title: COMPOOTIM: An Approach to Software Processes Composition and Optimization

abstract: Software process definition is a complex, time consuming and error prone activity. Such activity can be facilitated by a process reuse strategy. This strategy can be implemented through a context-based process line approach. Based on the data from this approach and following its systematic, this work presents a proposal to software processes composition and optimization (COMPOOTIM). The purpose is to support the project manager’s decisions and automate the composition of a project specific process. COMPOOTIM optimizes the suggestion of processes to the context of a particular project. A usage scenario for process selection exemplifies the use of COMPOOTIM.


title: Learning a Probabilistic Topology Discovering Model for Scene Categorization

abstract: A recent advance in scene categorization prefers a topological based modeling to capture the existence and relationships among different scene components. To that effect, local features are typically used to handle photographing variances such as occlusions and clutters. However, in many cases, the local features alone cannot well capture the scene semantics since they are extracted from tiny regions (e.g., $4\times 4$ patches) within an image. In this paper, we mine a discriminative topology and a low-redundant topology from the local descriptors under a probabilistic perspective, which are further integrated into a boosting framework for scene categorization. In particular, by decomposing a scene image into basic components, a graphlet model is used to describe their spatial interactions. Accordingly, scene categorization is formulated as an intergraphlet matching problem. The above procedure is further accelerated by introducing a probabilistic based representative topology selection scheme that makes the pairwise graphlet comparison trackable despite their exponentially increasing volumes. The selected graphlets are highly discriminative and independent, characterizing the topological characteristics of scene images. A weak learner is subsequently trained for each topology, which are boosted together to jointly describe the scene image. In our experiment, the visualized graphlets demonstrate that the mined topological patterns are representative to scene categories, and our proposed method beats state-of-the-art models on five popular scene data sets.


title: Image Analysis for Obturator Foramen Based on Marker-controlled Watershed Segmentation and Zernike Moments

abstract: Obturator Foramen is a specific structure in Pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as Obturator Foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template as a preprocessing step for computation of Pelvic bone rotation on hip radiographs. This method consists of integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor and it is used to detect Obturator Foramen accurately. Marker-controlled Watershed segmentation is applied to separate Obturator Foramen from the background effectively. Then, Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for final extraction of Obturator Foramens. Finally, Pelvic bone rotation rate calculation for each hip radiograph is performed automatically to select and eliminate hip radiographs for further studies which depend on Pelvic bone angle measurements. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results demonstrated that the proposed method is able to segment Obturator Foramen with 96% accuracy. Keywords—Medical image analysis, marker-controlled watershed segmentation, segmentation of bone structures on hip radiographs, pelvic bone rotation rate, zernike moment feature descriptor.


Brief Review of Vibration Based Machine Condition Monitoring


title: Enhancing Explainability of Neural Networks through Architecture Constraints

abstract: Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. The neural networks are known to possess good prediction performance, but lack of sufficient model explainability. In this paper, we propose to enhance the explainability of neural networks through the following architecture constraints: a) sparse additive subnetworks; b) orthogonal projection pursuit; and c) smooth function approximation. It leads to a sparse, orthogonal and smooth explainable neural network (SOSxNN). The multiple parameters in the SOSxNN model are simultaneously estimated by a modified mini-batch gradient descent algorithm based on the backpropagation technique for calculating the derivatives and the Cayley transform for preserving the projection orthogonality. The hyperparameters controlling the sparse and smooth constraints are optimized by the grid search. Through simulation studies, we compare the SOSxNN method to several benchmark methods including least absolute shrinkage and selection operator, support vector machine, random forest, and multi-layer perceptron. It is shown that proposed model keeps the flexibility of pursuing prediction accuracy while attaining the improved interpretability, which can be therefore used as a promising surrogate model for complex model approximation. Finally, the real data example from the Lending Club is employed as a showcase of the SOSxNN application.


title: SoK: Applying Machine Learning in Security - A Survey

abstract: The idea of applying machine learning(ML) to solve problems in security domains is almost 3 decades old. As information and communications grow more ubiquitous and more data become available, many security risks arise as well as appetite to manage and mitigate such risks. Consequently, research on applying and designing ML algorithms and systems for security has grown fast, ranging from intrusion detection systems(IDS) and malware classification to security policy management(SPM) and information leak checking. In this paper, we systematically study the methods, algorithms, and system designs in academic publications from 2008-2015 that applied ML in security domains. 98 percent of the surveyed papers appeared in the 6 highest-ranked academic security conferences and 1 conference known for pioneering ML applications in security. We examine the generalized system designs, underlying assumptions, measurements, and use cases in active research. Our examinations lead to 1) a taxonomy on ML paradigms and security domains for future exploration and exploitation, and 2) an agenda detailing open and upcoming challenges. Based on our survey, we also suggest a point of view that treats security as a game theory problem instead of a batch-trained ML problem.


title: Software quality and agile methods

abstract: Agile methods may produce software faster but we also need to know how they meet our quality requirements. In this paper we compare the waterfall model with agile processes to show how agile methods achieve software quality under time pressure and in an unstable requirements environment, i.e. we analyze agile software quality assurance. We present a detailed waterfall model showing its software quality support processes. We then show the quality practices that agile methods have integrated into their processes. This allows us to answer the question "can agile methods ensure quality even though they develop software faster and can handle unstable requirements?".


title: Advances and Future Prospects of Spin-Transfer Torque Random Access Memory

abstract: Spin-transfer torque random access memory (STT-RAM) is a potentially revolutionary universal memory technology that combines the capacity and cost benefits of DRAM, the fast read and write performance of SRAM, the non-volatility of Flash, and essentially unlimited endurance. In order to realize a small cell size, high speed and achieve a fully functional STT-RAM chip, the MgO-barrier magnetic tunnel junctions (MTJ) used as the core storage and readout element must meet a set of performance requirements on switching current density, voltage, magneto-resistance ratio (MR), resistance-area product (RA), thermal stability factor (?) , switching current distribution, read resistance distribution and reliability. In this paper, we report the progress of our work on device design, material improvement, wafer processing, integration with CMOS, and testing for a demonstration STT-RAM test chip, and projections based on modeling of the future characteristics of STT-RAM.


title: Sound-Proof: Usable Two-Factor Authentication Based on Ambient Sound

abstract: Two-factor authentication protects online accounts even if passwords are leaked. Most users, however, prefer password-only authentication. One reason why two-factor authentication is so unpopular is the extra steps that the user must complete in order to log in. Currently deployed two-factor authentication mechanisms require the user to interact with his phone to, for example, copy a verification code to the browser. Two-factor authentication schemes that eliminate user-phone interaction exist, but require additional software to be deployed. ::: ::: In this paper we propose Sound-Proof, a usable and deployable two-factor authentication mechanism. Sound-Proof does not require interaction between the user and his phone. In Sound-Proof the second authentication factor is the proximity of the user's phone to the device being used to log in. The proximity of the two devices is verified by comparing the ambient noise recorded by their microphones. Audio recording and comparison are transparent to the user, so that the user experience is similar to the one of password-only authentication. Sound-Proof can be easily deployed as it works with current phones and major browsers without plugins. We build a prototype for both Android and iOS. We provide empirical evidence that ambient noise is a robust discriminant to determine the proximity of two devices both indoors and outdoors, and even if the phone is in a pocket or purse. We conduct a user study designed to compare the perceived usability of Sound-Proof with Google 2-Step Verification. Participants ranked Sound-Proof as more usable and the majority would be willing to use Sound-Proof even for scenarios in which two-factor authentication is optional.


title: Bayesian analogy with relational transformations.

abstract: How can humans acquire relational representations that enable analogical inference and other forms of high-level reasoning? Using comparative relations as a model domain, we explore the possibility that bottom-up learning mechanisms applied to objects coded as feature vectors can yield representations of relations sufficient to solve analogy problems. We introduce Bayesian analogy with relational transformations (BART) and apply the model to the task of learning first-order comparative relations (e.g., larger, smaller, fiercer, meeker) from a set of animal pairs. Inputs are coded by vectors of continuousvalued features, based either on human magnitude ratings, normed feature ratings (De Deyne et al., 2008), or outputs of the topics model (Griffiths, Steyvers, & Tenenbaum, 2007). Bootstrapping from empirical priors, the model is able to induce first-order relations represented as probabilistic weight distributions, even when given positive examples only. These learned representations allow classification of novel instantiations of the relations and yield a symbolic distance effect of the sort obtained with both humans and other primates. BART then transforms its learned weight distributions by importance-guided mapping, thereby placing distinct dimensions into correspondence. These transformed representations allow BART to reliably solve 4-term analogies (e.g., larger:smaller::fiercer:meeker), a type of reasoning that is arguably specific to humans. Our results provide a proof-of-concept that structured analogies can be solved with representations induced from unstructured feature vectors by mechanisms that operate in a largely bottom-up fashion. We discuss potential implications for algorithmic and neural models of relational thinking, as well as for the evolution of abstract thought.


2-2017 Lightweight data race detection for production runs


title: An in vitro evaluation of anti-aging effect of guluronic acid (G2013) based on enzymatic oxidative stress gene expression using healthy individuals PBMCs

abstract: Abstract Background Aging is usually associated with increased levels of oxidants, and may result in damages caused by oxidative stress. There is a direct relationship between aging and increased incidence of inflammatory diseases. The present research intended to study the anti-aging and anti-inflammatory effects of the drug G2013 (guluronic acid) at low and high doses on the genes expression of a number of enzymes involved in oxidative stress (including SOD2, GPX1, CAT, GST, iNOS, and MPO) in peripheral blood mononuclear cells (PBMCs) of healthy individuals under in vitro conditions. Methods Venous blood samples were taken from 20 healthy individuals, the PBMCs were isolated and their RNAs extracted and their cDNAs were synthesized, and the genes expression levels were measured using the qRT-PCR technique. Results Our results indicated that this drug could, at both low and high doses, significantly reduce the expression of the genes for SOD2, GPX1, CAT, and GST compared to the LPS group ( p 0.0001). Moreover, it was noticed that the drug is able to significantly reduce gene expression levels at the high dose and at both doses (low and high), for iNOS and MPO compared to the LPS group ( p Conclusions The present research showed that G2013, as a novel NSAID drug with immunomodulatory properties, could modulate the expression levels of the genes for SOD2, GPX1, CAT, GST, iNOS, and MPO, to the level of healthy gene expression, and possibly it might reduce the pathological process of aging and age-related inflammatory diseases.


title: Systemic Complications of Complex Regional Pain Syndrome

abstract: Complex Regional Pain Syndrome (CRPS) is a neuropathic pain disorder that is characterized by: 1) Severe pain beyond the area of injury; 2) Autonomic dysregulation; 3) Neuropathic edema; 4) A movement disorder, atrophy and dystrophy. It is most often caused by a fracture, soft-tissue injury or surgical procedure and is divided into Type I, in which no nerve lesion is identified (classic reflex sympathetic dystrophy), and Type II where a specific nerve has been damaged (causalgia). In addition to the peripheral manifestations, there are many internal medical complications whose etiology is often not appreciated. This article will examine how CRPS affects the systems of: cognition; constitutional, cardiac, and respiratory complications; systemic autonomic dysregulation; neurogenic edema; musculoskeletal, endocrine and dermatological manifestations; as well as urological and gastrointestinal function.


title: Significant contribution of organically-bound Mg, Ca, and Fe to inorganic PM10 emission during the combustion of pulverized Victorian brown coal

abstract: Abstract This study deployed water-washing and dilute acid washing of a Victorian brown coal to prepare a set of coal samples with simplified occurrence forms of inorganic species. The resulted water-washed coal contains dominantly organically-bound species (primarily Mg, Ca, and Fe) and discrete mineral particles whereas the acid-washed coal only consists of discrete mineral particles. The coal samples (i.e., the raw, the water-washed and the acid-washed coals) were combusted in a lab-scale drop-tube furnace in air at 1400 °C to produce inorganic particulate matter (PM) with aerodynamic diameter less than 10 μm (PM 10 ). Benchmarking on yields, particle size distribution, and chemical properties of PM 10 produced from the combustion of the three coals clearly demonstrates that majority of PM 10 , including PM with aerodynamic diameter less than 1 μm (PM 1 ) and between 1 and 10 μm (PM 1 – 10 ), is contributed by organically-bound elements (mainly Mg, Ca, and Fe) whereas there is only limited contribution from discrete mineral particles due to their low contents in the brown coal. The results also clearly show that the water-soluble salts (e.g., NaCl) mainly contribute to the emission of PM with aerodynamic diameter less than 0.1 μm (PM 0.1 ).


title: Stochastic Implementation of LDPC Decoders

abstract: LDPC codes are found in many recent communications standards such as 10GBASE-T, DVB-S2 and IEEE 802.16 (WiMAX). We present a review of a new class of "stochastic" iterative decoding architectures. Stochastic decoders represent probabilistic messages by the frequency of ones in a binary stream. This results in a simple mapping of the factor graph of the code into silicon. An FPGA implementation of a LDPC decoder with 8 information bits and 8 coded bits is described. On an Altera Cyclone FPGA, the throughput is 5 Mbps when clocked at 100 MHz and is expected to increase nearly linearly with the code length. Simulations of the decoder on an Altera Stratix FPGA indicate a potential throughput of 8 Mbps


title: www.elsevier.com/locate/jss Experiences using systematic review guidelines

abstract: Systematic review is a method to identify, assess and analyse published primary studies to investigate research questions. We critique recently published guidelines for performing systematic reviews on software engineering, and comment on systematic review generally with respect to our experience conducting one. Overall we recommend the guidelines. We recommend researchers clearly and narrowly define research questions to reduce overall effort, and to improve selection and data extraction. We suggest that ''complementary'' research questions can help clarify the main questions and define selection criteria. We show our project timeline, and discuss possibilities for automating and increasing the acceptance of systematic review.


title: Design and analysis of different type of charge pump using CMOS technology

abstract: Basically charge pump are used to produce a higher voltage than the power supply voltage. The charge pump is a DC to DC converter which uses capacitor as energy storage elements to produce a higher or lower voltage. This paper present a comparison between two of the most admired charge pump structures such as Dickson charge pump and Charge pump circuit with cross connected NMOS cells. The comparison has been carried out considering the output voltage, power consumption, delay, output current and conversion ratio. The discussion is supported by practical analysis of both the charge pump. The two charge pumps are taken with the input clock signal, power supply voltage, the same storage capacitance per stage, and the same number of stages. The object of this paper is to compare the all the parameter of the charge pump to found best charge pump. The comparison showed that the charge pump circuit with cross connected NMOS cells is best rather than Dickson charge pump. All simulation and result of Dickson charge pump and Charge pump circuit with cross connected NMOS cells is done at tanner tool 14.1.


title: Risk management in ERP project introduction: Review of the literature

abstract: In recent years ERP systems have received much attention. However, ERP projects have often been found to be complex and risky to implement in business enterprises. The organizational relevance and risk of ERP projects make it important for organizations to focus on ways to make ERP implementation successful. We collected and analyzed a number of key articles discussing and analyzing ERP implementation. The different approaches taken in the literature were compared from a risk management point of view to highlight the key risk factors and their impact on project success. Literature was further classified in order to address and analyze each risk factor and its relevance during the stages of the ERP project life cycle.


title: Hierarchical Kinematic Design of Foldable Hexapedal Locomotion Platforms

abstract: Origami-inspired folding enables integrated design and manufacturing of intricate kinematic mechanisms and structures. Here, we present a hierarchical development process of foldable robotic platforms as combinations of fundamental building blocks to achieve arbitrary levels of complexity and functionality. Rooted in theoretical linkage kinematics, designs for static structures and functional units, respectively, offer rigidity and mobility for robotic systems. The proposed approach is demonstrated on the design, fabrication, and experimental verification of three distinct types of hexapedal locomotion platforms covering a broad range of features and use cases.


title: Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography

abstract: Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.


title: Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

abstract: While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectu ...


title: Primary Object Segmentation in Videos via Alternate Convex Optimization of Foreground and Background Distributions

abstract: An unsupervised video object segmentation algorithm, which discovers a primary object in a video sequence automatically, is proposed in this work. We introduce three energies in terms of foreground and background probability distributions: Markov, spatiotemporal, and antagonistic energies. Then, we minimize a hybrid of the three energies to separate a primary object from its background. However, the hybrid energy is nonconvex. Therefore, we develop the alternate convex optimization (ACO) scheme, which decomposes the nonconvex optimization into two quadratic programs. Moreover, we propose the forward-backward strategy, which performs the segmentation sequentially from the first to the last frames and then vice versa, to exploit temporal correlations. Experimental results on extensive datasets demonstrate that the proposed ACO algorithm outperforms the state-of-the-art techniques significantly.


title: A Novel Lane Detection System With Efficient Ground Truth Generation

abstract: A new night-time lane detection system and its accompanying framework are presented in this paper. The accompanying framework consists of an automated ground truth process and systematic storage of captured videos that will be used for training and testing. The proposed Advanced Lane Detector 2.0 (ALD 2.0) is an improvement over the ALD 1.0 or Layered Approach with integration of pixel remapping, outlier removal, and prediction with tracking. Additionally, a novel procedure to generate the ground truth data for lane marker locations is also proposed. The procedure consists of an original process called time slicing, which provides the user with unique visualization of the captured video and enables quick generation of ground truth information. Finally, the setup and implementation of a database hosting lane detection videos and standardized data sets for testing are also described. The ALD 2.0 is evaluated by means of the user-created annotations accompanying the videos. Finally, the planned improvements and remaining work are addressed.


title: Table frame line detection in low quality document images based on Hough transform

abstract: Table detection is of importance in the field of document images analysis and processing, especially table frame line detection. Although a great success has been achieved for high quality images during the past decade, table detection in low quality images still remains a challenge. To address this problem, we proposed a neoteric method to detect table frame line automatically in low quality document images. Firstly, Radon transform is adopted to detect skew of document images and then correct it. Secondly, run length smoothing algorithm (RLSA) is used to extract the lines longer than a predefined threshold. Thirdly, we locate table regions according to table features and detect frame lines of the detected tables using Hough transform method. The experimental results show that this method could obtain a better performance even in the low quality document images compared to the conventional method.


title: Gaussian Processes for Natural Language Processing

abstract: Gaussian Processes (GPs) are a powerful modelling framework incorporating kernels and Bayesian inference, and are recognised as stateof-the-art for many machine learning tasks. Despite this, GPs have seen few applications in natural language processing (notwithstanding several recent papers by the authors). We argue that the GP framework offers many benefits over commonly used machine learning frameworks, such as linear models (logistic regression, least squares regression) and support vector machines. Moreover, GPs are extremely flexible and can be incorporated into larger graphical models, forming an important additional tool for probabilistic inference. Notably, GPs are one of the few models which support analytic Bayesian inference, avoiding the many approximation errors that plague approximate inference techniques in common use for Bayesian models (e.g. MCMC, variational Bayes).1 GPs accurately model not just the underlying task, but also the uncertainty in the predictions, such that uncertainty can be propagated through pipelines of probabilistic components. Overall, GPs provide an elegant, flexible and simple means of probabilistic inference and are well overdue for consideration of the NLP community. This tutorial will focus primarily on regression and classification, both fundamental techniques of wide-spread use in the NLP community. Within NLP, linear models are near ubiquitous, because they provide good results for many tasks, support efficient inference (including dynamic programming in structured prediction) and support simple parameter interpretation. However, linear models are inherently limited in the types of relationships between variables they can model. Often


title: Intrusion detection in Bluetooth enabled mobile phones

abstract: Bluetooth plays a major role in expanding global spread of wireless technology. This predominantly happens through Bluetooth enabled mobile phones, which cover almost 60% of the Bluetooth market. Although Bluetooth mobile phones are equipped with built-in security modes and policies, intruders compromise mobile phones through existing security vulnerabilities and limitations. Information stored in mobile phones, whether it is personal or corporate, is significant to mobile phone users. Hence, the need to protect information, as well as alert mobile phone users of their incoming connections, is vital. An additional security mechanism was therefore conceptualized, at the mobile phone's user level, which is essential in improving the security. Bluetooth Logging Agent (BLA) is a mechanism that has been developed for this purpose. It alleviates the current security issues by making the users aware of their incoming Bluetooth connections and gives them an option to either accept or reject these connections. Besides this, the intrusion detection and verification module uses databases and rules to authenticate and verify all connections. BLA when compared to the existing security solutions is novel and unique in that it is equipped with a Bluetooth message logging module. This logging module reduces the security risks by monitoring the Bluetooth communication between the mobile phone and the remote device.


title: Hierarchically-Attentive RNN for Album Summarization and Storytelling

abstract: We address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the album. For this task, we make use of the Visual Storytelling dataset and a model composed of three hierarchically-attentive Recurrent Neural Nets (RNNs) to: encode the album photos, select representative (summary) photos, and compose the story. Automatic and human evaluations show our model achieves better performance on selection, generation, and retrieval than baselines.


title: Arsenic distribution along different hydrogeomorphic zones in parts of the Brahmaputra River Valley, Assam (India)

abstract: The spatial distribution of arsenic (As) concentrations along three classified hydrogeomorphological zones in the Brahmaputra River Valley in Assam (India) have been investigated: zone I, comprising the piedmont and alluvial fans; zone II, comprising the runoff areas; and zone III, comprising the discharge zones. Groundwater (150 samples) from shallow hand-pumped and public water supply wells (2–60 m in depth) was analysed for chemical composition to examine the geochemical processes controlling As mobilization. As concentrations up to 0.134 mg/L were recorded, with concentrations below the World Health Organization and the Bureau of Indian Standards drinking-water limits of 0.01 mg/L being found mainly in the proximal recharge areas. Eh and other redox indicators (i.e., dissolved oxygen, Fe, Mn and As) indicate that, except for samples taken in the recharge zone, groundwater is reducing and exhibits a systematic decrease in redox conditions along the runoff and discharge zones. Hydrogeochemical evaluation indicated that zone I, located along the proximal recharge areas, is characterized by low As concentration, while zones II and III are areas with high and moderate concentrations, respectively. Systematic changes in As concentrations along the three zones support the view that areas of active recharge with high hydraulic gradient are potential areas hosting low-As aquifers.


title: Amorphous silicon thin film transistor circuit integration for organic LED displays on glass and plastic

abstract: This paper presents design considerations along with measurement results pertinent to hydrogenated amorphous silicon (a-Si:H) thin film transistor (TFT) drive circuits for active matrix organic light emitting diode (AMOLED) displays. We describe both pixel architectures and TFT circuit topologies that are amenable for vertically integrated, high aperture ratio pixels. Here, the OLED layer is integrated directly above the TFT circuit layer, to provide an active pixel area that is at least 90% of the total pixel area with an aperture ratio that remains virtually independent of scaling. Both voltage-programmed and current-programmed drive circuits are considered. The latter provides compensation for shifts in device characteristics due to metastable shifts in the threshold voltage of the TFT. Various drive circuits on glass and plastic were fabricated and tested. Integration of on-panel gate drivers is also discussed where we present the architecture of an a-Si:H based gate de-multiplexer that is threshold voltage shift invariant. In addition, a programmable current mirror with good linearity and stability is presented. Programmable current sources are an essential requirement in the design of source driver output stages.


title: Multi-View Surveillance Video Summarization via Joint Embedding and Sparse Optimization

abstract: Most traditional video summarization methods are designed to generate effective summaries for single-view videos, and thus, they cannot fully exploit the complicated intra- and inter-view correlations in summarizing multi-view videos in a camera network. In this paper, with the aim of summarizing multi-view videos, we introduce a novel unsupervised framework via joint embedding and sparse representative selection. The objective function is twofold. The first is to capture the multi-view correlations via an embedding, which helps in extracting a diverse set of representatives. The second is to use a $\ell _{2,1}$ -norm to model the sparsity while selecting representative shots for the summary. We propose to jointly optimize both of the objectives, such that embedding can not only characterize the correlations, but also indicate the requirements of sparse representative selection. We present an efficient alternating algorithm based on half-quadratic minimization to solve the proposed non-smooth and non-convex objective with convergence analysis. A key advantage of the proposed approach with respect to the state-of-the-art is that it can summarize multi-view videos without assuming any prior correspondences/alignment between them, e.g., uncalibrated camera networks. Rigorous experiments on several multi-view datasets demonstrate that our approach clearly outperforms the state-of-the-art methods.


title: A discussion of the strengths and weaknesses of ‘reflection’ in nursing practice and education

abstract: • This paper begins by exploring the principles underpinning ‘reflective practice’. ::: ::: • The impact of reflection on the nursing profession is discussed, with particular attention being paid to nursing education. ::: ::: • The value of reflective practice in nursing has been subject to considerable debate. This paper reviews both the claimed strengths and shortcomings of ‘reflection’, particularly in relation to the use of reflective material in the assessment of students.


title: Is FLIP enough? Or should we use the FLIPPED model instead?

abstract: The flipped learning model, which "flips" traditional in-class lectures with collaborative activities, has gained many followers and converts in K-12 education. However, a review of previous studies shows that the flipped model is still underutilized and underexplored in the higher education context. Research and design models for flipped learning in higher education are also insufficient. This study attempts to fill this gap by developing a model that can provide a foundation for further research and practice for flipped learning in higher education. Building from the four pillars of F-L-I-P? (Flexible Environments, Learning Culture, Intentional Content, and Professional Educators), the proposed research and design model named "FLIPPED" adds three extra letters-P-E-D (Progressive Activities, Engaging Experiences, and Diversified Platforms)-to the F-L-I-P? acronym. This model was implemented in a "Holistic Flipped Classroom" environment and evaluated based on a student survey, interviews, and an analysis of computer system logs. Findings demonstrated that the proposed model was effective; students reported that they were satisfied with the course, their attendance improved, and their study efforts increased. Results also suggested that the transactional distance changed during the learning process: highly motivated students performed much better than less motivated students. However, some students retained their former passive learning habits, and this resulted in an obstruction to full adoption. Reflections on the achievements and challenges of the "FLIPPED" model have culminated in various examples, guidelines, and suggestions for practitioners as they consider their own design, implementation, and adoption.1 Develops the FLIPPED research & design model for higher education.Uses the Holistic Flipped Classroom approach to implement the model.Research was conducted using a survey, interviews, and system log analysis.Identifies challenges and problems during the implementation.Provides strategies, examples, and guidelines for future practitioners.


EFFECTIVE DESIGN OF AN ASSEMBLY LINE USING MODELING & SIMULATION


Badis as Theatre -- putting the drama perspective to the test


title: A Survey on Preprocessing Methods for Web Usage Data

abstract: World Wide Web is a huge repository of web pages and links. It provides abundance of information for the Internet users. The growth of web is tremendous as approximately one million pages are added daily. Users' accesses are recorded in web logs. Because of the tremendous usage of web, the web log files are growing at a faster rate and the size is becoming huge. Web data mining is the application of data mining techniques in web data. Web Usage Mining applies mining techniques in log data to extract the behavior of users which is used in various applications like personalized services, adaptive web sites, customer profiling, prefetching, creating attractive web sites etc., Web usage mining consists of three phases preprocessing, pattern discovery and pattern analysis. Web log data is usually noisy and ambiguous and preprocessing is an important process before mining. For discovering patterns sessions are to be constructed efficiently. This paper reviews existing work done in the preprocessing stage. A brief overview of various data mining techniques for discovering patterns, and pattern analysis are discussed. Finally a glimpse of various applications of web usage mining is also presented.


title: Input space versus feature space in kernel-based methods

abstract: This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In particular, we review what is known about the shape of the image of input space under the feature space map, and how this influences the capacity of SV methods. Following this, we describe how the metric governing the intrinsic geometry of the mapped surface can be computed in terms of the kernel, using the example of the class of inhomogeneous polynomial kernels, which are often used in SV pattern recognition. We then discuss the connection between feature space and input space by dealing with the question of how one can, given some vector in feature space, find a preimage (exact or approximate) in input space. We describe algorithms to tackle this issue, and show their utility in two applications of kernel methods. First, we use it to reduce the computational complexity of SV decision functions; second, we combine it with the kernel PCA algorithm, thereby constructing a nonlinear statistical denoising technique which is shown to perform well on real-world data.


title: Moir\'{e} Photo Restoration Using Multiresolution Convolutional Neural Networks

abstract: Digital cameras and mobile phones enable us to conveniently record precious moments. While digital image quality is constantly being improved, taking high-quality photos of digital screens still remains challenging because the photos are often contaminated with moiré patterns, a result of the interference between the pixel grids of the camera sensor and the device screen. Moiré patterns can severely damage the visual quality of photos. However, few studies have aimed to solve this problem. In this paper, we introduce a novel multiresolution fully convolutional network for automatically removing moiré patterns from photos. Since a moiré pattern spans over a wide range of frequencies, our proposed network performs a nonlinear multiresolution analysis of the input image before computing how to cancel moiré artefacts within every frequency band. We also create a large-scale benchmark dataset with 100,000+ image pairs for investigating and evaluating moiré pattern removal algorithms. Our network achieves state-of-the-art performance on this dataset in comparison to existing learning architectures for image restoration problems.


title: The impact of social media on software engineering practices and tools

abstract: Today's generation of software developers frequently make use of social media, either as an adjunct or integrated into a wide range of tools ranging from code editors and issue trackers, to IDEs and web-based portals. The role of social media usage in software engineering is not well understood, and yet the use of these mechanisms influences software development practices. In this position paper, we advocate for research that strives to understand the benefits, risks and limitations of using social media in software development at the team, project and community levels. Guided by the implications of current tools and social media features, we propose a set of pertinent research questions around community involvement, project coordination and management, as well as individual software development activities. Answers to these questions will guide future software engineering tool innovations and software development team practices.


Individual Creativity Towards Technology Business Incubator Performance Emergent Research Forum ( ERF )


FUZZY MULTI-CRITERIA DECISION MAKING MODEL FOR RISK ASSESSMENT


title: Immersive VR and Education: Embodied Design Principles That Include Gesture and Hand Controls

abstract: This article explores relevant applications of educational theory for the design of immersive virtual reality (VR). Two unique attributes associated with VR position the technology to positively affect education: 1) the sensation of presence, and 2) the embodied affordances of gesture and manipulation in the 3rd dimension. These are referred to as the two profound affordances of VR. The primary focus of this article is on the embodiment afforded by gesture and 3D for learning. The new generation of hand controllers induces embodiment and agency via meaningful and congruent movements with the content to be learned. Several examples of gesture-rich lessons are presented. The final section includes an extensive set of design principles for immersive VR in education, and finishes with the Necessary Nine which are hypothesized to optimize the pedagogy within a lesson.


title: Generalized Quantum Reinforcement Learning with Quantum Technologies

abstract: We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.


title: Application of honey-bee mating optimization algorithm on clustering

abstract: Cluster analysis is one of attractive data mining technique that use in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies done in clustering. Over the last decade, modeling the behavior of social insects, such as ants and bees, for the purpose of search and problem solving has been the context of the emerging area of swarm intelligence. Honey-bees are among the most closely studied social insects. Honey-bee mating may also be considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of marriage in real honey-bee. Honey-bee has been used to model agent-based systems. In this paper, we proposed application of honeybee mating optimization in clustering (HBMK-means). We compared HBMK-means with other heuristics algorithm in clustering, such as GA, SA, TS, and ACO, by implementing them on several well-known datasets. Our finding shows that the proposed algorithm works than the best one.


title: An investigation into round touchscreen wristwatch interaction

abstract: The wristwatch is a device that is quick to access, but is currently under-utilized as a platform for interaction. We investigate interaction on a circular touchscreen wristwatch, empirically determining the error rate for variously-sized buttons placed around the rim. We consider three types of inter-target movements, and derive a mathematical model for error rate given a movement type and angular and radial button widths.


title: 9.8 Receiver with integrated magnetic-free N-path-filter-based non-reciprocal circulator and baseband self-interference cancellation for full-duplex wireless

abstract: Full-duplex (FD) is an emergent wireless communication paradigm where the transmitter (TX) and the receiver (RX) operate at the same time and at the same frequency. The fundamental challenge with FD is the tremendous amount of TX self-interference (SI) at the RX. Low-power applications relax FD system requirements [1], but an FD system with −6dBm transmit power, 10MHz signal bandwidth and 12dB NF budget still requires 86dB of SI suppression to reach the −92dBm noise floor. Recent research has focused on techniques for integrated self-interference cancellation (SIC) in FD receivers [1–3]. Open challenges include achieving the challenging levels of SIC through multi-domain cancellation, and low-loss shared-antenna (ANT) interfaces with high TX-to-RX isolation. Sharedantenna interfaces enable compact form factor, translate easily to MIMO, and ease system design through channel reciprocity.


title: Signal validation for cardiac biometrics

abstract: Medical biometrics offer direct solutions to the liveness and impersonation detection risks which dominate traditional biometric modalities, like the iris, the face or the fingerprint. The electrocardiogram (ECG) is a cardiac signal which falls under this category, and which has lately drawn interest from the biometrics community. This paper presents a novel recognition method based on ECG signals, which enhances the AC/LDA feature extraction algorithm, by incorporating the periodicity transform (PT). It is demonstrated that PT is a powerful tool not only in assessing the matching validity of the signal, but also in handling heart rate changes. The performance of the system over 52 subjects is 92.3%.


title: Child Murder Committed by Severely Mentally Ill Mothers: An Examination of Mothers Found Not Guilty By Reason of Insanity

abstract: Forensic hospital records of 39 severely mentally ill mothers adjudicated Not Guilty by Reason of Insanity for filicide (child murder by parents) were analyzed to describe characteristics preceding this tragedy and to suggest prevention strategies. Almost three-quarters of the mothers (72%) had previous mental health treatment. Over two thirds (69%) of the mothers were experiencing auditory hallucinations, most frequently command hallucinations, and half (49%) were depressed at the time of the offense. Over one third (38%) of the filicides occurred during pregnancy or the postpartum period, and many had a history of postpartum psychosis. Almost three-quarters (72%) of the mothers had experienced considerable developmental stressors, such as death of their own mother or incest. Maternal motives for filicide were predominantly "altruistic" (meaning murder out of love) or "acutely psychotic" (occurring in the throes of psychosis, without rational motive). Psychiatrists should perform careful risk assessments for filicide in mothers with mental illnesses. Language: en


title: Implementation of N-way Gysel combiners using back to back microstrip structure

abstract: This paper presents a method for implementation of Gysel power combiners on back to back microstrip structure. In this method the topology of Gysel combiner is divided into two sections and then each section is implemented on one side of the back to back microstrip structure. An experimental prototype using proposed method was designed over frequency range from 2.9 to 3.1GHz. The results obtained from this prototype show a good agreement with EM simulation results.


title: Using dependency graphs to support collaboration over GitHub: The Neo4j graph database approach

abstract: Software Configuration Management Systems capture direct conflicts which arise due to concurrent editing of same shared artifact. However, indirect conflicts, arising due to concurrent editing of related artifacts by collaborative developers, might eventually evade the build process employed by the underlying SCM. Additional collaborative software development tools have been developed, that supplement existing SCM systems, by providing change awareness information about potential indirect conflicts. In this paper, we propose the tool named Collaboration Over GitHub (COG), that provides support for collaborative software development over GitHub and also captures indirect conflicts at finer levels of granularity. For this, COG converts project repositories into dependency graphs which are stored as Neo4j Graph Databases. These graphs store structural and dependency information obtained from codebases and are used for identifying conflicts and affected artifacts.


title: Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

abstract: Industry 4.0 can make a factory smart by applying intelligent information processing approaches, communication systems, future-oriented techniques, and more. However, the high complexity, automation, and flexibility of an intelligent factory bring new challenges to reliability and safety. Industrial big data generated by multisource sensors, intercommunication within the system and external-related information, and so on, might provide new solutions for predictive maintenance to improve system reliability. This paper puts forth attributes of industrial big data processing and actively explores industrial big data processing-based predictive maintenance. A novel framework is proposed for structuring multisource heterogeneous information, characterizing structured data with consideration of the spatiotemporal property, and modeling invisible factors, which would make the production process transparent and eventually implement predictive maintenance on facilities and energy saving in the industry 4.0 era. The effectiveness of the proposed scheme was verified by analyzing multisource heterogeneous industrial data for the remaining life prediction of key components of machining equipment.


title: An activity guideline for technology roadmapping implementation

abstract: To strive for sustainability under today's intense business competition, organisations apply technology roadmapping (TRM) as a strategic planning tool to align their technology strategies with business strategies. Many organisations desire to integrate TRM into an ongoing strategic planning process. The consequences of TRM implementation can lead to some changes in the business process, organisational structure, or even working culture. Applying a change management approach will help organisations to understand the basic elements that an individual needs so that some challenges can be addressed in advance before adopting the TRM process. This paper proposes a practical guideline to implement technology roadmapping along with a case example.


title: Color image segmentation

abstract: In this work, a new approach to fully automatic color image segmentation, called JSEG, is presented. First, colors in the image are quantized to several representing classes that can be used to differentiate regions in the image. Then, image pixel colors are replaced by their corresponding color class labels, thus forming a class-map of the image. A criterion for "good" segmentation using this class-map is proposed. Applying the criterion to local windows in the class-map results in the "J-image", in which high and low values correspond to possible region boundaries and region centers, respectively. A region growing method is then used to segment the image based on the multi-scale J-images. Experiments show that JSEG provides good segmentation results on a variety of images.


title: Cerebral palsy

abstract: Cerebral palsy is the most common cause of childhood-onset, lifelong physical disability in most countries, affecting about 1 in 500 neonates with an estimated prevalence of 17 million people worldwide. Cerebral palsy is not a disease entity in the traditional sense but a clinical description of children who share features of a non-progressive brain injury or lesion acquired during the antenatal, perinatal or early postnatal period. The clinical manifestations of cerebral palsy vary greatly in the type of movement disorder, the degree of functional ability and limitation and the affected parts of the body. There is currently no cure, but progress is being made in both the prevention and the amelioration of the brain injury. For example, administration of magnesium sulfate during premature labour and cooling of high-risk infants can reduce the rate and severity of cerebral palsy. Although the disorder affects individuals throughout their lifetime, most cerebral palsy research efforts and management strategies currently focus on the needs of children. Clinical management of children with cerebral palsy is directed towards maximizing function and participation in activities and minimizing the effects of the factors that can make the condition worse, such as epilepsy, feeding challenges, hip dislocation and scoliosis. These management strategies include enhancing neurological function during early development; managing medical co-morbidities, weakness and hypertonia; using rehabilitation technologies to enhance motor function; and preventing secondary musculoskeletal problems. Meeting the needs of people with cerebral palsy in resource-poor settings is particularly challenging.


title: Moving Object Detection and Tracking for Video Survelliance

abstract: Moving object detection and Tracking has been widely used in diverse discipline such as intelligent transportation system, airport security system, video surveillance applications, and so on. This paper presents the moving object detection and tracking using reference Background Subtraction. In this method, we used Static camera for video and first frame of video is directly consider as Reference Background Frame and this frame is subtract from current frame to detect moving object and then set threshold T value. If the pixel difference is greater than the set threshold T, then it determines that the pixels from moving object, otherwise, as the background pixels. But this fixed threshold suitable only for an ideal condition is not suitable for complex environment with lighting changes. So that in this paper we used dynamic optimization threshold method to obtain a more complete moving objects. This method can effectively eliminate the impact of light changes.


title: Automatic Calibration of Spinning Actuated Lidar Internal Parameters

abstract: Actuated lidar, where a scanning lidar is combined with an actuation mechanism to scan a three-dimensional volume rather than a single line, has been used heavily in a wide variety of field robotics applications. Common examples of actuated lidar include spinning/rolling and nodding/pitching configurations. Due to the construction of actuated lidar, the center of rotation of the lidar mirror may not coincide with the center of rotation of the actuation mechanism. To triangulate a precise point cloud representation of the environment, the centers of rotation must be brought into alignment using a suitable calibration procedure. We refer to this problem as estimating the internal parameters of actuated lidar. In this work, we focus on spinning/rolling lidar and present a fully automated algorithm for calibration using generic scenes without the need for specialized calibration targets. The algorithm is evaluated on a range of real and synthetic data and is shown to be robust, accurate, and have a large basin of convergence.


title: Vision-Based Multi-UAV Position Estimation Localization Based on Blob Features for Exploration Missions

abstract: This paper describes a method for vision-based unmanned aerial vehicle (UAV) motion estimation from multiple planar homographies. The paper also describes the determination of the relative displacement between different UAVs employing techniques for blob feature extraction and matching. It then presents and shows experimental results of the application of the proposed technique to multi-UAV detection of forest fires


title: Reducing Smartphone Application Delay through Read/Write Isolation

abstract: The smartphone has become an important part of our daily lives. However, the user experience is still far from being optimal. In particular, despite the rapid hardware upgrades, current smartphones often suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this paper, we investigate the behavior of reads and writes in smartphones. We conduct the first large-scale measurement study on the Android I/O delay using the data collected from our Android application running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. Additionally, we show the asymmetry of the slowdown of one I/O type due to another, and elaborate the speedup of concurrent I/Os over serial ones. We use this obtained knowledge to design and implement a system prototype called SmartIO that reduces the application delay by prioritizing reads over writes, and grouping them based on assigned priorities. SmartIO issues I/Os with optimized concurrency parameters. The system is implemented on the Android platform and evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%.


title: Record Linkage to Match Customer Names: A Probabilistic Approach

abstract: Consider the following problem: given a database of records indexed by names (e.g., name of companies, restaurants, businesses, or universities) and a new name, determine whether the new name is in the database, and if so, which record it refers to. This problem is an instance of record linkage problem and is a challenging problem because people do not consistently use the official name, but use abbreviations, synonyms, different order of terms, different spelling of terms, short form of terms, and the name can contain typos or spacing issues. We provide a probabilistic model using relational logistic regression to find the probability of each record in the database being the desired record for a given query and find the best record(s) with respect to the probabilities. Building on term-matching and translational approaches for search, our model addresses many of the aforementioned challenges and provides good results when existing baselines fail. Using the probabilities outputted by the model, we can automate the search process for a portion of queries whose desired documents get a probability higher than a trust threshold. We evaluate our model on a large real-world dataset from a telecommunications company and compare it to several state-of-the-art baselines. The obtained results show that our model is a promising probabilistic model for record linkage for names. We also test if the knowledge learned by our model on one domain can be effectively transferred to a new domain. For this purpose, we test our model on an unseen test set from the business names of the secondString dataset. Promising results show that our model can be effectively applied to unseen datasets. Finally, we study the sensitivity of our model to the statistics of datasets.


Congestion and Corruption Loss Detection with Enhanced-TCP


title: DoA estimation and capacity analysis for 3D massive-MIMO/FD-MIMO OFDM system

abstract: With the promise of meeting future capacity demands for mobile broadband communications, 3D massive-MIMO/Full Dimension MIMO (FD-MIMO) systems have gained much interest among the researchers in recent years. Apart from the huge spectral efficiency gain offered by the system, the reason for this great interest can also be attributed to significant reduction of latency, simplified multiple access layer, and robustness to interference. However, in order to completely extract the benefits of massive-MIMO systems, accurate channel state information is very critical. In this paper, a channel estimation method based on direction of arrival (DoA) estimation is presented for massive-MIMO OFDM systems. To be specific, the DoA is estimated using Estimation of Signal Parameter via Rotational Invariance Technique (ESPRIT) method, and the capacity of the system is analytically characterized. It has been shown that optimum precoding matrix for the massive MIMO OFDM system can be constructed from the DoA estimation at the base station.


Hysteresis and phase transitions for 1 D and 3 D models in shape memory alloys


title: Generating Long-term Trajectories Using Deep Hierarchical Networks

abstract: We study the problem of modeling spatiotemporal trajectories over long time horizons using expert demonstrations. For instance, in sports, agents often choose action sequences with long-term goals in mind, such as achieving a certain strategic position. Conventional policy learning approaches, such as those based on Markov decision processes, generally fail at learning cohesive long-term behavior in such high-dimensional state spaces, and are only effective when fairly myopic decision-making yields the desired behavior. The key difficulty is that conventional models are ``single-scale'' and only learn a single state-action policy. We instead propose a hierarchical policy class that automatically reasons about both long-term and short-term goals, which we instantiate as a hierarchical neural network. We showcase our approach in a case study on learning to imitate demonstrated basketball trajectories, and show that it generates significantly more realistic trajectories compared to non-hierarchical baselines as judged by professional sports analysts.


title: A covariance analysis model for DDoS attack detection

abstract: This paper discusses the effects of multivariate correlation analysis on the DDoS detection and proposes an example, a covariance analysis model for detecting SYN flooding attacks. The simulation results show that this method is highly accurate in detecting malicious network traffic in DDoS attacks of different intensities. This method can effectively differentiate between normal and attack traffic. Indeed, this method can detect even very subtle attacks only slightly different from the normal behaviors. The linear complexity of the method makes its real time detection practical. The covariance model in this paper to some extent verifies the effectiveness of multivariate correlation analysis for DDoS detection. Some open issues still exist in this model for further research.


title: Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

abstract: Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain operational to collect and transfer data from the environment to a base-station. However, sensor nodes have limited energy in their primary power storage unit, and this energy may be quickly drained if the sensor node remains operational over long periods of time. Therefore, the idea of harvesting ambient energy from the immediate surroundings of the deployed sensors, to recharge the batteries and to directly power the sensor nodes, has recently been proposed. The deployment of energy harvesting in environmental field systems eliminates the dependency of sensor nodes on battery power, drastically reducing the maintenance costs required to replace batteries. In this article, we review the state-of-the-art in energy-harvesting WSNs for environmental monitoring applications, including Animal Tracking, Air Quality Monitoring, Water Quality Monitoring, and Disaster Monitoring to improve the ecosystem and human life. In addition to presenting the technologies for harvesting energy from ambient sources and the protocols that can take advantage of the harvested energy, we present challenges that must be addressed to further advance energy-harvesting-based WSNs, along with some future work directions to address these challenges.


Six Sigma for Software


title: Interactive Arrangement of Botanical L-System Models

abstract: In this paper, we explore the problem of interactively mauipulat- ing plant models without sacrificing their botanical accuracy. The primary technical contribution of the paper is a method for interac- tively manipulating plant structures using a inverse-kinematics op- timizatitin technique. The branches of the plant are endowed with flexural and torsional stiffnesses, and these are used in the IK opti- mization. We demonstrate our approach with several examples of plant models arranged in this fashion. The power of L-systems lies in their ability to generate complicated structures from a small number of rules, but the cost of this brevity is the lack of precise control over the final form. This is a draw- back of L-system models in applications such as illustration, scene design, and animation - areas in which a designer's aesthetic vi- sion is the priority. In such areas, what is needed is a plant model that can grow and respond to its environment, yet that can be easily adjusted and controlled. Our application ilsa (interactive L-string arranger) allows a user to interactively manipulate a plant model while preserving its botanical accuracy and behavior, improving the usefulness of L-systems in domains beyond biological modeling. The ability to interact with L-system models extends their utility within the domain of botanical modeling as well. A model can be grown for a certain number of generations, arranged or pruned, and then grown some more. The final form of such a model expresses both the developmental behavior of the plant and the effects of hu- man intervention. Our project ties together threads of related work from two areas of computer graphics: plant modeling using L-systems and realistic model manipulation. The majority of work on L-system model- ing is that of Prusinkiewicz et al. (4, 10, 111. In the broad area of interactive manipulation, the most closely related work is that of Zhao and Badler, who developed a system for the interactive ma- nipulation of jointed figures using inverse-kinematics, and that of


title: LeCun Y: Dynamical factor graphs for time series modeling

abstract: This article presents a method for training Dynamic Factor Graphs (DFG) with continuous latent state variables. A DFG includes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden variables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observation sequence. Because the factors are designed to ensure a constant partition function, they can be trained by minimizing the expected energy over training sequences with respect to the factors' parameters. These alternated inference and parameter updates can be seen as a deterministic EM-like procedure. Using smoothing regularizers, DFGs are shown to reconstruct chaotic attractors and to separate a mixture of independent oscillatory sources perfectly. DFGs outperform the best known algorithm on the CATS competition benchmark for time series prediction. DFGs also successfully reconstruct missing motion capture data.


title: Transactional versus transformational leadership: An analysis of the MLQ

abstract: A questionnaire used often to measure transformational, transactional and laissez-faire leadership is che Multifactor Leadership Questionnaire developed by Bass (Bass & Avolio, 1989). This study aims to test the factor structure of the MLQ as developed by Bass & Avolio. The MLQ-8Y was analysed using data collected in Dutch organizations. Seven hundred employees from eight organizations rated their leader's behaviour with the MLQ. First, an indication of the internal consistency of the scales developed by Bass is reported. The results of subsequent factor analyses show that the three types of leadership can be found in the data; however, the scales found here are slightly different from Bass' scales. Especially, the transactional and laissez-faire scales have been altered on theoretical and empirical grounds. The adapted version of the MLQ covers the domain with fewer items.


title: Defect spectroscopy using 1/fγ noise of gate leakage current in ultrathin oxide MOSFETs

abstract: Abstract The electrical trap properties in ultrathin oxide MOSFETs are extracted using 1/ f γ low frequency noise components of gate leakage current. The technique is based on the low frequency noise measurements which are capable of probing trap states over the energy and space. The gate voltage is swept between 0.4 and 1.32 V to obtain the different power slopes γ of 1/ f γ noise spectrum. From the measured noise data, the trap parameters such as slow trap densities and capture cross-section are accurately determined as a function of trap energy and trap distance. The 1/ f γ noise measurement results confirm that the capture cross-sections depend strongly on the surface potential, and the slow trap densities estimated near the band edge and at the Si–SiO 2 interface are higher than those near the midgap and deeper distance. Moreover, the parallel conductance spectroscopy of slow traps is reproduced by 1/ f γ low frequency noise measurements, and is verified by the low frequency plateau in measured conductance loss G p ( f )/ w . For the purpose of investigating the dependency of trap parameters on the complex admittance of oxide traps, the Cole–Cole plots for different trap profiles, a uniform and exponentially decreasing trap distribution in space, are calculated using the theoretical model. The noise spectroscopy technique further reveal the dynamic evolution of trap states and gives a clear physical understanding of the pertinent low frequency noise sources.


title: Is the deconvolution layer the same as a convolutional layer?

abstract: In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented. Firstly, What is the relationship between our proposed layer and the deconvolution layer? And secondly, why are convolutions in low-resolution (LR) space a better choice? These are key questions we tried to answer in the paper, but we were not able to go into as much depth and clarity as we would have liked in the space allowance. To better answer these questions in this note, we first discuss the relationships between the deconvolution layer in the forms of the transposed convolution layer, the sub-pixel convolutional layer and our efficient sub-pixel convolutional layer. We will refer to our efficient sub-pixel convolutional layer as a convolutional layer in LR space to distinguish it from the common sub-pixel convolutional layer. We will then show that for a fixed computational budget and complexity, a network with convolutions exclusively in LR space has more representation power at the same speed than a network that first upsamples the input in high resolution space.


title: The KlattGrid speech synthesizer

abstract: We present a new speech synthesizer class, named KlattGrid, for the Praat program [3]. This synthesizer is based on the original description of Klatt [1, 2]. New aspects of a KlattGrid in comparison with other Klatt-type synthesizers are that a KlattGrid is not frame-based but time-based. You specify parameters as a function of time with any precision you like. has no limitations on the number of oral formants, nasal formants, nasal antiformants, tracheal formants or tracheal antiformants that can be defined. has separate formants for the frication part. allows varying the form of the glottal flow function as a function of time. allows for any number of formants and bandwidths to be modified during the open phase of the glottis. uses no beforehand quantization of amplitude parameters. is fully integrated into the freely available speech analysis program Praat [3].


title: The Milieu of Managerial Work: An Integrative Framework Linking Work Context to Role Requirements

abstract: Theoretical and empirical efforts focusing on the interplay between work context and managerial role requirements have been conspicuously absent in the scholarly literature. This paucity exists despite over 60 years of research concerning the requirements of managerial work and with the rather universal recognition that work context meaningfully shapes organizational behavior. The authors developed a theoretical model linking different types of role requirements to different forms of work context. They empirically tested this framework with a nationally representative sample of 8,633 incumbents spanning 52 managerial occupations. Findings from hierarchical linear modeling analyses demonstrated that discrete forms of context (task, social, and physical) exert significant and predictable effects on managerial role requirements.


title: Toward Automatic Mitotic Cell Detection and Segmentation in Multispectral Histopathological Images

abstract: The count of mitotic cells is a critical factor in most cancer grading systems. Extracting the mitotic cell from the histopathological image is a very challenging task. In this paper, we propose an efficient technique for detecting and segmenting the mitotic cells in the high-resolution multispectral image. The proposed technique consists of three main modules: discriminative image generation, mitotic cell candidate detection and segmentation, and mitotic cell candidate classification. In the first module, a discriminative image is obtained by linear discriminant analysis using ten different spectral band images. A set of mitotic cell candidate regions is then detected and segmented by the Bayesian modeling and local-region threshold method. In the third module, a 226 dimension feature is extracted from the mitotic cell candidates and their surrounding regions. An imbalanced classification framework is then applied to perform the classification for the mitotic cell candidates in order to detect the real mitotic cells. The proposed technique has been evaluated on a publicly available dataset of 35 × 10 multispectral images, in which 224 mitotic cells are manually labeled by experts. The proposed technique is able to provide superior performance compared to the existing technique, 81.5% sensitivity rate and 33.9% precision rate in terms of detection performance, and 89.3% sensitivity rate and 87.5% precision rate in terms of segmentation performance.


title: Enhancements of narrowband IoT in 3GPP Rel-14 and Rel-15

abstract: In 3GPP Rel-13, a narrowband system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. Further enhancements have been made to this system in subsequent 3GPP releases. In this paper, we describe Rel-14 enhancements that have been standardized for NB-IoT. They include positioning support, multicast support, higher data rates, and non-anchor carrier enhancements. In addition, we summarize upcoming Rel-15 enhancements and expected improvement. Representative performance results are also provided to illustrate potential improvements from the enhancements.


title: "Costs" and Short-Term Survivorship of Hornless Black Rhinos

abstract: The devastation of Africa's black rhinos (Diceros bicornis) by poaching (Western 1987) highlights the challenges found at the interface of biology and economics (Leader-Williams & Albon 1988). One of the most controversial conservation actions to date has been the removal of horns from black and white rhinos (Ceratotherium simum), tactics adopted in both Namibia (Lindeque 1990) and Zimbabwe (Kock 1991). While acts such as these have been debated for more than a decade, the efficacy of "dehorning" remains unknown (Western 1982; Leader-Williams 1989), in part due to an absence of data on (1) rates of horn regrowth and the subsequent monetary worth of the horns; and (2) whether the vulnerability of dehorned rhinos or their neonates is altered with respect to poachers and predators. Here we explore economic and biological implications of horn removal using data gathered in Namibia in 1991, 1992, and 1993. Specifically, we make three


title: Evolving Pac-Man Players: Can We Learn from Raw Input?

abstract: Pac-Man (and variant) computer games have received some recent attention in artificial intelligence research. One reason is that the game provides a platform that is both simple enough to conduct experimental research and complex enough to require non-trivial strategies for successful game-play. This paper describes an approach to developing Pac-Man playing agents that learn game-play based on minimal onscreen information. The agents are based on evolving neural network controllers using a simple evolutionary algorithm. The results show that neuroevolution is able to produce agents that display novice playing ability, with a minimal amount of onscreen information, no knowledge of the rules of the game and a minimally informative fitness function. The limitations of the approach are also discussed, together with possible directions for extending the work towards producing better Pac-Man playing agents


title: An Investigation of Fuel Cell / Battery and Fuel Cell / Supercapacitor Hybrid Sources for Vehicle Applications

abstract: This article presents the impact of the performance of an FC and control strategies on the benefits of hybridization of fuel cell/supercapacitor hybrid sources for vehicle applications. Then, the storage device can complement the main source to produce the compatibility and performance characteristics needed in a load. The studies of two hybrid power systems for vehicle applications, FC/battery and FC/supercapacitor hybrid power sources, are explained. Experimental results with small-scale devices (a PEMFC of 500 W, 40 A, and 13 V; a lead-xicid battery module of 33 Ah and 48 V; and a supercapacitor module of 292 F, 500 A, and 30 V) in laboratory will illustrate the performance of the system during motor-drive cycles.


title: Social Set Analysis: A Set Theoretical Approach to Big Data Analytics

abstract: Current analytical approaches in computational social science can be characterized by four dominant paradigms: text analysis (information extraction and classification), social network analysis (graph theory), social complexity analysis (complex systems science), and social simulations (cellular automata and agent-based modeling). However, when it comes to organizational and societal units of analysis, there exists no approach to conceptualize, model, analyze, explain, and predict social media interactions as individuals’ associations with ideas, values, identities, and so on. To address this limitation, based on the sociology of associations and the mathematics of set theory, this paper presents a new approach to big data analytics called social set analysis. Social set analysis consists of a generative framework for the philosophies of computational social science, theory of social data, conceptual and formal models of social data, and an analytical framework for combining big social data sets with organizational and societal data sets. Three empirical studies of big social data are presented to illustrate and demonstrate social set analysis in terms of fuzzy set-theoretical sentiment analysis, crisp set-theoretical interaction analysis, and event-studies-oriented set-theoretical visualizations. Implications for big data analytics, current limitations of the set-theoretical approach, and future directions are outlined.


title: Leveraging Context-Free Grammar for Efficient Inverted Index Compression

abstract: Large-scale search engines need to answer thousands of queries per second over billions of documents, which is typically done by querying a large inverted index. Many highly optimized integer encoding techniques are applied to compress the inverted index and reduce the query processing time. In this paper, we propose a new grammar-based inverted index compression scheme, which can improve the performance of both index compression and query processing. Our approach identifies patterns (common subsequences of docIDs) among different posting lists and generates a context-free grammar to succinctly represent the inverted index. To further optimize the compression performance, we carefully redesign the index structure. Experiments show a reduction up to 8.8% in space usage while decompression is up to 14% faster. We also design an efficient list intersection algorithm which utilizes the proposed grammar-based inverted index. We show that our scheme can be combined with common docID reassignment methods and encoding techniques, and yields about 14% to 27% higher throughput for AND queries by utilizing multiple threads.


title: Adversarial Multiclass Classification: A Risk Minimization Perspective

abstract: Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses. In contrast with empirical risk minimization (ERM) methods, which use convex surrogate losses to approximate the desired non-convex target loss function, adversarial methods minimize non-convex losses by treating the properties of the training data as being uncertain and worst case within a minimax game. Despite this difference in formulation, we recast adversarial classification under zero-one loss as an ERM method with a novel prescribed loss function. We demonstrate a number of theoretical and practical advantages over the very closely related hinge loss ERM methods. This establishes adversarial classification under the zero-one loss as a method that fills the long standing gap in multiclass hinge loss classification, simultaneously guaranteeing Fisher consistency and universal consistency, while also providing dual parameter sparsity and high accuracy predictions in practice.


title: Semantics-based Graph Approach to Complex Question-Answering

abstract: This paper suggests an architectural approach of representing knowledge graph for complex question-answering. There are four kinds of entity relations added to our knowledge graph: syntactic dependencies, semantic role labels, named entities, and coreference links, which can be effectively applied to answer complex questions. As a proof of concept, we demonstrate how our knowledge graph can be used to solve complex questions such as arithmetics. Our experiment shows a promising result on solving arithmetic questions, achieving the 3folds cross-validation score of 71.75%.


title: A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments

abstract: National exercises for the evaluation of research activity by universities are becoming regular practice in ever more countries. These exercises have mainly been conducted through the application of peer-review methods. Bibliometrics has not been able to offer a valid large-scale alternative because of almost overwhelming difficulties in identifying the true author of each publication. We will address this problem by presenting a heuristic approach to author name disambiguation in bibliometric datasets for large-scale research assessments. The application proposed concerns the Italian university system, comprising 80 universities and a research staff of over 60,000 scientists. The key advantage of the proposed approach is the ease of implementation. The algorithms are of practical application and have considerably better scalability and expandability properties than state-of-the-art unsupervised approaches. Moreover, the performance in terms of precision and recall, which can be further improved, seems thoroughly adequate for the typical needs of large-scale bibliometric research assessments. © 2011 Wiley Periodicals, Inc.


CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings


title: Generative Adversarial Networks

abstract: We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.


title: Starships and Cybersecurity: Teaching Security Concepts through Immersive Gaming Experiences

abstract: As the world grows more technology involved, teaching some measure of cybersecurity has become imperative. Our research lab aims to help increase the cybersecurity interest and awareness among those of all ages. To aid us in this endeavor, we decided to introduce an interesting method that gamifies cybersecurity. Our methodology tests whether our new method is a viable vehicle for cybersecurity education. To do this, we asked participants to take surveys and included our own observations. We conclude our paper with our analysis of survey results and future improvements for an effective educational tool.


title: TRANSRECTAL ULTRASONOGRAPHY OF EQUINE LUMBOSACRAL NERVES: PILOT STUDY IN 28 HEALTHY WARMBLOOD HORSES

abstract: A noninvasive method for visualizing lumbosacral nerves would be helpful for horses with suspected lumbosacral plexopathy or injury. The aim of this prospective descriptive pilot study was to characterize the ultrasonographic appearance of the lumbosacral nerves in a sample of healthy horses, and expand on the technique for image acquisition. Horses were recruited for inclusion if they were determined to be healthy and sound based on clinical and lameness evaluation. Transrectal ultrasound images of the lumbosacral nerves (L6, S1, and S2) were obtained for both sides. Landmarks for localization, and techniques for nerve identification and measurement were described. Effects of sex, age, side, and nerve on measured thickness were statistically tested. Twenty-eight warmblood horses were sampled (15 males and 13 females). Ages ranged from 5 to 15 years. Ventral nerve roots from L6 to S2 appeared as tubular structures with a characteristic hyperechoic linear echo pattern. There was no significant difference in nerve vertical diameter between left and right sides. A three-way interaction was found among sex, age and lumbosacral nerve. The L6 nerve in males was significantly larger than S1 only in the youngest group. The S2 nerve was significantly smaller than L6 or S1 regardless of age group or sex. In conclusion, transrectal ultrasound was a feasible method for visualizing and measuring equine lumbosacral nerves. Wide ranges of sizes for each nerve were found between horses, however nerves were bilaterally symmetrical within horses. Side comparison is therefore recommended when nerve pathology is suspected.


title: Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM

abstract: Abstract The detection of seizure activity in electroencephalogram (EEG) signals is crucial for the classification of epileptic seizures. However, epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. In this work, we present a new technique for seizure classification of EEG signals using Hilbert–Huang transform (HHT) and support vector machine (SVM). In our method, the HHT based time-frequency representation (TFR) has been considered as time-frequency image (TFI), the segmentation of TFI has been implemented based on the frequency-bands of the rhythms of EEG signals, the histogram of grayscale sub-images has been represented. Statistical features such as mean, variance, skewness and kurtosis of pixel intensity in the histogram have been extracted. The SVM with radial basis function (RBF) kernel has been employed for classification of seizure and nonseizure EEG signals. The classification accuracy and receiver operating characteristics (ROC) curve have been used for evaluating the performance of the classifier. Experimental results show that the best average classification accuracy of this algorithm can reach 99.125% with the theta rhythm of EEG signals.


title: Management of Status Asthmaticus in Children

abstract: Asthma is a common chronic inflammatory disorder of the airways characterized by recurrent wheezing, breathlessness, and coughing. Acute exacerbations of asthma can be life-threatening; annual worldwide estimated mortality is 250,000 and most of these deaths are preventable. While most of the acute exacerbations can be managed successfully in the emergency room, few children have severe exacerbations requiring intensive care. Mainstay of treatment for status asthmaticus are inhaled β2 agonist and anticholinergic agents, oxygen along with corticosteroids. Children who do not respond well to initial treatment require parenteral β2 agonist and magnesium. Rarely, sick children need parenteral aminophylline infusion and mechanical ventilation. Guidelines for diagnosis, treatment, ventilator management and supportive care for status asthmaticus in children are discussed in the protocol.


title: PAMM - a robotic aid to the elderly for mobility assistance and monitoring: a "helping-hand" for the elderly

abstract: Meeting the needs of the elderly presents important technical challenges. In this research, a system concept for a robotic aid to provide mobility assistance and monitoring for the elderly and its enabling technologies are being developed. The system, called PAMM (personal aid for mobility and monitoring), is intended to assist the elderly living independently or in senior assisted living facilities. It provides physical support and guidance, and it monitors the user's basic vital signs. An experimental test-bed used to evaluate the PAMM technology is described. This test-bed has a cane based configuration with a nonholonomic drive. Preliminary field trials at an Eldercare Facility are presented.


title: Power Factor Corrected Welding Power Supply Using Modified Zeta Converter

abstract: A power factor (PF) corrected single stage, two-switch isolated zeta converter is proposed for arc welding. This modified zeta converter is having two switches and two clamping diodes on the primary side of a high-frequency transformer. This, in turn, results in reduced switch stress. The proposed converter is designed to operate in a discontinuous inductor current mode (DICM) to achieve inherent PF correction at the utility. The DICM operation substantially reduces the complexity of the control and effectively regulates the output dc voltage. The proposed converter offers several features, such as inherent overload current limit and fast parametrical response, to the load and source voltage conditions. This, in turn, results in an improved performance in terms of power quality indices and an enhanced weld bead quality. The proposed modified zeta converter is designed and its performance is simulated in the MATLAB/Simulink environment. Simulated results are also verified experimentally on a developed prototype of the converter. The performance of the system is investigated in terms of its input PF, displacement PF, total harmonic distortion of ac mains current, voltage regulation, and robustness to prove its efficacy in overall performance.


title: MIMO State Feedback Controller for a Flexible Joint Robot with Strong Joint Coupling

abstract: The paper describes the modeling and control of a robot with flexible joints (the DLR medical robot), which has strong mechanical couplings between pairs of joints realized with a differential gear-box. Because of this coupling, controllers developed before for the DLR light-weight robots cannot be directly applied. The previous control approach is extended in order to allow a multi-input-multi-output (MIMO) design for the strongly coupled joints. Asymptotic stability is shown for the MIMO controller. Finally, experimental results with the DLR medical robot are presented.


title: Active Learning with Multi-Label SVM Classification

abstract: Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more time-consuming or expensive to obtain than annotations of single-label instances. Though active learning has been widely studied on reducing labeling effort for single-label problems, current research on multi-label active learning remains in a preliminary state. In this paper, we first propose two novel multi-label active learning strategies, a max-margin prediction uncertainty strategy and a label cardinality inconsistency strategy, and then integrate them into an adaptive framework of multi-label active learning. Our empirical results on multiple multilabel data sets demonstrate the efficacy of the proposed active instance selection strategies and the integrated active learning approach.


title: A Context-Aware Usability Model for Mobile Health Applications

abstract: Mobile healthcare is a fast growing area of research that capitalizes on mobile technologies and wearables to provide realtime and continuous monitoring and analysis of vital signs of users. Yet, most of the current applications are developed for general population without taking into consideration the context and needs of different user groups. Designing and developing mobile health applications and diaries according to the user context can significantly improve the quality of user interaction and encourage the application use. In this paper, we propose a user context model and a set of usability attributes for developing mobile applications in healthcare. The proposed model and the selected attributes are integrated into a mobile application development framework to provide user-centered and context-aware guidelines. To validate our framework, a mobile diary was implemented for patients undergoing Peritoneal Dialysis (PD) and tested with real users.


title: Smart-Cart for Smart-Cities

abstract: In the current scenario, smart devices and smart technology are gaining high momentum for Automatic Identification and Data Capture(AIDC). Radio Frequency Identification and Detection(RFID) are one of the promising solutions for AIDC. Here, we present a smart solution for automatic billing enabled by RFID. We have proposed new methodology to simplify the billing process and reduce the time spent in searching products in the supermarket. Every product and customers details is uniquely identified by the RFID. An Authenticated customer can choose the products from the list displayed on the user interface. Then the smart cart automatically routes itself to the location of the product. Every purchased products price and weight is to be read by the RFID reader on the Smart-Cart through RFID tag on the product. Concurrent billing and calculation is done by the website developed. When the shopping is completed, the amount gets debited from the customers prepaid account.


title: Story Recall and Narrative Coherence of High-Functioning Children with Autism Spectrum Disorders

abstract: Previous research has found few quantitative differences between children with high-functioning autism spectrum disorders (ASDs) and well-matched controls in the length, complexity, and structure of their narratives. Researchers have noted, however, that narratives of children with ASDs have an unusual and idiosyncratic nature. This study provides an analysis of narratives in 17 children with high-functioning ASDs and 17 typically developing children matched on age, gender, language abilities, and cognitive abilities. We examined story recall and narrative coherence. The study revealed no group differences in story length or syntactic complexity. Children with ASDs also did not differ from controls in their use of the gist of a story to aid recall, or in their sensitivity to the importance of story events. Children with ASDs did, however, produce narratives that were significantly less coherent than the narratives of controls. Children with ASDs appeared less likely to use the gist of the story to organize their narratives coherently. These findings are discussed with regard to their relationship to other cognitive and linguistic difficulties of children with ASDs.