title: Data deduplication techniques

abstract: With the information and network technology, rapid development, rapid increase in the size of the data center, energy consumption in the proportion of IT spending rising. In the great green environment many companies are eyeing the green store, hoping thereby to reduce the energy storage system. Data de-duplication technology to optimize the storage system can greatly reduce the amount of data, thereby reducing energy consumption and reduce heat emission. Data compression can reduce the number of disks used in the operation to reduce disk energy consumption costs. By studying the data de-duplication strategy, processes, and implementations for the following further lay the foundation of the work.


title: Extracting relevant named entities for automated expense reimbursement

abstract: Expense reimbursement is a time-consuming and labor-intensive process across organizations. In this paper, we present a prototype expense reimbursement system that dramatically reduces the elapsed time and costs involved, by eliminating paper from the process life cycle. Our complete solution involves (1) an electronic submission infrastructure that provides multi- channel image capture, secure transport and centralized storage of paper documents; (2) an unconstrained data mining approach to extracting relevant named entities from un-structured document images; (3) automation of auditing procedures that enables automatic expense validation with minimum human interaction. Extracting relevant named entities robustly from document images with unconstrained layouts and diverse formatting is a fundamental technical challenge to image-based data mining, question answering, and other information retrieval tasks. In many applications that require such capability, applying traditional language modeling techniques to the stream of OCR text does not give satisfactory result due to the absence of linguistic context. We present an approach for extracting relevant named entities from document images by combining rich page layout features in the image space with language content in the OCR text using a discriminative conditional random field (CRF) framework. We integrate this named entity extraction engine into our expense reimbursement solution and evaluate the system performance on large collections of real-world receipt images provided by IBM World Wide Reimbursement Center.


title: Sustainable competitive advantage: Combining institutional and resource-based views

abstract: This article suggests that the context and process of resource selection have an important influence on firm heterogeneity and sustainable competitive advantage. It is argued that a firm’s sustainable advantage depends on its ability to manage the institutional context of its resource decisions. A firm’s institutional context includes its internal culture as well as broader influences from the state, society, and interfirm relations that define socially acceptable economic behavior. A process model of firm heterogeneity is proposed that combines the insights of a resource-based view with the institutional perspective from organization theory. Normative rationality, institutional isolating mechanisms, and institutional sources of firm homogeneity are proposed as determinants of rent potential that complement and extend resource-based explanations of firm variation and sustainable competitive advantage. The article suggests that both resource capital and institutional capital are indispensable to sustainable competitive advantage. © 1997 John Wiley & Sons, Ltd.


title: Coinami: A Cryptocurrency with DNA Sequence Alignment as Proof-of-work

abstract: Rate of growth of the amount of data generated using the high throughput sequencing (HTS) platforms now exceeds the growth stipulated by Moore’s Law. The HTS data is expected to surpass those of other \big data" domains such as astronomy, before the year 2025. In addition to sequencing genomes for research purposes, genome and exome sequencing in clinical settings will be a routine part of health care. The analysis of such large amounts of data, however, is not without computational challenges. This burden is even more increased due to the periodic updates to reference genomes, which typically require re-analysis of existing data. Here we propose Coin-Application Mediator Interface (Coinami 1 ) to distribute the workload for mapping reads to reference genomes using a volunteer grid computer approach similar to Berkeley Open Infrastructure for Network Computing (BOINC). However, since HTS read mapping requires substantial computational resources and fast analysis turnout is desired, Coinami uses the HTS read mapping as proof-of-work to generate valid blocks to main its own cryptocurrency system, which may help motivate volunteers to dedicate more resources. The Coinami protocol includes mechanisms to ensure that jobs performed by volunteers are correct, and provides genomic data privacy. The prototype implementation of Coinami is available at http://coinami.github.io/.


title: Mobility Prediction and Routing in Ad Hoc Wireless Networks

abstract: By exploiting non-random behaviors for the mobility patterns that mobile users exhibit, we can predict the future state of network topology and perform route reconstruction proactively in a timely manner. Moreover, by using the predicted information on the network topology, we can eliminate transmissions of control packets otherwise needed to reconstruct the route and thus reduce overhead. In this paper, we propose various schemes to improve routing protocol performances by using mobility prediction. We then evaluate the effectiveness of using mobility prediction via simulation. Copyright © 2001 John Wiley & Sons, Ltd.


title: A comparison of approaches to large-scale data analysis

abstract: There is currently considerable enthusiasm around the MapReduce (MR) paradigm for large-scale data analysis [17]. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper, we describe and compare both paradigms. Furthermore, we evaluate both kinds of systems in terms of performance and development complexity. To this end, we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system's performance for various degrees of parallelism on a cluster of 100 nodes. Our results reveal some interesting trade-offs. Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures.


title: Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder

abstract: In recent years, a change in perspective in etiological models of attention deficit hyperactivity disorder (ADHD) has occurred in concordance with emerging concepts in other neuropsychiatric disorders such as schizophrenia and autism. These models shift the focus of the assumed pathology from regional brain abnormalities to dysfunction in distributed network organization. In the current contribution, we report findings from functional connectivity studies during resting and task states, as well as from studies on structural connectivity using diffusion tensor imaging, in subjects with ADHD. Although major methodological limitations in analyzing connectivity measures derived from noninvasive in vivo neuroimaging still exist, there is convergent evidence for white matter pathology and disrupted anatomical connectivity in ADHD. In addition, dysfunctional connectivity during rest and during cognitive tasks has been demonstrated. However, the causality between disturbed white matter architecture and cortical dysfunction remains to be evaluated. Both genetic and environmental factors might contribute to disruptions in interactions between different brain regions. Stimulant medication not only modulates regionally specific activation strength but also normalizes dysfunctional connectivity, pointing to a predominant network dysfunction in ADHD. By combining a longitudinal approach with a systems perspective in ADHD in the future, it might be possible to identify at which stage during development disruptions in neural networks emerge and to delineate possible new endophenotypes of ADHD. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc.


title: Human-like Clustering with Deep Convolutional Neural Networks

abstract: Classification and clustering have been studied separately in machine learning and computer vision. Inspired by the recent success of deep learning models in solving various vision problems (e.g., object recognition, semantic segmentation) and the fact that humans serve as the gold standard in assessing clustering algorithms, here, we advocate for a unified treatment of the two problems and suggest that hierarchical frameworks that progressively build complex patterns on top of the simpler ones (e.g., convolutional neural networks) offer a promising solution. We do not dwell much on the learning mechanisms in these frameworks as they are still a matter of debate, with respect to biological constraints. Instead, we emphasize on the compositionality of the real world structures and objects. In particular, we show that CNNs, trained end to end using back propagation with noisy labels, are able to cluster data points belonging to several overlapping shapes, and do so much better than the state of the art algorithms. The main takeaway lesson from our study is that mechanisms of human vision, particularly the hierarchal organization of the visual ventral stream should be taken into account in clustering algorithms (e.g., for learning representations in an unsupervised manner or with minimum supervision) to reach human level clustering performance. This, by no means, suggests that other methods do not hold merits. For example, methods relying on pairwise affinities (e.g., spectral clustering) have been very successful in many cases but still fail in some cases (e.g., overlapping clusters).


title: Microgrids in active network management—Part I: Hierarchical control, energy storage, virtual power plants, and market participation

abstract: The microgrid concept has been closely investigated and implemented by numerous experts worldwide. The first part of this paper describes the principles of microgrid design, considering the operational concepts and requirements arising from participation in active network management. Over the last several years, efforts to standardize microgrids have been made, and it is in terms of these advances that the current paper proposes the application of IEC/ISO 62264 standards to microgrids and Virtual Power Plants, along with a comprehensive review of microgrids, including advanced control techniques, energy storage systems, and market participation in both island and grid-connection operation. Finally, control techniques and the principles of energy-storage systems are summarized in a comprehensive flowchart.


title: IoT based water management

abstract: Objectives: This project focuses on monitoring of use of water, consider, by one block of house in a flat system, where at the partition of pipeline from where the water gets diverted to various part of a block. Methods/Statistical analysis: Water places a vital role for living beings in their day to day lives. The earth's 71% is covered by water is a ubiquitous fact. Among which Oceans has approximately 96.50% and 3% is considered to be freshwater, again out of which only 0.08% is accessible direct to human use and rest is preserved in tundra regions and in different form on and in the earth surface which is very difficult to abstract for the human purposes. From this it states that only 0.08% is available as fresh water for human being to make use for drinking, domestic purposes, sanitation, manufacturing, leisure, agriculture etc which gets recharged by rain and snowfall 1. Findings: According to scientists and organizations as IPCC (Intergovernmental Panel on Climate Change), state has come, since a long time, where water management as such implies to maximizing use of water and minimizing the wastage of water and thus preventing the domino effect cycle arises as wastage of water. The sensors will sense the flow of water to each pipe which ultimately tells the usage of water at one block ideally. This water usage data would be sent to cloud using the IOT (Internet of things) space. This cloud data would be sent to the concern resident's person's mobile app (application) reporting the water used and alerting the user to limit the water use if it gets extended to the limit usage set by municipal government or corporation. If the limit gets extended the user have to pay accordingly. This will be real time operation. The objective of doing so is for limiting and minimizing the usage of water for an average of per person. And secondly, the cloud data will be used as statistic data for use of water at every seasons that is winter, summer and monsoon so that measuring steps for water management can be taken with the appropriate statistics, yielding an avenue for predictive measure. Improvements/Applications: To appraise the IOT based water management, it can be ramified as diligent, frugal for water management in a symbiotic parity way, which will constrict the water resource evenly according to the in situ factors.


title: Technical Section: Automatic urban modeling using volumetric reconstruction with surface graph cuts

abstract: The demand for 3D city-scale models has been significantly increased due to the proliferation of urban planning, city navigation, and virtual reality applications. We present an approach to automatically reconstruct buildings densely spanning a large urban area. Our method takes as input calibrated aerial images and available GIS meta-data. Our computational pipeline computes a per-building 2.5D volumetric reconstruction by exploiting photo-consistency where it is highly sampled amongst the aerial images. Our building surface graph cut method overcomes errors of occlusion, geometry, and calibration in order to stitch together aerial images and yield a visually coherent texture-mapped result. Our comparisons show similar quality to the manually modeled buildings of Google Earth, and show improvements over naive texture mapping and over space-carving methods. We have tested our algorithms with a 12sqkm area of Boston, MA (USA), using 4667 images (i.e., 280GB of raw image data) and producing 1785 buildings.


title: The Cognitive Neuroscience of Creativity: A Critical Review

abstract: Cognitive neuroscience studies of creativity have appeared with increasing frequently in recent years. Yet to date, no comprehensive and critical review of these studies has yet been published. The first part of this article presents a quick overview of the 3 primary methodologies used by cognitive neuroscientists: electroencephalography (EEG), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI). The second part provides a comprehensive review of cognitive neuroscience studies of creativity-related cognitive processes. The third part critically examines these studies; the goal is to be extremely clear about exactly what interpretations can appropriately be made of these studies. The conclusion provides recommendations for future research collaborations between creativity researchers and cognitive neuroscientists.


title: Web Mining: Pattern discovery from World Wide Web transactions

abstract: Oil fence especially adapted for the defining of impurities on a water surface and made of a web of cloth shaped material. The web exhibits alternating connected portions and multiple layer portions made in such a manner that between the layers of the multiple layer portions pockets and/or channels are formed, which are defined by the single layer portions, in order to receive for one thing float bodies for the support of the oil fence in water, and for another thing sinking weights in order to give the oil fence the intended upright position in the water. The present cloth shaped web comprises a preferably impregnated and/or coated textile material, which is woven with alternating single layer portions forming said connected portions and portions with at least two layers, between which said pockets and/or channels are formed. The single layer portions are by means of a per se known weaving technique shaped from the interwoven weft of the converging layers of the adjacent multiple layer portions, thereby obtaining an extremely strong connection of said portions.


title: Effect of Initial Low Oxygen Concentration on Respiration and Quality of Fresh-cut Cabbages

abstract: The effects of perforated film package (PFP) on the gas concentrations and respiration rates of fresh~~ut cabbage with initial 02 5, 8, 10 and 210/0 were studied at 5 and 20'C. The microbial growih, appearance, flavor and ascorbic acid content were also determined. The respiration rates of fresh-cut cabbages were suppressed under initial low 02 during storage of 4 days, especially in 5 and 80/0 02 concentrations. The anaerobic respiration did not occur under low 02 Ievels. It was also found that fresh-cut cabbages had better color retention and quality, reduced respiration rate and microbial population in PFP. Although there was the difference of quality attributes between PFP and MAP, but no significant difference was found. The effect of initial low 02 50/0 was the most important among different levels of initial low 02 according to the oxidation of ascorbic acid (AA) and browning of fresh-cut cabbage. Microbial analysis also showed that total count on the surface of fresh-cut cabbage was lowest among initial low 02 treatments. Total ascorbic acid (TAA) decreased by AA oxidation after cutting. Loss in TAA was much lower in PFP than that in MAP. Moreover, The results of sensory evaluation showed that there were no differences in all treatments at 5 'C throughout storage. At 20'C, flavor reached 2-grade in all treatments and become 3-grade by 4 days in 210/0MAP. The appearances were reached 2-grades after 3 days in 100/0PFP and 210/0MAP. These suggest that better quality of fresh-cut cabbage could be obtained from the combination of PFP with initial O. 5-80/0 Ievel and 5 'C.


title: Biomass for Renewable Energy and Fuels

abstract: barrels of oil equivalent (boe) The total energy content of a non-petroleum-based product or fuel in GJ divided by 5.904 6.115 GJ/boe. biodiesel The methyl or ethyl esters of transesterified triglycerides (lipids, fats, cooking greases) from biomass. biofuel A solid, gaseous, or liquid fuel produced from biomass. biogas A medium-energy-content gaseous fuel, generally containing 40 to 80 volume percent methane, produced from biomass by methane fermentation (anaerobic digestion). biomass All non-fossil-based living or dead organisms and organic materials that have an intrinsic chemical energy content. biorefinery A processing plant for converting waste and virgin biomass feedstocks to energy, fuels, and other products. gasohol A blend of 10 volume percent ethanol and 90 volume percent gasoline. independent power producer (IPP) A nonutility generator of electricity, usually produced in a small capacity plant or industrial facility. integrated biomass production conversion system (IBPCS) A system in which all operations concerned with the production of virgin biomass feedstocks and their conversion to energy, fuels, or chemicals are integrated. landfill gas (LFG) Amedium-energy-content fuel gas high in methane and carbon dioxide produced by landfills that contain municipal solid wastes and other waste biomass. methyl t-butyl ether (MTBE) An organic compound used as an oxygenate and octane-enhancing additive in motor gasolines. oxygenated gasoline Gasolines that contain soluble oxygen-containing organic compounds such as fuel ethanol and MTBE. quad One quad is 10 (1 quadrillion) Btu. refuse-derived fuel (RDF) The combustible portion of municipal solid wastes. tonnes of oil equivalent (toe) The total energy content of a non-petroleum-based product or fuel in GJ divided by 43.395 44.945 GJ/toe.


title: bioLogic: Natto Cells as Nanoactuators for Shape Changing Interfaces

abstract: Nature has engineered its own actuators, as well as the efficient material composition, geometry and structure to utilize its actuators and achieve functional transformation. Based on the natural phenomenon of cells' hygromorphic transformation, we introduce the living Bacillus Subtilis natto cell as a humidity sensitive nanoactuator. In this paper, we unfold the process of exploring and comparing cell types that are proper for HCI use, the development of the composite biofilm, the development of the responsive structures, the control setup for actuating biofilms, and a simulation and fabrication platform. Finally, we provide a variety of application designs, with and without computer control to demonstrate the potential of our bio actuators. Through this paper, we intend to enable the use of natto cells and our platform technologies for HCI researchers, designers and bio-hackers. More generally, we try to encourage the research and use of biological responsive materials and interdisciplinary research in HCI.


title: Fundamentals of Computer Aided Geometric Design

abstract: A classic reference and text, this book introduces the foundations used to create an accurate computer screen image using mathematical tools. This comprehensive guide is a handbook for students and practitioners and includes an extensive bibliography for further study.


title: A survey on comparative analysis of tools for the detection of ARP poisoning

abstract: Giving security to the system in an amenable source has turned into a noteworthy test these days. Information inherited through the system is not thought to be protected. Different dangers like sniffing, phishing, caricaturing exist. This paper exhibits an outline of different system dangers and assaults to the system. There are so many apparatuses in the amenable source and many mitigation techniques which are produced as a way to tackle to these assaults. Devices like Wireshark, arpwatch, firewall has been talked about in this paper.


title: A cable driven upper arm exoskeleton for upper extremity rehabilitation

abstract: Conventional robotic rehabilitation devices for upper extremity are bulky, heavy or lack the ability to provide joint level rehabilitation. Some designs address these issues by replacing rigid links of the exoskeletons with light weight cables. However these designs are controlled in position mode instead of force control which is desirable for rehabilitation. In this paper, a 5 degree-of-freedom cable-driven upper arm exoskeleton, with control of force, is proposed. In this design, attachment points of cables on the arm are adjustable. The attachment points are optimized to achieve large workspace to perform activities of daily living. Simulation results of force field control for training and rehabilitation of the arm are presented. Experiments have been performed on a dummy robotic arm in the upper arm exoskeleton.


title: Coordination Control of Wheeled Mobile Robot Using MPC

abstract: In the last decades Model predictive control (MPC) is the leading paradigm for high-performance and cost-effective control of complex systems. MPC contains powerful technique for optimizing the performance of constrained systems on state and control variables. For this reason, we use a distributed controller to coordinate a fleet of moving robots with unicycle kinematics. The proposed algorithm is based on the concept of robust MPC control with tube based strategy (i.e. It imposes low complexity and conservativeness). Using this approach we are able to guarantee that the real controlled system, on which a bounded disturbance is supposed to act, remains constrained in a neighborhood of the trajectory of the ideal system, which is defined by neglecting the effect of uncertainties. Obstacle avoidance function introduced for prevent collision between robot coordination. Mat lab implementation and real robot results are used to show the efficacy of the proposed control strategy.


title: Design of a GaN-based, 9-level flying capacitor multilevel inverter with low inductance layout

abstract: Multilevel inverters such as the flying capacitor multilevel inverter (FCML) hold large potential benefit in applications where the size and weight of the inverter is constrained. This work presents the design and implementation of an inverter module which incorporates two individual 9-level FCML single-phase inverters in an interleaved design. Each inverter utilizes GaN FETs switching at 100 kHz, for an effective inductor ripple frequency of 800 kHz. The implementation features an innovative dual-sided integrated switching cell layout which decreases the effective commutation loop inductance of the inverter and enables fast switching with minimal ringing while also simplifying efficient double-sided cooling.


title: ERAOF: A new RPL protocol objective function for Internet of Things applications

abstract: Since its definition, RPL (the IPv6 Routing Protocol for Low-Power and Lossy Networks) has been emerging as the standard protocol for routing in Internet of Things (IoT) solutions. RPL is a proactive routing protocol that performs the process of route creation based on Objective Functions (OFs). The OFs are responsible for defining rules and constraints to select the best paths considering different routing metrics. In its definition, RPL does not impose the use of a default OF and indicates that an OF should be selected according to the application. Thus, this paper proposes an Energy Efficient and Path Reliability Aware Objective Function (ERAOF) for IoT applications that requires energy efficiency and reliability in data transmission. The ERAOF is based on the composition of energy and link quality routing metrics. Results show that ERAOF is able to improve the network performance when compared to other OFs available in the literature.


title: Automatically Extracting Word Relationships as Templates for Pun Generation

abstract: Computational models can be built to capture the syntactic structures and semantic patterns of human punning riddles. This model is then used as rules by a computer to generate its own puns. This paper presents T-PEG, a system that utilizes phonetic and semantic linguistic resources to automatically extract word relationships in puns and store the knowledge in template form. Given a set of training examples, it is able to extract 69.2% usable templates, resulting in computer-generated puns that received an average score of 2.13 as compared to 2.70 for human-generated puns from user feedback.


title: Automatic detection and tracking of oil spills in SAR imagery with level set segmentation

abstract: Automatic detection and monitoring of oil spills and illegal oil discharges is of fundamental importance in ensuring compliance with marine legislation and protection of the coastal environments, which are under considerable threat from intentional or accidental oil spills, uncontrolled sewage and wastewater discharged. In this paper, the level set based image segmentation was evaluated for the real-time detection and tracking of oil spills from SAR imagery. The processing scheme developed consists of a pre-processing step, in which an advanced image simplification takes place, followed by a geometric level set segmentation for the detection of possible oil spills. Finally, a classification was performed for the separation of look-alikes, leading to oil spill extraction. Experimental results demonstrate that the level set segmentation is a robust tool for the detection of possible oil spills, copes well with abrupt shape deformations and splits and outperforms earlier efforts that were based on different types of thresholds or edge detection techniques. The developed algorithm's efficiency for real-time oil spill detection and monitoring was also tested.


title: A Survey of User-centric Identity Management Technologies

abstract: Computing anytime anywhere is more and more the rule. In this ambient intelligent world, the choice of identity mechanisms will have a large impact on its social, cultural, business and political aspects. Privacy is a human need and the whole of society would suffer from its demise. Moreover, people have a hectic life and cannot spend their time administering their digital identities. In this paper, we survey how the requirements for identity management have evolved, and their associated technologies, with emphasis on the federated approaches and user-centricity.


title: Transfer learning using computational intelligence: A survey

abstract: Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, computational intelligence has recently been applied in transfer learning. This paper systematically examines computational intelligence-based transfer learning techniques and clusters related technique developments into four main categories: (a) neural network-based transfer learning; (b) Bayes-based transfer learning; (c) fuzzy transfer learning, and (d) applications of computational intelligence-based transfer learning. By providing state-of-the-art knowledge, this survey will directly support researchers and practice-based professionals to understand the developments in computational intelligence-based transfer learning research and applications.


title: Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

abstract: Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.


title: Feature Generating Networks for Zero-Shot Learning

abstract: Suffering from the extreme training data imbalance between seen and unseen classes, most of existing state-of-the-art approaches fail to achieve satisfactory results for the challenging generalized zero-shot learning task. To circumvent the need for labeled examples of unseen classes, we propose a novel generative adversarial network (GAN) that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution. Our proposed approach, pairing a Wasserstein GAN with a classification loss, is able to generate sufficiently discriminative CNN features to train softmax classifiers or any multimodal embedding method. Our experimental results demonstrate a significant boost in accuracy over the state of the art on five challenging datasets -- CUB, FLO, SUN, AWA and ImageNet -- in both the zero-shot learning and generalized zero-shot learning settings.


title: The role of venodilators in the perioperative management of heart failure.

abstract: A unifying hypothesis which satisfactorily explains the clinical syndrome of heart failure has proved elusive. A deeper understanding of the underlying pathophysiology has led to the development of more complex models and, as a result, the evolution of new treatment strategies. In patients undergoin


title: A database and challenge for acoustic scene classification and event detection

abstract: An increasing number of researchers work in computational auditory scene analysis (CASA). However, a set of tasks, each with a well-defined evaluation framework and commonly used datasets do not yet exist. Thus, it is difficult for results and algorithms to be compared fairly, which hinders research on the field. In this paper we will introduce a newly-launched public evaluation challenge dealing with two closely related tasks of the field: acoustic scene classification and event detection. We give an overview of the tasks involved; describe the processes of creating the dataset; and define the evaluation metrics. Finally, illustrations on results for both tasks using baseline methods applied on this dataset are presented, accompanied by open-source code.


title: AutoTutor: an intelligent tutoring system with mixed-initiative dialogue

abstract: AutoTutor simulates a human tutor by holding a conversation with the learner in natural language. The dialogue is augmented by an animated conversational agent and three-dimensional (3-D) interactive simulations in order to enhance the learner's engagement and the depth of the learning. Grounded in constructivist learning theories and tutoring research, AutoTutor achieves learning gains of approximately 0.8 sigma (nearly one letter grade), depending on the learning measure and comparison condition. The computational architecture of the system uses the .NET framework and has simplified deployment for classroom trials.


title: Effects of Honey Bee (Hymenoptera: Apidae) Foraging on Seed Set in Self-fertile Sunflowers (Helianthus annuus L)

abstract: Ten self-fertile commercial sunflowers cultivars were evaluated for seed set with and without exposure to bees. In the first planting, the number of foraging honey bees was smaller than in the second, and seed set for most cultivars did not differ between those bagged to exclude bees and ones that were open pollinated. In the second planting, however, a majority of cultivars had significantly greater seed set when capitula were exposed to bees compared with when they were not. The weight of seeds from open-pollinated capitula was greater than from those where bees were excluded. Environmental conditions also played a role in seed set as evidenced by differences between plantings in set on bagged capitula. In the first planting, average maximum and minimum temperatures were significantly higher than in the second, and overall seed set was significantly lower in capitula where bees were excluded compared with the second planting. Under the high temperature conditions, however, some cultivars set four times more seed on open-pollinated capitula compared with those that were bagged. These results suggest that foraging activity and cross-pollination by bees might mitigate reductions in seed set caused by high temperatures.


title: PROPOSED METHODOLOGY FOR EARTHQUAKE-INDUCED LOSS ASSESSMENT OF INSTRUMENTED STEEL FRAME BUILDINGS: BUILDING-SPECIFIC AND CITY-SCALE APPROACHES

abstract: The performance-based earthquake engineering framework utilizes probabilistic seismic demand models to obtain accurate estimates of building engineering demand parameters. These parameters are utilized to estimate earthquake-induced losses in terms of probabilistic repair costs, life-safety impact, and loss of function due to damage to a wide variety of building structural and non-structural components and content. Although nonlinear response history analysis is a reliable tool to develop probabilistic seismic demand models, it typically requires a considerable time investment in developing detailed building numerical model representations. In that respect, the challenge of city-scale damage assessment still remains. This paper proposes a simplified methodology that rapidly assesses the story-based engineering demand parameters (e.g., peak story drift ratios, residual story drift ratios, peak absolute floor accelerations) along the height of a steel frame building in the aftermath of an earthquake. The proposed methodology can be employed at a city-scale in order to facilitate rapid earthquake-induced loss assessment of steel frame buildings in terms of structural damage and monetary losses within a region. Therefore, buildings within an urban area that are potentially damaged after an earthquake event or scenario can be easily identified without a detailed engineering inspection. To illustrate the methodology for rapid earthquake-induced loss assessment at the city-scale we employ data from instrumented steel frame buildings in urban California that experienced the 1994 Northridge earthquake. Maps that highlight the expected structural and nonstructural damage as well as the expected earthquake-induced monetary losses are developed. The maps are developed with the geographical information system for steel frame buildings located in Los Angeles.


title: Waterloo Exploration Database: New Challenges for Image Quality Assessment Models

abstract: The great content diversity of real-world digital images poses a grand challenge to image quality assessment (IQA) models, which are traditionally designed and validated on a handful of commonly used IQA databases with very limited content variation. To test the generalization capability and to facilitate the wide usage of IQA techniques in real-world applications, we establish a large-scale database named the Waterloo Exploration Database, which in its current state contains 4744 pristine natural images and 94 880 distorted images created from them. Instead of collecting the mean opinion score for each image via subjective testing, which is extremely difficult if not impossible, we present three alternative test criteria to evaluate the performance of IQA models, namely, the pristine/distorted image discriminability test, the listwise ranking consistency test, and the pairwise preference consistency test (P-test). We compare 20 well-known IQA models using the proposed criteria, which not only provide a stronger test in a more challenging testing environment for existing models, but also demonstrate the additional benefits of using the proposed database. For example, in the P-test, even for the best performing no-reference IQA model, more than 6 million failure cases against the model are “discovered” automatically out of over 1 billion test pairs. Furthermore, we discuss how the new database may be exploited using innovative approaches in the future, to reveal the weaknesses of existing IQA models, to provide insights on how to improve the models, and to shed light on how the next-generation IQA models may be developed. The database and codes are made publicly available at: https://ece.uwaterloo.ca/~k29ma/exploration/ .


title: Validation of a smart shoe for estimating foot progression angle during walking gait

abstract: The foot progression angle is an important measurement related to knee loading, pain, and function for individuals with knee osteoarthritis, however current measurement methods require camera-based motion capture or floor-embedded force plates confining foot progression angle assessment to facilities with specialized equipment. This paper presents the validation of a customized smart shoe for estimating foot progression angle during walking. The smart shoe is composed of an electronic module with inertial and magnetometer sensing inserted into the sole of a standard walking shoe. The smart shoe charges wirelessly, and up to 160h of continuous data (sampled at 100Hz) can be stored locally on the shoe. For validation testing, fourteen healthy subjects were recruited and performed treadmill walking trials with small, medium, and large toe-in (internal foot rotation), small, medium, and large toe-out (external foot rotation) and normal foot progression angle at self-selected walking speeds. Foot progression angle calculations from the smart shoe were compared with measurements from a standard motion capture system. In general, foot progression angle values from the smart shoe closely followed motion capture values for all walking conditions with an overall average error of 0.1±1.9deg and an overall average absolute error of 1.7±1.0deg. There were no significant differences in foot progression angle accuracy across the seven different walking gait patterns. The presented smart shoe could potentially be used for knee osteoarthritis or other clinical applications requiring foot progression angle assessment in community settings or in clinics without specialized motion capture equipment.


title: Evaluation of direct attacks to fingerprint verification systems

abstract: The vulnerabilities of fingerprint-based recognition systems to direct attacks with and without the cooperation of the user are studied. Two different systems, one minutiae-based and one ridge feature-based, are evaluated on a database of real and fake fingerprints. Based on the fingerprint images quality and on the results achieved on different operational scenarios, we obtain a number of statistically significant observations regarding the robustness of the systems.


title: Multi-view Generative Adversarial Networks

abstract: Learning over multi-view data is a challenging problem with strong practical applications. Most related studies focus on the classification point of view and assume that all the views are available at any time. We consider an extension of this framework in two directions. First, based on the BiGAN model, the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from multi-view inputs. Second, it can deal with missing views and is able to update its prediction when additional views are provided. We illustrate these properties on a set of experiments over different datasets.


title: Path planning navigation of mobile robot with obstacles avoidance using fuzzy logic controller

abstract: Autonomous mobile robots are used in several application areas including manufacturing, mining, military, and transportation, search and protect missions, etc. For the navigation system it is necessary to locate the position of the mobile robot in surrounding environment. For avoiding obstacles efficiently and to reach the target under many different shapes of obstacle in environment, a fuzzy logic controller has been designed to improve the movement of mobile robot according to obstacles positions by defining or establishing input variables, output variables, fuzzy logic membership functions, fuzzy logic rule base ‘If-Then’ fuzzy inference system rules and defuzzification method. Then it has to plan a path towards desired goal. The navigation system of a mobile robot has to identify all potential obstacles in order to search for a collision free path. Obstacles avoidance and destination point can be achieved by changing the direction angle of the mobile robot. To make the mobile robot move in its environment, the basic path planning strategies have been used. While the mobile robot is navigating in its workspace environment, it avoids obstacles and look for the target. In this paper the simulation of path planning technique for an autonomous mobile robot is presented. The figure shows simulation of the mobile robot with four obstacles.


title: A statistical similarity measure for aggregate crowd dynamics

abstract: We present an information-theoretic method to measure the similarity between a given set of observed, real-world data and visual simulation technique for aggregate crowd motions of a complex system consisting of many individual agents. This metric uses a two-step process to quantify a simulator's ability to reproduce the collective behaviors of the whole system, as observed in the recorded real-world data. First, Bayesian inference is used to estimate the simulation states which best correspond to the observed data, then a maximum likelihood estimator is used to approximate the prediction errors. This process is iterated using the EM-algorithm to produce a robust, statistical estimate of the magnitude of the prediction error as measured by its entropy (smaller is better). This metric serves as a simulator-to-data similarity measurement. We evaluated the metric in terms of robustness to sensor noise, consistency across different datasets and simulation methods, and correlation to perceptual metrics.


title: Vehicle Make and Model classification system using bag of SIFT features

abstract: Vehicle Make and Model classification is an important part of Intelligent Transportation System. In this work, we have proposed a method based on Linear Support vector machine to solve this problem. Scale Invariant Transform Feature (SIFT) algorithm is used in this work to extract and represent local interest points. Bag of words model is used to represent the local features as fixed length vector to represent an image. The proposed method is evaluated on a publicly available vehicle make and model dataset and promising results are achieved with this method.


title: Large Margin Boltzmann Machines

abstract: Boltzmann Machines are a powerful class of undirected graphical models. Originally proposed as artificial neural networks, they can be regarded as a type of Markov Random Field in which the connection weights between nodes are symmetric and learned from data. They are also closely related to recent models such as Markov logic networks and Conditional Random Fields. A major challenge for Boltzmann machines (as well as other graphical models) is speeding up learning for large-scale problems. The heart of the problem lies in efficiently and effectively approximating the partition function. In this paper, we propose a new efficient learning algorithm for Boltzmann machines that allows them to be applied to problems with large numbers of random variables. We introduce a new large-margin variational approximation to the partition function that allows Boltzmann machines to be trained using a support vector machine (SVM) style learning algorithm. For discriminative learning tasks, these large margin Boltzmann machines provide an alternative approach to structural SVMs. We show that these machines have low sample complexity and derive a generalization bound. Our results demonstrate that on multilabel classification problems, large margin Boltzmann machines achieve orders of magnitude faster performance than structural SVMs and also outperform structural SVMs on problems with large numbers of labels.


title: Modelling and Simulation of Electric Vehicle Fast Charging Stations Driven by High Speed Railway Systems

abstract: The aim of this investigation is the analysis of the opportunity introduced by the use of railway infrastructures for the power supply of fast charging stations located in highways. Actually, long highways are often located far from urban areas and electrical infrastructure, therefore the installations of high power charging areas can be difficult. Specifically, the aim of this investigation is the analysis of the opportunity introduced by the use of railway infrastructures for the power supply of fast charging stations located in highways. Specifically, this work concentrates on fast-charging electric cars in motorway service areas by using high-speed lines for supplying the required power. Economic, security, safety and environmental pressures are motivating and pushing countries around the globe to electrify transportation, which currently accounts for a significant amount, above 70 percent of total oil demand. Electric cars require fast-charging station networks to allowing owners to rapidly charge their batteries when they drive relatively long routes. In other words, this means about the infrastructure towards building charging stations in motorway service areas and addressing the problem of finding solutions for suitable electric power sources. A possible and promising solution is proposed in the study that involves using the high-speed railway line, because it allows not only powering a high load but also it can be located relatively near the motorway itself. This paper presents a detailed investigation on the modelling and simulation of a 2 × 25 kV system to feed the railway. A model has been developed and implemented using the SimPower systems tool in MATLAB/Simulink to simulate the railway itself. Then, the model has been applied to simulate the battery charger and the system as a whole in two successive steps. The results showed that the concept could work in a real situation. Nonetheless if more than twenty 100 kW charging bays are required in each direction or if the line topology is changed for whatever reason, it cannot be guaranteed that the railway system will be able to deliver the additional power that is necessary.


title: Application of Composite Right/Left Handed (CRLH) Transmission Lines based on Complementary Split Ring Resonators (CSRRs) to the Design of Dual-Band Microwave Components

abstract: In this letter, composite right/left handed transmission lines based on complementary split ring resonators are applied to the design of dual-band microwave components. Specifically, impedance inverters operative at two (arbitrary) different frequencies (one of them within the left-handed band and the other within the right-handed band) are designed and applied to the implementation of a dual-band branch line hybrid coupler. The characterization of this device reveals good performance and dual-band operation. The fabricated device is small, fully planar, and vias are not required.


title: Learning graphical model structure using L1-regularization paths

abstract: Sparsity-promoting L1-regularization has recently been succesfully used to learn the structure of undirected graphical models. In this paper, we apply this technique to learn the structure of directed graphical models. Specifically, we make three contributions. First, we show how the decomposability of the MDL score, plus the ability to quickly compute entire regularization paths, allows us to efficiently pick the optimal regularization parameter on a per-node basis. Second, we show how to use L1 variable selection to select the Markov blanket, before a DAG search stage. Finally, we show how L1 variable selection can be used inside of an order search algorithm. The effectiveness of these L1-based approaches are compared to current state of the art methods on 10 datasets.


title: An online PPGI approach for camera based heart rate monitoring using beat-to-beat detection

abstract: In this work an online method for camera-based heart rate detection, also known as Photoplethysmography Imaging (PPGI), is presented. The pulse related signal is obtained from RGB videos of the human face, using an off the shelf camera under ambient light conditions. The algorithm for heart rate estimation is based on the beat-to-beat analysis of the PPGI signal, allowing the estimation of psychophysiological parameters like the heart rate variability. The system is tested indoors under laboratory conditions, and in field trials integrated in a car. Field tests are done as a step towards robust appraisal of psycho-physiological driver states. In measurements with eight subjects, a sensitivity of ∼93% for pulse peak detection is reached under resting and constant light conditions. First test drives show good results for heart rate estimation while driving on highways outside of city traffic. The results indicate that PPGI can be a suitable solution for providing vital sign information of drivers in cars.


title: Golden Eggs and Hyperbolic Discounting

abstract: Hyperbolic discount functions induce dynamically inconsistent preferences, implying a motive for consumers to constrain their own future choices. This paper analyzes the decisions of a hyperbolic consumer who has access to an imperfect commitment technology: an illiquid asset whose sale must be initiated one period before the sale proceeds are received. The model predicts that consumption tracks income, and the model explains why consumers have asset-specific marginal propensities to consume. The model suggests that financial innovation may have caused the ongoing decline in U. S. savings rates, since financial innovation in- creases liquidity, eliminating commitment opportunities. Finally, the model implies that financial market innovation may reduce welfare by providing â€œtoo muchâ€ liquidity.


title: Measures of Classroom Quality in Prekindergarten and Children’s Development of Academic, Language, and Social Skills

abstract: This study examined development of academic, language, and social skills among 4-year-olds in publicly supported prekindergarten (pre-K) programs in relation to 3 methods of measuring pre-K quality, which are as follows: (a) adherence to 9 standards of quality related to program infrastructure and design, (b) observations of the overall quality of classroom environments, and (c) observations of teachers’ emotional and instructional interactions with children in classrooms. Participants were 2,439 children enrolled in 671 pre-K classrooms in 11 states. Adjusting for prior skill levels, child and family characteristics, program characteristics, and state, teachers’ instructional interactions predicted academic and language skills and teachers’ emotional interactions predicted teacher-reported social skills. Findings suggest that policies, program development, and professional development efforts that improve teacher–child interactions can facilitate children’s school readiness.


title: One-Dimensional Oxide Nanostructures as Gas-Sensing Materials: Review and Issues

abstract: In this article, we review gas sensor application of one-dimensional (1D) metal-oxide nanostructures with major emphases on the types of device structure and issues for realizing practical sensors. One of the most important steps in fabricating 1D-nanostructure devices is manipulation and making electrical contacts of the nanostructures. Gas sensors based on individual 1D nanostructure, which were usually fabricated using electron-beam lithography, have been a platform technology for fundamental research. Recently, gas sensors with practical applicability were proposed, which were fabricated with an array of 1D nanostructures using scalable micro-fabrication tools. In the second part of the paper, some critical issues are pointed out including long-term stability, gas selectivity, and room-temperature operation of 1D-nanostructure-based metal-oxide gas sensors.


title: Timing of Menarche and First Birth in Relation to Risk of Breast Cancer in A-Bomb Survivors

abstract: BACKGROUND ::: The length of the interval between age at menarche and age at first birth is positively associated with breast cancer risk. We examined the risk of breast cancer in atomic bomb survivors to investigate whether women exposed to radiation between menarche and first birth had a higher risk of radiogenic breast cancer than women exposed at the same age but outside this interval. ::: ::: ::: METHODS ::: Women (n = 30,113) were classified into three reproductive status at the time of the bombings (ATB) categories (premenarche, between menarche and first birth, or after first birth). Poisson regression was used to test the primary hypothesis. ::: ::: ::: RESULTS ::: When the background rate of breast cancer was taken to depend on city, age ATB, and attained age only, the radiation-related excess relative risk (ERR) varied significantly among the three categories (P = 0.049). However, after controlling for significant heterogeneity in the baseline risk of breast cancer between reproductive status ATB groups (P < 0.001), no significant heterogeneity (P = 0.88) was observed in the ERR, with an ERR per Gy of 1.36 [95% confidence interval (CI), 0.54-2.75] for women exposed between menarche and first birth ATB, and 1.07 (95% CI, 0.22-3.62) and 1.53 (95% CI, 0.63-2.90) for those exposed premenarche or after first birth, respectively. ::: ::: ::: CONCLUSIONS ::: The radiation-associated risk of breast cancer does not vary significantly by reproductive status ATB. ::: ::: ::: IMPACT ::: It is possible that radiation exerts similar carcinogenic effects on the breast regardless of its stage of differentiation, or that the differences in radiosensitivity are too small to be detected in this cohort.


title: An Empirical Investigation of Statistical Significance in NLP

abstract: We investigate two aspects of the empirical behavior of paired significance tests for NLP systems. First, when one system appears to outperform another, how does significance level relate in practice to the magnitude of the gain, to the size of the test set, to the similarity of the systems, and so on? Is it true that for each task there is a gain which roughly implies significance? We explore these issues across a range of NLP tasks using both large collections of past systems' outputs and variants of single systems. Next, once significance levels are computed, how well does the standard i.i.d. notion of significance hold up in practical settings where future distributions are neither independent nor identically distributed, such as across domains? We explore this question using a range of test set variations for constituency parsing.


title: Probabilistic Databases of Universal Schema

abstract: In data integration we transform information from a source into a target schema. A general problem in this task is loss of fidelity and coverage: the source expresses more knowledge than can fit into the target schema, or knowledge that is hard to fit into any schema at all. This problem is taken to an extreme in information extraction (IE) where the source is natural language. To address this issue, one can either automatically learn a latent schema emergent in text (a brittle and ill-defined task), or manually extend schemas. We propose instead to store data in a probabilistic database of universal schema. This schema is simply the union of all source schemas, and the probabilistic database learns how to predict the cells of each source relation in this union. For example, the database could store Freebase relations and relations that correspond to natural language surface patterns. The database would learn to predict what freebase relations hold true based on what surface patterns appear, and vice versa. We describe an analogy between such databases and collaborative filtering models, and use it to implement our paradigm with probabilistic PCA, a scalable and effective collaborative filtering method.


title: The anterolateral supra-fibular-head approach for plating posterolateral tibial plateau fractures: A novel surgical technique.

abstract: OBJECTIVE ::: The posterolateral (PL) tibial plateau quadrant is laterally covered by the fibular head and posteriorly covered by a mass of muscle ligament and important neurovascular structures. There are several limitations in exposing and fixing the PL tibial plateau fractures using a posterior approach. The aim of this study is to present a novel anterolateral supra-fibular-head approach for plating PL tibial plateau fractures. ::: ::: ::: METHODS ::: Five fresh and ten preserved knee specimens were dissected to measure the following parameters:1) the vertical distance from the apex of the fibular head to the lateral plateau surface, 2) the transverse distance between the PL platform and fibula collateral ligament (FCL), and 3) the tension of the FCL in different knee flexion positions. Clinically, isolated PL quadrant tibial plateau fractures were treated via an anterolateral supra-fibular-head approach and lateral rafting plate fixation. The outcome of the patients was assessed after a short to medium follow-up period. ::: ::: ::: RESULTS ::: The distance from the apex of the fibular head to the lateral condylar surface was 12.2 ± 1.6 mm on average. With the knee extended and the FCL tensioned, the transverse distance between the PL platform and the FCL was 6.7 ± 1.1 mm. With the knee flexed to 60° and the FCL was in the most relaxed position, the distance increased to 21.1 ± 3.0 mm. Clinically, a series of 7 cases of PL tibial plateau fractures were treated via this anterolateral supra-fibular-head approach. The patient was placed in a lateral decubitus position with the knee flexed to approximately 60 degrees. After the posterior retraction of the FCL, the plate was placed more posteriorly to provide a raft or horizontal belt fixation of the PL tibial plateau fragment. After an average of 14.3 months of follow up, the knee range of motion(ROM) was 121.4° ± 8.8° (range: 105°-135°), the HSS score was 96.7 ± 2.6 (range: 90-100), and the SMFA dysfunction score was 22.4 ± 3.8 (range: 16-28) points. ::: ::: ::: CONCLUSION ::: The anterolateral supra-fibular-head approach can provide direct visualization of the posterolateral tibial plateau quadrant and put the plate more posteriorly to provide a raft for the fragments such that good clinical outcomes can be anticipated.


title: Significant positive correlation between the recombination rate and GC content in the human pseudoautosomal region

abstract: This paper establishes that recombination drives the evolution of GC content in a significant way. Because the human P-arm pseudoautosomal region (PAR1) has been shown to have a high recombination ...


title: "Please, Not Now!": A Model for Timing Recommendations

abstract: Proactive recommender systems push recommendations to users without their explicit request whenever a recommendation that suits a user is available. These systems strive to optimize the match between recommended items and users' preferences. We assume that recommendations might be reflected with low accuracy not only due to the recommended items' suitability to the user, but also because of the recommendations' timings. We therefore claim that it is possible to learn a model of good and bad contexts for recommendations that can later be integrated in a recommender system. Using mobile data collected during a three week user study, we suggest a two-phase model that is able to classify whether a certain context is at all suitable for any recommendation, regardless of its content. Results reveal that a hybrid model that first decides whether it should use a personal or a non-personal timing model, and then classifies accordingly whether the timing is proper for recommendations, is superior to both the personal or non-personal timing models.


title: Reliability in co-located 802.15.4 personal area networks

abstract: IEEE 802.5.4-based personal area networks (PANs) are expected to play a key role for emerging mobile health applications. In this work, we evaluate network performance and reliability in the case where several PANs are forced to share the same channel. Based on extensive simulations, we show that inter-network interaction may lead to long periods of disruption, even when the average network performance is very good and the channel is only lightly loaded. We demonstrate that this is mostly due to lack of coordination between networks, rather than limitations of duty cycle or memory.


title: Hybrid rotor position observer for wide speed-range sensorless PM motor drives including zero speed

abstract: This paper addresses the problem of wide speed-range sensorless control of a surface-mount permanent-magnet (SMPM) machine including zero-speed operation. A hybrid structure integrating a flux observer and signal-injection techniques is proposed, which results in a rotor position signal independent of motor parameters at low and zero speed. Although the SMPM machine typically has a very low geometric saliency, the injection technique is effective in tracking the saturation-induced saliency produced by the stator flux. Experimental results are presented showing an excellent performance for both the sensorless speed and position control using an off-the-shelf SMPM machine.


title: On the difficulty of software-based attestation of embedded devices

abstract: Device attestation is an essential feature in many security protocols and applications. The lack of dedicated hardware and the impossibility to physically access devices to be attested, makes attestation of embedded devices, in applications such as Wireless Sensor Networks, a prominent challenge. Several software-based attestation techniques have been proposed that either rely on tight time constraints or on the lack of free space to store malicious code. This paper investigates the shortcomings of existing software-based attestation techniques. We first present two generic attacks, one based on a return-oriented rootkit} and the other on code compression. We further describe specific attacks on two existing proposals, namely SWATT and ICE-based schemes, and argue about the difficulty of fixing them. All attacks presented in this paper were implemented and validated on commodity sensors.


title: Modelling foot height and foot shape-related dimensions.

abstract: The application of foot anthropometry to design good-fitting footwear has been difficult due to the lack of generalised models. This study seeks to model foot dimensions so that the characteristic shapes of feet, especially in the midfoot region, can be understood. Fifty Hong Kong Chinese adults (26 males and 24 females) participated in this study. Their foot lengths, foot widths, ball girths and foot heights were measured and then evaluated using mathematical models. The results showed that there were no significant allometry (p > 0.05) effects of foot length on ball girth and foot width. Foot height showed no direct relationship with foot length. However, a normalisation with respect to foot length and foot height resulted in a significant relationship for both males and females with R(2) greater than 0.97. Due to the lack of a direct relationship between foot height and foot length, the current practice of grading shoes with a constant increase in height or proportionate scaling in response to foot length is less than ideal. The results when validated with other populations can be a significant way forward in the design of footwear that has an improved fit in the height dimension.


title: Rectangular Slotted Patch Antenna for 5-6GHz Applications

abstract: A novel miniature wideband rectangular patch antenna is designed for wireless local area network (WLANs) applications and operating for 5-6 GHz ISM band, and wideband applications. The proposed antenna gives a bandwidth of 4.84 to 6.56 GHz for S11<-10dB. The antenna has the dimensions of 20 mm by 15 mm by 0.8 mm on FR4 substrate. Rectangular slot and step have been used for bandwidth improvement.


title: Understanding hand-object manipulation by modeling the contextual relationship between actions, grasp types and object attributes

abstract: This paper proposes a novel method for understanding daily hand-object manipulation by developing computer vision-based techniques. Specifically, we focus on recognizing hand grasp types, object attributes and manipulation actions within an unified framework by exploring their contextual relationships. Our hypothesis is that it is necessary to jointly model hands, objects and actions in order to accurately recognize multiple tasks that are correlated to each other in hand-object manipulation. In the proposed model, we explore various semantic relationships between actions, grasp types and object attributes, and show how the context can be used to boost the recognition of each component. We also explore the spatial relationship between the hand and object in order to detect the manipulated object from hand in cluttered environment. Experiment results on all three recognition tasks show that our proposed method outperforms traditional appearance-based methods which are not designed to take into account contextual relationships involved in hand-object manipulation. The visualization and generalizability study of the learned context further supports our hypothesis.


title: A simple boundary process technique for empirical mode decomposition

abstract: The empirical mode decomposition (EMD) proposed by Huang et al. in 1998 shows remarkably effective in analyzing nonlinear signals. It adaptively represents nonstationary signals as sums of zero-mean amplitude modulation-frequency modulation (AM-FM) components by iteratively conducting the sifting process. How to determine the boundary conditions of the cubic spline when constructing the envelopes of data is the critical issue of the sifting process. A simple bound hit process technique is presented in this paper which constructs two periodic series from the original data by even and odd extension and then builds the envelopes using cubic spline with periodic boundary condition. The EMD is conducted fluently without any assumptions of the processed data by this approach. An example is presented to pick out the weak modulation of internal waves from an Envisat ASAR image by EMD with the boundary process technique


title: I know what your packet did last hop: using packet histories to troubleshoot networks

abstract: The complexity of networks has outpaced our tools to debug them; today, administrators use manual tools to diagnose problems. In this paper, we show how packet histories--the full stories of every packet's journey through the network--can simplify network diagnosis. To demonstrate the usefulness of packet histories and the practical feasibility of constructing them, we built NetSight, an extensible platform that captures packet histories and enables applications to concisely and flexibly retrieve packet histories of interest. Atop NetSight, we built four applications that illustrate its flexibility: an interactive network debugger, a live invariant monitor, a path-aware history logger, and a hierarchical network profiler. On a single modern multi-core server, NetSight can process packet histories for the traffic of multiple 10 Gb/s links. For larger networks, NetSight scales linearly with additional servers and scales even further with straightforward additions to hardware- and hypervisor-based switches.


title: An Edge-based Clustering Algorithm to Detect Social Circles in Ego Networks

abstract: Organizing users’ friends in personal social networks, i.e., ego networks, into circles is an important task for online social networks. Social networking sites allow users to manually categorize their friends into social circles. However, it is time consuming and does not update automatically as a user adds more friends. In this paper, we propose an edge-based clustering algorithm to detect social circles in ego networks automatically. Firstly, we reconstruct ego networks by predicting the missing links. Then, we define the similarity of adjacent edges and cluster edges by single-linkage hierarchical clustering algorithm. Finally, we label each circle by abstracting its common properties to explain why this circle forms. The experimental results demonstrate it is a better way to characterize social circles from the respect of edges. Our algorithm outperforms the link community algorithm and low-rank embedding algorithm in terms of accuracy, and is more efficient than the probabilistic model algorithm. Our proposed method is validated as an effective algorithm in identifying social circles.


title: Dynamics of Intersubject Brain Networks during Anxious Anticipation

abstract: How do large-scale brain networks reorganize during the waxing and waning of anxious anticipation? Here, threat was dynamically modulated during human functional MRI as two circles slowly meandered on the screen; if they touched, an unpleasant shock was delivered. We employed intersubject correlation analysis, which allowed the investigation of network-level functional connectivity across brains, and sought to determine how network connectivity changed during periods of approach (circles moving closer) and periods of retreat (circles moving apart). Analysis of positive connection weights revealed that dynamic threat altered connectivity within and between the salience, executive, and task-negative networks. For example, dynamic functional connectivity increased within the salience network during approach and decreased during retreat. The opposite pattern was found for the functional connectivity between the salience and task-negative networks: decreases during approach and increases during approach. Functional connections between subcortical regions and the salience network also changed dynamically during approach and retreat periods. Subcortical regions exhibiting such changes included the putative periaqueductal gray, putative habenula, and putative bed nucleus of the stria terminalis. Additional analysis of negative functional connections revealed dynamic changes, too. For example, negative weights within the salience network decreased during approach and increased during retreat, opposite what was found for positive weights. Together, our findings unraveled dynamic features of functional connectivity of large-scale networks and subcortical regions across participants while threat levels varied continuously, and demonstrate the potential of characterizing emotional processing at the level of dynamic networks.


title: Characterizing dynamic functional connectivity in the resting brain using variable parameter regression and Kalman filtering approaches

abstract: Abstract The cognitive activity of the human brain benefits from the functional connectivity of multiple brain regions that form specific, functional brain networks. Recent studies have indicated that the relationship between brain regions can be investigated by examining the temporal interaction (known as functional connectivity) of spontaneous blood oxygen level-dependent (BOLD) signals derived from resting-state functional MRI. Most of these studies plausibly assumed that inter-regional interactions were temporally stationary. However, little is known about the dynamic characteristics of resting-state functional connectivity (RSFC). In this study, we thoroughly examined this question within and between multiple functional brain networks. Twenty-two healthy subjects were scanned in a resting state. Several of the RSFC networks observed, including the default-mode, motor, attention, memory, auditory, visual, language and subcortical networks, were first identified using a conventional voxel-wise correlation analysis with predefined region of interests (ROIs). Then, a variable parameter regression model combined with the Kalman filtering method was employed to detect the dynamic interactions between each ROI and all other brain voxels within each of the RSFC maps extracted above. Experimental results revealed that the functional interactions within each RSFC map showed time-varying properties, and that approximately 10–20% of the voxels within each RSFC map showed significant functional connectivity to each ROI during the scanning session. This dynamic pattern was also observed for the interactions between different functional networks. In addition, the spatial pattern of dynamic connectivity maps obtained from neighboring time points had a high similarity. Overall, this study provides insights into the dynamic properties of resting-state functional networks.


title: Crowdsourcing Literature Reviews in New Domains

abstract: A standard approach to exploring a domain is to conduct a literature review. However, conducting a literature review in a new domain presents unique challenges. Whereas in an existing domain, researchers can use established classifications of knowledge to guide their search for and interpretation of the literature, this is not the case for a new domain that lacks such classifications. In a new domain, the literature is typically broad, fragmented, and, at the same time, growing quickly. The task of the researcher is to make sense of evidence when it does not fit existing models and classifications. Encountering such evidence forces them to extend existing knowledge.


title: Training Deep AutoEncoders for Collaborative Filtering

abstract: This paper proposes a novel model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. We empirically demonstrate that: a) deep autoencoder models generalize much better than the shallow ones, b) non-linear activation functions with negative parts are crucial for training deep models, and c) heavy use of regularization techniques such as dropout is necessary to prevent over-fiting. We also propose a new training algorithm based on iterative output re-feeding to overcome natural sparseness of collaborate filtering. The new algorithm significantly speeds up training and improves model performance. Our code is available at this https URL


Persian Printed Numeral Character Recognition using Geometrical Central Moments and Fuzzy


title: Deep Hedging

abstract: We present a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, market impact, liquidity constraints or risk limits using modern deep reinforcement machine learning methods. We discuss how standard reinforcement learning methods can be applied to non-linear reward structures, i.e. in our case convex risk measures. As a general contribution to the use of deep learning for stochastic processes, we also show that the set of constrained trading strategies used by our algorithm is large enough to $\epsilon$-approximate any optimal solution. Our algorithm can be implemented efficiently even in high-dimensional situations using modern machine learning tools. Its structure does not depend on specific market dynamics, and generalizes across hedging instruments including the use of liquid derivatives. Its computational performance is largely invariant in the size of the portfolio as it depends mainly on the number of hedging instruments available. We illustrate our approach by showing the effect on hedging under transaction costs in a synthetic market driven by the Heston model, where we outperform the standard"complete market"solution.


title: Smart irrigation using internet of things

abstract: The Algerian economy is currently experiencing a significant deterioration because of its dependence on oil revenues, which drops in prices recently. Therefore, it is necessary to revive the Algerian economy with other important sectors, mainly agricultural sector especially in the south of Algeria. The southern Algeria Contain all necessary conditions for agriculture, which are the large agricultural areas, water resources and good illumination (sunlight).The mismanagement of irrigation water affects negatively the agricultural production of Algeria because of the shortage of irrigation water. Thus, with the benefit of the Internet of Things and the smart technologies, we will propose in this paper a new strategy for smart irrigation in southern Algeria regions, to optimize the water consumption, and to provide a remote control and monitoring for the irrigation system. Tests were realized to prove the validity of our proposed system by using Contiki-Cooja simulator.


title: Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

abstract: In this paper, we present a scheme based on feature mining and pattern classification to detect LSB matching steganography in grayscale images, which is a very challenging problem in steganalysis. Five types of features are proposed. In comparison with other well-known feature sets, the set of proposed features performs the best. We compare different learning classifiers and deal with the issue of feature selection that is rarely mentioned in steganalysis. In our experiments, the combination of a dynamic evolving neural fuzzy inference system (DENFIS) with a feature selection of support vector machine recursive feature elimination (SVMRFE) achieves the best detection performance. Results also show that image complexity is an important reference to evaluation of steganalysis performance.


title: Framework for Adaptive Computation Offloading in IoT Applications

abstract: The internet of things (IoT) attracts great interest in many application domains concerned with monitoring and control of physical phenomena. IoT applications try to provide more and more functionality and then they inevitably become so complex as to make the limits of devices worse, which may lead to poor performance of applications. Computation offloading is a promising way to improve the performance of an IoT application by executing some parts of the application on remote devices or servers. However, supporting such capability is not easy for application developers due to (1) adaptability: IoT applications often face changes of runtime environments so that the adaptation on offloading is needed. (2) effectiveness: when the device context changes, it needs to dynamically decide the deployment plan of computation tasks, and the reduced execution time must be greater than the network delay and extra overheads caused by offloading. This paper proposes a framework which supports IoT applications with adaptive computation offloading capability. First, a design pattern is proposed to enable an application to be computation offloaded on-demand. Second, an estimation model is presented to automatically decide the deployment plan for offloading. Third, a framework is implemented to support the design pattern and the estimation model. A thorough evaluation on the real-world application is proposed, and the results show that our approach can help reduce execution time by over 45% in most scenarios.


title: Risk profile and consumer shopping behavior in electronic and traditional channels

abstract: This paper develops an economic model that captures consumer shopping channel choices based on shopping channel characteristics and consumer risk profiles--risk-neutral or risk-averse. Analyses of results show that after making purchases through one channel, electronic or traditional, risk-averse consumers tend to be more loyal customers than risk-neutral consumers. Further, the two types of consumers may exhibit split channel behavior--risk-neutral consumers prefer one channel and risk-averse consumers prefer the other. However, risk-neutral consumers are not always more likely to prefer electronic channel than risk-averse consumers. Implications for retailer pricing strategies are discussed.


title: FlowNet: Learning Optical Flow with Convolutional Networks

abstract: Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks CNNs succeeded at. In this paper we construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a large synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.


title: Trust Me: Doubts and Concerns Living with the Internet of Things

abstract: An increasing number of everyday objects are now connected to the internet, collecting and sharing information about us: the "Internet of Things" (IoT). However, as the number of "social" objects increases, human concerns arising from this connected world are starting to become apparent. This paper presents the results of a preliminary qualitative study in which five participants lived with an ambiguous IoT device that collected and shared data about their activities at home for a week. In analyzing this data, we identify the nature of human and socio-technical concerns that arise when living with IoT technologies. Trust is identified as a critical factor - as trust in the entity/ies that are able to use their collected information decreases, users are likely to demand greater control over information collection. Addressing these concerns may support greater engagement of users with IoT technology. The paper concludes with a discussion of how IoT systems might be designed to better foster trust with their owners.


title: Dissecting Torsin/cofactor function at the nuclear envelope: a genetic study

abstract: The human genome encodes four Torsin ATPases, the functions of which are poorly understood. In this study, we use CRISPR/Cas9 engineering to delete all four Torsin ATPases individually and in combination. Using nuclear envelope (NE) blebbing as a phenotypic measure, we establish a direct correlation between the number of inactivated Torsin alleles and the occurrence of omega-shaped herniations within the lumen of the NE. A similar, although not identical, redundancy is observed for LAP1 and LULL1, which serve as regulatory cofactors for a subset of Torsin ATPases. Unexpectedly, deletion of Tor2A in a TorA/B/3A-deficient background results in a stark increase of bleb formation, even though Tor2A does not respond to LAP1/LULL1 stimulation. The robustness of the observed phenotype in Torsin-deficient cells enables a structural analysis via electron microscopy tomography and a compositional analysis via immunogold labeling. Ubiquitin and nucleoporins were identified as distinctively localizing components of the omega-shaped bleb structure. These findings suggest a functional link between the Torsin/cofactor system and NE/nuclear pore complex biogenesis or homeostasis and establish a Torsin-deficient cell line as a valuable experimental platform with which to decipher Torsin function.


title: Distributed and interactive brain mechanisms during emotion face perception: Evidence from functional neuroimaging

abstract: Brain imaging studies in humans have shown that face processing in several areas is modulated by the affective significance of faces, particularly with fearful expressions, but also with other social signals such gaze direction. Here we review haemodynamic and electrical neuroimaging results indicating that activity in the face-selective fusiform cortex may be enhanced by emotional (fearful) expressions, without explicit voluntary control, and presumably through direct feedback connections from the amygdala. fMRI studies show that these increased responses in fusiform cortex to fearful faces are abolished by amygdala damage in the ipsilateral hemisphere, despite preserved effects of voluntary attention on fusiform; whereas emotional increases can still arise despite deficits in attention or awareness following parietal damage, and appear relatively unaffected by pharmacological increases in cholinergic stimulation. Fear-related modulations of face processing driven by amygdala signals may implicate not only fusiform cortex, but also earlier visual areas in occipital cortex (e.g., V1) and other distant regions involved in social, cognitive, or somatic responses (e.g., superior temporal sulcus, cingulate, or parietal areas). In the temporal domain, evoked-potentials show a widespread time-course of emotional face perception, with some increases in the amplitude of responses recorded over both occipital and frontal regions for fearful relative to neutral faces (as well as in the amygdala and orbitofrontal cortex, when using intracranial recordings), but with different latencies post-stimulus onset. Early emotional responses may arise around 120 ms, prior to a full visual categorization stage indexed by the face-selective N170 component, possibly reflecting rapid emotion processing based on crude visual cues in faces. Other electrical components arise at later latencies and involve more sustained activities, probably generated in associative or supramodal brain areas, and resulting in part from the modulatory signals received from amygdala. Altogether, these fMRI and ERP results demonstrate that emotion face perception is a complex process that cannot be related to a single neural event taking place in a single brain regions, but rather implicates an interactive network with distributed activity in time and space. Moreover, although traditional models in cognitive neuropsychology have often considered that facial expression and facial identity are processed along two separate pathways, evidence from fMRI and ERPs suggests instead that emotional processing can strongly affect brain systems responsible for face recognition and memory. The functional implications of these interactions remain to be fully explored, but might play an important role in the normal development of face processing skills and in some neuropsychiatric disorders.


A fast and flexible architecture for very large word n-gram datasets


title: Semi-Supervised Learning on Riemannian Manifolds

abstract: We consider the general problem of utilizing both labeled and unlabeled data to improve classification accuracy. Under the assumption that the data lie on a submanifold in a high dimensional space, we develop an algorithmic framework to classify a partially labeled data set in a principled manner. The central idea of our approach is that classification functions are naturally defined only on the submanifold in question rather than the total ambient space. Using the Laplace-Beltrami operator one produces a basis (the Laplacian Eigenmaps) for a Hilbert space of square integrable functions on the submanifold. To recover such a basis, only unlabeled examples are required. Once such a basis is obtained, training can be performed using the labeled data set. ::: ::: Our algorithm models the manifold using the adjacency graph for the data and approximates the Laplace-Beltrami operator by the graph Laplacian. We provide details of the algorithm, its theoretical justification, and several practical applications for image, speech, and text classification.


title: One road to turnover: an examination of work exhaustion in technology professionals

abstract: The concept of work exhaustion (or job burnout) from the management and psychology research literature is examined in the context of technology professionals. Data were collected from 270 IT professionals and managers in various industries across the United States. Through structural equation modeling, work exhaustion was shown to partially mediate the effects of workplace factors on turnover intention. In addition, the results of the study revealed that: (1) technology professionals experiencing higher levels of exhaustion reported higher intentions to leave the job and, (2) of the variables expected to influence exhaus


title: Understanding Changes in Consumer Payment Habits - Do Mobile Payments and Electronic Invoices Attract Consumers?

abstract: Mobile payment services have, thus far, failed to entice consumers. An apparent conclusion is that these services have failed to meet consumers' payment needs. Deeper understanding of consumer adoption motivations is thus needed to be able to develop and launch mobile payment services successfully. Our paper seeks to find out if the generic technology adoption models are sufficient to explain factors consumers consider when they decide whether or not to adopt new payment services. In particular, we develop two models in the payment context. One of them models the determinants of the mobile payments services adoption while the other models the determinants of electronic invoicing adoption. Comparison of the model structures suggests that perceived ease of use seems to be the least common denominator for consumer adoption of these information technology based services while the context of technology adoption determines both the non-differentiating and the differentiating determinants of technology adoption


title: A statistical model-based voice activity detection

abstract: In this letter, we develop a robust voice activity detector (VAD) for the application to variable-rate speech coding. The developed VAD employs the decision-directed parameter estimation method for the likelihood ratio test. In addition, we propose an effective hang-over scheme which considers the previous observations by a first-order Markov process modeling of speech occurrences. According to our simulation results, the proposed VAD shows significantly better performances than the G.729B VAD in low signal-to-noise ratio (SNR) and vehicular noise environments.


title: Healthcare informatics and analytics framework

abstract: Healthcare industry is constantly undergoing changes owing to the advancements made in its medical and technological dimensions. Healthcare Informatics has evolved over decades by leveraging upon best of the computer technologies available. It has come a long way from being just a database to a comprehensive source of useful information for analytics and research. Emergence of technology paradigms like Big Data, Internet of Things (IoT), Complex Even Processing (CEP), Cloud Computing etc are changing the way things were. Healthcare informatics is now at juncture to find a complete solution to all its woes by adopting an integrated solution framework based on Big Data, IoT and CEP. This paper proposes one such framework which can act as reference for implementing a holistic healthcare informatics and analytics ecosystem.


title: Deep Transfer Learning with Joint Adaptation Networks

abstract: Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain. In this paper, we present joint adaptation networks (JAN), which learn a transfer network by aligning the joint distributions of multiple domain-specific layers across domains based on a joint maximum mean discrepancy (JMMD) criterion. Adversarial training strategy is adopted to maximize JMMD such that the distributions of the source and target domains are made more distinguishable. Learning can be performed by stochastic gradient descent with the gradients computed by back-propagation in linear-time. Experiments testify that our model yields state of the art results on standard datasets.


title: Automatic Rank Selection for High-Speed Convolutional Neural Network

abstract: Low-rank decomposition plays a central role in accelerating convolutional neural network (CNN), and the rank of decomposed kernel-tensor is a key parameter that determines the complexity and accuracy of a neural network. In this paper, we define rank selection as a combinatorial optimization problem and propose a methodology to minimize network complexity while maintaining the desired accuracy. Combinatorial optimization is not feasible due to search space limitations. To restrict the search space and obtain the optimal rank, we define the space constraint parameters with a boundary condition. We also propose a linearly-approximated accuracy function to predict the fine-tuned accuracy of the optimized CNN model during the cost reduction. Experimental results on AlexNet and VGG-16 show that the proposed rank selection algorithm satisfies the accuracy constraint. Our method combined with truncated-SVD outperforms state-of-the-art methods in terms of inference and training time at almost the same accuracy.


title: Carrier-based SVPWM through sub-sector identification in multi-level inverters

abstract: This paper proposes a simple method for identification of the sub-sectors of the space vector diagram in which the reference phasor tip lies and using that; carrier-based implementation of the Space vector pulse width modulation (SVPWM) is derived for multi-level PWM inverters. This method is based on the line equations of the n-level inverter space vector diagram. Sub-sector identification and thereby a common-mode offset determination for the carrier-based SVPWM can be used to reduce the complexity of implementation of SVPWM as well as modified SVPWM strategies. The logic of the proposed method is implemented for three-level and four-level inverters and can be further extended to higher level voltage-source inverters. The validity of the method is verified in simulation for the three-level and four-level inverters.


title: Reconfigurable liquid-metal antenna with integrated surface-tension actuation

abstract: A new design for a reconfigurable antenna using electrically actuated liquid-metal tuning elements is presented. Liquid-metal slugs vary the electrical length of a resonant slot aperture, changing the operational frequency of the antenna across a 36% tunable bandwidth. The fluidic channels are fabricated with polydimethylsiloxane and are fully sealed with integrated DC actuation pads, providing enhanced versatility over previous designs and enabling future miniaturization.


title: Design and implementation of spherical ultrasonic motor

abstract: We present a mechanical design and implementation of spherical ultrasonic motor (SUSM) that is an actuator with multiple rotational degrees of freedom (multi-DOF). The motor is constructed of 3 annular stators and a spherical rotor and is much smaller and simpler than conventional multi-DOF mechanisms such as gimbals using servomotors. We designed a novel SUSM using experimental data from a single annular stator and a finite element method. The SUSM using a spherical rotor of diameter 20 mm without any reduction gear has demonstrated advantages of high responsiveness, good accuracy, and high torque at low speed. The dynamic implementation of SUSM was consistent with the driving model of SUSM based on a friction drive.