title: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection

abstract: In this paper, we proposed a novel spam detection method that focused on reducing the false positive error of mislabeling nonspam as spam. First, we used the wrapper-based feature selection method to extract crucial features. Second, the decision tree was chosen as the classifier model with C4.5 as the training algorithm. Third, the cost matrix was introduced to give different weights to two error types, i.e., the false positive and the false negative errors. We define the weight parameter as a to adjust the relative importance of the two error types. Fourth, K-fold cross validation was employed to reduce out-of-sample error. Finally, the binary PSO with mutation operator (MBPSO) was used as the subset search strategy. Our experimental dataset contains 6000 emails, which were collected during the year of 2012. We conducted a Kolmogorov–Smirnov hypothesis test on the capital-run-length related features and found that all the p values were less than 0.001. Afterwards, we found a = 7 was the most appropriate in our model. Among seven meta-heuristic algorithms, we demonstrated the MBPSO is superior to GA, RSA, PSO, and BPSO in terms of classification performance. The sensitivity, specificity, and accuracy of the decision tree with feature selection by MBPSO were 91.02%, 97.51%, and 94.27%, respectively. We also compared the MBPSO with conventional feature selection methods such as SFS and SBS. The results showed that the MBPSO performs better than SFS and SBS. We also demonstrated that wrappers are more effective than filters with regard to classification performance indexes. It was clearly shown that the proposed method is effective, and it can reduce the false positive error without compromising the sensitivity and accuracy values.


title: Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

abstract: Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars. The source code and data to train and validate the networks described in this paper are made available for researchers.


title: Answering top-k representative queries on graph databases

abstract: Given a function that classifies a data object as relevant or irrelevant, we consider the task of selecting k objects that best represent all relevant objects in the underlying database. This problem occurs naturally when analysts want to familiarize themselves with the relevant objects in a database using a small set of k exemplars. In this paper, we solve the problem of top-k representative queries on graph databases. While graph databases model a wide range of scientific data, solving the problem in the context of graphs presents us with unique challenges due to the inherent complexity of matching structures. Furthermore, top-k representative queries map to the classic Set Cover problem, making it NP-hard. To overcome these challenges, we develop a greedy approximation with theoretical guarantees on the quality of the answer set, noting that a better approximation is not feasible in polynomial time. To further optimize the quadratic computational cost of the greedy algorithm, we propose an index structure called NB-Index to index the \theta-neighborhoods of the database graphs by employing a novel combination of Lipschitz embedding and agglomerative clustering. Extensive experiments on real graph datasets validate the efficiency and effectiveness of the proposed techniques that achieve up to two orders of magnitude speed-up over state-of-the-art algorithms.


Performance Optimization of Levenberg-Marquardt Algorithm with Parallelization


title: A GRU-Gated Attention Model for Neural Machine Translation

abstract: Neural machine translation (NMT) heavily relies on an attention network to produce a context vector for each target word prediction. In practice, we find that context vectors for different target words are quite similar to one another and therefore are insufficient in discriminatively predicting target words. The reason for this might be that context vectors produced by the vanilla attention network are just a weighted sum of source representations that are invariant to decoder states. In this paper, we propose a novel GRU-gated attention model (GAtt) for NMT which enhances the degree of discrimination of context vectors by enabling source representations to be sensitive to the partial translation generated by the decoder. GAtt uses a gated recurrent unit (GRU) to combine two types of information: treating a source annotation vector originally produced by the bidirectional encoder as the history state while the corresponding previous decoder state as the input to the GRU. The GRU-combined information forms a new source annotation vector. In this way, we can obtain translation-sensitive source representations which are then feed into the attention network to generate discriminative context vectors. We further propose a variant that regards a source annotation vector as the current input while the previous decoder state as the history. Experiments on NIST Chinese-English translation tasks show that both GAtt-based models achieve significant improvements over the vanilla attentionbased NMT. Further analyses on attention weights and context vectors demonstrate the effectiveness of GAtt in improving the discrimination power of representations and handling the challenging issue of over-translation.


title: Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks

abstract: We argue for network slicing as an efficient solution that addresses the diverse requirements of 5G mobile networks, thus providing the necessary flexibility and scalability associated with future network implementations. We elaborate on the challenges that emerge when we design 5G networks based on network slicing. We focus on the architectural aspects associated with the coexistence of dedicated as well as shared slices in the network. In particular, we analyze the realization options of a flexible radio access network with focus on network slicing and their impact on the design of 5G mobile networks. In addition to the technical study, this paper provides an investigation of the revenue potential of network slicing, where the applications that originate from such concept and the profit capabilities from the network operator's perspective are put forward.


title: Bypassing Captcha By Machine A Proof For Passing The Turing Test

abstract: For the last ten years, CAPTCHAs have been widely used by websites to prevent their data being automatically updated by machines. By supposedly allowing only humans to do so, CAPTCHAs take advantage of the reverse Turing test (TT), knowing that humans are more intelligent than machines. Generally, CAPTCHAs have defeated machines, but things are changing rapidly as technology improves. Hence, advanced research into optical character recognition (OCR) is overtaking attempts to strengthen CAPTCHAs against machine-based attacks. This paper investigates the immunity of CAPTCHA, which was built on the failure of the TT. We show that some CAPTCHAs are easily broken using a simple OCR machine built for the purpose of this study. By reviewing other techniques, we show that even more difficult CAPTCHAs can be broken using advanced OCR machines. Current advances in OCR should enable machines to pass the TT in the image recognition domain, which is exactly where machines are seeking to overcome CAPTCHAs. We enhance traditional CAPTCHAs by employing not only characters, but also natural language and multiple objects within the same CAPTCHA. The proposed CAPTCHAs might be able to hold out against machines, at least until the advent of a machine that passes the TT completely.


title: Security Analysis of ECC Based Authentication Protocols

abstract: In this work we consider two elliptic curve cryptography based authentication protocols for performing cryptanalysis and security enhancement. The first one by Moosavi et al., is a mutual authentication scheme for RFID implant systems. We perform a cryptanalysis on this protocol and observe that it is prone to the clogging attack, a kind of denial of service (DoS) attack. We then suggest an improvement on the protocol to prevent the clogging attack. The other protocol we consider for analysis is by Xu et al. This is a smart card based authentication protocol. We again perform the clogging (DoS) attack on this protocol via replay. We observe that all smart card based authentication protocols which precede the one by Xu et al., and require the server to compute the computationally intensive elliptic curve techniques are prone to the clogging attack. We suggest an alternative improvement on the protocol to prevent the clogging attack, which also applies to the protocol by Moosavi et al.


title: Choose your ‘ buddy icon ’ carefully : The influence of avatar androgyny , anthropomorphism and credibility in online interactions

abstract: In both online and offline interactions, the visual representation of people influences how others perceive them. In contrast to the offline body, an online visual representation of a person is consciously chosen and not stable. This paper reports the results of a 2 step examination of the influence of avatars on the person perception process. Specifically, this project examines the reliance on visual characteristics during the online perception process, and the relative influence of androgyny, anthropomorphism and credibility. In the first step, 255 participants fill out a survey where they rated a set of 30 static avatars on their credibility, androgyny, and anthropomorphism. The second step is a between subjects experiment with 230 participants who interact with partners represented by one of eight avatars (high and low androgyny, and anthropomorphism by high and low credibility). Results show that the characteristics of the avatar are used in the person perception process. Causal modeling techniques revealed that perceptions of avatar androgyny influence perceptions of anthropomorphism, which influences attributions of both avatar and partner credibility. Implications of these results for theory, future research, and users and designers of systems using avatars are discussed.


title: Foliage Plant Retrieval using Polar Fourier Transform, Color Moments and Vein Features

abstract: This paper proposed a method that combines Polar Fourier Transform, color moments, and vein features to retrieve leaf images based on a leaf image. The method is very useful to help people in recognizing foliage plants. Foliage plants are plants that have various colors and unique patterns in the leaf. Therefore, the colors and its patterns are information that should be counted on in the processing of plant identification. To compare the performance of retrieving system to other result, the experiments used Flavia dataset, which is very popular in recognizing plants. The result shows that the method gave better performance than PNN, SVM, and Fourier Transform. The method was also tested using foliage plants with various colors. The accuracy was 90.80% for 50 kinds of plants.


How Many Grapes went into the Wine: Stafford Beer on the Art and Science of Holistic Management


title: A Travel Recommender System for Combining Multiple Travel Regions to a Composite Trip

abstract: Internet-based services are available to recommend destinations and activities for organized trips. Only few systems support travelers when creating composite trips consisting of multiple destinations or activities. The idea in this work is to select travel regions that maximize the value of the composite trip for the user while still respecting her limitations in time and money. The value of a travel region can be determined by the similarity between a specified user query and the cases in a travel region database. The recommendation algorithm needs to find a decent routing between the regions while still satisfying diversity of the whole trip. We developed an algorithm based on an approximation for the knapsack problem and extended it to recognize dependencies between the regions while calculating best combinations. It is able to determine the optimal duration of stay per region and its performance improves when benefiting from the hierarchical structure of our travel database. In an expert study, we verified the results of our approach. The study proves that our algorithm for composite trips delivers good recommendations which satisfied an expert user more than baseline algorithms. Regions in the composite trip fit together better and a decent routing between the regions can be ensured. Nevertheless, the algorithm leaves room for improvement by combining less similar regions in a composite trip, thus leading to a higher diversity of the recommendation.


Spreadsheet Programming


title: Development of a Shipboard Remote Control and Telemetry Experimental System for Large-Scale Model’s Motions and Loads Measurement in Realistic Sea Waves

abstract: Wave-induced motion and load responses are important criteria for ship performance evaluation. Physical experiments have long been an indispensable tool in the predictions of ship’s navigation state, speed, motions, accelerations, sectional loads and wave impact pressure. Currently, majority of the experiments are conducted in laboratory tank environment, where the wave environments are different from the realistic sea waves. In this paper, a laboratory tank testing system for ship motions and loads measurement is reviewed and reported first. Then, a novel large-scale model measurement technique is developed based on the laboratory testing foundations to obtain accurate motion and load responses of ships in realistic sea conditions. For this purpose, a suite of advanced remote control and telemetry experimental system was developed in-house to allow for the implementation of large-scale model seakeeping measurement at sea. The experimental system includes a series of technique sensors, e.g., the Global Position System/Inertial Navigation System (GPS/INS) module, course top, optical fiber sensors, strain gauges, pressure sensors and accelerometers. The developed measurement system was tested by field experiments in coastal seas, which indicates that the proposed large-scale model testing scheme is capable and feasible. Meaningful data including ocean environment parameters, ship navigation state, motions and loads were obtained through the sea trial campaign.


title: On the features and challenges of security and privacy in distributed internet of things

abstract: In the Internet of Things, services can be provisioned using centralized architectures, where central entities acquire, process, and provide information. Alternatively, distributed architectures, where entities at the edge of the network exchange information and collaborate with each other in a dynamic way, can also be used. In order to understand the applicability and viability of this distributed approach, it is necessary to know its advantages and disadvantages - not only in terms of features but also in terms of security and privacy challenges. The purpose of this paper is to show that the distributed approach has various challenges that need to be solved, but also various interesting properties and strengths.


title: PoisonIvy: Safe speculation for secure memory

abstract: Encryption and integrity trees guard against physical attacks, but harm performance. Prior academic work has speculated around the latency of integrity verification, but has done so in an insecure manner. No industrial implementations of secure processors have included speculation. This work presents PoisonIvy, a mechanism which speculatively uses data before its integrity has been verified while preserving security and closing address-based side-channels. PoisonIvy reduces performance overheads from 40% to 20% for memory intensive workloads and down to 1.8%, on average.


title: Internet of Things Enhanced User Experience for Smart Water and Energy Management

abstract: Smart environments can engage a wide range of end users with different interests and priorities, from corporate managers looking to improve the performance of their business to school children who want to explore and learn more about the world around them. Creating an effective user experience within a smart environment (from smart buildings to smart cities) is an important factor to success. In this article, we reflect on our experience of developing Internet-of-Things-enabled applications within a smart home, school, office building, university, and airport, where the goal has been to engage a wide range of users (from building managers to business travelers) to increase water and energy awareness, management, and conservation.


title: Planning Formula One race strategies using discrete-event simulation

abstract: A discrete-event simulation model that imitates most on-track events, including car failures, passing manoeuvres and pit stops during a Formula One race, is presented. The model is intended for use by a specific team. It will enable decision-makers to plan and evaluate their race strategy, consequently providing them with a possible competitive advantage. The simulation modelling approach presented in this paper captures the mechanical complexities and physical interactions of a race car with its environment through a time-based approach. Model verification and validation are demonstrated using three races from the 2005 season. The application of the model is illustrated by evaluating the race strategies employed by a specific team during these three races.


title: BizSeeker: A hybrid semantic recommendation system for personalized government-to-business e-services

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to develop a hybrid semantic recommendation system to provide personalized government to business (G2B) e‐services, in particular, business partner recommendation e‐services for Australian small to medium enterprises (SMEs). ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – The study first proposes a product semantic relevance model. It then develops a hybrid semantic recommendation approach which combines item‐based collaborative filtering (CF) similarity and item‐based semantic similarity techniques. This hybrid approach is implemented into an intelligent business‐partner‐locator recommendation‐system prototype called BizSeeker. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – The hybrid semantic recommendation approach can help overcome the limitations of existing recommendation techniques. The recommendation system prototype, BizSeeker, can recommend relevant business partners to individual business users (e.g. exporters), which therefore will reduce the time, cost and risk of businesses involved in entering local and international markets. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: – The study would be of great value in e‐government personalization research. It would facilitate the transformation of the current G2B e‐services into a new stage wherein the e‐government agencies offer personalized e‐services to business users. The study would help government policy decision‐makers to increase the adoption of e‐government services. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – Providing personalized e‐services by e‐government can be seen as an evolution of the intentions‐based approach and will be one of the next directions of government e‐services. This paper develops a new recommender approach and systems to improve personalization of government e‐services.


title: Scheduling strategies and throughput optimization for the Uplink for IEEE 802.11ax and IEEE 802.11ac based networks

abstract: The new IEEE 802.11 standard, IEEE 802.11ax, has the challenging goal of serving more Uplink (UL) traffic and users as compared with his predecessor IEEE 802.11ac, en- abling consistent and reliable streams of data (average throughput) per station. In this paper we explore several new IEEE 802.11ax UL scheduling mechanisms and compare between the maximum throughputs of unidirectional UDP Multi Users (MU) triadic. The evaluation is conducted based on Multiple-Input-Multiple-Output (MIMO) and Orthogonal Frequency Division Multiple Acceess (OFDMA) transmission multiplexing format in IEEE 802.11ax vs. the CSMA/CA MAC in IEEE 802.11ac in the Single User (SU) and MU modes for 1, 4, 8, 16, 32 and 64 stations scenario in reliable and unreliable channels. The comparison is conducted as a function of the Modulation and Coding Schemes (MCS) in use. In IEEE 802.11ax we consider two new flavors of ac- knowledgment operation settings, where the maximum acknowledgment windows are 64 or 256 respectively. In SU scenario IEEE 802.11ax throughputs outperform IEEE 802.11ac by about 64% and 85% in reliable and unreliable channels respectively. In MU-MIMO scenario IEEE 802.11ax throughputs outperform IEEE 802.11ac by up to 263% and 270% in reliable and unreliable channels respectively. Also, as the number of stations increases, the advantage of IEEE 802.11ax in terms of the access delay also increases.


title: Nonliear model predictive control of ball-plate system based on gaussian particle swarm optimization

abstract: This paper presents a new nonlinear model predictive control (NMPC) strategy based on the Gaussian particle swarm optimization (GPSO). Through the Taylor expansion, NMPC transform to a quadratic programming problem with unknown parameters. Hence, for the global convergence character and higher optimization accuracy, GPSO is employed to dynamically perform nonlinear constraint optimization. Finally, the proposed control strategy is applied to Ball-Plate system to verify the effectiveness.


title: New Propeller-Type Tribocharging Device With Application to the Electrostatic Separation of Granular Insulating Materials

abstract: The aim of this paper is the development and functional optimization of a new propeller-type aerodynamic tribocharging device with application in the field of electrostatic separation. The originality of the system consists of its modular structure: one or several propellers can be stacked in the same device to provide appropriate tribocharging conditions to a wide variety of granular mixtures containing two or more of insulating materials. The study is conducted with samples of the following various insulating materials: polycarbonate, polyamide, acrylonitrile butadiene styrene, polyvinyl chloride, and high-impact polystyrene, grain sizes up to 4 mm in diameter, for several values of the propeller rotation speed, and of the mass of the particles in the tribocharging device, the wall of which are made of acetate, Polymethyl methacrylate (PMMA), or aluminum. The efficiency of the device is tested by processing the charged granular mixture in a metal-belt conveyor-type electrostatic separator. The aluminum-wall device enables better charging that PMMA and acetate. The mass introduced in the device has no significant effect on the outcome of the process, but the speed of the propellers does. Successful separation of a mixture of three insulating materials is reported.


title: Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU

abstract: We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. We analyze its computational traits and concentrate on aspects critical to leveraging the GPU's computational power. We introduce a system of queues and a dynamic scheduling strategy, potentially helpful for other asynchronous algorithms as well. Our hybrid CPU/GPU version of A3C, based on TensorFlow, achieves a significant speed up compared to a CPU implementation; we make it publicly available to other researchers at https://github.com/NVlabs/GA3C.


title: A theory of memory retrieval

abstract: A theory of memory retrieval is developed and is shown to apply over a range of experimental paradigms. Access to memory traces is viewed in terms of a resonance metaphor. The probe item evokes the search set on the basis of probe-memory item relatedness, just as a ringing tuning fork evokes sympathetic vibrations in other tuning forks. Evidence is accumulated in parallel from each probe-memory item comparison, and each comparison is modeled by a continuous random walk process. In item recognition, the decision process is self-terminating on matching comparisons and exhaustive on nonmatching comparisons. The mathematical model produces predictions about accuracy, mean reaction time, error latency, and reaction time distributions that are in good accord with experimental data. The theory is applied to four item recognition paradigms (Sternberg, prememorized list, study-test, and continuous) and to speed-accuracy paradigms; results are found to provide a basis for comparison of these paradigms. It is noted that neural network models can be interfaced to the retrieval theory with little difficulty and that semantic memory models may benefit from such a retrieval scheme.


title: Efficient global portfolios: big data and investment universes

abstract: In this analysis of the risk and return of stocks in the United States and global markets, we apply several portfolio construction and optimization techniques to U.S. and global stock universes. We find that (1) mean-variance techniques continue to produce portfolios capable of generating excess returns above transaction costs and statistically significant asset selection, (2) optimization techniques minimizing expected tail loss are statistically significant in portfolio construction, and (3) global markets offer the potential for greater returns relative to risk than domestic markets. In this experiment, mean-variance, enhanced-index-tracking techniques, and mean-expected tail-loss methodologies are examined. Global equity data and the vast quantity (and quality) of the data relative to U.S. equity modeling have been discussed in the literature. We estimate expected return models in the U.S. and global equity markets using a given stock-selection model and generate statistically significant active returns from various portfolio construction techniques.


Biomedical Event Extraction using Abstract Meaning Representation


title: Computer network intrusion detection using sequential LSTM Neural Networks autoencoders

abstract: In this paper, we introduce a sequential autoencoder framework using long short term memory (LSTM) neural network for computer network intrusion detection. We exploit the dimensionality reduction and feature extraction property of the autoencoder framework to efficiently carry out the reconstruction process. Furthermore, we use the LSTM networks to handle the sequential nature of the computer network data. We assign a threshold value based on cross-validation in order to classify whether the incoming network data sequence is anomalous or not. Moreover, the proposed framework can work on both fixed and variable length data sequence and works efficiently for unforeseen and unpredictable network attacks. We then also use the unsupervised version of the LSTM, GRU, Bi-LSTM and Neural Networks. Through a comprehensive set of experiments, we demonstrate that our proposed sequential intrusion detection framework performs well and is dynamic, robust and scalable.


title: Deep Co-Clustering for Unsupervised Audiovisual Learning

abstract: The seen birds twitter, the running cars accompany with noise, people talks by face-to-face, etc. These naturally audiovisual correspondences provide the possibilities to explore and understand the outside world. However, the mixed multiple objects and sounds make it intractable to perform efficient matching in the unconstrained environment. To settle this problem, we propose to adequately excavate audio and visual components and perform elaborate correspondence learning among them. Concretely, a novel unsupervised audiovisual learning model is proposed, named as Deep Co-Clustering (DCC), that synchronously performs sets of clustering with multimodal vectors of convolutional maps in different shared spaces for capturing multiple audiovisual correspondences. And such integrated multimodal clustering network can be effectively trained with max-margin loss in the end-to-end fashion. Amounts of experiments in feature evaluation and audiovisual tasks are performed. The results demonstrate that DCC can learn effective unimodal representation, with which the classifier can even outperform human. Further, DCC shows noticeable performance in the task of sound localization, multisource detection, and audiovisual understanding.


title: Implementation of K-means algorithm on FGGA

abstract: K-means algorithm is one of the clustering algorithms that increase in popularity day by day. The intensive mathematical operations and the continuous increase of the data size while clustering on large data using the K-means algorithm prevent the algorithm from operating at high performance. Therefore, the K-means algorithm that works on large data needs to be implemented on very fast hardware. FPGAs capable of parallel processing can be mathematically processed much faster than traditional processors. Therefore, realization of algorithms that require intensive mathematical computations such as K-means using FPGAs is of great importance for the performance of applications. In this study, an architecture is designed on the FPGA for the K-means algorithm and the accuracy and efficiency of the generated architecture are compared with the software applied in the standard processor and the performance is tested. When the results are examined, it is seen that the FPGA gives an average of 100X faster results than the standard processor.


title: Design & Development of a GSM Based Vehicle Theft Control System

abstract: This paper deals with the design & development of a theft control system for an automobile, which is being used to prevent/control the theft of a vehicle. The developed system makes use of an embedded system based on GSM technology. The designed & developed system is installed in the vehicle. An interfacing mobile is also connected to the microcontroller, which is in turn, connected to the engine. Once, the vehicle is being stolen, the information is being used by the vehicle owner for further processing. The information is passed onto the central processing insurance system, where by sitting at a remote place, a particular number is dialed by them to the interfacing mobile that is with the hardware kit which is installed in the vehicle. By reading the signals received by the mobile, one can control the ignition of the engine; say to lock it or to stop the engine immediately. Again it will come to the normal condition only after entering a secured password. The owner of the vehicle & the central processing system will know this secured password. The main concept in this design is introducing the mobile communications into the embedded system. The designed unit is very simple & low cost. The entire designed unit is on a single chip. When the vehicle is stolen, owner of vehicle may inform to the central processing system, then they will stop the vehicle by just giving a ring to that secret number and with the help of SIM tracking knows the location of vehicle and informs to the local police or stops it from further movement.


title: Understanding the influence of C2C communication on purchase decision in online communities from a perspective of information adoption model

abstract: We test the impact of C2C communication on purchase decision in online communities.The theoretical model is developed based on information adoption model.Argument quality, source credibility and tie strength are important predictors.The three predictors affect purchase decision through product usefulness evaluation.Relationship type is a moderator. Information derived from consumer-to-consumer (C2C) communication in online communities plays an important role in consumers' purchase decision making. Based on the information adoption model, the present study develops a research model to investigate the influence of C2C communication on consumers' purchase decision in online communities. Based on 324 effective data, the Partial Least Squares technique is used to test the proposed model. The results show that argument quality, source credibility, and tie strength positively influence purchase decision through product usefulness evaluation. In addition, tie strength exhibits difference effect on product usefulness evaluation between the context of consumers communicating with real and virtual relationships. Theoretical and managerial implications are discussed.


title: Discriminatively Trained Templates for 3D Object Detection: A Real Time Scalable Approach

abstract: In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.


title: Comparison between traditional plan-based and agile software processes according to team size & project domain (A systematic literature review)

abstract: Background: Agile Software development is becoming the most preferred approach for the process of software development. Since the traditional plan-based methods are rigorous and not flexible with changing requirements, some projects are postponed, go over budget, and are sometimes canceled or started from scratch. Questions remain on whether the traditional plan-based approaches will be replaced by agile. For effective, flexible and high-quality projects, organizations are shifting to flexible methods, where they can change the requirements at any stage of the development process. The purpose of this paper is to compare the plan-based and agile software development processes. The paper will discuss the art of deciding which methodology should be used with regard to the team size and the project domain. In the paper a systematic literature review covers 26 papers between 2000 and 2016. The papers are selected with reference to the size of the team and the domain of the project. The result of the paper illustrate that each methodology has a specific area which it best fits in. An organization should consider all factors and choose the methodology according to the situation. Finally, Agile best fits with small team sizes, for exploratory, and software & web-based projects. Traditional methods best fit large team sizes, for predictable, and reusable artifacts projects. However, they can co-exist.


title: A self-organizing neural network architecture for learning human-object interactions

abstract: The visual recognition of transitive actions comprising human-object interactions is a key component for artificial systems operating in natural environments. This challenging task requires jointly the recognition of articulated body actions as well as the extraction of semantic elements from the scene such as the identity of the manipulated objects. In this paper, we present a self-organizing neural network for the recognition of human-object interactions from RGB-D videos. Our model consists of a hierarchy of Grow-When-Required (GWR) networks that learn prototypical representations of body motion patterns and objects, accounting for the development of action-object mappings in an unsupervised fashion. We report experimental results on a dataset of daily activities collected for the purpose of this study as well as on a publicly available benchmark dataset. In line with neurophysiological studies, our self-organizing architecture exhibits higher neural activation for congruent action-object pairs learned during training sessions with respect to synthetically created incongruent ones. We show that our unsupervised model shows competitive classification results on the benchmark dataset with respect to strictly supervised approaches.


title: Infants' Ability to Distinguish between Purposeful and Non-Purposeful Behaviors

abstract: Prior studies (Gergely et al., 1995; Woodward, 1998) have found that infants focus on the goals of an action over other details. The current studies tested whether infants would distinguish between a behavior that seemed to be goal-directed and one that seemed not to be. Infants in one condition saw an actor grasp one of two toys that sat side by side on a stage. Infants in the other condition saw the actor drop her hand onto one of the toys in a manner that looked unintentional. Once infants had been habituated to these events, they were shown test events in which either the path of motion or the object that was touched had changed. Nine-month-olds differentiated between these two actions. When they saw the actor grasp the toy, they looked longer on trials with a change in goal object than on trials with a change in path. When they saw the actor drop her hand onto the toy, they looked equally at the two test events. These findings did not result from infants being more interested in grasping as compared to inert hands. In a second study, 5-month-old infants showed patterns similar to those seen in 9-month-olds. These findings have implications for theories of the development of the concept of intention. They argue against the claim that infants are innately predisposed to interpret any motion of an animate agent as intentional.


title: What Drives Cryptocurrency Prices?: An Investigation of Google Trends and Telegram Sentiment

abstract: The Google Trends1 search analysis service and the Telegram2 messaging platform are investigated to determine their respective relationships to cryptocurrency price behaviour. It is shown that, in contrast to earlier findings, the relationship between cryptocurrency price movements and internet search volumes obtained from Google Trends is no longer consistently positive, with strong negative correlations detected for Bitcoin and Ethereum during June 2018. Sentiment extracted from cryptocurrency investment groups on Telegram is found to be positively correlated to Bitcoin and Ethereum price movements, particularly during periods of elevated volatility. The number of messages posted on a Bitcoin-themed Telegram group is found to be an indicator of Bitcoin price action in the subsequent week. A long shortterm memory (LSTM) recurrent neural network is developed to predict the direction of cryptocurrency prices using data obtained from Google Trends and Telegram. It is shown that Telegram data is a better predictor of the direction of the Bitcoin market than Google Trends. The converse is true for Ethereum. The LSTM model produces the most accurate results when predicting price movements over a one-week period.


title: A 10MHz 80μW 67 ppm/°C CMOS reference clock oscillator with a temperature compensated feedback loop in 0.18μm CMOS

abstract: A 10MHz, 80μW CMOS reference clock oscillator is presented in 0.18μm CMOS. The proposed oscillator employs a supply-regulated ring-oscillator in a temperature compensated feedback loop, which minimizes the frequency sensitivity to supply and temperature variations. The clock oscillator achieves frequency variation of less than ±0.05% against supply variation of 1.2V ~ 3V and ±0.4% against temperature variation of −20°C ~ 120°C. In addition, low power consumption is achieved by using sub-threshold bias circuits.


title: How chunks, long-term working memory and templates offer a cognitive explanation for neuroimaging data on expertise acquisition: A two-stage framework

abstract: Abstract Our review of research on PET and fMRI neuroimaging of experts and expertise acquisition reveals two apparently discordant patterns in working-memory-related tasks. When experts are involved, studies show activations in brain regions typically activated during long-term memory tasks that are not observed with novices, a result that is compatible with functional brain reorganization. By contrast, when involving novices and training programs, studies show a decrease in brain regions typically activated during working memory tasks, with no functional reorganization. We suggest that the latter result is a consequence of practice periods that do not allow important structures to be completely acquired: knowledge structures (i.e., Ericsson and Kintsch’s retrieval structures; Gobet and Simon’s templates) and in a lesser way, chunks. These structures allow individuals to improve performance on working-memory tasks, by enabling them to use part of long-term memory as working memory, causing a cerebral functional reorganization. Our hypothesis is that the two brain activation patterns observed in the literature are not discordant, but involve the same process of expertise acquisition in two stages: from decreased activation to brain functional reorganization. The dynamic of these two physiological stages depend on the two above-mentioned psychological constructs: chunks and knowledge structures.


title: A Class-D outphasing RF amplifier with harmonic suppression in 90nm CMOS

abstract: This paper presents a low-power Class-D stage featuring a new harmonic reduction technique, which cancels the 3rd harmonic and reduces the 5th harmonic. The technique creates a voltage level of V DD /2 from a single supply voltage to shape the drain voltage, uses only digital circuits and eliminates the short-circuit current present in inverter-based Class-D stages. From a single Class-D stage operating at 900MHz, the measured output power is +5.1dBm with Drain Efficiency (DE) and Power-Added Efficiency (PAE) of 73% and 59% for a 1.2V supply, while 2nd to 4th harmonics are measured to be −37dBc without any filtering. Connecting two Class-D stages to a PCB-mounted transformer in an outphasing configuration, the overall amplifier is linear enough to amplify EDGE 8-PSK and WCDMA modulated signals at 900MHz without pre-distortion of the input signals or any other linearization technique.


Using Fast Weights to Deblur Old Memories


title: Revisiting Reuse for Approximate Query Processing

abstract: Visual data exploration tools allow users to quickly gather insights from new datasets. As dataset sizes continue to increase, though, new techniques will be necessary to maintain the interactivity guarantees that these tools require. Approximate query processing (AQP) attempts to tackle this problem and allows systems to return query results at "human speed." However, existing AQP techniques start to break down when confronted with ad hoc queries that target the tails of the distribution. ::: ::: We therefore present an AQP formulation that can provide low-error approximate results at interactive speeds, even for queries over rare subpopulations. In particular, our formulation treats query results as random variables in order to leverage the ample opportunities for result reuse inherent in interactive data exploration. As part of our approach, we apply a variety of optimization techniques that are based on probability theory, including new query rewrite rules and index structures. We implemented these techniques in a prototype system and show that they can achieve interactivity where alternative approaches cannot.


title: Sample size in factor analysis.

abstract: The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the


title: A kilohertz kilotaxel tactile sensor array for investigating spatiotemporal features in neuromorphic touch

abstract: The spatiotemporal structure of mechanoreceptor responses is known to facilitate rapid tactile processing in the brain. To investigate whether such mechanisms can be replicated by artificial systems, tactile sensors with a large number of elements and sub-millisecond response time are required. In this work, we present a 4096 element tactile sensor array that can be sampled at over 5kHz. Experimental results demonstrate that high spatiotemporal resolution aids to resolve force magnitude and direction of tactile events. Such a platform enables the development of time dependent neuromorphic algorithms for tactile learning and signal processing.


title: The IoT LoRa system design for tracking and monitoring patient with mental disorder

abstract: This paper aims to propose a system design, working on the basis of the Internet of Things (IoT) LoRa, for tracking and monitoring the patient with mental disorder. The system consists of a LoRa client, which is a tracking device on end devices installed on the patient, and LoRa gateways, installed in hospitals and other public locations. The LoRa gateways are connected to local servers and cloud servers by utilizing both mobile cellular and Wi-Fi networks as the communications media. The feasibility of the system design is developed by employing the results of our previous work on LoRa performance in the Line of Sight (LoS) and Non-Line of Sight (Non-LoS) environments. Discussions are presented concerning the LoRa network performance, battery power and scalability. The future work is to build the proposed the design in a real system scenarios.


title: Low-profile wideband high gain ME dipole antenna with SIW side walls surrounding

abstract: This paper introduces a simple method to improve the radiation pattern of the low profile magneto-electric (ME) dipole antenna by adding a substrate integrated waveguide (SIW) side-walls structure around. Compared with the original ME dipole antenna, gain enhancement of about 3dB on average is achieved without deteriorating the impedance bandwidth. The antenna operates at 15GHz with 63.3% −10dB impedance bandwidth from 10.8GHz to 18.4GHz and the gain is 12.3dBi at 17GHz on a substrate with fixed thickness of 3mm (0.15λ0) and aperture of 35mm×35mm (1.75λ0). This antenna is a good choice in the wireless communication application for its advantages of low-profile, wide bandwidth, high gain and low cost fabrication.


title: k-space undersampling in PROPELLER imaging

abstract: PROPELLER MRI (periodically rotated overlapping parallel lines with enhanced reconstruction) provides images with significantly fewer B0-related artifacts than echo-planar imaging (EPI), as well as reduced sensitivity to motion compared to conventional multiple-shot fast spin-echo (FSE). However, the minimum imaging time in PROPELLER is markedly longer than in EPI and 50% longer than in conventional multiple-shot FSE. Often in MRI, imaging time is reduced by undersampling k-space. In the present study, the effects of undersampling on PROPELLER images were evaluated using simulated and in vivo data sets. Undersampling using PROPELLER patterns with reduced number of samples per line, number of lines per blade, or number of blades per acquisition, while maintaining the same k-space field of view (FOVk) and uniform sampling at the edges of FOVk, reduced imaging time but led to severe image artifacts. In contrast, undersampling by means of removing whole blades from a PROPELLER sampling pattern that sufficiently samples k-space produced only minimal image artifacts, mainly manifested as blurring in directions parallel to the blades removed, even when reducing imaging time by as much as 50%. Finally, undersampling using asymmetric blades and taking advantage of Hermitian symmetries to fill-in the missing data significantly reduced imaging time without causing image artifacts. Magn Reson Med 53:675–683, 2005. © 2005 Wiley-Liss, Inc.


title: iOverbook: Intelligent Resource-Overbooking to Support Soft Real-Time Applications in the Cloud

abstract: Cloud service providers (CSPs) often overbook their resources with user applications despite having to maintain service-level agreements with their customers. Overbooking is attractive to CSPs because it helps to reduce power consumption in the data center by packing more user jobs in less number of resources while improving their profits. Overbooking becomes feasible because user applications tend to overestimate their resource requirements utilizing only a fraction of the allocated resources. Arbitrary resource overbooking ratios, however, may be detrimental to soft real-time applications, such as airline reservations or Netflix video streaming, which are increasingly hosted in the cloud. The changing dynamics of the cloud preclude an offline determination of overbooking ratios. To address these concerns, this paper presents iOverbook, which uses a machine learning approach to make systematic and online determination of overbooking ratios such that the quality of service needs of soft real-time systems can be met while still benefiting from overbooking. Specifically, iOverbook utilizes historic data of tasks and host machines in the cloud to extract their resource usage patterns and predict future resource usage along with the expected mean performance of host machines. To evaluate our approach, we have used a large usage trace made available by Google of one of its production data centers. In the context of the traces, our experiments show that iOverbook can help CSPs improve their resource utilization by an average of 12.5% and save 32% power in the data center.


title: A robust, real-time control scheme for multifunction myoelectric control

abstract: This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.


Design of Smart Factory Web Services Based on the Industrial Internet of Things


title: Content-Based Medical Image Retrieval: A Survey of Applications to Multidimensional and Multimodality Data

abstract: Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.


title: Dual band compact printed monopole antenna for Bluetooth (2.54GHz) and WLAN (5.2GHz) applications

abstract: The printed monopole antenna having dual band is proposed which operates at frequency 2.54 GHz (Bluetooth) and 5.2GHz (WLAN). Bluetooth and WLAN have been widely applied in all new trending laptops and smart phones. These two technologies are well known for its effective cost and high-speed data connection. The antenna comprises of two rectangular patches of different sizes for the required dual-band operations. The presented antenna is fed by corporate feed network which improves impedance bandwidth. The prime motto of this project is to make the smallest (compact) possible antenna so that it can be placed in the limited area of handheld devices. Simulated percentage impedance bandwidth of the antenna are 46.25 (1.958 GHz to 3.13 GHz) and 31.30 (4.15 GHz to 5.69 GHz) respectively. Good return loss and VSWR (less than 2) of the designed antenna is obtained by simulating on IE3D software.


title: Automatic Generation of Context-Based Fill-in-the-Blank Exercises Using Co-occurrence Likelihoods and Google n-grams

abstract: In this paper, we propose a method of automatically generating multiple-choice fill-inthe-blank exercises from existing text passages that challenge a reader’s comprehension skills and contextual awareness. We use a unique application of word co-occurrence likelihoods and the Google n-grams corpus to select words with strong contextual links to their surrounding text, and to generate distractors that make sense only in an isolated narrow context and not in the full context of the passage. Results show that our method is successful at generating questions with distractors that are semantically consistent in a narrow context but inconsistent given the full text, with larger n-grams yielding significantly better results.


title: An Improved LSB Based Steganography Technique for RGB Color Images

abstract: This paper proposes an improved LSB(least Significant bit) based Steganography technique for images imparting better information security .It presents an embedding algorithm for hiding encrypted messages in nonadjacent and random pixel locations in edges and smooth areas of images. It first encrypts the secret message, and detects edges in the cover-image using improved edge detection filter. Message bits are then, embedded in the least significant byte of randomly selected edge area pixels and 1-3-4 LSBs of red, green, blue components respectively across randomly selected pixels across smooth area of image. It ensures that the eavesdroppers will not have any suspicion that message bits are hidden in the image and standard steganography detection methods can not estimate the length of the secret message correctly. The Proposed approach is better in PSNR value and Capacity as shown experimentally than existing techniques.


title: Channel dispersion and moderate deviations limits for memoryless channels

abstract: Recently, Altug and Wagner [1] posed a question regarding the optimal behavior of the probability of error when channel coding rate converges to the capacity sufficiently slowly. They gave a sufficient condition for the discrete memoryless channel (DMC) to satisfy a moderate deviation property (MDP) with the constant equal to the channel dispersion. Their sufficient condition excludes some practically interesting channels, such as the binary erasure channel and the Z-channel. We extend their result in two directions. First, we show that a DMC satisfies MDP if and only if its channel dispersion is nonzero. Second, we prove that the AWGN channel also satisfies MDP with a constant equal to the channel dispersion. While the methods used by Altug and Wagner are based on the method of types and other DMC-specific ideas, our proofs (in both achievability and converse parts) rely on the tools from our recent work [2] on finite-blocklength regime that are equally applicable to non-discrete channels and channels with memory.


title: Cloacal exstrophy--pull-through or permanent stoma? A review of 53 patients.

abstract: PURPOSE ::: Patients with cloacal exstrophy have complex anomalies of the genitourinary and gastrointestinal tract with a spectrum of colonic length. Often, colon is lost during the initial management by use of ileostomies and for urologic and genital reconstruction. It is a common belief that these patients require permanent stomas, which we hypothesized is inaccurate, and therefore reviewed our experience with exstrophy, focusing specifically on a patient's potential to undergo a colonic pull-through. ::: ::: ::: METHODS ::: All patients with exstrophy or exstrophy variant treated by the authors were retrospectively reviewed. Their ability to form solid stool was assessed via bowel management involving a constipating diet, antidiarrheals, bulking agents, and a daily enema through the stoma. Patients who underwent successful bowel management through the stoma were offered a pull-through. ::: ::: ::: RESULTS ::: Fifty-three patients were treated over a 26-year period, including typical cloacal exstrophy (27), or a covered variant (16), and complex anorectal malformations with short colon (10). Newborn operations (48 done at other institutions, 5 by us) involved ileostomy in 11 or end colostomy in 42. Eight patients with ileostomies suffered acidosis and failure to thrive and underwent "rescue" operations to incorporate all defunctionalized colon into the fecal stream. Four had colon used for their urologic reconstruction and 6 for their genital reconstruction, leaving them borderline or unable to form solid stool. Twenty-three are undergoing bowel management or being observed for growth of the colonic pouch to determine if they are pull-through candidates. Of the others, 90% (27/30) underwent colonic pull-through. Ten percent (3/30) had a permanent stoma. Of 20 available for follow-up after pull-through, 17 are clean with bowel management (85%), 2 (10%) have voluntary bowel movements with occasional soiling, and 1 is incontinent but noncompliant. ::: ::: ::: CONCLUSIONS ::: Indication for pull-through depends on successful bowel management through the stoma, which depends on the ability to form solid stool. To maximize this potential, it is crucial to use all available hindgut for the initial colostomy and avoid use of colon for urologic or genital reconstruction. Most patients have poor prognosis for bowel control but can remain clean with bowel management. Our experience indicates that a permanent stoma is not required for the most of these patients and that bowel management can keep them clean, which we believe provides them with a better quality of life. Using these criteria, most exstrophy patients, contrary to popular belief, are candidates for a pull-through.


title: Pedestrian detection from a moving vehicle

abstract: This paper presents a prototype system for pedestrian detection on-board a moving vehicle. The system uses a generic two-step approach for efficient object detection. In the first step, contour features are used in a hierarchical template matching approach to efficiently "lock" onto candidate solutions. Shape matching is based on Distance Transforms. By capturing the objects shape variability by means of a template hierarchy and using a combined coarse-to-fine approach in shape and parameter space, this method achieves very large speed-ups compared to a brute-force method. We have measured gains of several orders of magnitude. The second step utilizes the richer set of intensity features in a pattern classification approach to verify the candidate solutions (i.e. using Radial Basis Functions). We present experimental results on pedestrian detection off-line and on-board our Urban Traffic Assistant vehicle and discuss the challenges that lie ahead.


title: Approximate TF-IDF based on topic extraction from massive message stream using the GPU

abstract: The Web is a constantly expanding global information space that includes disparate types of data and resources. Recent trends demonstrate the urgent need to manage the large amounts of data stream, especially in specific domains of application such as critical infrastructure systems, sensor networks, log file analysis, search engines and more recently, social networks. All of these applications involve large-scale data-intensive tasks, often subject to time constraints and space complexity. Algorithms, data management and data retrieval techniques must be able to process data stream, i.e., process data as it becomes available and provide an accurate response, based solely on the data stream that has already been provided. Data retrieval techniques often require traditional data storage and processing approach, i.e., all data must be available in the storage space in order to be processed. For instance, a widely used relevance measure is Term Frequency-Inverse Document Frequency (TF-IDF), which can evaluate how important a word is in a collection of documents and requires to a priori know the whole dataset.To address this problem, we propose an approximate version of the TF-IDF measure suitable to work on continuous data stream (such as the exchange of messages, tweets and sensor-based log files). The algorithm for the calculation of this measure makes two assumptions: a fast response is required, and memory is both limited and infinitely smaller than the size of the data stream. In addition, to face the great computational power required to process massive data stream, we present also a parallel implementation of the approximate TF-IDF calculation using Graphical Processing Units (GPUs).This implementation of the algorithm was tested on generated and real data stream and was able to capture the most frequent terms. Our results demonstrate that the approximate version of the TF-IDF measure performs at a level that is comparable to the solution of the precise TF-IDF measure.


title: Optimal design of an air-cooling system for a Li-Ion battery pack in Electric Vehicles with a genetic algorithm

abstract: This paper examines and optimizes parameters that affect the air cooling of a Lithium-Ion (Li-Ion) battery, used in Electric Vehicles (EVs). A battery pack containing 150 cylindrical type Li-Ion battery cells in a PVC casing is investigated. An equal number of tubes are used in the pack as a medium to cool the battery by using a fan when the vehicle is stationary or with ambient air when in motion. The parameters affecting the air cooling of battery are studied and optimized by considering their practical constraints. The objective function and Number of Transfer Unit (NTU) are developed. Finally, a genetic algorithm method is employed to optimize the decision variables. Analysing the results shows that NTU can be maximized by increasing the diameter of tubes on the battery and keeping the air velocity in a certain range.


title: Complications of the Neovagina in Male-to-Female Transgender Surgery: A Systematic Review and Meta-analysis with Discussion of Management

abstract: Gender-affirmation surgery is often the final gender-confirming medical intervention sought by those patients suffering from gender dysphoria. In the male-to-female (MtF) transgendered patient, the creation of esthetic and functional external female genitalia with a functional vaginal channel is of the utmost importance. The aim of this review and meta-analysis is to evaluate the epidemiology, presentation, management, and outcomes of neovaginal complications in the MtF transgender reassignment surgery patients. PUBMED was searched in accordance with PRISMA guidelines for relevant articles (n = 125). Ineligible articles were excluded and articles meeting all inclusion criteria went on to review and analysis (n = 13). Ultimately, studies reported on 1,684 patients with an overall complication rate of 32.5% and a reoperation rate of 21.7% for non-esthetic reasons. The most common complication was stenosis of the neo-meatus (14.4%). Wound infection was associated with an increased risk of all tissue-healing complications. Use of sacrospinous ligament fixation (SSL) was associated with a significantly decreased risk of prolapse of the neovagina. Gender-affirmation surgery is important in the treatment of gender dysphoric patients, but there is a high complication rate in the reported literature. Variability in technique and complication reporting standards makes it difficult to assess the accurately the current state of MtF gender reassignment surgery. Further research and implementation of standards is necessary to improve patient outcomes. Clin. Anat. 31:191-199, 2018. © 2017 Wiley Periodicals, Inc.


title: Lane detection based on spiking neural network and hough transform

abstract: In the field of the unmanned automobile and the automobile auxiliary driving system, the real-time and accurate detection of the lane is very important. Based on the previous research on the lane detection, the paper introduces the spiking neural network with the parallel mechanism to detect the lane. Firstly, the region of interests (ROI) is set on the origin image that collected by a vehicle on-board camera. In order to reduce processing time, areas outside the road are excluded in the ROI. Then the image preprocessing is applied to the ROI, including RGB to grayscale, gray stretch and median filtering to eliminate noise. Edge detection of the lane is the key to determine whether the Hough transform can detect the lane. In this paper, the spiking neural network is used to detect the edge of the lane. Finally, Hough transform is used to detect the lane. Experimental results show that this method is more accurate and robust than other methods.


title: GMCP-Tracker: Global Multi-object Tracking Using Generalized Minimum Clique Graphs

abstract: Data association is an essential component of any human tracking system. The majority of current methods, such as bipartite matching, incorporate a limited-temporal-locality of the sequence into the data association problem, which makes them inherently prone to IDswitches and difficulties caused by long-term occlusion, cluttered background, and crowded scenes.We propose an approach to data association which incorporates both motion and appearance in a global manner. Unlike limited-temporal-locality methods which incorporate a few frames into the data association problem, we incorporate the whole temporal span and solve the data association problem for one object at a time, while implicitly incorporating the rest of the objects. In order to achieve this, we utilize Generalized Minimum Clique Graphs to solve the optimization problem of our data association method. Our proposed method yields a better formulated approach to data association which is supported by our superior results. Experiments show the proposed method makes significant improvements in tracking in the diverse sequences of Town Center [1], TUD-crossing [2], TUD-Stadtmitte [2], PETS2009 [3], and a new sequence called Parking Lot compared to the state of the art methods.


title: Exploiting user profile information for answer ranking in cQA

abstract: Answer ranking is very important for cQA services due to the high variance in the quality of answers. Most existing works in this area focus on using various features or employing machine learning techniques to address this problem. Only a few of them noticed and involved user profile information in this particular task. In this work, we assume the close relationship between user profile information and the quality of their answers under the ground truth that user information records the user behaviors and histories as a summary. Thus, we exploited the effectiveness of three categories of user profile information, i.e. engagement-related, authority-related and level-related, on answer ranking in cQA. Different from previous work, we only employed the information which is easy to extract without any limitations, such as user privacy. Experimental results on Yahoo! Answers manner questions showed that our system by using the user profile information achieved comparable or even better results over the state-of-the-art baseline system. Moreover, we found that the picture existence of a user in cQA community contributed more than other information in the answer ranking task.


title: Hybrid Energy Storage System (HESS) in vehicular applications: A review on interfacing battery and ultra-capacitor units

abstract: One of the key components of every Electric Vehicle (EV)/Hybrid Electric Vehicle (HEV) is the Energy Storage System (ESS). The most widely-used ESS in electric drivetrains is based on batteries. As the specific power of batteries is normally low, they are hybridized with high-specific power storage elements such as ultra-capacitors in a Hybrid Energy Storage System (HESS) to meet harsh power requirements of the vehicle during acceleration and regenerative braking. This paper provides a thorough literature review on various configurations for interfacing battery and ultra-capacitor units to the DC bus forming a HESS in EV/HEV applications. It also reviews the energy management mechanisms used to split the power demand between battery and ultra-capacitor units.


title: Beyond Disagreement-based Agnostic Active Learning

abstract: We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithms for this problem are {\em{disagreement-based active learning}}, which has a high label requirement, and {\em{margin-based active learning}}, which only applies to fairly restricted settings. A major challenge is to find an algorithm which achieves better label complexity, is consistent in an agnostic setting, and applies to general classification problems. ::: In this paper, we provide such an algorithm. Our solution is based on two novel contributions -- a reduction from consistent active learning to confidence-rated prediction with guaranteed error, and a novel confidence-rated predictor.


title: Automated processing of shoeprint images based on the Fourier transform for use in forensic science

abstract: The development of a system for automatically sorting a database of shoeprint images based on the outsole pattern in response to a reference shoeprint image is presented. The database images are sorted so that those from the same pattern group as the reference shoeprint are likely to be at the start of the list. A database of 476 complete shoeprint images belonging to 140 pattern groups was established with each group containing two or more examples. A panel of human observers performed the grouping of the images into pattern categories. Tests of the system using the database showed that the first-ranked database image belongs to the same pattern category as the reference image 65 percent of the time and that a correct match appears within the first 5 percent of the sorted images 87 percent of the time. The system has translational and rotational invariance so that the spatial positioning of the reference shoeprint images does not have to correspond with the spatial positioning of the shoeprint images of the database. The performance of the system for matching partial-prints was also determined.


title: Origami: A 803 GOp/s/W Convolutional Network Accelerator

abstract: An ever increasing number of computer vision and image/video processing challenges are being approached using deep convolutional neural networks, obtaining state-of-the-art ::: results in object recognition and detection, semantic segmentation, action recognition, optical flow and superresolution. Hardware acceleration of these algorithms is essential to adopt ::: these improvements in embedded and mobile computer vision systems. We present a new architecture, design and implementation as well as the first reported silicon measurements of such an ::: accelerator, outperforming previous work in terms of power-, area- and I/Oefficiency. The manufactured device provides up to 196 GOp/s on 3.09 mm2 of silicon in UMC 65 nm technology and can ::: achieve a power efficiency of 803 GOp/s/W. The massively reduced bandwidth requirements make it the first architecture scalable to TOp/s performance.


title: A System for Smart Home Control of Appliances based on Timer and Speech Interaction

abstract: The main objective of this work is to design and construct a microcomputer based system: to control electric appliances such as light, fan, heater, washing machine, motor, TV, etc. The paper discusses two major approaches to control home appliances. The first involves controlling home appliances using timer option. The second approach is to control home appliances using voice command. Moreover, it is also possible to control appliances using Graphical User Interface. The parallel port is used to transfer data from computer to the particular device to be controlled. An interface box is designed to connect the high power loads to the parallel port. This system will play an important role for the elderly and physically disable people to control their home appliances in intuitive and flexible way. We have developed a system, which is able to control eight electric appliances properly in these three modes.


title: Current review of prepubertal vaginal bleeding

abstract: Purpose of reviewPrepubertal vaginal bleeding raises many concerns and evaluation and diagnosis may prove difficult for many providers. We aim to provide a comprehensive review and recent updates for those practitioners who care for these patients.Recent findingsPrompt management in the case of prep


title: Measuring user influence on Twitter: A survey

abstract: We survey the several measures that exists in literature to rank influential users in Twitter network.We propose a classification of these measures according to different criteria, such as the kind of metrics, the use of PageRank algorithm, the use of content analysis, among others.Besides the influential users, we also study measures of activity and popularity.We finish by mentioning some aspects of this topic related with computational complexity and correlation measures. Centrality is one of the most studied concepts in social network analysis. There is a huge literature regarding centrality measures, as ways to identify the most relevant users in a social network. The challenge is to find measures that can be computed efficiently, and that can be able to classify the users according to relevance criteria as close as possible to reality. We address this problem in the context of the Twitter network, an online social networking service with millions of users and an impressive flow of messages that are published and spread daily by interactions between users. Twitter has different types of users, but the greatest utility lies in finding the most influential ones. The purpose of this article is to collect and classify the different Twitter influence measures that exist so far in literature. These measures are very diverse. Some are based on simple metrics provided by the Twitter API, while others are based on complex mathematical models. Several measures are based on the PageRank algorithm, traditionally used to rank the websites on the Internet. Some others consider the timeline of publication, others the content of the messages, some are focused on specific topics, and others try to make predictions. We consider all these aspects, and some additional ones. Furthermore, we include measures of activity and popularity, the traditional mechanisms to correlate measures, and some important aspects of computational complexity for this particular context.


title: For Satellites, Think Small, Dream Big: A review of recent antenna developments for CubeSats.

abstract: Advances in modern technology have aided the development of a class of miniaturized satellites called SmallSats that typically weigh less than 500 kg. Key members of this family are CubeSats. CubeSats can weigh as little as 1.33 kg, with a typical volume of 10 ? 10 ? 10 cm3. Their potential has motivated the scientific community to revisit existing spacecraft technologies to make them suitable for CubeSats.


abstract: A Perceptual Colour Segmentation Algorithm Christopher G. Healey and James T. Enns This paper presents a simple method for segmenting colour regions into categories like red, green, blue, and yellow. We are interested in studying how colour categories influence colour selection during scientific visualization. The ability to name individual colours is also important in other problem domains like real-time displays, user-interface design, and medical imaging systems. Our algorithm uses the Munsell and CIE LUV colour models to automatically segment a colour space like RGB or CIE XYZ into ten colour categories. Users are then asked to name a small number of representative colours from each category. This provides three important results: a measure of the perceptual overlap between neighbouring categories, a measure of a category''s strength, and a user-chosen name for each strong category. We evaluated our technique by segmenting known colour regions from the RGB, HSV, and CIE LUV colour models. The names we obtained were accurate, and the boundaries between different colour categories were well defined. We concluded our investigation by conducting an experiment to obtain user-chosen names and perceptual overlap for ten colour categories along the circumference of a colour wheel in CIE LUV.


title: An autonomous robotic assistant for drinking

abstract: Stroke and neurodegenerative diseases, among a range of other neurologic disorders, can cause chronic paralysis. Patients suffering from paralysis may remain unable to achieve even basic everyday tasks such as liquid intake. Currently, there is a great interest in developing robotic assistants controlled via brain-machine interfaces (BMIs) to restore the ability to perform such tasks. This paper describes an autonomous robotic assistant for liquid intake. The components of the system include autonomous online detection both of the cup to be grasped and of the mouth of the user. It plans motions of the robot arm under the constraints that the cup stays upright while moving towards the mouth and that the cup stays in direct contact with the user's mouth while the robot tilts it during the drinking phase. To achieve this, our system also includes a technique for online estimation of the location of the user's mouth even under partial occlusions by the cup or robot arm. We tested our system in a real environment and in a shared-control setting using frequency-specific modulations recorded by electroencephalography (EEG) from the brain of the user. Our experiments demonstrate that our BMI-controlled robotic system enables a reliable liquid intake. We believe that our approach can easily be extended to other useful tasks including food intake and object manipulation.


title: A 60 GHz, 802.11ad/WiGig-Compliant Transceiver for Infrastructure and Mobile Applications in 130 nm SiGe BiCMOS

abstract: A fully integrated 802.11ad/WiGig compliant 60 GHz transceiver is presented in a 130 nm SiGe BiCMOS technology. Encompassing an area of 2.3 mm $\, \times \,$ 2.16 mm $=$ 4.97 mm $^{2}$ , the transceiver covers the entire 60 GHz band, from 57 to 66 GHz. Within this span, the RX NF, TX OP1dB, and PLL RMS jitter is better than 5.5 dB, ${+} $ 13.5 dBm, and 7 $^{\circ}$ , respectively. The transceiver is packaged in 1) a system-in-package substrate with industry standard WR-15 transition providing an approximate 1 dB insertion loss, and 2) a cost-effective 7 $\, \times \,$ 7 mm $^{{{2}}}$ organic BGA package with integrated transmit and receive antennas providing 8 dBi gain. In system-level testing, the transceiver is fully compliant with all TX EVM and RX sensitivity requirements of the WiGig standard up to the top-rate 16-QAM operating mode and across all standard channel frequencies. Link testing over the air with the antenna-integrated package shows a range of 5.9 m at 4.6 Gbps and over 20 m at 2.5 Gbps. This system achieves the highest performance 802.11ad/WiGig compliant wireless links of any reported single-element transceiver.


title: Detection of seam carving and localization of seam insertions in digital images

abstract: "Seam carving" is a recently introduced content aware image resizing algorithm. This method can also be used for image tampering. In this paper, we explore techniques to detect seam carving (or seam insertion) without knowledge of the original image. We employ a machine learning based framework to distinguish between seam-carved (or seam-inserted) and normal images. It is seen that the 324-dimensional Markov feature, consisting of 2D difference histograms in the block-based Discrete Cosine Transform domain, is well-suited for the classification task. The feature yields a detection accuracy of 80% and 85% for seam carving and seam insertion, respectively. For seam insertion, each new pixel that is introduced is a linear combination of its neighboring pixels. We detect seam insertions based on this linear relation, with a high detection accuracy of 94% even for very low seam insertion rates. We show that the Markov feature is also useful for scaling and rotation detection.


title: Three-dimensional plotting and printing of an implant drilling guide: simplifying guided implant surgery.

abstract: PURPOSE ::: To present an efficient workflow for the production of implant drilling guides using virtual planning tools. For this purpose, laser surface scanning, cone beam computed tomography, computer-aided design and manufacturing, and 3-dimensional (3D) printing were combined. ::: ::: ::: MATERIALS AND METHODS ::: Intraoral optical impressions (iTero, Align Technologies, Santa Clara, CA) and digital 3D radiographs (cone beam computed tomography) were performed at the first consultation of 1 exemplary patient. With image processing techniques, the intraoral surface data, acquired using an intraoral scanner, and radiologic 3D data were fused. The virtual implant planning process (using virtual library teeth) and the in-office production of the implant drilling guide was performed after only 1 clinical consultation of the patient. Implant surgery with a computer-aided design and manufacturing produced implant drilling guide was performed during the second consultation. The production of a scan prosthesis and multiple preoperative consultations of the patient were unnecessary. ::: ::: ::: CONCLUSIONS ::: The presented procedure offers another step in facilitating the production of drilling guides in dental implantology. Four main advantages are realized with this procedure. First, no additional scan prosthesis is needed. Second, data acquisition can be performed during the first consultation. Third, the virtual planning is directly transferred to the drilling guide without a loss of accuracy. Finally, the treatment cost and time required are reduced with this facilitated production process.


title: Large-scale image classification: Fast feature extraction and SVM training

abstract: Most research efforts on image classification so far have been focused on medium-scale datasets, which are often defined as datasets that can fit into the memory of a desktop (typically 4G∼48G). There are two main reasons for the limited effort on large-scale image classification. First, until the emergence of ImageNet dataset, there was almost no publicly available large-scale benchmark data for image classification. This is mostly because class labels are expensive to obtain. Second, large-scale classification is hard because it poses more challenges than its medium-scale counterparts. A key challenge is how to achieve efficiency in both feature extraction and classifier training without compromising performance. This paper is to show how we address this challenge using ImageNet dataset as an example. For feature extraction, we develop a Hadoop scheme that performs feature extraction in parallel using hundreds of mappers. This allows us to extract fairly sophisticated features (with dimensions being hundreds of thousands) on 1.2 million images within one day. For SVM training, we develop a parallel averaging stochastic gradient descent (ASGD) algorithm for training one-against-all 1000-class SVM classifiers. The ASGD algorithm is capable of dealing with terabytes of training data and converges very fast–typically 5 epochs are sufficient. As a result, we achieve state-of-the-art performance on the ImageNet 1000-class classification, i.e., 52.9% in classification accuracy and 71.8% in top 5 hit rate.


title: Hand Movements : A Window into Haptic Object Recognition

abstract: Abstract Two experiments establish links between desired knowledge about objects and hand movements during haptic object exploration. Experiment 1 used a match-to-sample task, in which blindfolded subjects were directed to match objects on a particular dimension (e.g., texture). Hand movements during object exploration were reliably classified as “exploratory procedures,” each procedure defined by its invariant and typical properties. The movement profile, i.e., the distribution of exploratory procedures, was directly related to the desired object knowledge that was required for the match. Experiment 2 addressed the reasons for the specific links between exploratory procedures and knowledge goals. Hand movements were constrained, and performance on various matching tasks was assessed. The procedures were considered in terms of their necessity, sufficiency, and optimality of performance for each task. The results establish that in free exploration, a procedure is generally used to acquire information about an object property, not because it is merely sufficient, but because it is optimal or even necessary. Hand movements can serve as “windows,” through which it is possible to learn about the underlying representation of objects in memory and the processes by which such representations are derived and utilized.


title: Implementation of a PV emulator using programmable DC power supply

abstract: Installation of a Photovoltaic (PV) system needs an accurate design and evaluation test for the performance of its modules before using them in a system. This can be fulfilled with high accuracy in the laboratory without using commercial PV modules by using a PV emulator. The PV emulator introduces a suitable tool which easily permits emulating PV system component with different parameters, teaching its characteristic and training researchers in this field. The main purpose of this paper is introducing the development of a Photovoltaics (PV) emulator has the capability of emulate commercial PV modules with different parameters and under different environmental condition (irradiance or temperature change), constructing a PV array based on the series and parallel numbers of modules. The PV emulator was implemented using a real-time algorithm representing the PV array mathematical model and generating the reference output power from a programmable DC power supply. The PV model is simulated in MATLAB and interfaced to the real world through dSPACE using an evolutionary graphic user interface (GUI). This laboratory PV emulator is tested with real-time execution of PV model for the steady state and dynamic operating conditions.


title: Diagram understanding in geometry questions

abstract: Automatically solving geometry questions is a long-standing AI problem. A geometry question typically includes a textual description accompanied by a diagram. The first step in solving geometry questions is diagram understanding, which consists of identifying visual elements in the diagram, their locations, their geometric properties, and aligning them to corresponding textual descriptions. In this paper, we present a method for diagram understanding that identifies visual elements in a diagram while maximizing agreement between textual and visual data. We show that the method's objective function is submodular; thus we are able to introduce an efficient method for diagram understanding that is close to optimal. To empirically evaluate our method, we compile a new dataset of geometry questions (textual descriptions and diagrams) and compare with baselines that utilize standard vision techniques. Our experimental evaluation shows an F1 boost of more than 17% in identifying visual elements and 25% in aligning visual elements with their textual descriptions.


title: A l1-Unified Variational Framework for Image Restoration

abstract: Among image restoration literature, there are mainly two kinds of approach. One is based on a process over image wavelet coefficients, as wavelet shrinkage for denoising. The other one is based on a process over image gradient. In order to get an edge-preserving regularization, one usually assume that the image belongs to the space of functions of Bounded Variation (BV). An energy is minimized, composed of an observation term and the Total Variation (TV) of the image.


title: Compressive-Projection Principal Component Analysis

abstract: Principal component analysis (PCA) is often central to dimensionality reduction and compression in many applications, yet its data-dependent nature as a transform computed via expensive eigendecomposition often hinders its use in severely resource-constrained settings such as satellite-borne sensors. A process is presented that effectively shifts the computational burden of PCA from the resource-constrained encoder to a presumably more capable base-station decoder. The proposed approach, compressive-projection PCA (CPPCA), is driven by projections at the sensor onto lower-dimensional subspaces chosen at random, while the CPPCA decoder, given only these random projections, recovers not only the coefficients associated with the PCA transform, but also an approximation to the PCA transform basis itself. An analysis is presented that extends existing Rayleigh-Ritz theory to the special case of highly eccentric distributions; this analysis in turn motivates a reconstruction process at the CPPCA decoder that consists of a novel eigenvector reconstruction based on a convex-set optimization driven by Ritz vectors within the projected subspaces. As such, CPPCA constitutes a fundamental departure from traditional PCA in that it permits its excellent dimensionality-reduction and compression performance to be realized in an light-encoder/heavy-decoder system architecture. In experimental results, CPPCA outperforms a multiple-vector variant of compressed sensing for the reconstruction of hyperspectral data.


title: Choice of Mel filter bank in computing MFCC of a resampled speech

abstract: Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in many speech and speaker recognition applications. In this paper, we study the effect of resampling a speech signal on these speech features. We first derive a relationship between the MFCC parameters of the resampled speech and the MFCC parameters of the original speech. We propose six methods of calculating the MFCC parameters of downsampled speech by transforming the Mel filter bank used to compute MFCC of the original speech. We then experimentally compute the MFCC parameters of the down sampled speech using the proposed methods and compute the Pearson coefficient between the MFCC parameters of the downsampled speech and that of the original speech to identify the most effective choice of Mel-filter band that enables the computed MFCC of the resampled speech to be as close as possible to the MFCC of the original speech.


title: Motivation Parameters for Engineering Managers Using Maslow's Theory

abstract: This paper emphasizes total self-development of the individual for improved motivation and organization management. It builds on Maslow's hierarchy of needs theory to examine motivational levels for four levels of engineering staff at a public construction agency. The researchers studied these engineering groups qualitatively through interviews and quantitatively using a questionnaire. Using a holistic approach, this study focused on 15 parameters from Maslow's five essential needs—the physical, safety, social, esteem, and self-actualization levels. Considerable emphasis was placed on the development of Maslow's principle of “self-actualization.” This difficult-to-grasp concept, as Maslow reported, is a prerequisite for enlightened management. The researchers analyzed engineers' perceptions regarding the fulfillment of need parameters and measured their perception of the importance of those parameters. Among specific findings were that junior project engineers had higher scores on self-actualization than ...


Deep Gaussian Process Regression ( DGPR )


title: A Hybrid Motion Estimation for Video Stabilization Based on an IMU Sensor

abstract: Recorded video data must be clear for accuracy and faster analysis during post-processing, which often requires video stabilization systems to remove undesired motion. In this paper, we proposed a hybrid method to estimate the motion and to stabilize videos by the switching function. This method switched the estimated motion between a Kanade⁻Lucus⁻Tomasi (KLT) tracker and an IMU-aided motion estimator. It facilitated the best function to stabilize the video in real-time as those methods had numerous advantages in estimating the motion. To achieve this, we used a KLT tracker to correct the motion for low rotations and an IMU-aided motion estimator for high rotation, owing to the poor performance of the KLT tracker during larger movements. Furthermore, a Kalman filter was used to remove the undesired motion and hence smoothen the trajectory. To increase the frame rate, a multi-threaded approach was applied to execute the algorithm in the array. Irrespective of the situations exposed to the experimental results of the moving camera from five video sequences revealed that the proposed algorithm stabilized the video efficiently.


title: Probabilistic analysis of dynamic and temporal fault trees using accurate stochastic logic gates

abstract: Abstract This paper presents accurate models for the analysis of fault trees based on stochastic logic. To produce the models, probabilistic analysis of static, dynamic and temporal gates is carried out and the probability models are converted to their equivalent stochastic logic gates. A hardware template is also designed for each stochastic logic gate. In the proposed method, users provide fault rates of basic events and immediately evaluate system reliability. Experimental results show that the proposed method is more accurate than previous methods using the proposed stochastic logic gates for dynamic and temporal fault trees. The formula was validated using the Markov model for exponential failure distribution events. The proposed model is applicable for both exponential and non-exponential distributions.


title: Two-axis bend sensor design, kinematics and control for a continuum robotic endoscope

abstract: Angular displacement sensing and contact force sensing in robotic surgery is important for finding the conformation of the tool and for closed loop control. Current techniques for measuring large deflections, such as electromagnetic tracking, involve bulky external reference equipment. This paper discusses the design, dynamic modeling, and kinematic simulation of a compact two-axis bend sensor constructed from layers of carbon black filled polyurethane for determining the bending angle of a cable-driven continuum robotic endoscope. The sensor is flexible ( 10% strain), compact, and does not require external reference equipment. The sensor is able to bend in 180 degrees in all directions, produces two-axis outputs that are linear up to 120 degrees of bending and has a response time less than 9 ms. This paper explores the use of the sensor for controlling the tip position of a robotic endoscope and detecting external forces. The design outlined in this paper has many possible applications in robotic endoscopes, surgical tools, snake-like robots and soft material robotics.