title: An External Capacitorless Low-Dropout Regulator With High PSR at All Frequencies From 10 kHz to 1 GHz Using an Adaptive Supply-Ripple Cancellation Technique

abstract: Herein is presented an external capacitorless low-dropout regulator (LDO) that provides high-power-supply rejection (PSR) at all low-to-high frequencies. The LDO is designed to have the dominant pole at the gate of the pass transistor to secure stability without the use of an external capacitor, even when the load current increases significantly. Using the proposed adaptive supply-ripple cancellation (ASRC) technique, in which the ripples copied from the supply are injected adaptively to the body gate, the PSR hump that appears in conventional gate-pole-dominant LDOs can be suppressed significantly. Since the ASRC circuit continues to adjust the magnitude of the injecting ripples to an optimal value, the LDO presented here can maintain high PSRs, irrespective of the magnitude of the load current $I_{L}$ , or the dropout voltage $V_{\mathrm {DO}}$ . The proposed LDO was fabricated in a 65-nm CMOS process, and it had an input voltage of 1.2 V. With a 240-pF load capacitor, the measured PSRs were less than −36 dB at all frequencies from 10 kHz to 1 GHz, despite changes of $I_{L}$ and $V_{\mathrm {DO}}$ as well as process, voltage, temperature (PVT) variations.


title: Visual objects in context

abstract: We see the world in scenes, where visual objects occur in rich surroundings, often embedded in a typical context with other related objects. How does the human brain analyse and use these common associations? This article reviews the knowledge that is available, proposes specific mechanisms for the contextual facilitation of object recognition, and highlights important open questions. Although much has already been revealed about the cognitive and cortical mechanisms that subserve recognition of individual objects, surprisingly little is known about the neural underpinnings of contextual analysis and scene perception. Building on previous findings, we now have the means to address the question of how the brain integrates individual elements to construct the visual experience.


title: Novel Broadband Monopole Antennas With Dual-Band Circular Polarization

abstract: Novel broadband monopole antenna designs with dual-band circular polarization (CP) are presented. The proposed antenna comprised of a ground plane embedded with an inverted-L slit, which is capable of generating a resonant mode for broadband impedance-bandwidth, and excites two orthogonal E vectors with equal amplitude and 90deg phase difference (PD) for radiating left-hand circular polarization (LHCP) at 2.5 GHz and right-hand circular polarization (RHCP) at 3.4 GHz. A bevel is cut in the rectangular radiator to increase the impedance-bandwidth. The measured result of the impedance-bandwidth is about 4.46 GHz from 2.12 to 6.58 GHz; the 3-dB axial ratio (AR) bandwidths are about 150 MHz at the lower band (2.5 GHz) and 230 MHz at the upper band (3.4 GHz). Furthermore, embedding an I-shaped slit in the rectangular radiator and adding an I-shaped stub in the ground plane, the impedance-bandwidth can be further increased to 6.30 GHz (2.17-8.47 GHz), and the 3-dB AR-bandwidth at the upper band is greatly enhanced from 230 to 900 MHz.


title: Knowledge Graph Completion via Complex Tensor Factorization

abstract: In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices---thus all possible relation/adjacency matrices---are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.


title: The flipped classroom experience

abstract: This paper describes the findings on ‘flipping the classroom’ pedagogy for one selected topic in the Software Engineering module. A flipped classroom is a form of blended learning where the lecture is moved outside the classroom with the help of technology and learning activities are moved inside the classroom. The students typically watch instructional videos outside of the classroom but participate in learning activities with other students in class. This allows students to learn on their own time and pace outside class time while enabling the lecturer to enhance interaction, collaboration and higher-order thinking of students by monitoring individual learning and task performance. Our past experience has shown that using the traditional approach to teach software engineering, which is a heavily conceptual and theoretical subject, can be challenging. Against this backdrop, we have devised a flipped class room trial to let the students carry out the learning activities before coming to class where they then share and apply the knowledge during face-to-face lessons for more engaging learning experience. As this is an initial trial, the author has chosen a topic that is relatively easy to understand and with support of interesting videos readily available online. The purpose is to motivate the use of flipped classroom for appropriate topics taught in Software Engineering curriculum.


title: Review on cybersickness in applications and visual displays

abstract: Cybersickness is an affliction common to users of virtual environments. Similar in symptoms to motion sickness, cybersickness can result in nausea, headaches, and dizziness. With these systems becoming readily available to the general public, reports of cybersickness have increased and there is a growing concern about the safety of these systems. This review presents the current state of research methods, theories, and known aspects associated with cybersickness. Current measurements of incidence of cybersickness are questionnaires, postural sway, and physiological state. Varying effects due to display and rendering modes, such as visual display type and stereoscopic or monoscopic rendering, are compared. The known and suspected application aspects that induce cybersickness are discussed. There are numerous potential contributing application design aspects, many of which have had limited study, but field of view and navigation are strongly correlated with cybersickness. The effect of visual displays is not well understood, and application design may be of greater importance.


title: The Relation of Humor and Child Development: Social, Adaptive, and Emotional Aspects

abstract: A sense of humor has been linked to social competence, popularity, and adaptability. The purpose of this review was to investigate the extant research in humor in childhood. Emerging work on the neuroanatomy of humor was discussed with findings of right hemispheric involvement for the comprehension and appreciation of humor for the affective network and the left hemisphere for cognitive understanding. These findings are intriguing when examining humor functioning in children with various disabilities, particularly the right hemisphere for children with autistic spectrum disorders or nonverbal learning disabilities. Examination of research in humor in childhood disabilities found most articles on humor in children with autistic spectrum disorder or mental retardation, with few to none in learning disabilities or attention deficit hyperactivity disorder. It was concluded that further study is needed to understand humor in children with disabilities and that such understanding will assist with interventions.


title: Evaluation of a Blade Force Measurement System for a Vertical Axis Wind Turbine Using Load Cells

abstract: Unique blade force measurements on an open site straight-bladed vertical axis wind turbine have been performed. This paper presents a method for measuring the tangential and normal forces on a 12-kW vertical axis wind turbine prototype with a three-bladed H-rotor. Four single-axis load cells were installed in-between the hub and the support arms on one of the blades. The experimental setup, the measurement principle, together with the necessary control and measurement system are described. The maximum errors of the forces and accompanying weather data that can be obtained with the system are carefully estimated. Measured forces from the four load cells are presented, as well as the normal and tangential forces derived from them and a comparison with theoretical data. The measured torque and bending moment are also provided. The influence of the load cells on the turbine dynamics has also been evaluated. For the aerodynamic normal force, the system provides periodic data in agreement with simulations. Unexpected mechanical oscillations are present in the tangential force, introduced by the turbine dynamics. The measurement errors are of an acceptable size and often depend on the measured variable. Equations are presented for the calculation of measurement errors.


title: Cybercrime profiling: Text mining techniques to detect and predict criminal activities in microblog posts

abstract: The exponential development in online social media allows users around the globe the possibility to share and communicate information and ideas freely in different formats of data via internet. This emerging media has become a dominant communication tool and it has been used as a communication channel in several events, especially “The Arab Spring” and BOSTON'S attack etc. In order to develop useful profiles of different cybercriminals, text mining techniques is an effective way to detect and predict criminal activities in microblog posts taking account the problems of data sparseness and semantic gap. The hashtags used on Twitter (e.g., #arabspring, #BostonAttack) contains outstanding indicators to detect events and trending topics especially to target and detect suspicious topics and eventual illegal events. Similarity approach is used in text analysis to detect suspicious posts in microblog publications. The evaluation of our proposed approach is done within real posts.


title: Secure Virtual Machine Execution under an Untrusted Management OS

abstract: Virtualization is a rapidly evolving technology that can be used to provide a range of benefits to computing systems, including improved resource utilization, software portability, and reliability. For security-critical applications, it is highly desirable to have a small trusted computing base (TCB), since it minimizes the surface of attacks that could jeopardize the security of the entire system. In traditional virtualization architectures, the TCB for an application includes not only the hardware and the virtual machine monitor (VMM), but also the whole management operating system (OS) that contains the device drivers and virtual machine (VM) management functionality. For many applications, it is not acceptable to trust this management OS, due to its large code base and abundance of vulnerabilities. In this paper, we address the problem of providing a secure execution environment on a virtualized computing platform under the assumption of an untrusted management OS. We propose a secure virtualization architecture that provides a secure run-time environment, network interface, and secondary storage for a guest VM. The proposed architecture significantly reduces the TCB of security-critical guest VMs, leading to improved security in an untrusted management environment. We have implemented a prototype of the proposed approach using the Xen virtualization system, and demonstrated how it can be used to facilitate secure remote computing services. We evaluate the performance penalties incurred by the proposed architecture, and demonstrate that the penalties are minimal.


title: Guidelines for Medical and Health Information Sites on the Internet

abstract: The Internet has permitted physicians, other health care professionals, patients, and other consumers to quickly access medical information in unprecedented volume. Such access has the potential to speed the transformation of the patient-physician relationship from that of physician authority ministering advice and treatment (with sometimes questionable patient understanding and adherence to recommendations) to that of shared decision making between patient and physician. However, several substantial barriers remain before this relationship can be realized. These barriers include equitable access to information, imbalance between patient health literacy and the information provided, extreme variability in the quality of the content, potential for commercial interests to influence content, and uncertain preservation of personal privacy.1-4


Sound, Sign and Meaning: Quinquagenary of the Prague Linguistic Circle


title: Planning High-Quality Grasps Using Mean Curvature Object Skeletons

abstract: In this letter, we present a grasp planner that integrates two sources of information to generate robust grasps for a robotic hand. First, the topological information of the object model is incorporated by building the mean curvature skeleton and segmenting the object accordingly in order to identify object regions which are suitable for grasping. We show how this information can be used to derive different grasping strategies, which also allows to distinguish between precision and power grasps. Second, the local surface structure is investigated to construct feasible and robust grasping poses by aligning the hand according to the local object shape. We apply the approach to a wide variety of object models of the KIT and the YCB real-world object model databases and evaluate it with several robotic hands. The results show that the skeleton-based grasp planner is capable to generate high-quality grasps in an efficient manner. In addition, we evaluate how robust the planned grasps are against hand positioning errors as they occur in real-world applications due to perception and actuation inaccuracies. The evaluation shows that the majority of the generated grasps are of high quality since they can be successfully applied even when the hand is not exactly positioned.


title: A novel credit scoring model based on optimized random forest

abstract: With the rapid development of the credit industry, credit scoring models has become a very important issue in the credit industry. Many credit scoring models based on machine learning have been widely used. Such as artificial neural network (ANN), rough set, support vector machine (SVM), and other innovative credit scoring models. However, in practical applications, a large amount of irrelevant and redundant features in the credit data, which leads to higher computational complexity and lower prediction accuracy. So, the face of a large number of credit data, effective feature selection method is necessary. In this paper, we propose a novel credit scoring model, called NCSM, based on feature selection and grid search to optimize random forest algorithm. The model reduces the influence of the irrelevant and redundant features and to get the higher prediction accuracy. In NCSM, the information entropy is regarded as the heuristic to select the optimal feature. Two credit data sets in UCI database are used as experimental data to demonstrate the accuracy of the NCSM. Compared with linear SVM, CART, MLP, H2O RF models, the experimental result shows that NCSM has a superior performance in improving the prediction accuracy.


title: Semi-parametric Topological Memory for Navigation

abstract: We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals. The proposed semi-parametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations. The graph stores no metric information, only connectivity of locations corresponding to the nodes. We use SPTM as a planning module in a navigation system. Given only 5 minutes of footage of a previously unseen maze, an SPTM-based navigation agent can build a topological map of the environment and use it to confidently navigate towards goals. The average success rate of the SPTM agent in goal-directed navigation across test environments is higher than the best-performing baseline by a factor of three. A video of the agent is available at this https URL


title: Anthropometry of Pretarsal Fullness and Eyelids in Oriental Women

abstract: Background ::: In Chinese physiognomy, lower-lid pretarsal fullness is considered a sign of beauty in women. However, there are no data regarding the normal measurements of pretarsal fullness. The purpose of this study was to evaluate the average appearance of lower-lid pretarsal fullness in oriental women of specific age groups with the intention of obtaining data that surgeons could use when performing lower blepharoplasty. The effects of the orbicularis oculi muscle on pretarsal fullness are also discussed.


A GENE-EXPRESSION SIGNATURE AS A PREDICTOR OF SURVIVAL IN BREAST CANCER


title: Automatic Extraction of Structured Web Data with Domain Knowledge

abstract: We present in this paper a novel approach for extracting structured data from the Web, whose goal is to harvest real-world items from template-based HTML pages (the structured Web). It illustrates a two-phase querying of the Web, in which an intentional description of the data that is targeted is first provided, in a flexible and widely applicable manner. The extraction process leverages then both the input description and the source structure. Our approach is domain-independent, in the sense that it applies to any relation, either flat or nested, describing real-world items. Extensive experiments on five different domains and comparison with the main state of the art extraction systems from literature illustrate its flexibility and precision. We advocate via our technique that automatic extraction and integration of complex structured data can be done fast and effectively, when the redundancy of the Web meets knowledge over the to-be-extracted data.


title: Wrist-card: PPG sensor based wrist wearable unit for low cost personalized cardio healthcare system

abstract: Of late, modern lifestyle has noticeably accentuated several health hazards related to cardiovascular diseases (CVDs). A simple, cost efficient but effective way out of this threatening health issue can be the prevention of these diseases beforehand by ensuring customized routine exercise and regular health checkup. In this context, we propose Wrist-Card, a photoplethysmogram (PPG) sensor based wrist wearable unit (WWU) and a personalized healthcare android application, to monitor the cardio-health of the user and guide with customized feedback measured from the health indexes for the effective prevention of CVDs at low cost. ‘Wrist-Card’ consists of three components: a low-cost PPG sensor, Wrist Wearable Unit (WWU) and an Android application. The WWU mainly consists of a microcontroller, Bluetooth module and power-supply, all embedded into a single printed circuit board (PCB). Our proposed system provides a concurrent monitoring of health indexes such as heart rate, heart rate variability, pressure, blood oxygen saturation and blood glucose level with the help of PPG. The proposed system provides an average absolute error (AAE) of 2.63 beats per minute (BPM) with a standard deviation of 1.42 BPM from the measurements taken from standardized measuring unit for 10 test subjects. However, sensor noise as well as some forms of motion artifacts can be attributed to the deviations of the obtained results from the ground truths. Considering the global acceleration of CVDs, our proposed low cost and affordable Wrist-Card would offer a smart and efficient preventive approach against CVDs, therefore working as a humanitarian platform to reduce the mortality rate across the world.


title: Shared decision making: really putting patients at the centre of healthcare

abstract: Although many clinicians feel they already use shared decision making, research shows a perception-reality gap. A M Stiggelbout and colleagues discuss why it is important and highlight some best practices


title: Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking

abstract: Advances in sensing and tracking technology enable location-based applications but they also create significant privacy risks. Anonymity can provide a high degree of privacy, save service users from dealing with service providers’ privacy policies, and reduce the service providers’ requirements for safeguarding private information. However, guaranteeing anonymous usage of location-based services requires that the precise location information transmitted by a user cannot be easily used to re-identify the subject. This paper presents a middleware architecture and algorithms that can be used by a centralized location broker service. The adaptive algorithms adjust the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area. Using a model based on automotive traffic counts and cartographic material, we estimate the realistically expected spatial resolution for different anonymity constraints. The median resolution generated by our algorithms is 125 meters. Thus, anonymous location-based requests for urban areas would have the same accuracy currently needed for E-911 services; this would provide sufficient resolution for wayfinding, automated bus routing services and similar location-dependent services.


title: Microstrip Parallel-Coupled Filters With Cascade Trisection and Quadruplet Responses

abstract: Microstrip parallel-coupled filters with generalized Chebyshev responses are presented. The basic structure of the proposed filter is a conventional parallel-coupled filter of which the physical dimensions can be easily obtained by the well-known analytical method. With the aid of the equivalent circuit corresponding to a conventional parallel-coupled filter, the relative insertion phase from source or load to each open end of resonators can be easily obtained by observing the two-port admittance matrix. Applying the cross coupling from source or load to a proper nonadjacent resonator, a trisection or a quadruplet coupling scheme can be realized with prescribed transmission zeros. More importantly, the proposed trisection can be designed to have a transmission zero on the lower or upper stopband by just adjusting the length of the cross-coupling strip. Using the proposed structure, the conventional time-consuming adjusting procedure to obtain initial physical dimensions of filters is no longer required. In this paper, a fourth-order parallel-coupled filter is used as the basic structure to demonstrate various combinations of transmission zeros. Simulated and measured results are well matched.


title: Predicting sentiment toward transportation in social media using visual and textual features

abstract: Social media platforms can be used by transportation agencies to receive feedback from their customers, thus creating two-way communication between the service provider and its consumers. Sentiment analysis is one method of aggregating overall polarity (positive or negative) towards a topic. However, most sentiment analysis methods rely on text processing, thus ignoring the large amount of image data present in popular social networks. The primary aim of this study is to exploit image data in conjunction with text and to evaluate this integrated approach for sentiment analysis for transportation. This study used image, captions, and comments data from the Instagram social network that were marked as being relevant to California Department of Transportation (Caltrans) and attempted to predict the expressed sentiment towards this agency. A set of high-level features were extracted from images using the web-based Microsoft Cognitive Services APIs. These features included the detection of faces and 86 categories which describe the images. Text features included the set of individual words and structural features. The experiment results of different machine learning techniques show a gain in precision when images and texts are combined compared to text-only approaches, thus confirming the relevance of visual content usage. The precision reaches a performance close to human classification agreement (typically approximately 80%). However, the results do not indicate that visual features are more informative than text features.


title: A Metrics Framework for Quantifying Autonomy in Complex Systems

abstract: Autonomous systems, often realized as multi-agent systems, are envisioned to deal with uncertain and dynamic environments. They are applied in dangerous situations, e.g. as rescue robots or to relieve humans from complex and tedious tasks like driving a car or infrastructure maintenance. But in order to further improve the technology a generic measurement and benchmarking of autonomy is required. Within this paper we present an improved understanding of autonomous systems. Based on this foundation we introduce our concept of a multi-dimensional autonomy metric framework that especially takes into account multi-system environments. Finally, our approach is illustrated by means of an example.


title: Mining User Rationale from Software Reviews

abstract: Rationale refers to the reasoning and justification behind human decisions, opinions, and beliefs. In software engineering, rationale management focuses on capturing design and requirements decisions and on organizing and reusing project knowledge. This paper takes a different view on rationale written by users in online reviews. We studied 32,414 reviews for 52 software applications in the Amazon Store. Through a grounded theory approach and peer content analysis, we investigated how users argue and justify their decisions, e.g. about upgrading, installing, or switching software applications. We also studied the occurrence frequency of rationale concepts such as issues encountered or alternatives considered in the reviews and found that assessment criteria like performance, compatibility, and usability represent the most pervasive concept. We then used the truth set of manually labeled review sentences to explore how accurately we can mine rationale concepts from the reviews. Support Vector Classifier, Naive Bayes, and Logistic Regression, trained on the review metadata, syntax tree of the review text, and influential terms, achieved a precision around 80% for predicting sentences with alternatives and decisions, with top recall values of 98%. On the review level, precision was up to 13% higher with recall values reaching 99%. We discuss the findings and the rationale importance for supporting deliberation in user communities and synthesizing the reviews for developers.


title: A Laser-Aided Inertial Navigation System (L-INS) for human localization in unknown indoor environments

abstract: This paper presents a novel 3D indoor Laser-aided Inertial Navigation System (L-INS) for the visually impaired. An Extended Kalman Filter (EKF) fuses information from an Inertial Measurement Unit (IMU) and a 2D laser scanner, to concurrently estimate the six degree-of-freedom (d.o.f.) position and orientation (pose) of the person and a 3D map of the environment. The IMU measurements are integrated to obtain pose estimates, which are subsequently corrected using line-to-plane correspondences between linear segments in the laser-scan data and orthogonal structural planes of the building. Exploiting the orthogonal building planes ensures fast and efficient initialization and estimation of the map features while providing human-interpretable layout of the environment. The L-INS is experimentally validated by a person traversing a multistory building, and the results demonstrate the reliability and accuracy of the proposed method for indoor localization and mapping.


Smooth Scan : One Access Path to Rule Them All


title: Continual and One-Shot Learning Through Neural Networks with Dynamic External Memory

abstract: Training neural networks to quickly learn new skills without forgetting previously learned skills is an important open challenge in machine learning. A common problem for adaptive networks that can learn during their lifetime is that the weights encoding a particular task are often overridden when a new task is learned. This paper takes a step in overcoming this limitation by building on the recently proposed Evolving Neural Turing Machine (ENTM) approach. In the ENTM, neural networks are augmented with an external memory component that they can write to and read from, which allows them to store associations quickly and over long periods of time. The results in this paper demonstrate that the ENTM is able to perform one-shot learning in reinforcement learning tasks without catastrophic forgetting of previously stored associations. Additionally, we introduce a new ENTM default jump mechanism that makes it easier to find unused memory location and therefor facilitates the evolution of continual learning networks. Our results suggest that augmenting evolving networks with an external memory component is not only a viable mechanism for adaptive behaviors in neuroevolution but also allows these networks to perform continual and one-shot learning at the same time.


title: (Quasi)Periodicity Quantification in Video Data, Using Topology

abstract: This work introduces a novel framework for quantifying the presence and strength of recurrent dynamics in video data. Specifically, we provide continuous measures of periodicity (perfect repetition) and quasiperiodicity (superposition of periodic modes with non-commensurate periods), in a way which does not require segmentation, training, object tracking or 1-dimensional surrogate signals. Our methodology operates directly on video data. The approach combines ideas from nonlinear time series analysis (delay embeddings) and computational topology (persistent homology), by translating the problem of finding recurrent dynamics in video data, into the problem of determining the circularity or toroidality of an associated geometric space. Through extensive testing, we show the robustness of our scores with respect to several noise models/levels, we show that our periodicity score is superior to other methods when compared to human-generated periodicity rankings, and furthermore, we show that our quasiperiodicity score clearly indicates the presence of biphonation in videos of vibrating vocal folds, which has never before been accomplished end to end quantitatively.


title: A case study on value-based requirements tracing

abstract: Project managers aim at keeping track of interdependencies between various artifacts of the software development lifecycle, to find out potential requirements conflicts, to better understand the impact of change requests, and to fulfill process quality standards, such as CMMI requirements. While there are many methods and techniques on how to technically store requirements traces, the economic issues of dealing with requirements tracing complexity remain open. In practice tracing is typically not an explicit systematic process, but occurs rather ad hoc with considerable hidden tracing-related quality costs. This paper reports a case study on value-based requirements tracing (VBRT) that systematically supports project managers in tailoring requirements tracing precision and effort based on the parameters stakeholder value, requirements risk/volatility, and tracing costs. Main results of the case study were: (a) VBRT took around 35% effort of full requirements tracing; (b) more risky or volatile requirements warranted more detailed tracing because of their higher change probability.


title: Video Surveillance Online Repository (ViSOR): an integrated framework

abstract: The availability of new techniques and tools for Video Surveillance and the capability of storing huge amounts of visual data acquired by hundreds of cameras every day call for a convergence between pattern recognition, computer vision and multimedia paradigms. A clear need for this convergence is shown by new research projects which attempt to exploit both ontology-based retrieval and video analysis techniques also in the field of surveillance. This paper presents the ViSOR (Video Surveillance Online Repository) framework, designed with the aim of establishing an open platform for collecting, annotating, retrieving, and sharing surveillance videos, as well as evaluating the performance of automatic surveillance systems. Annotations are based on a reference ontology which has been defined integrating hundreds of concepts, some of them coming from the LSCOM and MediaMill ontologies. A new annotation classification schema is also provided, which is aimed at identifying the spatial, temporal and domain detail level used. The ViSOR web interface allows video browsing, querying by annotated concepts or by keywords, compressed video previewing, media downloading and uploading. Finally, ViSOR includes a performance evaluation desk which can be used to compare different annotations.


Applications of Artificial Intelligence & Associated Technologies


title: Tunable Bandpass Filter With Independently Controllable Dual Passbands

abstract: This paper presents a two-pole dual-band tunable bandpass filter (BPF) with independently controllable dual passbands based on a novel tunable dual-mode resonator. This resonator principally comprises a λ/2 resonator and two varactor diodes. One varactor is placed at the center of the resonator to determine the dominant even-mode resonant frequency; the other is installed between two ends of the resonator to control the dominant odd-mode resonant frequency. These two distinct odd- and even-mode resonances can be independently generated, and they are used to realize the two separated passbands as desired. Detailed discussion is carried on to provide a set of closed-form design equations for determination of all of the elements involved in this tunable filter, inclusive of capacitively loaded quarter-wavelength or λ/2 resonators, external quality factor, and coupling coefficient. Finally, a prototype tunable dual-band filter is fabricated and measured. Measured and simulated results are found in good agreement with each other. The results show that the first passband can be tuned in a frequency range from 0.77 to 1.00 GHz with the 3-dB fractional-bandwidth of 20.3%-24.7%, whereas the second passband varies from 1.57 to 2.00 GHz with the 3-dB absolute-bandwidth of 120 ± 8 MHz.


title: The brainweb: phase synchronization and large-scale integration.

abstract: The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.


title: Team AnnieWAY’s Autonomous System

abstract: This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the DARPA Urban Challenge 2007 competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. A recent laser scanner plays the prominent role in the perception of the environment. It measures range and reflectivity for each pixel. While the former is used to provide 3D scene geometry, the latter allows robust lane marker detection. Mission and maneuver selection is conducted via a concurrent hierarchical state machine that specifically ascertains behavior in accordance with California traffic rules. We conclude with a report of the results achieved during the competition.


title: User-Pairing Based Non-Orthogonal Multiple Access (NOMA) System

abstract: Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for next generation wireless networks. This paper investigates the sum rate maximization problem for NOMA system over a frequency selective fading channel with its users being paired according to their channel powers. A hierarchical power allocation process is proposed, whereby the users are divided into two groups such that a closed form power allocation solution can be applied. This divide-and allocate approach is repeated until all users are allocated with a transmission power. The proposed architecture facilitates a large number of users for NOMA system. Simulation results show that the proposed scheme achieves comparable performances to the optimal one based on numerical solution, and outperforms other approaches.


title: EMC: Emotion-aware mobile cloud computing in 5G

abstract: With the development of 5G, the wireless world will be interconnected without barriers. This new technology will enable many challenging applications, and more personalized and interactive services are expected to be available with resourcelimited mobile terminals. Fortunately, mobile cloud computing (MCC) emerging in the context of 5G has the potential to overcome this bottleneck, which enables many resource-intensive services for mobile users with the support of mobile big data delivery and cloud-assisted computing. In this article we propose a novel framework named EMC in the context of 5G, which offers personalized emotionaware services by MCC and affective computing. With the proposed framework, the traditional MCC architecture is modified to achieve the required Quality of Experience in emotion-aware applications. Furthermore, we design a partitioning solution corresponding to the fundamental trade-off between the communication and computation in EMC. The framework would be helpful to provide personalized, human-centric, intelligent emotion-aware services in 5G.


title: PSLO: enforcing the Xth percentile latency and throughput SLOs for consolidated VM storage

abstract: It is desirable but challenging to simultaneously support latency SLO at a pre-defined percentile, i.e., the Xth percentile latency SLO, and throughput SLO for consolidated VM storage. Ensuring the Xth percentile latency contributes to accurately differentiating service levels in the metric of the application-level latency SLO compliance, especially for the application built on multiple VMs. However, the Xth percentile latency SLO and throughput SLO enforcement are the opposite sides of the same coin due to the conflicting requirements for the level of IO concurrency. To address this challenge, this paper proposes PSLO, a framework supporting the Xth percentile latency and throughput SLOs under consolidated VM environment by precisely coordinating the level of IO concurrency and arrival rate for each VM issue queue. It is noted that PSLO can take full advantage of the available IO capacity allowed by SLO constraints to improve throughput or reduce latency with the best effort. We design and implement a PSLO prototype in the real VM consolidation environment created by Xen. Our extensive trace-driven prototype evaluation shows that our system is able to optimize the Xth percentile latency and throughput for consolidated VMs under SLO constraints.


title: From 4G to 5G: Self-organized Network Management meets Machine Learning

abstract: Abstract Self-organization as applied to cellular networks is usually referred to Selforganizing Networks (SONs), and it is a key driver for improving Operations,Administration, and Management (OAM) activities. SON aims at reducing the cost of installation and management of 4G and future 5G networks, by simplifying operational tasks through the capability to configure, optimize and heal itself. To satisfy 5G network management requirements, this autonomous management vision has to be extended to the end to end network. In literature and also in some instances of products available in the market, Machine Learning (ML) has been identified as the key tool to implement autonomous adaptability and take advantage of experience when making decisions. In this paper, we survey how 5G network management, with an end-to-end perspective of the network, can significantly benefit from ML solutions. We review and provide the basic concepts and taxonomy for SON, network management and ML. We analyze the available state of the art in the literature, standardization, and in the market. We pay special attention to 3rd Generation Partnership Project (3GPP) evolution in the area of network management and to the data that can be extracted from 3GPP networks, in order to gain knowledge and experience in how the network is working, and improve network performance in a proactive way. Finally, we go through the main challenges associated with this line of research, in both 4G and in what 5G is getting designed, while identifying new directions for research.


title: zxcvbn: Low-Budget Password Strength Estimation

abstract: For over 30 years, password requirements and feedback have largely remained a product of LUDS: counts of lowerand uppercase letters, digits and symbols. LUDS remains ubiquitous despite being a conclusively burdensome and ineffective security practice. zxcvbn is an alternative password strength estimator that is small, fast, and crucially no harder than LUDS to adopt. Using leaked passwords, we compare its estimations to the best of four modern guessing attacks and show it to be accurate and conservative at low magnitudes, suitable for mitigating online attacks. We find 1.5 MB of compressed storage is sufficient to accurately estimate the best-known guessing attacks up to 105 guesses, or 104 and 103 guesses, respectively, given 245 kB and 29 kB. zxcvbn can be adopted with 4 lines of code and downloaded in seconds. It runs in milliseconds and works as-is on web, iOS and Android.


title: Anticipatory Robot Assistance for the Prevention of Human Static Joint Overloading in Human–Robot Collaboration

abstract: This letter proposes a novel human–robot collaboration (HRC) control approach to alert and reduce the static joint torque overloading of a human partner while executing shared tasks with a robot. Using a preidentified statically equivalent serial chain model, variations of the centre-of-pressure and ground reaction force are calculated, and the overloading joint torques are evaluated in real time to initially alert the human about consequent injuries. An online optimization technique is implemented to adjust the robot trajectories facilitating the human to achieve more ergonomic body poses throughout the HRC task. The optimized human configurations are calculated by taking into account the human stability, the robot, and the human workspaces, and the task constraints, and illustrated to the human in real time. The experimental evaluation of the proposed technique is achieved in a human–robot load sharing task as a representative example of coassembly or transportation scenarios in industrial settings.


title: Gender differences in the presentation of adult obstructive hypertrophic cardiomyopathy with resting gradient: a study of 122 patients.

abstract: The present study investigated gender differences among adult patients with obstructive hypertrophic cardiomyopathy (OHCM) and resting gradient. Using outflow gradients >10 mmHg and the presence of asymmetrical septal hypertrophy of the left ventricle as inclusion criteria, 122 patients were identified among patients referred for echocardiographic examinations between May 1990 and October 1996. Clinical, echocardiographical and follow-up data were compared between male and female patients. The female patients were significantly older than male patients (mean age +/-SD 66.7+/-10.5 vs 54.8+/-12.5 years). The female patients had a smaller interventricular septal wall thickness, less frequent systolic anterior movement of the mitral valve, more frequent association with hypertension, and less frequent association with ischemic heart disease (IHD) and giant T wave inversion. In this study population, adult female patients presented with OHCM 12 years later than males. Whether this represents female patients' reluctance to seek medical attention early, a different disease process that affects predominantly elderly females, or a gender-specific end organ response to aging, hypertension, IHD and other processes, or the protective effects of estrogen remains to be determined.


title: Using humans as sensors: An estimation-theoretic perspective

abstract: The explosive growth in social network content suggests that the largest "sensor network" yet might be human. Extending the participatory sensing model, this paper explores the prospect of utilizing social networks as sensor networks, which gives rise to an interesting reliable sensing problem. In this problem, individuals are represented by sensors (data sources) who occasionally make observations about the physical world. These observations may be true or false, and hence are viewed as binary claims. The reliable sensing problem is to determine the correctness of reported observations. From a networked sensing standpoint, what makes this sensing problem formulation different is that, in the case of human participants, not only is the reliability of sources usually unknown but also the original data provenance may be uncertain. Individuals may report observations made by others as their own. The contribution of this paper lies in developing a model that considers the impact of such information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a "sensor network" for observing events in the physical world. Evaluation, using Twitter-based case-studies, shows good correspondence between observations deemed correct by Apollo and ground truth.


title: Kinematical and Dynamical Models of KR 6 KUKA Robot, Including the Kinematic Control in a Parallel Processing Platform

abstract: This chapter presents the study and modelling of KR 6 KUKA Robot, of the Robotics Laboratory, Federal University of Rio de Janeiro, see fig 1. The chapter shows the CAD model (Computer Aided Design), the direct kinematics, the inverse kinematics and the inverse dynamical model. The direct kinematic is based in the use of homogeneous matrix. The inverse kinematics uses the quadratic equations model. The dynamical model is based on the use of Euler-Lagrange equations, using the D-H (Denavit-Hartenberg) algorithm and taking into account the inertia tensor, which was found with help of CAE tools (Computer Aided Engineering), On the other hand the Jacobian of robot manipulator is present, it‘s necessary for the kinematic control. The chapter finishes with the implementation of the inverse kinematic in one parallel processing platform and analyzes its performance. Fig. 1. KR 6 KUKA Robot, Robotics Laboratory, Federal University of Rio de Janeiro UFRJ.


title: Cutoff Scores in Neurocognitive Testing and Symptom Clusters That Predict Protracted Recovery From Concussions in High School Athletes

abstract: BACKGROUND ::: Many studies address diagnosing concussions, but few look at predicting prognosis. A previous discriminant function analysis showed that symptom clusters derived from the Post-Concussion Symptom Scale and Immediate Postconcussion Assessment and Cognitive Testing composite scores used together improved predictions of protracted recovery after a sports-related concussion. ::: ::: ::: OBJECTIVE ::: To determine cutoff scores in neurocognitive and Post-Concussion Symptom Scale symptom cluster scores when classifying protracted recovery in concussed athletes. ::: ::: ::: METHODS ::: 108 male high school football athletes completed a computer-based neurocognitive test battery (Immediate Postconcussion Assessment and Cognitive Testing) within a median of 2 days after injury. Patients completed graded exertional protocols requiring athletes to be symptom free at rest and during increasing levels of activity and had recovery of neurocognitive scores before return to play. After return to play, athletes were classified as protracted recovery (>14 days, n = 58) or short-recovery (≤14 days, n = 50). Receiver-operating characteristic curves analyzed each of the neurocognitive (verbal, visual, processing speed, and reaction time) and symptom cluster (migraine, cognitive, sleep, and neuropsychiatric) scores. ::: ::: ::: RESULTS ::: Cutoffs for migraine cluster, cognitive cluster, visual memory, and processing speed were statistically significant. Cutoffs at 75%, 80%, and 85% sensitivity to predict protracted recovery for the migraine symptom cluster were 15 or greater, 18, 20; cognitive symptom cluster 18 or greater, 19, 22; visual memory 48 or less, 46, 44.5; and processing speed 24.5 or less, 23.46, 22.5, respectively. Eighty-percent sensitivity indicates that the corresponding cutoff correctly identify 80% of concussed athletes requiring protracted recovery. ::: ::: ::: CONCLUSION ::: Specific cutoffs may help to set numerical thresholds for clinicians to predict which concussed athletes will have a protracted recovery.


title: Tessier No. 3 Incomplete Cleft Reconstruction with Alar Transposition and Irregular Z-Plasty

abstract: Facial clefts are extremely rare congenital deformities and there are only a few technique reports for surgical reconstruction of clefts in the literature. In this article, we report a Tessier no. 3 incomplete cleft reconstruction with alar transposition and irregular Z-plasty in a 2-year old female patient.


title: Toward Health Exercise Behavior Change for Teams Using Lifelog Sharing Models

abstract: Recent technological trends in mobile/wearable devices and sensors have been enabling an increasing number of people to collect and store their “lifelog” easily in their daily lives. Beyond exercise behavior change of individual users, our research focus is on the behavior change of teams, based on lifelogging technologies and lifelog sharing. In this paper, we propose and evaluate six different types of lifelog sharing models among team members for their exercise promotion, leveraging the concepts of “competition” and “collaboration.” According to our experimental mobile web application for exercise promotion and an extensive user study conducted with a total of 64 participants over a period of three weeks, the model with a “competition” technique resulted in the most effective performance for competitive teams, such as sports teams.


title: Overcoming Evolutionary Mismatch by Self-Treatment with Helminths: Current Practices and Experience

abstract: Background. Biome depletion, or loss of biodiversity from the ecosystem of the human body, is a major "evolutionary mismatch" underlying a variety of inflammatory diseases in Western populations. Enhancing biodiversity via exposure to helminths has effectively treated immune diseases in a variety of experimental animal models and in a few published studies involving human subjects. Purpose. This study probes another untapped resource for helminthic therapy: the methods and outcomes reported by individuals currently self-treating with helminths. Procedures. Helminth providers were interviewed, surveys were collected from self-treaters, and publically available information was compiled. Results. More than 250 anecdotal experiences of self-treatment were assessed, and the total number of individuals worldwide currently self-treating was estimated at between 6,000 and 7,000. A wide range of inflammation- related diseases, including inflammatory bowel disease, allergies, and autoimmunity, were effectively treated. Conclusions. This study finds that the therapy is being refined through experience and is now expanding to treat widespread neuropsychiatric problems such as depression, anxiety, migraine headaches, bipolar disorder, and perhaps Parkinson's disease.


title: Deep Nets: What have they ever done for Vision?

abstract: This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the center of recent progress on artificial intelligence and are of growing importance in cognitive science and neuroscience. They have enormous successes but also clear limitations. There is also only partial understanding of their inner workings. It seems unlikely that Deep Nets in their current form will be the best long-term solution either for building general purpose intelligent machines or for understanding the mind/brain, but it is likely that many aspects of them will remain. At present Deep Nets do very well on specific types of visual tasks and on specific benchmarked datasets. But Deep Nets are much less general purpose, flexible, and adaptive than the human visual system. Moreover, methods like Deep Nets may run into fundamental difficulties when faced with the enormous complexity of natural images which can lead to a combinatorial explosion. To illustrate our main points, while keeping the references small, this paper is slightly biased towards work from our group.


title: Monitoring vital signs using millimeter wave

abstract: Continuous monitoring of human's breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This paper presents mmVital, a system that uses 60 GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human's body and the RSS of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital relies on a novel human finding procedure where a human can be located within a room by reflection loss based object/human classification. We evaluate mmVital using a 60 GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 Bpm (breathing rate) and 2.15 bpm (heart rate). Also, it can locate the human subject with 98.4% accuracy within 100 ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind the wall.


title: Domains of Concern in Software Architectures and Architecture Description Languages

abstract: Software architectures shift the focus of developers from lines-of-code to coarser-grained elements and their interconnection structure. Architecture description languages (ADLs) have been proposed as domain-specific languages for the domain of software architecture. There is still little consensus in the research community on what problems are most important to address in a study of software architecture, what aspects of an architecture should be modeled in an ADL, or even what an ADL is. To shed light on these issues, we provide a framework of architectural domains, or areas of concern in the study of software architectures. We evaluate existing ADLs with respect to the framework and study the relationship between architectural and application domains. One conclusion is that, while the architectural domains perspective enables one to approach architectures and ADLs in a new, more structured manner, further understanding of architectural domains, their tie to application domains, and their specific influence on ADLs is needed.


title: Collision avoidance and path planning of multiple UAVs using flyable paths in 3D

abstract: This paper discusses path planning of a Micro Air Vehicle (UAV) with collision avoidance in a three dimensional space. The 3D space is chosen as an urban environment where the buildings are sparsely located. The process is divided into two phases: (i) Route planning where the buildings are isolated to form a route, which is followed by (ii) Path planning in which simple Dubins paths are employed to follow through the way-points generated by the route planner. The first phase creates a network of waypoints in between the buildings which meets the minimum distance required for safe manoeuvre. Delaunay triangulation is used to isolate the buildings from each other and to produce safe waypoints between them. Once the waypoints are assured safe to pass through, the path planner uses Dubins path to produce the flyable and safe paths.


title: Multivariate data analysis of NMR data.

abstract: Multivariate methods based on principal components (PCA and PLS) have been used to reduce NMR spectral information, to predict NMR parameters of complicated structures, and to relate shift data sets to dependent descriptors of biological significance. Noise reduction and elimination of instrumental artifacts are easily performed on 2D NMR data. Configurational classification of triterpenes and shift predictions in disubstituted benzenes can be obtained using PCA and PLS analysis. Finally, the shift predictions of tripeptides from descriptors of amino acids open the possibility of automatic analysis of multidimensional data of complex structures.


A universal VAD based on jointly trained deep neural networks


title: Practices and Problems in Communication - the Accountant and the Distance Educator

abstract: This paper argues that accountants and distance educators share a problem in communication with their clients in that, in many cases, interaction in the communication process is essentially precluded by the nature of the service they offer. This paper reviews the procedures employed by accountants and distance educators in communicating with their clients, with a view to determine how each group can benefit by employing practices utilised by the other. It is argued that developments in communication through distance education have been limited only by developments in communication technology and costs. As striking developments in communications technology have been made in the past decade, and costs of using such technology have been falling, distance educators have been exploring and exploiting a range of media options in communications between the teacher and the student. In contrast, accountants have confined themselves exclusively, or almost exclusively, to communication by print. It is argued that accountants have allowed themselves to become hidebound in this practice, and that the profession may look at adopting other media options to communicate financial reports to advantage. In contrast, the accountant works with an, albeit imperfect, formal, well-developed conceptual framework to structure communication 1 In this paper the term ‘distance educator’ is taken to encompass the roles of the subject specialist, and the specialist skills required to frame a message that can be readily comprehended by the recipient. As these skills are not necessarily vested in one person, the term ‘distance educator’ may refer to a team.


title: Introducing blockchains for healthcare

abstract: Blockchains as a technology emerged to facilitate money exchange transactions and eliminate the need for a trusted third party to notarize and verify such transactions as well as protect data security and privacy. New structures of Blockchains have been designed to accommodate the need for this technology in other fields such as e-health, tourism and energy. This paper is concerned with the use of Blockchains in managing and sharing electronic health and medical records to allow patients, hospitals, clinics, and other medical stakeholder to share data amongst themselves, and increase interoperability. The selection of the Blockchains used architecture depends on the entities participating in the constructed chain network. Although the use of Blockchains may reduce redundancy and provide caregivers with consistent records about their patients, it still comes with few challenges which could infringe patients' privacy, or potentially compromise the whole network of stakeholders. In this paper, we investigate different Blockchains structures, look at existing challenges and provide possible solutions. We focus on challenges that may expose patients' privacy and the resiliency of Blockchains to possible attacks.


title: Factors influencing food choices of adolescents: findings from focus-group discussions with adolescents.

abstract: Abstract Objective To assess adolescents' perceptions about factors influencing their food choices and eating behaviors. Design Data were collected in focus-group discussions. Subjects/setting The study population included 141 adolescents in 7th and 10th grade from 2 urban schools in St Paul, Minn, who participated in 21 focus groups. Analysis Data were analyzed using qualitative research methodology, specifically, the constant comparative method. Results Factors perceived as influencing food choices included hunger and food cravings, appeal of food, time considerations of adolescents and parents, convenience of food, food availability, parental influence on eating behaviors (including the culture or religion of the family), benefits of foods (including health), situation-specific factors, mood, body image, habit, cost, media, and vegetarian beliefs. Major barriers to eating more fruits, vegetables, and dairy products and eating fewer high-fat foods included a lack of sense of urgency about personal health in relation to other concerns, and taste preferences for other foods. Suggestions for helping adolescents eat a more healthful diet include making healthful food taste and look better, limiting the availability of unhealthful options, making healthful food more available and convenient, teaching children good eating habits at an early age, and changing social norms to make it "cool" to eat healthfully. Applications/conclusions The findings suggest that if programs to improve adolescent nutrition are to be effective, they need to address a broad range of factors, in particular environmental factors (eg, the increased availability and promotion of appealing, convenient foods within homes, schools, and restaurants). J Am Diet Assoc. 1999;99:929–934,937.


title: FPGA parallel-pipelined AES-GCM core for 100G Ethernet applications

abstract: The forthcoming IEEE 802.3ba Ethernet standard will provide data transmission at a bandwidth of 100 Gbit/s. Currently, the fastest cryptographic primitive approved by the U.S. National Institute for Standard and Technology, that combines data encryption and authentication, is the Galois/Counter Mode (GCM) of operation. If the feasibility to increase the speed of the GCM up to 100 Gbit/s on ASIC technologies has already been demonstrated, the FPGA implementation of the GCM in secure 100G Ethernet network systems arises some important structural issues. In this paper, we report on an efficient FPGA architecture of the GCM combined with the AES block cipher. With the parallelization of four pipelined AES-GCM cores we were able to reach the speed required by the new Ethernet standard. Furthermore, the time-critical binary field multiplication of the authentication process relies on four pipelined 2-step Karatsuba-Ofman multipliers.


title: The Misbehavior of Reinforcement Learning

abstract: Organisms modify their behavior in response to its consequences, a phenomenon referred to as operant learning. The computational principles and neural mechanisms underlying operant learning are a subject of extensive experimental and theoretical investigations. Theoretical approaches largely rely on concepts and algorithms from reinforcement learning. The dominant view is that organisms maintain a value function, that is, a set of estimates of the cumulative future rewards associated with the different behavioral options. These values are then used to select actions. Learning in this framework results from the update of these values depending on experience of the consequences of past actions. An alternative view questions the applicability of such a computational scheme to many real-life situations. Instead, it posits that organisms exploit the intrinsic variability in their action-selection mechanism(s) to modify their behavior, e.g., via stochastic gradient ascent, without the need of an explicit representation of values. In this review, we compare these two approaches in terms of their computational power and flexibility, their putative neural correlates, and, finally, in terms of their ability to account for behavior as observed in repeated-choice experiments. We discuss the successes and failures of these alternative approaches in explaining the observed patterns of choice behavior. We conclude by identifying some of the important challenges to a comprehensive theory of operant learning.


title: Robust 3D Reconstruction With an RGB-D Camera

abstract: We present a novel 3D reconstruction approach using a low-cost RGB-D camera such as Microsoft Kinect. Compared with previous methods, our scanning system can work well in challenging cases where there are large repeated textures and significant depth missing problems. For robust registration, we propose to utilize both visual and geometry features and combine SFM technique to enhance the robustness of feature matching and camera pose estimation. In addition, a novel prior-based multicandidates RANSAC is introduced to efficiently estimate the model parameters and significantly speed up the camera pose estimation under multiple correspondence candidates. Even when serious depth missing occurs, our method still can successfully register all frames together. Loop closure also can be robustly detected and handled to eliminate the drift problem. The missing geometry can be completed by combining multiview stereo and mesh deformation techniques. A variety of challenging examples demonstrate the effectiveness of the proposed approach.


title: A Multiword Expression Data Set: Annotating Non-Compositionality and Conventionalization for English Noun Compounds

abstract: Scarcity of multiword expression data sets raises a fundamental challenge to evaluating the systems that deal with these linguistic structures. In this work we attempt to address this problem for a subclass of multiword expressions by producing a large data set annotated by experts and validated by common statistical measures. We present a set of 1048 noun-noun compounds annotated as non-compositional, compositional, conventionalized and not conventionalized. We build this data set following common trends in previous work while trying to address some of the well known issues such as small number of annotated instances, quality of the annotations, and lack of availability of true negative instances.


title: An example-based procedural system for element arrangement

abstract: We present a method for synthesizing two dimensional (2D) element arrangements from an example. The main idea is to combine texture synthesis techniques based-on a local neighborhood comparison and procedural modeling systems based-on local growth. Given a user-specified reference pattern, our system analyzes neigh-borhood information of each element by constructing connectivity. Our synthesis process starts with a single seed and progressively places elements one by one by searching a reference element which has local features that are the most similar to the target place of the synthesized pattern. To support creative design activities, we introduce three types of interaction for controlling global features of the resulting pattern, namely a spray tool, a flow field tool, and a boundary tool. We also introduce a global optimization process that helps to avoid local error concentrations. We illustrate the feasibility of our method by creating several types of 2D patterns.


The Impact of Marketing-Induced vs . Word-of-Mouth Customer Acquisition on Customer Equity by


Scrum in Practice: an Overview of Scrum Adaptations


title: Associative Compression Networks for Representation Learning

abstract: This paper introduces Associative Compression Networks (ACNs), a new framework for variational autoencoding with neural networks. The system differs from existing variational autoencoders (VAEs) in that the prior distribution used to model each code is conditioned on a similar code from the dataset. In compression terms this equates to sequentially transmitting the dataset using an ordering determined by proximity in latent space. Since the prior need only account for local, rather than global variations in the latent space, the coding cost is greatly reduced, leading to rich, informative codes. Crucially, the codes remain informative when powerful, autoregressive decoders are used, which we argue is fundamentally difficult with normal VAEs. Experimental results on MNIST, CIFAR-10, ImageNet and CelebA show that ACNs discover high-level latent features such as object class, writing style, pose and facial expression, which can be used to cluster and classify the data, as well as to generate diverse and convincing samples. We conclude that ACNs are a promising new direction for representation learning: one that steps away from IID modelling, and towards learning a structured description of the dataset as a whole.


title: Drive Video Analysis for the Detection of Traffic Near-Miss Incidents

abstract: Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).


title: Comparison Study between NOMA and SCMA

abstract: In this paper, the performance and system complexity of the candidate multiple access (MA) techniques for the next generation of cellular systems, namely, non-orthogonal multiple access (NOMA) (in this paper, we consider power domain MA as NOMA) and sparse code multiple access (SCMA), are investigated. To this end, for each MA technique, a resource allocation problem considering heterogeneous cellular networks (HetNet) is formulated. We apply successive convex approximation (SCA) method to each problem and obtain their solutions. The simulation results show that SCMA-based system achieves better performance than NOMA-based one at the cost of more complexity.


title: Crowd Behavior Analysis: A Survey

abstract: Nowadays, one of the most interesting and active research topic in computer vision is the analysis of crowd behavior. Crowd is a set of individuals gathered in a particular physical area. Analyzing crowd behavior involves many ways viz., crowd density estimation, crowd motion detection, crowd tracking and crowd behavior recognition. We provide a brief literature survey on crowd behavior analysis from 2000 in this paper.


title: Extended Suicide With a Pet

abstract: The combination of the killing of a pet and a suicide is a perplexing scenario that is largely unexplored in the literature. Many forensic psychiatrists and psychologists may be unaccustomed to considering the significance of the killing of a pet. The subject is important, however, because many people regard their pets as members of their family. A case is presented of a woman who killed her pet dog and herself by carbon monoxide poisoning. The purpose of this article is to provide an initial exploration of the topic of extended suicide with a pet. Forensic mental health evaluations may have a role in understanding the etiology of this event and in opining as to the culpability of individuals who attempt to or successfully kill a pet and then commit suicide. Because the scientific literature is lacking, there is a need to understand this act from a variety of perspectives. First, a social and anthropological perspective will be presented that summarizes the history of the practice of killing of one's pet, with a focus on the ancient Egyptians. A clinical context will examine what relationship animals have to mental illness. A vast body of existing scientific data showing the relevance of human attachment to pets suggests that conclusions from the phenomena of homicide-suicide and filicide-suicide are applicable to extended suicide with a pet. Finally, recommendations will be proposed for both clinical and forensic psychiatrists faced with similar cases.


title: Towards a Digital Body: The Virtual Arm Illusion

abstract: The integration of the human brain with computers is an interesting new area of applied neuroscience, where one application is replacement of a person’s real body by a virtual representation. Here we demonstrate that a virtual limb can be made to feel part of your body if appropriate multisensory correlations are provided. We report an illusion that is invoked through tactile stimulation on a person’s hidden real right hand with synchronous virtual visual stimulation on an aligned 3D stereo virtual arm projecting horizontally out of their shoulder. An experiment with 21 male participants showed displacement of ownership towards the virtual hand, as illustrated by questionnaire responses and proprioceptive drift. A control experiment with asynchronous tapping was carried out with a different set of 20 male participants who did not experience the illusion. After 5 minutes of stimulation the virtual arm rotated. Evidence suggests that the extent of the illusion was also correlated with the degree of muscle activity onset in the right arm as measured by EMG during this period that the arm was rotating, for the synchronous but not the asynchronous condition. A completely virtual object can therefore be experienced as part of one’s self, which opens up the possibility that an entire virtual body could be felt as one’s own in future virtual reality applications or online games, and be an invaluable tool for the understanding of the brain mechanisms underlying body ownership.


title: Analysis of damper winding designs for direct-on-line synchronous reluctance motor

abstract: One of the essential parts of a Synchronous Reluctance Motor (SynRM) for direct-on-line (DOL) applications is a damper winding (DW) which makes the start of the motor possible. However, the DW can also be a considerable source of Joule losses caused by DW currents reacting on the stator current linkage and permeance harmonics. In order to get a comprehension of how DW bars' distribution influences the amount of induced rotor Joule losses at steady state different designs of DW of a SynRM are considered in the paper. Also the authors consider different methods of minimising stator current linkage distortion and permeance harmonics by stator part modification: magnetic wedges, reduction of slot opening and short pitching. A comparison with an induction motor with the same stator was done as well.


Application of Radial Basis Function Neural Network Based on Ant Colony Algorithm in Credit Evaluation of Real Estate Enterprises


title: Tensor Networks for Big Data Analytics and Large-Scale Optimization Problems

abstract: In this paper we review basic and emerging models and associated algorithms for large-scale tensor networks, especially Tensor Train (TT) decompositions using novel mathematical and graphical representations. We discus the concept of tensorization (i.e., creating very high-order tensors from lower-order original data) and super compression of data achieved via quantized tensor train (QTT) networks. The purpose of a tensorization and quantization is to achieve, via low-rank tensor approximations"super"compression, and meaningful, compact representation of structured data. The main objective of this paper is to show how tensor networks can be used to solve a wide class of big data optimization problems (that are far from tractable by classical numerical methods) by applying tensorization and performing all operations using relatively small size matrices and tensors and applying iteratively optimized and approximative tensor contractions. Keywords: Tensor networks, tensor train (TT) decompositions, matrix product states (MPS), matrix product operators (MPO), basic tensor operations, tensorization, distributed representation od data optimization problems for very large-scale problems: generalized eigenvalue decomposition (GEVD), PCA/SVD, canonical correlation analysis (CCA).


Treatment decision making for young people diagnosed with major depressive disorders


title: An efficient audio fingerprint search algorithm for music retrieval

abstract: The conventional audio fingerprinting system by Haitsma uses a lookup table to identify the candidate songs in the database, which contains the sub-fingerprints of songs, and searches the candidates to find a song whose bit error rate is the lowest. However, this approach has a drawback that the number of database accesses increases dramatically, especially when the database contains a large number of songs or when a matching sub-fingerprint is not found in the lookup table due to a heavily degraded input signal. In this paper, a novel search method is proposed to overcome these difficulties. The proposed method partitions each song found from the lookup table into blocks, assigns a weight to each block, and uses the weight as a search priority to speed up the search process while reducing the number of database accesses. Various results from our experiment show the significant improvement in search speed while maintaining the search accuracy comparable to the conventional method.


title: Form digitization in BPO: from outsourcing to crowdsourcing?

abstract: This paper describes an ethnographic study of an outsourced business process - the digitization of healthcare forms. The aim of the study was to understand how the work is currently organized, with an eye to uncovering the research challenges which need to be addressed if that work is to be crowdsourced. The findings are organised under four emergent themes: Workplace Ecology, Data Entry Skills and Knowledge, Achieving Targets and Collaborative Working. For each theme a description of how the work is undertaken in the outsourcer's Indian office locations is given, followed by the implications for crowdsourcing that work. This research is a first step in understanding how crowdsourcing might be applied to BPO activities. The paper examines features specific to form digitization - extreme distribution and form decomposition - and lightly touches on the crowdsourcing of BPO work more generally.


title: Parallel bubble sort using stream programming paradigm

abstract: Parallel computing has become the dominant paradigm in computer science, mainly in the form of multi core processors. One software approach for parallel programming, is writing programs in streaming model. A stream program is type of computer program such that the input data is stream of data. In this paper we have used stream programming model, and developed the parallel version of bubble sort algorithm. Our source codes are simple java code and used JStream java library. Our algorithm doesn't have any loop statement, witch is increases the time complexity of algorithms. We have chosen bubble sort, because it is the oldest and slowest sorting algorithm with time complexity level of O(n2). In this paper we have shown that our algorithm has lower time complexity. Also our code shows that writing parallel programs in streaming model would be very simple.


title: Global-Local and Trail-Making Tasks by Monolingual and Bilingual Children: Beyond Inhibition

abstract: In three experiments, a total of 151 monolingual and bilingual 6-year-olds performed similarly on measures of language and cognitive ability but bilinguals solved the global-local and trail-making tasks more rapidly than monolinguals. This bilingual advantage was found not only for the traditionally demanding conditions, incongruent global-local trials and Trails B, but also for the conditions not usually considered to be cognitively demanding, congruent global-local trials and Trails A. All the children performed similarly when congruent trials were presented in a single block or perceptually simple stimuli were used, ruling out speed differences between the groups. The results demonstrate a bilingual advantage in processing complex stimuli in tasks that require executive processing components for conflict resolution, including switching and updating, even when no inhibition appears to be involved. They also suggest that simple conditions of the trail-making and global-local tasks involve some level of effortful processing for young children. Finally, the bilingual advantage in the trail-making task suggests that the interpretation of standardized measures of executive control needs to be reconsidered for children with specific experiences such as bilingualism.


title: The effect of intramuscular interferon beta-1a on spinal cord volume in relapsing-remitting multiple sclerosis

abstract: Background ::: Spinal cord atrophy occurs early in multiple sclerosis (MS) and impacts disability. The therapeutic effect of interferon beta-1a (IFNβ-1a) on spinal cord atrophy in patients with relapsing-remitting (RR) MS has not been explored.


Importance sampling for unbiased on-demand evaluation of knowledge base population


title: Machine Reading at the University of Washington

abstract: Machine reading is a long-standing goal of AI and NLP. In recent years, tremendous progress has been made in developing machine learning approaches for many of its subtasks such as parsing, information extraction, and question answering. However, existing end-to-end solutions typically require substantial amount of human efforts (e.g., labeled data and/or manual engineering), and are not well poised for Web-scale knowledge acquisition. In this paper, we propose a unifying approach for machine reading by bootstrapping from the easiest extractable knowledge and conquering the long tail via a self-supervised learning process. This self-supervision is powered by joint inference based on Markov logic, and is made scalable by leveraging hierarchical structures and coarse-to-fine inference. Researchers at the University of Washington have taken the first steps in this direction. Our existing work explores the wide spectrum of this vision and shows its promise.


title: Spin-orbit proximity effect in graphene.

abstract: The development of spintronics devices relies on efficient generation of spin-polarized currents and their electric-field-controlled manipulation. While observation of exceptionally long spin relaxation lengths makes graphene an intriguing material for spintronics studies, electric field modulation of spin currents is almost impossible due to negligible intrinsic spin-orbit coupling of graphene. In this work, we create an artificial interface between monolayer graphene and few-layer semiconducting tungsten disulphide. In these devices, we observe that graphene acquires spin-orbit coupling up to 17 meV, three orders of magnitude higher than its intrinsic value, without modifying the structure of the graphene. The proximity spin-orbit coupling leads to the spin Hall effect even at room temperature, and opens the door to spin field effect transistors. We show that intrinsic defects in tungsten disulphide play an important role in this proximity effect and that graphene can act as a probe to detect defects in semiconducting surfaces.


title: Multi-class filter bank common spatial pattern for four-class motor imagery BCI

abstract: This paper investigates the classification of multi-class motor imagery for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm classifies EEG measurements from features constructed using subject-specific temporal-spatial filters. However, the FBCSP algorithm is limited to binary-class motor imagery. Hence, this paper proposes 3 approaches of multi-class extension to the FBCSP algorithm: One-versus-Rest, Pair-Wise and Divide-and-Conquer. These approaches decompose the multi-class problem into several binary-class problems. The study is conducted on the BCI Competition IV dataset IIa, which comprises single-trial EEG data from 9 subjects performing 4-class motor imagery of left-hand, right-hand, foot and tongue actions. The results showed that the multi-class FBCSP algorithm could extract features that matched neurophysiological knowledge, and yielded the best performance on the evaluation data compared to other international submissions.


title: Active Electromagnetic Suspension System for Improved Vehicle Dynamics

abstract: This paper offers motivations for an active suspension system which provides for both additional stability and maneuverability by performing active roll and pitch control during cornering and braking as well as eliminating road irregularities, hence increasing both vehicle and passenger safety and drive comfort. Various technologies are compared to the proposed electromagnetic suspension system which uses a tubular permanent magnet (PM) actuator together with a passive spring. Based upon on-road measurements and results from the literature, several specifications for the design of an electromagnetic suspension system are derived. The measured on-road movement of the passive suspension system is reproduced by electromagnetic actuation on a quarter car setup proving the dynamic capabilities of an electromagnetic suspension system.


title: Absolute Orientation for Word Embedding Alignment

abstract: We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e.g., GloVe or word2vec). Our methods are simple and have a closed form to optimally rotate, translate, and scale to minimize root mean squared errors or maximize the average cosine similarity between two embeddings of the same vocabulary into the same dimensional space. Our methods extend approaches known as Absolute Orientation, which are popular for aligning objects in three-dimensions, and generalize an approach by Smith etal (ICLR 2017). We prove new results for optimal scaling and for maximizing cosine similarity. Then we demonstrate how to evaluate the similarity of embeddings from different sources or mechanisms, and that certain properties like synonyms and analogies are preserved across the embeddings and can be enhanced by simply aligning and averaging ensembles of embeddings.


title: How Blind People Interact with Visual Content on Social Networking Services

abstract: In this paper, we explore blind peopleâ s motivations, challenges, interactions, and experiences with visual content on Social Networking Services (SNSs). We present findings from an interview study of 11 individuals and a survey study of 60 individuals, all with little to no functional vision. Compared to sighted SNS users, our blind participants faced profound accessibility challenges, including the prevalence of photos without sufficient text descriptions. To overcome the challenges, they developed creative strategies, including using a variety of methods to access SNS features (e.g., opening the mobile site on a desktop browser), and inferring photo content from textual cues and social interactions. When strategies failed, participants reached out for help from trusted friends, or avoided certain features. We discuss our findings in the context of CSCW research and SNS accessibility as a design value. We highlight the social significance of photo interactions for blind people and suggest design practices.


title: A Survey of Role Mining

abstract: Role-Based Access Control (RBAC) is the most widely used model for advanced access control deployed in diverse enterprises of all sizes. RBAC critically depends on defining roles, which are a functional intermediate between users and permissions. Thus, for RBAC to be effective, an appropriate set of roles needs to be identified. Since many organizations already have user-permission assignments defined in some form, it makes sense to identify roles from this existing information. This process, known as role mining, is one of the critical steps for successful RBAC adoption in any enterprise. In recent years, numerous role mining techniques have been developed, which take into account the characteristics of the core RBAC model, as well as its various extended features. In this article, we comprehensively study and classify the basic problem of role mining along with its several variants and the corresponding solution strategies. Categorization is done on the basis of the nature of the target RBAC system, the objective of role mining, and the type of solution. We then discuss the limitations of existing work and identify new areas of research that can lead to further enrichment of this field.


title: Crime Rate Inference with Big Data

abstract: Crime is one of the most important social problems in the country, affecting public safety, children development, and adult socioeconomic status. Understanding what factors cause higher crime is critical for policy makers in their efforts to reduce crime and increase citizens' life quality. We tackle a fundamental problem in our paper: crime rate inference at the neighborhood level. Traditional approaches have used demographics and geographical influences to estimate crime rates in a region. With the fast development of positioning technology and prevalence of mobile devices, a large amount of modern urban data have been collected and such big data can provide new perspectives for understanding crime. In this paper, we used large-scale Point-Of-Interest data and taxi flow data in the city of Chicago, IL in the USA. We observed significantly improved performance in crime rate inference compared to using traditional features. Such an improvement is consistent over multiple years. We also show that these new features are significant in the feature importance analysis.


title: UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory

abstract: In this work we introduce a convolutional neural network (CNN) that jointly handles low-, mid-, and high-level vision tasks in a unified architecture that is trained end-to-end. Such a universal network can act like a `swiss knife' for vision tasks; we call this architecture an UberNet to indicate its overarching nature. ::: We address two main technical challenges that emerge when broadening up the range of tasks handled by a single CNN: (i) training a deep architecture while relying on diverse training sets and (ii) training many (potentially unlimited) tasks with a limited memory budget. Properly addressing these two problems allows us to train accurate predictors for a host of tasks, without compromising accuracy. ::: Through these advances we train in an end-to-end manner a CNN that simultaneously addresses (a) boundary detection (b) normal estimation (c) saliency estimation (d) semantic segmentation (e) human part segmentation (f) semantic boundary detection, (g) region proposal generation and object detection. We obtain competitive performance while jointly addressing all of these tasks in 0.7 seconds per frame on a single GPU. A demonstration of this system can be found at this http URL


Brief Introduction of Virtual Reality & its Challenges


title: New approaches to nurse rostering benchmark instances

abstract: This paper presents the results of developing a branch and price algorithm and an ejection chain method for nurse rostering problems. The approach is general enough to be able to apply it to a wide range of benchmark nurse rostering instances. The majority of the instances are real world applications. They have been collected from a variety of sources including industrial collaborators, other researchers and various publications. The results of entering these algorithms in the 2010 International Nurse Rostering Competition are also presented and discussed. In addition, incorporated within both algorithms is a dynamic programming method which we present. The algorithm contains a number of heuristics and other features which make it very effective on the broad rostering model introduced.


title: A comparative study of different machine learning methods on microarray gene expression data

abstract: BackgroundSeveral classification and feature selection methods have been studied for the identification of differentially expressed genes in microarray data. Classification methods such as SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods have been used in recent studies. The accuracy of these methods has been calculated with validation methods such as v-fold validation. However there is lack of comparison between these methods to find a better framework for classification, clustering and analysis of microarray gene expression results.ResultsIn this study, we compared the efficiency of the classification methods including; SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods. The v-fold cross validation was used to calculate the accuracy of the classifiers. Some of the common clustering methods including K-means, DBC, and EM clustering were applied to the datasets and the efficiency of these methods have been analysed. Further the efficiency of the feature selection methods including support vector machine recursive feature elimination (SVM-RFE), Chi Squared, and CSF were compared. In each case these methods were applied to eight different binary (two class) microarray datasets. We evaluated the class prediction efficiency of each gene list in training and test cross-validation using supervised classifiers.ConclusionsWe presented a study in which we compared some of the common used classification, clustering, and feature selection methods. We applied these methods to eight publicly available datasets, and compared how these methods performed in class prediction of test datasets. We reported that the choice of feature selection methods, the number of genes in the gene list, the number of cases (samples) substantially influence classification success. Based on features chosen by these methods, error rates and accuracy of several classification algorithms were obtained. Results revealed the importance of feature selection in accurately classifying new samples and how an integrated feature selection and classification algorithm is performing and is capable of identifying significant genes.


title: Toward simulating entire cities with behavioral models of traffic

abstract: Resilient transportation systems enable quick evacuation, rescue, distribution of relief supplies, and other activities for reducing the impact of natural disasters and for accelerating the recovery from them. The resilience of a transportation system largely relies on the decisions made during a natural disaster. We developed an agent-based traffic simulator for predicting the results of potential actions taken with respect to the transportation system to quickly make appropriate decisions. For realistic simulation, we govern the behavior of individual drivers of vehicles with foundational principles learned from probe-car data. For example, we used the probe-car data to estimate the personality of individual drivers of vehicles in selecting their routes, taking into account various metrics of routes such as travel time, travel distance, and the number of turns. This behavioral model, which was constructed from actual data, constitutes a special feature of our simulator. We built this simulator using the X10 language, which enables massively parallel execution for simulating traffic in a large metropolitan area. We report the use cases of the simulator in three major cities in the context of disaster recovery and resilient transportation.