title: Robust Audio Event Recognition with 1-Max Pooling Convolutional Neural Networks

abstract: We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with multiple convolutional and pooling layers topped up with multiple fully connected layers, the proposed network consists of only three layers: convolutional, pooling, and softmax layer. Two further features distinguish it from the deep architectures that have been proposed for the task: varying-size convolutional filters at the convolutional layer and 1-max pooling scheme at the pooling layer. In intuition, the network tends to select the most discriminative features from the whole audio signals for recognition. Our proposed CNN not only shows state-of-the-art performance on the standard task of robust audio event recognition but also outperforms other deep architectures up to 4.5% in terms of recognition accuracy, which is equivalent to 76.3% relative error reduction.


title: Localization in wireless sensor networks

abstract: A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -- they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.


title: A Frequency-Division MIMO FMCW Radar System Based on Delta–Sigma Modulated Transmitters

abstract: In this paper, we present a hardware-efficient method, which allows to implement multiple-input multiple-output (MIMO) radars with a separation of the transmit (TX) signals in the frequency domain. The proposed architecture uses binary phase-shift keying (BPSK) modulators within the TX paths of a multi-channel frequency-modulated continuous-wave (FMCW) radar. These BPSK modulators are driven by 1-bit sequences, which are generated using ΔΣ-modulators to shift the quantization noise towards high frequencies. Thus, neither multi-bit digital-to-analog converters, nor vector modulators or phase shifters are required for the proposed approach. Since the FMCW principle relies on the evaluation of beat frequencies within a narrow frequency band, the shaped quantization noise is outside the frequencies of interest and can thus be filtered out. The chosen hardware setup is optimized for integration into monolithic microwave integrated circuits (MMICs), which is demonstrated by a prototype system based on 77-GHz chips. Several MMICs are combined to realize a radar frontend with six TX and eight receive channels resulting in 48 MIMO channels. A signal-processing approach is derived and a method to reduce ambiguities is presented. Measurements demonstrate a range resolution of 15 cm and a 3-dB beamwidth of 3 ::: ° ::: with position standard deviations better than 1 mm. Furthermore, a measurement example with a target placed outside the unambiguous range of the radar shows an 8-dB power reduction of the unwanted signal from the target at the ambiguous distance. This improvement is achieved by choosing nonidentical frequency spacings for the TX signals.


title: MILES: Multiple-Instance Learning via Embedded Instance Selection

abstract: Multiple-instance problems arise from the situations where training class labels are attached to sets of samples (named bags), instead of individual samples within each bag (called instances). Most previous multiple-instance learning (MIL) algorithms are developed based on the assumption that a bag is positive if and only if at least one of its instances is positive. Although the assumption works well in a drug activity prediction problem, it is rather restrictive for other applications, especially those in the computer vision area. We propose a learning method, MILES (multiple-instance learning via embedded instance selection), which converts the multiple-instance learning problem to a standard supervised learning problem that does not impose the assumption relating instance labels to bag labels. MILES maps each bag into a feature space defined by the instances in the training bags via an instance similarity measure. This feature mapping often provides a large number of redundant or irrelevant features. Hence, 1-norm SVM is applied to select important features as well as construct classifiers simultaneously. We have performed extensive experiments. In comparison with other methods, MILES demonstrates competitive classification accuracy, high computation efficiency, and robustness to labeling uncertainty


title: Sample size estimation and power analysis for clinical research studies

abstract: Determining the optimal sample size for a study assures an adequate power to detect statistical significance. Hence, it is a critical step in the design of a planned research protocol. Using too many participants in a study is expensive and exposes more number of subjects to procedure. Similarly, if study is underpowered, it will be statistically inconclusive and may make the whole protocol a failure. This paper covers the essentials in calculating power and sample size for a variety of applied study designs. Sample size computation for single group mean, survey type of studies, 2 group studies based on means and proportions or rates, correlation studies and for case-control for assessing the categorical outcome are presented in detail.


title: Tactons: Structured Tactile Messages for Non-Visual Information Display

abstract: Tactile displays are now becoming available in a form that can be easily used in a user interface. This paper describes a new form of tactile output. Tactons, or tactile icons, are structured, abstract messages that can be used to communicate messages non-visually. A range of different parameters can be used for Tacton construction including: frequency, amplitude and duration of a tactile pulse, plus other parameters such as rhythm and location. Tactons have the potential to improve interaction in a range of different areas, particularly where the visual display is overloaded, limited in size or not available, such as interfaces for blind people or in mobile and wearable devices. This paper describes Tactons, the parameters used to construct them and some possible ways to design them. Examples of where Tactons might prove useful in user interfaces are given.


title: Boosting KNN text classification accuracy by using supervised term weighting schemes

abstract: The increasing availability of digital documents in the last decade has prompted the development of machine learning techniques to automatically classify and organize text documents. The majority of text classification systems rely on the vector space model, which represents the documents as vectors in the term space. Each vector component is assigned a weight that reflects the importance of the term in the document. Typically, these weights are assigned using an information retrieval (IR) approach, such as the famous tf-idf function. In this work, we study two weighting schemes based on information gain and chi-square statistics. These schemes take advantage of the category label information to weight the terms according to their distributions across the different categories. We show that using these supervised weights instead of conventional unsupervised weights can greatly improve the performance of the k-nearest neighbor (KNN) classifier. Experimental evaluations, carried out on multiple text classification tasks, demonstrate the benefits of this approach in creating accurate text classifiers.


title: Design of A 1.5-V 3-5 GHz CMOS Low-Voltage Transformer-Based Low Noise Amplifier with TR Switch for Computational Intelligence Applications

abstract: An integrated T/R switch designed to protect low-noise amplifier (LNA) circuits in CMOS transceivers is reported. A 1.5-V 3-5 GHz low-noise amplifier utilizing on-chip transformer and gain boosting in cascode stage is fabricated by TSMC 0.18um CMOS process. The transformer-based LNA is realized with a LC ladder filter circuit to achieve the wideband input matching to the source impedance of antenna, whereas the cascode stage with a shunting peaking coil provides a flat and high power gain. The output stage with a diode-connected load matches the measured instrument. This proposed chip also merger T/R-Switch design on single RFIC solution on 3-5 GHz by electronic design automation (EDA) form computational intelligence techniques.


title: Boosting Scene Parsing Performance via Reliable Scale Prediction

abstract: Segmenting objects on suitable scales is a key factor to improve the scene parsing performance. Existing methods either simply average multi-scale results or predict scales by weakly-supervised models, due to the lack of scale labels. In this paper, we propose a novel fully-supervised Scale Prediction Model. On one hand, the proposed Scale Prediction Model learns parsing scales by the strong scale supervision, which is automatically generated from the scene parsing ground truth without any extra manually annotation. On the other hand, we explore the relationship between scale and object class, and propose to use the object class information to further improve the reliability of the scale prediction. The proposed Scale Prediction Model improves 23.1%, 20.1% and 29.3% scale prediction accuracies on the NYU Depth v2, PASCAL-Context and SIFT Flow datasets, respectively. Based on the Scale Prediction Model, we design a Scale Parsing Net (SPNet) for scene parsing, which segments each object on the scale predicted by the Scale Prediction Model. Moreover, SPNet leverages the intermediate result (i.e., the object class) to refine the parsing results. The experiment results show that SPNet outperforms many state-of-the-art methods on multiple scene parsing datasets.


title: Explore Multi-Step Reasoning in Video Question Answering

abstract: Video question answering (VideoQA) always involves visual reasoning. When answering questions composing of multiple logic correlations, models need to perform multi-step reasoning. In this paper, we formulate multi-step reasoning in VideoQA as a new task to answer compositional and logical structured questions based on video content. Existing VideoQA datasets are inadequate as benchmarks for the multi-step reasoning due to limitations such as lacking logical structure and having language biases. Thus we design a system to automatically generate a large-scale dataset, namely SVQA (Synthetic Video Question Answering). Compared with other VideoQA datasets, SVQA contains exclusively long and structured questions with various spatial and temporal relations between objects. More importantly, questions in SVQA can be decomposed into human readable logical tree or chain layouts, each node of which represents a sub-task requiring a reasoning operation such as comparison or arithmetic. Towards automatic question answering in SVQA, we develop a new VideoQA model. Particularly, we construct a new attention module, which contains spatial attention mechanism to address crucial and multiple logical sub-tasks embedded in questions, as well as a refined GRU called ta-GRU (temporal-attention GRU) to capture the long-term temporal dependency and gather complete visual cues. Experimental results show the capability of multi-step reasoning of SVQA and the effectiveness of our model when compared with other existing models.


title: Using SEM and EDX for a Simple Differentiation of α- and β-Alfesi-Phases in Wrought Aluminum Billets

abstract: Aluminum 6xxx extrusions have considerable potential to make cars lighter. Thus there is a growing demand for high quality aluminum billets. Additionally there is a requirement to improve the aluminum properties. An important issue to fulfill these increasing requirements is the optimization of the microstructure. While utilizing 6xxx-alloys the formation and optimization of the AlFeSi-phase is important. The determination of α- and β-Alfesi-phases is a challenge, because β-Alfesi-phases affect the mechanical properties negatively. A determination in wrought alloys via microscopy is often complicated and leads to questionable results due to the low amount of the phases. TRIMET has investigated one possibility of determining α- and β-Alfesi-phases with the help of a scanning electron microscope and energy dispersive X-ray. The paper describes the first tests, the phase simulations and the casting experiments. Finally it presents the tests of such phase determinations in the production of 6xxx-alloys like the new TRIMAL 52.


title: Cooperative navigation in robotic swarms

abstract: We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.


title: Neuro-fuzzy modeling and control

abstract: Fundamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which unifies both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called adaptive-network-based fuzzy inference system (ANFIS), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed. >


title: Theoretical analysis of xDAWN algorithm: Application to an efficient sensor selection in a p300 BCI

abstract: A Brain-Computer Interface (BCI) is a specific type of human-machine interface that enables communication between a subject/patient and a computer by a direct control from the decoding of brain activity. To improve the ergonomics and to minimize the cost of such a BCI, reducing the number of electrodes is mandatory. A theoretical analysis of the subjacent model induced by the BCI paradigm leads to derive a closed form theoretical expression of the spatial filters which maximize the signal to signal-plus-noise ratio. Moreover, this new formulation is useful to improve a previously introduced method to automatically select relevant sensors. Experimental results on 20 subjects show that the proposed method is efficient to select the most relevant sensors: from 32 down to 8 sensors, the loss in classification accuracy is less than 2%. Furethermore, the computational time required to rank the 32 sensors is reduced by a 4.6 speed up factor allowing dynamical monitoring of sensor relevance as a marker of the user's mental state.


title: Scaled Natural Gradient Algorithms for Instantaneous and Convolutive Blind Source Separation

abstract: This paper describes a novel modification to the well-known natural gradient or INFOMAX algorithm for blind source separation that largely mitigates its divergence problems. The modified algorithm imposes an a posteriori scalar gradient constraint that adds little computational complexity to the algorithm and exhibits fast convergence and excellent performance for fixed step size values that are largely independent of input signal magnitudes and initial separation matrix estimates. Evaluation of the approach for the separation of instantaneous and convolutive source mixtures using both time- and frequency-domain implementations shows its excellent separation behavior.


title: An identification and prevention of theft-of-service attack on cloud computing

abstract: Cloud computing, an emerging paradigm, has shifted the shape of computing models from technology to a utility. However, data integrity, privacy, trust and secure cloud infrastructure are amongst the major issues which can subvert the wide benefits and deployment of cloud computing. Cloud computing uses virtualization to provide scalable, efficient and low cost IT services to users. The back bone of virtualization is hypervisor which mapped logical hardware with physical hardware and make one computer appear as many. Hypervisor vulnerabilities can favor the attacker to use the VM (virtual machine) for a longer period and pay less amount by resetting/manipulating the variables (i.e. which store CPU and memory etc. usage of VM, used by user, to charge him at the end) resulting in an attack called theft-of-service attack. This attack, caused by stealing VM, can affect the cloud infrastructure financially as well as there will be no record of user's activity during his stay on cloud which lead to a greater risk to success of cloud computing. In this paper, we investigated the limitations of Kernel Virtual Machine together with the QEMU emulator in private cloud and provided the solution, in term of an API based VM's power consumption that will identify and prevent theft-of-service attack. This solution has been implemented in a private cloud to determine the efficiency of proposed API. We concluded that our API can detect and prevent theft-of-service attack in cloud with respect to different scenarios.


title: Keeping Users Engaged through Feature Updates: A Long-Term Study of Using Wearable-Based Exergames

abstract: Gamification and exergames in particular have been broadly employed in health and fitness as an attempt to promote exercise and more active life styles. Motivated by popularity and availability of wearable activity trackers, we present the design and findings of a study on the motivational effects of using activity tracker-based games to promote daily exercise. Furthermore, we have investigated user behaviors, usage patterns, engagement, and parameters that affect them. An exergame was developed with an accompanying wearable device, for which different variations of application updates were pushed out periodically over a 70-day period. The results of this long-term study show that the usage of wearable activity trackers during exercise, even when gamified for increased entertainment, sees a consistent decline over time. This decline, however, is observed to be reversible with periodic updates to the game. This work, we believe, can make a significant contribution to solving the user retention problem of wearable-based exergames.


title: Nonparametric semi-supervised learning of class proportions

abstract: The problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning. A common requirement in this setting is to approximate posterior probabilities of positive and negative classes for a previously unseen data point. This problem can be decomposed into two steps: (i) the development of accurate predictors that discriminate between positive and unlabeled data, and (ii) the accurate estimation of the prior probabilities of positive and negative examples. In this work we primarily focus on the latter subproblem. We study nonparametric class prior estimation and formulate this problem as an estimation of mixing proportions in two-component mixture models, given a sample from one of the components and another sample from the mixture itself. We show that estimation of mixing proportions is generally ill-defined and propose a canonical form to obtain identifiability while maintaining the flexibility to model any distribution. We use insights from this theory to elucidate the optimization surface of the class priors and propose an algorithm for estimating them. To address the problems of high-dimensional density estimation, we provide practical transformations to low-dimensional spaces that preserve class priors. Finally, we demonstrate the efficacy of our method on univariate and multivariate data.


title: Structure Learning in Motor Control:A Deep Reinforcement Learning Model

abstract: Motor adaptation displays a structure-learning effect: adaptation to a new perturbation occurs more quickly when the subject has prior exposure to perturbations with related structure. Although this `learning-to-learn' effect is well documented, its underlying computational mechanisms are poorly understood. We present a new model of motor structure learning, approaching it from the point of view of deep reinforcement learning. Previous work outside of motor control has shown how recurrent neural networks can account for learning-to-learn effects. We leverage this insight to address motor learning, by importing it into the setting of model-based reinforcement learning. We apply the resulting processing architecture to empirical findings from a landmark study of structure learning in target-directed reaching (Braun et al., 2009), and discuss its implications for a wider range of learning-to-learn phenomena.


title: Testing Exchangeability On-Line

abstract: The majority of theoretical work in machine learning is done under the assumption of exchangeability: essentially, it is assumed that the examples are generated from the same probability distribution independently. This paper is concerned with the problem of testing the exchangeability assumption in the on-line mode: examples are observed one by one and the goal is to monitor on-line the strength of evidence against the hypothesis of exchangeability. We introduce the notion of exchangeability martingales, which are online procedures for detecting deviations from exchangeability; in essence, they are betting schemes that never risk bankruptcy and are fair under the hypothesis of exchangeability. Some specific exchangeability martingales are constructed using Transductive Confidence Machine. We report experimental results showing their performance on the USPS benchmark data set of hand-written digits (known to be somewhat heterogeneous); one of them multiplies the initial capital by more than 1018; this means that the hypothesis of exchangeability is rejected at the significance level 10-18.


title: Visual Analysis of Eye State and Head Pose for Driver Alertness Monitoring

abstract: This paper presents visual analysis of eye state and head pose (HP) for continuous monitoring of alertness of a vehicle driver. Most existing approaches to visual detection of nonalert driving patterns rely either on eye closure or head nodding angles to determine the driver drowsiness or distraction level. The proposed scheme uses visual features such as eye index (EI), pupil activity (PA), and HP to extract critical information on nonalertness of a vehicle driver. EI determines if the eye is open, half closed, or closed from the ratio of pupil height and eye height. PA measures the rate of deviation of the pupil center from the eye center over a time period. HP finds the amount of the driver's head movements by counting the number of video segments that involve a large deviation of three Euler angles of HP, i.e., nodding, shaking, and tilting, from its normal driving position. HP provides useful information on the lack of attention, particularly when the driver's eyes are not visible due to occlusion caused by large head movements. A support vector machine (SVM) classifies a sequence of video segments into alert or nonalert driving events. Experimental results show that the proposed scheme offers high classification accuracy with acceptably low errors and false alarms for people of various ethnicity and gender in real road driving conditions.


title: A.V.: A data driven approach for algebraic loop invariants

abstract: We describe a Guess-and-Check algorithm for computing algebraic equation invariants of the form ∧ifi(x1,…,xn)=0, where each fi is a polynomial over the variables x1,…,xn of the program. The "guess" phase is data driven and derives a candidate invariant from data generated from concrete executions of the program. This candidate invariant is subsequently validated in a "check" phase by an off-the-shelf SMT solver. Iterating between the two phases leads to a sound algorithm. Moreover, we are able to prove a bound on the number of decision procedure queries which Guess-and-Check requires to obtain a sound invariant. We show how Guess-and-Check can be extended to generate arbitrary boolean combinations of linear equalities as invariants, which enables us to generate expressive invariants to be consumed by tools that cannot handle non-linear arithmetic. We have evaluated our technique on a number of benchmark programs from recent papers on invariant generation. Our results are encouraging --- we are able to efficiently compute algebraic invariants in all cases, with only a few tests.


title: Scalp Rotation Flap for Reconstruction of Complex Soft Tissue Defects

abstract: Importance Scalp reconstructions may be required after tumor resection or trauma. The inherent anatomy of the scalp presents challenges and may limit reconstructive options. Objective To describe and investigate the scalp rotation flap as a reconstructive technique for complex soft tissue defects. Design Retrospective case series with a mean follow-up of 13 months. Setting Tertiary academic center. Participants A total of 22 patients with large scalp soft tissue defects undergoing scalp rotation flap reconstruction. Interventions The flap is designed adjacent to the defect and elevated in the subgaleal plane. The flap is rotated into the defect, and a split-thickness skin graft is placed over the donor site periosteum. Main Outcomes and Measure Data points collected included defect size, operative time, hospital stay, and patient satisfaction with cosmetic outcome. Results Mean patient age was 71 years. Mean American Society of Anesthesiologist classification was 2.8. Mean defect size was 41 cm(2) (range: 7.8-120 cm(2)), and 19 of 22 defects resulted from a neoplasm resection. Mean operative time was 181 minutes, and mean hospital stay was 2.4 days. There were no intraoperative complications. Three patients with previous radiation therapy had distal flap necrosis. Twenty-one patients (95%) reported an acceptable cosmetic result. Conclusions and Relevance The scalp rotation flap is an efficient and reliable option for reconstructing complex soft tissue defects. This can be particularly important in patients with significant medical comorbidities who cannot tolerate a lengthy operative procedure.


title: Wired and wireless sensor networks for industrial applications

abstract: Distributed architectures for industrial applications are a new opportunity to realize cost-effective, flexible, scalable and reliable systems. Direct interfacing of sensors and actuators to the industrial communication network improves the system performance, because process data and diagnostics can be simultaneously available to many systems and also shared on the Web. However, sensors, especially low-cost ones, cannot use standard communication protocols suitable for computers and PLCs. In fact, sensors typically require a cyclic, isochronous and hard real-time exchange of few data, whereas PCs and PLCs exchange a large amount of data with soft real-time constrains. Looking at the industrial communication systems, this separation is clearly visible: several fieldbuses have been designed for specific sensor application areas, whereas high-level industrial equipments use wired/wireless Ethernet and Internet technologies. Recently, traditional fieldbuses were replaced by Real-Time Ethernet protocols, which are ''extended'' versions of Ethernet that meet real-time operation requirements. Besides, real-time wireless sensor networking seems promising, as demonstrated by the growing research activities. In this paper, an overview of the state-of-art of real-time sensor networks for industrial applications is presented. Particular attention has been paid to the description of methods and instrumentation for performance measurement in this kind of architectures.


title: A compact and efficient image retrieval approach based on border/interior pixel classification

abstract: This paper presents \bic (Border/Interior pixel Classification), a compact and efficient CBIR approach suitable for broad image domains. It has three main components: (1) a simple and powerful image analysis algorithm that classifies image pixels as either border or interior, (2) a new logarithmic distance (dLog) for comparing histograms, and (3) a compact representation for the visual features extracted from images. Experimental results show that the BIC approach is consistently more compact, more efficient and more effective than state-of-the-art CBIR approaches based on sophisticated image analysis algorithms and complex distance functions. It was also observed that the dLog distance function has two main advantages over vectorial distances (e.g., L 1 ): (1) it is able to increase substantially the effectiveness of (several) histogram-based CBIR approaches and, at the same time, (2) it reduces by 50% the space requirement to represent a histogram.


title: Rational design of a dry powder inhaler: device design and optimisation

abstract: OBJECTIVES ::: As part of the development of a dry powder inhalation system for the treatment of asthma and chronic obstructive pulmonary disease, this work specifically aimed at the systematic, however, cost-effective and efficient development of an inhalation device. ::: ::: ::: METHODS ::: Based on theoretical design considerations and an initial inhaler prototype, the concept of a modular inhaler was developed. The modular inhaler was used for the systematic evaluation of the influence of the inhaler's inner dimensions on the resistance to the air flow and the in-vitro deposition characteristics of the inhalation system by using statistical design of experiments and cascade impaction analysis. ::: ::: ::: KEY FINDINGS ::: A reliable statistical model enabled the accurate prediction of the device resistance of any combination of inner dimensions of the inhaler. In conjunction with results from in-vitro deposition studies, this allowed for the definition of optimised inner dimensions of the inhaler to maximise the fine particle fraction and minimise oropharyngeal deposition within the desired range of the inhaler's resistance to air flow. ::: ::: ::: CONCLUSIONS ::: The concept of the modular inhaler and statistical design and evaluation of experiments proved to be important tools for an efficient and successful product development. Eventually, the approaches described and the knowledge obtained enabled the cost-effective development and design of a technically feasible and competitive dry powder inhaler.


title: Family income, parental education and brain structure in children and adolescents

abstract: Socioeconomic disparities are associated with differences in cognitive development. The extent to which this translates to disparities in brain structure is unclear. We investigated relationships between socioeconomic factors and brain morphometry, independently of genetic ancestry, among a cohort of 1,099 typically developing individuals between 3 and 20 years of age. Income was logarithmically associated with brain surface area. Among children from lower income families, small differences in income were associated with relatively large differences in surface area, whereas, among children from higher income families, similar income increments were associated with smaller differences in surface area. These relationships were most prominent in regions supporting language, reading, executive functions and spatial skills; surface area mediated socioeconomic differences in certain neurocognitive abilities. These data imply that income relates most strongly to brain structure among the most disadvantaged children.


title: Cost-Aware Streaming Data Analysis: Distributed vs Single-Thread

abstract: Distributed systems have become the preferred solution for dealing with Big Data analysis tasks. These systems are able to achieve superior performance by managing a large pool of resources as a single entity. However, in many contexts, performance is not the only metric to consider. When comparing two performance equivalent solutions, their cost becomes an important factor. Distributed systems are usually more expensive to deploy than traditional single-threaded applications. In this work, we build on these considerations by presenting an empirical study that compares the cost of two performance equivalent solutions for a real streaming data analysis task for the Telecommunication industry. The first solution is built on popular distributed processing engines (Apache Spark), while the second solution is a single-threaded application built on an home-brew stream processing framework (Natron). We show that, in the case of continuous analysis, the benefits of distributed processing are outvalued by the distributed data ingestion costs. This is also the case for periodic analysis. However, if data ingestion costs are fixed and small, we show that the most cost-effective solution depends on the dataset size.


title: Document Embedding with Paragraph Vectors

abstract: Paragraph Vectors has been recently proposed as an unsupervised method for learning distributed representations for pieces of texts. In their work, the authors showed that the method can learn an embedding of movie review texts which can be leveraged for sentiment analysis. That proof of concept, while encouraging, was rather narrow. Here we consider tasks other than sentiment analysis, provide a more thorough comparison of Paragraph Vectors to other document modelling algorithms such as Latent Dirichlet Allocation, and evaluate performance of the method as we vary the dimensionality of the learned representation. We benchmarked the models on two document similarity data sets, one from Wikipedia, one from arXiv. We observe that the Paragraph Vector method performs significantly better than other methods, and propose a simple improvement to enhance embedding quality. Somewhat surprisingly, we also show that much like word embeddings, vector operations on Paragraph Vectors can perform useful semantic results.


title: When the network crumbles: an empirical study of cloud network failures and their impact on services

abstract: The growing demand for always-on and low-latency cloud services is driving the creation of globally distributed datacenters. A major factor affecting service availability is reliability of the network, both inside the datacenters and wide-area links connecting them. While several research efforts focus on building scale-out datacenter networks, little has been reported on real network failures and how they impact geo-distributed services. This paper makes one of the first attempts to characterize intra-datacenter and inter-datacenter network failures from a service perspective. We describe a large-scale study analyzing and correlating failure events over three years across multiple datacenters and thousands of network elements such as Access routers, Aggregation switches, Top-of-Rack switches, and long-haul links. Our study reveals several important findings on (a) the availability of network domains, (b) root causes, (c) service impact, (d) effectiveness of repairs, and (e) modeling failures. Finally, we outline steps based on existing network mechanisms to improve service availability.


title: Integrating Continuous Security Assessments in Microservices and Cloud Native Applications

abstract: Cloud Native Applications (CNA) consists of multiple collaborating microservice instances working together towards common goals. These microservices leverage the underlying cloud infrastructure to enable several properties such as scalability and resiliency. CNA are complex distributed applications, vulnerable to several security issues affecting microservices and traditional cloud-based applications. For example, each microservice instance could be developed with different technologies e.g. programming languages and databases. This diversity of technologies increases the chances for security vulnerabilities in microservices. Moreover, the fast-paced development cycles of (CNA) increases the probability of insufficient security tests in the development pipelines, and consequent deployment of vulnerable microservices. Furthermore, cloud native environments are ephemeral, microservices are dynamically launched and de-registered, this factor creates a discoverability challenge for traditional security assessment techniques. Hence, security assessments in such environments require new approaches which are specifically adapted and integrated to CNA. In fact, such techniques are to be cloud native i.e. well integrated into the cloud's fabric. In this paper, we tackle the above-mentioned challenges through the introduction of a novel Security Control concept - the Security Gateway. To support the Security Gateway concept, two other concepts are proposed: dynamic document store and security health endpoints. We have implemented these concepts using cloud-native design patterns and integrated them into the CNA workflow. Our experimental evaluations validate the efficiency of our proposals, the time overhead due to the security gateway is minimal and the vulnerability detection rate surpasses that of traditional security assessment approaches. Our proposal can therefore be employed to secure CNA and microservice-based implementations.


title: Article type: Focus Article Replaying History on Process Models for Conformance Checking and Performance Analysis

abstract: Process mining techniques use event data to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand-made models. Event logs are used to learn and enrich process models. By replaying history using the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quantified. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.


title: Edge-Preserving Decomposition-Based Single Image Haze Removal

abstract: Single image haze removal is under-constrained, because the number of freedoms is larger than the number of observations. In this paper, a novel edge-preserving decomposition-based method is introduced to estimate transmission map for a haze image so as to design a single image haze removal algorithm from the Koschmiedars law without using any prior. In particular, weighted guided image filter is adopted to decompose simplified dark channel of the haze image into a base layer and a detail layer. The transmission map is estimated from the base layer, and it is applied to restore the haze-free image. The experimental results on different types of images, including haze images, underwater images, and normal images without haze, show the performance of the proposed algorithm.


title: Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

abstract: Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER−) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER– patients, compared to the other six machine learning a...


title: Query Optimization in Oracle 12c Database In-Memory

abstract: Traditional on-disk row major tables have been the dominant storage mechanism in relational databases for decades. Over the last decade, however, with explosive growth in data volume and demand for faster analytics, has come the recognition that a different data representation is needed. There is widespread agreement that in-memory column-oriented databases are best suited to meet the realities of this new world. ::: ::: Oracle 12c Database In-memory, the industry's first dual-format database, allows existing row major on-disk tables to have complementary in-memory columnar representations. The new storage format brings new data processing techniques and query execution algorithms and thus new challenges for the query optimizer. Execution plans that are optimal for one format may be sub-optimal for the other. ::: ::: In this paper, we describe the changes made in the query optimizer to generate execution plans optimized for the specific format -- row major or columnar -- that will be scanned during query execution. With enhancements in several areas -- statistics, cost model, query transformation, access path and join optimization, parallelism, and cluster-awareness -- the query optimizer plays a significant role in unlocking the full promise and performance of Oracle Database In-Memory.


title: On track-to-track data association for automotive sensor fusion

abstract: When fusing data from more than one information source, it is important to associate the correct pair of the data available from the information sources to achieve an optimal fused result. The responsibility of the task of proper association lies on the data association method used in the system. The design of the data association method has to be done considering the requirements of the application, quality and quantity of the information provided by the sensors. Parameters such as: whether the fusion is carried out in a centralized or decentralized architecture, whether the data available from the sensors is raw and unprocessed data or already processed by the built-in signal processing system of the sensor, plays a role in the finalization of the data association problem. The data association problem in a decentralized sensor fusion setting, also known as track-to-track association, is discussed in detail in this paper. The information sources used in this paper are environment perception sensors based on different measurement principles used for automotive safety and autonomous driving functions. The sensors deliver kinematic and as well as non-kinematic information on the tracked targets. To make use of this non-kinematic target information, attribute based association methods in-addition to the traditional data association methods and results based on the real world data are presented in this paper. A video recorded under real world test conditions that include sensor data and results will be made available for the community.


title: Emotion recognition employing ECG and GSR signals as markers of ANS

abstract: Change in GSR and ECG signals are expression of Autonomic Nervous System response to change in environment and physiological system. In this study, we evaluate the effectiveness of GSR and ECG in emotional state recognition. We took into account three mutually exclusive emotional states-happy, sad and neutral and three different approaches to recognize these emotional states. The results indicate that to classify opposite valence emotion (happy and sad) GSR solely is capable of recognizing emotions with enhanced accuracy with a minimum accuracy of 99.44%. However, in classifying emotionally active state (happy or sad) in reference to neutral state, combined feature set of both ECG and GSR is more effective, especially the combination of PSD features and SVM classifier, providing a high accuracy for classifying all three emotional states.


title: Multi-agent based intrusion prevention and mitigation architecture for software defined networks

abstract: Software Defined Networking (SDN) has proved to be a promising approach for creating next generation software based network ecosystems. It has provided us with a centralized network provision, a holistic management plane and a well-defined level of abstraction. But, at the same time brings forth new security and management challenges. Research in the field of SDN is primarily focused on reconfiguration, forwarding and network management issues. However in recent times the interest has moved to tackling security and maintenance issues. This work is based on providing a means to mitigate security challenges in an SDN environment from a DDoS attack based point of view. This paper introduces a Multi-Agent based intrusion prevention and mitigation architecture for SDN. Thus allowing networks to govern their behavior and take appropriate measures when the network is under attack. The architecture is evaluated against filter based intrusion prevention architectures to measure efficiency and resilience against DDoS attacks and false policy based attacks.


title: A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks

abstract: Transfer and multi-task learning have traditionally focused on either a single source-target pair or very few, similar tasks. Ideally, the linguistic levels of morphology, syntax and semantics would benefit each other by being trained in a single model. We introduce such a joint many-task model together with a strategy for successively growing its depth to solve increasingly complex tasks. All layers include shortcut connections to both word representations and lower-level task predictions. We use a simple regularization term to allow for optimizing all model weights to improve one task's loss without exhibiting catastrophic interference of the other tasks. Our single end-to-end trainable model obtains state-of-the-art results on chunking, dependency parsing, semantic relatedness and textual entailment. It also performs competitively on POS tagging. Our dependency parsing layer relies only on a single feed-forward pass and does not require a beam search.


title: Potential for utilising head-mounted displays (HMDs) for augmenting laboratories

abstract: Laboratory-based experiments are an important learning tool in many disciplines. These experiments involve students exploring physical phenomena. In many cases however these phenomena are not directly visible (e.g. magnetism, heat, etc.) The recent emergence of augmented reality technologies provides us with a way to change the way in which we perceive the real world. It can therefore be postulated that augmented reality may be able to be used to change the way in which students perceive reality whilst undertaking real-world experimentation, and hence to improve the educational process and outcomes. In this work we evaluate the potential for utilising Augmented Reality (AR) technology in a laboratory context. We consider a range of illustrative scenarios for enhancing the laboratory experience, such as: annotations to the experimental configuration; making visible phenomena that are otherwise invisible; and changing the actual laboratory configuration. We then use these scenarios to propose a classification of laboratory augmentation types.


title: Fully collusion resistant traitor tracing with short ciphertexts and private keys

abstract: We construct a fully collusion resistant tracing traitors system with sublinear size ciphertexts and constant size private keys. More precisely, let N be the total number of users. Our system generates ciphertexts of size O(√N) and private keys of size O(1). We first introduce a simpler primitive we call private linear broadcast encryption (PLBE) and show that any PLBE gives a tracing traitors system with the same parameters. We then show how to build a PLBE system with O(√N) size ciphertexts. Our system uses bilinear maps in groups of composite order.


title: Mobile Networks Connected Drones: Field Trials, Simulations, and Design Insights

abstract: Drones are increasingly used in a wide variety of industries and services and are delivering profound socioeconomic benefits. Technology needs to be in place to ensure the safe operation and management of this growing fleet of drones. In the past decades, mobile networks have connected tens of billions of devices on the ground and are now ready to connect drones flying in the sky. In this article, we share some of our findings concerning cellular connectivity for low-altitude drones. We first present and analyze field measurement data collected during drone flights in a commercial LTE network. We then present simulation results to shed light on the performance of a network when it is serving many drones simultaneously over a wide area. The results, analysis, and design insights presented here help enhance understanding of the applicability of and performance concerns related to providing mobile connectivity for lowaltitude drones.


title: Quantification of phosphoinositides reveals strong enrichment of PIP2 in HIV-1 compared to producer cell membranes

abstract: Human immunodeficiency virus type 1 (HIV-1) acquires its lipid envelope during budding from the plasma membrane of the host cell. Various studies indicated that HIV-1 membranes differ from producer cell plasma membranes, suggesting budding from specialized membrane microdomains. The phosphoinositide PI(4,5)P2 has been of particular interest since PI(4,5)P2 is needed to recruit the viral structural polyprotein Gag to the plasma membrane and thus facilitates viral morphogenesis. While there is evidence for an enrichment of PIP2 in HIV-1, fully quantitative analysis of all phosphoinositides remains technically challenging and therefore has not been reported, yet. Here, we present a comprehensive analysis of the lipid content of HIV-1 and of plasma membranes from infected and non-infected producer cells, resulting in a total of 478 quantified lipid compounds, including molecular species distribution of 25 different lipid classes. Quantitative analyses of phosphoinositides revealed strong enrichment of PIP2, but also of PIP3, in the viral compared to the producer cell plasma membrane. We calculated an average of ca. 8,000 PIP2 molecules per HIV-1 particle, three times more than Gag. We speculate that the high density of PIP2 at the HIV-1 assembly site is mediated by transient interactions with viral Gag polyproteins, facilitating PIP2 concentration in this microdomain. These results are consistent with our previous observation that PIP2 is not only required for recruiting, but also for stably maintaining Gag at the plasma membrane. We believe that this quantitative analysis of the molecular anatomy of the HIV-1 lipid envelope may serve as standard reference for future investigations.


title: Simple Memory: A Theory for Archicortex

abstract: It is proposed that the most important characteristic of archicortex is its ability to perform a simple kind of memorizing task. It is shown that rather general numerical constraints roughly determine the dimensions of memorizing models for the mammalian brain, and from these is derived a general model for archicortex. The addition of further constraints leads to the notion of a simple representation, which is a way of translating a great deal of information into the firing of about 200 out of a population of 10 $^5$ cells. It is shown that if about 10 $^5$ simple representations are stored in such a population of cells, very little information about a single learnt event is necessary to provoke its recall. A detailed numerical examination is made of a particular example of this kind of memory, and various general conclusions are drawn from the analysis. The insight gained from these models is used to derive theories for various archicortical areas. A functional interpretation is given of the cells and synapses of the area entorhinalis, the prcsubiculum, the prosubiculum, the cornu ammonis and the fascia dentata. Many predictions are made, a substantial number of which must be true if the theory is correct. A general functional classification of typical archicortical cells is proposed.


title: Environments: Organizational Capability as Knowledge Integration

abstract: Unstable market conditions caused by innovation and increasing intensity and diversity of competition have resulted in organizational capabilities rather than served markets becoming the primary basis upon which firms establish their long-term strategies. If the strategically most important resource of the firm is knowledge , and if knowledge resides in specialized form among individual organizational members, then the essence of organizational capability is the integration of individuals' specialized knowledge.This paper develops a knowledge-based theory of organizational capability and draws upon research into competitive dynamics, the resource-based view of the firm, organizational capabilities, and organizational learning. Central to the theory is analysis of the mechanisms through which knowledge is integrated within firms in order to create capability. The theory is used to explore firms' potential for establishing competitive advantage in dynamic market settings, including the role of firm networks under conditions of unstable linkages between knowledge inputs and product outputs. The analysis points to the difficulties in creating the “dynamic” and “flexible-response capabilities” which have been deemed critical to success in hypercompetitive markets.


Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks


title: 21.5 dBm power-handling 5 GHz transmit/receive CMOS switch realized by voltage division effect of stacked transistor configuration with Depletion-layer-Extended Transistors (DETs)

abstract: This paper reports for the first time an over-20 dBm power-handling 5 GHz transmit/receive (T/R) CMOS switch. The Depletion-layer-Extended Transistor (DET), which possesses high effective substrate resistance, enables the voltage division effect of the stacked transistor configuration to work in CMOS, thus realizing this high power-handling capability. Furthermore, despite insertion-loss (I/sub L/) degradation due to double on-resistance with the stacked transistor configuration, a receive-mode I/sub L/ (I/sub L/@RX) of as low as 1.44 dB at 5 GHz is accomplished with the benefit of the I/sub L/ improvement effects in the DET, in addition to a very low transmit-mode I/sub L/ (I/sub L/@TX) of 0.95 dB at 5 GHz.


title: Behavior-Oriented Time Segmentation for Mining Individualized Rules of Mobile Phone Users

abstract: Mobile or cellular phones can record various types of context data related to a user's phone call activities. In this paper, we present an approach to discovering individualized behavior rules for mobile users from their phone call records, based on the temporal context in which a user accepts, rejects or misses a call. One of the determinants of an individual's phone behavior is the various activities undertaken at various times of a day and days of the week. In many cases, such behavior will follow temporal patterns. Currently, researchers modeling user behavior using temporal context statically segment time into arbitrary categories (e.g., morning, evening) or periods (e.g., 1 hour). However, such time categorization does not necessarily map to the patterns of individual user activity and subsequent behavior. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that dynamically identifies diverse time segments for an individual user's behaviors based on the phone call records. Experiments on real datasets show that our proposed technique better captures the user's dominant call response behavior at various times of the day and week, thereby enabling more appropriate rules to be created for the purpose of automated handling of incoming calls, in an intelligent call interruption management system.


title: A three-phase flyback current-fed push-pull bidirectional dc-dc converter for dc microgrid application

abstract: This paper proposes a new three-phase flyback current-fed push-pull bidirectional dc-dc converter, which in both senses operation of the proposed converter is analyzed and explained even as the DC transfer function in continuous, discontinuous and critical conduction mode has been presented. The proposed converter is suitable for the dc microgrids bus, such as those having fuel cells, PV array and wind turbine as the DC source power supply. Where bidirectional power flow related with battery or supercapacitor bank charge and discharge is necessary.


title: Multiclass SVM-based automated diagnosis of diabetic retinopathy

abstract: Diabetes related eye disease is growing as a major health concern worldwide. Diabetic retinopathy is an infirmity due to higher level of glucose in the retinal capillaries, resulting in cloudy vision and blindness eventually. With regular screening, pathology can be detected in the instigating stage and if intervened with in time medication could prevent further deterioration. This paper develops an automated diagnosis system to recognize retinal blood vessels, and pathologies, such as exudates and microaneurysms together with certain texture properties using image processing techniques. These anatomical and texture features are then fed into a multiclass support vector machine (SVM) for classifying it into normal, mild, moderate, severe and proliferative categories. Advantages include, it processes quickly a large collection of fundus images obtained from mass screening which lessens cost and increases efficiency for ophthalmologists. Our method was evaluated on two publicly available databases and got encouraging results with a state of the art in this area.


title: Building on women's strengths : a social work agenda for the twenty-first century

abstract: Contributing authors to Building on Women's Strengths examine major social issues affecting women today. The authors, women social work scholars, promote a new agenda for the profession. Through this new agenda, they show how policymakers and practitioners can address pertinent issues and transform women's lives today and in the coming century.Individual chapters challenge social workers to look anew at major areas of women's lives and to explore how, as social workers, they can engage in micro- and macro-level practice that empower women. This approach is the new agenda for the profession, an agenda that redresses the continuing inequalities that women face, one that is built on the strengths of women, and one that is firmly grounded in the values of the social work profession.Building on Women's Strengths is designed to enable social work students and faculty to get a fresh and exciting look at the interconnected ways in which women's lives can be transformed. It draws together practice and policy issues, looking at the close interconnections between the macro- and micro levels of practice. The contributors cover diverse issues, among them: a feminist overview of policy issues affecting women in the twentieth century social work roles in enabling women to become self-sufficient economically child welfare as a women's issue policy issues affecting older women how social workers can bring women's stories and their strengths into the openThroughout the book, social work scholars detail specifics of a needed social work agenda for women in the twenty-first century. The diverse topics they cover are tied together by the common commitment to rewriting women's stories to emancipate them from oppression. Readers are reminded of women's historic exclusion from the public domain and the struggles they experience today as they seek to find their own balance between their public and private lives.


title: Destination prediction by sub-trajectory synthesis and privacy protection against such prediction

abstract: Destination prediction is an essential task for many emerging location based applications such as recommending sightseeing places and targeted advertising based on destination. A common approach to destination prediction is to derive the probability of a location being the destination based on historical trajectories. However, existing techniques using this approach suffer from the “data sparsity problem”, i.e., the available historical trajectories is far from being able to cover all possible trajectories. This problem considerably limits the number of query trajectories that can obtain predicted destinations. We propose a novel method named Sub-Trajectory Synthesis (SubSyn) algorithm to address the data sparsity problem. SubSyn algorithm first decomposes historical trajectories into sub-trajectories comprising two neighbouring locations, and then connects the sub-trajectories into “synthesised” trajectories. The number of query trajectories that can have predicted destinations is exponentially increased by this means. Experiments based on real datasets show that SubSyn algorithm can predict destinations for up to ten times more query trajectories than a baseline algorithm while the SubSyn prediction algorithm runs over two orders of magnitude faster than the baseline algorithm. In this paper, we also consider the privacy protection issue in case an adversary uses SubSyn algorithm to derive sensitive location information of users. We propose an efficient algorithm to select a minimum number of locations a user has to hide on her trajectory in order to avoid privacy leak. Experiments also validate the high efficiency of the privacy protection algorithm.


Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees


title: CompactDFA: Scalable Pattern Matching Using Longest Prefix Match Solutions

abstract: A central component in all contemporary intrusion detection systems (IDSs) is their pattern matching algorithms, which are often based on constructing and traversing a deterministic finite automaton (DFA) that represents the patterns. While this approach ensures deterministic time guarantees, modern IDSs need to deal with hundreds of patterns, thus requiring to store very large DFAs, which usually do not fit in fast memory. This results in a major bottleneck on the throughput of the IDS, as well as its power consumption and cost. We propose a novel method to compress DFAs by observing that the name used by common DFA encoding is meaningless. While regular DFAs store separately each transition between two states, we use this degree of freedom and encode states in such a way that all transitions to a specific state are represented by a single prefix that defines a set of current states. Our technique applies to a large class of automata, which can be categorized by simple properties. Then, the problem of pattern matching is reduced to the well-studied problem of Longest Prefix Match (LPM), which can be solved either in ternary content-addressable memory (TCAM), in commercially available IP-lookup chips, or in software. Specifically, we show that with a TCAM our scheme can reach a throughput of 10 Gb/s with low power consumption.


title: Deep Canonical Time Warping

abstract: Machine learning algorithms for the analysis of timeseries often depend on the assumption that the utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture the properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards the temporal alignment of time-series are applied directly on the observation space, or utilise simple linear projections. Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e.g., aligning visual and acoustic information). To this end, we present the Deep Canonical Time Warping (DCTW), a method which automatically learns complex non-linear representations of multiple time-series, generated such that (i) they are highly correlated, and (ii) temporally in alignment. By means of experiments on four real datasets, we show that the representations learnt via the proposed DCTW significantly outperform state-of-the-art methods in temporal alignment, elegantly handling scenarios with highly heterogeneous features, such as the temporal alignment of acoustic and visual features.


title: Portfolios for assessment and learning: AMEE Guide no. 45

abstract: In 1990, Miller wrote that no tools were available for assessment of what a learner does when functioning independently at the clinical workplace (Miller ). Since then portfolios have filled this gap and found their way into medical education, not only as tools for assessment of performance in the workplace, but also as tools to stimulate learning from experience. We give an overview of the content and structure of various types of portfolios, describe the potential of electronic portfolios, present techniques and strategies for using portfolios as tools for stimulating learning and for assessment, and discuss factors that influence the success of the introduction. We conclude that portfolios have a lot of potential but that their introduction also often leads to disappointment, because they require a new perspective on education from mentors and learners and a significant investment of time and energy.


title: Bit-Planes: Dense Subpixel Alignment of Binary Descriptors

abstract: Binary descriptors have been instrumental in the recent evolution of computationally efficient sparse image alignment algorithms. Increasingly, however, the vision community is interested in dense image alignment methods, which are more suitable for estimating correspondences from high frame rate cameras as they do not rely on exhaustive search. However, classic dense alignment approaches are sensitive to illumination change. In this paper, we propose an easy to implement and low complexity dense binary descriptor, which we refer to as bit-planes, that can be seamlessly integrated within a multi-channel Lucas & Kanade framework. This novel approach combines the robustness of binary descriptors with the speed and accuracy of dense alignment methods. The approach is demonstrated on a template tracking problem achieving state-of-the-art robustness and faster than real-time performance on consumer laptops (400+ fps on a single core Intel i7) and hand-held mobile devices (100+ fps on an iPad Air 2).


title: Microsystem technologies for implantable applications

abstract: Microsystem technologies (MST) have become the basis of a large industry. The advantages of MST compared to other technologies provide opportunities for application in implantable biomedical devices. This paper presents a general and broad literature review of MST for implantable applications focused on the technical domain. A classification scheme is introduced to order the examples, basic technological building blocks relevant for implantable applications are described and finally a case study on the role of microsystems for one clinical condition is presented. We observe that the microfabricated parts span a wide range for implantable applications in various clinical areas. There are 94 active and 67 commercial 'end items' out of a total of 142. End item refers to the total concept, of which the microsystem may only be a part. From the 105 active end items 18 (13% of total number of end items) are classified as products. From these 18 products, there are only two for chronic use. The number of active end items in clinical, animal and proto phase for chronic use is 17, 13 and 20, respectively. The average year of first publication of chronic end items that are still in the animal or clinical phase is 1994 (n = 7) and 1993 (n = 11), respectively. The major technology–market combinations are sensors for cardiovascular, drug delivery for drug delivery and electrodes for neurology and ophthalmology. Together these form 51% of all end items. Pressure sensors form the majority of sensors and there is just one product (considered to be an implantable microsystem) in the neurological area. Micro-machined ceramic packages, glass sealed packages and polymer encapsulations are used. Glass to metal seals are used for feedthroughs. Interconnection techniques such as flip chip, wirebonding or conductive epoxy as used in the semiconductor packaging and assembly industry are also used for manufacturing of implantable devices. Coatings are polymers or metal. As an alternative to implantable primary batteries, rechargeable batteries were introduced or concepts in which energy is provided from the outside based on inductive coupling. Long-term developments aiming at autonomous power are, for example, based on electrostatic conversion of mechanical vibrations. Communication with the implantable device is usually done using an inductive link. A large range of materials commonly used in microfabrication are also used for implantable microsystems.


title: How to include the dependency of the R/sub DS(on/) of power MOSFETs on the instantaneous value of the drain current into the calculation of the conduction losses of high-frequency three-phase PWM inverters

abstract: In this paper, the conduction losses of power MOSFETs are calculated analytically for application in three-phase voltage DC-link pulsewidth modulation (PWM) power converter systems. Contrary to a conventional calculation, the dependency of the turn-on behavior on the drain current is considered in terms of a quadratic approximation. The derived relationships are represented graphically; they can be included directly into the dimensioning of the power transistors.


title: Hydroinformatics in 1999: what is to be done?

abstract: Some possible directions of future evolution of hydroinformatics as seen from the recently observed social demand point of view are indicated. Among key issues concerning water resources management, hydroinformatics can contribute by defining a coherent methodological approach for negotiation of the most relevant water-related conflicts. The latter require however equitable access of all stakeholders and parties in conflicting situations to reliable information and objective tools allowing assessment and evaluation of a family of feasible solutions. Rapidly changing Information and Communications Technologies (in particular Internet applications) will have also their impact on the way water-related industry will use the modelling software and an appropriate experience.


title: Automatic Extraction of Useful Facet Hierarchies from Text Databases

abstract: Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with ";context"; phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster.


title: Image Parsing: Unifying Segmentation, Detection, and Recognition

abstract: In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a “parsing graph”, in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches—generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns—generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. IEEE Trans. PAMI, 24(5):657–673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.


title: From movement to thought: the development of executive function.

abstract: Abstract This article presents a very simple definition of executive functioning (EF). Although EF is traditionally understood as a cognitive function dependent upon top-down cortical control, we challenge this model. We propose that the functional architecture of the brain evolved to meet the needs of interactive behavior and that cognition develops to control the motor system, which is of paramount importance in adaptation, essentially a manifestation of EF. We propose that traditional models of cognition are incomplete characterizations of EF and that procedural learning and “automatic” behaviors are the most basic, bottom-up functions that support all EF. We propose that motor development in children demonstrates how all knowledge is grounded in sensorimotor interaction and how interactive behavior generates both procedural and declarative knowledge, which later interact to generate EF. This model emphasizes the critical importance of motor behavior in children and stresses the importance of the pedia...


title: Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora

abstract: We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data.


Better than pre-commitment mean-variance portfolio allocation 1 strategies : a semi-self-financing


title: Detecting Anomaly in Videos from Trajectory Similarity Analysis

abstract: Trajectories of moving objects provide crucial clues for video event analysis especially in surveillance applications. In this paper, we study the problem of detecting anomalous events by analyzing the motion trajectories in videos. Different trajectories of the same category may have varying relative velocities, in addition to the variations and noises in location samples; hence the core of the problem is to provide a robust and accurate function for measuring the similarities of trajectory pairs. We propose a novel learning based algorithm for estimating the similarities of the multi-dimensional sequence pairs, and then an anomaly detection framework is presented to detect anomalous motion trajectories in surveillance videos. Our proposed algorithm offers several advantages over the traditional algorithms for dealing with the trajectories of moving objects. First, the similarity measurement is robust against data imperfections such as noise, algorithmic error and etc. Second, we introduce a learning algorithm which allows the similarity function to be adapted to the particular problems being solved. Third, the proposed anomaly detection framework is fully automatic and without parametric distribution assumption on the data. The experiments on both outdoor and indoor surveillance videos validate the effectiveness of our proposed framework in detecting anomalous trajectories.


title: A Communication Theoretical Modeling and Analysis of Underwater Magneto-Inductive Wireless Channels

abstract: Underwater physical medium is a challenging environment for communication using radio frequency (RF) or acoustic waves due to strong attenuation, delay, multi-path fading, power and cost limitations. Discovered a century ago, magneto-inductive (MI) communication technique stands as a strong alternative paradigm due to its independence of environmental impairments including multi-path fading, dynamic channels and high propagation delays experienced by acoustic waves. Furthermore, MI technique yields networking solutions exploiting low-cost, easily-deployable and flexible antenna structures, and the possibility of forming networks of magnetic waveguides defeating path loss. In this work, highly power efficient and fully connected underwater communication networks (UWCNs) composed of transceiver and relay induction coils are presented. Three dimensional (3D) UWCNs are analysed in terms of basic communication metrics, i.e, signal-to-noise ratio, bit-error rate, connectivity and communication bandwidth. The performance studies of realistic 3D networks covering hundreds of meters sea depths and a few km2 areas show that fully connected multi-coil networks with communication bandwidths extending from a few to tens of KHz are possible. Furthermore, the performance dependence on coil properties and network size is theoretically modelled. Results show that MI wireless communication is a promising alternative for UWCNs and future research challenges are pointed out.


title: A low-cost adaptive multi-mode digital control solution maximizing AC/DC power supply efficiency

abstract: This paper presents a new low-cost digital control solution that maximizes the AC/DC flyback power supply efficiency. This intelligent digital approach achieves the combined benefits of high performance, low cost and high reliability in a single controller. It introduces unique multiple PWM and PFM operational modes adaptively based on the power supply load changes. While the multi-mode PWM/PFM control significantly improves the light-load efficiency and thus the overall average efficiency, it does not bring compromise to other system performance, such as audible noise, voltage ripples or regulations. It also seamlessly integrated an improved quasi-resonant switching scheme that enables valley-mode turn on in every switching cycle without causing modification to the main PWM/PFM control schemes. A digital integrated circuit (IC) that implements this solution, namely iW1696, has been fabricated and introduced to the industry recently. In addition to outlining the approach, this paper provides experimental results obtained on a 3-W (5V/550mA) cell phone charger that is built with the iW1696.


title: ReNoun: Fact Extraction for Nominal Attributes

abstract: Search engines are increasingly relying on large knowledge bases of facts to provide direct answers to users’ queries. However, the construction of these knowledge bases is largely manual and does not scale to the long and heavy tail of facts. Open information extraction tries to address this challenge, but typically assumes that facts are expressed with verb phrases, and therefore has had difficulty extracting facts for noun-based relations. We describe ReNoun, an open information extraction system that complements previous efforts by focusing on nominal attributes and on the long tail. ReNoun’s approach is based on leveraging a large ontology of noun attributes mined from a text corpus and from user queries. ReNoun creates a seed set of training data by using specialized patterns and requiring that the facts mention an attribute in the ontology. ReNoun then generalizes from this seed set to produce a much larger set of extractions that are then scored. We describe experiments that show that we extract facts with high precision and for attributes that cannot be extracted with verb-based techniques.


title: Crowdsourcing Entity Resolution: a Short Overview and Open Issues

abstract: Entity resolution (ER) is a process to identify records that stand for the same real-world entity. Although automatic algorithms aiming at solving this problem have been developed for many years, their accuracy remains far from perfect. Crowdsourcing is a technology currently investigated, which leverages the crowd to solicit contributions to complete certain tasks via crowdsourced marketplaces. One of its advantages is to inject human reasoning to problems that are still hard to process for computers, which makes it suitable for ER and provides an opportunity to achieve a higher accuracy. As crowdsourcing ER is still a relatively new area in data processing, this paper provides an overview and a brief classification of current research state in crowdsourcing ER. Besides, some open issues are revealed that will be a starting point for our future research.


title: Application of Association Rules Mining in Employment Guidance

abstract: How to improve the employment rate of graduates is an important task for higher vocational colleges to solve. In order to effectively improve their Employment competitiveness, advice should be made to help students to enhance specific kinds of learning and ability. Association Rules Mining is one core of the Data Mining Association Rules, it’s helpful in finding useful information hidden in complex data. By using Association Rules Mining in finding the knowledge and ability which helps employed students to earn their jobs, necessary ability for each kind of job can be found, and then advice offered for students to target their employment career will be more exact and proper.


HDPsent : Incorporation of Latent Dirichlet Allocation for Aspect-Level Sentiment into Hierarchical Dirichlet Process-Based Topic Models


title: Geodesic Flow Kernel Support Vector Machine for Hyperspectral Image Classification by Unsupervised Subspace Feature Transfer

abstract: In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context of hyperspectral image classification via a geodesic Gaussian flow kernel based support vector machine (GFKSVM). To show the superior performance of the proposed approach, conventional support vector machines (SVMs) and state-of-the-art DA algorithms, including information-theoretical learning of discriminative cluster for domain adaptation (ITLDC), joint distribution adaptation (JDA), and joint transfer matching (JTM), are also considered. Additionally, unsupervised linear and nonlinear subspace feature transfer techniques including principal component analysis (PCA), randomized nonlinear principal component analysis (rPCA), factor analysis (FA) and non-negative matrix factorization (NNMF) are investigated and compared. Experiments on two real hyperspectral images show the cross-image classification performances of the GFKSVM, confirming its effectiveness and suitability when applied to hyperspectral images.


title: Development and pilot testing of HEXORR: Hand EXOskeleton Rehabilitation Robot

abstract: BackgroundFollowing acute therapeutic interventions, the majority of stroke survivors are left with a poorly functioning hemiparetic hand. Rehabilitation robotics has shown promise in providing patients with intensive therapy leading to functional gains. Because of the hand's crucial role in performing activities of daily living, attention to hand therapy has recently increased.MethodsThis paper introduces a newly developed Hand Exoskeleton Rehabilitation Robot (HEXORR). This device has been designed to provide full range of motion (ROM) for all of the hand's digits. The thumb actuator allows for variable thumb plane of motion to incorporate different degrees of extension/flexion and abduction/adduction. Compensation algorithms have been developed to improve the exoskeleton's backdrivability by counteracting gravity, stiction and kinetic friction. We have also designed a force assistance mode that provides extension assistance based on each individual's needs. A pilot study was conducted on 9 unimpaired and 5 chronic stroke subjects to investigate the device's ability to allow physiologically accurate hand movements throughout the full ROM. The study also tested the efficacy of the force assistance mode with the goal of increasing stroke subjects' active ROM while still requiring active extension torque on the part of the subject.ResultsFor 12 of the hand digits'15 joints in neurologically normal subjects, there were no significant ROM differences (P > 0.05) between active movements performed inside and outside of HEXORR. Interjoint coordination was examined in the 1st and 3rd digits, and no differences were found between inside and outside of the device (P > 0.05). Stroke subjects were capable of performing free hand movements inside of the exoskeleton and the force assistance mode was successful in increasing active ROM by 43 ± 5% (P < 0.001) and 24 ± 6% (P = 0.041) for the fingers and thumb, respectively.ConclusionsOur pilot study shows that this device is capable of moving the hand's digits through nearly the entire ROM with physiologically accurate trajectories. Stroke subjects received the device intervention well and device impedance was minimized so that subjects could freely extend and flex their digits inside of HEXORR. Our active force-assisted condition was successful in increasing the subjects' ROM while promoting active participation.


title: Fuzzy based speed control of BLDC motor with bidirectional DC-DC converter

abstract: In this paper we have designed a fuzzy logic controller for the speed control of bidirectional brushless DC motor (BLDC). The speed control is achieved by using fuzzy controller. The fuzzy logic control is used in three phase BLDC motor in order to control the four quadrants operations without any loss of power. In fact energy is conserved during the regenerative period and storage of excess energy created during regenerative braking condition in the battery. The bidirectional DC-DC converters are oftenly used for the battery charging and discharging application. The bidirectional dc-dc converter can be operated in buck boost mode. The stored energy in the battery provides maximum power to the BLDC motor, while during regenerative braking operation the energy is fed back to the source by the same bidirectional buck-boost converter. The simulation will be done for Brushless DC motor with fuzzy logic controller using MATLAB/Simulink software.


title: Data Analysis with Bayesian Networks: A Bootstrap Approach

abstract: In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the existence of an edge between two nodes warranted? Is the Markov blanket of a given node robust? Can we say something about the ordering of the variables? We should be able to address these questions, even when the amount of data is not enough to induce a high scoring network. In this paper we propose Efron's Bootstrap as a computationally efficient approach for answering these questions. In addition, we propose to use these confidence measures to induce better structures from the data, and to detect the presence of latent variables.


Visualizing Association Rules: Introduction to the R-extension Package arulesViz


title: Minimal test set generation for input stuck-at and bridging faults in reversible circuits

abstract: Research in reversible computing has gained importance because of its potential use in low-power design, and also quantum computing. Several works on reversible circuit testing have also been reported. Many fault models have been proposed, some of which have been borrowed from traditional logic. In this paper, we consider the problem of reversible circuit testing, specifically targeting test generation for stuck-at and bridging fault models. It has been shown that [log 2 N] number of test vectors are necessary and sufficient for detection of all inputs bridging faults of a reversible circuit with N inputs. Addition of only one more test vector to an existing test set of single input bridging faults can also detect all single input stuck-at faults. Finally, we provide our experimental results to verify our claim, and also show that the test size is smaller as compared to existing methods.


title: Pharmacotherapy for borderline personality disorder: Cochrane systematic review of randomised trials

abstract: Background ::: ::: Many patients with borderline personality disorder receive pharmacological treatment, but there is uncertainty about the usefulness of such therapies. ::: ::: Aims ::: ::: To evaluate the evidence of effectiveness of pharmacotherapy in treating different facets of the psychopathology of borderline personality disorder. ::: ::: Method ::: ::: A Cochrane Collaboration systematic review and meta-analysis of randomised comparisons of drug v. placebo, drug v. drug, or single drug v. combined drug treatment in adult patients with borderline personality disorder was conducted. Primary outcomes were overall disorder severity as well as specific core symptoms. Secondary outcomes comprised associated psychiatric pathology and drug tolerability. ::: ::: Results ::: ::: Twenty-seven trials were included in which first- and second-generation antipsychotics, mood stabilisers, antidepressants and omega-3 fatty acids were tested. Most beneficial effects were found for the mood stabilisers topiramate, lamotrigine and valproate semisodium, and the second-generation antipsychotics aripiprazole and olanzapine. However, the robustness of findings is low, since they are based mostly on single, small studies. Selective serotonin reuptake inhibitors so far lack high-level evidence of effectiveness. ::: ::: Conclusions ::: ::: The current evidence from randomised controlled trials suggests that drug treatment, especially with mood stabilisers and second-generation antipsychotics, may be effective for treating a number of core symptoms and associated psychopathology, but the evidence does not currently support effectiveness for overall severity of borderline personality disorder. Pharmacotherapy should therefore be targeted at specific symptoms.


title: Look into Person: Joint Body Parsing & Pose Estimation Network and a New Benchmark

abstract: Human parsing and pose estimation have recently received considerable interest due to their substantial application potentials. However, the existing datasets have limited numbers of images and annotations and lack a variety of human appearances and coverage of challenging cases in unconstrained environments. In this paper, we introduce a new benchmark named “Look into Person (LIP)” that provides a significant advancement in terms of scalability, diversity, and difficulty, which are crucial for future developments in human-centric analysis. This comprehensive dataset contains over 50,000 elaborately annotated images with 19 semantic part labels and 16 body joints, which are captured from a broad range of viewpoints, occlusions, and background complexities. Using these rich annotations, we perform detailed analyses of the leading human parsing and pose estimation approaches, thereby obtaining insights into the successes and failures of these methods. To further explore and take advantage of the semantic correlation of these two tasks, we propose a novel joint human parsing and pose estimation network to explore efficient context modeling, which can simultaneously predict parsing and pose with extremely high quality. Furthermore, we simplify the network to solve human parsing by exploring a novel self-supervised structure-sensitive learning approach, which imposes human pose structures into the parsing results without resorting to extra supervision. The datasets, code and models are available at http://www.sysu-hcp.net/lip/ .


title: Reinforcement learning by backpropagation through an LSTM model/critic

abstract: This paper describes backpropagation through an LSTM recurrent neural network model/critic, for reinforcement learning tasks in partially observable domains. This combines the advantage of LSTM's strength at learning long-term temporal dependencies to infer states in partially observable tasks, with the advantage of being able to learn high-dimensional and/or continuous actions with backpropagation's focused credit assignment mechanism


title: Are Autonomous Mobile Robots Able to Take Over Construction? A Review

abstract: Although construction has been known as a highly complex application field for autonomous robotic systems, recent advances in this field offer great hope for using robotic capabilities to develop automated construction. Today, space research agencies seek to build infrastructures without human intervention, and construction companies look to robots with the potential to improve construction quality, efficiency, and safety, not to mention flexibility in architectural design. However, unlike production robots used, for instance, in automotive industries, autonomous robots should be designed with special consideration for challenges such as the complexity of the cluttered and dynamic working space, human-robot interactions and inaccuracy in positioning due to the nature of mobile systems and the lack of affordable and precise self-positioning solutions. This paper briefly reviews state-ofthe-art research into automated construction by autonomous mobile robots. We address and classify the relevant studies in terms of applications, materials, and robotic systems. We also identify ongoing challenges and discuss about future robotic requirements for automated construction.


Organic Computing – Technical Systems for Survival in the Real World


Performance Evaluation of IoT Result for Machine Learning