title: Community Violence Exposure, Social Cognition, and Aggression Among Urban Elementary School Children

abstract: The effects of witnessing community violence on aggressive cognitions and behavior were investigated in an ethnically diverse sample of 4,458 children living in urban neighborhoods. Prior violence exposure had a significant effect in increasing aggression, normative beliefs about aggression, and aggressive fantasy. Although exposure to violence predicted aggressive behavior both in Grades 1 through 3 (ages 5–8) and Grades 4 through 6 (ages 9–12), the effects on social cognition were only evident in the later grades. Furthermore, the effect of violence exposure on aggression in the later grades was partially mediated by its effect on social cognition. These findings suggest that witnessing community violence has an effect on children’s aggressive behavior through both imitation of violence and the development of associated cognitions as children get older. One of the most robust findings in the literature on the etiology of aggression is the statistical continuity of aggression from early childhood into adulthood (Huesmann, Eron, Lefkowitz, & Walder, 1984; Olweus, 1979). Several researchers have argued that one of the mediating factors in maintaining this continuity is the pattern of social cognition the child develops supporting aggression. In other words, aggressive behavior is seen as being increasingly controlled by internal self-regulating processes that become more stable over time (Huesmann & Guerra, 1997). Huesmann (1988, 1998) has emphasized the importance of cognitive schemas used as models of the world, cognitive scripts stored in memory and used as guides for social behavior, and normative beliefs used to evaluate the appropriateness of scripts. The more aggressive child is presumed to have acquired cognitive schemas depicting the world as a more hostile place, to endorse normative beliefs that aggression is more acceptable, and to have encoded in memory more extensive, wellconnected networks of social scripts emphasizing aggressive responses.


title: Biomechanics of nodal, seminal and lateral roots of barley: effects of diameter, waterlogging and mechanical impedance

abstract: Background and aims ::: Biomechanical properties of cereal root systems largely control both resistance to root lodging and their ability to stabilise soil. Abiotic stresses can greatly modify root system growth and form. In this paper the effect of waterlogging and moderate mechanical impedance on root biomechanics is studied for both lateral roots and the main axes of barley.


title: CloudScope: Diagnosing and Managing Performance Interference in Multi-tenant Clouds

abstract: Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%.


title: Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience.

abstract: For over 25 years, personal assistant robots for severely disabled individuals have been in development. More recently, using robots to deliver rehabilitation therapy has been proposed. This paper summarizes the development and clinical testing of three mechatronic systems for post-stroke therapy conducted at the VA Palo Alto in collaboration with Stanford University. We describe the philosophy and experiences that guided their evolution. Unique to the Palo Alto approach is provision for bimanual, mirror-image, patient-controlled therapeutic exercise. Proof-of-concept was established with a 2-degree-of-freedom (DOF) elbow/forearm manipulator. Tests of a second-generation therapy robot producing planar forearm movements in 19 hemiplegic and control subjects confirmed the validity and reliability of interaction forces during mechanically assisted upper-limb movements. Clinical trials comparing 3-D robot-assisted therapy to traditional therapy in 21 chronic stroke subjects showed significant improvement in the Fugl-Meyer (FM) measure of motor recovery in the robot group, which exceeded improvements in the control group.


title: Characterization and discrimination of phenolic compounds using Fourier transform Raman spectroscopy and chemometric tools

abstract: Description of the subject. Phenolic compounds (PCs) are the most abundant secondary metabolites in plants. This work was part of a study that sought to develop rapid screening FT-Raman methods for identifying and quantifying classes and/or types of PCs in the dry extracts of plant products. Objectives. Fourier transform Raman spectroscopy (FT-Raman) coupled with chemometric tools was used to characterize and discriminate four families of phenolic compounds: two important classes of phenolic acids, hydroxybenzoic and hydroxycinnamic acids, as well as their derivatives and flavonoids. Method. For this study, 25 standards of phenolic compounds were used (47 standards in total, taking account of the different brands). Repeatability and reproducibility studies were conducted to verify the Raman assignments of gallic acid. Raman characterization with the most significant spectral bands of phenolic compounds was done using spectra ranging from 1,800 to 50 cm-1. A Fisher test was applied to pre-processed Raman spectra (SNV) and 20 Raman scattering signals were used to differentiate each class of phenolic compounds. Results. Hydroxybenzoic acids, hydroxycinnamic acids, their derivatives and flavonoids have been characterized by Raman spectroscopy; bands have been identified and differentiated within and between groups. Conclusions. The scattering intensities located around 1,600-1,699, 1,300-1,400 and below 200 cm-1 were responsible for differentiating 100% of phenolic compound families, classes and subclasses.


title: Learning Where to Attend with Deep Architectures for Image Tracking

abstract: We discuss an attentional model for simultaneous object tracking and recognition that is driven by gaze data. Motivated by theories of perception, the model consists of two interacting pathways, identity and control, intended to mirror the what and where pathways in neuroscience models. The identity pathway models object appearance and performs classification using deep (factored)-restricted Boltzmann machines. At each point in time, the observations consist of foveated images, with decaying resolution toward the periphery of the gaze. The control pathway models the location, orientation, scale, and speed of the attended object. The posterior distribution of these states is estimated with particle filtering. Deeper in the control pathway, we encounter an attentional mechanism that learns to select gazes so as to minimize tracking uncertainty. Unlike in our previous work, we introduce gaze selection strategies that operate in the presence of partial information and on a continuous action space. We show that a straightforward extension of the existing approach to the partial information setting results in poor performance, and we propose an alternative method based on modeling the reward surface as a gaussian process. This approach gives good performance in the presence of partial information and allows us to expand the action space from a small, discrete set of fixation points to a continuous domain.


title: Applications of irradiance tensors to the simulation of non-Lambertian phenomena

abstract: We present new techniques for computing illumination from non-diffuse luminaires and scattering from non-diffuse surfaces. The methods are based on new closed-form expressions derived using a generalization of irradiance known as irradiance tensors. The elements of these tensors are angular moments, weighted integrals of the radiation field that are useful in simulating a variety of non-diffuse phenomena. Applications include the computation of irradiance due to directionally-varying area light sources, reflections from glossy surfaces, and transmission through glossy surfaces. The principles apply to any emission, reflection, or transmission distribution expressed as a polynomial over the unit sphere. We derive expressions for a simple but versatile subclass of these functions, called axial moments, and present complete algorithms their exact evaluation in polyhedral environments. The algorithms are demonstrated by simulating Phong-like emission and scattering effects. CR


title: Nonparametric bayesian inverse reinforcement learning for multiple reward functions

abstract: We present a nonparametric Bayesian approach to inverse reinforcement learning (IRL) for multiple reward functions. Most previous IRL algorithms assume that the behaviour data is obtained from an agent who is optimizing a single reward function, but this assumption is hard to guarantee in practice. Our approach is based on integrating the Dirichlet process mixture model into Bayesian IRL. We provide an efficient Metropolis-Hastings sampling algorithm utilizing the gradient of the posterior to estimate the underlying reward functions, and demonstrate that our approach outperforms previous ones via experiments on a number of problem domains.


title: A Closed-Form Expression for Minimum Operating Voltage of CMOS D Flip-Flop

abstract: In this paper, a closed-form expression for estimating the minimum operating voltage ( $V_{\mathrm{ DDmin}}$ ) of D flip-flops (FFs) is proposed. $V_{\mathrm{ DDmin}}$ is defined as the minimum supply voltage at which the FFs are functional without errors. The proposed expression indicates that $V_{\mathrm{ DDmin}}$ of FFs is a linear function of the square root of logarithm of the number of FFs, and its slope depends on the within-die variation of the threshold voltage ( $V_{\mathrm{ TH}}$ ) and its intercept depends on the balance between nMOS and pMOS, which is mainly due to the die-to-die $V_{\mathrm{ TH}}$ variation. The proposed expression of $V_{\mathrm{ DDmin}}$ is validated by the simulation results as well as the silicon measurements. Finally, we discuss the dependence of $V_{\mathrm{ DDmin}}$ on the device parameters.


title: Reading Comprehension using Entity-based Memory Network

abstract: This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}. It enhances neural networks' ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities' states. These entities' states are continuously updated according to the input text. Questions with regard to the input text are used to search the memory pool for related entities and answers are further predicted based on the states of retrieved entities. Compared with previous memory network models, the proposed model is capable of handling fine-grained information and more sophisticated relations based on entities. We formulated several different tasks as question answering problems and tested the proposed model. Experiments reported satisfying results.


title: Comparative Analysis of Job Satisfaction in Agile and Non-agile Software Development Teams

abstract: Software engineering is fundamentally driven by economics. One of the issues that software teams face is employee turnover which has a serious economic impact. The effect of job dissatisfaction on high turnover is consistently supported by evidence from multiple disciplines. The study investigates if and how job satisfaction relates to development processes that are being used and the determinants of job satisfaction across a wide range of teams, regions and employees. A moderate positive correlation between the level of experience with agile methods and the overall job satisfaction was found. The evidence suggests that there are twice as many members of agile teams who are satisfied with their jobs (vs members of non-agile teams). The ability to influence decisions that affect you, the opportunity to work on interesting projects, and the relationships with users were found to be statisticcally significant satisfiers.


title: Co-hierarchical analysis of shape structures

abstract: We introduce an unsupervised co-hierarchical analysis of a set of shapes, aimed at discovering their hierarchical part structures and revealing relations between geometrically dissimilar yet functionally equivalent shape parts across the set. The core problem is that of representative co-selection. For each shape in the set, one representative hierarchy (tree) is selected from among many possible interpretations of the hierarchical structure of the shape. Collectively, the selected tree representatives maximize the within-cluster structural similarity among them. We develop an iterative algorithm for representative co-selection. At each step, a novel cluster-and-select scheme is applied to a set of candidate trees for all the shapes. The tree-to-tree distance for clustering caters to structural shape analysis by focusing on spatial arrangement of shape parts, rather than their geometric details. The final set of representative trees are unified to form a structural co-hierarchy. We demonstrate co-hierarchical analysis on families of man-made shapes exhibiting high degrees of geometric and finer-scale structural variabilities.


title: Question classification with log-linear models

abstract: Question classification has become a crucial step in modern question answering systems. Previous work has demonstrated the effectiveness of statistical machine learning approaches to this problem. This paper presents a new approach to building a question classifier using log-linear models. Evidence from a rich and diverse set of syntactic and semantic features is evaluated, as well as approaches which exploit the hierarchical structure of the question classes.


Inverse Planning in Brachytherapy: Radium to High Dose Rate 192 Iridium Afterloading


title: Directly addressable variable-length codes

abstract: We introduce a symbol reordering technique that implicitly synchronizes variable-length codes, such that it is possible to directly access the i -th codeword without need of any sampling method. The technique is practical and has many applications to the representation of ordered sets, sparse bitmaps, partial sums, and compressed data structures for suffix trees, arrays, and inverted indexes, to name just a few. We show experimentally that the technique offers a competitive alternative to other data structures that handle this problem.


title: Advancements in CAD/CAM technology: Options for practical implementation.

abstract: PURPOSE ::: The purpose of this review is to present a comprehensive review of the current published literature investigating the various methods and techniques for scanning, designing, and fabrication of CAD/CAM generated restorations along with detailing the new classifications of CAD/CAM technology. ::: ::: ::: STUDY SELECTION ::: I performed a review of a PubMed using the following search terms "CAD/CAM, 3D printing, scanner, digital impression, and zirconia". The articles were screened for further relevant investigations. The search was limited to articles written in English, published from 2001 to 2015. In addition, a manual search was also conducted through articles and reference lists retrieved from the electronic search and peer-reviewed journals. ::: ::: ::: RESULTS ::: CAD/CAM technology has advantages including digital impressions and models, and use of virtual articulators. However, the implementation of this technology is still considered expensive and requires highly trained personnel. Currently, the design software has more applications including complete dentures and removable partial denture frameworks. The accuracy of restoration fabrication can be best attained with 5 axes milling units. The 3D printing technology has been incorporated into dentistry, but does not include ceramics and is limited to polymers. In the future, optical impressions will be replaced with ultrasound impressions using ultrasonic waves, which have the capability to penetrate the gingiva non-invasively without retraction cords and not be affected by fluids. ::: ::: ::: CONCLUSION ::: The coming trend for most practitioners will be the use of an acquisition camera attached to a computer with the appropriate software and the capability of forwarding the image to the laboratory.


title: Comparison of three one-question, post-task usability questionnaires

abstract: Post-task ratings of difficulty in a usability test have the potential to provide diagnostic information and be an additional measure of user satisfaction. But the ratings need to be reliable as well as easy to use for both respondents and researchers. Three one-question rating types were compared in a study with 26 participants who attempted the same five tasks with two software applications. The types were a Likert scale, a Usability Magnitude Estimation (UME) judgment, and a Subjective Mental Effort Question (SMEQ). All three types could distinguish between the applications with 26 participants, but the Likert and SMEQ types were more sensitive with small sample sizes. Both the Likert and SMEQ types were easy to learn and quick to execute. The online version of the SMEQ question was highly correlated with other measures and had equal sensitivity to the Likert question type.


title: MapReduce and parallel DBMSs: friends or foes?

abstract: MapReduce complements DBMSs since databases are not designed for extract-transform-load tasks, a MapReduce specialty.


title: Emotional creativity

abstract: In collaboration with the Rapidform Print Research department at the Royal College of Art in London we developed a series of three-dimensional works. Some of the digital data was used for animations, some data was manipulated for printing and 3D sculptures. This research demonstrates an exciting moment of a new tangentiality of digital media. With this research a crossover zone was explored, where computer technology affects the material realm and where digitally driven processes interact with traditional ones describing a hybrid practice. 'InterFaced' and 'Thick Friendship' is a series of works that speak to evolutionary biology, emotions and hybridity. The work derives from a database of human faces and individual animal faces that where scanned with a 3D scanner to bring gestural and facial expressions of animals and humans to interact. The work suggests a discussion about feelings and consciousness that include animal and posthuman digital emotion.


title: E-learning models: The effectiveness of the cloud-based E-learning model over the traditional E-learning model

abstract: This paper sought to understand the effectiveness of the traditional and cloud-based e-learning model. The growth of internet has been a game changer in the education sector. The internet has made it easy to for learners and educators to share information interactively, which is vital in enhancing education. Nonetheless, the storage of the content of learning has led to the adoption of cloud computing in the education sector in general and in higher education in particular. The adoption of cloud computing is in line with the objective of saving resources/saving costs and improving the interaction between students and the educators. Cloud-based learning has been slowly gaining popularity, with researchers arguing that it has the potential of replacing/improving the traditional e-learning model. It is worth noting safety (privacy and security) of information and data remains a huge priority for individuals who want to the e-learning model the best learning model for individuals learning from home and for education providers as well. The study utilizes a quantitative technique, where participants were drawn from two institutions. The respondents answered and presented a questionnaire for evaluation. Based on the results of the study, it was clear that the cloud-based e-learning model could effectively address the problems related to the traditional e-learning model. There is a need to improve the safety of the information contained in the e-learning platforms.


title: A Deep Learning Model for Estimating Story Points

abstract: Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues. Story points are the most common unit of measure used for estimating the effort involved in implementing a user story or resolving an issue. In this paper, we offer for the \emph{first} time a comprehensive dataset for story points-based estimation that contains 23,313 issues from 16 open source projects. We also propose a prediction model for estimating story points based on a novel combination of two powerful deep learning architectures: long short-term memory and recurrent highway network. Our prediction system is \emph{end-to-end} trainable from raw input data to prediction outcomes without any manual feature engineering. An empirical evaluation demonstrates that our approach consistently outperforms three common effort estimation baselines and two alternatives in both Mean Absolute Error and the Standardized Accuracy.


title: A scalable deduplication and garbage collection engine for incremental backup

abstract: Very large block-level data backup systems need scalable data deduplication and garbage collection techniques to make efficient use of the storage space and to minimize the performance overhead of doing so. Although the deduplication and garbage collection logic is conceptually straight-forward, their implementations pose a significant technical challenge because only a small portion of their associated data structures could fit into memory. In this paper, we describe the design, implementation and evaluation of a data deduplication and garbage collection engine called Sungem that is designed to remove duplicate blocks in incremental data backup streams. Sungem features three novel techniques to maximize the deduplication throughput without compromising the deduplication ratio. First, Sungem puts related fingerprint sequences, rather than fingerprints from the same backup stream, into the same container in order to increase the fingerprint prefetching efficiency. Second, to make the most of the memory space reserved for storing fingerprints, Sungem varies the sampling rates for fingerprint sequences based on their stability. Third, Sungem combines reference count and expiration time in a unique way to arrive at the first known incremental garbage collection algorithm whose bookkeeping overhead is proportional to the size of a disk volume's incremental backup snapshot rather than its full backup snapshot. We evaluated the Sungem prototype using a real-world data backup trace, and showed that the average throughput of Sungem is more than 200,000 fingerprint lookups per second on a standard X86 server, including the garbage collection cost.


title: Finding similar users using category-based location history

abstract: In this paper, we aim to estimate the similarity between users according to their GPS trajectories. Our approach first models a user's GPS trajectories with a semantic location history (SLH), e.g., shopping malls → restaurants → cinemas. Then, we measure the similarity between different users' SLHs by using our maximal travel match (MTM) algorithm. The advantage of our approach lies in two aspects. First, SLH carries more semantic meanings of a user's interests beyond low-level geographic positions. Second, our approach can estimate the similarity between two users without overlaps in the geographic spaces, e.g., people living in different cities. We evaluate our method based on a real-world GPS dataset collected by 109 users in a period of 1 year. As a result, SLH-MTM outperforms the related works [4].


title: CMOS Startup Charge Pump With Body Bias and Backward Control for Energy Harvesting Step-Up Converters

abstract: A new low voltage charge pump is developed to help start up a step-up converter in energy harvesting applications. The proposed charge pump is the first to utilize both backward control scheme and two branches of charge transfer switches (CTSs) to direct charge flow. The backward control scheme uses the internal boosted voltage to dynamically control the CTSs' gate, and the two branches utilize both NMOS and PMOS to implement their switching structure. The combination of backward control scheme and two-branch operation allows the CTSs to be completely turned on and off. Thus, the reverse charge sharing phenomenon and switching loss are significantly reduced, which effectively improves pumping efficiency. The last stage is specially designed to improve the charge pump's charge and capacitance drivability. Using subthreshold operation and body-bias technique, the charge pump and its clock generator can operate under a low voltage supply. The proposed charge pump circuit is designed in a standard 0.18 $\mu$ m CMOS process. It consists of 6 stages, each with a 24 pF pumping capacitor (total 288 pF pumping capacitance area). Under a 320 mV supply, the measured output voltage of the proposed charge pump can rise from 0 to 2.04 V within 0.1 milliseconds.


title: Fingerprinting BitTorrent Traffic in Encrypted Tunnels Using Recurrent Deep Learning

abstract: Torrent connection blocking inside institutional networks can be bypassed using VPN and proxies, allowing Bit-Torrent applications to tunnel connections to a remote server. Consequently it has become extremely challenging to classify application traffic without resorting to stateful deep flow or deep packet inspection techniques. As an alternative, we present a deep learning implementation that takes a featureset based on the statistical behavior of TCP tunnels proxying BitTorrent traffic, transforms it to multiple timestep sequences, and uses it to train a recurrent neural network. Our work demonstrates an RNN model and featureset that accurately determines the presence of tunneled BitTorrent connections with high accuracy, precision, and recall. We present how Bidirectional LSTM can improve binary classifier metrics when used over regular LSTM in classifying network traffic time sequences.


title: A Vision-Based Counting and Recognition System for Flying Insects in Intelligent Agriculture

abstract: Rapid and accurate counting and recognition of flying insects are of great importance, especially for pest control. Traditional manual identification and counting of flying insects is labor intensive and inefficient. In this study, a vision-based counting and classification system for flying insects is designed and implemented. The system is constructed as follows: firstly, a yellow sticky trap is installed in the surveillance area to trap flying insects and a camera is set up to collect real-time images. Then the detection and coarse counting method based on You Only Look Once (YOLO) object detection, the classification method and fine counting based on Support Vector Machines (SVM) using global features are designed. Finally, the insect counting and recognition system is implemented on Raspberry PI. Six species of flying insects including bee, fly, mosquito, moth, chafer and fruit fly are selected to assess the effectiveness of the system. Compared with the conventional methods, the test results show promising performance. The average counting accuracy is 92.50% and average classifying accuracy is 90.18% on Raspberry PI. The proposed system is easy-to-use and provides efficient and accurate recognition data, therefore, it can be used for intelligent agriculture applications.


title: A feature-based robust digital image watermarking scheme

abstract: A robust digital image watermarking scheme that combines image feature extraction and image normalization is proposed. The goal is to resist both geometric distortion and signal processing attacks. We adopt a feature extraction method called Mexican hat wavelet scale interaction. The extracted feature points can survive a variety of attacks and be used as reference points for both watermark embedding and detection. The normalized image of an image (object) is nearly invariant with respect to rotations. As a result, the watermark detection task can be much simplified when it is applied to the normalized image. However, because image normalization is sensitive to image local variation, we apply image normalization to nonoverlapped image disks separately. The disks are centered at the extracted feature points. Several copies of a 16-bit watermark sequence are embedded in the original image to improve the robustness of watermarks. Simulation results show that our scheme can survive low-quality JPEG compression, color reduction, sharpening, Gaussian filtering, median filtering, row or column removal, shearing, rotation, local warping, cropping, and linear geometric transformations.


title: THE IMPACT OF POPULATION CHANGE ON ECONOMIC GROWTH IN KENYA

abstract: The debate on the relationship between population growth and economic growth has been undergoing and varies across countries. The first theory states that population growth stimulates economic growth. The second theory view population growth as a factor that adversely affects economic growth while a third school is that population growth is a neutral factor in economic growth and is determined outside standard growth models. Given this scenario there was a need to establish the relationship between economic growth and population growth in Kenya. The study employed Vector Auto Regression estimation technique and used annual time series data for the period 1963 to 2009. The results indicated population growth and economic growths are both positively correlated and that an increase in population will impact positively to the economic growth in the country. The study concludes that in Kenya population growth promotes economic growth and subsequently economic development.


title: Crossing-Line Crowd Counting with Two-Phase Deep Neural Networks

abstract: In this paper, we propose a deep Convolutional Neural Network (CNN) for counting the number of people across a line-of-interest (LOI) in surveillance videos. It is a challenging problem and has many potential applications. Observing the limitations of temporal slices used by state-of-the-art LOI crowd counting methods, our proposed CNN directly estimates the crowd counts with pairs of video frames as inputs and is trained with pixel-level supervision maps. Such rich supervision information helps our CNN learn more discriminative feature representations. A two-phase training scheme is adopted, which decomposes the original counting problem into two easier sub-problems, estimating crowd density map and estimating crowd velocity map. Learning to solve the sub-problems provides a good initial point for our CNN model, which is then fine-tuned to solve the original counting problem. A new dataset with pedestrian trajectory annotations is introduced for evaluating LOI crowd counting methods and has more annotations than any existing one. Our extensive experiments show that our proposed method is robust to variations of crowd density, crowd velocity, and directions of the LOI, and outperforms state-of-the-art LOI counting methods.


title: A fuzzy aid rear-end collision warning/avoidance system

abstract: Highlights? We have implemented a rear-end collision warning/avoidance system in a real car. ? The system decides how to perform the maneuver without leaving the road. ? A vehicle-to-infrastructure communication system is used to exchange data. ? Fuzzy logic is used both for the warning and for the avoidance systems. ? Experiments with real cars were conducted with propper results. To decrease traffic accidents is a declared target of Intelligent Transportation Systems (ITS). Among them, rear-end collisions are one of the most common and constitute one of the as yet unsolved topics in the automotive sector. This paper presents an approach to the avoidance of rear-end collisions in congested traffic situations. To this end, two fuzzy controllers, a Collision Warning System (CWS) and a Collision Avoidance System (CAS), have been developed. The former is in charge of alerting the driver in case of an impending rear-end collision to prevent or mitigate the crash. The latter is in charge of generating an output control signal for the steering wheel in order to avoid the collision. Both CWS and CAS have been tested with real cars using vehicle-to-infrastructure (V2I) communications to acquire data of vehicles. A system installed in the infrastructure capable of assessing road traffic conditions in real time is responsible for transmitting the data of the vehicles in the surrounding area. The systems have been tested at the Center for Automation and Robotics (CAR)'s facilities with two mass-produced cars.


Image smoothing viaL0gradient minimization


title: Chimeric antigen receptor T cells for sustained remissions in leukemia.

abstract: BACKGROUND ::: Relapsed acute lymphoblastic leukemia (ALL) is difficult to treat despite the availability of aggressive therapies. Chimeric antigen receptor-modified T cells targeting CD19 may overcome many limitations of conventional therapies and induce remission in patients with refractory disease. ::: ::: ::: METHODS ::: We infused autologous T cells transduced with a CD19-directed chimeric antigen receptor (CTL019) lentiviral vector in patients with relapsed or refractory ALL at doses of 0.76×10(6) to 20.6×10(6) CTL019 cells per kilogram of body weight. Patients were monitored for a response, toxic effects, and the expansion and persistence of circulating CTL019 T cells. ::: ::: ::: RESULTS ::: A total of 30 children and adults received CTL019. Complete remission was achieved in 27 patients (90%), including 2 patients with blinatumomab-refractory disease and 15 who had undergone stem-cell transplantation. CTL019 cells proliferated in vivo and were detectable in the blood, bone marrow, and cerebrospinal fluid of patients who had a response. Sustained remission was achieved with a 6-month event-free survival rate of 67% (95% confidence interval [CI], 51 to 88) and an overall survival rate of 78% (95% CI, 65 to 95). At 6 months, the probability that a patient would have persistence of CTL019 was 68% (95% CI, 50 to 92) and the probability that a patient would have relapse-free B-cell aplasia was 73% (95% CI, 57 to 94). All the patients had the cytokine-release syndrome. Severe cytokine-release syndrome, which developed in 27% of the patients, was associated with a higher disease burden before infusion and was effectively treated with the anti-interleukin-6 receptor antibody tocilizumab. ::: ::: ::: CONCLUSIONS ::: Chimeric antigen receptor-modified T-cell therapy against CD19 was effective in treating relapsed and refractory ALL. CTL019 was associated with a high remission rate, even among patients for whom stem-cell transplantation had failed, and durable remissions up to 24 months were observed. (Funded by Novartis and others; CART19 ClinicalTrials.gov numbers, NCT01626495 and NCT01029366.).


title: A 1.6 Gb/s, 3 mW CMOS receiver for optical communication

abstract: A 1.6 Gb/s receiver for optical communication has been designed and fabricated in a 0.25-/spl mu/m CMOS process. This receiver has no transimpedance amplifier and uses the parasitic capacitor of the flip-chip bonded photodetector as an integrating element and resolves the data with a double-sampling technique. A simple feedback loop adjusts a bias current to the average optical signal, which essentially "AC couples" the input. The resulting receiver resolves an 11 /spl mu/A input, dissipates 3 mW of power, occupies 80 /spl mu/m/spl times/50 /spl mu/m of area and operates at over 1.6 Gb/s.


title: Negotiating Biculturalism Cultural Frame Switching in Biculturals with Oppositional Versus Compatible Cultural Identities

abstract: The authors propose that cultural frame shifting—shifting between two culturally based interpretative lenses in response to cultural cues—is moderated by perceived compatibility (vs. opposition) between the two cultural orientations, or bicultural identity integration (BII). Three studies found that Chinese American biculturals who perceived their cultural identities as compatible (high BII) responded in culturally congruent ways to cultural cues: They made more external attributions (a characteristically Asian behavior) after being exposed to Chinese primes and more internal attributions (a characteristically Western behavior) after being exposed to American primes. However, Chinese American biculturals who perceived their cultural identities as oppositional (low BII) exhibited a reverse priming effect. This trend was not apparent for noncultural primes. The results show that individual differences in bicultural identity affect how cultural knowledge is used to interpret social events.


title: Flower species recognition system using convolution neural networks and transfer learning

abstract: Automatic identification and recognition of medicinal plant species in environments such as forests, mountains and dense regions is necessary to know about their existence. In recent years, plant species recognition is carried out based on the shape, geometry and texture of various plant parts such as leaves, stem, flowers etc. Flower based plant species identification systems are widely used. While modern search engines provide methods to visually search for a query image that contains a flower, it lacks in robustness because of the intra-class variation among millions of flower species around the world. Hence in this proposed research work, a Deep learning approach using Convolutional Neural Networks (CNN) is used to recognize flower species with high accuracy. Images of the plant species are acquired using the built-in camera module of a mobile phone. Feature extraction of flower images is performed using a Transfer Learning approach (i.e. extraction of complex features from a pre-trained network). A machine learning classifier such as Logistic Regression or Random Forest is used on top of it to yield a higher accuracy rate. This approach helps in minimizing the hardware requirement needed to perform the computationally intensive task of training a CNN. It is observed that, CNN combined with Transfer Learning approach as feature extractor outperforms all the handcrafted feature extraction methods such as Local Binary Pattern (LBP), Color Channel Statistics, Color Histograms, Haralick Texture, Hu Moments and Zernike Moments. CNN combined with Transfer Learning approach yields impressive Rank-1 accuracies of 73.05%, 93.41% and 90.60% using OverFeat, Inception-v3 and Xception architectures, respectively as Feature Extractors on FLOWERS102 dataset.


DOUBLE GATE-CONTROLLED DUAL BASE SOI BIPOLAR JUNCTION TRANSISTOR : A PROMISING CANDIDATE FOR HIGH SPEED ELECTRONICS


title: Automated Colour Grading using Colour Distribution Transfer

abstract: This article proposes an original method for grading the colours between different images or shots. The first stage of the method is to find a one-to-one colour mapping that transfers the palette of an example target picture to the original picture. This is performed using an original and parameter free algorithm that is able to transform any N-dimensional probability density function into another one. The proposed algorithm is iterative, non-linear and has a low computational cost. Applying the colour mapping on the original picture allows reproducing the same 'feel' as the target picture, but can also increase the graininess of the original picture, especially if the colour dynamic of the two pictures is very different. The second stage of the method is to reduce this grain artefact through an efficient post-processing algorithm that intends to preserve the gradient field of the original picture.


title: Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host’s metabolism

abstract: Major depressive disorder (MDD) is the result of complex gene-environment interactions. According to the World Health Organization, MDD is the leading cause of disability worldwide, and it is a major contributor to the overall global burden of disease. However, the definitive environmental mechanisms underlying the pathophysiology of MDD remain elusive. The gut microbiome is an increasingly recognized environmental factor that can shape the brain through the microbiota-gut-brain axis. We show here that the absence of gut microbiota in germ-free (GF) mice resulted in decreased immobility time in the forced swimming test relative to conventionally raised healthy control mice. Moreover, from clinical sampling, the gut microbiotic compositions of MDD patients and healthy controls were significantly different with MDD patients characterized by significant changes in the relative abundance of Firmicutes, Actinobacteria and Bacteroidetes. Fecal microbiota transplantation of GF mice with 'depression microbiota' derived from MDD patients resulted in depression-like behaviors compared with colonization with 'healthy microbiota' derived from healthy control individuals. Mice harboring 'depression microbiota' primarily exhibited disturbances of microbial genes and host metabolites involved in carbohydrate and amino acid metabolism. This study demonstrates that dysbiosis of the gut microbiome may have a causal role in the development of depressive-like behaviors, in a pathway that is mediated through the host's metabolism.


title: Anomaly Detection in Business Process Runtime Behavior -- Challenges and Limitations

abstract: Anomaly detection is generally acknowledged as an important problem that has already drawn attention to various domains and research areas, such as, network security. For such "classic" application domains a wide range of surveys and literature reviews exist already - which is not the case for the process domain. Hence, this systematic literature review strives to provide an organized holistic view on research related to business process runtime behavior anomaly detection. For this the unique challenges of the process domain are outlined along with the nature of the analyzed data and data sources. Moreover, existing work is identified and categorized based on the underlying fundamental technology applied by each work. Furthermore, this work describes advantages and disadvantages of each identified approach. Based on these information limitations and gaps in existing research are identified and recommendations are proposed to tackle them. This work aims to foster the understanding and development of the process anomaly detection domain.


title: 10-Gbps RSOA-based upstream transmission in WDM-PON with MZI-based equalizers

abstract: Proposed and demonstrated in this paper is the 10-Gbps uplink transmission in WDM-PON, in which band-limited RSOAs are applied to colorless ONUs and Mach-Zehnder Interferometers (MZIs) are used as two-tap optical equalizers before the receivers.


title: Stock Prediction based on Bayesian-LSTM

abstract: Fluctuations in stock market represent the changes in national economic objectively. Some machine learning algorithms, such as linear fitting and sequence mining, are employed to predict the stock market. However, linear fitting faces issue of over-fitting and black relationships with historical data, while sequence mining is short in efficiency and lack dynamic adaptations. This paper builds a modified Bayesian-LSTM (B-LSTM) model for stock prediction. Six indicators of the Chinese stock market in every day are the basic input for LSTM. In order to represent the economic wave, we defined a data set unit by week which means the basic unit in LSTM is data in one week. Furthermore, a Bayesian optimization model is proposed to estimate the unit number dynamically in different economic cycle. The experimental results demonstrated that the B-LSTM increase over 25% prediction than the conventional LSTM.


title: Composite secure MQTT for Internet of Things using ABE and dynamic S-box AES

abstract: The Publish-Subscribe messaging paradigm is widely used as one of the communication models for Internet of Things (IoT). Here, Users are more interested in sharing and retrieving information and careless about which specific end point is holding the information. of various publish-subscribe protocol promoted, MQTT is extremely lightweight and widely used Publish-Subscribe connectivity protocol for Internet of Things. The major drawback of MQTT protocol is that it has limited security features. Hence, these protocols need to address security issues for IoT. In this paper, we present a solution to provide access control and confidentiality of the information exchanged in an MQTT based IoT system. Our approach provides a solution of applying Attribute-based Encryption (ABE) and Dynamic S-Box Advanced Encryption Standard (AES) for payload encryption in MQTT. Further, we evaluated our proposed scheme through simulation.


title: Building classification models for customer credit scoring

abstract: Credit scoring is a quantitative method. It has been used in the banking industry. The major challenge of credit scoring is essentially to classify credit customers into different risk groups. As known methods, Hidden Markov Model (HMM) have been proposed as a classification technique. It can be utilized to build a classification model. The developed process consists to distinguish preciously the profitable customers from bankrupt customers using all possible characteristics describing the applicant. The important and the difficult task in credit scoring problems arises when a banker decide whether to grant or not grant a loan. In this order, the aim of this work is to investigate the performance of HMM for building a good classifier for credit scoring. In this paper, we also provide a set of classification models to score customer by combining the HMM and Baum-Welch procedure. There are two phases in this model: firstly, we train the initial model by HMM techniques estimations, secondly we use the iterative procedure Baum-Welch to generate HMMs parameters and building new models. Experimental results show that the proposed model can build the highest accuracy classifier for credit scoring datasets. The implementation of HMM with Baum-Welch model allows lenders and bankers to develop techniques to measure customer credit risk.


Logic, arithmetic, and automata


title: Identification of Thai characters and numbers on plate number

abstract: This paper proposes a method of Thai characters and numeral identification on plate number. The proposed method consists of license plate detection and identification of characters and numbers. In the license plate detection, the vertical edges, the vertical projection, and some operations of morphology are used to searching for license plate location. Once plate location is known, the identification of characters and numbers starts from inversion of binary image, character and number segmentation, normalization, computation of the horizontal and vertical DCT coefficients. Finally, the impulse response of FIR system of character and number are determined using the horizontal and vertical DCT coefficients as input and output of FIR system. The unknown characters or numbers on the plate number can be identified by comparison of their impulse response with the impulse responses of known characters and numbers in database, if the sum square error of them is smallest. On experiments, it found that the correct detection of license plate is about 95.52% and the accuracy of characters and numeral identification is about 81.25%.


title: Learning to Speed Up Query Planning in Graph Databases

abstract: Querying graph structured data is a fundamental operation that enables important applications including knowledge graph search, social network analysis, and cyber-network security. However, the growing size of real-world data graphs poses severe challenges for graph databases to meet the response-time requirements of the applications. Planning the computational steps of query processing - Query Planning - is central to address these challenges. In this paper, we study the problem of learning to speedup query planning in graph databases towards the goal of improving the computational-efficiency of query processing via training queries.We present a Learning to Plan (L2P) framework that is applicable to a large class of query reasoners that follow the Threshold Algorithm (TA) approach. First, we define a generic search space over candidate query plans, and identify target search trajectories (query plans) corresponding to the training queries by performing an expensive search. Subsequently, we learn greedy search control knowledge to imitate the search behavior of the target query plans. We provide a concrete instantiation of our L2P framework for STAR, a state-of-the-art graph query reasoner. Our experiments on benchmark knowledge graphs including DBpedia, YAGO, and Freebase show that using the query plans generated by the learned search control knowledge, we can significantly improve the speed of STAR with negligible loss in accuracy.


title: AEMS: An Anytime Online Search Algorithm for Approximate Policy Refinement in Large POMDPs

abstract: Solving large Partially Observable Markov Decision Processes (POMDPs) is a complex task which is often intractable. A lot of effort has been made to develop approximate offline algorithms to solve ever larger POMDPs. However, even state-of-the-art approaches fail to solve large POMDPs in reasonable time. Recent developments in online POMDP search suggest that combining offline computations with online computations is often more efficient and can also considerably reduce the error made by approximate policies computed offline. In the same vein, we propose a new anytime online search algorithm which seeks to minimize, as efficiently as possible, the error made by an approximate value function computed offline. In addition, we show how previous online computations can be reused in following time steps in order to prevent redundant computations. Our preliminary results indicate that our approach is able to tackle large state space and observation space efficiently and under real-time constraints.


title: Adaptive RGB-D Localization

abstract: The advent of RGB-D cameras which provide synchronized range and video data creates new opportunities for exploiting both sensing modalities for various robotic applications. This paper exploits the strengths of vision and range measurements and develops a novel robust algorithm for localization using RGB-D cameras. We show how correspondences established by matching visual SIFT features can effectively initialize the generalized ICP algorithm as well as demonstrate situations where such initialization is not viable. We propose an adaptive architecture which computes the pose estimate from the most reliable measurements in a given environment and present thorough evaluation of the resulting algorithm against a dataset of RGB-D benchmarks, demonstrating superior or comparable performance in the absence of the global optimization stage. Lastly we demonstrate the proposed algorithm on a challenging indoor dataset and demonstrate improvements where pose estimation from either pure range sensing or vision techniques perform poorly.


title: Adaptive LQR stabilization control of reaction wheel for satellite systems

abstract: Spacecrafts, which are used for stereoscopic mapping, imaging and telecommunication applications, require fine attitude and stabilization control which has an important role in high precision pointing and accurate stabilization. The conventional techniques for attitude and stabilization control are thrusters, reaction wheels, control moment gyroscopes (CMG) and magnetic torquers. Since reaction wheel can generate relatively smaller torques, they provide very fine stabilization and attitude control. Although conventional PID framework solves many stabilization problems, it is reported that many PID feedback loops are poorly tuned. In this paper, a model reference adaptive LQR control for reaction wheel stabilization problem is implemented. The tracking performance and disturbance rejection capability of proposed controller is found to give smooth motion after abnormal disruptions.


title: A Clustering Based Adaptive Sequence-to-Sequence Model for Dialogue Systems

abstract: Dialogue systems which can communicate with people in natural language is popularly used in entertainments and language learning tools. As the development of deep neural networks, Sequence-to-Sequence models become the main stream models of conversation generation tasks which are the key part of dialogue systems, because Sequence-to-Sequence models is good at dealing with the tasks like machine translation and conversation generation whose input's length and output's length is unknown previously. However, recent works find that Sequence-to-Sequence models tend to respond in dull sentences. We propose a clustering based adaptive Sequence-to-Sequence model to improve the performance of dialogue systems. Different with previous models who treat all the dialogue data as input of a single model, we cluster the dialogue data and use several Sequence-to-Sequence models to train different cluster of data to catch different characteristic in different cluster. Our experiments show that our models can improve the performance of dialogue systems.


title: Beyond sentence-level semantic role labeling: linking argument structures in discourse

abstract: Semantic role labeling is traditionally viewed as a sentence-level task concerned with identifying semantic arguments that are overtly realized in a fairly local context (i.e., a clause or sentence). However, this local view potentially misses important information that can only be recovered if local argument structures are linked across sentence boundaries. One important link concerns semantic arguments that remain locally unrealized (null instantiations) but can be inferred from the context. In this paper, we report on the SemEval 2010 Task-10 on "Linking Events and Their Participants in Discourse", that addressed this problem. We discuss the corpus that was created for this task, which contains annotations on multiple levels: predicate argument structure (FrameNet and PropBank), null instantiations, and coreference. We also provide an analysis of the task and its difficulties.


Hidden Markov induced Dynamic Bayesian Network for recovering time evolving gene regulatory networks


Parry Romberg syndrome with a wide range of ocular manifestations: a case report


title: Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs

abstract: In this paper, the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide the fly wireless communications to a given geographical area is analyzed. In particular, the coexistence between the UAV, that is transmitting data in the downlink, and an underlaid device-to-device (D2D) communication network is considered. For this model, a tractable analytical framework for the coverage and rate analysis is derived. Two scenarios are considered: a static UAV and a mobile UAV. In the first scenario, the average coverage probability and the system sum-rate for the users in the area are derived as a function of the UAV altitude and the number of D2D users. In the second scenario, using the disk covering problem, the minimum number of stop points that the UAV needs to visit in order to completely cover the area is computed. Furthermore, considering multiple retransmissions for the UAV and D2D users, the overall outage probability of the D2D users is derived. Simulation and analytical results show that, depending on the density of D2D users, the optimal values for the UAV altitude, which lead to the maximum system sum-rate and coverage probability, exist. Moreover, our results also show that, by enabling the UAV to intelligently move over the target area, the total required transmit power of UAV while covering the entire area, can be minimized. Finally, in order to provide full coverage for the area of interest, the tradeoff between the coverage and delay, in terms of the number of stop points, is discussed.


title: Chemical–gene relation extraction using recursive neural network

abstract: In this article, we describe our system for the CHEMPROT task of the BioCreative VI challenge. Although considerable research on the named entity recognition of genes and drugs has been conducted, there is limited research on extracting relationships between them. Extracting relations between chemical compounds and genes from the literature is an important element in pharmacological and clinical research. The CHEMPROT task of BioCreative VI aims to promote the development of text mining systems that can be used to automatically extract relationships between chemical compounds and genes. We tested three recursive neural network approaches to improve the performance of relation extraction. In the BioCreative VI challenge, we developed a tree-Long Short-Term Memory networks (tree-LSTM) model with several additional features including a position feature and a subtree containment feature, and we also applied an ensemble method. After the challenge, we applied additional pre-processing steps to the tree-LSTM model, and we tested the performance of another recursive neural network model called Stack-augmented Parser Interpreter Neural Network (SPINN). Our tree-LSTM model achieved an F-score of 58.53% in the BioCreative VI challenge. Our tree-LSTM model with additional pre-processing and the SPINN model obtained F-scores of 63.7 and 64.1%, respectively.Database URL: https://github.com/arwhirang/recursive_chemprot.


title: Multi-Sensor Obstacle Detection System Via Model-Based State-Feedback Control in Smart Cane Design for the Visually Challenged

abstract: Smart canes are usually developed to alert visually challenged users of any obstacles beyond the canes’ physical lengths. The accuracy of the sensors and their actuators’ positions are equally crucial to estimate the locations of the obstacles with respect to the users so as to ensure only correct signals are sent through the associated audio or tactile feedbacks. For implementations with low-cost sensors, however, the users are very likely to get false alerts due to the effects from noise and their erratic readings, and the performance degradation will be more noticeable when the positional fluctuations of the actuators get amplified. In this paper, a multi-sensor obstacle detection system for a smart cane is proposed via a model-based state-feedback control strategy to regulate the detection angle of the sensors and minimize the false alerts to the user. In this approach, the overall system is first restructured into a suitable state-space model, and a linear quadratic regulator (LQR)-based controller is then synthesized to further optimize the actuator’s control actions while ensuring its position tracking. We also integrate dynamic feedback compensators into the design to increase the accuracy of the user alerts. The performance of the resulting feedback system was evaluated via a series of real-time experiments, and we showed that the proposed method provides significant improvements over conventional methods in terms of error reductions.


title: Graph Convolutional Neural Networks via Motif-based Attention

abstract: Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural networks on arbitrary graphs. Different from previous convolutional neural networks on graphs, we first design a motif-matching guided subgraph normalization method to capture neighborhood information. Then we implement subgraph-level self-attentional layers to learn different importances from different subgraphs to solve graph classification problems. Analogous to image-based attentional convolution networks that operate on locally connected and weighted regions of the input, we also extend graph normalization from one-dimensional node sequence to two-dimensional node grid by leveraging motif-matching, and design self-attentional layers without requiring any kinds of cost depending on prior knowledge of the graph structure. Our results on both bioinformatics and social network datasets show that we can significantly improve graph classification benchmarks over traditional graph kernel and existing deep models.


title: CIRCULAR CODED TARGET FOR AUTOMATION OF OPTICAL 3D-MEASUREMENT AND CAMERA CALIBRATION

abstract: One of the primary, but tedious, tasks for the user and developer of an optical 3D-measurement system is to find the homologous image points in multiple images, a task that is frequently referred to as the correspondence problem. Along with the solution, error-free correspondence and accurate measurement of image points are of great importance, on which the qualitative results of succeeding camera calibration and 3D-measurements are immediately dependent. In fact, the automation of measurement processes is getting more important with progresses in production, and hence, is of increasing topical interest. In this paper, we present a circular coded target for automatic image point measurement and identification. The applied image processing method will be described in detail, and we will show some application examples of the circular coded target to optical 3D-measurement techniques.


title: A MULTI-BEAM AND MULTI-RANGE RADAR WITH FMCW AND DIGITAL BEAM FORMING FOR AUTOMOTIVE APPLICATIONS

abstract: In this paper, we propose a multi-beam and multi-range (MBMR) radar with frequency modulated continuous wave (FMCW) waveform and digital beam forming (DBF) algorithm to cover a detection area of long range and narrow angle (150m, §10 - ) as well as short range and wide angle (60m, §30 - ) as a single 24GHz sensor. The developed radar is highly integrated with multiple phased-array antennas, a two-channel transmitter and a four-channel receiver using K-band GaAs RF ICs, and back-end processing board with subspace- based DBF algorithm. The proposed 24GHz MBMR radar can be used for an adaptive cruise control (ACC) stop-and-go system which typically consists of three radars, such as two 24GHz short-range radars for object detection in an adjacent lane and one 77GHz long- range radar for object detection in the center lane.


title: Toward Human Parity in Conversational Speech Recognition

abstract: Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure a human error rate on the widely used NIST 2000 test set for commercial bulk transcription. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion, where friends and family members have open-ended conversations. In both cases, our automated system edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively. The key to our system's performance is the use of various convolutional and long-short-term memory acoustic model architectures, combined with a novel spatial smoothing method and lattice-free discriminative acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination. Comparing frequent errors in our human and machine transcripts, we find them to be remarkably similar, and highly correlated as a function of the speaker. Human subjects find it very difficult to tell which errorful transcriptions come from humans. Overall, this suggests that, given sufficient matched training data, conversational speech transcription engines are approximating human parity in both quantitative and qualitative terms.


title: Power challenges may end the multicore era

abstract: Starting in 2004, the microprocessor industry has shifted to multicore scaling---increasing the number of cores per die each generation---as its principal strategy for continuing performance growth. Many in the research community believe that this exponential core scaling will continue into the hundreds or thousands of cores per chip, auguring a parallelism revolution in hardware or software. However, while transistor count increases continue at traditional Moore's Law rates, the per-transistor speed and energy efficiency improvements have slowed dramatically. Under these conditions, more cores are only possible if the cores are slower, simpler, or less utilized with each additional technology generation. This paper brings together transistor technology, processor core, and application models to understand whether multicore scaling can sustain the historical exponential performance growth in this energy-limited era. As the number of cores increases, power constraints may prevent powering of all cores at their full speed, requiring a fraction of the cores to be powered off at all times. According to our models, the fraction of these chips that is "dark" may be as much as 50% within three process generations. The low utility of this "dark silicon" may prevent both scaling to higher core counts and ultimately the economic viability of continued silicon scaling. Our results show that core count scaling provides much less performance gain than conventional wisdom suggests. Under (highly) optimistic scaling assumptions---for parallel workloads---multicore scaling provides a 7.9× (23% per year) over ten years. Under more conservative (realistic) assumptions, multicore scaling provides a total performance gain of 3.7× (14% per year) over ten years, and obviously less when sufficiently parallel workloads are unavailable. Without a breakthrough in process technology or microarchitecture, other directions are needed to continue the historical rate of performance improvement.


title: Preliminary studies on the Good, the Bad, and the Ugly face recognition challenge problem

abstract: Face recognition has made significant advances over the last twenty years. State-of-the-art algorithms push the performance envelope to near perfect recognition rates on many face databases. Recently, the Good, the Bad, and the Ugly (GBU) face challenge problem has been introduced to focus on hard aspects of face recognition from still frontal images. In this paper, we introduce the CohortLDA baseline algorithm, which is an Linear Discriminant Analysis (LDA) algorithm with color spaces and cohort normalization. CohortLDA greatly outperforms some well known face recognition algorithms on the GBU challenge problem. The GBU protocol includes rules for creating training sets. We investigate the effect on performance of violating the rules for creating training sets. This analysis shows that violating the GBU protocol can substantially over estimate performance on the GBU challenge problem.


title: Group Device Pairing based Secure Sensor Association and Key Management for Body Area Networks

abstract: Body Area Networks (BAN) is a key enabling technology in E-healthcare such as remote health monitoring. An important security issue during bootstrap phase of the BAN is to securely associate a group of sensor nodes to a patient, and generate necessary secret keys to protect the subsequent wireless communications. Due to the the ad hoc nature of the BAN and the extreme resource constraints of sensor devices, providing secure, fast, efficient and user-friendly secure sensor association is a challenging task. In this paper, we propose a lightweight scheme for secure sensor association and key management in BAN. A group of sensor nodes, having no prior shared secrets before they meet, establish initial trust through \textit{group device pairing} (GDP), which is an authenticated group key agreement protocol where the legitimacy of each member node can be visually verified by a human. Various kinds of secret keys can be generated on demand after deployment. The GDP supports batch deployment of sensor nodes to save setup time, does not rely on any additional hardware devices, and is mostly based on symmetric key cryptography, while allowing batch node addition and revocation. We implemented GDP on a sensor network testbed and evaluated its performance. Experimental results show that that GDP indeed achieves the expected design goals.


title: Value-Difference based Exploration: Adaptive Control between epsilon-Greedy and Softmax

abstract: This paper proposes "Value-Difference Based Exploration combined with Softmax action selection" (VDBE-Softmax) as an adaptive exploration/exploitation policy for temporal-difference learning. The advantage of the proposed approach is that exploration actions are only selected in situations when the knowledge about the environment is uncertain, which is indicated by fluctuating values during learning. The method is evaluated in experiments having deterministic rewards and a mixture of both deterministic and stochastic rewards. The results show that a VDBE-Softmax policy can outperform e-greedy, Softmax and VDBE policies in combination with on- and off-policy learning algorithms such as Q-learning and Sarsa. Furthermore, it is also shown that VDBE-Softmax is more reliable in case of value-function oscillations.


title: Design of Boost-Flyback Single-Stage PFC converter for LED power supply without electrolytic capacitor for energy-storage

abstract: Light emitting diodes (LEDs) are likely to be used for general lighting applications due to their high efficiency and longer life. This paper presents the concept of applying large voltage ripple for energy storage into the Boost-Flyback Single -Stage PFC converter for the elimination of the electrical capacitor. Following proposed design procedure, the single stage PFC circuit with small energy storage capacitance can still achieve good output voltage regulation while preserving desired input power factor. The detailed theoretical analysis and design procedure of the Single-Stage PFC converter is presented. The experimental results obtained on a 60W prototype converter along with waveforms are presented.


title: Age-related differences in performance with touchscreens compared to traditional mouse input

abstract: Despite the apparent popularity of touchscreens for older adults, little is known about the psychomotor performance of these devices. We compared performance between older adults and younger adults on four desktop and touchscreen tasks: pointing, dragging, crossing and steering. On the touchscreen, we also examined pinch-to-zoom. Our results show that while older adults were significantly slower than younger adults in general, the touchscreen reduced this performance gap relative to the desktop and mouse. Indeed, the touchscreen resulted in a significant movement time reduction of 35% over the mouse for older adults, compared to only 16% for younger adults. Error rates also decreased.


title: Basic Properties of the Blockchain: (Invited Talk)

abstract: As the first decentralized cryptocurrency, Bitcoin [1] has ignited much excitement, not only for its novel realization of a central bank-free financial instrument, but also as an alternative approach to classical distributed computing problems, such as reaching agreement distributedly in the presence of misbehaving parties, as well as to numerous other applications-contracts, reputation systems, name services, etc. The soundness and security of these applications, however, hinges on the thorough understanding of the fundamental properties of its underlying blockchain data structure, which parties ("miners") maintain and try to extend by generating "proofs of work" (POW, aka "cryptographic puzzle"). In this talk we follow the approach introduced in [2], formulating such fundamental properties of the blockchain, and then showing how applications such as consensus and a robust public transaction ledger can be built ``on top'' of them. The properties are as follows, assuming the adversary's hashing power (our analysis holds against arbitrary attacks) is strictly less than ½ and high network synchrony: Common prefix: The blockchains maintained by the honest parties possess a large common prefix. More specifically, if two honest parties "prune" (i.e., cut off) k blocks from the end of their local chains, the probability that the resulting pruned chains will not be mutual prefixes of each other drops exponentially in the that parameter. Chain quality: We show a bound on the ratio of blocks in the chain of any honest party contributed by malicious parties. In particular, as the adversary's hashing power approaches ½, we show that blockchains are only guaranteed to have few, but still some, blocks contributed by honest parties. Chain growth: We quantify the number of blocks that are added to the blockchain during any given number of rounds during the execution of the protocol. (N.B.: This property, which in [2] was proven and used directly in the form of a lemma, was explicitly introduced in [3]. Identifying it as a separate property enables modular proofs of applications' properties.) The above properties hold assuming that all parties-honest and adversarial-"wake up" and start computing at the same time, or, alternatively, that they compute on a common random string (the "genesis" block) only made available at the exact time when the protocol execution is to begin. In this talk we also consider the question of whether such a trusted setup/behavioral assumption is necessary, answering it in the negative by presenting a Bitcoin-like blockchain protocol that is provably secure without trusted setup, and, further, overcomes such lack in a scalable way-i.e., with running time independent of the number of parties [4]. A direct consequence of our construction above is that consensus can be solved directly by a blockchain protocol without trusted setup assuming an honest majority (in terms of computational power).


title: Computational Analysis and Simulation of Empathic Behaviors: a Survey of Empathy Modeling with Behavioral Signal Processing Framework

abstract: Empathy is an important psychological process that facilitates human communication and interaction. Enhancement of empathy has profound significance in a range of applications. In this paper, we review emerging directions of research on computational analysis of empathy expression and perception as well as empathic interactions, including their simulation. We summarize the work on empathic expression analysis by the targeted signal modalities (e.g., text, audio, and facial expressions). We categorize empathy simulation studies into theory-based emotion space modeling or application-driven user and context modeling. We summarize challenges in computational study of empathy including conceptual framing and understanding of empathy, data availability, appropriate use and validation of machine learning techniques, and behavior signal processing. Finally, we propose a unified view of empathy computation and offer a series of open problems for future research.


title: Fast Registration Based on Noisy Planes With Unknown Correspondences for 3-D Mapping

abstract: We present a robot-pose-registration algorithm, which is entirely based on large planar-surface patches extracted from point clouds sampled from a three-dimensional (3-D) sensor. This approach offers an alternative to the traditional point-to-point iterative-closest-point (ICP) algorithm, its point-to-plane variant, as well as newer grid-based algorithms, such as the 3-D normal distribution transform (NDT). The simpler case of known plane correspondences is tackled first by deriving expressions for least-squares pose estimation considering plane-parameter uncertainty computed during plane extraction. Closed-form expressions for covariances are also derived. To round-off the solution, we present a new algorithm, which is called minimally uncertain maximal consensus (MUMC), to determine the unknown plane correspondences by maximizing geometric consistency by minimizing the uncertainty volume in configuration space. Experimental results from three 3-D sensors, viz., Swiss-Ranger, University of South Florida Odetics Laser Detection and Ranging, and an actuated SICK S300, are given. The first two have low fields of view (FOV) and moderate ranges, while the third has a much bigger FOV and range. Experimental results show that this approach is not only more robust than point- or grid-based approaches in plane-rich environments, but it is also faster, requires significantly less memory, and offers a less-cluttered planar-patches-based visualization.


title: Example-dependent cost-sensitive decision trees

abstract: Example-dependent cost-sensitive tree algorithm.Each example is assumed to have different financial cost.Application on credit card fraud detection, credit scoring and direct marketing.Focus on maximizing the financial savings instead of accuracy.Code is open source and available at albahnsen.com/CostSensitiveClassification. Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. State-of-the-art example-dependent cost-sensitive techniques only introduce the cost to the algorithm, either before or after training, therefore, leaving opportunities to investigate the potential impact of algorithms that take into account the real financial example-dependent costs during an algorithm training. In this paper, we propose an example-dependent cost-sensitive decision tree algorithm, by incorporating the different example-dependent costs into a new cost-based impurity measure and a new cost-based pruning criteria. Then, using three different databases, from three real-world applications: credit card fraud detection, credit scoring and direct marketing, we evaluate the proposed method. The results show that the proposed algorithm is the best performing method for all databases. Furthermore, when compared against a standard decision tree, our method builds significantly smaller trees in only a fifth of the time, while having a superior performance measured by cost savings, leading to a method that not only has more business-oriented results, but also a method that creates simpler models that are easier to analyze.


title: A Robust Blind Watermarking Using Convolutional Neural Network

abstract: This paper introduces a blind watermarking based on a convolutional neural network (CNN). We propose an iterative learning framework to secure robustness of watermarking. One loop of learning process consists of the following three stages: Watermark embedding, attack simulation, and weight update. We have learned a network that can detect a 1-bit message from a image sub-block. Experimental results show that this learned network is an extension of the frequency domain that is widely used in existing watermarking scheme. The proposed scheme achieved robustness against geometric and signal processing attacks with a learning time of one day.


title: Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

abstract: This work was partially supported by the Spanish CICyT through the project TIN2013-48152-C2-1-R, the Consejeria de Educacion de la Comunidad Valenciana through Project PROMETEO/2012/017 and a FPU fellowship (AP2012-0939) from the Spanish Ministerio de Educacion Cultura y Deporte.


title: Visone -- Analysis and Visualization of Social Networks

abstract: Social network analysis is a subdiscipline of the social sciences using graph-theoretic concepts to understand and explain social structure.We describe the main issues in social network analysis. General principles are laid out for visualizing network data in a way that conveys structural information relevant to specific research questions. Based on these innovative graph drawing techniques integrating the analysis and visualization of social networks are introduced.


title: Open source simulation of smart antenna systems in network simulator-2 using Octave

abstract: This paper presents a simulation platform that exploits the scalability properties of network simulator-2 (ns-2) for the discrete-event simulation of a telecommunication network, and adopts the open source tool Octave for the discrete-time implementation of adaptive antenna arrays. The physical antenna system, the beamforming algorithm, and the multipath statistic are modeled in detail to accurately estimate the Signal to Interference Ratio (SIR) at each receiving node. This study describes the methods that can be used to interface Octave with ns-2 and compares the developed solution to an existing one, based on the proprietary tool MATLAB, discussing the advantages and disadvantages that characterize the two platforms.


title: Understanding the Roles of Signs and Norms in Organisations-A semiotic approach to information systems design

abstract: To apply semiotics to organizational analysis and information systems design, it is essential to unite two basic concepts: the sign and the norm. A sign is anything that stands for something else for some community. A norm is a generalized disposition to the world shared by members of a community. When its condition is met, a norm generates a propositional attitude which may, but not necessarily will, affect the subject's behaviour. Norms reflect regularities in the behaviour of members in an organization, allowing them to coordinate their actions. Organized behaviour is norm-governed behaviour. Signs trigger the norms leading to more signs being produced. Both signs and norms lend themselves to empirical study. The focus in this paper is on the properties of norms since those for signs are relatively well known. The paper discusses a number of different taxonomies of norms: formal, informal, technical; evaluative, perceptual, behavioural, cognitive; structure, action; substantive, communication and control. A semiotic analysis of information systems is adduced in this paper from the social, pragmatic, semantic, syntactic, empiric and physical perspectives. The paper finally presents a semiotic approach to information systems design, by discussing the method of information modelling and systems architecture. This approach shows advantages over other traditional ones in a higher degree of separation of knowledge, and hence in the consistency, integrity and maintainability of systems.


title: A Nonlinear-Disturbance-Observer-Based DC-Bus Voltage Control for a Hybrid AC/DC Microgrid

abstract: DC bus voltage control is an important task in the control of a dc microgrid or hybrid ac/dc microgrid system. To improve the dc bus voltage control dynamics, traditional approaches are mainly based on feeding forward the load or source power. However, in the microgrid system with distributed dc sources and loads, the traditional feedforward based methods need remote measurement or communications. In this paper, a nonlinear disturbance observer (NDO) based dc bus voltage control is proposed, which does not need the remote measurement or communication with a true “plug-and-play” feature. Based on this observer, a novel dc bus voltage control scheme with pulse-width-modulation (PWM) dead-time compensation is developed. The proposed method can suppress the transient fluctuations of dc bus voltage, and improve the power quality in such a hybrid microgrid system. The performance of the presented control strategy has been successfully verified in a 30 kVA dc subgrid including battery energy storage system, photovoltaic power generation system and a dc load.


title: Motorcycle detection and classification in urban Scenarios using a model based on Faster R-CNN

abstract: This paper introduces a Deep Learning Convolutional Neural Network model based on Faster-RCNN for motorcycle detection and classification on urban environments. The model is evaluated in occluded scenarios where more than 60% of the vehicles present a degree of occlusion. For training and evaluation, we introduce a new dataset of 7500 annotated images, captured under real traffic scenes, using a drone mounted camera. Several tests were carried out to design the network, achieving promising results of 75% in average precision (AP), even with the high number of occluded motorbikes, the low angle of capture and the moving camera. The model is also evaluated on low occlusions datasets, reaching results of up to 92% in AP.


title: Fully digital workflow, integrating dental scan, smile design and CAD-CAM: case report

abstract: BACKGROUND ::: This report is a presentation of a clinical case that follows a full digital workflow. ::: ::: ::: CASE PRESENTATION ::: A 47-year old man presented with pain in the TMJ (temporomandibular joint) and whose aesthetic concern was having a chipped maxillary central incisor veneer. The concern was solved following a fully digital workflow: it was applied the digital smile design protocol, as well as CAD-CAM monolithic lithium disilicate ceramic veneers and crowns (following a minimal invasive preparation approach). The aim of this rehabilitation was to solve a loss of vertical dimension, subsequent aesthetics and temporomandibular joint disorders. ::: ::: ::: CONCLUSION ::: Thanks to the evolution of technology in dentistry, it is possible to do a full digital case and solve problems such as loss of vertical dimension successfully. Nevertheless, more clinical studies are needed to obtain consistent results about the digital work flow compared to the conventional technique in loss of vertical dimension cases.


title: Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

abstract: Along with the development of cloud computing, an increasing number of enterprises start to adopt cloud service, which promotes the emergence of many cloud service providers. For cloud service providers, how to configure their cloud service platforms to obtain the maximum profit becomes increasingly the focus that they pay attention to. In this paper, we take customer satisfaction into consideration to address this problem. Customer satisfaction affects the profit of cloud service providers in two ways. On one hand, the cloud configuration affects the quality of service which is an important factor affecting customer satisfaction. On the other hand, the customer satisfaction affects the request arrival rate of a cloud service provider. However, few existing works take customer satisfaction into consideration in solving profit maximization problem, or the existing works considering customer satisfaction do not give a proper formalized definition for it. Hence, we first refer to the definition of customer satisfaction in economics and develop a formula for measuring customer satisfaction in cloud computing. And then, an analysis is given in detail on how the customer satisfaction affects the profit. Lastly, taking into consideration customer satisfaction, service-level agreement, renting price, energy consumption, and so forth, a profit maximization problem is formulated and solved to get the optimal configuration such that the profit is maximized.


title: Design and implementation of smart wireless street lighting system with ad-hoc network configuration

abstract: In this research, a smart wireless street lighting system (SWSL) based on wireless communication using ZigBee configuration is designed, implemented, and analyzed. The proposed SWSL system which is equipped with sensors (light sensor, motion sensor, current and voltage sensor), time readers and the microcontroller so that the system is able to regulate its function automatically according to the time and environmental conditions. The SWSL communication system is using ad-hoc network configuration to transmit the data information of each point of lights to the server. Ad-hoc network configuration makes the system more flexible because each node can communicate with each other directly without having to go through an access point. In addition, this system also uses LED lights and apply on-grid technology that uses the energy of sunlight as its primary power source, so it is able to save on energy consumption. It is shown from the results, the system has been able to work in accordance with designed algorithms (resources switching, work function of light and sensor, and data communication). On data communications testing for transmitting data from router node to coordinator node, is obtained data reception success rate of 82.085%. Then, from the results of the calculating simulation of the efficiency of the system obtained the total power usage of SWSL per year only amounted to 59.09 KWh / lamp. Therefore, SWSL system is much more efficient in terms of energy consumption compared to existing street lighting system.


title: Visualizing Social Networks

abstract: The use of visual images is common in many branches of science. And reviewers often suggest that such images are important for progress in the various fields (Koestler, 1964; Arnheim, 1970; Taylor, 1971; Tukey, 1972; Klovdahl, 1981; Tufte, 1983; Belien and Leenders). The historian Alfred Crosby (1997) has gone much further. He has proposed that visualization is one of only two factors that are responsible for the explosive development of all of modern science. The other is measurement.


title: Feature Extraction and Summarization of Recipes Using Flow Graph

abstract: These days, there are more than a million recipes on the Web. When you search for a recipe with one query such as "nikujaga," the name of a typical Japanese food, you can find thousands of "nikujaga" recipes as the result. Even if you focus on only the top ten results, it is still difficult to find out the characteristic feature of each recipe because a cooking is a work-flow including parallel procedures. According to our survey, people place the most importance on the differences of cooking procedures when they compare the recipes. However, such differences are difficult to be extracted just by comparing the recipe texts as existing methods. Therefore, our system extracts (i) a general way to cook as a summary of cooking procedures and (ii) the characteristic features of each recipe by analyzing the work-flows of the top ten results. In the experiments, our method succeeded in extracting 54% of manually extracted features while the previous research addressed 37% of them.


title: An IoT based 6LoWPAN enabled experiment for water management

abstract: During the past decade, water needs have risen exponentially to an unprecedented scale in India. The demand for water supply is ever increasing and satisfying this requirement has been a major challenge for many countries around the world. Urbanization, climatic changes and wasteful usage has further depleted the resource. Water being one of the major requirements for human survival, conservation and management of the resource must be given utmost importance. Ensuring access to safe and clean water is another issue that requires attention. In this paper, we present an IPv6 network connected IoT design for real-time water flow metering and quality monitoring. Our prototype implementation uses CoAP for monitoring and control approach which supports internet based data collection. The system addresses new challenges in the water sector - ease of billing, fair billing and the need for a study of supply versus consumption of water in order to create awareness to curb water wastage and encourage its conservation. Automatic detection of leakage through any of the outlets is notified to the user. We also measure the quality of water distributed to every household by deploying pH and ORP sensors. The traditional water metering systems require periodic manual intervention for both metering and maintenance making it inconvenient and often least effective. Shortcomings of the existing models call for a ubiquitous usage of wireless systems for smart water flow metering and quality monitoring. We propose to do this with the aid of CC2538 motes programmed using ContikiOS to monitor the water consumption and communicate the data to a gateway wirelessly.


title: The Next Stage of Operational Business Intelligence: Creating New Challenges for Business Process Management

abstract: Current practices in the area of business intelligence (BI) and business performance management (BPerM) confirm the need for better integration of BI and business processes (BPs). This is especially the case with operational BI that aims to unify strategic and tactical decision making, by integrating BI solutions with organisation's constantly evolving BPs. However, operational BI has a very limited view of BP and business process management (BProM) systems. In essence, it focuses on a limited number of core, transactional BPs that are, by definition, highly structured and repetitive. This paper argues that in order to support customer-facing employees in service-oriented industries, it is necessary to consider knowledge intensive BPs and their possible integration with operational BI. This paper offers a critical analysis of case-handling BPs in the context of operational BI. It then identifies a number of research challenges related to a new type of case-handling BProM system


title: A Design of Greenhouse Monitoring & Control System Based on ZigBee Wireless Sensor Network

abstract: The wireless sensor network (WSN) is one of the most significant technologies in the 21st century. As an open and global standard for WSN, ZigBee shows advantages on low-cost, low power consumption and self-forming. The current researches of ZigBee wireless sensor network on industrial automation, electronic products, smart buildings and medical care were presented and, as an explorative application of ZigBee wireless sensor network in protected agriculture overcoming the limits of wire connection, its applied design for greenhouse management was proposed by introducing both the hardware and software architectures. The node power consumption was also discussed. Finally, the architecture of ZigBee-based mesh network greenhouse management was indicated.


title: Internet of Things for Cultural Heritage of Smart Cities and Smart Regions

abstract: Different from big cities, small towns call for culture preservation in addition to revitalization. IoT technologies could potentially serve this need. This article develops an IoT architec- ture, and choose best IoT enabling technologies, and IoT services, applications, and standards, towards this goal. In this article, we focus on the opportunities and challenges of applying IoT to culture preservation and revitalization of smart towns. We expect that the intelligent use of IoT could breathe new life into traditional, close-knit culture of small towns.


title: Citation count prediction as a link prediction problem

abstract: The citation count is an important factor to estimate the relevance and significance of academic publications. However, it is not possible to use this measure for papers which are too new. A solution to this problem is to estimate the future citation counts. There are existing works, which point out that graph mining techniques lead to the best results. We aim at improving the prediction of future citation counts by introducing a new feature. This feature is based on frequent graph pattern mining in the so-called citation network constructed on the basis of a dataset of scientific publications. Our new feature improves the accuracy of citation count prediction, and outperforms the state-of-the-art features in many cases which we show with experiments on two real datasets.


title: A CNN Based Approach for Garments Texture Design Classification

abstract: Identifying garments texture design automatically for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several Hand-Engineered feature coding exists for identifying garments design classes. Recently, Deep Convolutional Neural Networks (CNNs) have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data more accurately. In this paper, a CNN model for identifying garments design classes has been proposed. Experimental results on two different datasets show better results than existing two well-known CNN models (AlexNet and VGGNet) and some state-of-the-art Hand-Engineered feature extraction methods.