title: Oral stimulation for promoting oral feeding in preterm infants

abstract: BACKGROUND ::: Preterm infants (< 37 weeks' postmenstrual age) are often delayed in attaining oral feeding. Normal oral feeding is suggested as an important outcome for the timing of discharge from the hospital and can be an early indicator of neuromotor integrity and developmental outcomes. A range of oral stimulation interventions may help infants to develop sucking and oromotor co-ordination, promoting earlier oral feeding and earlier hospital discharge. ::: ::: ::: OBJECTIVES ::: To determine the effectiveness of oral stimulation interventions for attainment of oral feeding in preterm infants born before 37 weeks' postmenstrual age (PMA).To conduct subgroup analyses for the following prespecified subgroups.• Extremely preterm infants born at < 28 weeks' PMA.• Very preterm infants born from 28 to < 32 weeks' PMA.• Infants breast-fed exclusively.• Infants bottle-fed exclusively.• Infants who were both breast-fed and bottle-fed. ::: ::: ::: SEARCH METHODS ::: We used the standard search strategy of the Cochrane Neonatal Review Group to search the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE via PubMed (1966 to 25 February 2016), Embase (1980 to 25 February 2016) and the Cumulative Index to Nursing and Allied Health Literature (CINAHL; 1982 to 25 February 2016). We searched clinical trials databases, conference proceedings and the reference lists of retrieved articles. ::: ::: ::: SELECTION CRITERIA ::: Randomised and quasi-randomised controlled trials comparing a defined oral stimulation intervention with no intervention, standard care, sham treatment or non-oral intervention in preterm infants and reporting at least one of the specified outcomes. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: One review author searched the databases and identified studies for screening. Two review authors screened the abstracts of these studies and full-text copies when needed to identify trials for inclusion in the review. All review authors independently extracted the data and analysed each study for risk of bias across the five domains of bias. All review authors discussed and analysed the data and used the GRADE system to rate the quality of the evidence. Review authors divided studies into two groups for comparison: intervention versus standard care and intervention versus other non-oral or sham intervention. We performed meta-analysis using a fixed-effect model. ::: ::: ::: MAIN RESULTS ::: This review included 19 randomised trials with a total of 823 participants. Almost all included trials had several methodological weaknesses. Meta-analysis showed that oral stimulation reduced the time to transition to oral feeding compared with standard care (mean difference (MD) -4.81, 95% confidence interval (CI) -5.56 to -4.06 days) and compared with another non-oral intervention (MD -9.01, 95% CI -10.30 to -7.71 days), as well as the duration of initial hospitalisation compared with standard care (MD -5.26, 95% CI -7.34 to -3.19 days) and compared with another non-oral intervention (MD -9.01, 95% CI -10.30 to -7.71 days).Investigators reported shorter duration of parenteral nutrition for infants compared with standard care (MD -5.30, 95% CI -9.73 to -0.87 days) and compared with another non-oral intervention (MD -8.70, 95% CI -15.46 to -1.94 days). They could identify no effect on breast-feeding outcomes nor on weight gain. ::: ::: ::: AUTHORS' CONCLUSIONS ::: Although the included studies suggest that oral stimulation shortens hospital stay, days to exclusive oral feeding and duration of parenteral nutrition, one must interpret results of these studies with caution, as risk of bias and poor methodological quality are high overall. Well-designed trials of oral stimulation interventions for preterm infants are warranted. Such trials should use reliable methods of randomisation while concealing treatment allocation, blinding caregivers to treatment when possible and paying particular attention to blinding of outcome assessors.


title: SDN-Based Application-Aware Networking on the Example of YouTube Video Streaming

abstract: Application-Aware Networking is a promising approach to provide good application quality to users in scenarios with limited network resources, like today's access networks. With SDN, a particularly interesting method to enable flowbased traffic management in networks has become available. In this work we take a look at how a specific application, i.e., YouTube Streaming, can benefit from such an SDN-based Application-Aware Network. We implement and investigate an approach based on Deep Packet Inspection (DPI) and one based on direct information input from the application in an OpenFlow testbed in order to show, how these different types of application information can be exploited to enhance the Quality of Experience (QoE). Furthermore, we determine the overhead caused by each of the presented approaches.


title: Optimized cascade pi controllers of buck converters using particle swarm optimization algorithm

abstract: This paper presents the optimal controller design of buck converters. The particle swarm optimization algorithm is applied to tune the controller parameters until the best output response is achieved. The output response from the system with the controllers designed by the proposed technique is compared with those from the classical method. The transient simulations using the commercial software package show that the controllers designed from the particle swarm optimization method can provide the better output performance compared with the result from the classical method. The proposed design technique is very flexible and useful for electrical engineers.


title: Application of Machine Learning for Channel based Message Authentication in Mission Critical Machine Type Communication

abstract: The design of robust wireless communication systems for industrial applications such as closed loop control processes has been considered manifold recently. Additionally, the ongoing advances in the area of connected mobility have similar or even higher requirements regarding system reliability and availability. Beside unfulfilled reliability requirements, the availability of a system can further be reduced, if it is under attack in the sense of violation of information security goals such as data authenticity or integrity. In order to guarantee the safe operation of an application, a system has at least to be able to detect these attacks. Though there are numerous techniques in the sense of conventional cryptography in order to achieve that goal, these are not always suited for the requirements of the applications mentioned due to resource inefficiency. In the present work, we show how the goal of message authenticity based on physical layer security (PHYSEC) can be achieved. The main idea for such techniques is to exploit user specific characteristics of the wireless channel, especially in spatial domain. Additionally, we show the performance of our machine learning based approach and compare it with other existing approaches.


title: Comparative evaluation of the biopharmaceutical and chemical equivalence of some commercially available brands of ciprofloxacin hydrochloride tablets

abstract: Purpose: This study was undertaken with the objective of evaluating the biopharmaceutical equivalency of ten brands of ciprofloxacin tablets and the chemical equivalency with the use of an analytical method, which will be easy to use, accurate, reproducible, simple, and inexpensive. ::: ::: Method: The biopharmaceutical and chemical equivalence of ten brands of ciprofloxacin hydrochloride tablets were assessed through the evaluation of the uniformity of weight, friability test, hardness, disintegration test, dissolution rate, thin layer chromatography and non-aqueous titration procedure with the use of crystal violet solution indicator. ::: Result: All the brands complied with the official specification for uniformity of weight and friability test, while one of the brands failed the disintegration test. The dissolution rate profile revealed that four of the brands did not attain 70% dissolution throughout the period of the determination, while the other brands had above 70% dissolution at less than 45minutes. The non-aqueous titrimetric procedure showed that the excipients did not affect the procedure; with seven brands having values within the range specified in the USP, while the remaining three brands gave lower values. ::: ::: Conclusion: Six of the brands evaluated in this study could be regarded as being biopharmaceutically and chemically equivalent, while a particular brand is obviously a fake product. The non-aqueous titrimetric procedure used in this study is simple, inexpensive, and easy to use and could be used in routine monitoring of the quality of ciprofloxacin HCl tablets, especially in the absence of high technology equipments that are not easily available in most developing countries. Keywords : Ciprofloxacin hydrochloride tablets, non-aqueous titration, chemical equivalence, biopharmaceutical equivalence > Tropical Journal of Pharmaceutical Research Vol. 6 (3) 2007: pp. 737-745


title: MoFA: Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction

abstract: In this work we propose a novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image. To this end, we combine a convolutional encoder network with an expert-designed generative model that serves as decoder. The core innovation is our new differentiable parametric decoder that encapsulates image formation analytically based on a generative model. Our decoder takes as input a code vector with exactly defined semantic meaning that encodes detailed face pose, shape, expression, skin reflectance and scene illumination. Due to this new way of combining CNN-based with model-based face reconstruction, the CNN-based encoder learns to extract semantically meaningful parameters from a single monocular input image. For the first time, a CNN encoder and an expert-designed generative model can be trained end-to-end in an unsupervised manner, which renders training on very large (unlabeled) real world data feasible. The obtained reconstructions compare favorably to current state-of-the-art approaches in terms of quality and richness of representation.


title: Pattern matching for extraction of core contents from news web pages

abstract: Web pages, besides core contents, consist of other elements, such as banners, navigational elements, copyright information, external links, etc. This noisy content covers more area of web pages and is typically not related to the main subjects of the web pages. Most of the information available on web pages is either represented in XML, or HTML, or XHTML format that mostly contains semi-structured text documents, which lacks formatted document structure. This document does not discriminate between the text and the schema, and the amount of structure used to represent the text depends on the purpose. No semantic is applied to semi-structured documents. This requires extracting core contents of text document to analyse words or sentences for retrieving relevant information. Although there are many existing methods that formulate the actual content identification problem as a DOM tree node selection problem, each one has some sort of lacunae. Here we proposed an approach based on pattern matching technique. This technique uses simple heuristic for extraction of core contents from web pages which are mostly semi-structured in nature. It requires visiting the appropriate news web site using their URL, accessing the links related to each news page of specified category, extracting the data including metadata from each of these news web pages. The approach uses devised algorithm that applies regular expressions (regexes) to identify the correct pattern for extracting the actual text contents from these news documents. Proposed approach deals with news web pages of any size and extracts core contents with efficiency and high accuracy.


title: The Theory and Practice of Simulated Annealing

abstract: Simulated annealing is a popular local search meta-heuristic used to address discrete and, to a lesser extent, continuous optimization problems. The key feature of simulated annealing is that it provides a means to escape local optima by allowing hill-climbing moves (i.e., moves which worsen the objective function value) in hopes of finding a global optimum. A brief history of simulated annealing is presented, including a review of its application to discrete and continuous optimization problems. Convergence theory for simulated annealing is reviewed, as well as recent advances in the analysis of finite time performance. Other local search algorithms are discussed in terms of their relationship to simulated annealing. The chapter also presents practical guidelines for the implementation of simulated annealing in terms of cooling schedules, neighborhood functions, and appropriate applications.


Brain 4 Cars : Sensory-Fusion Recurrent Neural Models for Driver Activity Anticipation


Would You Do as a Robot Commands ? An Obedience Study for Human-Robot Interaction


Evaluation of Phytochemical Constituents and Antibacterial Activity of Chromolaena Odorata L. Leaf Extract against Selected Multidrug Resistant Bacteria Isolated from Wounds


title: Eliciting security requirements with misuse cases

abstract: Use case diagrams (L. Jacobson et al., 1992) have proven quite helpful in requirements engineering, both for eliciting requirements and getting a better overview of requirements already stated. However, not all kinds of requirements are equally well supported by use case diagrams. They are good for functional requirements, but poorer at e.g., security requirements, which often concentrate on what should not happen in the system. With the advent of e- and m-commerce applications, security requirements are growing in importance, also for quite simple applications where a short lead time is important. Thus, it would be interesting to look into the possibility for applying use cases on this arena. The paper suggests how this can be done, extending the diagrams with misuse cases. This new construct makes it possible to represent actions that the system should prevent, together with those actions which it should support.


title: Lifting scheme on graphs with application to image representation

abstract: We propose a new multiscale transform for scalar functions defined on the vertex set of a general undirected weighted graph. The transform is based on an adaption of the lifting scheme to graphs. One of the difficulties in applying directly the lifting scheme to graphs is the partitioning of the vertex set. We follow a recent greedy approach and extend it to a multilevel transform. We carefully examine each step of the algorithm, in particular its effect on the underlying basis. We finally investigate the use of the proposed transform to image representation by computing M-term nonlinear approximation errors. We provide a comparison with standard orthogonal and biorthogonal wavelet transforms.


title: Human Pose Estimation and Activity Recognition From Multi-View Videos: Comparative Explorations of Recent Developments

abstract: This paper presents a review and comparative study of recent multi-view approaches for human 3D pose estimation and activity recognition. We discuss the application domain of human pose estimation and activity recognition and the associated requirements, covering: advanced human–computer interaction (HCI), assisted living, gesture-based interactive games, intelligent driver assistance systems, movies, 3D TV and animation, physical therapy, autonomous mental development, smart environments, sport motion analysis, video surveillance, and video annotation. Next, we review and categorize recent approaches which have been proposed to comply with these requirements. We report a comparison of the most promising methods for multi-view human action recognition using two publicly available datasets: the INRIA Xmas Motion Acquisition Sequences (IXMAS) Multi-View Human Action Dataset, and the i3DPost Multi-View Human Action and Interaction Dataset. To compare the proposed methods, we give a qualitative assessment of methods which cannot be compared quantitatively, and analyze some prominent 3D pose estimation techniques for application, where not only the performed action needs to be identified but a more detailed description of the body pose and joint configuration. Finally, we discuss some of the shortcomings of multi-view camera setups and outline our thoughts on future directions of 3D body pose estimation and human action recognition.


title: Validating Literary Theories Using Automatic Social Network Extraction

abstract: In this paper, we investigate whether longstanding literary theories about nineteenthcentury British novels can be verified using computational techniques. Elson et al. (2010) previously introduced the task of computationally validating such theories, extracting conversational networks from literary texts. Revisiting their work, we conduct a closer reading of the theories themselves, present a revised and expanded set of hypotheses based on a divergent interpretation of the theories, and widen the scope of networks for validating this expanded set of hypotheses.


title: Factors Influencing the Successful Adoption of Human Resource Information System: The Content of Aqaba Special Economic Zone Authority

abstract: This study examines the key factors that have impact on the successful adoption of Human Resource Information System (HRIS) within the Aqaba Special Economic Zone Authority (ASEZA)/Jordan. In order to accomplish the purpose of the study four critical factors are inquired. So, four critical factors are inquired: First, TAM Model (Perceived Ease of Use (PEOU) and Perceived Usefulness (PU)). Second, Information Technology Infrastructure (ITI). Third, Top Management Support (TMS). Finally, Individual Experience with Computer (IEC). The research model was applied to collect data from the questionnaires answered by 45 users of HRIS as a source of primary data, based on a convenience sample the response rate was about 91%. In addition, the results were analyzed by utilizing the Statistical Package for Social Software (SPSS). Furthermore, the findings were analyzed; multiple Regression analysis indicated that all research variables have significant relationship on successful adoption of HRIS. The findings indicated IT infrastructures have a positive and significant effect on the successful adoption of HRIS. But there is no significant of PU, PEOU, TMS, and IEC on the successful adoption of HRIS. Finally, the results indicated that no significant statistical differences of demographic characteristics on HRIS adoption. Depending on the research’s findings; the researchers proposed a set of recommendations for better adoption of HRIS in SEZA.


title: Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic

abstract: Model-free deep reinforcement learning (RL) methods have been successful in a wide variety of simulated domains. However, a major obstacle facing deep RL in the real world is their high sample complexity. Batch policy gradient methods offer stable learning, but at the cost of high variance, which often requires large batches. TD-style methods, such as off-policy actor-critic and Q-learning, are more sample-efficient but biased, and often require costly hyperparameter sweeps to stabilize. In this work, we aim to develop methods that combine the stability of policy gradients with the efficiency of off-policy RL. We present Q-Prop, a policy gradient method that uses a Taylor expansion of the off-policy critic as a control variate. Q-Prop is both sample efficient and stable, and effectively combines the benefits of on-policy and off-policy methods. We analyze the connection between Q-Prop and existing model-free algorithms, and use control variate theory to derive two variants of Q-Prop with conservative and aggressive adaptation. We show that conservative Q-Prop provides substantial gains in sample efficiency over trust region policy optimization (TRPO) with generalized advantage estimation (GAE), and improves stability over deep deterministic policy gradient (DDPG), the state-of-the-art on-policy and off-policy methods, on OpenAI Gym's MuJoCo continuous control environments.


title: Joint trilateral filtering for depth map super-resolution

abstract: Depth map super-resolution is an emerging topic due to the increasing needs and applications using RGB-D sensors. Together with the color image, the corresponding range data provides additional information and makes visual analysis tasks more tractable. However, since the depth maps captured by such sensors are typically with limited resolution, it is preferable to enhance its resolution for improved recognition. In this paper, we present a novel joint trilateral filtering (JTF) algorithm for solving depth map super-resolution (SR) problems. Inspired by bilateral filtering, our JTF utilizes and preserves edge information from the associated high-resolution (HR) image by taking spatial and range information of local pixels. Our proposed further integrates local gradient information of the depth map when synthesizing its HR output, which alleviates textural artifacts like edge discontinuities. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of our approach over prior depth map upsampling works.


title: Making Tree Kernels Practical For Natural Language Learning

abstract: In recent years tree kernels have been proposed for the automatic learning of natural language applications. Unfortunately, they show (a) an inherent super linear complexity and (b) a lower accuracy than traditional attribute/value methods. In this paper, we show that tree kernels are very helpful in the processing of natural language as (a) we provide a simple algorithm to compute tree kernels in linear average running time and (b) our study on the classification properties of diverse tree kernels show that kernel combinations always improve the traditional methods. Experiments with Support Vector Machines on the predicate argument classification task provide empirical support to our thesis.


title: Creative thinking as orchestrated by semantic processing vs. cognitive control brain networks

abstract: Creativity is primarily investigated within the neuroscientific perspective as a unitary construct. While such an approach is beneficial when trying to infer the general picture regarding creativity and brain function, it is insufficient if the objective is to uncover the information processing brain mechanisms by which creativity occurs. As creative thinking emerges through the dynamic interplay between several cognitive processes, assessing the neural correlates of these operations would enable the development and characterization of an information processing framework from which to better understand this complex ability. This article focuses on two aspects of creative cognition that are central to generating original ideas. "Conceptual expansion" refers to the ability to widen one's conceptual structures to include unusual or novel associations, while "overcoming knowledge constraints" refers to our ability to override the constraining influence imposed by salient or pertinent knowledge when trying to be creative. Neuroimaging and neuropsychological evidence is presented to illustrate how semantic processing and cognitive control networks in the brain differentially modulate these critical facets of creative cognition.


title: A class-specific copy network for handling the rare word problem in neural machine translation

abstract: Neural machine translation (NMT) has shown promising results and rapidly gained adoption in many large-scale settings. With the NMT model being widely used in empirical productions, its long-standing weakness in handling the rare and out of vocabulary words has been amplified a lot. In order to release the model from the stress of “understanding” the rare words, copy mechanism has been proposed to deal with the rare and unseen words for the neural network models using attention. However the negative side of the copy mechanism is that the model is only able to decide whether to copy or not. It is unable to detect which class should the rare word be copied to, such as person, location, and organization. This paper deeply investigates this limitation of the NMT model. As a result, we propose a new NMT model by novelly incorporating a class-specific copy network. With the network, the proposed NMT model is able to decide which class the words in the target belong to and which class in the source should be copied to. Experimental results on Chinese-English translation tasks show that the proposed model outperforms the traditional NMT model with a large margin especially for sentences containing the rare words.


title: Investigation of safety in human-robot-interaction for a series elastic, tendon-driven robot arm

abstract: This paper presents the design of the lightweight BioRob manipulator with spring-loaded tendon-driven actuation developed for safe physical human-robot interaction. The safety of the manipulator is analyzed by an analytical worst-case estimation of impact and clamping forces in the absence of collision detection. As intrinsic joint compliance can pose a threat by storing energy, a safety evaluation method is proposed taking the potential energy stored in the elastic actuation into account. The evaluation shows that the robot arm design constrains the worst case clamping forces to only 25 N, while being able to handle loads up to 2 kg, and inherits extremely low impact properties, such as an effective mass of less than 0.4 kg in non near-singular configurations, enabling safe operation even in case of high velocities. The results are validated in simulation and experiments.


title: Predicting Physical Features and Diseases by DNA Analysis: Current Advances and Future Challenges

abstract: The 'omics' era and its concomitant technological advances have brought great insight into genetics. One of the most promising fields within human genetics is the prediction of physical traits from analysis of genetic material. Besides the predictive potential of DNA, the traceability of pathogenic agents in the human body through molecular analysis is also a field to be further exploited. In this review, we aim to discuss specific aspects of phenotypic prediction by analysing DNA, with special emphasis on normal variation, and the application of a technology known as ‘Forensic DNA Phenotyping’ (FDP). We also suggest the term ‘Phenotype Informative Markers’ (PIMs) to designate any molecular markers responsible for normal or pathological human phenotypic variation. In addition, we raise some recommendations related to forensic genetics, the molecular diagnosis of human diseases, and the traceability of pathogens in the human body, giving special emphasis to the need for validation of these tests with strict protocols. Some relevant concerns about privacy, ethics, and legality of such predictions have also been discussed. Finally, we look at perspectives on the use of epigenetic tools, and quote some examples of what has been done in this specific field.


title: SLA-Aware Provisioning and Scheduling of Cloud Resources for Big Data Analytics

abstract: The stunning growth in data has immensely impacted organizations. Their infrastructure and traditional data management system could not keep up to scale of Big Data. They have to either invest heavily on their infrastructure or move their Big Data analytics to Cloud where they can benefit from both on-demand scalability and contemporary data management techniques. However, to make Cloud hosted Big Data analytics available to wider range of enterprises, we have to carefully capture their preferences in terms of budget and service level objectives. Therefore, this study aims at proposing a SLA and cost-aware resource provisioning and task scheduling approach tailored for Big Data applications in the Cloud. Current approaches assume that data is pre-stored in cluster nodes prior to deployment of Big Data applications. In addition, their focus is purely on task scheduling, and not virtual machine provisioning. We argue that in the Cloud computing context this is not applicable, because the nodes are provisioned dynamically (data cannot be pre-stored) and leaving provisioning to user may lead to under or over provisioning that can both lead to SLA or budget constraint violations. Therefore,in this study we first model the user request, which consist of Big Data analytics jobs with budget and deadline. Then, we model infrastructures as a list of data centers, virtual machines (offered in a pay-as-you-go model), data sources, and network throughputs. After that, to address the aforementioned issues, we propose and compare cost-aware and SLA-based algorithms which provision cloud resources and schedule analytics tasks.


title: A NOVEL HYBRID APPROACH TO ESTIMATING MISSING VALUES IN DATABASES USING K-NEAREST NEIGHBORS AND NEURAL NETWORKS

abstract: Abstract. Missing values in datasets and databases can be estimated via statistics, ma-chine learning and arti cial intelligence methods. This paper uses a novel hybrid neuralnetwork and weighted nearest neighbors to estimate missing values and provides goodresults with high performance. In this work, four ﬀent characteristic datasets wereused and missing values were estimated. Error ratio, correlation coﬃ predictionaccuracy were calculated between actual and estimated values and the results were com-pared with basic neural network-genetic algorithm estimation method. Keywords: Missing data, Missing value, K-nearest neighbors, Neural networks, Impu-tation, Auto-associative neural networks, Genetic algorithm 1. Introduction. Biological or medical experimentation results, chemical analysis re-sults, meteorology, microarray gene monitoring technology or survey databases or datasetscontain missing values. Missing values can occur due to a sensor faults, not reacting ex-periments, not recovering some situation of work, measuring a faulty value, transferringdata to digital system problem or participants skip some survey question [4,5].Data quality is a major criterion for machine learning, data mining, information andknowledge [13]. Data cleaning aims to produce quality data; thus, missing values are apart of information and knowledge [14]. Deleting, ignoring records containing missingvalues or imputation mean, mode or the most common value instead of missing valuesigni cantly bias datasets and make datasets noisy and low quality [6,16]. Most compu-tational intelligence techniques such as neural networks, support vector machines, anddecision trees are predictive models that take discovered data as inputs and estimate anoutput class. This model fails if one or more inputs are missing. Consequently, theycannot be used for decision-making purposes if the data variables are not complete [15].2. Literature Review. In recent years, many applications and methods of missing valueestimation have been developed by researchers.Troyanskaya et al. presented missing value estimation methods for DNA Microarrayswith weighted k-NN [1]. Thompson studied the contractive nature of auto-encoders, thatis, the practice of missing sensor restoration by using neural network [2]. Qiao et al.also presented a missing data estimator based on a neural network coupled with particleswarm optimization [3] and used it in tracking the dynamics of a plant in the presence ofmissing sensor measurements. Abdella et al. proposed the use of genetic algorithms andneural networks to approximate missing data in databases [4]. Mohamed et al. ﬀan approach, which is both agent-based and neural network-based, to estimating missing


title: HSV-2 Vaccine: Current Status and Insight into Factors for Developing an Efficient Vaccine

abstract: Herpes simplex virus type 2 (HSV-2), a globally sexually transmitted virus, and also one of the main causes of genital ulcer diseases, increases susceptibility to HIV-1. Effective vaccines to prevent HSV-2 infection are not yet available, but are currently being developed. To facilitate this process, the latest progress in development of these vaccines is reviewed in this paper. A summary of the most promising HSV-2 vaccines tested in animals in the last five years is presented, including the main factors, and new ideas for developing an effective vaccine from animal experiments and human clinical trials. Experimental results indicate that future HSV-2 vaccines may depend on a strategy that targets mucosal immunity. Furthermore, estradiol, which increases the effectiveness of vaccines, may be considered as an adjuvant. Therefore, this review is expected to provide possible strategies for development of future HSV-2 vaccines.


title: Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems

abstract: Security, privacy, and fairness have become critical in the era of data science and machine learning. More and more we see that achieving universally secure, private, and fair systems is practically impossible. We have seen for example how generative adversarial networks can be used to learn about the expected private training data; how the exploitation of additional data can reveal private information in the original one; and how what looks like unrelated features can teach us about each other. Confronted with this challenge, in this paper we open a new line of research, where the security, privacy, and fairness is learned and used in a closed environment. The goal is to ensure that a given entity (e.g., the company or the government), trusted to infer certain information with our data, is blocked from inferring protected information from it. For example, a hospital might be allowed to produce diagnosis on the patient (the positive task), without being able to infer the gender of the subject (negative task). Similarly, a company can guarantee that internally it is not using the provided data for any undesired task, an important goal that is not contradicting the virtually impossible challenge of blocking everybody from the undesired task. We design a system that learns to succeed on the positive task while simultaneously fail at the negative one, and illustrate this with challenging cases where the positive task is actually harder than the negative one being blocked. Fairness, to the information in the negative task, is often automatically obtained as a result of this proposed approach. The particular framework and examples open the door to security, privacy, and fairness in very important closed scenarios, ranging from private data accumulation companies like social networks to law-enforcement and hospitals.


title: Accelerometer signal pre-processing influence on human activity recognition

abstract: A study of data pre-processing influence on accelerometer-based human activity recognition algorithms is presented. The frequency band used to filter-out the accelerometer signals and the number of accelerometers involved were considered in terms of their influence on the recognition accuracy.


title: Personalized Classifier for Food Image Recognition

abstract: Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often has content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework, which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.


title: Higher-Order Minimum Cost Lifted Multicuts for Motion Segmentation

abstract: Most state-of-the-art motion segmentation algorithms draw their potential from modeling motion differences of local entities such as point trajectories in terms of pairwise potentials in graphical models. Inference in instances of minimum cost multicut problems defined on such graphs allows to optimize the number of the resulting segments along with the segment assignment. However, pairwise potentials limit the discriminative power of the employed motion models to translational differences. More complex models such as Euclidean or affine transformations call for higher-order potentials and a tractable inference in the resulting higher-order graphical models. In this paper, we (1) introduce a generalization of the minimum cost lifted multicut problem to hypergraphs, and (2) propose a simple primal feasible heuristic that allows for a reasonably efficient inference in instances of higher-order lifted multicut problem instances defined on point trajectory hypergraphs for motion segmentation. The resulting motion segmentations improve over the state-of-the-art on the FBMS-59 dataset.


title: Hierarchical Interactive Learning for a HUman-Powered Augmentation Lower EXoskeleton

abstract: Learning by demonstration methods have gained considerable interest in human-coupled robot control. It aims at modeling the goal motion trajectories through human demonstration. However, in lower exoskeleton control, the physical human-robot interaction is changing from pilot to pilot or even for one pilot in different walking patterns. This characteristic requires that the exoskeletons should have the ability to learn and adapt the motion trajectories as well as controllers online. This paper presents a novel Hierarchical Interactive Learning (HIL) strategy which reduces the complexity of the exoskeleton sensory system and is able to handle varying interaction dynamics. The proposed HIL strategy is composed of two learning hierarchies, namely, high-level motion learning and low-level controller learning. The Dynamic Movement Primitives (DMPs) combined with Locally Weighted Regression (LWR) are employed to model and learn the motion trajectories, while reinforcement learning (RL) is used to learn the model-based controller. We demonstrate the efficiency of proposed HIL strategy on a single degree-of-freedom (DOF) platform as well as a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system. Experimental results indicate that the proposed HIL strategy is able to handle the varying interaction dynamics with less interaction force between the pilot and the exoskeleton when compared to traditional model-based control algorithms.


title: Time Series Prediction Via Recurrent Neural Networks with the Information Bottleneck Principle

abstract: In this paper, we propose a novel method for probabilistic time series prediction based on Recurrent Information Bottleneck (RIB). We propose to incorporate the stochastic latent states for modeling complex and non-linear time series optimized by RIB objective. Compared with previous work, the proposed method can yield better prediction and uncertainty estimation. It's built on the extension of information bottleneck principle to recurrent setting, to find the stochastic latent state maximally informative about the target with low complexity. The experiments over real-world datasets show the proposed method can outperform the state-of-the-art prediction performance on single and multi-dimensional data.


title: Manipulation Planning with Probabilistic Roadmaps

abstract: This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems.


title: Multi-Modal Hybrid Deep Neural Network for Speech Enhancement

abstract: Deep Neural Networks (DNN) have been successful in en- hancing noisy speech signals. Enhancement is achieved by learning a nonlinear mapping function from the features of the corrupted speech signal to that of the reference clean speech signal. The quality of predicted features can be improved by providing additional side channel information that is robust to noise, such as visual cues. In this paper we propose a novel deep learning model inspired by insights from human audio visual perception. In the proposed unified hybrid architecture, features from a Convolution Neural Network (CNN) that processes the visual cues and features from a fully connected DNN that processes the audio signal are integrated using a Bidirectional Long Short-Term Memory (BiLSTM) network. The parameters of the hybrid model are jointly learned using backpropagation. We compare the quality of enhanced speech from the hybrid models with those from traditional DNN and BiLSTM models.


title: Leading Agile Teams: An Exploratory Study of Leadership Styles in Agile Software Development

abstract: Leadership is crucial for agile team effectiveness and performance. The roles played by project leaders have significantly changed in agile methodologies, but very limited studies on leadership have been conducted in the agile software development context. Motivated by this gap, this study aims to investigate the roles of various leadership styles in agile methodologies and understand how different types of leadership can collaboratively improve an agile project’s performance. We will conduct an exploratory case study to develop a research model. Our study will contribute to the theoretical understanding of leadership roles in agile projects. The findings will also provide guidance to project management practices.


title: Virtual function placement and traffic steering in flexible and dynamic software defined networks

abstract: The integration of network function virtualization (NFV) and software defined networks (SDN) seeks to create a more flexible and dynamic software-based network environment. The line between entities involved in forwarding and those involved in more complex middle box functionality in the network is blurred by the use of high-performance virtualized platforms capable of performing these functions. A key problem is how and where network functions should be placed in the network and how traffic is routed through them. An efficient placement and appropriate routing increases system capacity while also minimizing the delay seen by flows. In this paper, we formulate the problem of network function placement and routing as a mixed integer linear programming problem. This formulation not only determines the placement of services and routing of the flows, but also seeks to minimize the resource utilization. We develop heuristicsto solve the problem incrementally, allowing us to support a large number of flows and to solve the problem for incoming flows without impacting existing flows.


title: Permutohedral Lattice CNNs

abstract: This paper presents a convolutional layer that is able to process sparse input features. As an example, for image recognition problems this allows an efficient filtering of signals that do not lie on a dense grid (like pixel position), but of more general features (such as color values). The presented algorithm makes use of the permutohedral lattice data structure. The permutohedral lattice was introduced to efficiently implement a bilateral filter, a commonly used image processing operation. Its use allows for a generalization of the convolution type found in current (spatial) convolutional network architectures.


title: Reflection Analysis for Java: Uncovering More Reflective Targets Precisely

abstract: Reflection, which is widely used in practice and abused by many security exploits, poses a significant obstacle to program analysis. Reflective calls can be analyzed statically or dynamically. Static analysis is more sound but also more imprecise (by introducing many false reflective targets and thus affecting its scalability). Dynamic analysis can be precise but often miss many true reflective targets due to low code coverage.We introduce MIRROR, the first automatic reflection analysis for Java that increases significantly the code coverage of dynamic analysis while keeping false reflective targets low. In its static analysis, a novel reflection-oriented slicing technique is applied to identify a small number of small path-based slices for a reflective call so that different reflective targets are likely exercised along these different paths. This preserves the soundness of pure static reflection analysis as much as possible, improves its scalability, and reduces substantially its false positive rate. In its dynamic analysis, these slices are executed with automatically generated test cases to report the reflective targets accessed. This significantly improves the code coverage of pure dynamic analysis. We evaluate MIRROR against a state-of-the-art dynamic reflection analysis tool, TAMIFLEX, by using 10 large real-world Java applications. MIRROR detects 12.5% - 933.3% more reflective targets efficiently (in 362.8 seconds on average) without producing any false positives. These new targets enable 5 - 174949 callgraph edges to be reachable in the application code.


title: Recognizing RGB Images by Learning from RGB-D Data

abstract: In this work, we propose a new framework for recognizing RGB images captured by the conventional cameras by leveraging a set of labeled RGB-D data, in which the depth features can be additionally extracted from the depth images. We formulate this task as a new unsupervised domain adaptation (UDA) problem, in which we aim to take advantage of the additional depth features in the source domain and also cope with the data distribution mismatch between the source and target domains. To effectively utilize the additional depth features, we seek two optimal projection matrices to map the samples from both domains into a common space by preserving as much as possible the correlations between the visual features and depth features. To effectively employ the training samples from the source domain for learning the target classifier, we reduce the data distribution mismatch by minimizing the Maximum Mean Discrepancy (MMD) criterion, which compares the data distributions for each type of feature in the common space. Based on the above two motivations, we propose a new SVM based objective function to simultaneously learn the two projection matrices and the optimal target classifier in order to well separate the source samples from different classes when using each type of feature in the common space. An efficient alternating optimization algorithm is developed to solve our new objective function. Comprehensive experiments for object recognition and gender recognition demonstrate the effectiveness of our proposed approach for recognizing RGB images by learning from RGB-D data.


title: VLocNet++: Deep Multitask Learning for Semantic Visual Localization and Odometry

abstract: Semantic understanding and localization are fundamental enablers of robot autonomy that have for the most part been tackled as disjoint problems. While deep learning has enabled recent breakthroughs across a wide spectrum of scene understanding tasks, its applicability to state estimation tasks has been limited due to the direct formulation that renders it incapable of encoding scene-specific constrains. In this work, we propose the VLocNet++ architecture that employs a multitask learning approach to exploit the inter-task relationship between learning semantics, regressing 6-DoF global pose and odometry, for the mutual benefit of each of these tasks. Our network overcomes the aforementioned limitation by simultaneously embedding geometric and semantic knowledge of the world into the pose regression network. We propose a novel adaptive weighted fusion layer to aggregate motion-specific temporal information and to fuse semantic features into the localization stream based on region activations. Furthermore, we propose a self-supervised warping technique that uses the relative motion to warp intermediate network representations in the segmentation stream for learning consistent semantics. Finally, we introduce a first-of-a-kind urban outdoor localization dataset with pixel-level semantic labels and multiple loops for training deep networks. Extensive experiments on the challenging Microsoft 7-Scenes benchmark and our DeepLoc dataset demonstrate that our approach exceeds the state-of-the-art outperforming local feature-based methods while simultaneously performing multiple tasks and exhibiting substantial robustness in challenging scenarios.


title: Falling for Fake News: Investigating the Consumption of News via Social Media

abstract: In the so called 'post-truth' era, characterized by a loss of public trust in various institutions, and the rise of 'fake news' disseminated via the internet and social media, individuals may face uncertainty about the veracity of information available, whether it be satire or malicious hoax. We investigate attitudes to news delivered by social media, and subsequent verification strategies applied, or not applied, by individuals. A survey reveals that two thirds of respondents regularly consumed news via Facebook, and that one third had at some point come across fake news that they initially believed to be true. An analysis task involving news presented via Facebook reveals a diverse range of judgement forming strategies, with participants relying on personal judgements as to plausibility and scepticism around sources and journalistic style. This reflects a shift away from traditional methods of accessing the news, and highlights the difficulties in combating the spread of fake news.


title: N ov 2 01 6 Assembly pointers for variable binding in networks of spiking neurons

abstract: Humans possess the capability to reason at an abstract level and to structure information into abstract categories, but the underlying neural processes have remained unknown. Experimental evidence has recently emerged for the organization of an important aspect of abstract reasoning: for assigning words to semantic roles in a sentence, such as agent (or subject) and patient (or object). Using minimal assumptions, we show how such a binding of words to semantic roles emerges in a generic spiking neural network through Hebbian plasticity. The resulting model is consistent with the experimental data and enables new computational functionalities such as structured information retrieval, copying data, and comparisons. It thus provides a basis for the implementation of more demanding cognitive computations by networks of spiking neurons.


title: Real-time fingertip tracking and gesture recognition

abstract: Augmented desk interfaces and other virtual reality systems depend on accurate, real-time hand and fingertip tracking for seamless integration between real objects and associated digital information. We introduce a method for discerning fingertip locations in image frames and measuring fingertip trajectories across image frames. We also propose a mechanism for combining direct manipulation and symbolic gestures based on multiple fingertip motions. Our method uses a filtering technique, in addition to detecting fingertips in each image frame, to predict fingertip locations in successive image frames and to examine the correspondences between the predicted locations and detected fingertips. This lets us obtain multiple complex fingertip trajectories in real time and improves fingertip tracking. This method can track multiple fingertips reliably even on a complex background under changing lighting conditions without invasive devices or color markers.


title: Gazetteer-independent toponym resolution using geographic word profiles

abstract: Toponym resolution, or grounding names of places to their actual locations, is an important problem in analysis of both historical corpora and present-day news and web content. Recent approaches have shifted from rule-based spatial minimization methods to machine learned classifiers that use features of the text surrounding a toponym. Such methods have been shown to be highly effective, but they crucially rely on gazetteers and are unable to handle unknown place names or locations. We address this limitation by modeling the geographic distributions of words over the earth's surface: we calculate the geographic profile of each word based on local spatial statistics over a set of geo-referenced language models. These geo-profiles can be further refined by combining in-domain data with background statistics from Wikipedia. Our resolver computes the overlap of all geo-profiles in a given text span; without using a gazetteer, it performs on par with existing classifiers. When combined with a gazetteer, it achieves state-of-the-art performance for two standard toponym resolution corpora (TR-CoNLL and Civil War). Furthermore, it dramatically improves recall when toponyms are identified by named entity recognizers, which often (correctly) find non-standard variants of toponyms.


title: Modeling a complex global service delivery system

abstract: Enterprises and IT service providers are increasingly challenged with improving the quality of service while reducing the cost of service delivery. Effectively balancing dynamic customer workload, strict service level constraints, and diverse service personnel skills challenges the most experienced management teams. In this paper we describe a modeling framework for analyzing complex service delivery systems. The interaction among various key factors are included in the model to allow decision-making around staffing skill levels, scheduling, and service level constraints in system design. We demonstrate the applicability of the proposed approach in a large IT services delivery environment.


title: Illicit substance use among university students from seven European countries: A comparison of personal and perceived peer use and personal and perceived peer attitudes towards illicit substance use

abstract: Objective ::: ::: To compare European students’ personal use and approval of illicit substance use with their perceptions of peer behaviours and attitudes, and investigate whether perceptions of peer norms are associated with personal use of illicit substances and attitudes. ::: ::: Method ::: ::: This study used baseline data from the Social Norms Intervention for the prevention of Polydrug usE (SNIPE) project involving 4,482 students from seven European countries in 2012. Students completed an online survey which included questions on personal and perceived peer illicit substance use and personal and perceived peer attitude towards illicit substances. ::: ::: Results ::: ::: 8.3% of students reported having used illicit substances at least once in their life. 29.9% of students perceived that the majority of their peers have used illicit substances more frequently than themselves. The perception was significantly associated with higher odds for personal illicit substance use (OR: 1.97, CI: 1.53-2.54). The perception that the majority of peers approve illicit substance use was significantly associated with higher odds for personal approval of illicit substance use (OR: 3.47, CI: 2.73-4.41). ::: ::: Conclusion ::: ::: Students commonly perceived that their peers used illicit drugs more often than themselves. We found an association between the perceived peer norms/attitudes and reported individual behaviour/attitudes. ::: ::: Keywords ::: ::: Illicit drugs; Students; Europe; Health behavior


title: Robust Rigid Point Registration based on Convolution of Adaptive Gaussian Mixture Models

abstract: Matching 3D rigid point clouds in complex environments robustly and accurately is still a core technique used in many applications. This paper proposes a new architecture combining error estimation from sample covariances and dual global probability alignment based on the convolution of adaptive Gaussian Mixture Models (GMM) from point clouds. Firstly, a novel adaptive GMM is defined using probability distributions from the corresponding points. Then rigid point cloud alignment is performed by maximizing the global probability from the convolution of dual adaptive GMMs in the whole 2D or 3D space, which can be efficiently optimized and has a large zone of accurate convergence. Thousands of trials have been conducted on 200 models from public 2D and 3D datasets to demonstrate superior robustness and accuracy in complex environments with unpredictable noise, outliers, occlusion, initial rotation, shape and missing points.


title: PROGgene: gene expression based survival analysis web application for multiple cancers

abstract: BackgroundIdentification of prognostic mRNA biomarkers has been done for various cancer types. The data that are published from such studies are archived in public repositories. There are hundreds of such datasets available for multiple cancer types in public repositories. Wealth of such data can be utilized to study prognostic implications of mRNA in different cancers as well as in different populations or subtypes of same cancer.DescriptionWe have created a web application that can be used for studying prognostic implications of mRNA biomarkers in a variety of cancers. We have compiled data from public repositories such as GEO, EBI Array Express and The Cancer Genome Atlas for creating this tool. With 64 patient series from 18 cancer types in our database, this tool provides the most comprehensive resource available for survival analysis to date. The tool is called PROGgene and it is available at http://www.compbio.iupui.edu/proggene.ConclusionsWe present this tool as a hypothesis generation tool for researchers to identify potential prognostic mRNA biomarkers to follow up with further research. For this reason, we have kept the web application very simple and straightforward. We believe this tool will be useful in accelerating biomarker discovery in cancer and quickly providing results that may indicate disease-specific prognostic value of specific biomarkers.


title: Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations

abstract: Null hypothesis statistical significance tests (NHST) are widely used in quantitative research in the empirical sciences including scientometrics. Nevertheless, since their introduction nearly a century ago significance tests have been controversial. Many researchers are not aware of the numerous criticisms raised against NHST. As practiced, NHST has been characterized as a `null ritual' that is overused and too often misapplied and misinterpreted. NHST is in fact a patchwork of two fundamentally different classical statistical testing models, often blended with some wishful quasi-Bayesian interpretations. This is undoubtedly a major reason why NHST is very often misunderstood. But NHST also has intrinsic logical problems and the epistemic range of the information provided by such tests is much more limited than most researchers recognize. In this article we introduce to the scientometric community the theoretical origins of NHST, which is mostly absent from standard statistical textbooks, and we discuss some of the most prevalent problems relating to the practice of NHST and trace these problems back to the mix-up of the two different theoretical origins. Finally, we illustrate some of the misunderstandings with examples from the scientometric literature and bring forward some modest recommendations for a more sound practice in quantitative data analysis.


title: Design requirements for technologies that encourage physical activity

abstract: Overweight and obesity are a global epidemic, with over one billion overweight adults worldwide (300+ million of whom are obese). Obesity is linked to several serious health problems and medical conditions. Medical experts agree that physical activity is critical to maintaining fitness, reducing weight, and improving health, yet many people have difficulty increasing and maintaining physical activity in everyday life. Clinical studies have shown that health benefits can occur from simply increasing the number of steps one takes each day and that social support can motivate people to stay active. In this paper, we describe Houston, a prototype mobile phone application for encouraging activity by sharing step count with friends. We also present four design requirements for technologies that encourage physical activity that we derived from a three-week long in situ pilot study that was conducted with women who wanted to increase their physical activity.


title: Authentic learning of mobile security with case studies

abstract: This work-in-progress paper presents an approach to authentic learning of mobile security through real-world-scenario case studies. Five sets of case studies are being developed to cover the state-of-the-art of mobile security knowledge and practices. Some of the developed case studies are being implemented in related courses and the preliminary feedback is positive.


title: Examining electronic surveillance in the workplace: A review of theoretical perspectives and research findings. Paper presented at

abstract: Almost three fourths of major U.S. firms admit that they engage in one form or another of electronic surveillance (American Management Association, 2000). At the same time, concern over the negative effects of electronic surveillance is raising. The paradox of electronic surveillance is that it is much used and little understood. This paper is an attempt to facilitate and stimulate research into electronic surveillance. It summarizes up-to-date information on the pervasiveness and the noted effects of electronic surveillance. It proceeds to review academic studies that have focused on this phenomenon and finally, it proposes two other theoretical perspectives that may explain behavior-related effects of electronic surveillance – equity theory, and a communication-oriented approach that focuses on surveillance and responses to it as socially constructed acts of discipline and antidiscipline. Examining Electronic Surveillance In The Workplace: A Review Of Theoretical Perspectives And Research Findings


title: Gait phase-based smoothed sliding mode control for a rotary series elastic actuator installed on the knee joint

abstract: Actuators for physical human-robot interaction (pHRI), such as series elastic actuators, should generate the desired torque precisely. However, the resistive and inertia loads inherent in the actuators (e.g., friction, damping, and inertia) set challenges in the control of actuators in a force (torque) mode. The resistive factors include nonlinear effects and should be considered in the controller design to generate the desired force accurately. Moreover, the uncertainties in the plant dynamics make the precise torque control difficult. In this paper, nonlinear control algorithms are exploited for a rotary series elastic actuator to generate the desired torque precisely in the presence of nonlinear resistive factors and modeling uncertainty. The sliding mode control smoothed by a boundary layer is applied to enhance the robustness for the modeling uncertainty without chattering phenomenon. In this paper, the rotary series elastic actuator (RSEA) is installed on the knee joint of an orthosis, and the thickness of the boundary layer is changed by gait phases in order to minimize the torque error without the chattering phenomenon. The performance of the proposed controller is verified by experiments with actual walking motions.


title: Fast Multi-Swarm Optimization for Dynamic Optimization Problems

abstract: In the real world, many applications are non-stationary optimization problems. This requires that the optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a multi-swarm algorithm based on fast particle swarm optimization for dynamic optimization problems. The algorithm employs a mechanism to track multiple peaks by preventing overcrowding at a peak and a fast particle swarm optimization algorithm as a local search method to find the near optimal solutions in a local promising region in the search space. The moving peaks benchmark function is used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for dynamic optimization problems.


Forecasting Non-Stationary Time Series : From Theory to Algorithms


title: Imaging of neonatal child abuse with an emphasis on abusive head trauma.

abstract: Child abuse and neglect is a serious clinical and socioeconomic problem that is sometimes underestimated. One of the most devastating forms is abusive head trauma. This review addresses the radiological workup in cases of suspected child abuse. The use of all modalities, and their advantages and disadvantages, is discussed. A special section is devoted to the radiological report in cases of child abuse, as a clinical record and a legal document.


title: Light field superresolution

abstract: Light field cameras have been recently shown to be very effective in applications such as digital refocusing and 3D reconstruction. In a single snapshot these cameras provide a sample of the light field of a scene by trading off spatial resolution with angular resolution. Current methods produce images at a resolution that is much lower than that of traditional imaging devices. However, by explicitly modeling the image formation process and incorporating priors such as Lambertianity and texture statistics, these types of images can be reconstructed at a higher resolution. We formulate this method in a variational Bayesian framework and perform the reconstruction of both the surface of the scene and the (superresolved) light field. The method is demonstrated on both synthetic and real images captured with our light-field camera prototype.


title: Big Data fraud detection using multiple medicare data sources

abstract: In the United States, advances in technology and medical sciences continue to improve the general well-being of the population. With this continued progress, programs such as Medicare are needed to help manage the high costs associated with quality healthcare. Unfortunately, there are individuals who commit fraud for nefarious reasons and personal gain, limiting Medicare’s ability to effectively provide for the healthcare needs of the elderly and other qualifying people. To minimize fraudulent activities, the Centers for Medicare and Medicaid Services (CMS) released a number of “Big Data” datasets for different parts of the Medicare program. In this paper, we focus on the detection of Medicare fraud using the following CMS datasets: (1) Medicare Provider Utilization and Payment Data: Physician and Other Supplier (Part B), (2) Medicare Provider Utilization and Payment Data: Part D Prescriber (Part D), and (3) Medicare Provider Utilization and Payment Data: Referring Durable Medical Equipment, Prosthetics, Orthotics and Supplies (DMEPOS). Additionally, we create a fourth dataset which is a combination of the three primary datasets. We discuss data processing for all four datasets and the mapping of real-world provider fraud labels using the List of Excluded Individuals and Entities (LEIE) from the Office of the Inspector General. Our exploratory analysis on Medicare fraud detection involves building and assessing three learners on each dataset. Based on the Area under the Receiver Operating Characteristic (ROC) Curve performance metric, our results show that the Combined dataset with the Logistic Regression (LR) learner yielded the best overall score at 0.816, closely followed by the Part B dataset with LR at 0.805. Overall, the Combined and Part B datasets produced the best fraud detection performance with no statistical difference between these datasets, over all the learners. Therefore, based on our results and the assumption that there is no way to know within which part of Medicare a physician will commit fraud, we suggest using the Combined dataset for detecting fraudulent behavior when a physician has submitted payments through any or all Medicare parts evaluated in our study.


title: KNOWLEDGE BASED 3D BUILDING MODEL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS FROM LIDAR AND AERIAL IMAGERIES

abstract: In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings’ roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings’ roofs automatically considering the complementary nature of height and RGB information.


title: Recovering traceability links between code and documentation

abstract: Software system documentation is almost always expressed informally in natural language and free text. Examples include requirement specifications, design documents, manual pages, system development journals, error logs, and related maintenance reports. We propose a method based on information retrieval to recover traceability links between source code and free text documents. A premise of our work is that programmers use meaningful names for program items, such as functions, variables, types, classes, and methods. We believe that the application-domain knowledge that programmers process when writing the code is often captured by the mnemonics for identifiers; therefore, the analysis of these mnemonics can help to associate high-level concepts with program concepts and vice-versa. We apply both a probabilistic and a vector space information retrieval model in two case studies to trace C++ source code onto manual pages and Java code to functional requirements. We compare the results of applying the two models, discuss the benefits and limitations, and describe directions for improvements.


title: Social Media Mining to Understand Public Mental Health

abstract: In this paper, we apply text mining and topic modelling to understand public mental health. We focus on identifying common mental health topics across two anonymous social media platforms: Reddit and a mobile journalling/mood-tracking app. Furthermore, we analyze journals from the app to uncover relationships between topics, journal visibility (private vs. visible to other users of the app), and user-labelled sentiment. Our main findings are that (1) anxiety and depression are shared on both platforms; (2) users of the journalling app keep routine topics such as eating private, and these topics rarely appear on Reddit; and (3) sleep was a critical theme on the journalling app and had an unexpectedly negative sentiment.


Automating the ICU: facing the realities.


title: 1.5MVA grid-connected interleaved inverters using coupled inductors for wind power generation system

abstract: In this paper, grid-connected interleaved voltage source inverters for PMSG wind power generation system with coupled inductors is introduced. In parallel operation, the undesirable circulating current flows between inverters. There are some differences in circulating currents according to source configuration. It is mathematically analyzed and simulated for the verification of analysis. In case of interleaved inverters, the uncontrollable circulating current in switching frequency level flows between inverter modules regardless of source configuration such as common or separated if filter inductance which is placed between inverters is significantly low. In order to suppress high frequency circulating current, the coupled inductors are employed in each phase. A case of 1.5MVA grid-connected interleaved inverters using coupled inductors prototype for the PMSG wind power generation Back To Back converters in parallel are introduced and experimentally verified the proposed topology.


title: Blockchain and Principles of Business Process Re-Engineering for Process Innovation

abstract: Blockchain has emerged as one of the most promising and revolutionary technologies in the past years. Companies are exploring implementation of use cases in hope of significant gains in efficiencies. However, to achieve the impact hoped for, it is not sufficient to merely replace existing technologies. The current business processes must also be redesigned and innovated to enable realization of hoped for benefits. This conceptual paper provides a theoretical contribution on how blockchain technology and smart contracts potentially can, within the framework of the seven principles of business process re-engineering (BPR), enable process innovations. In this paper, we analyze the BPR principles in light of their applicability to blockchain-based solutions. We find these principles to be applicable and helpful in understanding how blockchain technology could enable transformational redesign of current processes. However, the viewpoint taken, should be expanded from intra- to inter-organizational processes operating within an ecosystem of separate organizational entities. In such a blockchain powered ecosystem, smart contracts take on a pivotal role, both as repositories of data and executioner of activities.


title: Training Faster by Separating Modes of Variation in Batch-normalized Models

abstract: Batch Normalization (BN) is essential to effectively train state-of-the-art deep Convolutional Neural Networks (CNN). It normalizes the layer outputs during training using the statistics of each mini-batch. BN accelerates training procedure by allowing to safely utilize large learning rates and alleviates the need for careful initialization of the parameters. In this work, we study BN from the viewpoint of Fisher kernels that arise from generative probability models. We show that assuming samples within a mini-batch are from the same probability density function, then BN is identical to the Fisher vector of a Gaussian distribution. That means batch normalizing transform can be explained in terms of kernels that naturally emerge from the probability density function that models the generative process of the underlying data distribution. Consequently, it promises higher discrimination power for the batch-normalized mini-batch. However, given the rectifying non-linearities employed in CNN architectures, distribution of the layer outputs show an asymmetric characteristic. Therefore, in order for BN to fully benefit from the aforementioned properties, we propose approximating underlying data distribution not with one, but a mixture of Gaussian densities. Deriving Fisher vector for a Gaussian Mixture Model (GMM), reveals that batch normalization can be improved by independently normalizing with respect to the statistics of disentangled sub-populations. We refer to our proposed soft piecewise version of batch normalization as Mixture Normalization (MN). Through extensive set of experiments on CIFAR-10 and CIFAR-100, using both a 5-layers deep CNN and modern Inception-V3 architecture, we show that mixture normalization reduces required number of gradient updates to reach the maximum test accuracy of the batch normalized model by ∼31%-47% across a variety of training scenarios. Replacing even a few BN modules with MN in the 48-layers deep Inception-V3 architecture is sufficient to not only obtain considerable training acceleration but also better final test accuracy. We show that similar observations are valid for 40 and 100-layers deep DenseNet architectures as well. We complement our study by evaluating the application of mixture normalization to the Generative Adversarial Networks (GANs), where "mode collapse" hinders the training process. We solely replace a few batch normalization layers in the generator with our proposed mixture normalization. Our experiments using Deep Convolutional GAN (DCGAN) on CIFAR-10 show that mixture normalized DCGAN not only provides an acceleration of ∼58% but also reaches lower (better) "Frechet Inception Distance" (FID) of 33.35 compared to 37.56 of its batch normalized counterpart.


title: Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry

abstract: We are concerned with the discovery of hierarchical relationships from large-scale unstructured similarity scores. For this purpose, we study different models of hyperbolic space and find that learning embeddings in the Lorentz model is substantially more efficient than in the Poincar\'e-ball model. We show that the proposed approach allows us to learn high-quality embeddings of large taxonomies which yield improvements over Poincar\'e embeddings, especially in low dimensions. Lastly, we apply our model to discover hierarchies in two real-world datasets: we show that an embedding in hyperbolic space can reveal important aspects of a company's organizational structure as well as reveal historical relationships between language families.


title: Diachronic word embeddings and semantic shifts: a survey

abstract: Recent years have witnessed a surge of publications aimed at tracing temporal changes in lexical semantics using distributional methods, particularly prediction-based word embedding models. However, this vein of research lacks the cohesion, common terminology and shared practices of more established areas of natural language processing. In this paper, we survey the current state of academic research related to diachronic word embeddings and semantic shifts detection. We start with discussing the notion of semantic shifts, and then continue with an overview of the existing methods for tracing such time-related shifts with word embedding models. We propose several axes along which these methods can be compared, and outline the main challenges before this emerging subfield of NLP, as well as prospects and possible applications.


title: The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention

abstract: Abstract In a study of telecommunications services, the authors examine the effects of customer satisfaction, affective commitment, and calculative commitment on retention. The study further examines the potential for situational and reactional trigger conditions to moderate the satisfaction–retention relationship. The results support consistent effects of customer satisfaction, calculative commitment, and prior churn on retention. Prior churn also moderates the satisfaction–retention relationship. The results have implications for both customer relationship managers and researchers who use satisfaction surveys to predict behavior.


title: Disulfide bonds in a recombinant protein modeled after a core repeat in an aquatic insect's silk protein.

abstract: We constructed a gene encoding rCAS, recombinant constant and subrepeat protein, modeled after tandem repeats found in the major silk proteins synthesized by aquatic larvae of the midge, Chironomus tentans. Bacterially synthesized rCAS was purified to near homogeneity and characterized by several biochemical and biophysical methods including amino-terminal sequencing, amino acid compositional analysis, sedimentation equilibrium ultracentrifugation, and mass spectrometry. Complementing these techniques with quantitative sulfhydryl assays, we discovered that the four cysteines present in rCAS form two intramolecular disulfide bonds. Mapping studies revealed that the disulfide bonds are heterogeneous. When reduced and denatured rCAS was allowed to refold and its disulfide bonding state monitored, it again adopted a conformation with two intramolecular disulfide bonds. The inherent ability of rCAS to quantitatively form two intramolecular disulfide bonds may reflect a previously unknown feature of the in vivo silk proteins from which it is derived.


title: Towards computer-assisted language learning with robots, wikipedia and CogInfoCom

abstract: Computer-Assisted Language Learning is being transformed by the use of humanoid robots combined with Wikipedia-based information access and CogInfoCom systems. The learner's engagement in language-learning dialogues is strengthened by the robot's non-verbal behaviour such as nodding, gesturing and face-tracking, and the learner's level of interest can be estimated by monitoring the learner's non-verbal signals through gaze-tracking and proximity measurement. In this paper we discuss new approaches in which robotassisted language learning is further enhanced by the use of CogInfoCom channels to extend the learner's engagement and interest. We focus on the robot's use of Wikipedia-based information access to greatly extend the range of topics that the robot can talk about during language-learning spoken dialogues, and describe natural language processing techniques to generate questions from Wikipedia information, so the robot can ask new questions about the learner's preferred topics. We predict that the learner's interest level is increased when the learner can navigate in Wikipedia to dialogue topics of personal interest rather than being restricted to topics chosen by the teacher.


title: Embodied conversational agents as a ui paradigm: A framework for evaluation

abstract: Research on embodied conversational agent interfaces has produced widely divergent results. We suggest that this is due to insufficient consideration of key factors that influence the perception and effectiveness of agent-based interfaces. Thus, we propose a framework for the evaluation of conversational agent interfaces that can systematize the research. The framework emphasizes features of the agent, characteristics of the user, and the task the user is performing. We have conducted experiments within this framework. The first study manipulated the agent's appearance (lifelike versus iconic) and the nature of the user's task (carrying out procedures versus providing opinions). We found that the perception of the agent was strongly influenced by the task while features of the agent that we manipulated had little effect. The second study (in progress) manipulates the initiative of the agent (proactive versus reactive). Initial analysis of the data showed that the participants strongly preferred proactive agents while initiative of the agent had little effect on their task performance.


title: Three-dimensional reconstructions from non-deparaffinized tissue sections

abstract: Three-dimensional (3-D) reconstruction from microscopic images represents a useful tool for the study of biological structures in embryology and developmental biology. However, it is usually necessary to cope with many difficulties connected with the preparation of specimens. In order to minimize mutual displacement of structures in successive sections, the applicability of non-deparaffinized tissue sections for 3-D reconstruction was tested. Chicken embryos were fixed and stained in toto with eosin and then embedded in paraffin. About 30-mum-thick non-deparaffinized serial sections were used for obtaining initial data for 3-D reconstruction of larger stacks of embryonic bodies using either fluorescence or confocal microscope. The same sections served for both collecting optical serial sections of mesonephros as source images for its 3-D reconstruction, and immunohistochemical detection of fibronectin, laminin and vimentin. It was found that sections with retained paraffin preserve the mutual spatial relationships of tissue components as well as provide an excellent differentiation of structure. It makes the process of 3-D reconstruction easier. The localization of the products of immunohistochemical reactions demonstrated the co-localization of fibronectin and laminin in basal laminas and the presence of vimentin in glomeruli and mesenchymal tissue. The use of non-deparaffinized sections represents a less time consuming and more effective alternative to thin histological sections for the purpose of 3-D reconstruction, and enables further application of material.


title: Model Predictive Control for Power Converters and Drives: Advances and Trends

abstract: Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities.


title: OpenHRP: Open Architecture Humanoid Robotics Platform

abstract: This paper presents the open architecture humanoid robotics platform (OpenHRP) which consists of a simulator and motion control library of humanoid robots. The binary software developed on OpenHRP can be applied to the real counterpart as is, thank to the proposed hardware abstraction and synchronization mechanism. The compatibility between the simulation and corresponding experiment has been successfully examined. OpenHRP is expected to initiate the exploration of humanoid robotics on open architecture software and hardware.


title: Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation

abstract: The popularity of image sharing on social media and the engagement it creates between users reflects the important role that visual context plays in everyday conversations. We present a novel task, Image-Grounded Conversations (IGC), in which natural-sounding conversations are generated about a shared image. To benchmark progress, we introduce a new multiple-reference dataset of crowd-sourced, event-centric conversations on images. IGC falls on the continuum between chit-chat and goal-directed conversation models, where visual grounding constrains the topic of conversation to event-driven utterances. Experiments with models trained on social media data show that the combination of visual and textual context enhances the quality of generated conversational turns. In human evaluation, the gap between human performance and that of both neural and retrieval architectures suggests that multi-modal IGC presents an interesting challenge for dialogue research.


Code Obfuscation Literature Survey


title: Towards Understanding of Factors Influencing User Acceptance of Mobile Payment Systems

abstract: M-payments, which refer to payments over a mobile device, have not taken off as fast as predicted. Their slow adoption rates raise many questions about what drives consumer behaviour. The aim of this study is to find out what influences the user acceptance of a new system. The user is seen here as the key to acceptance of a new m-payment procedure, hence the focus on understanding their motivations and attitudes. To predict acceptance, Technology Acceptance Model (TAM) has been extensively used in information systems. This study is based on the belief that TAM needs to be expanded and customized to suit specific characteristics of mobile payments. This paper draws from prior studies that used TAM in fields related to mobile payments. Extensions of TAM in related fields are depicted. Analysis of suitability of various acceptance factors for mobile payments follows. The result of the analysis is a set of factors that are believed to influence the user acceptance of mobile payments: perceived ease of use, usefulness, mobility, cost, trust, and expressiveness. To measure each of these factors, multi-item scales using specific criteria are proposed. Further research will involve pilot study and possible improvement of the measuring scales, as well as further empirical validation of the model. The proposed model illustrates the level of fulfilment of each of the six acceptance factors in an m-payment system, and therefore predicts its adoption, as well as indicates areas of improvement.


title: Performance Analysis of LS and LMMSE Channel Estimation Techniques for LTE Downlink Systems

abstract: The main purpose of this paper is to study the performance of two linear channel estimators for LTE Downlink systems, the Least Square Error (LSE) and the Linear Minimum Mean Square Error (LMMSE). As LTE is a MIMO-OFDM based system, a cyclic prefix is inserted at the beginning of each transmitted OFDM symbol in order to completely suppress both inter-carrier interference (ICI) and inter-symbol interference (ISI). Usually, the cyclic prefix is equal to or longer than the channel length but in some cases and because of some unforeseen channel behaviour, the cyclic prefix can be shorter. Therefore, we propose to study the performance of the two linear estimators under the effect of the channel length. Computer simulations show that, in the case where the cyclic prefix is equal to or longer than the channel length,LMMSE performs better than LSE but at the cost of computational complexity.In the other case, LMMSE continue to improve its performance only for low SNR values but it degrades for high SNR values in which LS shows better performance for LTE Downlink systems. MATLAB Monte – Carlo simulations are used to evaluate the performance of the studied estimators in terms of Mean Square Error (MSE) and Bit Error Rate (BER) for 2�2 LTE Downlink systems.


title: EXPLORING MOBILE PAYMENT ADOPTION IN CHINA

abstract: With the development of the integration between mobile communication and Internet technology, China is expected to have a large number of M-payment users due to its population size with a large number of mobile users. However, the number of M-payment users in China is still low and currently there are limited in-depth studies exploring the adoption of M-payment in China. This study aims to explore reasons for individuals to use M-payment in China through a qualitative study. The research results indicated that M-payment adoption was influenced by various reasons related to system quality, service quality, usefulness, social influence, trust, among others. The study findings indicate that the influence of system quality and service quality on individual’s decision to use in China appear to be the most important. A particular individual lifestyle, need and promotion offered by service providers have also been identified as important reasons for using M-payment in China. The outcomes of this study enhance the current knowledge about the M-payment adoption particularly in China. They can also be used by service providers to devise appropriate strategies to encourage wider adoption of M-payment.


title: Wearable accessory robot for context aware apprise of personal information

abstract: A new type of wearable computing artifact for apprise of personal information - wearable accessory robot has been developed. It was selected as the most attractive research platform for ubiquitous information access among a group of previously developed devices with different shape, functionality and modality. The main purpose of the wearable robot is divided into functional and aesthetic: to assist the users in their various context-based information retrieval needs in every-day life while being a decorative accessory depicting the users' taste. Visual observation and use-case scenario self-report studies have been performed in order to analyze the users' cognitive response towards the robot. A significant difference between the passive and the active perception of the robot interface was obtained. It was shown that the development of a pleasant and personal wearable robot communication device is an important step toward a new era of wearable computing in the future. The compelling embodiment of ubiquitous products which the users can personalize, were shown to be a major differentiating factor beyond the technical specifications.


title: Graph-Based Semi-Supervised Learning

abstract: While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of Artificial Intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / Learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index


title: Gas Pressure Sensor Based on CO 2-Laser-Induced Long-Period Fiber Grating in Air-Core Photonic Bandgap Fiber

abstract: We reported a gas pressure sensor based on CO ::: 2 ::: -laser-induced long-period fiber grating (LPFG) in an air-core photonic bandgap fiber (PBF). The LPFG was inscribed in an air-core PBF by the use of an improved CO ::: 2 ::: laser system with an ultraprecision 2-D scanning technique, which induced periodic collapses of air holes along the axis of the PBF. Such an LPFG could be used to develop a promising gas pressure sensor with a sensitivity of -137 pm/MPa. Moreover, a simplified fiber model with a relatively similar elastic response was developed to qualitatively study the gas pressure response of the LPFG inscribed in the air-core PBF. A simulated stress distribution along the LPFG revealed that the gas pressure leads to a stress concentration at the collapsed area of the air holes in the fiber cladding, which finally results in a resonant wavelength shift of the LPFG through an elastooptical effect.


title: Crisis communication, learning and responding: Best practices in social media

abstract: As noted by Seeger (2006) the notion of best practices is often use to improve professional practice; to create research and functional recommendations to use in a specific situation. This essay describes best practices in crisis communication specifically through the use of social media. It provides suggestions and approaches for improving the effectiveness of crisis communication and learning with and between organizations, governments and citizens. Seven best practices for effective crisis communication using social media are outlined. Research on social media and crisis communication is synthesized.Results from experiments and applied research are discussed.Best practices for crisis communication are offered.


title: Design and fabrication of materials with desired deformation behavior

abstract: This paper introduces a data-driven process for designing and fabricating materials with desired deformation behavior. Our process starts with measuring deformation properties of base materials. For each base material we acquire a set of example deformations, and we represent the material as a non-linear stress-strain relationship in a finite-element model. We have validated our material measurement process by comparing simulations of arbitrary stacks of base materials with measured deformations of fabricated material stacks. After material measurement, our process continues with designing stacked layers of base materials. We introduce an optimization process that finds the best combination of stacked layers that meets a user's criteria specified by example deformations. Our algorithm employs a number of strategies to prune poor solutions from the combinatorial search space. We demonstrate the complete process by designing and fabricating objects with complex heterogeneous materials using modern multi-material 3D printers.