title: A Fault Detection and Reconfigurable Control Architecture for Unmanned Aerial Vehicles

abstract: The past decade has seen the development of several reconfigurable flight control strategies for unmanned aerial vehicles. Although the majority of the research is dedicated to fixed wing vehicles, simulation results do support the application of reconfigurable flight control to unmanned rotorcraft. This paper develops a fault tolerant control architecture that couples techniques for fault detection and identification with reconfigurable flight control to augment the reliability and autonomy of an unmanned aerial vehicle. The architecture is applicable to fixed and rotary wing aircraft. An adaptive neural network feedback linearization technique is employed to stabilize the vehicle after the detection of a fault. Actual flight test results support the validity of the approach on an unmanned helicopter. The fault tolerant control architecture recovers aircraft performance after the occurrence of four different faults in the flight control system: three swash-plate actuator faults and a collective actuator fault. All of these faults are catastrophic under nominal conditions


title: Seeing Stars of Valence and Arousal in Blog Posts

abstract: Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two dimensions, based on the psychological model of Russell's circumplex model of affect and label a previously available corpus with multidimensional, real-valued annotations. Experimental results using regression and one-versus-all approaches of support vector machine classifiers show that although the latter approach provides better exact ordinal class prediction accuracy, regression techniques tend to make smaller scale errors.


title: Stakeholder analysis for R&D project management

abstract: R&D projects affect the interests of different stakeholders in different ways. Understanding the stakeholders and analysing their interests helps in the better management of R&D projects. In this paper we discuss a methodology to systematically analyse the stakeholders of R&D projects. This methodology includes Freeman's (1984) three levels of analysis: rational, process and transactional. Based on these three levels, the stakeholder management capability of an R&D project is determined. The final stage is based on Mitchell et al. (1997) approach to analysing the dynamics of stakeholders. This methodology is illustrated using a New Zealand case relating to a road pricing R&D project.


Provably secure session key distribution: the three party case


title: DART: Dropouts meet Multiple Additive Regression Trees

abstract: Multiple Additive Regression Trees (MART), an ensemble model of boosted regression trees, is known to deliver high prediction accuracy for diverse tasks, and it is widely used in practice. However, it suffers an issue which we call over-specialization, wherein trees added at later iterations tend to impact the prediction of only a few instances, and make negligible contribution towards the remaining instances. This negatively affects the performance of the model on unseen data, and also makes the model over-sensitive to the contributions of the few, initially added tress. We show that the commonly used tool to address this issue, that of shrinkage, alleviates the problem only to a certain extent and the fundamental issue of over-specialization still remains. In this work, we explore a different approach to address the problem that of employing dropouts, a tool that has been recently proposed in the context of learning deep neural networks. We propose a novel way of employing dropouts in MART, resulting in the DART algorithm. We evaluate DART on ranking, regression and classification tasks, using large scale, publicly available datasets, and show that DART outperforms MART in each of the tasks, with a significant margin. We also show that DART overcomes the issue of over-specialization to a considerable extent.


title: A survey of activity recognition in egocentric lifelogging datasets

abstract: With the appearance of many devices that everyday captured a large number of images. The rapid access to these huge collections of images and automatically characterizing an activity or an experience from this huge collection of unlabeled and unstructured egocentric data presents major challenges and requires novel and efficient algorithmic solutions. One of the big challenges of egocentric vision and lifelogging is to develop automatic algorithms to automatically characterize everyday activities. Such information is of high interest to predict migraines attacks or assure healthy behavior of patients and individuals of high healthy risk. In this work, we first conduct a comprehensive survey of existing egocentric datasets and we will present our future contribution to automatically characterize everyday activities.


title: Fuzzy Aided Ant Colony Optimization Algorithm to Solve Optimization Problem

abstract: In ant colony optimization technique (ACO), the shortest path is identified based on the pheromones deposited on the way by the traveling ants and the pheromones evaporate with the passage of time. Because of this nature, the technique only provides possible solutions from the neighboring node and cannot provide the best solution. By considering this draw back, this paper introduces a fuzzy integrated ACO technique which reduces the iteration time and also identifies the best path. The proposed technique is tested for travelling sales man problem and the performance is observed from the test results.


LANDSAT 7 SCAN LINE CORRECTOR-OFF GAP-FILLED PRODUCT DEVELOPMENT


title: Revisiting correlation-based filters for low-resolution and long-term visual tracking

abstract: In this paper, we revisit the problem of visual tracking by introducing a novel low-dimensional descriptor based on gradient distribution and specifically focus our attention on the problem of low-resolution and long-term visual tracking. We show that our tracking solution empowered by our proposed descriptor can effectively address the existing challenges in low-resolution and long-term visual tracking. Compared to the existing descriptors, the proposed method provides better robustness against local geometric and photometric variations. It adopts a new approach for aggregating information in a local neighborhood such that the sensitivity of the descriptor to noise and unreliable texture information is reduced. Integrating the proposed descriptor into a correlation-based tracking framework results in a robust and fast visual tracker. An extensive set of experiments on a number of large-scale benchmark datasets shows that the proposed method outperforms the state-of-the-art methods on low-resolution and long-term challenges, while achieving state-of-the-art performance in generic tracking.


title: Recovery of Interdependent Networks

abstract: Recent network research has focused on the cascading failures in a system of interdependent networks and the necessary preconditions for system collapse. An important question that has not been addressed is how to repair a failing system before it suffers total breakdown. Here we introduce a recovery strategy for nodes and develop an analytic and numerical framework for studying the concurrent failure and recovery of a system of interdependent networks based on an efficient and practically reasonable strategy. Our strategy consists of repairing a fraction of failed nodes, with probability of recovery γ, that are neighbors of the largest connected component of each constituent network. We find that, for a given initial failure of a fraction 1 − p of nodes, there is a critical probability of recovery above which the cascade is halted and the system fully restores to its initial state and below which the system abruptly collapses. As a consequence we find in the plane γ − p of the phase diagram three distinct phases. A phase in which the system never collapses without being restored, another phase in which the recovery strategy avoids the breakdown, and a phase in which even the repairing process cannot prevent system collapse.


title: Clone detection using abstract syntax trees

abstract: Existing research suggests that a considerable fraction (5-10%) of the source code of large scale computer programs is duplicate code ("clones"). Detection and removal of such clones promises decreased software maintenance costs of possibly the same magnitude. Previous work was limited to detection of either near misses differing only in single lexems, or near misses only between complete functions. The paper presents simple and practical methods for detecting exact and near miss clones over arbitrary program fragments in program source code by using abstract syntax trees. Previous work also did not suggest practical means for removing detected clones. Since our methods operate in terms of the program structure, clones could be removed by mechanical methods producing in-lined procedures or standard preprocessor macros. A tool using these techniques is applied to a C production software system of some 400 K source lines, and the results confirm detected levels of duplication found by previous work. The tool produces macro bodies needed for clone removal, and macro invocations to replace the clones. The tool uses a variation of the well known compiler method for detecting common sub expressions. This method determines exact tree matches; a number of adjustments are needed to detect equivalent statement sequences, commutative operands, and nearly exact matches. We additionally suggest that clone detection could also be useful in producing more structured code, and in reverse engineering to discover domain concepts and their implementations.


title: A market approach for handling power emergencies in multi-tenant data center

abstract: Power oversubscription in data centers may occasionally trigger an emergency when the aggregate power demand exceeds the capacity. Handling such an emergency requires a graceful power capping solution that minimizes the performance loss. In this paper, we study power capping in a multi-tenant data center where the operator supplies power to multiple tenants that manage their own servers. Unlike owner-operated data centers, the operator lacks control over tenants' servers. To address this challenge, we propose a novel market mechanism based on supply function bidding, called COOP, to financially incentivize and coordinate tenants' power reduction for minimizing total performance loss (quantified in performance cost) while satisfying multiple power capping constraints. We build a prototype to show that COOP is efficient in terms of minimizing the total performance cost, even compared to the ideal but infeasible case that assumes the operator has full control over tenants' servers. We also demonstrate that COOP is "win-win", increasing the operator's profit (through oversubscription) and reducing tenants' cost (through financial compensation for their power reduction during emergencies).


title: An autonomic system for intelligent truck parking

abstract: Traffic and road transport conditions are strongly influenced by the decisions of drivers. The more information they receive, the better decisions may be taken about their behavior. This is especially important in the transport of goods, since drivers are subject to traffic laws about maximum driving time and minimum time rest. In this paper a multi-agent system for negotiated management of parking spaces in road rest areas is presented. This system dynamically adapts itself to the preferences and needs of the drivers of goods about parking requests. The system is shown to be robust to incidents regarding the closure of road rest areas and an increased volume of freight traffic. The results also show that the number of illegal parkings is reduced resulting in greater road safety.


title: Deep Learning Inference in Facebook Data Centers: Characterization, Performance Optimizations and Hardware Implications

abstract: The application of deep learning techniques resulted in remarkable improvement of machine learning models. In this paper provides detailed characterizations of deep learning models used in many Facebook social network services. We present computational characteristics of our models, describe high performance optimizations targeting existing systems, point out their limitations and make suggestions for the future general-purpose/accelerated inference hardware. Also, we highlight the need for better co-design of algorithms, numerics and computing platforms to address the challenges of workloads often run in data centers.


title: k-ary search on modern processors

abstract: This paper presents novel tree-based search algorithms that exploit the SIMD instructions found in virtually all modern processors. The algorithms are a natural extension of binary search: While binary search performs one comparison at each iteration, thereby cutting the search space in two halves, our algorithms perform k comparisons at a time and thus cut the search space into k pieces. On traditional processors, this so-called k-ary search procedure is not beneficial because the cost increase per iteration offsets the cost reduction due to the reduced number of iterations. On modern processors, however, multiple scalar operations can be executed simultaneously, which makes k-ary search attractive. In this paper, we provide two different search algorithms that differ in terms of efficiency and memory access patterns. Both algorithms are first described in a platform independent way and then evaluated on various state-of-the-art processors. Our experiments suggest that k-ary search provides significant performance improvements (factor two and more) on most platforms.


title: Mutual coupling reduction in MIMO antennas at 5.6 GHz (WiMAX) based on an proposed uniplanar easily fabricated structure

abstract: In this paper a new technique for mutual coupling reduction of uniplanar MIMO antennas is presented. The only necessary information needed, is the transmission coefficient of a plane wave which is propagating over a specific structure, of a new proposed geometry.


title: State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements

abstract: The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a kinematic decoder. We recorded simultaneous neuronal ensemble and local field potential (LFP) activity from microelectrode arrays in primary motor cortex (M1) and dorsal (PMd) and ventral (PMv) premotor areas of two male rhesus monkeys performing a center-out reach-and-grasp task, while upper limb kinematics were tracked with a motion capture system with markers on the dorsal aspect of the forearm, hand, and fingers. A state decoder was trained to distinguish four behavioral states (baseline, reaction, movement, hold), while a kinematic decoder was trained to continuously decode hand end point position and 18 joint ...


title: Organoids as an in vitro model of human development and disease

abstract: The in vitro organoid model is a major technological breakthrough that has already been established as an essential tool in many basic biology and clinical applications. This near-physiological 3D model facilitates an accurate study of a range of in vivo biological processes including tissue renewal, stem cell/niche functions and tissue responses to drugs, mutation or damage. In this Review, we discuss the current achievements, challenges and potential applications of this technique.


title: Vanishing point detection with convolutional neural networks

abstract: Inspired by the finding that vanishing point (road tangent) guides driver's gaze, in our previous work we showed that vanishing point attracts gaze during free viewing of natural scenes as well as in visual search (Borji et al., Journal of Vision 2016). We have also introduced improved saliency models using vanishing point detectors (Feng et al., WACV 2016). Here, we aim to predict vanishing points in naturalistic environments by training convolutional neural networks in an end-to-end manner over a large set of road images downloaded from Youtube with vanishing points annotated. Results demonstrate effectiveness of our approach compared to classic approaches of vanishing point detection in the literature.


title: Oriented Response Networks

abstract: Deep Convolution Neural Networks (DCNNs) are capable of learning unprecedentedly effective image representations. However, their ability in handling significant local and global image rotations remains limited. In this paper, we propose Active Rotating Filters (ARFs) that actively rotate during convolution and produce feature maps with location and orientation explicitly encoded. An ARF acts as a virtual filter bank containing the filter itself and its multiple unmaterialised rotated versions. During back-propagation, an ARF is collectively updated using errors from all its rotated versions. DCNNs using ARFs, referred to as Oriented Response Networks (ORNs), can produce within-class rotation-invariant deep features while maintaining inter-class discrimination for classification tasks. The oriented response produced by ORNs can also be used for image and object orientation estimation tasks. Over multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we consistently observe that replacing regular filters with the proposed ARFs leads to significant reduction in the number of network parameters and improvement in classification performance. We report the best results on several commonly used benchmarks.


title: Elliptic and Hyperbolic Dielectric Lens Antennas in mm-Waves

abstract: Dielectric lenses can substantially improve antenna parameters, especially the planarity of radiated waves and the antenna gain. The paper deals with their application in millimeter-wave band. The main goal concerns the introduction of characteristics and differences between the most commonly used types of dielectric lens antennas, i.e. elliptic and hyperbolic. Their particular features as well as behavior of radiating systems incorporating the lenses are investigated. Specific features of these lenses are discussed for both, near-field and farfield based on simulation and measurement results.


title: The Web, the Users, and the MOS: Influence of HTTP/2 on User Experience

abstract: This work focuses on the evaluation of Web quality of experience as perceived by actual users and in particular on the impact of HTTP/1 vs HTTP/2. We adopt an experimental methodology that uses real web pages served through a realistic testbed where we control network, protocol, and application configuration. Users are asked to browse such pages and provide their subjective feedback, which we leverage to obtain the Mean Opinion Score (MOS), while the testbed records objective metrics.


title: The gut microbiota: A treasure for human health.

abstract: Abstract The interplay between the host and host-associated gut microbiota is an area of increasing interest during the recent decade. From young infants to elderly people, from primitive tribes to modern societies, accumulating evidence has suggested the association of critical physiological roles of gut microbiota in the pathogenesis of a variety of human metabolic, immunological and neurological diseases. Importantly, it appears that the relationship between the gut microbiota and disease is bidirectional, instead of causal or consequential. Personalized nutritional and therapeutic strategies targeting the gut microbiota such as prebiotics, probiotics, drugs and fecal microbiota transplantation may create a new era in the human health.


title: A dielectric lens antenna feeding with microstrip patch antennas for 77GHz long range radar application

abstract: A dielectric lens antenna feeding with microstrip patch antennas for long range radar application is presented. Using the microstrip patch antenna to feed the lens antenna is easy to implement due to its simple and compact size. Besides, the lens antenna can be easily achieved high gain and low side lobes. To achieve broader field of view for the radar application, two feeding microstrip patch antennas are utilized, and each of which has 10 degree of half-power beamwidth covering field of view of 20 degree. The measurement peak gain, half-power beamwidth, and side-lobe level are 20.2 dBi, 9 degree, and −16 dB, respectively. The measurement results show the input impedance of the proposed lens antenna is well matched to 50 Ω. All the measurement and simulated results are in good agreement.


title: Neural Architectures for Named Entity Recognition

abstract: State-of-the-art named entity recognition systems rely heavily on hand-crafted features and domain-specific knowledge in order to learn effectively from the small, supervised training corpora that are available. In this paper, we introduce two new neural architectures---one based on bidirectional LSTMs and conditional random fields, and the other that constructs and labels segments using a transition-based approach inspired by shift-reduce parsers. Our models rely on two sources of information about words: character-based word representations learned from the supervised corpus and unsupervised word representations learned from unannotated corpora. Our models obtain state-of-the-art performance in NER in four languages without resorting to any language-specific knowledge or resources such as gazetteers.


title: Extending space colonization tree modeling for artistic control and environmental interactions

abstract: There are a growing number of modeling techniques and algorithms for creating and modifying tree structures, both quickly and realistically. Many of these tools are part of larger software packages used for architecture or landscaping designs. In these tools the tree models are static and come from a library selection. In other areas, such as 3D modeling and design in film and animation, the tree creation tools are more open for artistic creativity and freedom of control. Much research has also been done on growing trees that are sensitive to some environment. That is, growing tree models where genus type, and natural and artificial environment factors are taken into heavy consideration. In this paper, we present an approach that facilitates user creativity while still providing a realistic response to environment factors. Our system adapts the Space Colonization Tree Modeling approach to allow for continued branch addition as well as environmental interaction. Feedback from experienced modelers who participated in a user study revealed that our approach generated the tree skeleton structures they intended and also provided realistic interaction with the environment.


title: A Robot Localization System Combining RSSI and Phase Shift in UHF-RFID Signals

abstract: This paper proposes a global indoor localization system based on the radio frequency identification (RFID) technology. A reader, installed on the robot, measures the received signal strength indication (RSSI) and the phase shift of UHF-RFID signals coming from a set of passive tags deployed on the ceiling of the environment. The position of the tags in the environment is only roughly known at the beginning. Exploiting the complementary features of RSSI and phase-shift information in RFID signals (together with odometry data), a multihypothesis extended and unscented Kalman filter is proposed to localize the robot and to simultaneously improve the initial estimate on the tag coordinates. The simulative and experimental results are reported to illustrate the effectiveness of the proposed approach.


A survey of parallel distributed genetic algorithms


title: Brain structural and functional abnormalities in mood disorders: implications for neurocircuitry models of depression

abstract: AbstractThe neural networks that putatively modulate aspects of normal emotional behavior have been implicated in the pathophysiology of mood disorders by converging evidence from neuroimaging, neuropathological and lesion analysis studies. These networks involve the medial prefrontal cortex (MPFC) and closely related areas in the medial and caudolateral orbital cortex (medial prefrontal network), amygdala, hippocampus, and ventromedial parts of the basal ganglia, where alterations in grey matter volume and neurophysiological activity are found in cases with recurrent depressive episodes. Such findings hold major implications for models of the neurocircuits that underlie depression. In particular evidence from lesion analysis studies suggests that the MPFC and related limbic and striato-pallido-thalamic structures organize emotional expression. The MPFC is part of a larger “default system” of cortical areas that include the dorsal PFC, mid- and posterior cingulate cortex, anterior temporal cortex, and entorhinal and parahippocampal cortex, which has been implicated in self-referential functions. Dysfunction within and between structures in this circuit may induce disturbances in emotional behavior and other cognitive aspects of depressive syndromes in humans. Further, because the MPFC and related limbic structures provide forebrain modulation over visceral control structures in the hypothalamus and brainstem, their dysfunction can account for the disturbances in autonomic regulation and neuroendocrine responses that are associated with mood disorders. This paper discusses these systems together with the neurochemical systems that impinge on them and form the basis for most pharmacological therapies.


title: Compact Substrate Integrated Waveguide (SIW) Monopulse Network for $Ku$ -Band Tracking System Applications

abstract: A compact single-layer substrate integrated waveguide (SIW) monopulse network for Ku-band tracking system applications is investigated and demonstrated in this work. The feeding network and the monopulse comparator are integrated into a substrate with a structure size of 8.2λ0×9λ0 at center frequency while 32 output ports are welded perpendicularly for connecting active transmit/receive modules. The optimal design of a 3-dB coupler with metallic slots and phase shifter with dual steps is presented. The relative frequency bandwidth ( | S11|, | S22|, and ) of the network is 6%, in which the phase imbalance of all ports is less than 5.5° [root mean square (rms)], the power imbalance is about 0.5 dB (rms). The insertion losses are about 3-5 dB and the isolations between the three channels are all better than 25 dB. The good performances of this SIW-based monopulse network are validated by measurements, showing the advantages of low cost, light weight, easy fabrication, etc. It presents an excellent candidate for Ku-band tracking systems or up-band millimeter-wave systems.


title: Incentivizing Energy Trading for Interconnected Microgrids

abstract: In this paper, we study the interactions among interconnected autonomous microgrids, and develop a joint energy trading and scheduling strategy. Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network. Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits. Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading. We design an incentive mechanism using Nash bargaining theory to encourage proactive energy trading and fair benefit sharing. We solve the bargaining problem by decomposing it into two sequential problems on social cost minimization and trading benefit sharing, respectively. For practical implementation, we propose a decentralized solution method with minimum information exchange overhead. Numerical studies based on realistic data demonstrate that the total cost of the interconnected-microgrids operation can be reduced by up to 13.2% through energy trading, and an individual participating microgrid can achieve up to 29.4% reduction in its cost through energy trading.


title: Remaining Useful Life Estimation of Critical Components With Application to Bearings

abstract: Prognostics activity deals with the estimation of the Remaining Useful Life (RUL) of physical systems based on their current health state and their future operating conditions. RUL estimation can be done by using two main approaches, namely model-based and data-driven approaches. The first approach is based on the utilization of physics of failure models of the degradation, while the second approach is based on the transformation of the data provided by the sensors into models that represent the behavior of the degradation. This paper deals with a data-driven prognostics method, where the RUL of the physical system is assessed depending on its critical component. Once the critical component is identified, and the appropriate sensors installed, the data provided by these sensors are exploited to model the degradation's behavior. For this purpose, Mixture of Gaussians Hidden Markov Models (MoG-HMMs), represented by Dynamic Bayesian Networks (DBNs), are used as a modeling tool. MoG-HMMs allow us to represent the evolution of the component's health condition by hidden states by using temporal or frequency features extracted from the raw signals provided by the sensors. The prognostics process is then done in two phases: a learning phase to generate the behavior model, and an exploitation phase to estimate the current health state and calculate the RUL. Furthermore, the performance of the proposed method is verified by implementing prognostics performance metrics, such as accuracy, precision, and prediction horizon. Finally, the proposed method is applied to real data corresponding to the accelerated life of bearings, and experimental results are discussed.


title: Modelling and numerical analysis of rotating antennas and rotating scatterers applied to aeronautical system simulations

abstract: This paper deals with the system distortions of radar and navigation systems by objects, in particular by rotation effects. This rotation can be a mechanically or electronically rotating antenna (e.g. VOR/DVOR antenna) or a simple rotating scatterer in the radiation field of a system (e.g. rotating radar antenna, rotating wind turbine). Effects are analyzed and evaluated by state of the art 3D simulations.


title: Motion Artifacts in Capacitive ECG Measurements: Reducing the Combined Effect of DC Voltages and Capacitance Changes Using an Injection Signal

abstract: Capacitive electrodes are a promising alternative to the conventional adhesive electrodes for ECG measurements. They provide more comfort to the patient when integrated in everyday objects (e.g., beds or seats) for long-term monitoring. However, the application of capacitive sensors is limited by their high sensitivity to motion artifacts. For example, motion at the body-electrode interface causes variations of the coupling capacitance which, in the presence of a dc voltage across the coupling capacitor, create strong artifacts in the measurements. The origin, relevance, and reduction of this specific and important type of artifacts are studied here. An injection signal is exploited to track the variations of the coupling capacitance in real time. This information is then used by an identification scheme to estimate the artifacts and subtract them from the measurements. The method was evaluated in simulations, lab environments, and in a real-life recording on an adult's chest. For the type of artifact under study, a strong artifact reduction ranging from 40 dB for simulated data to 9 dB for a given real-life recording was achieved. The proposed method is automated, does not require any knowledge about the measurement system parameters, and provides an online estimate for the dc voltage across the coupling capacitor.


title: Haemodynamic Effects in Healthy Horses Treated with an ACE-Inhibitor (Ramipril)

abstract: ACE-inhibitors are widely used in humans and in small animals for the treatment of CHF and systemic hypertension (Brown and Vaughan 1998; Wolfel 1998). Data regarding the haemodynamic effects and therapeutic use of these drugs are scarce for the horse. In an experimental study, it was demonstrated that the ACE-inhibitor ramipril is useful in contrasting the hypertensive effects of angiotensin I (De Luna et al. 1995). The clinical effectiveness of ramipril on the clinical signs of CHF in a horse with chronic valvular disease was described. The effects of the drug on arterial blood pressure were also demonstrated (Guglielmini et al. 2002).The aim of this study was to evaluate the haemodynamic effects of ramipril in healthy horses.


title: Vertical dust concentration measurements within the boundary layer to assess regional source–sink relations of dust in semi-arid grasslands of Inner Mongolia, China

abstract: Whether grazed semi-arid grassland can be regarded as a net-sink or net-source of aeolian dust is difficult to detect since deposition and emission processes are in gradual transition. In grassland, dust arrives from sources far away or is directly emitted and immediately part of the suspension load. The processes of dust emission or deposition can be mainly identified by vertical concentration measurements close to the ground and close to the sources. Often the concentration differences at the used measuring heights are too small to derive the direction of vertical fluxes precisely, especially at dust storms of light-to-medium intensities. For this reason, particle mass (PM10, PM1) and particle number concentrations were measured simultaneously at ground level and at heights of up to 80 m using a dust monitoring system lifted by a kite. The measured data of dust concentration gradients are used to calculate the vertical fluxes with the necessary certainty to derive the direction of the dust fluxes. Additionally, dust arriving from different directions and crossing different land use/land cover patterns was used to interpret the interaction of source/sink relationships along the transport direction upstream of the measuring point. Results show that dust measurements above grassland have to span greater height differences to derive vertical fluxes certainly, i.e., concentration gradients (Δc/Δz) of PM10 and PM1 provide an indication of the direction of the dust flux. Furthermore, the results indicate a close relationship between the particle size compositions of the dust and land use/cover patterns. Arable land in the surroundings causes an emission flux (vertical upward), especially of the coarser fractions >8 µm, whereas grassland causes deposition fluxes of size classes >2 µm during a dust storm of low intensity. Sources from far away are reflected by higher concentrations of finer particles, measured during a super-regional dust storm. Both, dust concentration gradients and particle size composition reflect the potential source areas and deposition processes adequately with regard to different land use/cover patterns up to 50 km away.


title: Making the Transition: Helping Teachers To Teach Online.

abstract: Teaching in the cyberspace classroom requires that we move beyond old models of pedagogy into new practices that are more facilitative. Teaching in cyberspace involves much more than simply taking old models of pedagogy and transferring them to a different medium. Unlike the face-to-face classroom, in online distance education, attention needs to be paid to the development of a sense of community within the group of participants in order for the learning process to be successful.


title: Optimizing coverage in a K-covered and connected sensor network using genetic algorithms

abstract: Sensing coverage of a sensor network characterizes how well an area is monitored or tracked by sensors. Connectivity is an important requirement that shows how nodes in a sensor network can effectively communicate. Some hotspot areas in the network are more important than other areas and need to be covered by more sensors. We are interested in an initial deployment strategy that maximizes the coverage area of wireless sensor network while preserving connectivity between nodes provided that all given hotspot regions are covered by at least k sensors. We propose a genetic algorithm based solution to find an optimal sensor node distribution. Experimental results are presented to evaluate our algorithm.


title: Denoising auto-encoders for learning of objects and tools affordances in continuous space

abstract: The concept of affordances facilitates the encoding of relations between actions and effects in an environment centered around the agent. Such an interpretation has important impacts on several cognitive capabilities and manifestations of intelligence, such as prediction and planning. In this paper, a new framework based on denoising Auto-encoders (dA) is proposed which allows an agent to explore its environment and actively learn the affordances of objects and tools by observing the consequences of acting on them. The dA serves as a unified framework to fuse multi-modal data and retrieve an entire missing modality or a feature within a modality given information about other modalities. This work has two major contributions. First, since training the dA is done in continuous space, there will be no need to discretize the dataset and higher accuracies in inference can be achieved with respect to approaches in which data discretization is required (e.g. Bayesian networks). Second, by fixing the structure of the dA, knowledge can be added incrementally making the architecture particularly useful in online learning scenarios. Evaluation scores of real and simulated robotic experiments show improvements over previous approaches while the new model can be applied in a wider range of domains.


title: Snake venom causes apoptosis by increasing the reactive oxygen species in colorectal and breast cancer cell lines

abstract: Snake venom possesses various kinds of proteins and neurotoxic polypeptides, which can negatively interfere with the neurotransmitter signaling cascade. This phenomenon occurs mainly due to the blocking of ion channels in the body system. Envenomation prevents or severely interrupts nerve impulses from being transmitted, inhibition of adenosine triphosphate synthesis, and proper functioning of the cardiac muscles. However, some beneficial properties of venoms have also been reported. The aim of this study was to examine the snake venom as an anticancer agent due to its inhibitory effects on cancer progression such as cell motility, cell invasion, and colony formation. In this study, the effect of venoms on phenotypic changes and the change on molecular level in colorectal and breast cancer cell lines were examined. A reduction of 60%–90% in cell motility, colony formation, and cell invasion was observed when these cell lines were treated with different concentrations of snake venom. In addition, the increase in oxidative stress that results in an increase in the number of apoptotic cancer cells was significantly higher in the venom-treated cell lines. Further analysis showed that there was a decrease in the expression of pro-inflammatory cytokines and signaling proteins, strongly suggesting a promising role for snake venom against breast and colorectal cancer cell progression. In conclusion, the snake venoms used in this study showed significant anticancer properties against colorectal and breast cancer cell lines.


Automotive Software Architectures


title: Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study

abstract: Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.


ASR Confidence Estimation with Speaker-Adapted Recurrent Neural Networks.


title: Conversational Actions and Discourse Situations

abstract: eve, Switzerland We use the idea that actions performed in a conversation become part of the common ground as the basis for a model of context that reconciles in a general and systematic fashion the differences between the theories of discourse context used for reference resolution, intention recognition, and dialogue management. We start from the treatment of anaphoric accessibility developed in discourse representation theory (DRT), and we show first how to obtain a discourse model that, while preserving DRT’s basic ideas about referential accessibility, includes information about the occurrence of speech acts and their relations. Next, we show how the different kinds of ‘structure’ that play a role in conversation—discourse segmentation, turn-taking, and grounding—can be formulated in terms of information about speech acts, and use this same information as the basis for a model of the interpretation of fragmentary input. 1. MOTIVATIONS Although the slogan “language is (joint) action” is accepted by almost everyone working in semantics or pragmatics, in practice this idea has resulted in theories of the common ground that differ in almost all essential details. We intend to show that this need not be the case, i.e., that the hypothesis that speech act occurrences are recorded in the common ground can serve as the basis for a model of language processing in context that reconciles in a general and systematic fashion the differences between the theories of the common ground adopted in current theories of reference resolution, intention recognition, and dialogue management. Our model is meant to be usable by an agent engaging in conversations as an internal, on-line representation of context. Our proposal is motivated by work on the Trains Project at the University of Rochester, one of whose aims is the development of a planning assistant able to engage with its user in spoken conversations in the domain of transportation by train (Allen et al. 1995). The Trains prototype must perform several kinds of linguistic activities that depend on a context, including reference resolution, intention recognition, and dialogue management. The problem is that although much has been written about individual contextual problems, many of the proposed representations are mutually incompatible, not usable by an agent involved in a conversation, or both. One example of poor fit between existing theories of context is the contrast between, on the one hand, linguistically motivated theories of context developed to account for the semantics of anaphora (e.g., Kamp and Reyle 1993); and on the other hand, the models of context proposed for intention recognition and dialogue management, whose emphasis is on capturing the effects of speech acts on the beliefs, intentions, and obligations of the participating agents (Allen 1983; Carberry 1990; Cohen and Levesque 1990; Perrault 1990; Traum and Allen 1994). These traditions resulted in very detailed proposals about context and context update; 1 but the resulting models of context differ significantly. It is not possible


title: Understanding Indoor Scenes Using 3D Geometric Phrases

abstract: Visual scene understanding is a difficult problem interleaving object detection, geometric reasoning and scene classification. We present a hierarchical scene model for learning and reasoning about complex indoor scenes which is computationally tractable, can be learned from a reasonable amount of training data, and avoids oversimplification. At the core of this approach is the 3D Geometric Phrase Model which captures the semantic and geometric relationships between objects which frequently co-occur in the same 3D spatial configuration. Experiments show that this model effectively explains scene semantics, geometry and object groupings from a single image, while also improving individual object detections.


title: Gameplay Design Patterns for Believable Non-Player Characters

abstract: Descriptions of humans require several qualities for people to experience them as believable: human body; selfawareness, intentional states, and self impelled actions; expression of emotions; ability to use natural language; and persistent traits. Based on these we analyze non-player character Claudette Perrick in The Elders Scroll IV: Oblivion to detect how these qualities can be created in the interactive environment of a game. We derive the gameplay design patterns Awareness of Surrounding, Visual Body Damage, Dissectible Bodies, Initiative, Own Agenda, Sense of Self, Emotional Attachment, Contextual Conversational Responses, and Goal-Driven Personal Development, which point to design choices that can be made when designing believable non-player characters in games.


title: Diurnal effects of prior heat stress exposure on sprint and endurance exercise capacity in the heat.

abstract: ABSTRACTActive individuals often perform exercises in the heat following heat stress exposure (HSE) regardless of the time-of-day and its variation in body temperature. However, there is no informa...


title: Serum alkaline phosphatase activity is regulated by a chromosomal region containing the alkaline phosphatase 2 gene (Akp2) in C57BL/6J and DBA/2J mice.

abstract: Quantitative trait locus (QTL) analyses were conducted to identify chromosomal regions that contribute to variability in serum alkaline phosphatase (AP) enzyme activity in mice derived from the C57BL/6J (B6) and DBA/2J (D2) inbred strains. Serum AP was measured in 400 B6D2 F2 mice at 5 mo and 400 B6D2 F2 mice at 15 mo of age that were genotyped at 96 microsatellite markers, and in 19 BXD recombinant inbred (RI) strains at 5 mo of age. A QTL on the distal end of chromosome 4 was present in all sex- and age-specific analyses with a peak logarithm of odds (LOD) score of 20.36 at 58.51 cM. The Akp2 gene, which encodes the major serum AP isozyme, falls within this QTL region at 70.2 cM where the LOD score reached 13.2 (LOD significance level set at 4.3). Serum AP activity was directly related to the number of D2 alleles of a single nucleotide polymorphism in the 5′-flanking region of the Akp2 gene, although no strain-related differences in hepatic expression of Akp2 RNA were found. A variety of sequence polymo...


title: A Closer Look at Spatiotemporal Convolutions for Action Recognition

abstract: In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition. Our motivation stems from the observation that 2D CNNs applied to individual frames of the video have remained solid performers in action recognition. In this work we empirically demonstrate the accuracy advantages of 3D CNNs over 2D CNNs within the framework of residual learning. Furthermore, we show that factorizing the 3D convolutional filters into separate spatial and temporal components yields significantly advantages in accuracy. Our empirical study leads to the design of a new spatiotemporal convolutional block "R(2+1)D" which gives rise to CNNs that achieve results comparable or superior to the state-of-the-art on Sports-1M, Kinetics, UCF101 and HMDB51.


title: Robust possibilistic programming for socially responsible supply chain network design: A new approach

abstract: The importance of social responsibility of corporate and business units is increasingly emphasized by researchers and practitioners in recent years. Since supply chains play important roles in today's business environment, the issue of social responsibility should be considered carefully when designing and planning of supply chains to move towards sustainability. This paper addresses the problem of socially responsible supply chain network design under uncertain conditions. To this aim, first a bi-objective mathematical programming model is developed wherein its objective functions include minimizing the total cost and maximizing the supply chain social responsibility. Then, for coping with uncertain parameters effectively, a novel possibilistic programming approach, called robust possibilistic programming (RPP), is proposed. Several varieties of RPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. A real industrial case study is provided to illustrate the performance and applicability of the proposed RPP models in practice.


title: A scalable verification solution for blockchains

abstract: Bitcoin and Ethereum, whose miners arguably collectively comprise the most powerful computational resource in the history of mankind, offer no more power for processing and verifying transactions than a typical smart phone. The system described herein bypasses this bottleneck and brings scalable computation to Ethereum. Our new system consists of a financial incentive layer atop a dispute resolution layer where the latter takes form of a versatile"verification game."In addition to secure outsourced computation, immediate applications include decentralized mining pools whose operator is an Ethereum smart contract, a cryptocurrency with scalable transaction throughput, and a trustless means for transferring currency between disjoint cryptocurrency systems.


title: CANDY: Conditional Adversarial Networks based End-to-End System for Single Image Haze Removal

abstract: Single image haze removal is a challenging and ill-posed problem. The existing haze removal methods in literature, including the recently introduced deep learning methods, model the problem of haze removal as that of estimating intermediate parameters, viz., scene transmission map and atmospheric light. These are used to compute the haze-free image from the hazy input image. Such an approach only focuses on accurate estimation of intermediate parameters, while the aesthetic quality of the haze-free image is unaccounted for in the optimization framework. Thus, errors in the estimation of intermediate parameters often lead to generation of inferior quality haze-free images. In this paper, we present CANDY (Conditional Adversarial Networks based Dehazing of hazY images), a fully end-to-end model which directly generates a clean haze-free image from a hazy input image. CANDY also incorporates the visual quality of haze-free image into the optimization function; thus, generating a superior quality haze-free image. This is one of the first works in literature to propose a fully end-to-end model for single image haze removal. Also, this is the first work to explore the concept of generative adversarial networks for the problem of single image haze removal. CANDY was trained on a synthetically created haze image dataset, while evaluation was performed on challenging synthetic as well as real haze image datasets. The extensive evaluation and comparison results of CANDY reveal that it significantly outperforms existing state-of-the-art haze removal methods in literature, both quantitatively as well as qualitatively.


title: Montreal Neural Machine Translation Systems for WMT'15

abstract: Neural machine translation (NMT) systems have recently achieved results comparable to the state of the art on a few translation tasks, including English→French and English→German. The main purpose of the Montreal Institute for Learning Algorithms (MILA) submission to WMT’15 is to evaluate this new approach on a greater variety of language pairs. Furthermore, the human evaluation campaign may help us and the research community to better understand the behaviour of our systems. We use the RNNsearch architecture, which adds an attention mechanism to the encoderdecoder. We also leverage some of the recent developments in NMT, including the use of large vocabularies, unknown word replacement and, to a limited degree, the inclusion of monolingual language models.


title: TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics

abstract: Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the temporal patterns of retweet activity for an original tweet. We model the system by Time-Dependent Hawkes process (TiDeH), which properly takes into account the circadian nature of the users and the aging of information. The input of the prediction model are observed retweet times and structural information about the underlying social network. We develop a procedure for parameter optimization and for predicting the future profiles of retweet activity at different time resolutions. We validate our methodology on a large corpus of Twitter data and demonstrate its systematic improvement over existing approaches in all the time regimes.


title: Semi-supervised learning by sparse representation

abstract: In this paper, we present a novel semi-supervised learning framework based on `1 graph. The `1 graph is motivated by that each datum can be reconstructed by the sparse linear superposition of the training data. The sparse reconstruction coefficients, used to deduce the weights of the directed `1 graph, are derived by solving an `1 optimization problem on sparse representation. Different from conventional graph construction processes which are generally divided into two independent steps, i.e., adjacency searching and weight selection, the graph adjacency structure as well as the graph weights of the `1 graph is derived simultaneously and in a parameter-free manner. Illuminated by the validated discriminating power of sparse representation in [16], we propose a semi-supervised learning framework based on `1 graph to utilize both labeled and unlabeled data for inference on a graph. Extensive experiments on semi-supervised face recognition and image classification demonstrate the superiority of our proposed semi-supervised learning framework based on `1 graph over the counterparts based on traditional graphs.


title: Millimeter-wave ink-jet printed RF energy harvester for next generation flexible electronics

abstract: In this effort, the authors present the first demonstration of a flexible, inkjet-printed mm-wave rectenna. The design considerations and processes of the individual antenna array and rectifier are described, demonstrating a realized gain of 5 dBi, and a single-lumped-component via-less rectifier with up to a 2.5V dc voltage for an input power of 18 dBm, at the operation frequency of 24 GHz. The printed system is finally demonstrated in a wireless operation test, in a bent condition, thereby setting the foundation for the emergence of mm-wave-powered smart skins and wearables for the Internet of Things (IoT).


title: A Reconfigurable Partially Reflective Surface (PRS) Antenna for Beam Steering

abstract: The design of a novel partially reflective surface (PRS) antenna with the capability of beam steering is presented in this paper. The beam steering is realized by employing a reconfigurable PRS structure to achieve a changeable reflection phase as well as using a phased array as the source to excite the PRS antenna. A prototype antenna including the biasing network is fabricated and measured. It achieves a consistent beam steering from – 15° to 15° with respect to the broadside direction across an overlapped frequency range from 5.5 to 5.7 GHz with measured realized gains over 12 dBi. Good agreement between the simulated and measured results for the input reflection coefficients and radiation patterns is achieved, which validates the feasibility of the design principle. Compared with other beam steering PRS antennas, the proposed one enables a larger beam steering angle with comparable gains, requires a simpler biasing network, and is more compact.


title: On-the-Fly calibration of low-cost gas sensors

abstract: Air quality monitoring is extremely important as air pollution has a direct impact on human health. Low-cost gas sensors are used to effectively perceive the environment by mounting them on top of mobile vehicles, for example, using a public transport network. Thus, these sensors are part of a mobile network and perform from time to time measurements in each others vicinity. In this paper, we study three calibration algorithms that exploit co-located sensor measurements to enhance sensor calibration and consequently the quality of the pollution measurements on-the-fly. Forward calibration, based on a traditional approach widely used in the literature, is used as performance benchmark for two novel algorithms: backward and instant calibration. We validate all three algorithms with real ozone pollution measurements carried out in an urban setting by comparing gas sensor output to high-quality measurements from analytical instruments. We find that both backward and instant calibration reduce the average measurement error by a factor of two compared to forward calibration. Furthermore, we unveil the arising difficulties if sensor calibration is not based on reliable reference measurements but on sensor readings of low-cost gas sensors which is inevitable in a mobile scenario with only a few reliable sensors. We propose a solution and evaluate its effect on the measurement accuracy in experiments and simulation.


title: Teach me to dance: exploring player experience and performance in full body dance games

abstract: We present a between-subjects user study designed to compare a dance instruction video to a rhythm game interface. The goal of our study is to answer the question: can these games be an effective learning tool for the activity they simulate? We use a body controlled dance game prototype which visually emulates current commercial games. Our research explores the player's perceptions of their own capabilities, their capacity to deal with a high influx of information, and their preferences regarding body-controlled video games. Our results indicate that the game-inspired interface elements alone were not a substitute for footage of a real human dancer, but participants overall preferred to have access to both forms of media. We also discuss the dance rhythm game as abstracted entertainment, exercise motivation, and realistic dance instruction.


title: Transport-based single frame super resolution of very low resolution face images

abstract: Extracting high-resolution information from highly degraded facial images is an important problem with several applications in science and technology. Here we describe a single frame super resolution technique that uses a transport-based formulation of the problem. The method consists of a training and a testing phase. In the training phase, a nonlinear Lagrangian model of high-resolution facial appearance is constructed fully automatically. In the testing phase, the resolution of a degraded image is enhanced by finding the model parameters that best fit the given low resolution data. We test the approach on two face datasets, namely the extended Yale Face Database B and the AR face datasets, and compare it to state of the art methods. The proposed method outperforms existing solutions in problems related to enhancing images of very low resolution.


title: An F-band active phase shifter in 28nm CMOS

abstract: In this paper an active phase shifter with high linear frequency dependency is presented for a FSK quadrature demodulator. The circuit is based on a coupled resonator to increase the linear phase shifting range and bandwidth. A capacitor bank, implemented as a digitally controlled artificial dielectric transmission line, is used to realize the phase shifting operation. The design methodology is explained and measurements of an implementation in a 28nm bulk CMOS are given. The design has a phase range of 123° and a phase resolution of 7.2° with a frequency dependent phase difference of 12.2 °/GHz. The circuit occupies an active area of 0.01 mm2 and draws a current of 9.5 mA from a 900mV power supply.


title: COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

abstract: This research has been supported by the project “Desarrollo de Tecnicas Inteligentes e Interactivas de Mineria de Textos” (PROMETEO/2009/119) and the project reference ACOMP/2011/001 from the Valencian Government, as well as by the Spanish Government (grant no. TIN2009-13391-C04-01).


title: Attack Tree Generation by Policy Invalidation

abstract: Attacks on systems and organisations increasingly exploit human actors, for example through social engineering, complicating their formal treatment and automatic identification. Formalisation of human behaviour is difficult at best, and attacks on socio-technical systems are still mostly identified through brainstorming of experts. In this work we formalize attack tree generation including human factors; based on recent advances in system models we develop a technique to identify possible attacks analytically, including technical and human factors. Our systematic attack generation is based on invalidating policies in the system model by identifying possible sequences of actions that lead to an attack. The generated attacks are precise enough to illustrate the threat, and they are general enough to hide the details of individual steps.


title: Joint optimization of energy efficiency and spectrum efficiency in 5G ultra-dense networks

abstract: The heterogeneous deployment of ultra dense small cells such as femtocells in the coverage area of the traditional macrocells is seen as a cost-efficient solution to provide network capacity, indoor coverage and green communications towards sustainable environments in the fifth generation wireless network. However, the unplanned and ultra-dense deployment of femtocells will lead to increase in total energy consumption, cross-tier interference (interference between macrocells and femtocells), co-tier interference (interference between neighbouring femtocells) and inadequate QoS provisioning. Therefore, there is a need to develop a radio resource allocation algorithm that will jointly maximize the energy efficiency (EE) and spectrum efficiency (SE) of the overall networks. Unfortunately, maximizing the EE results in low performance of the SE and vice versa. This paper investigates how to balance the trade-off that arises when maximizing both the EE and the SE simultaneously. The joint EE and SE maximization problem is formulated as a multi-objective optimization problem, which is later converted into a single-objective optimization problem using the weighted sum method. An iterative algorithm based on the Lagrangian dual decomposition method is proposed. Simulation results show that the proposed algorithm achieves an optimal trade-off between the EE and the SE with fast convergence.


title: Effective hyperparameter optimization using Nelder-Mead method in deep learning

abstract: In deep learning, deep neural network (DNN) hyperparameters can severely affect network performance. Currently, such hyperparameters are frequently optimized by several methods, such as Bayesian optimization and the covariance matrix adaptation evolution strategy. However, it is difficult for non-experts to employ these methods. In this paper, we adapted the simpler coordinate-search and Nelder-Mead methods to optimize hyperparameters. Several hyperparameter optimization methods were compared by configuring DNNs for character recognition and age/gender classification. Numerical results demonstrated that the Nelder-Mead method outperforms the other methods and achieves state-of-the-art accuracy for age/gender classification.


title: SAM: enabling practical spatial multiple access in wireless LAN

abstract: Spatial multiple access holds the promise to boost the capacity of wireless networks when an access point has multiple antennas. Due to the asynchronous and uncontrolled nature of wireless LANs, conventional MIMO technology does not work efficiently when concurrent transmissions from multiple stations are uncoordinated. In this paper, we present the design and implementation of a crosslayer system, called SAM, that addresses the challenges of enabling spatial multiple access for multiple devices in a random access network like WLAN. SAM uses a chain-decoding technique to reliably recover the channel parameters for each device, and iteratively decode concurrent frames with misaligned symbol timings and frequency offsets. We propose a new MAC protocol, called CCMA, to enable concurrent transmissions by different mobile stations while remaining backward compatible with 802.11. Finally, we implement the PHY and MAC layer of SAM using the Sora high-performance software radio platform. Our evaluation results under real wireless conditions show that SAM can improve network uplink throughput by 70% with two antennas over 802.11.


title: Study on load and aerodynamic characteristics for rotating wind turbine rotor

abstract: Based on the Blade Element Momentum theory, the model of load and aerodynamic characteristics for wind turbine blades is carried out that the blade tip loss is fully considered. Applying the structural dynamic equations of blades, the aeroelastic model of the rotating rotor and the numerical simulation method of the deflections are presented under the aerodynamic loads and centrifugal forces. Coupling the aerodynamic characteristics and the structure stress, the flapwise and edgewise loads, deflections and accelerations of the rotating blades are calculated and plotted. Finally, one 5MW wind turbine rotor is chosen that the aerodynamic loads and output power are simulated. The bending displacement and vibration response are investigated and compared between different wind speed and working time to prove the accuracy and reliability of the model presented in this paper. The results have important theoretical significance for the fatigue life prediction and vibration noise estimate of wind turbine blades.


title: A Prospective Longitudinal Study Testing Relationships between Meaningful Activities, Basic Psychological Needs Fulfillment, and Meaning in Life

abstract: The current study used a prospective longitudinal design to determine whether change in meaningful activity over an 11-month period could help explain change in meaning in life in a sample of 174 undergraduate and graduate students. The Engagement in Meaningful Activities Survey, Basic Psychological Needs Scales (i.e., autonomy, competence, relatedness), and the Meaning in Life Questionnaire were used as indicators of the constructs of meaningful activity, basic psychological needs fulfillment, and meaning and purpose in life. The findings were in support of the study hypotheses and indicated that change in meaningful activity explained both change in basic psychological needs fulfillment (i.e., autonomy, competence, relatedness) and change in meaning in life. Further, this study reports findings consistent with results from cross-sectional studies in support of the hypothesis that change in meaningful activity may influence change in meaning in life through two pathways: a direct path of influence from m...


title: Halftone Image Watermarking by Content Aware Double-sided Embedding Error Diffusion

abstract: In this paper, we carry out a performance analysis from a probabilistic perspective to introduce the EDHVW methods' expected performances and limitations. Then, we propose a new general error diffusion based halftone visual watermarking (EDHVW) method, Content aware Double-sided Embedding Error Diffusion (CaDEED), via considering the expected watermark decoding performance with specific content of the cover images and watermark, different noise tolerance abilities of various cover image content and the different importance levels of every pixel (when being perceived) in the secret pattern (watermark). To demonstrate the effectiveness of CaDEED, we propose CaDEED with expectation constraint (CaDEED-EC) and CaDEED-NVF&IF (CaDEED-N&I). Specifically, we build CaDEED-EC by only considering the expected performances of specific cover images and watermark. By adopting the noise visibility function (NVF) and proposing the importance factor (IF) to assign weights to every embedding location and watermark pixel, respectively, we build the specific method CaDEED-N&I. In the experiments, we select the optimal parameters for NVF and IF via extensive experiments. In both the numerical and visual comparisons, the experimental results demonstrate the superiority of our proposed work.


title: Cross-Platform Development: Software that Lasts

abstract: The design of software that is easy to port or deliberately targeted for multiple platforms is a neglected area of software engineering. A promising solution is to link components and toolkits through XML and reflection. One of the more compelling definitions of software engineering is, the multiperson construction of multiversion software. The popular view of software engineering focuses on the first part of this definition - managing teams to produce a large product. But just as important is the view inherent in the second part of the definition - identifying specific parts of a product so that experts can design them and organizations can mass-produce them free of language and environment dependencies. Component-based software engineering has made tremendous strides toward satisfying both parts of the definition. Through the use of middleware constructed using reflection and controlled through XML specifications, it is possible to give the components in a large software system a high degree of platform and even language independence. The result is long lived software that can migrate gracefully as platforms improve and change


title: Rough Sets and Boolean Reasoning

abstract: Abstract In this article, we discuss methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis.


title: Implementation in Fpgas of Jacobi Method to Solve the Eigenvalue and Eigenvector Problem

abstract: This work shows a modular architecture based on FPGA?s to solve the eigenvalue problem according to the Jacobi method. This method is able to solve the eigenvalues and eigenvectors concurrently. The main contribution of this work is the low execution time compared with other sequential algorithms, and minimal internal FPGA consumed resources, mainly due to the fact of using the CORDIC algorithm. Two CORDIC modules have been designed to solve the trigonometric operations involved. A parallel CORDIC architecture is proposed as it is the best option to compute the eigenvalues with this method. Both CORDIC modules can work in rotation and vector mode. The whole system has been done in VHDL language, attempting to optimize the design.


title: PT-LDA: A latent variable model to predict personality traits of social network users

abstract: Online social network presents a great opportunity to analyze user behavior and mine the implicit personality traits from the social network data. Considering the personality recognition as a multi-label classification problem, this paper proposes a new probabilistic topic model (PT-LDA model) to predict the personality traits within the framework of Five Factor Model. The proposed model extends the Latent Dirichlet Allocation (LDA) model to integrate the n-gram features into few latent topics and each topic is characterized by not only the multinomial distribution over words but also the Gaussian distributions over personality traits. This paper develops a Gibbs-EM algorithm to solve the proposed model iteratively based on Gibbs sampling and expectation maximization. Quantitative evaluation shows that PT-LDA is more accurate, efficient and robust than several baselines. Our experiment also shows that the proposed model can be used to extract the interpretable topics associated with each personality trait, which provides a new way to uncover user behaviors in online social network.


“Nice Picture Comment!” Graphicons in Facebook Comment Threads


title: Tree-maps: a space-filling approach to the visualization of hierarchical information structures

abstract: A method for visualizing hierarchically structured information is described. The tree-map visualization technique makes 100% use of the available display space, mapping the full hierarchy onto a rectangular region in a space-filling manner. This efficient use of space allows very large hierarchies to be displayed in their entirety and facilitates the presentation of semantic information. Tree-maps can depict both the structure and content of the hierarchy. However, the approach is best suited to hierarchies in which the content of the leaf nodes and the structure of the hierarchy are of primary importance, and the content information associated with internal nodes is largely derived from their children.<<ETX>>


title: Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding

abstract: t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for dimensionality reduction and visualization that has become widely popular in recent years. Efficient implementations of t-SNE are available, but they scale poorly to datasets with hundreds of thousands to millions of high dimensional data-points. We present Fast Fourier Transform-accelerated Interpolation-based t-SNE (FIt-SNE), which dramatically accelerates the computation of t-SNE. The most time-consuming step of t-SNE is a convolution that we accelerate by interpolating onto an equispaced grid and subsequently using the fast Fourier transform to perform the convolution. We also optimize the computation of input similarities in high dimensions using multi-threaded approximate nearest neighbors. We further present a modification to t-SNE called"late exaggeration,"which allows for easier identification of clusters in t-SNE embeddings. Finally, for datasets that cannot be loaded into the memory, we present out-of-core randomized principal component analysis (oocPCA), so that the top principal components of a dataset can be computed without ever fully loading the matrix, hence allowing for t-SNE of large datasets to be computed on resource-limited machines.


title: Incidence of hepatitis A in Israel following universal immunization of toddlers.

abstract: CONTEXT ::: In Israel, the mean annual incidence of hepatitis A disease was 50.4 per 100 000 during 1993-1998. A 2-dose universal hepatitis A immunization program aimed at children aged 18 and 24 months (without a catch-up campaign) was started in 1999. ::: ::: ::: OBJECTIVE ::: To observe the impact of toddlers-only universal vaccination on hepatitis A virus disease in Israel. ::: ::: ::: DESIGN AND SETTING ::: Ongoing passive national surveillance of hepatitis A cases in Israel has been conducted since 1993 by the Ministry of Health. An active surveillance program in the Jerusalem district in 1999-2003 provided validation for the passive program. ::: ::: ::: MAIN OUTCOME MEASURE ::: Incidence of reported hepatitis A disease, 1993-2004. ::: ::: ::: RESULTS ::: Overall vaccine coverage in Israel in 2001-2002 was 90% for the first dose and 85% for the second dose. A decline in disease rates was observed before 1999 among the Jewish but not the non-Jewish population. After initiation of the program, a sharp decrease in disease rates was observed in both populations. The annual incidence of 2.2 to 2.5 per 100 000 during 2002-2004 represents a 95% or greater reduction for each year with respect to the mean incidence during 1993-1998 (P<.001). For children aged 1 through 4 years, a 98.2% reduction in disease was observed in 2002-2004, compared with the prevaccination period (P<.001). However, a sharp decline was also observed in all other age groups (84.3% [<1 year], 96.5% [5-9 years], 95.2% [10-14 years], 91.3% [15-44 years], 90.6% [45-64 years], and 77.3% [>or=65 years]). Among the Jewish population in the Jerusalem district, in whom the active surveillance program was successfully conducted, a more than 90% reduction of disease was demonstrated. Of the 433 cases reported nationwide in 2002-2004 in whom vaccination status could be ascertained, 424 (97.9%) received no vaccine and none received 2 doses. ::: ::: ::: CONCLUSION ::: This universal toddlers-only immunization program in Israel demonstrated not only high effectiveness of hepatitis A vaccination but also marked herd protection, challenging the need for catch-up hepatitis A vaccination programs.


title: A fast expert system for electrocardiogram arrhythmia detection

abstract: A fast expert system for electrocardiogram (ECG) arrhythmia detection has been designed in this study. Selecting proper wavelet details, the ECG signals are denoised and beat locations are detected. Beat locations are later used to locate the peaks of the individual waves present in each cardiac cycle. Onsets and offsets of the P and T waves are also detected. These are considered as ECG features which are later used for arrhythmia detection utilizing a novel fuzzy classifier. Fourteen types of arrhythmias and abnormalities can be detected implementing the proposed procedure. We have evaluated the algorithm on the MIT-BIH arrhythmia database. Application of the wavelet filter with the scaling function which closely resembles the shape of the ECG signal has been shown to provide precise results in this study.


title: When Online Dating Meets Nash Social Welfare

abstract: Mobile dating applications such as Coffee Meets Bagel, Tantan, and Tinder, have become significant for young adults to meet new friends and discover romantic relationships. From a system designer’s perspective, in order to achieve better user experience in these applications, we should take both the efficiency and fairness of a dating market into consideration, so as to increase the overall satisfaction for all users. Towards this goal, we investigate the nature of diminishing marginal returns for online dating markets (i.e., captured by the submodularity), and trade-off between the efficiency and fairness of the market with Nash social welfare. We further design effective online algorithms to the apps. We verify our models and algorithms through sound theoretical analysis and empirical studies by using real data and show that our algorithms can significantly improve the ecosystems of the online dating applications.


title: Experimental evaluation of functional splits for 5G cloud-RANs

abstract: Centralized RAN processing has been identified as one of the major enablers for 5G mobile network access. By moving the baseband units (BBU) to the Cloud, multiple instances can be instantiated on the fly, serving several Remote Radio Head (RRH) units. The goal is to satisfy the existing demand of particular geographical areas, whereas drastically reducing the overall CAPEX and OPEX costs of the mobile operators. In this work, we present an experimental study of real Cloud-RAN deployments, with respect to different functional splits. We use as a reference architecture the 3GPP LTE stack, and argue about the functional split applicability in contemporary networks. We evaluate Layer 2 functional splits, that can be used for the convergence of multiple heterogeneous wireless technologies in an all-in-one unit. By deploying our approach in a real testbed setup, we extract the backhaul network transfer requirements for the different splits and present our experimental findings, compared with the respective simulation results.


The discovery of effective ploidy barriers between diploid solanums.


title: Training restricted Boltzmann machines

abstract: Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. They have attracted much attention as building blocks for the multi-layer learning systems called deep belief networks, and variants and extensions of RBMs have found application in a wide range of pattern recognition tasks. This tutorial introduces RBMs from the viewpoint of Markov random fields, starting with the required concepts of undirected graphical models. Different learning algorithms for RBMs, including contrastive divergence learning and parallel tempering, are discussed. As sampling from RBMs, and therefore also most of their learning algorithms, are based on Markov chain Monte Carlo (MCMC) methods, an introduction to Markov chains and MCMC techniques is provided. Experiments demonstrate relevant aspects of RBM training. HighlightsWe review the state-of-the-art in training restricted Boltzmann machines (RBMs) from the perspective of graphical models.Variants and extensions of RBMs are used in a wide range of pattern recognition tasks.The required background on graphical models and Markov chain Monte Carlo methods is provided.Theoretical and experimental results are presented.


title: Progress towards implementing the e-Bug Project in Poland

abstract: The e-Bug Project provides useful educational tools that can be implemented in primary and secondary schools, and plays important role in reducing the burden of community-acquired infections. Consideration of the Polish education system and educational programmes in schools facilitated the selection of the best age groups for the implementation of e-Bug materials in Poland. The Polish version of the resources has been reviewed by a special group of experts and teachers from pilot schools. The pilot implementation is currently provided by demonstration lessons for educators and recorded as a demonstration material. The e-Bug teaching pack is a valuable resource that is useful for improving knowledge about microorganisms, hygiene and antimicrobial agents, and is aimed at pupils, teachers and parents.


title: Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors

abstract: The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.


title: Digital forensics: exploring validation, verification & certification

abstract: Digital forensic teams and laboratories are now common place within Australia, particularly associated with law enforcement and intelligence agencies. The digital forensics discipline is rapidly evolving to become a scientific practice with domain-specific guideline. These guidelines are still under discussion in an attempt to progress the discipline so as to become as solid and robust in its scientific underpinnings as other forensic disciplines. Influential players, practitioners and observers all agree that rigorous standards need to be adopted to align this science with other forensic sciences. How does one assess the scientific nature of digital forensics with so many independent computing and IT elements combined, and what are the outcomes of each assessment method? Solutions are proposed regularly justifying their use but to date no one international or national standard exists. This paper does not propose a solution but rather explores the concept of Validation and Verification (V&V) with particular respect to digital forensic tools. The paper also explores ISO17025 "General requirements for the competence of testing and calibration laboratories" and develops the testing process to satisfy this standard to allow for Australian digital forensic laboratories to be eligible for certification.


title: A low-power high-sensitivity analog front-end for PPG sensor

abstract: This paper presents a low-power analog front-end (AFE) photoplethysmography (PPG) sensor fabricated in 0.35 µm CMOS process. The AFE amplifies the weak photocurrent from the photodiode (PD) and converts it to a strong voltage at the output. In order to decrease the power consumption, the circuits are designed in subthreshold region; so the total biasing current of the AFE is 10 µ A. Since the large input DC photocurrent is a big issue for the PPG sensing circuit, we apply a DC photocurrent rejection technique by adding a DC current-cancellation loop to reject the large DC photocurrent up to 10 µA. In addition, a pseudo resistor is used to reduce the high-pass corner frequency below 0.5 Hz and Gm-C filter is adapted to reject the out-of-band noise higher than 16 Hz. For the whole sensor, the amplifier chain can achieve a total gain of 140 dBμ and an input integrated noise current of 68.87 pA rms up to 16 Hz.


title: Applying deep learning based automatic bug triager to industrial projects

abstract: Finding the appropriate developer for a bug report, so called `Bug Triage', is one of the bottlenecks in the bug resolution process. To address this problem, many approaches have proposed various automatic bug triage techniques in recent studies. We argue that most previous studies focused on open source projects only and did not consider deep learning techniques. In this paper, we propose to use Convolutional Neural Network and word embedding to build an automatic bug triager. The results of the experiments applied to both industrial and open source projects reveal benefits of the automatic approach and suggest co-operation of human and automatic triagers. Our experience in integrating and operating the proposed system in an industrial development environment is also reported.


title: Gait and Trajectory Optimization for Legged Systems Through Phase-Based End-Effector Parameterization

abstract: We present a single trajectory optimization formulation for legged locomotion that automatically determines the gait sequence, step timings, footholds, swing-leg motions, and six-dimensional body motion over nonflat terrain, without any additional modules. Our phase-based parameterization of feet motion and forces allows to optimize over the discrete gait sequence using only continuous decision variables. The system is represented using a simplified centroidal dynamics model that is influenced by the feet's location and forces. We explicitly enforce friction cone constraints, depending on the shape of the terrain. The nonlinear programming problem solver generates highly dynamic motion plans with full flight phases for a variety of legged systems with arbitrary morphologies in an efficient manner. We validate the feasibility of the generated plans in simulation and on the real quadruped robot ANYmal. Additionally, the entire solver software TOWR , which used to generate these motions is made freely available.


title: The use of visual feedback, in particular mirror visual feedback, in restoring brain function

abstract: This article reviews the potential use of visual feedback, focusing on mirror visual feedback, introduced over 15 years ago, for the treatment of many chronic neurological disorders that have long been regarded as intractable such as phantom pain, hemiparesis from stroke and complex regional pain syndrome. Apart from its clinical importance, mirror visual feedback paves the way for a paradigm shift in the way we approach neurological disorders. Instead of resulting entirely from irreversible damage to specialized brain modules, some of them may arise from short-term functional shifts that are potentially reversible. If so, relatively simple therapies can be devised—of which mirror visual feedback is an example—to restore function.


title: The Latium acute stroke registry: data quality and validation of the NIH Stroke Scale in predicting in-hospital death

abstract: Background and Purpose : The experimental phase of the stroke registry for the region of Lazio, completed in 2003, collected clinical data using an ad hoc survey on emergency room (ER) patients who presented with suspected stroke. We evaluated the accuracy and completeness of the data collected and assessed the validity of the National Institute of Health Stroke Scale (NIHSS) in predicting in-hospital mortality. Methods and Results : Two different indicators were developed to evaluate the quality of the registry: the response (% ER patients surveyed with suspected stroke) and the coverage rates (% ER patients surveyed discharged with stroke). Their values by type of emergency service were respectively: first aid ER =59.3% and 45.5%; level one ER =39.7% and 27.6%; level two ER =17.2% e 12.0%. Stroke severity, age, type of emergency facility and time of arrival at the ER were associated with the accuracy and completeness of the registry. The in-hospital mortality predictive model showed good discriminative ability (area under the receiveroperating characteristic (ROC) curve (AUC)=0.79 (95%CI 0.75, 0.84)) and good calibration (Hosmer- Lemeshow’s goodness-of-fit (HL) test p=0.48). AUC and HL tests, estimated after applying the linear predictor to an external validation cohort, were 0.79 (95%CI 0.74, 0.84) and p=0.23 respectively. Conclusions : Survey completeness and accuracy were associated with patient and facility characteristics, suggesting that it could be improved. The NIHSS calculated at ER admission is a good predictor of inhospital mortality.


title: Fault detection of eccentricity and bearing damage in a PMSM by means of wavelet transforms decomposition of the stator current

abstract: This paper presents the study of permanent magnet synchronous machines (PMSM) running with eccentricity and bearings damage. The objective is to detect and identify the fault through the current signature analysis. The stator current has been analyzed by means of both Fourier (FFT) and Discrete Wavelet (DWT) transforms. Simulations have been carried out with a two-dimensional (2-D) finite element analysis (FEA), and they have been also compared with experiments. The results prove that the method proposed can be used to identify mechanical faults in a PMSM.


title: Semiconducting layered blue phosphorus: A computational study

abstract: We investigate a previously unknown phase of phosphorus that shares its layered structure and high stability with the black phosphorus allotrope. We find the in-plane hexagonal structure and bulk layer stacking of this structure, which we call "blue phosphorus," to be related to graphite. Unlike graphite and black phosphorus, blue phosphorus displays a wide fundamental band gap. Still, it should exfoliate easily to form quasi-two-dimensional structures suitable for electronic applications. We study a likely transformation pathway from black to blue phosphorus and discuss possible ways to synthesize the new structure.


Multi-task , Multi-Kernel Learning for Estimating Individual Wellbeing


title: DeepQA Jeopardy! Gamification: A Machine-Learning Perspective

abstract: DeepQA is a large-scale natural language processing (NLP) question-and-answer system that responds across a breadth of structured and unstructured data, from hundreds of analytics that are combined with over 50 models, trained through machine learning. After the 2011 historic milestone of defeating the two best human players in the Jeopardy! game show, the technology behind IBM Watson, DeepQA, is undergoing gamification into real-world business problems. Gamifying a business domain for Watson is a composite of functional, content, and training adaptation for nongame play. During domain gamification for medical, financial, government, or any other business, each system change affects the machine-learning process. As opposed to the original Watson Jeopardy!, whose class distribution of positive-to-negative labels is 1:100, in adaptation the computed training instances, question-and-answer pairs transformed into true-false labels, result in a very low positive-to-negative ratio of 1:100 000. Such initial extreme class imbalance during domain gamification poses a big challenge for the Watson machine-learning pipelines. The combination of ingested corpus sets, question-and-answer pairs, configuration settings, and NLP algorithms contribute toward the challenging data state. We propose several data engineering techniques, such as answer key vetting and expansion, source ingestion, oversampling classes, and question set modifications to increase the computed true labels. In addition, algorithm engineering, such as an implementation of the Newton-Raphson logistic regression with a regularization term, relaxes the constraints of class imbalance during training adaptation. We conclude by empirically demonstrating that data and algorithm engineering are complementary and indispensable to overcome the challenges in this first Watson gamification for real-world business problems.


Autoencoders with Variable Sized Latent Vector for Image Compression


title: Soft wearable device for lower limb assistance: Assessment of an optimized energy efficient actuation prototype

abstract: There is a shared trend, in engineering fields related to robotics, to privilege soft systems on behalf of hard ones. This direction stems from the desire of designing and implementing energetically efficient systems that are light, soft, wieldy, reconfigurable and with augmented dexterity. Soft wearable devices will also improve very important characteristics such as wearability and comfort. The design and implementation of a wearable system meeting these requirements is being developed in the XoSoft EU project. In this work we present the design of a soft modular energy-efficient lower limb exoskeleton. It is based on an energy efficiency analysis optimization. Novelty of this work is given by the integration of quasi-passive actuations in a soft exoskeleton. The goal is to assist-during daily tasks-subjects with low to moderate mobility impairments reducing by 10% to 30% the mechanical energy requirements and improving the gait (stability, tiredness, etc). Assistance is meant to be on hip, knee and ankle, in a unilateral or bilateral configuration. A first modular single-joint prototype is described, developed and assessed on a post-stroke patient gait. The comparison between the validation and the simulation showed a similar behavior and an energy reduction of 7.8%. The overall measured assistance given by the exoskeleton on the user's hip segment in terms of power is 9.3%.


title: ON-BOARD LANE DETECTION SYSTEM FOR INTELLIGENT VEHICLE BASED ON MONOCULAR VISION

abstract: The objective of this research is to develop a monocular vision system that can locate the positions of the road lane in real time. First, Canny approach is used to obtain edge map from the road image acquired from monocular camera mount on vehicle; Second, a matching process is conducted to normalize the candidates of road line; Third, a searching method is used for reinforce potential road lines while degraded those impossible ones; Forth, a linking condition is used to further enhance the confidence of the potential lane lines, and a K-means cluster algorithm is employed to localize the lane lines; Finally, a on board system is designed for experiment. The proposed system is shown to work well under various conditions on the roadway. Besides, the computation cost is inexpensive and the system's response is almost real time.