title: Improving user authentication on mobile devices: a touchscreen graphical password

abstract: Typing text passwords is challenging when using touchscreens on mobile devices and this is becoming more problematic as mobile usage increases. We designed a new graphical password scheme called Touchscreen Multi-layered Drawing (TMD) specifically for use with touchscreens. We conducted an exploratory user study of three existing graphical passwords on smart phones and tablets with 31 users. From this, we set our design goals for TMD to include addressing input accuracy issues without having to memorize images, while maintaining an appropriately secure password space. Design features include warp cells which allow TMD users to continuously draw their passwords across multiple layers in order to create more complex passwords than normally possible on a small screen. We compared the usability of TMD to Draw A Secret (DAS) on a tablet computer and a smart phone with 90 users. Results show that TMD improves memorability, addresses the input accuracy issues, and is preferred as a replacement for text passwords on mobile devices.


title: Ovulation as a male mating prime: Subtle signs of women's fertility influence men's mating cognition and behavior.

abstract: Women’s reproductive fertility peaks for a few days in the middle of their cycle around ovulation. Because conception is most likely to occur inside this brief fertile window, evolutionary theories suggest that men possess adaptations designed to maximize their reproductive success by mating with women during their peak period of fertility. In this article, we provide evidence from 3 studies that subtle cues of fertility prime mating motivation in men, thus facilitating psychological and behavioral processes associated with the pursuit of a sexual partner. In Study 1, men exposed to the scent of a woman near peak levels of fertility displayed increased accessibility to sexual concepts. Study 2 demonstrated that, among men who reported being sensitive to odors, scent cues of fertility triggered heightened perceptions of women’s sexual arousal. Study 3 revealed that, in a face-to-face interaction, high levels of female fertility were associated with a greater tendency for men to make risky decisions and to behaviorally mimic a female partner. Hence, subtle cues of fertility led to a cascade of mating-related processes—from lower order cognition to overt behavior—that reflected heightened mating motivation. Implications for theories of goal pursuit, romantic attraction, and evolutionary psychology are discussed.


title: On joint design with intrinsic variable compliance: derivation of the DLR QA-Joint

abstract: In this paper we introduce a classification of intrinsically compliant joint mechanisms. Furthermore, we outline design considerations for realizing such devices in order to match the requirements for robust and performant actuation. Based on this elaboration, a new design concept is presented, the DLR QA-Joint. Its performance is investigated by various experiments, covering velocity increase using the elastic energy, joint protection capabilities, and control performance.


title: Prediction of Peak ground acceleration for earthquakes by using intelligent methods

abstract: Peak ground acceleration (PGA) is equal to the maximum ground acceleration that occurred during earthquake shaking at a location and the design basis earthquake ground motion is often defined in terms of PGA. In this paper, three intelligent methods are proposed for predicting of PGA in regions where PGA value is greater than 0.5g. These knowledge base methods are Adaptive Network Based Fuzzy Inference System (ANFIS), Support Vector Regression Neural Network (SVRNN) and Radial Basis Function Network (RBFN). For this purpose, four key parameters of the past earthquakes are used, such as Moment Magnitude, Rupture distance, Site class and Style of faulting. The performed simulations determine the performance of the discussed networks and the obtained results of the methods by using Matlab Software show that SVRNN is better for PGA prediction in future earthquakes.


SanssouciDB: An In-Memory Database for Processing Enterprise Workloads.


title: Measuring Message Propagation and Social Influence on Twitter.com

abstract: Although extensive studies have been conducted on online social networks (OSNs), it is not clear how to characterize information propagation and social influence, two types of important but not well defined social behavior. This paper presents a measurement study of 58M messages collected from 700K users on Twitter.com, a popular social medium. We analyze the propagation patterns of general messages and show how breaking news (Michael Jackson's death) spread through Twitter. Furthermore, we evaluate different social influences by examining their stabilities, assessments, and correlations. This paper addresses the complications as well as challenges we encounter when measuring message propagation and social influence on OSNs. We believe that our results here provide valuable insights for future OSN research.


title: Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification

abstract: Improving speech system performance in noisy environments remains a challenging task, and speech enhancement (SE) is one of the effective techniques to solve the problem. Motivated by the promising results of generative adversarial networks (GANs) in a variety of image processing tasks, we explore the potential of conditional GANs (cGANs) for SE, and in particular, we make use of the image processing framework proposed by Isola et al. [1] to learn a mapping from the spectrogram of noisy speech to an enhanced counterpart. The SE cGAN consists of two networks, trained in an adversarial manner: a generator that tries to enhance the input noisy spectrogram, and a discriminator that tries to distinguish between enhanced spectrograms provided by the generator and clean ones from the database using the noisy spectrogram as a condition. We evaluate the performance of the cGAN method in terms of perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and equal error rate (EER) of speaker verification (an example application). Experimental results show that the cGAN method overall outperforms the classical short-time spectral amplitude minimum mean square error (STSA-MMSE) SE algorithm, and is comparable to a deep neural network-based SE approach (DNN-SE).


title: pKa Modulation of the Acid/Base Catalyst within GH32 and GH68: A Role in Substrate/Inhibitor Specificity?

abstract: Glycoside hydrolases of families 32 (GH32) and 68 (GH68) belong to clan GH-J, containing hydrolytic enzymes (sucrose/fructans as donor substrates) and fructosyltransferases (sucrose/fructans as donor and acceptor substrates). In GH32 members, some of the sugar substrates can also function as inhibitors, this regulatory aspect further adding to the complexity in enzyme functionalities within this family. Although 3D structural information becomes increasingly available within this clan and huge progress has been made on structure-function relationships, it is not clear why some sugars bind as inhibitors without being catalyzed. Conserved aspartate and glutamate residues are well known to act as nucleophile and acid/bases within this clan. Based on the available 3D structures of enzymes and enzyme-ligand complexes as well as docking simulations, we calculated the pKa of the acid-base before and after substrate binding. The obtained results strongly suggest that most GH-J members show an acid-base catalyst that is not sufficiently protonated before ligand entrance, while the acid-base can be fully protonated when a substrate, but not an inhibitor, enters the catalytic pocket. This provides a new mechanistic insight aiming at understanding the complex substrate and inhibitor specificities observed within the GH-J clan. Moreover, besides the effect of substrate entrance on its own, we strongly suggest that a highly conserved arginine residue (in the RDP motif) rather than the previously proposed Tyr motif (not conserved) provides the proton to increase the pKa of the acid-base catalyst.


title: Monocular Localization of a moving person onboard a Quadrotor MAV

abstract: In this paper, we propose a novel method to recover the 3D trajectory of a moving person from a monocular camera mounted on a quadrotor micro aerial vehicle (MAV). The key contribution is an integrated approach that simultaneously performs visual odometry (VO) and persistent tracking of a person automatically detected in the scene. All computation pertaining to VO, detection and tracking runs onboard the MAV from a front-facing monocular RGB camera. Given the gravity direction from an inertial sensor and the knowledge of the individual's height, a complete 3D trajectory of the person within the reconstructed scene can be estimated. When the ground plane is detected from the triangulated 3D points, the absolute metric scale of the trajectory and the 3D map is also recovered. Our extensive indoor and outdoor experiments show that the system can localize a person moving naturally within a large area. The system runs at 17 frames per second on the onboard computer. A walking person was successfully tracked for two minutes and an accurate trajectory was recovered over a distance of 140 meters with our system running onboard.


title: How Learning to Read Changes the Cortical Networks for Vision and Language

abstract: Does literacy improve brain function? Does it also entail losses? Using functional magnetic resonance imaging, we measured brain responses to spoken and written language, visual faces, houses, tools, and checkers in adults of variable literacy (10 were illiterate, 22 became literate as adults, and 31 were literate in childhood). As literacy enhanced the left fusiform activation evoked by writing, it induced a small competition with faces at this location, but also broadly enhanced visual responses in fusiform and occipital cortex, extending to area V1. Literacy also enhanced phonological activation to speech in the planum temporale and afforded a top-down activation of orthography from spoken inputs. Most changes occurred even when literacy was acquired in adulthood, emphasizing that both childhood and adult education can profoundly refine cortical organization.


title: Supply chain risk management in French companies

abstract: The risk thematic is not new in management, but it is a recent and growing subject in supply chain management. Supply Chain Risk Management (SCRM) plays a major role in successfully managing business processes in a proactive manner. Supply chain risk has multiple sources including process, control, demand, supply and environment. Supply chain management, faced with these risks, requires specific and adequate responses such as techniques, attitude and strategies for management of risk. This paper is based on an empirical study of 142 general managers and logistics and supply chain managers in 50 different French companies. It demonstrates that for organizations to be effective, SCRM must be a management function that is inter-organizational in nature and closely related to strategic and operational realities of the activity in question. Moreover, the findings of our empirical study suggest that effective SCRM is based on collaboration (collaborative meetings, timely and relevant information exchanges) and the establishment of joint and common transverse processes with industrial partners.


title: HyperST-Net: Hypernetworks for Spatio-Temporal Forecasting

abstract: Spatio-temporal (ST) data, which represent multiple time series data corresponding to different spatial locations, are ubiquitous in real-world dynamic systems, such as air quality readings. Forecasting over ST data is of great importance but challenging as it is affected by many complex factors, including spatial characteristics, temporal characteristics and the intrinsic causality between them. In this paper, we propose a general framework (HyperST-Net) based on hypernetworks for deep ST models. More specifically, it consists of three major modules: a spatial module, a temporal module and a deduction module. Among them, the deduction module derives the parameter weights of the temporal module from the spatial characteristics, which are extracted by the spatial module. Then, we design a general form of HyperST layer as well as different forms for several basic layers in neural networks, including the dense layer (HyperST-Dense) and the convolutional layer (HyperST-Conv). Experiments on three types of real-world tasks demonstrate that the predictive models integrated with our framework achieve significant improvements, and outperform the state-of-the-art baselines as well.


title: On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks

abstract: Compression is a key step to deploy large neural networks on resource-constrained platforms. As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight. In this paper, we study the representation power of quantized neural networks. First, we prove the universal approximability of quantized ReLU networks on a wide class of functions. Then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bit-width of weights for function-independent and function-dependent structures. Our results reveal that, to attain an approximation error bound of $\epsilon$, the number of weights needed by a quantized network is no more than $\mathcal{O}\left(\log^5(1/\epsilon)\right)$ times that of an unquantized network. This overhead is of much lower order than the lower bound of the number of weights needed for the error bound, supporting the empirical success of various quantization techniques. To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.


title: IoT Based Low Cost Smart Irrigation System

abstract: One of the essential and basic services to survive on earth is Water. Recent time increasing the scarcity of water due to growing in population. So this is becoming as a universal obstacle. The old irrigation system which demands a lot of water, so it needs smart techniques for reducing the percentage of wasting available water for the irrigation. We have been seeing the increasing of huge demand for Internet of things in every domain from small and simple applications to large and complex applications. Practically implementation of a Smart Irrigation is very complex deal, but association with IoT using Smart wireless sensors it brings a great management system. The Humidity and Temperature Sensor sense the both water vapor content and temperature around the plant. The Soil Moisture Sensor sense the soil moisture of a plant, if water content is below minimum requirement then water will supply from water reservoir using relay and Ultrasonic sensor measures the water level of reservoir after that sends the data to ESP8266 NodeMCU. ESP8266 NodeMCU is a Microcontroller gets the data from smart wireless sensors, process the data and send to destination through A Message Queue Telemetry Transport (MQTT)protocol.


title: Managing Configurable Business Process as a Service to Satisfy Client Transactional Requirements

abstract: Business Process as a Service (BPaaS) has emerged in recent years as a new paradigm for hosting and offering complex business operations using a cloud service delivery model. BPaaS providers can increase the diversity of their market by enabling their services to be configured to meet individual client needs. However, in order for the clients to trust external services for sensitive business processes, they must be assured that the transactional integrity of the service complies with their own business policies. To provide this trust, we propose a BPaaS configuration method that allows clients to ensure their transactional requirements will not be violated, while configuring the activities, resources, and data objects used in the service. Clients formalize transactional requirements using an expressive and easy-to-use template set. The service is then configured and verified in a three-step process that applies binary decision diagram analysis and model checking. An experimental study using a configurable Web store checkout BPaaS demonstrates the feasibility of our approach. Our state space reduction methods allow verification of models with hundreds of configurable features and requirements.


title: Image ranking and retrieval based on multi-attribute queries

abstract: We propose a novel approach for ranking and retrieval of images based on multi-attribute queries. Existing image retrieval methods train separate classifiers for each word and heuristically combine their outputs for retrieving multiword queries. Moreover, these approaches also ignore the interdependencies among the query terms. In contrast, we propose a principled approach for multi-attribute retrieval which explicitly models the correlations that are present between the attributes. Given a multi-attribute query, we also utilize other attributes in the vocabulary which are not present in the query, for ranking/retrieval. Furthermore, we integrate ranking and retrieval within the same formulation, by posing them as structured prediction problems. Extensive experimental evaluation on the Labeled Faces in the Wild(LFW), FaceTracer and PASCAL VOC datasets show that our approach significantly outperforms several state-of-the-art ranking and retrieval methods.


title: Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics.

abstract: The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.


title: The Relationship between Excessive Internet Use and Depression: A Questionnaire-Based Study of 1,319 Young People and Adults

abstract: Background: There is a growing awareness of a psychiatric construct that needs to be better defined and understood: Internet addiction (IA). Recently there has been much public conc


title: The effect of avatar realism in immersive social virtual realities

abstract: This paper investigates the effect of avatar realism on embodiment and social interactions in Virtual Reality (VR). We compared abstract avatar representations based on a wooden mannequin with high fidelity avatars generated from photogrammetry 3D scan methods. Both avatar representations were alternately applied to participating users and to the virtual counterpart in dyadic social encounters to examine the impact of avatar realism on self-embodiment and social interaction quality. Users were immersed in a virtual room via a head mounted display (HMD). Their full-body movements were tracked and mapped to respective movements of their avatars. Embodiment was induced by presenting the users' avatars to themselves in a virtual mirror. Afterwards they had to react to a non-verbal behavior of a virtual interaction partner they encountered in the virtual space. Several measures were taken to analyze the effect of the appearance of the users' avatars as well as the effect of the appearance of the others' avatars on the users. The realistic avatars were rated significantly more human-like when used as avatars for the others and evoked a stronger acceptance in terms of virtual body ownership (VBO). There also was some indication of a potential uncanny valley. Additionally, there was an indication that the appearance of the others' avatars impacts the self-perception of the users.


title: Structured-light 3D surface imaging: a tutorial

abstract: We provide a review of recent advances in 3D surface imaging technologies. We focus particularly on noncontact 3D surface measurement techniques based on structured illumination. The high-speed and high-resolution pattern projection capability offered by the digital light projection technology, together with the recent advances in imaging sensor technologies, may enable new generation systems for 3D surface measurement applications that will provide much better functionality and performance than existing ones in terms of speed, accuracy, resolution, modularization, and ease of use. Performance indexes of 3D imaging system are discussed, and various 3D surface imaging schemes are categorized, illustrated, and compared. Calibration techniques are also discussed, since they play critical roles in achieving the required precision. Numerous applications of 3D surface imaging technologies are discussed with several examples.


MULTIHOP ROUTING OF TELEMETRY DATA IN DRONE SWARMS


title: Making a case for the teaching of reading across the curriculum in higher education

abstract: Over the past two decades there has been much written in the literature about the importance of reading and the importance of teaching students reading strategies to improve their reading comprehension. Reading is one of the most important academic tasks encountered by students. In higher education, students are exposed to a number of texts and textbooks that require independent reading. At this level they are expected to comprehend what they read so that they can analyse, critique, evaluate and synthesize information from various sources. Many students entering higher education are not adequately prepared to meet these challenges. This article highlights the literacy situation in South Africa with a particular focus on reading both in school and in higher education. In addition, the article highlights the importance of teaching students reading strategies across the curriculum in order to improve their reading comprehension, thereby enhancing their chances of academic success. The implications of this research for policy makers and academics in higher education institutions are outlined and some suggestions are made.


title: Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RL

abstract: Humans and animals can learn complex predictive models that allow them to accurately and reliably reason about real-world phenomena, and they can adapt such models extremely quickly in the face of unexpected changes. Deep neural network models allow us to represent very complex functions, but lack this capacity for rapid online adaptation. The goal in this paper is to develop a method for continual online learning from an incoming stream of data, using deep neural network models. We formulate an online learning procedure that uses stochastic gradient descent to update model parameters, and an expectation maximization algorithm with a Chinese restaurant process prior to develop and maintain a mixture of models to handle non-stationary task distributions. This allows for all models to be adapted as necessary, with new models instantiated for task changes and old models recalled when previously seen tasks are encountered again. Furthermore, we observe that meta-learning can be used to meta-train a model such that this direct online adaptation with SGD is effective, which is otherwise not the case for large function approximators. In this work, we apply our meta-learning for online learning (MOLe) approach to model-based reinforcement learning, where adapting the predictive model is critical for control; we demonstrate that MOLe outperforms alternative prior methods, and enables effective continuous adaptation in non-stationary task distributions such as varying terrains, motor failures, and unexpected disturbances.


title: Compact windings design for traction induction motors and SCIGs

abstract: In this article winding schemes and calculation methods for original energy efficient electric machines are introduced. They are intended for application in electrical equipment of autonomous systems. These machines winding conductors have a rectangular cross-section. Slot connection spots and overhang parts are reduced. This allows one to reduce copper intensity and the machine length and improve efficiency. Specific end-face schemes and the conductors connection diagram for the stator windings are given. Overhang deviation and machine characteristics calculation features are described. The thermal simulation was undertaken for original conductors. The calculation results are compared with serial produced electric machines.


title: A prototype electrical actuator for aircraft flaps and slats

abstract: This paper considers the electrical actuation of aircraft wing surfaces, with particular emphasis upon flap systems. It discusses existing electro-hydraulic systems and proposes an electrical alternative, examining the potential system benefits in terms of increased functionality, maintenance and life cycle costs. The paper then progresses to describe a full scale actuation demonstrator of the flap system, including the high speed electrical drive, step down gearbox and flaps. Detailed descriptions are given of the fault tolerant motor, power electronics, control architecture and position sensor systems, along with a range of test results, demonstrating the system in operation


title: RFID-based smart parking management system

abstract: AbstractIn this paper, the adoption of passive Radio Frequency Identification (RFID) tag-to-tag communication paradigm within the context of a smart parking system is evangelised, in terms of achieving improved energy-efficiency and operational effectiveness. To demonstrate that the joint routing and RFID readers’ transmission power minimisation problem is studied, considering tag-to-tag communication. The superiority of the proposed framework against conventional direct RFID reader-tag communication is demonstrated in terms of: (i) reduction of RFID readers’ transmission power to the minimum required to guarantee connectivity, and (ii) expansion of RFID reader’s coverage area towards communicating with more distant tags, otherwise unreachable through direct communication.


title: Filtering of laser altimetry data using a slope adaptive filter

abstract: A point set obtained by laser altimetry represents points from not only the ground surface but also objects found on it. For civil works applications points representing the surface of non-ground objects have to be removed from the point set in a filtering process. This paper describes modifications made to an existing “slope based” filtering algorithm, and presents some results obtained from the use of the filter. The “slope based” filter operates on the assumption that terrain slopes do not rise above a certain threshold, and that features in the data that have slopes above this threshold do not belong to the natural terrain surface. However, this assumption limits the use of the filter to terrain with gentle slopes. To overcome this limitation, the filter was modified in manner that the threshold varies with respect to the slope of the terrain. The results of tests carried out using the modified filter confirm that the modification reduces the number of Type I errors (ground points in steep terrain are not filtered off). Further numerical comparison of the filter output with a reference data set for the same site (obtained photogrammetrically) show that the filter generates relatively minimal Type II errors. The output of the modified slope filter was also compared with the output from a filtering found in the commercial software package, “Terrascan”.


title: Tensor completion via optimization on the product of matrix manifolds

abstract: We present a method for tensor completion using optimization on low-rank matrix manifolds. Our notion of tensor-rank is based on the recently proposed framework of tensor- Singular Value Decomposition (t-SVD) in [1], [2]. In contrast to convex optimization methods used in [1] that operate in a high-dimensional space, in the manifold setting, one works directly in the reduced dimensionality space and is thus able to significantly reduce the computational costs [3], [4]. In this paper we focus on 3-D data and under the tensor algebraic framework of [1], [2] we show that a 3-D tensor of fixed tubal-rank can be seen as an element of the product manifold of fixed low-rank matrices in the Fourier domain. The tensor completion problem then reduces to finding the best approximation to the sampled data on this product manifold. Further, for 3-D data we consider and compare recovery performance under two approaches. In the first approach one samples entire mode-3 fibers of the tensor, which we refer to as tubal-sampling. The second approach employs element-wise sampling and we simply refer to this method as sampling. For these two types of sampling approaches, we present simulation results for surveillance video data and show that recovery under random sampling has better performance compared to the random tubal-sampling.


title: New Reno and SACK TCP implemented in UMTS

abstract: Mobile Telecommunication System shortly UMTS is seen as the future mobile network. The use of the existing Transmission control protocol (TCP) as transport protocol in an UMTS environment is an understandable choice. TCP is the most commonly used transport protocol on the internet, and if it will be used as transport protocol in an UMTS environment, the UMTS devices will be compatible with today most used internet services such as mail and HTTP. To implement TCP in a third generation network such as UMTS there has been made several recommendations. In this paper the performances of two TCP variants are evaluated and compared, namely TCP New Reno and TCP SACK. When the results are analyzed there can be concluded that with the recommendations a positive performance difference is achieved. There can also be concluded that in most cases TCP SACK performs better than TCP New Reno.


title: A comparative study of traditional and newly proposed features for recognition of speech under stress

abstract: It is well known that the performance of speech recognition algorithms degrade in the presence of adverse environments where a speaker is under stress, emotion, or Lombard (1911) effect. This study evaluates the effectiveness of traditional features in recognition of speech under stress and formulates new features which are shown to improve stressed speech recognition. The focus is on formulating robust features which are less dependent on the speaking conditions rather than applying compensation or adaptation techniques. The stressed speaking styles considered are simulated angry and loud. Lombard effect speech, and noisy actual stressed speech from the SUSAS database which is available on a CD-ROM through the NATO IST/TG-01 research group and LDC. In addition, this study investigates the immunity of the linear prediction power spectrum and fast Fourier transform power spectrum to the presence of stress. Our results show that unlike fast Fourier transform's (FFT) immunity to noise, the linear prediction power spectrum is more immune than FFT to stress as well as to a combination of a noisy and stressful environment. Finally, the effect of various parameter processing such as fixed versus variable preemphasis, liftering, and fixed versus cepstral mean normalization are studied. Two alternative frequency partitioning methods are proposed and compared with traditional mel-frequency cepstral coefficients (MFCC) features for stressed speech recognition. It is shown that the alternate filterbank frequency partitions are more effective for recognition of speech under both simulated and actual stressed conditions.


title: Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not

abstract: Deep-learning has dramatically changed the world overnight. It greatly boosted the development of visual perception, object detection, and speech recognition, etc. That was attributed to the multiple convolutional processing layers for abstraction of learning representations from massive data. The advantages of deep convolutional structures in data processing motivated the applications of artificial intelligence methods in robotic problems, especially perception and control system, the two typical and challenging problems in robotics. This paper presents a survey of the deep-learning research landscape in mobile robotics. We start with introducing the definition and development of deep-learning in related fields, especially the essential distinctions between image processing and robotic tasks. We described and discussed several typical applications and related works in this domain, followed by the benefits from deep-learning, and related existing frameworks. Besides, operation in the complex dynamic environment is regarded as a critical bottleneck for mobile robots, such as that for autonomous driving. We thus further emphasize the recent achievement on how deep-learning contributes to navigation and control systems for mobile robots. At the end, we discuss the open challenges and research frontiers.


title: InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines.

abstract: In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published updated standards and guidelines for the clinical interpretation of sequence variants with respect to human diseases on the basis of 28 criteria. However, variability between individual interpreters can be extensive because of reasons such as the different understandings of these guidelines and the lack of standard algorithms for implementing them, yet computational tools for semi-automated variant interpretation are not available. To address these problems, we propose a suite of methods for implementing these criteria and have developed a tool called InterVar to help human reviewers interpret the clinical significance of variants. InterVar can take a pre-annotated or VCF file as input and generate automated interpretation on 18 criteria. Furthermore, we have developed a companion web server, wInterVar, to enable user-friendly variant interpretation with an automated interpretation step and a manual adjustment step. These tools are especially useful for addressing severe congenital or very early-onset developmental disorders with high penetrance. Using results from a few published sequencing studies, we demonstrate the utility of InterVar in significantly reducing the time to interpret the clinical significance of sequence variants.


title: Gate Driver Circuit Using Pre-Charge Structure and Time-Division Multiplexing Driving Scheme for Active-Matrix LCDs Integrated with In-Cell Touch Structures

abstract: This paper presents a new gate driver circuit for active-matrix liquid crystal displays with an in-cell touch structure designed by hydrogenated amorphous silicon thin-film transistors. To increase the reporting rate of a touch panel, the proposed circuit can be used to pause display operations to perform touch sensing operations several times per frame. The proposed circuit exploits a pre-charge structure that alleviates the leakage current and long-term stress of a driving TFT during the touch sensing operation. Simulation results confirm that the proposed gate driver circuit can generate a highly uniform output waveform after each touch sensing operation that lasts for 200 μs when the circuit is operate at 85 °C. The variations of the rising and falling time are suppressed below 3.71%, confirming the feasibility of use of the proposed gate driver circuit for an in-cell touch panel.


title: Removing gamification from an enterprise SNS

abstract: Gamification, the use of game mechanics in non-gaming applications, has been applied to various systems to encourage desired user behaviors. In this paper, we examine patterns of user activity in an enterprise social network service after the removal of a points-based incentive system. Our results reveal that the removal of the incentive scheme did reduce overall participation via contribution within the SNS. We also describe the strategies by point leaders and observe that users geographically distant from headquarters tended to comment on profiles outside of their home country. Finally, we describe the implications of the removal of extrinsic rewards, such as points and badges, on social software systems, particularly those deployed within an enterprise.


title: Culture and Computer-Mediated Communication: Toward New Understandings

abstract: This collection of articles was originally inspired by several presentations at CATaC'041 and subsequent critical discussion of their use of the frameworks for cultural analyses developed by Edward T. Hall (1966, 1976) and Gert Hofstede (e.g., 1980, 1991). In response to these presentations and discussion, we developed this special thematic section for the Journal of Computer-Mediated Communication.


title: Summing up evidence: one answer is not always enough

abstract: Are meta-analyses the brave new world, or are the critics of such combined analyses right to say that the biases inherent in clinical trials make them uncombinable? Negative trials are often unreported, and hence can be missed by meta-analysts. And how much heterogeneity between trials is acceptable? A recent major criticism is that large randomised trials do not always agree with a prior meta-analysis. Neither individual trials nor meta-analyses, reporting as they do on population effects, tell how to treat the individual patient. Here we take a more rounded approach to meta-analyses, arguing that their strengths outweigh their weaknesses, although the latter must not be brushed aside.


title: Power Factor Correction Boost Converter Based on the Three-State Switching Cell

abstract: The need for solid-state ac-dc converters to improve power quality in terms of power factor correction, reduced total harmonic distortion at input ac mains, and precisely regulated dc output has motivated the investigation of several topologies based on classical converters such as buck, boost, and buck-boost converters. Boost converters operating in continuous-conduction mode have become particularly popular because reduced electromagnetic interference levels result from their utilization. Within this context, this paper introduces a bridgeless boost converter based on a three-state switching cell (3SSC), whose distinct advantages are reduced conduction losses with the use of magnetic elements with minimized size, weight, and volume. The approach also employs the principle of interleaved converters, as it can be extended to a generic number of legs per winding of the autotransformers and high power levels. A literature review of boost converters based on the 3SSC is initially presented so that key aspects are identified. The theoretical analysis of the proposed converter is then developed, while a comparison with a conventional boost converter is also performed. An experimental prototype rated at 1 kW is implemented to validate the proposal, as relevant issues regarding the novel converter are discussed.


title: New Sliding-Mode Observer for Position Sensorless Control of Permanent-Magnet Synchronous Motor

abstract: This paper proposes a novel sliding-mode observer (SMO) to achieve the sensorless control of permanent-magnet synchronous motor (PMSM). An observer is built according to the back electromotive force (EMF) model after the back EMF equivalent signal is obtained. In this way, not only are low-pass filter and phase compensation module eliminated, but also estimation accuracy is improved. Numerical simulations and experiments with an 11-kW low-speed PMSM are carried out. The results demonstrate that the novel SMO can effectively estimate rotor position and speed and achieve good static and dynamic performance.


title: Deep Convolutional Neural Networks for Fire Detection in Images.

abstract: Detecting fire in images using image processing and computer vision techniques has gained a lot of attention from researchers during the past few years. Indeed, with sufficient accuracy, such systems may outperform traditional fire detection equipment. One of the most promising techniques used in this area is Convolutional Neural Networks (CNNs). However, the previous research on fire detection with CNNs has only been evaluated on balanced datasets, which may give misleading information on real-world performance, where fire is a rare event. Actually, as demonstrated in this paper, it turns out that a traditional CNN performs relatively poorly when evaluated on the more realistically balanced benchmark dataset provided in this paper. We therefore propose to use even deeper Convolutional Neural Networks for fire detection in images, and enhancing these with fine tuning based on a fully connected layer. We use two pretrained state-of-the-art Deep CNNs, VGG16 and Resnet50, to develop our fire detection system. The Deep CNNs are tested on our imbalanced dataset, which we have assembled to replicate real world scenarios. It includes images that are particularly difficult to classify and that are deliberately unbalanced by including significantly more non-fire images than fire images. The dataset has been made available online. Our results show that adding fully connected layers for fine tuning indeed does increase accuracy, however, this also increases training time. Overall, we found that our deeper CNNs give good performance on a more challenging dataset, with Resnet50 slightly outperforming VGG16. These results may thus lead to more successful fire detection systems in practice.


title: Numerically Grounded Language Models for Semantic Error Correction

abstract: Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction. Current approaches generally focus on relatively shallow semantics and do not account for numeric quantities. Our approach uses language models grounded in numbers within the text. Such groundings are easily achieved for recurrent neural language model architectures, which can be further conditioned on incomplete background knowledge bases. Our evaluation on clinical reports shows that numerical grounding improves perplexity by 33% and F1 for semantic error correction by 5 points when compared to ungrounded approaches. Conditioning on a knowledge base yields further improvements.


title: Computer security in the real world

abstract: Most computers today are insecure because security is costly in terms of user inconvenience and foregone features, and people are unwilling to pay the price. Real-world security depends more on punishment than on locks, but it's hard to even find network attackers, much less punish them. The basic elements of security are authentication, authorization, and auditing: the gold standard. The idea of one principal speaking for another is the key to doing these uniformly across the Internet.


title: Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features

abstract: Named entity recognition (NER) is an important task in clinical natural language processing (NLP) research. Machine learning (ML) based NER methods have shown good performance in recognizing entities in clinical text. Algorithms and features are two important factors that largely affect the performance of ML-based NER systems. Conditional Random Fields (CRFs), a sequential labelling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to clinical NER tasks. For features, syntactic and semantic information of context words has often been used in clinical NER systems. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, and word representation features, which contain word-level back-off information over large unlabelled corpus by unsupervised algorithms, have not been extensively investigated for clinical text processing. Therefore, the primary goal of this study is to evaluate the use of SSVMs and word representation features in clinical NER tasks. In this study, we developed SSVMs-based NER systems to recognize clinical entities in hospital discharge summaries, using the data set from the concept extration task in the 2010 i2b2 NLP challenge. We compared the performance of CRFs and SSVMs-based NER classifiers with the same feature sets. Furthermore, we extracted two different types of word representation features (clustering-based representation features and distributional representation features) and integrated them with the SSVMs-based clinical NER system. We then reported the performance of SSVM-based NER systems with different types of word representation features. Using the same training (N = 27,837) and test (N = 45,009) sets in the challenge, our evaluation showed that the SSVMs-based NER systems achieved better performance than the CRFs-based systems for clinical entity recognition, when same features were used. Both types of word representation features (clustering-based and distributional representations) improved the performance of ML-based NER systems. By combining two different types of word representation features together with SSVMs, our system achieved a highest F-measure of 85.82%, which outperformed the best system reported in the challenge by 0.6%. Our results show that SSVMs is a great potential algorithm for clinical NLP research, and both types of unsupervised word representation features are beneficial to clinical NER tasks.


title: Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO

abstract: Conventional differential gene expression analysis by methods such as student’s t-test, SAM, and Empirical Bayes often searches for statistically significant genes without considering the interactions among them. Network-based approaches provide a natural way to study these interactions and to investigate the rewiring interactions in disease versus control groups. In this paper, we apply weighted graphical LASSO (wgLASSO) algorithm to integrate a data-driven network model with prior biological knowledge (i.e., protein-protein interactions) for biological network inference. We propose a novel differentially weighted graphical LASSO (dwgLASSO) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups. Through simulation, we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models (e.g., neighbor selection, graphical LASSO), even when only a moderate level of information is available as prior biological knowledge. We evaluated the performance of dwgLASSO for survival time prediction using two microarray breast cancer datasets previously reported by Bild et al. and van de Vijver et al. Compared with the top 10 significant genes selected by conventional differential gene expression analysis method, the top 10 significant genes selected by dwgLASSO in the dataset from Bild et al. led to a significantly improved survival time prediction in the independent dataset from van de Vijver et al. Among the 10 genes selected by dwgLASSO, UBE2S, SALL2, XBP1 and KIAA0922 have been confirmed by literature survey to be highly relevant in breast cancer biomarker discovery study. Additionally, we tested dwgLASSO on TCGA RNA-seq data acquired from patients with hepatocellular carcinoma (HCC) on tumors samples and their corresponding non-tumorous liver tissues. Improved sensitivity, specificity and area under curve (AUC) were observed when comparing dwgLASSO with conventional differential gene expression analysis method. The proposed network-based differential gene expression analysis algorithm dwgLASSO can achieve better performance than conventional differential gene expression analysis methods by integrating information at both gene expression and network topology levels. The incorporation of prior biological knowledge can lead to the identification of biologically meaningful genes in cancer biomarker studies.


title: Dual Graph Regularized Latent Low-Rank Representation for Subspace Clustering

abstract: Low-rank representation (LRR) has received considerable attention in subspace segmentation due to its effectiveness in exploring low-dimensional subspace structures embedded in data. To preserve the intrinsic geometrical structure of data, a graph regularizer has been introduced into LRR framework for learning the locality and similarity information within data. However, it is often the case that not only the high-dimensional data reside on a non-linear low-dimensional manifold in the ambient space, but also their features lie on a manifold in feature space. In this paper, we propose a dual graph regularized LRR model (DGLRR) by enforcing preservation of geometric information in both the ambient space and the feature space. The proposed method aims for simultaneously considering the geometric structures of the data manifold and the feature manifold. Furthermore, we extend the DGLRR model to include non-negative constraint, leading to a parts-based representation of data. Experiments are conducted on several image data sets to demonstrate that the proposed method outperforms the state-of-the-art approaches in image clustering.


title: BURROWS WHEELER BASED DATA COMPRESSION AND SECURE TRANSMISSION

abstract: Now days, computer technology mostly focusing on storage space and speed With the rapid growing of important data and increased number of applications, devising new approach for efficient compression and encryption methods are playing a vital role in performance. In this work, burrows wheeler transformation is introduced for pre processing of the input data and made several performance analysis experiments over different compression techniques for various types of text files and improved compression ratio has been found by applying burrows wheeler transform as pre-processing step.


title: Feature Space Transfer for Data Augmentation

abstract: The problem of data augmentation in feature space is considered. A new architecture, denoted the FeATure TransfEr Network (FATTEN), is proposed for the modeling of feature trajectories induced by variations of object pose. This architecture exploits a parametrization of the pose manifold in terms of pose and appearance. This leads to a deep encoder/decoder network architecture, where the encoder factors into an appearance and a pose predictor. Unlike previous attempts at trajectory transfer, FATTEN can be efficiently trained end-to-end, with no need to train separate feature transfer functions. This is realized by supplying the decoder with information about a target pose and the use of a multi-task loss that penalizes category- and pose-mismatches. In result, FATTEN discourages discontinuous or non-smooth trajectories that fail to capture the structure of the pose manifold, and generalizes well on object recognition tasks involving large pose variation. Experimental results on the artificial ModelNet database show that it can successfully learn to map source features to target features of a desired pose, while preserving class identity. Most notably, by using feature space transfer for data augmentation (w.r.t. pose and depth) on SUN-RGBD objects, we demonstrate considerable performance improvements on one/few-shot object recognition in a transfer learning setup, compared to current state-of-the-art methods.


title: Vehicle Routing Problems for Drone Delivery

abstract: Unmanned aerial vehicles, or drones, have the potential to significantly reduce the cost and time of making last-mile deliveries and responding to emergencies. Despite this potential, little work has gone into developing vehicle routing problems (VRPs) specifically for drone delivery scenarios. Existing VRPs are insufficient for planning drone deliveries: either multiple trips to the depot are not permitted, leading to solutions with excess drones, or the effect of battery and payload weight on energy consumption is not considered, leading to costly or infeasible routes. We propose two multitrip VRPs for drone delivery that address both issues. One minimizes costs subject to a delivery time limit, while the other minimizes the overall delivery time subject to a budget constraint. We mathematically derive and experimentally validate an energy consumption model for multirotor drones, demonstrating that energy consumption varies approximately linearly with payload and battery weight. We use this approximation to derive mixed integer linear programs for our VRPs. We propose a cost function that considers our energy consumption model and drone reuse, and apply it in a simulated annealing (SA) heuristic for finding suboptimal solutions to practical scenarios. To assist drone delivery practitioners with balancing cost and delivery time, the SA heuristic is used to show that the minimum cost has an inverse exponential relationship with the delivery time limit, and the minimum overall delivery time has an inverse exponential relationship with the budget. Numerical results confirm the importance of reusing drones and optimizing battery size in drone delivery VRPs.


' s personal copy What ’ s magic about magic numbers ? Chunking and data compression in short-term memory


THE MISTLETOES OF THE BAHAMAS


title: SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization

abstract: Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first derivatives are available and that the constraint gradients are sparse. Second derivatives are assumed to be unavailable or too expensive to calculate. ::: We discuss an SQP algorithm that uses a smooth augmented Lagrangian merit function and makes explicit provision for infeasibility in the original problem and the QP subproblems. The Hessian of the Lagrangian is approximated using a limited-memory quasi-Newton method. ::: SNOPT is a particular implementation that uses a reduced-Hessian semidefinite QP solver (SQOPT) for the QP subproblems. It is designed for problems with many thousands of constraints and variables but is best suited for problems with a moderate number of degrees of freedom (say, up to 2000). Numerical results are given for most of the CUTEr and COPS test collections (about 1020 examples of all sizes up to 40000 constraints and variables, and up to 20000 degrees of freedom).


title: A Fast and Stable Incremental Clustering Algorithm

abstract: Clustering is a pivotal building block in many data mining applications and in machine learning in general. Most clustering algorithms in the literature pertain to off-line (or batch) processing, in which the clustering process repeatedly sweeps through a set of data samples in an attempt to capture its underlying structure in a compact and efficient way. However, many recent applications require that the clustering algorithm be online, or incremental, in the that there is no a priori set of samples to process but rather samples are provided one iteration at a time. Accordingly, the clustering algorithm is expected to gradually improve its prototype (or centroid) constructs. Several problems emerge in this context, particularly relating to the stability of the process and its speed of convergence. In this paper, we present a fast and stable incremental clustering algorithm, which is computationally modest and imposes minimal memory requirements. Simulation results clearly demonstrate the advantages of the proposed framework in a variety of practical scenarios.


title: IoT in Precision Agriculture applications using Wireless Moisture Sensor Network

abstract: Internet of Things (IoT) is a network of sensors and connectivity to enable application like agriculture optimum irrigation. Wireless sensor network (WSN) and Wireless Moisture Sensor Network (WMSN) are components of IoT. One of the important processes in agriculture is irrigation. Improper irrigation will result in waste of water. Proper irrigation system could be achieved by using WSN technology. Monitoring and control applications have been tremendously improved by using WSN technology. It enabled efficient communication with many sensors. WMSN is a WSN with moisture sensors. In this study, Precision Agriculture (PA) used WMSN to enable efficient irrigation. In this paper, we describe about IoT and WMSN in agriculture applications particularly in greenhouse environment. This paper explained and proved the efficiency of feedback control method in greenhouse crop irrigation. A test was conducted to see the different these two methods. The methods are irrigation by schedule or feedback based irrigation. Irrigation by schedule is to supply water to the plant at specific time periods. Feedback based irrigation is to irrigate plant when the moisture or level of media wetness reached predefined value. The test shows that there is an average savings of 1,500 ml per day per tree.


title: Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

abstract: In this paper, we show dynamic locomotion strategies for wheeled quadrupedal robots which combine the advantages of walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a Zero-Moment Point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which optimizes the generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on the torque-controllable robot ANYmal equipped with non-steerable, torque-controlled wheels. We conducted experiments on flat, inclined and rough terrain, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots.


title: Internet of Things (IoT) Applied to an Urban Garden

abstract: The constant increase of migration to the urban area has caused that the rural sector not have the manpower for agricultural production and is not able to supply food to the world's population, which has generated the need to expand the crops towards the cities. To encourage agricultural production in urban sectors, the use of technologies capable of automating and increasing production performance is proposed. This paper presents the develop of a system for the measurement, monitoring and automatic irrigation of an urban garden using Internet of Things (IoT) technology, it has sensors of CO2, humidity, luminosity, temperature, detection of plants and a hybrid application for its remote monitoring connected to a local area network. Also, the communication process used here is tested in order to provide a great connection service between the proposed system and the network.


title: Review on Detection Techniques against DDoS Attacks on a Software-Defined Networking Controller

abstract: The evolution of information and communication technologies has brought new challenges in managing the Internet. Software-Defined Networking (SDN) aims to provide easily configured and remotely controlled networks based on centralized control. Since SDN will be the next disruption in networking, SDN security has become a hot research topic because of its importance in communication systems. A centralized controller can become a focal point of attack, thus preventing attack in controller will be a priority. The whole network will be affected if attacker gain access to the controller. One of the attacks that affect SDN controller is DDoS attacks. This paper reviews different detection techniques that are available to prevent DDoS attacks, characteristics of these techniques and issues that may arise using these techniques.


title: Natural Customer Ranking of Banks in Terms of Credit Risk by Using Data Mining A Case Study: Branches of Mellat Bank of Iran

abstract: ith the development of trade and business throughout the world, needs to become more widespread financial dealings that led to the development of the business activities of banks and new banks were established. Banking and financial systems, credit risk management is a major problem. So check applicants' credit facility to repay the process is important and many methods have been proposed for this work is the study of data mining techniques to identify genuine bank customer credit risk will be used. Numeric and non-numeric data is the selected data set consisting of 1000. This data set contains 20 features. 7 of the 20 features and 13 features a numerical and non-numerical attributes are nominal. Actually this is 20 characters are input. There's also a feature called Class, category and class of a row of characters in the show. (Ie the output of the problem) that are included in this category are 2 classes, good and bad. In the first 300 customer data poorly (Y = 1) and 700, proper and good customer (Y = 0) exist.


title: Improving literacy in developing countries using speech recognition-supported games on mobile devices

abstract: Learning to read in a second language is challenging, but highly rewarding. For low-income children in developing countries, this task can be significantly more challenging because of lack of access to high-quality schooling, but can potentially improve economic prospects at the same time. A synthesis of research findings suggests that practicing recalling and vocalizing words for expressing an intended meaning could improve word reading skills - including reading in a second language - more than silent recognition of what the given words mean. Unfortunately, many language learning software do not support this instructional approach, owing to the technical challenges of incorporating speech recognition support to check that the learner is vocalizing the correct word. In this paper, we present results from a usability test and two subsequent experiments that explore the use of two speech recognition-enabled mobile games to help rural children in India read words with understanding. Through a working speech recognition prototype, we discuss two major contributions of this work: first, we give empirical evidence that shows the extent to which productive training (i.e. vocalizing words) is superior to receptive vocabulary training, and discuss the use of scaffolding hints to ""unpack"" factors in the learner's linguistic knowledge that may impact reading. Second, we discuss what our results suggest for future research in HCI.


title: Toward Open Set Recognition

abstract: To date, almost all experimental evaluations of machine learning-based recognition algorithms in computer vision have taken the form of “closed set” recognition, whereby all testing classes are known at training time. A more realistic scenario for vision applications is “open set” recognition, where incomplete knowledge of the world is present at training time, and unknown classes can be submitted to an algorithm during testing. This paper explores the nature of open set recognition and formalizes its definition as a constrained minimization problem. The open set recognition problem is not well addressed by existing algorithms because it requires strong generalization. As a step toward a solution, we introduce a novel “1-vs-set machine,” which sculpts a decision space from the marginal distances of a 1-class or binary SVM with a linear kernel. This methodology applies to several different applications in computer vision where open set recognition is a challenging problem, including object recognition and face verification. We consider both in this work, with large scale cross-dataset experiments performed over the Caltech 256 and ImageNet sets, as well as face matching experiments performed over the Labeled Faces in the Wild set. The experiments highlight the effectiveness of machines adapted for open set evaluation compared to existing 1-class and binary SVMs for the same tasks.


title: UWB Antenna With Single or Dual Band-Notches for Lower WLAN Band and Upper WLAN Band

abstract: To prevent interference problems due to existing nearby communication systems within an ultrawideband (UWB) operating frequency, the significance of an efficient band-notched design is increased. Here, the band-notches are realized by adding independent controllable strips in terms of the notch frequency and the width of the band-notches to the fork shape of the UWB antenna. The size of the flat type band-notched UWB antenna is etched on 24 times 36 mm2 substrate. Two novel antennas are presented. One antenna is designed for single band-notch with a separated strip to cover the 5.15-5.825 GHz band. The second antenna is designed for dual band-notches using two separated strips to cover the 5.15-5.35 GHz band and 5.725-5.825 GHz band. The simulation and measurement show that the proposed antenna achieves a wide bandwidth from 3 to 12 GHz with the dual band-notches successfully.


Generalizing Value Estimation over Timescale


title: Bayesian Conditional Generative Adverserial Networks

abstract: Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input $y'$ to a sample $\mathbf{x}$. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outperforms the state-of-the-arts.


title: Effects of WENO flux reconstruction order and spatial resolution on reshocked two-dimensional Richtmyer-Meshkov instability

abstract: Weighted essentially non-oscillatory (WENO) simulations of the reshocked two-dimensional single-mode Richtmyer-Meshkov instability using third-, fifth- and ninth-order spatial flux reconstruction and uniform grid resolutions corresponding to 128, 256 and 512 points per initial perturbation wavelength are presented. The dependence of the density, vorticity, simulated density Schlieren and baroclinic production fields, mixing layer width, circulation deposition, mixing profiles, production and mixing fractions, energy spectra, statistics, probability distribution functions, numerical turbulent kinetic energy and enstrophy production/dissipation rates, numerical Reynolds numbers, and numerical viscosity on the order and resolution is investigated to long evolution times. The results are interpreted using the implicit numerical dissipation in the characteristic projection-based, finite-difference WENO method. It is shown that higher order higher resolution simulations have lower numerical dissipation. The sensitivity of the quantities considered to the order and resolution is further amplified following reshock, when the energy deposition by the second shock-interface interaction induces the formation of small-scale structures. Lower-order lower-resolution simulations preserve large-scale structures and flow symmetry to late times, while higher-order higher-resolution simulations exhibit fragmentation of the structures, symmetry breaking and increased mixing. Similar flow features are qualitatively and quantitatively captured by either approximately doubling the order or the resolution. Additionally, the computational scaling showsmore » that increasing the order is more advantageous than increasing the resolution for the flow considered here. The present investigation suggests that the ninth-order WENO method is well-suited for the simulation and analysis of complex multi-scale flows and mixing generated by shock-induced hydrodynamic instabilities.« less


title: A Context-Based Infrastructure for Smart Environments

abstract: In order for a smart environment to provide services to its occupants, it must be able to detect its current state or context and determine what actions to take based on the context. We discuss the requirements for dealing with context in a smart environment and present a software infrastructure solution we have designed and implemented to help application designers build intelligent services and applications more easily. We describe the benefits of our infrastructure through applications that we have built.


title: CLPsych 2015 Shared Task: Depression and PTSD on Twitter

abstract: This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.


title: Semantic Photometric Bundle Adjustment on Natural Sequences

abstract: The problem of obtaining dense reconstruction of an object in a natural sequence of images has been long studied in computer vision. Classically this problem has been solved through the application of bundle adjustment (BA). More recently, excellent results have been attained through the application of photometric bundle adjustment (PBA) methods -- which directly minimize the photometric error across frames. A fundamental drawback to BA & PBA, however, is: (i) their reliance on having to view all points on the object, and (ii) for the object surface to be well textured. To circumvent these limitations we propose semantic PBA which incorporates a 3D object prior, obtained through deep learning, within the photometric bundle adjustment problem. We demonstrate state of the art performance in comparison to leading methods for object reconstruction across numerous natural sequences.


title: Modeling and control of 100kW three-phase grid-connected photovoltaic inverter

abstract: This paper presents mathematical modeling procedure of three-phase grid-connected photovoltaic inverter. It presents synchronous PI current control strategy and the method for adjuster design. The typical waveforms of grid voltage, grid current and harmonics of grid current are carried out on a 100kW photovoltaic inverter, which can provide some guidelines for engineers to analyze, design and implement.


title: Improved tools for biological sequence comparison.

abstract: We have developed three computer programs for comparisons of protein and DNA sequences. They can be used to search sequence data bases, evaluate similarity scores, and identify periodic structures based on local sequence similarity. The FASTA program is a more sensitive derivative of the FASTP program, which can be used to search protein or DNA sequence data bases and can compare a protein sequence to a DNA sequence data base by translating the DNA data base as it is searched. FASTA includes an additional step in the calculation of the initial pairwise similarity score that allows multiple regions of similarity to be joined to increase the score of related sequences. The RDF2 program can be used to evaluate the significance of similarity scores using a shuffling method that preserves local sequence composition. The LFASTA program can display all the regions of local similarity between two sequences with scores greater than a threshold, using the same scoring parameters and a similar alignment algorithm; these local similarities can be displayed as a "graphic matrix" plot or as individual alignments. In addition, these programs have been generalized to allow comparison of DNA or protein sequences based on a variety of alternative scoring matrices.


title: Absolute fingerprint pre-alignment in minutiae-based cryptosystems

abstract: Most biometric cryptosystems that have been proposed to protect fingerprint minutiae make use of public alignment helper data. This, however, has the inadvertent effect of information leakage about the protected templates. A countermeasure to avoid auxiliary alignment data is to protect absolutely pre-aligned fingerprints. As a proof of concept, we run performance evaluations of a minutiae fuzzy vault with an automatic method for absolute pre-alignment. Therefore, we propose a new method for estimating a fingerprint's directed reference point by modeling the local orientation around the core as a tented arch.


title: Infrared face recognition: A literature review

abstract: Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in AFR continues to improve, benefiting from advances in a range of different fields including image processing, pattern recognition, computer graphics and physiology. However, systems based on visible spectrum images continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease their accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject.


title: A linear space algorithm for computing maximal common subsequences

abstract: The problem of finding a longest common subsequence of two strings has been solved in quadratic time and space. An algorithm is presented which will solve this problem in quadratic time and in linear space.


title: Performance comparison of NSGA-II and NSGA-III on various many-objective test problems

abstract: Recently NSGA-III has been frequently used for performance comparison of newly proposed evolutionary many-objective optimization algorithms. That is, NSGA-III has been used as a benchmark algorithm for evolutionary many-objective optimization. However, unfortunately, its source code is not available from the authors of the NSGA-III paper. This leads to an undesirable situation where a different implementation is used in a different study. Moreover, comparison is usually performed on DTLZ and WFG test problems. As a result, the performance of NSGA-III on a wide variety of many-objective test problems is still unclear whereas it has been frequently used for performance comparison in the literature. In this paper, we evaluate the performance of NSGA-III in comparison with NSGA-II on four totally different types of many-objective test problems with 3–10 objectives: DTLZ1-4 problems, their maximization variants, distance minimization problems, and knapsack problems. We use two different implementations of NSGA-II and NSGA-III. We show through computational experiments that NSGA-III does not always outperform NSGA-II even for ten-objective problems. That is, their comparison results depend not only on the number of objectives but also on the type of test problems. The choice of test problems has a larger effect on their comparison results than the number of objectives in our computational experiments. We also demonstrate that totally different results are obtained from different implementations of NSGA-III for some test problems.


title: Discriminative learning of visual words for 3D human pose estimation

abstract: This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an appearance and position context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.


abstract: catch phrase from the 1996 film Jerry Maguire. All of us want to see evidence as to whether something is worth investing in or not. I remember one public health informatician who was my boss at the time of my post-doc fellowship telling the fellows: “If you figure out how to evaluate health information technology (HIT) well enough, you’ll never have to worry about your career in the future.” At the time I had to wonder if that could be true. Even now, I certainly do not know. My boss probably said so because we are living in an outcomeoriented world and we expect that the evaluation of HIT will be able to demonstrate how well HIT works in order to justify the investment of money, time, and resources. Hence, knowledge and skill in the field of HIT evaluation is one competency that a health informatician must have. Evaluation has been considered a difficult task owing to the complexity of the environment in which information systems are implemented and the busy schedule related to working with patients, especially in a hospital setting [1]. On top of that, we often need to know the baseline data, in order to compare it with the results after the deployment of a new HIT system for evaluation. This requires thoughtful planning ahead; if not, it is often too late to perform evaluation once the IT system is implemented. HIT evaluation is defined as “the act of measuring or assessing the properties of a health information system (in terms of planning, development, implementation, or operation), the result of which informs a decision to be made concerning that system in a specific context” [2]. As the title, Handbook of Evaluation Methods for Health Informatics, suggests, this is not a textbook but a reference book [3]. Handbooks are usually designed to be easily consulted and to provide quick answers in a certain field [4]. The author claimed the purpose of this book was to illusHandbook of Evaluation Methods for Health Informatics


title: Deep learning for radar

abstract: Motivated by the recent advances in deep learning, we lay out a vision of how deep learning techniques can be used in radar. Specifically, our discussion focuses on the use of deep learning to advance the state-of-the-art in radar imaging. While deep learning can be directly applied to automatic target recognition (ATR), the relevance of these techniques in other radar problems is not obvious. We argue that deep learning can play a central role in advancing the state-of-the-art in a wide range of radar imaging problems, discuss the challenges associated with applying these methods, and the potential advancements that are expected. We lay out an approach to design a network architecture based on the specific structure of the synthetic aperture radar (SAR) imaging problem that augments learning with traditional SAR modelling. This framework allows for capture of the non-linearity of the SAR forward model. Furthermore, we demonstrate how this process can be used to learn and compensate for trajectory based phase error for the autofocus problem.


title: Visualizing the Loss Landscape of Neural Nets

abstract: Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well-chosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, is not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple "filter normalization" method that helps us visualize loss function curvature, and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.


title: Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

abstract: BACKGROUND ::: The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. ::: ::: ::: OBJECTIVE ::: The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. ::: ::: ::: METHODS ::: We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. ::: ::: ::: RESULTS ::: The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. ::: ::: ::: CONCLUSIONS ::: To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.


title: Balancing exploration and exploitation in reinforcement learning using a value of information criterion

abstract: In this paper, we consider an information-theoretic approach for addressing the exploration-exploitation dilemma in reinforcement learning. We employ the value of information, a criterion that provides the optimal trade-off between the expected returns and a policy's degrees of freedom. As the degrees of freedom are reduced, an agent will exploit more than explore. As the policy degrees of freedom increase, an agent will explore more than exploit. We provide an efficient computational procedure for constructing policies using the value of information. The performance is demonstrated on a standard reinforcement learning benchmark problem.


title: Interpersonal and genetic origins of adult attachment styles: a longitudinal study from infancy to early adulthood.

abstract: During the last 30 years, attachment theory has become one of the leading theoretical frameworks for the social psychological study of close relationships and personality dynamics (Mikulincer & Shaver, 2007). The majority of attachment research has emphasized individual differences in self-reported attachment styles—relatively enduring patterns of thoughts, feelings, and behaviors in close relationships. Research has demonstrated that adult attachment styles have broad consequences for interpersonal functioning, emotion regulation, and well-being. For example, people who are relatively secure with respect to attachment are more likely than those who are insecure to experience satisfaction and to report high levels of commitment in their marital and dating relationships (e.g., Frei & Shaver, 2002; Tucker & Anders, 1999). In addition, their relationships are characterized by less conflict (e.g., Campbell, Simpson, Boldry, & Kashy, 2005; Simpson, Rholes, & Phillips, 1996) and tend to be more resistant to dissolution and divorce (Davila & Bradbury, 2001; Kirkpatrick & Davis, 1994). Individuals who are relatively secure in their attachment orientation are also less likely than others to report depressive symptoms (e.g., Carnelley, Pietromonaco, & Jaffe, 1994; Hankin, Kassel, & Abela, 2005), more likely to report higher levels of self-esteem (e.g., Bartholomew & Horowitz, 1991), and to cope more effectively in response to stressful events (e.g., Berant, Mikulincer, & Shaver, 2008). ::: ::: Although the consequences of attachment styles for psychological functioning have been well documented, the developmental antecedents of attachment styles have not. To be clear, it is not the case that the antecedents of attachment are merely of tangential interest to adult attachment theorists. Indeed, one of the core assumptions of attachment theory is that individual differences in adult attachment styles are a function of variation in peoples’ developmental histories (Hazan & Shaver, 1987; Mikulincer & Shaver, 2007). However, prospective research on the antecedents of self-reported attachment style has been lacking due to the time and expense involved in conducting prospective research. As such, one of the foundational assumptions of the social psychological approach to adult attachment remains largely untested. ::: ::: Our objective in this article is to help fill this gap by examining the antecedents of adult attachment styles using longitudinal data from the NICHD Study of Early Child Care and Youth Development (SECCYD). The SECCYD is particularly well suited for this purpose because it is based on a large, longitudinal sample of children who were assessed on multiple occasions from birth to age 15, with a follow-up study of participants’ attachment styles at age 18 (see Booth-LaForce & Roisman, 2012). Moreover, the study contains measures of a number of constructs (e.g., maternal sensitivity, the quality of peer relationships) that have been hypothesized to predict variation in adult attachment styles. It is our hope that this research can help fill a gap that has been present for decades in the social-psychological literature on adult attachment while also helping to advance the field’s understanding of the origins of adult attachment styles.


title: GECKA: Game Engine for Commonsense Knowledge Acquisition.

abstract: Commonsense knowledge representation and reasoning is key for tasks such as natural language understanding. Since common-sense consists of information that humans take for granted, however, gathering it is an extremely difficult task.The game engine for common-sense knowledge acquisition (GECKA) aims to collect common-sense from game designers through the development of serious games. GECKA merges, as never before, the potential of serious games and games with a purpose. This not only provides a platform for the acquisition of re-usable and multi-purpose knowledge, but also enables the development of games that can, apart from providing entertainment value, also teach gamers something meaningful about the world they live in.


title: Robust edge-based visual odometry using machine-learned edges

abstract: In this work, we present a real-time robust edge-based visual odometry framework for RGBD sensors (REVO). Even though our method is independent of the edge detection algorithm, we show that the use of state-of-the-art machine-learned edges gives significant improvements in terms of robustness and accuracy compared to standard edge detection methods. In contrast to approaches that heavily rely on the photo-consistency assumption, edges are less influenced by lighting changes and the sparse edge representation offers a larger convergence basin while the pose estimates are also very fast to compute. Further, we introduce a measure for tracking quality, which we use to determine when to insert a new key frame. We show the feasibility of our system on real-world datasets and extensively evaluate on standard benchmark sequences to demonstrate the performance in a wide variety of scenes and camera motions. Our framework runs in real-time on the CPU of a laptop computer and is available online.


Is neuroplasticity in the central nervous system the missing link to our understanding of chronic musculoskeletal disorders?


title: On the Module of Internet Banking System

abstract: Because of the speed, flexibility, and efficiency that it offers, the Internet has become the means for conducting growing numbers of transactions between suppliers and large international corporations. In this way, the Internet has opened new markets to the world and has accelerated the diffusion of knowledge. The meaning of Internet markets or online business has been widely used in these days. The success of the business depends on its flexibility, availability and security. Since that the web-based systems should have a special way to design the system and implement it. Nowadays, the Internet Banking System widely used and the banks looking to provide the best quality system with highly available, fast response, secure and safe to use. The Unified Modelling Language (UML) is the uniquely language which is used to analyse and design any system. In this paper, the UML diagrams has been proposed to illustrate the design phase for any banking system. The authors, presented two types of architecture which is used for the Internet Banking System.


title: More Symmetric Four-Phase Inverse Coupled Inductor for Low Current Ripples & High-Efficiency Interleaved Bidirectional Buck/Boost Converter

abstract: In order to substantially improve multiphase interleaved bidirectional buck/boost converter's (IBC) current ripples and efficiency, this paper proposes a more symmetric four-phase inverse coupled inductor (ICI) structure and its comprehensive design solution. First, this paper presents a more symmetric four-phase ICI structure which has the advantages of larger inductance values and more symmetric inductance parameters comparing to existing four-phase ICI of similar volume. Second, this paper presents the four-phase ICI's two magnetic circuit analytical models, the four-phase ICI's inductance calculation results from the two models are verified with Maxwell 3-D simulation and experiments. Third, this paper presents the four-phase ICI's comprehensive design solution with the two models and Maxwell 3-D piecewise linear transient simulation. Finally, the advantages of the four-phase ICI are verified with experiments. When the four-phase ICI is used in multiphase IBC, the IBC will have smaller current ripples, more symmetric phase current waveforms, lower output voltage ripple, and higher efficiency both in buck and boost modes.


title: A novel soft manipulator based on beehive structure

abstract: This paper presents a novel design for the soft continuum manipulator based on beehive structure. Our design inspiration derives from the beehive hexagon structure, which has some characteristics of compact layout, low material consumption, thinner thickness of wall, and higher structure strength. The manipulator includes several same modules which composed of many hexagon cell units, the deformation of these cell units synthesize the module macroscopic movement. By installing several modules in series, we can form the manipulator arm. For module fabrication, we adopt 3D printing technology and use the silicone rubber as the flexible material; these measures ensure that the complex shape of the module can be achieved and make the module easier to deform elastically. Based on pneumatic technology, we build the control system and verify its operation via some experiments. In the gravity environment, three-section (about 180mm long at uninflated state) manipulator's maximum bending angle reaches about 90°, its elongation is close to 2∶1. We also do a five-section experiment to acquire the manipulator's motion under different section combinations. The experiments verified that the manipulator has both good flexibility and operational capabilities.


title: Comparison of two battery equivalent circuit models for state of charge estimation in electric vehicles

abstract: This study compares two battery equivalent circuits for state of charge (SOC) estimation in electric vehicles (EVs), namely the RC circuit and Thevenin circuit. The state space representations of each circuit are used to develop the adaptive gain sliding mode observers (AGSMO) for the SOC estimations, which are compared with the true SOC. The results show that the Thevenin circuit provides more accurate SOC estimation than the RC circuit. A lithium-ion battery is chosen for experimental verification under constant current discharge and variable current discharge based on EV driving cycles.


title: Deep cross-domain flying object classification for robust UAV detection

abstract: Recent progress in the development of unmanned aerial vehicles (UAVs) causes serious safety issues for mass events and safety-sensitive locations like prisons or airports. To address these concerns, robust UAV detection systems are required. In this work, we propose an UAV detection framework based on video images. Depending on whether the video images are recorded by static cameras or moving cameras, we initially detect regions that are likely to contain an object by median background subtraction or a deep learning based object proposal method, respectively. Then, the detected regions are classified into UAV or distractors, such as birds, by applying a convolutional neural network (CNN) classifier. To train this classifier, we use our own dataset comprised of crawled and self-acquired drone images, as well as bird images from a publicly available dataset. We show that, even across a significant domain gap, the resulting classifier can successfully identify UAVs in our target dataset. We evaluate our UAV detection framework on six challenging video sequences that contain UAVs at different distances as well as birds and background motion.


title: Impact of the 26 December 2003 Bam Earthquake on Activities of Daily Living and Instrumental Activities of Daily Living of Older People

abstract: INTRODUCTION: This study compares self-reported Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) status among elderly survivors of the 2003 Bam Earthquake before, two months after, and five years after the event, and explores related determinants. METHODS: A two-stage cluster survey was conducted on 210 elderly survivors in the earthquake-stricken area five years after the event. RESULTS: Both ADL and IADL scores decreased two months after earthquake compared to prior status (p 0.05). Access to medical services were not related to level of ADL or IADL (p = 0.52 and p = 0.74, respectively). Elderly survivors with lower functional capability in terms of ADL experienced more problems in access to relief items (p = 0.04), but no similar association was found for IADL (p = 0.26). CONCLUSION: The Bam earthquake adversely affected functional capacity of the elderly. Disaster responders must take into account functional capacity of elders when planning for medical and relief operations. Language: en