title: LOCUS: learning object classes with unsupervised segmentation

abstract: We address the problem of learning object class models and object segmentations from unannotated images. We introduce LOCUS (learning object classes with unsupervised segmentation) which uses a generative probabilistic model to combine bottom-up cues of color and edge with top-down cues of shape and pose. A key aspect of this model is that the object appearance is allowed to vary from image to image, allowing for significant within-class variation. By iteratively updating the belief in the object's position, size, segmentation and pose, LOCUS avoids making hard decisions about any of these quantities and so allows for each to be refined at any stage. We show that LOCUS successfully learns an object class model from unlabeled images, whilst also giving segmentation accuracies that rival existing supervised methods. Finally, we demonstrate simultaneous recognition and segmentation in novel images using the learned models for a number of object classes, as well as unsupervised object discovery and tracking in video.


title: Functional Interdependence Theory An Evolutionary Account of Social Situations

abstract: Social interactions are characterized by distinct forms of interdependence, each of which has unique effects on how behavior unfolds within the interaction. Despite this, little is known about the psychological mechanisms that allow people to detect and respond to the nature of interdependence in any given interaction. We propose that interdependence theory provides clues regarding the structure of interdependence in the human ancestral past. In turn, evolutionary psychology offers a framework for understanding the types of information processing mechanisms that could have been shaped under these recurring conditions. We synthesize and extend these two perspectives to introduce a new theory: functional interdependence theory (FIT). FIT can generate testable hypotheses about the function and structure of the psychological mechanisms for inferring interdependence. This new perspective offers insight into how people initiate and maintain cooperative relationships, select social partners and allies, and ident...


title: Challenges in creating long continuum robots

abstract: We discuss the design and control issues in creating continuous backbone continuum robots at lengths of multiple meters. At this scale, many of the design techniques and materials properties which have proved advantageous in creating continuum robots at smaller scales are not applicable. This leads to problems with reduced load capacity and poor internal stability. Using two example cases, a thin tendril and an extensible pipe, we illustrate the issues and discuss potential successful design alternatives.


title: Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables

abstract: Deep learning has revolutionized the way sensor data are analyzed and interpreted. The accuracy gains these approaches offer make them attractive for the next generation of mobile, wearable and embedded sensory applications. However, state-of-the-art deep learning algorithms typically require a significant amount of device and processor resources, even just for the inference stages that are used to discriminate high-level classes from low-level data. The limited availability of memory, computation, and energy on mobile and embedded platforms thus pose a significant challenge to the adoption of these powerful learning techniques. In this paper, we propose SparseSep, a new approach that leverages the sparsification of fully connected layers and separation of convolutional kernels to reduce the resource requirements of popular deep learning algorithms. As a result, SparseSep allows large-scale DNNs and CNNs to run efficiently on mobile and embedded hardware with only minimal impact on inference accuracy. We experiment using SparseSep across a variety of common processors such as the Qualcomm Snapdragon 400, ARM Cortex M0 and M3, and Nvidia Tegra K1, and show that it allows inference for various deep models to execute more efficiently; for example, on average requiring 11.3 times less memory and running 13.3 times faster on these representative platforms.


title: Robust Large-Scale Localization in 3D Point Clouds Revisited

abstract: We tackle the problem of getting a full 6-DOF pose estimation of a query image inside a given point cloud. This technical report re-evaluates the algorithms proposed by Y. Li et al."Worldwide Pose Estimation using 3D Point Cloud". Our code computes poses from 3 or 4 points, with both known and unknown focal length. The results can easily be displayed and analyzed with Meshlab. We found both advantages and shortcomings of the methods proposed. Furthermore, additional priors and parameters for point selection, RANSAC and pose quality estimate (inlier test) are proposed and applied.


Critical Discourse Analysis


title: An improved Artificial Potential Field approach to real-time mobile robot path planning in an unknown environment

abstract: This paper deals with the navigation of a mobile robot in an unknown environment. The approach developed is based on the Artificial Potential Field (APF) method in which the target creates a virtual potential that attracts the robot while obstacles create a virtual potential that repels the robot. A new form of repelling potential is proposed in order to reduce oscillations and to avoid conflicts when the target is close to obstacles. A rotational force is integrated as well, allowing for a smoother trajectory around the obstacles. Experiment results show the effectiveness of the proposed approach.


Phytochemical Analysis and Antimicrobial Screening Of Moringa Oleifera Leaves Extract OJIAKO


title: Requirements Engineering with Use Cases - a Basis for Software Development

abstract: Successful development of software systems depends on the quality of the requirements engineering process. Use cases and scenarios are promising vehicles for eliciting, specifying and validating requirements. This thesis investigates the role of use case modelling in requirements engineering and its relation to system verification and validation. The thesis includes studies of concepts and representations in use case modelling. Semantic issues are discussed and notations based on natural and graphical languages are provided, which allow a hierarchical structure and enable representation at different abstraction levels. Two different strategies for integrating use case modelling with system testing are presented and evaluated, showing how use cases can be a basis for test case generation and reliability assessment. An experiment on requirements validation using inspections with perspective-based reading is also reported, where one of the perspectives applies use case modelling. The results of the experiment indicate that a combination of multiple perspectives may not give higher defect detection rate compared to single perspective reading. Pilot studies of the transition from use case based requirements to high-level design are described, where use cases are successfully applied for documenting how functional requirements are distributed on architectural elements. The investigation of an industrial requirements engineering process improvement programme is also reported, where the introduction of a release-driven prioritisation method contributed to a measurable improvement in delivery precision and product quality. The results presented in the thesis provide further support for how to successfully apply requirements engineering with use cases as an important basis for software development. (Less)


title: ECG beat detection using filter banks

abstract: The authors have designed a multirate digital signal processing algorithm to detect heartbeats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.


title: Refined slanted-edge measurement practical camera and scanner testing

abstract: It has been almost five years since the ISO adopted a standard for measurement of image resolution of digital still cameras using slanted-edge gradient analysis. The method has also been applied to the spatial frequency response and MTF of film and print scanners, and CRT displays. Each of these applications presents challenges to the use of the method. Previously, we have described causes of both bias and variation error in terms of the various signal processing steps involved. This analysis, when combined with observations from practical systems testing, has suggested improvements and interpretation of results. Specifically, refinements in data screening for signal encoding problems, edge feature location and slope estimation, and noise resilience will be addressed.


title: Automatic measurement of fetal head structures using ultrasound

abstract: Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement of calipers on the structure of interest. The process is tedious, time consuming and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain(AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. Input image is preprocessed and filtered. Hybrid mean filter is used in order to perform filtering, though there are different types of filters available, hybrid mean filter is commonly used in medical applications because it preserves edges and boundaries. Segmentation is performed by gray level thersholding and threshold value is chosen based on k-mean clustering algorithm. Feature extraction is done to mark and measure OFD, BPD&HC. The feature here are texture (brain structures) which include lateral ventricle, cisterna magna and cerebellm. The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use.


title: Challenges to learning and schooling in the digital networked world of the 21st century

abstract: This article elaborates on the competencies, often referred to as 21st century competencies, that are needed to be able to live in and contribute to our current (and future) society. We begin by describing, analysing and reflecting on international frameworks describing 21st century competencies, giving special attention to digital literacy as one of the core competencies for the 21st century. This is followed by an analysis of the learning approaches that are considered appropriate for acquiring 21st century competencies, and the specific role of technology in these learning processes. Despite some consensus about what 21st century competencies are and how they can be acquired, results from international studies indicate that teaching strategies for 21st century competencies are often not well implemented in actual educational practice. The reasons for this include a lack of integration of 21st century competencies in curriculum and assessment, insufficient preparation of teachers and the absence of any systematic attention for strategies to adopt at scale innovative teaching and learning practices. The article concludes with a range of specific recommendations for the implementation of 21st century competencies.


title: Mining Arabic Business Reviews

abstract: For languages with rich content over the web, business reviews are easily accessible via many known websites, e.g., Yelp.com. For languages with poor content over the web like Arabic, there are very few websites (we are actually aware of only one that is indeed unpopular) that provide business reviews. However, this does not mean that such reviews do not exist. They indeed exist unstructured in websites not originally intended for reviews, e.g., Forums and Blogs. Hence, there is a need to mine for those Arabic reviews from the web in order to provide them in the search results when a user searches for a business or a category of businesses. In this paper, we show how to extract the business reviews scattered on the web written in the Arabic language. The mined reviews are analyzed to also provide their sentiments (positive, negative or neutral). This way, we provide our users the information they need about the local businesses in the language they understand, and therefore provide a better search experience for the Middle East region, which mostly speaks Arabic.


title: 500-MS/s 5-bit ADC in 65-nm CMOS With Split Capacitor Array DAC

abstract: A 500-MS/s 5-bit ADC for UWB applications has been fabricated in a 65-nm CMOS technology using no analog-specific processing options. The time-interleaved successive approximation register (SAR) architecture has been chosen due to its simplicity versus flash and its amenability to scaled technologies versus pipelined, which relies on operational amplifiers. Six time-interleaved channels are used, sharing a single clock operating at the composite sampling rate. Each channel has a split capacitor array that reduces switching energy, increases speed, and has similar INL and decreased DNL, as compared to a conventional binary-weighted array. A variable delay line adjusts the instant of latch strobing to reduce preamplifier currents. The ADC achieves Nyquist performance, with an SNDR of 27.8 and 26.1 dB for 3.3 and 239 MHz inputs, respectively. The total active area is 0.9 mm 2 , and the ADC consumes 6 mW from a 1.2-V supply.


title: SCOPE: easy and efficient parallel processing of massive data sets

abstract: Companies providing cloud-scale services have an increasing need to store and analyze massive data sets such as search logs and click streams. For cost and performance reasons, processing is typically done on large clusters of shared-nothing commodity machines. It is imperative to develop a programming model that hides the complexity of the underlying system but provides flexibility by allowing users to extend functionality to meet a variety of requirements. ::: ::: In this paper, we present a new declarative and extensible scripting language, SCOPE (Structured Computations Optimized for Parallel Execution), targeted for this type of massive data analysis. The language is designed for ease of use with no explicit parallelism, while being amenable to efficient parallel execution on large clusters. SCOPE borrows several features from SQL. Data is modeled as sets of rows composed of typed columns. The select statement is retained with inner joins, outer joins, and aggregation allowed. Users can easily define their own functions and implement their own versions of operators: extractors (parsing and constructing rows from a file), processors (row-wise processing), reducers (group-wise processing), and combiners (combining rows from two inputs). SCOPE supports nesting of expressions but also allows a computation to be specified as a series of steps, in a manner often preferred by programmers. We also describe how scripts are compiled into efficient, parallel execution plans and executed on large clusters.


title: From Selective to Adaptive Security in Functional Encryption

abstract: In a functional encryption (FE) scheme, the owner of the secret key can generate restricted decryption keys that allow users to learn specific functions of the encrypted messages and nothing else. In many known constructions of FE schemes, security is guaranteed only for messages that are fixed ahead of time (i.e., before the adversary even interacts with the system). This so-called selective security is too restrictive for many realistic applications. Achieving adaptive security (also called full security), where security is guaranteed even for messages that are adaptively chosen at any point in time, seems significantly more challenging. The handful of known adaptively-secure schemes are based on specifically tailored techniques that rely on strong assumptions (such as obfuscation or multilinear maps assumptions).


title: 14.6 A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems

abstract: Transmitting massive amounts of image and audio data acquired by Internet-of-Everything (IoE) devices to data center servers for intelligent recognition processes is impractical for energy reasons, requiring in-situ processing of such data. However, algorithms accelerated by previous recognition processors [1, 2] are limited to specific applications, therefore, each IoE device may require an application-specific accelerator. On the other hand, deep convolutional neural networks (CNNs) [3] are a promising machine-learning approach, showing state-of-the-art recognition accuracy in a wide variety of applications, including both image and audio recognition. This makes CNNs a suitable candidate for a universal recognition platform for IoE devices, as described in Fig. 14.6.1. Due to the computational complexity and significant memory requirements of CNNs, a microcontroller unit (MCU) typically used for IoE devices is incapable of producing a meaningful recognition result in an energy-efficient way. Hence, the implementation of an energy-efficient CNN processor is desired to realize intelligent IoE systems.


title: Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

abstract: While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results in very good prognosis and as such makes early detection crucial. While radiomics have shown considerable promise as a powerful diagnostic tool for significantly improving oncological diagnostic accuracy and efficiency, current radiomics-driven methods have largely rely on pre-defined, hand-crafted quantitative features, which can greatly limit the ability to fully characterize unique cancer phenotype that distinguish it from healthy tissue. Recently, the notion of discovery radiomics was introduced, where a large amount of custom, quantitative radiomic features are directly discovered from the wealth of readily available medical imaging data. In this study, we present a novel discovery radiomics framework for skin cancer detection, where we leverage novel deep multi-column radiomic sequencers for high-throughput discovery and extraction of a large amount of custom radiomic features tailored for characterizing unique skin cancer tissue phenotype. The discovered radiomic sequencer was tested against 9,152 biopsy-proven clinical images comprising of different skin cancers such as melanoma and basal cell carcinoma, and demonstrated sensitivity and specificity of 91% and 75%, respectively, thus achieving dermatologist-level performance and \break hence can be a powerful tool for assisting general practitioners and dermatologists alike in improving the efficiency, consistency, and accuracy of skin cancer diagnosis.


title: A lecture transcription system combining neural network acoustic and language models

abstract: This paper presents a new system for automatic transcription of lectures. The system combines a number of novel features, including deep neural network acoustic models using multi-level adaptive networks to incorporate out-of-domain information, and factored recurrent neural network language models. We demonstrate that the system achieves large improvements on the TED lecture transcription task from the 2012 IWSLT evaluation ‐ our results are currently the best reported on this task, showing an relative WER reduction of more than 16% compared to the closest competing system from the evaluation.


title: Architectures for evaluating the quality of information models : A meta and an object level comparison

abstract: The evaluation of information models is an outstanding research field in information systems engineering. From an theoretically oriented research viewpoint, it has to be questioned whether it is possible to evaluate artefacts with respect to philosophical and decision theory oriented aspects. Basing on the theoretical assumption that information modeling is a decision problem, this article deals with model evaluation approaches discussed in literature. Approaches will be compared not only on a meta level (ontological and epistemological assumptions) but also on an object level (evaluation of information models). The Frameworks of Moody/Shanks, Krogstie/Lindland/Sindre and the Guidelines of Modeling will be described and compared.


Music Education and Its Impact on Students with Special Needs


title: Learning to extract information from semi-structured text using a discriminative context free grammar

abstract: In recent work, conditional Markov chain models (CMM) have been used to extract information from semi-structured text (one example is the Conditional Random Field [10]). Applications range from finding the author and title in research papers to finding the phone number and street address in a web page. The CMM framework combines a priori knowledge encoded as features with a set of labeled training data to learn an efficient extraction process. We will show that similar problems can be solved more effectively by learning a discriminative context free grammar from training data. The grammar has several distinct advantages: long range, even global, constraints can be used to disambiguate entity labels; training data is used more efficiently; and a set of new more powerful features can be introduced. The grammar based approach also results in semantic information (encoded in the form of a parse tree) which could be used for IR applications like question answering. The specific problem we consider is of extracting personal contact, or address, information from unstructured sources such as documents and emails. While linear-chain CMMs perform reasonably well on this task, we show that a statistical parsing approach results in a 50% reduction in error rate. This system also has the advantage of being interactive, similar to the system described in [9]. In cases where there are multiple errors, a single user correction can be propagated to correct multiple errors automatically. Using a discriminatively trained grammar, 93.71% of all tokens are labeled correctly (compared to 88.43% for a CMM) and 72.87% of records have all tokens labeled correctly (compared to 45.29% for the CMM).


title: Heat transfer inside emulsions. Determination of the DSC thermograms. Part 2. Melting of the crystallized droplets

abstract: A model is presented to explain the shape of the thermogram obtained by differential scanning calorimetry of the melting of the crystallized droplets of an emulsion. This model is entirely different from the model presented in Part 1 of this study concerning the crystallization of undercooled liquid droplets. In spite of the purity of the dispersed substance, significant temperature gradients are found, in contrast to the classical assumption of a uniform melting temperature. The model also gives the kinetics of the melting of the droplets which depends on their location.


title: Reduction Techniques for Instance-Based Learning Algorithms

abstract: Instance-based learning algorithms are often faced with the problem of deciding which instances to store for use during generalization. Storing too many instances can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce storage requirements in instance-based learning algorithms and other exemplar-based algorithms. Second, it proposes six additional reduction algorithms called DROP1–DROP5 and DEL (three of which were first described in Wilson & Martinez, 1997c, as RT1–RT3) that can be used to remove instances from the concept description. These algorithms and 10 algorithms from the survey are compared on 31 classification tasks. Of those algorithms that provide substantial storage reduction, the DROP algorithms have the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise.


title: Archetypal Game Recommender Systems

abstract: Contemporary users (players, consumers) of digital games have thousands of products to choose from, which makes finding games that fit their interests challenging. Towards addressing this challenge, in this paper two different formulations of Archetypal Analysis for Top-L recommender tasks using implicit feedback are presented: factorand neighborhood-oriented models. These form the first application of recommender systems to digital games. Both models are tested on a dataset of 500,000 users of the game distribution platform Steam, covering game ownership and playtime data across more than 3000 games. Compared to four other recommender models (nearest neighbor, two popularity models, random baseline), the archetype based models provide the highest recall rates showing that Archetypal Analysis can be successfully applied for Top-L recommendation purposes.


title: Single underwater image restoration by blue-green channels dehazing and red channel correction

abstract: Restoring underwater image from a single image is know to be ill-posed, and some assumptions made in previous methods are not suitable for many situations. In this paper, we propose a method based on blue-green channels dehazing and red channel correction for underwater image restoration. Firstly, blue-green channels are recovered via dehazing algorithm based on an extension and modification of Dark Channel Prior algorithm. Then, red channel is corrected following the Gray-World assumption theory. Finally, in order to resolve the problem which some recovered image regions may look too dim or too bright, an adaptive exposure map is built. Qualitative analysis demonstrates that our method significantly improves visibility and contrast, and reduces the effects of light absorption and scattering. For quantitative analysis, our results obtain best values in terms of entropy, local feature points and average gradient, which outperform three existing physical model available methods.


title: Extendible hashing—a fast access method for dynamic files

abstract: Extendible hashing is a new access technique, in which the user is guaranteed no more than two page faults to locate the data associated with a given unique identifier, or key. Unlike conventional hashing, extendible hashing has a dynamic structure that grows and shrinks gracefully as the database grows and shrinks. This approach simultaneously solves the problem of making hash tables that are extendible and of making radix search trees that are balanced. We study, by analysis and simulation, the performance of extendible hashing. The results indicate that extendible hashing provides an attractive alternative to other access methods, such as balanced trees.


title: A Literature Review on Enterprise Social Media Collaboration in Virtual Teams: Challenges, Determinants, Implications and Impacts

abstract: This literature review focusses research on Enterprise Social Media (ESM) and its use within virtual teams. The paper includes results from 38 articles outlining the existing body of knowledge and showing the steady progress in this research field. Previous literature often thematizes the external use of social media and technological aspects, but this research concentrates on the internal collaborative use, its challenges, related determinants, managerial implications and its impact on business. Existing research illustrates key factors in each of the previously mentioned subjects. However, as research should consider the entire field, a research agenda for further studies in this ESM topic is developed.


title: An adaptive mechanism to achieve learning rate dynamically

abstract: Gradient descent is prevalent for large-scale optimization problems in machine learning; especially it nowadays plays a major role in computing and correcting the connection strength of neural networks in deep learning. However, many gradient-based optimization methods contain more sensitive hyper-parameters which require endless ways of configuring. In this paper, we present a novel adaptive mechanism called adaptive exponential decay rate (AEDR). AEDR uses an adaptive exponential decay rate rather than a fixed and preconfigured one, and it can allow us to eliminate one otherwise tuning sensitive hyper-parameters. AEDR also can be used to calculate exponential decay rate adaptively by employing the moving average of both gradients and squared gradients over time. The mechanism is then applied to Adadelta and Adam; it reduces the number of hyper-parameters of Adadelta and Adam to only a single one to be turned. We use neural network of long short-term memory and LeNet to demonstrate how learning rate adapts dynamically. We show promising results compared with other state-of-the-art methods on four data sets, the IMDB (movie reviews), SemEval-2016 (sentiment analysis in twitter) (IMDB), CIFAR-10 and Pascal VOC-2012.


title: Inverse Risk-Sensitive Reinforcement Learning

abstract: This work addresses the problem of inverse reinforcement learning in Markov decision processes where the decision-making agent is risk-sensitive. In particular, a risk-sensitive reinforcement learning algorithm with convergence guarantees that makes use of coherent risk metrics and models of human decision-making which have their origins in behavioral psychology and economics is presented. The risk-sensitive reinforcement learning algorithm provides the theoretical underpinning for a gradient-based inverse reinforcement learning algorithm that seeks to minimize a loss function defined on the observed behavior. It is shown that the gradient of the loss function with respect to the model parameters is well defined and computable via a contraction map argument. Evaluation of the proposed technique is performed on a Grid World example, a canonical benchmark problem.


title: Enhanced operating system security through efficient and fine-grained address space randomization

abstract: In recent years, the deployment of many application-level countermeasures against memory errors and the increasing number of vulnerabilities discovered in the kernel has fostered a renewed interest in kernel-level exploitation. Unfortunately, no comprehensive and well-established mechanism exists to protect the operating system from arbitrary attacks, due to the relatively new development of the area and the challenges involved. ::: ::: In this paper, we propose the first design for fine-grained address space randomization (ASR) inside the operating system (OS), providing an efficient and comprehensive countermeasure against classic and emerging attacks, such as return-oriented programming. To motivate our design, we investigate the differences with application-level ASR and find that some of the well-established assumptions in existing solutions are no longer valid inside the OS; above all, perhaps, that information leakage becomes a major concern in the new context. We show that our ASR strategy outperforms state-of-the-art solutions in terms of both performance and security without affecting the software distribution model. Finally, we present the first comprehensive live rerandomization strategy, which we found to be particularly important inside the OS. Experimental results demonstrate that our techniques yield low run-time performance overhead (less than 5% on average on both SPEC and syscall-intensive benchmarks) and limited run-time memory footprint increase (around 15% during the execution of our benchmarks). We believe our techniques can greatly enhance the level of OS security without compromising the performance and reliability of the OS.


title: Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding

abstract: Classifying single image patches is important in many different applications, such as road detection or scene understanding. In this paper, we present convolutional patch networks, which are convolutional networks learned to distinguish different image patches and which can be used for pixel-wise labeling. We also show how to incorporate spatial information of the patch as an input to the network, which allows for learning spatial priors for certain categories jointly with an appearance model. In particular, we focus on road detection and urban scene understanding, two application areas where we are able to achieve state-of-the-art results on the KITTI as well as on the LabelMeFacade dataset. ::: Furthermore, our paper offers a guideline for people working in the area and desperately wandering through all the painstaking details that render training CNs on image patches extremely difficult.


title: Towards a true neural stance on consciousness

abstract: Consciousness is traditionally defined in mental or psychological terms. In trying to find its neural basis, introspective or behavioral observations are considered the gold standard, to which neural measures should be fitted. I argue that this poses serious problems for understanding the mind–brain relationship. To solve these problems, neural and behavioral measures should be put on an equal footing. I illustrate this by an example from visual neuroscience, in which both neural and behavioral arguments converge towards a coherent scientific definition of visual consciousness. However, to accept this definition, we need to let go of our intuitive or psychological notions of conscious experience and let the neuroscience arguments have their way. Only by moving our notion of mind towards that of brain can progress be made.


title: Recognizing Eating from Body-Worn Sensors

abstract: Automated dietary monitoring solutions that can find when, what, and how much individuals consume are needed for many applications such as providing feedback to individuals with chronic disease. Advances in body-worn sensors have led to systems with high accuracy for finding meals and even which foods are consumed in each bite. However, most tests are done in controlled lab settings with restricted meal choices, little background noise, and subjects focused on eating. For these systems to be adopted by users it is critical that they work well in realistic situations and be able to handle confounding factors such as background noise, shared meals, and multi-tasking. Work in realistic environments usually has lower accuracy, but has challenges in determining ground truth. Most critically, there has been a significant gap between lab and free-living environments. This is compounded by data usually being collected for different individuals in each setting, making it difficult to determine how the accuracy gap can be closed. We present a multi-modality study on eating recognition, using body-worn motion (head, wrists) and audio (earbud microphone) sensors for 12 participants (6 from the lab study, 6 new to test generalizability). In contrast to the lab, where audio alone has the highest accuracy, we find now that a combination of sensing modalities (audio, motion) is needed; yet sensor placement (head vs. wrist) is not critical. We further find that lab data does generalize to other participants, but while personal free-living data improves accuracy, more data from others can actually lead to worse performance.


title: Geographical prioritization of social network messages in near real-time using sensor data streams: an application to floods.

abstract: Social networks have been used to overcome the problem of incomplete oﬃ cial data, and to provide a more detailed description of a disaster. However, the ﬁ ltering of relevant messages on-the-ﬂ y remains challenging due to the large amount of misleading, outdated or inaccurate information. This paper presents an approach for the automated geographic prioritization of social network messages for ﬂ ood risk management based on sensor data streams. It was evaluated using data from Twitter and a monitoring agency of Brazil. The results revealed that the proposed approach is useful for identifying valuable ﬂ ood-related messages in near real-time.


title: Will Doherty continue to rule for 5G?

abstract: High data rate 5G systems are expected to operate at frequencies between 15GHz and 100GHz, and utilize large numbers of antennas in both base-stations and handsets. Representative power amplifier scenarios differ from those in commercial wireless today: there will be many more power amplifiers operating at much low power levels. Yet signal peak-to-average ratios will remain high, and efficiency will remain a critical consideration. Doherty amplifiers will be a prime contender for these applications, although they are not the only possibility. This paper reviews the emerging requirements for power amplifiers in mm-wave 5G systems, describes the status of mm-wave Doherty ICs in different device technologies, lists challenges for amplifier designs, and provides a glimpse of approaches other than Doherty amplifiers to meet the needs.


title: Pseudo-CMOS: A Design Style for Low-Cost and Robust Flexible Electronics

abstract: Thin-film transistors (TFTs) are a key element of flexible electronics implemented on low-cost substrates. Most TFT technologies, however, have only monotype-either n- or p-type-devices. In this paper, we propose a novel design style Pseudo-CMOS for flexible electronics that uses only monotype single-VT TFTs but has comparable performance with the complementary-type or dual-VT designs. The manufacturing cost and complexity can therefore be significantly reduced, whereas the circuit yield and reliability are enhanced with built-in postfabrication tunability. Digital cells are fabricated in two different TFT technologies, i.e., p-type self-assembled-monolayer-organic TFTs and n-type metal-oxide InGaZnO TFTs, to validate the proposed Pseudo-CMOS design style. To the best of our knowledge, this is the first design solution that has been experimentally proven to achieve superior performance for both types of TFT technologies.


title: Blockchain exhumed

abstract: A blockchain is a distributed, decentralised database of records of digital events (transactions) that took place and were shared among the participating parties. Each transaction in the public ledger is verified by consensus of a majority of the participants in the system. Bitcoin may not be that important in the future, but blockchain technology's role in Financial and Non-financial world can't be undermined. In this paper, we provide a holistic view of how Blockchain technology works, its strength and weaknesses, and its role to change the way the business happens today and tomorrow.


title: Design and Realization of a Linearly Polarized Eleven Feed for 1-10 GHz

abstract: A new linearly polarized eleven feed for operation between 1 and 10 GHz is presented. This frequency band is higher than the realized before, and the input reflection coefficient is better than ${-}8$ dB over the frequency range, which also is an improvement over previous models. The log-periodic dipole petals have been designed by using a simple one-by-one parameter optimization scheme with a simulation tool based on moment method. The final analysis has been improved by applying an finite-difference time-domain (FDTD)-based solver for the central part of the feed and then a circuit network program to combine the results of the center part with the result of the dipole panels. The antenna has been manufactured by printed circuit board (PCB) technology on a metalized Kevlar sheet in order to obtain better tolerances and smaller dimensions than in previous models. Measurements show agreement with the analysis.


title: Mental models and human reasoning

abstract: To be rational is to be able to reason. Thirty years ago psychologists believed that human reasoning depended on formal rules of inference akin to those of a logical calculus. This hypothesis ran into difficulties, which led to an alternative view: reasoning depends on envisaging the possibilities consistent with the starting point—a perception of the world, a set of assertions, a memory, or some mixture of them. We construct mental models of each distinct possibility and derive a conclusion from them. The theory predicts systematic errors in our reasoning, and the evidence corroborates this prediction. Yet, our ability to use counterexamples to refute invalid inferences provides a foundation for rationality. On this account, reasoning is a simulation of the world fleshed out with our knowledge, not a formal rearrangement of the logical skeletons of sentences.


title: A wearable system that knows who wears it

abstract: Body-area networks of pervasive wearable devices are increasingly used for health monitoring, personal assistance, entertainment, and home automation. In an ideal world, a user would simply wear their desired set of devices with no configuration necessary: the devices would discover each other, recognize that they are on the same person, construct a secure communications channel, and recognize the user to which they are attached. In this paper we address a portion of this vision by offering a wearable system that unobtrusively recognizes the person wearing it. Because it can recognize the user, our system can properly label sensor data or personalize interactions. Our recognition method uses bioimpedance, a measurement of how tissue responds when exposed to an electrical current. By collecting bioimpedance samples using a small wearable device we designed, our system can determine that (a)the wearer is indeed the expected person and (b)~the device is physically on the wearer's body. Our recognition method works with 98% balanced-accuracy under a cross-validation of a day's worth of bioimpedance samples from a cohort of 8 volunteer subjects. We also demonstrate that our system continues to recognize a subset of these subjects even several months later. Finally, we measure the energy requirements of our system as implemented on a Nexus~S smart phone and custom-designed module for the Shimmer sensing platform.


title: A Novel Approach for HCI Using a MEMS Based Device

abstract: The main objective of this paper is to introduce a MEMS based device for handwritten digit and hand gesture recognition applications. This MEMS based device consists of triaxial accelerometer, an atmega8 micro controller and a ZigBee wireless transmission module. The accelerometer is used to recognize hand movements. It converts the acceleration signals into analog voltage signals. The analog voltage signals will be converted into digital signals in atmega8 micro controller. The ZigBee module will transmit those signals to personal computer and it will be processed. Finally the digits will be displayed in PC monitor. Hence human computer interaction can be done in an easy manner similar to write in a notebook by using a pen.


title: A continuum among logarithmic, linear, and exponential functions, and its potential to improve generalization in neural networks

abstract: We present the soft exponential activation function for artificial neural networks that continuously interpolates between logarithmic, linear, and exponential functions. This activation function is simple, differentiable, and parameterized so that it can be trained as the rest of the network is trained. We hypothesize that soft exponential has the potential to improve neural network learning, as it can exactly calculate many natural operations that typical neural networks can only approximate, including addition, multiplication, inner product, distance, polynomials, and sinusoids.


title: Wandering minds: the default network and stimulus-independent thought

abstract: Gilbert et al. suggest that activity in the default network may be due to the emergence of stimulus-oriented rather than stimulus-independent thought. Although both kinds of thought likely emerge during familiar tasks, we argue—and report data suggesting—that stimulus-independent thought dominates unconstrained cognitive periods.


title: Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss.

abstract: STUDY OBJECTIVES ::: The psychomotor vigilance test (PVT) is among the most widely used measures of behavioral alertness, but there is large variation among published studies in PVT performance outcomes and test durations. To promote standardization of the PVT and increase its sensitivity and specificity to sleep loss, we determined PVT metrics and task durations that optimally discriminated sleep deprived subjects from alert subjects. ::: ::: ::: DESIGN ::: Repeated-measures experiments involving 10-min PVT assessments every 2 h across both acute total sleep deprivation (TSD) and 5 days of chronic partial sleep deprivation (PSD). ::: ::: ::: SETTING ::: Controlled laboratory environment. ::: ::: ::: PARTICIPANTS ::: 74 healthy subjects (34 female), aged 22-45 years. ::: ::: ::: INTERVENTIONS ::: TSD experiment involving 33 h awake (N = 31 subjects) and a PSD experiment involving 5 nights of 4 h time in bed (N = 43 subjects). ::: ::: ::: MEASUREMENTS AND RESULTS ::: In a paired t-test paradigm and for both TSD and PSD, effect sizes of 10 different PVT performance outcomes were calculated. Effect sizes were high for both TSD (1.59-1.94) and PSD (0.88-1.21) for PVT metrics related to lapses and to measures of psychomotor speed, i.e., mean 1/RT (response time) and mean slowest 10% 1/RT. In contrast, PVT mean and median RT outcomes scored low to moderate effect sizes influenced by extreme values. Analyses facilitating only portions of the full 10-min PVT indicated that for some outcomes, high effect sizes could be achieved with PVT durations considerably shorter than 10 min, although metrics involving lapses seemed to profit from longer test durations in TSD. ::: ::: ::: CONCLUSIONS ::: Due to their superior conceptual and statistical properties and high sensitivity to sleep deprivation, metrics involving response speed and lapses should be considered primary outcomes for the 10-min PVT. In contrast, PVT mean and median metrics, which are among the most widely used outcomes, should be avoided as primary measures of alertness. Our analyses also suggest that some shorter-duration PVT versions may be sensitive to sleep loss, depending on the outcome variable selected, although this will need to be confirmed in comparative analyses of separate duration versions of the PVT. Using both sensitive PVT metrics and optimal test durations maximizes the sensitivity of the PVT to sleep loss and therefore potentially decreases the sample size needed to detect the same neurobehavioral deficit. We propose criteria to better standardize the 10-min PVT and facilitate between-study comparisons and meta-analyses.


title: Human-inspired robotic exoskeleton (HuREx) for lower limb rehabilitation

abstract: A robot exoskeleton which is inspired by the human musculoskeletal system has been developed for lower limb rehabilitation. The device was manufactured using a novel technique employing 3D printing and fiber reinforcement to make one-of-a-kind form fitting human-robot connections. Actuation of the exoskeleton is achieved using PMAs (pneumatic air muscles) and cable actuation to give the system inherent compliance while maintaining a very low mass. The entire system was modeled including a new hybrid model for PMAs. Simulation and experimental results for a force and impedance based trajectory tracking controller demonstrate the feasibility for using the HuREx system for gait and rehabilitation training.


title: Skin Reading: encoding text in a 6-channel haptic display

abstract: This paper investigates the communication of natural language messages using a wearable haptic display. Our research spans both the design of the haptic display, as well as the methods for communication that use it. First, three wearable configurations are proposed basing on haptic perception fundamentals. To encode symbols, we devise an overlapping spatiotemporal stimulation (OST) method, that distributes stimuli spatially and temporally with a minima gap. An empirical study shows that, compared with spatial stimulation, OST is preferred in terms of recall. Second, we propose an encoding for the entire English alphabet and a training method for letters, words and phrases. A second study investigates communication accuracy. It puts four participants through five sessions, for an overall training time of approximately 5 hours per participant. Results reveal that after one hour of training, participants were able to discern 16 letters, and identify two- and three-letter words. They could discern the full English alphabet (26 letters, 92% accuracy) after approximately three hours of training, and after five hours participants were able to interpret words transmitted at an average duration of 0.6s per word.


title: Social media for disaster situations: Methods, opportunities and challenges

abstract: During disasters people start using social media as a platform to share and disseminate real-time disaster information to wider audiences. In order to understand the type of information that is being shared during disasters and how communities are using the technologies to respond to the disasters, we analyze two-different case studies on natural disasters using Twitter as a platform for gathering and sharing information. In both the case studies, we applied different content analysis methods, both manual and automated, to analyze the valuable information from the user-generated content produced during disaster situations. Based on our findings, we argue that social media platforms are facilitating collective level situation awareness among people and valuable information for disaster management agencies. However, in order to integrate social media in organizational work routines and processes, understanding the opportunities along with challenges is a key.


title: Hodgkin Lymphoma revealed by epidural spinal cord compression.

abstract: Context: Hodgkin Lymphoma is rarely diagnosed as spinal cord compression syndrome. Caused by an epidural mass, this complication is often encountered in a late stage of the disease. We report the c...


title: BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems

abstract: We present a new algorithm that significantly improves the efficiency of exploration for deep Q-learning agents in dialogue systems. Our agents explore via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop neural network. Our algorithm learns much faster than common exploration strategies such as $\epsilon$-greedy, Boltzmann, bootstrapping, and intrinsic-reward-based ones. Additionally, we show that spiking the replay buffer with experiences from just a few successful episodes can make Q-learning feasible when it might otherwise fail.


title: A transmission line circuit-oriented approach for miniaturization of a log-periodic dipole array (LPDA) antenna

abstract: A miniaturized planar log-periodic dipole array (LPDA) antenna is presented. Antenna miniaturization is accomplished by replacing the uniform dipole elements of the conventional LPDA antenna with elements that possess variable- impedance profiles governed by a truncated Fourier series. Conventional and miniaturized LPDA antennas exhibiting 7 dB end-fire gains are designed to operate over the frequency range of 2 to 4 GHz. A 32 % reduction in dipole arm length was achieved. Full-wave simulation results show stable directional radiation patterns, excellent front-to-back ratio over the designed impedance bandwidth, and minimal degradation in the overall electrical performance.


title: Offline Recognition of Devanagari Script: A Survey

abstract: In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.


Analysis of physiological signals for recognition of boredom, pain, and surprise emotions


A broadband polarization-insensitive cloak based on mode conversion


title: A Memristor-Based Optimization Framework for Artificial Intelligence Applications

abstract: Memristors have recently received significant attention as device-level components for building a novel generation of computing systems. These devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability. The ability to control and modify biasing voltages at memristor terminals make them promising candidates to efficiently perform matrix-vector multiplications and solve systems of linear equations. In this article, we discuss how networks of memristors arranged in crossbar arrays can be used for efficiently solving optimization and machine learning problems. We introduce a new memristor-based optimization framework that combines the computational merits of memristor crossbars with the advantages of an operator splitting method, the alternating direction method of multipliers (ADMM). Here, ADMM helps in splitting a complex optimization problem into subproblems that involve the solution of systems of linear equations. The strength of this framework is shown by applying it to linear programming, quadratic programming, and sparse optimization. In addition to ADMM, implementation of a customized power iteration method for eigenvalue/eigenvector computation using memristor crossbars is discussed. The memristor-based power iteration method can further be applied to principal component analysis. The use of memristor crossbars yields a significant speed-up in computation, and thus, we believe, has the potential to advance optimization and machine learning research in artificial intelligence.


title: A simple preparation of a stable CdS-polyacrylamide nanocomposite: structure, thermal and optical properties.

abstract: A facile approach was employed for the preparation of stable luminescent nanocomposites of CdS quantum dots (QDs) and polyacrylamide (PAM) through the cross coupling chemistry. Initially, CdS QDs were synthesized in a well controlled manner using 3-mercaptopropionic acid as a capping agent. Then, carboxylic acid groups on CdS QDs were chemically incorporated into PAM matrices with the assistance of 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride and N-hydroxysulfosuccinimide coupling agents. FT-IR analysis was used to investigate the chemical incorporation of CdS QDs in PAM matrices via the covalent protocol. The XPS elemental mapping studies further suggested the formation of CdS-PAM nanocomposites. FE-SEM and TEM images were engaged to study the morphologies, and distribution of CdS QDs in the PAM networks. The physical structure of the CdS-PAM nanocomposites was investigated by XRD analysis. Thermal stability of the nanocomposites was observed to be enhanced in compare to PAM as evidenced from TGA data. The UV-vis and photoluminescence studies of the CdS-PAM nanocomposites suggested their promising optical applications.


title: Relationship Between Procrastination and Academic Performance Among a Group of Undergraduate Dental Students in India

abstract: Procrastination, generally defined as a voluntary, irrational delay of behavior, is a prevalent phenomenon among college students throughout the world and occurs at alarmingly high rates. For this study, a survey was conducted of 209 second-, third-, and fourth-year undergraduate dental students of Bapuji Dental College and Hospital, Davangere, India, to identify the relationship between their level of procrastination and academic performance. A sixteen-item questionnaire was used to assess the level of procrastination among these students. Data related to their academic performance were also collected. Spearman’s correlation coefficient test was used to assess the relationship between procrastination and academic performance. It showed a negative correlation of −0.63 with a significance level of p


title: Perfect Isolation: Dealing with Self-Jamming in Passive RFID Systems

abstract: Passive RF identification (RFID) readers transmit a continuous wave (CW) powering signal to remotely power up batteryless transponders, while simultaneously receiving a backscattered signal at the same frequency [1]. To separate the transmitter and receiver paths, readers use either a bistatic or monostatic antenna configuration. The first configuration employs two separate antennas, one to transmit and one to receive; in the second, the transmitter and receiver share a common antenna by means of a coupler or circulator. Both cases, however, suffer from poor transmitter-to-receiver isolation: strong CW signals can leak into the receiver, degrading its sensitivity to the weak transponder backscattered signals. Receiver sensitivity degradation occurs due to the leaked local oscillator (LO) phase noise [1] and also to the saturation of the front end caused by strong CW leakage. This requires high-dynamic-range capability in the RF stage [the low-noise amplifier (LNA) and mixer] as well as in the baseband stage [the amplifiers and analog-to-digital converters (ADCs)].


abstract: Full article available online at Healio.com/Orthopedics. Search: 20130624-26 Intramedullary nailing is one of the most convenient biological options for treating distal femoral fractures. Because the distal medulla of the femur is wider than the middle diaphysis and intramedullary nails cannot completely fill the intramedullary canal, intramedullary nailing of distal femoral fractures can be difficult when trying to obtain adequate reduction. Some different methods exist for achieving reduction. The purpose of this study was determine whether the use of blocking screws resolves varus or valgus and translation and recurvatum deformities, which can be encountered in antegrade and retrograde intramedullary nailing. Thirty-four patients with distal femoral fractures underwent intramedullary nailing between January 2005 and June 2011. Fifteen patients treated by intramedullary nailing and blocking screws were included in the study. Six patients had distal diaphyseal fractures and 9 had distal diaphyseo-metaphyseal fractures. Antegrade nailing was performed in 7 patients and retrograde nailing was performed in 8. Reduction during surgery and union during follow-up were achieved in all patients with no significant complications. Mean follow-up was 26.6 months. Mean time to union was 12.6 weeks. The main purpose of using blocking screws is to achieve reduction, but they are also useful for maintaining permanent reduction. When inserting blocking screws, the screws must be placed 1 to 3 cm away from the fracture line to avoid from propagation of the fracture. When applied properly and in an adequate way, blocking screws provide an efficient solution for deformities encountered during intramedullary nailing of distal femur fractures. The authors are from the Department of Orthopaedics and Traumatology (MS, FD, AG), Acibadem Kadikoy Hospital; the Department of Orthopaedics and Traumatology (SC), GATA Haydarpasa Hospital, Istanbul, Turkey; and Harris Orthopaedic Laboratory (SC), Massachuasetts General Hospital, Boston, Massachusetts. The authors have no relevant financial relationships to disclose. Correspondence should be addressed to: Selami Cakmak, MD, Harris Orthopaedic Laboratory, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114 (selamicakmak@gmail.com). doi: 10.3928/01477447-20130624-26 Blocking Screws for the Treatment of Distal Femur Fractures Mustafa seyhan, MD; selaMi CakMak, MD; ferDi DonMez, MD; arel Gereli, MD


title: mmWave mobile broadband (MMB): Unleashing the 3–300GHz spectrum

abstract: Almost all cellular mobile communications including first generation analog systems, second generation digital systems, third generation WCDMA, and fourth generation OFDMA systems use Ultra High Frequency (UHF) band of radio spectrum with frequencies in the range of 300MHz-3GHz. This band of spectrum is becoming increasingly crowded due to spectacular growth in mobile data and other related services. The portion of the RF spectrum above 3GHz has largely been uxexploited for commercial mobile applications. In this paper, we reason why wireless community should start looking at 3–300GHz spectrum for mobile broadband applications. We discuss propagation and device technology challenges associated with this band as well as its unique advantages such as spectrum availability and small component sizes for mobile applications.


title: Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm

abstract: In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and selecting salient features. The main goal of using MGSA-SVM is to decrease the number of features and to improve the SVM classification accuracy. Finally, the selected features and the tuned SVM classifier are used for detecting tumors. The experimental results show that the proposed method is able to optimize both feature selection and the SVM parameters for the breast cancer tumor detection.


title: Natural Image Bases to Represent Neuroimaging Data

abstract: Visual inspection of neuroimagery is susceptible to human eye limitations. Computerized methods have been shown to be equally or more effective than human clinicians in diagnosing dementia from neuroimages. Nevertheless, much of the work involves the use of domain expertise to extract handcrafted features. The key technique in this paper is the use of cross-domain features to represent MRI data. We used a sparse autoencoder to learn a set of bases from natural images and then applied convolution to extract features from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Using this new representation, we classify MRI instances into three categories: Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Healthy Control (HC). Our approach, in spite of being very simple, achieved high classification performance, which is competitive with or better than other approaches.


title: Toward the Coevolution of Novel Vertical-Axis Wind Turbines

abstract: The production of renewable and sustainable energy is one of the most important challenges currently facing mankind. Wind has made an increasing contribution to the world’s energy supply mix, but remains a long way from reaching its full potential. In this paper, we investigate the use of artificial evolution to design vertical-axis wind turbine prototypes that are physically instantiated and evaluated under approximated wind tunnel conditions. Initially, a conventional evolutionary algorithm is used to explore the design space of a single wind turbine and later a cooperative coevolutionary algorithm is used to explore the design space of an array of wind turbines. Artificial neural networks are used throughout as surrogate models to assist learning and found to reduce the number of fabrications required to reach a higher aerodynamic efficiency. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations are used and no model assumptions are made.


Identification of Bacteria by Gas Chromatography of Cellular Fatty Acids Technical Note # 101


title: Kinematic design and commutation of a spherical stepper motor

abstract: This paper addresses the design and commutation of a novel kind of spherical stepper motor in which the poles of the stator are electromagnets and the poles of the rotor (rotating ball) are permanent magnets. Due to the fact that points on a sphere can only be arranged with equal spacing in a limited number of cases (corresponding to the Platonic solids), design of spherical stepper motors with fine rotational increments is fundamentally geometrical in nature. We address this problem and the related problem of how rotor and stator poles should be arranged in order to interact to cause motion. The resulting design has a much wider range of unhindered motion than other spherical stepper motor designs in the literature. We also address the problem of commutation, i.e., we determine the sequence of stator polarities in time that approximate a desired spherical motion.


title: Architecture for Customer Relationship Management Approaches in Financial Services

abstract: The majority of financial services companies in Germany and Switzerland have, with varying objectives and success, conducted customer relationship management (CRM) implementation projects. In this paper we present a framework for the analysis of CRM approaches in financial services companies. Building on previous research and using comprehensive literature research, we develop a CRM reference architecture that focuses on the process and system level for the description and classification of CRM approaches in companies. Moreover, we analyze three CRM case studies in Swiss and German financial services companies and derive different types of CRM approaches in the financial services industry: Customer Satisfaction Management, Customer Contact Management, and Customer Profitability Management. We describe each type in accordance with the CRM architecture and a case example.


title: Simulation model verification and validation: increasing the users' confidence

abstract: This paper sets simulation model verification and validation (V&V) in the context of the process of performing a simulation study. Various different forms of V&V need to take place depending on the stage that has been reached. Since the phases of a study are performed in an iterative manner, so too are the various forms of V&V. A number of difficulties with verifying and validating models are discussed, after which a series of V&V methods are described. V&V is seen as a process of increasing confidence in a model, and not one of demonstrating absolute accuracy.


title: Affective Analysis of Professional and Amateur Abstract Paintings Using Statistical Analysis and Art Theory

abstract: When artists express their feelings through the artworks they create, it is believed that the resulting works transform into objects with “emotions” capable of conveying the artists' mood to the audience. There is little to no dispute about this belief: Regardless of the artwork, genre, time, and origin of creation, people from different backgrounds are able to read the emotional messages. This holds true even for the most abstract paintings. Could this idea be applied to machines as well? Can machines learn what makes a work of art “emotional”? In this work, we employ a state-of-the-art recognition system to learn which statistical patterns are associated with positive and negative emotions on two different datasets that comprise professional and amateur abstract artworks. Moreover, we analyze and compare two different annotation methods in order to establish the ground truth of positive and negative emotions in abstract art. Additionally, we use computer vision techniques to quantify which parts of a painting evoke positive and negative emotions. We also demonstrate how the quantification of evidence for positive and negative emotions can be used to predict which parts of a painting people prefer to focus on. This method opens new opportunities of research on why a specific painting is perceived as emotional at global and local scales.


title: Learning to Grade Short Answers using Machine Learning Techniques

abstract: In this work, we are attempting to grade short answer automatically which can be efficient and helpful to both students and teachers. It uses a combination of many semantic and graph alignment features and is implemented in the Microsoft Azure Machine Learning using Two-class Averaged Perceptron, Linear and Isotonic Regression. We also provide first attempt to use graph alignment features at sentence level. We compare the results of two machine learning algorithms like Two-class Averaged Perceptron and Two-class Support Vector Machine in the results of grading short answers. We have devised novel techniques to apply the concept of Random Projection for grading 150 algorithmic answers on a coding question using our own domain specific corpus which gives precise classification of right and wrong answers.


title: FlexLevel: A novel NAND flash storage system design for LDPC latency reduction

abstract: LDPC code is introduced in NAND flash memory to handle high BER (bit error rate) incurred by technology scaling. Despite strong error correction capability, LDPC decoding induces long NAND flash read latency. In this work, we propose FlexLevel - a robust NAND flash storage system design to improve data reliability and read efficiency affected by the LDPC operations. FlexLevel first reduces BER by enlarging noise margins via Vth (threshold voltage) level reduction. It reduces the sensing levels of LDPC but also causes loss of storage capacity. To compensate this capacity loss with minimum impact on read performance, FlexLevel identifies the data with high LDPC overhead and only applies the V th level reduction technique to those data. Experimental results show that compared with state-of-the-art, FlexLevel can achieve up to 33% read speedup with very moderate capacity loss.


Formation and Dielectric Properties of Al-Ti-O Nanocomposite Oxide Film on Aluminum Using Sol-Gel and Anodizing


Deep Pyramid Convolutional Neural Networks for Text Categorization


title: Software clones in scratch projects: on the presence of copy-and-paste in computational thinking learning

abstract: Computer programming is being introduced in schools worldwide as part of a movement that promotes Computational Thinking (CT) skills among young learners. In general, learners use visual, block-based programming languages to acquire these skills, with Scratch being one of the most popular ones. Similar to professional developers, learners also copy and paste their code, resulting in duplication. In this paper we present the findings of correlating the assessment of the CT skills of learners with the presence of software clones in over 230,000 projects obtained from the Scratch platform. Specifically, we investigate i) if software cloning is an extended practice in Scratch projects, ii) if the presence of code cloning is independent of the programming mastery of learners, iii) if code cloning can be found more frequently in Scratch projects that require specific skills (as parallelism or logical thinking), and iv) if learners who have the skills to avoid software cloning really do so. The results show that i) software cloning can be commonly found in Scratch projects, that ii) it becomes more frequent as learners work on projects that require advanced skills, that iii) no CT dimension is to be found more related to the absence of software clones than others, and iv) that learners -even if they potentially know how to avoid cloning- still copy and paste frequently. The insights from this paper could be used by educators and learners to determine when it is pedagogically more effective to address software cloning, by educational programming platform developers to adapt their systems, and by learning assessment tools to provide better evaluations.


title: Syntactic ambiguity resolution in discourse: modeling the effects of referential context and lexical frequency

abstract: Sentences with temporarily ambiguous reduced relative clauses (e.g., The actress selected by the director believed that...) were preceded by discourse contexts biasing a main clause or a relative clause. Eye movements in the disambiguating region (by the director) revealed that, in the relative clause biasing contexts, ambiguous reduced relatives were no more difficult to process than unambiguous reduced relatives or full (unreduced) relatives. Regression analyses demonstrated that the effects of discourse context at the point of ambiguity (e.g., selected) interacted with the past participle frequency of the ambiguous verb. Reading times were modeled using a constraint-based competition framework in which multiple constraints are immediately integrated during parsing and interpretation. Simulations suggested that this framework reconciles the superficially conflicting results in the literature on referential context effects on syntactic ambiguity resolution.


title: Multi-Task Deep Neural Networks for Natural Language Understanding

abstract: In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from a regularization effect that leads to more general representations in order to adapt to new tasks and domains. MT-DNN extends the model proposed in Liu et al. (2015) by incorporating a pre-trained bidirectional transformer language model, known as BERT (Devlin et al., 2018). MT-DNN obtains new state-of-the-art results on ten NLU tasks, including SNLI, SciTail, and eight out of nine GLUE tasks, pushing the GLUE benchmark to 82.7% (2.2% absolute improvement). We also demonstrate using the SNLI and SciTail datasets that the representations learned by MT-DNN allow domain adaptation with substantially fewer in-domain labels than the pre-trained BERT representations. The code and pre-trained models are publicly available at this https URL.


title: Equivariant Transformer Networks

abstract: How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups. Through the use of specially-derived canonical coordinate systems, ETs incorporate functions that are equivariant by construction with respect to these transformations. We show empirically that ETs can be flexibly composed to improve model robustness towards more complicated transformation groups in several parameters. On a real-world image classification task, ETs improve the sample efficiency of ResNet classifiers, achieving relative improvements in error rate of up to 15% in the limited data regime while increasing model parameter count by less than 1%.


title: Power aware reconfigurable multiprocessor for elliptic curve cryptography

abstract: Reconfigurable architectures are being increasingly used for their flexibility and extensive parallelism to achieve accelerations for computationally intensive applications. Although these architectures provide easy adaptability, it is so with an overhead in terms of area, power and timing, as compared to non-reconfigurable ASICs. Here, we propose a low overhead reconfigurable multiprocessor, which provides both parallelism and flexibility. The architecture has been evaluated for its energy efficiency for a computational intensive algorithm used in elliptic curve cryptography (ECC). Typically, algorithms in ECC exhibit task-level parallelism and demand large amount of computational resources for custom implementations to achieve a significant speedup. A finite field multiplication in GF (2233) was chosen as a sample application to evaluate the performance on the QuadroCore reconfigurable multiprocessor architecture. A three-fold performance improvement as compared to a single processor implementation was observed. Further, via reconfiguration to suit the application, power savings of about 24% were noted in UMC's 90nm standard cell technology.


title: Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles

abstract: Parsing accuracy using efficient greedy transition systems has improved dramatically in recent years thanks to neural networks. Despite striking results in dependency parsing, however, neural models have not surpassed state-of-the-art approaches in constituency parsing. To remedy this, we introduce a new shift-reduce system whose stack contains merely sentence spans, represented by a bare minimum of LSTM features. We also design the first provably optimal dynamic oracle for constituency parsing, which runs in amortized O(1) time, compared to O(n^3) oracles for standard dependency parsing. Training with this oracle, we achieve the best F1 scores on both English and French of any parser that does not use reranking or external data.


title: iVisDesigner: Expressive Interactive Design of Information Visualizations

abstract: We present the design, implementation and evaluation of iVisDesigner, a web-based system that enables users to design information visualizations for complex datasets interactively, without the need for textual programming. Our system achieves high interactive expressiveness through conceptual modularity, covering a broad information visualization design space. iVisDesigner supports the interactive design of interactive visualizations, such as provisioning for responsive graph layouts and different types of brushing and linking interactions. We present the system design and implementation, exemplify it through a variety of illustrative visualization designs and discuss its limitations. A performance analysis and an informal user study are presented to evaluate the system.


title: A survey and evaluation of promising approaches for automatic image-based defect detection of bridge structures

abstract: Automatic health monitoring and maintenance of civil infrastructure systems is a challenging area of research. Nondestructive evaluation techniques, such as digital image processing, are innovative approaches for structural health monitoring. Current structure inspection standards require an inspector to travel to the structure site and visually assess the structure conditions. A less time consuming and inexpensive alternative to current monitoring methods is to use a robotic system that could inspect structures more frequently. Among several possible techniques is the use of optical instrumentation (e.g. digital cameras) that relies on image processing. The feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading experts in the field. A survey and evaluation of relevant studies that appear promising and practical for this purpose is presented in this study. Several image processing techniques, including enhancement, noise removal, registratio...


title: Activity recognition and monitoring for smart wheelchair users

abstract: In recent years, the elderly population is increasing enormously, from 9% in 1994 to 12% in 2014, and is expected to reach 21% by 2050. Elderly live often alone today and even conducting an independent daily life, some of them move with the aid of walkers or using wheelchairs. Monitoring elderly activity in mobility has become a major priority to provide them an effective care service. This paper focuses on an enhancement of a smart wheelchair based on pressure sensors to monitor users sitting on the wheelchair. If the wheelchair user assumes a dangerous posture, the system triggers audio/visual alarms to avoid critical consequences such as wheelchair overturn. The paper discusses the hardware design of the system, then analyzes and compares posture recognition methods that have been applied on pressure data we collected. The experiments demonstrate the effectiveness of the proposed method and 99.5% posture recognition accuracy has been observed.


title: Music Emotion Recognition by Multi-label Multi-layer Multi-instance Multi-view Learning

abstract: Music emotion recognition, which aims to automatically recognize the affective content of a piece of music, has become one of the key components of music searching, exploring, and social networking applications. Although researchers have given more and more attention to music emotion recognition studies, the recognition performance has come to a bottleneck in recent years. One major reason is that experts' labels for music emotion are mostly song-level, while music emotion usually varies within a song. Traditional methods have considered each song as a single instance and have built models based on song-level features. However, they ignored the dynamics of music emotion and failed to capture accurate emotion-feature correlations. In this paper, we model music emotion recognition as a novel multi-label multi-layer multi-instance multi-view learning problem: music is formulated as a hierarchical multi-instance structure (e.g., song-segment-sentence) where multiple emotion labels correspond to at least one of the instances with multiple views of each layer. We propose a Hierarchical Music Emotion Recognition model (HMER) -- a novel hierarchical Bayesian model using sentence-level music and lyrics features. It captures music emotion dynamics with a song-segment-sentence hierarchical structure. HMER also considers emotion correlations between both music segments and sentences. Experimental results show that HMER outperforms several state-of-the-art methods in terms of $F_1$ score and mean average precision.


title: Personality Traits Predict Music Taxonomy Preferences

abstract: Music streaming services increasingly incorporate additional music taxonomies (i.e., mood, activity, and genre) to provide users different ways to browse through music collections. However, these additional taxonomies can distract the user from reaching their music goal, and influence choice satisfaction. We conducted an online user study with an application called "Tune-A-Find," where we measured participants' music taxonomy choice (mood, activity, and genre). Among 297 participants, we found that the chosen taxonomy is related to personality traits. We found that openness to experience increased the choice for browsing music by mood, while conscientiousness increased the choice for browsing music by activity. In addition, those high in neuroticism were most likely to browse for music by activity or genre. Our findings can support music streaming services to further personalize user interfaces. By knowing the user's personality, the user interface can adapt to the user's preferred way of music browsing.


title: Abstractive Summarization of Product Reviews Using Discourse Structure

abstract: We propose a novel abstractive summarization system for product reviews by taking advantage of their discourse structure. First, we apply a discourse parser to each review and obtain a discourse tree representation for every review. We then modify the discourse trees such that every leaf node only contains the aspect words. Second, we aggregate the aspect discourse trees and generate a graph. We then select a subgraph representing the most important aspects and the rhetorical relations between them using a PageRank algorithm, and transform the selected subgraph into an aspect tree. Finally, we generate a natural language summary by applying a template-based NLG framework. Quantitative and qualitative analysis of the results, based on two user studies, show that our approach significantly outperforms extractive and abstractive baselines.


title: An Empirical Model of Large-Batch Training

abstract: In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency. However the limits of this massive data parallelism seem to differ from domain to domain, ranging from batches of tens of thousands in ImageNet to batches of millions in RL agents that play the game Dota 2. To our knowledge there is limited conceptual understanding of why these limits to batch size differ or how we might choose the correct batch size in a new domain. In this paper, we demonstrate that a simple and easy-to-measure statistic called the gradient noise scale predicts the largest useful batch size across many domains and applications, including a number of supervised learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word), reinforcement learning domains (Atari and Dota), and even generative model training (autoencoders on SVHN). We find that the noise scale increases as the loss decreases over a training run and depends on the model size primarily through improved model performance. Our empirically-motivated theory also describes the tradeoff between compute-efficiency and time-efficiency, and provides a rough model of the benefits of adaptive batch-size training.


title: Affective touch gesture recognition for a furry zoomorphic machine

abstract: Over the last decade, the surprising fact has emerged that machines can possess therapeutic power. Due to the many healing qualities of touch, one route to such power is through haptic emotional interaction, which requires sophisticated touch sensing and interpretation. We explore the development of touch recognition technologies in the context of a furry artificial lap-pet, with the ultimate goal of creating therapeutic interactions by sensing human emotion through touch. In this work, we build upon a previous design for a new type of fur-based touch sensor. Here, we integrate our fur sensor with a piezoresistive fabric location/pressure sensor, and adapt the combined design to cover a curved creature-like object. We then use this interface to collect synchronized time-series data from the two sensors, and perform machine learning analysis to recognize 9 key affective touch gestures. In a study of 16 participants, our model averages 94% recognition accuracy when trained on individuals, and 86% when applied to the combined set of all participants. The model can also recognize which participant is touching the prototype with 79% accuracy. These results promise a new generation of emotionally intelligent machines, enabled by affective touch gesture recognition.


title: Visual saliency detection based on Bayesian model

abstract: Image saliency detection is very useful in many computer vision tasks while it still remains a challenging problem. In this paper, we propose a new computational saliency detection model which is implemented with a coarse to fine strategy under the Bayesian framework. First, saliency points are applied to get a coarse location of the saliency region. And then, based on the rough region, we compute a prior map for the Bayesian model to achieve the final saliency map. Experimental results on a public available dataset show the effectiveness of the proposed prior map and the strength of our saliency map compared with several previous method.


title: Leukotrienes: mediators of immediate hypersensitivity reactions and inflammation.

abstract: Arachidonic acid plays a central role in a biological control system where such oxygenated derivatives as prostaglandins, thromboxanes, and leukotrienes are mediators. The leukotrienes are formed by transformation of arachidonic acid into an unstable epoxide intermediate, leukotriene A4, which can be converted enzymatically by hydration to leukotriene B4, and by addition of glutathione to leukotriene C4. This last compound is metabolized to leukotrienes D4 and E4 by successive elimination of a gamma-glutamyl residue and glycine. Slow-reacting substance of anaphylaxis consists of leukotrienes C4, D4, and E4. The cysteinyl-containing leukotrienes are potent bronchoconstrictors, increase vascular permeability in postcapillary venules, and stimulate mucus secretion. Leukotriene B4 causes adhesion and chemotactic movement of leukocytes and stimulates aggregation, enzyme release, and generation of superoxide in neutrophils. Leukotrienes C4, D4, and E4, which are released from the lung tissue of asthmatic subjects exposed to specific allergens, seem to play a pathophysiological role in immediate hypersensitivity reactions. These leukotrienes, as well as leukotriene B4, have pro-inflammatory effects.


title: Aerobic activity before and following short- duration static stretching improves range of motion and performance vs. a traditional warm-up

abstract: Many activities necessitate a high degree of static joint range of motion (ROM) for an extended duration. The objective of this study was to examine whether ROM could be improved with a short duration and volume of static stretching within a warm-up, without negatively impacting performance. Ten male recreationally active participants completed 2 separate protocols to examine changes in ROM and performance, respectively, with different warm-ups. The warm-up conditions for the ROM protocol were static stretching (SS), consisting of 6 repetitions of 6 s stretches; 10 min of running prior to the SS (AS); and 5 min of running before and after the SS (ASA). The performance protocol included a control condition of 10 min of running. Measures for the ROM protocol included hip flexion ROM, passive leg extensor tension, and hamstring electromyographic (EMG) activity at pre-warm-up, and at 1, 10, 20, and 30 min post-warm-up. Performance measures included countermovement jump (CMJ) height, reaction time (RT), moveme...


title: Perceptual qualities and material classes.

abstract: Under typical viewing conditions, we can easily group materials into distinct classes (e.g., woods, plastics, textiles). Additionally, we can also make many other judgments about material properties (e.g., hardness, rigidity, colorfulness). Although these two types of judgment (classification and inferring material properties) have different requirements, they likely facilitate one another. We conducted two experiments to investigate the interactions between material classification and judgments of material qualities in both the visual and semantic domains. In Experiment 1, nine students viewed 130 images of materials from 10 different classes. For each image, they rated nine subjective properties (glossiness, transparency, colorfulness, roughness, hardness, coldness, fragility, naturalness, prettiness). In Experiment 2, 65 subjects were given the verbal names of six material classes, which they rated in terms of 42 adjectives describing material qualities. In both experiments, there was notable agreement between subjects, and a relatively small number of factors (weighted combinations of different qualities) were substantially independent of one another. Despite the difficulty of classifying materials from images (Liu, Sharan, Adelson, & Rosenholtz, 2010), the different classes were well clustered in the feature space defined by the subjective ratings. K-means clustering could correctly identify class membership for over 90% of the samples, based on the average ratings across subjects. We also found a high degree of consistency between the two tasks, suggesting subjects access similar information about materials whether judging their qualities visually or from memory. Together, these findings show that perceptual qualities are well defined, distinct, and systematically related to material class membership. Introduction


title: A Through-Dielectric Ultrawideband (UWB) Switched-Antenna-Array Radar Imaging System

abstract: A through-dielectric switched-antenna-array radar imaging system is shown that produces near real-time imagery of targets on the opposite side of a lossy dielectric slab. This system operates at S-band, provides a frame rate of 0.5 Hz, and operates at a stand-off range of 6 m or greater. The antenna array synthesizes 44 effective phase centers in a linear array providing $\lambda/2$ element-to-element spacing by time division multiplexing the radar's transmit and receive ports between 8 receive elements and 13 transmit elements, producing 2D (range vs. cross-range) imagery of what is behind a slab. Laboratory measurements agree with simulations, the air-slab interface is range gated out of the image, and target scenes consisting of cylinders and soda cans are imaged through the slab. A 2D model of a slab, a cylinder, and phase centers shows that blurring due to the slab and bistatic phase centers on the array is negligible when the radar sensor is located at stand-off ranges of 6 m or greater.


title: Design and Experiment of An E-band Folded Waveguide Traveling Wave Tube

abstract: An E-band folded waveguide traveling wave tube (FWGTWT) was designed, fabricated and tested. Experimental result shows that the prototype tube covers the bandwidth of 83-86GHz with the output power of above 30W, meanwhile, the peak power is 35W in 85.5GHz. The maximum electron efficiency is up to 3.3%. The tube is tested when the electron gun voltage is 17kV and the beam current is 62mA.


title: A deep learning approach towards pore extraction for high-resolution fingerprint recognition

abstract: As high-resolution fingerprint images are becoming more common, the pores have been found to be one of the promising candidates in improving the performance of automated fingerprint identification systems (AFIS). This paper proposes a deep learning approach towards pore extraction. It exploits the feature learning and classification capability of convolutional neural networks (CNNs) to detect pores on fingerprints. Besides, this paper also presents a unique affine Fourier moment-matching (AFMM) method of matching and fusing the scores obtained for three different fingerprint features to deal with both local and global linear distortions. Combining the two aforementioned contributions, an EER of 3.66% can be observed from the experimental results.


title: Computer-Aided Breast Cancer Detection Using Mammograms: A Review

abstract: The American Cancer Society (ACS) recommends women aged 40 and above to have a mammogram every year and calls it a gold standard for breast cancer detection. Early detection of breast cancer can improve survival rates to a great extent. Inter-observer and intra-observer errors occur frequently in analysis of medical images, given the high variability between interpretations of different radiologists. Also, the sensitivity of mammographic screening varies with image quality and expertise of the radiologist. So, there is no golden standard for the screening process. To offset this variability and to standardize the diagnostic procedures, efforts are being made to develop automated techniques for diagnosis and grading of breast cancer images. A few papers have documented the general trend of computer-aided diagnosis of breast cancer, making a broad study of the several techniques involved. But, there is no definitive documentation focusing on the mathematical techniques used in breast cancer detection. This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts in the field.


title: Physically-Based Facial Modeling , Analysis , and Animation

abstract: We develop a new 3D hierarchical model of the human face. The model incorporates a physically-based approximation to facial tissue and a set of anatomically-motivated facial muscle actuators. Despite its sophistication, the model is efficient enough to produce facial animation at interactive rates on a high-end graphics workstation. A second contribution of this paper is a technique for estimating muscle contractions from video sequences of human faces performing expressive articulations. These estimates may be input as dynamic control parameters to the face model in order to produce realistic animation. Using an example, we demonstrate that our technique yields sufficiently accurate muscle contraction estimates for the model to reproduce expressions from dynamic images of faces.


title: An integrated strategy for a production planning and warehouse layout problem: Modeling and solution approaches

abstract: We study a real-world production warehousing case, where the company always faces the challenge to find available space for its products and to manage the items in the warehouse. To resolve the problem, an integrated strategy that combines warehouse layout with the capacitated lot-sizing problem is presented, which have been traditionally treated separately in the existing literature. We develop a mixed integer linear programming model to formulate the integrated optimization problem with the objective of minimizing the total cost of production and warehouse operations. The problem with real data is a large-scale instance that is beyond the capability of optimization solvers. A novel Lagrangian relax-and-fix heuristic approach and its variants are proposed to solve the large-scale problem. The preliminary numerical results from the heuristic approaches are reported.


The Combinatorics Of Object Recognition In Cluttered Environments Using Constrained Search