title: Smarter log analysis

abstract: Modern computer systems generate an enormous number of logs. IBM Mining Effectively Large Output Data Yield (MELODY) is a unique and innovative solution for handling these logs and filtering out the anomalies and failures. MELODY can detect system errors early on and avoid subsequent crashes by identifying the root causes of such errors. By analyzing the logs leading up to a problem, MELODY can pinpoint when and where things went wrong and visually present them to the user, ensuring that corrections are accurately and effectively done. We present the MELODY solution and describe its architecture, algorithmic components, functions, and benefits. After being trained on a large portion of relevant data, MELODY provides alerts of abnormalities in newly arriving log files or in streams of logs. The solution is being used by IBM services groups that support IBM xSeries® servers on a regular basis. MELODY was recently tested with ten large IBM customers who use zSeries® machines and was found to be extremely useful for the information technology experts in those companies. They found that the solution's ability to reduce extensively large log data to manageable sets of highlighted messages saved them time and helped them make better use of the data.


title: Fabricating 3D Figurines with Personalized Faces

abstract: We present a semi-automated system for fabricating figurines with faces that are personalised to the individual likeness of the customer. The efficacy of the system has been demonstrated by commercial deployments at Walt Disney World Resort and Star Wars Celebration VI in Orlando Florida. Although the system is semi automated, human intervention is limited to a few simple tasks to maintain the high throughput and consistent quality required for commercial application. In contrast to existing systems that fabricate custom heads that are assembled to pre-fabricated plastic bodies, our system seamlessly integrates 3D facial data with a predefined figurine body into a unique and continuous object that is fabricated as a single piece. The combination of state-of-the-art 3D capture, modelling, and printing that are the core of our system provide the flexibility to fabricate figurines whose complexity is only limited by the creativity of the designer.


Feature Selection for Classification: A Review


title: Social Media Analysis for Product Safety using Text Mining and Sentiment Analysis

abstract: The growing incidents of counterfeiting and associated economic and health consequences necessitate the development of active surveillance systems capable of producing timely and reliable information for all stake holders in the anti-counterfeiting fight. User generated content from social media platforms can provide early clues about product allergies, adverse events and product counterfeiting. This paper reports a work in progress with contributions including: the development of a framework for gathering and analyzing the views and experiences of users of drug and cosmetic products using machine learning, text mining and sentiment analysis; the application of the proposed framework on Facebook comments and data from Twitter for brand analysis, and the description of how to develop a product safety lexicon and training data for modeling a machine learning classifier for drug and cosmetic product sentiment prediction. The initial brand and product comparison results signify the usefulness of text mining and sentiment analysis on social media data while the use of machine learning classifier for predicting the sentiment orientation provides a useful tool for users, product manufacturers, regulatory and enforcement agencies to monitor brand or product sentiment trends in order to act in the event of sudden or significant rise in negative sentiment.


title: LQR, double-PID and pole placement stabilization and tracking control of single link inverted pendulum

abstract: This paper presents the dynamic behaviour of a nonlinear single link inverted pendulum-on-cart system based on Lagrange Equation. The nonlinear model linearization was presented based on Taylor series approximation. LQR, double-PID and simple pole placement control techniques were proposed for upright stabilization and tracking controls of the system. Simulations results for the various control techniques subjected to a unity magnitude pulse input torque with and without disturbance were compared. The performances of the proposed controllers were investigated based on response time specifications and level of disturbance rejection. Thus, the performance of LQR is more reliable and satisfactory. Finally, future work suggestions were made.


title: Business Models for Electronic Markets

abstract: Electronic commerce over the Internet may be either complementary to traditional business or represent a whole new line of the new features of the Internet, critical questions to be answered include: ♦ what are the emerging business models; and related to this ♦ which strategic marketing approaches are applied, or emerging. This article addresses the first question above by providing a framework for the classification of Internet electronic commerce business models. This framework has been developed on the basis of current commercial Internet business and experimental work in European R&D programmes.


title: Efficient and robust automated machine learning

abstract: The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts. To be effective in practice, such systems need to automatically choose a good algorithm and feature preprocessing steps for a new dataset at hand, and also set their respective hyperparameters. Recent work has started to tackle this automated machine learning (AutoML) problem with the help of efficient Bayesian optimization methods. Building on this, we introduce a robust new AutoML system based on scikit-learn (using 15 classifiers, 14 feature preprocessing methods, and 4 data preprocessing methods, giving rise to a structured hypothesis space with 110 hyperparameters). This system, which we dub AUTO-SKLEARN, improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Our system won the first phase of the ongoing ChaLearn AutoML challenge, and our comprehensive analysis on over 100 diverse datasets shows that it substantially outperforms the previous state of the art in AutoML. We also demonstrate the performance gains due to each of our contributions and derive insights into the effectiveness of the individual components of AUTO-SKLEARN.


title: The optimum received power levels of uplink non-orthogonal multiple access (NOMA) signals

abstract: Non-orthogonal multiple access (NOMA) has been recently considered as a promising multiple access technique for fifth generation (5G) mobile networks as an enabling technology to meet the demands of low latency, high reliability, massive connectivity, and high throughput. The two dominants types of NOMA are: power-domain and code-domain. The key feature of power-domain NOMA is to allow different users to share the same time, frequency, and code, but with different power levels. In code-domain NOMA, different spread-spectrum codes are assigned to different users and are then multiplexed over the same time-frequency resources. This paper concentrates on power-domain NOMA. In power-domain NOMA, Successive Interference Cancellation (SIC) is employed at the receiver. In this paper, the optimum received uplink power levels using a SIC detector is determined analytically for any number of transmitters. The optimum uplink received power levels using the SIC decoder in NOMA strongly resembles the μ-law encoding used in pulse code modulation (PCM) speech companders.


Static In-wheel Wireless Charging Systems for Electric Vehicles


title: Customers’ Influence Makes or Breaks Your Brand’s Success Story – Accounting for Positive and Negative Social Influence in Online Customer Networks

abstract: The ongoing proliferation of digital technologies is reshaping the customer-firm relationship by providing new possibilities for companies and customers to interact with each other. Companies try to involve customers in firm-sponsored online customer networks to connect them more deeply with the brand. In this context, the impact of positive social influence induced among customers on their value contribution has been acknowledged, however, research often neglects the impact of negative social influence. We propose therefore a novel approach to account for direct and indirect as well as positive and negative social influence between customers in online customer networks to calculate customers’ integrated value contribution. We demonstrate the applicability of our approach using an illustrative online customer network. Our approach allows practitioners to evaluate customers’ “true” value in online customer networks by preventing over- and underestimation of customers’ value contribution.


title: Learning to Write with Cooperative Discriminators

abstract: Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models, but when used to generate natural language their output tends to be overly generic, repetitive, and self-contradictory. We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the notion of communicative goals described by linguistic principles such as Grice's Maxims. We propose learning a mixture of multiple discriminative models that can be used to complement the RNN generator and guide the decoding process. Human evaluation demonstrates that text generated by our system is preferred over that of baselines by a large margin and significantly enhances the overall coherence, style, and information content of the generated text.


title: Estimation and measurement of flat or solenoidal coil inductance for radiofrequency NMR coil design.

abstract: The inductance of a radiofrequency coil determines its compatibility with a given NMR probe circuit. However, calculation (or estimation) of inductance for radiofrequency coils of dimensions suitable for use in an NMR probe is not trivial, particularly for flat-coils. A comparison of a number of formulae for calculation of inductance is presented through the use of a straightforward inductance measurement circuit. This technique relies upon instrumentation available in many NMR laboratories rather than upon more expensive and specialized instrumentation often utilized in the literature. Inductance estimation methods are suggested and validated for both flat-coils and solenoids. These have proven very useful for fabrication of a number of new coils in our laboratory for use in static solid-state NMR probes operating at (1)H frequencies of 300 and 600MHz. Solenoidal coils with very similar measured and estimated inductances having inner diameters from 1 to 5mm are directly compared as an example of the practical application of inductance estimation for interchange of coils within an existing solid-state NMR probe.


title: ScentBar: A Query Suggestion Interface Visualizing the Amount of Missed Relevant Information for Intrinsically Diverse Search

abstract: For intrinsically diverse tasks, in which collecting extensive information from different aspects of a topic is required, searchers often have difficulty formulating queries to explore diverse aspects and deciding when to stop searching. With the goal of helping searchers discover unexplored aspects and find the appropriate timing for search stopping in intrinsically diverse tasks, we propose ScentBar, a query suggestion interface visualizing the amount of important information that a user potentially misses collecting from the search results of individual queries. We define the amount of missed information for a query as the additional gain that can be obtained from unclicked search results of the query, where gain is formalized as a set-wise metric based on aspect importance, aspect novelty, and per-aspect document relevance and is estimated by using a state-of-the-art algorithm for subtopic mining and search result diversification. Results of a user study involving 24 participants showed that the proposed interface had the following advantages when the gain estimation algorithm worked reasonably: (1) ScentBar users stopped examining search results after collecting a greater amount of relevant information; (2) they issued queries whose search results contained more missed information; (3) they obtained higher gain, particularly at the late stage of their sessions; and (4) they obtained higher gain per unit time. These results suggest that the simple query visualization helps make the search process of intrinsically diverse tasks more efficient, unless inaccurate estimates of missed information are visualized.


title: Semi-supervised time series classification

abstract: The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other domain specialists. As in many other domains, there are often copious amounts of unlabeled data available. For example, the PhysioBank archive contains gigabytes of ECG data. In this work we propose a semi-supervised technique for building time series classifiers. While such algorithms are well known in text domains, we will show that special considerations must be made to make them both efficient and effective for the time series domain. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, handwritten documents, and video datasets. The experimental results demonstrate that our approach requires only a handful of labeled examples to construct accurate classifiers.


title: Biometric recognition of newborns: Identification using palmprints

abstract: We present some results on newborn identification through high-resolution images of palmar surfaces. To our knowledge, there is no biometric system currently available that can be effectively used for newborn identification. The manual procedure of capturing inked footprints in practice for this purpose is limited for use inside hospitals and is not an effective solution for identification purposes. The use of friction ridge patterns on the hands of newborns is challenging due to both the small size of newborn's papillary ridges, which are, on average, 2.5 to 3 times smaller than the ridges in adult fingerprints, and their fragility, making them amenable to deformation. The proposed palmprint based automatic system for newborn identification is relatively easy to use and shows the feasibility of this approach. Experiments were performed on images collected from 250 newborns at the University Hospital (Universidade Federal do Parana). An image acquisition protocol was developed in order to collect suitable images. When considering the good quality palmar images, the results show that the proposed approach is promising.


title: Dynamic control flow in large-scale machine learning

abstract: Many recent machine learning models rely on fine-grained dynamic control flow for training and inference. In particular, models based on recurrent neural networks and on reinforcement learning depend on recurrence relations, data-dependent conditional execution, and other features that call for dynamic control flow. These applications benefit from the ability to make rapid control-flow decisions across a set of computing devices in a distributed system. For performance, scalability, and expressiveness, a machine learning system must support dynamic control flow in distributed and heterogeneous environments. This paper presents a programming model for distributed machine learning that supports dynamic control flow. We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system. Our approach extends the use of dataflow graphs to represent machine learning models, offering several distinctive features. First, the branches of conditionals and bodies of loops can be partitioned across many machines to run on a set of heterogeneous devices, including CPUs, GPUs, and custom ASICs. Second, programs written in our model support automatic differentiation and distributed gradient computations, which are necessary for training machine learning models that use control flow. Third, our choice of non-strict semantics enables multiple loop iterations to execute in parallel across machines, and to overlap compute and I/O operations. We have done our work in the context of TensorFlow, and it has been used extensively in research and production. We evaluate it using several real-world applications, and demonstrate its performance and scalability.


title: One Trillion Edges: Graph Processing at Facebook-Scale

abstract: Analyzing large graphs provides valuable insights for social networking and web companies in content ranking and recommendations. While numerous graph processing systems have been developed and evaluated on available benchmark graphs of up to 6.6B edges, they often face significant difficulties in scaling to much larger graphs. Industry graphs can be two orders of magnitude larger - hundreds of billions or up to one trillion edges. In addition to scalability challenges, real world applications often require much more complex graph processing workflows than previously evaluated. In this paper, we describe the usability, performance, and scalability improvements we made to Apache Giraph, an open-source graph processing system, in order to use it on Facebook-scale graphs of up to one trillion edges. We also describe several key extensions to the original Pregel model that make it possible to develop a broader range of production graph applications and workflows as well as improve code reuse. Finally, we report on real-world operations as well as performance characteristics of several large-scale production applications.


title: Shape, albedo, and illumination from a single image of an unknown object

abstract: We address the problem of recovering shape, albedo, and illumination from a single grayscale image of an object, using shading as our primary cue. Because this problem is fundamentally underconstrained, we construct statistical models of albedo and shape, and define an optimization problem that searches for the most likely explanation of a single image. We present two priors on albedo which encourage local smoothness and global sparsity, and three priors on shape which encourage flatness, outward-facing orientation at the occluding contour, and local smoothness. We present an optimization technique for using these priors to recover shape, albedo, and a spherical harmonic model of illumination. Our model, which we call SAIFS (shape, albedo, and illumination from shading) produces reasonable results on arbitrary grayscale images taken in the real world, and outperforms all previous grayscale “intrinsic image” — style algorithms on the MIT Intrinsic Images dataset.


title: MAKHA—A New Hybrid Swarm Intelligence Global Optimization Algorithm

abstract: The search for efficient and reliable bio-inspired optimization methods continues to be an active topic of research due to the wide application of the developed methods. In this study, we developed a reliable and efficient optimization method via the hybridization of two bio-inspired swarm intelligence optimization algorithms, namely, the Monkey Algorithm (MA) and the Krill Herd Algorithm (KHA). The hybridization made use of the efficient steps in each of the two original algorithms and provided a better balance between the exploration/diversification steps and the exploitation/intensification steps. The new hybrid algorithm, MAKHA, was rigorously tested with 27 benchmark problems and its results were compared with the results of the two original algorithms. MAKHA proved to be considerably more reliable and more efficient in tested problems.


title: The NETSPEAK WORDGRAPH: Visualizing keywords in context

abstract: NETSPEAK helps writers in choosing words while writing a text. It checks for the commonness of phrases and allows for the retrieval of alternatives by means of wildcard queries. To support such queries, we implement a scalable retrieval engine, which returns high-quality results within milliseconds using a probabilistic retrieval strategy. The results are displayed as WORDGRAPH visualization or as a textual list. The graphical interface provides an effective means for interactive exploration of search results using filter techniques, query expansion and navigation. Our observations indicate that, of three investigated retrieval tasks, the textual interface is sufficient for the phrase verification task, wherein both views support context-sensitive word choice, and the WORDGRAPH best supports the exploration of a phrase's context or the underlying corpus. The preferred view for context-sensitive word choice seems to depend on query complexity (i.e. the number of wildcards in a query).


title: A state-of-the-art 3D sensor for robot navigation

abstract: This paper relates first experiences using a state-of-the-art, time-of-flight sensor that is able to deliver 3D images. The properties and capabilities of the sensor make it a potential powerful tool for applications within mobile robotics especially for real-time tasks, as the sensor features a frame rate of up to 30 frames per second. Its capabilities in terms of basic obstacle avoidance and local path-planning are evaluated and compared to the performance of a standard laser scanner.


title: Automated valet parking and charging for e-mobility

abstract: Automated valet parking services provide great potential to increase the attractiveness of electric vehicles by mitigating their two main current deficiencies: reduced driving ranges and prolonged refueling times. The European research project V-Charge aims at providing this service on designated parking lots using close-to-market sensors only. For this purpose the project developed a prototype capable of performing fully automated navigation in mixed traffic on designated parking lots and GPS-denied parking garages with cameras and ultrasonic sensors only. This paper summarizes the work of the project, comprising advances in network communication and parking space scheduling, multi-camera calibration, semantic mapping concepts, visual localization and motion planning. The project pushed visual localization, environment perception and automated parking to centimetre precision. The developed infrastructure-based camera calibration and semi-supervised semantic mapping concepts greatly reduce maintenance efforts. Results are presented from extensive month-long field tests.


Complex Trauma and Disorders of Extreme Stress (DESNOS) Diagnosis, Part One: Assessment


title: LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees

abstract: We present a method to generate a robot control strategy that maximizes the probability to accomplish a task. The task is given as a Linear Temporal Logic (LTL) formula over a set of properties that can be satisfied at the regions of a partitioned environment. We assume that the probabilities with which the properties are satisfied at the regions are known, and the robot can determine the truth value of a proposition only at the current region. Motivated by several results on partitioned-based abstractions, we assume that the motion is performed on a graph. To account for noisy sensors and actuators, we assume that a control action enables several transitions with known probabilities. We show that this problem can be reduced to the problem of generating a control policy for a Markov Decision Process (MDP) such that the probability of satisfying an LTL formula over its states is maximized. We provide a complete solution for the latter problem that builds on existing results from probabilistic model checking. We include an illustrative case study.


title: High-Selectivity Dual-Band Bandpass Filter Using a Quad-Mode Resonator With Source-Load Coupling

abstract: This letter presents a novel dual-band bandpass filter with controllable frequencies and bandwidths as well as a high out-of-band rejection level. The proposed filter utilizes a novel stub-loaded quad-mode resonator. Every two modes, which can be flexibly controlled, are utilized to form a passband with controllable frequency and bandwidth. Source-load coupling and hook-shape feed lines are introduced and high selectivity is achieved. An experimental filter is implemented and the experimental results are presented for validation.


title: Molecular and morphological phylogeny of hooded shrimps, genera Betaeus and Betaeopsis (Decapoda, Alpheidae): testing the center of origin biogeographic model and evolution of life history traits.

abstract: The phylogenetic relationships of the alpheid shrimp genera Betaeus (Dana, 1852) (15 species) and Betaeopsis (Yaldwyn, 1971) (three species), collectively known as hooded shrimps, are analyzed with morphological, molecular (16S and H3) and combined "total evidence" (morphology+DNA) datasets. The tree topology resulting from morphological and combined analyses places Betaeus jucundus as sister to all the remaining species of Betaeus and Betaeopsis, rendering Betaeus paraphyletic. On the other hand, Betaeopsis is recovered as monophyletic. Betaeus australis is positioned as sister to the remaining species of Betaeus s. str. (excluding B. jucundus), which is composed of three well-supported and resolved clades. Mapping of biogeographic traits on the combined tree suggests at least two possible historic scenarios. In the first scenario, the North-East Pacific harboring the highest diversity of hooded shrimps (seven species of Betaeus), acted as the "center of origin", where species appeared, matured and eventually migrated toward peripheral regions. In the second scenario, Betaeus+Betaeopsis originated in the southern Indo-West Pacific and subsequently colonized the North-East Pacific, where a major radiation involving dispersal/vicariance events took place. The mapping of life history traits (symbiosis vs. free living and gregariousness vs. single/pair living) in the combined tree suggests (1) that different types of symbioses with dissimilar host organisms (sea urchins, abalones, other decapods, spoon worms) evolved independently more than once in the group (in B. jucundus and in various lineages of Betaeus s. str.), and (2) that gregariousness was ancestral in the Betaeus s. str. -Betaeopsis clade and later shifted toward single/pair living in several lineages.


title: A component model for hard real-time systems: CCM with ARINC-653

abstract: The size and complexity of software in safety-critical systems is increasing at a rapid pace. One technology that can be used to mitigate this complexity is component-based software development. However, in spite of the apparent benefits of a component-based approach to development, little work has been done in applying these concepts to hard real-time systems. This paper improves the state of the art by making three contributions: (1) we present a component model for hard real-time systems and define the semantics of different types of component interactions; (2) we present an implementation of a middleware that supports this component model. This middleware combines an open-source CORBA Component Model (CCM) implementation (MICO) with ARINC-653: a state-of-the-art real-time operating systems (RTOS) standard, (3) finally; we describe a modeling environment that enables design, analysis, and deployment of component assemblies. We conclude with a discussion of the lessons learned during this exercise. Our experiences point toward extending both the CCM as well as revising the ARINC-653. Copyright © 2011 John Wiley & Sons, Ltd.


title: From Factors to Actors: Computational Sociology and Agent-Based Modeling

abstract: ■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach.


title: Consensus of Multiagent Systems and Synchronization of Complex Networks: A Unified Viewpoint

abstract: This paper addresses the consensus problem of multiagent systems with a time-invariant communication topology consisting of general linear node dynamics. A distributed observer-type consensus protocol based on relative output measurements is proposed. A new framework is introduced to address in a unified way the consensus of multiagent systems and the synchronization of complex networks. Under this framework, the consensus of multiagent systems with a communication topology having a spanning tree can be cast into the stability of a set of matrices of the same low dimension. The notion of consensus region is then introduced and analyzed. It is shown that there exists an observer-type protocol solving the consensus problem and meanwhile yielding an unbounded consensus region if and only if each agent is both stabilizable and detectable. A multistep consensus protocol design procedure is further presented. The consensus with respect to a time-varying state and the robustness of the consensus protocol to external disturbances are finally discussed. The effectiveness of the theoretical results is demonstrated through numerical simulations, with an application to low-Earth-orbit satellite formation flying.


title: Learning Parameters and Constitutive Relationships with Physics Informed Deep Neural Networks

abstract: We present a physics informed deep neural network (DNN) method for estimating parameters and unknown physics (constitutive relationships) in partial differential equation (PDE) models. We use PDEs in addition to measurements to train DNNs to approximate unknown parameters and constitutive relationships as well as states. The proposed approach increases the accuracy of DNN approximations of partially known functions when a limited number of measurements is available and allows for training DNNs when no direct measurements of the functions of interest are available. We employ physics informed DNNs to estimate the unknown space-dependent diffusion coefficient in a linear diffusion equation and an unknown constitutive relationship in a non-linear diffusion equation. For the parameter estimation problem, we assume that partial measurements of the coefficient and states are available and demonstrate that under these conditions, the proposed method is more accurate than state-of-the-art methods. For the non-linear diffusion PDE model with a fully unknown constitutive relationship (i.e., no measurements of constitutive relationship are available), the physics informed DNN method can accurately estimate the non-linear constitutive relationship based on state measurements only. Finally, we demonstrate that the proposed method remains accurate in the presence of measurement noise.


title: SEGCloud: Semantic Segmentation of 3D Point Clouds

abstract: 3D semantic scene labeling is fundamental to agents operating in the real world. In particular, labeling raw 3D point sets from sensors provides fine-grained semantics. Recent works leverage the capabilities of Neural Networks (NNs), but are limited to coarse voxel predictions and do not explicitly enforce global consistency. We present SEGCloud, an end-to-end framework to obtain 3D point-level segmentation that combines the advantages of NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields (FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are transferred back to the raw 3D points via trilinear interpolation. Then the FC-CRF enforces global consistency and provides fine-grained semantics on the points. We implement the latter as a differentiable Recurrent NN to allow joint optimization. We evaluate the framework on two indoor and two outdoor 3D datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance comparable or superior to the state-of-the-art on all datasets.


title: Automatic mood detection of indian music using mfccs and k-means algorithm

abstract: This paper proposes a method of identifying the mood underlying a piece of music by extracting suitable and robust features from music clip. To recognize the mood, K- means clustering and global thresholding was used. Three features were amalgamated to decide the mood tag of the musical piece. Mel frequency cepstral coefficients, frame energy and peak difference are the features of interest. These features were used for clustering and further achieving silhouette plot which formed the basis of deciding the limits of threshold for classification. Experiments were performed on a database of audio clips of various categories. The accuracy of the mood extracted is around 90% indicating that the proposed technique provides encouraging results.


title: Natural Language Acquisition and Grounding for Embodied Robotic Systems

abstract: We present a cognitively plausible novel framework capable of learning the grounding in visual semantics and the grammar of natural language commands given to a robot in a table top environment. The input to the system consists of video clips of a manually controlled robot arm, paired with natural language commands describing the action. No prior knowledge is assumed about the meaning of words, or the structure of the language, except that there are different classes of words (corresponding to observable actions, spatial relations, and objects and their observable properties). The learning process automatically clusters the continuous perceptual spaces into concepts corresponding to linguistic input. A novel relational graph representation is used to build connections between language and vision. As well as the grounding of language to perception, the system also induces a set of probabilistic grammar rules. The knowledge learned is used to parse new commands involving previously unseen objects.


Says Who...? Identification of Expert versus Layman Critics' Reviews of Documentary Films


The complexity of the matrix eigenproblem


title: FlowDroid: precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps

abstract: Today's smartphones are a ubiquitous source of private and confidential data. At the same time, smartphone users are plagued by carelessly programmed apps that leak important data by accident, and by malicious apps that exploit their given privileges to copy such data intentionally. While existing static taint-analysis approaches have the potential of detecting such data leaks ahead of time, all approaches for Android use a number of coarse-grain approximations that can yield high numbers of missed leaks and false alarms. In this work we thus present FlowDroid, a novel and highly precise static taint analysis for Android applications. A precise model of Android's lifecycle allows the analysis to properly handle callbacks invoked by the Android framework, while context, flow, field and object-sensitivity allows the analysis to reduce the number of false alarms. Novel on-demand algorithms help FlowDroid maintain high efficiency and precision at the same time. We also propose DroidBench, an open test suite for evaluating the effectiveness and accuracy of taint-analysis tools specifically for Android apps. As we show through a set of experiments using SecuriBench Micro, DroidBench, and a set of well-known Android test applications, FlowDroid finds a very high fraction of data leaks while keeping the rate of false positives low. On DroidBench, FlowDroid achieves 93% recall and 86% precision, greatly outperforming the commercial tools IBM AppScan Source and Fortify SCA. FlowDroid successfully finds leaks in a subset of 500 apps from Google Play and about 1,000 malware apps from the VirusShare project.


title: Concept-based Short Text Classification and Ranking

abstract: Most existing approaches for text classification represent texts as vectors of words, namely ``Bag-of-Words.'' This text representation results in a very high dimensionality of feature space and frequently suffers from surface mismatching. Short texts make these issues even more serious, due to their shortness and sparsity. In this paper, we propose using ``Bag-of-Concepts'' in short text representation, aiming to avoid the surface mismatching and handle the synonym and polysemy problem. Based on ``Bag-of-Concepts,'' a novel framework is proposed for lightweight short text classification applications. By leveraging a large taxonomy knowledgebase, it learns a concept model for each category, and conceptualizes a short text to a set of relevant concepts. A concept-based similarity mechanism is presented to classify the given short text to the most similar category. One advantage of this mechanism is that it facilitates short text ranking after classification, which is needed in many applications, such as query or ad recommendation. We demonstrate the usage of our proposed framework through a real online application: Channel-based Query Recommendation. Experiments show that our framework can map queries to channels with a high degree of precision (avg. precision=90.3%), which is critical for recommendation applications.


title: Disruption management in the airline industry - Concepts, models and methods

abstract: This paper provides a thorough review of the current state-of-the-art within airline disruption management of resources, including aircraft, crew, passenger and integrated recovery. An overview of model formulations of the aircraft and crew scheduling problems is presented in order to emphasize similarities between solution approaches applied to the planning and recovery problems. A brief overview of research within schedule robustness in airline scheduling is included in the review, since this proactive measure is a natural complement to disruption management.


title: Impedance-based fault location in transmission networks: theory and application

abstract: A number of impedance-based fault location algorithms have been developed for estimating the distance to faults in a transmission network. Each algorithm has specific input data requirements and makes certain assumptions that may or may not hold true in a particular fault location scenario. Without a detailed understanding of the principle of each fault-locating method, choosing the most suitable fault location algorithm can be a challenging task. This paper, therefore, presents the theory of one-ended (simple reactance, Takagi, modified Takagi, Eriksson, and Novosel et al. ) and two-ended (synchronized, unsynchronized, and current-only) impedance-based fault location algorithms and demonstrates their application in locating real-world faults. The theory details the formulation and input data requirement of each fault-locating algorithm and evaluates the sensitivity of each to the following error sources: 1) load; 2) remote infeed; 3) fault resistance; 4) mutual coupling; 5) inaccurate line impedances; 6) DC offset and CT saturation; 7) three-terminal lines; and 8) tapped radial lines. From the theoretical analysis and field data testing, the following criteria are recommended for choosing the most suitable fault-locating algorithm: 1) data availability and 2) fault location application scenario. Another objective of this paper is to assess what additional information can be gleaned from waveforms recorded by intelligent electronic devices (IEDs) during a fault. Actual fault event data captured in utility networks is exploited to gain valuable feedback about the transmission network upstream from the IED device, and estimate the value of fault resistance.


title: Image and Video Abstraction by Anisotropic Kuwahara Filtering

abstract: We present a non-photorealistic rendering technique to transform color images and videos into painterly abstractions. It is based on a generalization of the Kuwahara filter that is adapted to the local shape of features, derived from the smoothed structure tensor. Contrary to conventional edge-preserving filters, our filter generates a painting-like flattening effect along the local feature directions while preserving shape boundaries. As opposed to conventional painting algorithms, it produces temporally coherent video abstraction without extra processing. The GPU implementation of our method processes video in real-time. The results have the clearness of cartoon illustrations but also exhibit directional information as found in oil paintings.


title: Digital implementation of fuzzy logic controller for wide range speed control of brushless DC motor

abstract: The brushless DC motors find wide applications such as in battery operated vehicles, wheel chairs, automotive fuel pumps, robotics, machine tools, aerospace and in many industrial applications due to their superior electrical and mechanical characteristics and its capability to operate in hazardous environment. Conventional controllers fail to yield desired performance in BLDC motor control systems due to the non-linearity arising out of variation in the system parameters and change in load. The main focus is now on the application of artificial intelligent techniques such as fuzzy logic to solve this problem. Another great challenge is to reduce the size and cost of the drive system without compromising the performance. In this paper, the design and digital implementation of fuzzy logic controller using a versatile ADUC812 microcontroller, and low-cost, compact, superior performance components are used in order to reduce the cost and size of the drive system. The experimental results are presented to prove the flexibility of the control scheme in real time.


IC Online: Ubiquitous Computing: The Future of Development?


title: Cognitive debiasing 1: origins of bias and theory of debiasing

abstract: Numerous studies have shown that diagnostic failure depends upon a variety of factors. Psychological factors are fundamental in influencing the cognitive performance of the decision maker. In this first of two papers, we discuss the basics of reasoning and the Dual Process Theory (DPT) of decision making. The general properties of the DPT model, as it applies to diagnostic reasoning, are reviewed. A variety of cognitive and affective biases are known to compromise the decision-making process. They mostly appear to originate in the fast intuitive processes of Type 1 that dominate (or drive) decision making. Type 1 processes work well most of the time but they may open the door for biases. Removing or at least mitigating these biases would appear to be an important goal. We will also review the origins of biases. The consensus is that there are two major sources: innate, hard-wired biases that developed in our evolutionary past, and acquired biases established in the course of development and within our working environments. Both are associated with abbreviated decision making in the form of heuristics. Other work suggests that ambient and contextual factors may create high risk situations that dispose decision makers to particular biases. Fatigue, sleep deprivation and cognitive overload appear to be important determinants. The theoretical basis of several approaches towards debiasing is then discussed. All share a common feature that involves a deliberate decoupling from Type 1 intuitive processing and moving to Type 2 analytical processing so that eventually unexamined intuitive judgments can be submitted to verification. This decoupling step appears to be the critical feature of cognitive and affective debiasing.


title: Recovering the Missing Link: Predicting Class-Attribute Associations for Unsupervised Zero-Shot Learning

abstract: Collecting training images for all visual categories is not only expensive but also impractical. Zero-shot learning (ZSL), especially using attributes, offers a pragmatic solution to this problem. However, at test time most attributebased methods require a full description of attribute associations for each unseen class. Providing these associations is time consuming and often requires domain specific knowledge. In this work, we aim to carry out attributebased zero-shot classification in an unsupervised manner. We propose an approach to learn relations that couples class embeddings with their corresponding attributes. Given only the name of an unseen class, the learned relationship model is used to automatically predict the classattribute associations. Furthermore, our model facilitates transferring attributes across data sets without additional effort. Integrating knowledge from multiple sources results in a significant additional improvement in performance. We evaluate on two public data sets: Animals with Attributes and aPascal/aYahoo. Our approach outperforms state-of the-art methods in both predicting class-attribute associations and unsupervised ZSL by a large margin.


title: The Impact of ICT on Intermediation in the Microfinance Industry

abstract: The microfinance industry provides financial services to the world's poor in hopes of moving individuals and families out of poverty. This research examines how information and communication technologies (ICTs) are changing the microfinance industry given recent advancements in mobile banking, Internet usage, and connectivity. By examining the microfinance market structure, we determine that ICTs impact intermediation and market structure among various players in the microfinance industry. We use a recent industry risk report to inform predictions of changes to the intermediation structure of the industry.


title: MIMO-OFDM Channel Estimation Using Pilot Carries

abstract: A multiple-input multiple-output (MIMO) communication system combined with the orthogonal frequency division multiplexing (OFDM) modulation technique can achieve reliable high data rate transmission over broadband wireless channels. Channel state information for both single-input single-output (SISO) and MIMO systems based on pilot aided arrangement is investigated in this paper. The estimation of channel at pilot frequencies with conventional Least Square (LS) and Minimum Mean Square (MMSE) estimation algorithms is carried out through Matlab simulation. The performance of MIMO OFDM and SISO OFDM are evaluated on the basis of Bit Error Rate (BER) and Mean Square Error (MSE) level. Further enhancement of performance can be achieved through maximum diversity Space Time Block Coding (STBC) and Maximum Likelihood Detection at transmission and reception ends respectively. MMSE estimation has been shown to perform much better than LS but is more complex than LS for the MIMO system using pilot carriers.


title: A recommendation engine for travel products based on topic sequential patterns

abstract: Travel products recommendation has become one of emerging issues in the realm of recommendation systems. The widely-used collaborative filtering algorithms are usually difficult to be used for recommending travel products due to a number of reasons, including (1) the content of travel products is very complex, (2) the user-item matrix is extremely sparse, and (3) the cold-start users are widely existing. To tackle these issues, we try to exploit Web server logs for generating recommendation, and present a novel recommendation engine (SECT for short) for travel products based on topic sequential patterns. In detail, we first extract topics from semantic description of every Web page. Then, we mine topic frequent sequential patterns and their target products to form click patterns library. At last, we propose a Markov n-gram model for matching the real-time click-stream of users with the click patterns library and thus computing recommendation scores. Experimental results on a real-world travel dataset demonstrate that the SECT prevails over the state-of-art baseline algorithms. In particular, SECT shows merits in improving the both coverage and accuracy for recommending products to cold-start users. Also, SECT is effective to recommend long tail items and outperform baseline algorithms.


Dynamic Movement Primitives ( DMPs ) encode a desired movement trajectory in terms of the attractor


title: Primary-Side Power Flow Control of Wireless Power Transfer for Electric Vehicle Charging

abstract: Various noncontacting methods of plug-in electric vehicle charging are either under development or now deployed as aftermarket options in the light-duty automotive market. Wireless power transfer (WPT) is now the accepted term for wireless charging and is used synonymously for inductive power transfer and magnetic resonance coupling. WPT technology is in its infancy; standardization is lacking, especially on interoperability, center frequency selection, magnetic fringe field suppression, and the methods employed for power flow regulation. This paper proposes a new analysis concept for power flow in WPT in which the primary provides frequency selection and the tuned secondary, with its resemblance to a power transmission network having a reactive power voltage control, is analyzed as a transmission network. Analysis is supported with experimental data taken from Oak Ridge National Laboratory’s WPT apparatus. This paper also provides an experimental evidence for frequency selection, fringe field assessment, and the need for low-latency communications in the feedback path.


title: CFaaS: bilaterally agreed evidence collection

abstract: A common cloud forensic model proposed by researchers is `Cloud-Forensic-as-a-Service' where consumers have to access it as a service to collect forensic data from cloud environments. The `Cloud-Forensic-as-a-Service' model raises the question of how it collects digital evidence pertaining to an incident which occurred in the cloud. Currently, types of `Cloud-Forensic-as-a-Service' systems in the literature show that the system is controlled and implemented by the cloud provider, where they unilaterally define the type of evidence that can be collected by the system. A serious limitation of this approach is that it does not offer the consumer sufficient means of performing reasonableness checks to verify that the provider is not accidentally or maliciously contaminating the evidence. To address the problem, the paper proposes a conceptual bilateral Cloud-Forensic-as-a-Service model where both consumers and providers can independently collect, verify the equity of the forensic analysis process and try to resolve potential disputes emerging from the independently collected results. The authors have developed a cloud forensic process model to lead common and significant aspects of a bilateral Cloud-Forensics-as-a-Service model. The paper explicitly discusses the concept of a bilateral Cloud-Forensic-as-a-Service model.


Personality Prediction with Social Behavior by Analyzing Social Media Data – A Survey


title: Efficient Large-Scale Sequence Comparison by Locality-Sensitive Hashing

abstract: Motivation: Comparison of multimegabase genomic DNA sequences is a popular technique for finding and annotating conserved genome features. Performing such comparisons entails finding many short local alignments between sequences up to tens of megabases in length. To process such long sequences efficiently, existing algorithms find alignments by expanding around short runs of matching bases with no substitutions or other differences. Unfortunately, exact matches that are short enough to occur often in significant alignments also occur frequently by chance in the background sequence. Thus, these algorithms must trade off between efficiency and sensitivity to features without long exact matches. Results: We introduce a new algorithm, LSH-ALL-PAIRS, to find ungapped local alignments in genomic sequence with up to a specified fraction of substitutions. The length and substitution rate of these alignments can be chosen so that they appear frequently in significant similarities yet still remain rare in the background sequence. The algorithm finds ungapped alignments efficiently using a randomized search technique, locality-sensitive hashing. We have found LSH-ALL-PAIRS to be both efficient and sensitive for finding local similarities with as little as 63% identity in mammalian genomic sequences up to tens of megabases in length Availability: Contact the author at the address below.


Modeling and Monitoring of Cardiovascular Dynamics for Patients in Critical Care


title: Exercise as a mediator of hepcidin activity in athletes

abstract: Iron is a trace mineral used by the body in many physiological processes that are essential for athletic performance. However, it is common that an athlete’s iron stores are compromised via several well-established exercise-related mechanisms such as hemolysis, hematuria, sweating and gastrointestinal bleeding. Recently, however, a new mechanism for athletics-induced iron deficiency has been proposed, involving the influence of physical activity on the post-exercise hepcidin response. Hepcidin is a liver-produced hormone that regulates iron metabolism in the gut and macrophages. This hormone has become the focus of recent investigations into altered iron metabolism in athletes, and may be a mitigating factor implicated in athletics-induced iron deficiency. This review attempts to summarize and disseminate the collective knowledge currently held regarding exercise and hepcidin expression, in addition to suggesting the direction for future research in this area.


title: Deep Cropping via Attention Box Prediction and Aesthetics Assessment

abstract: We model the photo cropping problem as a cascade of attention box regression and aesthetic quality classification, based on deep learning. A neural network is designed that has two branches for predicting attention bounding box and analyzing aesthetics, respectively. The predicted attention box is treated as an initial crop window where a set of cropping candidates are generated around it, without missing important information. Then, aesthetics assessment is employed to select the final crop as the one with the best aesthetic quality. With our network, cropping candidates share features within full-image convolutional feature maps, thus avoiding repeated feature computation and leading to higher computation efficiency. Via leveraging rich data for attention prediction and aesthetics assessment, the proposed method produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results demonstrate the competitive results and fast processing speed (5 fps with all steps).


title: Tech United Eindhoven Team Description 2014 Middle Size League

abstract: In this paper we discuss improvements in mechanical, electrical and software design of our middle-size league robots. Regarding hardware and control recent progress includes a prototype design of a ball clamping system, and first steps towards improved passing accuracy via velocity feedback control on the shooting lever. In terms of intelligent gameplay we have worked on creating possibilities for in-game optimization of strategic decisions. Via qr-code detection we can pass coaching instructions to our robots and with a basic machine learning algorithm success and failure after free-kicks is taken into account. In the final part of this paper we briefly discuss progress we have made in designing a four-wheeled soccer robot with a suspension system and on a smartphone application which real-time visualizes robot status and game state.


title: Human behavior analysis in video surveillance: A Social Signal Processing perspective

abstract: The analysis of human activities is one of the most intriguing and important open issues for the automated video surveillance community. Since few years ago, it has been handled following a mere Computer Vision and Pattern Recognition perspective, where an activity corresponded to a temporal sequence of explicit actions (run, stop, sit, walk, etc.). Even under this simplistic assumption, the issue is hard, due to the strong diversity of the people appearance, the number of individuals considered (we may monitor single individuals, groups, crowd), the variability of the environmental conditions (indoor/outdoor, different weather conditions), and the kinds of sensors employed. More recently, the automated surveillance of human activities has been faced considering a new perspective, that brings in notions and principles from the social, affective, and psychological literature, and that is called Social Signal Processing (SSP). SSP employs primarily nonverbal cues, most of them are outside of conscious awareness, like face expressions and gazing, body posture and gestures, vocal characteristics, relative distances in the space and the like. This paper is the first review analyzing this new trend, proposing a structured snapshot of the state of the art and envisaging novel challenges in the surveillance domain where the cross-pollination of Computer Science technologies and Sociology theories may offer valid investigation strategies.


Do Ridesharing Services Affect Traffic Congestion ? An Empirical Study of Uber Entry


title: A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing

abstract: The purpose of this paper is to develop a novel hybrid optimization method (HRABC) based on artificial bee colony algorithm and Taguchi method. The proposed approach is applied to a structural design optimization of a vehicle component and a multi-tool milling optimization problem. A comparison of state-of-the-art optimization techniques for the design and manufacturing optimization problems is presented. The results have demonstrated the superiority of the HRABC over the other techniques like differential evolution algorithm, harmony search algorithm, particle swarm optimization algorithm, artificial immune algorithm, ant colony algorithm, hybrid robust genetic algorithm, scatter search algorithm, genetic algorithm in terms of convergence speed and efficiency by measuring the number of function evaluations required.


title: Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition

abstract: A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.


title: Hybrid data labeling algorithm for clustering large mixed type data

abstract: Due to enormous growth in both volume and variety of data, clustering a very large database is a time-consuming process. To speed up clustering process, sampling has been recognized as a very utilitarian approach to reduce the dataset size in which a collection of data points are taken as a sample and then a clustering algorithm is applied to partitioning the data points in that sample into clusters. In this approach, the data points, that are not sampled, do not get their cluster labels. The process of allocating unlabeled data points into proper clusters has been well explored purely in numerical or categorical domain only, but not the both. In this paper, we propose a hybrid similarity coefficient to find the resemblance between an unlabeled data point and a cluster, based on the importance of categorical attribute values and the mean values of numerical attributes. Furthermore, we propose a Hybrid Data Labeling Algorithm (HDLA), based on this similarity coefficient to designate an appropriate cluster label to each unlabeled data point. We analyze its time complexity and perform various experiments using synthetic and real world datasets to demonstrate the efficacy of HDLA.


Measuring the Impacts of FDI


title: SRDA: Generating Instance Segmentation Annotation via Scanning, Reasoning and Domain Adaptation

abstract: Instance segmentation is a problem of significance in computer vision. However, preparing annotated data for this task is extremely time-consuming and costly. By combining the advantages of 3D scanning, reasoning, and GAN-based domain adaptation techniques, we introduce a novel pipeline named SRDA to obtain large quantities of training samples with very minor effort. Our pipeline is well-suited to scenes that can be scanned, i.e. most indoor and some outdoor scenarios. To evaluate our performance, we build three representative scenes and a new dataset, with 3D models of various common objects categories and annotated real-world scene images. Extensive experiments show that our pipeline can achieve decent instance segmentation performance given very low human labor cost.


title: Politics and health in eight European countries: A comparative study of mortality decline under social democracies and right-wing governments

abstract: Recent publications have argued that the welfare state is an important determinant of population health, and that social democracy in office and higher levels of health expenditure promote health progress. In the period 1950-2000, Greece, Portugal, and Spain were the poorest market economies in Europe, with a fragmented system of welfare provision, and many years of military or authoritarian right-wing regimes. In contrast, the five Nordic countries were the richest market economies in Europe, governed mostly by center or center-left coalitions often including the social democratic parties, and having a generous and universal welfare state. In spite of the socioeconomic and political differences, and a large gap between the five Nordic and the three southern nations in levels of health in 1950, population health indicators converged among these eight countries. Mean decadal gains in longevity of Portugal and Spain between 1950 and 2000 were almost three times greater than gains in Denmark, and about twice as great as those in Iceland, Norway and Sweden during the same period. All this raises serious doubts regarding the hypothesis that the political regime, the political party in office, the level of health care spending, and the type of welfare state exert major influences on population health. Either these factors are not major determinants of mortality decline, or their impact on population health in Nordic countries was more than offset by other health-promoting factors present in Southern Europe.


title: Social networks users: fear of missing out in preservice teachers

abstract: As mobile computing and smartphones become an integrated part of our lives, the time individuals spend on social networks has significantly increased. Moreover, a link has been established between the uncontrolled use of social networks to the development of undesirable habits and behaviors including addictions. One such behavior, namely, fear of missing out (FOMO) is of particular interest and concern especially because of the widespread use of smartphones and computers, and thereby extensive use of social networks by the younger generation. This study establishes the relationships between FOMO and various social networks in an attempt to identify the problematic use of social media in Turkey and discover variables relevant to FOMO. The main objective of the study is to examine the prevalence of FOMO in preservice teachers. A total number of 363 preservice teachers on various academic courses were employed for the purpose of this study. The design was a survey which utilized a standardized questionnaire on FOMO together with a demographic questionnaire to explore the impact of the usage of smartphones and social networks by preservice teachers. The data were analyzed using descriptive statistics, independent samples t-tests, and analysis of variance (ANOVA). The overall findings of the study showed that the spread of FOMO amongst preservice teachers is at intermediate level. Significant differences were also found between groups based on gender, age, and usage of social networks. Furthermore, data from males were indicative of higher FOMO levels than female preservice teaches and again, under 21s were found to have higher levels of FOMO compared to the other age groups. A significant relationship was reported between being online on social networks throughout the day and active use of social media over seven hours, and FOMO. Furthermore, preservice teachers using Twitter, Instagram, Swarm, or Snapchat have increased levels of FOMO. Implications are discussed within current models. Keywords: FOMO, social networks, smartphones, university students.


title: Nutrition and the transplant candidate

abstract: Cirrhosis is the most common indication for liver transplantation (LT) worldwide. Malnutrition is present in at least two-thirds of patients with cirrhosis awaiting LT. It negatively impacts survival, quality of life, and the ability to respond to stressors, such as infection and surgery. Muscle wasting or sarcopenia is the most objective feature of chronic protein malnutrition in cirrhosis, and this condition is associated with increased morbidity and mortality before and after LT. In addition to its objectivity, muscularity assessment with cross-sectional imaging studies is a useful marker of nutritional status in LT candidates, as sarcopenia reflects a chronic decline in the general physical condition, rather than acute severity of the liver disease. Despite the high prevalence and important prognostic role, malnutrition and sarcopenia are frequently overlooked because standards for nutritional assessment are lacking and challenges such as fluid retention and obesity are prevalent. In this review, current diagnostic methods to evaluate malnutrition, including muscle abnormalities in cirrhosis, are discussed and current knowledge regarding the incidence and clinical impact of malnutrition in cirrhosis and its impact after LT are presented. Existing and potential novel therapeutic strategies for malnutrition in cirrhosis are also discussed, emphasizing the treatment of muscle wasting in the LT candidate in an effort to improve survival while waiting for LT and to reduce morbidity and mortality after LT.Liver Transplantation 23 1451–1464 2017 AASLD.


title: Using balanced scorecards for the evaluation of "Software-as-a-service"

abstract: To overcome the problem of limited resources, increasing numbers of small- and medium-sized companies (SMEs) are adopting ''Software-as-a-service'' (Saas) as an efficient tool for IS implementation. The balanced scorecard (BSC) has been adopted by SMEs to evaluate Saas via four measures: learning and growth, internal business processes, customer performance, and financial performance. The survey results for 101 Software-as-a-service adopters indicate that learning and growth, internal business processes, and customer performance are causally related to financial performance. The results show that these four key elements for Saas success are interrelated, supporting the core premise of the BSC.


title: Using one-sided RDMA reads to build a fast, CPU-efficient key-value store

abstract: Recent technological trends indicate that future datacenter networks will incorporate High Performance Computing network features, such as ultra-low latency and CPU bypassing. How can these features be exploited in datacenter-scale systems infrastructure? In this paper, we explore the design of a distributed in-memory key-value store called Pilaf that takes advantage of Remote Direct Memory Access to achieve high performance with low CPU overhead. ::: ::: In Pilaf, clients directly read from the server's memory via RDMA to perform gets, which commonly dominate key-value store workloads. By contrast, put operations are serviced by the server to simplify the task of synchronizing memory accesses. To detect inconsistent RDMA reads with concurrent CPU memory modifications, we introduce the notion of self-verifying data structures that can detect read-write races without client-server coordination. Our experiments show that Pilaf achieves low latency and high throughput while consuming few CPU resources. Specifically, Pilaf can surpass 1.3 million ops/sec (90% gets) using a single CPU core compared with 55K for Memcached and 59K for Redis.


Author ' s personal copy Modeling the transmission dynamics and control of hepatitis B virus in China $


title: Toward automated discovery of artistic influence

abstract: Considering the huge amount of art pieces that exist, there is valuable information to be discovered. Examining a painting, an expert can determine its style, genre, and the time period that the painting belongs. One important task for art historians is to find influences and connections between artists. Is influence a task that a computer can measure? The contribution of this paper is in exploring the problem of computer-automated suggestion of influences between artists, a problem that was not addressed before in a general setting. We first present a comparative study of different classification methodologies for the task of fine-art style classification. A two-level comparative study is performed for this classification problem. The first level reviews the performance of discriminative vs. generative models, while the second level touches the features aspect of the paintings and compares semantic-level features vs. low-level and intermediate-level features present in the painting. Then, we investigate the question "Who influenced this artist?" by looking at his masterpieces and comparing them to others. We pose this interesting question as a knowledge discovery problem. For this purpose, we investigated several painting-similarity and artist-similarity measures. As a result, we provide a visualization of artists (Map of Artists) based on the similarity between their works


title: MUSEUMFINLAND -- Finnish museums on the semantic web

abstract: This article presents the semantic portal MuseumFinland for publishing heterogeneous museum collections on the Semantic Web. It is shown how museums with their semantically rich and interrelated collection content can create a large, consolidated semantic collection portal together on the web. By sharing a set of ontologies, it is possible to make collections semantically interoperable, and provide the museum visitors with intelligent content-based search and browsing services to the global collection base. The architecture underlying MuseumFinland separates generic search and browsing services from the underlying application dependent schemas and metadata by a layer of logical rules. As a result, the portal creation framework and software developed has been applied successfully to other domains as well. MuseumFinland got the Semantic Web Challence Award (second prize) in 2004.


title: Smartphones Based Crowdsourcing for Indoor Localization

abstract: Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m $^2$ , and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.


title: A Real-Time State-Observer-Based Controller for a Stochastic Robotic Manipulator

abstract: In this paper, a generalized linear feedback matrix controller is investigated for the state of n -link robot to track the desired trajectory in the presence of stochastic noise. The novel feature of the control algorithm is that it is based on $Ito^{\prime }s$ stochastic calculus for the minimization of the conditional expectation of the instantaneous tracking error energy differential with respect to the feedback matrix subject to energy constraints. The proposed control algorithm enables the adaptive features for tracking of the robotic manipulator. Additionally, the effects of feedback coefficients and parametric uncertainty on the error energies of the system are also studied using sensitivity analysis. Finally, the experimental results conducted using the “Phantom Omni™ Bundle” robot manipulator demonstrate and validate the potential application of the proposed control algorithm on a real system.


title: Partial Functional Correspondence

abstract: In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes. We use perturbation analysis to show how removal of shape parts changes the Laplace-Beltrami eigenfunctions, and exploit it as a prior on the spectral representation of the correspondence. Corresponding parts are optimization variables in our problem and are used to weight the functional correspondence; we are looking for the largest and most regular in the Mumford-Shah sense parts that minimize correspondence distortion. We show that our approach can cope with very challenging correspondence settings.


title: Leader progression model application for calculation of lightning critical flashover voltage of overhead transmission line insulators

abstract: The paper presents a method for calculation of lightning critical flashover voltage (CFO) of overhead transmission insulators. The method is based on the application of leader progression model (LPM). CFO was calculated and measured in high voltage laboratory for different polymer and glass insulator strings equipped with arcing horns and grading rings fittings. Comparison of laboratory measurements and calculation results showed that CFO can be determined with good accuracy by using the presented method.


title: Determining the dispersive and polar contributions to the surface tension of water-based printing ink as a function of surfactant surface excess

abstract: The surface tension of a model, water-based, flexographic printing ink was measured at a range of surfactant concentrations along with the equilibrium contact angle formed with polymer substrates. The surface excess of surfactant at each concentration was calculated using the Gibbs adsorption isotherm and assumed equal to the concentration of surfactant at the interface. The change in the surface tension of the ink formulation was assumed to be determined entirely by the surface concentration of surfactant. This allowed the estimation of the surface tension at the solid–liquid and solid–vapour boundaries when in contact with substrate based on the values obtained for pendant drops. The associated polar and dispersive contributions to the surface tension were then calculated using the Young–Dupre equation. The values of the polar and dispersive surface tension components extracted in this manner were compared with those calculated using the approach of van Oss, Chaudhury and Good. The use of surface excess in estimating the contributions to surface tension was found to give far better agreement with experimental data than the van Oss approach which is intended for use with pure liquids.


title: Detecting malicious URLs using machine learning techniques

abstract: The World Wide Web supports a wide range of criminal activities such as spam-advertised e-commerce, financial fraud and malware dissemination. Although the precise motivations behind these schemes may differ, the common denominator lies in the fact that unsuspecting users visit their sites. These visits can be driven by email, web search results or links from other web pages. In all cases, however, the user is required to take some action, such as clicking on a desired Uniform Resource Locator (URL). In order to identify these malicious sites, the web security community has developed blacklisting services. These blacklists are in turn constructed by an array of techniques including manual reporting, honeypots, and web crawlers combined with site analysis heuristics. Inevitably, many malicious sites are not blacklisted either because they are too recent or were never or incorrectly evaluated. In this paper, we address the detection of malicious URLs as a binary classification problem and study the performance of several well-known classifiers, namely Naive Bayes, Support Vector Machines, Multi-Layer Perceptron, Decision Trees, Random Forest and k-Nearest Neighbors. Furthermore, we adopted a public dataset comprising 2.4 million URLs (instances) and 3.2 million features. The numerical simulations have shown that most classification methods achieve acceptable prediction rates without requiring either advanced feature selection techniques or the involvement of a domain expert. In particular, Random Forest and Multi-Layer Perceptron attain the highest accuracy.


Characteristic and Counting Polynomials of Nonane Isomers


title: Developing a typology of teacher beliefs and practices concerning classroom use of ICT

abstract: The study explored uses of educational technology as echoed in teachers' beliefs and practices with ICT. The research is situated in Cyprus where ICT in education is still at an early stage, and is premised on the argument that teachers' limited use of technology in the classroom can be explained by the lack of incorporation of an effective pedagogy. On the basis that understanding teachers' rationales and behaviours in depth is crucial for the successful implementation of an ICT initiative, a multi-case study of primary teachers was conducted. Following within- and cross-case analyses, four distinct foci were identified across the sample in terms of the value of technology that resulted in varied aims for practice. Synthesis of the findings led to the development of a typology depicting a relationship between beliefs and practices and adding to our knowledge about teacher response to the introduction of ICT in schools.


title: A Taxonomy of Botnet Structures

abstract: We propose a taxonomy of botnet structures, based on their utility to the botmaster. We propose key metrics to measure their utility for various activities (e.g., spam, ddos). Using these performance metrics, we consider the ability of different response techniques to degrade or disrupt botnets. In particular, our models show that targeted responses are particularly effective against scale free botnets and efforts to increase the robustness of scale free networks comes at a cost of diminished transitivity. Botmasters do not appear to have any structural solutions to this problem in scale free networks. We also show that random graph botnets (e.g., those using P2P formations) are highly resistant to both random and targeted responses. We evaluate the impact of responses on different topologies using simulation and demonstrate the utility of our proposed metrics by performing novel measurements of a P2P network. Our analysis shows how botnets may be classified according to structure and given rank or priority using our proposed metrics. This may help direct responses and suggests which general remediation strategies are more likely to succeed.


title: The Computerized Adaptive Diagnostic Test for Major Depressive Disorder (CAD-MDD)

abstract: Objective ::: To develop a computerized adaptive diagnostic (CAD) screening tool for depression that decreases patient and clinician burden and increases sensitivity and specificity for clinician-based DSM-IV diagnosis of major depressive disorder (MDD).


title: Snapify: capturing snapshots of offload applications on xeon phi manycore processors

abstract: Intel Xeon Phi coprocessors provide excellent performance acceleration for highly parallel applications and have been deployed in several top-ranking supercomputers. One popular approach of programming the Xeon Phi is the offload model, where parallel code is executed on the Xeon Phi, while the host system executes the sequential code. However, Xeon Phi's Many Integrated Core Platform Software Stack (MPSS) lacks fault-tolerance support for offload applications. This paper introduces Snapify, a set of extensions to MPSS that provides three novel features for Xeon Phi offload applications: checkpoint and restart, process swapping, and process migration. The core technique of Snapify is to take consistent process snapshots of the communicating offload processes and their host processes. To reduce the PCI latency of storing and retrieving process snapshots, Snapify uses a novel data transfer mechanism based on remote direct memory access (RDMA). Snapify can be used transparently by single-node and MPI applications, or be triggered directly by job schedulers through Snapify's API. Experimental results on OpenMP and MPI offload applications show that Snapify adds a runtime overhead of at most 5%, and this overhead is low enough for most use cases in practice.


title: A general supervised approach to segmentation of clinical texts

abstract: Segmentation of clinical texts is critical for all sorts of tasks such as medical coding for billing, auto drafting of discharge summaries, patient problem list generation and many such applications. While there have been previous studies on using supervised approaches to segmentation of clinical texts, these existing approaches were trained and tested on a fairly limited data set showing low adaptibility to new unseen documents. We propose a highly generalized supervised model for segmenting clinical texts, based on a set of line-wise predictions by a classifier with constraints imposing their coherence. Evaluation results on 5 independent test sets show that our approach can work on all sorts of note types and performs consistently across enterprises.


title: The Study of the Relationship between Emotional Intelligence, Reading Motivation, And Anxiety with Reading Comprehension among Iranian EFL Learners

abstract: The present study was carried out to investigate the relationship between the emotional intelligence, reading motivation, and anxiety with reading Comprehension. The participants in the study were selected from six classes consisting of 75 intermediate students studying English in English Institute Their level of English proficiency was determined on the basis of their scores on the PET proficiency test.. After all participants in both groups were given PET proficiency test, those participants placing between +_ 1 SD were considered as the main Finally, there were 64 EFL learners selected for the purpose of this study. A correlational design was the most appropriate design for this study because the study explored the relationship between emotional intelligence, reading motivation, and reading anxiety and reading correlation study seeks to determine relationships between variables or to use those relationships to make predictions. Then, three questionnaires including Emotional Intelligence Questionnaire and the Reading Motivations questionnaire, and reading anxiety questionnaire were given to the selected participants. Finally, the results of the test and three questionnaires were analyzed to see the relationship among variables. The results of the study indicated that there is a positive relationship between the students? level of reading comprehension and their emotional intelligence. Also, there is a positive relationship between emotional intelligence and reading motivation. Finally, there is a negative relationship between reading anxiety and reading comprehension.


title: Integrated Pedestrian and Direction Classification Using a Random Decision Forest

abstract: For analysing the behaviour of pedestrians in a scene, it is common practice that pedestrian localization, classification, and tracking are conducted consecutively. The direction of a pedestrian, being part of the pose, implies the future path. This paper proposes novel Random Decision Forests (RDFs) to simultaneously classify pedestrians and their directions, without adding an extra module for direction classification to the pedestrian classification module. The proposed algorithm is trained and tested on the TUD multi-view pedestrian and Daimler Mono Pedestrian Benchmark data-sets. The proposed integrated RDF classifiers perform comparable to pedestrian or direction trained separated RDF classifiers. The integrated RDFs yield results comparable to those of state-of-the-art and baseline methods aiming for pedestrian classification or body direction classification, respectively.


title: Dynamic radial view based culling for continuous self-collision detection

abstract: The radial view-based culling (RVBC) method has been presented for continuous self-collision detection to efficiently cull away non-colliding regions. While this technique mainly relies on the segmented clusters of the reference pose and the associated fixed observer points, it has several drawbacks during the animation and the reduced cost of executing collision detection is limited. We thus present a modified framework to improve the culling efficiency of RVBC. At the preprocessing stage, we segment the closed deformable mesh according to not only the attached skeleton but also the triangle orientations, in order to minimize the collision checks of triangles in a cluster. At the runtime stage, we dynamically merge adjacent clusters and update the positions of observer points if the merged shape is nearly convex. This strategy minimizes the number of triangles in different clusters that required collision check. Our framework can be easily integrated with bounding volume hierarchies to boost the culling efficiency. Experimental results show that our framework achieves up to 5.2 times speedup over the original RVBC method and even more times over the recent techniques.


title: Routing in Socially Selfish Delay Tolerant Networks

abstract: Existing routing algorithms for Delay Tolerant Networks(DTNs) assume that nodes are willing to forward packets for others. In the real world, however, most people are socially selfish; i.e., they are willing to forward packets for nodes with whom they have social ties but not others, and such willingness varies with the strength of the social tie. Following the philosophy of design for user, we propose a Social Selfishness Aware Routing (SSAR) algorithm to allow user selfishness and provide better routing performance in an efficient way. To select a forwarding node, SSAR considers both users' willingness to forward and their contact opportunity, resulting in a better forwarding strategy than purely contact-based approaches. Moreover, SSAR formulates the data forwarding process as a Multiple Knapsack Problem with Assignment Restrictions (MKPAR) to satisfy user demands for selfishness and performance. Trace-driven simulations show that SSAR allows users to maintain selfishness and achieves better routing performance with low transmission cost.


title: Search-Based Web Service Antipatterns Detection

abstract: Service Oriented Architecture (SOA) is widely used in industry and is regarded as one of the preferred architectural design technologies. As with any other software system, service-based systems (SBSs) may suffer from poor design, i.e., antipatterns, for many reasons such as poorly planned changes, time pressure or bad design choices. Consequently, this may lead to an SBS product that is difficult to evolve and that exhibits poor quality of service (QoS). Detecting web service antipatterns is a manual, time-consuming and error-prone process for software developers. In this paper, we propose an automated approach for detection of web service antipatterns using a cooperative parallel evolutionary algorithm (P-EA). The idea is that several detection methods are combined and executed in parallel during an optimization process to find a consensus regarding the identification of web service antipatterns. We report the results of an empirical study using eight types of common web service antipatterns. We compare the implementation of our cooperative P-EA approach with random search, two single population-based approaches and one state-of-the-art detection technique not based on heuristic search. Statistical analysis of the obtained results demonstrates that our approach is efficient in antipattern detection, with a precision score of 89 percent and a recall score of 93 percent.


title: Proofreading Tile Sets: Error Correction for Algorithmic Self-Assembly

abstract: For robust molecular implementation of tile-based algorithmic self-assembly, methods for reducing errors must be developed. Previous studies suggested that by control of physical conditions, such as temperature and the concentration of tiles, errors (e) can be reduced to an arbitrarily low rate – but at the cost of reduced speed (r) for the self-assembly process. For tile sets directly implementing blocked cellular automata, it was shown that r ≈ βe 2 was optimal. Here, we show that an improved construction, which we refer to as proofreading tile sets, can in principle exploit the cooperativity of tile assembly reactions to dramatically improve the scaling behavior to r ≈ βe and better. This suggests that existing DNA-based molecular tile approaches may be improved to produce macroscopic algorithmic crystals with few errors. Generalizations and limitations of the proofreading tile set construction are discussed.


Automated Bone Age Classification with Deep Neural Networks


title: Monitoring and controlling rice diseases using Image processing techniques

abstract: Development of an automated system for identifying and classifying different diseases of the contaminated plants is an emerging research area in precision agriculture. Identification of the diseases is the key to prevent qualitative and quantitative loss of agricultural yields. Rice (Oryza Sative) is one of the essential crops in India and losses due to the diseases badly impact the economy. Manual detection of the diseases is very difficult and not accurate. This creates a need for Image processing techniques which will help in accurate and timely detection of the diseases and overcome the limitations of the human vision. A new technique to diagnose and classify rice diseases has been proposed in this paper. Four diseases namely rice bacterial blight, rice blast, rice brown spot and rice sheath rot have been identified and classified. Different features like shape, the color of a diseased portion of the leaf have been extracted by developing an algorithm. All the extracted features have been combined as per the diseases and diseases have been classified using Minimum Distance Classifier (MDC) and k-Nearest Neighbor classifier (k-NN). The performance of the proposed technique has been evaluated with the help of 115 rice leaf images of four diseases and 70 percent image data has been used for training the classifier and 30 percent has been used for testing. Classification accuracy has been calculated for each disease using both classifiers. The overall accuracy achieved by using k-NN and MDC is 87.02 percent and 89.23 percent respectively.


title: Design of scalable directory service for future IoT applications

abstract: Unprecedentedly a massive number of devices are getting connected in the coming era of the Internet of Things (IoT). For discovery, remote access and management of these IoT devices, an IoT directory service is needed to store and provide their various attributes such as location, generated data types, owner's name, and security keys. In this paper, we present the architectural design of the IoT directory service that is capable to store a huge number of heterogeneous records and provide fast lookup (latency of few milliseconds) and dynamic update (latency of few seconds), while fully complying with owner- or user-centric security and privacy policy. To meet the performance requirements despite fluctuations in the workload and networking environment, we leverage tools of network function and resource virtualization to dynamically allocate and adjust the computational and network resources assigned to the directory service.


title: The neocortical microcircuit as a tabula rasa

abstract: The neocortex has a high capacity for plasticity. To understand the full scope of this capacity, it is essential to know how neurons choose particular partners to form synaptic connections. By using multineuron whole-cell recordings and confocal microscopy we found that axons of layer V neocortical pyramidal neurons do not preferentially project toward the dendrites of particular neighboring pyramidal neurons; instead, axons promiscuously touch all neighboring dendrites without any bias. Functional synaptic coupling of a small fraction of these neurons is, however, correlated with the existence of synaptic boutons at existing touch sites. These data provide the first direct experimental evidence for a tabula rasa-like structural matrix between neocortical pyramidal neurons and suggests that pre- and postsynaptic interactions shape the conversion between touches and synapses to form specific functional microcircuits. These data also indicate that the local neocortical microcircuit has the potential to be differently rewired without the need for remodeling axonal or dendritic arbors.


title: Generic semantics of feature diagrams

abstract: Feature Diagrams (FDs) are a family of popular modelling languages used to address the feature interaction problem, particularly in software product lines, FDs were first introduced by Kang as part of the FODA (Feature-Oriented Domain Analysis) method back in 1990. Afterwards, various extensions of FODA FDs were introduced to compensate for a purported ambiguity and lack of precision and expressiveness. However, they never received a formal semantics, which is the hallmark of precision and unambiguity and a prerequisite for efficient and safe tool automation. The reported work is intended to contribute a more rigorous approach to the definition, understanding, evaluation, selection and implementation of FD languages. First, we provide a survey of FD variants. Then, we give them a formal semantics, thanks to a generic construction that we call Free Feature Diagrams (FFDs). This demonstrates that FDs can be precise and unambiguous. This also defines their expressiveness. Many variants are expressively complete, and thus the endless quest for extensions actually cannot be justified by expressiveness. A finer notion is thus needed to compare these expressively complete languages. Two solutions are well-established: succinctness and embeddability, that express the naturalness of a language. We show that the expressively complete FDs fall into two succinctness classes, of which we of course recommend the most succinct. Among the succinct expressively complete languages, we suggest a new, simple one that is not harmfully redundant: Varied FD (VFD). Finally, we study the execution time that tools will need to solve useful problems in these languages.