title: Dynamic simulation of splashing fluids

abstract: We describe a method for modeling the dynamic behavior of splashing fluids. The model simulates the behavior of a fluid when objects impact or float on its surface. The forces generated by the objects create waves and splashes on the surface of the fluid. To demonstrate the realism and limitations of the model, images from a computer-generated animation are presented and compared with video frames of actual splashes occurring under similar initial conditions. >


title: Characteristics of hand motion of eye surgeons

abstract: Overall manual accuracy and motion frequency in simulated microsurgery have been studied. Eye surgeons were tested in two tasks: attempting to hold an instrument still, and repeatedly actuating. The RMS error and overall motion range were measured. Spectral analysis was also performed. The average RMS errors for the two test conditions were 49 /spl mu/m and 133 /spl mu/m, respectively, and the average range of motion while trying to hold still was 202 /spl mu/m. Substantial low-frequency motion occurred under both conditions. On average, 98.9% of the total power of voluntary movements tested was found to be below 2 Hz.


title: AGEING NEUROREPORT Meditation experience is associated with increased cortical thickness

abstract: Previous research indicates that long-term meditation practice is associated with altered resting electroencephalogram patterns, suggestive of long lasting changes in brain activity. We hypothesized that meditation practice might also be associated with changes in the brain's physical structure. Magnetic resonance imaging was used to assess cortical thickness in 20 participants with extensive Insight meditation experience, which involves focused attention to internal experiences. Brain regions associated with attention, interoception and sensory processing were thicker in meditation participants than matched controls, including the prefrontal cortex and right anterior insula. Between-group differences in prefrontal cortical thickness were most pronounced in older participants, suggesting that meditation might offset age-related cortical thinning. Finally, the thickness of two regions correlated with meditation experience. These data provide the first structural evidence for experience-dependent cortical plasticity associated with meditation practice.


title: Mobility-Aware Application Scheduling in Fog Computing

abstract: Fog computing provides a distributed infrastructure at the edges of the network, resulting in low-latency access and faster response to application requests when compared to centralized clouds. With this new level of computing capacity introduced between users and the data center-based clouds, new forms of resource allocation and management can be developed to take advantage of the Fog infrastructure. A wide range of applications with different requirements run on end-user devices, and with the popularity of cloud computing many of them rely on remote processing or storage. As clouds are primarily delivered through centralized data centers, such remote processing/storage usually takes place at a single location that hosts user applications and data. The distributed capacity provided by Fog computing allows execution and storage to be performed at different locations. The combination of distributed capacity, the range and types of user applications, and the mobility of smart devices require resource management and scheduling strategies that takes into account these factors altogether. We analyze the scheduling problem in Fog computing, focusing on how user mobility can influence application performance and how three different scheduling policies, namely concurrent, FCFS, and delay-priority, can be used to improve execution based on application characteristics.


title: Software defined radar studies of human motion signatures

abstract: The detection and monitoring of human motion with radar has numerous applications in surveillance, urban military operations, search-and-rescue, and other areas. Recent studies have shown that movements of humans generate unique micro-Doppler signatures that can be exploited to classify human motions. This motivates an improved understanding of human Doppler signatures. Numerous simulations and measurements of human “dismount” signatures has been performed in the past, but most have been focused on a single radar center-frequency and have not taken polarization effects into consideration. In this paper, human modeling and motion measurements using multiple radar frequencies are proposed to explore the impact of the radar frequency on human range/Doppler signatures. Furthermore, ground effects on human targets are investigated using a four path model. The OSU Software defined radar (SDR) system, which can be tuned from 2GHz to 18 GHz with 500MHz bandwidth, was used for the measurements. This radar can operate at two frequencies simultaneously, allowing for dual frequency human measurements. Also, different polarizations are considered to understand human Doppler signatures. Modeling efforts are based on a finite dielectric cylinder approximation, so that the human body is modeled as a collection of dielectric cylinders. Scattering signatures are computed neglecting scattering interactions among these cylinders.


title: Determining the Effects of LMS Learning Behaviors on Academic Achievement in a Learning Analytic Perspective

abstract: Two of the most important outcomes of learning analytics are predicting students’ learning and providing effective feedback. Learning Management Systems (LMS), which are widely used to support online and face-to-face learning, provide extensive research opportunities with detailed records of background data regarding users’ behaviors. The purpose of this study was to investigate the effects of undergraduate students’ LMS learning behaviors on their academic achievements. In line with this purpose, the participating students’ online learning behaviors in LMS were examined by using learning analytics for 14 weeks, and the relationship between students’ behaviors and their academic achievements was analyzed, followed by an analysis of their views about the influence of LMS on their academic achievement. The present study, in which quantitative and qualitative data were collected, was carried out with the explanatory mixed method. A total of 71 undergraduate students participated in the study. The results revealed that the students used LMSs as a support to face-to-face education more intensively on course days (at the beginning of the related lessons and at nights on course days) and that they activated the content elements the most. Lastly, almost all the students agreed that LMSs helped increase their academic achievement only when LMSs included such features as effectiveness, interaction, reinforcement, attractive design, social media support, and accessibility.


title: Error recovery in a micro-electrode-dot-array digital microfluidic biochip?

abstract: A digital microfluidic biochip (DMFB) is an attractive technology platform for automating laboratory procedures in biochemistry. However, today's DMFBs suffer from several limitations: (i) constraints on droplet size and the inability to vary droplet volume in a fine-grained manner; (ii) the lack of integrated sensors for real-time detection; (iii) the need for special fabrication processes and the associated reliability/yield concerns. To overcome the above problems, DMFBs based on a micro-electrode-dot-array (MEDA) architecture have been proposed recently, and droplet manipulation on these devices has been experimentally demonstrated. Errors are likely to occur due to defects, chip degradation, and the lack of precision inherent in biochemical experiments. Therefore, an efficient error-recovery strategy is essential to ensure the correctness of assays executed on MEDA biochips. By exploiting MEDA-specific advances in droplet sensing, we present a novel error-recovery technique to dynamically reconfigure the biochip using real-time data provided by on-chip sensors. Local recovery strategies based on probabilistic-timed-automata are presented for various types of errors. A control flow is also proposed to connect local recovery procedures with global error recovery for the complete bioassay. Laboratory experiments using a fabricated MEDA chip are used to characterize the outcomes of key droplet operations. The PRISM model checker and three analytical chemistry benchmarks are used for an extensive set of simulations. Our results highlight the effectiveness of the proposed error-recovery strategy.


title: Design of an X-band low phase noise oscillator using a complementary spiral-resonator

abstract: In this paper, a proposed high-Q resonator is presented and it is applied to the design of an X-band oscillator. The measurement of the resonator itself shows that it provides a higher loaded quality factor compared to the hair-pin and complementary spiral-resonator. The oscillator is designed to operate at 10 GHz using the complementary spiral resonator to compare the phase noise performance. The measurement results show the output power of 9.80 dBm and phase noise of −123.82 dBc/Hz at 1 MHz offset, respectively.


title: The psychology of word use in depression forums in English and in Spanish: Testing two text analytic approaches

abstract: The present studies demonstrate two computerized approaches to examining the expression of depression on the Internet. Study 1 observed linguistic markers of depression in English and Spanish forums. English and Spanish posts by depressed (N=160) and non-depressed individuals (N=160) were collected from Internet forums using bulletin board systems (bbs). A computer program (LIWC2001) was used to compare linguistic categories across groups within the English and Spanish language. Study 2 analyzed the themes people use when talking about their depression in forums in English and Spanish. Posts by English speaking (N=404) and Spanish speaking depressed people (N=400) were collected from bbs. An automated inductive approach to content analysis was used to compare how the expression of depression varies across cultures. The results for Study 1 showed that linguistic cues previously associated with depression were found to be higher in depressed than in nondepressed posts in English and Spanish. Study 2 showed that depressed people who wrote in Spanish were more likely to mention relational concerns than depressed people who wrote in English, who were more likely to mention medicinal concerns. Implications for informational and support needs and concerns in psychotherapy vs. online support across cultures are addressed and discussed.


title: Introducing Linggle: From Concordance to Linguistic Search Engine

abstract: We introduce a Web-scale linguistics search engine, Linggle, that retrieves lexical bundles in response to a given query. Unlike a typical concordance, Linggle accepts queries with keywords, wildcard, wild part of speech (PoS), synonymous words, and additional regular expression (RE) operators, and returns bundles with frequency counts. In our approach, we argument Google Web 1T corpus with inverted file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation. The method involves parsing the query to transforming the query to several keyword retrieval commands, retrieving word chunks with counts, filtering the chunks again the query as a RE, and finally displaying the results according the count, similarity, and topic. Clusters of synonymous or conceptually related words are also provided. In addition, Linggle provide example sentences from The New York Times on demand. The current implementation of Linggle is the most comprehensive functionally, and is in principle language and dataset independent. We plan to extend Linggle to provide a fast and convenient access to a wealth of linguistic information embodied in Web scale datasets including Google Web 1T and Google Books Ngram for many major languages in the World. For non-native speakers, doubts concerning the usage of a preposition, the mandatory presence of a determiner, the correctness of the association of a verb with an object or the need for synonyms of a term in a given context are problems that arise frequently when writing in English. Printed collocation dictionaries and reference tools based on compiled corpora offer limited coverage of word usage while knowledge of collocations is vital for the competent use of a language. We propose to address these limitations with a comprehensive system that truly aims at letting learners “know a word by the company it keeps”. Linggle (linggle.com) is a broad coverage language reference tool for English as Second Language learners (ESL). The system is designed to access words in context under various forms. First, we build inverted file index for the Google Web 1T Ngram to support queries with RE-like patterns including PoS and synonym matches. For example, for the query “$V $D +important role”, Linggle retrieve 4-gram chunks that start with a PACLIC-27


title: PV cell modeling on single-diode equivalent circuit

abstract: PV cell model is necessary both for software and hardware simulators in analyzing and testing the performance of PV generation systems. To get the characteristic non-linear I-V Curve of a PV cell, 5 characteristic parameters Rs, Rsh, I0 and vt should be extracted from the three remarkable operation points of the manufacture's datasheets, Isc, Voc, IMPP and VMPP. This paper analyzes conventional three representative PV modeling algorithms by comparison, and proposes a novel PV modeling algorithm which is fast, accurate and applicable to all kinds of PV cells such as Cr-Si type and thin-film type PV cells. Proposed theory is verified by simulations for various PV cell types such as Cr-Si type and thin-film type PV cells.


title: Predicting the Temperature and Flow Distribution in a Direct Oil-Cooled Electrical Machine With Segmented Stator

abstract: This paper presents a computationally efficient thermo-fluid model to predict the temperature and flow distribution in an oil-cooled electrical machine with a segmented stator. The Yokeless and Segmented Armature axial flux machine was used as a case study in which a numerical model was set up and validated to within 6% of experimental results. The model was adapted to predict the temperature distribution of the segmented stator of a machine, identifying the hotspot temperatures and their location. Changes to the flow geometry on the stator temperature distribution were investigated. It was shown how by carefully controlling the flow distribution in the stator, the temperature distribution is improved and the hot spot temperature is reduced by 13 K. This benefits the machine by doubling the insulation lifetime or by increasing the current density by approximately 7%.


title: An Improved Method for Semantic Image Inpainting with GANs: Progressive Inpainting

abstract: Semantic image inpainting is getting more and more attention due to its increasing usage. Existing methods make inference based on either local data or external information. Generating Adversarial Networks, as a research focus in recent years, has been proven to be useful in inpainting work. One of the most representative is the deep-generative-model-based approach, which use undamaged images for training and repair the corrupted image with the trained networks. However, this method is too dependent on the training process, easily resulting in the completed image blurry in details. In this paper, we propose an improved method named progressive inpainting. With the trained networks, we use back-propagation to find the most appropriate input distribution and use the generator to repair the corrupted image. Instead of repairing the image in one step, we take a pyramid strategy from a low-resolution image to higher one, with the purpose of getting a clear completed image and reducing the reliance on the training process. The advantage of progressive inpainting is that we can predict the general distribution of the corrupted image and then gradually refine the details. Experiment results on two datasets show that our method successfully reconstructs the image and outperforms most existing methods.


title: Geometric Approach in Solving Inverse Kinematics of PUMA Robots

abstract: A geometric approach for deriving a consistent joint solution of a six-point PUMA1 robot is presented. The approach calls for the definition of various possible arm configurations based on the link coordinate systems and human arm geometry. These arm configurations are then expressed in an exact mathematical way to allow the construction of arm configuration indicators and their corresponding decision equations. The arm configuration indicators are prespecified by a user for finding the joint solution. These indicators enable one to find a solution from the possible four solutions for the first three joints, a solution from the possible two solutions for the last three joints. The solution is calculated in two stages. First a position vector pointing from the shoulder to the wrist is derived. This is used to derive the solution of the first three joints by looking at the projection of the position vector onto the xi-1-yi-1(i = 1,2,3) plane. The last three joints are solved using the calculated joint solution from the first three joints, the orientation matrices, and the projection of the link coordinate frames onto the xi-1-yi-1 (i = 4,5,6) plane. From the geometry, one can easily find the arm solution consistently. A computer simulation study conducted on a VAX-11/780 computer demonstrated the validity of the arm solution.


title: 3-D eye movement analysis.

abstract: This paper presents a novel three-dimensional (3-D) eye movement analysis algorithm for binocular eye tracking within virtualreality (VR). The user’s gaze direction, head position, and orientation are tracked in order to allow recording of the user’s fixations within the environment. Although the linear signal analysis approach is itself not new, its application to eye movement analysis in three dimensions advances traditional two-dimensional approaches, since it takes into account the six degrees of freedom of head movements and is resolution independent. Results indicate that the 3-D eye movement analysis algorithm can successfully be used for analysis of visual process measures in VR. Process measures not only can corroborate performance measures, but also can lead to discoveries of the reasons for performance improvements. In particular, analysis of users’ eye movements in VR can potentially lead to further insights into the underlying cognitive processes of VR subjects.


title: A review and taxonomy of distortion-oriented presentation techniques

abstract: One of the common problems associated with large computer-based information systems is the relatively small window through which an information space can be viewed. Increasing interest in recent years has been focused on the development of distortion-oriented presentation techniques to address this problem. However, the growing number of new terminologies and techniques developed have caused considerable confusion to the graphical user interface designer, consequently making the comparison of these presentation techniques and generalization of empirical results of experiments with them very difficult, if not impossible. This article provides a taxonomy of distortion-oriented techniques which demonstrates clearly their underlying relationships. A unified theory is presented to reveal their roots and origins. Issues relating to the implementation and performance of these techniques are also discussed.


title: [Clothing and heat disorder].

abstract: The influence of the clothing material properties(like water absorbency and rapid dryness, water vapor absorption, water vapor permeability and air permeability) and the design factor of the clothing(like opening condition and fitting of clothing), which contributed to prevent heat disorder, was outlined. WBGT(wet-bulb globe temperature) is used to show a guideline for environmental limitation of activities to prevent heat disorder. As the safety function is more important than thermal comfort for some sportswear and protective clothing with high cover area, clothing itself increases the risk of heat disorder. WBGT is corrected by CAF (clothing adjustment factor) in wearing such kind of protective clothing.


title: Automatic Annotation of Structured Facts in Images

abstract: Motivated by the application of fact-level image understanding, we present an automatic method for data collection of structured visual facts from images with captions. Example structured facts include attributed objects (e.g., ), actions (e.g., ), interactions (e.g., ), and positional information (e.g., ). The collected annotations are in the form of fact-image pairs (e.g., and an image region containing this fact). With a language approach, the proposed method is able to collect hundreds of thousands of visual fact annotations with accuracy of 83% according to human judgment. Our method automatically collected more than 380,000 visual fact annotations and more than 110,000 unique visual facts from images with captions and localized them in images in less than one day of processing time on standard CPU platforms.


title: The Effects of Online Brand Communities on Brand Equity in the Luxury Fashion Industry

abstract: Quoting the fashion genius Coco Chanel: “Fashion is not something that exists in dresses only. Fashion is in the sky, in the street, fashion has to do with ideas, the way we live, what is happening.” This is even more true today than it was back then. Indeed, Web 2.0 technologies have made luxury fashion more accessible to everyone. Nowadays, consumers can instantly access a wide variety of fashion goods on the Internet and share brand experiences with other consumers. In this scenario, Online Brand Communities (OBCs) allow fashion customers to communicate with each other without any restrictions of time and place. For this reason, OBCs are viewed by fashion marketers as powerful instruments to influence customers' purchasing behaviour. The aim of the current study is to analyse the effects of OBC dynamics on brand equity. The proposed research model is applied to eight spontaneous OBCs of the luxury fashion industry.


title: Stackelberg game for utility-based cooperative cognitiveradio networks

abstract: With the development of cognitive radio technologies, dynamic spectrum access becomes a promising approach to increase the efficiency of spectrum utilization and solve spectrum scarcity problem. Under dynamic spectrum access, unlicensed wireless users (secondary users) can dynamically access the licensed bands from legacy spectrum holders (primary users) on an opportunistic basis. While most primary users in existing works assume secondary transmissions as negative interference and don't actively involve them into the primary transmission, in this paper, motivated by the idea of cooperative communication, we propose a cooperative cognitive radio framework, where primary users, aware of the existence of secondary users, may select some of them to be the cooperative relay, and in return lease portion of the channel access time to them for their own data transmission. Secondary users cooperating with primary transmissions have the right to decide their payment made for primary user in order to achieve a proportional access time to the wireless media. Both primary and secondary users target at maximizing their utilities in terms of their transmission rate and revenue/payment. This model is formulated as a Stackelberg game and a unique Nash Equilibrium point is achieved in analytical format. Based on the analysis we discuss the condition under which cooperation will increase the performance of the whole system. Both analytical result and numerical result show that the cooperative cognitive radio framework is a promising framework under which the utility of both primary and secondary system are maximized.


title: TextDraw: a prototype for gestural typesetting

abstract: This paper considers a new approach that allows for the creation of typographic works through gestural interaction. It describes a prototype application called TextDraw, which is designed to introduce new techniques for typesetting opened up by gesture and tangible manipulation. Previous work explored the implications of tangible-surface interaction in the context of Surface Games; specifically, how the combination of multi-touch and tangible objects can extend the paradigm of board games in new directions. TextDraw also looks toward tangible-surface interaction and the idea that associating functions to gestures combined with new tangible tools will result in radically new methodologies for design, artistic and poetic expression of typographic works. Furthermore, while artworks created with TextDraw are presented, this paper focuses on the introduction / implication of our prototype.


title: Negative Sampling Improves Hypernymy Extraction Based on Projection Learning

abstract: We present a new approach to extraction of hypernyms based on projection learning and word embeddings. In contrast to classification-based approaches, projection-based methods require no candidate hyponym-hypernym pairs. While it is natural to use both positive and negative training examples in supervised relation extraction, the impact of positive examples on hypernym prediction was not studied so far. In this paper, we show that explicit negative examples used for regularization of the model significantly improve performance compared to the state-of-the-art approach of Fu et al. (2014) on three datasets from different languages.


title: Suboptimal variants of the conflict-based search algorithm for the multi-agent pathfinding problem

abstract: The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Solving MAPF optimally is proven to be NP-hard, hence CBS and all other optimal solvers do not scale up. We propose several ways to relax the optimality conditions of CBS trading solution quality for runtime as well as bounded-suboptimal variants, where the returned solution is guaranteed to be within a constant factor from optimal solution cost. Experimental results show the benefits of our new approach; a massive reduction in running time is presented while sacrificing a minor loss in solution quality. Our new algorithms outperform other existing algorithms in most of the cases.


title: 28 GHz wideband filter using quartz crystal waveguide for massive MIMO antenna unit

abstract: Massive MIMO technology and mm-wave band radio communication are effective solution of a high data rate for 5G. This paper presents a novel 28GHz wideband filter for those antenna units. To realize a wide fractional bandwidth of 10%, a new terminal configuration of “Y-shaped” structure is proposed. A TEM-TE mode transition function using the new structure was analyzed, and the shape was optimized. Applying the new terminal structure to an 8-pole with 2 trap quartz waveguide filter, high performances such as very low loss of 1.2 dB, wide relative passband of 10.4% and high attenuation of more than 50dB were obtained with a small size and SMD structure.


title: An efficient method for segmentation of images based on fractional calculus and natural selection

abstract: Image segmentation has been widely used in document image analysis for extraction of printed characters, map processing in order to find lines, legends, and characters, topological features extraction for extraction of geographical information, and quality inspection of materials where defective parts must be delineated among many other applications. In image analysis, the efficient segmentation of images into meaningful objects is important for classification and object recognition. This paper presents two novel methods for segmentation of images based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) and Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image. The efficiency of the proposed methods is compared with other well-known thresholding segmentation methods. Experimental results show that the proposed methods perform better than other methods when considering a number of different measures.


title: Electric power steering system matching and hardware-in-the-loop simulation of economical cars

abstract: On the basis of the market demand, our company proposed to upgrade the power steering system of some cars, from hydraulic power steering to electric power steering (EPS). First of all, the system match of key parts was discussed. For determine of assist characteristics curve, the handling characteristics of the car were tested, and then the speed coefficient was fitted out according to the results. EPS system simulation model was established based on MATLAB/SIMULINK, the aligning and damping control strategies were formulated and the parameters were optimized. EPS controller was designed and the hardware-in-the-loop simulation test-bed based on DSPACE was built. Some tests were implemented on the bed and the results showed that the control strategies were effective and the controller can meet the requirements.


title: Advantages of the HEXACO Model of Personality Structure

abstract: The authors argue that a new six-dimensional framework for personality structure—the HEXACO model— constitutes a viable alternative to the well-known Big Five or five-factor model. The new model is consistent with the cross-culturally replicated finding of a common six-dimensional structure containing the factors Honesty-Humility (H), Emotionality (E), eExtraversion (X), Agreeableness (A), Conscientiousness (C), and Openness to Experience (O). Also, the HEXACO model predicts several personality phenomena that are not explained within the B5/FFM, including the relations of personality factors with theoretical biologists' constructs of reciprocal and kin altruism and the patterns of sex differences in personality traits. In addition, the HEXACO model accommodates several personality variables that are poorly assimilated within the B5/FFM.


title: Estimating Continuous Distributions in Bayesian Classifiers

abstract: When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.


title: The evaluation technique of logistics’ system cargo transportation efficiency development

abstract: Summary.The paper presents model to optimize the number of different types of transport used in different stations of transport-storehouse’s complex and also to determine the optimal traffic volume, storage and handling of materials, which provide minimum costs on them at which the efficiency of transport and storehouse complex is estimated


title: GUNREAL: GPU-accelerated UNsupervised REinforcement and Auxiliary Learning

abstract: Recent state-of-the-art Deep Reinforcement Learning algorithms, such as A3C and UNREAL, are designed to train on a single device with only CPU's. Using GPU acceleration for these algorithms results in low GPU utilization, which means the full performance of the GPU is not reached. Motivated by the architecture changes made by the GA3C algorithm, which gave A3C better GPU acceleration, together with the high learning efficiency of the UNREAL algorithm, this paper extends GA3C with the auxiliary tasks from UNREAL to create a Deep Reinforcement Learning algorithm, GUNREAL, with higher learning efficiency and also benefiting from GPU acceleration. We show that our GUNREAL system reaches higher scores on several games in the same amount of time than GA3C.


title: Optimalization Stock Portfolio Based on Single Index Model over Bullish and Bearish Market: Case Study on LQ45 Manufacturing Company

abstract: This study aimed to determine the optimalization stock portfolio as a basis of investment inside company stocks in LQ45 on Indonesia Stock Exchange. In this research, we use data observation from January 2013 to December 2017 with quantitative method to obtain population over 11 manufacturing companies based single index model. The results showed 5 stocks became to portfolio candidates from the 11 candidate stocks studied which is 5 optimal portfolio shares had a return of 0.019 or equal 1.98% per month. Here, the 5 optimal portfolio have a risk and must be faced by investors shares around 0.007 or equal to 0.07%. The 5 optimal portfolio namely UNVR, ICBP, GGRM, KLBF, and INDF to share over investors. The fund proportion can be invested by 5 shares such as UNVR, ICBP, GGRM, KLBF, and INDF are equal 56.06%, 16.14%, 7.86%, 16.42% and 3.52%, respectively. The Return on bullish conditions shows that the portfolio returns is calculated from selected five stocks to obtain optimal portfolio formation reached 0.021. Furthermore, the return on bearish conditions have risk value more than 0.008. Thus, the optimal portfolio in bullish conditions can reduced investment risk more than 0.011 poins. Thus, the optimal portfolio can be reduced risk value over low level in bearish conditions in LQ45 manufacturing company in near future


title: An Architecture for the Integration of Robots and Sensors for the Care of the Elderly in an Ambient Assisted Living Environment

abstract: In this paper, the development of an assistance system for the elderly that combines robots with a network of sensors and actuators is described. The architecture was developed with the aim of interaction. With this reason, the system incorporates particular solutions that are adaptable to the needs of the user, such as a pyramid interaction system, a telepresence robot, a biometric bracelet, and others. In the software, the system is composed of two servers: local and web. The local server is in charge of different modules that interact with direct users and their needs. The web server provides different facilities to external users to access the system remotely. A use case is analyzed where the proposed system is validated.


title: Supply networks and complex adaptive systems: control versus emergence

abstract: In much of the current literature on supply chain management, supply networks are recognized as a system. In this paper, we take this observation to the next level by arguing the need to recognize supply networks as a complex adaptive system (CAS). We propose that many supply networks emerge rather than result from purposeful design by a singular entity. Most supply chain management literature emphasizes negative feedback for purposes of control; however, the emergent patterns in a supply network can much better be managed through positive feedback, which allows for autonomous action. Imposing too much control detracts from innovation and flexibility; conversely, allowing too much emergence can undermine managerial predictability and work routines. Therefore, when managing supply networks, managers must appropriately balance how much to control and how much to let emerge. © 2001 Elsevier Science B.V. All rights reserved.


title: Building Emotional Machines: Recognizing Image Emotions Through Deep Neural Networks

abstract: An image is a very effective tool for conveying emotions. Many researchers have investigated in computing the image emotions by using various features extracted from images. In this paper, we focus on two high level features, the object and the background, and assume that the semantic information of images is a good cue for predicting emotion. An object is one of the most important elements that define an image, and we find out through experiments that there is a high correlation between the object and the emotion in images. Even with the same object, there may be slight difference in emotion due to different backgrounds, and we use the semantic information of the background to improve the prediction performance. By combining the different levels of features, we build an emotion based feed forward deep neural network which produces the emotion values of a given image. The output emotion values in our framework are continuous values in the 2-dimensional space (Valence and Arousal), which are more effective than using a few number of emotion categories in describing emotions. Experiments confirm the effectiveness of our network in predicting the emotion of images.


title: Visual Programming Languages and the Empirical Evidence For and Against

abstract: Abstract The past decade has witnessed the emergence of an active visual programming research community. Yet, there has also been a noteworthy shortage of empirical evidence supporting the resulting research. This paper summarizes empirical data relevant to visual programming languages, both to show the current empirical status and to act as a call to arms for further empirical work.


title: Blood Cell Identification Using Emotional Neural Networks

abstract: The idea of machines having emotions sounds like science fiction, however, few decades ago the idea of machines with intelligence seemed also like fiction, but today we are developing intelligent machines with successful applications. We have always overlooked the emotional factors during machine learning and decision making; however, it is quite conceivable to artificially model certain emotions in machine learning. This paper presents an emotional neural network (EmNN) that is based on the emotional back propagation (EmBP) learning algorithm. The EmNN has emotional weights and two emotional parameters; anxiety and confidence, which are updated during learning. The performances of the EmNN and a conventional BP-based neural network, using two topologies for each network, will be compared when applied to a blood cell type identification problem. Experimental results show that the additional emotional parameters and weights improved the identification rate as well as the classification time.


title: An Empirical Exploration of Recurrent Network Architectures

abstract: The Recurrent Neural Network (RNN) is an extremely powerful sequence model that is often difficult to train. The Long Short-Term Memory (LSTM) is a specific RNN architecture whose design makes it much easier to train. While wildly successful in practice, the LSTM's architecture appears to be ad-hoc so it is not clear if it is optimal, and the significance of its individual components is unclear. ::: ::: In this work, we aim to determine whether the LSTM architecture is optimal or whether much better architectures exist. We conducted a thorough architecture search where we evaluated over ten thousand different RNN architectures, and identified an architecture that outperforms both the LSTM and the recently-introduced Gated Recurrent Unit (GRU) on some but not all tasks. We found that adding a bias of 1 to the LSTM's forget gate closes the gap between the LSTM and the GRU.


title: $Ka$-Band Low-Loss and High-Isolation Switch Design in 0.13-$\mu{\hbox {m}}$ CMOS

abstract: This paper presents designs and measurements of Ka-band single-pole single-throw (SPST) and single-pole double-throw (SPDT) 0.13-CMOS switches. Designs based on series and shunt switches on low and high substrate resistance networks are presented. It is found that the shunt switch and the series switch with a high substrate resistance network have a lower insertion loss than a standard designs. The shunt SPST switch shows an insertion loss of 1.0 dB and an isolation of 26 dB at >35 GHz. The series SPDT switch with a high substrate resistance network shows excellent performance with 2.2-dB insertion loss and isolation at 35 GHz, and this is achieved using two parallel resonant networks. The series-shunt SPDT switch using deep n-well nMOS transistors for a high substrate resistance network results in an insertion loss and isolation of 2.6 and 27 dB, respectively, at 35 GHz. For series switches, the input 1-dB compression point (1P1) can be significantly increased to with the use of a high substrate resistance design. In contrast, of shunt switches is limited by the self-biasing effect to 12 dBm independent of the substrate resistance network. The paper shows that, with good design, several 0.13- CMOS designs can be used for state-of-the-art switches at 26-40 GHz.


title: Autonomous Intersection Management for Semi-Autonomous Vehicles

abstract: Recent advances in autonomous vehicle technology will open the door to highly efficient transportation systems in the future, as demonstrated by Autonomous Intersection Management, an intersection control protocol designed for fully autonomous vehicles. We, however, anticipate there will be a long transition period during which most vehicles have some but not all capabilities of fully autonomous vehicles. This paper introduces a new protocol called Semi-Autonomous Intersection Management, which allows vehicles with partially-autonomous features such as adaptive cruise control to enter an intersection from different directions simultaneously. Our experiments show that this protocol can greatly decrease traffic delay when most vehicles are semi-autonomous. Our incremental deployment study reveals that traffic delay keeps decreasing as more and more vehicles employ features of autonomy.


title: A comparative study of elasticsearch and CouchDB document oriented databases

abstract: With the advent of large complex datasets, NOSQL databases have gained immense popularity for their efficiency to handle such datasets in comparison to relational databases. There are a number of NOSQL data stores for e.g. Mongo DB, Apache Couch DB etc. Operations in these data stores are executed quickly. In this paper we aim to get familiar with 2 most popular NoSQL databases: Elasticsearch and Apache CouchDB. This paper also aims to analyze the performance of Elasticsearch and CouchDB on image data sets. This analysis is based on the results carried out by instantiate, read, update and delete operations on both document-oriented stores and thus justifying how CouchDB is more efficient than Elasticsearch during insertion, updation and deletion operations but during selection operation Elasticsearch performs much better than CouchDB. The implementation has been done on LINUX platform.


title: Context-aware local abnormality detection in crowded scene

abstract: In this paper, we propose a novel algorithm by jointly modeling motion and context information targeting at detecting abnormal events in crowded scenes. In our algorithm, context pattern information, extracted through volume local binary patterns computation on three orthogonal planes (LBP-TOP) between local target areas with surrounding areas, is explicitly taken into consideration for localizing abnormality. To capture motion information, a novel feature descriptor named Multi-scale Histogram of Frequency Coefficient is explored by taking Fourier Transform on the extracted dense trajectories. For detection of abnormality, sparse reconstruction cost from a learned event dictionary is adopted to classify local normal and abnormal events. Experiments conducted on three benchmark datasets show superior performance to many related state-of-the-art methods.摘要创新点本论文通过对运动特征和上下文感知联合建模, 提出了一种新颖的算法用于拥挤场景下的异常事件检测。 在本算法中, 通过目标区域和周围区域的三个交叉垂直平面计算时空局部二值模式信义, 以提取上下文信息, 用于精确定位异常事件位置。 另外, 通过稠密轨迹的傅里叶变换系数, 提取则多尺度频率系数直方图用于运动特征。 最后基于已训练字典的重构误差来判别异常事件。 本算法, 在三个基准数据上取得很好效果, 证明了算法的有效性。


title: Learning to score the figure skating sports videos

abstract: This paper targets at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global sequential information in each video. Furthermore, we present a large-scale figure skating sports video dataset -- FisV dataset. This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds. Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our proposed model is validated on FisV and MIT-skate datasets. The experimental results show the effectiveness of our models in learning to score the figure skating videos.


title: Two Fatty Acid Elongases Possessing C18-Δ6/C18-Δ9/C20-Δ5 or C16-Δ9 Elongase Activity in Thraustochytrium sp. ATCC 26185

abstract: Thraustochytrids, unicellular eukaryotic marine protists, accumulate polyunsaturated fatty acids. Here, we report the molecular cloning and functional characterization of two fatty acid elongase genes (designated tselo1 and tselo2), which could be involved in the desaturase/elongase (standard) pathway in Thraustochytrium sp. ATCC 26185. TsELO1, the product of tselo1 and classified into a Δ6 elongase group by phylogenetic analysis, showed strong C18-Δ6 elongase activity and relatively weak C18-Δ9 and C20-Δ5 activities when expressed in the budding yeast Saccharomyces cerevisiae. TsELO2, classified into a Δ9 elongase subgroup, showed only C16-Δ9 activity. When expressed in Aurantiochytrium limacinum mh0186 using a thraustochytrid-derived promoter and a terminator, TsELO1 exhibited almost the same specificity as expressed in the yeast but TsELO2 showed weak C18-Δ9 activity, in addition to its main C16-Δ9 activity. These results suggest that TsELO1 functions not only as a C18-Δ6 and a C20-Δ5 elongase in the main route but also as a C18-Δ9 elongase in the alternative route of standard pathway, while TsELO2 functions mainly as a C16-Δ9 elongase generating vaccenic acid (C18:1n−7) in thraustochytrids. This is the first report describing a fatty acid elongase harboring C16-Δ9 activity in thraustochytrids.


title: Thai car license plate recognition using essential-elements-based method

abstract: There has been several research on car license plate recognition (LPR). However, the number of research on Thai LPR is limited, besides, most of which are published in Thai. The existing work on Thai LPR have faced problems caused by low- quality license plates and a great number of similar characters that exist in Thai alphabets. Generally, car license plates in Thailand come in different conditions, ranging from new license plates of excellent quality to low-quality ones with screws on or even some paint already peeled off. Thai characters that appear on Thai license plates are also generally shape-complicated. Area-based methods, such as conventional template matching, are ineffective to low-quality or resembling characters. To cope with these problems, this paper presents a new method, which recognizes the character patterns relying only on essential elements of characters. This method lies on the concept that Thai characters can be distinguished by essential elements that form up different shapes. Similar characters are handled by directly focusing on their obvious differences among them. The overall success rate of the proposed method, tested on 300 actual license plates of various qualities, is 85.33%.


title: Aneuploidy screening in the first trimester

abstract: This article reviews the performance of first trimester screening for chromosomal anomalies using various combinations of ultrasound and maternal serum biochemical modalities. Detection rates in excess of 90% can be routinely achieved for Trisomy 21, Trisomy 13, Trisomy 18 using a combination of fetal nuchal translucency (NT) thickness and maternal serum free ss-hCG and PAPP-A at 11 + 0 to 13 + 6 weeks of gestation.


title: A multiple device approach for supporting whiteboard-based interactions

abstract: In this paper, we propose a multiple-device approach for supporting informal meetings using a digital whiteboard. Traditional digital whiteboard systems often suffer from a limited capability to enter text and the handling of existing data. The large display surface of the whiteboard also makes traditional GUI design ineffective. Our proposed approach provides a hand-held computer for each participant which serves as a tool palette and data entry palette for the whiteboard. Just as an oil painter effectively uses a palette in his/her hand, this hand-held device offers an easy way to create a new text/stroke object, to select existing data from a network, to select pen attributes, and to control the whiteboard application. This paper also reports our experience with the digital whiteboard systems using a proposed multi-device architecture.


title: Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images

abstract: Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using ${\cal L}^{2}$ Lebesgue measure of the $\gamma$ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., ${\cal H}^{1}$ Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.


title: Interpretable Distribution Features with Maximum Testing Power

abstract: Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i.e, features). The features are chosen so as to maximize the distinguishability of the distributions, by optimizing a lower bound on test power for a statistical test using these features. The result is a parsimonious and interpretable indication of how and where two distributions differ locally. An empirical estimate of the test power criterion converges with increasing sample size, ensuring the quality of the returned features. In real-world benchmarks on high-dimensional text and image data, linear-time tests using the proposed semimetrics achieve comparable performance to the state-of-the-art quadratic-time maximum mean discrepancy test, while returning human-interpretable features that explain the test results.


title: A critical review of the literature on social and leisure activity and wellbeing in later life

abstract: An engaged lifestyle is seen as an important component of successful ageing. Many older adults with high participation in social and leisure activities report positive wellbeing, a fact that fuelled the original activity theory and that continues to influence researchers, theorists and practitioners. This study's purpose is to review the conceptualisation and measurement of activity among older adults and the associations reported in the gerontological literature between specific dimensions of activity and wellbeing. We searched published studies that focused on social and leisure activity and wellbeing, and found 42 studies in 44 articles published between 1995 and 2009. They reported from one to 13 activity domains, the majority reporting two or three, such as informal , formal and solitary , or productive versus leisure . Domains associated with subjective wellbeing, health or survival included social, leisure, productive, physical, intellectual, service and solitary activities. Informal social activity has accumulated the most evidence of an influence on wellbeing. Individual descriptors such as gender or physical functioning sometimes moderate these associations, while contextual variables such as choice, meaning or perceived quality play intervening roles. Differences in definitions and measurement make it difficult to draw inferences about this body of evidence on the associations between activity and wellbeing. Activity theory serves as shorthand for these associations, but gerontology must better integrate developmental and psychological constructs into a refined, comprehensive activity theory.


title: Emotion in learning: a neglected dynamic

abstract: A ridge rafter connector formed from a strip of sheet metal which encapsulates the sides of the rafter and is normally connected to the side and top of the ridge beam. The top flange of the connector is configured so that it provides a locator function for the rafter to be connected on the opposite side of the ridge beam. The connecting flange attached to the holding beam may be planar or bent to different angles for use at various locations and for different purposes.


title: Emergence of online communities: Empirical evidence and theory

abstract: Online communities, which have become an integral part of the day-to-day life of people and organizations, exhibit much diversity in both size and activity level; some communities grow to a massive scale and thrive, whereas others remain small, and even wither. In spite of the important role of these proliferating communities, there is limited empirical evidence that identifies the dominant factors underlying their dynamics. Using data collected from seven large online platforms, we observe a relationship between online community size and its activity which generally repeats itself across platforms: First, in most platforms, three distinct activity regimes exist-one of low-activity and two of high-activity. Further, we find a sharp activity phase transition at a critical community size that marks the shift between the first and the second regime in six out of the seven online platforms. Essentially, we argue that it is around this critical size that sustainable interactive communities emerge. The third activity regime occurs above a higher characteristic size in which community activity reaches and remains at a constant and higher level. We find that there is variance in the steepness of the slope of the second regime, that leads to the third regime of saturation, but that the third regime is exhibited in six of the seven online platforms. We propose that the sharp activity phase transition and the regime structure stem from the branching property of online interactions.


title: The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions

abstract: Background ::: CONSORT guidelines call for precise reporting of behavior change interventions: we need rigorous methods of characterizing active content of interventions with precision and specificity.


title: State of the art in ultra-wideband antennas

abstract: Ultra wide-band (UWB) technology is one of the most promising solutions for future communication systems due to its high-speed data rate and excellent immunity to multipath interference. In this context, the UWB antenna design plays a unique role because it behaves like a band pass filter and reshapes the spectra of the pulses, so it should be designed to avoid undesired distortions; some of the critical requirements in UWB antenna design are: ultra wide bandwidth, directional or omni-directional radiation patterns, constant gain and group delay over the entire band, high radiation efficiency and low profile. This work reviews the state of the art in UWB antennas, where planar monopole antennas show a special interest because they exhibit excellent performance in matching impedance bandwidth as well as pattern. Some experimental and simulation results on a proposed UWB antenna are also given.


Generalized canonical correlations and their applications to experimental data.


title: Toward automating a human behavioral coding system for married couples' interactions using speech acoustic features

abstract: Observational methods are fundamental to the study of human behavior in the behavioral sciences. For example, in the context of research on intimate relationships, psychologists' hypotheses are often empirically tested by video recording interactions of couples and manually coding relevant behaviors using standardized coding systems. This coding process can be time-consuming, and the resulting coded data may have a high degree of variability because of a number of factors (e.g., inter-evaluator differences). These challenges provide an opportunity to employ engineering methods to aid in automatically coding human behavioral data. In this work, we analyzed a large corpus of married couples' problem-solving interactions. Each spouse was manually coded with multiple session-level behavioral observations (e.g., level of blame toward other spouse), and we used acoustic speech features to automatically classify extreme instances for six selected codes (e.g., ''low'' vs. ''high'' blame). Specifically, we extracted prosodic, spectral, and voice quality features to capture global acoustic properties for each spouse and trained gender-specific and gender-independent classifiers. The best overall automatic system correctly classified 74.1% of the instances, an improvement of 3.95% absolute (5.63% relative) over our previously reported best results. We compare performance for the various factors: across codes, gender, classifier type, and feature type.


title: Apathy in nursing home residents with dementia: Results from a cluster-randomized controlled trial §

abstract: Abstract Purpose Here we evaluate an interdisciplinary occupational and sport therapy intervention for dementia patients suffering from apathy. Subjects and methods A prospective, controlled, rater-blinded, clinical trial with two follow-ups was conducted as part of a larger cluster-randomized trial in 18 nursing homes in Berlin. n =117 dementia patients with apathy, defined as a score of 40 or more on the apathy evaluation scale (AES) or presence of apathy on the Neuropsychiatric Inventory (NPI), were randomly assigned to intervention or control group. The intervention included 10 months of brief activities, provided once a week. The primary outcome measure was the total score on the AES scale measured directly after the intervention period and again after 12 months. Results We found significant group differences with respect to apathy during the 10 month intervention period (F 2,82 =7.79, P P =0.55). Conclusions Our intervention was effective for the therapy of apathy in dementia, when applied, but not one year after cessation of therapy.


title: Ethos' Deeply Integrated Distributed Types

abstract: Programming languages have long incorporated type safety, increasing their level of abstraction and thus aiding programmers. Type safety eliminates whole classes of security-sensitive bugs, replacing the tedious and error-prone search for such bugs in each application with verifying the correctness of the type system. Despite their benefits, these protections often end at the process boundary, that is, type safety holds within a program but usually not to the file system or communication with other programs. Existing operating system approaches to bridge this gap require the use of a single programming language or common language runtime. We describe the deep integration of type safety in Ethos, a clean-slate operating system which requires that all program input and output satisfy a recognizer before applications are permitted to further process it. Ethos types are multilingual and runtime-agnostic, and each has an automatically generated unique type identifier. Ethos bridges the type-safety gap between programs by (1) providing a convenient mechanism for specifying the types each program may produce or consume, (2) ensuring that each type has a single, distributed-system-wide recognizer implementation, and (3) inescapably enforcing these type constraints.


title: Trojan of Things: Embedding Malicious NFC Tags into Common Objects

abstract: We present a novel proof-of-concept attack named Trojan of Things (ToT), which aims to attack NFC- enabled mobile devices such as smartphones. The key idea of ToT attacks is to covertly embed maliciously programmed NFC tags into common objects routinely encountered in daily life such as banknotes, clothing, or furniture, which are not considered as NFC touchpoints. To fully explore the threat of ToT, we develop two striking techniques named ToT device and Phantom touch generator. These techniques enable an attacker to carry out various severe and sophisticated attacks unbeknownst to the device owner who unintentionally puts the device close to a ToT. We discuss the feasibility of the attack as well as the possible countermeasures against the threats of ToT attacks.


title: Vacant parking space detector for outdoor parking lot by using surveillance camera and FCM classifier

abstract: The most prevailing approach now for parking lot vacancy detecting system is to use sensor-based techniques. The main impediments to the camera-based system in applying to parking lots on rooftop and outside building are the glaring sun light and dark shadows in the daytime, and low-light intensity and back-lighting in the nighttime. To date, no camera-based detecting systems for outdoor parking lots have been in practical use. A few engineering firms provide the camera-based system, which is only for underground and indoor parking lots. This paper reports on the new camera based system called ParkLotD for detecting vacancy/occupancy in parking lots. ParkLotD uses a classifier based on fuzzy c-means (FCM) clustering and hyper-parameter tuning by particle swarm optimization (PSO). The test result of the detection error rate for the indoor multi-story parking lot has improved by an order of magnitude compared to the current system based on the edge detection approach. ParkLotD demonstrates high detection performance and enables the camera-based system to achieve the practical use in outdoor parking lots.


title: Component-Enhanced Chinese Character Embeddings

abstract: Distributed word representations are very useful for capturing semantic information and have been successfully applied in a variety of NLP tasks, especially on English. In this work, we innovatively develop two component-enhanced Chinese character embedding models and their bigram extensions. Distinguished from English word embeddings, our models explore the compositions of Chinese characters, which often serve as semantic indictors inherently. The evaluations on both word similarity and text classification demonstrate the effectiveness of our models.


title: Sudden-onset anisocoria in a patient with upper respiratory tract infection

abstract: A 33-year-old woman presented after sudden onset of a right-sided throbbing, severe headache and drooping of her right upper eyelid while recovering from a 5-day upper respiratory tract infection with persistent dry cough. She had no history of neck trauma or manipulation (e.g., massage or


title: The Construction Of Reality In The Child

abstract: An electrical conducting, fluid tight, tubular assembly comprising a number of elongate, interchangeable, reusable tubular sections that may be removably connected end to end and extended downwardly in an oil well bore hole adjacent a tubing string situated therein to supply electrical power to a down hole electrical pump or other electrically operated device that may be situated in the bore hole. The assembly is concurrently made up as the tubing string is lowered into the bore hole, with the sections being clamped to the tubing string and supported therefrom. The sections of the tubing string are longitudinally movable relative to one another within predetermined limits and as a result the assembly may elongate and contract in conformity with changes in the longitudinal length of the tubing string due to temperature changes in the bore hole. When the tubing string is pulled from the oil well bore hole the sections of the tubular assembly are sequentially unclamped therefrom and disengaged from one another. The disengaging of the sections one from the other is extremely simple, as the sections are in slidable engagement and the uppermost sections on an assembly simply has to be lifted upwardly to effect such disengagement. The tubular sections when assembled provides protection for a number of longitudinally extending laterally spaced electrical conductors or cables therein, both from mechanical damage, as well as damage that may result from excessive temperatures or gas pressure in the bore hole.


title: Understanding the Robustness of SSDs under Power Fault

abstract: Modern storage technology (SSDs, No-SQL databases, commoditized RAID hardware, etc.) bring new reliability challenges to the already complicated storage stack. Among other things, the behavior of these new components during power faults--which happen relatively frequently in data centers--is an important yet mostly ignored issue in this dependability-critical area. Understanding how new storage components behave under power fault is the first step towards designing new robust storage systems. ::: ::: In this paper, we propose a new methodology to expose reliability issues in block devices under power faults. Our framework includes specially-designed hardware to inject power faults directly to devices, workloads to stress storage components, and techniques to detect various types of failures. Applying our testing framework, we test fifteen commodity SSDs from five different vendors using more than three thousand fault injection cycles in total. Our experimental results reveal that thirteen out of the fifteen tested SSD devices exhibit surprising failure behaviors under power faults, including bit corruption, shorn writes, unserializable writes, metadata corruption, and total device failure.


title: Security challenges in internet of things: survey

abstract: The Internet of Things is part of our everyday life, which applies to all aspects of human life; from smart phones and environmental sensors to smart devices used in the industry. Although the Internet of Things has many advantages, there are risks and dangers as well that need to be addressed. The information used and transmitted on Internet of Things contain important info about the daily lives of people, banking information, location and geographical information, environmental and medical information, together with many other sensitive data. Therefore, it is critical to identify and address the security issues and challenges of Internet of Things. In this article, considering the broad scope of this field and its literature, we are going to express some comprehensive information on security challenges of the Internet of Things.


title: EEG-BASED SPEECH RECOGNITION Impact of Temporal Effects

abstract: In this paper, we investigate the use of electroencephalograhic signals for the purpose of recognizing unspoken speech. The term unspoken speech refers to the process in which a subject imagines speaking a given word without moving any articulatory muscle or producing any audible sound. Early work by Wester (Wester, 2006) presented results which were initially interpreted to be related to brain activity patterns due to the imagination of pronouncing words. However, subsequent investigations lead to the hypothesis that the good recognition performance might instead have resulted from temporal correlated artifacts in the brainwaves since the words were presented in blocks. In order to further investigate this hypothesis, we run a study with 21 subjects, recording 16 EEG channels using a 128 cap montage. The vocabulary consists of 5 words, each of which is repeated 20 times during a recording session in order to train our HMM-based classifier. The words are presented in blockwise, sequential, and random order. We show that the block mode yields an average recognition rate of 45.50%, but it drops to chance level for all other modes. Our experiments suggest that temporal correlated artifacts were recognized instead of words in block recordings and back the above-mentioned hypothesis.


title: Research and development of the 4X-variable reluctance resolver

abstract: The paper presents the results of the development of the variable reluctance resolver with the multiplication factor of 4X. Some common design characteristics of the resolver are disclosed in the paper as well as the review of the resolver's signal processing methodic. Attention is paid to the research of the sources of the resolver's angular position errors. In the study, the simulation based results and the real 4X-variable reluctance resolver prototype parameters are presented and compared.


title: Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)

abstract: Two-photon laser scanning microscopy of calcium dynamics using fluorescent indicators is a widely used imaging method for large scale recording of neural activity in vivo. Here we introduce volumetric Two-photon Imaging of Neurons using Stereoscopy (vTwINS), a volumetric calcium imaging method that employs an elongated, V-shaped point spread function to image a 3D brain volume. Single neurons project to spatially displaced image pairs in the resulting 2D image, and the separation distance between images is proportional to depth in the volume. To demix the fluorescence time series of individual neurons, we introduce a novel orthogonal matching pursuit algorithm that also infers source locations within the 3D volume. We illustrate vTwINS by imaging neural population activity in mouse primary visual cortex and hippocampus. Our results demonstrate that vTwINS provides an effective method for volumetric two-photon calcium imaging that increases the number of neurons recorded while maintaining a high frame-rate.


title: Drone sound detection

abstract: Nowadays small size UAV (Unmanned Aerial Vehicle) systems are really popular. We can find so many of them on video sharing portals and nowadays there are many articles on the media with the advantages and disadvantages of them. We can use “drones” for so many useful task, but on the other hand the risk is increasing with the growing popularity. We can found articles about the unethical and unlawful usages of them. They can be used for smuggling, observation, violation of privacy etc. As a consequence of the growing popularity, the price of these things is quickly becoming lower. High quality cameras are also widespread and it's also easy to install one onto a commercially available drone. We have to admit that these factors are become increasingly critical.


title: Restoration of impulse noise corrupted images using long-range correlation

abstract: We present a new algorithm that can remove impulse noise from corrupted images while preserving details. The algorithm is fundamentally different from the traditional methods in that it can utilize information not just of a local window centered about the corrupted pixel, but also of some remote regions in the image. Computer simulations indicate that our algorithm outperforms many existing techniques.


title: Frequency band widening of negative permeability using split ring resonators

abstract: In recent years, the medium which has negative index of refraction is widely researched. The medium has both the negative permittivity and the negative permeability. In this paper, we have researched the frequency range widening of negative permeability using split ring resonators.


title: A deep learning framework for segmentation and pose estimation of pedicle screw implants based on C-arm fluoroscopy

abstract: Pedicle screw fixation is a challenging procedure with a concerning rates of reoperation. After insertion of the screws is completed, the most common intraoperative verification approach is to acquire anterior–posterior and lateral radiographic images, based on which the surgeons try to visually assess the correctness of insertion. Given the limited accuracy of the existing verification techniques, we identified the need for an accurate and automated pedicle screw assessment system that can verify the screw insertion intraoperatively. For doing so, this paper offers a framework for automatic segmentation and pose estimation of pedicle screws based on deep learning principles. Segmentation of pedicle screw X-ray projections was performed by a convolutional neural network. The network could isolate the input X-rays into three classes: screw head, screw shaft and background. Once all the screw shafts were segmented, knowledge about the spatial configuration of the acquired biplanar X-rays was used to identify the correspondence between the projections. Pose estimation was then performed to estimate the 6 degree-of-freedom pose of each screw. The performance of the proposed pose estimation method was tested on a porcine specimen. The developed machine learning framework was capable of segmenting the screw shafts with 93% and 83% accuracy when tested on synthetic X-rays and on clinically realistic X-rays, respectively. The pose estimation accuracy of this method was shown to be $$1.93^{\circ } \pm 0.64^{\circ }$$ 1.93∘±0.64∘ and $$1.92 \pm 0.55\,\hbox {mm}$$ 1.92±0.55mm on clinically realistic X-rays. The proposed system offers an accurate and fully automatic pedicle screw segmentation and pose assessment framework. Such a system can help to provide an intraoperative pedicle screw insertion assessment protocol with minimal interference with the existing surgical routines.


title: HS-Nets: Estimating Human Body Shape from Silhouettes with Convolutional Neural Networks

abstract: We represent human body shape estimation from binary silhouettes or shaded images as a regression problem, and describe a novel method to tackle it using CNNs. Utilizing a parametric body model, we train CNNs to learn a global mapping from the input to shape parameters used to reconstruct the shapes of people, in neutral poses, with the application of garment fitting in mind. This results in an accurate, robust and automatic system, orders of magnitude faster than methods we compare to, enabling interactive applications. In addition, we show how to combine silhouettes from two views to improve prediction over a single view. The method is extensively evaluated on thousands of synthetic shapes and real data and compared to state of-art approaches, clearly outperforming methods based on global fitting and strongly competing with more expensive local fitting based ones.


title: Towards Linguistically Generalizable NLP Systems: A Workshop and Shared Task

abstract: This paper presents a summary of the first Workshop on Building Linguistically Generalizable Natural Language Processing Systems, and the associated Build It Break It, The Language Edition shared task. The goal of this workshop was to bring together researchers in NLP and linguistics with a shared task aimed at testing the generalizability of NLP systems beyond the distributions of their training data. We describe the motivation, setup, and participation of the shared task, provide discussion of some highlighted results, and discuss lessons learned.


title: A new algorithm for detecting text line in handwritten documents

abstract: Curvilinear text line detection and segmentation in handwritten documents is a significant challenge for handwriting recognition. Given no prior knowledge of script, we model text line detection as an image segmentation problem by enhancing text line structure using a Gaussian window, and adopting the level set method to evolve text line boundaries. Experiments show that the proposed method achieves high accuracy for detecting text lines in both handwritten and machine printed documents with many scripts.


title: NEW TOOL FOR VISUALIZATION OF TIME SERIES AND ANOMALIES IN STREAMING DATA

abstract: A new software tool for simultaneous visualization of multiple time dependent signals, featuring a novel and highly useful combination of capabilities, and published as an open source solution is presented in this paper. The tool is designed to meet the needs of its users who expect lightweight, interactive & intuitive use and ease of deployment in current setups, including live monitoring systems with anomaly detection, highlighting and streaming data processing abilities. The functionality and motivation for our system is derived from various signal analysis applications, our research activities related to design and evaluation of neural network models, and from systems for continuous monitoring and anomaly detection (e.g. in IT or medical domains), which is demonstrated on simple use case examples.


Subependymal glioma with hypertension: diagnostic problem.


title: Auxiliary Beam Pair Enabled AoD and AoA Estimation in mmWave FD-MIMO Systems

abstract: In this paper, auxiliary beam pair design is developed to provide high-resolution estimates of channel's elevation/azimuth angle-of-departure (AoD) and angle-of-arrival (AoA) for millimeter- wave full-dimension MIMO (FD-MIMO) systems. Pairs of custom designed analog beams are formed at both the transmitter and receiver to help acquire channel information. It is shown via simulation results that by employing the proposed method, promising elevation/azimuth AoD and AoA estimation performance can be achieved under various signal- to-noise ratio levels and channel conditions.


title: The Volcano optimizer generator: extensibility and efficient search

abstract: The Volcano project, which provides efficient, extensible tools for query and request processing, particularly for object-oriented and scientific database systems, is reviewed. In particular, one of its tools, the optimizer generator, is discussed. The data model, logical algebra, physical algebra, and optimization rules are translated by the optimizer generator into optimizer source code. It is shown that, compared with the EXODUS optimizer generator prototype, the search engine of the Volcano optimizer generator is more extensible and powerful. It provides effective support for non-trivial cost models and for physical properties such as sorting order. At the same time, it is much more efficient, as it combines dynamic programming with goal-directed searching and branch-and-bound pruning. Compared with other rule-based optimization systems, it provides complete data model independence and more natural extensibility. >


title: Data marketplace for Internet of Things

abstract: The data generated by Internet of Things (IoT) devices is mostly owned by device owners and is often private in nature. There are however third parties that could benefit from using that data, and the challenge is in allowing them to access it under the conditions that data owners find acceptable. An opportunity presents itself to create a marketplace where device owners could sell that data and data consumers could buy it. Some properties of IoT data make it difficult to trade in traditional data markets. We hypothesise that it is possible to develop an efficient algorithm that responds to data consumer's queries in a way that it's budget constraints are respected and that device owners are compensated for the data they provide. In this paper we create a model of such a market, analyse it's query performance, expected earnings of device owners and utility achieved by data consumers and conclude that data markets in IoT are a viable method of distributing generated measurements.


title: Implementation of electrical rim driven fan technology to small unmanned aircraft

abstract: Aircraft propeller performance is significantly reduced when tip speeds become sonic causing the maximum attainable airspeed of the vehicle to be limited by the propeller diameter. There are also performance losses attributable to miniature Unmanned Aerial Vehicles as the propeller to hub diameter ratio is reduced. The research conducted indicated that re-arranging a Brushless DC Motor and propeller configuration, so that it becomes rim-driven rather than hub-driven, would provide some performance and operational advantages and could inspire the design of novel high-speed Unmanned Aerial Vehicle configurations powered by hub-less, multi-stage contra-rotating electrical fan-compressors. This investigation involved analysis, design and testing a prototype, low cost, concept demonstrator Rim Driven Fan device in order to assess the feasibility of applying this technology to Small Unmanned Aircraft. It was demonstrated that Rim Driven Fan technology could be successfully applied to lift and propel a Small Unmanned Aircraft. However, the performance testing of the Rim Driven Fan demonstrated that in its prototype configuration it would not be as efficient as a conventional Brushless DC motor and propeller.


title: Computational Learning Theory and Language Acquisition

abstract: Computational learning theory explores the limits of learnability. Studying language acquisition from this perspective involves identifying classes of languages that are learnable from the available data, within the limits of time and computational resources available to the learner. Different models of learning can yield radically different learnability results, where these depend on the assumptions of the model about the nature of the learning process, and the data, time, and resources that learners have access to. To the extent that such assumptions accurately reflect human language learning, a model that invokes them can offer important insights into the formal properties of natural languages, and the way in which their representations might be efficiently acquired. In this chapter we consider several computational learning models that have been applied to the language learning task. Some of these have yielded results that suggest that the class of natural languages cannot be efficiently learned from the primary linguistic data (PLD) available to children, through


title: Comparative Analysis of Data Mining Tools and Techniques for Evaluating Performance of Database System

abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Nowadays, large amount of data and information are available, Data can now be stored in many different kinds of databases and information repositories, being available on the Internet. There is a need for powerful techniques for better interpretation of these data that exceeds the human's ability for comprehension and making decision in a better way. There are data mining, web mining and knowledge discovery tools and software packages such as WEKA Tool and RapidMiner tool. The work deals with analysis of WEKA, RapidMiner and NetTools spider tools KNIME and Orange. There are various tools available for data mining and web mining. Therefore awareness is required about the quantitative investigation of these tools. This paper focuses on various functional, practical, cognitive as well as analysis aspects that users may be looking for in the tools. Complete study addresses the usefulness and importance of these tools including various aspects. Analysis presents various benefits of these data mining tools along with desired aspects and the features of current tools.


title: Virtual ads insertion in street building views for augmented reality

abstract: This paper is proposing a new framework of virtual ads insertion for Augmented Reality (AR) in a specific real scene, building facades of the street view videos. In this framework, specific region detection, camera orientation estimation, visual tracking, motion filtering and virtual-real blending are discussed. For the building facade, vanishing points are fully identified to detect a rectangular planar structure. Meanwhile, dynamic registration and color harmonization are designed to improve visual acuity of virtual ads insertion. Experimental results are given to show the good performance of the proposed framework.


title: The Dialog State Tracking Challenge

abstract: In a spoken dialog system, dialog state tracking deduces information about the user’s goal as the dialog progresses, synthesizing evidence such as dialog acts over multiple turns with external data sources. Recent approaches have been shown to overcome ASR and SLU errors in some applications. However, there are currently no common testbeds or evaluation measures for this task, hampering progress. The dialog state tracking challenge seeks to address this by providing a heterogeneous corpus of 15K human-computer dialogs in a standard format, along with a suite of 11 evaluation metrics. The challenge received a total of 27 entries from 9 research groups. The results show that the suite of performance metrics cluster into 4 natural groups. Moreover, the dialog systems that benefit most from dialog state tracking are those with less discriminative speech recognition confidence scores. Finally, generalization is a key problem: in 2 of the 4 test sets, fewer than half of the entries out-performed simple baselines. 1 Overview and motivation Spoken dialog systems interact with users via natural language to help them achieve a goal. As the interaction progresses, the dialog manager maintains a representation of the state of the dialog in a process called dialog state tracking (DST). For example, in a bus schedule information system, the dialog state might indicate the user’s desired bus route, origin, and destination. Dialog state tracking is difficult because automatic speech ∗Most of the work for the challenge was performed when the second and third authors were with Honda Research Institute, Mountain View, CA, USA recognition (ASR) and spoken language understanding (SLU) errors are common, and can cause the system to misunderstand the user’s needs. At the same time, state tracking is crucial because the system relies on the estimated dialog state to choose actions – for example, which bus schedule information to present to the user. Most commercial systems use hand-crafted heuristics for state tracking, selecting the SLU result with the highest confidence score, and discarding alternatives. In contrast, statistical approaches compute scores for many hypotheses for the dialog state (Figure 1). By exploiting correlations between turns and information from external data sources – such as maps, bus timetables, or models of past dialogs – statistical approaches can overcome some SLU errors. Numerous techniques for dialog state tracking have been proposed, including heuristic scores (Higashinaka et al., 2003), Bayesian networks (Paek and Horvitz, 2000; Williams and Young, 2007), kernel density estimators (Ma et al., 2012), and discriminative models (Bohus and Rudnicky, 2006). Techniques have been fielded which scale to realistically sized dialog problems and operate in real time (Young et al., 2010; Thomson and Young, 2010; Williams, 2010; Mehta et al., 2010). In end-to-end dialog systems, dialog state tracking has been shown to improve overall system performance (Young et al., 2010; Thomson and Young, 2010). Despite this progress, direct comparisons between methods have not been possible because past studies use different domains and system components, for speech recognition, spoken language understanding, dialog control, etc. Moreover, there is little agreement on how to evaluate dialog state tracking. Together these issues limit progress in this research area. The Dialog State Tracking Challenge (DSTC) provides a first common testbed and evaluation


title: Adaptive Intrusion Detection: A Data Mining Approach

abstract: In this paper we describe a data mining framework for constructingintrusion detection models. The first key idea is to mine system auditdata for consistent and useful patterns of program and user behavior.The other is to use the set of relevant system features presented inthe patterns to compute inductively learned classifiers that canrecognize anomalies and known intrusions. In order for the classifiersto be effective intrusion detection models, we need to have sufficientaudit data for training and also select a set of predictive systemfeatures. We propose to use the association rules and frequentepisodes computed from audit data as the basis for guiding the auditdata gathering and feature selection processes. We modify these twobasic algorithms to use axis attribute(s) and referenceattribute(s) as forms of item constraints to compute only therelevant patterns. In addition, we use an iterative level-wiseapproximate mining procedure to uncover the low frequency butimportant patterns. We use meta-learning as a mechanism to makeintrusion detection models more effective and adaptive. We report ourextensive experiments in using our framework on real-world audit data.


title: Dynamic Tracking of Joint Motion with Antagonized Smart Braids

abstract: Our lab has developed a way to measure the contraction of Pneumatic Artificial Muscles (PAMs) using the reinforcing fibers themselves as sensing elements. We achieve this by using electrically-conductive wire to form the fiber braid. The inductance of this “Smart Braid” increases as the actuator contracts. We are interested in using these sensors for the feedback control of fluid systems that rely on PAM actuators. Toward that end, we have begun to explore highly portable inductance measurement techniques. Here, we demonstrate the forcecompensated tracking of a rotary joint angle at 1 kHz. This is an important step towards the utilization of these sensors in feedback control systems.


title: A novel topology design approach using an integrated deep learning network architecture

abstract: Topology design optimization offers tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the topology design problem using a standard topology optimization process, such as SIMP (Simplified Isotropic Material with Penalization) is possible. In reality, an estimated over thousands of design iterations is often required for just a few design variables, the conventional optimization approach is in general impractical or computationally unachievable for real world applications significantly diluting the development of the topology optimization technology. There is, therefore, a need for a different approach that will be able to optimize the initial design topology effectively and rapidly. Therefore, this work presents a new topology design procedure to generate optimal structures using an integrated Generative Adversarial Networks (GANs) and convolutional neural network architecture.


title: Unsupervised Multi-Modal Neural Machine Translation

abstract: Unsupervised neural machine translation (UNMT) has recently achieved remarkable results \cite{lample2018phrase} with only large monolingual corpora in each language. However, the uncertainty of associating target with source sentences makes UNMT theoretically an ill-posed problem. This work investigates the possibility of utilizing images for disambiguation to improve the performance of UNMT. Our assumption is intuitively based on the invariant property of image, i.e., the description of the same visual content by different languages should be approximately similar. We propose an unsupervised multi-modal machine translation (UMNMT) framework based on the language translation cycle consistency loss conditional on the image, targeting to learn the bidirectional multi-modal translation simultaneously. Through an alternate training between multi-modal and uni-modal, our inference model can translate with or without the image. On the widely used Multi30K dataset, the experimental results of our approach are significantly better than those of the text-only UNMT on the 2016 test dataset.


title: Predicting customer purchase behavior in the e-commerce context

abstract: Predicting customer purchase behavior is an interesting and challenging task. In the e-commerce context, meeting this challenge requires confronting many problems not observed in the traditional business context. Recommender system technology has been widely adopted by e-commerce websites. However, a traditional recommendation algorithm cannot perform well the predictive task in this context. This study intends to build a predictive framework for customer purchase behavior in the e-commerce context. This framework, known as CustOmer purchase pREdiction modeL (COREL), may be understood as a two-stage process. First, associations among products are investigated and exploited to predicate customer's motivations, i.e., to build a candidate product collection. Next, customer preferences for product features are learned and subsequently used to identify the candidate products most likely to be purchased. This study investigates three categories of product features and develops methods to detect customer preferences for each of these three categories. When a product purchased by a particular consumer is submitted to COREL, the program can return the top n products most likely to be purchased by that customer in the future. Experiments conducted on a real dataset show that customer preference for particular product features plays a key role in decision-making and that COREL greatly outperforms the baseline methods.


title: A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning

abstract: We consider the situation in semi-supervised learning, where the "label sampling" mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method of moments for estimating this stochastic dependence using the unlabeled data. This is potentially useful for two distinct purposes: a. As an input to a supervised learning procedure which can be used to "de-bias" its results using labeled data only and b. As a potentially interesting learning task in itself. We present several examples to illustrate the practical usefulness of our method.


title: Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

abstract: In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators.


title: An Enhanced Hypercube-Based Encoding for Evolving the Placement, Density, and Connectivity of Neurons

abstract: Intelligence in nature is the product of living brains, which are themselves the product of natural evolution. Although researchers in the field of neuroevolution NE attempt to recapitulate this process, artificial neural networks ANNs so far evolved through NE algorithms do not match the distinctive capabilities of biological brains. The recently introduced hypercube-based neuroevolution of augmenting topologies HyperNEAT approach narrowed this gap by demonstrating that the pattern of weights across the connectivity of an ANN can be generated as a function of its geometry, thereby allowing large ANNs to be evolved for high-dimensional problems. Yet the positions and number of the neurons connected through this approach must be decided a priori by the user and, unlike in living brains, cannot change during evolution. Evolvable-substrate HyperNEAT ES-HyperNEAT, introduced in this article, addresses this limitation by automatically deducing the node geometry from implicit information in the pattern of weights encoded by HyperNEAT, thereby avoiding the need to evolve explicit placement. This approach not only can evolve the location of every neuron in the network, but also can represent regions of varying density, which means resolution can increase holistically over evolution. ES-HyperNEAT is demonstrated through multi-task, maze navigation, and modular retina domains, revealing that the ANNs generated by this new approach assume natural properties such as neural topography and geometric regularity. Also importantly, ES-HyperNEAT's compact indirect encoding can be seeded to begin with a bias toward a desired class of ANN topographies, which facilitates the evolutionary search. The main conclusion is that ES-HyperNEAT significantly expands the scope of neural structures that evolution can discover.