title: State-of-the-Art Vietnamese Word Segmentation

abstract: Word segmentation is the first step of any tasks in Vietnamese language processing. This paper reviews state-of-the-art approaches and systems for word segmentation in Vietnamese. To have an overview of all stages from building corpora to developing toolkits, we discuss building the corpus stage, approaches applied to solve the word segmentation and existing toolkits to segment words in Vietnamese sentences. In addition, this study shows clearly the motivations on building corpus and implementing machine learning techniques to improve the accuracy for Vietnamese word segmentation. According to our observation, this study also reports a few of achievements and limitations in existing Vietnamese word segmentation systems.


title: GRAMMAR-SUPPORTED 3D INDOOR RECONSTRUCTION FROM POINT CLOUDS FOR "AS-BUILT" BIM

abstract: Abstract. The paper presents a grammar-based approach for the robust automatic reconstruction of 3D interiors from raw point clouds. The core of the approach is a 3D indoor grammar which is an extension of our previously published grammar concept for the modeling of 2D floor plans. The grammar allows for the modeling of buildings whose horizontal, continuous floors are traversed by hallways providing access to the rooms as it is the case for most office buildings or public buildings like schools, hospitals or hotels. The grammar is designed in such way that it can be embedded in an iterative automatic learning process providing a seamless transition from LOD3 to LOD4 building models. Starting from an initial low-level grammar, automatically derived from the window representations of an available LOD3 building model, hypotheses about indoor geometries can be generated. The hypothesized indoor geometries are checked against observation data - here 3D point clouds - collected in the interior of the building. The verified and accepted geometries form the basis for an automatic update of the initial grammar. By this, the knowledge content of the initial grammar is enriched, leading to a grammar with increased quality. This higher-level grammar can then be applied to predict realistic geometries to building parts where only sparse observation data are available. Thus, our approach allows for the robust generation of complete 3D indoor models whose quality can be improved continuously as soon as new observation data are fed into the grammar-based reconstruction process. The feasibility of our approach is demonstrated based on a real-world example.


title: CNTFET basics and simulation

abstract: This paper provides an overview of current types of CNTFETs and of some compact models. Using the available models, the influence of the parameters on the device characteristics was simulated and analyzed. The conclusion is that the tube diameter influences not only the current level, but also the threshold voltage of the CNTFET, while the contact resistance influences only the current level. From a designer's point of view, taking care of the parameter variations and in particular of the nanotube diameters is crucial to achieve reliable circuits


title: Offloading in Mobile Cloudlet Systems with Intermittent Connectivity

abstract: The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users’ local load and availability of cloudlets. We examine users’ mobility patterns and cloudlets’ admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.


title: Regional cerebral blood flow correlates of visuospatial tasks in Alzheimer's disease.

abstract: This study investigated the role of visuospatial tasks in identifying cognitive decline in patients with Alzheimer's disease (AD), by correlating neuropsychological performance with cerebral perfusion measures. There were 157 participants: 29 neurologically healthy controls (age: 70.3 ± 6.6, MMSE ≥ 27), 86 patients with mild AD (age: 69.18 ± 8.28, MMSE ≥ 21) and 42 patients moderate/severe AD (age: 68.86 ± 10.69, MMSE 8–20). Single Photon Emission Computerized Tomography (SPECT) was used to derive regional perfusion ratios, and correlated using partial least squares (PLS) with neuropsychological test scores from the Benton Line Orientation (BLO) and the Rey-Osterrieth Complex Figure (RO). Cross-sectional analysis demonstrated that mean scores differed in accordance with disease status: control group (BLO 25.5, RO 33.3); mild AD (BLO 20.1, RO 25.5); moderate/severe AD (BLO 10.7, RO 16). Correlations were observed between BLO/RO and right parietal SPECT regions in the AD groups. Visuospatial performance, often undersampled in cognitive batteries for AD, is clearly impaired even in mild AD and correlates with functional deficits as indexed by cerebral perfusion ratios on SPECT implicating right hemisphere circuits. Furthermore, PLS reveals that usual spatial tasks probe a distributed brain network in both hemispheres including many areas targeted by early AD pathology. ( JINS , 2008, 14 , 1034–1045.)


title: Improving Ontology-Based User Profiles

abstract: Personalized Web browsing and search hope to provide Web information that matches a user's personal interests and thus provide more effective and efficient information access. A key feature in developing successful personalized Web applications is to build user profiles that accurately represent a user's interests. The main goal of this research is to investigate techniques that implicitly build ontology-based user profiles. We build the profiles without user interaction, automatically monitoring the user's browsing habits. After building the initial profile from visited Web pages, we investigate techniques to improve the accuracy of the user profile. In particular, we focus on how quickly we can achieve profile stability, how to identify the most important concepts, the effect of depth in the concept-hierarchy on the importance of a concept, and how many levels from the hierarchy should be used to represent the user. ::: ::: Our major findings are that ranking the concepts in the profiles by number of documents assigned to them rather than by accumulated weights provides better profile accuracy. We are also able to identify stable concepts in the profile, thus allowing us to detect long-term user interests. We found that the accuracy of concept detection decreases as we descend them in the concept hierarchy, however this loss of accuracy must be balanced against the detailed view of the user available only through the inclusion of lower-level concepts.


From PID to L 1 Adaptive Control for Automatic Balancing of a Spacecraft Three-axis Simulator


title: Linearly convergent stochastic heavy ball method for minimizing generalization error

abstract: In this work we establish the first linear convergence result for the stochastic heavy ball method. The method performs SGD steps with a fixed stepsize, amended by a heavy ball momentum term. In the analysis, we focus on minimizing the expected loss and not on finite-sum minimization, which is typically a much harder problem. While in the analysis we constrain ourselves to quadratic loss, the overall objective is not necessarily strongly convex.


title: Psychiatric disorders in children with autism spectrum disorders: prevalence, comorbidity, and associated factors in a population-derived sample

abstract: Abstract Objective Autism spectrum disorders are now recognized to occur in up to 1% of the population and to be a major public health concern because of their early onset, lifelong persistence, and high levels of associated impairment. Little is known about the associated psychiatric disorders that may contribute to impairment. We identify the rates and type of psychiatric comorbidity associated with ASDs and explore the associations with variables identified as risk factors for child psychiatric disorders. Method A subgroup of 112 ten- to 14-year old children from a population-derived cohort was assessed for other child psychiatric disorders (3 months' prevalence) through parent interview using the Child and Adolescent Psychiatric Assessment. DSM-IV diagnoses for childhood anxiety disorders, depressive disorders, oppositional defiant and conduct disorders, attention-deficit/hyperactivity disorder, tic disorders, trichotillomania, enuresis, and encopresis were identified. Results Seventy percent of participants had at least one comorbid disorder and 41% had two or more. The most common diagnoses were social anxiety disorder (29.2%, 95% confidence interval [Cl)] 13.2-45.1), attention-deficit/hyperactivity disorder (28.2%, 95% Cl 13.3-43.0), and oppositional defiant disorder (28.1 %, 95% Cl 13.9-42.2). Of those with attention-deficit/hyperactivity disorder, 84% received a second comorbid diagnosis. There were few associations between putative risk factors and psychiatric disorder. Conclusions Psychiatric disorders are common and frequently multiple in children with autism spectrum disorders. They may provide targets for intervention and should be routinely evaluated in the clinical assessment of this group. J. Am. Acad. Child Adolesc. Psychiatry , 2008;47(8):921-929.


title: Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition

abstract: Recognizing facial action units (AUs) from spontaneous facial expressions is still a challenging problem. Most recently, CNNs have shown promise on facial AU recognition. However, the learned CNNs are often overfitted and do not generalize well to unseen subjects due to limited AU-coded training images. We proposed a novel Incremental Boosting CNN (IB-CNN) to integrate boosting into the CNN via an incremental boosting layer that selects discriminative neurons from the lower layer and is incrementally updated on successive mini-batches. In addition, a novel loss function that accounts for errors from both the incremental boosted classifier and individual weak classifiers was proposed to fine-tune the IB-CNN. Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition. The improvement is more impressive for the AUs that have the lowest frequencies in the databases.


title: Pneumatic position servo control of magnetic resonance compatible needle insertion robot

abstract: This paper presents a control methodology that enables nonlinear model-base control of pulse width modulated (PWM) pneumatic position servo control system of a magnetic resonance (MR) compatible needle insertion robot used for MRI-guided prostate brachytherapy. Sliding mode control with a boundary layer is investigated based on the equivalent continuous-time dynamic model. Considering chattering in sliding mode control applications, especially in motion control of the robot, a fuzzy method is developed to tune the boundary layer of the sliding mode controller. Simulation and experimental results indicate that the controller is effective in both position control and trajectory tracking. With the boundary layer increasing, the steady state error of the response is becoming larger; however the maximum error is less than 0.2 mm. This steady state performance meets the accuracy requirement.


title: An Unbounded Nonblocking Double-Ended Queue

abstract: We introduce a new algorithm for an unbounded concurrent double-ended queue (deque). Like the bounded deque of Herlihy, Luchangco, and Moir on which it is based, the new algorithm is simple and obstruction free, has no pathological long-latency scenarios, avoids interference between operations at opposite ends, and requires no special hardware support beyond the usual compare-and-swap. To the best of our knowledge, no prior concurrent deque combines these properties with unbounded capacity, or provides consistently better performance across a wide range of concurrent workloads.


title: Power Management and Power Flow Control With Back-to-Back Converters in a Utility Connected Microgrid

abstract: This paper proposes a method for power flow control between utility and microgrid through back-to-back converters, which facilitates desired real and reactive power flow between utility and microgrid. In the proposed control strategy, the system can run in two different modes depending on the power requirement in the microgrid. In mode-1, specified amount of real and reactive power are shared between the utility and the microgrid through the back-to-back converters. Mode-2 is invoked when the power that can be supplied by the distributed generators (DGs) in the microgrid reaches its maximum limit. In such a case, the rest of the power demand of the microgrid has to be supplied by the utility. An arrangement between DGs in the microgrid is proposed to achieve load sharing in both grid connected and islanded modes. The back-to-back converters also provide total frequency isolation between the utility and the microgrid. It is shown that the voltage or frequency fluctuation in the utility side has no impact on voltage or power in microgrid side. Proper relay-breaker operation coordination is proposed during fault along with the blocking of the back-to-back converters for seamless resynchronization. Both impedance and motor type loads are considered to verify the system stability. The impact of dc side voltage fluctuation of the DGs and DG tripping on power sharing is also investigated. The efficacy of the proposed control arrangement has been validated through simulation for various operating conditions. The model of the microgrid power system is simulated in PSCAD.


A Large-Scale Study of File Prevalence


title: Hilbert Space Methods for Reduced-Rank Gaussian Process Regression

abstract: This paper proposes a novel scheme for reduced-rank Gaussian process regression. The method is based on an approximate series expansion of the covariance function in terms of an eigenfunction expansion of the Laplace operator in a compact subset of $$\mathbb {R}^d$$ ::: . On this approximate eigenbasis, the eigenvalues of the covariance function can be expressed as simple functions of the spectral density of the Gaussian process, which allows the GP inference to be solved under a computational cost scaling as $$\mathcal {O}(nm^2)$$ ::: (initial) and $$\mathcal {O}(m^3)$$ ::: (hyperparameter learning) with m basis functions and n data points. Furthermore, the basis functions are independent of the parameters of the covariance function, which allows for very fast hyperparameter learning. The approach also allows for rigorous error analysis with Hilbert space theory, and we show that the approximation becomes exact when the size of the compact subset and the number of eigenfunctions go to infinity. We also show that the convergence rate of the truncation error is independent of the input dimensionality provided that the differentiability order of the covariance function increases appropriately, and for the squared exponential covariance function it is always bounded by $${\sim }1/m$$ ::: regardless of the input dimensionality. The expansion generalizes to Hilbert spaces with an inner product which is defined as an integral over a specified input density. The method is compared to previously proposed methods theoretically and through empirical tests with simulated and real data.


title: Latent dirichlet allocation based diversified retrieval for e-commerce search

abstract: Diversified retrieval is a very important problem on many e-commerce sites, e.g. eBay and Amazon. Using IR approaches without optimizing for diversity results in a clutter of redundant items that belong to the same products. Most existing product taxonomies are often too noisy, with overlapping structures and non-uniform granularity, to be used directly in diversified retrieval. To address this problem, we propose a Latent Dirichlet Allocation (LDA) based diversified retrieval approach that selects diverse items based on the hidden user intents. Our approach first discovers the hidden user intents of a query using the LDA model, and then ranks the user intents by making trade-offs between their relevance and information novelty. Finally, it chooses the most representative item for each user intent to display. To evaluate the diversity in the search results on e-commerce sites, we propose a new metric, average satisfaction, measuring user satisfaction with the search results. Through our empirical study on eBay, we show that the LDA model discovers meaningful user intents and the LDA-based approach provides significantly higher user satisfaction than the eBay production ranker and three other diversified retrieval approaches.


title: IoT based smart greenhouse

abstract: This work is primarily about the improvement of current agricultural practices by using modern technologies for better yield. This work provides a model of a smart greenhouse, which helps the farmers to carry out the work in a farm automatically without the use of much manual inspection. Greenhouse, [1], [2] being a closed structure protects the plants from extreme weather conditions namely: wind, hailstorm, ultraviolet radiations, and insect and pest attacks. The irrigation of agriculture field is carried out using automatic drip irrigation, which operates according to the soil moisture threshold set accordingly so as optimal amount of water is applied to the plants. Based on data from soil health card, proper amount of nitrogen, phosphorus, potassium and other minerals can be applied by using drip fertigation techniques. Proper water management tanks are constructed and they are filled with water after measuring the current water level using an ultrasonic sensor. Plants are also provided the requisite wavelength light during the night using growing lights. Temperature and air humidity are controlled by humidity and temperature sensors and a fogger is used to control the same. A tube well is controlled using GSM [3] module (missed call or sms). Bee-hive boxes are deployed for pollination and boxes are monitored using ultrasonic sensors to measure honey and send mails to the buyers when they are filled. Further, the readings collected from storage containers are uploaded to cloud service (Google drive) and can be forwarded to an e-commerce company.


A MULTI-OBJECTIVE APPROACH ON THE OPTIMAL PRODUCTION AND MAINTENANCE PLANNING OF BIOPHARMACEUTICAL PROCESSES UNDER PERFORMANCE DECAY USING A CONTINUOUS-TIME FORMULATION


title: Multi-Fiber Networks for Video Recognition

abstract: In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks. To this end, we present the novel Multi-Fiber architecture that slices a complex neural network into an ensemble of lightweight networks or fibers that run through the network. To facilitate information flow between fibers we further incorporate multiplexer modules and end up with an architecture that reduces the computational cost of 3D networks by an order of magnitude, while increasing recognition performance at the same time. Extensive experimental results show that our multi-fiber architecture significantly boosts the efficiency of existing convolution networks for both image and video recognition tasks, achieving state-of-the-art performance on UCF-101, HMDB-51 and Kinetics datasets. Our proposed model requires over 9x and 13x less computations than the I3D and R(2+1)D models, respectively, yet providing higher accuracy.


title: Correlation between Photovoltaic Performance and Interchain Ordering Induced Delocalization of Electronics States in Conjugated Polymer Blends

abstract: In this paper we correlate the solar cell performance with bimolecular packing of donor:acceptor bulk heterojunction (BHJ) organic solar cells (OSCs), where interchain ordering of the donor molecule and its influence on morphology, optical properties, and charge carrier dynamics of BHJ solar cells are studied in detail. Solar cells that are fabricated using more ordered defect free 100% regioregular poly(3-hexylthiophene) (DF-P3HT) as the donor polymer show ca. 10% increase in the average power conversion efficiency (PCE) when compared to that of the solar cell fabricated using 92% regioregularity P3HT, referred to as rr-P3HT. EQE and UV–vis absorption spectrum show a clear increase in the 607 nm vibronic shoulder of the DF-P3HT blend suggesting better interchain ordering which was also reflected in the less Urbach energy (Eu) value for this system. The increase in ordering inside the blend has enhanced the hole-mobility which is calculated from the single carrier device J–V characteristics. Electrolumina...


title: Faster Neural Network Training with Approximate Tensor Operations

abstract: We propose a novel technique for faster Neural Network (NN) training by systematically approximating all the constituent matrix multiplications and convolutions. This approach is complementary to other approximation techniques, requires no changes to the dimensions of the network layers, hence compatible with existing training frameworks. We first analyze the applicability of the existing methods for approximating matrix multiplication to NN training, and extend the most suitable column-row sampling algorithm to approximating multi-channel convolutions. We apply approximate tensor operations to training MLP, CNN and LSTM network architectures on MNIST, CIFAR-100 and Penn Tree Bank datasets and demonstrate 30%-80% reduction in the amount of computations while maintaining little or no impact on the test accuracy. Our promising results encourage further study of general methods for approximating tensor operations and their application to NN training.


title: Joint Chinese Word Segmentation and POS Tagging on Heterogeneous Annotated Corpora with Multiple Task Learning

abstract: Chinese word segmentation and part-ofspeech tagging (S&T) are fundamental steps for more advanced Chinese language processing tasks. Recently, it has attracted more and more research interests to exploit heterogeneous annotation corpora for Chinese S&T. In this paper, we propose a unified model for Chinese S&T with heterogeneous annotation corpora. We first automatically construct a loose and uncertain mapping between two representative heterogeneous corpora, Penn Chinese Treebank (CTB) and PKU’s People’s Daily (PPD). Then we regard the Chinese S&T with heterogeneous corpora as two “related” tasks and train our model on two heterogeneous corpora simultaneously. Experiments show that our method can boost the performances of both of the heterogeneous corpora by using the shared information, and achieves significant improvements over the state-of-the-art methods.


title: Finding the adequate resolution for grid mapping - Cell sizes locally adapting on-the-fly

abstract: For robot mapping occupancy grid maps are the most common representation of the environment. However, most existing algorithms for creating such maps assume a fixed resolution of the grid cells. In this paper we present a novel mapping technique that chooses the resolution of each cell adaptively by merging and splitting cells depending on the measurements. The splitting of the cells is based on a statistical measure that we derive in this paper. In contrast to other approaches the adaption of the resolution is done online during the mapping process itself. Additionally, we introduce the Nd-Tree, a generalization of quadtrees and octrees that allows to subdivide any d-dimensional volume recursively with Nd children per node. Using this data structure our approach can be implemented in a very generic way and allows the creation of 2D, 3D and even higher dimensional maps using the same algorithm. Finally, we show results of our proposed method for 2D and 3D mapping using different kinds of range sensors.


title: An empirical comparison of rhythm in language and music

abstract: Musicologists and linguists have often suggested that the prosody of a culture's spoken language can influence the structure of its instrumental music. However, empirical data supporting this idea have been lacking. This has been partly due to the difficulty of developing and applying comparable quantitative measures to melody and rhythm in speech and music. This study uses a recently-developed measure for the study of speech rhythm to compare rhythmic patterns in English and French language and classical music. We find that English and French musical themes are significantly different in this measure of rhythm, which also differentiates the rhythm of spoken English and French. Thus, there is an empirical basis for the claim that spoken prosody leaves an imprint on the music of a culture.


title: Automatic Musical Genre Classification of Audio Signals

abstract: Musical genres are categorical descriptions that are used to describe music. They are commonly used to structure the increasing amounts of music available in digital form on the Web and are important for music information retrieval. Genre categorization for audio has traditionally been performed manually. A particular musical genre is characterized by statistical properties related to the instrumentation, rhythmic structure and form of its members. In this work, algorithms for the automatic genre categorization of audio signals are described. More specifically, we propose a set of features for representing texture and instrumentation. In addition a novel set of features for representing rhythmic structure and strength is proposed. The performance of those feature sets has been evaluated by training statistical pattern recognition classifiers using real world audio collections. Based on the automatic hierarchical genre classification two graphical user interfaces for browsing and interacting with large audio collections have been developed.


title: Electric drive system with BLDC motor

abstract: With the resource supply becoming severe recently, the use of hybrid car or electric car is a necessity that it can reduce the need of oil. This paper presents BLDC motor is the best choice for high-efficiency motor and shows a design of control system for BLDC motor. With the comparison of DC motor, Induction Motor and BLDC motor, Analysis will be done to determine which type of electric motor has the best characteristics and the motor will be modeled in MATLAB™ /Simulink™ to determine which type of motor has the best performances and the highest efficiency. A control system will be designed in the electric drive system.


title: Guess my object: an 'objects first' game on objects' behavior and implementation with bluej

abstract: Introductory programming education following the Objects First approach introduces the concepts of object-oriented programming early on. Objects with state (fields) and behavior (methods) that offer services to their clients (via their public interface) and hide the way these services are implemented (in their implementation) are the building blocks of any larger object system. These basic properties of objects are so crucial for understanding object-oriented programming (and later on object-oriented design) that diverse approaches to teaching them should be offered. In this paper we introduce Guess My Object (GMO) as a new approach to getting in contact with objects early that can complement existing teaching approaches. In essence, GMO is a way of using BlueJ for an interactive round-based game, each consisting of two stages, behavior exploration and behavior implementation.


title: Comparison between testicular volumes as measured with prader orchidometer and ultrasonography in Healthy Nigerian Newborns

abstract: Background: We compared the testicular volume (TV) measured with Prader orchidometer (PO) to the volume measured with ultrasonography in male neonates and their relationships with some selected neonatal characteristics. Subjects and Methods: A cross-sectional study of all term male neonates who had clinical examination of their external genitalia performed and TV was measured using PO and ultrasonography. Information about the gestational age, birth weight, and birth length was also recorded to determine their relationships with TV measured. Results: The mean TV measured with PO was 1.06 (standard deviation [SD] ± 0.24) ml for both sides. With ultrasonography, the mean left TV was 0.273 (SD ± 0.081) ml and the mean right TV was 0.272 (SD ± 0.079) ml. There were significant correlations between TV using both methods with the birth weight and length. Conclusion: TV measured with the two methods was comparable and correlates well with their birth weight and birth length.


title: Comparing Rule-Based and Deep Learning Models for Patient Phenotyping

abstract: Objective: We investigate whether deep learning techniques for natural language processing (NLP) can be used efficiently for patient phenotyping. Patient phenotyping is a classification task for determining whether a patient has a medical condition, and is a crucial part of secondary analysis of healthcare data. We assess the performance of deep learning algorithms and compare them with classical NLP approaches. ::: Materials and Methods: We compare convolutional neural networks (CNNs), n-gram models, and approaches based on cTAKES that extract pre-defined medical concepts from clinical notes and use them to predict patient phenotypes. The performance is tested on 10 different phenotyping tasks using 1,610 discharge summaries extracted from the MIMIC-III database. ::: Results: CNNs outperform other phenotyping algorithms in all 10 tasks. The average F1-score of our model is 76 (PPV of 83, and sensitivity of 71) with our model having an F1-score up to 37 points higher than alternative approaches. We additionally assess the interpretability of our model by presenting a method that extracts the most salient phrases for a particular prediction. ::: Conclusion: We show that NLP methods based on deep learning improve the performance of patient phenotyping. Our CNN-based algorithm automatically learns the phrases associated with each patient phenotype. As such, it reduces the annotation complexity for clinical domain experts, who are normally required to develop task-specific annotation rules and identify relevant phrases. Our method performs well in terms of both performance and interpretability, which indicates that deep learning is an effective approach to patient phenotyping based on clinicians' notes.


title: Feedforward Sequential Memory Neural Networks without Recurrent Feedback

abstract: We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback. The proposed FSMN is a standard feedforward neural networks equipped with learnable sequential memory blocks in the hidden layers. In this work, we have applied FSMN to several language modeling (LM) tasks. Experimental results have shown that the memory blocks in FSMN can learn effective representations of long history. Experiments have shown that FSMN based language models can significantly outperform not only feedforward neural network (FNN) based LMs but also the popular recurrent neural network (RNN) LMs.


title: A Survey of Music Recommendation Aids

abstract: paper provides a review of explanations, visualizations and interactive elements of user interfaces (UI) in music recommendation systems. We call these UI features "recommendation aids". Explanations are elements of the interface that inform the user why a certain recommendation was made. We highlight six possible goals for explanations, resulting in overall satisfaction towards the system. We found that the most of the existing music recommenders of popular systems provide no explanations, or very limited ones. Since explanations are not independent of other UI elements in recommendation process, we consider how the other elements can be used to achieve the same goals. To this end, we evaluated several existing music recommenders. We wanted to discover which of the six goals (transparency, scrutability, effectiveness, persuasiveness, efficiency and trust) the different UI elements promote in the existing music recommenders, and how they could be measured in order to create a simple framework for evaluating recommender UIs. By using this framework designers of recommendation systems could promote users' trust and overall satisfaction towards a recommender system thereby improving the user experience with the system.


title: A2iA Check Reader: a family of bank check recognition systems

abstract: The paper presents new A2iA bank check recognition systems designed to process handwritten and/or printed checks issued in France, UK or USA. All the systems have identical architecture and design principles. However, each of them contains a country-specific part and is trained with country-specific data. Each system performs location, extraction, segmentation and recognition of courtesy and legal amounts in a document image, as well as deciding whether to accept or reject the check. The recognition rate is 80-90%. In the production mode, the check acceptance rate is 60-75%, with the misread rate corresponding to that of a human operator (close to 1%).


title: Frequent Pattern Compression: A Significance-Based Compression Scheme for L2 Caches

abstract: With the widening gap between processor and memory speeds, memory system designers may find cache compression beneficial to increase cache capacity and reduce off-chip bandwidth. Most hardware compression algorithms fall into the dictionary-based category, which depend on building a dictionary and using its entries to encode repeated data values. Such algorithms are effective in compressing large data blocks and files. Cache lines, however, are typically short (32-256 bytes), and a per-line dictionary places a significant overhead that limits the compressibility and increases decompression latency of such algorithms. For such short lines, significance-based compression is an appealing alternative. We propose and evaluate a simple significance-based compression scheme that has a low compression and decompression overhead. This scheme, Frequent Pattern Compression (FPC) compresses individual cache lines on a word-by-word basis by storing common word patterns in a compressed format accompanied with an appropriate prefix. For a 64-byte cache line, compression can be completed in three cycles and decompression in five cycles, assuming 12 FO4 gate delays per cycle. We propose a compressed cache design in which data is stored in a compressed form in the L2 caches, but are uncompressed in the L1 caches. L2 cache lines are compressed to predetermined sizes that never exceed their original size to reduce decompression overhead. This simple scheme provides comparable compression ratios to more complex schemes that have higher cache hit latencies.


title: A Three-Stage Matting Method

abstract: Natural image matting is an important image processing task. How to leverage the advantages of both sampling matting and propagation matting is a challenge issue. In this paper, we propose a novel sampling-propagation matting method. First, in an overall framework, we propose a three-stage method for sampling-propagation matting, in which the sampling matting stage and propagation matting stage are bridged by a new stage (stage 2). Second, in the sampling matting stage (stage 1), a new gradient sampling matting is presented to cover more diversified samples, and a new equation is proposed to calculate the impact of sample-pair overlap. Third, in bridge stage (stage 2), we propose a judgment criterion to distinguish each pixel of matte after stage 1. For pixels that fail to meet the criterion, we propose an automatic labeling method. Fourth, in the propagation stage (stage 3), we discriminatingly process non-labeled pixels and labeled pixels with separate weight coefficients. The non-labeled pixels are smoothed by propagation, and the labeled pixels are solved by propagation matting. Finally, the proposed method is compared with other methods on public available benchmark. The results show that our method outperforms many other methods and achieves a good ranking.


title: Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing

abstract: Hyperspectral unmixing is one of the most important problems in analyzing remote sensing images, which aims to decompose a mixed pixel into a collection of constituent materials named endmembers and their corresponding fractional abundances. Recently, various methods have been proposed to incorporate sparse constraints into hyperspectral unmixing and achieve advanced performance. However, most of them ignore the complex distribution of substances in hyperspectral data so that they are only effective in limited cases. In this paper, the concept of substance dependence is introduced to help hyperspectral unmixing. Generally, substance dependence can be considered in a local region by $K$ - nearest neighbors method. However, since substances of hyperspectral images are complicatedly distributed, number $K$ of the most similar substances to each substance is difficult to decide. In this case, substance dependence should be considered in the whole data space, and the number of the $K$ most similar substances to each substance can be adaptively determined by searching from the whole space. Through maintaining the substance dependence during unmixing, the abundances resulted from the proposed method are closer to the real fractions, which lead to better unmixing performance. The following contributions can be summarized. 1) The concept of substance dependence is proposed to describe the complicated relationship between substances in the hyperspectral image. 2) We propose substance dependence constrained sparse nonnegative matrix factorization (SDSNMF) for hyperspectral unmixing. Using SDSNMF, we meet or exceed state-of-the-art unmixing performance. 3) Adequate experiments on both synthetic and real hyperspectral data have been tested. Compared with the state-of-the-art methods, the experimental results prove the superiority of the proposed method.


title: Growing a Graph Matching from a Handful of Seeds

abstract: In many graph--mining problems, two networks from different domains have to be matched. In the absence of reliable node attributes, graph matching has to rely on only the link structures of the two networks, which amounts to a generalization of the classic graph isomorphism problem. Graph matching has applications in social--network reconciliation and de-anonymization, protein--network alignment in biology, and computer vision. ::: ::: The most scalable graph--matching approaches use ideas from percolation theory, where a matched node pair "infects" neighbouring pairs as additional potential matches. This class of matching algorithm requires an initial seed set of known matches to start the percolation. The size and correctness of the matching is very sensitive to the size of the seed set. ::: ::: In this paper, we give a new graph--matching algorithm that can operate with a much smaller seed set than previous approaches, with only a small increase in matching errors. We characterize a phase transition in matching performance as a function of the seed set size, using a random bigraph model and ideas from bootstrap percolation theory. We also show the excellent performance in matching several real large-scale social networks, using only a handful of seeds.


title: A humanistic approach to the study of social media: combining social network analysis & case study research

abstract: Humanistic research into social media is presently diverse in approach, but rich in theoretical underpinnings. It is unsurprising that there is some difficulty in translating often text-based approaches to multi-media rich, rapidly-evolving social networking environments. We explore theoretical issues for studying social media with respect to one popular research methodology: case study research (CSR). Here we examine the challenges that social media pose to CSR in the humanities and then advance an approach using social network analysis (SNA) to assist in selecting case studies. This approach, we argue, improves selection of case studies by considering the network structures of social media.


title: Efficient Quantum Algorithms for Analyzing Large Sparse Electrical Networks

abstract: Analyzing large sparse electrical networks is a fundamental task in physics, electrical engineering and computer science. We propose two classes of quantum algorithms for this task. The first class is based on solving linear systems, and the second class is based on using quantum walks. These algorithms compute various electrical quantities, including voltages, currents, dissipated powers and effective resistances, in time $\operatorname{poly}(d, c, \operatorname{log}(N), 1/\lambda, 1/\epsilon)$, where $N$ is the number of vertices in the network, $d$ is the maximum unweighted degree of the vertices, $c$ is the ratio of largest to smallest edge resistance, $\lambda$ is the spectral gap of the normalized Laplacian of the network, and $\epsilon$ is the accuracy. Furthermore, we show that the polynomial dependence on $1/\lambda$ is necessary. This implies that our algorithms are optimal up to polynomial factors and cannot be significantly improved.


title: New sensorless control for brushless DC motors using disturbance observers and adaptive velocity estimations

abstract: A brushless DC motor has been represented by a nonlinear equation. Therefore, it has been difficult to apply linear control theory to brushless DC motor systems. In this paper, to apply the linear control theory to brushless DC motor systems, the authors propose considering the nonlinear term of the equation as a disturbance and to realize a sensorless control of the brushless DC motor using both the disturbance observer and the adaptive velocity estimation. With proper pole locations of the disturbance observer, stability of the position estimation is guaranteed, and stability of the adaptive velocity estimation is also guaranteed by Popov's hyperstability theory. The experimental results show that the proposed method is very useful.


title: VFDETECT: A vulnerable code clone detection system based on vulnerability fingerprint

abstract: Vulnerable code reuse in open source software is a serious threat to software security. However, the existing high-efficiency methods for vulnerable code clone detection have a large number of false-negatives when the code is modified, which results in limited application scenarios. In this paper, we present an innovative fingerprint model to describe the vulnerability code and propose VFDETECT, an efficient system to detect vulnerable code clones based on the fingerprints. Firstly, the fingerprint is constructed by applying hash function to appropriate code blocks in the diff which are preprocessed. Then, VFDETECT detects the vulnerable code clone by matching the preprocessed code blocks in target project with the fingerprint, which is mapped to a bitmap so that can be identified efficiently. VFDETECT could maintain better performance and acquire higher robustness under multiple code modification methods such as variable renaming, code sequence changing and redundancy inserting, which is difficult to achieve in existing research work. Our results in real-world datasets detection demonstrate that it is of practical values.


title: Design, control and implementation of a ball on plate balancing system

abstract: The ball balancing system in a 2-degree of freedom platform is of unique importance in understanding the control system applications. It is a platform to test and identify different aspects of controls, as the non-linearities increases with the degree of freedoms. So far many techniques have been applied to sense the position of the ball on a plate in real time, most common of them is touchpad and audio video camera system. This paper describes the design, development and control strategy of balancing a ball on a plate using low cost phototransistor sensors. These Phototransistors are triggered by monochromatic sharp beams of laser light. The plate area is 1sq. foot and an array of 11 sensors each 1 inch apart is used on each axis making total of 121 points on the plate.


title: The effect of five prosthetic feet on the gait and loading of the sound limb in dysvascular below-knee amputees.

abstract: The purpose of this study was to determine the effects of prosthetic foot design on the vertical ground reaction forces experienced by the sound and amputated limbs in a group of persons with dysvascular below-knee amputations. Stride characteristics, joint motion, and ground reaction forces were recorded simultaneously during a self-selected free walking velocity in seven subjects wearing five different prosthetic feet (SACH, Flex-Foot, Carbon Copy II, Seattle, Quantum). Subjects used each foot for one month prior to testing. Results indicated that the sound limb was exposed to higher vertical ground reaction forces than normal despite a reduced walking velocity. Use of the Flex-Foot resulted in the lowest sound limb vertical forces, which appears to be related to its large arc of dorsiflexion motion. In addition, there was increased loading response knee flexion of the sound limb indicating an attempt by these subjects to modulate floor impact. These results suggest that the intact lower extremity is susceptible to excessive floor impact, and that prosthetic foot design can have an effect on the magnitude of the vertical forces experienced by the limb.


title: Substrate Integrated Waveguide Quasi-Elliptic Filter Using Slot-Coupled and Microstrip-Line Cross-Coupled Structures

abstract: This paper proposes a quasi-elliptic filter with slot coupling and nonadjacent cross coupling based on the substrate integrated waveguide (SIW) cavity. The slots etched on the top metal plane of SIWcavity are used to produce electrical coupling, and the cross coupling is realized by the microstrip transmission line above the SIW cavity. The coupling strength is mainly controlled by the width and height of the slot. The length of the open-ended microstrip line controls the sign of the cross coupling. The cross coupling with different signs are used in the filter to produce a pair of transmission zeros (TZs) at both sides of the passband. In order to prove the validity, a fourth-order SIW quasi-elliptic filter with TZsat both sides of the passband is fabricated in a two-layer printed circuit board. The measured insertion loss at a center frequency of 3.7 GHz is 1.1 dB. The return loss within the passband is below −18 dB with a fractional bandwidth of 16%. The measured results are in good agreement with the simulated results.


title: A discriminative model approach for accurate duplicate bug report retrieval

abstract: Bug repositories are usually maintained in software projects. Testers or users submit bug reports to identify various issues with systems. Sometimes two or more bug reports correspond to the same defect. To address the problem with duplicate bug reports, a person called a triager needs to manually label these bug reports as duplicates, and link them to their "master" reports for subsequent maintenance work. However, in practice there are considerable duplicate bug reports sent daily; requesting triagers to manually label these bugs could be highly time consuming. To address this issue, recently, several techniques have be proposed using various similarity based metrics to detect candidate duplicate bug reports for manual verification. Automating triaging has been proved challenging as two reports of the same bug could be written in various ways. There is still much room for improvement in terms of accuracy of duplicate detection process. In this paper, we leverage recent advances on using discriminative models for information retrieval to detect duplicate bug reports more accurately. We have validated our approach on three large software bug repositories from Firefox, Eclipse, and OpenOffice. We show that our technique could result in 17--31%, 22--26%, and 35--43% relative improvement over state-of-the-art techniques in OpenOffice, Firefox, and Eclipse datasets respectively using commonly available natural language information only.


title: Cross-platform model-driven development of mobile applications with md2

abstract: Mobile applications usually need to be provided for more than one operating system. Developing native apps separately for each platform is a laborious and expensive undertaking. Hence, cross-platform approaches have emerged, most of them based on Web technologies. While these enable developers to use a single code base for all platforms, resulting apps lack a native look & feel. This, however, is often desired by users and businesses. Furthermore, they have a low abstraction level. We propose MD2, an approach for model-driven cross-platform development of apps. With MD2, developers specify an app in a high-level (domain-specific) language designed for describing business apps succinctly. From this model, purely native apps for Android and iOS are automatically generated. MD2 was developed in close cooperation with industry partners and provides means to develop data-driven apps with a native look and feel. Apps can access the device hardware and interact with remote servers.


title: An evaluation of sequential model-based optimization for expensive blackbox functions

abstract: We benchmark a sequential model-based optimization procedure, SMAC-BBOB, on the BBOB set of blackbox functions. We demonstrate that with a small budget of 10xD evaluations of D-dimensional functions, SMAC-BBOB in most cases outperforms the state-of-the-art blackbox optimizer CMA-ES. However, CMA-ES benefits more from growing the budget to 100xD, and for larger number of function evaluations SMAC-BBOB also requires increasingly large computational resources for building and using its models.


title: Research Review: The neurobiology and genetics of maltreatment and adversity

abstract: The neurobiological mechanisms by which childhood maltreatment heightens vulnerability to psychopathology remain poorly understood. It is likely that a complex interaction between environmental experiences (including poor caregiving) and an individual's genetic make-up influence neurobiological development across infancy and childhood, which in turn sets the stage for a child's psychological and emotional development. This review provides a concise synopsis of those studies investigating the neurobiological and genetic factors associated with childhood maltreatment and adversity. We first provide an overview of the neuroendocrine findings, drawing from animal and human studies. These studies indicate an association between early adversity and atypical development of the hypothalamic-pituitary-adrenal (HPA) axis stress response, which can predispose to psychiatric vulnerability in adulthood. We then review the neuroimaging findings of structural and functional brain differences in children and adults who have experienced childhood maltreatment. These studies offer evidence of several structural differences associated with early stress, most notably in the corpus callosum in children and the hippocampus in adults; functional studies have reported atypical activation of several brain regions, including decreased activity of the prefrontal cortex. Next we consider studies that suggest that the effect of environmental adversity may be conditional on an individual's genotype. We also briefly consider the possible role that epigenetic mechanisms might play in mediating the impact of early adversity. Finally we consider several ways in which the neurobiological and genetic research may be relevant to clinical practice and intervention.


title: Target classification in oceanographic SAR images with deep neural networks: Architecture and initial results

abstract: Synthetic Aperture Radar (SAR) provides detailed information of Ocean's surface and man-made floating structures. Advances in SAR technology and deployment of new SAR satellites have contributed to an increasing number of remote sensing data available. Handle this large amount of data with human operators is infeasible. Therefore, the use of automated tools to process remote sensing images, identify regions of interest, and select relevant information are needed. The use of neural networks to solve SAR image classification problems is well known. A typical architecture consists of a shallow feed-forward neural network with an input layer, a hidden layer, and an output layer. This type of neural network, combined with back-propagation and a gradient-based training algorithm, is able to solve complex problems in SAR image analysis. However, this architecture is unable to take advantage of unlabeled data during its training process, and in many cases the input features need to be carefully tuned in order to reduce the overall network complexity. This paper proposes the application of Deep Neural Networks (DNN) to perform oceanographic-object classification.


title: Mobile Augmented Reality Survey: From Where We Are to Where We Go

abstract: The boom in the capabilities and features of mobile devices, like smartphones, tablets, and wearables, combined with the ubiquitous and affordable Internet access and the advances in the areas of cooperative networking, computer vision, and mobile cloud computing transformed mobile augmented reality (MAR) from science fiction to a reality. Although mobile devices are more constrained computationalwise from traditional computers, they have a multitude of sensors that can be used to the development of more sophisticated MAR applications and can be assisted from remote servers for the execution of their intensive parts. In this paper, after introducing the reader to the basics of MAR, we present a categorization of the application fields together with some representative examples. Next, we introduce the reader to the user interface and experience in MAR applications and continue with the core system components of the MAR systems. After that, we discuss advances in tracking and registration, since their functionality is crucial to any MAR application and the network connectivity of the devices that run MAR applications together with its importance to the performance of the application. We continue with the importance of data management in MAR systems and the systems performance and sustainability, and before we conclude this survey, we present existing challenging problems.


Co-training based algorithm for gender detection from emotional speech


title: A System Software Quality Model using DeLone and McLean Model and ISO/IEC 9126

abstract: It is important work to exactly know what is major quality and their sub-criterion on QoS model. Furthermore, when the system developer or designer construct or make system software, they need to get an accurate guide line of the quality. However, the quality research of system software area lacked. This research aims to identify the criteria and to make quality model for system software. For the model, we extract criteria fitting on system software from DeLone & McLean’s quality model and ISO/IEC 9126 characteristics. The major quality has six criteria, and they affect to user and system satisfaction.


title: On the effectiveness of anonymizing networks for web search privacy

abstract: Web search has emerged as one of the most important applications on the internet, with several search engines available to the users. There is a common practice among these search engines to log and analyse the user queries, which leads to serious privacy implications. One well known solution to search privacy involves issuing the queries via an anonymizing network, such as Tor, thereby hiding one's identity from the search engine. A fundamental problem with this solution, however, is that user queries are still obviously revealed to the search engine, although they are "mixed" among the queries issued by other users of the same anonymization service. In this paper, we consider the problem of identifying the queries of a user of interest (UOI) within a pool of queries received by a search engine over an anonymizing network. We demonstrate that an adversarial search engine can extract the UOI's queries, when it is equipped with only a short-term user search query history, by utilizing only the query content information and off-the-shelf machine learning classifiers. More specifically, by treating a selected set of 60 users --- from the publicly-available AOL search logs --- as the users of interest performing web search over an anonymizing network, we show that each user's queries can be identified with 25.95% average accuracy, when mixed with queries of 99 other users of the anonymization service. This average accuracy drops to 18.95% when queries of 999 other users of the anonymization service are mixed together. Though the average accuracies are not so high, our results indicate that few users of interest could be identified with accuracies as high as 80--98%, even when their queries are mixed among queries of 999 other users. Our results cast serious doubts on the effectiveness of anonymizing web search queries by means of anonymizing networks.


title: A Vector Space Approach for Aspect Based Sentiment Analysis

abstract: Vector representations for language has been shown to be useful in a number of Natural Language Processing tasks. In this paper, we aim to investigate the effectiveness of word vector representations for the problem of Aspect Based Sentiment Analysis. In particular, we target three sub-tasks namely aspect term extraction, aspect category detection, and aspect sentiment prediction. We investigate the effectiveness of vector representations over different text data and evaluate the quality of domain-dependent vectors. We utilize vector representations to compute various vectorbased features and conduct extensive experiments to demonstrate their effectiveness. Using simple vector based features, we achieve F1 scores of 79.91% for aspect term extraction, 86.75% for category detection, and the accuracy 72.39% for aspect sentiment prediction.


title: Detection of fatigue crack anomaly: a symbolic dynamics approach

abstract: This paper presents a novel method for early detection of fatigue crack anomaly in complex mechanical structures, which is built upon the concepts of symbolic dynamics and finite state machines. The experimental apparatus, on which the crack detection method is tested, is a multi-degree of freedom mass-beam structure excited by oscillatory motion of two vibrators. The evolution of fatigue crack at one or more of the three failure sites causes slow variations in the natural frequencies of the mechanical structure, which are detected at an early stage from the time series data of displacement sensor signals. The proposed anomaly detection method has been validated by comparison with existing pattern recognition techniques.


title: Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text

abstract: We compare policy differences across institutions by embedding representations of the entire legal corpus of each institution and the vocabulary shared across all corpora into a continuous vector space. We apply our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and official summaries of Congressional bills. The model discerns meaningful differences between government branches. We also learn representations for more fine-grained word sources: individual Presidents and (2-year) Congresses. The similarities between learned representations of Congresses over time and sitting Presidents are negatively correlated with the bill veto rate, and the temporal ordering of Presidents and Congresses was implicitly learned from only text. With the resulting vectors we answer questions such as: how does Obama and the 113th House differ in addressing climate change and how does this vary from environmental or economic perspectives? Our work illustrates vector-arithmetic-based investigations of complex relationships between word sources based on their texts. We are extending this to create a more comprehensive legal semantic map.


title: Lane detection and tracking in challenging environments based on a weighted graph and integrated cues

abstract: Real-time lane detection and localization is one of the key issues for many intelligent transportation systems. In this paper, we present a lane detection and tracking approach designed to work in challenging environments where lane boundaries may be low-contrast and changeful with noise due to a number of factors such as wear, type, lighting and weather conditions, etc. In the method, a sophisticated cascade lane feature detector is applied to cope with challenging environments at the very beginning of the detection and a weighted graph is subsequently constructed by integrating intensity as well as geometry cues, reflecting the confidence of each pixel as a lane feature. In order to deal with complex road geometry, we employ Catmull-Rom splines to represent lane boundaries and the left and right lane boundaries are estimated separately in a tracking process using particle filter based on the weighted graph. In the proposed framework, unlike most of previous methods we lay a strong emphasis on accurate and effective lane feature detection since the challenges happen in the very first step of lane detection, and accurately detected lane features can be expected to reduce the complexity and difficulty, as well as improve the accuracy of lane detection in the following steps.


title: A low-friction passive fluid transmission and fluid-tendon soft actuator

abstract: We present a passive fluid transmission based on antagonist pairs of rolling diaphragm cylinders. The transmission fluid working volume is completely sealed, forming a closed, passive system, ensuring input-output symmetry and complete backdrivability. Rolling diaphragm-sealed cylinders provide leak-free operation without the stiction of a traditional sliding seal. Fluid pressure preloading allows for bidirectional operation and also serves to preload the gears or belts in the linear-to-rotary output coupler, eliminating system backlash end-to-end. A prototype transmission is built and tested for stiffness, bandwidth, and frictional properties using either air or water as working fluids. Torque transmission is smooth over the entire stroke and stiction is measured to be one percent of full-range torque or less. We also present a tendon-coupled design where the rolling diaphragm is inverted from its normal orientation; this design does not require shaft support bushings, tolerates misalignment, and can be made out of substantially soft materials. Actuator units and a passive transmission are demonstrated using this new soft cylinder design.


title: Adult separation anxiety in treatment nonresponders with anxiety disorders: delineation of the syndrome and exploration of attachment-based psychotherapy and biomarkers

abstract: Abstract Clinically significant separation anxiety [SA] has been identified as being common among patients who do not respond to psychiatric interventions, regardless of intervention type (pharmacological or psychotherapeutic), across anxiety and mood disorders. An attachment formation and maintenance domain has been proposed as contributing to anxiety disorders. We therefore directly determined prevalence of SA in a population of adult treatment non-responders suffering from primary anxiety. In these separation anxious nonresponders, we pilot-tested an SA-focused, attachment-based psychotherapy for anxiety, Panic-Focused Psychodynamic Psychotherapy-eXtended Range [PFPP-XR], and assessed whether hypothesized biomarkers of attachment were engaged. We studied separation anxiety [SA] in 46 adults (ages 23–70 [mean 43.9 (14.9)]) with clinically significant anxiety symptoms (Hamilton Anxiety Rating Scale [HARS]≥15), and reporting a history of past non-response to psychotherapy and/or medication treatments. Thirty-seven (80%) had clinically significant symptoms of separation anxiety (Structured Clinical Interview for Separation Anxiety Symptoms [SCI-SAS] score≥8). Five of these subjects completed an open clinical trial of Panic Focused Psychodynamic Psychotherapy eXtended Range [PFPP-XR], a 21–24 session, 12-week manualized attachment-focused anxiolytic psychodynamic psychotherapy for anxiety. Patients improved on "adult threshold" SCI-SAS (current separation anxiety) ( p = . 016), HARS ( p =0.002), and global severity, assessed by the Clinical Global Impression Scale ( p =.0006), at treatment termination. Salivary oxytocin levels decreased 67% after treatment ( p =.12). There was no significant change in high or low frequency HRV after treatment, but change in high frequency HRV inversely correlated with treatment change in oxytocin ( p p SA is surprisingly prevalent among non-responders to standard anti-anxiety treatments, and it may represent a novel transdiagnostic target for treatment intervention in this population. Anxiety and global function improved in a small trial of a brief, manualized, attachment-focused psychodynamic psychotherapy, potentially supporting the clinical relevance of attachment dysfunction in this sample. The large decrease in oxytocin levels with treatment, although not statistically significant in this very small sample, suggests the need for further study of oxytocin as a putative biomarker or mediator of SA response. These pilot data generate testable hypotheses supporting an attachment domain underlying treatment-resistant anxiety, and new treatment strategies.


title: More generality in efficient multiple kernel learning

abstract: Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient descent based optimization schemes has made it possible to tackle large scale problems. Simultaneously, MKL based algorithms have achieved very good results on challenging real world applications. Yet, despite their successes, MKL approaches are limited in that they focus on learning a linear combination of given base kernels. In this paper, we observe that existing MKL formulations can be extended to learn general kernel combinations subject to general regularization. This can be achieved while retaining all the efficiency of existing large scale optimization algorithms. To highlight the advantages of generalized kernel learning, we tackle feature selection problems on benchmark vision and UCI databases. It is demonstrated that the proposed formulation can lead to better results not only as compared to traditional MKL but also as compared to state-of-the-art wrapper and filter methods for feature selection.


title: Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

abstract: Sustainable urban planning and management require reliable land change models, which can be used to improve decision making. The objective of this study was to test a random forest-cellular automata (RF-CA) model, which combines random forest (RF) and cellular automata (CA) models. The Kappa simulation (KSimulation), figure of merit, and components of agreement and disagreement statistics were used to validate the RF-CA model. Furthermore, the RF-CA model was compared with support vector machine cellular automata (SVM-CA) and logistic regression cellular automata (LR-CA) models. Results show that the RF-CA model outperformed the SVM-CA and LR-CA models. The RF-CA model had a Kappa simulation (KSimulation) accuracy of 0.51 (with a figure of merit statistic of 47%), while SVM-CA and LR-CA models had a KSimulation accuracy of 0.39 and −0.22 (with figure of merit statistics of 39% and 6%), respectively. Generally, the RF-CA model was relatively accurate at allocating “non-built-up to built-up” changes as reflected by the correct “non-built-up to built-up” components of agreement of 15%. The performance of the RF-CA model was attributed to the relatively accurate RF transition potential maps. Therefore, this study highlights the potential of the RF-CA model for simulating urban growth.


title: Cognitive Load Estimation in the Wild

abstract: Cognitive load has been shown, over hundreds of validated studies, to be an important variable for understanding human performance. However, establishing practical, non-contact approaches for automated estimation of cognitive load under real-world conditions is far from a solved problem. Toward the goal of designing such a system, we propose two novel vision-based methods for cognitive load estimation, and evaluate them on a large-scale dataset collected under real-world driving conditions. Cognitive load is defined by which of 3 levels of a validated reference task the observed subject was performing. On this 3-class problem, our best proposed method of using 3D convolutional neural networks achieves 86.1% accuracy at predicting task-induced cognitive load in a sample of 92 subjects from video alone. This work uses the driving context as a training and evaluation dataset, but the trained network is not constrained to the driving environment as it requires no calibration and makes no assumptions about the subject's visual appearance, activity, head pose, scale, and perspective.


title: A system of portable ECG monitoring based on Bluetooth mobile phone

abstract: Aiming at diagnosing and preventing the cardiovascular disease, a portable ECG monitoring system based on Bluetooth mobile phones is presented. The system consists of some novel dry skin electrodes, an ECG monitoring circuit and a smart phone. The weak ECG signals extracted from the dry electrode can be amplified, band-pass filtered, analog-digital converted and so on. Finally it is sent to the mobile phone by Bluetooth technology for real-time display on screen. The core ECG monitoring circuit is composed of a CMOS preamplifier ASIC designed by ourselves, a band-pass filter, a microcontroller and a Bluetooth module. The volume is 5.5 cm × 3.4 cm × 1.6 cm, weight is only 20.76 g (without batteries), and power consumption is 115 mW. The tests show that the system can operate steadily, precisely and display the ECG in real time.


title: Online gaming and risks predict cyberbullying perpetration and victimization in adolescents

abstract: Objectives ::: The present study examined factors associated with the emergence and cessation of youth cyberbullying and victimization in Taiwan.


title: Development of a Fuzzy Expert System based Liveliness Detection Scheme for Biometric Authentication

abstract: Liveliness detection acts as a safe guard against spoofing attacks. Most of the researchers used vision based techniques to detect liveliness of the user, but they are highly sensitive to illumination effects. Therefore it is very hard to design a system, which will work robustly under all circumstances. Literature shows that most of the research utilize eye blink or mouth movement to detect the liveliness, while the other group used face texture to distinguish between real and imposter. The classification results of all these approaches decreases drastically in variable light conditions. Hence in this paper we are introducing fuzzy expert system which is sufficient enough to handle most of the cases comes in real time. We have used two testing parameters, (a) under bad illumination and (b) less movement in eyes and mouth in case of real user to evaluate the performance of the system. The system is behaving well in all, while in first case its False Rejection Rate (FRR) is 0.28, and in second case its FRR is 0.4.


title: Implementation of a 32-bit MIPS based RISC processor using Cadence

abstract: This paper presents implementation of a 5-stage pipelined 32-bit High performance MIPS based RISC Core. MIPS (Microprocessor without Interlocked Pipeline Stages) is a RISC (Reduced Instruction Set Computer) architecture. A RISC is a microprocessor that had been designed to perform a small set of instructions, with the aim of increasing the overall speed of the processor. MIPS have 5 stages of pipeline viz. Instruction Fetch (IF), Instruction Decode (ID), Execution (EX), Memory Access (MEM) and Write Back (WB) modules. The various modules being used are Instruction Memory, Data Memory, ALU, Registers etc. The aim of this paper is to include Hazard detection unit and Data forwarding unit for efficient implementation of the pipeline. The design is developed using Verilog-HDL. The main goal is to do the complete ASIC flow (RTL to GDS II), using Cadence tool. The module functionality and performance issues like area, power dissipation and propagation delay are analyzed using Cadence RTL complier using typical libraries of tsmc 0.18 um technology.


title: Streaming Active Learning Strategies for Real-Life Credit Card Fraud Detection: Assessment and Visualization

abstract: Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active learning process, as every day human investigators contact only a small number of cardholders (associated to the riskiest transactions) and obtain the class (fraud or genuine) of the related transactions. An adequate selection of the set of cardholders is therefore crucial for an efficient fraud detection process. In this paper, we present a number of active learning strategies and we investigate their fraud detection accuracies. We compare different criteria (supervised, semi-supervised and unsupervised) to query unlabeled transactions. Finally, we highlight the existence of an exploitation/exploration trade-off for active learning in the context of fraud detection, which has so far been overlooked in the literature.


title: Intent pattern recognition of lower-limb motion based on mechanical sensors

abstract: Based on the regularity nature of lower-limb motion, an intent pattern recognition approach for above-knee prosthesis is proposed in this paper. To remedy the defects of recognizer based on electromyogram U+0028 EMG U+0029, we develop a pure mechanical sensor architecture for intent pattern recognition of lower-limb motion. The sensor system is composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. To compensate the delay in the control of prosthesis, the signals in the stance phase are used to predict the terrain and speed in the swing phase. Specifically, the intent pattern recognizer utilizes intraclass correlation coefficient U+0028 ICC U+0029 according to the Cartesian product of walking speed and terrain. Moreover, the sensor data are fused via Dempster-Shafer U+02BC s theory. And hidden Markov model U+0028 HMM U+0029 is used to recognize the realtime motion state with the reference of the prior step. The proposed method can infer the prosthesis user U+02BC s intent of walking on different terrain, which includes level ground, stair ascent, stair descent, up and down ramp. The experiments demonstrate that the intent pattern recognizer is capable of identifying five typical terrain-modes with the rate of 95.8%. The outcome of this investigation is expected to substantially improve the control performance of powered above-knee prosthesis.


title: Relational Autoencoder for Feature Extraction

abstract: Feature extraction becomes increasingly important as data grows high dimensional. Autoencoder as a neural network based feature extraction method achieves great success in generating abstract features of high dimensional data. However, it fails to consider the relationships of data samples which may affect experimental results of using original and new features. In this paper, we propose a Relation Autoencoder model considering both data features and their relationships. We also extend it to work with other major autoencoder models including Sparse Autoencoder, Denoising Autoencoder and Variational Autoencoder. The proposed relational autoencoder models are evaluated on a set of benchmark datasets and the experimental results show that considering data relationships can generate more robust features which achieve lower construction loss and then lower error rate in further classification compared to the other variants of autoencoders.


title: Modern Small Satellites-Changing the Economics of Space

abstract: Earth orbiting satellites come in a wide range of shapes and sizes to meet a diverse variety of uses and applications. Large satellites with masses over 1000 kg support high-resolution remote sensing of the Earth, high bandwidth communications services, and world-class scientific studies but take lengthy developments and are costly to build and launch. The advent of commercially available, high-volume, and hence low-cost microelectronics has enabled a different approach through miniaturization. This results in physically far smaller satellites that dramatically reduce timescales and costs and that are able to provide operational and commercially viable services. This paper charts the evolution and rise of small satellites from being an early curiosity with limited utility through to the present where small satellites are a key element of modern space capabilities.


Bugbench: Benchmarks for evaluating bug detection tools


of II Complex Trauma in Children and Adolescents


title: Teaching the Basics of NLP and ML in an Introductory Course to Information Science

abstract: In this paper we discuss our experience of teaching basic Natural Language Processing (NLP) and Machine Learning (ML) in an introductory course to Information Science. We discuss the challenges we faced while incorporating NLP and ML to the curriculum followed by a presentation of how we met these challenges. The overall response (of students) to the inclusion of this new topic to the curriculum has been positive. Students this semester are pursuing NLP/ML projects, formulating their own tasks (some of which are novel and presented towards the end of the paper), collecting and annotating data and building models for their task.


title: 16 FACTOR STRUCTURE OF THE “ATTITUDES TOWARD RESEARCH ” SCALE 6

abstract: SUMMARY Students at the undergraduate level usually tend to view research methods courses negatively. However, an understanding of these attitudes is necessary to help instructors facilitate the learning of research for their students, by enabling them to create more positive attitudes toward such courses. The aim of this study is to describe the development of an “attitudes toward research” scale and verify the dimensions of attitudes toward research among undergraduate students enrolled in introductory research courses. The basic hypothesis of this research study is that the concept of attitudes is multidimensional in nature. The sample of the study consisted of 226 students who had completed a research methods course. Based on a factor analysis, five factors of student attitudes toward research were identified. These were the factors of usefulness of research, anxiety, affect indicating positive feelings about research, life relevancy of research to the students’ daily lives, and difficulty of research.


title: Railway Track Finding System with RFID Application

abstract: The Indian Railways is world’s fourth largest railway network in the world after USA, Russia and China. There is a severe problem of collisions of trains. So Indian railway is working in this aspect to promote the motto of “SAFE JOURNEY”. A RFID based railway track finding system for railway has been proposed in this paper. In this system the RFID tags and reader are used which are attached in the tracks and engine consecutively. So Train engine automatically get the data of path by receiving it from RFID tag and detect it. If path is correct then train continue to run on track and if it is wrong then a signal is generated and sent to the control station and after this engine automatically stop in a minimum time and the display of LCD show the “WRONG PATH”. So the collision and accident of train can be avoided. With the help of this system the train engine would be programmed to move according to the requirement. The another feature of this system is automatic track changer by which the track jointer would move automatically according to availability of trains.


title: Towards analysing the rationale of information security non-compliance: Devising a Value-Based Compliance analysis method

abstract: We develop a method for Value Based Compliance analysis of information security.We develop a set of design principles for a Value Based Compliance analysis method.We analyse value conflicts behind information security non-compliance.We provide a hands-on guide to Value Based Compliance analysis. Employees poor compliance with information security policies is a perennial problem. Current information security analysis methods do not allow information security managers to capture the rationalities behind employees compliance and non-compliance. To address this shortcoming, this design science research paper suggests: (a) a Value-Based Compliance analysis method and (b) a set of design principles for methods that analyse different rationalities for information security. Our empirical demonstration shows that the method supports a systematic analysis of why employees comply/do not comply with policies. Thus we provide managers with a tool to make them more knowledgeable about employees information security behaviours.


title: Metal Oxide Gas Sensors, a Survey of Selectivity Issues Addressed at the SENSOR Lab, Brescia (Italy)

abstract: This work reports the recent results achieved at the SENSOR Lab, Brescia (Italy) to address the selectivity of metal oxide based gas sensors. In particular, two main strategies are being developed for this purpose: (i) investigating different sensing mechanisms featuring different response spectra that may be potentially integrated in a single device; (ii) exploiting the electronic nose (EN) approach. The former has been addressed only recently and activities are mainly focused on determining the most suitable configuration and measurements to exploit the novel mechanism. Devices suitable to exploit optical (photoluminescence), magnetic (magneto-optical Kerr effect) and surface ionization in addition to the traditional chemiresistor device are here discussed together with the sensing performance measured so far. The electronic nose is a much more consolidated technology, and results are shown concerning its suitability to respond to industrial and societal needs in the fields of food quality control and detection of microbial activity in human sweat.


title: An Automatic Learning-Based Framework for Robust Nucleus Segmentation

abstract: Computer-aided image analysis of histopathology specimens could potentially provide support for early detection and improved characterization of diseases such as brain tumor, pancreatic neuroendocrine tumor (NET), and breast cancer. Automated nucleus segmentation is a prerequisite for various quantitative analyses including automatic morphological feature computation. However, it remains to be a challenging problem due to the complex nature of histopathology images. In this paper, we propose a learning-based framework for robust and automatic nucleus segmentation with shape preservation. Given a nucleus image, it begins with a deep convolutional neural network (CNN) model to generate a probability map, on which an iterative region merging approach is performed for shape initializations. Next, a novel segmentation algorithm is exploited to separate individual nuclei combining a robust selection-based sparse shape model and a local repulsive deformable model. One of the significant benefits of the proposed framework is that it is applicable to different staining histopathology images. Due to the feature learning characteristic of the deep CNN and the high level shape prior modeling, the proposed method is general enough to perform well across multiple scenarios. We have tested the proposed algorithm on three large-scale pathology image datasets using a range of different tissue and stain preparations, and the comparative experiments with recent state of the arts demonstrate the superior performance of the proposed approach.


title: An Architecture for Autonomy

abstract: An autonomous robot offers a challenging and ideal field for the study of intelligent architectures. Autonomy within a rational be havior could be evaluated by the robot's effectiveness and robust ness in carrying out tasks in different and ill-known environments. It raises major requirements on the control architecture. Further more, a robot as a programmable machine brings up other archi tectural needs, such as the ease and quality of its specification and programming.This article describes an integrated architecture that allows a mobile robot to plan its tasks—taking into account temporal and domain constraints, to perform corresponding actions and to con trol their execution in real-time—while being reactive to possible events. The general architecture is composed of three levels: a de cision level, an execution level, and a functional level. The latter is composed of modules that embed the functions achieving sensor- data processing and effector control. The decision level is goal and event driven, a...


title: Expected Noise Levels For Interferometric Sensors Multiplexed Using Partially Coherent Light

abstract: A method for multiplexing interferometric optical sensors is reviewed, and theoretical results are presented which predict the phase sensitivity of sensors multiplexed using this method. The multiplexing technique--"coherence multiplexing' is based on the use of a source whose coherence length is short compared to most system optical path length differences. The use of a single-mode semiconductor laser diode source typically leads to a min-imum detectable phase on the order of a milliradian per VHz. Small improvements result from the use of a noise-canceling balanced detection scheme. Sensitivity in the microradian per VHz regime might be achieved through the use of a shorter coherence length source such as a superluminescent diode.


title: The Influence of Shared Mental Models on Team Process and Performance

abstract: The influence of teammates' shared mental models on team processes and performance was tested using 56 undergraduate dyads who flew a series of missions on a personal-computer-based flight-combat simulation. The authors both conceptually and empirically distinguished between teammates' task- and team-based mental models and indexed their convergence or sharedness using individually completed paired-comparisons matrices analyzed using a network-based algorithm. The results illustrated that both shared-team- and task-based mental models related positively to subsequent team process and performance. Furthermore, team processes fully mediated the relationship between mental model convergence and team effectiveness. Results are discussed in terms of the role of shared cognitions in team effectiveness and the applicability of different interventions designed to achieve such convergence.


title: Episodic Memory: From Mind to Brain

abstract: ▪ Abstract Episodic memory is a neurocognitive (brain/mind) system, uniquely different from other memory systems, that enables human beings to remember past experiences. The notion of episodic memory was first proposed some 30 years ago. At that time it was defined in terms of materials and tasks. It was subsequently refined and elaborated in terms of ideas such as self, subjective time, and autonoetic consciousness. This chapter provides a brief history of the concept of episodic memory, describes how it has changed (indeed greatly changed) since its inception, considers criticisms of it, and then discusses supporting evidence provided by (a) neuropsychological studies of patterns of memory impairment caused by brain damage, and (b) functional neuroimaging studies of patterns of brain activity of normal subjects engaged in various memory tasks. I also suggest that episodic memory is a true, even if as yet generally unappreciated, marvel of nature.


title: Repurposed high-throughput images enable biological activity prediction for drug discovery

abstract: We repurpose a High-Throughput (cell) Imaging (HTI) screen of a glucocorticoid receptor assay to predict target protein activity in multiple other seemingly unrelated assays. In two ongoing drug discovery projects, our repurposing approach increased hit rates by 60- to 250-fold over that of the primary project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from available HTI screens are a rich source of information that can be reused to empower drug discovery efforts.


title: Emotion Regulation and the Quality of Social Interaction: Does the Ability to Evaluate Emotional Situations and Identify Effective Responses Matter?

abstract: ABSTRACT We examined self and friends' ratings of social relationship quality and everyday social interactions in 3 studies involving 544 college students in Germany, Spain, and the United States. Scores on a situational judgment test measuring strategic emotion regulation ability (SERA) were negatively related to conflict with others. SERA was more consistently and strongly related to conflict with others than to the positive dimension of relationship quality (support, companionship, and nurturance). The relationship between SERA and conflict was generally not mediated by trait positive or negative affect, and it remained significant or marginally significant controlling for the Big Five personality traits. These findings highlight the importance of the ability to evaluate emotional situations and identify effective responses to these in interpersonal emotion regulation. Furthermore, they suggest that situational judgment and flexible response selection may help people to manage conflicts more than to bond with others.


title: Type-safe observable sharing in Haskell

abstract: Haskell is a great language for writing and supporting embedded Domain Specific Languages (DSLs). Some form of observable sharing is often a critical capability for allowing so-called deep DSLs to be compiled and processed. In this paper, we describe and explore uses of an IO function for reification which allows direct observation of sharing.


title: ROUTING TECHNIQUES IN COGNITIVE RADIO NETWORKS: A SURVEY

abstract: Cognitive Radio Networks (CRNs) are being studied intensively. The major motivation for this is the heavily underutilized frequency spectrum. CRN has the capability to utilize the unutilized frequency spectrum. Routing in CRN is a challenging task due the diversity in the available channels and data rates. In this paper, we present a survey of the state-of-the-art routing techniques in CRNs. We first outline the design challenges for routing protocols in CRNs followed by a comprehensive survey of different routing techniques. Furthermore we classified these routing protocols into spectrum aware-based, multipathbased, local coordination-based, reactive source-based and tree-based routing techniques depending on the protocol operation.


title: Environmental Noise Pollution in the United States: Developing an Effective Public Health Response

abstract: BACKGROUND ::: Tens of millions of Americans suffer from a range of adverse health outcomes due to noise exposure, including heart disease and hearing loss. Reducing environmental noise pollution is achievable and consistent with national prevention goals, yet there is no national plan to reduce environmental noise pollution. ::: ::: ::: OBJECTIVES ::: We aimed to describe some of the most serious health effects associated with noise, summarize exposures from several highly prevalent noise sources based on published estimates as well as extrapolations made using these estimates, and lay out proven mechanisms and strategies to reduce noise by incorporating scientific insight and technological innovations into existing public health infrastructure. ::: ::: ::: DISCUSSION ::: We estimated that 104 million individuals had annual LEQ(24) levels > 70 dBA (equivalent to a continuous average exposure level of >70 dBA over 24 hr) in 2013 and were at risk of noise-induced hearing loss. Tens of millions more may be at risk of heart disease, and other noise-related health effects. Direct regulation, altering the informational environment, and altering the built environment are the least costly, most logistically feasible, and most effective noise reduction interventions. ::: ::: ::: CONCLUSION ::: Significant public health benefit can be achieved by integrating interventions that reduce environmental noise levels and exposures into the federal public health agenda.


title: Unilateral upper-limb loss: Satisfaction and prosthetic-device use in veterans and servicemembers from Vietnam and OIF/OEF conflicts

abstract: Prosthetic use and satisfaction in wounded servicemembers and veterans with unilateral upper-limb loss has not been thoroughly explored. Through a national survey, we enrolled 47 participants from the Vietnam conflict and 50 from Operation Iraqi Freedom/Operation Enduring Freedom (OIF/OEF) with combat-associated major unilateral upper-limb loss. Upper-limb prosthetic devices were used by 70% of the Vietnam group and 76% of the OIF/OEF group. Mechanical/body-powered upper-limb devices were favored by the Vietnam group, while a combination of myoelectric/hybrid and mechanical/body-powered devices were favored by the OIF/OEF group. Upper-limb devices were completely abandoned in 30% of the Vietnam and 22% of the OIF/OEF groups. Abandonment was more frequent for transhumeral and more proximal levels (42% of Vietnam and 40% of OIF/OEF) than more distal limb-loss levels. Upper-limb prostheses were rejected because of dissatisfaction with the device by significantly fewer (23%) members of the Vietnam group than the OIF/OEF group (45%) (p < 0.001). Most common reasons for rejection included pain, poor comfort, and lack of functionality. A significant paradigm shift has been noted in the OIF/OEF group, who use a greater number and diversity of upper-limb prostheses than the Vietnam group.


title: lnference from Iterative Simulation Using Multiple Sequences

abstract: The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were contin- ued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random- effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.


Gap Analysis in Enterprise Architecture using Semantic Web Technologies


title: An FPGA-based implementation of HW/SW architecture for CFAR radar target detector

abstract: This paper presents an efficient HW/SW Codesign FPGA-based architecture of B-ACOSD CFAR target detector in log normal distribution for radar system. All CFAR system modules are analyzed in order to identify the critical ones to be optimized so that the detection process will be conducted in realtime. To compel the design optimization of CFAR Architecture, we have considered the custom instruction approach offered by Altera environment. Furthermore HW/SW architecture of the CFAR detector is carried out where the NIOS II execute the software part and communicate via the Avalon switch fabric with the hardware modules represented by the custom logic components, on-chip memories, UART and JTAG interfaces. The proposed system-on-chip is validated and tested using the Stratix IV EP4SGX230KF4C2 of Altera operating at 250MHz. Using the HW/SW approach for our embedded target detection system, we improved the performance of the architecture compared to the pure software one with a total delay of 0.45 μs.