title: Towards Bin Packing (preliminary problem survey, models with multiset estimates)

abstract: The paper described a generalized integrated glance to bin packing problems including a brief literature survey and some new problem formulations for the cases of multiset estimates of items. A new systemic viewpoint to bin packing problems is suggested: (a) basic element sets (item set, bin set, item subset assigned to bin), (b) binary relation over the sets: relation over item set as compatibility, precedence, dominance; relation over items and bins (i.e., correspondence of items to bins). A special attention is targeted to the following versions of bin packing problems: (a) problem with multiset estimates of items, (b) problem with colored items (and some close problems). Applied examples of bin packing problems are considered: (i) planning in paper industry (framework of combinatorial problems), (ii) selection of information messages, (iii) packing of messages/information packages in WiMAX communication system (brief description).


title: Performance and perception in the flipped classroom

abstract: Changes in the conceptualisation of higher education have led to instructional methods that embrace technology as a teaching medium. These changes have led to the flipped classroom phenomenon - where content is delivered outside class, through media such as video and podcast, and engagement with the content, through problem-solving and/or group work, occurs in class. Studies investigating the impact of the flipped classroom have mainly looked at the student experience with little focus on whether exam outcomes are enhanced by flipping. An undergraduate Material Technology course at The University of the West Indies was taught in two formats over two successive years. The course was taught during the 2012/13 academic year in a `traditional' format but, after reflecting on student feedback and personal pedagogy, the lecturer restructured the class and taught it in a flipped format during the 2013/14 academic year. This research examines whether the flipped format improved the learning experience in relation to exam performance and student perception. Data was gathered through analysis of course grades and student evaluation questionnaires. The lecturer's reflective comments were also reviewed before and after the study. Analysis of the qualitative data shows that the flipped format led to a slight improvement in how students perceived the course and the lecturer's reflection shows that they are keen to continue with the flipped format as it allowed more time for them to work with students at an individual level. While no significant change in relation to average cohort exam performance was found, fewer students in the flipped classroom achieved marks at the highest level. It is therefore recommended that practitioners who intend to flip their classroom pay as much attention to student performance as they do to student perception.


LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORK


title: Information governance in the banking industry

abstract: As a consequence of explosive growth in the amount of information generated, stored, accessed, and used by organizations, the concept of Information Governance (IG) is becoming important in the information systems management profession. The paper focuses on information governance in the banking industry. It involves a study of thirteen banks in three countries, all with highly developed financial systems: Brazil, Hong Kong (SAR of China) and the United States. In addition to reporting on the perceived need for IG in today's banks, the study proposes and verifies the possibilities of an information governance framework (IGF) with banking industry executives.


title: Dynamic Selection of Classifiers in Bug Prediction: An Adaptive Method

abstract: In the last decades, the research community has devoted a lot of effort in the definition of approaches able to predict the defect proneness of source code files. Such approaches exploit several predictors (e.g., product or process metrics) and use machine learning classifiers to predict classes into buggy or not buggy, or provide the likelihood that a class will exhibit a fault in the near future. The empirical evaluation of all these approaches indicated that there is no machine learning classifier providing the best accuracy in any context, highlighting interesting complementarity among them. For these reasons ensemble methods have been proposed to estimate the bug-proneness of a class by combining the predictions of different classifiers. Following this line of research, in this paper we propose an adaptive method, named ASCI (Adaptive Selection of Classifiers in bug prediction), able to dynamically select among a set of machine learning classifiers the one which better predicts the bug-proneness of a class based on its characteristics. An empirical study conducted on 30 software systems indicates that ASCI exhibits higher performances than five different classifiers used independently and combined with the majority voting ensemble method.


title: The Presentation of Self in the Age of Social Media: Distinguishing Performances and Exhibitions Online

abstract: Presentation of self (via Goffman) is becoming increasingly popular as a means for explaining differences in meaning and activity of online participation. This article argues that self-presentation can be split into performances, which take place in synchronous “situations,” and artifacts, which take place in asynchronous “exhibitions.” Goffman’s dramaturgical approach (including the notions of front and back stage) focuses on situations. Social media, on the other hand, frequently employs exhibitions, such as lists of status updates and sets of photos, alongside situational activities, such as chatting. A key difference in exhibitions is the virtual “curator” that manages and redistributes this digital content. This article introduces the exhibitional approach and the curator and suggests ways in which this approach can extend present work concerning online presentation of self. It introduces a theory of “lowest common denominator” culture employing the exhibitional approach.


title: HOPE: Enabling Efficient Service Orchestration in Software-Defined Data Centers

abstract: The functional scope of today's software-defined data centers (SDDC) has expanded to such an extent that servers face a growing amount of critical background operational tasks like load monitoring, logging, migration, and duplication, etc. These ancillary operations, which we refer to as management operations, often nibble the stringent data center power envelope and exert a tremendous amount of pressure on front-end user tasks. However, existing power capping, peak shaving, and time shifting mechanisms mainly focus on managing data center power demand at the "macro level" -- they do not distinguish ancillary background services from user tasks, and therefore often incur significant performance degradation and energy overhead. In this study we explore "micro-level" power management in SDDC: tuning a specific set of critical loads for the sake of overall system efficiency and performance. Specifically, we look at management operations that can often lead to resource contention and energy overhead in an IaaS SDDC. We assess the feasibility of this new power management paradigm by characterizing the resource and power impact of various management operations. We propose HOPE, a new system optimization framework for eliminating the potential efficiency bottleneck caused by the management operations in the SDDC. HOPE is implemented on a customized OpenStack cloud environment with heavily instrumented power infrastructure. We thoroughly validate HOPE models and optimization efficacy under various user workload scenarios. Our deployment experiences show that the proposed technique allows SDDC to reduce energy consumption by 19%, reduce management operation execution time by 25.4%, and in the meantime improve workload performance by 30%.


title: Analyzing Titanic disaster using machine learning algorithms

abstract: Titanic disaster occurred 100 years ago on April 15, 1912, killing about 1500 passengers and crew members. The fateful incident still compel the researchers and analysts to understand what can have led to the survival of some passengers and demise of the others. With the use of machine learning methods and a dataset consisting of 891 rows in the train set and 418 rows in the test set, the research attempts to determine the correlation between factors such as age, sex, passenger class, fare etc. to the chance of survival of the passengers. These factors may or may not have impacted the survival rates of the passengers. In this research paper, various machine learning algorithms namely Logistic Regression, Naive Bayes, Decision Tree, Random Forest have been implemented to predict the survival of passengers. In particular, this research work compares the algorithm on the basis of the percentage of accuracy on a test dataset.


Performance based seismic design of structure: A review


title: Theory of Relativity, Time and Truth

abstract: I. I NTRODUCTION Theory of relativity is invented by famous scientist Albert Einstein to envisage a new direction in the physical world. First he had published Special Relativity 1905 A.D. in paper "On the Electrodynamics of Moving Bodies"and finally brought General Relativity in 1916 A.D. [1]-[5]. Special Relativity deals with elementary particles, and their interactions like motion, dimensions etc., especially when speed or motion is uniform in nature. It is widely used in the field of atomic physics, nuclear physics and quantum physics etc. from 1920 A.D. onwards. General Relativity is designed for non uniform motion, i.e., when the bodies are subjected to accelerating or retarding motion and is applied in cosmological and astronomical field like heavenly bodies, satellites, space research, gravitational force changing etc. It is appeared that minor corrections can be made to Newtonian gravitation theory, but after 1930 A.D. General Relativity is predominant in the field of space research and satellite communications. The main theme with mathematics of relativity theory was quite a complex one and in the first stage, only few scientists could understand this. When Einstein was going to deliberate and explain Relativity theory, most of the scientists were not understood it clearly and they were also uncertain in which field this theory could be applied. The great scientist Einstein indicates the very reason of Relativity Theory. He tells that whatever things we are doing, that is time and space dependent, since everything is changing in this world as well as universe. Einstein had shown the variation in speed of light at the time of solar eclipse, it is happening due to change of earth’s gravitation at the time of solar eclipse and light is composed by quantum of energy particles, called photon as per Max Planck’s invention. Planck stated that Energy of


title: Designing a learning analytics dashboard for twitter-facilitated teaching

abstract: Social media sites are increasingly being adopted to support teaching practice in higher education. Learning Analytics (LA) dashboards can be used to reveal how students engage with course material and others in the class. However, research on the best practices of designing, developing, and evaluating such dashboards to support teaching and learning with social media has been limited. Considering the increasing use of Twitter for both formal and informal learning processes, this paper presents our design process and a LA prototype dashboard developed based on a comprehensive literature review and an online survey among 54 higher education instructors who have used Twitter in their teaching.


title: DeepTraffic: Driving Fast through Dense Traffic with Deep Reinforcement Learning

abstract: We present a micro-traffic simulation (named "DeepTraffic") where the perception, control, and planning systems for one of the cars are all handled by a single neural network as part of a model-free, off-policy reinforcement learning process. The primary goal of DeepTraffic is to make the hands-on study of deep reinforcement learning accessible to thousands of students, educators, and researchers in order to inspire and fuel the exploration and evaluation of DQN variants and hyperparameter configurations through large-scale, open competition. This paper investigates the crowd-sourced hyperparameter tuning of the policy network that resulted from the first iteration of the DeepTraffic competition where thousands of participants actively searched through the hyperparameter space with the objective of their neural network submission to make it onto the top-10 leaderboard.


title: The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage

abstract: Abstract Most theories of choice assume that decisions derive from an assessment of the future outcomes of various options and alternatives through some type of cost-benefit analyses. The influence of emotions on decision-making is largely ignored. The studies of decision-making in neurological patients who can no longer process emotional information normally suggest that people make judgments not only by evaluating the consequences and their probability of occurring, but also and even sometimes primarily at a gut or emotional level. Lesions of the ventromedial (which includes the orbitofrontal) sector of the prefrontal cortex interfere with the normal processing of “somatic” or emotional signals, while sparing most basic cognitive functions. Such damage leads to impairments in the decision-making process, which seriously compromise the quality of decisions in daily life. The aim of this paper is to review evidence in support of “The Somatic Marker Hypothesis,” which provides a systems-level neuroanatomical and cognitive framework for decision-making and suggests that the process of decision-making depends in many important ways on neural substrates that regulate homeostasis, emotion, and feeling. The implications of this theoretical framework for the normal and abnormal development of the orbitofrontal cortex are also discussed.


title: Functional Equivalence between Radial Basis Function Networks and Fuzzy Inference Systems

abstract: It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent. This functional equivalence makes it possible to apply what has been discovered (learning rule, representational power, etc.) for one of the models to the other, and vice versa. It is of interest to observe that two models stemming from different origins turn out to be functionally equivalent. >


title: My science tutor: A conversational multimedia virtual tutor for elementary school science

abstract: This article describes My Science Tutor (MyST), an intelligent tutoring system designed to improve science learning by students in 3rd, 4th, and 5th grades (7 to 11 years old) through conversational dialogs with a virtual science tutor. In our study, individual students engage in spoken dialogs with the virtual tutor Marni during 15 to 20 minute sessions following classroom science investigations to discuss and extend concepts embedded in the investigations. The spoken dialogs in MyST are designed to scaffold learning by presenting open-ended questions accompanied by illustrations or animations related to the classroom investigations and the science concepts being learned. The focus of the interactions is to elicit self-expression from students. To this end, Marni applies some of the principles of Questioning the Author, a proven approach to classroom conversations, to challenge students to think about and integrate new concepts with prior knowledge to construct enriched mental models that can be used to explain and predict scientific phenomena. In this article, we describe how spoken dialogs using Automatic Speech Recognition (ASR) and natural language processing were developed to stimulate students' thinking, reasoning and self explanations. We describe the MyST system architecture and Wizard of Oz procedure that was used to collect data from tutorial sessions with elementary school students. Using data collected with the procedure, we present evaluations of the ASR and semantic parsing components. A formal evaluation of learning gains resulting from system use is currently being conducted. This paper presents survey results of teachers' and children's impressions of MyST.


title: Wearable device for swim assessment: a new ecologic approach for communication and analysis

abstract: This paper describes a wearable system for the assessment of swim performance focusing on the description of the system and especially on the novel method for data transmission in water, and the algorithm used for extracting some parameters for the qualitative assessment of the performance. We placed a 3-axes Inertial Measurement Unit (IMU) on the athlete’s back in a specific swimsuit, which also allows for recording 1-lead ECG. The system and the algorithm have been tested on 13 trials with 10 subjects comparing the results with data extracted from video recording. The system demonstrated able and reliable to measure time and kinematic parameters of swimming.


title: A survey on comparison of electric motor types and drives used for electric vehicles

abstract: In this study, Switched Reluctance Motor (SRM), Induction Motor (IM), Brushless DC Motor, and Permanent Magnet Motor (PM), and their drives have been compared with the efficiency, cost, weight, cooling, maximum speed, reliability, fault tolerance, power ratings, and vehicle acceleration time. Hence, a comprehensive literature research on motor types and their drives used in EV has been made. According to these researches, some conclusions have been obtained. It has been seen that PM BLDC motors and their drives are the most efficient and have high power density, brushless DC motors and their drives have low cost, IM is appropriate for controllability and cost, the weight of SRM is low, its reliability is high, it operates fault-tolerance and according to the acceleration time, its performance is better than IM and BLDC. Hence, SRM is the most appropriate motor for EV.


title: Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic

abstract: Fuzzy logic is used to define a new quantity called the battery working state (BWS), which is based on both battery terminal voltage and state of charge (SOC), to overcome the problem of battery over-discharge and associated damage resulting from inaccurate estimates of the SOC. The BWS is used by a fuzzy logic energy-management system of a plug-in series hybrid electric vehicle (HEV) to make a decision on the power split between the battery and the engine, based on the BWS and vehicle power demand, while controlling the engine to work in its fuel economic region. The fuzzy logic management system was tested in real time using an HEV simulation test bench with a real battery in the loop. Simulation results are presented to demonstrate the performance of the proposed fuzzy logic energy-management system under different driving conditions and battery SOCs. The results indicate that the fuzzy logic energy-management system using the BWS was effective in ensuring that the engine operates in the vicinity of its maximum fuel efficiency region while preventing the battery from over-discharging.


title: Transfer Learning for Cross-Dataset Recognition: A Survey

abstract: This paper summarises and analyses the cross-dataset recognition transfer learning techniques with the emphasis on what kinds of methods can be used when the available source and target data are presented in different forms for boosting the target task. This paper for the first time summarises several transferring criteria in details from the concept level, which are the key bases to guide what kind of knowledge to transfer between datasets. In addition, a taxonomy of cross-dataset scenarios and problems is proposed according the properties of data that define how different datasets are diverged, thereby review the recent advances on each specific problem under different scenarios. Moreover, some real world applications and corresponding commonly used benchmarks of cross-dataset recognition are reviewed. Lastly, several future directions are identified.


title: A large scale exploratory analysis of software vulnerability life cycles

abstract: Software systems inherently contain vulnerabilities that have been exploited in the past resulting in significant revenue losses. The study of vulnerability life cycles can help in the development, deployment, and maintenance of software systems. It can also help in designing future security policies and conducting audits of past incidents. Furthermore, such an analysis can help customers to assess the security risks associated with software products of different vendors. In this paper, we conduct an exploratory measurement study of a large software vulnerability data set containing 46310 vulnerabilities disclosed since 1988 till 2011. We investigate vulnerabilities along following seven dimensions: (1) phases in the life cycle of vulnerabilities, (2) evolution of vulnerabilities over the years, (3) functionality of vulnerabilities, (4) access requirement for exploitation of vulnerabilities, (5) risk level of vulnerabilities, (6) software vendors, and (7) software products. Our exploratory analysis uncovers several statistically significant findings that have important implications for software development and deployment.


title: Optimizing Failure Prediction to Maximize Availability

abstract: Availability of autonomous systems can be enhanced with self-monitoring and fault-tolerance methods based on failures prediction. With each correct prediction, proactive actions may be taken to prevent or to mitigate a failure. On the other hand, incorrect predictions will introduce additional downtime associated with the overhead of a proactive action that may decrease availability. The total effect on availability will depend on the quality of prediction (measured with precision and recall), the overhead of proactive actions (penalty), and the benefit of proactive actions when prediction is correct (reward). In this paper, we quantify the impact of failure prediction and proactive actions on steady-state availability. Furthermore, we provide guidelines for optimizing failure prediction to maximize availability by selecting a proper precision and recall trade-off with respect to penalty and reward. A case study to demonstrate the approach is also presented.


title: HFH: Homologically Functional Hashing for Compressing Deep Neural Networks

abstract: As the complexity of deep neural networks (DNNs) trends to grow to absorb the increasing sizes of data, memory and energy consumption has been receiving more and more attentions for industrial applications, especially on mobile devices. This paper presents a novel structure based on homologically functional hashing to compress DNNs, shortly named as HFH. For each weight entry in a deep net, HFH uses multiple low-cost hash functions to fetch values in a compression space, and then employs a small reconstruction network to recover that entry. The compression space is homological because all layers fetch hashed values from it. The reconstruction network is plugged into the whole network and trained jointly. On several benchmark datasets, HFH demonstrates high compression ratios with little loss on prediction accuracy. Particularly, HFH includes the recently proposed HashedNets as a degenerated scenario and shows significantly improved performance. Moreover, the homological hashing essence facilitates us to efficiently figure out one single desired compression ratio, instead of exhaustive searching throughout a combinatory space configured by all layers.


title: A mixture ofEscherichia coli(DSM 17252) andEnterococcus faecalis(DSM 16440) for treatment of the irritable bowel syndrome - A randomized controlled trial with primary care physicians

abstract: Therapy trials with bacterial compounds in irritable bowel syndrome (IBS) have produced conflicting results. This study was performed in 1988 and 1989, and was re-analysed according to current IBS standards. Two hundred ninety-seven patients with lower abdominal symptoms diagnosed as IBS were treated for 8 weeks by the compound ProSymbioflor((R)) (Symbiopharm GmbH, Herborn, Germany), an autolysate of cells and cell fragments of Enterococcus faecalis and Escherichia coli, or placebo in a double-blinded, randomized fashion. Patients were seen weekly by the physician, who assessed the presence of core IBS symptoms. Responders had at least a 50% decrease in global symptom score (GSS) and in abdominal pain score (APS) reports at >/=1 visit during treatment. The responder rate in GSS to the drug was 102/149 (68.5%) in comparison to placebo with 56/148 (37.8%) (P < 0.001), the improvement in APS was 108/149 (72.5%) and 66/148 (44.6%) respectively (P = 0.001). The number-needed-to-treat was 3.27 for GSS and 3.59 for the APS report. Kaplan-Meier analysis revealed a mean response time of 4-5 weeks for active treatment and more than 8 weeks for placebo (P < 0.0001). Treatment of IBS with the bacterial lysate ProSymbioflor is effective and superior to placebo in reducing typical symptoms of IBS patients seen by general practitioners.


title: DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

abstract: Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task. However, a large body of work has highlighted the brittleness of these systems, showing that there is much work left to be done. We introduce a new English reading comprehension benchmark, DROP, which requires Discrete Reasoning Over the content of Paragraphs. In this crowdsourced, adversarially-created, 96k-question benchmark, a system must resolve references in a question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was necessary for prior datasets. We apply state-of-the-art methods from both the reading comprehension and semantic parsing literature on this dataset and show that the best systems only achieve 32.7% F1 on our generalized accuracy metric, while expert human performance is 96.0%. We additionally present a new model that combines reading comprehension methods with simple numerical reasoning to achieve 47.0% F1.


title: Vision Based Autonomous Robot Navigation: Motion Segmentation,

abstract: The ability to acquire and respond appropriately to targets or obstacles, moving or stationary, while underway, is critical for all unmanned mobile robot applications. This is achieved by most animate systems, but has proven difficult for artificial systems. We propose that efficient and extensible solutions to the target acquisition, discrimination and maintenance problem may be found when the machine sensor-effector control algorithms emulate the mechanisms employed by biological systems. In nature, visual motion provides the basis for these functions. Because visual motion can be due either to target motion or to platform motion, a method of motion segmentation must be found. We present a solution to this problem that emulates natural strategies, and describe its implementation in an autonomous visually controlled mobile robot. The Problem of Motion Segmentation Motion segmentation is used here as the process of identifying the 3-D spatial location of a unique source of motion from the optic flows created by one or more independently moving objects in the visual field. Motion segmentation is a greater problem than identifying and localizing a source of motion because the observer himself may also be moving in a complex visual space causing all other objects, whether stationary or moving, to contribute to the optic flow though induced motion. Consider a person driving his car down a busy street. In the course of driving, the driver uses saccades to select targets upon which to fix his gaze, and pursuit eye movements to hold his gaze upon his selected target. These targets may be other moving cars, pedestrians, billboards or other objects resting on the ground. While the relative target motion is minimized by the fixation mechanisms, images of the objects in the foreground and background relative to the target will continue to contribute to the optic flow on his retina during target fixation due to the motion of the car as well as to the pursuit eye movements. This flow can be non-uniform in direction as well as speed, depending upon the distance to the objects and their position with respect to the target and the relative direction of gaze with respect to the direction of travel of the car. Still, new targets are quickly noticed in the driver’s peripheral vision, especially if they are moving independently of their surround. The mechanism that draws attention to these new targets is the focus of our interest in motion segmentation. Studies of eye movements of drivers have shown that unusual motion is a strong attractor of attention (Thomas, 1969). For example, a car that takes off from the parked position is irresistible, so is a blinking light, like a turn indicator, or a bouncing ball that appears across the traffic. While the driver most often notices objects that are themselves moving, motion in itself is inadequate to


title: Enactive Robot Vision

abstract: Enactivism claims that sensory-motor activity and embodiment are crucial in perceiving the environment and that machine vision could be a much simpler business if considered in this context. However, computational models of enactive vision are very rare and often rely on handcrafted control systems. In this article, we argue that the apparent complexity of the environment and of the robot brain can be significantly simplified if perception, behavior, and learning are allowed to co-develop on the same timescale. In doing so, robots become sensitive to, and actively exploit, characteristics of the environment that they can tackle within their own computational and physical constraints. We describe the application of this methodology in three sets of experiments: shape discrimination, car driving, and wheeled robot navigation. A further set of experiments, where the visual system can develop the receptive fields by means of unsupervised Hebbian learning, demonstrates that the receptive fields are consistently and significantly affected by the behavior of the system and differ from those predicted by most computational models of the visual cortex. Finally, we show that our robots can also replicate the performance deficiencies observed in experiments of motor deprivation with kittens.


title: Operation of a multiagent system for microgrid control

abstract: This paper presents the operation of a multiagent system (MAS) for the control of a Microgrid. The approach presented utilizes the advantages of using the MAS technology for controlling a Microgrid and a classical distributed algorithm based on the symmetrical assignment problem for the optimal energy exchange between the production units of the Microgrid and the local loads, as well the main grid.


Try the Blue Pill: What’s Wrong with Life in a Simulation?


title: Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

abstract: In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered.


title: The RRADS platform: a real road autonomous driving simulator

abstract: This platform paper introduces a methodology for simulating an autonomous vehicle on open public roads. The paper outlines the technology and protocol needed for running these simulations, and describes an instance where the Real Road Autonomous Driving Simulator (RRADS) was used to evaluate 3 prototypes in a between-participant study design. 35 participants were interviewed at length before and after entering the RRADS. Although our study did not use overt deception---the consent form clearly states that a licensed driver is operating the vehicle---the protocol was designed to support suspension of disbelief. Several participants who did not read the consent form clearly strongly believed that they were interacting with a fully autonomous vehicle. The RRADS platform provides a lens onto the attitudes and concerns that people in real-world autonomous vehicles might have, and also points to ways that a protocol deliberately using misdirection can gain ecologically valid reactions from study participants.


Statistical mechanics and phase transitions in clustering.


title: ShmCaffe: A Distributed Deep Learning Platform with Shared Memory Buffer for HPC Architecture

abstract: One of the reasons behind the tremendous success of deep learning theory and applications in the recent days is advances in distributed and parallel high performance computing (HPC). This paper proposes a new distributed deep learning platform, named ShmCaffe, which utilizes remote shared memory for communication overhead reduction in massive deep neural network training parameter sharing. ShmCaffe is designed based on Soft Memory Box (SMB), a virtual shared memory framework. In the SMB framework, the remote shared memory is used as a shared buffer for asynchronous massive parameter sharing among many distributed deep learning processes. Moreover, a hybrid method that combines asynchronous and synchronous parameter sharing methods is also discussed in this paper for improving scalability. As a result, ShmCaffe is 10.1 times faster than Caffe and 2.8 times faster than Caffe-MPI for deep neural network training when Inception\_v1 is trained with 16 GPUs. We verify the convergence of the Inception\_v1 model training using ShmCaffe-A and ShmCaffe-H by varying the number of workers. Furthermore, we evaluate scalability of ShmCaffe by analyzing the computation and communication times per one iteration of deep learning training in four convolutional neural network (CNN) models.


title: Accurate and Practical 3D Measurement for Translucent Objects by Dashed Lines and Complementary Gray Code Projection

abstract: We propose a practical 3D method for measuring translucent objects with high accuracy. We improve the accuracy of the measurement by a high-frequency dashed line projection to a target object, which eliminates the influence of subsurface scattering while maintaining higher image contrast. For efficient measurement, multiple dashed lines are projected at the same time and we identify them without error by a proposed complementary Gray code, thus reducing the number of projections to less than 1/10 of that by conventional light sheet scanning. With regard to performance, we show that the frequency of the dashed line largely determines the accuracy and that there is a trade off between depth offset as a systematic error and random error. We validate the proposed method by measuring both canonical translucent samples with various translucencies and actual engineering parts.


title: Finding All Justifications of OWL DL

abstract: Finding the justifications of an entailment (that is, all the minimal set of axioms sufficient to produce an entailment) has emerged as a key inference service for the Web Ontology Language (OWL). Justifications are essential for debugging unsatisfiable classes and contradictions. The availability of justifications as explanations of entailments improves the understandability of large and complex ontologies. In this paper, we present several algorithms for computing all the justifications of an entailment in an OWL-DL Ontology and show, by an empirical evaluation, that even a reasoner independent approach works well on real ontologies.


title: Crowd Sourcing, with a Few Answers: Recommending Commuters for Traffic Updates

abstract: Real-time traffic awareness applications are playing an ever increasing role understanding and tackling traffic congestion in cities. First-hand accounts from drivers witnessing an incident is an invaluable source of information for traffic managers. Nowadays, drivers increasingly contact control rooms through social media to report on journey times, accidents or road weather conditions. These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying participants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user. We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station.


title: Analysis of a Multilevel Dual Active Bridge (ML-DAB) DC-DC Converter Using Symmetric Modulation

abstract: Dual active bridge (DAB) converters have been popular in high voltage, low and medium power DC-DC applications, as well as an intermediate high frequency link in solid state transformers. In this paper, a multilevel DAB (ML-DAB) has been proposed in which two active bridges produce two-level (2L)-5L, 5L-2L and 3L-5L voltage waveforms across the high frequency transformer. The proposed ML-DAB has the advantage of being used in high step-up/down converters, which deal with higher voltages, as compared to conventional two-level DABs. A three-level neutral point diode clamped (NPC) topology has been used in the high voltage bridge, which enables the semiconductor switches to be operated within a higher voltage range without the need for cascaded bridges or multiple two-level DAB converters. A symmetric modulation scheme, based on the least number of angular parameters rather than the duty-ratio, has been proposed for a different combination of bridge voltages. This ML-DAB is also suitable for maximum power point tracking (MPPT) control in photovoltaic applications. Steady-state analysis of the converter with symmetric phase-shift modulation is presented and verified using simulation and hardware experiments.


title: Camera-Assisted Video Saliency Prediction and Its Applications

abstract: Video saliency prediction is an indispensable yet challenging technique which can facilitate various applications, such as video surveillance, autonomous driving, and realistic rendering. Based on the popularity of embedded cameras, we in this paper predict region-level saliency from videos by leveraging human gaze locations recorded using a camera, (e.g., those equipped on an iMAC and laptop PC). Our proposed camera-assisted mechanism improves saliency prediction by discovering human attended regions inside a video clip. It is orthogonal to the current saliency models, i.e., any existing video/image saliency model can be boosted by our mechanism. First of all, the spatial-and temporal-level visual features are exploited collaboratively for calculating an initial saliency map. We notice that the current saliency models are not sufficiently adaptable to the variations in lighting, different view angles, and complicated backgrounds. Therefore, assisted by a camera tracking human gaze movements, a non-negative matrix factorization algorithm is designed to accurately localize the semantically/visually salient video regions perceived by humans. Finally, the learned human gaze locations as well as the initial saliency map are integrated to optimize video saliency calculation. Empirical results thoroughly demonstrated that: 1) our approach achieves the state-of-the-art video saliency prediction accuracy by outperforming 11 mainstream algorithms considerably and 2) our method can conveniently and successfully enhance video retargeting, quality estimation, and summarization.


title: Multiple Classifier System for Remote Sensing Image Classification: A Review

abstract: Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+). Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community.


title: MOCA : Mobile Certificate Authority for Wireless Ad Hoc

abstract: An authentication service is one of the the most fundamental building blocks for providing communication security. In this paper, we present the MOCA (MObile Certificate Authority) key management framework designed to provide authentication service for ad hoc wireless networks. MOCA is a distributed certificate authority (CA) based on threshold cryptography. We present a set of guidelines for a secure configuration of threshold cryptography to maintain strong security. MOCA utilizes a carefully selected set of mobile nodes to function as a collective certificate authority while the MOCA nodes are kept anonymous. Equipped with a novel routing protocol designed to support the unique communication pattern for certification traffic, MOCA achieves high availability without sacrificing security. Both the security of the framework and the operational performance is evaluated with rigorous analysis and extensive simulation study.


title: Generic Pattern Trees for Exhaustive Exceptional Model Mining

abstract: Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation, and present the general GP-growth algorithm based on FP-growth. Furthermore, we discuss the scope of the proposed approach by drawing an analogy to data stream mining and provide examples for several different model classes. Runtime experiments show improvements of more than an order of magnitude in comparison to a naive exhaustive depth-first search.


title: Principal Warps : Thin-Plate Splines and the Decomposition of Deformations

abstract: The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current in computer vision. How they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images in indicated. >


title: An aspect representation for object manipulation based on convolutional neural networks

abstract: We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning sequences of actions to solve tasks. In previous work, we introduced the aspect transition graph (ATG) which represents how actions lead from one observation to another using a directed multi-graph. In this work, we propose a novel aspect representation based on hierarchical CNN features, learned with convolutional neural networks, that supports manipulation and captures the essential affordances of an object based on RGB-D images. In a traditional planning system, robots are given a pre-defined set of actions that take the robot from one symbolic state to another. However symbolic states often lack the flexibility to generalize across similar situations. Our proposed representation is grounded in the robot's observations and lies in a continuous space that allows the robot to handle similar unseen situations. The hierarchical CNN features within a representation also allow the robot to act precisely with respect to the spatial location of individual features. We evaluate the robustness of this representation using the Washington RGB-D Objects Dataset and show that it achieves state of the art results for instance pose estimation. We then test this representation in conjunction with an ATG on a drill grasping task on Robonaut-2. We show that given grasp, drag, and turn demonstrations on the drill, the robot is capable of planning sequences of learned actions to compensate for reachability constraints.


title: HaptoMime: mid-air haptic interaction with a floating virtual screen

abstract: We present HaptoMime, a mid-air interaction system that allows users to touch a floating virtual screen with hands-free tactile feedback. Floating images formed by tailored light beams are inherently lacking in tactile feedback. Here we propose a method to superpose hands-free tactile feedback on such a floating image using ultrasound. By tracking a fingertip with an electronically steerable ultrasonic beam, the fingertip encounters a mechanical force consistent with the floating image. We demonstrate and characterize the proposed transmission scheme and discuss promising applications with an emphasis that it helps us 'pantomime' in mid-air.


An Emerging Market for Corporate Control


title: Perceived effectiveness of text vs. multimedia Location-Based Advertising messaging

abstract: The emergence of mobile communication and positioning technologies has presented advertisers and marketers with a radically innovative advertising channel: Location-Based Advertising (LBA). Despite the growing attention given to LBA, little is understood about the differential effects of text and multimedia advertising formats on the mobile consumer perceptions and behaviours. This exploratory study empirically examines the effects of multimedia advertisements vis-a-vis text-based advertisements on consumer perceptions and behaviours in a simulated LBA environment. A structural model was formulated to test their effects on consumer perceptions of entertainment, informativeness and irritation. Results show that multimedia LBA messages lead to more favourable attitude, increase the intention to use the LBA application, and have significant impact on purchase intention. Furthermore, this study indicates the role of multimedia as a double-edged sword: on the one hand, it suggests that multimedia impose a higher level of irritation; on the other hand, it suggests that multimedia enhance the informativeness and entertainment value of LBA. Implications for theory and practice are discussed.


title: A Framework for Feature Selection in Clustering

abstract: We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set of features. We propose a novel framework for sparse clustering, in which one clusters the observations using an adaptively chosen subset of the features. The method uses a lasso-type penalty to select the features. We use this framework to develop simple methods for sparse K-means and sparse hierarchical clustering. A single criterion governs both the selection of the features and the resulting clusters. These approaches are demonstrated on simulated and genomic data.


title: Novel wide-band coplanar waveguide-to-rectangular waveguide transition

abstract: A new coplanar waveguide (CPW)-to-rectangular waveguide transition is proposed for wide-band millimeter-wave applications. The transition has an in-line structure, which provides easy fabrication of the CPW circuit and waveguide block. An X-band (8.2-12.4 GHz) model has been designed and tested. Measurements of a back-to-back double transition have yielded a return loss of over 17 dB and an insertion loss of less than 0.5 dB over the full X-band. These results agree well with the simulated results obtained with a commercial electromagnetic structure simulator. In addition, this type of a transition has been designed and used for a local oscillator feed with a full F-band (90-140 GHz) coverage of a submillimeter-wave general-purpose harmonic mixer.


CONTINUOUS AUDITING : THE USA EXPERIENCE AND CONSIDERATIONS FOR ITS IMPLEMENTATION IN BRAZIL


title: Exploring how technology complements constructivism using a lesson plan

abstract: Constructivism approach to learning is known to support multiple perspectives in interpretations of reality, knowledge construction and context rich, experience based activities. Technology comes in handy to offer incredible amounts of information and tools for creativity and development as well as diverse environments and communication forums where students can build knowledge in an engaged setting. However, technophobia has been reported among teachers as inhibiting integration of technology in the classroom. Teachers feel confident about their knowledge of the pedagogy. They are also confident about their knowledge of the content but less confident when it comes to technology knowledge. There are many models of instruction informed by constructivist theories to implement technology into teaching. These models do not demonstrate how a teacher can develop a lesson plan using the Constructivist Learning Perspective and integrate technology in the plan particularly using SAMR Model of technology integration. This paper explores how technology can be used to complement constructivism approach to learning as part of building teacher's confidence in using technology in the classroom. This will be possible through developing a lesson plan and using the Substitution Augmentation Modification Redefinition Model (SAMR) technology integration model. Fifteen (15) PhD students taking a course in education in May-August 2016 were engaged in developing a lesson plan based on constructivism approach to learning and they were required to use SAMR model of ICT integration in order to demonstrate how technology complements constructivism. The SMAR supports and allows trainers and teachers to plan, develop, and infuse learning experiences that utilize technology in their teaching. Specific practices in the lesson plan that specified tasks to be done using cognitive terminology such as classify, analyze, predict and create, coupled with various levels of technology integration model enabled students to explore learning.


title: New Digital-Controlled Technique for Battery Charger With Constant Current and Voltage Control Without Current Feedback

abstract: The main theme of this paper is to present a new digital-controlled technique for battery charger to achieve constant current and voltage control while not requiring current feedback. The basic idea is to achieve constant current charging control by limiting the duty cycle of charger. Therefore, the current feedback signal is not required and thereby reducing the cost of A/D converter, current sensor, and computation complexity required for current control. Moreover, when the battery voltage is increased to the preset voltage level using constant current charge, the charger changes the control mode to constant voltage charge. A digital-controlled charger is designed and implemented for uninterrupted power supply (UPS) applications. The charger control is based upon the proposed control method in software. As a result, the UPS control, including boost converter, charger, and inverter control can be realized using only one low cost MCU. Experimental results demonstrate that the effectiveness of the design and implementation.


title: A Wideband PET Inkjet-Printed Antenna for UHF RFID

abstract: A new antenna, designed on a polyethylene terephthalate (PET) substrate and implemented by inkjet printing using a conductive ink, is proposed as a passive tag antenna for UHF radio frequency identification (RFID). The operating bandwidth of the proposed antenna is very large since it encompasses all worldwide UHF RFID bands and extends well beyond at both edges. Moreover, it has a very simple geometry, can be easily tuned to feed many of the commercial RFID chips, and is very robust with respect to realization tolerances. The antenna has been designed using a general-purpose 3-D computer-aided design (CAD), CST Microwave Studio, and measured results are in very good agreement with simulations. The proposed passive RFID tag meets both the objectives of low-cost and size reduction.


Multiple Hypothesis Testing


title: RCMC: Recognizing Crowd-Mobility Patterns in Cities Based on Location Based Social Networks Data

abstract: During the past few years, the analysis of data generated from Location-Based Social Networks (LBSNs) have aided in the identification of urban patterns, understanding activity behaviours in urban areas, as well as producing novel recommender systems that facilitate users’ choices. Recognizing crowd-mobility patterns in cities is very important for public safety, traffic managment, disaster management, and urban planning. In this article, we propose a framework for Recognizing the Crowd Mobility Patterns in Cities using LBSN data. Our proposed framework comprises four main components: data gathering, recurrent crowd-mobility patterns extraction, temporal functional regions detection, and visualization component. More specifically, we employ a novel approach based on Non-negative Matrix Factorization and Gaussian Kernel Density Estimation for extracting the recurrent crowd-mobility patterns in cities illustrating how crowd shifts from one area to another during each day across various time slots. Moreover, the framework employs a hierarchical clustering-based algorithm for identifying what we refer to as temporal functional regions by modeling functional areas taking into account temporal variation by means of check-ins’ categories. We build the framework using a spatial-temporal dataset crawled from Twitter for two entire years (2013 and 2014) for the area of Manhattan in New York City. We perform a detailed analysis of the extracted crowd patterns with an exploratory visualization showing that our proposed approach can identify clearly obvious mobility patterns that recur over time and location in the urban scenario. Using same time interval, we show that correlating the temporal functional regions with the recognized recurrent crowd-mobility patterns can yield to a deeper understanding of city dynamics and the motivation behind the crowd mobility. We are confident that our proposed framework not only can help in managing complex city environments and better allocation of resources based on the expected crowd mobility and temporal functional regions but also can have a direct implication on a variety of applications such as personalized recommender systems, anomalous event detection, disaster resilience management systems, and others.


title: On beamforming with finite rate feedback in multiple-antenna systems

abstract: We study a multiple-antenna system where the transmitter is equipped with quantized information about instantaneous channel realizations. Assuming that the transmitter uses the quantized information for beamforming, we derive a universal lower bound on the outage probability for any finite set of beamformers. The universal lower bound provides a concise characterization of the gain with each additional bit of feedback information regarding the channel. Using the bound, it is shown that finite information systems approach the perfect information case as (t-1)2/sup -B/t-1/, where B is the number of feedback bits and t is the number of transmit antennas. The geometrical bounding technique, used in the proof of the lower bound, also leads to a design criterion for good beamformers, whose outage performance approaches the lower bound. The design criterion minimizes the maximum inner product between any two beamforming vectors in the beamformer codebook, and is equivalent to the problem of designing unitary space-time codes under certain conditions. Finally, we show that good beamformers are good packings of two-dimensional subspaces in a 2t-dimensional real Grassmannian manifold with chordal distance as the metric.


title: Understanding and executing instructions for everyday manipulation tasks from the World Wide Web

abstract: Service robots will have to accomplish more and more complex, open-ended tasks and regularly acquire new skills. In this work, we propose a new approach to the problem of generating plans for such household robots. Instead composing them from atomic actions — the common approach in robot planning — we propose to transform task descriptions on web sites like ehow.com into executable robot plans. We present methods for automatically converting the instructions from natural language into a formal, logic-based representation, for resolving the word senses using the WordNet database and the Cyc ontology, and for exporting the generated plans into the mobile robot's plan language RPL. We discuss the problem of inferring information that is missing in these descriptions and the problem of grounding the abstract task descriptions in the perception and action system, and we propose techniques for solving them. The whole system works autonomously without human interaction. It has successfully been tested with a set of about 150 natural language directives, of which up to 80% could be correctly transformed.


title: A modular architecture for building automation systems

abstract: The deployment of building automation systems (BAS) allows to increase comfort, safety and security and to reduce operational cost. Today such systems typically follow a two-layered hierarchical approach. While control networks interconnect distributed sensors, actuators and controllers, a backbone provides the necessary infrastructure for management tasks hosted by configuration and management devices. In addition, devices interconnecting the control network with the backbone and the backbone with further networks (e.g., the Internet) play a strategic role. All BAS devices contributing to a particular functionality differ in their requirements for hardware. This paper discusses requirements for devices used in the building automation domain and presents our work in progress to assemble platforms with different purposes relying on a modular architecture.


title: Mean-Conditional Value-at-Risk Optimal Energy Storage Operation in the Presence of Transaction Costs

abstract: This paper addresses the formulation and solution of an optimal energy storage management problem under risk consideration and transaction costs of trading energy with the power grid. The price evolves as a stochastic process, capable of correctly explaining the seasonality effects as well as the tail fatness and spikiness in its distribution. Transaction costs capture the price impact of the storage operation on the electricity spot price. A risk analysis of an optimal risk neutral deterministic policy as well as the simple myopic policy indicates that the realized operational cost may notably differ from the expected cost by a considerable probability. This difference suggests that we need to consider risk. Using the downside risk measure of conditional value-at-risk, an optimal risk averse conversion and transmission strategy, among the grid, the renewable power generation source, and an energy storage is proposed to fully satisfy the electricity demand and minimize the expected operational cost as well as the risk. Our numerical study using data from NYISO demonstrates the impacts of risk consideration and the transaction cost parameters on the optimal strategy structure, its expected cost, and its risk.


title: Millimeter-Wave Radar Sensor for Snow Height Measurements

abstract: A small and lightweight frequency-modulated continuous-wave (FMCW) radar system is used for the determination of snow height by measuring the distance to the snow surface from a platform. The measurements have been performed at the Centre des Etudes de la Neige (Col de Porte), which is located near Grenoble in the French Alps. It is shown that the FMCW radar at millimeter-wave frequencies is an extremely promising approach for distance measurements to snow surfaces, e.g., in the mountains or in an Arctic environment. The characteristics of the radar sensor are described in detail. The relevant accuracy to measure the distance to a snow layer is shown at different heights and over an extended time duration. A dedicated laser snow telemeter is used as reference. In addition, the reflection from different types of snow is shown.


title: MEASURING PERFORMANCE IN ENTREPRENEURSHIP RESEARCH

abstract: Abstract Accurate performance measurement is critical to understanding new venture and small business success and failure. The first phase of this study surveys the entrepreneurship literature from 1987–1993 and evaluates the dimensions and measures of performance used. Little consistency in performance measurement across studies was found; rather, a wide diversity of measures are relied upon. Moreover, 60% of the studies used only one or two dimensions of performance, generally without justification for selection. The second phase examines relationships between performance variables and illustrates the extent to which dimensions of performance exist. We argue that research in the entrepreneurial context could benefit by: (1) explicitly stating the specific performance dimension(s) under investigation, (2) providing a theorybased rationale for examining the given dimension(s), (3) including multiple dimensions of performance where possible, and (4) including consideration of several critical control variables such as industry, age, and size of the firm.


title: Learning designs and learning analytics

abstract: Government and institutionally-driven reforms focused on quality teaching and learning in universities emphasize the importance of developing replicable, scalable teaching approaches that can be evaluated. In this context, learning design and learning analytics are two fields of research that may help university teachers design quality learning experiences for their students, evaluate how students are learning within that intended learning context and support personalized learning experiences for students. Learning Designs are ways of describing an educational experience such that it can be applied across a range of disciplinary contexts. Learning analytics offers new approaches to investigating the data associated with a learner's experience. This paper explores the relationship between learning designs and learning analytics.


title: Missing Subjects in Early Child Language

abstract: This paper focuses on early grammatical development through the prism of missing subjects in child language. Central work on this topic is reviewed and an attempt is made to draw out the more general implications of missing subjects for parameter-setting models of development (e.g. HyamsAQ: Kindly confirm the references included in Abstract are appropriate as citations are not allowed in Abstract., Language acquisition and the theory of parameters. Reidel, Dordrecht, 1986; Hyams, A reanalysis of null subjects in child language. In: Weissenborn J, Goodluck H, Roeper T (eds) Theoretical issues in language Acquisiton. Lawrence Erlbaum Associates, Inc., New Jersey, 1991) and maturational models (Borer and Wexler, Nat Linguist Linguist Theory 10:147–189, 1992). Also discussed are more recent analytical directions, including truncation and root null subjects (RizziAQ: Please specify whether it is “2005a” or “2005b” for the citation “Rizzi 2005”., On the grammatical basis of language development: A case study. In Cinque, G, Kayne, R (eds) The oxford handbook of comparative syntax. Oxford University Press, New York, pp 70–109, 2005a; Rizzi, Grammatically-based target-inconsistencies in child language. Proceedings of the inaugural conference of GALANA. UCONN/MIT Working Papers in Linguistics, Cambridge, MA, 2005b) and variationalist models of null subjects (Yang, Knowledge and learning in natural language. Oxford University Press, Oxford, 2002). The review of findings from spontaneous production and imitation studies (e.g. Valian et al., Dev Psychol 32(1):153–164, 1996) leads to a discussion of the converging results of the different methodologies used to explore null subjects in early language. In this context, the results of a recent comprehension study on null subjects in English (Orfitelli, Null subjects in child language: the competing roles of competence and performance. Master’s thesis, UCLA, Department of Linguistics, 2008; OrfitelliAQ: The citation “Orfitelli and Hyams 2007, 2009” is not listed in the References list. Please provide complete details and include in the References list if appropriate and Hyams 2007, 2009) are presented and the implications of those results for competence and performance models (Bloom, Linguist Inq 21:491–504, 1990; Hyams and Wexler, Linguist Inq 24(3):421–459, 1993) are discussed. Finally, some recent (and not so recent) findings are discussed that illustrate children’s pragmatic knowledge in choosing specific subject types (null, pronominal, lexical) (Hughes and Allen, A discourse-pragmatic analysis of subject omission in child English. Proceedings of the 30th annual BUCLD, vol 1. Cascadilla Press, Somerville, pp 293–304, 2006; Hughes and Allen, Child-directed speech and the development of referential choice in child English. Talk presented at the International Association for the Study of Child Language Conference, Edinburgh, Scotland, 2008). These results show early sensitivity to information structure (IS) interacts with grammatical knowledge (and potentially production output), and suggest that pragmatic principles may in fact account for certain results that have thus far been attributed to processing limitations in early language.


title: A Critical Review of Centrality Measures in Social Networks

abstract: Social networks are currently gaining increasing impact in the light of the ongoing growth of web-based services like facebook.com. One major challenge for the economically successful implementation of selected management activities such as viral marketing is the identification of key persons with an outstanding structural position within the network. For this purpose, social network analysis provides a lot of measures for quantifying a member’s interconnectedness within social networks. In this context, our paper shows the state of the art with regard to centrality measures for social networks. Due to strongly differing results with respect to the quality of different centrality measures, this paper also aims at illustrating the tremendous importance of a reflected utilization of existing centrality measures. For this purpose, the paper analyzes five centrality measures commonly discussed in literature on the basis of three simple requirements for the behavior of centrality measures.


title: Stock market co-movement assessment using a three-phase clustering method

abstract: An automatic stock market categorization system would be invaluable to individual investors and financial experts, providing them with the opportunity to predict the stock price changes of a company with respect to other companies. In recent years, clustering all companies in the stock markets based on their similarities in the shape of the stock market has increasingly become a common scheme. However, existing approaches are impractical because the stock price data are high-dimensional data and the changes in the stock price usually occur with shift, which makes the categorization more complex. Moreover, no stock market categorization method that can cluster companies down to the sub-cluster level, which are very meaningful to end users, has been developed. Therefore, in this paper, a novel three-phase clustering model is proposed to categorize companies based on the similarity in the shape of their stock markets. First, low-resolution time series data are used to approximately categorize companies. Then, in the second phase, pre-clustered companies are split into some pure sub-clusters. Finally, sub-clusters are merged in the third phase. The accuracy of the proposed method is evaluated using various published data sets in different domains. We show that this approach has good performance in efficiency and effectiveness compared to existing conventional clustering algorithms.


title: IIIT-CFW: A Benchmark Database of Cartoon Faces in the Wild

abstract: In this paper, we introduce the cartoon faces in the wild (IIIT-CFW) database and associated problems. This database contains 8,928 annotated images of cartoon faces of 100 public figures. It will be useful in conducting research on spectrum of problems associated with cartoon understanding. Note that to our knowledge, such realistic and large databases of cartoon faces are not available in the literature.


title: IoT based smart museum using Bluetooth Low Energy

abstract: In today's world everything is becoming smarter through the development of heterogeneous technologies. To match this smartness, our environment needs to become smarter through a technology that is we called Internet of Things. Nowadays, People are switched to follow our ancient culture. Museum is the one of a place where ancient culture and cultural heritage exist, hence it is considered to make a smart museum. To get the attention of visitor in museum it is being proposed as IoT based Smart Museum Environment which will automatically provide the information about artwork without any user intervention. IoT based smart museum relies on a wearable device(Raspberry PI) that will capture the user's movement, does the background subtraction algorithm to perform Image processing and it gets the localization information from a Bluetooth Low Energy (BLE) which is fixed in the museum. Hence, this wearable device will increase the performance of the whole system by sending only matched frame to cloud processing center. To start with all the artwork and related multimedia contents need to be uploaded to cloud. Finally, everyone can easily access the arts profile and history through smart phone by using the mobile application.


title: 3D street art illusions: embedding chalk stylized rendering of 3D objects into a pavement photo

abstract: Three dimensional street art illusions have become more popular in recent years. Many of them are drawn on pavement such as streets, sidewalks, and town squares. It is often known as 3D chalk art, where a 2D artwork is drawn on the street, giving the viewer a 3D optical illusion from a certain perspective. This paper supplies chalk stylized renderings for 3D models and synthesizes 3D objects into a realistic photograph. Users can input one photo for the 3D model. First, a camera position is achieved by using a camera calibration algorithm. Second, a chalk stylized rendering was applied to create an artistic image of the 3D models using the same camera position. Then, the non-photorealistic rendering image is composited into a source photograph using a modified Poisson approach. All of the enhanced pavement texture details are also blended into the object's image. The major contribution of this paper is providing a user to create any interesting and attractive 3D illusionary art without physically drawing pictures on the pavement. Finally, the proposed method is demonstrated using various experimental 3D street art illusion images. Copyright © 2014 John Wiley & Sons, Ltd.


title: Memory encoding in Alzheimer's disease: an fMRI study of explicit and implicit memory

abstract: Summary Alzheimer’s disease is the most common cause of dementia in older adults. Although the cognitive deficits and pathologic hallmarks of Alzheimer’s disease have been well characterized, few functional imaging studies have examined the functional competency of specific brain regions and their relationship to specific behavioural memory deficits in Alzheimer’s disease. We used functional MRI (fMRI) to examine seven early stage Alzheimer’s disease patients and seven healthy age-matched neurologically normal control subjects during intentional encoding of scenes. Subjects viewed blocks of novel scenes, repeated scenes or baseline. Data were analysed using whole-brain statistical parametric mapping and region of interest approaches. The Alzheimer’s disease group demonstrated impaired explicit recognition memory, but intact implicit memory (repetition priming), for the scenes. Alzheimer’s disease patients demonstrated a graded deficit in activation for novel versus repeated scenes along the ventral visual stream, with most impaired activation changes in the mesial temporal lobe (MTL) and fusiform regions, most preserved activations in primary visual cortex and variably affected activations in secondary visual areas. Group-level correlations with behavioural measures of explicit memory were found in MTL, lingual and fusiform areas, whereas correlations with priming were found in lateral occipital, parietal and frontal areas. Together, these fMRI findings indicate a dissociation in Alzheimer’s disease between impaired explicit memory encoding in MTL and fusiform regions and intact implicit encoding in earlier-stage occipital cortex.


title: Surface EMG Signal Amplification and Filtering

abstract: Electromyographic (EMG) signals have been widely employed as a control signal in rehabilitation and a means of diagnosis in health care. Signal amplification and filtering is the first step in surface EMG signal processing and application systems. The characteristics of the amplifiers and filters determine the quality of EMG signals. Up until now, searching for better amplification and filtering circuit design that is able to accurately capture the features of surface EMG signals for the intended applications is still a challenging. With the fast development in computer sciences and technologies, EMG signals are expected to be used and integrated within small or even tiny intelligent, automatic, robotic, and mechatronics systems. This research focused on small size amplification and filtering circuit design for processing surface EMG signals from an upper limb and aimed to fix the amplifiers and filters inside a robotic hand with limited space to command and control the robot hand movement. The research made a study on the commonly used methodologies for EMG signal processing and circuitry design and proposed a circuit design for EMG signal amplification and filtering. High-pass filters including secondorder and fourth-order with the suppression to low frequency noises are studied. The analysis, verification and experiment showed that a second-order high-pass filter can adequately suppress the low frequency noises. The proposed amplification and filtering circuit design is able to effectively clean the noises and collect the useful surface EMG signals from an upper limb. The experiment also clearly revealed that power line interference needs to be carefully handled for higher signalnoise-ratio (SNR) as a notch-filter might cause the loss of useful signal components. Commercial computer software such as LabView and Matlab were used for data acquisition software development and data analysis. General Terms EMG data acquisition, Bio-signal processing, Bio-informatics


title: An Optimized Recurrent Unit for Ultra-Low-Power Keyword Spotting

abstract: There is growing interest in being able to run neural networks on sensors, wearables and internet-of-things (IoT) devices. However, the computational demands of neural networks make them difficult to deploy on resource-constrained edge devices. ::: ::: To meet this need, our work introduces a new recurrent unit architecture that is specifically adapted for on-device low power acoustic event detection (AED). The proposed architecture is based on the gated recurrent unit ('GRU' -- introduced by Cho et al. [9]) but features optimizations that make it implementable on ultra-low power micro-controllers such as the Arm Cortex M0+. ::: ::: Our new architecture, the Embedded Gated Recurrent Unit (eGRU) is demonstrated to be highly efficient and suitable for short-duration AED and keyword spotting tasks. A single eGRU cell is 60× faster and 10× smaller than a GRU cell. Despite its optimizations, eGRU compares well with GRU across tasks of varying complexities. ::: ::: The practicality of eGRU is investigated in a wearable acoustic event detection application. An eGRU model is implemented and tested on the Arm Cortex M0-based Atmel ATSAMD21E18 processor. The Arm M0+ implementation of the eGRU model compares favorably with a full precision GRU that is running on a workstation. The embedded eGRU model achieves a classification accuracy 95.3%, which is only 2% less than the full precision GRU.


THEMIS: Framework for Automated Testing of Graphical User Interfaces


title: Towards a next generation data center architecture: scalability and commoditization

abstract: Applications hosted in today's data centers suffer from internal fragmentation of resources, rigidity, and bandwidth constraints imposed by the architecture of the network connecting the data center's servers. Conventional architectures statically map web services to Ethernet VLANs, each constrained in size to a few hundred servers owing to control plane overheads. The IP routers used to span traffic across VLANs and the load balancers used to spray requests within a VLAN across servers are realized via expensive customized hardware and proprietary software. Bisection bandwidth is low, severly constraining distributed computation Further, the conventional architecture concentrates traffic in a few pieces of hardware that must be frequently upgraded and replaced to keep pace with demand - an approach that directly contradicts the prevailing philosophy in the rest of the data center, which is to scale out (adding more cheap components) rather than scale up (adding more power and complexity to a small number of expensive components). Commodity switching hardware is now becoming available with programmable control interfaces and with very high port speeds at very low port cost, making this the right time to redesign the data center networking infrastructure. In this paper, we describe monsoon, a new network architecture, which scales and commoditizes data center networking monsoon realizes a simple mesh-like architecture using programmable commodity layer-2 switches and servers. In order to scale to 100,000 servers or more,monsoon makes modifications to the control plane (e.g., source routing) and to the data plane (e.g., hot-spot free multipath routing via Valiant Load Balancing). It disaggregates the function of load balancing into a group of regular servers, with the result that load balancing server hardware can be distributed amongst racks in the data center leading to greater agility and less fragmentation. The architecture creates a huge, flexible switching domain, supporting any server/any service and unfragmented server capacity at low cost.


title: PIEJoin: Towards Parallel Set Containment Joins

abstract: The efficient computation of set containment joins (SCJ) over set-valued attributes is a well-studied problem with many applications in commercial and scientific fields. Nevertheless, there still exists a number of open questions: An extensive comparative evaluation is still missing, the two most recent algorithms have not yet been compared to each other, and the exact impact of item sort order and properties of the data on algorithms performance still is largely unknown. Furthermore, all previous works only considered sequential join algorithms, although modern servers offer ample opportunities for parallelization. We present PIEJoin, a novel algorithm for computing SCJ based on intersecting prefix trees built at runtime over the to-be-joined attributes. We also present a highly optimized implementation of PIEJoin which uses tree signatures for saving space and interval labeling for improving runtime of the basic method. Most importantly, PIEJoin can be parallelized easily by partitioning the tree intersection. A comprehensive evaluation on eight data sets shows that PIEJoin already in its sequential form clearly outperforms two of the three most important competitors (PRETTI and PRETTI+). It is mostly yet not always slower than the third, LIMIT+(opj) but requires significantly less space. The parallel version of PIEJoin we present here achieves significant further speed-ups, yet our evaluation also shows that further research is needed as finding the best way of partitioning the join turns out to be non-trivial.


title: Pages 1–8 Fast and sensitive mapping of bisulfite-treated sequencing data

abstract: Motivation: Cytosine DNA methylation is one of the major epigenetic modifications and influences gene expression, developmental processes, X-chromosome inactivation, and genomic imprinting. Aberrant methylation is furthermore known to be associated with several diseases including cancer. The gold standard to determine DNA methylation on genome-wide scales is ‘bisulfite sequencing’: DNA fragments are treated with sodium bisulfite resulting in the conversion of unmethylated cytosines into uracils, whereas methylated cytosines remain unchanged. The resulting sequencing reads thus exhibit asymmetric bisulfite-related mismatches and suffer from an effective reduction of the alphabet size in the unmethylated regions, rendering the mapping of bisulfite sequencing reads computationally much more demanding. As a consequence, currently available read mapping software often fails to achieve high sensitivity and in many cases requires unrealistic computational resources to cope with large real-life datasets. ::: ::: Results: In this study, we present a seed-based approach based on enhanced suffix arrays in conjunction with Myers bit-vector algorithm to efficiently extend seeds to optimal semi-global alignments while allowing for bisulfite-related substitutions. It outperforms most current approaches in terms of sensitivity and performs time-competitive in mapping hundreds of millions of sequencing reads to vertebrate genomes. ::: ::: Availability: The software segemehl is freely available at http://www.bioinf.uni-leipzig.de/Software/segemehl. ::: ::: Contact: E-mail: steve@bioinf.uni-leipzig.de ::: ::: Supplementary information:Supplementary data are available at Bioinformatics online.


The Dark Side of Socially Mediated Rewards : How Narcissism and Social Status Affect Managerial Reporting


title: Synthesis and evaluation of N-3 substituted phenoxypropyl piperidine benzimidazol-2-one analogues as NOP receptor agonists with analgesic and sedative properties.

abstract: A series of 3-phenoxypropyl piperidine benzimidazol-2-one analogues have been discovered as novel NOP receptor agonists. Structure-activity relationships have been explored via N-3 substitution of the benzimidazol-2-one with a range of functionality. The N-methyl acetamide derivative (+)-7f was found to be a high-affinity, potent NOP agonist with greater than 100-fold selectivity over the MOP receptor. Furthermore (+)-7f was shown to be both antinociceptive and sedative when administered iv to rodents.


title: Kasapovic Scheduling Multiprocessor Tasks with Genetic Algorithms, - Applied InformaticsProceedings

abstract: In this paper, an efficient method based on genetic algorithms is developed to solve the multiprocessor scheduling problem. To efficiently execute programs in parallel on multiprocessor scheduling problem must be solved to determine the assignment of tasks to the processors and the execution order of the tasks so that the execution time is minimized. Even when the target processors is fully connected and no communication delay is considered among tasks in the task graph the scheduling problem is NP-complete. Complexity of scheduling problems dependent of number of processors (P), task processing time Ti and precedence constraints. This problem has been known as strong NP-hard intractable optimisation problem when it assumes arbitrary number of processors, arbitrary task processing time and arbitrary precedence constraints. We assumed fixed number of processors and tasks are represented by a directed acyclic graph (DAG) called “task graph”.


title: Micromachined biomimetic artificial haircell sensors

abstract: The biological haircell is a modular building block of a rich variety of biological sensors. Using micro- and nanofabrication technology, an engineering equivalent artificial haircell sensor can be developed, imitating the structure and transfer function of the biological haircell. The artificial haircells can be made of hybrid semiconductor, metal and polymers. This paper discusses a number of strategies, using representative material systems, for building artificial haircell sensors and briefly outlines their fabrication method and performance. The motivation for imitating the biological haircell is also discussed to provide a background for this work.


title: A high-voltage level shifter with sub-nano-second propagation delay for switching power converters

abstract: A level-shifting circuit with sub-nano-second propagation delay for high input voltage switched-mode power converters is presented. The proposed circuit uses isolated low-voltage NMOS transistors and capacitive coupling to shift the control signal of the high-side power switch from a low-voltage logic domain (VLogic ~ 5V) up to a high-voltage power domain (VIN ~ 65V) with less than 115ps propagation delay. As a result, the non-overlap time inserted between the control signals of the high-side and low-side power switches can be minimized, leading to higher efficiency. Moreover, the control signal is shifted to the high-voltage power domain without reducing its voltage swing, which helps minimizing the on-resistance of the switch and further improves efficiency. The proposed circuit is fully integrated in a 0.18μm technology with no off-chip components. It occupies less than 0.75mm2 and provides built-in protection for the low-voltage devices with no additional protection circuitry. Transistor level simulations demonstrate the circuit's functionality and performance.


The unified modeling language for object-oriented development


title: A study on agility and testing processes in software organizations

abstract: In this paper, we studied the differences in testing activities between software organizations which apply agile development methods and organizations which take the traditional plan-driven approach. Our focus was on the concepts which allow the software organization to successfully apply agile development methods or plan-driven methods. We also observed the test process enhancements and hindrances, which originate in the selected development method. We interviewed 12 organizations, which were selected to represent different polar types of software production. The interviews were tape-recorded and transcribed for further analysis. The study yielded hypotheses which were derived by applying the qualitative grounded theory method. The results indicated that in practice, agile methods can improve the position of testing through the early involvement of testing activities in development, and also have a positive influence on end-product satisfaction. By applying these results, organizations can improve their processes and avoid pitfalls when transitioning to agile methods.


title: Densely Connected Progressive Learning for LSTM-Based Speech Enhancement

abstract: Recently, we proposed a novel progressive learning (PL) framework for deep neural network (DNN) based speech enhancement to improve the performance in low signal-to-noise ratio (SNR) environments. In this study, several new contributions are made to this framework. First, the advanced long short-term memory (LSTM) architecture is adopted to achieve better results, namely LSTM-PL, where each LSTM layer is guided to explicitly learn an intermediate target with a specific SNR gain. However, we observe that the performance of LSTM-PL architecture is easily degraded by increasing the number of intermediate targets due to the possible information loss when involving more target layers. Accordingly, we propose densely connected progressive learning in which the input and the estimations of intermediate targets are spliced together to learn the next target. This new structure can fully utilize the rich set of information from the multiple learning targets and alleviate the information loss problem. Experimental results demonstrate that the dense structure with deeper LSTM layers can yield significant gains of speech intelligibility measure for all noise types and levels. Moreover, the post-processing with more targets tends to achieve better performance.


title: Color-Distortion Filtering for Remote Photoplethysmography

abstract: This paper introduces a powerful filtering method that exploits the physiological and optical properties of skin reflections to improve the performance of remote photoplethysmography (rPPG). Based on the fact that the pulsatile and nonpulsatile (e.g., intensity and specular changes) components have different reflection-spectra in a multi-wavelength camera, we propose to use their different characteristic color changes as a soft criterion to filter the RGB-signals in the frequency domain, such that the AC-components containing clear color distortions can be suppressed before the actual pulse extraction. This leads to a novel “Color-Distortion Filter” (CDF) that can be used as a common pre-processing step for arbitrary rPPG algorithms to increase their robustness. The benchmark in challenging fitness recordings shows that CDF brings significant and consistent improvements to all benchmarked rPPG algorithms, and drives all multi-channel approaches to a similar high quality-level.


title: Characterization of hydration products of mineral trioxide aggregate

abstract: Objective To characterize the hydration products of white mineral trioxide aggregate (MTA). ::: ::: ::: ::: Methodology Mineral trioxide aggregate, white Portland cement and bismuth oxide were evaluated using X-ray diffraction (XRD) analysis and Rietveld XRD. The cements were tested un-hydrated and after hydration and curing for 30 days at 37 °C. Analysis of hydrated cement leachate was performed weekly for five consecutive weeks from mixing using inductively coupled plasma atomic emission spectroscopy after which the cements were viewed under the scanning electron microscope to evaluate the cement microstructure. Quantitative energy dispersive analysis with X-ray was performed and atomic ratios were plotted. ::: ::: ::: ::: Results Both Portland cement and MTA produced calcium silicate hydrate (C-S-H) and calcium hydroxide (CH) on hydration. The tricalcium aluminate levels were low for MTA which resulted in reduced production of ettringite and monosulphate. On hydration the bismuth level in the hydrated MTA decreased; bismuth oxide replaced the silica in the C-S-H and was leached out once the C-S-H decomposed with time. Both MTA and Portland cement released a high amount of calcium ions which decreased in amount over the 5-week period. ::: ::: ::: ::: Conclusions The hydration mechanism of MTA is different to that of Portland cement. In MTA the bismuth oxide is bound to the C-S-H and is leached out from the cement with time as the C-S-H decomposes. MTA produces a high proportion of calcium ions from CH a by-product of hydration and also by decomposition of C-S-H. The release of calcium ions reduces with time.


title: The Origins of Plant Cultivation in South America

abstract: A rectangular shaped food products storage and transfer vessel having an integral sanitary top rail structure triangular in cross-section and formed by being bent downward from the upper edges of the respective side-wall panels of the vessel, the lowermost depending edges of the rail members thus formed being joined to the upper external sidewalls of the vessel by means of a continuous exposed weld and the sidewall rail structure terminal positions at the respective upper external corners of the vessel being closed by separately formed corner members weldably affixed thereto to form a continuous closed rail structure about the upper open periphery of the vessel and thereby provide an easily cleanable reinforced protective top edge member for the vessel.


Tutorials for the WGCNA package for R : WGCNA Background and glossary


title: Generalized k-Labelsets Ensemble for Multi-Label and Cost-Sensitive Classification

abstract: Label powerset (LP) method is one category of multi-label learning algorithm. This paper presents a basis expansions model for multi-label classification, where a basis function is an LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the ground truth. We derive an analytic solution to learn the coefficients efficiently. We further extend this model to handle the cost-sensitive multi-label classification problem, and apply it in social tagging to handle the issue of the noisy training set by treating the tag counts as the misclassification costs. We have conducted experiments on several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. Experimental results on both multi-label classification and cost-sensitive social tagging demonstrate that our method has better performance than other methods.


title: A New Framework for Sign Language Recognition based on 3D Handshape Identification and Linguistic Modeling

abstract: Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success. Here we propose a new framework that (1) provides a new tracking method less dependent than others on laboratory conditions and able to deal with variations in background and skin regions (such as the face, forearms, or other hands); (2) allows for identification of 3D hand configurations that are linguistically important in American Sign Language (ASL); and (3) incorporates statistical information reflecting linguistic constraints in sign production. For purposes of large-scale computer-based sign language recognition from video, the ability to distinguish hand configurations accurately is critical. Our current method estimates the 3D hand configuration to distinguish among 77 hand configurations linguistically relevant for ASL. Constraining the problem in this way makes recognition of 3D hand configuration more tractable and provides the information specifically needed for sign recognition. Further improvements are obtained by incorporation of statistical information about linguistic dependencies among handshapes within a sign derived from an annotated corpus of almost 10,000 sign tokens.