title: W-Band High-Gain Circularly Polarized Aperture-Coupled Magneto-Electric Dipole Antenna Array With Gap Waveguide Feed Network

abstract: A 1 × 8 circularly polarized (CP) aperture-coupled magneto-electric (ME) dipole antenna array excited by low-loss gap waveguide (GWG) feed network is proposed in this study. This linear antenna array is composed of two layers, upper CP ME-dipole printed circuit board (PCB) antenna subarrays and a lower metallic GWG feed network. The proposed compact GWG feed network with wideband impedance matching is composed of a ridge gap waveguide (RGWG) transmission structure and two short-ended vertical polarized groove gap waveguide (VP-GGWG) cavities. The transmission loss of this feed network caused by transition, such as coupling and fabrication tolerance between two layers, can be reduced by combining RGWG and VP-GGWG in the same layer. In addition, this feed network is fabricated all in metal with no substrate loss. Transverse slots are etched at the right position of the upper PCB layer above VP-GGWG cavities to excite the CP ME-dipole antennas. The antenna element has stable radiation pattern and wide axial-ratio (AR) bandwidth, which is desirable for CP antenna array. The proposed 1 × 8 linear antenna array has been simulated and fabricated to validate our design. Measured results show that antenna array achieves a maximum boresight gain of 18 dBi and an AR bandwidth of 14.5% from 89 to 103 GHz for AR <; 3 dB, with a boresight gain above 16.5 dBi.


title: From Pictorial Structures to deformable structures

abstract: Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connected with pairwise constraints that define the prior probability of part configurations. These models are widely used to represent non-rigid articulated objects such as humans and animals despite the fact that such objects have parts that deform non-rigidly. Here we define a new Deformable Structures (DS) model that is a natural extension of previous PS models and that captures the non-rigid shape deformation of the parts. Each part in a DS model is represented by a low-dimensional shape deformation space and pairwise potentials between parts capture how the shape varies with pose and the shape of neighboring parts. A key advantage of such a model is that it more accurately models object boundaries. This enables image likelihood models that are more discriminative than previous PS likelihoods. This likelihood is learned using training imagery annotated using a DS “puppet.” We focus on a human DS model learned from 2D projections of a realistic 3D human body model and use it to infer human poses in images using a form of non-parametric belief propagation.


title: Enhancing IT governance practices: A model and case study of an organization's efforts

abstract: For many organizations, Information Technology (IT) enabled business initiatives and IT infrastructure constitute major investments that, if not managed properly, may impair rather than enhance the organization's competitive position. Especially since the advent of Sarbanes–Oxley (SOX), both management and IT professionals are concerned with design, implementation, and assessment of IT governance strategies to ensure that technology truly serves the needs of the business. Via an in-depth study within one organisation, this research explores the factors influencing IT governance structures, processes, and outcome metrics. Interview responses to open-ended questions indicated that more effective IT governance performance outcomes are associated with a shared understanding of business and IT objectives; active involvement of IT steering committees; a balance of business and IT representatives in IT decisions; and comprehensive and well-communicated IT strategies and policies. IT governance also plays a prominent role in fostering project success and delivering business value.


title: FPGA Implementation of 8, 16 and 32 Bit LFSR with Maximum Length Feedback Polynomial Using VHDL

abstract: LFSR based PN Sequence Generator technique is used for various cryptography applications and for designing encoder, decoder in different communication channel. It is more important to test and verify by implementing on any hardware for getting better efficient result. As FPGAs is used to implement any logical function for faster prototype development, it is necessary to implement the existing design of LFSR on FPGA to test and verify the simulated & synthesis result between different lengths. The total number of random state generated on LFSR depends on the feedback polynomial. As it is simple counter so it can count maximum of 2n-1 by using maximum feedback polynomial. Here in this paper we implemented 8, 16 and 32-bit LFSR on FPGA by using VHDL to study the performance and analysis the behavior of randomness. The analysis is conceded out to find number of gates, memory and speed requirement in FPGA as the number of bits is increased. The comparative study of 8, 16 and 32 bit LFSR on FPGA is shown here to understand the on chip verification. Also the simulation problem for long bit LFSR on FPGA is presented.


title: Brain–computer interface using a simplified functional near-infrared spectroscopy system

abstract: A brain–computer interface (BCI) is a device that allows a user to communicate with external devices through thought processes alone. A novel signal acquisition tool for BCIs is near-infrared spectroscopy (NIRS), an optical technique to measure localized cortical brain activity. The benefits of using this non-invasive modality are safety, portability and accessibility. A number of commercial multi-channel NIRS system are available; however we have developed a straightforward custom-built system to investigate the functionality of a fNIRS-BCI system. This work describes the construction of the device, the principles of operation and the implementation of a fNIRS-BCI application, 'Mindswitch' that harnesses motor imagery for control. Analysis is performed online and feedback of performance is presented to the user. Mindswitch presents a basic 'on/off' switching option to the user, where selection of either state takes 1 min. Initial results show that fNIRS can support simple BCI functionality and shows much potential. Although performance may be currently inferior to many EEG systems, there is much scope for development particularly with more sophisticated signal processing and classification techniques. We hope that by presenting fNIRS as an accessible and affordable option, a new avenue of exploration will open within the BCI research community and stimulate further research in fNIRS-BCIs.


Using context-window overlapping in synonym discovery and ontology extension


title: The Social Internet of Things (SIoT) - When social networks meet the Internet of Things: Concept, architecture and network characterization

abstract: Recently there has been quite a number of independent research activities that investigated the potentialities of integrating social networking concepts into Internet of Things (IoT) solutions. The resulting paradigm, named Social Internet of Things (SIoT), has the potential to support novel applications and networking services for the IoT in more effective and efficient ways. In this context, the main contributions of this paper are the following: (i) we identify appropriate policies for the establishment and the management of social relationships between objects in such a way that the resulting social network is navigable; (ii) we describe a possible architecture for the IoT that includes the functionalities required to integrate things into a social network; (iii) we analyze the characteristics of the SIoT network structure by means of simulations.


title: Learning Latent Variable Gaussian Graphical Models

abstract: Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical models (LVGGM), where the model is conditionally sparse given latent variables, but marginally non-sparse. In LVGGM, the inverse covariance matrix has a low-rank plus sparse structure, and can be learned in a regularized maximum likelihood framework. We derive novel parameter estimation error bounds for LVGGM under mild conditions in the high-dimensional setting. These results complement the existing theory on the structural learning, and open up new possibilities of using LVGGM for statistical inference.


title: Multi-criteria Shortest Paths in Time-Dependent Train Networks

abstract: We study the problem of finding all Pareto-optimal solutions in a multi-criteria setting of the shortest path problem in time-dependent graphs. This has important applications in timetable information systems for train schedules. We present a new prototype to solve this problem in a fully realistic scenario based on a multi-criteria generalization of Dijkstra's algorithm. As optimization criteria we use travel time and number of train changes, as well as a new criterion "reliability of transfers". ::: ::: The performance of the prototype and various speed-up techniques are analyzed experimentally on a large set of real test instances. In comparison with a base-line implementation, our prototype achieves significant speed-up factors of 20 with respect to the number of label creations and of 138 with respect to label insertions into the priority queue. We also compare our prototype with a time-expanded graph model.


title: Yago: A Large Ontology from Wikipedia and WordNet

abstract: This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO's precision at 95%-as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO's data.


title: Design of High-Performing Microstrip Receiving GPS Antennas With Multiple Feeds

abstract: In this letter, we present the design of two new high-performing microstrip receiving antennas for the Global Positioning System (GPS). Both layouts proposed here are based on the employment of multiple feeds in order to improve the polarization purity and the pattern symmetry, thus leading to an improved accuracy of the system. The first antenna configuration consists of a truncated-corner squared patch with concentric rings of miniaturized magnetic resonators in order to suppress the surface wave contribution and reduce the back-radiation of the antenna. The second radiator consists of a circular patch antenna with four feeds and loaded with a number of vias in order to improve the front-to-back ratio. Full-wave numerical simulations are presented to show the effectiveness of the proposed design in terms of impedance matching, polarization purity, antenna gain, front-to-back ratio, and shape of the radiation pattern.


title: Agency mechanics: gameplay design in survival horror video games

abstract: This article proposes the notion of ‘agency mechanics’ as an innovative design element of video games. It begins by exploring the well-documented interactive elements of horror cinema, and goes on to explore how strategies such as the sadistic/masochistic gaze and narrative perspective have been further developed in video games. It then explores work to date on the manipulation of interactivity in video games before closely analysing how agency and control are managed in the survival horror franchise Dead Space. It concludes with reflections on further research in the nexus between film and games, and further possibilities for game design through the explicit management of agency mechanics.


Supplementary material for the paper : Bayesian Optimization with Robust Bayesian Neural Networks


A Lane Detection, Tracking and Recognition System for Smart Vehicles


title: Segmentation of White Blood Cells From Microscopic Images Using a Novel Combination of K-Means Clustering and Modified Watershed Algorithm

abstract: Recognition of white blood cells (WBCs) is the first step to diagnose some particular diseases such as acquired immune deficiency syndrome, leukemia, and other blood-related diseases that are usually done by pathologists using an optical microscope. This process is time-consuming, extremely tedious, and expensive and needs experienced experts in this field. Thus, a computer-aided diagnosis system that assists pathologistsin the diagnostic process can be so effective. Segmentation of WBCsis usually a first step in developing a computer-aided diagnosis system. The main purpose of this paper is to segment WBCs from microscopic images. For this purpose, we present a novel combination of thresholding, kmeans clustering, and modified watershed algorithms in three stages including (1) segmentation of WBCs from a microscopic image, (2) extraction of nuclei from cellâ€™s image, and (3) separation of overlapping cells and nuclei. The evaluation results of the proposed method show that similarity measures, precision, and sensitivity respectively were 92.07, 96.07, and 94.30% for nucleus segmentation and 92.93, 97.41, and 93.78% for cell segmentation. In addition, statistical analysis presents high similarity between manual segmentation and the results obtained by the proposed method.


title: Face identification by fitting a 3D morphable model using linear shape and texture error functions

abstract: This paper presents a novel algorithm aiming at analysis and identification of faces viewed from different poses and illumination conditions. Face analysis from a single image is performed by recovering the shape and textures parameters of a 3D Morphable Model in an analysis-by-synthesis fashion. The shape parameters are computed from a shape error estimated by optical flow and the texture parameters are obtained from a texture error. The algorithm uses linear equations to recover the shape and texture parameters irrespective of pose and lighting conditions of the face image. Identification experiments are reported on more than 5000 images from the publicly available CMU-PIE database which includes faces viewed from 13 different poses and under 22 different illuminations. Extensive identification results are available on our web page for future comparison with novel algorithms.


title: The Web as a Knowledge-base for Answering Complex Questions

abstract: Answering complex questions is a time-consuming activity for humans that requires reasoning and integration of information. Recent work on reading comprehension made headway in answering simple questions, but tackling complex questions is still an ongoing research challenge. Conversely, semantic parsers have been successful at handling compositionality, but only when the information resides in a target knowledge-base. In this paper, we present a novel framework for answering broad and complex questions, assuming answering simple questions is possible using a search engine and a reading comprehension model. We propose to decompose complex questions into a sequence of simple questions, and compute the final answer from the sequence of answers. To illustrate the viability of our approach, we create a new dataset of complex questions, ComplexWebQuestions, and present a model that decomposes questions and interacts with the web to compute an answer. We empirically demonstrate that question decomposition improves performance from 20.8 precision@1 to 27.5 precision@1 on this new dataset.


title: Electromagnetic wave absorption and compressive behavior of a three-dimensional metamaterial absorber based on 3D printed honeycomb

abstract: Lightweight structures with multi-functions such as electromagnetic wave absorption and excellent mechanical properties are required in spacecraft. A three-dimensional metamaterial absorber consisting of honeycomb and resistive films was proposed and fabricated through 3D printing and silk-screen printing technology. According to simulation and experiment results, the present three-dimensional metamaterial absorber can realize an absorptivity of more than 90% in a wide band of 3.53–24.00 GHz, and improve absorbing efficiency for transverse magnetic (TM) waves of oblique incidence angle from 0° to 70°. The compression test results reveal that compressive strength of the 3D printed honeycomb can reach 10.7 MPa with density of only 254.91 kg/m3, and the energy absorption per volume W ::: v ::: and per unit mass W ::: m ::: are 4.37 × 103 KJ/m3 and 17.14 KJ/Kg, respectively. The peak compressive strength and energy absorption per mass are at least 2.2 and 3 times comparing to metallic lattice cores with the same density. Outstanding electromagnetic wave absorption and mechanical performance make the present three-dimensional metamaterial absorber more competitive in engineering applications.


title: OSCAR: Object security architecture for the Internet of Things

abstract: Abstract In this paper, we propose OSCAR, an architecture for end-to-end security in the Internet of Things. It is based on the concept of object security that relates security with the application payload. The architecture includes Authorization Servers that provide clients with Access Secrets that enable them to request resources from constrained CoAP nodes. The nodes reply with the requested resources that are signed and encrypted. The scheme intrinsically supports multicast, asynchronous traffic, and caching. We have evaluated OSCAR in two cases: 802.15.4 Low Power and Lossy Networks (LLN) and Machine-to-Machine (M2M) communication on two different hardware platforms and MAC layers on a real testbed and using the Cooja emulator. The results show that OSCAR outperforms a security scheme based on DTLS when the number of nodes increases. OSCAR also results in low energy consumption and latency.


title: Reconfigurable Leaky-Wave Antenna Based on Periodic Water Grating

abstract: A reconfigurable leaky-wave antenna using periodic water grating on the top of a grounded glass slab is presented in this letter. A probe-fed unidirectional surface-wave launcher is designed to feed the antenna. Two prototypes of the proposed antenna are fabricated and tested to demonstrate its radiation performance and tuning capability. Measured results of the first static antenna show that a maximum gain of 16 dB, a wide impedance bandwidth (|S11|<;-10 dB) from 4.1 to 6.6 GHz (46.7%), and a large beam-scanning range over the impedance band from the back-fire to nearly broadside direction are obtained. Measured results of the second dynamic antenna show that the beam-pointing angle can be tuned from -32° to 18 ° at 5.5 GHz, while the return loss maintains better than 10 dB.


Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews


title: Hybrid Inertial and Vision Tracking for Augmented Reality Registration

abstract: The biggest single obstacle to building effective augmented reality (AR) systems is the lack of accurate wide-area sensors for trackers that report the locations and orientations of objects in an environment. Active (sensor-emitter) tracking technologies require powered-device installation. Limiting their use to prepared areas that are relatively free of natural or man-made interference sources. Vision-based systems can use passive landmarks, but they are more computationally demanding and often exhibit erroneous behavior due to occlusion or numerical instability. Inertial sensors are completely passive, requiring no external devices or targets, however, the drift rates in portable strapdown configurations are too great for practical use. In this paper, we present a hybrid approach to AR tracking that integrates inertial and vision-based technologies. We exploit the complementary nature of the two technologies to compensate for the weaknesses in each component. Analysis and experimental results demonstrate this system's effectiveness.


title: Communication and Security in Health Monitoring Systems -- A Review

abstract: The fast development of sensing devices and radios enables more powerful and flexible remote health monitoring systems. Considering the future vision of the Internet of Things (IoT), many requirements and challenges rise to the design and implementation of such systems. Bridging the gap between sensor nodes on the human body and the Internet becomes a challenging task in terms of reliable communications. Additionally, the systems will not only have to provide functionality, but also be highly secure. In this paper, we provide a survey on existing communication protocols and security issues related to pervasive health monitoring, describing their limitations, challenges, and possible solutions. We propose a generic protocol stack design as a first step toward handling interoperability in heterogeneous low-power wireless body area networks.


title: Refining image retrieval based on context-driven methods

abstract: A prototype of the content-based image retrieval system is implemented, based on the algorithms introduced in this paper. The image contents at the high levels are extracted. The fuzzy C means classifier is employed to compute the object clusters and provide useful information for overlapped clusters. Automatic image segmentation and categorization are achieved. To obtain the context for image retrieval, the subjective and objective contexts are modeled by means of the fuzzy sets theory. The system is able to trace the users' interactions during retrieval. The refinements of the retrieval results can be made, while the users are submitting the queries telling the specific requirements.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


Machine Learning-based Software Testing: Towards a Classification Framework


title: 77-GHz Dual-Layer Transmit-Array for Automotive Radar Applications

abstract: A 77-GHz transmit-array on dual-layer printed circuit board (PCB) is proposed for automotive radar applications. Coplanar patch unit-cells are etched on opposite sides of the PCB and connected by through-via. The unit-cells are arranged in concentric rings to form the transmit-array for 1-bit in-phase transmission. When combined with four-substrate-integrated waveguide (SIW) slot antennas as the primary feeds, the transmit-array is able to generate four beams with a specific coverage of $\pm 15^{\circ}$ . The simulated and measured results of the antenna prototype at 76.5 GHz agree well, with gain greater than 18.5 dBi. The coplanar structure significantly simplifies the transmit-array design and eases the fabrication, in particular, at millimeter-wave frequencies.


Statistical notes for clinical researchers: A one-way repeated measures ANOVA for data with repeated observations


title: Reflectance and Illumination Recovery in the Wild

abstract: The appearance of an object in an image encodes invaluable information about that object and the surrounding scene. Inferring object reflectance and scene illumination from an image would help us decode this information: reflectance can reveal important properties about the materials composing an object; the illumination can tell us, for instance, whether the scene is indoors or outdoors. Recovering reflectance and illumination from a single image in the real world, however, is a difficult task. Real scenes illuminate objects from every visible direction and real objects vary greatly in reflectance behavior. In addition, the image formation process introduces ambiguities, like color constancy, that make reversing the process ill-posed. To address this problem, we propose a Bayesian framework for joint reflectance and illumination inference in the real world. We develop a reflectance model and priors that precisely capture the space of real-world object reflectance and a flexible illumination model that can represent real-world illumination with priors that combat the deleterious effects of image formation. We analyze the performance of our approach on a set of synthetic data and demonstrate results on real-world scenes. These contributions enable reliable reflectance and illumination inference in the real world.


title: Artificial intelligence techniques for driving safety and vehicle crash prediction

abstract: Accident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. Artificial intelligence (AI) is used in many real world applications, especially where outcomes and data are not same all the time and are influenced by occurrence of random changes. This paper presents a study on the existing approaches for the detection of unsafe driving patterns of a vehicle used to predict accidents. The literature covered in this paper is from the past 10 years, from 2004 to 2014. AI techniques are surveyed for the detection of unsafe driving style and crash prediction. A number of statistical methods which are used to predict the accidents by using different vehicle and driving features are also covered in this paper. The approaches studied in this paper are compared in terms of datasets and prediction performance. We also provide a list of datasets and simulators available for the scientific community to conduct research in the subject domain. The paper also identifies some of the critical open questions that need to be addressed for road safety using AI techniques.


Mirroring, Social Learning and Dance Movement Therapy with Childhood Autism Spectrum Disorder: A Literature Review


title: The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task

abstract: This paper extends previous research on two approaches to human-centred automation: (1) intermediate levels of automation (LOAs) for maintaining operator involvement in complex systems control and facilitating situation awareness; and (2) adaptive automation (AA) for managing operator workload through dynamic control allocations between the human and machine over time. Some empirical research has been conducted to examine LOA and AA independently, with the objective of detailing a theory of human-centred automation. Unfortunately, no previous work has studied the interaction of these two approaches, nor has any research attempted to systematically determine which LOAs should be used in adaptive systems and how certain types of dynamic function allocations should be scheduled over time. The present research briefly reviews the theory of human-centred automation and LOA and AA approaches. Building on this background, an initial study was presented that attempts to address the conjuncture of these two approa...


title: Large-scale taxonomy categorization for noisy product listings

abstract: E-commerce catalogs include a continuously growing number of products that are constantly updated. Each item in a catalog is characterized by several attributes and identified by a taxonomy label. Categorizing products with their taxonomy labels is fundamental to effectively search and organize listings in a catalog. However, manual and/or rule based approaches to categorization are not scalable. In this paper, we compare several classifiers to product taxonomy categorization of top-level categories. We first investigate a number of feature sets and observe that a combination of word unigrams from product names and navigational breadcrumbs work best for categorization. Secondly, we apply correspondence topic models to detect noisy data and introduce a lightweight manual process to improve dataset quality. Finally, we evaluate linear models, gradient boosted trees (GBTs) and convolutional neural networks (CNNs) with pre-trained word embeddings demonstrating that, compared to other baselines, GBTs and CNNs yield the highest gains in error reduction.


title: Neural Query Performance Prediction using Weak Supervision from Multiple Signals

abstract: Predicting the performance of a search engine for a given query is a fundamental and challenging task in information retrieval. Accurate performance predictors can be used in various ways, such as triggering an action, choosing the most effective ranking function per query, or selecting the best variant from multiple query formulations. In this paper, we propose a general end-to-end query performance prediction framework based on neural networks, called NeuralQPP. Our framework consists of multiple components, each learning a representation suitable for performance prediction. These representations are then aggregated and fed into a prediction sub-network. We train our models with multiple weak supervision signals, which is an unsupervised learning approach that uses the existing unsupervised performance predictors using weak labels. We also propose a simple yet effective component dropout technique to regularize our model. Our experiments on four newswire and web collections demonstrate that NeuralQPP significantly outperforms state-of-the-art baselines, in nearly every case. Furthermore, we thoroughly analyze the effectiveness of each component, each weak supervision signal, and all resulting combinations in our experiments.


Gas Identification Using Passive UHF RFID Sensor Platform


Lexical Sememe Prediction via Word Embeddings and Matrix Factorization


title: ANFIS-Based Control of a Grid-Connected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries

abstract: This paper describes and evaluates an adaptive neuro-fuzzy inference system (ANFIS)-based energy management system (EMS) of a grid-connected hybrid system. It presents a wind turbine (WT) and photovoltaic (PV) solar panels as primary energy sources, and an energy storage system (ESS) based on hydrogen (fuel cell -FC-, hydrogen tank and electrolyzer) and battery. All of the energy sources use dc/dc power converters in order to connect them to a central DC bus. An ANFIS-based supervisory control system determines the power that must be generated by/stored in the hydrogen and battery, taking into account the power demanded by the grid, the available power, the hydrogen tank level and the state-of-charge (SOC) of the battery. Furthermore, an ANFIS-based control is applied to the three-phase inverter, which connects the hybrid system to grid. Otherwise, this new EMS is compared with a classical EMS composed of state-based supervisory control system based on states and inverter control system based on PI controllers. Dynamic simulations demonstrate the right performance of the ANFIS-based EMS for the hybrid system under study and the better performance with respect to the classical EMS.


title: 14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks

abstract: Recently, deep learning with convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has become universal in all-around applications. CNNs are used to support vision recognition and processing, and RNNs are able to recognize time varying entities and to support generative models. Also, combining both CNNs and RNNs can recognize time varying visual entities, such as action and gesture, and to support image captioning [1]. However, the computational requirements in CNNs are quite different from those of RNNs. Fig. 14.2.1 shows a computation and weight-size analysis of convolution layers (CLs), fully-connected layers (FCLs) and RNN-LSTM layers (RLs). While CLs require a massive amount of computation with a relatively small number of filter weights, FCLs and RLs require a relatively small amount of computation with a huge number of filter weights. Therefore, when FCLs and RLs are accelerated with SoCs specialized for CLs, they suffer from high memory transaction costs, low PE utilization, and a mismatch of the computational patterns. Conversely, when CLs are accelerated with FCL- and RL-dedicated SoCs, they cannot exploit reusability and achieve required throughput. So far, works have considered acceleration of CLs, such as [2–4], or FCLs and RLs like [5]. However, there has been no work on a combined CNN-RNN processor. In addition, a highly reconfigurable CNN-RNN processor with high energy-efficiency is desirable to support general-purpose deep neural networks (DNNs).


title: Assessment of Millennial Nursesʼ Job Satisfaction and Professional Practice Environment

abstract: OBJECTIVE: The purpose of this study was to assess the relationship between demographic factors (age, gender, race, ethnicity, work status, and experience), the professional practice environment, and work satisfaction to increase understanding of millennial nurses. BACKGROUND: Millennials comprise 30% of the nursing workforce and are more likely to experience burnout, stress, high turnover, and less dedication to their workplace than other counterparts. Understanding how to retain these nurses is important to ensure work satisfaction and high-quality patient outcomes. METHODS: This descriptive study was a secondary analysis of data using the Professional Practice Work Environment Inventory survey. Descriptive statistics were used to describe the variables. Descriptive statistics and standard t tests were used. RESULTS: Demographics accounted for only 2.6% of the variance in work satisfaction, whereas supportive leadership accounted for nearly 63%. CONCLUSION: Findings demonstrate that supportive leadership is the primary factor contributing to millennial nurses' work satisfaction. This suggests that efforts to retain millennial nurses should focus on developing supportive leaders.


title: Achieving Controllability of Electric Loads

abstract: This paper discusses conceptual frameworks for actively involving highly distributed loads in power system control actions. The context for load control is established by providing an overview of system control objectives, including economic dispatch, automatic generation control, and spinning reserve. The paper then reviews existing initiatives that seek to develop load control programs for the provision of power system services. We then discuss some of the challenges to achieving a load control scheme that balances device-level objectives with power system-level objectives. One of the central premises of the paper is that, in order to achieve full responsiveness, direct load control (as opposed to price response) is required to enable fast time scale, predictable control opportunities, especially for the provision of ancillary services such as regulation and contingency reserves. Centralized, hierarchical, and distributed control architectures are discussed along with benefits and disadvantages, especially in relation to integration with the legacy power system control architecture. Implications for the supporting communications infrastructure are also considered. Fully responsive load control is illustrated in the context of thermostatically controlled loads and plug-in electric vehicles.


title: Cloud Computing: The impact on digital forensic investigations

abstract: Cloud Computing (CC) as a concept and business opportunity is likely to see many organisations experiencing the 'credit crunch', embrace the relatively low cost option of CC to ensure continued business viability and sustainability. The pay-as-you-go structure of the CC business model is typically suited to SME's who do not have the resources to completely fulfil their IT requirements. Private end users will also look to utilise the colossal pool of resources that CC offers in an attempt to provide mobile freedom of information. However, as with many opportunities that offer legitimate users enormous benefits, unscrupulous and criminal users will also look to use CC to exploit the loopholes that may exist within this new concept, design and business model. This paper will outline the tasks that the authors undertook for the CLOIDIFIN project and highlight where the Impact of CC will diversely effect digital forensic investigations.


title: Transition-based Dependency DAG Parsing Using Dynamic Oracles

abstract: In most of the dependency parsing studies, dependency relations within a sentence are often presented as a tree structure. Whilst the tree structure is sufficient to represent the surface relations, deep dependencies which may result to multi-headed relations require more general dependency structures, namely Directed Acyclic Graphs (DAGs). This study proposes a new dependency DAG parsing approach which uses a dynamic oracle within a shift-reduce transitionbased parsing framework. Although there is still room for improvement on performance with more feature engineering, we already obtain competitive performances compared to static oracles as a result of our initial experiments conducted on the ITU-METU-Sabanci Turkish Treebank (IMST).


title: Investigation of the random forest framework for classification of hyperspectral data

abstract: Statistical classification of byperspectral data is challenging because the inputs are high in dimension and represent multiple classes that are sometimes quite mixed, while the amount and quality of ground truth in the form of labeled data is typically limited. The resulting classifiers are often unstable and have poor generalization. This work investigates two approaches based on the concept of random forests of classifiers implemented within a binary hierarchical multiclassifier system, with the goal of achieving improved generalization of the classifier in analysis of hyperspectral data, particularly when the quantity of training data is limited. A new classifier is proposed that incorporates bagging of training samples and adaptive random subspace feature selection within a binary hierarchical classifier (BHC), such that the number of features that is selected at each node of the tree is dependent on the quantity of associated training data. Results are compared to a random forest implementation based on the framework of classification and regression trees. For both methods, classification results obtained from experiments on data acquired by the National Aeronautics and Space Administration (NASA) Airborne Visible/Infrared Imaging Spectrometer instrument over the Kennedy Space Center, Florida, and by Hyperion on the NASA Earth Observing 1 satellite over the Okavango Delta of Botswana are superior to those from the original best basis BHC algorithm and a random subspace extension of the BHC.


title: A Structural SVM Based Approach for Optimizing Partial AUC

abstract: The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking and biometric screening to medical diagnosis, performance is measured not in terms of the full area under the ROC curve, but instead, in terms of the partial area under the ROC curve between two specified false positive rates. In this paper, we develop a structural SVM framework for directly optimizing the partial AUC between any two false positive rates. Our approach makes use of a cutting plane solver along the lines of the structural SVM based approach for optimizing the full AUC developed by Joachims (2005). Unlike the full AUC, where the combinatorial optimization problem needed to find the most violated constraint in the cutting plane solver can be decomposed easily to yield an efficient algorithm, the corresponding optimization problem in the case of partial AUC is harder to decompose. One of our key technical contributions is an efficient algorithm for solving this combinatorial optimization problem that has the same computational complexity as Joachims' algorithm for optimizing the usual AUC. This allows us to efficiently optimize the partial AUC in any desired false positive range. We demonstrate the approach on a variety of real-world tasks.


title: Evaluating Colour-Based Object Recognition Algorithms Using the SOIL-47 Database

abstract: In this paper, a new image set, called the Surrey Object Image Library (SOIL-47) is introduced, on which the performance of two colour-based object recognition methods is evaluated. The data was collected specifically for testing colour-based recognition algorithm and is publicly available. In the conducted experiments on SOIL-47, we evaluate two recognition algoritms; the Multimodal Neighbourhood Signature (MNS) approach and a method based on a Attributed Relational Graph (ARG). The MNS approach represents object appearance by measurements computed from image neighbourhoods with a multimodal colour density function. The ARG approach computes a graph of affine invariant measurements of the colour and shape of segmented image regions. Using only a single model image of each of the 47 objects, MNS performed well even for extreme test views close to degrees. The ARG method assumes a locally planar surface, therefore a second experiment was conducted on a subset of box-like objects of SOIL-47. MNS performance was fairly stable, outperforming ARG for most viewing angles.. Note, that this is the fir st systematic test of MNS with controlled 3D viewpoint change.


title: The Meaning of Growing Plants: Contributions to the Elderly Living in Sheltered Housing

abstract: In this qualitative research conducted in Finland, 12 residents in sheltered housing for aged people were interviewed to explore the meanings they associate with the growing of plants. Growing plants had both individual and social meanings for the interviewees. The individual meanings were categorized into three groups: one’s own growing skills, the continuity of time, and creating experiences. The category “one’s own growing skills” was coded into three subcategories: individual settings and growing methods, interpretation of the plants’ needs and responses, and adaptation to current situation. The social meanings identified in the data were also divided into three categories: significant acts undertaken for other people, indications about the gardener, and the feeling of togetherness. The results of the research suggest that growing plants may have an effect on the wellbeing of the elderly who have a rural background and are living in institutional settings, especially for those aspects threatened by institutional environments: autonomy, a sense of control, identity, and the opportunity to form social relationships. G ardening has long been one of the most common spare-time activities among elderly people (Hill and Relf, 1982; Sarola, 1994). However, in modern institutional care-facilities for the elderly, the residents’ opportunities for continuing their hobbies are limited. This raises the question of whether or not growing plants could contribute to the well-being of the elderly living in institutional settings. Health facilities have customarily been designed to be effective careenvironments, but these environments are also often psychologically stressful (Ulrich, 1991). The design of the grounds is usually highly institutional, which can aggravate the residents’ problems of isolation, loneliness, and loss of capability and identity (Stoneham and Thoday, 1996). Moreover, conflicts between the residents’ autonomy and security are particularly prominent in homes for the elderly (Paivarinta, 1997). The life of the elderly in long-term care is often thus marked by loneliness, social isolation and by the feelings of worthlessness (Noro, 1998). According to Grahn (1991), an elderly person in institutional care may often adopt a passive role as a dependent. The abilities of the elderly become impaired, when the environment accepts them as decrepit instead of supporting their identity. Furthermore, the emphasis placed on diseases in health care work strengthens this role of decrepitude in the elderly (Liukkonen, 1995; Stein, 1997). Does gardening have qualities which could affect positively the institutional environments of elderly people? Gardening, when regarded as interaction between people and plants, includes both physical and psychological activities. The main content of gardening is the fostering of a living thing (Browne, 1992; Lewis, 1992, 1996; Matsuo, 1995). Horticultural activity enables individual creativity, social interaction (Browne, 1992) and the reciprocal exchange relationships with other people (Lewis, 1996; Stein, 1997). Stein (1997) states that by gardening, residents in a nursing home may create new memories and meanings for their lives. Plants may also trigger pleasant memories from the past (Browne, 1992; Lewis, 1996). Gardening may also increase opportunities for independent choice (Stein 1997). Ulrich (1991) suggests that to promote wellness, the environment in healthcare facilities should foster a sense of control, access to social support and access to positive distractions. In addition, plants give the elderly a hope for the future (Gaskins and Forte, 1995; Lewis, 1996). The change, which appears in the growth of the plants, can be experienced as something favorable, not threatening like the change in health of the elderly (Lewis, 1996). To assess whether growing plants contributes to the well-being of elderly living in institutions, we examined whether the growing of plants has meanings for the elderly that can be associated with their well-being. It is widely accepted that nature and plants contribute to human well-being (Relf, 1992). The effects of this positive benefit are often studied by measuring blood pressure and pulse and by using other tests (e.g., Hartig et al., 1991; Ulrich et al., 1991; Lohr et al., 1996; Ottosson and Grahn, 1998). However, these methods cannot reveal all the aspects of how the experience of growing plants affects human wellbeing. This study, therefore, applies a qualitative approach to clarify how elderly people themselves experience plants and nurture of plants in an institutional environment of sheltered housing. Rowles (1984) has defined the concept of environmental experience as “physical, cognitive, and emotional transactions with the physical setting”. In this study, the experience of growing plants is defined analogous to Rowles’ (1984) definition as a physical, cognitive, and emotional interaction of the elderly with plants. The interaction of the elderly with plants means either the actual growing or the mental activity involved in nurturing a plant. The question of whether or not the meanings the elderly give for growing plants can be associated with the well-being of residents is explored in this study. Especially those aspects of human well-being threatened in institutional environments, such as autonomy, identity, and social relationships are emphasized in the present analysis.


title: Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

abstract: This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features that can be extracted from the face to infer the level of drowsiness. These include eye blinks, head movements and yawning. However, the development of a drowsiness detection system that yields reliable and accurate results is a challenging task as it requires accurate and robust algorithms. A wide range of techniques has been examined to detect driver drowsiness in the past. The recent rise of deep learning requires that these algorithms be revisited to evaluate their accuracy in detection of drowsiness. As a result, this paper reviews machine learning techniques which include support vector machines, convolutional neural networks and hidden Markov models in the context of drowsiness detection. Furthermore, a meta-analysis is conducted on 25 papers that use machine learning techniques for drowsiness detection. The analysis reveals that support vector machine technique is the most commonly used technique to detect drowsiness, but convolutional neural networks performed better than the other two techniques. Finally, this paper lists publicly available datasets that can be used as benchmarks for drowsiness detection.


title: Concept and Implementation of a PLL-Controlled Interlaced Chirp Sequence Radar for Optimized Range–Doppler Measurements

abstract: In this paper, we introduce a novel concept for frequency-modulated continuous-wave radar. The approach is based on an interlaced chirp sequence waveform, which is controlled by phase-locked loops (PLLs). The use of PLLs improves frequency chirp linearity and reduces phase noise compared with open-loop voltage-controlled oscillators commonly used to generate the fast chirps. In contrast to basic range–Doppler processing, it enables higher target velocities to be detected unambiguously at the same resolution, which makes it suitable for automotive applications. Separate frequency synthesizers generate the interlaced ramps in the system. An RF switch combines these two signals to suppress transients caused by oscillator overshoot and to avoid incoherencies due to programming times of the PLL ICs. We built a prototype radar system in K-band. The test results are promising and bode well for other applications.


title: Hi, magic closet, tell me what to wear!

abstract: In this paper, we aim at a practical system, magic closet, for automatic occasion-oriented clothing recommendation. Given a user-input occasion, e.g., wedding, shopping or dating, magic closet intelligently suggests the most suitable clothing from the user's own clothing photo album, or automatically pairs the user-specified reference clothing (upper-body or lower-body) with the most suitable one from online shops. Two key criteria are explicitly considered for the magic closet system. One criterion is to wear properly, e.g., compared to suit pants, it is more decent to wear a cocktail dress for a banquet occasion. The other criterion is to wear aesthetically, e.g., a red T-shirt matches better white pants than green pants. To narrow the semantic gap between the low-level features of clothing and the high-level occasion categories, we adopt middle-level clothing attributes (e.g., clothing category, color, pattern) as a bridge. More specifically, the clothing attributes are treated as latent variables in our proposed latent Support Vector Machine (SVM) based recommendation model. The wearing properly criterion is described in the model through a feature-occasion potential and an attribute-occasion potential, while the wearing aesthetically criterion is expressed by an attribute-attribute potential. To learn a generalize-well model and comprehensively evaluate it, we collect a large clothing What-to-Wear (WoW) dataset, and thoroughly annotate the whole dataset with 7 multi-value clothing attributes and 10 occasion categories via Amazon Mechanic Turk. Extensive experiments on the WoW dataset demonstrate the effectiveness of the magic closet system for both occasion-oriented clothing recommendation and pairing.


title: Phonological Awareness, Vocabulary, and Reading in Deaf Children With Cochlear Implants

abstract: Purpose To explore the phonological awareness skills of deaf children with cochlear implants (CIs) and relationships with vocabulary and reading development. Method Forty-three deaf children with i...


title: Balancing strategic interests and technological requirements for mobile services

abstract: Designing business models for mobile services is a complex undertaking because it requires multiple actors to balance different design requirements. A business model can be seen as a blueprint of four interrelated components or domains: service, technology, organization and finance domain. Little attention has been paid to how these different domains are related to one another. This knowledge is needed to enhance our understanding of what constitutes a viable business model. In this paper the interdependencies between two of these domains, namely organization and technology domain, are explored by analyzing critical design issues in business models for mobile services, i.e. partner selection, network openness, network governance, and network complexity in the organization domain, and security, quality of service, management of service profiles, system integration and accessibility in the technology domain. A causal framework is developed, which links these critical design issues to expected network value and business model viability.


title: An Ensemble Approach for Incremental Learning in Nonstationary Environments

abstract: We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different snapshot of a distribution that is drifting at an unknown rate. Furthermore, we assume that the algorithm must learn the new environment in an incremental manner, that is, without having access to previously available data. Instead of a time window over incoming instances, or an aged based forgetting - as used by most ensemble based nonstationary learning algorithms - a strategic weighting mechanism is employed that tracks the classifiers' performances over drifting environments to determine appropriate voting weights. Specifically, the proposed approach generates a single classifier for each dataset that becomes available, and then combines them through a dynamically modified weighted majority voting, where the voting weights themselves are computed as weighted averages of classifiers' individual performances over all environments. We describe the implementation details of this approach, as well as its initial results on simulated non-stationary environments.


d.tools: Integrated Prototyping for Physical Interaction Design


title: The “quiet eye” and motor performance: Task demands matter!

abstract: Evidence suggests that superior motor performance coincides with a longer duration of the last fixation before movement initiation, an observation called “quiet eye” (QE). Although the empirical findings over the last two decades underline the robustness of the phenomenon, little is known about its functional role in motor performance. Therefore, a novel paradigm is introduced, testing QE duration as an independent variable by experimentally manipulating the onset of the last fixation before movement unfolding. Furthermore, this paradigm is employed to investigate the functional mechanisms behind the QE phenomenon by manipulating the predictability of the target position and thereby the amount of information to be processed over the QE period. The results further support the assumption that QE affects motor performance, with experimentally prolonged QE durations increasing accuracy in a throwing task. However, it is only under a high information-processing load that a longer QE duration is beneficial for throwing performance. Therefore, the optimization of information processing, particularly in motor execution, turns out to be a promising candidate for explaining QE benefits on a functional level.


title: System-level performance of downlink NOMA for future LTE enhancements

abstract: This paper investigates the system-level performance of downlink non-orthogonal multiple access (NOMA) with power-domain user multiplexing at the transmitter side and successive interference canceller (SIC) on the receiver side. The goal is to clarify the performance gains of NOMA for future LTE (Long-Term Evolution) enhancements, taking into account design aspects related to the LTE radio interface such as, frequency-domain scheduling with adaptive modulation and coding (AMC), and NOMA specific functionalities such as error propagation of SIC receiver, multi-user pairing and transmit power allocation. In particular, a pre-defined user grouping and fixed per-group power allocation are proposed to reduce the overhead associated with power allocation signalling. Based on computer simulations, we show that for both wideband and subband scheduling and both low and high mobility scenarios, NOMA can still provide a hefty portion of its expected gains even with error propagation, and also when the proposed simplified user grouping and power allocation are used.


title: How many vehicles in the LTE-V2V awareness range with half or full duplex radios?

abstract: Given the increasing interest for future connected vehicles, the long term evolution (LTE) specifications are being enhanced by 3GPP to cope with the vehicle-to-everything (V2X) scenarios starting from the upcoming Release 14, which will include, among others, vehicle-to-vehicle (V2V) direct communications. Since the main service expected for connected vehicles is the cooperative awareness, in this work we aim at deriving the number of neighbors that can be managed by LTE-V2V. In addition to the normal half duplex (HD) radios, which strictly limit the granularity of resource allocation, advanced full duplex (FD) radios are also considered. Results show that LTE-V2V with HD radios is able to manage up to few tens of neighbors, whereas FD can increase this limit significantly at the expense of an increase of complexity.


title: WireWarping: A fast surface flattening approach with length-preserved feature curves

abstract: This paper presents a novel approach - WireWarping for computing a flattened planar piece with length-preserved feature curves from a 3D piecewise linear surface patch. The property of length-preservation on feature curves is very important to industrial applications for controlling the shape and dimension of products fabricated from planar pieces. WireWarping simulates warping a given 3D surface patch onto plane with the feature curves as tendon wires to preserve the length of their edges. During warping, the surface-angle variations between edges on wires are minimized so that the shape of a planar piece is similar to its corresponding 3D patch. Two schemes - the progressive warping and the global warping schemes are developed, where the progressive scheme is flexible for local shape control and the global scheme gives good performance on highly distorted patches. Experimental results show that WireWarping can successfully flatten surface patches into planar pieces while preserving the length of edges on feature curves.


title: Semantic Parsing on Freebase from Question-Answer Pairs

abstract: In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs. The main challenge in this setting is narrowing down the huge number of possible logical predicates for a given question. We tackle this problem in two ways: First, we build a coarse mapping from phrases to predicates using a knowledge base and a large text corpus. Second, we use a bridging operation to generate additional predicates based on neighboring predicates. On the dataset of Cai and Yates (2013), despite not having annotated logical forms, our system outperforms their state-of-the-art parser. Additionally, we collected a more realistic and challenging dataset of question-answer pairs and improves over a natural baseline.


title: Online maneuver recognition and multimodal trajectory prediction for intersection assistance using non-parametric regression

abstract: Maneuver recognition and trajectory prediction of moving vehicles are two important and challenging tasks of advanced driver assistance systems (ADAS) at urban intersections. This paper presents a continuing work to handle these two problems in a consistent framework using non-parametric regression models. We provide a feature normalization scheme and present a strategy for constructing three-dimensional Gaussian process regression models from two-dimensional trajectory patterns. These models can capture spatio-temporal characteristics of traffic situations. Given a new, partially observed and unlabeled trajectory, the maneuver can be recognized online by comparing the likelihoods of the observation data for each individual regression model. Furthermore, we take advantage of our representation for trajectory prediction. Because predicting possible trajectories at urban intersection involves obvious multimodalities and non-linearities, we employ the Monte Carlo method to handle these difficulties. This approach allows the incremental prediction of possible trajectories in situations where unimodal estimators such as Kalman Filters would not work well. The proposed framework is evaluated experimentally in urban intersection scenarios using real-world data.


title: Efficient software-based fault isolation

abstract: One way to provide fault isolation among cooperating software modules is to place each in its own address space. However, for tightly-coupled modules, this solution incurs prohibitive context switch overhead. In this paper, we present a software approach to implementing fault isolation within a single address space.Our approach has two parts. First, we load the code and data for a distrusted module into its own fault do main, a logically separate portion of the application's address space. Second, we modify the object code of a distrusted module to prevent it from writing or jumping to an address outside its fault domain. Both these software operations are portable and programming language independent.Our approach poses a tradeoff relative to hardware fault isolation: substantially faster communication between fault domains, at a cost of slightly increased execution time for distrusted modules. We demonstrate that for frequently communicating modules, implementing fault isolation in software rather than hardware can substantially improve end-to-end application performance.


title: Alveolar Oxidative Stress is Associated with Elevated Levels of Nonenzymatic Low-Molecular-Weight Antioxidants in Patients with Different Forms of Chronic Fibrosing Interstitial Lung Diseases

abstract: Increasing evidence indicates that disequilibrium of the alveolar oxidant–antioxidant balance may play a role in the pathogenesis of chronic fibrosing lung diseases. Excessive production of oxidants and a differential regulation of antioxidant enzymes have been described under these conditions. We characterized for the first time numerous nonenzymatic low-molecular-weight antioxidants in bronchoalveolar lavage fluids from patients with different forms of lung fibrosis initiated either by injury to the alveolar epithelium (idiopathic pulmonary fibrosis, IPF) or by inflammation (chronic sarcoidosis/hypersensitivity pneumonitis). Footprints of oxidative stress accompanied by an increase in the majority of antioxidants assessed were observed in all patient groups: elevated levels of uric acid, ascorbic acid, retinol, and α-tocopherol were noted, whereas glutathione levels were unchanged. The expression of Nrf2, an important redox-sensitive transcriptional regulator of antioxidants, was increased in IPF lungs....


title: Open source software development and Lotka's law: bibliometric patterns in programming

abstract: This research applies Lotka's Law to metadata on open source software development. Lotka's Law predicts the proportion of authors at different levels of productivity. Open source software development harnesses the creativity of thousands of programmers worldwide, is important to the progress of the Internet and many other computing environments, and yet has not been widely researched. We examine metadata from the Linux Software Map (LSM), which documents many open source projects, and Sourceforge, one of the largest resources for open source developers. Authoring patterns found are comparable to prior studies of Lotka's Law for scientific and scholarly publishing. Lotka's Law was found to be effective in understanding software development productivity patterns, and offer promise in predicting aggregate behavior of open source developers.


title: The moral challenges of driverless cars

abstract: Autonomous vehicles will need to decide on a course of action when presented with multiple less-than-ideal outcomes.


title: Analysis of eye-tracking experiments performed on a Tobii T60

abstract: Commercial eye-gaze trackers have the potential to be an important tool for quantifying the benefits of new visualization ::: techniques. The expense of such trackers has made their use relatively infrequent in visualization studies. As such, it is ::: difficult for researchers to compare multiple devices - obtaining several demonstration models is impractical in cost and ::: time, and quantitative measures from real-world use are not readily available. In this paper, we present a sample protocol ::: to determine the accuracy of a gaze-tacking device.


title: Hilbert space embeddings of conditional distributions with applications to dynamical systems

abstract: In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connection to ordinary embeddings. Conditional embeddings largely extend our ability to manipulate distributions in Hilbert spaces, and as an example, we derive a nonparametric method for modeling dynamical systems where the belief state of the system is maintained as a conditional embedding. Our method is very general in terms of both the domains and the types of distributions that it can handle, and we demonstrate the effectiveness of our method in various dynamical systems. We expect that conditional embeddings will have wider applications beyond modeling dynamical systems.


title: Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission

abstract: Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.


title: A MODEL TO OPTIMIZE A CRYOGENIC SEPARATION SYSTEM WITH INNOVATIVE HYBRID DISTILLATION MEMBRANE IN SERIES

abstract: The study developed a model for hybrid distillation membrane that optimized the energy usage in binary cryogenic separation. In separating air mixture components, distillation columns are often used and these columns consumes very large energy during operation. From analysis, the exergy efficiency and heat transfer of a cryogenic air separation double diabatic column in the distillation process is greater than that of the conventional adiabatic double columns. There is need to discover alternative separation technologies with lesser energy consumption such as membrane separation. However, use of membrane separation alone is constrained to small separation due to large areas needed with the attendant costs. Thus, a hybrid system comprising of distillation column and membrane separator offers the best compromise. To optimize the process, the overhead product from the distillation column was fed to a membrane separator in series in this study. A mathematical Model approach was proposed to improve a hybrid separation system comprising of a distillation column and a Serial novel membrane separation unit. First, a model was introduced that validated if the hybrid system could optimize the process and the order of magnitude of energy that can be expected. Secondly, a superstructure optimization approach was applied and it uses rigorous models for both the column and the membrane. A process simulator, excel and visual basic were used to solve and program the equations. The result showed that significant energy savings was achieved using a novel hybrid separation system with a material membrane.


title: How neuroscience will change our view on consciousness

abstract: Is there consciousness in machines? Or in animals? What happens to consciousness when we are asleep, or in vegetative state? These are just a few examples of the many questions about consciousness that are troubling scientists and laypersons alike. Moreover, these questions share a striking feature: They seem to have been around forever, yet neither science nor philosophy has been able to provide an answer. Why is that? In my view, the main reason is that the study of consciousness is dominated by what we know from introspection and behavior. This has fooled us into thinking that we know what we are conscious of. The scientific equivalent of this is Global Workspace theory. But in fact we don't know what we are conscious of, as I will explain from a simple experiment in visual perception. Once we acknowledge that, it is clear that we need other evidence about the presence or absence of a conscious sensation than introspection or behavior. Assuming the brain has something to do with it, I will demonstrate ...


title: Crowdsourcing High Quality Labels with a Tight Budget

abstract: In the past decade, commercial crowdsourcing platforms have revolutionized the ways of classifying and annotating data, especially for large datasets. Obtaining labels for a single instance can be inexpensive, but for large datasets, it is important to allocate budgets wisely. With limited budgets, requesters must trade-off between the quantity of labeled instances and the quality of the final results. Existing budget allocation methods can achieve good quantity but cannot guarantee high quality of individual instances under a tight budget. However, in some scenarios, requesters may be willing to label fewer instances but of higher quality. Moreover, they may have different requirements on quality for different tasks. To address these challenges, we propose a flexible budget allocation framework called Requallo. Requallo allows requesters to set their specific requirements on the labeling quality and maximizes the number of labeled instances that achieve the quality requirement under a tight budget. The budget allocation problem is modeled as a Markov decision process and a sequential labeling policy is produced. The proposed policy greedily searches for the instance to query next as the one that can provide the maximum reward for the goal. The Requallo framework is further extended to consider worker reliability so that the budget can be better allocated. Experiments on two real-world crowdsourcing tasks as well as a simulated task demonstrate that when the budget is tight, the proposed Requallo framework outperforms existing state-of-the-art budget allocation methods from both quantity and quality aspects.


At the Border of Acoustics and Linguistics: Bag-of-Audio-Words for the Recognition of Emotions in Speech.


Deep Neural Networks and Hidden Markov Models in i-vector-based Text-Dependent Speaker Verification


title: Web wallet: preventing phishing attacks by revealing user intentions

abstract: We introduce a new anti-phishing solution, the Web Wallet. The Web Wallet is a browser sidebar which users can use to submit their sensitive information online. It detects phishing attacks by determining where users intend to submit their information and suggests an alternative safe path to their intended site if the current site does not match it. It integrates security questions into the user's workflow so that its protection cannot be ignored by the user. We conducted a user study on the Web Wallet prototype and found that the Web Wallet is a promising approach. In the study, it significantly decreased the spoof rate of typical phishing attacks from 63% to 7%, and it effectively prevented all phishing attacks as long as it was used. A majority of the subjects successfully learned to depend on the Web Wallet to submit their login information. However, the study also found that spoofing the Web Wallet interface itself was an effective attack. Moreover, it was not easy to completely stop all subjects from typing sensitive information directly into web forms.


title: A Combined Deep Learning GRU-Autoencoder for the Early Detection of Respiratory Disease in Pigs Using Multiple Environmental Sensors

abstract: We designed and evaluated an assumption-free, deep learning-based methodology for animal health monitoring, specifically for the early detection of respiratory disease in growing pigs based on environmental sensor data. Two recurrent neural networks (RNNs), each comprising gated recurrent units (GRUs), were used to create an autoencoder (GRU-AE) into which environmental data, collected from a variety of sensors, was processed to detect anomalies. An autoencoder is a type of network trained to reconstruct the patterns it is fed as input. By training the GRU-AE using environmental data that did not lead to an occurrence of respiratory disease, data that did not fit the pattern of "healthy environmental data" had a greater reconstruction error. All reconstruction errors were labelled as either normal or anomalous using threshold-based anomaly detection optimised with particle swarm optimisation (PSO), from which alerts are raised. The results from the GRU-AE method outperformed state-of-the-art techniques, raising alerts when such predictions deviated from the actual observations. The results show that a change in the environment can result in occurrences of pigs showing symptoms of respiratory disease within 1⁻7 days, meaning that there is a period of time during which their keepers can act to mitigate the negative effect of respiratory diseases, such as porcine reproductive and respiratory syndrome (PRRS), a common and destructive disease endemic in pigs.


title: Automatic construction of travel itineraries using social breadcrumbs

abstract: Vacation planning is one of the frequent---but nonetheless laborious---tasks that people engage themselves with online; requiring skilled interaction with a multitude of resources. This paper constructs intra-city travel itineraries automatically by tapping a latent source reflecting geo-temporal breadcrumbs left by millions of tourists. For example, the popular rich media sharing site, Flickr, allows photos to be stamped by the time of when they were taken and be mapped to Points Of Interests (POIs) by geographical (i.e. latitude-longitude) and semantic (e.g., tags) metadata. Leveraging this information, we construct itineraries following a two-step approach. Given a city, we first extract photo streams of individual users. Each photo stream provides estimates on where the user was, how long he stayed at each place, and what was the transit time between places. In the second step, we aggregate all user photo streams into a POI graph. Itineraries are then automatically constructed from the graph based on the popularity of the POIs and subject to the user's time and destination constraints. We evaluate our approach by constructing itineraries for several major cities and comparing them, through a "crowd-sourcing" marketplace (Amazon Mechanical Turk), against itineraries constructed from popular bus tours that are professionally generated. Our extensive survey-based user studies over about 450 workers on AMT indicate that high quality itineraries can be automatically constructed from Flickr data.


title: Action Recognition from RGB-D Data: Comparison and Fusion of Spatio-Temporal Handcrafted Features and Deep Strategies

abstract: In this work, multimodal fusion of RGB-D data are analyzed for action recognition by using scene flow as early fusion and integrating the results of all modalities in a late fusion fashion. Recently, there is a migration from traditional handcrafting to deep learning. However, handcrafted features are still widely used owing to their high performance and low computational complexity. In this research, Multimodal dense trajectories (MMDT) is proposed to describe RGB-D videos. Dense trajectories are pruned based on scene flow data. Besides, 2DCNN is extended to multimodal (MM2DCNN) by adding one more stream (scene flow) as input and then fusing the output of all models. We evaluate and compare the results from each modality and their fusion on two action datasets. The experimental result shows that the new representation improves the accuracy. Furthermore, the fusion of handcrafted and learning-based features shows a boost in the final performance, achieving state of the art results.


title: Mass Cytometry: Single Cells, Many Features

abstract: Technology development in biological research often aims to either increase the number of cellular features that can be surveyed simultaneously or enhance the resolution at which such observations are possible. For decades, flow cytometry has balanced these goals to fill a critical need by enabling the measurement of multiple features in single cells, commonly to examine complex or hierarchical cellular systems. Recently, a format for flow cytometry has been developed that leverages the precision of mass spectrometry. This fusion of the two technologies, termed mass cytometry, provides measurement of over 40 simultaneous cellular parameters at single-cell resolution, significantly augmenting the ability of cytometry to evaluate complex cellular systems and processes. In this Primer, we review the current state of mass cytometry, providing an overview of the instrumentation, its present capabilities, and methods of data analysis, as well as thoughts on future developments and applications.


title: Development and Preliminary Evaluation of the Transsexual Voice Questionnaire for Male-to-Female Transsexuals

abstract: Summary Objective This study reports on the preliminary psychometric evaluation of the Transsexual Voice Questionnaire for Male-to-Female Transsexuals (MtF)—TVQ MtF , a tool designed to measure the perceptions of MtF transsexuals regarding their voice. The TVQ MtF evolved from an extensive review of the existing Transgender Self-evaluation Questionnaire (TSEQ). Study Design This study was conducted in two phases. In phase I, the TSEQ was reviewed. In phase II, classical test theory was applied to examine two psychometric properties (internal consistency and test-retest reliability) of the TVQ MtF . Method In phase I, two speech pathologists and two MtF individuals reviewed the TSEQ, and a comparison was made with the results of a thematic analysis of 137 voice-related extracts from the transcripts of in-depth interviews with 14 MtF individuals. In phase II, 29 Australian and six Canadian MtF transsexuals completed the new tool, the TVQ MtF twice with an interim period of 4–6 weeks to enable psychometric evaluation. Results Phase I led to substantial changes to the content and structure of the original measure, the TSEQ, and the subsequent development of the TVQ MtF . In phase II, internal consistency of the TVQ MtF was found to be high (time 1 Cronbach's α = .964; time 2 α = .974). Excellent test-retest reliability was demonstrated (intraclass correlation coefficient = 0.979; 95% confidence interval = 0.968–0.988). Conclusions This study provides psychometric evidence of the reliability of the TVQ MtF . The MtF participants in this study reported a wide range of vocal functioning and voice-related difficulties. Voice pitch was reported as the most problematic aspect of vocal functioning, and psychosocial issues were the most frequently reported voice-related impacts.


title: Risk Mining: Company-Risk Identification from Unstructured Sources

abstract: Risk permeates all aspects of doing business. However, support tools capable of systematically identifying the complete spectrum of risks that a company might face are currently lacking. Such a tool would need to reliably identify company-risk relationships from unstructured sources, therefore providing a qualitative assessment of risk exposure. We propose a supervised learning approach that combines a weakly-supervised risk taxonomy, named entity tagging and dependency tree analysis in order to perform company-risk relationship classification. We demonstrate that a support vector machine using a tree kernel, trained on hand-annotated articles from Reuters News Archive, is capable of significantly outperforming a selection of alternative classification algorithms. To our knowledge, this is the first example of company-risk relationship extraction.


Complimentary Memory Technologies to Reform the Homogeneity , Energy Landscape , and Volatility of Future Memory Hierarchies


title: Detecting Simulated Attacks in Computer Networks Using Resilient Propagation Artificial Neural Networks

abstract: In a large network, it is extremely difficult for an administrator or security personnel to detect which computers are being attacked and from where intrusions come. Intrusion detection systems using neural networks have been deemed a promising solution to detect such attacks. The reason is that neural networks have some advantages such as learning from training and being able to categorize data. Many studies have been done on applying neural networks in intrusion detection systems. This work presents a study of applying resilient propagation neural networks to detect simulated attacks. The approach includes two main components: the Data Pre- processing module and the Neural Network. The Data Pre- processing module performs normalizing data function while the Neural Network processes and categorizes each connection to find out attacks. The results produced by this approach are compared with present approaches.


title: Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks

abstract: This paper provides an overview of cognitive radio (CR) networks, with focus on the recent advances in resource allocation techniques and the CR networks architectural design. The contribution of this work is threefold. First, a systematic way to study the resource allocation problem is presented; various design approaches are introduced, such as signal-to-interference-and-noise ratio (SINR) or transmission power-based, and centralized or distributed methods. Second, CR optimization methods are presented, accompanied by a comprehensive study of the resource allocation problem formulations. Furthermore, quality of service criteria of the physical or/and the medium access control layers are investigated. Third, challenges in spectrum assignment are discussed, focusing on dynamic spectrum allocation, spectrum aggregation and frequency mobility. Such approaches constitute an emerging trend in efficient spectrum sharing and affect the performance of resource allocation techniques. The open issues for future research in this area are finally discussed, including adaptability-reconfigurability, dual accessibility, and energy efficiency.


title: An operational measure of information leakage

abstract: Given two discrete random variables X and Y, an operational approach is undertaken to quantify the “leakage” of information from X to Y. The resulting measure ℒ(X→Y ) is called maximal leakage, and is defined as the multiplicative increase, upon observing Y, of the probability of correctly guessing a randomized function of X, maximized over all such randomized functions. It is shown to be equal to the Sibson mutual information of order infinity, giving the latter operational significance. Its resulting properties are consistent with an axiomatic view of a leakage measure; for example, it satisfies the data processing inequality, it is asymmetric, and it is additive over independent pairs of random variables. Moreover, it is shown that the definition is robust in several respects: allowing for several guesses or requiring the guess to be only within a certain distance of the true function value does not change the resulting measure.


title: Vertebrate origins: riding the crest of a new wave, or the wave of a new crest?

abstract: Natural scientists in the nineteenth century were fascinated by the problem of the ancestry and origin of the vertebrates. Indeed, searching for ancestors, kinship, and relationships (systematics and classification) was as major a spur to Darwin’s life work as was his desire to understand how new forms arise. From around the mid-nineteenth century, evidence from adult and embryonic animals was used to support a large number of potential (and more or less likely, as it turns out) vertebrate ancestorsFannelids, echinoderms, hemichordates, and cephalochordates being some but by no means all. Different theories and theorists placed their emphasis on individual but different characters, some in embryos some in adults. Careers were built around the importance for vertebrate genealogy of a notochord in cephalochordates (Branchiostoma spp. and Asymmetron spp., commonly known as amphioxus) or of ciliary bands on ascidian larvae. By the mid-twentieth century, analyses based on sets of characters placed the cephalochordates as the closest relatives (sister taxon) to vertebrates. Consequently (i) the neural crest (NC)Fa synapomorphy of vertebratesFwas presumed to have arisen in a cephalochordate-like ancestor, and (ii) evidence for the antecedent of the NC (a protoneural crest) was sought in the extant cephalochordate, amphioxus, which was regarded as a surrogate for the prevertebrate ancestor. The presence in amphioxus embryos of migratory cells associated with the crests of the dorsal neural tube, and the expression in those cells of genes also expressed in vertebrate neural crest cells, opened a window onto the prevertebrate condition. Extant cephalochordates lack pigment cells, which are a NC-derivative in vertebrates. Therefore, the ancestral cell type associated with a protoneural crest has been regarded widely as a neural cell. Although ascidians were not regarded as the closest relatives to vertebrates, their cells and genes have been explored for signs of a protoneural crest. Such studies have acquired a greatly increased importance with the publication of several molecular phylogenies that place ascidians rather than cephalochordates as the sister group to vertebrates. In a recent paper on chordate ancestry of the neural crest, Jeffery (2007) laid out the consequences of these revised phylogenetic relationships. The gene Snail1 illustrates how the new phylogeny requires us to take a new approach to the origin of the NC. Snail1, an important marker for NC cells in vertebrates, is not expressed in mesoderm. AmphiSnail1 (i.e., Snail1 in amphioxus) is expressed in presomitic mesoderm. If the prevertebrate was cephalochordate-like, the dorsal neural tube (NC) expression of Snail1 was enhanced and co-opted from mesoderm, and the phylogenetically older mesodermal expression was lost with the origin of the vertebrates. Snail1-positive cells also are expressed in ascidians. In the sea vase, Ciona intestinalis, these cells form the ependyma, a layer of cells of neural ectodermal origin that line the spinal cord of the central nervous system. In ascidians, therefore, and unlike cephalochordates, Snail1-positive cells are neuronal not mesodermal. Furthermore, the primary function of migratory neural-tube-derived cells in ascidians is as pigment cell precursors; extant cephalochordates lack pigment cells. Ascidians possess a band of pigmented cells along the dorsal aspect of the neural tube. These cells arise in the neural tube fromHNK-1and Zic3-positive cells (HNK-1 is a NCmarker and Zic3 is required for neural induction in vertebrates) that then migrate into the body wall. Jeffery (2007) does not take the ‘‘pre-adaptive’’ view of identifying pigment cell precursors in ascidians as evidence of protoneural crest cells. Rather, he identifies them as an evolved lineage in their own rightF‘‘neural crest-like cells,’’ whose primary role is to provide pigment cells. With ascidians as the sister group to vertebrates, and neural crest-like cells as a lineage established and functioning in ascidians before the origin of NC cells in vertebrates, we have to reevaluate the data on gene expression patterns in amphioxus; reconsider whether the neural crest-like cells in ascidians functioned as a protoneural crest that co-opted genes and gene pathways from mesoderm, non-neural ectoderm or pharyngeal endoderm as basal cephalochordates are proposed to have done (Meulemans and Bronner-Fraser EVOLUTION & DEVELOPMENT 10:3, 261 –262 (2008)


title: The Shift of Environmental Debates in Russia

abstract: This article presents the results of long-term research aimed at identifying the major trends in environmental debates conditioned by Russian reforms and by the changing geopolitical situation of the Russian nation-state. The main topics of the article are changes in the very subject of these debates, the actors involved, the political opportunity structure of the debates and the character of the languages used by their participants. Four major shifts have been identified and analysed: first, from long-term to short-term issues; second, from nationwide to ‘insular’ debates conditioned by the division of Russians into those who live in time and in space; third, from value-centred to economically oriented; and fourth, from humanistic to social-technological. Epistemologically, these shifts indicate a process of transition from instructive to discursive production of scientific knowledge that should take into account local knowledge; in terms of sociology of social knowledge, this means a change in the relat...


title: [Family dynamics: sleep quality of women caregivers of family members with Alzheimer disease].

abstract: This replication study/Riccio 1996) sets out to explore the Quality of sleep of 34 women primary caregivers of Alzheimer's Disease patients at home. A descriptive, correlational and longitudinal design has been used. Key aspects considered were the quality of nocturnal sleep, the daytime sleepiness, the hours of caring, the educational level, the employment, the time for personal needs and the levels of depression and anxiety. After an initial interview, the caregivers were asked to fill in several instruments to measure the variables taken into consideration. The caregivers presented poor quality in their sleep. Quality of nocturnal sleep was negatively affected by more hours of caring (p < 0.02), high depression and anxiety levels (p < 0.05 and 0.007 respectively). The education level, the employment outside home and the amount of time spent for themselves, positively affected the quality of sleep (p < 0.01, 0.04, 0.05 respectively). This research pointed out that hours of caring, directly and indirectly, cause sleep problem to Alzheimer's Disease caregivers enhancing nursing care implications.


title: A Novel Near-Transparent ASK-Reconfigurable Inkjet-Printed Chipless RFID Tag

abstract: The design of a novel chipless radio frequency identification (RFID) tag, which is inkjet-printed on a polyimide substrate, is presented. It is shown that a silver nanoparticles-based ink can be combined with a resistive organic ink to create a tag having a “near-transparent” configuration. By adding some transparent elements, we obtain various electromagnetic responses for tags visually similar. The tag is composed of three dual-rhombic loop resonators for a total size of 7 × 4 cm2. It operates within the ultrawideband frequency range of 3-6 GHz. The resistive properties of the organic ink enable the implementation of a novel coding technique based on amplitude shift keying. Simulation and measurement results validate the concept and the novel coding technique.


title: Broca's Area, Sentence Comprehension, and Working Memory: An fMRI Study

abstract: The role of Broca's area in sentence processing remains controversial. According to one view, Broca's area is involved in processing a subcomponent of syntactic processing. Another view holds that it contributes to sentence processing via verbal working memory. Sub-regions of Broca's area have been identified that are more active during the processing of complex (object-relative clause) sentences compared to simple (subject-relative clause) sentences. The present study aimed to determine if this complexity effect can be accounted for in terms of the articulatory rehearsal component of verbal working memory. In a behavioral experiment, subjects were asked to comprehend sentences during concurrent speech articulation which minimizes articulatory rehearsal as a resource for sentence comprehension. A finger-tapping task was used as a control concurrent task. Only the object-relative clause sentences were more difficult to comprehend during speech articulation than during the manual task, showing that articulatory rehearsal does contribute to sentence processing. A second experiment used fMRI to document the brain regions underlying this effect. Subjects judged the plausibility of sentences during speech articulation, a finger-tapping task, or without a concurrent task. In the absence of a secondary task, Broca's area (pars triangularis and pars opercularis) demonstrated an increase in activity as a function of syntactic complexity. However, during concurrent speech articulation (but not finger-tapping) this complexity effect was eliminated in the pars opercularis suggesting that this region supports sentence comprehension via its role in articulatory rehearsal. Activity in the pars triangularis was modulated by the finger-tapping task, but not the speech articulation task.


title: A Dual-Band Rectenna Using Broadband Yagi Antenna Array for Ambient RF Power Harvesting

abstract: This letter presents a dual-band rectenna that can harvest ambient RF power of GSM-1800 and UMTS-2100 bands efficiently. The novel rectenna is based on a broadband 1 × 4 quasi-Yagi antenna array with bandwidth from 1.8 to 2.2 GHz and high gains of 10.9 and 13.3 dBi at 1.85 and 2.15 GHz, respectively. Also, a dual-band rectifier that can sufficiently enhance the RF-to-dc power conversion efficiency (PCE) at ambient RF power level is designed for the rectenna. Measurement results show that a PCE of 40% and an output dc voltage of 224 mV have been achieved over a 5-k Ω resistor when the dual-tone input power density is 455 μW/m 2. Additionally, output dc voltage varying between 300-400 mV can be obtained by collecting the relatively low ambient RF power.


title: PicToSeek : Combining Color and Shape Invariant Features for Image Retrieval

abstract: We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-dimensional invariant feature set for discriminatory object retrieval. Experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. From the theoretical and experimental results it is concluded that object retrieval based on composite color and shape invariant features provides excellent retrieval accuracy. Object retrieval based on color invariants provides very high retrieval accuracy whereas object retrieval based entirely on shape invariants yields poor discriminative power. Furthermore, the image retrieval scheme is highly robust to partial occlusion, object clutter and a change in the object's pose. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.


title: Recognizing Soldier Activities in the Field

abstract: We describe the activity recognition component of the Soldier Assist System (SAS), which was built to meet the goals of DARPA’s Advanced Soldier Sensor Information System and Technology (ASSIST) program. As a whole, SAS provides an integrated solution that includes on-body data capture, automatic recognition of soldier activity, and a multimedia interface that combines data search and exploration. The recognition component analyzes readings from six on-body accelerometers to identify activity. The activities are modeled by boosted 1D classifiers, which allows efficient selection of the most useful features within the learning algorithm. We present empirical results based on data collected at Georgia Tech and at the Army’s Aberdeen Proving Grounds during official testing by a DARPA appointed NIST evaluation team. Our approach achieves 78.7% for continuous event recognition and 70.3% frame level accuracy. The accuracy increases to 90.3% and 90.3% respectively when considering only the modeled activities. In addition to standard error metrics, we discuss error division diagrams (EDDs) for several Aberdeen data sequences to provide a rich visual representation of the performance of our system.


title: Cybercafés and their potential as Community Development Tools in India

abstract: The following article examines the role of Cybercafes in urban, semiurban and suburban areas of India. Cybercafes have become an important access point for different urban communities in India, and the paper discusses their role in an urban development context. ::: To examine the role of Cybercafes, a broad quantitative and qualitative analysis of cybercafes has been made in different urban, semiurban and suburban areas in India, including a user survey of 1500 users and 30 interviews. ::: This paper discusses some of the findings, shows some up to date trends of the Indian Cybercafe Scene and shows some interesting potentials that Cybercafes in urban areas have to serve different communities.