title: Multimodal Convolutional Neural Networks for Matching Image and Sentence

abstract: In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities. More specifically, it consists of one image CNN encoding the image content and one matching CNN modeling the joint representation of image and sentence. The matching CNN composes different semantic fragments from words and learns the inter-modal relations between image and the composed fragments at different levels, thus fully exploit the matching relations between image and sentence. Experimental results demonstrate that the proposed m-CNNs can effectively capture the information necessary for image and sentence matching. More specifically, our proposed m-CNNs significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets.


title: Quote Recommendation in Dialogue using Deep Neural Network

abstract: Quotes, or quotations, are well known phrases or sentences that we use for various purposes such as emphasis, elaboration, and humor. In this paper, we introduce a task of recommending quotes which are suitable for given dialogue context and we present a deep learning recommender system which combines recurrent neural network and convolutional neural network in order to learn semantic representation of each utterance and construct a sequence model for the dialog thread. We collected a large set of twitter dialogues with quote occurrences in order to evaluate proposed recommender system. Experimental results show that our approach outperforms not only the other state-of-the-art algorithms in quote recommendation task, but also other neural network based methods built for similar tasks.


title: Stochastic evolutionary game dynamics

abstract: Traditional game theory studies strategic interactions in which the agents make rational decisions. Evolutionary game theory differs in two key respects: the focus is on large populations of individuals who interact at random rather than on small numbers of players; and individuals are assumed to employ simple adaptive rules rather than to engage in perfectly rational behavior. In such a setting, an equilibrium is a rest point of the population-level dynamical process rather than a form of consistency between beliefs and strategies. This chapter shows how the theory of stochastic dynamical systems can be used to characterize the equilibria that are most likely to be selected when the evolutionary process is subject to small persistent perturbations. Such equilibria are said to be stochastically stable. The implications of stochastic stability are discussed in a variety of settings, including 2 A— 2 games, bargaining games, public-goods games, potential games, and network games. Stochastic stability often selects equilibria that are familiar from traditional game theory: in 2 A— 2 games one obtains the risk-dominant equilibrium, in bargaining games the Nash bargaining solution, and in potential games the potential-maximizing equilibrium. However, the justification for these solution concepts differs between the two approaches. In traditional game theory, equilibria are justified in terms of rationality, common knowledge of the game, and common knowledge of rationality. Evolutionary game theory dispenses with all three of these assumptions; nevertheless, some of the main solution concepts survive in a stochastic evolutionary setting.


title: Discrete Cosserat Approach for Multi-Section Soft Robots Dynamics

abstract: Nowadays, the most adopted model for the design and control of soft robots is the piecewise constant curvature model, with its consolidated benefits and drawbacks. In this work, an alternative model for multisection soft manipulator dynamics is presented based on a discrete Cosserat approach, in which the continuous Cosserat model is discretized by assuming a piecewise constant strain along the soft arm. As a consequence, the soft manipulator state is described by a finite set of constant strains. This approach has several advantages with respect to the existing models. First, it takes into account shear and torsional deformations, which are both essential to cope with out-of-plane external loads. Furthermore, it inherits desirable geometrical and mechanical properties of the continuous Cosserat model, such as intrinsic parameterization and greater generality. Finally, this approach allows to extend to soft manipulators, the recursive composite-rigid-body and articulated-body algorithms, whose performances are compared through a cantilever beam simulation. The soundness of the model is demonstrated through extensive simulation and experimental results.


title: A Cognitive Model of Semantic Network Learning

abstract: Child semantic development includes learning the meaning of words as well as the semantic relations among words. A presumed outcome of semantic development is the formation of a semantic network that reflects this knowledge. We present an algorithm for simultaneously learning word meanings and gradually growing a semantic network, which adheres to the cognitive plausibility requirements of incrementality and limited computations. We demonstrate that the semantic connections among words in addition to their context is necessary in forming a semantic network that resembles an adult’s semantic knowledge.


title: Incremental and batch planar simplification of dense point cloud maps

abstract: Dense RGB-D SLAM techniques and high-fidelity LIDAR scanners are examples from an abundant set of systems capable of providing multi-million point datasets. These datasets quickly become difficult to process due to the sheer volume of data, typically containing significant redundant information, such as the representation of planar surfaces with millions of points. In order to exploit the richness of information provided by dense methods in real-time robotics, techniques are required to reduce the inherent redundancy of the data. In this paper we present a method for incremental planar segmentation of a gradually expanding point cloud map and a method for efficient triangulation and texturing of planar surface segments. Experimental results show that our incremental segmentation method is capable of running in real-time while producing a segmentation faithful to what would be achieved using a batch segmentation method. Our results also show that the proposed planar simplification and triangulation algorithm removes more than 90% of the input planar points, leading to a triangulation with only 10% of the original quantity of triangles per planar segment. Additionally, our texture generation algorithm preserves all colour information contained within planar segments, resulting in a visually appealing and geometrically accurate simplified representation. Online incremental planar segmentation algorithm capable of running in real-time.Point cloud plane segment triangulation that preserves principle geometric features.Texture generation algorithm that maintains visual appearance of planar surfaces.


title: On stochastic proximal gradient algorithms

abstract: We study a perturbed version of the proximal gradient algorithm for which the gradient is not known in closed form and should be approximated. We address the convergence and derive a non-asymptotic bound on the convergence rate for the perturbed proximal gradient, a perturbed averaged version of the proximal gradient algorithm and a perturbed version of the fast iterative shrinkage-thresholding (FISTA) of \cite{becketteboulle09}. When the approximation is achieved by using Monte Carlo methods, we derive conditions involving the Monte Carlo batch-size and the step-size of the algorithm under which convergence is guaranteed. In particular, we show that the Monte Carlo approximations of some averaged proximal gradient algorithms and a Monte Carlo approximation of FISTA achieve the same convergence rates as their deterministic counterparts. To illustrate, we apply the algorithms to high-dimensional generalized linear mixed models using $\ell_1$-penalization.


title: PERSUASIVE DECISION SUPPORT: IMPROVING RELIANCE ON DECISION SUPPORT SYSTEMS

abstract: The primary role of a decision support system is to guide and support a decision maker. As reliance on a decision support system is largely discretionary the persuasiveness of the system becomes critically important. In this paper characteristics thought to affect systems persuasiveness are examined. This paper asserts that the target and source of a decision support message, along with the design of the message itself, act to influence the persuasiveness of the decision support provided. Using a purpose built experimental platform with seventy subjects the research finds that the persuasiveness of a decision support message is varied by the perceived difficulty of the task being undertaken, and the perceived usefulness of the decision support provided. The type of decisional guidance provided also affects persuasiveness of the system; in particular, providing suggestive decisional guidance is shown to significantly improve system persuasiveness. The implications of these findings relate to the appropriate design of decision support systems, and the contexts within which a decision support system can be expected to persuade decision makers to reply on the support provided.


title: Generative linguistics and neural networks at 60: Foundation, friction, and fusion

abstract: Abstract:The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This article traces the development of these two approaches to cognitive science, from their largely autonomous early development in the first thirty years, through their collision in the 1980s around the past-tense debate (Rumelhart & McClelland 1986, Pinker & Prince 1988) and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines of research is likely impeding progress in both: on learning in generative linguistics, and on the representation of language in neural modeling. The article concludes with a brief argument that generative linguistics is unlikely to fulfill its promise of accounting for language learning if it continues to maintain its distance from neural and statistical approaches to learning.


title: An Appraisal of Lung Nodules Automatic Classification Algorithms for CT Images

abstract: Lung cancer is one of the most deadly diseases around the world representing about 26% of all cancers in 2017. The five-year cure rate is only 18% despite great progress in recent diagnosis and treatment. Before diagnosis, lung nodule classification is a key step, especially since automatic classification can help clinicians by providing a valuable opinion. Modern computer vision and machine learning technologies allow very fast and reliable CT image classification. This research area has become very hot for its high efficiency and labor saving. The paper aims to draw a systematic review of the state of the art of automatic classification of lung nodules. This research paper covers published works selected from the Web of Science, IEEEXplore, and DBLP databases up to June 2018. Each paper is critically reviewed based on objective, methodology, research dataset, and performance evaluation. Mainstream algorithms are conveyed and generic structures are summarized. Our work reveals that lung nodule classification based on deep learning becomes dominant for its excellent performance. It is concluded that the consistency of the research objective and integration of data deserves more attention. Moreover, collaborative works among developers, clinicians, and other parties should be strengthened.


title: A wearable device for monitoring galvanic skin response to accurately predict changes in blood pressure indexes and cardiovascular dynamics

abstract: Predicting acute hypotensive episode (AHE) in patients in emergency rooms and in intensive care units (ICU) is a difficult challenge. As it is well accepted that physiological compensatory adaptations to circulatory shock involve blood flow redistribution and increase in sympathetic stimulation, we recently investigated if galvanic skin response (GSR) or electro-dermal activity (EDA), a measure of sympathetic stimulation, could give information about the impending danger of acute hypotensive episode or circulatory collapse (Subramanya and Mudol, 2012). In this current study, a low-cost wearable device was developed and tested to help progress towards a system for predicting blood pressure (BP) and cardiovascular dynamics. In a pilot study, we examined hypotheses about the relation between GSR values and four BP indexes (systolic BP, diastolic BP, mean arterial pressure and pulse pressure) in apparently healthy human volunteers before and immediately after treadmill exercise. All four BP indexes had significant relationship with GSR, with pulse pressure possibly the strongest predictor of variations in the GSR and vice-versa. This paper opens up opportunities for future investigations to evaluate the utility of continuous monitoring of GSR to forecast imminent cardiovascular collapse, AHE and shock, and could have far-reaching implications for ICU, trauma and critical care management.


Revisiting Data , Information , Knowledge and Wisdom ( DIKW ) Model and Introducing the Green Leaf Model


title: On Plenoptic Multiplexing and Reconstruction

abstract: Photography has been striving to capture an ever increasing amount of visual information in a single image. Digital sensors, however, are limited to recording a small subset of the desired information at each pixel. A common approach to overcoming the limitations of sensing hardware is the optical multiplexing of high-dimensional data into a photograph. While this is a well-studied topic for imaging with color filter arrays, we develop a mathematical framework that generalizes multiplexed imaging to all dimensions of the plenoptic function. This framework unifies a wide variety of existing approaches to analyze and reconstruct multiplexed data in either the spatial or the frequency domain. We demonstrate many practical applications of our framework including high-quality light field reconstruction, the first comparative noise analysis of light field attenuation masks, and an analysis of aliasing in multiplexing applications.


title: A review of missing values handling methods on time-series data

abstract: Missing values becomes one of the problems that frequently occur in the data observation or data recording process. The needs of data completeness of the observation data for the uses of advanced analysis becomes important to be solved. Conventional method such as mean and mode imputation, deletion, and other methods are not good enough to handle missing values as those method can caused bias to the data. Estimation or imputation to the missing data with the values produced by some procedures or algorithms can be the best possible solution to minimized the bias effect of the conventional method of the data. So that at last, the data will be completed and ready to use for another step of analysis or data mining. In this paper, we will explain and describe several previous studies about missing values handling methods or approach on time series data. This paper also discuss some plausible option of methods to estimate missing values to be used by other researchers in this field of study. The discussion's aim is to help them to figure out what method is commonly used now along with its advantages and drawbacks.


title: A case study of OpenCL on an Android mobile GPU

abstract: An observation in supercomputing in the past decade illustrates the transition of pervasive commodity products being integrated with the world's fastest system. Given today's exploding popularity of mobile devices, we investigate the possibilities for high performance mobile computing. Because parallel processing on mobile devices will be the key element in developing a mobile and computationally powerful system, this study was designed to assess the computational capability of a GPU on a low-power, ARM-based mobile device. The methodology for executing computationally intensive benchmarks on a handheld mobile GPU is presented, including the practical aspects of working with the existing Android-based software stack and leveraging the OpenCL-based parallel programming model. The empirical results provide the performance of an OpenCL N-body benchmark and an auto-tuning kernel parameterization strategy. The achieved computational performance of the low-power mobile Adreno GPU is compared with a quad-core ARM, an x86 Intel processor, and a discrete AMD GPU.


title: Collaborative filtering with privacy via factor analysis

abstract: Collaborative filtering (CF) is valuable in e-commerce, and for direct recommendations for music, movies, news etc. But today's systems have several disadvantages, including privacy risks. As we move toward ubiquitous computing, there is a great potential for individuals to share all kinds of information about places and things to do, see and buy, but the privacy risks are severe. In this paper we describe a new method for collaborative filtering which protects the privacy of individual data. The method is based on a probabilistic factor analysis model. Privacy protection is provided by a peer-to-peer protocol which is described elsewhere, but outlined in this paper. The factor analysis approach handles missing data without requiring default values for them. We give several experiments that suggest that this is most accurate method for CF to date. The new algorithm has other advantages in speed and storage over previous algorithms. Finally, we suggest applications of the approach to other kinds of statistical analyses of survey or questionaire data.


title: Energy aware dwelling: a critical survey of interaction design for eco-visualizations

abstract: Eco-visualizations (EVs) are any kind of interactive device targeted at revealing energy use in order to promote sustainable behaviours or foster positive attitudes towards sustainable practices. There are some interesting, informative, highly creative, and delightful EVs now available. This paper provides a critical survey of several noteworthy EVs and classifies them in terms of scale and contexts of use. The paper attempts to provide a foundation for practitioners to design new EVs in more varied scales and contexts and for researchers to continue to refine understandings of how effective EVs can be and how EVs can be made to be more effective. The paper describes (i) feedback types and use-contexts for classifying EVs and (ii) strategies for designing effective EVs.


title: Constrained multi-objective trajectory planning of parallel kinematic machines

abstract: This paper presents a new approach to multi-objective dynamic trajectory planning of parallel kinematic machines (PKM) under task, workspace and manipulator constraints. The robot kinematic and dynamic model, (including actuators) is first developed. Then the proposed trajectory planning system is introduced. It minimizes electrical and kinetic energy, robot traveling time separating two sampling periods, and maximizes a measure of manipulability allowing singularity avoidance. Several technological constraints such as actuator, link length and workspace limitations, and some task requirements, such as passing through imposed poses are simultaneously satisfied. The discrete augmented Lagrangean technique is used to solve the resulting strong nonlinear constrained optimal control problem. A decoupled formulation is proposed in order to cope with some difficulties arising from dynamic parameters computation. A systematic implementation procedure is provided along with some numerical issues. Simulation results proving the effectiveness of the proposed approach are given and discussed.


title: Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users

abstract: Internet advertising is a fast growing business which has proved to be significantly important in digital economics. It is vitally important for both web search engines and online content providers and publishers because web advertising provides them with major sources of revenue. Its presence is increasingly important for the whole media industry due to the influence of the Web. For advertisers, it is a smarter alternative to traditional marketing media such as TVs and newspapers. As the web evolves and data collection continues, the design of methods for more targeted, interactive, and friendly advertising may have a major impact on the way our digital economy evolves, and to aid societal development. Towards this goal mathematically well-grounded Computational Advertising methods are becoming necessary and will continue to develop as a fundamental tool towards the Web. As a vibrant new discipline, Internet advertising requires effort from different research domains including Information Retrieval, Machine Learning, Data Mining and Analytic, Statistics, Economics, and even Psychology to predict and understand user behaviours. In this paper, we provide a comprehensive survey on Internet advertising, discussing and classifying the research issues, identifying the recent technologies, and suggesting its future directions. To have a comprehensive picture, we first start with a brief history, introduction, and classification of the industry and present a schematic view of the new advertising ecosystem. We then introduce four major participants, namely advertisers, online publishers, ad exchanges and web users; and through analysing and discussing the major research problems and existing solutions from their perspectives respectively, we discover and aggregate the fundamental problems that characterise the newly-formed research field and capture its potential future prospects.


title: Pathology, pseudopathology, and the Dark Triad of personality

abstract: The Dark Triad traits (i.e., psychopathy, Machiavellianism, and narcissism) have traditionally been viewed as undesirable and pathological. In contrast, an evolutionary perspective suggests that traits like these might be pseudopathologies; traits that society actively dislikes in that they pose a threat to the collective good. We examined (N = 290) how the Dark Triad traits related to intrapersonal (i.e., behavioral dysfunction), quasibehavioral (i.e., reactive and proactive aggression), and interpersonal (i.e., communal and exchange orientation) factors. Psychopathy predicted high rates of behavioral dysregulation and both forms of aggression. Psychopathy and Machiavellianism showed an aversion towards communalism but an exchange orientation to social relationships. Lastly, individual differences in the Dark Triad traits accounted for part (5–22%) of the sex differences in social strategies and aggression. The theoretical implications of these findings are discussed in, and in support of, an evolutionary paradigm.


title: Classifying Collisions with Spatio-Temporal Action Graph Networks

abstract: Events defined by the interaction of objects in a scene often are of critical importance, yet such events are typically rare and available labeled examples insufficient to train a conventional deep model that performs well across expected object appearances. Most deep learning activity recognition models focus on global context aggregation and do not explicitly consider object interactions inside the video, potentially overlooking important cues relevant to interpreting activity in the scene. In this paper, we show that a new model for explicit representation of object interactions significantly improves deep video activity classification for driving collision detection. We propose a Spatio-Temporal Action Graph (STAG) network, which incorporates spatial and temporal relations of objects. The network is automatically learned from data, with a latent graph structure inferred for the task. As a benchmark to evaluate performance on collision detection tasks, we introduce a novel data set based on data obtained from real life driving collisions and near-collisions. This data set reflects the challenging task of detecting and classifying accidents in a richly varying but yet highly constrained setting, that is very relevant to the evaluation of autonomous driving and alerting systems. Our experiments confirm that our STAG model offers significantly improved results for collision activity classification.


title: Blending the lid/cheek junction.

abstract: BACKGROUND ::: Rejuvenation of the periorbital area requires an understanding of the morphologic changes that occur with aging. Blending the lid/cheek junction is a goal of periorbital rejuvenation. ::: ::: ::: OBJECTIVE ::: Transblepharoplasty techniques are proposed to achieve aesthetic periorbital rejuvenation with predictable improvement and minimal morbidity. ::: ::: ::: METHODS ::: Techniques of blending the lid/cheek junction are presented that include septal advancement with both septal tightening and subseptal fat pad contouring when indicated, or complete release of the orbital orbicularis oculi muscle, along with vascularized and/or free fat transfer over the inferior orbital rim, to produce a youthful contour. Lid support was enhanced with a medially based orbicularis oculi muscle sling, which further improves the contour of the lid/cheek junction through muscle release to the medial inferior orbital rim. ::: ::: ::: RESULTS ::: Fifty-eight consecutive cases were reviewed that included lower lid blepharoplasties with or without a concomitant midface lift. Four cases of midface lift did not require any lower eyelid procedures. Septal reset or aggressive orbicularis oculi release with fat transfer were performed in 30 patients. A medially based orbicularis oculi flap was developed to support the lid, and canthopexy or canthoplasty was performed as indicated. Complete release of the medial fibers of the orbital orbicularis oculi muscle allowed for tension-free placement of vascularized fat or free fat grafts and a more profound blending of the lid/cheek junction, since the flap advancement acted as a handle to carry the upper midface soft tissues. ::: ::: ::: CONCLUSIONS ::: Transblepharoplasty options for repositioning periorbital soft tissues are reliable means for blending the lid/cheek junction.


title: PRIMME_SVDS: A High-Performance Preconditioned SVD Solver for Accurate Large-Scale Computations

abstract: The increasing number of applications requiring the solution of large scale singular value problems have rekindled interest in iterative methods for the SVD. Some promising recent ad- vances in large scale iterative methods are still plagued by slow convergence and accuracy limitations for computing smallest singular triplets. Furthermore, their current implementations in MATLAB cannot address the required large problems. Recently, we presented a preconditioned, two-stage method to effectively and accurately compute a small number of extreme singular triplets. In this research, we present a high-performance software, PRIMME SVDS, that implements our hybrid method based on the state-of-the-art eigensolver package PRIMME for both largest and smallest singular values. PRIMME SVDS fills a gap in production level software for computing the partial SVD, especially with preconditioning. The numerical experiments demonstrate its superior performance compared to other state-of-the-art software and its good parallel performance under strong and weak scaling.


title: Learning Granger Causality for Hawkes Processes

abstract: Learning Granger causality for general point processes is a very challenging task. In this paper, we propose an effective method, learning Granger causality, for a special but significant type of point processes --- Hawkes process. We reveal the relationship between Hawkes process's impact function and its Granger causality graph. Specifically, our model represents impact functions using a series of basis functions and recovers the Granger causality graph via group sparsity of the impact functions' coefficients. We propose an effective learning algorithm combining a maximum likelihood estimator (MLE) with a sparse-group-lasso (SGL) regularizer. Additionally, the flexibility of our model allows to incorporate the clustering structure event types into learning framework. We analyze our learning algorithm and propose an adaptive procedure to select basis functions. Experiments on both synthetic and real-world data show that our method can learn the Granger causality graph and the triggering patterns of the Hawkes processes simultaneously.


title: Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

abstract: Due to limited computational power and energy resources, aggregation of data from multiple sensor nodes done at the aggregating node is usually accomplished by simple methods such as averaging. However such aggregation is known to be highly vulnerable to node compromising attacks. Since WSN are usually unattended and without tamper resistant hardware, they are highly susceptible to such attacks. Thus, ascertaining trustworthiness of data and reputation of sensor nodes is crucial for WSN. As the performance of very low power processors dramatically improves, future aggregator nodes will be capable of performing more sophisticated data aggregation algorithms, thus making WSN less vulnerable. Iterative filtering algorithms hold great promise for such a purpose. Such algorithms simultaneously aggregate data from multiple sources and provide trust assessment of these sources, usually in a form of corresponding weight factors assigned to data provided by each source. In this paper we demonstrate that several existing iterative filtering algorithms, while significantly more robust against collusion attacks than the simple averaging methods, are nevertheless susceptive to a novel sophisticated collusion attack we introduce. To address this security issue, we propose an improvement for iterative filtering techniques by providing an initial approximation for such algorithms which makes them not only collusion robust, but also more accurate and faster converging.


title: Discriminating Subsequence Discovery for Sequence Clustering ∗

abstract: In this paper, we explore the discriminating subsequencebased clustering problem. First, several effective optimization techniques are proposed to accelerate the sequence mining process and a new algorithm, CONTOUR, is developed to efficiently and directly mine a subset of discriminating frequent subsequences which can be used to cluster the input sequences. Second, an accurate hierarchical clustering algorithm, SSC, is constructed based on the result of CONTOUR. The performance study evaluates the efficiency and scalability of CONTOUR, and the clustering quality of SSC.


title: Hyperspectral remote sensing for shallow waters. I. A semianalytical model.

abstract: For analytical or semianalytical retrieval of shallow-water bathymetry andyor optical properties of the water column from remote sensing, the contribution to the remotely sensed signal from the water column has to be separated from that of the bottom. The mathematical separation involves three diffuse attenuation coefficients: one for the downwelling irradiance ~Kd!, one for the upwelling radiance of the water column ~Ku C !, and one for the upwelling radiance from bottom reflection ~Ku B !. Because of the differences in photon origination and path lengths, these three coefficients in general are not equal, although their equality has been assumed in many previous studies. By use of the Hydrolight radiativetransfer numerical model with a particle phase function typical of coastal waters, the remote-sensing reflectance above ~Rrs! and below ~rrs! the surface is calculated for various combinations of optical properties, bottom albedos, bottom depths, and solar zenith angles. A semianalytical ~SA! model for rrs of shallow waters is then developed, in which the diffuse attenuation coefficients are explicitly expressed as functions of in-water absorption ~a! and backscattering ~bb!. For remote-sensing inversion, parameters connecting Rrs and rrs are also derived. It is found that rrs values determined by the SA model agree well with the exact values computed by Hydrolight ~;3% error!, even for Hydrolight rrs values calculated with different particle phase functions. The Hydrolight calculations included bbya values as high as 1.5 to simulate high-turbidity situations that are occasionally found in coastal regions. © 1998 Optical Society of America OCIS codes: 010.0010, 280.0280.


title: Spectral-Spatial Hyperspectral Image Classification via Boundary-Adaptive Deep Learning

abstract: Deep learning based hyperspectral image (HSI) classification have recently shown promising performance. However, complex network architecture, tedious training process and effective utilization of spatial/contextual information in deep network limits the application and performance of deep learning. In this paper, for an effective spectral-spatial feature extraction , an improved deep network, spatial adaptive network (SANet) approach is proposed which exploits spatial contextual information and spectral characteristics to construct a more simplified deep network which leads to more powerful feature representation for effective HSI classification. SANet is established from the simple structure of a principal component analysis network. First spatial structural information is extracted and combined with informative spectral channels followed by an object-level classification using SANet based decision fusion approach. It integrates spatial-contextual outcome and spectral characteristics into a SANet framework for robust spectral-spatial HSI classification. Integration of local structural regularity and spectral similarity into simplified deep SANet has significant effect on the classification performance. Experimental results on popular standard HSI datasets reveal that proposed SANet technique produce better classification results than existing well known techniques.


title: Kinect v2 Sensor-Based Mobile Terrestrial Laser Scanner for Agricultural Outdoor Applications

abstract: Mobile terrestrial laser scanners (MTLS), based on light detection and ranging sensors, are used worldwide in agricultural applications. MTLS are applied to characterize the geometry and the structure of plants and crops for technical and scientific purposes. Although MTLS exhibit outstanding performance, their high cost is still a drawback for most agricultural applications. This paper presents a low-cost alternative to MTLS based on the combination of a Kinect v2 depth sensor and a real time kinematic global navigation satellite system (GNSS) with extended color information capability. The theoretical foundations of this system are exposed along with some experimental results illustrating their performance and limitations. This study is focused on open-field agricultural applications, although most conclusions can also be extrapolated to similar outdoor uses. The developed Kinect-based MTLS system allows to select different acquisition frequencies and fields of view (FOV), from one to 512 vertical slices. The authors conclude that the better performance is obtained when a FOV of a single slice is used, but at the price of a very low measuring speed. With that particular configuration, plants, crops, and objects are reproduced accurately. Future efforts will be directed to increase the scanning efficiency by improving both the hardware and software components and to make it feasible using both partial and full FOV.


title: A compact chipless RFID tag with environment sensing capability

abstract: This paper presents a chipless RFID tag having both identification and sensing capability. It is based on 5 resonant scatterers that behave as signal processing antennas in the band from 2.5 to 7.5 GHz. Only one scatterer is used to monitor a physical parameter variation, while the four others allow identifying the remote sensor with 13 bits. To make a resonator sensitive to the temperature or humidity, a material based on Silicon Nanowire is deposited on the tag surface using a very simple process. The tag needs only one conductive layer so that it can be directly printed on product making by the way a unit cost potentially very low. Measurements done using a bi static radar configuration in the frequency domain validate this new concept.


title: Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks

abstract: Mobile-edge computing (MEC) is an emerging paradigm that provides a capillary distribution of cloud computing capabilities to the edge of the wireless access network, enabling rich services and applications in close proximity to the end users. In this paper, an MEC enabled multi-cell wireless network is considered where each base station (BS) is equipped with a MEC server that assists mobile users in executing computation-intensive tasks via task offloading. The problem of joint task offloading and resource allocation is studied in order to maximize the users’ task offloading gains, which is measured by a weighted sum of reductions in task completion time and energy consumption. The considered problem is formulated as a mixed integer nonlinear program (MINLP) that involves jointly optimizing the task offloading decision, uplink transmission power of mobile users, and computing resource allocation at the MEC servers. Due to the combinatorial nature of this problem, solving for optimal solution is difficult and impractical for a large-scale network. To overcome this drawback, we propose to decompose the original problem into a resource allocation (RA) problem with fixed task offloading decision and a task offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem. We address the RA problem using convex and quasi-convex optimization techniques, and propose a novel heuristic algorithm to the TO problem that achieves a suboptimal solution in polynomial time. Simulation results show that our algorithm performs closely to the optimal solution and that it significantly improves the users’ offloading utility over traditional approaches.


title: SDN-based solutions for Moving Target Defense network protection

abstract: Software-Defined Networking (SDN) allows network capabilities and services to be managed through a central control point. Moving Target Defense (MTD) on the other hand, introduces a constantly adapting environment in order to delay or prevent attacks on a system. MTD is a use case where SDN can be leveraged in order to provide attack surface obfuscation. In this paper, we investigate how SDN can be used in some network-based MTD techniques. We first describe the advantages and disadvantages of these techniques, the potential countermeasures attackers could take to circumvent them, and the overhead of implementing MTD using SDN. Subsequently, we study the performance of the SDN-based MTD methods using Cisco's One Platform Kit and we show that they significantly increase the attacker's overheads.


title: SDD: A New Canonical Representation of Propositional Knowledge Bases

abstract: We identify a new representation of propositional knowledge bases, the Sentential Decision Diagram (SDD), which is interesting for a number of reasons. First, it is canonical in the presence of additional properties that resemble reduction rules of OBDDs. Second, SDDs can be combined using any Boolean operator in polytime. Third, CNFs with n variables and treewidth w have canonical SDDs of size O(n2w), which is tighter than the bound on OBDDs based on pathwidth. Finally, every OBDD is an SDD. Hence, working with the latter does not preclude the former.


title: THE FORGETRON: A KERNEL-BASED PERCEPTRON ON A BUDGET

abstract: The Perceptron algorithm, despite its simplicity, often performs well in online classification tasks. The Perceptron becomes especially effective when it is used in conjunction with kernel functions. However, a common difficulty encountered when implementing kernel-based online algorithms is the amount of memory required to store the online hypothesis, which may grow unboundedly as the algorithm progresses. Moreover, the running time of each online round grows linearly with the amount of memory used to store the hypothesis. In this paper, we present the Forgetron family of kernel-based online classification algorithms, which overcome this problem by restricting themselves to a predefined memory budget. We obtain different members of this family by modifying the kernel-based Perceptron in various ways. We also prove a unified mistake bound for all of the Forgetron algorithms. To our knowledge, this is the first online kernel-based learning paradigm which, on one hand, maintains a strict limit on the amount of memory it uses and, on the other hand, entertains a relative mistake bound. We conclude with experiments using real datasets, which underscore the merits of our approach.


title: Towards correct network virtualization

abstract: In SDN, the underlying infrastructure is usually abstracted for applications that can treat the network as a logical or virtual entity. Commonly, the ``mappings" between virtual abstractions and their actual physical implementations are not one-to-one, e.g., a single "big switch" abstract object might be implemented using a distributed set of physical devices. A key question is, what abstractions could be mapped to multiple physical elements while faithfully preserving their native semantics? E.g., can an application developer always expect her abstract "big switch" to act exactly as a physical big switch, despite being implemented using multiple physical switches in reality? ::: We show that the answer to that question is "no" for existing virtual-to-physical mapping techniques: behavior can differ between the virtual "big switch" and the physical network, providing incorrect application-level behavior. We also show that that those incorrect behaviors occur despite the fact that the most pervasive and commonly-used correctness invariants, such as per-packet consistency, are preserved throughout. These examples demonstrate that for practical notions of correctness, new systems and a new analytical framework are needed. We take the first steps by defining end-to-end correctness, a correctness condition that focuses on applications only, and outline a research vision to obtain virtualization systems with correct virtual to physical mappings.


Developing a Framework for Assessing Information Quality on the World Wide Web


title: Adding Chinese Captions to Images

abstract: This paper extends research on automated image captioning in the dimension of language, studying how to generate Chinese sentence descriptions for unlabeled images. To evaluate image captioning in this novel context, we present Flickr8k-CN, a bilingual extension of the popular Flickr8k set. The new multimedia dataset can be used to quantitatively assess the performance of Chinese captioning and English-Chinese machine translation. The possibility of re-using existing English data and models via machine translation is investigated. Our study reveals to some extent that a computer can master two distinct languages, English and Chinese, at a similar level for describing the visual world. Data is publicly available at http://tinyurl.com/flickr8kcn


title: Feature Selection and the Class Imbalance Problem in Predicting Protein Function from Sequence

abstract: When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place: identifying discriminatory features, and overcoming the challenge of a large imbalance in the training data. We show that by applying feature subset selection followed by undersampling of the majority class, significantly better support vector machine (SVM) classifiers are generated compared with standard machine learning approaches. As well as revealing that the features selected could have the potential to advance our understanding of the relationship between sequence and function, we also show that undersampling to produce fully balanced data significantly improves performance. The best discriminating ability is achieved using SVMs together with feature selection and full undersampling; this approach strongly outperforms other competitive learning algorithms. We conclude that this combined approach can generate powerful machine learning classifiers for predicting protein function directly from sequence.


title: Visual working memory capacity : from psychophysics and neurobiology to individual differences

abstract: Visual working memory capacity is of great interest because it is strongly correlated with overall cognitive ability, can be understood at the level of neural circuits, and is easily measured. Recent studies have shown that capacity influences tasks ranging from saccade targeting to analogical reasoning. A debate has arisen over whether capacity is constrained by a limited number of discrete representations or by an infinitely divisible resource, but the empirical evidence and neural network models currently favor a discrete item limit. Capacity differs markedly across individuals and groups, and recent research indicates that some of these differences reflect true differences in storage capacity whereas others reflect variations in the ability to use memory capacity efficiently.


title: Fingertip Detection: A Fast Method with Natural Hand

abstract: Many vision based applications have used fingertips to track or manipulate gestures in their applications. Gesture identification is a natural way to pass the signals to the machine, as the human express its feelings most of the time with hand expressions. Here a novel time efficient algorithm has been described for fingertip detection. This method is invariant to hand direction and in preprocessing it cuts only hand part from the full image, hence further computation would be much faster than processing full image. Binary silhouette of the input image is generated using HSV color space based skin filter and hand cropping done based on intensity histogram of the hand image.


title: Towards semantic maps for mobile robots

abstract: Intelligent autonomous action in ordinary environments calls for maps. 3D geometry is generally required for avoiding collision with complex obstacles and to self-localize in six degrees of freedom (6 DoF) (x, y, z positions, roll, yaw, and pitch angles). Meaning, in addition to geometry, becomes inevitable if the robot is supposed to interact with its environment in a goal-directed way. A semantic stance enables the robot to reason about objects; it helps disambiguate or round off sensor data; and the robot knowledge becomes reviewable and communicable. The paper describes an approach and an integrated robot system for semantic mapping. The prime sensor is a 3D laser scanner. Individual scans are registered into a coherent 3D geometry map by 6D SLAM. Coarse scene features (e.g., walls, floors in a building) are determined by semantic labeling. More delicate objects are then detected by a trained classifier and localized. In the end, the semantic maps can be visualized for human inspection. We sketch the overall architecture of the approach, explain the respective steps and their underlying algorithms, give examples based on a working robot implementation, and discuss the findings.


title: Multi-source data analysis for bike sharing systems

abstract: Bike sharing systems (BSSs) have become common in many cities worldwide, providing a new transportation mode for residents' commutes. However, the management of these systems gives rise to many problems. As the bike pick-up demands at different places are unbalanced at times, the systems have to be rebalanced frequently. Rebalancing the bike availability effectively, however, is very challenging as it demands accurate prediction for inventory target level determination. In this work, we propose two types of regression models using multi-source data to predict the hourly bike pick-up demand at cluster level: Similarity Weighted K-Nearest-Neighbor (SWK) based regression and Artificial Neural Network (ANN). SWK-based regression models learn the weights of several meteorological factors and/or taxi usage and use the correlation between consecutive time slots to predict the bike pick-up demand. The ANN is trained by using historical trip records of BSS, meteorological data, and taxi trip records. Our proposed methods are tested with real data from a New York City BSS: Citi Bike NYC. Performance comparison between SWK-based and ANN-based methods is provided. Experimental results indicate the high accuracy of ANN-based prediction for bike pick-up demand using multisource data.


title: End-to-End Neural Ad-hoc Ranking with Kernel Pooling

abstract: This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score. The whole model is trained end-to-end. The ranking layer learns desired feature patterns from the pairwise ranking loss. The kernels transfer the feature patterns into soft-match targets at each similarity level and enforce them on the translation matrix. The word embeddings are tuned accordingly so that they can produce the desired soft matches. Experiments on a commercial search engine's query log demonstrate the improvements of K-NRM over prior feature-based and neural-based states-of-the-art, and explain the source of K-NRM's advantage: Its kernel-guided embedding encodes a similarity metric tailored for matching query words to document words, and provides effective multi-level soft matches.


title: Relational Models

abstract: We provide a survey on relational models. Relational models describe complete networked {domains by taking into account global dependencies in the data}. Relational models can lead to more accurate predictions if compared to non-relational machine learning approaches. Relational models typically are based on probabilistic graphical models, e.g., Bayesian networks, Markov networks, or latent variable models. Relational models have applications in social networks analysis, the modeling of knowledge graphs, bioinformatics, recommendation systems, natural language processing, medical decision support, and linked data.


title: Reading Damaged Scripts: Partial Packet Recovery Based on Compressive Sensing for Efficient Random Linear Coded Transmission

abstract: Random linear coding (RLC) can improve the performance of multicast transmissions in terms of throughput and energy efficiency. However, RLC and linear codes in general cannot necessarily attain the optimal performance in arbitrary networks. In this regard, partial packet recovery can be considered as a nonlinear strategy to complement such approaches for more general networks. In this paper, we propose a partial packet recovery scheme that benefits from the sparsity of bit errors in partially corrupted RLC packets. As opposed to many previous schemes, it performs without introducing preliminary checksums or preambles, demanding physical layer soft information, or requesting post-redundancy from the transmitter. It relies only on algebraic coding and data processing techniques, the existing knowledge at the receiver, and the conventional acknowledgment messages in RLC. By reconstructing and utilizing the partially corrupted packets that are usually discarded, it can reduce the average number of transmitted RLC packets required for successful decoding by typically 50%, which improves throughput and energy efficiency at the transmitter. We formulate our partial packet recovery in the form of a sparse recovery problem, present its different solutions using compressive sensing theory, discuss their complexity, and present and evaluate a Markov chain model for its performance.


title: Image compression with neural networks - A survey

abstract: Apart from the existing technology on image compression represented by series of JPEG, MPEG and H.26x standards, new technology such as neural networks and genetic algorithms are being developed to explore the future of image coding. Successful applications of neural networks to vector quantization have now become well established, and other aspects of neural network involvement in this area are stepping up to play significant roles in assisting with those traditional technologies. This paper presents an extensive survey on the development of neural networks for image compression which covers three categories: direct image compression by neural networks; neural network implementation of existing techniques, and neural network based technology which provide improvement over traditional algorithms.


Comparative Study of VANET and MANET Routing Protocols


title: Concepts for dynamic obstacle avoidance and their extended application in underground navigation

abstract: An effective approach to dynamic obstacle avoidance for mobile robots is described in this paper. The main concepts in this approach include local Polar Object Chart (POC) for defining possible ways out, obstacle definition, detour mode, anchor for preventing getting lost, steering delimiting and velocity delimiting. The presented concepts and methods were tested and verified both by simulations and experiments on a developed prototype industrial Automated Guided Vehicle (AGV). This paper also presents the extended application of some of the concepts in underground navigation. © 2002 Elsevier Science B.V. All rights reserved.


title: Optimizing direct threaded code by selective inlining

abstract: Achieving good performance in bytecoded language interpreters is difficult without sacrificing both simplicity and portability. This is due to the complexity of dynamic translation ("just-in-time compilation") of bytecodes into native code, which is the mechanism employed universally by high-performance interpreters.We demonstrate that a few simple techniques make it possible to create highly-portable dynamic translators that can attain as much as 70% the performance of optimized C for certain numerical computations. Translators based on such techniques can offer respectable performance without sacrificing either the simplicity or portability of much slower "pure" bytecode interpreters.


title: Whole arm planning for a soft and highly compliant 2D robotic manipulator

abstract: Soft continuum manipulators have the advantage of being more compliant and having more degrees of freedom than rigid redundant manipulators. This attribute should allow soft manipulators to autonomously execute highly dexterous tasks. However, current approaches to motion planning, inverse kinematics, and even design limit the capacity of soft manipulators to take full advantage of their inherent compliance. We provide a computational approach to whole arm planning for a soft planar manipulator that advances the arm's end effector pose in task space while simultaneously considering the arm's entire envelope in proximity to a confined environment. The algorithm solves a series of constrained optimization problems to determine locally optimal inverse kinematics. Due to inherent limitations in modeling the kinematics of a highly compliant soft robot and the local optimality of the planner's solutions, we also rely on the increased softness of our newly designed manipulator to accomplish the whole arm task, namely the arm's ability to harmlessly collide with the environment. We detail the design and fabrication of the new modular manipulator as well as the planner's central algorithm. We experimentally validate our approach by showing that the robotic system is capable of autonomously advancing the soft arm through a pipe-like environment in order to reach distinct goal states.


title: Formalizing cardinality-based feature models and their specialization

abstract: Feature modeling is an important approach to capture the commonalities and variabilities in system families and product lines. Cardinality-based feature modeling integrates a number of existing extensions of the original feature-modeling notation from Feature-Oriented Domain Analysis. Staged configuration is a process that allows the incremental configuration of cardinality-based feature models. It can be achieved by performing a step-wise specialization of the feature model. In this article, we argue that cardinality-based feature models can be interpreted as a special class of context-free grammars. We make this precise by specifying a translation from a feature model into a context-free grammar. Consequently, we provide a semantic interpretation for cardinality-based feature models by assigning an appropriate semantics to the language recognized by the corresponding grammar. Finally, we give an account on how feature model specialization can be formalized as transformations on the grammar equivalent of feature models. Copyright © 2005 John Wiley & Sons, Ltd.


title: The Future of Memory: Remembering, Imagining, and the Brain

abstract: During the past few years, there has been a dramatic increase in research examining the role of memory in imagination and future thinking. This work has revealed striking similarities between remembering the past and imagining or simulating the future, including the finding that a common brain network underlies both memory and imagination. Here, we discuss a number of key points that have emerged during recent years, focusing in particular on the importance of distinguishing between temporal and nontemporal factors in analyses of memory and imagination, the nature of differences between remembering the past and imagining the future, the identification of component processes that comprise the default network supporting memory-based simulations, and the finding that this network can couple flexibly with other networks to support complex goal-directed simulations. This growing area of research has broadened our conception of memory by highlighting the many ways in which memory supports adaptive functioning.


Structured approaches for exploring interpersonal relationships in natural language text


title: Chirp sensing codes: Deterministic compressed sensing measurements for fast recovery

abstract: Abstract Compressed sensing is a novel technique to acquire sparse signals with few measurements. Normally, compressed sensing uses random projections as measurements. Here we design deterministic measurements and an algorithm to accomplish signal recovery with computational efficiency. A measurement matrix is designed with chirp sequences forming the columns. Chirps are used since an efficient method using FFTs can recover the parameters of a small superposition. We show that this type of matrix is valid as compressed sensing measurements. This is done by bounding the eigenvalues of sub-matrices, as well as an empirical comparison with random projections. Further, by implementing our algorithm, simulations show successful recovery of signals with sparsity levels similar to those possible by matching pursuit with random measurements. For sufficiently sparse signals, our algorithm recovers the signal with computational complexity O ( K log K ) for K measurements. This is a significant improvement over existing algorithms.


title: Gradient Descent Efficiently Finds the Cubic-Regularized Non-Convex Newton Step

abstract: We consider the minimization of non-convex quadratic forms regularized by a cubic term, which exhibit multiple saddle points and poor local minima. Nonetheless, we prove that, under mild assumptions, gradient descent approximates the $\textit{global minimum}$ to within $\varepsilon$ accuracy in $O(\varepsilon^{-1}\log(1/\varepsilon))$ steps for large $\varepsilon$ and $O(\log(1/\varepsilon))$ steps for small $\varepsilon$ (compared to a condition number we define), with at most logarithmic dependence on the problem dimension. When we use gradient descent to approximate the Nesterov-Polyak cubic-regularized Newton step, our result implies a rate of convergence to second-order stationary points of general smooth non-convex functions.


A Scalable Clustering-Based Local Multi-Label Classification Method.


title: Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks

abstract: Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this manuscript, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state of- the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.


How Useful Is the Theory of Disruptive Innovation ?


title: Is the Pain Visual Analogue Scale Linear and Responsive to Change? An Exploration Using Rasch Analysis

abstract: OBJECTIVES ::: Pain visual analogue scales (VAS) are commonly used in clinical trials and are often treated as an interval level scale without evidence that this is appropriate. This paper examines the internal construct validity and responsiveness of the pain VAS using Rasch analysis. ::: ::: ::: METHODS ::: Patients (n = 221, mean age 67, 58% female) with chronic stable joint pain (hip 40% or knee 60%) of mechanical origin waiting for joint replacement were included. Pain was scored on seven daily VASs. Rasch analysis was used to examine fit to the Rasch model. Responsiveness (Standardized Response Means, SRM) was examined on the raw ordinal data and the interval data generated from the Rasch analysis. ::: ::: ::: RESULTS ::: Baseline pain VAS scores fitted the Rasch model, although 15 aberrant cases impacted on unidimensionality. There was some local dependency between items but this did not significantly affect the person estimates of pain. Daily pain (item difficulty) was stable, suggesting that single measures can be used. Overall, the SRMs derived from ordinal data overestimated the true responsiveness by 59%. Changes over time at the lower and higher end of the scale were represented by large jumps in interval equivalent data points; in the middle of the scale the reverse was seen. ::: ::: ::: CONCLUSIONS ::: The pain VAS is a valid tool for measuring pain at one point in time. However, the pain VAS does not behave linearly and SRMs vary along the trait of pain. Consequently, Minimum Clinically Important Differences using raw data, or change scores in general, are invalid as these will either under- or overestimate true change; raw pain VAS data should not be used as a primary outcome measure or to inform parametric-based Randomised Controlled Trial power calculations in research studies; and Rasch analysis should be used to convert ordinal data to interval data prior to data interpretation.


title: Large-scale Bayesian multi-label learning via topic-based label embeddings

abstract: We present a scalable Bayesian multi-label learning model based on learning low-dimensional label embeddings. Our model assumes that each label vector is generated as a weighted combination of a set of topics (each topic being a distribution over labels), where the combination weights (i.e., the embeddings) for each label vector are conditioned on the observed feature vector. This construction, coupled with a Bernoulli-Poisson link function for each label of the binary label vector, leads to a model with a computational cost that scales in the number of positive labels in the label matrix. This makes the model particularly appealing for real-world multi-label learning problems where the label matrix is usually very massive but highly sparse. Using a data-augmentation strategy leads to full local conjugacy in our model, facilitating simple and very efficient Gibbs sampling, as well as an Expectation Maximization algorithm for inference. Also, predicting the label vector at test time does not require doing an inference for the label embeddings and can be done in closed form. We report results on several benchmark data sets, comparing our model with various state-of-the art methods.


title: A WiFi-based method to count and locate pedestrians in urban traffic scenarios

abstract: Estimating the number of people in a given environment is an attractive tool with a wide range of applications. Urban environments are not an exception. Counting pedestrians and locate them properly in a road traffic scenario can facilitate the design of intelligent systems for traffic control that take into account more actors and not only vehicle-based optimizations. In this work, we present a new WiFi-based passive method to estimate the number of pedestrians in an urban traffic scenario formed by signaled intersections. Particularly, we are able i) to distinguish between pedestrians walking and pedestrians waiting in a pedestrian crossing and ii) to estimate the exact location where static pedestrians are waiting to cross. By doing so, the pedestrian factor could be included in intelligent control management systems for traffic optimization in real time. The performance analysis carried out shows that our method is able to achieve a significant level of accuracy while presenting an easy implementation.


title: Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

abstract: Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadratically with respect to kernel size. In this paper, we present a parameter-free, FLOP-free "shift" operation as an alternative to spatial convolutions. We fuse shifts and point-wise convolutions to construct end-to-end trainable shift-based modules, with a hyperparameter characterizing the tradeoff between accuracy and efficiency. To demonstrate the operation's efficacy, we replace ResNet's 3x3 convolutions with shift-based modules for improved CIFAR10 and CIFAR100 accuracy using 60% fewer parameters; we additionally demonstrate the operation's resilience to parameter reduction on ImageNet, outperforming ResNet family members. We finally show the shift operation's applicability across domains, achieving strong performance with fewer parameters on classification, face verification and style transfer.


title: Mobile banking adoption: A literature review

abstract: Abstract Electronic commerce (e-commerce) continues to have a profound impact on the global business environment, but technologies and applications also have begun to focus more on mobile computing, the wireless Web, and mobile commerce. Against this backdrop, mobile banking (m-banking) has emerged as an important distribution channel, with considerable research devoted to its adoption. However, this research stream has lacked a clear roadmap or agenda. Therefore, the present article analyzes and synthesizes existing studies of m-banking adoption and maps the major theories that researchers have used to predict consumer intentions to adopt it. The findings indicate that the m-banking adoption literature is fragmented, though it commonly relies on the technology acceptance model and its modifications, revealing that compatibility (with lifestyle and device), perceived usefulness, and attitude are the most significant drivers of intentions to adopt m-banking services in developed and developing countries. Moreover, the extant literature appears limited by its narrow focus on SMS banking in developing countries; virtually no studies address the use of m-banking applications via smartphones or tablets or consider the consequences of such usage. This study makes several recommendations for continued research in the area of mobile banking.


title: A RFID/NFC Fusion based Smart Refrigerator for Wellness Service

abstract: In recent, the diet is one of a trend of the world for making a wellness life. However, the diet management depending on the characteristics of each person has a limitation. Thus, in this paper, we propose a smart dietary management not only patients but also regular users for supporting a wellness service. And then, we develop a smart phone application for providing a wellness service to each person. According to the experiment results, we can improve the utility of common smart refrigerator.


title: Improving the noise-robustness of mel-frequency cepstral coefficients for speech processing,” in proc of

abstract: In this paper we study the noise-robustness of mel-frequency cepstral coefficients (MFCCs) and explore ways to improve their performance in noisy conditions. Improvements based on a more accurate model of the early auditory system are suggested to make the MFCC features more robust to noise while preserving their class discrimination ability. Speech versus non-speech classification and speech recognition are chosen to evaluate the performance gains afforded by the modifications.


title: 3-D Reconstruction from Sparse Views using Monocular Vision

abstract: We consider the task of creating a 3-d model of a large novel environment, given only a small number of images of the scene. This is a difficult problem, because if the images are taken from very different viewpoints or if they contain similar-looking structures, then most geometric reconstruction methods will have great difficulty finding good correspondences. Further, the reconstructions given by most algorithms include only points in 3-d that were observed in two or more images; a point observed only in a single image would not be reconstructed. In this paper, we show how monocular image cues can be combined with triangulation cues to build a photo-realistic model of a scene given only a few images-even ones taken from very different viewpoints or with little overlap. Our approach begins by over-segmenting each image into small patches (superpixels). It then simultaneously tries to infer the 3-d position and orientation of every superpixel in every image. This is done using a Markov random field (MRF) which simultaneously reasons about monocular cues and about the relations between multiple image patches, both within the same image and across different images (triangulation cues). MAP inference in our model is efficiently approximated using a series of linear programs, and our algorithm scales well to a large number of images.


title: An overview of text-independent speaker recognition: from features to supervectors

abstract: This paper gives an overview of automatic speaker recognition technology, with an emphasis on text-independent recognition. Speaker recognition has been studied actively for several decades. We give an overview of both the classical and the state-of-the-art methods. We start with the fundamentals of automatic speaker recognition, concerning feature extraction and speaker modeling. We elaborate advanced computational techniques to address robustness and session variability. The recent progress from vectors towards supervectors opens up a new area of exploration and represents a technology trend. We also provide an overview of this recent development and discuss the evaluation methodology of speaker recognition systems. We conclude the paper with discussion on future directions.


title: Fuzzy portfolio selection using fuzzy analytic hierarchy process

abstract: Financial problems have been the subject of much research. A widely used approach in recent work on these problems is the use of fuzzy set theory, where fuzzy terms are used to model the uncertain environments. The purpose of this work is to combine the fuzzy analytic hierarchy process (AHP) with the portfolio selection problem. More specifically, the decision-making problem is to decide which stocks are to be chosen for investment and in what proportions they will be bought. To do this, we first dealt with two constrained fuzzy AHP methods given by Enea and Piazza [M. Enea, T. Piazza, Project selection by constrained fuzzy AHP, Fuzzy Optimization and Decision Making 3 (2004) 39-62]. We revised the first of these methods, addressing some of its fallacies, and called it revised constrained fuzzy AHP method (RCFAHP). Then we applied these two methods, namely, RCFAHP and the second- method of Enea and Piazza (2004), to the problem of choosing stocks on the Istanbul Stock Exchange (ISE). The methodology used for the hierarchy construction is based on the paper of Satty et al. [T.L. Saaty, P.C. Rogers, R. Bell, Portfolio selection through hierarchies, The Journal of Portfolio Management (1980) 16-21]. In this paper, we show that both of the models provide both ranking and weighting information, via fuzzy AHP, to the investors in this financial scenario. Finally, we discuss the relative advantages and disadvantages of these methods in comparison to existing methods in the literature.


title: Design and evaluation of a delay-efficient RPL routing metric

abstract: The Routing Protocol for Low power and Lossy Networks (RPL) is the IETF standard for IPv6 routing in low-power wireless sensor networks. It is a distance vector routing protocol that builds a Destination Oriented Directed Acyclic Graph (DODAG) rooted towards one sink (the DAG root), using an objective function and a set of metrics/constraints to compute the best path. In this paper, we propose a routing metric which minimizes the delay towards the DAG root, assuming that nodes run with very low duty cycles (e.g., under 1%) at the MAC layer. We evaluate the proposed routing metric with the Contiki operating system and compare its performance with that of the Expected Transmission Count (ETX) metric. Moreover, we propose some extensions to the ContikiMAC radio duty cycling protocol to support different sleeping periods of the nodes.


title: Hierarchical Spatio-Temporal Pattern Discovery and Predictive Modeling

abstract: We propose a new approach, CCRBoost, to identify the hierarchical structure of spatio-temporal patterns at different resolution levels and subsequently construct a predictive model based on the identified structure. To accomplish this, we first obtain indicators within different spatio-temporal spaces from the raw data. A distributed spatio-temporal pattern (DSTP) is extracted from a distribution, which consists of the locations with similar indicators from the same time period, generated by multi-clustering. Next, we use a greedy searching and pruning algorithm to combine the DSTPs in order to form an ensemble spatio-temporal pattern (ESTP). An ESTP can represent the spatio-temporal pattern of various regularities or a non-stationary pattern. To consider all the possible scenarios of a real-world ST pattern, we then build a model with layers of weighted ESTPs. By evaluating all the indicators of one location, this model can predict whether a target event will occur at this location. In the case study of predicting crime events, our results indicate that the predictive model can achieve 80 percent accuracy in predicting residential burglary, which is better than other methods.


title: Quantifying Phishing Susceptibility for Detection and Behavior Decisions

abstract: Objective:We use signal detection theory to measure vulnerability to phishing attacks, including variation in performance across task conditions.Background:Phishing attacks are difficult to prevent with technology alone, as long as technology is operated by people. Those responsible for managing security risks must understand user decision making in order to create and evaluate potential solutions.Method:Using a scenario-based online task, we performed two experiments comparing performance on two tasks: detection, deciding whether an e-mail is phishing, and behavior, deciding what to do with an e-mail. In Experiment 1, we manipulated the order of the tasks and notification of the phishing base rate. In Experiment 2, we varied which task participants performed.Results:In both experiments, despite exhibiting cautious behavior, participants’ limited detection ability left them vulnerable to phishing attacks. Greater sensitivity was positively correlated with confidence. Greater willingness to treat e-mails a...


title: Findings of the 2016 WMT Shared Task on Cross-lingual Pronoun Prediction

abstract: We describe the design, the evaluation setup, and the results of the 2016 WMT shared task on cross-lingual pronoun prediction. This is a classification task in which participants are asked to provi ...


title: PDGM: Percolation-based directed graph matching in social networks

abstract: Linking multiple accounts owned by the same user across different online social networks (OSNs) is an important issue in social networks, known as identity reconciliation. Graph matching is one of popular techniques to solve this problem by identifying a map that matches a set of vertices across different OSNs. Among them, percolation-based graph matching (PGM) has been explored to identify entities belonging to a same user across two different networks based on a set of initial pre-matched seed nodes and graph structural information. However, existing PGM algorithms have been applied in only undirected networks while many OSNs are represented by directional relationships (e.g., followers or followees in Twitter or Facebook). For PGM to be applicable in real world OSNs represented by directed networks with a small set of overlapping vertices, we propose a percolation-based directed graph matching algorithm, namely PDGM, by considering the following two key features: (1) similarity of two nodes based on directional relationships (i.e., outgoing edges vs. incoming edges); and (2) celebrity penalty such as penalty given for nodes with a high in-degree. Through the extensive simulation experiments, our results show that the proposed PDGM outperforms the baseline PGM counterpart that does not consider either directional relationships or celebrity penalty.


title: A very high frequency dc-dc converter based on a class Φ2 resonant inverter

abstract: This paper introduces a new dc-dc converter suitable for operation at very high frequencies under on-off control. The converter power stage is based on a resonant inverter (the Phi2 inverter) providing low switch voltage stress and fast settling time. A new multi-stage resonant gate driver suited for driving large, high-voltage rf MOSFETS at VHF frequencies is also introduced. Experimental results are presented from a prototype dc-dc converter operating at 30 MHz at input voltages up to 200 V and power levels above 200 W. These results demonstrate the high performance achievable with the proposed design.


title: A 0.13 µm CMOS laser radar receiver with leading edge detection and time domain error compensation

abstract: This paper presents a receiver channel for a pulsed time-of-flight laser rangefinder with dual threshold leading edge timing discrimination. The timing error dependent on amplitude variations is corrected by measuring the slew rate of the arrival optical pulse. In this way the correction can be continued after the pulse is clipped so that the residual error can be minimized. The measured bandwidth, transimpedance and input rms noise current of the receiver channel are 300 MHz, 23 k Ω and 95 nA, respectively. The timing detection accuracy of the receiver channel is under ±30 ps which is ±4.5 mm in distance within a dynamic range of 1:10000.


Diverse Beam Search for Improved Description of Complex Scenes


title: Traditional values of virginity and sexual behaviour in rural Ethiopian youth: results from a cross-sectional study

abstract: Background ::: Delaying sexual initiation has been promoted as one of the methods of decreasing risks of HIV among young people. In traditional countries, such as Ethiopia, retaining virginity until marriage is the norm. However, no one has examined the impact of this traditional norm on sexual behaviour and risk of HIV in marriage. This study examined the effect of virginity norm on having sex before marriage and sexual behaviour after marriage among rural Ethiopian youth.


title: Performance optimization of applications based on non-relational databases

abstract: This paper is focused on performance optimization of applications based on non-relational databases. Firstly there will be explained differences between relational and non-relational databases and also compared the types of non-relational databases. Then the advantages and disadvantages between MongoDB and ElasticSearch (SQL) database systems will be described and compared. After both database systems were implemented on the web server, their speed is compared in order to determine which system is better to use in different conditions and tasks.


title: Adult Psychiatric Outcomes of Bullying and Being Bullied by Peers in Childhood and Adolescence

abstract: Importance Both bullies and victims of bullying are at risk for psychiatric problems in childhood, but it is unclear if this elevated risk extends into early adulthood. Objective To test whether bullying and/or being bullied in childhood predicts psychiatric problems and suicidality in young adulthood after accounting for childhood psychiatric problems and family hardships. Design Prospective, population-based study. Setting Community sample from 11 counties in Western North Carolina. Participants A total of 1420 participants who had being bullied and bullying assessed 4 to 6 times between the ages of 9 and 16 years. Participants were categorized as bullies only, victims only, bullies and victims (hereafter referred to as bullies/victims), or neither. Main Outcome Measure Psychiatric outcomes, which included depression, anxiety, antisocial personality disorder, substance use disorders, and suicidality (including recurrent thoughts of death, suicidal ideation, or a suicide attempt), were assessed in young adulthood (19, 21, and 24-26 years) by use of structured diagnostic interviews. Results Victims and bullies/victims had elevated rates of young adult psychiatric disorders, but also elevated rates of childhood psychiatric disorders and family hardships. After controlling for childhood psychiatric problems or family hardships, we found that victims continued to have a higher prevalence of agoraphobia (odds ratio [OR], 4.6 [95% CI, 1.7-12.5]; P Conclusions and Relevance The effects of being bullied are direct, pleiotropic, and long-lasting, with the worst effects for those who are both victims and bullies.


title: Homotopy type theory: unified foundations of mathematics and computation

abstract: Homotopy type theory is a recently-developed unification of previously disparate frameworks, which can serve to advance the project of formalizing and mechanizing mathematics. One framework is based on a computational conception of the type of a construction, the other is based on a homotopical conception of the homotopy type of a space. The computational notion of type has its origins in Brouwer's program of intuitionism, and Church's λ-calculus, both of which sought to ground mathematics in computation (one would say "algorithm" these days). The homotopical notion comes from Grothendieck's late conception of homotopy types of spaces as represented by ∞-groupoids [Grothendieck 1983]. The computational perspective was developed most fully by Per Martin-Lof, leading in particular to his Intuitionistic Theory of Types [Martin-Lof and Sambin 1984], on which the formal system of homotopy type theory is based. The connection to homotopy theory was first hinted at in the groupoid interpretation of Hofmann and Streicher [Hofmann and Streicher 1994; 1995]. It was then made explicit by several researchers, roughly simultaneously. The connection was clinched by Voevodsky's introduction of the univalence axiom, which is motivated by the homotopical interpretation, and which relates type equality to homotopy equivalence [Kapulkin et al. 2012; Awodey et al. 2013].


Building an intelligent assistant for digital forensics


title: PLC code generation based on a formal specification language

abstract: The complexity and quality needs of PLC-based control system software have largely increased. Formal specification methods can help to cope with these needs. Besides formal verification, another benefit of a formal specification language is the possibility to provide automatic generation of the final source code. This paper overviews PLCspecif, our formal specification language for PLC programs and presents a code generation method for the language. The result of the code generator is a Structured Text (ST) code that not only corresponds to the formal semantics of the specification, but is also configurable, readable, understandable, and follows development conventions and standards. The code generation method shows that PLC-specif is applicable and well-adapted to the PLC domain.


title: Broadband CPW-Fed Circularly Polarized Square Slot Antenna With Three Inverted-L-Shape Grounded Strips

abstract: A new form CPW-fed broadband circularly polarized square slot antenna (CPSSA) with three inverted-L-shape grounded strips around the corners of the slot is proposed and fabricated. In comparison to the previous CPSSA structures, the axial-ratio (AR) bandwidth of the antenna is increased, which is two times wider than the previous proposed design. By embedding the L-shape strip to the ground plane and using the vertical stub to the feed line, very good axial-ratio bandwidth is obtained. The measured impedance bandwidth and AR of the realized CPSS antenna with optimized parameters is from 2 to 7 GHz (3.5:1, 110%) for VSWR ≤ 2 and 2-5 GHz (2.5:1; 85%) for AR ≤ 3 dB respectively. Throughout this letter, the improvement process of the AR and S11 properties is completely presented and discussed.


title: Computing Word-Pair Antonymy

abstract: Knowing the degree of antonymy between words has widespread applications in natural language processing. Manually-created lexicons have limited coverage and do not include most semantically contrasting word pairs. We present a new automatic and empirical measure of antonymy that combines corpus statistics with the structure of a published thesaurus. The approach is evaluated on a set of closest-opposite questions, obtaining a precision of over 80%. Along the way, we discuss what humans consider antonymous and how antonymy manifests itself in utterances.


title: Automation of attendance system using RFID, biometrics, GSM Modem with .Net framework

abstract: The purpose of this paper is to develop a wireless system to detect and maintain the attendance of a student and locate a student. For, this the students ID (identification) card is tagged with an Radio-frequency identification (RFID) passive tag which is matched against the database and only finalized once his fingerprint is verified using the biometric fingerprint scanner. The guardian is intimated by a sms (short message service) sent using the GSM (Global System for Mobile Communications) Modem of the same that is the student has reached the university or not on a daily basis, in the present day every guardian is worried whether his child has reached safely or not. In every classroom, laboratory, libraries, staffrooms etc. a RFID transponder is installed through which we will be detecting the location of the student and staff. There will be a website through which the student, teacher and the guardians can view the status of attendance and location of a student at present in the campus. A person needs to be located can be done by two means that is via the website or by sending the roll number of the student as an sms to the GSM modem which will reply by taking the last location stored of the student in the database.


title: Molecular mechanisms responsible for the atheroprotective effects of laminar shear stress.

abstract: The endothelium lining the inner surface of blood vessels of the cardiovascular system is constantly exposed to hemodynamic shear stress. The interaction between endothelial cells and hemodynamic shear stress has critical implications for atherosclerosis. Regions of arterial narrowing, curvatures, and bifurcations are especially susceptible to atherosclerotic lesion formation. In such areas, endothelial cells experience low, or oscillatory, shear stress. Corresponding changes in endothelial cell structure and function make them susceptible to the initiation and development of atherosclerosis. In contrast, blood flow with high laminar shear stress activates signal transductions as well as gene and protein expressions that play important roles in vascular homeostasis. In response to laminar shear stress, the release of vasoactive substances such as nitric oxide and prostacyclin decreases permeability to plasma lipoproteins as well as the adhesion of leukocytes, and inhibits smooth muscle cell proliferation and migration. In summary, different flow patterns directly determine endothelial cell morphology, metabolism, and inflammatory phenotype through signal transduction and gene and protein expression. Thus, high laminar shear stress plays a key role in the prevention of atherosclerosis through its regulation of vascular tone and long-term maintenance of the integrity and function of endothelial cells.


title: Comprehensive Optimization Model for Sizing and Siting of DG Units, EV Charging Stations, and Energy Storage Systems

abstract: The sizing and siting of renewable resources-based distributed generation (DG) units has been a topic of growing interest, especially during the last decade due to the increasing interest in renewable energy systems and the possible impacts of their volatility on distribution system operation. This paper goes beyond the existing literature by presenting a comprehensive optimization model for the sizing and siting of different renewable resources-based DG units, electric vehicle charging stations, and energy storage systems within the distribution system. The proposed optimization model is formulated as a second order conic programming problem, considering also the time-varying nature of DG generation and load consumption, in contrast with the majority of the relevant studies that have been based on static values.


title: Lovasz Convolutional Networks

abstract: Semi-supervised learning on graph structured data has received significant attention with the recent introduction of Graph Convolution Networks (GCN). While traditional methods have focused on optimizing a loss augmented with Laplacian regularization framework, GCNs perform an implicit Laplacian type regularization to capture local graph structure. In this work, we propose Lovasz Convolutional Network (LCNs) which are capable of incorporating global graph properties. LCNs achieve this by utilizing Lovasz’s orthonormal embeddings of the nodes. We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs. We validate the proposed method on standard random graph models such as stochastic block models (SBM) and certain community structure based graphs where LCNs outperform GCNs and learn more intuitive embeddings. We also perform extensive binary and multi-class classification experiments on real world datasets to demonstrate LCN’s effectiveness. In addition to simple graphs, we also demonstrate the use of LCNs on hyper-graphs by identifying settings where they are expected to work better than GCNs.


title: Influence of Er concentration on the emission properties of Er-doped Si-rich silica films obtained by reactive magnetron co-sputtering

abstract: Abstract Er-doped silicon-rich silicon oxide (SRSO) thin films have been fabricated by reactive magnetron sputtering of a pure silica target topped by chips of Er 2 O 3 . The incorporation of silicon excess in the films is due to the introduction of hydrogen in the plasma which reduces accordingly the oxygen released by the silica target. The incorporated Er content [Er] in the films was controlled by the sputtered surface ratio of Er 2 O 3 chips with respect to that of silica target. The photoluminescence (PL) intensity at 1.54 μm from samples obtained with different [Er] values and annealing temperature were compared for the same amount of Si excess and interpreted in terms of sensitizing action of Si nanoclusters (Si–NCs) towards the neighboring Er ions. Some onset of concentration-related effects (up conversion, quenching) was suspected for [Er] ∼3 × 10 20 cm −3 through the decrease of the lifetime emission from ∼6 ms for [Er] = 5 × 10 19 cm −3 to about 3.5 ms. These effects were compensated by a better coupling rate between the Si–NCs and the neighbouring Er ions. This was achieved through some adjustments of the processing parameters: the decrease of the plasma pressure that led to the improvement of the PL by a factor of 3, and the decrease of the annealing temperature from 1100 °C to 900 °C that induced a 2.5 times increase of the 1.54 μm emission.


title: A CNN-Based Approach to Detecting Text from Images of Whiteboards and Handwritten Notes

abstract: Detecting handwritten text from images of whiteboards and handwritten notes is an important yet under-researched topic. In this paper, we propose a convolutional neural network (CNN) based approach to address this problem. First, to detect text instances of different scales, a feature pyramid network is adopted as a backbone network to extract three feature maps of different scales from a given input image, where a scale-specific detection module is attached to each feature map. Then, for a pixel on each feature map, a detection module is used to predict whether there exists a text instance at its corresponding location in the input image. For positive prediction, the bounding box of the detected text segment and the links between the concerned pixel and its 8 neighbors on the feature map are predicted simultaneously. Based on the linkage information, text segments extracted from each feature map are grouped into text-lines respectively and wrongly grouped text-lines are separated by a graph-based text-line segmentation method. Finally, detection results from three different feature maps are aggregated by a skewed non-maximum suppression algorithm. Our proposed approach has achieved superior results on a testing set consisting of 285 natural scene images of whiteboards and handwritten notes.


title: Learning Multi-View Neighborhood Preserving Projections

abstract: We address the problem of metric learning for multi-view data, namely the construction of embedding projections from data in different representations into a shared feature space, such that the Euclidean distance in this space provides a meaningful within-view as well as between-view similarity. Our motivation stems from the problem of cross-media retrieval tasks, where the availability of a joint Euclidean distance function is a prerequisite to allow fast, in particular hashing-based, nearest neighbor queries. ::: ::: We formulate an objective function that expresses the intuitive concept that matching samples are mapped closely together in the output space, whereas non-matching samples are pushed apart, no matter in which view they are available. The resulting optimization problem is not convex, but it can be decomposed explicitly into a convex and a concave part, thereby allowing efficient optimization using the convex-concave procedure. Experiments on an image retrieval task show that nearest-neighbor based cross-view retrieval is indeed possible, and the proposed technique improves the retrieval accuracy over baseline techniques.


title: A 3D Morphable Model Learnt from 10,000 Faces

abstract: We present Large Scale Facial Model (LSFM) — a 3D Morphable Model (3DMM) automatically constructed from 9,663 distinct facial identities. To the best of our knowledge LSFM is the largest-scale Morphable Model ever constructed, containing statistical information from a huge variety of the human population. To build such a large model we introduce a novel fully automated and robust Morphable Model construction pipeline. The dataset that LSFM is trained on includes rich demographic information about each subject, allowing for the construction of not only a global 3DMM but also models tailored for specific age, gender or ethnicity groups. As an application example, we utilise the proposed model to perform age classification from 3D shape alone. Furthermore, we perform a systematic analysis of the constructed 3DMMs that showcases their quality and descriptive power. The presented extensive qualitative and quantitative evaluations reveal that the proposed 3DMM achieves state-of-the-art results, outperforming existing models by a large margin. Finally, for the benefit of the research community, we make publicly available the source code of the proposed automatic 3DMM construction pipeline. In addition, the constructed global 3DMM and a variety of bespoke models tailored by age, gender and ethnicity are available on application to researchers involved in medically oriented research.