title: Auxiliary Guided Autoregressive Variational Autoencoders

abstract: Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of both models. Our contribution is to train such hybrid models using an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder. In contrast, prior work on such hybrid models needed to limit the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables and only rely on autoregressive modeling. Our approach results in models with meaningful latent variable representations, and which rely on powerful autoregressive decoders to model image details. Our model generates qualitatively convincing samples, and yields state-of-the-art quantitative results.


title: Making Deep Belief Networks effective for large vocabulary continuous speech recognition

abstract: To date, there has been limited work in applying Deep Belief Networks (DBNs) for acoustic modeling in LVCSR tasks, with past work using standard speech features. However, a typical LVCSR system makes use of both feature and model-space speaker adaptation and discriminative training. This paper explores the performance of DBNs in a state-of-the-art LVCSR system, showing improvements over Multi-Layer Perceptrons (MLPs) and GMM/HMMs across a variety of features on an English Broadcast News task. In addition, we provide a recipe for data parallelization of DBN training, showing that data parallelization can provide linear speed-up in the number of machines, without impacting WER.


title: Development of Mind Control System for Humanoid Robot through a Brain Computer Interface

abstract: We develop a mind control system for humanoid robot through a brain-computer-interface (BCI), consisting of a 32 channel electroencephalograph (EEG), a humanoid robot, and a CCD camera. We present two types of humanoid robots in the BCI control system: KT-X PC robot with 20 degrees of freedom (DOFs) or NAO H25 robot with 25 DOFs. The CCD camera takes video clips of a subject or an instructor hand postures to identify mental activities when the subject is thinking "turning right, " "turning left, " or "walking forward." As an initial test, we implement three types of robot walking behaviors: turning right, turning left and walking forward, and report the neural signals correlated to these three mental activities.


title: Threats to Networking Cloud and Edge Datacenters in the Internet of Things

abstract: Several application domains are collecting data using Internet of Things sensing devices and shipping it to remote cloud datacenters for analysis (fusion, storage, and processing). Data analytics activities raise a new set of technical challenges from the perspective of ensuring end-to-end security and privacy of data as it travels from an edge datacenter (EDC) to a cloud datacenter (CDC) (or vice versa). This article discusses the security threats in EDCs and CDCs by dividing the complete network structure into three layers: perception layer, network layer, and application layer.


title: Parameter Estimation for Condition Monitoring of PMSM Stator Winding and Rotor Permanent Magnets

abstract: Winding resistance and rotor flux linkage are important to controller design and condition monitoring of a surface-mounted permanent-magnet synchronous machine (PMSM) system. In this paper, an online method for simultaneously estimating the winding resistance and rotor flux linkage of a PMSM is proposed, which is suitable for application under constant load torque. It is based on a proposed full-rank reference/variable model. Under constant load torque, a short pulse of id ≠ 0 is transiently injected into the d-axis current, and two sets of machine rotor speeds, currents, and voltages corresponding to id = 0 and id ≠ 0 are then measured for estimation. Since the torque is kept almost constant during the transient injection, owing to the moment of system inertia and negligible reluctance torque, the variation of rotor flux linkage due to injected id ≠ 0 can be taken into account by using the equation of constant torque without measuring the load torque and is then associated with the two sets of machine equations for simultaneously estimating the winding resistance and rotor flux linkage. Furthermore, the proposed method does not need the values of the dq-axis inductances, while the influence from the nonideal voltage measurement, which will cause an ill-conditioned problem in the estimation, has been taken into account and solved by error analysis. This method is finally verified on two prototype PMSMs and shows good performance.


title: Using Natural Language Generation in Automatic Route Description

abstract: In this paper we tackle the problem of generating natural route descriptions on the basis of input obtained from a commercially available way-finding system. Our framework and architecture incorporates the use of general principles drawn from the domain of natural language generation. Through examples we demonstrate that it is possible to bridge the gap between underlying data representations and natural sounding linguistic descriptions. The work presented contributes both to the area of natural language generation and to the improvement of way-finding system interfaces.


title: Relationships between meteorological parameters and criteria air pollutants in three megacities in China

abstract: Meteorological conditions play a crucial role in ambient air pollution by affecting both directly and indirectly the emissions, transport, formation, and deposition of air pollutants. In this study, the relationships between meteorological parameters and ambient air pollutants concentrations in three megacities in China, Beijing, Shanghai, and Guangzhou were investigated. A systematic analysis of air pollutants including PM2.5, PM10, CO, SO2, NO2, and O3 and meteorological parameters including temperature, wind speed (WS), wind direction (WD) and relative humanity (RH) was conducted for a continuous period of 12 months from March 2013 to February 2014. The results show that all three cities experienced severe air quality problems. Clear seasonal trends were observed for PM2.5, PM10, CO, SO2 and NO2 with the maximum concentrations in the winter and the minimum in the summer, while O3 exhibited an opposite trend. Substantially different correlations between air pollutants and meteorological parameters were observed among these three cities. WS reversely correlated with air pollutants, and temperature positively correlated with O3. Easterly wind led to the highest PM2.5 concentrations in Beijing, westerly wind led to high PM2.5 concentrations in Shanghai, while northern wind blew air parcels with the highest PM2.5 concentrations to Guangzhou. In Beijing, days of top 10% PM2.5, PM10, CO, and NO2 concentrations were with higher RH compared to days of bottom 10% concentrations, and SO2 and O3 showed no distinct RH dependencies. In Guangzhou, days of top 10% PM2.5, PM10, CO, SO2, NO2 and O3 concentrations were with lower RH compared to days of bottom 10% concentrations. Shanghai showed less fluctuation in RH between top and bottom 10%. These results confirm the important role of meteorological parameters in air pollution formation with large variations in different seasons and geological areas. These findings can be utilized to improve the understanding of the mechanisms that produce air pollution, enhance the forecast accuracy of the air pollution under different meteorological conditions, and provide effective measures for mitigating the pollution.


title: Street and traffic simulation: traffic flow simulation using CORSIM

abstract: This paper presents an overview of the concept and features of the CORSIM traffic simulation program and the TSIS package that provides an integrated, user-friendly interface and environment for executing CORSIM. The new functions and feature of CORSIM and TSIS in the most recent release are highlighted. Several successful TSIS/CORSIM applications are used to demonstrate that CORSIM is a very effective tool to perform traffic operations analysis and to evaluate new ITS technologies.


title: Large-Scale YouTube-8M Video Understanding with Deep Neural Networks

abstract: Video classification problem has been studied many years. The success of Convolutional Neural Networks (CNN) in image recognition tasks gives a powerful incentive for researchers to create more advanced video classification approaches. As video has a temporal content Long Short Term Memory (LSTM) networks become handy tool allowing to model long-term temporal clues. Both approaches need a large dataset of input data. In this paper three models provided to address video classification using recently announced YouTube-8M large-scale dataset. The first model is based on frame pooling approach. Two other models based on LSTM networks. Mixture of Experts intermediate layer is used in third model allowing to increase model capacity without dramatically increasing computations. The set of experiments for handling imbalanced training data has been conducted.


title: Using views for retrieving data from extremely heterogeneous databanks.

abstract: Information in molecular biology or biology in general is contained in a multitude of different databanks each specializing in a certain area. This specialization is useful since it improves the maintainability of the data and the flexibility of the overall structure which until now manages to exist without almost any standards. However, information gathering from these extremely heterogeneous sources is difficult since the desired data may be scattered over many databanks. This paper presents the concept of views implemented in the retrieval system SRS that uses links between databanks and a sophisticated parsing engine for information extraction to provide a flexible way of searching and obtaining data across databank boundaries. The implementation provides homogeneous access to all databanks using two types of views: the list-view which displays selected data-fields in their original format and the table-view available for HTML and plain ASCII text which tries to represent the data in a format independent from that of the source databank. 15 refs., 4 figs.


title: Recombinant activated factor VII in treatment of bleeding complications following hematopoietic stem cell transplantation

abstract: BACKGROUND ::: Bleeding is a common complication following hematopoietic stem cell transplantation (HSCT) and standard hemostatic treatment is often ineffective. We conducted a multicentre, randomized trial of the efficacy and safety of activated recombinant factor VII (rFVIIa, NovoSeven) in the treatment of bleeding following HSCT. ::: ::: ::: METHODS ::: 100 patients with moderate or severe bleeding (52 gastrointestinal; 26 hemorrhagic cystitis; seven pulmonary; one cerebral; 14 other) were included from days +2 to +180 post-transplant (97 allogeneic; three autologous) to receive seven doses of rFVIIa (40, 80 or 160 microg kg(-1)) or placebo every 6 h. The primary efficacy endpoint was the change in bleeding score between the first administration and 38 h. ::: ::: ::: RESULTS ::: No significant effect of increasing rFVIIa dose was observed on the primary endpoint. A post hoc analysis comparing each rFVIIa dose with placebo showed that 80 microg kg(-1) rFVIIa improved the bleeding score at the 38 h time point (81% vs. 57%, P = 0.021). This effect was not seen at 160 microg kg(-1). There were no differences in transfusion requirements across dose groups. There was no trend in the type or number of severe adverse events observed. Six thromboembolic events were observed in the active treatment groups: three during, and three following the 96-h observation period. ::: ::: ::: CONCLUSIONS ::: Despite no overall effect of rFVIIa treatment on primary endpoint, post hoc analysis showed an improvement in the control of bleeding for 80 microg kg(-1) rFVIIa vs. standard hemostatic treatment. The heterogeneity of the population may have contributed to the lack of an increasing effect with increased dose. Further trials should focus upon identifying the patient populations that may benefit from treatment with rFVIIa.


title: A deep convolutional neural network trained on representative samples for circulating tumor cell detection

abstract: The number of Circulating Tumor Cells (CTCs) in blood indicates the tumor response to chemotherapeutic agents and disease progression. In early cancer diagnosis and treatment monitoring routine, detection and enumeration of CTCs in clinical blood samples have significant applications. In this paper, we design a Deep Convolutional Neural Network (DCNN) with automatically learned features for image-based CTC detection. We also present an effective training methodology which finds the most representative training samples to define the classification boundary between positive and negative samples. In the experiment, we compare the performance of auto-learned feature from DCNN and hand-crafted features, in which the DCNN outperforms hand-crafted feature. We also prove that the proposed training methodology is effective in improving the performance of DCNN classifiers.


title: Dynamically scaling applications in the cloud

abstract: Scalability is said to be one of the major advantages brought by the cloud paradigm and, more specifically, the one that makes it different to an "advanced outsourcing" solution. However, there are some important pending issues before making the dreamed automated scaling for applications come true. In this paper, the most notable initiatives towards whole application scalability in cloud environments are presented. We present relevant efforts at the edge of state of the art technology, providing an encompassing overview of the trends they each follow. We also highlight pending challenges that will likely be addressed in new research efforts and present an ideal scalable cloud system.


title: Performance Comparison of VxWorks, Linux, RTAI and Xenomai in a Hard Real-time Application

abstract: We report on a set of performance measurements executed on VMEbus MVME5500 boards equipped with MPC7455 PowerPC processor, running four different operating systems: Wind River VxWorks, Linux, RTAI, and Xenomai. Some components of RTAI and Xenomai have been ported to the target architecture. Interrupt latency, rescheduling and inter-process communication times are compared in the framework of a sample real-time application. Performance measurements on Gigabit Ethernet network communication have also been carried out on the target boards. To this purpose, we have considered the Linux IP stack and RTnet, an open-source hard real-time network protocol stack for Xenomai and RTAI, which was ported to the considered architecture. Performance measurements show that the tested open-source software is suitable for hard real-time applications.


title: Image processing based obstacle detection with laser measurement in railways

abstract: Intelligent transport and transportation systems are becoming indispensable systems of today. Continuous new investments and work are being carried out to ensure safety in all transport ways and to reduce accident rates. In this study, a simulation is designed to increase the safety of railways. Most of the railway accidents are caused by the obstacles on the rails. These obstacles means, trees, rocks and consists of similar structures. In this application, the railway has been developed on the detection of any obstacles on the rails, the introduction of the emergency system, and the transmission of information to the movement center. The camera and the laser distance meter installed on the train are used to scan the image with the image processing method, and the results are verified and the obstacle is detected. Emergency braking system, warning system is inserted into the circuit to prevent the obstacle from crashing. In addition to this, the main computer status message is sent to the movement center with the help of the created network. As a result, accident rates will be reduced, and intelligent train systems will be further developed.


title: Integrated system of face recognition and sound localization for a smart door phone

abstract: This paper proposes a smart system using both face recognition and sound localization techniques to identify foreign faces through a door phone in a more efficient and accurate way. In particular, a visitor's voice is used to localize the proper location of the speaker. The location information is then used to adjust the door phone camera position to frame the face in its field of view. In order to accurately locate the facial position of the visitor, 4 microphones are positioned in a cross configuration. Furthermore, face detection and face recognition algorithms as well as a wireless interface are used to recognize the visitor and send the information to the home owner's phone. The entire control system is built using an FPGA chip and tested for actual use in door phone environments1.


title: Information Theoretic-Learning Auto-Encoder

abstract: We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a partition function. This paper also formalizes, generative moment matching networks under the ITL framework.


An Adaptive Dataset for the Evaluation of Android Malware Detection Techniques


Inequalities in Open Source Software Development: Analysis of Contributor’s Commits in Apache Software Foundation Projects


Differential Wide Temperature Range CMOS Interface Circuit for Capacitive MEMS Pressure Sensors


title: Bayesian Exploration: Incentivizing Exploration in Bayesian Games

abstract: We consider a ubiquitous scenario in the Internet economy when individual decision-makers (henceforth, agents) both produce and consume information as they make strategic choices in an uncertain environment. This creates a three-way trade-off between exploration (trying out insufficiently explored alternatives to help others in the future), exploitation (making optimal decisions given the information discovered by other agents), and incentives of the agents (who are myopically interested in exploitation, while preferring the others to explore). We posit a principal who controls the flow of information from agents that came before to the ones that arrive later, and strives to coordinate the agents towards a socially optimal balance between exploration and exploitation, not using any monetary transfers. The goal is to design a recommendation policy for the principal which respects agents' incentives and minimizes a suitable notion of regret. We extend prior work in this direction to allow the agents to interact with one another in a shared environment: at each time step, multiple agents arrive to play a Bayesian game, receive recommendations, choose their actions, receive their payoffs, and then leave the game forever. The agents now face two sources of uncertainty: the actions of the other agents and the parameters of the uncertain game environment. Our main contribution is to show that the principal can achieve constant regret when the utilities are deterministic (where the constant depends on the prior distribution, but not on the time horizon), and logarithmic regret when the utilities are stochastic. As a key technical tool, we introduce the concept of explorable actions, the actions which some incentive-compatible policy can recommend with non-zero probability. We show how the principal can identify (and explore) all explorable actions, and use the revealed information to perform optimally. In particular, our results significantly improve over the prior work on the special case of a single agent per round, which relies on assumptions to guarantee that all actions are explorable. Interestingly, we do not require the principal's utility to be aligned with the cumulative utility of the agents; instead, the principal can optimize an arbitrary notion of per-round reward.


title: Multimodal diffusion geometry by joint diagonalization of Laplacians

abstract: We construct an extension of diffusion geometry to multiple modalities through joint approximate diagonalization of Laplacian matrices. This naturally extends classical data analysis tools based on spectral geometry, such as diffusion maps and spectral clustering. We provide several synthetic and real examples of manifold learning, retrieval, and clustering demonstrating that the joint diffusion geometry frequently better captures the inherent structure of multi-modal data. We also show that many previous attempts to construct multimodal spectral clustering can be seen as particular cases of joint approximate diagonalization of the Laplacians.


title: Learning Bayesian networks: approaches and issues

abstract: Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks-in particular their structure-from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial-for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.


title: Metrics Feedback Cycle: measuring and improving user engagement in gamified eLearning systems

abstract: This paper presents the identification, design and implementation of a set of metrics of user engagement in a gamified eLearning application. The 'Metrics Feedback Cycle' (MFC) is introduced as a formal process prescribing the iterative evaluation and improvement of application-wide engagement, using data collected from metrics as input to improve related engagement features. This framework was showcased using a gamified eLearning application as a case study. In this paper, we designed a prototype and tested it with thirty-six (N=36) students to validate the effectiveness of the MFC. The analysis and interpretation of metrics data shows that the gamification features had a positive effect on user engagement, and helped identify areas in which this could be improved. We conclude that the MFC has applications in gamified systems that seek to maximise engagement by iteratively evaluating implemented features against a set of evolving metrics.


title: Female pelvic medicine and reconstructive surgery practice patterns: IUGA member survey

abstract: Introduction and hypothesis ::: The purpose of this study is to describe the current practice patterns of the International Urogynecological Association (IUGA) members regarding the diagnosis, evaluation, and surgical management of stress urinary incontinence (SUI) and pelvic organ prolapse (POP).


title: On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection

abstract: Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.


title: Histogram of Oriented Lines for Palmprint Recognition

abstract: Subspace learning methods are very sensitive to the illumination, translation, and rotation variances in image recognition. Thus, they have not obtained promising performance for palmprint recognition so far. In this paper, we propose a new descriptor of palmprint named histogram of oriented lines (HOL), which is a variant of histogram of oriented gradients (HOG). HOL is not very sensitive to changes of illumination, and has the robustness against small transformations because slight translations and rotations make small histogram value changes. Based on HOL, even some simple subspace learning methods can achieve high recognition rates.


title: SDN Partitioning: A Centralized Control Plane for Distributed Routing Protocols

abstract: Hybrid IP networks that use both control paradigms—distributed and centralized—promise the best of two worlds: 1) programmability of software-defined networking (SDN) and 2) reliability and fault tolerance of distributed routing protocols, like open shortest path first (OSPF). Typically, a hybrid network deploys SDN to control prioritized traffic and OSPF to assure care-free operation of best effort traffic. We propose a new hybrid network architecture, called SDN partitioning (SDNp), which establishes centralized control over the distributed routing protocol by partitioning the topology into sub-domains with SDN-enabled border nodes. In our approach, OSPF’s routing updates have to traverse SDN border nodes to reach neighboring sub-domains. This allows the central controller to modify how sub-domains view one another, which in turn allows to steer inter-sub-domain traffic. The degree of dynamic control against simplicity of OSPF can be traded off by adjusting the size of the sub-domains. This paper studies the technical requirements, presents a novel scheme for balanced topology partitioning, and provides the corresponding network management models for SDNp. Our performance evaluation shows that—already in its minimum configuration with two sub-domains—SDNp provides significant improvements in all respects compared to legacy routing protocols, whereas smaller sub-domains provide network control capabilities comparable to full SDN deployment.


title: A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets

abstract: BACKGROUND ::: Deep learning methods for radiomics/computer-aided diagnosis (CADx) are often prohibited by small datasets, long computation time, and the need for extensive image preprocessing. ::: ::: ::: AIMS ::: We aim to develop a breast CADx methodology that addresses the aforementioned issues by exploiting the efficiency of pre-trained convolutional neural networks (CNNs) and using pre-existing handcrafted CADx features. ::: ::: ::: MATERIALS & METHODS ::: We present a methodology that extracts and pools low- to mid-level features using a pretrained CNN and fuses them with handcrafted radiomic features computed using conventional CADx methods. Our methodology is tested on three different clinical imaging modalities (dynamic contrast enhanced-MRI [690 cases], full-field digital mammography [245 cases], and ultrasound [1125 cases]). ::: ::: ::: RESULTS ::: From ROC analysis, our fusion-based method demonstrates, on all three imaging modalities, statistically significant improvements in terms of AUC as compared to previous breast cancer CADx methods in the task of distinguishing between malignant and benign lesions. (DCE-MRI [AUC = 0.89 (se = 0.01)], FFDM [AUC = 0.86 (se = 0.01)], and ultrasound [AUC = 0.90 (se = 0.01)]). ::: ::: ::: DISCUSSION/CONCLUSION ::: We proposed a novel breast CADx methodology that can be used to more effectively characterize breast lesions in comparison to existing methods. Furthermore, our proposed methodology is computationally efficient and circumvents the need for image preprocessing.


title: Structured light and stereo vision for underwater 3D reconstruction

abstract: Stereo vision and structured light are compared in a common underwater environment with known dimensions and objects. Two different sensors are mounted on top of a Cartesian robot that moves with a known and programmed trajectory. The resulting point clouds from each sensor are compared in terms of distance from point to point, and measurements in the scanned objects, to determine which sensor is best suited depending on the environment and the survey purpose. The conclusions show that a stereo based reconstruction is best suited for long, high altitude surveys, always depending on having enough texture and light, whereas a structured light reconstruction can be better fitted in a short, close distance approach where accurate dimensions of an object or structure are needed.


title: Facial action recognition for facial expression analysis from static face images

abstract: Automatic recognition of facial gestures (i.e., facial muscle activity) is rapidly becoming an area of intense interest in the research field of machine vision. In this paper, we present an automated system that we developed to recognize facial gestures in static, frontal- and/or profile-view color face images. A multidetector approach to facial feature localization is utilized to spatially sample the profile contour and the contours of the facial components such as the eyes and the mouth. From the extracted contours of the facial features, we extract ten profile-contour fiducial points and 19 fiducial points of the contours of the facial components. Based on these, 32 individual facial muscle actions (AUs) occurring alone or in combination are recognized using rule-based reasoning. With each scored AU, the utilized algorithm associates a factor denoting the certainty with which the pertinent AU has been scored. A recognition rate of 86% is achieved.


title: Energy characterization of a tiled architecture processor with on-chip networks

abstract: Tiled architectures provide a paradigm for designers to turn silicon resources into processors with burgeoning quantities of programmable functional units and memories. The architecture has a dual responsibility: first, it must expose these resources in a way that is programmable. Second, it needs to manage the power associated with such resources. We present the power management facilities of the 16-tile Raw microprocessor. This design selectively turns on and off 48 SRAM macros, 96 functional unit clusters, 32 fetch units,and over 250 unique processor pipeline, stages, all according to the needs of the computation and environment at hand.


title: Dynamic Query Modeling for Related Content Finding

abstract: While watching television, people increasingly consume additional content related to what they are watching. We consider the task of finding video content related to a live television broadcast for which we leverage the textual stream of subtitles associated with the broadcast. We model this task as a Markov decision process and propose a method that uses reinforcement learning to directly optimize the retrieval effectiveness of queries generated from the stream of subtitles. Our dynamic query modeling approach significantly outperforms state-of-the-art baselines for stationary query modeling and for text-based retrieval in a television setting. In particular we find that carefully weighting terms and decaying these weights based on recency significantly improves effectiveness. Moreover, our method is highly efficient and can be used in a live television setting, i.e., in near real time.


title: A distributed IDS architecture model for Smart Home systems

abstract: The common use of smart devices encourages potential attackers to violate privacy. Sometimes taking control of one device allows the attacker to obtain secret data (such as password for home WiFi network) or tools to carry out DoS attack, and this, despite the limited resources of such devices. One of the solutions for gaining users’ confidence is to assign responsibility for detecting attacks to the service provider, particularly Internet Service Provider (ISP). It is possible, since ISP often provides also the Home Gateway (HG)—device that has multiple roles: residential router, entertainment center, and home’s “command and control” center which allows to manage the Smart Home entities. The ISP may extend this set of functionalities by implementing an intrusion detection software in HG provisioned to their customers. In this article we propose an Intrusion Detection System (IDS) distributed between devices residing at user’s and ISP’s premises. The Home Gateway IDS and the ISP’s IDS constitute together a distributed structure which allows spreading computations related to attacks against Smart Home ecosystem. On the other hand, it also leverages the operator’s knowledge of security incidents across the customer premises. This distributed structure is supported by the ISP’s expert system that helps to detect distributed attacks i.e., using botnets.


title: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

abstract: Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.


title: Extent of social media usage by students for improved learning in Tertiary Institution

abstract: This study was carried out to ascertain the perception of students on the use of social media for educational purposes. It was conducted in three tertiary institutions in Rivers State. The sample comprised 200 respondents randomly selected from three tertiary institutions in Rivers State. Five research objectives and four null hypotheses were used for the study. Mean scores, ANOVA, multiple comparison test using schaffer model, and Correlational Analysis were the statistical tools used in the study. The study found that social media is used in the study. The study found that social is used for educational purposes in terms of communication, sharing and exchanging ideas with improved engagement with technology. The study also revealed that social media is frequently used for interactions with friends for classroom assignment, communication in and outside classroom, promoting interactions during lectures, promoting informal learning, enhance course grades, facilitating language exchange, video conferencing, creating room for educational and video sharing. In terms of attitude of students, respondents generally have favourable attitude towards use of social media. The study revealed significant differences exist in social media usage across the three universities involved in the study and significant differences exist in usage of social media between male and female respondents; and in attitude of students towards social media. Based on these findings, the study recommend that the use of social media should be incorporated into the curriculum for enhanced universities education.


title: Verifying an Arbiter Circuit

abstract: This paper presents the verification of an asynchronous arbiter modeled at the circuit level with non-linear ordinary differential equations. We use Brockett's annulus to represent the allowed families of continuous waveforms for input and output signals and show that the metastability filter of the arbiter can be understood as a "Brockett annulus transformer." Improvements to the Coho verification tool are described that reduce the over approximation errors when working with non- convex reachable regions. The verification shows that the arbiter observes a four-phase handshake protocol with its clients and maintains mutual exclusion. We also show several liveness properties including bounded time response to uncontested requests and that grants are issued fairly.


title: Design of a spherical robot arm with the Spiral Zipper prismatic joint

abstract: A novel prismatic joint called a Spiral Zipper is used to create a 3DOF robot arm in a spherical robot configuration. The Spiral Zipper can be very compact as it has a large extension to compression ratio. An initial prototype has shown a ratio of over 14∶1. The Spiral Zipper is very strong in compression, but maybe loose under tension and moments. A tether based system ensures the prismatic joint is always in compression while enabling spherical coordinate positioning with a long reach, high force, low mass design. While having typically an order magnitude higher strength to weight ratio for a given reach compared to standard industrial robot arms, the arm is slower and was not designed for high precision. These characteristics may be applicable for mounting on mobile robots or flying vehicles. This paper introduces the design and testing of several prototypes.


title: Dashbell: A Low-cost Smart Doorbell System for Home Use

abstract: Author(s): Quadros, Bradley; Kadam, Ronit; Saxena, Kartik; Shen, Wen; Kobsa, Alfred | Abstract: Smart doorbells allow home owners to receive alerts when a visitor is at the door, see who the guest is, and communicate with the visitor from a smart device. They greatly improve people's life quality and contribute to the evolution of smart homes. However, the commercial smart doorbells are quite expensive, usually cost more than 190 US dollars, which is a substantial impediment on the pervasiveness of smart doorbells. To solve this problem, we introduce the Dashbell-a budget smart doorbell system for home use. It connects a WiFi-enabled device, the Amazon Dash Button, to a network and enables the home owner to answer the bell triggered by the dash button using a smartphone. The Dashbell system also enables fast fault detection and diagnosis due to its distributed framework.


title: A decade of progress in tissue engineering

abstract: Tremendous progress has been achieved in the field of tissue engineering in the past decade. Several major challenges laid down 10 years ago, have been studied, including renewable cell sources, biomaterials with tunable properties, mitigation of host responses, and vascularization. Here we review advancements in these areas and envision directions of further development.


title: Text summarization techniques: SVM versus neural networks

abstract: Automated text summarization is important to for humans to better manage the massive information explosion. Several machine learning approaches could be successfully used to handle the problem. This paper reports the results of our study to compare the performance between neural networks and support vector machines for text summarization. Both models have the ability to discover non-linear data and are effective model when dealing with large datasets.


title: Self-ensembling for domain adaptation

abstract: This paper explores the use of self-ensembling with random image augmentation -- a technique that has achieved impressive results in the area of semi-supervised learning -- for visual domain adaptation problems. We modify the approach of Laine et al. to improve stability and ease of use. Our approach demonstrates state of the art results when performing adaptation between the following pairs of datasets: MNIST and USPS, CIFAR-10 and STL, SVHN and MNIST, Syn-Digits to SVHN and Syn-Signs to GTSRB. We also explore the use of richer data augmentation to solve the challenging MNIST to SVHN adaptation path.


title: Accuracy of dermoscopic criteria for the diagnosis of psoriasis, dermatitis, lichen planus and pityriasis rosea

abstract: Summary ::: Background Dermoscopy is useful in evaluating skin tumours, but its applicability extends also to the field of inflammatory skin disorders. Plaque psoriasis (PP), dermatitis, lichen planus (LP) and pityriasis rosea (PR) are common inflammatory skin diseases, but little is currently known about their dermoscopic features. ::: ::: Objectives To determine and compare the dermoscopic patterns associated with PP, dermatitis, LP and PR and to assess the validity of certain dermoscopic criteria in the diagnosis of PP. ::: ::: Methods Patients with PP, dermatitis, LP and PR were prospectively enrolled. The single most recently developed lesion was examined dermoscopically and histopathologically. Variables included vascular morphology, vascular arrangement, background colour, scale colour, scale distribution and presence of Wickham striae. Univariate and adjusted odds ratios were calculated. Discriminant functions were used to plot receiver-operator characteristic curves. ::: ::: Results Eighty-three patients with PP and 86 patients with either dermatitis, LP or PR were included in the study. Dotted vessels in a regular arrangement over a light red background and white scales were highly predictive for the diagnosis of PP, whereas dermatitis more commonly showed yellow scales and dotted vessels in a patchy arrangement. PR was characterized by yellowish background, dotted vessels and peripheral scales; whitish lines (Wickham striae) were seen exclusively in LP. ::: ::: Conclusions PP, LP, PR and dermatitis show specific dermoscopic patterns that may aid their clinical diagnosis. Certain combinations of dermoscopic features can reliably predict the diagnosis of PP.


title: Compact and printed, coupled-fed, 2.4 GHz loop antenna

abstract: A novel, coupled-fed loop antenna with a small form factor for wireless local area network (WLAN) operation in the 2.4 GHz band is presented. The antenna has a very compact structure with the dimensions 29.75 mm × 5 mm and consists of a half-wavelength loop and a coupling feed, all printed in a coplanar configuration on a 0.4-mm thick FR4 substrate. The antenna was easily fed by using a 50-Ω mini-coaxial cable, allowing it to have much flexibility in the placement in a defined location. The results show that good input matching over the 2400–2484 MHz band can be achieved by tuning the coupling-pad size and the loop width. Good omnidirectional radiation patterns were also observed. Details of a design prototype are described and discussed.


title: A 100–300-GHz Free-Space Scalar Network Analyzer Using Compact Tx and Rx Modules

abstract: This paper presents a 100–300-GHz quasi-optical network analyzer using compact transmitter and receiver modules. The transmitter includes a wideband double bow-tie slot antenna and employs a Schottky diode as a frequency harmonic multiplier. The receiver includes a similar antenna, a Schottky diode used as a subharmonic mixer, and an LO/IF diplexer. The 100–300-GHz RF signals are the 5th–11th harmonics generated by the frequency multiplier when an 18–27-GHz LO signal is applied. The measured transmitter conversion gain with $P_{\textrm {in}} = 18$ dBm is from −35 to −59 dB for the 5th–11th harmonic, respectively, and results in a transmitter EIRP from +3 to −20 dBm up to 300 GHz. The measured mixer conversion gain is from −30 to −47 dB at the 5th–11th harmonic, respectively. The system has a dynamic range > 60 dB at 200 GHz in a 100-Hz bandwidth for a transmit and receive system based on 12-mm lenses and spaced 60 cm from each other. Frequency-selective surfaces at 150 and 200 GHz are tested by the proposed design and their measured results agree with simulations. Application areas are low-cost scalar network analyzers for wideband quasi-optical 100 GHz–1 THz measurements.


title: Printable elastic conductors with a high conductivity for electronic textile applications

abstract: Printable electronics is highly desirable for high throughput device manufacture. Here, Matsuhisa et al. report an electric ink, made of a self-assembled network of sliver flakes on the surface of a fluorine rubber matrix, which exhibits high conductivity and mechanical durability to achieve this goal.


title: Active deception model for securing cloud infrastructure

abstract: The proliferation of cloud computing over the past several years has led to a variety of new use cases and enabling technologies for enterprise and consumer applications. Increased reliance on cloud-based platforms has also necessitated an increased emphasis on securing the services and data hosted within those platforms. From a security standpoint, an advantage of cloud platforms over traditional production networks is that they have a dynamic, mutable structure that can change as a result of a variety of factors, so reconnaissance on the part of an attacker is far less predictable. In this work, we propose a novel technique that leverages the amorphous nature of cloud architectures to deceive and redirect potential intruders with decoy assets implanted among production hosts. In this way, attackers encounter and probe decoys that lead them to reveal their motives and cause them to be less likely to compromise their intended target, particularly once they have revealed their tactics against decoy assets. We show that our technique, after having been exposed to live traffic for approximately one month, detected 1,255 highly malicious hosts and was able to divert 97.5% of malicious traffic from these hosts. This traffic would have otherwise reached production hosts and potentially led to compromise. Index terms Intrusion Deception, Cloud Computing, Information Security, Network Decoys, Honeynets


title: Real-Time Progressive 3D Semantic Segmentation for Indoor Scenes

abstract: The widespread adoption of autonomous systems such as drones and assistant robots has created a need for real-time high-quality semantic scene segmentation. In this paper, we propose an efficient yet robust technique for on-the-fly dense reconstruction and semantic segmentation of 3D indoor scenes. To guarantee (near) real-time performance, our method is built atop an efficient super-voxel clustering method and a conditional random field with higher-order constraints from structural and object cues, enabling progressive dense semantic segmentation without any precomputation. We extensively evaluate our method on different indoor scenes including kitchens, offices, and bedrooms in the SceneNN and ScanNet datasets and show that our technique consistently produces state-of-the-art segmentation results in both qualitative and quantitative experiments.


title: Autonomous Surface Vehicle Docking Manoeuvre with Visual Information

abstract: This work presents a hybrid coordinated manoeuvre for docking an autonomous surface vehicle with an autonomous underwater vehicle. The control manoeuvre uses visual information to estimate the AUV relative position and attitude in relation to the ASV and steers the ASV in order to dock with the AUV. The AUV is assumed to be at surface with only a small fraction of its volume visible. The system implemented in the autonomous surface vehicle ROAZ, developed by LSA-ISEP to perform missions in river environment, test autonomous AUV docking capabilities and multiple AUV/ASV coordinated missions is presented. Information from a low cost embedded robotics vision system (LSAVision), along with inertial navigation sensors is fused in an extended Kalman filter and used to determine AUV relative position and orientation to the surface vehicle The real time vision processing system is described and results are presented in operational scenario.


title: Developmental Care Does Not Alter Sleep and Development of Premature Infants

abstract: Objective. The Neonatal Individualized Developmental Care Program (NIDCAP) for very low birth weight (VLBW) preterm infants has been suggested by Als et al to improve several medical outcome variables such as time on ventilator, time to nipple feed, the duration of hospital stay, better behavioral performance on Assessment of Preterm Infants9 Behavior (APIB), and improved neurodevelopmental outcomes. We have tested the hypothesis of whether the infants who had received NIDCAP would show advanced sleep-wake pattern, behavioral, and neurodevelopmental outcome. Methods. Thirty-five VLBW infants were randomly assigned to receive NIDCAP or routine infant care. The goals for NIDCAP intervention were to enhance comfort and stability and to reduce stress and agitation for the preterm infants by: a) altering the environment by decreasing excess light and noise in the neonatal intensive care unit (NICU) and by using covers over the incubators and cribs; b) use of positioning aids such as boundary supports, nests, and buntings to promote a balance of flexion and extension postures; c) modification of direct hands-on caregiving to maximize preparation of infants for, tolerance of, and facilitation of recovery from interventions; d) promotion of self-regulatory behaviors such as holding on, grasping, and sucking; e) attention to the readiness for and the ability to take oral feedings; and f) involving parents in the care of their infants as much as possible. The infants9 sleep was recorded at 36 weeks postconceptional age (PCA) and at 3 months corrected age (CA) using the Motility Monitoring System (MMS), an automated, nonintrusive procedure for determining sleep state from movement and respiration patterns. Behavioral and developmental outcome was assessed by the Neurobehavioral Assessment of the Preterm Infant (NAPI) at 36 weeks PCA, the APIB at 42 weeks PCA, and by the Bayley Scales of Infant Development (BSID) at 4, 12, and 24 months CA. Results. Sleep developmental measures at 3 months CA showed a clear developmental change compared with 36 weeks PCA. These include: increased amount of quiet sleep, reduced active sleep and indeterminate sleep, decreased arousal, and transitions during sleep. Longest sleep period at night showed a clear developmental effect (increased) when comparing nighttime sleep pattern of infants at 3 months with those at 36 weeks of age. Day-night rhythm of sleep-wake increased significantly from 36 weeks PCA to 3 months CA. However, neither of these sleep developmental changes showed any significant effects of NIDCAP intervention. Although all APIB measures showed better organized behavior in NIDCAP patients, neither NAPI nor Bayley showed any developmental advantages for the intervention group. The neurodevelopmental outcome measured by the Bayley at 4, 12, and 24 months CA showed 64% of the NIDCAP intervention group at the lowest possible score compared with 33% of the control group. These findings could not be explained by the occurrence of intraventricular hemorrhage or the socioeconomic status of the parents, which showed no significant group effect. Conclusion. The results of this study, including measures of sleep maturation and neurodevelopmental outcome up to 2 years of age did not demonstrate that the NIDCAP intervention results in increased maturity or development. Buehler et al ( Pediatrics. 1995;96:923–932) have reported that premature infants (N = 12; mean gestational age 32 weeks, mean birth weight 1700 g) who received developmental care compared with a similar group of infants who received routine care showed better organized behavioral performance on an APIB assessment at 42 weeks PCA. None of the medical outcome measures were significantly different in this study. Although our APIB results are in agreement, the results of the NAPI, the Bayley and sleep measures do not show an increase in neurodevelopmental maturation. In the earlier report by Als et al ( Journal of the American Medical Association. 1994;272:853–858), both medical and neurofunctional improvements were found in very low birth weight premature infants (mean gestational age 27 weeks, mean birth weight ∼870 g) in which 20 infants who received NIDCAP were compared with 18 infants who received routine care. At 42 weeks PCA the APIB was better in the intervention group as was the Bayley at 6 months CA. Later neurodevelopmental assessments in this study population have not been reported. Furthermore, as was indicated in the editorial by Merenstein in the same issue of the Journal of the American Medical Association , a significant problem with the study was that the number of intraventricular hemorrhages was higher in the control group (10 of 18 vs 1 of 20) and the study was conducted before the widespread use of surfactant and prenatal steroids. The study was performed in a single nursery with nurses who volunteered for developmental intervention and cared for the experimental group. No assessment was performed on differences in nursing, intervention, lighting, or sound between the two groups. Apnea, bradycardia, and desaturation data were not reported also. NIDCAP has been shown to reduce stress and agitation in the infants in our study (Heller C, et al. Journal of Perinatology. 1997;17:107–112); however, there was no difference in the incidence of apnea or bradycardia. Additional studies are needed to determine which specific interventions facilitate recovery in the high-risk preterm infant when interventions are efficacious, what may be adverse or ineffective, and what mechanisms are involved. Distinctions should be made between medical improvement, neurobehavioral responses (APIB), and neurodevelopmental maturation. Not only the duration of NICU hospitalization, but indeed, long-term outcomes must be carefully evaluated. We recommend that clinicians should be aware that preterm infants who have received NIDCAP during their hospitalization do not appear to be more mature at the time of discharge home.


title: Comparing Data Mining Techniques in HIV Testing Prediction

abstract: Introduction: The present work compared the ::: prediction power of the different data mining techniques used to develop the ::: HIV testing prediction model. Four popular data mining algorithms (Decision ::: tree, Naive Bayes, Neural network, logistic regression) were used to build the ::: model that predicts whether an individual was being tested for HIV among adults ::: in Ethiopia using EDHS 2011. The final experimentation results indicated that ::: the decision tree (random tree algorithm) performed the best with accuracy of ::: 96%, the decision tree induction method (J48) came out to be the second best ::: with a classification accuracy of 79%, followed by neural network (78%). ::: Logistic regression has also achieved the least classification accuracy of 74%. ::: Objectives: The objective of this study is to compare the prediction power of ::: the different data mining techniques used to develop the HIV testing ::: prediction model. Methods: Cross-Industry Standard Process for Data Mining ::: (CRISP-DM) was used to predict the model for HIV testing and explore ::: association rules between HIV testing and the selected attributes. Data ::: preprocessing was performed and missing values for the categorical variable ::: were replaced by the modal value of the variable. Different data mining ::: techniques were used to build the predictive model. Results: The target dataset ::: contained 30,625 study participants. Out of which 16,515 (54%) participants ::: were women while the rest 14,110 (46%) were men. The age of the participants in ::: the dataset ranged from 15 to 59 years old with modal age of 15 - 19 years old. ::: Among the study participants, 17,719 (58%) have never been tested for HIV while ::: the rest 12,906 (42%) had been tested. Residence, educational level, wealth ::: index, HIV related stigma, knowledge related to HIV, region, age group, risky ::: sexual behaviour attributes, knowledge about where to test for HIV and ::: knowledge on family planning through mass media were found to be predictors for ::: HIV testing. Conclusion and Recommendation: The results obtained from this ::: research reveal that data mining is crucial in extracting relevant information ::: for the effective utilization of HIV testing services which has clinical, ::: community and public health importance at all levels. It is vital to apply ::: different data mining techniques for the same settings and compare the model ::: performances (based on accuracy, sensitivity, and specificity) with each other. ::: Furthermore, this study would also invite interested researchers to explore ::: more on the application of data mining techniques in healthcare industry or ::: else in related and similar settings for the future.


title: Robust Manhattan Frame estimation from a single RGB-D image

abstract: This paper proposes a new framework for estimating the Manhattan Frame (MF) of an indoor scene from a single RGB-D image. Our technique formulates this problem as the estimation of a rotation matrix that best aligns the normals of the captured scene to a canonical world axes. By introducing sparsity constraints, our method can simultaneously estimate the scene MF, the surfaces in the scene that are best aligned to one of three coordinate axes, and the outlier surfaces that do not align with any of the axes. To test our approach, we contribute a new set of annotations to determine ground truth MFs in each image of the popular NYUv2 dataset. We use this new benchmark to experimentally demonstrate that our method is more accurate, faster, more reliable and more robust than the methods used in the literature. We further motivate our technique by showing how it can be used to address the RGB-D SLAM problem in indoor scenes by incorporating it into and improving the performance of a popular RGB-D SLAM method.


title: Real-Time Projection-Based Augmented Reality System for Dynamic Objects in the Performing Arts

abstract: This paper describes the case study of applying projection-based augmented reality, especially for dynamic objects in live performing shows, such as plays, dancing, or musicals. Our study aims to project imagery correctly inside the silhouettes of flexible objects, in other words, live actors or the surface of actor’s costumes; the silhouette transforms its own shape frequently. To realize this work, we implemented a special projection system based on the real-time masking technique, that is to say real-time projection-based augmented reality system for dynamic objects in performing arts. We installed the sets on a stage for live performance, and rehearsed particular scenes of a musical. In live performance, using projection-based augmented reality technology enhances technical and theatrical aspects which were not possible with existing video projection techniques. The projected images on the surfaces of actor’s costume could not only express the particular scene of a performance more effectively, but also lead the audience to an extraordinary visual experience.


title: Boolean Programs: A Model and Process for Software Analysis

abstract: A fundamental issue in model checking of software is the choice of a model for software. We present a model called boolean programs that is expressive enough to represent features in common programming languages and is amenable to model checking. We present a model checking algorithm for boolean programs using context-free-language reachability. The model checking algorithm allows procedure calls with unbounded recursion, exploits locality of variable scopes, and gives short error traces. Furthermore, we give a process for incrementally re ning an initial skeletal boolean program B (representing a source program P ) with respect to a particular reachability query in P . The presence of infeasible paths in P may lead to the model checker reporting false positive errors in B. We show how to re ne B by introducing boolean variables to rule out the infeasible paths. The process uses ideas from model checking, symbolic execution, and program slicing.


title: Run-Time Risk Mitigation in Automated Vehicles: A Model for Studying Preparatory Steps

abstract: We assume that autonomous or highly automated driving (AD) will be accompanied by tough assurance obligations exceeding the requirements of even recent revisions of ISO 26262 or SOTIF. Hence, automotive control and safety engineers have to (i) comprehensively analyze the driving process and its control loop, (ii) identify relevant hazards stemming from this loop, (iii) establish feasible automated measures for the effective mitigation of these hazards or the alleviation of their consequences. ::: By studying an example, this article investigates some achievements in the modeling for the steps (i), (ii), and (iii), amenable to formal verification of desired properties derived from potential assurance obligations such as the global existence of an effective mitigation strategy. In addition, the proposed approach is meant for step-wise refinement towards the automated synthesis of AD safety controllers implementing such properties.


title: Image Style Transfer Using Convolutional Neural Networks

abstract: Rendering the semantic content of an image in different styles is a difficult image processing task. Arguably, a major limiting factor for previous approaches has been the lack of image representations that explicitly represent semantic information and, thus, allow to separate image content from style. Here we use image representations derived from Convolutional Neural Networks optimised for object recognition, which make high level image information explicit. We introduce A Neural Algorithm of Artistic Style that can separate and recombine the image content and style of natural images. The algorithm allows us to produce new images of high perceptual quality that combine the content of an arbitrary photograph with the appearance of numerous wellknown artworks. Our results provide new insights into the deep image representations learned by Convolutional Neural Networks and demonstrate their potential for high level image synthesis and manipulation.


title: An approach for offensive text detection and prevention in Social Networks

abstract: Social Network has become a place where people from every corner of the world has established a virtual civilization. In this virtual community, people used to share their views, express their feelings, photos, videos, blogs, etc. Social Networking Sites like Facebook, Twitters, etc. has given a platform to share innumerable contents with just a click of a button. However, there is no restriction applied by them for the uploaded content. These uploaded content may contains abusive words, explicit images which may be unsuitable for social platforms. As such there is no defined mechanism for restricting offensive contents from publishing on social sites. To solve this problem we have used our proposed approach. In our approach we are developing a social network prototype for implementing our approach for automatic filtering of offensive content in social network. Many popular social networking sites today don't have proper mechanism for restricting offensive contents. They use reporting methods in which user report if the content is abuse. This requires substantial human efforts and time. In this paper, we applied pattern matching algorithm for offensive keyword detection from social networking comments and prevent it from publishing on social platform. Apart from conventional method of reporting abusive contents by users our approach does not requires any human intervention and thus restrict offensive words by detecting and preventing it automatically.


title: Towards a science of integrated AI and Robotics

abstract: The early promise of the impact of machine intelligence did not involve the partitioning of the nascent field of Artificial Intelligence. The founders of AI envisioned the notion of embedded intell ...


title: High-Efficiency Dual-Band On-Chip Rectenna for 35- and 94-GHz Wireless Power Transmission in 0.13-$\mu{\hbox {m}}$ CMOS Technology

abstract: This paper proposes a high-efficiency dual-band on-chip rectifying antenna (rectenna) at 35 and 94 GHz for wireless power transmission. The rectenna is designed in slotline (SL) and finite-width ground coplanar waveguide (FGCPW) transmission lines in a CMOS 0.13-μm process. The rectenna comprises a high gain linear tapered slot antenna (LTSA), an FGCPW to SL transition, a bandpass filter, and a full-wave rectifier. The LTSA achieves a VSWR=2 fractional bandwidth of 82% and 41%, and a gain of 7.4 and 6.5 dBi at the frequencies of 35 and 94 GHz. The measured power conversion efficiencies are 53% and 37% in free space at 35 and 94 GHz, while the incident radiation power density is 30 mW/cm2 . The fabricated rectenna occupies a compact size of 2.9 mm2.


title: Defining and Designing Mixed Research Synthesis Studies.

abstract: Mixed research synthesis is the latest addition to the repertoires of mixed methods research and systematic review. Mixed research synthesis requires that the problems generated by the methodological diversity within and between qualitative and quantitative studies be resolved. Three basic research designs accommodate this diversity, including the segregated, integrated, and contingent designs. Much work remains to be done before mixed research synthesis can secure its place in the repertoires of mixed methods research and systematic review, but the effort is well worth it as it has the potential to enhance both the significance and utility for practice of the many qualitative and quantitative studies constituting shared domains of research.


title: The global k-means clustering algorithm

abstract: We present the global k-means algorithm which is an incremental approach to clustering that dynamically adds one cluster center at a time through a deterministic global search procedure consisting of N (with N being the size of the data set) executions of the k-means algorithm from suitable initial positions. We also propose modifications of the method to reduce the computational load without significantly affecting solution quality. The proposed clustering methods are tested on well-known data sets and they compare favorably to the k-means algorithm with random restarts.


Autonomous Multilateral Surgical Tumor Resection with Interchangeable Instrument Mounts and Fluid Injection Device


title: Product aspect ranking using sentiment analysis and TOPSIS

abstract: The explosive growth of customer reviews on e-commerce websites has inspired many researchers to explore the problem of identifying the product aspects that have been mentioned in online reviews. Most of the conducted research studies extract the product aspects based on three main criteria: 1) extracting the aspects that have been commented repeatedly in online reviews, 2) determining of important aspects as those that have been described positively and negatively by many customers in their reviews, and 3) the association between the domain product aspect (like ‘camera’) and the other aspects contained in a specific product review. However, a lacuna remains as how to efficiently investigate online reviews to identify the most important product aspects by considering all the three criteria jointly. In response, this paper proposes a novel product aspect ranking framework using sentiment analysis and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution). The proposed work is decomposed into two stages: aspect extraction and aspect ranking. In aspect extraction stage, sentiment analysis is used to identify the product aspects from customer reviews in an unsupervised manner based on the three criteria of extraction. In the second stage, the extracted product aspects from the previous criteria have been involved simultaneously in TOPSIS to produce a ranked list of the most representative product aspects. The empirical evaluation of the proposed work using online reviews of four products shows its effectiveness in finding representative aspects.


title: Character-level Convolutional Network for Text Classification Applied to Chinese Corpus

abstract: This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem. We constructed a large-scale Chinese language dataset, and the result shows that character-level convolutional neural network works better on Chinese corpus than its corresponding pinyin format dataset. This is the first time that character-level convolutional neural network applied to text classification problem.


title: Automatic language identification using ergodic-HMM

abstract: Recently, we established the equivalence of an ergodic HMM (EHMM) to a parallel sub-word recognition (PSWR) framework for language identification (LID). The states of EHMM correspond to acoustic units of a language and its state-transitions represent the bigram language model of unit sequences. We consider two alternatives to represent the state-observation densities of EHMM, namely, the Gaussian mixture model (GMM) and hidden Markov model (HMM). We present a segmental K-means algorithm for the training of both these types of EHMM (EHMM of GMM and EHMM of HMM) and compare their performance on a 6 language LID task in the OGI-TS database. EHMM of GMM has a performance comparable to PSWR and superior to EHMM of HMM; we provide reasons for the performance difference between EHMM(G) and EHMM(H), and identify ways of enhancing the performance of EHMM(H) which is a novel and powerful architecture, ideal for spoken language modeling.


title: Drug development in Alzheimer’s disease: the path to 2025

abstract: The global impact of Alzheimer's disease (AD) continues to increase, and focused efforts are needed to address this immense public health challenge. National leaders have set a goal to prevent or effectively treat AD by 2025. In this paper, we discuss the path to 2025, and what is feasible in this time frame given the realities and challenges of AD drug development, with a focus on disease-modifying therapies (DMTs). Under the current conditions, only drugs currently in late Phase 1 or later will have a chance of being approved by 2025. If pipeline attrition rates remain high, only a few compounds at best will meet this time frame. There is an opportunity to reduce the time and risk of AD drug development through an improvement in trial design; better trial infrastructure; disease registries of well-characterized participant cohorts to help with more rapid enrollment of appropriate study populations; validated biomarkers to better detect disease, determine risk and monitor disease progression as well as predict disease response; more sensitive clinical assessment tools; and faster regulatory review. To implement change requires efforts to build awareness, educate and foster engagement; increase funding for both basic and clinical research; reduce fragmented environments and systems; increase learning from successes and failures; promote data standardization and increase wider data sharing; understand AD at the basic biology level; and rapidly translate new knowledge into clinical development. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets. The opportunity for more than a few new therapies by 2025 is small. Accelerating research and clinical development efforts and bringing DMTs to market sooner would have a significant impact on the future societal burden of AD. As these steps are put in place and plans come to fruition, e.g., approval of a DMT, it can be predicted that momentum will build, the process will be self-sustaining, and the path to 2025, and beyond, becomes clearer.


title: Online AUC Maximization

abstract: Most studies of online learning measure the performance of a learner by classification accuracy, which is inappropriate for applications where the data are unevenly distributed among different classes. We address this limitation by developing online learning algorithm for maximizing Area Under the ROC curve (AUC), a metric that is widely used for measuring the classification performance for imbalanced data distributions. The key challenge of online AUC maximization is that it needs to optimize the pairwise loss between two instances from different classes. This is in contrast to the classical setup of online learning where the overall loss is a sum of losses over individual training examples. We address this challenge by exploiting the reservoir sampling technique, and present two algorithms for online AUC maximization with theoretic performance guarantee. Extensive experimental studies confirm the effectiveness and the efficiency of the proposed algorithms for maximizing AUC.


title: Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition

abstract: This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and sparsity with respect to two latent variables, model parameters and grouping information, for joint optimization. To handle this non-convex optimization, we decompose it into two sub-tasks, multi-task learning and task relatedness discovery. First, we convert this non-convex objective function into the convex formulation by fixing the latent grouping information. This new objective function focuses on multi-task learning by strengthening the shared-action relationship and action-specific feature learning. Second, we leverage the learned model parameters for the task relatedness measure and clustering. In this way, HC-MTL can attain both optimal action models and group discovery by alternating iteratively. The proposed method is validated on three kinds of challenging datasets, including six realistic action datasets (Hollywood2, YouTube, UCF Sports, UCF50, HMDB51 $\&$ UCF101), two constrained datasets (KTH $\&$ TJU), and two multi-view datasets (MV-TJU $\&$ IXMAS). The extensive experimental results show that: 1) HC-MTL can produce competing performances to the state of the arts for action recognition and grouping; 2) HC-MTL can overcome the difficulty in heuristic action grouping simply based on human knowledge; 3) HC-MTL can avoid the possible inconsistency between the subjective action grouping depending on human knowledge and objective action grouping based on the feature subspace distributions of multiple actions. Comparison with the popular clustered multi-task learning further reveals that the discovered latent relatedness by HC-MTL aids inducing the group-wise multi-task learning and boosts the performance. To the best of our knowledge, ours is the first work that breaks the assumption that all actions are either independent for individual learning or correlated for joint modeling and proposes HC-MTL for automated, joint action grouping and modeling.


title: Part level transfer regularization for enhancing exemplar SVMs

abstract: EE-SVM, a part based transfer regularization method that boosts E-SVM, is introduced.EE-SVM is further improved by transferring the statistics between the parts.All the proposed objectives result in convex formulations.Experimentally shown that EE-SVM has better performance for pose matched retrieval.All objectives are conveniently optimized by mapping to a classical SVM formulation. Exemplar SVMs (E-SVMs, Malisiewicz et al., ICCV 2011), where an SVM is trained with only a single positive sample, have found applications in the areas of object detection and content-based image retrieval (CBIR), amongst others.In this paper we introduce a method of part based transfer regularization that boosts the performance of E-SVMs, with a negligible additional cost. This enhanced E-SVM (EE-SVM) improves the generalization ability of E-SVMs by softly forcing it to be constructed from existing classifier parts cropped from previously learned classifiers. In CBIR applications, where the aim is to retrieve instances of the same object class in a similar pose, the EE-SVM is able to tolerate increased levels of intra-class variation, including occlusions and truncations, over E-SVM, and thereby increases precision and recall.In addition to transferring parts, we introduce a method for transferring the statistics between the parts and also show that there is an equivalence between transfer regularization and feature augmentation for this problem and others, with the consequence that the new objective function can be optimized using standard libraries.EE-SVM is evaluated both quantitatively and qualitatively on the PASCAL VOC 2007 and ImageNet datasets for pose specific object retrieval. It achieves a significant performance improvement over E-SVMs, with greater suppression of negative detections and increased recall, whilst maintaining the same ease of training and testing.


title: Provable Subspace Clustering: When LRR Meets SSC

abstract: Sparse Subspace Clustering (SSC) and Low-Rank Representation (LRR) are both considered as the state-of-the-art methods for subspace clustering. The two methods are fundamentally similar in that both are convex optimizations exploiting the intuition of "Self-Expressiveness". The main difference is that SSC minimizes the vector l1 norm of the representation matrix to induce sparsity while LRR minimizes nuclear norm (aka trace norm) to promote a low-rank structure. Because the representation matrix is often simultaneously sparse and low-rank, we propose a new algorithm, termed Low-Rank Sparse Subspace Clustering (LRSSC), by combining SSC and LRR, and develops theoretical guarantees of when the algorithm succeeds. The results reveal interesting insights into the strength and weakness of SSC and LRR and demonstrate how LRSSC can take the advantages of both methods in preserving the "Self-Expressiveness Property" and "Graph Connectivity" at the same time.


title: Ultra-miniature high-Q filters and duplexers using FBAR technology

abstract: An ultra-miniature PCS duplexer uses thin-film bulk wave acoustic resonator (FBAR) technology. FBAR resonators are made using aluminum nitride for the piezoelectric material and silicon as the substrate. It has better than -52 dB rejection for the receive (Rx) filter in the transmit (Tx) band and pass-band insertion losses are on the order of 2 dB (Tx) and 3 dB (Rx). Performance is comparable to that of much larger ceramic duplexers.


Liveness Detection for Embedded Face Recognition System


title: Sensitivity Analysis for Direction of Arrival Estimation using a Root-MUSIC Algorithm

abstract: An improved disk drive includes an improved operating system, an improved eject system, an improved head retract system and an improved motor loading system. In a preferred embodiment of the operating system it includes a motor, an eject crank, a head crank and an actuator. The output of the motor can be selectively controlled by the actuator to power either the eject crank or the head crank. Powering the head crank, causes the disk drive heads to move, and powering the eject crank causes a disk cartridge to be ejected from the disk drive. The motor loading system may have a disk drive motor having a threaded exterior and a member extending from the exterior to interface with the eject system. Additionally, this motor loading system may include an aperture in the chassis of the disk drive that has a threaded ring running around the circumference of the aperture and a spring extending from the threaded ring. The disk drive motor can be inserted into the aperture in the chassis with the member depressing the spring. The disk drive motor can then be rotated to mate the threads of the disk drive motor with the threaded ring. When rotated, the slotted member releases the spring. The spring prevents rotation of the disk drive motor past a predetermined point and thereby prevents the motor from becoming dislodged from the chassis. When inserted into the disk drive, the motor can be moved between a loaded and an unloaded position.


title: Sample size selection in optimization methods for machine learning

abstract: This paper presents a methodology for using varying sample sizes in batch-type optimization methods for large-scale machine learning problems. The first part of the paper deals with the delicate issue of dynamic sample selection in the evaluation of the function and gradient. We propose a criterion for increasing the sample size based on variance estimates obtained during the computation of a batch gradient. We establish an $${O(1/\epsilon)}$$ complexity bound on the total cost of a gradient method. The second part of the paper describes a practical Newton method that uses a smaller sample to compute Hessian vector-products than to evaluate the function and the gradient, and that also employs a dynamic sampling technique. The focus of the paper shifts in the third part of the paper to L 1-regularized problems designed to produce sparse solutions. We propose a Newton-like method that consists of two phases: a (minimalistic) gradient projection phase that identifies zero variables, and subspace phase that applies a subsampled Hessian Newton iteration in the free variables. Numerical tests on speech recognition problems illustrate the performance of the algorithms.


title: Web-based framework for spatiotemporal screen real estate management of interactive public displays

abstract: In this paper we present a web-based framework for spatiotemporal screen real estate management of interactive public displays. The framework facilitates dynamic partitioning of the screen real estate into virtual screens assigned for multiple concurrent web applications. The framework is utilized in the implementation of so-called UBI-hotspot, which provides various information services via different interaction modalities including mobile. The framework facilitates seamless integration of third party web applications residing anywhere in the public Internet into the UBI-hotspot, thus catering for a scalable and open architecture. We report the deployment of a network of indoor and outdoor UBI-hotspots at downtown Oulu, Finland. The quantitative data on the usage of the UBI-hotspots implicitly speaks in favor of the practical applicability of the framework.


title: A Lyapunov Analysis of Momentum Methods in Optimization

abstract: Momentum methods play a significant role in optimization. Examples include Nesterov's accelerated gradient method and the conditional gradient algorithm. Several momentum methods are provably optimal under standard oracle models, and all use a technique called estimate sequences to analyze their convergence properties. The technique of estimate sequences has long been considered difficult to understand, leading many researchers to generate alternative, "more intuitive" methods and analyses. We show there is an equivalence between the technique of estimate sequences and a family of Lyapunov functions in both continuous and discrete time. This connection allows us to develop a simple and unified analysis of many existing momentum algorithms, introduce several new algorithms, and strengthen the connection between algorithms and continuous-time dynamical systems.


title: Fault-Tolerant In-Wheel Motor Topologies for High-Performance Electric Vehicles

abstract: The use of in-wheel motors, often referred to as hub motors, as a source of propulsion for pure electric or hybrid electric vehicles has recently received a lot of attention. Since the motor is housed in the limited space within the wheel rim, it must have high torque density and efficiency and survive the rigors of being in-wheel in terms of environmental cycling, ingress, shock and vibration, and driver abuse. Finally, to ensure that adequate levels of functional safety are met, it is essential that failures do not lead to the loss of control of the vehicle. This paper presents studies of a fault-tolerant concept for the design of in-wheel motors. The study focused on achieving a high torque density and the ability to sustain an adequate level of performance following a failure. A series of failures is simulated and then compared with experimental tests on a demonstrator motor.


title: Alternative Modernity: The Technical Turn in Philosophy and Social Theory

abstract: In this new collection of essays, Andrew Feenberg argues that conflicts over the design and organization of the technical systems that structure our society shape deep choices for the future. A pioneer in the philosophy of technology, Feenberg demonstrates the continuing vitality of the critical theory of the Frankfurt School. He calls into question the anti-technological stance commonly associated with its theoretical legacy and argues that technology contains potentialities that could be developed as the basis for an alternative form of modern society. Feenberg's critical reflections on the ideas of Jurgen Habermas, Herbert Marcuse, Jean-Francois Lyotard, and Kitaro Nishida shed new light on the philosophical study of technology and modernity. He contests the prevalent conception of technology as an unstoppable force responsive only to its own internal dynamic and politicizes the discussion of its social and cultural construction. This argument is substantiated in a series of compelling and well-grounded case studies. Through his exploration of science fiction and film, AIDS research, the French experience with the 'information superhighway', and the Japanese reception of Western values, he demonstrates how technology, when subjected to public pressure and debate, can incorporate ethical and aesthetic values.


title: Object proposal on RGB-D images via elastic edge boxes

abstract: As a fundamental preprocessing of various multimedia applications, object proposal aims to detect the candidate windows possibly containing arbitrary objects in images with two typical strategies, window scoring and grouping. In this paper, we first analyze the feasibility of improving object proposal performance by integrating window scoring and grouping strategies. Then, we propose a novel object proposal method for RGB-D images, named elastic edge boxes. The initial bounding boxes of candidate object regions are efficiently generated by edge boxes, and further adjusted by grouping the super-pixels within elastic range to obtain more accurate candidate windows. To validate the proposed method, we construct the largest RGB-D image data set NJU1800 for object proposal with balanced object number distribution. The experimental results show that our method can effectively and efficiently generate the candidate windows of object regions and it outperforms the state-of-the-art methods considering both accuracy and efficiency.


title: A case of cluster headaches preceded by back pain

abstract: This is the report of a patient with cluster headaches preceded by back pain. The patient’s back pain was provoked by arm and shoulder movement. Mild back pressure caused severe back pain and, as a result, he could not lie in a supine position. His back pain was usually mild, however he sometimes experienced severe stabbing and aching pain. We diagnosed the back pain as allodynia. We herein discuss the diagnosis of back pain in this case and its relationship with cluster headaches.


title: Concentration of organic sun-blocking agents in seawater of beaches and coral reefs of Okinawa Island, Japan

abstract: Abstract The concentration of UV filters (UVFs) and UV light stabilizers (UVLSs) were measured in seawater and river water collected from sites at four beaches, two reefs, and one river on Okinawa Island, Japan. UVFs and/or UVLSs of 8–10 types were detected in beaches samples and 6–9 types were detected in reef samples. The total UVF concentrations at the beach sites were highest either in July or August with a maximum of 1.4 μg L−1. The concentrations at the reef sites did not show peaks in summer and the maximum values were close to 10 ng L−1. The detected UVF profiles reflected the ingredients of sunscreens used in each region. The highest UVLS concentrations at the reefs were observed not only in summer but also in June and September. The UVLS concentrations at the reefs were similar to or even higher than that at the beaches or in the river.


title: End-to-end visual speech recognition with LSTMS

abstract: Traditional visual speech recognition systems consist of two stages, feature extraction and classification. Recently, several deep learning approaches have been presented which automatically extract features from the mouth images and aim to replace the feature extraction stage. However, research on joint learning of features and classification is very limited. In this work, we present an end-to-end visual speech recognition system based on Long-Short Memory (LSTM) networks. To the best of our knowledge, this is the first model which simultaneously learns to extract features directly from the pixels and perform classification and also achieves state-of-the-art performance in visual speech classification. The model consists of two streams which extract features directly from the mouth and difference images, respectively. The temporal dynamics in each stream are modelled by an LSTM and the fusion of the two streams takes place via a Bidirectional LSTM (BLSTM). An absolute improvement of 9.7% over the base line is reported on the OuluVS2 database, and 1.5% on the CUAVE database when compared with other methods which use a similar visual front-end.


title: Measuring agreement in method comparison studies

abstract: Agreement between two methods of clinical measurement can be quantified using the differences between observations made using the two methods on the same subjects. The 95% limits of agreement, estimated by mean difference +/- 1.96 standard deviation of the differences, provide an interval within which 95% of differences between measurements by the two methods are expected to lie. We describe how graphical methods can be used to investigate the assumptions of the method and we also give confidence intervals. We extend the basic approach to data where there is a relationship between difference and magnitude, both with a simple logarithmic transformation approach and a new, more general, regression approach. We discuss the importance of the repeatability of each method separately and compare an estimate of this to the limits of agreement. We extend the limits of agreement approach to data with repeated measurements, proposing new estimates for equal numbers of replicates by each method on each subject, for unequal numbers of replicates, and for replicated data collected in pairs, where the underlying value of the quantity being measured is changing. Finally, we describe a nonparametric approach to comparing methods.


title: Learning Patterns from Unix Process Execution Traces for Intrusion Detection

abstract: In this paper we describe our preliminary experiments to extend the work pioneered by Forrest (see Forrest et al. 1996) on learning the (normal abnormal) patterns of Unix processes. These patterns can be used to identify misuses of and intrusions in Unix systems. We formulated machine learning tasks on operating system call sequences of normal and abnormal (intrusion) executions of the Unix sendmail program. We show that our methods can accurately distinguish all abnormal executions of sendmail from the normal ones provided in a set of test traces. These preliminary results indicate that machine learning can play an important role by generalizing stored sequence information to perhaps provide broader intrusion detection services. The experiments also reveal some interesting and challenging problems for future research. much effort has been devoted to the problem of detecting intrusions as quickly as possible. There are two basic approaches to intrusion detection: ¯ Misuse Intrusion Detection: known patterns of (past


title: The Occlusion Spectrum for Volume Classification and Visualization

abstract: Despite the ever-growing improvements on graphics processing units and computational power, classifying 3D volume data remains a challenge.In this paper, we present a new method for classifying volume data based on the ambient occlusion of voxels. This information stems from the observation that most volumes of a certain type, e.g., CT, MRI or flow simulation, contain occlusion patterns that reveal the spatial structure of their materials or features. Furthermore, these patterns appear to emerge consistently for different data sets of the same type. We call this collection of patterns the occlusion spectrum of a dataset. We show that using this occlusion spectrum leads to better two-dimensional transfer functions that can help classify complex data sets in terms of the spatial relationships among features. In general, the ambient occlusion of a voxel can be interpreted as a weighted average of the intensities in a spherical neighborhood around the voxel. Different weighting schemes determine the ability to separate structures of interest in the occlusion spectrum. We present a general methodology for finding such a weighting. We show results of our approach in 3D imaging for different applications, including brain and breast tumor detection and the visualization of turbulent flow.


title: Derivation of Voltage Source Multilevel Converter Topologies

abstract: Multilevel converters have gained popularity in both medium voltage and low voltage applications. To find out the connections between various multilevel voltage-source converter topologies and to reveal how to obtain new topologies, this paper has presented four methods to derive multilevel converter topologies. Many existing topologies as well as new topologies can be derived with the methods presented in this paper. The fundamental characteristics of the multilevel converters which determine their usability such as dc-link neutral point voltage balancing and flying capacitor voltage control are also investigated in this paper with a mathematical model and an example. It is expected that more new topologies will be invented based on the work in this paper for emerging applications.