Successfully Managing Impending Skin Necrosis following Hyaluronic Acid Filler Injection, using High-Dose Pulsed Hyaluronidase

Facial fillers are becoming increasingly popular as aesthetic procedures to temporarily reduce the depth of wrinkles or to contour faces. However, even in the hands of very experienced injectors, there is always a small possibility of vascular complications like intra-arterial injection of filler substance. We present a case report of a patient who developed features of vascular obstruction in right infraorbital artery and tell-tale signs of impending skin necrosis, after hyaluronic acid filler injection by an experienced injector. The diagnosis of a vascular complication was made quickly with the help of clinical features like blanching, livedo reticularis, and poor capillary refill. Patient was treated promptly with "high-dose pulsed hyaluronidase protocol" comprising three 1,000-unit pulses of hyaluronidase, administered hourly. There was no further increase in size of the involved area after the first dose of hyaluronidase. All of the involved area, along with 1 cm overlapping in uninvolved skin area, was injected during each injection pulse, using a combination of cannula and needle. Complete reperfusion and good capillary filling were achieved after completion of 3 pulses, and these were taken as the end-point of high-dose pulsed hyaluronidase treatment. Immediate skin changes after filler injections, as well as after hyaluronidase injections and during the 3-week recovery period, were documented with photographs and clinical notes. Involved skin was found to have been fully recovered from this vascular episode, thus indicating that complete recovery of the ischemic skin changes secondary to possible intra-arterial injection could be achieved using high-dose pulsed hyaluronidase protocol.


Characterizing and visualizing physical world accessibility at scale using crowdsourcing, computer vision, and machine learning

Poorly maintained sidewalks and street intersections pose considerable accessibility challenges for people with mobility-impairments [13,14]. According to the most recent U.S. Census (2010), roughly 30.6 million adults have physical disabilities that affect their ambulatory activities [22]. Of these, nearly half report using an assistive aid such as a wheelchair (3.6 million) or a cane, crutches, or walker (11.6 million) [22]. Despite comprehensive civil rights legislation for Americans with Disabilities (e.g., [25,26]), many city streets, sidewalks, and businesses in the U.S. remain inaccessible. The problem is not just that street-level accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. Indeed, in a recent report, the National Council on Disability noted that they could not find comprehensive information on the "degree to which sidewalks are accessible" across the US [15]. This lack of information can have a significant negative impact on the independence and mobility of citizens [13,16] For example, in our own initial formative interviews with wheelchair users, we uncovered a prevailing view about navigating to new areas of a city: "I usually don't go where I don't know [about accessible routes]" (Interviewee 3, congenital polyneuropathy). Our overarching research vision is to transform the way in which street-level accessibility information is collected and used to support new types of assistive map-based tools.


Capacitance and Force Computation Due to Direct and Fringing Effects in MEMS/NEMS Arrays

An accurate computation of electrical force is significant in analyzing the performance of microelectromechanical systems and nanoelectromechanical systems. Many analytical and empirical models are available for computing the forces, especially, for a single set of parallel plates. In general, these forces are computed based on the direct electric field between the overlapping areas of the plates and the fringing field effects. Most of the models, which are based on direct electric field effect, consider only the trivial cases of the fringing field effects. In this paper, we propose different models which are obtained from the numerical simulations. It is found to be useful in computing capacitance as well force in simple and complex configurations consisting of an array of beams and electrodes. For the given configurations, the analytical models are compared with the available models and numerical results. While the percentage error of the proposed model is found to be under 6% with respect to the numerical results, the error associated with the analytical model without the fringing field effects is ~50 %. The proposed model can be applied to the devices in which the fringing field effects are dominant.


Self-Supervision for Reinforcement Learning by Parsa Mahmoudieh

Reinforcement learning optimizes policies for expected cumulative reward. Need the supervision be so narrow? Reward is delayed and sparse for many tasks, making it a difficult and impoverished signal for end-to-end optimization. To augment reward, we consider a range of self-supervised tasks that incorporate states, actions, and successors to provide auxiliary losses. These losses offer ubiquitous and instantaneous supervision for representation learning even in the absence of reward. While current results show that learning from reward alone is feasible, pure reinforcement learning methods are constrained by computational and data efficiency issues that can be remedied by auxiliary losses. Self-supervised pretraining and joint optimization improve the data efficiency and policy returns of end-to-end reinforcement learning.


Vertical and horizontal elasticity for dynamic virtual machine reconfiguration

Today, cloud computing applications are rapidly constructed by services belonging to different cloud providers and service owners. This work presents the inter-cloud elasticity framework, which focuses on cloud load balancing based on dynamic virtual machine reconfiguration when variations on load or on user requests volume are observed. We design a dynamic reconfiguration system, called inter-cloud load balancer (ICLB), that allows scaling up or down the virtual resources (thus providing automatized elasticity), by eliminating service downtimes and communication failures. It includes an inter-cloud load balancer for distributing incoming user HTTP traffic across multiple instances of inter-cloud applications and services and we perform dynamic reconfiguration of resources according to the real time requirements. The experimental analysis includes different topologies by showing how real-time traffic variation (using real world workloads) affects resource utilization and by achieving better resource usage in inter-cloud.


Harmony Search Algorithm for Solving Sudoku

Harmony search (HS) algorithm was applied to solving Sudoku puzzle. The HS is an evolutionary algorithm which mimics musicians’ behaviors such as random play, memory-based play, and pitch-adjusted play when they perform improvisation. Sudoku puzzles in this study were formulated as an optimization problem with number-uniqueness penalties. HS could successfully solve the optimization problem after 285 function evaluations, taking 9 seconds. Also, sensitivity analysis of HS parameters was performed to obtain a better idea of algorithm parameter values.


Introduction Advances in Text Comprehension : Model , Process and Development

To a very large extent, children learn in and out of school from written text. Information Communications Technologies (ICT) offers many possibilities to facilitate learning by confronting children with multimodal texts. In order to be able to implement learning environments that optimally facilitate children’s learning, insight is needed into the cognitive processes underlying text comprehension. In this light, the aim of this special issue is to report on new advances in text comprehension research in perspective of educational implications. Starting from recent theoretical frameworks on the cognitive processes underlying text comprehension, the online processing of text will be discussed in adults and in school children. Copyright # 2008 John Wiley & Sons, Ltd.


Food Image Recognition Using Very Deep Convolutional Networks

We evaluated the effectiveness in classifying food images of a deep-learning approach based on the specifications of Google's image recognition architecture Inception. The architecture is a deep convolutional neural network (DCNN) having a depth of 54 layers. In this study, we fine-tuned this architecture for classifying food images from three well-known food image datasets: ETH Food-101, UEC FOOD 100, and UEC FOOD 256. On these datasets we achieved, respectively, 88.28%, 81.45%, and 76.17% as top-1 accuracy and 96.88%, 97.27%, and 92.58% as top-5 accuracy. To the best of our knowledge, these results significantly improve the best published results obtained on the same datasets, while requiring less computation power, since the number of parameters and the computational complexity are much smaller than the competitors?. Because of this, even if it is still rather large, the deep network based on this architecture appears to be at least closer to the requirements for mobile systems.


Patterns of HCI Design and HCI Design of Patterns

This chapter introduces the concept of human–computer interaction (HCI) design pattern—also called UI design pattern, interaction design patterns, HCI patterns, user experience pattern and usability engineering pattern. In this book, we mainly use the term HCI Design pattern, but we also use all these terms interchangeably to refer to HCI design pattern. HCI design patterns—have been introduced as a medium to capture and represent users’ experiences as well as to disseminate it as design knowledge. Patterns have the potential of transferring the design expertise of HCI designers to software engineers, who are usually unfamiliar with UI design and usability principles. What is the difference between design patterns and HCI design patterns? Why are they important among HCI designers and SE practitioners? Why design patterns have been considered as a HCI design tool? Why and how HCI design patterns can make a difference? This chapter provides a first answer to these questions that are the key objectives of this book. 1.1 Original Ideas About Design Pattern Among the early attempts to capture and use design knowledge in the format of design patterns, the first major milestone is often attributed to the architect Christopher Alexander, in the late 1970s. In his two books, A Pattern Language (Alexander 1977) and A Timeless Way of Building (Alexander 1979), Alexander, the father of patterns, discusses the avenues to capture and use design knowledge, and presents a large collection of pattern examples to help architects and engineers with the design of buildings, towns, and other urban entities. To illustrate the concept of pattern, Alexander proposes an architectural pattern called Wings of Light (Alexander 1977), where the problem is: Modern buildings are often shaped with no concern for natural light—they depend almost entirely on artificial light. But, buildings which displace natural light as the major source of illumination are not fit places to spend the day. Amongst other information such as design rationale, examples, and links to related patterns, the solution statement to this problem is: 1 The Patterns of HCI Design 2 Arrange each building so that it breaks down into wings which correspond, approximately, to the most important natural social groups within the building. Make each wing long and as narrow as you can—never more than 25 ft wide. According to Alexander, every pattern has three essential elements illustrated in Fig. 1.1, which are: a context, a problem, and a solution. The context describes a recurring set of situations in which the pattern can be applied. The problem refers to a set of forces, i.e., goals and constraints, which occur in the context. Generally, the problem describes when to apply the pattern. The solution refers to a design form or a design rule that can be applied to resolve the forces. It also describes the elements that constitute a pattern, relationships among these elements, as well as responsibilities and collaboration. All of Alexander’s patterns address recurrent problems that designers face by providing a possible solution within a specific context. They follow a similar structure, and the presented information is organized into pattern attributes, such as Problem and Design Rationale. Most noteworthy, the presented solution statement is abstract enough to capture only the invariant properties of good design. The specific pattern implementation is dependent on the design details and the designer’s creativity (Dix et al. 2003). In the example above, there is no mention of specific details such as the corresponding positions of wings to one another, or even the number of wings. These implementation details are left to the designer, allowing different instances of the same pattern solution. In addition, Alexander (1977) recognized that the design and construction of buildings required all stakeholders to make use of a common language for facilitating the implementation of the project from its very beginnings to its completion. If organized properly, patterns could achieve this for all the participants of a design project, acting as a communication tool for design. The pattern concept was not well known until 1987 when patterns appeared again at Object-Oriented Programming, Systems, Languages & Applications (OOPSLA), the object orientation conference in Orlando. There Kent Beck and Ward Cunningham (1987) introduced pattern languages for object-oriented program construction in a seminal paper. Since then many papers and presentations have appeared, authored by renowned software design practitioners such as Grady Booch, Richard Helm, Erich Gamma, and Kent Beck. In 1993, the formation of Hildside Group (1993) by Beck, Cunningham, Coplien, Booch, Johnson and others was the first step forward to forming a design patterns community in the field of software engineering.


Construct validity of the Self-Compassion ScaleShort Form among psychotherapy clients Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke To cite this article: Jeffrey A. Hayes, Allison J. Lockard, Rebecca A. Janis & Benjamin D. Locke (2016): Construct validity of the Self-Compassion Scale-Short Form among psychotherapy clients, Counselling Psychology Quarterly, DOI: 10.1080/09515070.2016.1138397 To link to this article: http://dx.doi.org/10.1080/09515070.2016.1138397


An evolutionary game-theoretic modeling for heterogeneous information diffusion

In this paper, we model and analyze the information diffusion in heterogeneous social networks from an evolutionary game perspective. Users interact with each other according to their individual fitness, which are heterogeneous among different user types. We first study a model where in each social interaction the payoff of a user is independent of the type of the interacted user. In such a case, we derive the information diffusion dynamics of each type of users as well as that of the overall network. The evolutionarily stable states (ESSs) of the dynamics are determined accordingly. Afterwards, we investigate a more general model where in each interaction the payoff of a user depends on the type of the interacted user. We show that the local influence dynamics change much more quickly than the global strategy population dynamics and the former keeps track of the latter throughout the information diffusion process. Based on this observation, the global strategy population dynamics are derived. Finally, simulations are conducted to verify the theoretical results.


Comparative study on power conversion methods for wireless battery charging platform

In this paper, four different power conversion methods (voltage control, duty-cycle control, frequency control and phase-shift control) are compared for wireless power transfer applications by considering the energy transfer efficiency, electromagnetic interference, stability, and implementation complexity. The phase-shift control is found to be the optimal scheme with good efficiency and low electromagnetic interference. Its constant frequency feature is also within the framework of the new international wireless charging standard called ‘Qi’. A high system efficiency of 72% for 5W wireless charging applications has been achieved practically.


A general procedure for the construction of Gorges polygons for multi-phase windings of electrical machines

This paper presents a simple and effective procedure for the determination of the Gorges polygon, suitable for all possible winding configurations in electrical machines. This methodology takes into account the determination of a Winding Distribution Table (WDT), in which all the information about the distribution of the currents along the stator periphery is computed and from which the Görges polygon are easily derived. The proposed method can be applied to both symmetrical and asymmetrical multi-phase windings, including concentrated, fractional, reduced and dead-coil ones. The examples provided in this paper demonstrate the versatility of the proposed method.


Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles. We create SCIERC, a dataset that includes annotations for all three tasks and develop a unified framework called Scientific Information Extractor (SCIIE) for with shared span representations. The multi-task setup reduces cascading errors between tasks and leverages cross-sentence relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, which we use to analyze information in scientific literature.1


Topic discovery and future trend forecasting for texts

Finding topics from a collection of documents, such as research publications, patents, and technical reports, is helpful for summarizing large scale text collections and the world wide web. It can also help forecast topic trends in the future. This can be beneficial for many applications, such as modeling the evolution of the direction of research and forecasting future trends of the IT industry. In this paper, we propose using association analysis and ensemble forecasting to automatically discover topics from a set of text documents and forecast their evolving trend in a near future. In order to discover meaningful topics, we collect publications from a particular research area, data mining and machine learning, as our data domain. An association analysis process is applied to the collected data to first identify a set of topics, followed by a temporal correlation analysis to help discover correlations between topics, and identify a network of topics and communities. After that, an ensemble forecasting approach is proposed to predict the popularity of research topics in the future. Our experiments and validations on data with 9 years of publication records validate the effectiveness of the proposed design.


Fake News in Social Networks

We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary’s attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents’ network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary’s attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users’ private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.


Comparative Study on Vision Based Rice Seed Varieties Identification

This paper presents a system for automated classification of rice variety for rice seed production using computer vision and image processing techniques. Rice seeds of different varieties are visually very similar in color, shape and texture that make the classification of rice seed varieties at high accuracy challenging. We investigated various feature extraction techniques for efficient rice seed image representation. We analyzed the performance of powerful classifiers on the extracted features for finding the robust one. Images of six different rice seed varieties in northern Vietnam were acquired and analyzed. Our experiments have demonstrated that the average accuracy of our classification system can reach 90.54% using Random Forest method with a simple feature extraction technique. This result can be used for developing a computer-aided machine vision system for automated assessment of rice seeds purity.


Anatomical Variants in Prostate Artery Embolization: A Pictorial Essay

Prostate artery embolization (PAE) has emerged as a new treatment option for patients with symptomatic benign prostatic hyperplasia. The main challenges related to this procedure are navigating arteries with atherosclerosis and anatomical variations, and the potential risk of non-target embolization to pelvic structures due to the presence of collateral shunts and reflux of microparticles. Knowledge of classical vascular anatomy and the most common variations is essential for safe embolization, good clinical practice, and optimal outcomes. The aim of this pictorial essay is to illustrate the pelvic vascular anatomy relevant to PAE in order to provide a practical guide that includes the most common anatomical variants as well as to discuss the technical details related to each.


Lock-free linked lists and skip lists

Lock-free shared data structures implement distributed objects without the use of mutual exclusion, thus providing robustness and reliability. We present a new lock-free implementation of singly-linked lists. We prove that the worst-case amortized cost of the operations on our linked lists is linear in the length of the list plus the contention, which is better than in previous lock-free implementations of this data structure. Our implementation uses backlinks that are set when a node is deleted so that concurrent operations visiting the deleted node can recover. To avoid performance problems that would arise from traversing long chains of backlink pointers, we introduce flag bits, which indicate that a deletion of the next node is underway. We then give a lock-free implementation of a skip list dictionary data structure that uses the new linked list algorithms to implement individual levels. Our algorithms use the single-word C&S synchronization primitive.


A study into the believability of animated characters in the context of bullying intervention

The VICTEC (Virtual ICT with Empathic Characters) project provides an opportunity to explore the use of animated characters in a virtual environment for educational issues such as bullying behaviour. Our research aim was to evaluate whether an early prototype of the VICTEC demonstrator could provide useful information about character and story believability, physical aspects of the characters and story comprehensibility. Results from an evaluation with 76 participants revealed high levels of bullying story believability and character conversation was rated as convincing and interesting. In contrast, character movement was poorly rated. Overall the results imply that poor physical aspects of characters do not have detrimental effects on story believability and interest levels with the demonstrator. It is concluded that even at this early design phase, the demonstrator provides a suitable means to explore how virtual environments in terms of character and storyline believability may assist in the development of systems to deal with the educational issue of bullying.


Multi-class AdaBoost ∗

Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm to the multi-class case without reducing it to multiple two-class problems. We show that the proposed multi-class AdaBoost algorithm is equivalent to a forward stagewise additive modeling algorithm that minimizes a novel exponential loss for multi-class classification. Furthermore, we show that the exponential loss is a member of a class of Fisher-consistent loss functions for multi-class classification. As shown in the paper, the new algorithm is extremely easy to implement and is highly competitive in terms of misclassification error rate.


Predicting Movie Sales from Blogger Sentiment

The volume of discussion about a product in weblogs has recently been shown to correlate with the product’s financial performance. In this paper, we study whether applying sentiment analysis methods to weblog data results in better correlation than volume only, in the domain of movies. Our main finding is that positive sentiment is indeed a better predictor for movie success when applied to a limited context around references to the movie in weblogs, posted prior to its release. If my film makes one more person miserable, I’ve done my job.


Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.


A Live-User Study of Opinionated Explanations for Recommender Systems

This paper describes an approach for generating rich and compelling explanations in recommender systems, based on opinions mined from user-generated reviews. The explanations highlight the features of a recommended item that matter most to the user and also relate them to other recommendation alternatives and the user's past activities to provide a context.


MO-SLAM: Multi object SLAM with run-time object discovery through duplicates

In this paper, we present MO-SLAM, a novel visual SLAM system that is capable of detecting duplicate objects in the scene during run-time without requiring an offline training stage to pre-populate a database of objects. Instead, we propose a novel method to detect landmarks that belong to duplicate objects. Further, we show how landmarks belonging to duplicate objects can be converted to first-order entities which generate additional constraints for optimizing the map. We evaluate the performance of MO-SLAM with extensive experiments on both synthetic and real data, where the experimental results verify the capabilities of MO-SLAM in detecting duplicate objects and using these constraints to improve the accuracy of the map.


Speed planning for solar-powered electric vehicles

Electric vehicles (EVs) are the trend for future transportation. The major obstacle is range anxiety due to poor availability of charging stations and long charging time. Solar-powered EVs, which mostly rely on solar energy, are free of charging limitations. However, the range anxiety problem is more severe due to the availability of sun light. For example, shadings of buildings or trees may cause a solar-powered EV to stop halfway in a trip. In this paper, we show that by optimally planning the speed on different road segments and thus balancing energy harvesting and consumption, we can enable a solar-powered EV to successfully reach the destination using the shortest travel time. The speed planning problem is essentially a constrained non-linear programming problem, which is generally difficult to solve. We have identified an optimality property that allows us to compute an optimal speed assignment for a partition of the path; then, a dynamic programming method is developed to efficiently compute the optimal speed assignment for the whole trip with significantly low computation overhead compared to the state-of-the-art non-linear programming solver. To evaluate the usability of the proposed method, we have also developed a solar-powered EV prototype. Experiments show that the predictions by the proposed technique match well with the data collected from the physical EV. Issues on practical implementation are also discussed.


Increase Physical Fitness and Create Health Awareness through Exergames and Gamification - The Role of Individual Factors, Motivation and Acceptance

Demographic change and the aging population push health and welfare system to its limits. Increased physical fitness and increased awareness for health issues will help elderly to live independently for longer and will thereby reduce the costs in the health care system. Exergames seem to be a promising solution for promoting physical fitness. Still, there is little evidence under what conditions Exergames will be accepted and used by elderly. To investigate promoting and hindering factors we conducted a user study with a prototype of an Exergame. We contrasted young vs. elderly players and investigated the role of gamer types, personality factors and technical expertise on the performance within the game and changes in the attitude towards individual health after the game. Surprisingly, performance within the game is not affected by performance motivation but by gamer type. More importantly, a universal positive effect on perceived pain is detected after the Exergame


A CNN-based segmentation model for segmenting foreground by a probability map

This paper proposes a CNN-based segmentation model to segment foreground from an image and a prior probability map. Our model is constructed based on the FCN model that we simply replace the original RGB-based three channel input layer by a four channel, i.e., RGB and prior probability map. We then train the model by constructing various image, prior probability maps and the groundtruths from the PASCAL VOC dataset, and finally obtain a CNN-based foreground segmentation model that is suitable for general images. Our proposed method is motivated by the observation that the classical graphcut algorithm using GMM for modeling the priors can not capture the semantic segmentation from the prior probability, and thus leads to low segmentation performance. Furthermore, the efficient FCN segmentation model is for specific objects rather than general objects. We therefore improve the graph-cut like foreground segmentation by extending FCN segmentation model. We verify the proposed model by various prior probability maps such as artifical maps, saliency maps, and discriminative maps. The ICoseg dataset that is different from the PASCAL Voc dataset is used for the verification. Experimental results demonstrates the fact that our method obviously outperforms the graphcut algorithms and FCN models.


Clothes Co-Parsing Via Joint Image Segmentation and Labeling With Application to Clothing Retrieval

This paper aims at developing an integrated system for clothing co-parsing (CCP), in order to jointly parse a set of clothing images (unsegmented but annotated with tags) into semantic configurations. A novel data-driven system consisting of two phases of inference is proposed. The first phase, referred as “image cosegmentation,” iterates to extract consistent regions on images and jointly refines the regions over all images by employing the exemplar-SVM technique [1]. In the second phase (i.e., “region colabeling”), we construct a multiimage graphical model by taking the segmented regions as vertices, and incorporating several contexts of clothing configuration (e.g., item locations and mutual interactions). The joint label assignment can be solved using the efficient Graph Cuts algorithm. In addition to evaluate our framework on the Fashionista dataset [2], we construct a dataset called the SYSU-Clothes dataset consisting of 2098 high-resolution street fashion photos to demonstrate the performance of our system. We achieve 90.29%/88.23% segmentation accuracy and 65.52%/63.89% recognition rate on the Fashionista and the SYSU-Clothes datasets, respectively, which are superior compared with the previous methods. Furthermore, we apply our method on a challenging task, i.e., cross-domain clothing retrieval: given user photo depicting a clothing image, retrieving the same clothing items from online shopping stores based on the fine-grained parsing results.


Face recognition based on convolutional neural network and support vector machine

Face recognition is an important embodiment of human-computer interaction, which has been widely used in access control system, monitoring system and identity verification. However, since face images vary with expressions, ages, as well as poses of people and illumination conditions, the face images of the same sample might be different, which makes face recognition difficult. There are two main requirements in face recognition, the high recognition rate and less training time. In this paper, we combine Convolutional Neural Network (CNN) and Support Vector Machine (SVM) to recognize face images. CNN is used as a feature extractor to acquire remarkable features automatically. We first pre-train our CNN by ancillary data to get the updated weights, and then train the CNN by the target dataset to extract more hidden facial features. Finally we use SVM as our classifier instead of CNN to recognize all the classes. With the input of facial features extracted from CNN, SVM will recognize face images more accurately. In our experiments, some face images in the Casia-Webfaces database are used for pre-training, and FERET database is used for training and testing. The results in experiments demonstrate the efficiency with high recognition rate and less training time.


Progress on a cognitive-motivational-relational theory of emotion.

The 2 main tasks of this article are 1st, to examine what a theory of emotion must do and basic issues that it must address. These include definitional issues, whether or not physiological activity should be a defining attribute, categorical versus dimensional strategies, the reconciliation of biological universals with sociocultural sources of variability, and a classification of the emotions. The 2nd main task is to apply an analysis of appraisal patterns and the core relational themes that they produce to a number of commonly identified emotions. Anger, anxiety, sadness, and pride (to include 1 positive emotion) are used as illustrations. The purpose is to show the capability of a cognitive-motivational-relational theory to explain and predict the emotions. The role of coping in emotion is also discussed, and the article ends with a response to criticisms of a phenomenological, folk-theory outlook.


A Survey of Logical Models for OLAP Databases

In this paper, we present different proposals for multidimensional data cubes, which are the basic logical model for OLAP applications. We have grouped the work in the field in two categories: commercial tools (presented along with terminology and standards) and academic efforts. We further divide the academic efforts in two subcategories: the relational model extensions and the cube-oriented approaches. Finally, we attempt a comparative analysis of the various efforts.


Who Is Going to Get Hurt? Predicting Injuries in Professional Soccer

Injury prevention has a fundamental role in professional soccer due to the high cost of recovery for players and the strong influence of injuries on a club’s performance. In this paper we provide a predictive model to prevent injuries of soccer players using a multidimensional approach based on GPS measurements and machine learning. In an evolutive scenario, where a soccer club starts collecting the data for the first time and updates the predictive model as the season goes by, our approach can detect around half of the injuries, allowing the soccer club to save 70% of a season’s economic costs related to injuries. The proposed approach can be a valuable support for coaches, helping the soccer club to reduce injury incidence, save money and increase team performance.


Answering Schrödinger's question: A free-energy formulation

The free-energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery.


Reconnaissance with ultra wideband UHF synthetic aperture radar


Unseen Noise Estimation Using Separable Deep Auto Encoder for Speech Enhancement

Unseen noise estimation is a key yet challenging step to make a speech enhancement algorithm work in adverse environments. At worst, the only prior knowledge we know about the encountered noise is that it is different from the involved speech. Therefore, by subtracting the components which cannot be adequately represented by a well defined speech model, the noises can be estimated and removed. Given the good performance of deep learning in signal representation, a deep auto encoder (DAE) is employed in this work for accurately modeling the clean speech spectrum. In the subsequent stage of speech enhancement, an extra DAE is introduced to represent the residual part obtained by subtracting the estimated clean speech spectrum (by using the pre-trained DAE) from the noisy speech spectrum. By adjusting the estimated clean speech spectrum and the unknown parameters of the noise DAE, one can reach a stationary point to minimize the total reconstruction error of the noisy speech spectrum. The enhanced speech signal is thus obtained by transforming the estimated clean speech spectrum back into time domain. The above proposed technique is called separable deep auto encoder (SDAE). Given the under-determined nature of the above optimization problem, the clean speech reconstruction is confined in the convex hull spanned by a pre-trained speech dictionary. New learning algorithms are investigated to respect the non-negativity of the parameters in the SDAE. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the conventional baselines.


A variable splitting augmented Lagrangian approach to linear spectral unmixing

This paper presents a new linear hyperspectral unmixing method of the minimum volume class, termed simplex identification via split augmented Lagrangian (SISAL). Following Craig's seminal ideas, hyperspectral linear unmixing amounts to finding the minimum volume simplex containing the hyperspectral vectors. This is a nonconvex optimization problem with convex constraints. In the proposed approach, the positivity constraints, forcing the spectral vectors to belong to the convex hull of the endmember signatures, are replaced by soft constraints. The obtained problem is solved by a sequence of augmented Lagrangian optimizations. The resulting algorithm is very fast and able so solve problems far beyond the reach of the current state-of-the art algorithms. The effectiveness of SISAL is illustrated with simulated data.


Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation

We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that consists of two interconnected pathways, a convolutional pathway, which learns increasingly more abstract and higher-level image features, and a deconvolutional pathway, which predicts the final segmentation at the voxel level. The joint training of the feature extraction and prediction pathways allows for the automatic learning of features at different scales that are optimized for accuracy for any given combination of image types and segmentation task. In addition, shortcut connections between the two pathways allow high- and low-level features to be integrated, which enables the segmentation of lesions across a wide range of sizes. We have evaluated our method on two publicly available data sets (MICCAI 2008 and ISBI 2015 challenges) with the results showing that our method performs comparably to the top-ranked state-of-the-art methods, even when only relatively small data sets are available for training. In addition, we have compared our method with five freely available and widely used MS lesion segmentation methods (EMS, LST-LPA, LST-LGA, Lesion-TOADS, and SLS) on a large data set from an MS clinical trial. The results show that our method consistently outperforms these other methods across a wide range of lesion sizes.


How to put probabilities on homographies

We present a family of "normal" distributions over a matrix group together with a simple method for estimating its parameters. In particular, the mean of a set of elements can be calculated. The approach is applied to planar projective homographies, showing that using priors defined in this way improves object recognition.


From Skimming to Reading: A Two-stage Examination Model for Web Search

User's examination of search results is a key concept involved in all the click models. However, most studies assumed that eye fixation means examination and no further study has been carried out to better understand user's examination behavior. In this study, we design an experimental search engine to collect both the user's feedback on their examinations and the eye-tracking/click-through data. To our surprise, a large proportion (45.8%) of the results fixated by users are not recognized as being "read". Looking into the tracking data, we found that before the user actually "reads" the result, there is often a "skimming" step in which the user quickly looks at the result without reading it. We thus propose a two-stage examination model which composes of a first "from skimming to reading" stage (Stage 1) and a second "from reading to clicking" stage (Stage 2). We found that the biases (e.g. position bias, domain bias, attractiveness bias) considered in many studies impact in different ways in Stage 1 and Stage 2, which suggests that users make judgments according to different signals in different stages. We also show that the two-stage examination behaviors can be predicted with mouse movement behavior, which can be collected at large scale. Relevance estimation with the two-stage examination model also outperforms that with a single-stage examination model. This study shows that the user's examination of search results is a complex cognitive process that needs to be investigated in greater depth and this may have a significant impact on Web search.


Artificial Intelligence Technique Applied to Intrusion Detection

Communication network is facilitated with different protocol .Each protocol supported to increase the network performance in a secured manner. In communication process, user’s connectivity, violations of policy on access of information are handles through intrusion. Intrusion prevention is the process of performing intrusion detection and attempting to stop detected possible incidents. It focused on identifying possible incidents, logging information about them, attempting to stop them, and reporting them to security administrators. However, organizations use Intrusion detection and prevention system (IDPS) for other purposes, such as identifying problems with security policies, documenting existing threats, and determining individuals from violating security policies. Communication architecture is built up on IP.Internet Control Message Protocol (ICMP ) protocol is tightly integrated with IP. ICMP messages, delivered in IP packets, are used for out-of-band messages related to network operation or mis-operation.In this paper describes about HDLC , ICMP protocol sequences which is used to detect the intrusion on hybrid network and its attributes are recommend the standardized protocol for the intrusion detection process. This standardization protocol compare the previous part of the research of HDLC and ICMP protocols.


Enhanced LSB technique for audio steganography

The idea of this paper is to invent a new strategy in Steganography to get the minimum effect in audio which is used to hide data into it. “Progress always involves risk” Fredrick Wilcox observed that technological progress of computer science and the Internet altered the way we lived, and will continue to cast our life[1] . In this paper we have presented a Steganography method of embedding text data in an audio file. The basic approach behind this paper is to provide a good, well-organized method for hiding the data and sent to the destination in safer manner. In the proposed technique first the audio file is sampled and then appropriate bit is modified. In selected sample one bit is modified at least significant bit .The remaining bits may be used but it may be cause noise. We have attempted to provide an overview, theoretical framework about audio Steganography techniques and a novel approach to hide data in an audio using least significant bit (LSB).


A stable-isotope dilution GC-MS approach for the analysis of DFRC (derivatization followed by reductive cleavage) monomers from low-lignin plant materials.

The derivatization followed by reductive cleavage (DFRC) method is a well-established tool to characterize the lignin composition of plant materials. However, the application of the original procedure, especially the chromatographic determination of the DFRC monomers, is problematic for low-lignin foods. To overcome these problems a modified sample cleanup and a stable-isotope dilution approach were developed and validated. To quantitate the diacetylated DFRC monomers, their corresponding hexadeuterated analogs were synthesized and used as internal standards. By using the selected-ion monitoring mode, matrix-associated interferences can be minimized resulting in higher selectivity and sensitivity. The modified method was applied to four low-lignin samples. Lignin from carrot fibers was classified as guaiacyl-rich whereas the lignins from radish, pear, and asparagus fibers where classified as balanced lignins (guaiacyl/syringyl ratio=1-2).


Action Design Research and Visualization Design

In applied visualization research, artifacts are shaped by a series of small design decisions, many of which are evaluated quickly and informally via methods that often go unreported and unverified. Such design decisions are influenced not only by visualization theory, but also by the people and context of the research. While existing applied visualization models support a level of reliability throughout the design process, they fail to explicitly account for the influence of the research context in shaping the resulting design artifacts. In this work, we look to action design research (ADR) for insight into addressing this issue. In particular, ADR offers a framework along with a set of guiding principles for navigating and capitalizing on the disruptive, subjective, human-centered nature of applied design work, while aiming to ensure reliability of the process and design, and emphasizing opportunities for conducting research. We explore the utility of ADR in increasing the reliability of applied visualization design research by: describing ADR in the language and constructs developed within the visualization community; comparing ADR to existing visualization methodologies; and analyzing a recent design study retrospectively through the lens of ADR's framework and principles.


Econometric Modelling of Markov-Switching Vector Autoregressions using MSVAR for Ox


An X-band to Ka-band SPDT switch using 200 nm SiGe HBTs

This paper presents the design and measured performance of an X-band to Ka-band SiGe HBT SPDT switch. The proposed SPDT switch was fabricated using a 200 nm, 150 GHz peak fT silicon-germanium (SiGe) heterojunction bipolar transistor (HBT) BiCMOS technology. The SPDT switch design uses diode-connected SiGe HBTs in a series-shunt configuration to improve the switch bandwidth and isolation. Between 8 and 40 GHz, this SPDT switch achieves an insertion loss of less than 4.3 dB, an isolation of more than 20.3 dB, and a return loss of more than 9 dB.


Measuring dimensions of intergenerational contact: factor analysis of the Queen's University Scale.

OBJECTIVES
Intergenerational contact has been linked to a range of health outcomes, including greater engagement and lower depression. Measures of contact are limited. Informed by Allport's contact theory, the Queen's University Scale consists of items rating contact with elders. We administered the survey to a young adult sample (N = 606) to identify factors that may optimize intervention programming and enhance young persons' health as they age.

METHODS
We conducted exploratory factor analysis (EFA) in the structural equation modeling framework and then confirmatory factor analysis with items pertaining to the general elder population.

RESULTS
EFAs did not yield an adequate factor structure. We tested two alternative confirmatory models based on findings from the EFA. Neither a second-order model nor a first-order model allowing double loadings and correlated errors proved adequate.

CONCLUSION
Difficulty finding an adequate factor solution reflects challenges to measuring intergenerational contact with this scale. Items reflect relevant topics but subscale models are limited in interpretability. Knox and colleagues' analyses led them to recommend a brief, global scale, but we did not find empirical support for such a measure. Next steps include development and testing of a reliable, valid scale measuring dimensions of contact as perceived by both youth and elders.


End of Moore ’ s law : thermal ( noise ) death of integration in micro and nano electronics

The exponential growth of memory size and clock frequency in computers has a great impact on everyday life. The growth is empirically described by Moore’s law of miniaturization. Physical limitations of this growth would have a serious impact on technology and economy. A thermodynamical effect, the increasing thermal noise voltage (Johnson–Nyquist noise) on decreasing characteristic capacitances, together with the constrain of using lower supply voltages to keep power dissipation manageable on the contrary of increasing clock frequency, has the potential to break abruptly Moore’s law within 6–8 years, or earlier.  2002 Elsevier Science B.V. All rights reserved.


Low-power 4-2 and 5-2 compressors

This paper explores various low power higher order compressors such as 4-2 and 5-2 compressor units. These compressors are building blocks for binary multipliers. Various circuit architectures for 4-2 compressors are compared with respect to their delay and power consumption. The different circuits are simulated using HSPICE. A new circuit for a 5-2 compressor is then presented which is 12% faster and consumes 37% less power.


Analysis of jitter due to power-supply noise in phase-locked loops

Phase-locked loops (PLL) in RF and mixed signal VLSI circuits experience supply noise which translates to a timing jitter. In this paper an analysis of the timing jitter due to the noise on the power supply rails is presented. Stochastic models of the power supply noise in VLSI circuits for different values of on-chip decoupling capacitances are presented first. This is followed by calculation of the phase noise of the voltage-controlled oscillator (VCO) in terms of the statistical properties of supply noise. Finally the timing jitter of PLL is predicted in response to the VCO phase noise. A PLL circuit has been designed in 0.35μ CMOS process, and our mathematical model was applied to determine the timing jitter. Experimental results prove the accuracy of the predicted model.


Performance analysis of low-power 1-bit CMOS full adder cells

A performance analysis of 1-bit full-adder cell is presented. The adder cell is anatomized into smaller modules. The modules are studied and evaluated extensively. Several designs of each of them are developed, prototyped, simulated and analyzed. Twenty different 1-bit full-adder cells are constructed (most of them are novel circuits) by connecting combinations of different designs of these modules. Each of these cells exhibits different power consumption, speed, area, and driving capability figures. Two realistic circuit structures that include adder cells are used for simulation. A library of full-adder cells is developed and presented to the circuit designers to pick the full-adder cell that satisfies their specific applications.


Harvesting Big Data to Enhance Supply Chain Innovation Capabilities : An Analytic Infrastructure Based on Deduction Graph

Today, firms can access to big data (tweets, videos, click streams, and other unstructured sources) to extract new ideas or understanding about their products, customers, and markets. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm’s existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.


Capturing the mood: facebook and face-to-face encounters in the workplace

What makes people feel happy, engaged and challenged at work? We conducted an in situ study of Facebook and face-to-face interactions examining how they influence people's mood in the workplace. Thirty-two participants in an organization were each observed for five days in their natural work environment using automated data capture and experience sampling. Our results show that online and offline social interactions are associated with different moods, suggesting that they serve different purposes at work. Face-to-face interactions are associated with a positive mood throughout the day whereas Facebook use and engagement in work contribute to a positive feeling at the end of the day. Email use is associated with negative affect and along with multitasking, is associated with a feeling of engagement and challenge throughout the day. Our findings provide initial evidence of how online and offline interactions affect workplace mood, and could inform practices to improve employee morale.


A Survey of Non-conventional Techniques for Low-voltage Low-power Analog Circuit Design

Designing integrated circuits able to work under low-voltage (LV) low-power (LP) condition is currently undergoing a very considerable boom. Reducing voltage supply and power consumption of integrated circuits is crucial factor since in general it ensures the device reliability, prevents overheating of the circuits and in particular prolongs the operation period for battery powered devices. Recently, non-conventional techniques i.e. bulkdriven (BD), floating-gate (FG) and quasi-floating-gate (QFG) techniques have been proposed as powerful ways to reduce the design complexity and push the voltage supply towards threshold voltage of the MOS transistors (MOST). Therefore, this paper presents the operation principle, the advantages and disadvantages of each of these techniques, enabling circuit designers to choose the proper design technique based on application requirements. As an example of application three operational transconductance amplifiers (OTA) based on these non-conventional techniques are presented, the voltage supply is only ±0.4 V and the power consumption is 23.5 μW. PSpice simulation results using the 0.18 μm CMOS technology from TSMC are included to verify the design functionality and correspondence with theory.


Listening to music and physiological and psychological functioning: the mediating role of emotion regulation and stress reactivity.

Music listening has been suggested to have short-term beneficial effects. The aim of this study was to investigate the association and potential mediating mechanisms between various aspects of habitual music-listening behaviour and physiological and psychological functioning. An internet-based survey was conducted in university students, measuring habitual music-listening behaviour, emotion regulation, stress reactivity, as well as physiological and psychological functioning. A total of 1230 individuals (mean = 24.89 ± 5.34 years, 55.3% women) completed the questionnaire. Quantitative aspects of habitual music-listening behaviour, i.e. average duration of music listening and subjective relevance of music, were not associated with physiological and psychological functioning. In contrast, qualitative aspects, i.e. reasons for listening (especially 'reducing loneliness and aggression', and 'arousing or intensifying specific emotions') were significantly related to physiological and psychological functioning (all p = 0.001). These direct effects were mediated by distress-augmenting emotion regulation and individual stress reactivity. The habitual music-listening behaviour appears to be a multifaceted behaviour that is further influenced by dispositions that are usually not related to music listening. Consequently, habitual music-listening behaviour is not obviously linked to physiological and psychological functioning.


Product type and consumers' perception of online consumer reviews

Consumers hesitate to buy experience products online because it is hard to get enough information about experience products via the Internet. Online consumer reviews may change that, as they offer consumers indirect experiences about dominant attributes of experience products, transforming them into search products. When consumers are exposed to an online consumer review, it should be noted that there are different kinds of review sources. This study investigates the effects of review source and product type on consumers’ perception of a review. The result of the online experiment suggests that product type canmoderate consumers’ perceived credibility of a review from different review sources, and the major findings are: (1) consumers are more influenced by a review for an experience product than for a search product when the review comes from consumer-developed review sites, and (2) a review from an online community is perceived to be the most credible for consumers seeking information about an experience product. The findings provide managerial implications for marketers as to how they can better manage online consumer reviews.


High-Throughput and Language-Agnostic Entity Disambiguation and Linking on User Generated Data

The Entity Disambiguation and Linking (EDL) task matches entity mentions in text to a unique Knowledge Base (KB) identifier such as a Wikipedia or Freebase id. It plays a critical role in the construction of a high quality information network, and can be further leveraged for a variety of information retrieval and NLP tasks such as text categorization and document tagging. EDL is a complex and challenging problem due to ambiguity of the mentions and real world text being multi-lingual. Moreover, EDL systems need to have high throughput and should be lightweight in order to scale to large datasets and run on off-the-shelf machines. More importantly, these systems need to be able to extract and disambiguate dense annotations from the data in order to enable an Information Retrieval or Extraction task running on the data to be more efficient and accurate. In order to address all these challenges, we present the Lithium EDL system and algorithm a high-throughput, lightweight, language-agnostic EDL system that extracts and correctly disambiguates 75% more entities than state-of-the-art EDL systems and is significantly faster than them.


A hybrid factor analysis and probabilistic PCA-based system for dictionary learning and encoding for robust speaker recognition

Probabilistic Principal Component Analysis (PPCA) based low dimensional representation of speech utterances is found to be useful for speaker recognition. Although, performance of the FA (Factor Analysis)-based total variability space model is found to be superior, hyperparameter estimation procedure in PPCA is computationally efficient. In this work, recent insight on the FA-based approach as a combination of dictionary learning and encoding is explored to use its encoding procedure in the PPCA framework. With the use of an alternate encoding technique on dictionaries learnt using PPCA, performance of state-of-the-art FA-based i-vector approach is matched by using the proposed procedure. A speed up of 4x is obtained while estimating the hyperparameter at the cost of 0.51% deterioration in performance in terms of the Equal Error Rate (EER) in the worst case. Compared to the conventional PPCA model, absolute improvements of 2.1% and 2.8% are observed on two telephone conditions of NIST 2008 SRE database. Using Canonical Correlational Analysis, it is shown that the i-vectors extracted from the conventional FA model and the proposed approach are highly correlated.


Remarkable archaeal diversity detected in a Yellowstone National Park hot spring environment.

Of the three primary phylogenetic domains--Archaea (archaebacteria), Bacteria (eubacteria), and Eucarya (eukaryotes)--Archaea is the least understood in terms of its diversity, physiologies, and ecological panorama. Although many species of Crenarchaeota (one of the two recognized archaeal kingdoms sensu Woese [Woese, C. R., Kandler, O. & Wheelis, M. L. (1990) Proc. Natl. Acad. Sci. USA 87, 4576-4579]) have been isolated, they constitute a relatively tight-knit cluster of lineages in phylogenetic analyses of rRNA sequences. It seemed possible that this limited diversity is merely apparent and reflects only a failure to culture organisms, not their absence. We report here phylogenetic characterization of many archaeal small subunit rRNA gene sequences obtained by polymerase chain reaction amplification of mixed population DNA extracted directly from sediment of a hot spring in Yellowstone National Park. This approach obviates the need for cultivation to identify organisms. The analyses document the existence not only of species belonging to well-characterized crenarchaeal genera or families but also of crenarchaeal species for which no close relatives have so far been found. The large number of distinct archaeal sequence types retrieved from this single hot spring was unexpected and demonstrates that Crenarchaeota is a much more diverse group than was previously suspected. The results have impact on our concepts of the phylogenetic organization of Archaea.


Interfacing industrial robots using Realtime Primitives

Today, most industrial robots are interfaced using text-based programming languages. These languages offer the possibility to declare robotic-specific data types, to specify simple motions, and to interact with tools and sensors via I/O operations. While tailored to the underlying robot controller, they usually only offer a fixed and controller-specific set of possible instructions. The specification of complex motions, the synchronization of cooperating robots and the advanced use of sensors is often very difficult or not even feasible. To overcome these limitations, this paper presents a generic and extensible interface for industrial robots, the Realtime Primitives Interface, as part of a larger software architecture. It allows a flexible specification of complex control instructions and can facilitate the development of sustainable robot controllers. The advantages of this approach are illustrated with several examples.


Adaptive Quantization for Deep Neural Network

In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNNs generally comes with high computational costs and large memory consumption, which may not be affordable for mobile platforms. Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we propose an optimization framework for deep model quantization. First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy. Then, we propose an optimization process based on this measurement for finding optimal quantization bit-width for each layer. This is the first work that theoretically analyse the relationship between parameter quantization errors of individual layers and model accuracy. Our new quantization algorithm outperforms previous quantization optimization methods, and achieves 20-40% higher compression rate compared to equal bit-width quantization at the same model prediction accuracy.


Toroidal skin drive for snake robot locomotion

Small robots have the potential to access confined spaces where humans cannot go. However, the mobility of wheeled and tracked systems is severely limited in cluttered environments. Snake robots using biologically inspired gaits for locomotion can provide better access in many situations, but are slow and can easily snag. This paper introduces an alternative approach to snake robot locomotion, in which the entire surface of the robot provides continuous propulsive force to significantly improve speed and mobility in many environments.


X-band 200W AlGaN/GaN HEMT for high power application

A 200 Watts GaN high electron mobility transistor (HEMT) has been developed for X-band applications. The device consists of four dies of 0.35 um-gate GaN HEMT of 16 mm gate periphery together with input and output 2-stage impedance transformers assembled into a low thermal resistance package. The developed GaN HEMT provides 204W output power and 12dB small signal gain at 9.3 GHz with power added efficiency of 32% under pulse condition at a duty of 10% with a pulse width of 100 usec.


Implementation of Sugeno FIS in model reference adaptive system adaptation scheme for speed sensorless control of PMSM

Model reference adaptive system (MRAS) is one of the popular methods to observe the speed and the angle information of the Permanent magnet synchronous motor (PMSM). This paper proposes a new adaptation scheme for MRAS to replace the conventional PI controller based on Popov Hyperstability theorem. In this project, the speed of PMSM is controlled by using field oriented control (FOC) method and the MRAS is used to observe the speed and the rotor position of the motor. The reference model for the MRAS is the PMSM itself while the adjustable model is the current model in the rotor reference frame. The Sugeno Fuzzy Logic Inference System (FIS) is implemented in the adaptation scheme in order to tune the error between the reference and the adjustable model. The new proposed method shows it is capable to track the motor speed efficiently at wide range of operating speeds.


Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment

In addition to aerial imagery, point clouds are important remote sensing data in urban environment studies. It is essential to extract semantic information from both images and point clouds for such purposes; thus, this study aims to automatically classify 3-D point clouds generated using oblique aerial imagery (OAI)/vertical aerial imagery (VAI) into various urban object classes, such as roof, facade, road, tree, and grass. A multicamera airborne imaging system that can simultaneously acquire VAI and OAI is suggested. The acquired small-format images contain only three RGB spectral bands and are used to generate photogrammetric point clouds through a multiview-stereo dense matching technique. To assign each 3-D point cloud to a corresponding urban object class, we first analyzed the original OAI through object-based image analyses. A rule-based hierarchical semantic classification scheme that utilizes spectral information and geometry- and topology-related features was developed, in which the object height and gradient features were derived from the photogrammetric point clouds to assist in the detection of elevated objects, particularly for the roof and facade. Finally, the photogrammetric point clouds were classified into the aforementioned five classes. The classification accuracy was assessed on the image space, and four experimental results showed that the overall accuracy is between 82.47% and 91.8%. In addition, visual and consistency analyses were performed to demonstrate the proposed classification scheme's feasibility, transferability, and reliability, particularly for distinguishing elevated objects from OAI, which has a severe occlusion effect, image-scale variation, and ambiguous spectral characteristics.


Exploring the Placement and Design of Word-Scale Visualizations

We present an exploration and a design space that characterize the usage and placement of word-scale visualizations within text documents. Word-scale visualizations are a more general version of sparklines-small, word-sized data graphics that allow meta-information to be visually presented in-line with document text. In accordance with Edward Tufte's definition, sparklines are traditionally placed directly before or after words in the text. We describe alternative placements that permit a wider range of word-scale graphics and more flexible integration with text layouts. These alternative placements include positioning visualizations between lines, within additional vertical and horizontal space in the document, and as interactive overlays on top of the text. Each strategy changes the dimensions of the space available to display the visualizations, as well as the degree to which the text must be adjusted or reflowed to accommodate them. We provide an illustrated design space of placement options for word-scale visualizations and identify six important variables that control the placement of the graphics and the level of disruption of the source text. We also contribute a quantitative analysis that highlights the effect of different placements on readability and text disruption. Finally, we use this analysis to propose guidelines to support the design and placement of word-scale visualizations.


A deep learning-based method for relative location prediction in CT scan images

Relative location prediction in computed tomography (CT) scan images is a challenging problem. In this paper, a regression model based on one-dimensional convolutional neural networks is proposed to determine the relative location of a CT scan image both robustly and precisely. A public dataset is employed to validate the performance of the study’s proposed method using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with the state-of-the-art techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm.


Parametric Shape-from-Shading by Radial Basis Functions

In this paper, we present a new method of shape from shading by using radial basis functions to parameterize the object depth. The radial basis functions are deformed by adjusting their centers, widths, and weights such that the intensity errors are minimized. The initial centers and widths are arranged hierarchically to speed up convergence and to stabilize the solution. Although the smoothness constraint is used, it can be eventually dropped out without causing instabilities in the solution. An important feature of our parametric shape-from-shading method is that it offers a unified framework for integration of multiple sensory information. We show that knowledge about surface depth and/or surface normals anywhere in the image can be easily incorporated into the shape from shading process. It is further demonstrated that even qualitative knowledge can be used in shape from shading to improve 3D reconstruction. Experimental comparisons of our method with several existing ones are made by using both synthetic and real images. Results show that our solution is more accurate than the others.


Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching

End-to-end deep-learning networks recently demonstrated extremely good performance for stereo matching. However, existing networks are difficult to use for practical applications since (1) they are memory-hungry and unable to process even modest-size images, (2) they have to be trained for a given disparity range. The Practical Deep Stereo (PDS) network that we propose addresses both issues: First, its architecture relies on novel bottleneck modules that drastically reduce the memory footprint in inference, and additional design choices allow to handle greater image size during training. This results in a model that leverages large image context to resolve matching ambiguities. Second, a novel sub-pixel crossentropy loss combined with a MAP estimator make this network less sensitive to ambiguous matches, and applicable to any disparity range without re-training. We compare PDS to state-of-the-art methods published over the recent months, and demonstrate its superior performance on FlyingThings3D and KITTI sets.


Digital Infrastructures : The Missing IS Research Agenda

S the inauguration of information systems research (ISR) two decades ago, the information systems (IS) field’s attention has moved beyond administrative systems and individual tools. Millions of users log onto Facebook, download iPhone applications, and use mobile services to create decentralized work organizations. Understanding these new dynamics will necessitate the field paying attention to digital infrastructures as a category of IT artifacts. A state-of-the-art review of the literature reveals a growing interest in digital infrastructures but also confirms that the field has yet to put infrastructure at the centre of its research endeavor. To assist this shift we propose three new directions for IS research: (1) theories of the nature of digital infrastructure as a separate type of IT artifact, sui generis; (2) digital infrastructures as relational constructs shaping all traditional IS research areas; (3) paradoxes of change and control as salient IS phenomena. We conclude with suggestions for how to study longitudinal, large-scale sociotechnical phenomena while striving to remain attentive to the limitations of the traditional categories that have guided IS research.


Running head: Working memory in L2 input processing The role of working memory in processing L2 input: Insights from eye-tracking

Our study investigated how attention paid to a target syntactic construction causative had is related the storage capacity and attention regulation function of working memory (WM) and how these WM abilities moderate the change of knowledge of the target construction in different input conditions. 80 Sri Lankan learners of English were exposed to examples of the target construction in explicit and implicit learning conditions and their eye movements were tracked as they read the input. Correlational and multiple regression analyses indicated a very strong relationship between WM abilities and gains in the knowledge of the target construction. WM scores were closely associated with gains in receptive knowledge in all input conditions, but they had a weaker link to the improvement of productive knowledge in the implicit learning conditions. The amount of attention paid to input was also strongly related to WM abilities.


Comparing Alzheimer’s and Parkinson’s diseases networks using graph communities structure

Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of “omics” data to shed light on Alzheimer’s and Parkinson’s diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson’s Disease (PD) and Alzheimer’s Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences. In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine). This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.


Design of Multimode Net-Type Resonators and Their Applications to Filters and Multiplexers

Net-type resonators with dual- and tri-mode electrical behaviors have been presented and analyzed theoretically in this paper. The proposed net-type resonator is constructed by a short-ended and several open-ended transmission-line sections, which has the advantages of small size and more flexible resonant frequency allocation and is therefore particularly suitable for applications to the design of microwave devices. To verify the usefulness of the proposed resonators, three experimental examples, including a dual-mode bandpass filter, a dual-passband filter, and a triplexer, have been designed and fabricated with microstrip technology. Each of the designed circuits occupies a very small size and has a good upper stop and performance. All measured results are in good agreement with the full-wave simulation results.


The coming paradigm shift in forensic identification science.

Converging legal and scientific forces are pushing the traditional forensic identification sciences toward fundamental change. The assumption of discernible uniqueness that resides at the core of these fields is weakened by evidence of errors in proficiency testing and in actual cases. Changes in the law pertaining to the admissibility of expert evidence in court, together with the emergence of DNA typing as a model for a scientifically defensible approach to questions of shared identity, are driving the older forensic sciences toward a new scientific paradigm.


Combining minutiae descriptors for fingerprint matching

A novel minutiae-based fingerprint matching algorithm is proposed. A minutiae matching algorithm has to solve two problems: correspondence and similarity computation. For the correspondence problem, we assign each minutia two descriptors: texture-based and minutiae-based descriptors, and use an alignment-based greedy matching algorithm to establish the correspondences between minutiae. For the similarity computation, we extract a 17-D feature vector from the matching result, and convert the feature vector into a matching score using support vector classifier. The proposed algorithm is tested on FVC2002 databases and compared to all participators in FVC2002. According to equal error rate, the proposed algorithm ranks 1st on DB3, the most difficult database in FVC2002, and on the average ranks 2nd on all 4 databases. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.


Handbook of Fingerprint Recognition

label(s) Label Class c) b) a) 7.3 Integration Strategies 241 Combination strategy A combination strategy (also called combination scheme) is a technique used to combine the output of the individual classifiers. The most popular combination strategies at the abstract level are based on majority vote rules, which simply assign an input pattern to the most voted class (see Section 7.2). When two classifiers are combined, either a logical AND or a logical OR operator is typically used. When more than two classifiers are integrated, the AND/OR rules can be combined. For example, a biometric system may work on “fingerprint OR (face AND hand geometry)”; that is, it requires a user to present either a fingerprint or both face and hand geometry for recognition. Class set reduction, logistic regression, and Borda counts are the most commonly used approaches in combining classifiers based on the rank labels (Ho, Hull, and Srihari, 1994). In class set reduction, a subset of the classes is selected with the aim that the subset be as small as possible and still contain the true class. Multiple subsets from multiple modalities are typically combined using either a union or an intersection of the subsets. The logistic regression and Borda count methods are collectively called the class set reordering methods. The objective here is to derive a consensus ranking of the given classes such that the true class is ranked at the top. Rank labels are very useful for integration in an indexing/retrieval system. A biometric retrieval system typically outputs an ordered list of candidates (most likely matches). The top element of this ordered list is the most likely to be a correct match and the bottom of the list is the least likely match. The most popular combination schemes for combining confidence values from multiple modalities are sum, mean, median, product, minimum, and maximum rules. Kittler et al. (1998) have developed a theoretical framework in an attempt to understand the underlying mathematical basis of these popular schemes. Their experiments demonstrated that the sum or mean scheme typically performs very well in practice. A problem in using the sum rule is that the confidences (or scores) from different modalities should be normalized. This normalization typically involves mapping the confidence measures from different modalities into a common domain. For example, a biometric system may output a distance score (the lower the score, the more similar the patterns) whereas another may output a similarity score (the higher the score, the more similar the patterns) and thus the scores cannot be directly combined using the sum rule. In its simplest form, this normalization may only include inverting the sign of distance scores such that a higher score corresponds to a higher similarity. In a more complex form, the normalization may be non-linear which can be learned from training data by estimating distributions of confidence values from each modality. The scores are then translated and scaled to have zero mean, unit variance, and then remapped to a fixed interval of (0,1) using a hyperbolic tangent function. Note that it is tempting to parameterize the estimated distributions for normalization. However, such parameterization of distributions should be used with care, because the error rates of biometric systems are typically very small and a small error in estimating the tails of the distributions may result in a significant change in the error estimates (see Figure 7.3). Another common practice is to compute different scaling factors (weights) for each modality from training data, such that the accuracy of the combined classifier is maxi7 Multimodal Biometric Systems 242 mized. This weighted sum rule is expected to work better than the simple sum rule when the component classifiers have different strengths (i.e., different error rates). Figure 7.3. a) Genuine and impostor distributions for a fingerprint verification system (Jain et al., 2000) and a Normal approximation for the impostor distribution. Visually, the Normal approximation seems to be good, but causes significant decrease in performance compared to the non-parametric estimate as shown in the ROCs in b), where FMR is referred to as FAR (False Acceptance Rate) and (1-FNMR) as Genuine Acceptance Rate. ©Elsevier. Some schemes to combine multiple modalities in biometric systems have also been studied from a theoretical point of view. Through a theoretical analysis, Daugman (1999b) showed that if a strong biometric and a weak biometric are combined with an abstract level combination using either the AND or the OR voting rules, the performance of the combination will be worse than the better of the two individual biometrics. Hong, Jain, and Pankanti’s (1999) theoretical analysis that AND/OR voting strategies can improve performance only when certain conditions are satisfied confirmed Daugman’s findings. Their analysis further showed that a confidence level fusion is expected to significantly improve overall performance even in the case of combining a weak and a strong biometric. Kittler et al. (1998) introduced a sensitivity analysis to explain why the sum (or average) rule outperforms the other rules. They showed that the sum rule is less sensitive than the other similar rules (such as the “product” rule) to the error rates of individual classifiers in estimating posterior probabilities (confidence values). They claim that the sum rule is the most appropriate for combining different estimates of the same posterior probabilities (e.g., resulting from different classifier initializations). Prabhakar and Jain (2002) compared the sum and the product rules with the Neyman−Pearson combination scheme and showed that the product rule is worse than the sum rule when combining correlated features and both the sum rule and the product rules are inferior to the Neyman− Pearson combination scheme when combining weak and strong classifiers. 0 20 40 60 80 100 0 1 2 3 4 5 6 7 Normalized Matching Score Pe rc en ta ge ( % ) Imposter Genuine Nonparametric Imposter Distribution Normal Imposter Distribution Genuine Distribution 0 1 2 3 4 5 50 55 60 65 70 75 80 85 90 95 100 False Acceptance Rate (%) G en ui ne A cc ep ta nc e R at e (% ) Using Nonparametric Imposter Distribution Using Normal Imposter Distribution


A Real-Time Matching System for Large Fingerprint Databases

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniclues to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated iri a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexincl large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-,4SIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the


Review of Farm Management Information Systems ( FMIS )

There have been considerable advancements in the field of MIS over the years, and it continues to grow and develop in response to the changing needs of the business and marketing environment. Professionals and academicians are contributing and serving the field of MIS through the dissemination of their knowledge and ideas in professional journals. Thus, changes and trends that likely have an impact on MIS concepts, processes, and implementation can be determined by reviewing the articles published in journals. Content of the articles published in journals can also give us an idea about the types of research and themes that are popular during a given period. To see the evolutionary change in the field of MIS, the present study examined the content of articles published in business and marketing journals. [New York Science Journal 2010;3(5):87-95]. (ISSN 1554 – 0200).


Is it really about me?: message content in social awareness streams

In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.


DC Motor Speed Control using PID Controllers

An Implementation of PID controllers for the speed control of a DC motor is given in this report. The motor is modeled as a first order system and its response is studied. The speed control using PI and PID control modes is explained and an implementation of the controller using OP-AMPs is given. The response of the controller to load variations is looked at.


Echo: An Edge-Centric Code Offloading System With Quality of Service Guarantee

Code offloading is a promising way to accelerate mobile applications and reduce the energy consumption of mobile devices by shifting some computation to the cloud. However, existing code offloading systems suffer from a long communication delay between mobile devices and the cloud. To address this challenge, in this paper, we consider to deploy edge nodes in close proximity to mobile devices and study how they benefit code offloading. We design an edge-centric code offloading system, called Echo, over a three-layer computing hierarchy consisting of mobile devices, the edge, and the cloud. A critical problem needs to be addressed by Echo is to decide which methods should be offloaded to which computing platform (the edge or the cloud). Different from existing offloading systems that let mobile devices individually make offloading decisions, Echo implements a centralized decision engine at the edge. This edge-centric design can fully exploit limited hardware resources at the edge to provide offloading services with the quality-of-service guarantee. Furthermore, we propose some novel mechanisms, e.g., lazy object transmission and differential object update, to further improve system performance. The results of a small-scale real deployment and trace-driven simulations show that Echo significantly outperforms existing code offloading systems at both execution time and energy consumption.


A Load Balancing Model based for Cloud Computing using Public Cloud

It is difficult to maintain all the resources and services on single system. So by using Load balancing strategy we distribute load on single system to multiple system providing efficient response to user, resulting in improve user satisfaction. Load balancing in the cloud computing has an important impact on the performance. To improve user satisfaction there is a need of efficient cloud computing for that we need a good load balancing algorithm. This article shows


An international qualitative study of ability and disability in ADHD using the WHO-ICF framework

This is the third in a series of four cross-cultural empirical studies designed to develop International Classification of Functioning, Disability and Health (ICF, and Children and Youth version, ICF(-CY) Core Sets for Attention-Deficit Hyperactivity Disorder (ADHD). To explore the perspectives of individuals diagnosed with ADHD, self-advocates, immediate family members and professional caregivers on relevant areas of impairment and functional abilities typical for ADHD across the lifespan as operationalized by the ICF(-CY). A qualitative study using focus group discussions or semi-structured interviews of 76 participants, divided into 16 stakeholder groups. Participants from five countries (Brazil, India, Saudi Arabia, South Africa and Sweden) were included. A deductive qualitative content analysis was conducted to extract meaningful functioning and disability concepts from verbatim material. Extracted concepts were then linked to ICF(-CY) categories by independent researchers using a standardized linking procedure. In total, 82 ICF(-CY) categories were identified, of which 32 were related to activities and participation, 25 to environmental factors, 23 to body functions and 2 to body structures. Participants also provided opinions on experienced positive sides to ADHD. A high level of energy and drive, creativity, hyper-focus, agreeableness, empathy, and willingness to assist others were the most consistently reported strengths associated with ADHD. Stakeholder perspectives highlighted the need to appraise ADHD in a broader context, extending beyond diagnostic criteria into many areas of ability and disability as well as environmental facilitators and barriers. This qualitative study, along with three other studies (comprehensive scoping review, expert survey and clinical study), will provide the scientific basis to define ICF(-CY) Core Sets for ADHD, from which assessment tools can be derived for use in clinical and research setting, as well as in health care administration.