title: Retrieving real world clothing images via multi-weight deep convolutional neural networks

abstract: Clothing images are abundantly available from the Internet, especially from the e-commercial platform. Retrieving those images is of importance for commercial and social applications and has recently been received tremendous attention from communities, such as multimedia processing and computer vision. However, the large variations in clothing of their appearance and style, and even the large quantity of multiple categories and attributes make those problems challenging. Furthermore, for real world images their labels provided by shop retailers from webpages are largely erroneous or incomplete. And the imbalance among those image categories prevents the effective learning. To overcome those problems, in this paper, we adopt a multi-task deep learning framework to learn the representation. And we propose multi-weight deep convolutional neural networks for imbalance learning. The topology of this network contains two groups of layers, shared layers at the bottom and task dependent ones at the top. Furthermore, category-relevant parameters are incorporated to regularize the backward gradients for categories. Mathematical proof shows its relationship to regulating the learning rates. Experiments demonstrate that our proposed joint framework and multi-weight neural networks can effectively learn robust representations and achieve better performance.


title: Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks

abstract: The potential for collaborative, robust networks of microsensors has attracted a great deal of research attention. For the most part, this is due to the compelling applications that will be enabled once wireless microsensor networks are in place; location-sensing, environmental sensing, medical monitoring and similar applications are all gaining interest. However, wireless microsensor networks pose numerous design challenges. For applications requiring long-term, robust sensing, such as military reconnaissance, one important challenge is to design sensor networks that have long system lifetimes. This challenge is especially difficult due to the energy-constrained nature of the devices. In order to design networks that have extremely long lifetimes, we propose a physical layer driven approach to designing protocols and algorithms. We first present a hardware model for our wireless sensor node and then introduce the design of physical layer aware protocols, algorithms, and applications that minimize energy consumption of the system. Our approach prescribes methods that can be used at all levels of the hierarchy to take advantage of the underlying hardware. We also show how to reduce energy consumption of non-ideal hardware through physical layer aware algorithms and protocols.


title: The affine particle-in-cell method

abstract: Hybrid Lagrangian/Eulerian simulation is commonplace in computer graphics for fluids and other materials undergoing large deformation. In these methods, particles are used to resolve transport and topological change, while a background Eulerian grid is used for computing mechanical forces and collision responses. Particle-in-Cell (PIC) techniques, particularly the Fluid Implicit Particle (FLIP) variants have become the norm in computer graphics calculations. While these approaches have proven very powerful, they do suffer from some well known limitations. The original PIC is stable, but highly dissipative, while FLIP, designed to remove this dissipation, is more noisy and at times, unstable. We present a novel technique designed to retain the stability of the original PIC, without suffering from the noise and instability of FLIP. Our primary observation is that the dissipation in the original PIC results from a loss of information when transferring between grid and particle representations. We prevent this loss of information by augmenting each particle with a locally affine, rather than locally constant, description of the velocity. We show that this not only stably removes the dissipation of PIC, but that it also allows for exact conservation of angular momentum across the transfers between particles and grid.


title: Semantic sentiment analysis of twitter

abstract: Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. "Apple product") as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.


title: Antibiotic perturbation of the murine gut microbiome enhances the adiposity, insulin resistance, and liver disease associated with high-fat diet

abstract: Background ::: Obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD) are serious health concerns, especially in Western populations. Antibiotic exposure and high-fat diet (HFD) are important and modifiable factors that may contribute to these diseases.


title: The Beautiful and the Picturesque

abstract: IN 1841 Wiley & Putnam published a book which soon brought its previously unknown writer international fame, and which, before it had spent its force, radically altered the American scene.* The author was Andrew Jackson Downing, a landscape gardener of Newburgh, New York, then only twenty-six years old; the book bore the rather formidable title of A Treatise on the Theory and Practice of Landscape Gardening Adapted to North America-hardly a topic which might have been expected to elicit much popular enthusiasm from the American people, then or now. Yet so interesting and useful did Downing contrive to make his subject that not only were four editions of the Treatise required during his lifetime, but six more appeared after his death, the last in 1921. As the New York journalist, Clarence Cook, later remarked, "perhaps never in the field of literature had so young a man met with so sudden and extended a reputation."' Heartened by his initial success, Downing the following year brought out a second, though smaller, work devoted entirely to the question of rural architecture (a subject which had occupied only a chapter of the Treatise). This he called simply Cottage Residences, and while its popularity never equaled the Treatise on Landscape Gardening, its success justified the publication in 1850 of a larger volume on The Architecture of Country Houses. Unlike the earlier "Builders' Guides" which had stressed structural details often best suited to important


Improving the Usefulness of Alerts Generated by Automated Static Analysis Tools


title: Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation

abstract: Holistic 3D indoor scene understanding refers to jointly recovering the i) object bounding boxes, ii) room layout, and iii) camera pose, all in 3D. The existing methods either are ineffective or only tackle the problem partially. In this paper, we propose an end-to-end model that simultaneously solves all three tasks in real-time given only a single RGB image. The essence of the proposed method is to improve the prediction by i) parametrizing the targets (\eg, 3D boxes) instead of directly estimating the targets, and ii) cooperative training across different modules in contrast to training these modules individually. Specifically, we parametrize the 3D object bounding boxes by the predictions from several modules, \ie, 3D camera pose and object attributes. The proposed method provides two major advantages: i) The parametrization helps maintain the consistency between the 2D image and the 3D world, thus largely reducing the prediction variances in 3D coordinates. ii) Constraints can be imposed on the parametrization to train different modules simultaneously. We call these constraints"cooperative losses"as they enable the joint training and inference. We employ three cooperative losses for 3D bounding boxes, 2D projections, and physical constraints to estimate a geometrically consistent and physically plausible 3D scene. Experiments on the SUN RGB-D dataset shows that the proposed method significantly outperforms prior approaches on 3D object detection, 3D layout estimation, 3D camera pose estimation, and holistic scene understanding.


title: Accelerating Decision Tree Based Traffic Classification on FPGA and Multicore Platforms

abstract: Machine learning (ML) algorithms have been shown to be effective in classifying a broad range of applications in the Internet traffic. In this paper, we propose algorithms and architectures to realize online traffic classification using flow level features. First, we develop a traffic classifier based on C4.5 decision tree algorithm and Entropy-MDL (Minimum Description Length) discretization algorithm. It achieves an overall accuracy of 97.92 percent for classifying eight major applications. Next we propose approaches to accelerate the classifier on FPGA (Field Programmable Gate Array) and multicore platforms. We optimize the original classifier by merging it with discretization. Our implementation of this optimized decision tree achieves 7500+ Million Classifications Per Second (MCPS) on a state-of-the-art FPGA platform and 75-150 MCPS on two state-of-the-art multicore platforms. We also propose a divide and conquer approach to handle imbalanced decision trees. Our implementation of the divide-and-conquer approach achieves 10,000+ MCPS on a state-of-the-art FPGA platform and 130-340 MCPS on two state-of-the-art multicore platforms. We conduct extensive experiments on both platforms for various application scenarios to compare the two approaches.


title: E-commerce quality and adoptive elements of e-ticketing for entertainment and sporting events

abstract: The present study is an attempt to discover some of the statistically significant outlines motivations and factors that influence the quality in e-ticketing, which affects customers' perceptions, preferences, and intentions. Consumers, especially business professions - the subjects of this study - are constantly demanding higher quality e-commerce services. All three hypotheses were found to be highly significant and positively related to promoting the perceived value of e-ticketing technologies, especially for sport-related events. Based on technology adoption models, e-ticketing does provide significant levels of perceived value and its linkage to customer satisfaction are important factors as well as operational costs. It seems obvious that box office staffs will become smaller in size as more e-ticketing devices and acceptance increases. Technological applications should continue to grow, and eventual acceptance of ticket kiosks, wireless ticket purchases, will undoubtedly change from being an industry rarity to an industry standard.


title: A Digitally Controlled Switching Regulator With Reduced Conductive EMI Spectra

abstract: This paper presents the use of a combined random pulse position modulation (RPPM)/digital pulsewidth modulation (DPWM) digital controller for a buck converter to simultaneously achieve low-conductive electromagnetic interference (EMI) and a fast transient response. An area-efficient RPPM/DPWM controller suitable for advanced system-on-chip integration is proposed and implemented in a field-programmable gate array-based prototype to verify its EMI-suppression capability. The experimental results for a 1.2-V digital buck converter show that the proposed digital switching regulator can switch between RPPM and DPWM modes smoothly and automatically. Closed-loop system measurements demonstrate that, compared to DPWM, up to 17.27 and 14.99 dB power spectra reductions are obtained in the input current and switching node, respectively.


title: Highly Efficient Algorithms for AES Key Retrieval in Cache Access Attacks

abstract: Leakage of information between two processes sharing the same processor cache has been exploited in many novel approaches targeting various cryptographic algorithms. The software implementation of AES is an specially attractive target since it makes extensive use of cache-resident table lookups. We consider two attack scenarios where either the plaintext or ciphertext is known. We employ a multi-threaded spy process and ensure that each time slice provided to the victim (running AES) is small enough so that it makes a very limited number of table accesses. We design and implement a suite of algorithms to deduce the 128-bit AES key using as input the set of (unordered) cache line numbers captured by the spy threads in an access-driven cache-based side channel attack. Our algorithms are expressed using simple relational algebraic operations and run in under a minute. Above all, our attack is highly efficient - we demonstrate recovery of the full AES key given only about 6 - 7 blocks of plaintext or ciphertext (theoretically even a single block would suffice). This is a substantial improvement over previous cache-based side channel attacks that require between 100 and a million encryptions. Moreover, our attack supports varying cache hit/miss observation granularities, does not need frequent interruptions of the victim and will work even if the victim makes up to 60 cache accesses before being interrupted. Finally, we develop analytic models to estimate the number of encryptions/decryptions required as a function of access granularity and compare model results with those obtained from our experiments.


title: A Fourth Order Accurate Discretization for the Laplace and Heat Equations on Arbitrary Domains, with Applications to the Stefan Problem

abstract: In this paper, we first describe a fourth order accurate finite difference discretization for both the Laplace equation and the heat equation with Dirichlet boundary conditions on irregular domains. In the case of the heat equation we use an implicit discretization in time to avoid the stringent time step restrictions associated with requirements for explicit schemes. We then turn our focus to the Stefan problem and construct a third order accurate method that also includes an implicit time discretization. Multidimensional computational results are presented to demonstrate the order accuracy of these numerical methods.


title: IMAGE BASED 3D FACE RECONSTRUCTION: A SURVEY

abstract: The use of 3D data in face image processing applications has received considerable attention during the last few years. A major issue for the implementation of 3D face processing systems is the accurate and real time acquisition of 3D faces using low cost equipment. In this paper we provide a survey of 3D reconstruction methods used for generating the 3D appearance of a face using either a single or multiple 2D images captured with ordinary equipment such as digital cameras and camcorders. In this context we discuss various issues pertaining to the general problem of 3D face reconstruction such as the existence of suitable 3D face databases, correspondence of 3D faces, feature detection, deformable 3D models and typical assumptions used during the reconstruction process. Different approaches to the problem of 3D reconstruction are presented and for each category the most important advantages and disadvantages are outlined. In particular we describe example-based methods, stereo methods, video-based methods and silhouette-based methods. The issue of performance evaluation of 3D face reconstruction algorithms, the state of the art and future trends are also discussed.


title: Cooperative gestures: effective signaling for humanoid robots

abstract: Cooperative gestures are a key aspect of human-human pro-social interaction. Thus, it is reasonable to expect that endowing humanoid robots with the ability to use such gestures when interacting with humans would be useful. However, while people are used to responding to such gestures expressed by other humans, it is unclear how they might react to a robot making them. To explore this topic, we conducted a within-subjects, video based laboratory experiment, measuring time to cooperate with a humanoid robot making interactional gestures. We manipulated the gesture type (beckon, give, shake hands), the gesture style (smooth, abrupt), and the gesture orientation (front, side). We also employed two measures of individual differences: negative attitudes toward robots (NARS) and human gesture decoding ability (DANVA2-POS). Our results show that people cooperate with abrupt gestures more quickly than smooth ones and front-oriented gestures more quickly than those made to the side, people's speed at decoding robot gestures is correlated with their ability to decode human gestures, and negative attitudes toward robots is strongly correlated with a decreased ability in decoding human gestures.


LncRNA CASC 15 promotes colon cancer cell proliferation and metastasis by regulating the miR ‐ 4310 / LGR 5 / Wnt / β ‐ catenin signaling pathway


title: Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection

abstract: Activity recognition from an on-body sensor network enables context-aware applications in wearable computing. A guaranteed classification accuracy is desirable while optimizing power consumption to ensure the system's wearability. In this paper, we investigate the benefits of dynamic sensor selection in order to use efficiently available energy while achieving a desired activity recognition accuracy. For this purpose we introduce and characterize an activity recognition method with an underlying run-time sensor selection scheme. The system relies on a meta-classifier that fuses the information of classifiers operating on individual sensors. Sensors are selected according to their contribution to classification accuracy as assessed during system training. We test this system by recognizing manipulative activities of assembly-line workers in a car production environment. Results show that the system's lifetime can be significantly extended while keeping high recognition accuracies. We discuss how this approach can be implemented in a dynamic sensor network by using the context-recognition framework Titan that we are developing for dynamic and heterogeneous sensor networks.


Chicago Linguistic Society 29: Papers From the Parasession on Conceptual Representations


title: Detecting Parkinsons' symptoms in uncontrolled home environments: A multiple instance learning approach

abstract: In this paper, we propose to use a weakly supervised machine learning framework for automatic detection of Parkinson's Disease motor symptoms in daily living environments. Our primary goal is to develop a monitoring system capable of being used outside of controlled laboratory settings. Such a system would enable us to track medication cycles at home and provide valuable clinical feedback. Most of the relevant prior works involve supervised learning frameworks (e.g., Support Vector Machines). However, in-home monitoring provides only coarse ground truth information about symptom occurrences, making it very hard to adapt and train supervised learning classifiers for symptom detection. We address this challenge by formulating symptom detection under incomplete ground truth information as a multiple instance learning (MIL) problem. MIL is a weakly supervised learning framework that does not require exact instances of symptom occurrences for training; rather, it learns from approximate time intervals within which a symptom might or might not have occurred on a given day. Once trained, the MIL detector was able to spot symptom-prone time windows on other days and approximately localize the symptom instances. We monitored two Parkinson's disease (PD) patients, each for four days with a set of five triaxial accelerometers and utilized a MIL algorithm based on axis parallel rectangle (APR) fitting in the feature space. We were able to detect subject specific symptoms (e.g. dyskinesia) that conformed with a daily log maintained by the patients.


title: Reinforcement Learning in First Person Shooter Games

abstract: Reinforcement learning (RL) is a popular machine learning technique that has many successes in learning how to play classic style games. Applying RL to first person shooter (FPS) games is an interesting area of research as it has the potential to create diverse behaviors without the need to implicitly code them. This paper investigates the tabular Sarsa (λ) RL algorithm applied to a purpose built FPS game. The first part of the research investigates using RL to learn bot controllers for the tasks of navigation, item collection, and combat individually. Results showed that the RL algorithm was able to learn a satisfactory strategy for navigation control, but not to the quality of the industry standard pathfinding algorithm. The combat controller performed well against a rule-based bot, indicating promising preliminary results for using RL in FPS games. The second part of the research used pretrained RL controllers and then combined them by a number of different methods to create a more generalized bot artificial intelligence (AI). The experimental results indicated that RL can be used in a generalized way to control a combination of tasks in FPS bots such as navigation, item collection, and combat.


title: Embodied Cognition and the Simulation of Action to Understand Others

abstract: Understanding the goals or intentions of other people requires a broad range of evaluative processes including the decoding of biological motion, knowing about object properties, and abilities for recognizing task space requirements and social contexts. It is becoming increasingly evident that some of this decoding is based in part on the simulation of other people's behavior within our own nervous system. This review focuses on aspects of action understanding that rely on embodied cognition, that is, the knowledge of the body and how it interacts with the world. This form of cognition provides an essential knowledge base from which action simulation can be used to decode at least some actions performed by others. Recent functional imaging studies or action understanding are interpreted with a goal of defining conditions when simulation operations occur and how this relates with other constructs, including top-down versus bottom-up processing and the functional distinctions between action observation and social networks. From this it is argued that action understanding emerges from the engagement of highly flexible computational hierarchies driven by simulation, object properties, social context, and kinematic constraints and where the hierarchy is driven by task structure rather than functional or strict anatomic rules.


title: Pneumogastric (Vagus) Nerve Activity Indexed by Heart Rate Variability in Chronic Pain Patients Compared to Healthy Controls: A Systematic Review and Meta-Analysis.

abstract: BACKGROUND ::: A large body of scientific literature derived from experimental studies emphasizes the vital role of vagal-nociceptive networks in acute pain processing. However, research on vagal activity, indexed by vagally-mediated heart rate variability (vmHRV) in chronic pain patients (CPPs), has not yet been summarized. ::: ::: ::: OBJECTIVES ::: To systematically investigate differences in vagus nerve activity indexed by time- and frequency-domain measures of vmHRV in CPPs compared to healthy controls (HCs). ::: ::: ::: STUDY DESIGN ::: A systematic review and meta-analysis, including meta-regression on a variety of populations (i.e., clinical etiology) and study-level (i.e., length of HRV recording) covariates. ::: ::: ::: SETTING ::: Not applicable (variety of studies included in the meta-analysis). ::: ::: ::: METHODS ::: Eight computerized databases (PubMed via MEDLINE, PsycNET, PsycINFO, Embase, CINAHL, Web of Science, PSYNDEX, and the Cochrane Library) in addition to a hand search were systematically screened for eligible studies based on pre-defined inclusion criteria. A meta-analysis on all empirical investigations reporting short- and long-term recordings of continuous time- (root-mean-square of successive R-R-interval differences [RMSSD]) and frequency-domain measures (high-frequency [HF] HRV) of vmHRV in CPPs and HCs was performed. True effect estimates as adjusted standardized mean differences (SMD; Hedges g) combined with inverse variance weights using a random effects model were computed. ::: ::: ::: RESULTS ::: CPPs show lower vmHRV than HCs indexed by RMSSD (Z = 5.47, P < .0001; g = -0.24;95% CI [-0.33, -0.16]; k = 25) and HF (Z = 4.54, P < .0001; g = -0.30; 95% CI [-0.44, -0.17]; k = 61).Meta-regression on covariates revealed significant differences by clinical etiology, age, gender, and length of HRV recording. ::: ::: ::: LIMITATIONS ::: We did not control for other potential covariates (i.e., duration of chronic pain, medication intake) which may carry potential risk of bias. ::: ::: ::: CONCLUSION(S) ::: The present meta-analysis is the most extensive review of the current evidence on vagal activity indexed by vmHRV in CPPs. CPPs were shown to have lower vagal activity, indexed by vmHRV, compared to HCs. Several covariates in this relationship have been identified. Further research is needed to investigate vagal activity in CPPs, in particular prospective and longitudinal follow-up studies are encouraged.


title: Assessing the Readability of Stacked Graphs

abstract: Stacked graphs are a visualization technique popular in casual scenarios for representing multiple time-series. Variations of stacked graphs have been focused on reducing the distortion of individual streams because foundational perceptual studies suggest that variably curved slopes may make it difficult to accurately read and compare values. We contribute to this discussion by formally comparing the relative readability of basic stacked area charts, ThemeRivers, streamgraphs and our own interactive technique for straightening baselines of individual streams in a ThemeRiver. We used both real-world and randomly generated datasets and covered tasks at the elementary, intermediate and overall information levels. Results indicate that the decreased distortion of the newer techniques does appear to improve their readability, with streamgraphs performing best for value comparison tasks. We also found that when a variety of tasks is expected to be performed, using the interactive version of the themeriver leads to more correctness at the cost of being slower for value comparison tasks.


title: A framework for validating the merit of properties that predict the influence of a twitter user

abstract: Classification algorithm separating between influential and non-influential users.Identification of properties that characterize influential users.Use of community structure to separate influential and non-influential users.Interactive annotation tool for labeling users and tweets regarding influence.Workflow for building a ground truth for influential users and tweets. What characterizes an influential user? While there is much research on finding the concrete influential members of a social network, there are less findings about the properties distinguishing between an influential and a non-influential user. A major challenge is the absence of a ground truth, on which supervised learning can be performed. In this study, we propose a complete framework for supervised separation between influential and non-influential users in a social network. The first component of our framework, the InfluenceLearner, extracts a Relation Graph and an Interaction Graph from a social network, computes network properties from them and then uses them for supervised learning. The second component of our framework, the SNAnnotator, serves for the establishment of a ground truth through manual annotation of tweets and users: it contains a crawling mechanism that produces a batch of tweets to be annotated offline, as well as an interactive interface that the annotators can use to acquire additional information about the users and the tweets. On this basis, we have created a ground truth dataset of Twitter users, upon which we study which properties characterize the influential ones. Our findings show that there are predictive properties associated with the activity level of users and their involvement in communities, but also that writing influential tweets is not a prerequisite for being an influential user.


title: Requirements elicitation issues and project performance: A test of a contingency model

abstract: A contingency model that depicted the influence of requirements elicitation issues on project performance suggest that lower degree of requirement elicitation issues result in a higher degree of project performance and vice versa. The empirical study of this model led to two critical observations — firstly, it was predicted that increasing degree of elicitation issues is directly proportional to residual performance risk; secondly, higher levels of residual performance risk is inversely proportional to project performance, thereby confirming that a decrease (increase) in the effects of residual performance risk will increase (decrease) the effects of project performance. This study is an extension to the author's research related to the empirical validation of the theoretical model that establishes a relationship between elicitation issues and overall project performance. It is important to validate and confirm the effectiveness of the outcomes from the empirical validation; this study attempts to apply the key outcomes from the empirical validation of the contingency model to real-time projects of large globally distributed organizations. 10 projects were randomly selected based on predefined criteria. The survey results provided reasonable support for the contingency model predictions. The statistical validation of responses indicated positive influence on the requirements engineering phase by minimizing key elicitation issues and thereby contributing towards a good project performance. Based on such statistical observations and feedbacks obtained from project stakeholders on the contingency model observations, it was concluded that the outcomes and recommendations of this contingency model can be extended in a large scale in research and practice, for stakeholders to experience benefits of effective elicitation in software developments projects.


title: Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis

abstract: Patent maps showing competition trends in technological development can provide valuable input for decision support on research and development (RD the structures of a patent encode the key findings of the invention and expertise of its inventors. Therefore, this paper introduces a method of constructing dynamic patent maps using SAO-based content analysis of patents and presents several types of dynamic patent maps by combining patent bibliographic information and patent mapping and clustering techniques. Building on the maps, this paper provides further analyses to identify technological areas in which patents have not been granted ("patent vacuums"), areas in which many patents have actively appeared ("technological hot spots"), R&D overlap of technological competitors, and characteristics of patent clusters. The proposed analyses of dynamic patent maps are illustrated using patents related to the synthesis of carbon nanotubes. We expect that the proposed method will aid experts in understanding technological competition trends in the process of formulating R&D strategies.


title: A Comparative Analysis on Smart Home System to Control, Monitor and Secure Home, based on technologies like GSM, IOT, Bluetooth and PIC Microcontroller with ZigBee Modulation

abstract: In this busy life schedule everyone wants to get some comfort and secure life as well. Home Automation used to control home appliances remotely to reduce efforts. Home security system is beneficial to secure your home from fire and trespass. Wireless smart home system plays a vital role in human life and increases popularity due to its flexibility, portability and low cost installation charges. Smart home system is very beneficial in everyday life as it reduces human workload, save electricity and reduces worries about home security for working peoples. Main focus in automation is to control light ON/OFF status, fan speed and other home appliances remotely. Home security includes services like gas leakage and trace pass protection. This system is very beneficial for old ages and handicapped people as well for working people, it is a blessing as it alert the person if any nasty situation raised at home in their absence. Though there are various technologies used but in every technology mobile plays important role, to automate home appliances or to get alert in risky situations. Attractive GUI can be used for smart home system, accessible with smart phones, tablet, laptop and PC. A comparative study of smart home system based on technologies like GSM, Bluetooth, IOT and PIC Microcontroller with ZigBee modulation is discussed here.


title: A new perspective on Twitter hashtag use: Diffusion of innovation theory

abstract: Twitter is a fast growing real-time social media tool. As Twitter evolves, more and more people are partaking in sharing what is happening around the world through various Twitter applications. Hashtag use has become a unique tagging convention to help associate Twitter messages with certain events or contexts. Prefixed by a # symbol with a keyword, a Twitter hashtag serves as a bottom-up user-proposed tagging convention. It also embodies user participation in the process of hashtag innovation, especially as it pertains to information organization tasks. Diffusion of innovation (DoI) is a theory that helps to explain the adoption process of an innovation by modeling its entire life cycle according to the aspects of communications and human information interactions. Hence, diffusion theory offers valuable insights into interface design that supports Twitter hashtag use and access. It also assists in evaluating hashtag life cycles and thus offering information required for decision-making, in regard to hashtag management.


title: ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient

abstract: Stochastic gradient algorithms have been the main focus of large-scale learning problems and they led to important successes in machine learning. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose a new adaptive learning rate algorithm, which utilizes curvature information for automatically tuning the learning rates. The information about the element-wise curvature of the loss function is estimated from the local statistics of the stochastic first order gradients. We further propose a new variance reduction technique to speed up the convergence. In our preliminary experiments with deep neural networks, we obtained better performance compared to the popular stochastic gradient algorithms.


title: Free drainage in aqueous foams: Model and experimental study

abstract: Free drainage in compressed-air foams were studied experimentally and theoretically. The time evolution of liquid holdup profiles in a 0.2-m-high by 0.29-m-diameter foam column was determined at various heights by measuring sonic velocity. A new experimental technique was devised to measure the true drainage rate of surfactant solution leaving the foam column. A drainage model was outlined to predict the discharge rate and evolution of the liquid-fraction profile in aqueous foams. The model led to the formulation of a nonlinear partial differential equation in which the liquid fraction was used explicitly as a dependent variable. The model was applied with one adjustable parameter to simulate drainage in foams made from fluorocarbon surfactants containing mobile plateau border and film walls. The liquid-fraction profiles, drainage rates, and final equilibrium liquid profiles depended strongly on the surface mobility of plateau border and film walls; the bubble size; and therefore on the coarsening history in the foam under study. Over longer time periods this model needs to be coupled with inter-bubble gas diffusion to account for coarsening-induced drainage. ::: Free drainage in compressed-air foams were studied experimentally and theoretically. The time evolution of liquid holdup profiles in a 0.2-m-high by 0.29-m-diameter foam column was determined at various heights by measuring sonic velocity. A new experimental technique was devised to measure the true drainage rate of surfactant solution leaving the foam column. A drainage model was outlined to predict the discharge rate and evolution of the liquid-fraction profile in aqueous foams. The model led to the formulation of a nonlinear partial differential equation in which the liquid fraction was used explicitly as a dependent variable. The model was applied with one adjustable parameter to simulate drainage in foams made from fluorocarbon surfactants containing mobile plateau border and film walls. The liquid-fraction profiles, drainage rates, and final equilibrium liquid profiles depended strongly on the surface mobility of plateau border and film walls; the bubble size; and therefore on the coarsening history in the foam under study. Over longer time periods this model needs to be coupled with inter-bubble gas diffusion to account for coarsening-induced drainage.


title: Performance of ROCOF protection in PV system

abstract: Due to deregulation and the use of renewable power, especially solar power, the proportion of PV system is rapidly increasing and most of them are connected to distribution networks to supply power to the grid as well as local loads. There are many power quality issues to be considered with PV systems and one of the main issues is islanding. It is important to estimate and detect an islanding situation quickly and accurately as it can lead to serious plant damage if the grid is suddenly reconnected. This paper investigates the characteristics of ROCOF relays which affect the operation responses and analyze the ROCOF performance for PV systems islanding detection. The equivalent impedance value V2/P is introduced as an interlock function to cooperate with ROCOF relays to avoid false operation during the non-islanding situation. It is shown that it can block nuisance tripping signals quickly and accurately. The range of effectiveness is established for the small power imbalances where there is a small potentially non-detection zone due to the overlap range of the impedance value.


title: On dominant characteristics of residential broadband internet traffic

abstract: While residential broadband Internet access is popular in many parts of the world, only a few studies have examined the characteristics of such traffic. In this paper we describe observations from monitoring the network activity for more than 20,000 residential DSL customers in an urban area. To ensure privacy, all data is immediately anonymized. We augment the anonymized packet traces with information about DSL-level sessions, IP (re-)assignments, and DSL link bandwidth. Our analysis reveals a number of surprises in terms of the mental models we developed from the measurement literature. For example, we find that HTTP - not peer-to-peer - traffic dominates by a significant margin; that more often than not the home user's immediate ISP connectivity contributes more to the round-trip times the user experiences than the WAN portion of the path; and that the DSL lines are frequently not the bottleneck in bulk-transfer performance.


title: Privacy Attitudes of Mechanical Turk Workers and the U.S. Public

abstract: Mechanical Turk (MTurk) is a crowdsourcing platform widely used to conduct behavioral research, including studies of online privacy and security. We studied how well the privacy attitudes of MTurk workers mirror the privacy attitudes of the larger user population. We report results from an MTurk survey of attitudes about managing one's personal information online and policy preferences about anonymity. We compare these attitudes with those of a representative U.S. adult sample drawn from a separate survey a few months earlier. MTurk respondents were younger and better educated, and more likely to use social media than the representative US adult sample. Although they reported a similar amount of personal information online, U.S. MTurk workers put a higher value on anonymity and hiding information, were more likely to do so, had more privacy concerns than the larger U.S. public. Indian MTurk workers were much less concerned than American workers about their privacy and more tolerant of government monitoring. Our analyses show that these findings hold even when controlling for age, education, gender, and social media use. Our findings suggest that privacy studies using MTurk need to account for differences between MTurk samples and the general population.


title: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008

abstract: Estimates of the worldwide incidence and mortality from 27 cancers in 2008 have been prepared for 182 countries as part of the GLOBOCAN series published by the International Agency for Research on Cancer. In this article, we present the results for 20 world regions, summarizing the global patterns for the eight most common cancers. Overall, an estimated 12.7 million new cancer cases and 7.6 million cancer deaths occur in 2008, with 56% of new cancer cases and 63% of the cancer deaths occurring in the less developed regions of the world. The most commonly diagnosed cancers worldwide are lung (1.61 million, 12.7% of the total), breast (1.38 million, 10.9%) and colorectal cancers (1.23 million, 9.7%). The most common causes of cancer death are lung cancer (1.38 million, 18.2% of the total), stomach cancer (738,000 deaths, 9.7%) and liver cancer (696,000 deaths, 9.2%). Cancer is neither rare anywhere in the world, nor mainly confined to high-resource countries. Striking differences in the patterns of cancer from region to region are observed.


title: A Powered Finger–Thumb Wearable Hand Exoskeleton With Self-Aligning Joint Axes

abstract: In recent years, the robotic research area has become extremely prolific in terms of wearable active exoskeletons for human body motion assistance, with the presentation of many novel devices, for upper limbs, lower limbs, and the hand. The hand shows a complex morphology, a high intersubject variability, and offers limited space for physical interaction with a robot: as a result, hand exoskeletons usually are heavy, cumbersome, and poorly usable. This paper introduces a novel device designed on the basis of human kinematic compatibility, wearability, and portability criteria. This hand exoskeleton, briefly HX, embeds several features as underactuated joints, passive degrees of freedom ensuring adaptability and compliance toward the hand anthropometric variability, and an ad hoc design of self-alignment mechanisms to absorb human/robot joint axes misplacement, and proposes a novel mechanism for the thumb opposition. The HX kinematic design and actuation are discussed together with theoretical and experimental data validating its adaptability performances. Results suggest that HX matches the self-alignment design goal and is then suited for close human-robot interaction.


title: On the inconsistency of bootstrap distribution estimators

abstract: We show that bootstrap distribution estimators of a ranked parameter value are consistent if and only if there are no ties for the rank in question. When inconsistency occurs, the bootstrap distribution estimator does not even converge in probability. This asymptotic result has important implications for small to moderate sample sizes, where poor distribution estimators can result when there are no ties but there are two or more closely spaced parameter values competing to the same rank. Several ways of alleviating the problem of inconsistency are suggested. © 1993.


title: 5G: Adaptable Networks Enabled by Versatile Radio Access Technologies

abstract: The requirements and key areas for 5G are gradually becoming more apparent, and it is becoming clear that 5G will need to be able to deal with increased levels of diversity in both the requirements it must fulfil and the technologies that it uses to fulfil them. The diverse and demanding requirements for 5G necessitate a shift away from the rigid networks of previous generations, toward a more versatile and adaptable network. Essential to enabling this level of adaptability in 5G networks will be the new radio access technologies that are employed. In previous generations, the radio access network (RAN) was composed of technologies and techniques that were tailored to satisfy the killer application of that era. In contrast, 5G will require versatile solutions that can be adapted to satisfy many different services and applications. The core network will also undergo fundamental changes, with increased levels of abstraction allowing for further reconfiguration of the network. The relationship between the RAN and core network will have a key role to play in managing and enabling adaptable networks. In this paper, we survey the choices and adaptability afforded by some of the radio access technologies being considered for 5G and explore how several system-level techniques, such as software-defined networking and cloud-RAN, can be utilized to enable and manage versatile 5G networks. Specifically, we focus on the relationship between new radio access technologies and emerging system-level techniques, examining how they may assist and complement each other. In this regard, we examine some tools such as virtualization and cognitive networks that can bridge this relationship. This paper is not intended to be a general survey on 5G, but rather a survey on how the requirements of flexibility and adaptability may be achieved in 5G through the coupling of versatile radio access technologies and emerging system-level techniques.


title: Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering

abstract: Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle.


title: Hierarchical object log format for normalisation of security events

abstract: The differences in log file formats employed in a variety of services and applications remain to be a problem for security analysts and developers of intrusion detection systems. The proposed solution, i.e. the usage of common log formats, has a limited utilization within existing solutions for security management. In our paper, we reveal the reasons for this limitation. We show disadvantages of existing common log formats for normalisation of security events. To deal with it we have created a new log format that fits for intrusion detection purposes and can be extended easily. Taking previous work into account, we would like to propose a new format as an extension to existing common log formats, rather than a standalone specification.


Techniques for text classification: Literature review and current trends


title: Croatian HMM based speech synthesis

abstract: The paper describes the development of a trainable speech synthesis system, based on hidden Markov models. An approach to speech signal generation using a source-filter model is presented. Inputs into the synthesis system are speech utterances and their phone level transcriptions. A method using context dependent acoustic models and Croatian phonetic rules for speech synthesis is proposed. Croatian HMM based speech synthesis experiments are presented and generated speech results are discussed


title: Targeted Online Password Guessing: An Underestimated Threat

abstract: While trawling online/offline password guessing has been intensively studied, only a few studies have examined targeted online guessing, where an attacker guesses a specific victim's password for a service, by exploiting the victim's personal information such as one sister password leaked from her another account and some personally identifiable information (PII). A key challenge for targeted online guessing is to choose the most effective password candidates, while the number of guess attempts allowed by a server's lockout or throttling mechanisms is typically very small. We propose TarGuess, a framework that systematically characterizes typical targeted guessing scenarios with seven sound mathematical models, each of which is based on varied kinds of data available to an attacker. These models allow us to design novel and efficient guessing algorithms. Extensive experiments on 10 large real-world password datasets show the effectiveness of TarGuess. Particularly, TarGuess I~IV capture the four most representative scenarios and within 100 guesses: (1) TarGuess-I outperforms its foremost counterpart by 142% against security-savvy users and by 46% against normal users; (2) TarGuess-II outperforms its foremost counterpart by 169% on security-savvy users and by 72% against normal users; and (3) Both TarGuess-III and IV gain success rates over 73% against normal users and over 32% against security-savvy users. TarGuess-III and IV, for the first time, address the issue of cross-site online guessing when given the victim's one sister password and some PII.


title: Integrating non-functional requirements into data modeling

abstract: Complex information systems need conceptual models to deal with aspects beyond the usual entity and activity framework. Recent research has pointed out that conceptual models need to deal with goals, in order to model complex situations that happen in the real world. Our work investigated a new strategy that allows the integration of non-functional requirements (NFR) into data models. Integration is done by representing the NFRs in a data model and investigating how NFR's satisfaction affects the data model. We adopted the widely used ER conceptual model as the data modeling representation schema. In order to start the validation of our strategy we applied it to a real system. The results so far confirm our hypothesis that NFR's integration into data models do help in achieving better understanding of what is being modeled.


Circumventing side effects of immersive virtual environments


title: WSN- and IOT-Based Smart Homes and Their Extension to Smart Buildings

abstract: Our research approach is to design and develop reliable, efficient, flexible, economical, real-time and realistic wellness sensor networks for smart home systems. The heterogeneous sensor and actuator nodes based on wireless networking technologies are deployed into the home environment. These nodes generate real-time data related to the object usage and movement inside the home, to forecast the wellness of an individual. Here, wellness stands for how efficiently someone stays fit in the home environment and performs his or her daily routine in order to live a long and healthy life. We initiate the research with the development of the smart home approach and implement it in different home conditions (different houses) to monitor the activity of an inhabitant for wellness detection. Additionally, our research extends the smart home system to smart buildings and models the design issues related to the smart building environment; these design issues are linked with system performance and reliability. This research paper also discusses and illustrates the possible mitigation to handle the ISM band interference and attenuation losses without compromising optimum system performance.


title: Knowledge Collaboration in Online Communities

abstract: Online communities (OCs) are a virtual organizational form in which knowledge collaboration can occur in unparalleled scale and scope, in ways not heretofore theorized. For example, collaboration can occur among people not known to each other, who share different interests and without dialogue. An exploration of this organizational form can fundamentally change how we theorize about knowledge collaboration among members of organizations. We argue that a fundamental characteristic of OCs that affords collaboration is their fluidity. This fluidity engenders a dynamic flow of resources in and out of the community---resources such as passion, time, identity, social disembodiment of ideas, socially ambiguous identities, and temporary convergence. With each resource comes both a negative and positive consequence, creating a tension that fluctuates with changes in the resource. We argue that the fluctuations in tensions can provide an opportunity for knowledge collaboration when the community responds to these tensions in ways that encourage interactions to be generative rather than constrained. After offering numerous examples of such generative responses, we suggest that this form of theorizing---induced by online communities---has implications for theorizing about the more general case of knowledge collaboration in organizations.


title: Fast object localization and pose estimation in heavy clutter for robotic bin picking

abstract: We present a practical vision-based robotic bin-picking system that performs detection and three-dimensional pose estimation of objects in an unstructured bin using a novel camera design, picks up parts from the bin, and performs error detection and pose correction while the part is in the gripper. Two main innovations enable our system to achieve real-time robust and accurate operation. First, we use a multi-flash camera that extracts robust depth edges. Second, we introduce an efficient shape-matching algorithm called fast directional chamfer matching (FDCM), which is used to reliably detect objects and estimate their poses. FDCM improves the accuracy of chamfer matching by including edge orientation. It also achieves massive improvements in matching speed using line-segment approximations of edges, a three-dimensional distance transform, and directional integral images. We empirically show that these speedups, combined with the use of bounds in the spatial and hypothesis domains, give the algorithm sub...


title: Goals and Habits in the Brain

abstract: An enduring and richly elaborated dichotomy in cognitive neuroscience is that of reflective versus reflexive decision making and choice. Other literatures refer to the two ends of what is likely to be a spectrum with terms such as goal-directed versus habitual, model-based versus model-free or prospective versus retrospective. One of the most rigorous traditions of experimental work in the field started with studies in rodents and graduated via human versions and enrichments of those experiments to a current state in which new paradigms are probing and challenging the very heart of the distinction. We review four generations of work in this tradition and provide pointers to the forefront of the field’s fifth generation.


title: Territoriality in collaborative tabletop workspaces

abstract: Researchers seeking alternatives to traditional desktop computers have begun exploring the potential collaborative benefits of digital tabletop displays. However, there are still many open issues related to the design of collaborative tabletop interfaces, such as whether these systems should automatically orient workspace items or enforce ownership of workspace content. Understanding the natural interaction practices that people use during tabletop collaboration with traditional media (e.g., pen and paper) can help to address these issues. Interfaces that are modeled on these practices will have the additional advantage of supporting the interaction skills people have developed over years of collaborating at traditional tables. To gain a deeper understanding of these interaction practices we conducted two observational studies of traditional tabletop collaboration in both casual and formal settings. Our results reveal that collaborators use three types of tabletop territories to help coordinate their interactions within the shared tabletop workspace: personal, group, and storage territories. Findings from a spatial analysis of collaborators' tabletop interactions reveal important properties of these tabletop territories. In order to provide a comprehensive picture of the role of tabletop territoriality in collaboration, we conclude with a synthesis of our findings and previous research findings and with several relevant design implications.


title: Transfer Learning for Video Recognition with Scarce Training Data

abstract: Unconstrained video recognition and Deep Convolution Network (DCN) are two active topics in computer vision recently. In this work, we apply DCNs as frame-based recognizers for video recognition. Our preliminary studies, however, show that video corpora with complete ground truth are usually not large and diverse enough to learn a robust model. The networks trained directly on the video data set suffer from significant overfitting and have poor recognition rate on the test set. The same lack-of-training-sample problem limits the usage of deep models on a wide range of computer vision problems where obtaining training data are difficult. To overcome the problem, we perform transfer learning from images to videos to utilize the knowledge in the weakly labeled image corpus for video recognition. The image corpus help to learn important visual patterns for natural images, while these patterns are ignored by models trained only on the video corpus. Therefore, the resultant networks have better generalizability and better recognition rate. We show that by means of transfer learning from image to video, we can learn a frame-based recognizer with only 4k videos. Because the image corpus is weakly labeled, the entire learning process requires only 4k annotated instances, which is far less than the million scale image data sets required by previous works. The same approach may be applied to other visual recognition tasks where only scarce training data is available, and it improves the applicability of DCNs in various computer vision problems. Our experiments also reveal the correlation between meta-parameters and the performance of DCNs, given the properties of the target problem and data. These results lead to a heuristic for meta-parameter selection for future researches, which does not rely on the time consuming meta-parameter search.


title: Fixed-camera visual servoing of moving object using an event-based path generation

abstract: Visual servo control of robots has been recently considered and developed by many researchers. In this paper we have used a fixed camera for obtaining location and orientation of a moving object on a conveyor belt and then tracking the object. Tracking trajectory is obtained using a new event-based real-time path generation. Using the error signal obtained, which is difference between location of moving object and location of end-effector of the custom designed robot, a PID controller is used to track the generated trajectory. All programming problems are accomplished by Mathlab® and Simulik®. Two personal computers are used, one for image processing and other for control and path generation problem. Simulation results are shown to confirm our theory in tracking the object smoothly.


title: Dynamics and Performance of Tailless Micro Aerial Vehicle with Flexible Articulated Wings

abstract: The purpose of this paper is to analyze and discuss the performance and stability of a tailless micro aerial vehicle ::: with flexible articulated wings. The dihedral angles can be varied symmetrically on both wings to control the aircraft ::: speed independently of the angle of attack and flight-path angle, while an asymmetric dihedral setting can be used to ::: control yaw in the absence of a vertical tail.Anonlinear aero-elastic model is derived, and it is used to study the steady-state performance and flight stability of the micro aerial vehicle. The concept of the effective dihedral is introduced, which allows for a unified treatment of rigid and flexible wing aircraft. It also identifies the amount of elasticity that is necessary to obtain tangible performance benefits over a rigid wing. The feasibility of using axial tension to stiffen the wing is discussed, and, at least in the context of a linear model, it is shown that adding axial tension is effective but undesirable. The turning performance of an micro aerial vehicle with flexible wings is compared to an otherwise identical micro aerial vehicle with rigid wings. The wing dihedral alone can be varied asymmetrically to perform rapid turns and regulate sideslip. The maximum attainable turn rate for a given elevator setting, however, does not increase unless antisymmetric wing twisting is employed.


Study of Viola-Jones Real Time Face Detector


title: Consensus Recommendations on the Aesthetic Usage of Botulinum Toxin Type A in Asians

abstract: BACKGROUND ::: The use of botulinum toxin has rapidly expanded into various aesthetic applications. Any guideline representing a consensus for aesthetic treatments using botulinum toxin type A (BTA) in Asians has not been published. ::: ::: ::: OBJECTIVES ::: To provide consensus recommendations on common aesthetic problems which are treated by neurotoxin in Asians. ::: ::: ::: METHODS ::: A panel of experienced Korean dermatologists was convened to develop a clinical consensus on common aesthetic problems involving the face, neck, and calves in Asians, based on their own extensive experience. ::: ::: ::: RESULTS ::: The consensus recommendations address general questions regarding treatment and provide specific guidelines on each common aesthetic indication. The recommended final concentration of BTA was 50 U/mL after reconstitution with physiologic saline. For horizontal forehead lines, the members recommended nine injections in two rows into the frontalis with 1 U/point. For glabellar lines, the members recommended three injection points (a total of 8 U). For crow's feet, the members recommended three injections per side (7 U/side) at the lateral part of the orbicularis oculi. For infraorbital wrinkles, one to two points per side in the superficial subcutaneous space approximately 1 cm below the lash line were recommended (1-2 U/side). For nasal flare, one injection point in the middle of each ala nasi was recommended (a total of 2 U). For depressed nasal tip, a single injection deep within the columella was recommended, with a dose of 3 U. For benign masseter hypertrophy, the members recommended a six-point injection to the masseter (three points per side for a total of 50-60 U). For the treatment of calf hypertrophy, the members recommended a total dose of 100 to 120 U (50-60 U/side), divided between six injection points (approximately 8-10 U/point). ::: ::: ::: CONCLUSION ::: This guideline provides a framework for physicians who wish to perform safe and efficacious injections of BTA in Asians.


T How To Fail In Project Management ( Without Really Trying )


title: Cellular IP: a new approach to Internet host mobility

abstract: This paper describes a new approach to Internet host mobility. We argue that by separating local and wide area mobility, the performance of existing mobile host protocols (e.g. Mobile IP) can be significantly improved. We propose Cellular IP, a new lightweight and robust protocol that is optimized to support local mobility but efficiently interworks with Mobile IP to provide wide area mobility support. Cellular IP shows great benefit in comparison to existing host mobility proposals for environments where mobile hosts migrate frequently, which we argue, will be the rule rather than the exception as Internet wireless access becomes ubiquitous. Cellular IP maintains distributed cache for location management and routing purposes. Distributed paging cache coarsely maintains the position of 'idle' mobile hosts in a service area. Cellular IP uses this paging cache to quickly and efficiently pinpoint 'idle' mobile hosts that wish to engage in 'active' communications. This approach is beneficial because it can accommodate a large number of users attached to the network without overloading the location management system. Distributed routing cache maintains the position of active mobile hosts in the service area and dynamically refreshes the routing state in response to the handoff of active mobile hosts. These distributed location management and routing algorithms lend themselves to a simple and low cost implementation of Internet host mobility requiring no new packet formats, encapsulations or address space allocation beyond what is present in IP.


title: Off-chip beam steering with a one-dimensional optical phased array on silicon-on-insulator

abstract: Optical phased arrays are versatile components enabling rapid and precise beam steering. An integrated approach is followed in which a 1D optical phased array is fabricated on silicon-on-insulator. The optical phased array consists of 16 parallel grating couplers spaced 2 μm apart. Steering in one direction is done thermo-optically by means of a titanium electrode on top of the structure using the phased array principle, while steering in the other direction is accomplished by wavelength tuning. At a wavelength of 1550 nm, continuous thermo-optical steering of 2.3° and wavelength steering of 14.1° is reported.


title: ITSA: Internet traffic statistics archive

abstract: Motivated by the fact that comprehensive and long term Internet traffic measurements can be hard to obtain, we have proposed and developed the Internet Traffic Statistics Archive (ITSA). Since 2013, ITSA concentrates reports on highlevel statistics of Internet traffic worldwide. Examples of statistics are shares of traffic per port, protocols and distribution of packets and bytes. Such information can be of great help to those that need to support claims and assumptions on what Internet traffic actually looks like. The reports containing traffic statistics are generated on a weekly basis from NetFlow data, and publicly published on the ITSA website. In this demo we will present the ITSA architecture, how it can be used, and how the available data can help to visualize and understand trends in Internet traffic.


title: Open Rectangle-of-Influence Drawings of Inner Triangulated Plane Graphs

abstract: A straight-line drawing of a plane graph is called an open rectangle-of-influence drawing if there is no vertex in the proper inside of the axis-parallel rectangle defined by the two ends of every edge. In an inner triangulated plane graph, every inner face is a triangle although the outer face is not always a triangle. In this paper, we first obtain a sufficient condition for an inner triangulated plane graph G to have an open rectangle-of-influence drawing; the condition is expressed in terms of a labeling of angles of a subgraph of G. We then present an O(n1.5/log n)-time algorithm to examine whether G satisfies the condition and, if so, construct an open rectangle-of-influence drawing of G on an (n-1)×(n-1) integer grid, where n is the number of vertices in G.


title: Bayesian Supervised Hashing

abstract: Among learning based hashing methods, supervised hashing seeks compact binary representation of the training data to preserve semantic similarities. Recent years have witnessed various problem formulations and optimization methods for supervised hashing. Most of them optimize a form of loss function with a regulization term, which can be viewed as a maximum a posterior (MAP) estimation of the hashing codes. However, these approaches are prone to overfitting unless hyperparameters are tuned carefully. To address this problem, we present a novel fully Bayesian treatment for supervised hashing problem, named Bayesian Supervised Hashing (BSH), in which hyperparameters are automatically tuned during optimization. Additionally, by utilizing automatic relevance determination (ARD), we can figure out relative discriminating ability of different hashing bits and select most informative bits among them. Experimental results on three real-world image datasets with semantic information show that BSH can achieve superior performance over state-of-the-art methods with comparable training time.


title: Intelligent distributed fuzzy logic control system (IDFLCS) of a mecanum wheeled autonomous guided vehicle

abstract: A truly autonomous guided vehicle (AGV) must sense its surrounding environment and react accordingly. Tn order to manoeuvre an AGV autonomously, it has to overcome navigational and collision avoidanc e problems. Previous AGV control systems have relied on hand-coded algorithms for processing sensor information. An intelligent distributed fuzzy logic control systems (TDFLCS) has been implemented in a mecanum wheeled AGV system in order to achieve improved reliability and to reduce complexity of the development of control systems. Fuzzy logic controllers have been used to achieve robust control of mechatronic systems by fusing multiple signals from noisy sensors, integrating the representation of human knowledge and implementing behaviour-based control using IF-THEN rules. This paper presents an intelligent distributed controller that implements fuzzy logic on an AGV that uses four independently driven mecanum wheels, incorporating laser, inertial and ultrasound sensors. Distributed control system, fuzzy control strategy, navigation and motion control ofsuch an AGV are presented.


Cladosporium keratitis – a case report and literature review


title: Intriguing Properties of Adversarial Examples

abstract: It is becoming increasingly clear that many machine learning classifiers are vulnerable to adversarial examples. In attempting to explain the origin of adversarial examples, previous studies have typically focused on the fact that neural networks operate on high dimensional data, they overfit, or they are too linear. Here we argue that the origin of adversarial examples is primarily due to an inherent uncertainty that neural networks have about their predictions. We show that the functional form of this uncertainty is independent of architecture, dataset, and training protocol; and depends only on the statistics of the logit differences of the network, which do not change significantly during training. This leads to adversarial error having a universal scaling, as a power-law, with respect to the size of the adversarial perturbation. We show that this universality holds for a broad range of datasets (MNIST, CIFAR10, ImageNet, and random data), models (including state-of-the-art deep networks, linear models, adversarially trained networks, and networks trained on randomly shuffled labels), and attacks (FGSM, step l.l., PGD). Motivated by these results, we study the effects of reducing prediction entropy on adversarial robustness. Finally, we study the effect of network architectures on adversarial sensitivity. To do this, we use neural architecture search with reinforcement learning to find adversarially robust architectures on CIFAR10. Our resulting architecture is more robust to white \emph{and} black box attacks compared to previous attempts.


title: Search-based planning for dual-arm manipulation with upright orientation constraints

abstract: Dual-arm manipulation is an increasingly important skill for robots operating in home, retail and industrial environments. Dual-arm manipulation is especially essential for tasks involving large objects which are harder to grasp and manipulate using a single arm. In this work, we address dual-arm manipulation of objects in indoor environments. We are particularly focused on tasks that involve an upright orientation constraint on the grasped object. Such constraints are often present in human environments, e.g. when manipulating a tray of food or a container with fluids. In this paper, we present a search-based approach that is capable of planning dual-arm motions, often within one second, in cluttered environments while adhering to the orientation constraints. Our approach systematically constructs a graph in task space and generates motions that are consistent across runs with similar start/goal configurations and are low-cost. These motions come with guarantees on completeness and bounds on the suboptimality with respect to the graph that encodes the planning problem. For many problems, the consistency of the generated motions is important as it helps make the actions of the robot more predictable for a human interacting with the robot.


title: Are Emojis Predictable?

abstract: Emojis are ideograms which are naturally combined with plain text to visually complement or condense the meaning of a message. Despite being widely used in social media, their underlying semantics have received little attention from a Natural Language Processing standpoint. In this paper, we investigate the relation between words and emojis, studying the novel task of predicting which emojis are evoked by text-based tweet messages. We train several models based on Long Short-Term Memory networks (LSTMs) in this task. Our experimental results show that our neural model outperforms two baselines as well as humans solving the same task, suggesting that computational models are able to better capture the underlying semantics of emojis.


title: Using CloudSim to Model and Simulate Cloud Computing Environment

abstract: Cloud computing will be a major technology in the development of the future Internet of Services. Service providers want to remove the bottle neck of the cloud computing system in order to satisfy user requirement. And in order to save energy consumption they also need to apply new energy-efficiency mechanism and observe its effects on a data center. As it is difficult to test new mechanism in real cloud computing environment and researchers often cannot reach the real cloud environment, simulation to model the mechanism and evaluate the results is necessary. Simulating a data center avoids spending time and effort to configure a real testing environment. Moreover, as real machines are not used for testing purposes, their computational power can be allocated to profit-making applications. This paper introduces a simulation framework called CloudSim which provides simulation, power to manage services and modeling of cloud infrastructure. And we have also discussed about how to extend it to simulation your own mechanism in cloud computing.


title: Dynamic Privacy Pricing: A Multi-Armed Bandit Approach With Time-Variant Rewards

abstract: Recently, the conflict between exploiting the value of personal data and protecting individuals’ privacy has attracted much attention. Personal data market provides a promising solution to this conflict, while determining the price of privacy is a tough issue. In this paper, we study the pricing problem in a setting where a data collector sequentially buys data from multiple data owners whose valuations of privacy are randomly drawn from an unknown distribution. To maximize the total payoff, the collector needs to dynamically adjust the prices offered to owners. We model the sequential decision-making problem of the collector as a multi-armed bandit problem with each arm representing a candidate price. Specifically, the privacy protection technique adopted by the collector is taken into account. Protecting privacy generally causes a negative effect on the value of data, and this effect is embodied by the time-variant distributions of the rewards associated with arms. Based on the classic upper confidence bound policy, we propose two learning policies for the bandit problem. The first policy estimates the expected reward of a price by counting how many times the price has been accepted by data owners. The second policy treats the time-variant data value as a context and uses ridge regression to estimate the rewards in different contexts. Simulation results on real-world data demonstrate that by applying the proposed policies, the collector can get a payoff which is close to that he can get by setting a fixed price, which is the best in hindsight, for all data owners.


title: Frontal predominance of a relative increase in sleep delta and theta EEG activity after sleep loss in humans.

abstract: The effect of sleep deprivation (40 h) on topographic and temporal aspects of electroencephalographic (EEG) activity during sleep was investigated by all night spectral analysis in six young volunteers. The sleep-deprivation-induced increase of EEG power density in the delta and theta frequencies (1-7 Hz) during nonREM sleep, assessed along the antero-posterior axis (midline: Fz, Cz, Pz, Oz), was significantly larger in the more frontal derivations (Fz, Cz) than in the more parietal derivations (Pz, Oz). This frequency-specific frontal predominance was already present in the first 30 min of recovery sleep, and dissipated in the course of the 8-h sleep episode. The data demonstrate that the enhancement of slow wave EEG activity during sleep following extended wakefulness is most pronounced in frontal cortical areas.


title: Chasing Professional Phronesis in Safety and Well-Being: Teacher Education Curriculum as a Case

abstract: Enhancing the safety culture in school context sets new challenges to prospective teachers, their need for safety skills and for the teacher education. This paper discusses firstly, how skill and knowledge oriented safety and well-being contents in the teacher education curriculum could be distinguished. Further on, it is explored, how these two repertoires are presented in the curriculum text of one teacher education unit and how they reflect the Aristotelian ideal of achieving phronesis, practical wisdom. The data pointed out that safety was included in the curriculum and could therefore be seen as a value in teacher education, hence the focus was very strongly in the interactive skills and the group dynamics.


title: Online patient websites for electronic health record access among vulnerable populations: portals to nowhere?

abstract: Objective: With the rapid rise in the adoption of patient portals, many patients are gaining access to their personal health information online for the first time. The objective of this study was to examine specific usability barriers to patient portal engagement among a diverse group of patients and caregivers. ::: ::: Materials and Methods: We conducted interviews using performance testing and think-aloud methods with 23 patients and 2 caregivers as they first attempted to use features of a newly launched patient portal. ::: ::: Results: In navigating the portal, participants experienced basic computer barriers (eg, difficulty using a mouse), routine computer barriers (eg, mistyping, navigation issues), reading/writing barriers, and medical content barriers. Compared to participants with adequate health literacy, participants with limited health literacy required 2 additional minutes to complete each task and were more likely to experience each type of navigational barrier. They also experienced more inaccuracies in interpreting a test result and finding a treatment plan within an after-visit summary. ::: ::: Discussion: When using a patient portal for the first time, participants with limited health literacy completed fewer tasks unassisted, had a higher prevalence of encountering barriers, took longer to complete tasks, and had more problems accurately interpreting medical information. ::: ::: Conclusion: Our findings suggest a strong need for tailored and accessible training and support to assist all vulnerable patients and/or caregivers with portal registration and use. Measuring the health literacy of a patient population might serve as a strong proxy for identifying patients who need the most support in using health technologies.


title: The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services

abstract: The purposes of this study are to construct an instrument to evaluate service quality of mobile value-added services and have a further discussion of the relationships among service quality, perceived value, customer satisfaction, and post-purchase intention. Structural equation modeling and multiple regression analysis were used to analyze the data collected from college and graduate students of 15 major universities in Taiwan. The main findings are as follows: (1) service quality positively influences both perceived value and customer satisfaction; (2) perceived value positively influences on both customer satisfaction and post-purchase intention; (3) customer satisfaction positively influences post-purchase intention; (4) service quality has an indirect positive influence on post-purchase intention through customer satisfaction or perceived value; (5) among the dimensions of service quality, ''customer service and system reliability'' is most influential on perceived value and customer satisfaction, and the influence of ''content quality'' ranks second; (6) the proposed model is proven with the effectiveness in explaining the relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile added-value services.


title: Multiscale Geometric Methods for Data Sets II: Geometric Multi-Resolution Analysis

abstract: Data sets are often modeled as point clouds in $R^D$, for $D$ large. It is often assumed that the data has some interesting low-dimensional structure, for example that of a $d$-dimensional manifold $M$, with $d$ much smaller than $D$. When $M$ is simply a linear subspace, one may exploit this assumption for encoding efficiently the data by projecting onto a dictionary of $d$ vectors in $R^D$ (for example found by SVD), at a cost $(n+D)d$ for $n$ data points. When $M$ is nonlinear, there are no"explicit"constructions of dictionaries that achieve a similar efficiency: typically one uses either random dictionaries, or dictionaries obtained by black-box optimization. In this paper we construct data-dependent multi-scale dictionaries that aim at efficient encoding and manipulating of the data. Their construction is fast, and so are the algorithms that map data points to dictionary coefficients and vice versa. In addition, data points are guaranteed to have a sparse representation in terms of the dictionary. We think of dictionaries as the analogue of wavelets, but for approximating point clouds rather than functions.


title: Understanding the role of the family physician in early psychosis intervention

abstract: Background The family physician is key to facilitating access to psychiatric treatment for young people with first-episode psychosis, and this involvement can reduce aversive events in pathways to care. Those who seek help from primary care tend to have longer intervals to psychiatric care, and some people receive ongoing psychiatric treatment from the family physician. Aims Our objective is to understand the role of the family physician in help-seeking, recognition and ongoing management of first-episode psychosis. Method We will use a mixed-methods approach, incorporating health administrative data, electronic medical records (EMRs) and qualitative methodologies to study the role of the family physician at three points on the pathway to care. First, help-seeking: we will use health administrative data to examine access to a family physician and patterns of primary care use preceding the first diagnosis of psychosis; second, recognition: we will identify first-onset cases of psychosis in health administrative data, and look back at linked EMRs from primary care to define a risk profile for undetected cases; and third, management: we will examine service provision to identified patients through EMR data, including patterns of contacts, prescriptions and referrals to specialised care. We will then conduct qualitative interviews and focus groups with key stakeholders to better understand the trends observed in the quantitative data. Discussion These findings will provide an in-depth description of first-episode psychosis in primary care, informing strategies to build linkages between family physicians and psychiatric services to improve transitions of care during the crucial early stages of psychosis. Declaration of interest None.


title: FUNDAMENTALS OF EEG MEASUREMENT

abstract: Electroencephalographic measurements are commonly used in medical and research areas. This review article presents an introduction into EEG measurement. Its purpose is to help with orientation in EEG field and with building basic knowledge for performing EEG recordings. The article is divided into two parts. In the first part, background of the subject, a brief historical overview, and some EEG related research areas are given. The second part explains EEG recording. Modern medicine applies variety of imaging techniques of the human body. The group of electrobiological measurements comprises items as electrocardiography (ECG, heart), electromyography (EMG, muscular contractions), electroencephalography (EEG, brain), magnetoencephalography (MEG, brain), electrogastrography (EGG, stomach), electrooptigraphy (EOG, eye dipole field). Imaging techniques based on different physical principles include computer tomography (CT), magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET), and single photon emission computed tomography (SPECT). Electroencephalography is a medical imaging technique that reads scalp electrical activity generated by brain structures. The electroencephalogram (EEG) is defined as electrical activity of an alternating type recorded from the scalp surface after being picked up by metal electrodes and conductive media (1). The EEG measured directly from the cortical surface is called electrocortiogram while when using depth probes it is called electrogram. In this article, we will refer only to EEG measured from the head surface. Thus electroencephalographic reading is a completely non-invasive procedure that can be applied repeatedly to patients, normal adults, and children with virtually no risk or limitation. When brain cells (neurons) are activated, local current flows are produced. EEG measures mostly the currents that flow during synaptic excitations of the dendrites of many pyramidal neurons in the cerebral cortex. Differences of electrical potentials are caused by summed postsynaptic graded potentials from pyramidal cells that create electrical dipoles between soma (body of neuron) and apical dendrites (neural branches). Brain electrical current consists mostly of Na+, K+, Ca++, and Cl- ions that are pumped through channels in neuron membranes in the direction governed by membrane potential (2). The detailed microscopic picture is more sophisticated, including different types of synapses involving variety of neurotransmitters. Only large populations of active neurons can generate electrical activity recordable on the head surface. Between electrode and neuronal layers current penetrates through skin, skull and several other layers. Weak electrical signals detected by the scalp electrodes are massively amplified, and then displayed on paper or stored to computer memory (3). Due to capability to reflect both the normal and abnormal electrical activity of the brain, EEG has been found to be a very powerful tool in the field of neurology and clinical neurophysiology. The human brain electric activity starts around the 17-23 week of prenatal development. It is assumed that at birth the full number of neural cells is already developed, roughly 10 11 neurons (4). This makes an average density of 10 4 neurons per cubic mm. Neurons are mutually connected into neural nets through synapses. Adults have about 500 trillion (5.10 14 ) synapses. The number of synapses per one neuron with age increases, however the number of neurons with age decreases, thus the total number of synapses decreases with age too. From the anatomical point of view, the brain can be divided into three sections: cerebrum, cerebellum, and brain stem. The cerebrum consists of left and right hemisphere with highly convoluted surface layer called cerebral cortex. The cortex is a


title: Consensus in the presence of partial synchrony

abstract: The concept of partial synchrony in a distributed system is introduced. Partial synchrony lies between the cases of a synchronous system and an asynchronous system. In a synchronous system, there is a known fixed upper bound Δ on the time required for a message to be sent from one processor to another and a known fixed upper bound P on the relative speeds of different processors. In an asynchronous system no fixed upper bounds Δ and P exist. In one version of partial synchrony, fixed bounds Δ and P exist, but they are not known a priori. The problem is to design protocols that work correctly in the partially synchronous system regardless of the actual values of the bounds Δ and P. In another version of partial synchrony, the bounds are known, but are only guaranteed to hold starting at some unknown time T , and protocols must be designed to work correctly regardless of when time T occurs. Fault-tolerant consensus protocols are given for various cases of partial synchrony and various fault models. Lower bounds that show in most cases that our protocols are optimal with respect to the number of faults tolerated are also given. Our consensus protocols for partially synchronous processors use new protocols for fault-tolerant “distributed clocks” that allow partially synchronous processors to reach some approximately common notion of time.


title: An efficient image matching method for multi-view stereo

abstract: Most existing Multi-View Stereo (MVS) algorithms employ the image matching method using Normalized Cross-Correlation (NCC) to estimate the depth of an object. The accuracy of the estimated depth depends on the step size of the depth in NCC-based window matching. The step size of the depth must be small for accurate 3D reconstruction, while the small step significantly increases computational cost. To improve the accuracy of depth estimation and reduce the computational cost, this paper proposes an efficient image matching method for MVS. The proposed method is based on Phase-Only Correlation (POC), which is a high-accuracy image matching technique using the phase components in Fourier transforms. The advantages of using POC are (i) the correlation function is obtained only by one window matching and (ii) the accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. Thus, using POC-based window matching for MVS makes it possible to estimate depth accurately from the correlation function obtained only by one window matching. Through a set of experiments using the public MVS datasets, we demonstrate that the proposed method performs better in terms of accuracy and computational cost than the conventional method.


title: Finance and the Sources of Growth

abstract: The authors evaluate whether the level of development in the banking sector exerts a causal impact on economic growth and its sources-total factor productivity growth, physical capital accumulation, and private saving. They use (1) a pure cross-country instrumental variable estimator to extract the exogenous component of banking development and (2) a new panel technique that controls for country-specific effects and endogeneity. They find that: Banks do exert a large, causal impact on total factor productivity growth, which feeds through to overall GDP (Gross Domestic Product) growth. The long-run links between banking development and both capital growth and private savings are more tenuous.


title: Design of retuning fractional PID controllers for a closed-loop magnetic levitation control system

abstract: In this paper, we study the problem of fractional-order PID controller design for an unstable plant — a laboratory model of a magnetic levitation system. To this end, we apply model based control design. A model of the magnetic levitation system is obtained by means of a closed-loop experiment. Several stable fractional-order controllers are identified and optimized by considering isolated stability regions. Finally, a nonintrusive controller retuning method is used to incorporate fractional-order dynamics into the existing control loop, thereby enhancing its performance. Experimental results confirm the effectiveness of the proposed approach. Control design methods offered in this paper are general enough to be applicable to a variety of control problems.


title: Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children

abstract: SUMMARYBackgroundThe relationship among physical activity (PA), fitness, cognitive function, and academic achievement in children is receiving considerable attention. The utility of PA to improve cognition and academic achievement is promising but uncertain; thus, this position stand will provide cl


title: Exploring dynamic research interest and academic influence for scientific collaborator recommendation

abstract: In many cases, it is time-consuming for researchers to find proper collaborators who can provide researching guidance besides simply collaborating. The Most Beneficial Collaborators (MBCs), who have high academic level and relevant research topics, can genuinely help researchers to enrich their research. However, how can we find the MBCs? In this paper, we propose a most Beneficial Collaborator Recommendation model called BCR. BCR learns on researchers’ publications and associates three academic features: topic distribution of research interest, interest variation with time and researchers’ impact in collaborators network. We run a topic model on researchers’ publications in each year for topic clustering. The generated topic distribution matrix is fixed by a time function to fit the interest dynamic transformation. The academic social impact is also considered to mine the most prolific researchers. Finally, a TopN MBCs recommendation list is generated according to the computed score. Extensive experiments on a dataset with citation network demonstrate that, in comparison to relevant baseline approaches, our BCR performs better in terms of precision, recall, F1 score and the recommendation quality.


title: Pretty Good BGP: Improving BGP by Cautiously Adopting Routes

abstract: The Internet's interdomain routing protocol, BGP, is vulnerable to a number of damaging attacks, which often arise from operator misconfiguration. Proposed solutions with strong guarantees require a public-key infrastructure, accurate routing registries, and changes to BGP. However, BGP routers can avoid selecting and propagating these routes if they are cautious about adopting new reachability information. We describe a protocol- preserving enhancement to BGP, Pretty Good BGP (PGBGP), that slows the dissemination of bogus routes, providing network operators time to respond before problems escalate into large- scale Internet attacks. Simulation results show that realistic deployments of PGBGP could provide 99% of Autonomous Systems with 24 hours to investigate and repair bogus routes without affecting prefix reachability. We also show that without PGBGP, 40% of ASs cannot avoid selecting bogus routes; with PGBGP, this number drops to less than 1%. Finally, we show that PGBGP is incrementally deployable and offers significant security benefits to early adopters and their customers.


title: Case for Automated Detection of Diabetic Retinopathy

abstract: Diabetic retinopathy, an eye disorder caused by diabetes, is the primary cause of blindness in America and over 99% of cases in India. India and China currently account for over 90 million diabetic patients and are on the verge of an explosion of diabetic populations. This may result in an unprecedented number of persons becoming blind unless diabetic retinopathy can be detected early. Aravind Eye Hospitals is the largest eye care facility in the world, handling over 2 million patients per year. The hospital is on a massive drive throughout southern India to detect diabetic retinopathy at an early stage. To that end, a group of 10 − 15 physicians are responsible for manually diagnosing over 2 million retinal images per year to detect diabetic retinopathy. While the task is extremely laborious, a large fraction of cases turn out to be normal indicating that much of this time is spent diagnosing completely normal cases. This paper describes our early experiences working with Aravind Eye Hospitals to develop an automated system to detect diabetic retinopathy from retinal images. The automated diabetic retinopathy problem is a hard computer vision problem whose goal is to detect features of retinopathy, such as hemorrhages and exudates, in retinal color fundus images. We describe our initial efforts towards building such a system using a range of computer vision techniques and discuss the potential impact on early detection of diabetic retinopathy.