title: Observer studies of cardiac lesion detectability with triple-head 360/spl deg/ vs. dual-head 180/spl deg/ SPECT acquisition using simulated projection data

abstract: The purpose of this study is to evaluate cardiac lesion detectability with triple-head 360-degree versus dual-head 180-degree myocardial perfusion SPECT scans with equal acquisition time. The channelized Hotelling observer (CHO) and human observers were used. A male and a female voxelized four-dimensional cardiac-torso phantoms were used to generate the 360-degree and the 180-degree projection data. A cold lesion was placed in 8 different locations of myocardium and had a lesion contrast of 25%. Sufficient poisson noise was added to set the area under the ROC curve (A/sub z/) to be between 0.75 to 0.85 in a pilot study, and to simulate the clinical case where dual-head 180-degree and triple-head 360-degree both have the same total scan time. For each lesion location, 100 realizations of lesion present and of lesion-absent data were generated. Five iteration OSEM was used to reconstruct the data with no attenuation correction. A 3D Hann filter with 0.7 times the Nyquist frequency was used to smooth the reconstructed images. The CHO showed a higher lesion detectability index d' for the 180-degree data. The human observer study showed a slightly better detection performance of the 180-degree scan, especially for the female phantom, but the differences were not statistically significant (at the P = 0.05 level).


title: Ear Detection in the Wild Using Faster R-CNN Deep Learning

abstract: Ear recognition has its advantages in identifying non-cooperative individuals in unconstrained environments. Ear detection is a major step within the ear recognition algorithmic process. While conventional approaches for ear detection have been used in the past, Faster Region-based Convolutional Neural Network (Faster R-CNN) based detection methods have recently achieved superior detection performance in various benchmark studies, including those on face detection. In this work, we propose an ear detection system that uses Faster R-CNN. The training of the system is performed on two stages: First, an AlexNet model is trained for classifying ear vs. non-ear segments. Second, the unified Region Proposal Network (RPN) with the AlexNet, that shares the convolutional features, are trained for ear detection. The proposed system operates in real-time and accomplishes 98 % detection rate on a test set, composed of data coming from different ear datasets. In addition, the system's ear detection performance is high even when the test images are coming from un-controlled settings with a wide variety of images in terms of image quality, illumination and ear occlusion.


title: Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion.

abstract: Understanding how the immune system affects cancer development and progression has been one of the most challenging questions in immunology. Research over the past two decades has helped explain why the answer to this question has evaded us for so long. We now appreciate that the immune system plays a dual role in cancer: It can not only suppress tumor growth by destroying cancer cells or inhibiting their outgrowth but also promote tumor progression either by selecting for tumor cells that are more fit to survive in an immunocompetent host or by establishing conditions within the tumor microenvironment that facilitate tumor outgrowth. Here, we discuss a unifying conceptual framework called "cancer immunoediting," which integrates the immune system's dual host-protective and tumor-promoting roles.


title: Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose

abstract: This paper addresses the challenge of 3D human pose estimation from a single color image. Despite the general success of the end-to-end learning paradigm, top performing approaches employ a two-step solution consisting of a Convolutional Network (ConvNet) for 2D joint localization and a subsequent optimization step to recover 3D pose. In this paper, we identify the representation of 3D pose as a critical issue with current ConvNet approaches and make two important contributions towards validating the value of end-to-end learning for this task. First, we propose a fine discretization of the 3D space around the subject and train a ConvNet to predict per voxel likelihoods for each joint. This creates a natural representation for 3D pose and greatly improves performance over the direct regression of joint coordinates. Second, to further improve upon initial estimates, we employ a coarse-to-fine prediction scheme. This step addresses the large dimensionality increase and enables iterative refinement and repeated processing of the image features. The proposed approach outperforms all state-of-the-art methods on standard benchmarks achieving a relative error reduction greater than 30% on average. Additionally, we investigate using our volumetric representation in a related architecture which is suboptimal compared to our end-to-end approach, but is of practical interest, since it enables training when no image with corresponding 3D groundtruth is available, and allows us to present compelling results for in-the-wild images.


title: Enabling Vertical Handover Decisions in Heterogeneous Wireless Networks: A State-of-the-Art and A Classification

abstract: Wireless networks are passing through a transition phase for the past few years now and this transition is giving a way towards the convergence of all IP-based networks to form the Next Generation Networks (NGNs). With the proliferation of these networks in daily life, users' needs are also increasing and service operators are offering different services to satisfy their customers for a better grade of service and an elevated quality of experience (QoE). However, a single operator cannot fulfill the huge demands of the users especially, if a user is nomadic. In nomadism, a user traverses number of available networks that might contain cellular or wireless data networks, usually known as heterogeneous wireless networks. These networks offer various services from email to live video streaming depending upon their capacity and nature. During this traversing procedure, a user switches among different networks to satisfy his/her needs in terms of quality of service. This process is commonly known as a vertical handover or handoff (VHO) due to the involvement of heterogeneous wireless networks in it. An extensive work has been carried out in this field in order to fulfill user demands for better QoS and QoE. In this paper, we give a detailed state-of-the-art of these existing vertical handover decision mechanisms that aim at providing ubiquitous connectivity to the mobile users. We have categorized these vertical handover measurement and decision schemes on the basis of their employed techniques and parameters. Also, we present a comprehensive summary of their advantages and drawbacks. This paper gives its readers an overview of the active research initiatives in the area of handover decision making process in heterogeneous wireless networks and identifies the challenges behind the seamless services provisioning during mobility.


title: Mixed-Tenancy in the Wild - Applicability of Mixed-Tenancy for Real-World Enterprise SaaS-Applications

abstract: Software-as-a-Service (SaaS) is a delivery model whose basic idea is to provide applications to the customer on demand over the Internet. SaaS thereby promotes multi-tenancy as a tool to exploit economies of scale. This means that a single application instance serves multiple customers. However, a major drawback of SaaS is the customers' hesitation of sharing infrastructure, application code, or data with other tenants. This is due to the fact that one of the major threats of multi-tenancy is information disclosure due to a system malfunction, system error, or aggressive actions. So far the only approach in research to counteract on this hesitation has been to enhance the isolation between tenants using the same instance. Our approach (presented in earlier work) tackles this hesitation differently. It allows customers to choose if or even with whom they want to share the application. The approach enables the customer to define their constraints for individual application components and the underlying infrastructure. The contribution of this paper is an analysis of real-world applicability of the mixed-tenancy approach. This is done experimentally by applying the mixed-tenancy approach to OpenERP, an open source enterprise resource planning system used in industry. The conclusion gained from this experiment is that the mixed-tenancy approach is technically realizable for cases of the real world. However, there are scenarios where the mixed-tenancy approach is not economically worthwhile for the operator.


title: MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks

abstract: Wireless sensor networks (WSNs) pose novel challenges compared with traditional networks. To answer such challenges a new communication paradigm, data-centric communication, is emerging. One form of data-centric communication is the publish/subscribe messaging system. Compared with other data-centric variants, publish/subscribe systems are common and wide-spread in distributed computing. Thus, extending publish/subscribe systems intoWSNs will simplify the integration of sensor applications with other distributed applications. This paper describes MQTT-S [1], an extension of the open publish/subscribe protocol message queuing telemetry transport (MQTT) [2] to WSNs. MQTT-S is designed in such a way that it can be run on low-end and battery-operated sensor/actuator devices and operate over bandwidth-constraint WSNs such as ZigBee-based networks. Various protocol design points are discussed and compared. MQTT-S has been implemented and is currently being tested on the IBM wireless sensor networking testbed [3]. Implementation aspects, open challenges and future work are also presented.


title: Pay Attention to Those Sets! Learning Quantification from Images

abstract: Major advances have recently been made in merging language and vision representations. But most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however, that humans (even pre-school children) can abstract over raw data to perform certain types of higher-level reasoning, expressed in natural language by function words. A case in point is given by their ability to learn quantifiers, i.e. expressions like 'few', 'some' and 'all'. From formal semantics and cognitive linguistics, we know that quantifiers are relations over sets which, as a simplification, we can see as proportions. For instance, in 'most fish are red', most encodes the proportion of fish which are red fish. In this paper, we study how well current language and vision strategies model such relations. We show that state-of-the-art attention mechanisms coupled with a traditional linguistic formalisation of quantifiers gives best performance on the task. Additionally, we provide insights on the role of 'gist' representations in quantification. A 'logical' strategy to tackle the task would be to first obtain a numerosity estimation for the two involved sets and then compare their cardinalities. We however argue that precisely identifying the composition of the sets is not only beyond current state-of-the-art models but perhaps even detrimental to a task that is most efficiently performed by refining the approximate numerosity estimator of the system.


title: Human Question Answering Performance Using an Interactive Document Retrieval System

abstract: Every day, people answer their questions by using document retrieval systems. Compared to document retrieval systems, question answering (QA) systems aim to speed the rate at which users find answers by retrieving answers rather than documents. To better understand how document retrieval systems compare to QA systems, we measured the performance of humans using an interactive document retrieval system to answer questions. We first measured the ability of users to answer their questions using an interactive document retrieval system, and then compared the users' performance with the document retrieval system to question answering systems. We found that while users can successfully answer their questions using a document retrieval system, question answering systems have the potential to significantly increase the rate at which users find answers.


title: Top-Down Neural Attention by Excitation Backprop

abstract: We aim to model the top-down attention of a Convolutional Neural Network (CNN) classifier for generating task-specific attention maps. Inspired by a top-down human visual attention model, we propose a new backpropagation scheme, called Excitation Backprop, to pass along top-down signals downwards in the network hierarchy via a probabilistic Winner-Take-All process. Furthermore, we introduce the concept of contrastive attention to make the top-down attention maps more discriminative. In experiments, we demonstrate the accuracy and generalizability of our method in weakly supervised localization tasks on the MS COCO, PASCAL VOC07 and ImageNet datasets. The usefulness of our method is further validated in the text-to-region association task. On the Flickr30k Entities dataset, we achieve promising performance in phrase localization by leveraging the top-down attention of a CNN model that has been trained on weakly labeled web images.


title: Speech Recognition for Machine Translation in Quaero

abstract: This paper describes the speech-to-text systems used to provide automatic transcriptions used in the Quaero 2010 evaluation of Machine Translation from speech. Quaero (www.quaero.org) is a large research and industrial innovation program focusing on technologies for automatic analysis and classification of multimedia and multilingual documents. The ASR transcript is the result of a Rover combination of systems from three teams ( KIT, RWTH, LIMSI+VR) for the French and German languages. The casesensitive word error rates (WER) of the combined systems were respectively 20.8% and 18.1% on the 2010 evaluation data, relative WER reductions of 14.6% and 17.4% respectively over the best component system.


title: Predicting Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory

abstract: In this paper, we introduce Long ShortTerm Memory (LSTM) recurrent network for twitter sentiment prediction. With the help of gates and constant error carousels in the memory block structure, the model could handle interactions between words through a flexible compositional function. Experiments on a public noisy labelled data show that our model outperforms several feature-engineering approaches, with the result comparable to the current best data-driven technique. According to the evaluation on a generated negation phrase test set, the proposed architecture doubles the performance of non-neural model based on bag-of-word features. Furthermore, words with special functions (such as negation and transition) are distinguished and the dissimilarities of words with opposite sentiment are magnified. An interesting case study on negation expression processing shows a promising potential of the architecture dealing with complex sentiment phrases.


title: E-eyes: device-free location-oriented activity identification using fine-grained WiFi signatures

abstract: Activity monitoring in home environments has become increasingly important and has the potential to support a broad array of applications including elder care, well-being management, and latchkey child safety. Traditional approaches involve wearable sensors and specialized hardware installations. This paper presents device-free location-oriented activity identification at home through the use of existing WiFi access points and WiFi devices (e.g., desktops, thermostats, refrigerators, smartTVs, laptops). Our low-cost system takes advantage of the ever more complex web of WiFi links between such devices and the increasingly fine-grained channel state information that can be extracted from such links. It examines channel features and can uniquely identify both in-place activities and walking movements across a home by comparing them against signal profiles. Signal profiles construction can be semi-supervised and the profiles can be adaptively updated to accommodate the movement of the mobile devices and day-to-day signal calibration. Our experimental evaluation in two apartments of different size demonstrates that our approach can achieve over 96% average true positive rate and less than 1% average false positive rate to distinguish a set of in-place and walking activities with only a single WiFi access point. Our prototype also shows that our system can work with wider signal band (802.11ac) with even higher accuracy.


title: Anterolateral Ligament Reconstruction Technique: An Anatomic-Based Approach

abstract: Restoration of anteroposterior laxity after an anterior cruciate ligament reconstruction has been predictable with traditional open and endoscopic techniques. However, anterolateral rotational stability has been difficult to achieve in a subset of patients, even with appropriate anatomic techniques. Therefore, differing techniques have attempted to address this rotational laxity by augmenting or reconstructing lateral-sided structures about the knee. In recent years, there has been a renewed interest in the anterolateral ligament as a potential contributor to residual anterolateral rotatory instability in anterior cruciate ligament–deficient patients. Numerous anatomic and biomechanical studies have been performed to further define the functional importance of the anterolateral ligament, highlighting the need for surgical techniques to address these injuries in the unstable knee. This article details our technique for an anatomic anterolateral ligament reconstruction using a semitendinosus tendon allograft.


title: AIDA2: A Hybrid Approach for Token and Sentence Level Dialect Identification in Arabic

abstract: In this paper, we present a hybrid approach for performing token and sentence levels Dialect Identification in Arabic. Specifically we try to identify whether each token in a given sentence belongs to Modern Standard Arabic (MSA), Egyptian Dialectal Arabic (EDA) or some other class and whether the whole sentence is mostly EDA or MSA. The token level component relies on a Conditional Random Field (CRF) classifier that uses decisions from several underlying components such as language models, a named entity recognizer and and a morphological analyzer to label each word in the sentence. The sentence level component uses a classifier ensemble system that relies on two independent underlying classifiers that model different aspects of the language. Using a featureselection heuristic, we select the best set of features for each of these two classifiers. We then train another classifier that uses the class labels and the confidence scores generated by each of the two underlying classifiers to decide upon the final class for each sentence. The token level component yields a new state of the art F-score of 90.6% (compared to previous state of the art of 86.8%) and the sentence level component yields an accuracy of 90.8% (compared to 86.6% obtained by the best state of the art system).


title: Prediction of weather-induced airline delays based on machine learning algorithms

abstract: The primary goal of the model proposed in this paper is to predict airline delays caused by inclement weather conditions using data mining and supervised machine learning algorithms. US domestic flight data and the weather data from 2005 to 2015 were extracted and used to train the model. To overcome the effects of imbalanced training data, sampling techniques are applied. Decision trees, random forest, the AdaBoost and the k-Nearest-Neighbors were implemented to build models which can predict delays of individual flights. Then, each of the algorithms' prediction accuracy and the receiver operating characteristic (ROC) curve were compared. In the prediction step, flight schedule and weather forecast were gathered and fed into the model. Using those data, the trained model performed a binary classification to predicted whether a scheduled flight will be delayed or on-time.


title: Supporting Private Data on Hyperledger Fabric with Secure Multiparty Computation

abstract: Hyperledger Fabric is a "permissioned" blockchain architecture, providing a consistent distributed ledger, shared by a set of "peers." As with every blockchain architecture, the core principle of Hyperledger Fabric is that all the peers must have the same view of the shared ledger, making it challenging to support private data for the different peers. Extending Hyperledger Fabric to support private data (that can influence transactions) would open the door to many exciting new applications, in areas from healthcare to commerce, insurance, finance, and more. In this work we explored adding private-data support to Hyperledger Fabric using secure multiparty computation (MPC). Specifically, in our solution the peers store on the chain encryption of their private data, and use secure MPC whenever such private data is needed in a transaction. This solution is very general, allowing in principle to base transactions on any combination of public and private data. We created a demo of our solution over Hyperledger Fabric v1.0, implementing a bidding system where sellers can list assets on the ledger with a secret reserve price, and bidders publish their bids on the ledger but keep secret the bidding price itself. We implemented a smart contract (aka "chaincode") that runs the auction on this secret data, using a simple secure-MPC protocol that was built using the EMP-toolkit library. The chaincode itself was written in Go, and we used the SWIG library to make it possible to call our protocol implementation in C++. We identified two basic services that should be added to Hyperledger Fabric to support our solution, and are now working on implementing them.


title: Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks

abstract: This work exploits the tolerance of Deep Neural Networks (DNNs) to reduced precision numerical representations and specifically, their recently demonstrated ability to tolerate representations of different precision per layer while maintaining accuracy. This flexibility enables improvements over conventional DNN implementations that use a single, uniform representation. This work proposes Proteus, which reduces the data traffic and storage footprint needed by DNNs, resulting in reduced energy and improved area efficiency for DNN implementations. Proteus uses a different representation per layer for both the data (neurons) and the weights (synapses) processed by DNNs. Proteus is a layered extension over existing DNN implementations that converts between the numerical representation used by the DNN execution engines and the shorter, layer-specific fixed-point representation used when reading and writing data values to memory be it on-chip buffers or off-chip memory. Proteus uses a novel memory layout for DNN data, enabling a simple, low-cost and low-energy conversion unit. We evaluate Proteus as an extension to a state-of-the-art accelerator [7] which uses a uniform 16-bit fixed-point representation. On five popular DNNs Proteus reduces data traffic among layers by 43% on average while maintaining accuracy within 1% even when compared to a single precision floating-point implementation. As a result, Proteus improves energy by 15% with no performance loss. Proteus also reduces the data footprint by at least 38% and hence the amount of on-chip buffering needed resulting in an implementation that requires 20% less area overall. This area savings can be used to improve cost by building smaller chips, to process larger DNNs for the same on-chip area, or to incorporate an additional three execution engines increasing peak performance bandwidth by 18%.


Adapting Sorting Algorithms for Routing AGVs on a Mesh-like Path Topology


title: Fast Food Consumption and Breakfast Skipping: Predictors of Weight Gain from Adolescence to Adulthood in a Nationally Representative Sample

abstract: Abstract Purpose To investigate whether fast food consumption and breakfast skipping are associated with weight gain during the transition from adolescence to adulthood. Methods A prospective study of 9919 adolescents participating in Waves II (age range 11–21 years) and III (age range 18–27 years) of the National Longitudinal Study of Adolescent Health. BMI z scores (zBMI) were computed using the 2000 Centers for Disease Control and Prevention growth charts. Multivariate regression models assessed the relationship between Wave II fast food and breakfast consumption and change in fast food and breakfast consumption between Waves II and III and weight gain during the transition to adulthood. Results Marked increases in fast food consumption and decreases in breakfast consumption occurred over the 5-year interval. Greater days of fast food consumption at Wave II predicted increased zBMI at Wave III. Fewer days of breakfast consumption at Wave II and decreases in breakfast consumption between Waves II and III predicted increased zBMI at Wave III. Conclusions Fast food consumption and breakfast skipping increased during the transition to adulthood, and both dietary behaviors are associated with increased weight gain from adolescence to adulthood. These behaviors may be appropriate targets for intervention during this important transition.


title: A 10–100-GHz Double-Ridged Horn Antenna and Coax Launcher

abstract: The design, fabrication, and measurement of a coax to double-ridged waveguide launcher and horn antenna is presented. The novel launcher design employs two symmetric field probes across the ridge gap to minimize spreading inductance in the transition, and achieves better than 15 dB return loss over a 10:1 bandwidth. The aperture-matched horn uses a half-cosine transition into a linear taper for the outer waveguide dimensions and ridge width, and a power-law scaled gap to realize monotonically varying cutoff frequencies, thus avoiding the appearance of trapped mode resonances. It achieves a nearly constant beamwidth in both E- and H-planes for an overall directivity of about 16.5 dB from 10–100 GHz.


title: Relighting Humans: Occlusion-Aware Inverse Rendering for Full-Body Human Images

abstract: Relighting of human images has various applications in image synthesis. For relighting, we must infer albedo, shape, and illumination from a human portrait. Previous techniques rely on human faces for this inference, based on spherical harmonics (SH) lighting. However, because they often ignore light occlusion, inferred shapes are biased and relit images are unnaturally bright particularly at hollowed regions such as armpits, crotches, or garment wrinkles. This paper introduces the first attempt to infer light occlusion in the SH formulation directly. Based on supervised learning using convolutional neural networks (CNNs), we infer not only an albedo map, illumination but also a light transport map that encodes occlusion as nine SH coefficients per pixel. The main difficulty in this inference is the lack of training datasets compared to unlimited variations of human portraits. Surprisingly, geometric information including occlusion can be inferred plausibly even with a small dataset of synthesized human figures, by carefully preparing the dataset so that the CNNs can exploit the data coherency. Our method accomplishes more realistic relighting than the occlusion-ignored formulation.


title: Avenanthramides are bioavailable and have antioxidant activity in humans after acute consumption of an enriched mixture from oats.

abstract: The consumption of polyphenols is associated with a decreased risk of cardiovascular disease. Avenanthramides (AV), alkaloids occurring only in oats, may have anti-atherosclerotic activity, but there is no information concerning their bioavailability and bioactivity in humans. We characterized the pharmacokinetics and antioxidant action of avenanthramide A, B, and C in healthy older adults in a randomized, placebo-controlled, 3-way crossover trial with 1-wk washout periods. Six free-living subjects (3 mol/L, 3 F; 60.8 +/- 3.6 y) consumed 360 mL skim milk alone (placebo) or containing 0.5 or 1 g avenanthramide-enriched mixture (AEM) extracted from oats. Plasma samples were collected over a 10-h period. Concentrations of AV-A, AV-B, and AV-C in the AEM were 154, 109, and 111 micromol/g, respectively. Maximum plasma concentrations of AV (free + conjugated) after consumption of 0.5 and 1 g AEM were 112.9 and 374.6 nmol/L for AV-A, 13.2 and 96.0 nmol/L for AV-B, and 41.4 and 89.0 nmol/L for AV-C, respectively. Times to reach the C(max) for both doses were 2.30, 1.75, and 2.15 h for AV-A, AV-B, and AV-C and half times for elimination were 1.75, 3.75, and 3.00 h, respectively. The elimination kinetics of plasma AV appeared to follow first-order kinetics. The bioavailability of AV-A was 4-fold larger than that of AV-B at the 0.5 g AEM dose. After consumption of 1 g AEM, plasma reduced glutathione was elevated by 21% at 15 min (P < or = 0.005) and by 14% at 10 h (P < or = 0.05). Thus, oat AV are bioavailable and increase antioxidant capacity in healthy older adults.


title: Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis

abstract: Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual’s state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.


title: A Novel High-Gain Tetrahedron Origami

abstract: In this letter, a novel high-gain tetrahedron origami antenna is introduced. The antenna comprises a triangular-shaped monopole, a reflector, and two parasitic strip directors on a paper substrate. The directors and the reflector are employed to increase the antenna gain. The step-by-step origami folding procedure is presented in detail. The proposed design of antenna is verified by both simulations and measurements with a fabricated prototype. The antenna exhibits a 10-dB impedance bandwidth of 66% (2-4 GHz) and a peak gain of 9.5 dBi at 2.6 GHz.


title: A Face in any Form: New Challenges and Opportunities for Face Recognition Technology

abstract: Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains will require solving these problems through focused interdisciplinary efforts among developers, researchers, and policymakers.


title: A Method of Feature Selection Based on Word2Vec in Text Categorization

abstract: In text categorization, the performance of classifier decreases with the increase of feature dimension. The main purpose of feature selection is to remove irrelevant features and redundant features in features and reduce feature dimension. Traditional methods of feature selection, such as CHI, IG, DF and so on, take into account only the number of appearances of features and ignore the feature semantics and part-of-speech features. The vector representations of words learned by word2vec models have been shown to carry semantic meanings and are useful in various NLP tasks. Based on the word vectors generated by Word2Vec, the paper proposes the algorithm Word2Vec-SM to reduce the dimensionality of the features. Experimental proof word2vec-SM algorithm.


title: On the embodiment of emotion regulation: interoceptive awareness facilitates reappraisal

abstract: The ability to cognitively regulate emotional responses to aversive events is essential for mental and physical health. One prerequisite of successful emotion regulation is the awareness of emotional states, which in turn is associated with the awareness of bodily signals [interoceptive awareness (IA)]. This study investigated the neural dynamics of reappraisal of emotional responses in 28 participants who differed with respect to IA. Electroencephalography was used to characterize the time course of emotion regulation. We found that reappraisal was accompanied by reduced arousal and significant modulation of late neural responses. What is more, higher IA facilitated downregulation of affect and was associated with more pronounced modulation of underlying neural activity. Therefore, we conclude that IA not only advances the consolidation of somatic markers required for guiding individual behaviour but also creates processing advantages in tasks referring to these bodily markers.


title: Functional foods: psychological and behavioural functions

abstract: It is easier to demonstrate the consistent effects of foods on satiety than on cognitive performance. This is understandable since the satiety system incorporates physiological signalling systems that mediate the effects of foods on function. Specific manipulations of proteins, carbohydrates and fats have the potential to act as functional foods for appetite control. Because of the importance of the optimal functioning of cognitions for survival, these functions are quite strongly protected against short-term dietary and physiological perturbances. Therefore, food manipulations may be better detected through the degree of effort exerted to maintain performance rather than via changes in the actual performance itself. This procedure has not been widely used hitherto. The concept of biomarkers may have to be interpreted differently from research on physiological systems or clinical endpoints. For satiety, adjustments in the profile of hunger could serve as a biomarker or surrogate endpoint. For cognitions, correlated physiological variables may be more difficult to measure than the functional endpoint itself. Changes related to unitary functions (such as tracking) could serve as biomarkers for more complex, integrated skills (such as car driving). Since food manipulations may affect multiple functions, the challenge is to design foods with good satiety control that do not impair mental performance; or alternatively to engineer foods that optimise cognitive performance without compromising satiety. This rapidly developing field has great potential for close collaboration between academia and industry in the production of commercially successful products that show clear improvements in human functioning with the capacity to protect against disease or impairment.


title: Ethics, Privacy, and Self-Restraint in Social Networking

abstract: Privacy is a basic human need. It is anthropologically and psychologically rooted in the sense of shame and the need for bodily integrity, personal space, and intimacy in interpersonal relationships. Especially in modern Western cultures, it is understood as a necessary condition for individual autonomy, identity, and integrity (Altman 1975; Westin 1967; see also Margulis, this volume, Chap. 2). The desire for privacy is historically variable and has increased noticeably throughout the process of modernization. As Jurgen Habermas (1962) has shown in his seminal study The Transformation of the Public Sphere, this process led to the emergence of the private sphere as a corollary to the public sphere: the private sphere offers the protection and freedom necessary for the undisturbed growth and self-fulfillment of the modern subject, who then, as a citizen, can participate in exchanging opinions and forming public discourse in the communicative space of the public sphere.


title: Cryptanalysis and Security Improvements of ‘Two-Factor User Authentication in Wireless Sensor Networks’

abstract: User authentication in wireless sensor networks (WSN) is a critical security issue due to their unattended and hostile deployment in the field. Since sensor nodes are equipped with limited computing power, storage, and communication modules; authenticating remote users in such resource-constrained environments is a paramount security concern. Recently, M.L. Das proposed a two-factor user authentication scheme in WSNs and claimed that his scheme is secure against different kinds of attack. However, in this paper, we show that the M.L. Das-scheme has some critical security pitfalls and cannot be recommended for real applications. We point out that in his scheme: users cannot change/update their passwords, it does not provide mutual authentication between gateway node and sensor node, and is vulnerable to gateway node bypassing attack and privileged-insider attack. To overcome the inherent security weaknesses of the M.L. Das-scheme, we propose improvements and security patches that attempt to fix the susceptibilities of his scheme. The proposed security improvements can be incorporated in the M.L. Das-scheme for achieving a more secure and robust two-factor user authentication in WSNs.


abstract: New text indexing functionalities of the compressed suffix arrays are proposed. The compressed suffix array proposed by Grossi and Vitter is a space-efficient data structure for text indexing. It occupies only O(n) bits for a text of length η; however it also uses the text itself that occupies n log2 |A| bits for the alphabet A. In this paper we modify the data structure so that pattern matching can be done without any access to the text. In addition to the original functions of the compressed suffix array, we add new operations search, decompress and inverse to the compressed suffix arrays. We show that the new index can find occ occurrences of any substring P of the text in O(|P| log n + occ loge n) time for any fixed 1 ≥ e > 0 without access to the text. The index also can decompress a part of the text of length m in O(m + loge n) time. For a text of length n on an alphabet A such that |A| = polylog(n), our new index occupies only O(nH0 + n log log |A|) bits where H0 ≤ log |A| is the order-0 entropy of the text. Especially for e = 1 the size is nH0 + O(n log log |A|) bits. Therefore the index will be smaller than the text, which means we can perform fast queries from compressed texts.


title: Wireless Mesh Networks for In-Home IPTV Distribution

abstract: IPTV is considered to be the next killer application. A key, challenging issue is in-home IPTV distribution with affordable deployment cost and sufficient flexibility, scalability, and reliability. In this article, we first survey and compare the emerging wired and wireless communication technologies for broadband home networks, including transmission over power lines, phone lines, coaxial cables or Ethernet cables, and IEEE 802.1 In, ultra wideband and millimeter wave wireless technologies. Considering these promising candidates for future broadband home networks, we propose three wireless mesh network architectures. These enable consumers to enjoy anywhere, anytime IPTV services without rewiring their homes. We compare the cost, reliability, and scalability of the three architectures. We further study their admission regions for IPTV, that is, the number of IPTV connections that can be supported simultaneously with satisfactory QoS. Analytical and simulation results with H.264 HDTV sources over wired, single hop wireless, and multi-hop wireless paths are given. These results can provide important guidelines for future broadband home network design supporting IPTV services.


title: Long-term study of safe Internet use of young children

abstract: The Internet is an evolving medium that continuously presents new functionalities. Accordingly, also children's Internet usage changes continuously. This requires being vigilant about related Internet risk behavior and safe Internet use. The present article presents a structured overview of Internet risks and summarizes approaches to foster safe Internet behavior. Based on a long-term cross-sectional study, Internet usage of young children has been researched. The focus is on contact risks and content risks. Based on the analysis of survey data from 10000 children, trends in their (un)safe Internet usage are studied in the years 2005-2006, 2007-2008, and 2008-2009. An Unsafe Internet Usage Index (UIUI) is calculated. The average results point at a rather low level of unsafe Internet usage. But these average results hide between-subject differences and a number of children clearly reflect risky unsafe Internet usage. Also, no consistent reduction in unsafe Internet behavior is being observed over the years. Parental and teacher control hardly increase, and hardly seem to impact the level of unsafe Internet behavior. Considering a number of limitations of the present study, directions for future research are discussed.


title: Damage to Ventromedial Prefrontal Cortex Impairs Judgment of Harmful Intent

abstract: Summary Moral judgments, whether delivered in ordinary experience or in the courtroom, depend on our ability to infer intentions. We forgive unintentional or accidental harms and condemn failed attempts to harm. Prior work demonstrates that patients with damage to the ventromedial prefrontal cortex (VMPC) deliver abnormal judgments in response to moral dilemmas and that these patients are especially impaired in triggering emotional responses to inferred or abstract events (e.g., intentions), as opposed to real or actual outcomes. We therefore predicted that VMPC patients would deliver abnormal moral judgments of harmful intentions in the absence of harmful outcomes, as in failed attempts to harm. This prediction was confirmed in the current study: VMPC patients judged attempted harms, including attempted murder, as more morally permissible relative to controls. These results highlight the critical role of the VMPC in processing harmful intent for moral judgment.


title: Cluster-Based Web Service Recommendation

abstract: The concept of Web services has become a widely applied paradigm in research and industry, with the number of services published on the Internet increasing rapidly over the last few years. Thus, service recommendation is becoming a challenging and time-consuming task due to large search space. Organizing the Web services into clusters is a one of very efficient approach for search space pruning process. In this paper, we proposed cluster-based service recommendation approach. User may want to interact with services that have similar functionalities, which they used to interact. Thus, we consider semantic similarity between services as one factor in the clustering process. Further, user may be interesting to identify the appropriate services to generate value-added service. Thus, we consider the association between services as the second factor. Our approach recommend services for currently invoked service using the generated clusters and services with better QoS values selected by a filtering process. Experimental results show that our approach works effectively.


title: Convention, Form, and Figurative Language Processing

abstract: There are 2 basic controversies concerning how metaphors are processed. First, are metaphoric mappings more akin to literal comparisons or to literal categorizations? And second, is metaphor comprehension indirect or direct? We believe that these controversies are more apparent than real and that a unified theoretical framework can be offered that reconciles these opposing views. The central idea is that all metaphors involve structural alignment of the target and base domains. Whether these alignments are more akin to comparisons or to categorizations, and whether they are computed directly or indirectly, depends on 2 factors: the type of representation invoked by the base term and the linguistic form of the metaphor.


title: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses

abstract: A new technique for three-dimensional (3D) camera calibration for machine vision metrology using off-the-shelf TV cameras and lenses is described. The two-stage technique is aimed at efficient computation of camera external position and orientation relative to object reference coordinate system as well as the effective focal length, radial lens distortion, and image scanning parameters. The two-stage technique has advantage in terms of accuracy, speed, and versatility over existing state of the art. A critical review of the state of the art is given in the beginning. A theoretical framework is established, supported by comprehensive proof in five appendixes, and may pave the way for future research on 3D robotics vision. Test results using real data are described. Both accuracy and speed are reported. The experimental results are analyzed and compared with theoretical prediction. Recent effort indicates that with slight modification, the two-stage calibration can be done in real time.


Fuzzy kernel C-means algorithm for intrusion detection systems


title: Orthomode Transducer and Dual-Polarized Horn Antenna in Substrate Integrated Technology

abstract: A dual-polarized system fully implemented in substrate integrated technology is presented. It comprises an Orthomode Transducer (OMT) with planar excitation schemes for both orthogonal TE10 and TE01 modes and a dual-polarized horn antenna. The whole structure requires only 2 layers of substrate and it is based on the Extended Substrate Integrated Waveguide (ESIW), which allows the propagation of TEm0 and TE0n modes. A compact OMT design is proposed along with a procedure to optimize it. The matching and radiation characteristics of a dual-polarized horn antenna fed with this OMT are also studied. The complete structure was manufactured exhibiting an 8.6% bandwidth (13.9-15.15 GHz) where both modes are well matched ( 28 dB). The horn radiation patterns within the frequency range of operation are also similar for both polarizations. The proposed design demonstrates the feasibility of dualpolarized Substrate Integrated Circuits (SICs). This opens the door to new substrate integrated devices as well as to the use of SICs in applications requiring polarization diversity at endfire. All together, this combined design becomes a promising approach to tackle the increasing demand for capacity in modern communication systems.


title: Computer games with intelligence

abstract: Abstract—Artificial Intelligence (AI) is playing an increasingly important role in the success or failure of computer games and the quality and complexity of the AI techniques employed in games is steadily increasing. The paper explores the use of AI in games focusing on difficulties faced by game developers and techniques that have proven useful. Particular attention is given to a recently released popular game that makes extensive use of AI. The paper highlights the potential benefits of collaboration between academic AI researchers and the games industry and specific focuses for potential collaboration are suggested.


title: A Robust Feature Extraction and Selection Method for the Recognition of Lymphocytes versus Acute Lymphoblastic Leukemia

abstract: An essential part of the diagnosis and treatment of leukemia is the visual examination of the patient's peripheral blood smear under the microscope. Morphological changes in the white blood cells are commonly used to determine the nature of the malignant cells, namely blasts. Manual techniques are labor intensive slow, subjected to error and costly. A computerized system can be used as an aiding tool for the specialist in order to improve and accelerate the morphological analysis process. This paper presents and application of feature extraction, selection and cell classification to the recognition and differentiation of normal lymphocytes versus abnormal lymphoblast cells on the image of peripheral blood smears. This is considered as a very useful procedure in the initial treatment process of leukemia patients. A computerized recognition system has been developed, and the results of its numerical verification are presented and discussed. The methodology demonstrates that the application of pattern recognition is a powerful tool for the differentiation of normal lymphocytes and acute lymphoblastic leukemia, leading to the improvement in the early effective treatment for leukemia.


title: MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles

abstract: We introduce a novel semi-automated metric, MEANT, that assesses translation utility by matching semantic role fillers, producing scores that correlate with human judgment as well as HTER but at much lower labor cost. As machine translation systems improve in lexical choice and fluency, the shortcomings of widespread n-gram based, fluency-oriented MT evaluation metrics such as BLEU, which fail to properly evaluate adequacy, become more apparent. But more accurate, non-automatic adequacy-oriented MT evaluation metrics like HTER are highly labor-intensive, which bottlenecks the evaluation cycle. We first show that when using untrained monolingual readers to annotate semantic roles in MT output, the non-automatic version of the metric HMEANT achieves a 0.43 correlation coefficient with human adequacy judgments at the sentence level, far superior to BLEU at only 0.20, and equal to the far more expensive HTER. We then replace the human semantic role annotators with automatic shallow semantic parsing to further automate the evaluation metric, and show that even the semi-automated evaluation metric achieves a 0.34 correlation coefficient with human adequacy judgment, which is still about 80% as closely correlated as HTER despite an even lower labor cost for the evaluation procedure. The results show that our proposed metric is significantly better correlated with human judgment on adequacy than current widespread automatic evaluation metrics, while being much more cost effective than HTER.


title: Scale-stack bar charts

abstract: It is difficult to create appropriate bar charts for data that cover large value ranges. The usual approach for these cases employs a logarithmic scale, which, however, suffers from issues inherent to its non-linear mapping: for example, a quantitative comparison of different values is difficult. We present a new approach for bar charts that combines the advantages of linear and logarithmic scales, while avoiding their drawbacks. Our scale-stack bar charts use multiple scales to cover a large value range, while the linear mapping within each scale preserves the ability to visually compare quantitative ratios. Scale-stack bar charts can be used for the same applications as classic bar charts; in particular, they can readily handle stacked bar representations and negative values. Our visualization technique is demonstrated with results for three different application areas and is assessed by an expert review and a quantitative user study confirming advantages of our technique for quantitative comparisons.


title: Using an Ensemble of One-Class SVM Classifiers to Harden Payload-based Anomaly Detection Systems

abstract: Unsupervised or unlabeled learning approaches for network anomaly detection have been recently proposed. In particular, recent work on unlabeled anomaly detection focused on high speed classification based on simple payload statistics. For example, PAYL, an anomaly IDS, measures the occurrence frequency in the payload of n-grams. A simple model of normal traffic is then constructed according to this description of the packets' content. It has been demonstrated that anomaly detectors based on payload statistics can be "evaded" by mimicry attacks using byte substitution and padding techniques. In this paper we propose a new approach to construct high speed payload-based anomaly IDS intended to be accurate and hard to evade. We propose a new technique to extract the features from the payload. We use a feature clustering algorithm originally proposed for text classification problems to reduce the dimensionality of the feature space. Accuracy and hardness of evasion are obtained by constructing our anomaly-based IDS using an ensemble of one-class SVM classifiers that work on different feature spaces.


title: Polygenic scores for schizophrenia and educational attainment are associated with behavioural problems in early childhood in the general population

abstract: Background ::: Genome-wide association studies in adults have identified numerous genetic variants related to psychiatric disorders and related traits, such as schizophrenia and educational attainment. However, the effects of these genetic variants on behaviour in the general population remain to be fully understood, particularly in younger populations. We investigated whether polygenic scores of five psychiatric disorders and educational attainment are related to emotional and behaviour problems during early childhood. ::: ::: Methods ::: From the Generation R Study, we included participants with available genotype data and behavioural problems measured with the Child Behavior Checklist (CBCL) at the age of 3 (n = 1,902), 6 (n = 2,202) and 10 years old (n = 1,843). Polygenic scores were calculated for five psychiatric disorders and educational attainment. These polygenic scores were tested for an association with the broadband internalizing and externalizing problem scales and the specific CBCL syndrome scale scores. ::: ::: Results ::: Analysis of the CBCL broadband scales showed that the schizophrenia polygenic score was associated with significantly higher internalizing scores at 3, 6 and 10 years and higher externalizing scores at age 3 and 6. The educational attainment polygenic score was associated with lower externalizing scores at all time points and lower internalizing scores at age 3. No associations were observed for the polygenic scores of bipolar disorder, major depressive disorder and autism spectrum disorder. Secondary analyses of specific syndrome scores showed that the schizophrenia polygenic score was strongly related to the Thought Problems scores. A negative association was observed between the educational attainment polygenic score and Attention Problems scores across all age groups. ::: ::: Conclusions ::: Polygenic scores for adult psychiatric disorders and educational attainment are associated with variation in emotional and behavioural problems already at a very early age.


title: Objective Measurement of Compressed Digital Television Picture Quality

abstract: We have introduced criticality as a quantitative measure of the difficulty in digitally coding for the purpose of analyzing picture quality of television programs statistically. Picture quality in digital coding depends greatly on picture characteristics such as spatial details and motion. Therefore it is not easy to confirm how well a system performs from only a subjective assessment test, in which a limited number of test sequences are used. A definition of criticality is proposed, and results of criticality measurement for broadcast television programs are reported. Also proposed is a method to obtain a statistical distribution of picture quality for digital television programs. By measuring the frequency of occurrence of criticality and relating it to subjective picture quality, it becomes possible to obtain statistical quality distribution in digital television broadcasting.


title: Adaptive Dynamics with Efficient Contact Handling for Articulated Robots

abstract: We present a novel adaptive dynamics algorithm with efficient contact handling for articulated robots. Our algorithm automatically computes a fraction of the joints whose motion provides a good approximation to overall robot dynamics. We extend Featherstone’s Divide-and-Conquer algorithm and are able to efficiently handle all contacts and collisions with the obstacles in the environment. Overall, our approach provides a time-critical collision detection and resolution algorithm for highly articulated bodies and its complexity is sub-linear in the number of degrees-of-freedom. We demonstrate our algorithm on several complex articulated robots consisting of hundreds of joints.


Alidade: IP Geolocation without Active Probing


title: A Yoga-Based Exercise Program for People With Chronic Poststroke Hemiparesis

abstract: Background and Purpose. This was a preliminary investigation of the effects of a yoga-based exercise program on people with chronic (greater than 9 months) poststroke hemiparesis. Many people who have had a stroke report an impaired health status because of a reduced level of activity. Proponents of yoga contend that it offers a gentle alternative exercise program that can be easily adapted for people who have had a stroke. Subjects and Methods. Four subjects with chronic poststroke hemiparesis participated in this single-case study. The primary outcome measures were the Berg Balance Scale (BBS) and the Timed Movement Battery (TMB). A secondary outcome measure was the Stroke Impact Scale (SIS). The baseline testing phase varied for each subject and ranged from 4 to 7 weeks. The 8-week intervention phase consisted of 1.5-hour yoga sessions, 2 times per week, in the subject's home. The primary outcome data were collected each week, and the secondary outcome data were collected before the baseline testing phase and before and after the intervention phase. Results. Subjects 1, 2, and 4 had improved TMB scores, and subjects 3 and 4 had improved BBS scores. Discussion and Conclusion. The results suggest that yoga may be beneficial to people who have had a stroke. Further investigation is warranted to further examine the effects of yoga in this population.


title: Automatic Detection of Diabetes Diagnosis using Feature Weighted Support Vector Machines based on Mutual Information and Modified Cuckoo Search

abstract: Diabetes is a major health problem in both developing and developed countries and its incidence is rising dramatically. In this study, we investigate a novel automatic approach to diagnose Diabetes disease based on Feature Weighted Support Vector Machines (FW-SVMs) and Modified Cuckoo Search (MCS). The proposed model consists of three stages: Firstly, PCA is applied to select an optimal subset of features out of set of all the features. Secondly, Mutual Information is employed to construct the FWSVM by weighting different features based on their degree of importance. Finally, since parameter selection plays a vital role in classification accuracy of SVMs, MCS is applied to select the best parameter values. The proposed MI-MCS-FWSVM method obtains 93.58% accuracy on UCI dataset. The experimental results demonstrate that our method outperforms the previous methods by not only giving more accurate results but also significantly speeding up the classification procedure.


title: Engineering Complex Synthetic Transcriptional Programs with CRISPR RNA Scaffolds

abstract: Summary Eukaryotic cells execute complex transcriptional programs in which specific loci throughout the genome are regulated in distinct ways by targeted regulatory assemblies. We have applied this principle to generate synthetic CRISPR-based transcriptional programs in yeast and human cells. By extending guide RNAs to include effector protein recruitment sites, we construct modular scaffold RNAs that encode both target locus and regulatory action. Sets of scaffold RNAs can be used to generate synthetic multigene transcriptional programs in which some genes are activated and others are repressed. We apply this approach to flexibly redirect flux through a complex branched metabolic pathway in yeast. Moreover, these programs can be executed by inducing expression of the dCas9 protein, which acts as a single master regulatory control point. CRISPR-associated RNA scaffolds provide a powerful way to construct synthetic gene expression programs for a wide range of applications, including rewiring cell fates or engineering metabolic pathways.


title: SNS LINAC Wire Scanner System : Signal Levels and Accuracy.

abstract: The linac wire scanner system for the Spallation Neutron Source (SNS) at Oak Ridge, TN, USA, calls for 5 units in the medium energy beam transport (MEBT), 5 in the drift tube linac (DTL), and 10 in the coupled cavity linac (CCL). In this paper we present expected signal levels and an analysis of the error in the beam size measurement as functions of wire position and electrical signal errors. 1 OVERVIEW In the Spallation Neutron Source (SNS) facility, H - beams will be accelerated to 2.5 MeV in an RFQ, to 87 MeV in a drift tube linac (DTL), to 186 MeV in a coupled cavity linac (CCL), and finally to 1000 MeV in a superconducting linac (SCL). The 60 Hz, 1-ms, 36 mA peak current beam pulses are chopped into 690-ns long segments with a 1 µs period to give an average current of 1.4 mA and an average beam power of 1.4 MW. The wire scanner actuators in the SNS linac are based on custom-designed linear actuators from Huntington Mechanical Laboratories, Inc. Three carbon wires, 32 microns in diameter, are mounted to each wire scanner fork to measure the beam profile in three different planes. The wires are offset from one another so that no more than one wire at a time is within ±2 rms of the beam center.


title: Firefly Algorithm with Artificial Neural Network for Time Series Problems

abstract: Time series classification is a supervised learning method maps the input to the output using historical data. The primary objective is to discover interesting patterns hidden in the data. For the purpose of solving time series classification problems used the multi-layered perceptrons Artificial Neural Networks (ANN). The weights in the ANN are modified to provide the output values of the net, which are much closer to the values of the preferred output. For this reason, several algorithms had been proposed to train the parameters of the neural network for time series classification problems. This study attempts to hybrid the Firefly Algorithm (FA) with the ANN in order to minimize the error rate of classification (coded as FA-ANN). The FA is employed to optimize the weights of the ANN model based on the processes. The proposed FA-ANN algorithm was tested on 6 benchmark UCR time series data sets. The experimental results have revealed that the proposed FA-ANN can effectively solve time series classification problems.


title: A Greedy Approach for Budgeted Maximum Inner Product Search

abstract: Maximum Inner Product Search (MIPS) is an important task in many machine learning applications such as the prediction phase of a low-rank matrix factorization model for a recommender system. There have been some works on how to perform MIPS in sub-linear time recently. However, most of them do not have the flexibility to control the trade-off between search efficient and search quality. In this paper, we study the MIPS problem with a computational budget. By carefully studying the problem structure of MIPS, we develop a novel Greedy-MIPS algorithm, which can handle budgeted MIPS by design. While simple and intuitive, Greedy-MIPS yields surprisingly superior performance compared to state-of-the-art approaches. As a specific example, on a candidate set containing half a million vectors of dimension 200, Greedy-MIPS runs 200x faster than the naive approach while yielding search results with the top-5 precision greater than 75\%.


title: A SPAD-Based QVGA Image Sensor for Single-Photon Counting and Quanta Imaging

abstract: A CMOS single-photon avalanche diode (SPAD)-based quarter video graphics array image sensor with 8- $\mu \text{m}$ pixel pitch and 26.8% fill factor (FF) is presented. The combination of analog pixel electronics and scalable shared-well SPAD devices facilitates high-resolution, high-FF SPAD imaging arrays exhibiting photon shot-noise-limited statistics. The SPAD has 47 counts/s dark count rate at 1.5 V excess bias (EB), 39.5% photon detection probability (PDP) at 480 nm, and a minimum of 1.1 ns dead time at 1 V EB. Analog single-photon counting imaging is demonstrated with maximum 14.2-mV/SPAD event sensitivity and 0.06e− minimum equivalent read noise. Binary quanta image sensor (QIS) 16-kframes/s real-time oversampling is shown, verifying single-photon QIS theory with $4.6\times $ overexposure latitude and 0.168e− read noise.


title: Semantic Proto-Roles

abstract: We present the first large-scale, corpus based verification of the seminal work of Dowty on the notion of thematic proto-roles. Our results demonstrate both the need for and the feasibility of a property-based annotation scheme of semantic relationships, as opposed to the currently dominant notion of categorical roles.


Arduino Etoys A programming platform for Arduino on Physical Etoys


title: Sensation Seeking, Overconfidence, and Trading Activity

abstract: This study analyzes the role that two psychological attributes%u2014sensation seeking and overconfidence%u2014play in the tendency of investors to trade stocks. Equity trading data are combined with data from an investor%u2019s tax filings, driving record, and psychological profile. We use the data to construct measures of overconfidence and sensation seeking tendencies. Controlling for a host of variables, including wealth, income, age, number of stocks owned, marital status, and occupation, we find that overconfident investors and those investors most prone to sensation seeking trade more frequently.


title: Changes and challenges of technical debt and its management during ongoing digital transformation

abstract: In this paper, we look how changes and challenges in technical debt and its management that could emerge from the ongoing digital transformation. The digital transformation ensues from companies' opportunities to join various digital ecosystems and use external digital infrastructures to extend business operations and capabilities. Those external infrastructures cannot be controlled by their users, which is interesting especially from the service, systems and software development viewpoint. This will have also impact on technical debt and its management. We outline these changes and challenges with possible scenarios that could occur with technical debt during this change.


title: Edge Computing Gateway of the Industrial Internet of Things Using Multiple Collaborative Microcontrollers

abstract: An Internet of Things gateway serves as a key intermediary between numerous smart things and their corresponding cloud networking servers. A typical conventional gateway system uses a high-level embedded microcontroller (MCU) as its core; that MCU performs low-level perception-layer device network management, upper-level cloud server functions, and remote mobile computation services. However, in edge computing, many factors need to be considered when designing an IoT gateway, such as minimizing the response time, the power consumption, and the bandwidth cost. Regarding system scalability, computational efficiency, and communication efficiency, solutions that use a single MCU cannot deliver IoT functionality such as big data collection, management, real-time communication, expandable peripherals, and various other services. Therefore, this article proposes an innovative multi-MCU system framework combining a field-programmable- gate-array-based hardware bridge and multiple scalable MCUs to realize an edge gateway of a smart sensor fieldbus network. Through distributed and collaborative computing, the multi-MCU edge gateway can efficiently perform fieldbus network management, embedded data collection, and networking communication, thereby considerably reducing the real-time power consumption and improving scalability compared to the existing industrial IoT solutions.


title: Using EM for Reinforcement Learning

abstract: We discsus Hinton’s (1989) relative payoff procedure (RPP), a static reinforcement learning algorithm whose foundation is not stochastic gradient ascent. We show circumstances under which applying the RPP is guaranteed to increase the mean return, even though it can make large changes in the values of the parameters. The proof is based on a mapping between the RPP and a form of the expectation-maximisation procedure of Dempster, Laird & Rubin (1976).


title: Interface degradation of Al heavy wire bonds on power semiconductors during active power cycling measured by the shear test

abstract: Wire bonding is still the dominant interconnection technology for power semiconductors in power modules, e.g. for automotive or photovoltaic applications. In the past, many research activities have occurred in the field of reliability of power modules, where the life time of the complete module is affected by bond wire lift offs, heel cracks and other failures. Less effort was spent for investigating the degradation process at the wire bond itself. This paper addresses a new approach and focuses on the investigation of the cracks in the interface region between the die and the wire by using the shear test. These cracks form after several thousand temperature swings due to CTE mismatch and ultimately lead to wire bond lift off. New results of active power cycling with different temperature amplitudes and medium temperatures will be discussed. Shear tests have been carried out in regular intervals to monitor the degradation of the 400 μm wire bonds on power MOS-FETs. It was found out that the rate of the shear force reduction was mostly dependent on the amplitude of the temperature cycling. A significant effect of different medium temperatures could not be identified. These results will contribute to the development of an enhanced life time model for heavy wire bonds on power semiconductors.


title: Efficient Automatic Speech Recognition on the GPU

abstract: Publisher Summary This chapter provides an understanding of specific implementation challenges when working with the speech inference process, weighted finite state transducer (WFST) based methods, and the Viterbi algorithm. It illustrates an efficient reference implementation on the GPU that could be productively customized to meet the needs of specific usage scenarios. Automatic speech recognition (ASR) allows multimedia content to be transcribed from acoustic waveforms to word sequences. This technology is emerging as a critical component in data analytics for a wealth of media data that is being generated every day. ASR is a challenging application to parallelize. Specifically, on the GPU an efficient implementation of ASR involves resolving a series of implementation challenges specific to the data-parallel architecture of the platform. There are efficient solutions for resolving the implementation challenges of speech recognition on the GPU that achieve more than an order of magnitude speedup compared to sequential execution. This chapter identifies and resolves four types of algorithmic challenges encountered during in the implementation of speech recognition on GPUs. The techniques presented here, when used together, are capable of delivering 10.6X speedup for this challenging application when compared to an optimized sequential implementation on the CPU. This kind of application framework provides an optimized infrastructure that incorporates all the techniques discussed in this chapter to allow efficient execution of the speech inference process on the GPU.


title: Data Mining with an Ant Colony Optimization Algorithm

abstract: The paper proposes an algorithm for data mining called Ant-Miner (ant-colony-based data miner). The goal of Ant-Miner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts as well as principles. We compare the performance of Ant-Miner with CN2, a well-known data mining algorithm for classification, in six public domain data sets. The results provide evidence that: 1) Ant-Miner is competitive with CN2 with respect to predictive accuracy, and 2) the rule lists discovered by Ant-Miner are considerably simpler (smaller) than those discovered by CN2.


title: Mental workload during n-back task—quantified in the prefrontal cortex using fNIRS

abstract: When interacting with technical systems, users experience mental workload. Particularly in multitasking scenarios (e.g. interacting with the car navigation system while driving) it is desired to not distract the users from their primary task. For such purposes, human-machine interfaces (HCIs) are desirable which continuously monitor the users' workload and dynamically adapt the behavior of the interface to the measured workload. While memory tasks have been shown to illicit hemodynamic responses in the brain when averaging over multiple trials, a robust single trial classification is a crucial prerequisite for the purpose of dynamically adapting HCIs to the workload of its user.The prefrontal cortex (PFC) plays an important role in the processing of memory and the associated workload. In this study of 10 subjects, we used functional Near-Infrared Spectroscopy (fNIRS), a non-invasive imaging modality, to sample workload activity in the PFC. The results show up to 78% accuracy for single-trial discrimination of three levels of workload from each other. We use an n-back task (n ∈ {1, 2, 3}) to induce different levels of workload, forcing subjects to continuously remember the last one, two or three of rapidly changing items.Our experimental results show that measuring hemodynamic responses in the PFC with fNIRS, can be used to robustly quantify and classify mental workload.Single trial analysis is still a young field that suffers from a general lack of standards. To increase comparability of fNIRS methods and results, the data corpus for this study is made available online.


title: A self-supervised learning system for object detection using physics simulation and multi-view pose estimation

abstract: Progress has been achieved recently in object detection given advancements in deep learning. Nevertheless, such tools typically require a large amount of training data and significant manual effort to label objects. This limits their applicability in robotics, where solutions must scale to a large number of objects and variety of conditions. This work proposes an autonomous process for training a Convolutional Neural Network (CNN) for object detection and pose estimation in robotic setups. The focus is on detecting objects placed in cluttered, tight environments, such as a shelf with multiple objects. In particular, given access to 3D object models, several aspects of the environment are physically simulated. The models are placed in physically realistic poses with respect to their environment to generate a labeled synthetic dataset. To further improve object detection, the network self-trains over real images that are labeled using a robust multi-view pose estimation process. The proposed training process is evaluated on several existing datasets and on a dataset collected for this paper with a Motoman robotic arm. Results show that the proposed approach outperforms popular training processes relying on synthetic — but not physically realistic — data and manual annotation. The key contributions are the incorporation of physical reasoning in the synthetic data generation process and the automation of the annotation process over real images.


title: Parents’ and Teens’ Perspectives on Privacy In a Technology-Filled World

abstract: The life of a teenager today is far dierent than in past decades. Through semi-structured interviews with 10 teenagers and 10 parents of teenagers, we investigate parent-teen privacy decision making in these uncharted waters. Parents and teens generally agreed that teens had a need for some degree of privacy from their parents and that respecting teens’ privacy demonstrated trust and fostered independence. We explored the boundaries of teen privacy in both the physical and digital worlds. While parents commonly felt none of their children’s possessions should ethically be exempt from parental monitoring, teens felt strongly that cell phones, particularly text messages, were private. Parents discussed struggling to keep up with new technologies and to understand teens’ technology-mediated socializing. While most parents said they thought similarly about privacy in the physical and digital worlds, half of teens said they thought about these concepts dierently. We present cases where parents made privacy decisions using false analogies with the physical world or outdated assumptions. We also highlight directions for more usable digital parenting tools.


title: Extensible intraprocedural flow analysis at the abstract syntax tree level

abstract: We have developed a new approach for implementing precise intraprocedural control-flow and dataflow analyses at the abstract syntax tree level. Our approach is declarative, making use of reference attribute grammars augmented with circular attributes and collection attributes. This results in concise executable specifications of the analyses, allowing extensions both to the language and with further source code analyses. To evaluate the new approach, we have implemented control flow, dataflow and dead assignment analyses for Java, by extending the JastAdd Extensible Java Compiler. We have compared our results to several well-known analysis frameworks and tools, using a set of Java programs as benchmarks. These results show that our approach performs well concerning both efficiency and preciseness.


title: Sequential Design of Experiments via Linear Programming

abstract: The celebrated multi-armed bandit problem in decision theory models the basic trade-off between exploration, or learning about the state of a system, and exploitation, or utilizing the system. In this paper we study the variant of the multi-armed bandit problem where the exploration phase involves costly experiments and occurs before the exploitation phase; and where each play of an arm during the exploration phase updates a prior belief about the arm. The problem of finding an inexpensive exploration strategy to optimize a certain exploitation objective is NP-Hard even when a single play reveals all information about an arm, and all exploration steps cost the same. ::: We provide the first polynomial time constant-factor approximation algorithm for this class of problems. We show that this framework also generalizes several problems of interest studied in the context of data acquisition in sensor networks. Our analyses also extends to switching and setup costs, and to concave utility objectives. ::: Our solution approach is via a novel linear program rounding technique based on stochastic packing. In addition to yielding exploration policies whose performance is within a small constant factor of the adaptive optimal policy, a nice feature of this approach is that the resulting policies explore the arms sequentially without revisiting any arm. Sequentiality is a well-studied concept in decision theory, and is very desirable in domains where multiple explorations can be conducted in parallel, for instance, in the sensor network context.


title: Comparison of architectural design decisions for resource-constrained self-driving cars - A multiple case-study

abstract: Context: Self-Driving cars are getting more and more attention with public demonstration from all important automotive OEMs but also from companies, which do not have a long history in the automotive industry. Fostered by large international competitions in the last decade, several automotive OEMs have already announced to bring this technology to the market around 2020. ::: Objective: International competitions like the 2007 DARPA Urban Challenge did not focus on efficient usage of resources to realize the self-driving vehicular functionality. Since the automotive industry is very cost-sensitive, realizing reliable and robust self- driving functionality is challenging when expensive and sophisticated sensors mounted very visibly on the vehicle’s roof for example cannot be used. Therefore, the goal for this study is to investigate how architectural design decisions of recent self-driving vehicular technology consider resource-efficiency. ::: Method: In a multiple case study, the architectural design decisions derived for resource- constrained self-driving miniature cars for the international competition CaroloCup are compared with architectural designs from recent real-scale self-driving cars. ::: Results: Scaling down available resources for realizing self-driving vehicular technol- ogy puts additional constraints on the architectural design; especially reusability of software components in platform-independent algorithmic concepts are prevailing. Conclusion: Software frameworks like the robotic operating system (ROS) enable fast prototypical solutions; however, architectural support for resource-constrained devices is limited. Here, architectural design drivers as realized in AUTOSAR are more suitable.


title: secCloudSim: Secure Cloud Simulator

abstract: Cloud Computing is an emerging field of technology that relies on sharing of resources over the Internet to achieve economy of scales. It has gained momentum among both individuals and corporate sectors due to a variety of applications such as ease of administration, mobility and collaboration, reduction of hardware and licensing cost, and scalability. Researchers are also trying to explore and analyze the Cloud environment, but due to the lack of resources for performing large-scale experiments, they mostly rely on Cloud simulators. In this regards, several Cloud simulators have been developed to analyze different features as evaluation of fetching a virtual machine, power consumption of network equipment's, resource consumptions and allocation of memory. However, according to the best of our knowledge, none of these simulators provide any support for analyzing the security aspects of the Cloud. In this paper, we have designed and implemented a new Cloud simulator, entitled as secure Cloud Simulator (sec Cloud Sim). The sec Cloud Sim is an extension of existing iCan Cloud simulator, which provides the basic security features of authentication and authorization. After the successful implementation, we have evaluated the performance of sec Cloud Sim and iCan Cloud in terms of packet rate, processing time, response time, and time delay. Our performance evaluation results revealed that sec Cloud Sim has a low performance impact as compared to iCan Cloud. This low performance impact of sec Cloud Sim is due to the integration of security features to iCan Cloud architecture.


Towards a Physiological Model of Emotions: First Steps


Spectral reconstruction of signals from periodic nonuniform subsampling based on a Nyquist folding scheme


title: Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

abstract: Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.


title: Fruit Feature Recognition Based on Unsupervised Competitive Learning and Backpropagation Algorithms

abstract: This paper presents Fruit Feature Recognition (FFR) based on Unsupervised Competitive Learning (UCL) and Back-propagation Neural Network (BPNN) techniques. Fruit image data set comprising 300 samples categorized into three fruit classes namely Apple, Banana and Mango were used. Fruit attributes were extracted using Gray Level Co-occurrence Matrix (GLCM) functions. The applied simulations showed that UCL and BPNN algorithms were able to recognize the fruit images. The performance comparison tests further showed that the UCL was able to achieve a higher accuracy compared to the BPNN explorations which attained acceptable degree of recognition. The experimental simulation analysis of fruit recognition system achieved an accuracy of 94% for UCL and 90% for the BPNN.


title: ORIGINAL ARTICLE Service Innovation Through Touch-points: Development of an Innovation Toolkit for the First Stages of New Service Development

abstract: This paper reviews one of the central areas of service design, the area of touch-point innovation. Specifically, it describes the development and use of a card-based toolkit developed in the AT-ONE project - the AT-ONE touch-point cards. These cards have been developed to assist cross-functional teams during the first phases of the New Service Development (NSD) process. This paper describes and analyses the development of the tools, their intended use and their evaluation following actual uptake by several commercial service providers. The results show that the toolkit assists the innovation process during the first phases of the new service development process and helps develop team cohesiveness. The card-based approach offers a tangibility that teams find useful, and that offers multiple usage alternatives. In addition, the paper describes the multiple functions that tools used in service innovation need to accommodate, and how design makes an important contribution to this. The work also reflects upon the materials of service design and suggests that touch-points are one of the materials used by designers to understand, explore and develop innovative service solutions. Suggestions for further work are included that include aspects of toolkit tangibility, usage areas and touch-point innovation.


title: Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning.

abstract: Effective parenting is the most powerful way to reduce adolescent problem behaviors. Dissemination of research-based family interventions has been slow, with most practitioners still implementing ineffective programs. This article reviews 2 federal studies that involved national searches for effective family interventions targeting prebirth to adolescence: Preventing Substance Abuse Among Children and Adolescents: Family-Centered Approaches (Center for Substance Abuse Prevention, 1998) and Strengthening America's Families (R. Alvarado, K. L. Kumpfer, K. Kendall, S. Beesley, & C. Lee-Cavaness, 2000). RESULTS identified 3 effective prevention approaches, 13 principles of effectiveness, and 35 programs. Recommendations include increased dissemination research on training and technical assistance systems, adoption with fidelity and quality, and gender-, age-, and culturally sensitive adaptations. (PsycINFO Database Record (c) 2013 APA, all rights reserved) Language: en


title: A single layer dual linearly polarized microstrip patch antenna array for automotive applications in the 77 GHz band student submission

abstract: A dual linearly polarized antenna array for automotive applications in the 77 GHz band is presented. The array is composed of 10 single microstrip patch radiators excited by a sophisticated series circuit feed network. The realized amplitude taper and the carefully adjusted excitation phases lead to a low sidelobe level (SLL) and boresight radiation in broadside direction. The ports of the antenna array are precisely matched to 50 Q over the complete frequency band from 76 to 79 GHz. With an extremely low cross-talk of below — 35 dB, purely linearly polarized waves are radiated in both channels. The radiation behavior is measured and shows a strong main beam in broadside direction, low sidelobes, and low cross polarized components.


Assessment of availability of Terra Preta Sanitation precursors in Arba Minch , Ethiopia


title: The Effects of School Reform under NCLB Waivers: Evidence from Focus Schools in Kentucky

abstract: Under waivers to the No Child Left Behind (NCLB) Act, the federal government required states to identify schools where targeted subgroups of students have the lowest achievement and to implement reforms in these “Focus Schools.” In this study, we examine the Focus School reforms in the state of Kentucky. The reforms in this state are uniquely interesting for several reasons. One is that the state developed unusually explicit guidance for Focus Schools centered on a comprehensive school-planning process. Second, the state identified Focus Schools using a “super subgroup” measure that combined traditionally low-performing subgroups into an umbrella group. This design feature may have catalyzed broader whole-school reforms and attenuated the incentives to target reform efforts narrowly. Using regression discontinuity designs, we find that these reforms led to substantial improvements in school performance, raising math proficiency rates by 17 percent and reading proficiency rates by 9 percent.


title: Managing demand uncertainty in supply chain planning

abstract: In this work, we provide an overview of our previously published works on incorporating demand uncertainty in midterm planning of multisite supply chains. A stochastic programming based approach is described to model the planning process as it reacts to demand realizations unfolding over time. In the proposed bilevel-framework, the manufacturing decisions are modeled as ‘here-and-now’ decisions, which are made before demand realization. Subsequently, the logistics decisions are postponed in a ‘waitand-see’ mode to optimize in the face of uncertainty. In addition, the trade-off between customer satisfaction level and production costs is also captured in the model. The proposed model provides an effective tool for evaluating and actively managing the exposure of an enterprises assets (such as inventory levels and profit margins) to market uncertainties. The key features of the proposed framework are highlighted through a supply chain planning case study. # 2003 Elsevier Science Ltd. All rights reserved.


title: Social features of online networks: the strength of intermediary ties in online social media

abstract: An increasing fraction of today social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite of this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties), events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties).


title: Recent system applications of short-pulse ultra-wideband (UWB) technology

abstract: Developed in the early 1960s, time-domain electromagnetics, the study of electromagnetic-wave propagation from a time-domain perspective, has given birth to a fascinating new technology, which today is commonly referred to as ultra-wideband (UWB). It has now been slightly more than 25 years since the 1978 seminal paper of Bennett and Ross, which summarized UWB's early applications. It thus seems appropriate, given the tremendous increase in interest in the technology since the Federal Communications Commission modified its Part 15 rules to accommodate UWB transmissions, to take a look at more recent system applications of this unique technology. This paper provides a brief historical perspective of UWB, discusses recent techniques for the generation and reception of short-pulse electromagnetic waveforms, and examines a number of recently developed UWB systems in the communications, radar, and precision-positioning fields. Finally, a brief assessment of future trends for the technology is provided.


A Hybrid Architecture for Plagiarism Detection


title: META II a syntax-oriented compiler writing language

abstract: META II is a compiler writing language which consists of syntax equations resembling Backus normal form and into which instructions to output assembly language commands are inserted. Compilers have been written in this language for VALGOL I and VALGOL II. The former is a simple algebraic language designed for the purpose of illustrating META II. The latter contains a fairly large subset of ALGOL 60. The method of writing compilers which is given in detail in the paper may be explained briefly as follows. Each syntax equation is translated into a recursive subroutine which tests the input string for a particular phrase structure, and deletes it if found. Backup is avoided by the extensive use of factoring in the syntax equations. For each source language, an interpreter is written and programs are compiled into that interpretive language.


title: Shield: A stackable secure storage system for file sharing in public storage

abstract: With the increasing amount of personal data stored in public storage, users are losing control of their physical data, putting their data information at risk of theft or being compromised. Traditional secure storage systems either require users to completely trust the storage provider or impose the considerable burden of managing files on file owners; such systems are inapplicable in the practical cloud environment. This paper addresses these challenging problems by proposing a new secure system architecture and implementing a stackable secure storage system named Shield, in which a proxy server is introduced to be in charge of authentication and access control. We propose a new variant of the Merkle Hash Tree to support efficient integrity checking and file content update; further, we have designed a hierarchical key organization to achieve convenient keys management and efficient permission revocation. Shield supports concurrent write access by employing a virtual linked list; it also provides secure file sharing without any modification to the underlying file systems. A series of evaluations over various real benchmarks show that Shield causes about 7%~13% performance degradation when compared with eCryptfs but provides enhanced security for user's data.


title: Quaddirectional 2D-Recurrent Neural Networks For Image Labeling

abstract: We adopt Convolutional Neural Networks (CNN) to learn discriminative features for local patch classification. We further introduce quaddirectional 2D Recurrent Neural Networks to model the long range dependencies among pixels. Our quaddirectional 2D-RNN is able to embed the global image context into the compact local representation, which significantly enhance their discriminative power. Our experiments demonstrate that the integration of CNN and quaddirectional 2D-RNN achieves very promising results which are comparable to state-of-the-art on real-world image labeling benchmarks.


title: An interactive approach to semantic modeling of indoor scenes with an RGBD camera

abstract: We present an interactive approach to semantic modeling of indoor scenes with a consumer-level RGBD camera. Using our approach, the user first takes an RGBD image of an indoor scene, which is automatically segmented into a set of regions with semantic labels. If the segmentation is not satisfactory, the user can draw some strokes to guide the algorithm to achieve better results. After the segmentation is finished, the depth data of each semantic region is used to retrieve a matching 3D model from a database. Each model is then transformed according to the image depth to yield the scene. For large scenes where a single image can only cover one part of the scene, the user can take multiple images to construct other parts of the scene. The 3D models built for all images are then transformed and unified into a complete scene. We demonstrate the efficiency and robustness of our approach by modeling several real-world scenes.


TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo supplementary materials