Bengali dramas of Rabindranath Tagore. Weighted degree, closeness, and betweenness centrality analyze the correlation among the characters. We propose an edge contribution-based centrality and diversity metric of a node to determine the influence of one character over others. High diverse nodes show low clustering coefficient and vice versa. We propose a novel idea to analyze the characteristics of protagonist and antagonist from the influential nodes based on the complex graph. We also present a game theory-based community detection method that clusters the actors with a high degree of relationship. Evaluation on real-world networks demonstrates the superiority of the proposed method over the other existing algorithms. Interrelationship of the actors within the drama is also shown from the detected communities, as underlying theme of the narrations is identical. The analytical results show that our method efficiently finds the protagonist and antagonist from the literature network. The method is unique, and the analytical results are more accurate and unbiased than the human perspective. Our approach establishes similar results compared with the benchmark analysis available in Tagore’s Bengali literature.


CALCULATIONAL STUDY OF ARRAYS OF CYLINDERS OF FISSILE SOLUTION.


title: Surreptitiously Weakening Cryptographic Systems.

abstract: Revelations over the past couple of years highlight the importance of understanding malicious and surreptitious weakening of cryptographic systems. We provide an overview of this domain, using a number of historical examples to drive development of a weaknesses taxonomy. This allows comparing different approaches to sabotage. We categorize a broader set of potential avenues for weakening systems using this taxonomy, and discuss what future research is needed to provide sabotage-resilient cryptography.


title: On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control

abstract: Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.


title: Highlighter: Automatic Highlighting of Electronic Learning Documents

abstract: Electronic textual documents are among the most popular teaching content accessible through e-learning platforms. Teachers or learners with different levels of knowledge can access the platform and highlight portions of textual content which are deemed as particularly relevant. The highlighted documents can be shared with the learning community in support of oral lessons or individual learning. However, highlights are often incomplete or unsuitable for learners with different levels of knowledge. This paper addresses the problem of predicting new highlights of partly highlighted electronic learning documents. With the goal of enriching teaching content with additional features, text classification techniques are exploited to automatically analyze portions of documents enriched with manual highlights made by users with different levels of knowledge and to generate ad hoc prediction models. Then, the generated models are applied to the remaining content to suggest highlights. To improve the quality of the learning experience, learners may explore highlights generated by models tailored to different levels of knowledge. We tested the prediction system on real and benchmark documents highlighted by domain experts and we compared the performance of various classifiers in generating highlights. The achieved results demonstrated the high accuracy of the predictions and the applicability of the proposed approach to real teaching documents.


title: Depth super-resolution via fully edge-augmented guidance

abstract: Recently, convolutional neural networks (CNNs) have been widely used for image processing problems. In this work, we present an end-to-end depth map super-resolution method based on CNN. Standing on a residual learning architecture, the proposed network learns joint features to get a high-resolution (HR) depth map from a low-resolution (LR) one with the multi-layers guidance of a HR color image. Furthermore, in order to focus on the boundaries of depth map, we generate an edge-attention map from the associated HR color images as a guidance. Experimental results show that the proposed network outperforms the state-of-the-art depth map super-resolution methods.


title: Physician-Patient Relationship and Medication Compliance: A Primary Care Investigation

abstract: PURPOSE We assessed the relationship between 4 attributes of the physician- patient relationship and medication compliance. METHODS We conducted a waiting room survey of patients consulting 22 general practitioners in 14 randomly selected practices in Auckland, New Zealand (81% response rate). A total of 370 consecutive patients (75% response rate) completed survey instruments about 4 attributes of the physician-patient relationship. Continu- ity of care (assessed from use of a usual physician, length of continuity, and per- ceived importance of continuity) and trust in the physician were ascertained before the consultation. After the consultation the Patient Enablement Index measured the physician's ability to enable patients in self-care, and concordance between the patient and physician was measured by a 6-item inventory of perceived agreement about the presenting problem and management, were ascertained immediately after the consultation. Compliance with prescribed medication therapy was ascer- tained by telephone follow-up 4 days after the consultation. RESULTS Overall, 220 patients (61%) received a prescription, and 79% of these patients were taking the medication at follow-up. In a univariate analysis adjusted for clustering, only trust and physician-patient concordance were signifi cantly related to compliance. In analysis further adjusted for health and demographic factors, physician-patient concordance was independently related to compliance (odds ratio = 1.34, 95% confi dence interval, 1.04-1.72). CONCLUSIONS Primary care consultations with higher levels of patient-reported physician-patient concordance were associated with one-third greater medication compliance. An emphasis on understanding and facilitating agreement between physician and patient may benefi t outcomes in primary care.


title: The feasible workspace analysis of a set point control for a cable-suspended robot with input constraints and disturbances

abstract: This paper deals with the characterization of reachable domain of a set-point controller for a cable-suspended robot under disturbances and input constraints. The main contribution of the paper is to calculate the feasible domain analytically through the choice of a control law, starting from a given initial condition. This analytical computation is then recursively used to find a second feasible domain starting from a boundary point of the first feasible domain. Hence, this procedure allows to expand the region of feasible reference signals by connecting adjacent domains through common points. Finally, the effectiveness of the proposed method is illustrated by numerical simulations on a kinematically determined cable robot with six cables.


title: Measurement of Human Locomotion

abstract: The importance of measurements to properly assess human locomotion is increasingly recognized. Already well established as an experimental scientific research tool, human locomotion measurements are frequently a routine clinical application. Fields of application encompass both healthy and pathological locomotion encountered in rehabilitation medicine, orthopedics, kinesiology, sports science, and other related fields. This volume provides comprehensive description of instrument systems for measurement of kinematics of human movement, kinetic quantities experienced by the human body in contact with the ground, and myoelectric changes associated with locomotor activity. Physical principles governing the operation of several measurement devices and relevant mathematics and engineering are presented, as well as signal processing issues that must be addressed in order to obtain and use quantitative measurement variables in the biomechanical context. Measurement data acquisiton, processing, and presentation to the user in a computer-based laboratory environment are explained. The ultimate goal is to contribute to the diagnostics and treatment of specific locomotion patterns. References to major historical landmarks in the development of measurement methodology are provided as well. Selected experimental data are shown and interpreted to illustrate the methods, some originating from the author's own research. Consequently, the reader will gain insight into the working principles, typical uses, and comparative advantages of a number of instruments, such as simple electrogoniometers, sophisticated stereometric instruments to capture human body kinematics, imbedded force plates, distributed pressure measurement systems, wire and telemetry electromyographs, etc. Systems oriented and interdisciplinary in character, this volume addresses biomedical engineers, active in industry or the clinical environment, physicians, kinesiologists, physical therapists, and students and researchers of human movement in clinics and academia. By focusing on locomotion measurements, the volume attempts to complement classical biomechanics, neurophysiology, and motor control-oriented texts.


title: A Password Manager that Doesn't Remember Passwords

abstract: The problems with passwords are well-known: secure passwords are difficult to remember, users have too many passwords, and users have difficulty matching their passwords to accounts. Password managers and cued graphical passwords are two password solutions that address the issues of memorability and keeping track of of passwords. We have developed Versipass, a password manager that incorporates key elements of password managers and cued graphical passwords to avoid existing problems of password memorability and associating passwords with accounts. Instead of remembering passwords, Versipass remembers image cues for graphical passwords. These cues help users to better remember their passwords and to more easily link passwords with accounts. Versipass also facilitates safe password reuse by allowing users to use the same image cue for multiple accounts.


title: Multi-Criteria Decision Analysis: A Framework for Structuring Remedial Decisions at the Contaminated Sites". Comparative Risk Assessment and Environmental Decision Making

abstract: Decision-making in environmental projects is typically a complex and confusing exercise, characterized by trade-offs between socio-political, environmental, and economic impacts. Cost-benefit analyses are often used, occasionally in concert with comparative risk assessment, to choose between competing project alternatives. The selection of appropriate remedial and abatement policies for contaminated sites, land-use planning and other regulatory decision-making problems for contaminated sites involves multiple criteria such as cost, benefit, environmental impact, safety, and risk. Some of these criteria cannot easily be condensed into a monetary value, which complicates the integration problem inherent to making comparisons and trade-offs. Even if it were possible to convert criteria rankings into a common unit this approach would not always be desirable since stakeholder preferences may be lost in the process. Furthermore, environmental concerns often involve ethical and moral principles that may not be related to any economic use or value.


title: An ontological framework for knowledge modeling and decision support in cyber-physical systems

abstract: Our work is concerned with the development of knowledge structures to support correct-by-design cyber-physical systems (CPS). This class of systems is defined by a tight integration of software and physical processes, the need to satisfy stringent constraints on performance and safety, and a reliance on automation for the management of system functionality and decision making. To assure correctness of functionality with respect to requirements, there is a strong need for system models to account for semantics of the domains involved. This paper introduces a new ontological-based knowledge and reasoning framework for decision support for CPS. It enables the development of determinate, provable and executable CPS models supported by sound semantics strengthening the model-driven approach to CPS design. An investigation into the structure of basic description logics (DL) has identified the needed semantic extensions to enable the web ontology language (OWL) as the ontological language for our framework. The SROIQ DL has been found to be the most appropriate logic-based knowledge formalism as it maps to OWL 2 and ensures its decidability. Thus, correct, stable, complete and terminating reasoning algorithms are guaranteed with this SROIQ -backed language. The framework takes advantage of the commonality of data and information processing in the different domains involved to overcome the barrier of heterogeneity of domains and physics in CPS. Rules-based reasoning processes are employed. The framework provides interfaces for semantic extensions and computational support, including the ability to handle quantities for which dimensions and units are semantic parameters in the physical world. Together, these capabilities enable the conversion of data to knowledge and their effective use for efficient decision making and the study of system-level properties, especially safety. We exercise these concepts in a traffic light time-based reasoning system.


title: Decentralized Power Management of a PV/Battery Hybrid Unit in a Droop-Controlled Islanded Microgrid

abstract: In this paper, a control strategy is proposed to achieve decentralized power management of a PV/battery hybrid unit in a droop-controlled islanded microgrid. In contrast to the common approach of controlling the PV unit as a current source, in the proposed strategy, the PV unit is controlled as a voltage source that follows a multi-segment adaptive power/frequency characteristic curve. The proposed power/frequency characteristics, of the hybrid unit and of the whole microgrid, adapt autonomously to the microgrid operating conditions so that the hybrid unit may supply the maximum PV power, match the load, and/or charge the battery, while maintaining the power balance in the microgrid and respecting the battery state-of-charge limits. These features are achieved without relying on a central management system and communications, as most of the existing algorithms do. The control strategy is implemented using multi-loop controllers, which provide smooth and autonomous transitions between the operating scenarios. Small-signal stability of the proposed control loops is investigated, and the system performance is experimentally validated on a 3.5 kVA microgrid.


title: Tentative clinical diagnosis of Lujan-Fryns syndrome-A conglomeration of different genetic entities?

abstract: The clinical diagnosis of Lujan-Fryns syndrome (LFS) comprises X-linked intellectual disability (XLID) with marfanoid habitus, distinct combination of minor facial anomalies and nasal speech. However the definition of syndrome was significantly broadened since the original report and implies ID with marfanoid habitus. Mutations of three genes (MED12, UPF3B, and ZDHHC9) have been reported in “broadly defined” LFS. We examined these genes in 28 individuals with a tentative clinical diagnosis of LFS but we did not identify any causative mutation. By molecular karyotyping we detected other disorders, i.e., Phelan-McDermid syndrome and 16p11.2 microduplication, each in one patient. One affected individual was carrier of a different recurrent duplication on 16p11.2 that has been reported several times to the DECIPHER and ISCA databases in individuals with autism, intellectual disability (ID), and developmental delay. It may represent a new duplication syndrome. We also identified previously unreported de novo duplication on chromosome 12p13.31 which we considered to be disease-causing. X-exome sequencing of four individuals revealed private or non-recurrent mutations in NKAP and LAS1L in one patient each. While LFS is defined as a form of XLID, there seem to be various conditions that have rather similar phenotypes. Therefore, the combination of ID and marfanoid habitus in a male patient is not sufficient for the diagnosis of LFS. We suggest that the diagnosis of LFS in patients with ID and marfanoid habitus should be made only in presence of specific facial features, nasal speech and obvious X-linked segregation of the disorder or an unambiguously pathogenic mutation in the MED12. © 2015 Wiley Periodicals, Inc.


Automatic Melody Generation


title: Long ties accelerate noisy threshold-based contagions

abstract: Network structure can affect when and how widely new ideas, products, and behaviors are adopted. In widely-used models of biological contagion, interventions that randomly rewire edges (generally making them "longer") accelerate spread. However, there are other models relevant to social contagion, such as those motivated by myopic best-response in games with strategic complements, in which an individual's behavior is described by a threshold number of adopting neighbors above which adoption occurs (i.e., complex contagions). Recent work has argued that highly clustered, rather than random, networks facilitate spread of these complex contagions. Here we show that minor modifications to this model, which make it more realistic, reverse this result: we allow very rare below-threshold adoption, i.e., rarely adoption occurs when there is only one adopting neighbor. To model the trade-off between long and short edges we consider networks that are the union of cycle-power-k graphs and random graphs on n nodes. Allowing adoptions below threshold to occur with order 1/\sqrt(n) probability along some "short" cycle edges is enough to ensure that random rewiring accelerates spread. Simulations illustrate the robustness of these results to other commonly-posited models for noisy best-response behavior. Hypothetical interventions that randomly rewire existing edges or add random edges (versus adding "short", triad-closing edges) in hundreds of empirical social networks reduce time to spread. This revised conclusion suggests that those wanting to increase spread should induce formation of long ties, rather than triad-closing ties. More generally, this highlights the importance of noise in game-theoretic analyses of behavior.


title: Approximate convex decomposition of polyhedra and its applications

abstract: Decomposition is a technique commonly used to partition complex models into simpler components. While decomposition into convex components results in pieces that are easy to process, such decompositions can be costly to construct and can result in representations with an unmanageable number of components. In this paper we explore an alternative partitioning strategy that decomposes a given model into ''approximately convex'' pieces that may provide similar benefits as convex components, while the resulting decomposition is both significantly smaller (typically by orders of magnitude) and can be computed more efficiently. Indeed, for many applications, an approximate convex decomposition (acd) can more accurately represent the important structural features of the model by providing a mechanism for ignoring less significant features, such as surface texture. We describe a technique for computing acds of three-dimensional polyhedral solids and surfaces of arbitrary genus. We provide results illustrating that our approach results in high quality decompositions with very few components and applications showing that comparable or better results can be obtained using acd decompositions in place of exact convex decompositions (ecd) that are several orders of magnitude larger.


title: Diagnosis and compensation of amplitude imbalance, imperfect quadrant and offset in resolver signals

abstract: In this paper, a new method for detecting some errors in resolver signals is presented. Sin and cosine signals in resolver to digital converter's (RDC's) input might have errors, such as amplitude imbalance, imperfect quadrant or DC offset. Like other sensors some defects in mechanical structure and electrical or magnetic circuits cause to these errors in resolver output signals. The amplitude and phase of resolver output signals and consequently calculated angle are affected by these errors at any moment. Resolver output signals are more affected in their peak points than the other points. These peak points are easier and more accurate than the other points for analyzing, too. It is obvious that any error has unique effects on the signals. Therefore, by analyzing the effect on signals' peak points the error type and the value can be detected. To avoid wrong detection, error signal is defined, which detects the per unit signals deviation from the unit circle. Also, for error detection, there is no need to interrupt converter's operation. The proposed method is simulated in Matlab/Simulink environment, and the obtained results validate its effectiveness. Also, it is simulated on TMS320F2812 DSP using CCS 3.3, and the results are presented. Furthermore, by implementing the present RDC on motor controller DSP, it will be a low-cost approach.


title: Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language☆

abstract: Abstract A new method for extracting common themes from written text is introduced and applied to 1165 open-ended self-descriptive narratives. Drawing on a lexical approach to personality, the most commonly-used adjectives within narratives written by college students were identified using computerized text analytic tools. A factor analysis on the use of these adjectives in the self-descriptions produced a 7-factor solution consisting of psychologically meaningful dimensions. Some dimensions were unipolar (e.g., Negativity factor, wherein most loaded items were negatively valenced adjectives); others were dimensional in that semantically opposite words clustered together (e.g., Sociability factor, wherein terms such as shy, outgoing, reserved, and loud all loaded in the same direction). The factors exhibited modest reliability across different types of writing samples and were correlated with self-reports and behaviors consistent with the dimensions. Similar analyses with additional content words (adjectives, adverbs, nouns, and verbs) yielded additional psychological dimensions associated with physical appearance, school, relationships, etc. in which people contextualize their self-concepts. The results suggest that the meaning extraction method is a promising strategy that determines the dimensions along which people think about themselves.


title: Effects of Physical Exercise Intervention on Psychological and Physical Fitness in Lymphoma Patients

abstract: Background and objectives: Lymphoma patients experience a psychological and physiological decline that could be reversed by exercise. However, little is known about the effects of the exercise on psychological and physical fitness variables. Therefore, the purpose of this longitudinal study was to assess self-efficacy, fatigue and physical fitness before and after an eight-week exercise intervention. Materials and Methods: Thirty-six participants (54.4 ± 19.1 years) performed a supervised exercise program (~60 min, 2d·wk−1). Each session included a combined progressive training of cardiorespiratory, resistance, flexibility and postural education exercises. Self-efficacy and fatigue were measured with the Regulatory Emotional Self-Efficacy scale and 0–10 rating scale, respectively. Physical fitness was assessed with the body mass index, lower back flexibility, static balance, muscle strength and functional mobility. Results: Adherence to exercise was high (91.2% ± 4.8%) and no major health problems were noted in the patients over the intervention period. At baseline, significant differences were found between Hodgkin’s lymphoma and non-Hodgkin’s lymphoma patients by age and all dependent measures (p < 0.05). Fatigue significantly decreased and the perceived capability to regulate negative affect and to express positive emotions improved after exercise (p < 0.001). Significant improvements were found for body mass index, trunk lateral flexibility, monopodalic balance, isometric handgrip force and functional mobility (p < 0.001). Fatigue was significantly correlated with handgrip force (r = −0.56, p < 0.001) and functional mobility (r = −0.69, p < 0.001). Conclusions: The supervised exercise program improved psychological and physical fitness without causing adverse effects and health problems. Therefore, exercise to improve fitness levels and reduce perceived fatigue should be considered in the management of lymphoma patients.


title: Three-Dimensional Printable Origami Twisted Tower: Design, Fabrication, and Robot Embodiment

abstract: This letter presents a novel robotic mechanism inspired by origami and its fabrication method using three-dimensional (3D) printing. The selected twisted tower design shows unique structural and geometrical properties that enable bending and linear motions, and therefore, can be suitable for various robotic applications. However, fabrication of this origami design requires a complex sequence of paper folding and assembling by hand, which cannot be automated by existing manufacturing technologies. To fabricate this structure via 3D printing, the twisted tower is reconstructed in a computer aided design (CAD) model. The CAD model is then 3D printed using two different materials—a flexible material for creases and a rigid material for surfaces. The printed structure preserves the geometrical properties of the original tower. Associated kinematics and workspace analysis are also presented in this letter. The 3D printed tower is embodied into a robotic arm to physically demonstrate its potential utility.


title: A novel cubic-order algorithm for approximating principal direction vectors

abstract: There are a number of applications in computer graphics that require as a first step the accurate estimation of principal direction vectors at arbitrary vertices on a triangulated surface. Although several methods for calculating principal directions over such models have been previously proposed, we have found in practice that all exhibit unexplained large errors in some cases. In this article, we describe our theoretical and experimental investigations into possible sources of errors in the approximation of principal direction vectors from triangular meshes, and suggest a new method for estimating principal directions that can yield better results under some circumstances.


title: Not all M&As are alike--and that matters.

abstract: Despite all that's been written about mergers and acquisitions, even the experts know surprisingly little about them. The author recently headed up a year-long study sponsored by Harvard Business School on the subject of M&A activity. In-depth findings will emerge over the next few years, but the research has already revealed some interesting results. Most intriguing is the notion that, although academics, consultants, and businesspeople lump M&As together, they represent very different strategic activities. Acquisitions occur for the following reasons: to deal with overcapacity through consolidation in mature industries; to roll up competitors in geographically fragmented industries; to extend into new products and markets; as a substitute for R&D; and to exploit eroding industry boundaries by inventing an industry. The different strategic intents present distinct integration challenges. For instance, if you acquire a company because your industry has excess capacity, you have to determine which plants to shut down and which people to let go. If, on the other hand, you buy a company because it has developed an important technology, your challenge is to keep the acquisition's best engineers from jumping ship. These scenarios require the acquiring company to engage in nearly opposite managerial behaviors. The author explores each type of M&A--its strategic intent and the integration challenges created by that intent. He underscores the importance of the acquiring company's assessment of the acquired group's culture. Depending on the type of M&A, approaches to the culture in place must vary, as will the level to which culture interferes with integration. He draws from the experiences of such companies as Cisco, Viacom, and BancOne to exemplify the different kinds of M&As.


title: Nonlinear control of VTOL UAVs incorporating flapping dynamics

abstract: This paper presents the design and evaluation of a nonlinear control scheme for multirotor helicopters that takes first-order drag effects into account explicitly. A dynamic model including the blade flapping and induced drag forces is presented. Based on this model, a hierarchical nonlinear controller is designed to actively compensates for the nonlinear effects these drag forces. Reported simulation and experimental results indicate the significant performance improvement of the proposed drag-augmented control scheme with respect to a conventional nonlinear controller. For completeness, an offline procedure allowing for efficiently identifying the drag parameters is proposed.


Common ground in Attention Deficit Hyperactivity Disorder (ADHD) and Borderline Personality Disorder (BPD)–review of recent findings


title: The Fourth International Chinese Language Processing Bakeoff: Chinese Word Segmentation, Named Entity Recognition and Chinese POS Tagging

abstract: The Fourth International Chinese Language Processing Bakeoff was held in 2007 to assess the state of the art in three important tasks: Chinese word segmentation, named entity recognition and Chinese POS tagging. Twenty-eight groups submitted result sets in the three tasks across two tracks and a total of seven corpora. Strong results have been found in all the tasks as well as continuing challenges.


title: K-band orthomode transducer with waveguide ports and balanced coaxial probes

abstract: We describe the design and test results of a K-band orthomode transducer consisting of orthogonal balanced coaxial probes in a 0.455-in-diameter circular waveguide linked by coaxial transmission lines to balanced coaxial probes in orthogonal WR42 waveguides. We built this device to model at large scale (/spl sim/10 : 1) the performance of a compact millimeter-wave transducer for use in an astronomical receiver. The identical construction of the dual channels results in the same phase and insertion-loss characteristics for both polarizations. Measurements show that, from 18 to 26 GHz, the orthomode transducer has a reflection coefficient of below -15dB (standing-wave ratio <1.4) and a cross-polarization coupling from -20to-40dB. The insertion loss over most of the band is /spl sim/0.4dB at room temperature. To scale this device for operation at 200-270 GHz, one must connect corresponding probes by coaxial lines with phase imbalance less than 15/spl deg/, equivalent to a line-length difference less than 0.0015 in, to mitigate periodical resonances. Probes must be fabricated and positioned with 0.001-in accuracy. The predicted insertion loss would be 0.25 dB at 4 K.


title: System supporting money laundering detection

abstract: Abstract Criminal analysis is a complex process involving information gathered from different sources, mainly of quantitative character, such as billings or bank account transactions, but also of qualitative character such as eyewitnesses' testimonies. Due to the massive nature of this information, operational or investigation activities can be vastly improved when supported by dedicated techniques and tools. The system supporting the police analyst in the process of detecting money laundering is presented in this paper. The main parts of the system are data importer and analyzing algorithms, such as transaction mining algorithm and frequent pattern mining algorithms. The results obtained with the use of these algorithms can be visualized, so that they can be easily explored by the police analyst. The transactions found can be treated as suspected operations. The frequent patterns found are mainly used to identify the roles of suspected entities. For the transaction mining algorithm a performance study is also presented.


title: Temporal and Spatial Clustering for a Parking Prediction Service

abstract: It has been estimated that in urban scenarios up to 30% of the traffic is due to vehicles looking for a free parking space. Thanks to recent technological evolutions, it is now possible to have at least a partial coverage of real-time data of parking space availability, and some preliminary mobile services are able to guide drivers towards free parking spaces. Nevertheless, the integration of this data within car navigators is challenging, mainly because (I) current In-Vehicle Telematic systems are not connected, and (II) they have strong limitations in terms of storage capabilities. To overcome these issues, in this paper we present a back-end based approach to learn historical models of parking availability per street. These compact models can then be easily stored on the map in the vehicle. In particular, we investigate the trade-off between the granularity level of the detailed spatial and temporal representation of parking space availability vs. The achievable prediction accuracy, using different spatio-temporal clustering strategies. The proposed solution is evaluated using five months of parking availability data, publicly available from the project Spark, based in San Francisco. Results show that clustering can reduce the needed storage up to 99%, still having an accuracy of around 70% in the predictions.


title: Single View Modeling of Free-Form Scenes

abstract: This paper presents a novel approach for reconstructing free-form, texture-mapped, 3D scene models from a single painting or photograph. Given a sparse set of user-specified constraints on the local shape of the scene, a smooth 3D surface that satisfies the constraints is generated. This problem is formulated as a constrained variational optimization problem. In contrast to previous work in single-view reconstruction, our technique enables high-quality reconstructions of free-form curved surfaces with arbitrary reflectance properties. A key feature of the approach is a novel hierarchical transformation technique for accelerating convergence on a non-uniform, piecewise continuous grid. The technique is interactive and updates the model in real time as constraints are added, allowing fast reconstruction of photorealistic scene models. The approach is shown to yield high-quality results on a large variety of images. Copyright © 2002 John Wiley & Sons, Ltd.


title: Location and Time Aware Social Collaborative Retrieval for New Successive Point-of-Interest Recommendation

abstract: In location-based social networks (LBSNs), new successive point-of-interest (POI) recommendation is a newly formulated task which tries to regard the POI a user currently visits as his POI-related query and recommend new POIs the user has not visited before. While carefully designed methods are proposed to solve this problem, they ignore the essence of the task which involves retrieval and recommendation problem simultaneously and fail to employ the social relations or temporal information adequately to improve the results. In order to solve this problem, we propose a new model called location and time aware social collaborative retrieval model (LTSCR), which has two distinct advantages: (1) it models the location, time, and social information simultaneously for the successive POI recommendation task; (2) it efficiently utilizes the merits of the collaborative retrieval model which leverages weighted approximately ranked pairwise (WARP) loss for achieving better top-n ranking results, just as the new successive POI recommendation task needs. We conducted some comprehensive experiments on publicly available datasets and demonstrate the power of the proposed method, with 46.6% growth in Precision@5 and 47.3% improvement in Recall@5 over the best previous method.


title: Predicting new molecular targets for known drugs

abstract: Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the β1 receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H4 receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug–target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs. Most drugs are intended to be selective for a single protein target, but will commonly bind to several other targets too. Some 'off-target' events induce side effects of varying degrees of severity, though some may be essential for a drug's efficacy. A new strategy to identify potential off-target effects for known drugs is reported in this issue. The structures of 3,665 FDA-approved and investigational drugs were computationally screened against hundreds of protein targets as defined by the ligands that bind to them. Chemical similarities between the drugs and various sets of ligands predicted thousands of off-target associations, some of which were confirmed in pharmacological assays. This approach may help predict and explain the side effects of known drugs and drug candidates, and may also lead to the identification of new clinical applications for drugs that have been previously approved for use in humans. Drugs that are chemically quite similar often bind to biologically diverse protein targets, and it is unclear how selective many of these compounds are. Because many drug–target combinations exist, it would be useful to explore possible interactions computationally. Here, 3,665 drugs are tested against hundreds of targets; chemical similarities between drugs and ligand sets are found to predict thousands of unanticipated associations.


title: Novelty search creates robots with general skills for exploration

abstract: Novelty Search, a new type of Evolutionary Algorithm, has shown much promise in the last few years. Instead of selecting for phenotypes that are closer to an objective, Novelty Search assigns rewards based on how different the phenotypes are from those already generated. A common criticism of Novelty Search is that it is effectively random or exhaustive search because it tries solutions in an unordered manner until a correct one is found. Its creators respond that over time Novelty Search accumulates information about the environment in the form of skills relevant to reaching uncharted territory, but to date no evidence for that hypothesis has been presented. In this paper we test that hypothesis by transferring robots evolved under Novelty Search to new environments (here, mazes) to see if the skills they've acquired generalize. Three lines of evidence support the claim that Novelty Search agents do indeed learn general exploration skills. First, robot controllers evolved via Novelty Search in one maze and then transferred to a new maze explore significantly more of the new environment than non-evolved (randomly generated) agents. Second, a Novelty Search process to solve the new mazes works significantly faster when seeded with the transferred controllers versus randomly-generated ones. Third, no significant difference exists when comparing two types of transferred agents: those evolved in the original maze under (1) Novelty Search vs. (2) a traditional, objective-based fitness function. The evidence gathered suggests that, like traditional Evolutionary Algorithms with objective-based fitness functions, Novelty Search is not a random or exhaustive search process, but instead is accumulating information about the environment, resulting in phenotypes possessing skills needed to explore their world.


title: A Fine-Grained Control Flow Integrity Approach Against Runtime Memory Attacks for Embedded Systems

abstract: Runtime attacks on memory, such as buffer overflow based stack smashing and code reuse attacks, are common in embedded systems. Control flow integrity (CFI) has been acknowledged as one promising approach to protect against such runtime attacks. However, previous CFI implementations suffer from coarse granularity (which can be circumvented by an advanced attack model) and high-performance overhead. In this paper, first, we present an approach to enforce fine-grained CFI at a basic block level, named basic block CFI (BB-CFI), which aims to defend against aforesaid attacks. The key idea is to verify the target address (TA) of control flow instructions (CFINs) (e.g., $\mathtt{call}$ , $\mathtt{ret}$ , and $\mathtt{jmp}$ ), which may be modified by the adversary. BB-CFI contains two stages: 1) offline profiling of the program—to extract the control flow information and 2) runtime control flow checking—to verify the TA of CFINs using the extracted information. We also handle the exceptional cases (e.g., multithreading, C++ exception, and longjump) that are found in complex binaries. Second, we propose an architectural design of control flow checker (CFC), which monitors the program execution during runtime to enforce BB-CFI. For proof of concept, we implement the CFC in field-programmable gate array (FPGA). Our method does not require the modification of the source code or the instruction set architecture. The experimental results demonstrate that BB-CFI is effective against runtime attacks, with 100% verification accuracy. The CFC implementation on FPGA shows <1% performance overhead and a small dynamic power consumption of 78 mW, with very small area footprint.


title: Multi-Scale Feature Learning Based on RICA for Single Training Sample Face Recognition

abstract: In many face recognition scenarios, there are three main challenges for face recognition include single training sample, partial occlusion and uneven illumination. While conventional face descriptors performed unsatisfied and deep learning networks are always time-consuming and resource-consuming. To address these challenges, a novel multi-scale feature is proposed to encoding method to extract multi-scale feature representation for face recognition in complex scenarios with single training sample. Firstly, we divide each face image into multi-scale regions and take dense samples with multi-scale patches in each region. Secondly, we introduce to learn multi-scale encoding matrixes for different face regions based on RICA. Finally, the face features will be extracted for comparing. Experimental results prove that our approach perform stably to expression, partial occlusion and uneven illumination conditions compared with the existing unsupervised methods.


title: Advanced Persistent threats and how to monitor and deter them

abstract: Advanced Persistent Threat (APT) is a term coined over the past couple of years for a new breed of insidious threats that use multiple attack techniques and vectors and that are conducted by stealth to avoid detection so that hackers can retain control over target systems unnoticed for long periods of time. Traditional defences aimed at keeping known threats out of the network are no longer sufficient against the exploits being used to conduct such attacks. The focus should be on developing a defence in depth strategy that aims to constantly monitor networks and security controls for their effectiveness, explains Colin Tankard of Digital Pathways. The UK Government has recently estimated that cybercrime costs the country some £27bn per year and, according to some estimates, the global cost is $1 trillion every year. This crime wave has been greatly facilitated by the rise of electronic communications, primarily those making use of the Internet. The purpose of electronic communications is to make it more efficient and easier to communicate – but they are also relatively easy to attack or intercept. No-one is immune – such attacks are aimed at individuals, small firms, multinationals and governments.


title: Dynamic Friction Models for Road/Tire Longitudinal Interaction

abstract: In this paper we derive a new dynamic friction force model for the longitudinal road/tire interaction for wheeled ground vehicles. The model is based on a dynamic friction model developed previously for contact-point friction problems, called the LuGre model. By assuming a contact patch between the tire and the ground we develop a partial differential equation for the distribution of the friction force along the patch. An ordinary differential equation (the lumped model) for the friction force is developed, based on the patch boundary conditions and the normal force distribution along the contact patch. This lumped model is derived to approximate closely the distributed friction model. Contrary to common static friction/slip maps, it is shown that this new dynamic friction model is able to capture accurately the transient behaviour of the friction force observed during transitions between braking and acceleration. A velocity-dependent, steady-state expression of the friction force versus the slip coefficient is also developed that allows easy tuning of the model parameters by comparison with steady-state experimental data. Experimental results validate the accuracy of the new tire friction model in predicting the friction force during transient vehicle motion. It is expected that this new model will be very helpful for tire friction modeling as well as for anti-lock braking (ABS) and traction control design.


title: SQL server column store indexes

abstract: The SQL Server 11 release (code named "Denali") introduces a new data warehouse query acceleration feature based on a new index type called a column store index. The new index type combined with new query operators processing batches of rows greatly improves data warehouse query performance: in some cases by hundreds of times and routinely a tenfold speedup for a broad range of decision support queries. Column store indexes are fully integrated with the rest of the system, including query processing and optimization. This paper gives an overview of the design and implementation of column store indexes including enhancements to query processing and query optimization to take full advantage of the new indexes. The resulting performance improvements are illustrated by a number of example queries.


title: Wordnet improves Text Document Clustering

abstract: Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with the problem, we integrate background knowledge — in our application Wordnet — into the process of clustering text documents. We cluster the documents by a standard partitional algorithm. Our experimental evaluation on Reuters newsfeeds compares clustering results with pre-categorizations of news. In the experiments, improvements of results by background knowledge compared to the baseline can be shown for many interesting tasks.


title: Estimating psychological networks and their accuracy: A tutorial paper

abstract: The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online.


title: Simple photovoltaic solar cell dynamic sliding mode controlled maximum power point tracker for battery charging applications

abstract: In this paper, we present a maximum power point tracker and estimator for a PV system to estimate the point of maximum power, to track this point and force it to reach this point in finite time and to stay there for all future time in order to provide the maximum power available to the load. The load will be composed of a battery bank. This is obtained by controlling the duty cycle of a DC-DC converter using sliding mode control. The sliding mode controller is given the estimated maximum power point as a reference for it to track that point and force the PV system to operate in this point. This method has the advantage that it will guarantee the maximum output power possible by the array configuration while considering the dynamic parameters temperature and solar irradiance and delivering more power to charge the battery. The procedure of designing, simulating and results are presented in this paper.


title: Connecting minds and sharing emotions through mimicry: A neurocognitive model of emotional contagion

abstract: HIGHLIGHTSMotor mimicry allows humans to recognize emotions and empathize with others.Autonomous mimicry is proposed as an alternative pathway to emotional contagion.A Neurocognitive Model of Emotional Contagion (NMEC) is proposed.The NMEC explains how empathy emerges between two interacting brains and bodies.The NMEC provides implications for human health and pathologies. ABSTRACT During social interactions, people tend to automatically align with, or mimic their interactor's facial expressions, vocalizations, postures and other bodily states. Automatic mimicry might be implicated in empathy and affiliation and is impaired in several pathologies. Despite a growing body of literature on its phenomenology, the function and underlying mechanisms of mimicry remain poorly understood. The current review puts forward a new Neurocognitive Model of Emotional Contagion (NMEC), demonstrating how basic automatic mimicry can give rise to emotional contagion. We combine neurological, developmental and evolutionary insights to argue that automatic mimicry is a precursor to healthy social development. We show that (i) strong synchronization exists between people, (ii) that this resonates on different levels of processing and (iii) demonstrate how mimicry translates into emotional contagion. We conclude that our synthesized model, built upon integrative knowledge from various fields, provides a promising avenue for future research investigating the role of mimicry in human mental health and social development.


Word Embeddings and Linguistic Metadata at the CLEF 2018 Tasks for Early Detection of Depression and Anorexia


title: A Biomechanically Motivated Two-Phase Strategy for Biped Upright Balance Control

abstract: Balance maintenance and upright posture recovery under unexpected environmental forces are key requirements for safe and successful co-existence of humanoid robots in normal human environments. In this paper we present a two-phase control strategy for robust balance maintenance under a force disturbance. The first phase, called the reflex phase, is designed to withstand the immediate effect of the force. The second phase is the recovery phase where the system is steered back to a statically stable “home” posture. The reflex control law employs angular momentum and is characterized by its counter-intuitive quality of “yielding” to the disturbance. The recovery control employs a general scheme of seeking to maximize the potential energy and is robust to local ground surface feature. Biomechanics literature indicates a similar strategy in play during human balance maintenance.


title: The potential of sustainable algal biofuel production using wastewater resources.

abstract: The potential of microalgae as a source of renewable energy has received considerable interest, but if microalgal biofuel production is to be economically viable and sustainable, further optimization of mass culture conditions are needed. Wastewaters derived from municipal, agricultural and industrial activities potentially provide cost-effective and sustainable means of algal growth for biofuels. In addition, there is also potential for combining wastewater treatment by algae, such as nutrient removal, with biofuel production. Here we will review the current research on this topic and discuss the potential benefits and limitations of using wastewaters as resources for cost-effective microalgal biofuel production.


title: Web Services Based On SOAP and REST Principles

abstract: Interest in Web services is rapidly increased from their start of use. To exchange information among the application in standard way is the main goal of web services. This communication between the applications is based on SOAP and REST principle. SOAP communications causes network traffic, higher latency and processing delays. To overcome this limitations the REST'ful architecture is used. REST is a lightweight, easy and better alternative for the SOAP. In this paper comparison on performance of SOAP based and REST'ful web services based on different metric for mobile environment and multimedia conference is taken into consideration. revious technologies such as RMI, CORBA and DCOM were used to create client and server applications. These are used in highly coupled distributed systems, in which both server and client are dependent on each other. Compatibility and security problems arise when we use these technologies. This kind of traffic will block by firewall and proxy servers. Highly coupled systems are mostly used for intranet applications because platform and technology used is already known or same on both server and client side. Therefore we are using a web service which is standard way to distribute services over the internet. The clients don't have any prior knowledge of the web services before they actually use it, therefore WEB SERVICE are platform independent and loosely coupled. There are two types of web services based on SOAP principle and REST principle. Various applications such as conferencing, web application can be developed using SOAP and REST'ful web services. In SOAP based web services XML is used to define simple object access protocol (SOAP). REST'ful web services follows REST principle for distributed hypermedia systems. REST design style is defined as network architectural style because REST'ful web services depend on HTTP, HTML and other web technologies. The goal of this paper is to make survey on SOAP and RESTful web services to show that RESTful web services having better performance. We give an overview of SOAP, REST, with multimedia conferencing, mobile computing services as an example for illustration. We then conclude the paper.


Naive Bayes Classifiers for User Modeling


title: Stochastic Discrimination

abstract: A general method is introduced for separating points in multidimensional spaces through the use of stochastic processes. This technique is called stochastic discrimination.


Information Exchange and Technological Innovation


title: Spot On: Action Localization from Pointly-Supervised Proposals

abstract: We strive for spatio-temporal localization of actions in videos. The state-of-the-art relies on action proposals at test time and selects the best one with a classifier trained on carefully annotated box annotations. Annotating action boxes in video is cumbersome, tedious, and error prone. Rather than annotating boxes, we propose to annotate actions in video with points on a sparse subset of frames only. We introduce an overlap measure between action proposals and points and incorporate them all into the objective of a non-convex Multiple Instance Learning optimization. Experimental evaluation on the UCF Sports and UCF 101 datasets shows that (i) spatio-temporal proposals can be used to train classifiers while retaining the localization performance, (ii) point annotations yield results comparable to box annotations while being significantly faster to annotate, (iii) with a minimum amount of supervision our approach is competitive to the state-of-the-art. Finally, we introduce spatio-temporal action annotations on the train and test videos of Hollywood2, resulting in Hollywood2Tubes, available at http://tinyurl.com/hollywood2tubes.


title: Calibration of stochastic cellular automata: the application to rural-urban land conversions

abstract: Despite the recognition of cellular automata (CA) as a flexible and powerful tool for urban growth simulation, the calibration of CA had been largely heuristic until recent efforts to incorporate multi-criteria evaluation and artificial neural network into rule definition. This study developed a stochastic CA model, which derives its initial probability of simulation from observed sequential land use data. Furthermore, this initial probability is updated dynamically through local rules based on the strength of neighbourhood development. Consequentially the integration of global (static) and local (dynamic) factors produces more realistic simulation results. The procedure of calibrated CA can be applied in other contexts with minimum modification. In this study we applied the procedure to simulate rural-urban land conversions in the city of Guangzhou, China. Moreover, the study suggests the need to examine the result of CA through spatial, tabular and structural validation.


title: A 10 000 Frames/s CMOS Digital Pixel Sensor

abstract: A 352/spl times/288 pixel CMOS image sensor chip with per-pixel single-slope ADC and dynamic memory in a standard digital 0.18-/spl mu/m CMOS process is described. The chip performs "snapshot" image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4/spl times/9.4 /spl mu/m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 /spl mu/V/e/sup -/. At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.


title: AudioQuilt: 2D Arrangements of Audio Samples using Metric Learning and Kernelized Sorting

abstract: The modern musician enjoys access to a staggering number of audio samples. Composition software can ship with many gigabytes of data, and there are many more to be found online. However, conventional methods for navigating these libraries are still quite rudimentary, and often involve scrolling through alphabetical lists. We present AudioQuilt, a system for sample exploration that allows audio clips to be sorted according to user taste, and arranged in any desired 2D formation such that similar samples are located near each other. Our method relies on two advances in machine learning. First, metric learning allows the user to shape the audio feature space to match their own preferences. Second, kernelized sorting finds an optimal arrangement for the samples in 2D. We demonstrate our system with three new interfaces for exploring audio samples, and evaluate the technology qualitatively and quantitatively via a pair of user studies.