title: On the combination of domain-specific heuristics for author name disambiguation: the nearest cluster method

abstract: Author name disambiguation has been one of the hardest problems faced by digital libraries since their early days. Historically, supervised solutions have empirically outperformed those based on heuristics, but with the burden of having to rely on manually labeled training sets for the learning process. Moreover, most supervised solutions just apply some type of generic machine learning solution and do not exploit specific knowledge about the problem. In this article, we follow a similar reasoning, but in the opposite direction. Instead of extending an existing supervised solution, we propose a set of carefully designed heuristics and similarity functions, and apply supervision only to optimize such parameters for each particular dataset. As our experiments show, the result is a very effective, efficient and practical author name disambiguation method that can be used in many different scenarios. In fact, we show that our method can beat state-of-the-art supervised methods in terms of effectiveness in many situations while being orders of magnitude faster. It can also run without any training information, using only default parameters, and still be very competitive when compared to these supervised methods (beating several of them) and better than most existing unsupervised author name disambiguation solutions.


title: A systematic literature review: Information security culture

abstract: Human behavior inside organizations is considered the main threat to organizations. Moreover, in information security the human element consider the most of weakest link in general. Therefore it is crucial to create an information security culture to protect the organization's assets from inside and to influence employees' security behavior. This paper focuses on identifying the definitions and frameworks for establishing and maintaining information security culture inside organizations. It presents work have been done to conduct a systematic literature review of papers published on information security culture from 2003 to 2016. The review identified 68 papers that focus on this area, 18 of which propose an information security culture framework. An analysis of these papers indicate there is a positive relationship between levels of knowledge and how employees behave. The level of knowledge significantly affects information security behavior and should be considered as a critical factor in the effectiveness of information security culture and in any further work that is carried out on information security culture. Therefore, there is a need for more studies to identity the security knowledge that needs to be incorporated into organizations and to find instances of best practice for building an information security culture within organizations.


title: Fast, accurate simulation for SDN prototyping

abstract: Thorough test and evaluation of new software-defined network (SDN)-based applications and configurations present many challenges. Examples of these challenges include scaling to large networks, accuracy, and efficiency in evaluation along with the ability to easily transition between prototype and test environments. Current methods for test and evaluation include new programming languages and frameworks, debugging and static analysis techniques, and VM- and container-based emulation tools. In this paper we describe a simulation-based tool called fs-sdn that complements and expands upon these existing approaches. Our work is designed to address the problem of prototyping and evaluating new SDN-based applications accurately, at large scale, and in a way that enables easy translation to real controller platforms like POX and NOX. We describe the design, implementation and use of fs-sdn, and demonstrate its capability by carrying out a series of experiments using fs-sdn and the Mininet platform in nearly identical configurations. We show that the measurements derived from fs-sdn are accurate compared with Mininet, but offer significant speed and scalability advantages.


title: Measuring the quality of web content using factual information

abstract: Nowadays, many decisions are based on information found in the Web. For the most part, the disseminating sources are not certified, and hence an assessment of the quality and credibility of Web content became more important than ever. With factual density we present a simple statistical quality measure that is based on facts extracted from Web content using Open Information Extraction. In a first case study, we use this measure to identify featured/good articles in Wikipedia. We compare the factual density measure with word count, a measure that has successfully been applied to this task in the past. Our evaluation corroborates the good performance of word count in Wikipedia since featured/good articles are often longer than non-featured. However, for articles of similar lengths the word count measure fails while factual density can separate between them with an F-measure of 90.4%. We also investigate the use of relational features for categorizing Wikipedia articles into featured/good versus non-featured ones. If articles have similar lengths, we achieve an F-measure of 86.7% and 84% otherwise.


title: Modeling Spatial-Temporal Dynamics for Traffic Prediction.

abstract: Spatial-temporal prediction has many applications such as climate forecasting and urban planning. In particular, traffic prediction has drawn increasing attention in data mining research field for the growing traffic related datasets and for its impacts in real-world applications. For example, an accurate taxi demand prediction can assist taxi companies to pre-allocate taxis to meet with commuting demands. The key challenge of traffic prediction lies in how to model the complex spatial and temporal dependencies. In this paper, we make two important observations which have not been considered by previous studies: (1) the spatial dependency between locations are dynamic; and (2) the temporal dependency follows strong periodicity but is not strictly periodic for its dynamic temporal shifting. Based on these two observations, we propose a novel Spatial-Temporal Dynamic Network (STDN) framework. In this framework, we propose a flow gating mechanism to learn the dynamic similarity between locations via traffic flow. A periodically shifted attention mechanism is designed to handle long-term periodic dependency and periodic temporal shifting. Furthermore, we extend our framework from region-based traffic prediction to traffic prediction for road intersections by using graph convolutional structure. We conduct extensive experiments on several large-scale real traffic datasets and demonstrate the effectiveness of our approach over state-of-the-art methods.


title: Dry electrodes for electrocardiography.

abstract: Patient biopotentials are usually measured with conventional disposable Ag/AgCl electrodes. These electrodes provide excellent signal quality but are irritating for long-term use. Skin preparation is usually required prior to the application of electrodes such as shaving and cleansing with alcohol. To overcome these difficulties, researchers and caregivers seek alternative electrodes that would be acceptable in clinical and research environments. Dry electrodes that operate without gel, adhesive or even skin preparation have been studied for many decades. They are used in research applications, but they have yet to achieve acceptance for medical use. So far, a complete comparison and evaluation of dry electrodes is not well described in the literature. This work compares dry electrodes for biomedical use and physiological research, and reviews some novel systems developed for cardiac monitoring. Lastly, the paper provides suggestions to develop a dry-electrode-based system for mobile and long-term cardiac monitoring applications.


title: A broadband achromatic metalens in the visible

abstract: Metalenses consist of an array of optical nanoantennas on a surface capable of manipulating the properties of an incoming light wavefront. Various flat optical components, such as polarizers, optical imaging encoders, tunable phase modulators and a retroreflector, have been demonstrated using a metalens design. An open issue, especially problematic for colour imaging and display applications, is the correction of chromatic aberration, an intrinsic effect originating from the specific resonance and limited working bandwidth of each nanoantenna. As a result, no metalens has demonstrated full-colour imaging in the visible wavelength. Here, we show a design and fabrication that consists of GaN-based integrated-resonant unit elements to achieve an achromatic metalens operating in the entire visible region in transmission mode. The focal length of our metalenses remains unchanged as the incident wavelength is varied from 400 to 660 nm, demonstrating complete elimination of chromatic aberration at about 49% bandwidth of the central working wavelength. The average efficiency of a metalens with a numerical aperture of 0.106 is about 40% over the whole visible spectrum. We also show some examples of full-colour imaging based on this design. Integrating the Pancharatnam–Berry phase with integrated resonant nanoantennas in a metalens design produces an achromatic device capable of full-colour imaging in the visible range in transmission mode.


title: Applying Machine Learning to Reduce Overhead in DTN Vehicular Networks

abstract: VANETs benefit from Delay Tolerant Networks (DTNs) routing algorithms when connectivity is intermittent because of the fast movement of vehicles. Multi-copy DTN algorithms spread message copies to increase the delivery probability but increasing network overhead. In this work we apply machine learning algorithms to reduce network overhead by discriminating the worst intermediate nodes for the ransmission of copies. The scenario is a VANET of public buses that follow specific routes and schedules. This repetitive behavior creates an opportunity for applying trained classifiers to predict the occurrence of performance-related events. As the main contribution, our method decreases overhead without degrading delivery probability.


title: A novel variable reluctance resolver for HEV/EV applications

abstract: In order to simplify the manufacturing process of variable reluctance (VR) resolvers for hybrid electric vehicle/electric vehicle (HEV/EV) applications, a novel VR resolver with nonoverlapping tooth–coil windings is proposed in this paper. A comparison of the winding configurations is first carried out between the existing and the proposed designs, followed by the description of the operating principle. Furthermore, the influence of actual application conditions is investigated by finite-element (FE) analyses, including operating speed and assembling eccentricity. In addition, identical stator and windings of the novel design can be employed in three resolvers of different rotor saliencies. The voltage difference among the three rotor combinations, as well as the detecting accuracy, is further investigated. Finally, prototypes are fabricated and tested to verify the analyses.


title: Variable radius pulley design methodology for pneumatic artificial muscle-based antagonistic actuation systems

abstract: There is a growing interest in utilizing pneumatic artificial muscles (PAMs) as actuators for human-friendly robots. However, several performance drawbacks prevent the widespread use of PAMs. Although many approaches have been proposed to overcome the low control bandwidth of PAMs, some limitations of PAMs such as restricted workspace and torque capacity remain to be addressed. This paper analyzes the limitations of conventional circular pulley joints and subsequently proposes a design methodology to synthesize a pair of variable radius pulleys to improve joint torque capacity over a large workspace. Experimental results show that newly synthesized variable radius pulleys significantly improve position tracking performance in the enlarged workspace.


title: Minimum-energy translational trajectory planning for battery-powered three-wheeled omni-directional mobile robots

abstract: The minimum-energy translational trajectory planning algorithm is proposed for battery-powered three-wheeled omni-directional mobile robots (TOMRs). We have chosen a practical cost function as the total energy drawn from the batteries, in order to lengthen the operational time of a mobile robot with given batteries. After establishing the dynamic equations of TOMRs, the optimal control theory is used to solve the minimum-energy trajectory, which gives the velocity profile in analytic form. Various simulations are performed and the consumed energy is compared to other velocity trajectories. Simulation results reveal that the energy saving is achieved of up to 2.4% compared with loss-minimization control, and up to 4.3% compared with conventional trapezoidal velocity profile.


Experiences with workflows for automating data-intensive bioinformatics


title: Virtual reality and consciousness inference in dreaming

abstract: This article explores the notion that the brain is genetically endowed with an innate virtual reality generator that – through experience-dependent plasticity – becomes a generative or predictive model of the world. This model, which is most clearly revealed in rapid eye movement (REM) sleep dreaming, may provide the theater for conscious experience. Functional neuroimaging evidence for brain activations that are time-locked to rapid eye movements (REMs) endorses the view that waking consciousness emerges from REM sleep – and dreaming lays the foundations for waking perception. In this view, the brain is equipped with a virtual model of the world that generates predictions of its sensations. This model is continually updated and entrained by sensory prediction errors in wakefulness to ensure veridical perception, but not in dreaming. In contrast, dreaming plays an essential role in maintaining and enhancing the capacity to model the world by minimizing model complexity and thereby maximizing both statistical and thermodynamic efficiency. This perspective suggests that consciousness corresponds to the embodied process of inference, realized through the generation of virtual realities (in both sleep and wakefulness). In short, our premise or hypothesis is that the waking brain engages with the world to predict the causes of sensations, while in sleep the brain’s generative model is actively refined so that it generates more efficient predictions during waking. We review the evidence in support of this hypothesis – evidence that grounds consciousness in biophysical computations whose neuronal and neurochemical infrastructure has been disclosed by sleep research.


title: Control of a 9-DoF Wheelchair-mounted robotic arm system using a P300 Brain Computer Interface: Initial experiments

abstract: An apparatus for maintaining the temperature of material contained in fluid conveying devices. The apparatus may include an enclosure having an improved door, mounting bracket, locking mechanism, heating device and a secure structure for accessing the upper portion of the enclosure. An alignment guide and integral tabs are also disclosed which facilitate assembly of the enclosure. A shield-support-bottom enclosure, which allows greater access for maintenance, is disclosed.


title: Toward Rapid Development of Multiparty Virtual Human Negotiation Scenarios

abstract: This paper reports on an ongoing effort to enable the rapid development of multi-party virtual human negotiation scenarios. We present a case study in which a new scenario supporting negotiation between two human role players and two virtual humans was developed over a period of 12 weeks. We discuss the methodology and development process that were employed, from storyline design through role play and iterative development of the virtual humans’ semantic and task representations and natural language processing capabilities. We analyze the effort, expertise, and time required for each development step, and discuss opportunities to further streamline the development process.


title: Low power data-aware STT-RAM based hybrid cache architecture

abstract: Static Random Access Memories (SRAMs) occupy a large area of today's microprocessors, and are a prime source of leakage power in highly scaled technologies. Low leakage and high density Spin-Transfer Torque RAMs (STT-RAMs) are ideal candidates for a power-efficient memory. However, STT-RAM suffers from high write energy and latency, especially when writing ‘one’ data. In this paper we propose a novel data-aware hybrid STT-RAM/SRAM cache architecture which stores data in the two partitions based on their bit counts. To exploit the new resultant data distribution in the SRAM partition, we employ an asymmetric low-power 5T-SRAM structure which has high reliability for majority ‘one’ data. The proposed design significantly reduces the number of writes and hence dynamic energy in both STT-RAM and SRAM partitions. We employed a write cache policy and a small swap memory to control data migration between cache partitions. Our evaluation on UltraSPARC-III processor shows that utilizing STT-RAM/6T-SRAM and STT-RAM/5T-SRAM architectures for the L2 cache results in 42% and 53% energy efficiency, 9.3% and 9.1% performance improvement and 16.9% and 20.3% area efficiency respectively, with respect to SRAM-based cache running SPEC CPU 2006 benchmarks.


title: On the utility of extended fingerprint features: A study on pores

abstract: Extended fingerprint features are routinely used by latent examiners in forensic applications. They are now being considered for inclusion in automatic fingerprint identification systems (AFIS), particularly with the adoption of 1000ppi resolution in the Next Generation Identification (NGI) system. Earlier studies on this topic suffered from two limitations: (i) experiments were based on live scan images that are generally of good quality and contain smaller intra-class variations compared to ink images and (ii) the baseline minutiae matcher used to measure the additive value of extended features was not a state-of-the-art matcher. In this paper, we study the utility of pores, one of the most prevalent extended fingerprint features, on rolled ink fingerprint images at both 500ppi and 1000ppi resolution in the NIST SD30 database. The results show that the fingerprint image quality significantly affects the automatic extraction and matching accuracy of pores. Furthermore, the contribution of pores to the overall fingerprint recognition accuracy is miniscule when a COTS matcher is used for 500ppi rolled ink fingerprint images. The fusion between pore matcher and COTS minutiae matcher is a bit more effective on 1000ppi good quality rolled ink fingerprint images. We believe that these results will be useful in the design of next generation AFIS.


title: Extended Target Tracking using a Gaussian-Mixture PHD Filter

abstract: We comment on the errors in the formulation of Theorem 1 given in Extended Target Tracking Using a Gaussian-Mixture PHD Filter by K. Granstrom, C. Lundquist, and U. Orguner, and give a correct formulation.


title: Integrating Palmprint and Fingerprint for Identity Verification

abstract: In this paper, we propose a multimodal biometrics system that combines fingerprint and palmprint features to overcome several limitations of unimodal biometrics—such as the inability to tolerate noise, distorted data and etc.—and thus able to improve the performance of biometrics for personal verification. The quality of fingerprint and palmprint images are first enhanced using a series of pre-processing techniques. Following, a bank of 2D Gabor filters is used to independently extract fingerprint and palmprint features, which are then concatenated into a single feature vector. We conclude that the proposed methodology has better performance and is more reliable compared to unimodal approaches using solely fingerprint or palmprint biometrics. This is supported by our experiments which are able to achieve equal error rate (EER) as low as 0.91% using the combined biometrics features.


title: A New Vision for Smart Objects and the Internet of Things: Mobile Robots and Long-Range UHF RFID Sensor Tags

abstract: We present a new vision for smart objects and the Internet of Things wherein mobile robots interact with wirelessly-powered, long-range, ultra-high frequency radio frequency identification (UHF RFID) tags outfitted with sensing capabilities. We explore the technology innovations driving this vision by examining recently-commercialized sensor tags that could be affixed-to or embedded-in objects or the environment to yield true embodied intelligence. Using a pair of autonomous mobile robots outfitted with UHF RFID readers, we explore several potential applications where mobile robots interact with sensor tags to perform tasks such as: soil moisture sensing, remote crop monitoring, infrastructure monitoring, water quality monitoring, and remote sensor deployment.


title: Mixed factorization for collaborative recommendation with heterogeneous explicit feedbacks

abstract: Collaborative recommendation (CR) is a fundamental enabling technology for providing high-quality personalization services in various online and offline applications. Collaborative recommendation with heterogeneous explicit feedbacks (CR-HEF) such as 5-star grade scores and like/dislike binary ratings is a new and important problem, because it provides a rich and accurate source for learning users' preferences. However, most previous works on collaborative recommendation only focus on exploiting homogeneous explicit feedbacks such as grade scores or homogeneous implicit feedbacks such as clicks or purchases. In this paper, we study the CR-HEF problem, and design a novel and generic mixed factorization based transfer learning framework to fully exploit those two different types of explicit feedbacks. Experimental results on two CR-HEF tasks with real-world data sets show that our TMF is able to perform significantly better than the state-of-the-art methods.


title: A LOW-COST AND PORTABLE SYSTEM FOR 3D RECONSTRUCTION OF TEXTURE-LESS OBJECTS

abstract: The optical methods for 3D modelling of objects can be classified into two categories including image-based and range-based methods. Structure from Motion is one of the image-based methods implemented in commercial software. In this paper, a low-cost and portable system for 3D modelling of texture-less objects is proposed. This system includes a rotating table designed and developed by using a stepper motor and a very light rotation plate. The system also has eight laser light sources with very dense and strong beams which provide a relatively appropriate pattern on texture-less objects. In this system, regarding to the step of stepper motor, images are semi automatically taken by a camera. The images can be used in structure from motion procedures implemented in Agisoft software.To evaluate the performance of the system, two dark objects were used. The point clouds of these objects were obtained by spraying a light powders on the objects and exploiting a GOM laser scanner. Then these objects were placed on the proposed turntable. Several convergent images were taken from each object while the laser light sources were projecting the pattern on the objects. Afterward, the images were imported in VisualSFM as a fully automatic software package for generating an accurate and complete point cloud. Finally, the obtained point clouds were compared to the point clouds generated by the GOM laser scanner. The results showed the ability of the proposed system to produce a complete 3D model from texture-less objects.


title: DTI Tractography of the Human Brain's Language Pathways

abstract: Diffusion Tensor Imaging (DTI) tractography has been used to detect leftward asymmetries in the arcuate fasciculus, a pathway that links temporal and inferior frontal language cortices. In this study, we more specifically define this asymmetry with respect to both anatomy and function. Twenty right-handed male subjects were scanned with DTI, and the arcuate fasciculus was reconstructed using deterministic tractography. The arcuate was divided into 2 segments with different hypothesized functions, one terminating in the posterior superior temporal gyrus (STG) and another terminating in the middle temporal gyrus (MTG). Tractography results were compared with peak activation coordinates from prior functional neuroimaging studies of phonology, lexical-semantic processing, and prosodic processing to assign putative functions to these pathways. STG terminations were strongly left lateralized and overlapped with phonological activations in the left but not the right hemisphere, suggesting that only the left hemisphere phonological cortex is directly connected with the frontal lobe via the arcuate fasciculus. MTG terminations were also strongly left lateralized, overlapping with left lateralized lexical-semantic activations. Smaller right hemisphere MTG terminations overlapped with right lateralized prosodic activations. We combine our findings with a recent model of brain language processing to explain 6 aphasia syndromes.


title: A Novel Vital-Sign Sensor Based on a Self-Injection-Locked Oscillator

abstract: A novel vital-sign sensor with a self-injection-locked oscillator and a frequency demodulator to reduce system com- plexity and improve sensitivity is proposed. The theory provides a delta-sigma model to account for the excellent signal-to-noise spectral density ratio in a parametric study of the sensitivity performance. Then, the experiments verify the sensing principle and the predicted performance. Accordingly, a prototype sensor with high sensitivity is realized for noncontact cardiopulmonary monitoring, achieving a long sensing distance without the need for a low-noise amplifier. The sensing distance can grow four times longer by doubling the operating frequency. Furthermore, the sensor using a swept frequency can eliminate the null detection points and the external radio interference. As an experimental result, the sensor, which is placed 4 m away from the subject, can reliably detect the heartbeat signal an the operating frequency of 3.6 GHz and an output power level of 0 dBm.


title: Validation of a Brief Version of the Difficulties in Emotion Regulation Scale (DERS-18) in Five Samples

abstract: The field of affective clinical science has expanded dramatically over the past several decades (Rottenberg and Gross 2003; Tracy et al. 2014). An important part of this research is understanding emotion regulation and dysregulation, in particular, how individuals differ in their ability to identify, accept, and manage their emotional experiences. One of the most common and widely cited measures of emotion dysregulation is the Difficulties with Emotion Regulation Scale (DERS; Gratz and Roemer 2004), which has been extensively used to facilitate understanding of how emotion dysregulation is associated with psychiatric symptoms, other emotion-related constructs, and treatment progress. While this scale has contributed greatly to our understanding of emotion regulation problems, its length makes its inclusion in brief study protocols difficult, limiting its utility and increasing participant burden. In order to address this issue, we developed a short form version of the DERS (DERS-18) composed of the strongest items from each of the measure’s six subscales from the original DERS publication (Gratz and Roemer 2004), and then validated this measure in five datasets that vary in age and sample type. Our results demonstrate that an 18-item short-form of the DERS exhibits a similar structure as the original 36-item DERS, demonstrates excellent reliability and validity, and performs similarly to the original DERS despite comprising half the items. The DERS-18 has the potential to improve and expand emotion regulation assessment while reducing demands on research participants.


The Disabilities of the Arm , Shoulder and Hand Questionnaire ( DASH ) can measure the International Classification of Functioning , Disability and Health ( ICF )


title: Novel Wideband DOA Estimation Based on Sparse Bayesian Learning With Dirichlet Process Priors

abstract: Direction of arrival (DOA) estimation methods based on joint sparsity are attractive due to their superiority of high resolution with a limited number of snapshots. However, the common assumption that signals from different directions share the spectral band is inappropriate when they occupy different bands. To flexibly deal with this situation, a novel wideband DOA estimation algorithm is proposed to simultaneously infer the band occupation and estimate high-resolution DOAs by leveraging the sparsity in the angular domain. The band occupation is exploited by exerting a Dirichlet process (DP) prior over the latent parametric space. Moreover, the proposed method is extended to deal with the off-grid problem by two schemes. One applies a linear approximation to the true dictionary and infers the hidden variables and parameters by the variational Bayesian expectation-maximization (VBEM) in an integrated manner. The other is the separated scheme where DOA is refined by a postsearching procedure based on the reconstructed results. Since the proposed schemes can automatically partition the sub-bands into clusters according to their underlying occupation, more accurate DOA estimation can be achieved by using the measurements within one cluster. Results of comprehensive simulations demonstrate that the proposed schemes outperform other reported ones.


title: Estimating the photorealism of images: distinguishing paintings from photographs

abstract: Automatic classification of an image as a photograph of a real-scene or as a painting is potentially useful for image retrieval and Web site filtering applications. The main contribution of the paper is the proposition of several features derived from the color, edge, and gray-scale-texture information of the image that effectively discriminate paintings from photographs. For example, we found that paintings contain significantly more pure-color edges, and that certain gray-scale-texture measurements (mean and variance of Gabor filters) are larger for photographs. Using a large set of images (12000) collected from different Web sites, the proposed features exhibit very promising classification performance (over 90%). A comparative analysis of the automatic classification results and psychophysical data is reported, suggesting that the proposed automatic classifier estimates the perceptual photorealism of a given picture.


title: Playing Hide and Seek With Stored Keys

abstract: In this paper we consider the problem of efficiently locating cryptographic keys hidden in gigabytes of data, such as the complete file system of a typical PC. We describe efficient algebraic attacks which can locate secret RSA keys in long bit strings, and more general statistical attacks which can find arbitrary cryptographic keys embedded in large programs. These techniques can be used to apply \lunchtime attacks" on signature keys used by financial institutes, or to defeat \authenticode" type mechanisms in software packages.


title: Predictive markers for AD in a multi-modality framework: An analysis of MCI progression in the ADNI population

abstract: Abstract Alzheimer's Disease (AD) and other neurodegenerative diseases affect over 20 million people worldwide, and this number is projected to significantly increase in the coming decades. Proposed imaging-based markers have shown steadily improving levels of sensitivity/specificity in classifying individual subjects as AD or normal. Several of these efforts have utilized statistical machine learning techniques, using brain images as input, as means of deriving such AD-related markers. A common characteristic of this line of research is a focus on either (1) using a single imaging modality for classification, or (2) incorporating several modalities, but reporting separate results for each. One strategy to improve on the success of these methods is to leverage all available imaging modalities together in a single automated learning framework. The rationale is that some subjects may show signs of pathology in one modality but not in another—by combining all available images a clearer view of the progression of disease pathology will emerge. Our method is based on the Multi-Kernel Learning (MKL) framework, which allows the inclusion of an arbitrary number of views of the data in a maximum margin, kernel learning framework. The principal innovation behind MKL is that it learns an optimal combination of kernel (similarity) matrices while simultaneously training a classifier. In classification experiments MKL outperformed an SVM trained on all available features by 3%–4%. We are especially interested in whether such markers are capable of identifying early signs of the disease. To address this question, we have examined whether our multi-modal disease marker (MMDM) can predict conversion from Mild Cognitive Impairment (MCI) to AD. Our experiments reveal that this measure shows significant group differences between MCI subjects who progressed to AD, and those who remained stable for 3 years. These differences were most significant in MMDMs based on imaging data. We also discuss the relationship between our MMDM and an individual's conversion from MCI to AD.


title: Joint sentiment/topic model for sentiment analysis

abstract: Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.


title: Improved Knowledge Base Completion by Path-Augmented TransR Model

abstract: Knowledge base completion aims to infer new relations from existing information. In this paper, we propose path-augmented TransR (PTransR) model to improve the accuracy of link prediction. In our approach, we build PTransR based on TransR, which is the best one-hop model at present. Then we regularize TransR with information of relation paths. In our experiment, we evaluate PTransR on the task of entity prediction. Experimental results show that PTransR outperforms previous models.


title: Context-Based Pedestrian Path Prediction

abstract: We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.


title: A Survey on Generic Scada Simulators

abstract: SCADA acronym for supervisory control and data acquisition, a computer system for gathering and analyzing real-time data. SCADA systems are used to monitor and control a plant or equipment in industries such as water and waste control, telecommunications, oil and gas refining, transportation, energy. As these systems monitor critical and industrial infrastructures, so evaluation of their execution or performance and security is crucial. They are complex systems passed on over wide domains, from now on their modeling and simulation is far from piddling. Really, consistently it requires the co-simulation of the processes directed, furthermore the network communication between components of SCADA. Selecting the best possible modeling and simulation tool is segregating, because the blend of domain specific simulators with network simulators while considering the basic piece of time synchronization, engages the plan of dependable diversion circumstances. We outline here in this research article the SCADA network structural design, evaluate the widespread network simulators distinctiveness and study the on-hand simulator realizations in multiple or manifold SCADA application realms.


title: ReSEED: social event dEtection dataset

abstract: Nowadays, digital cameras are very popular among people and quite every mobile phone has a build-in camera. Social events have a prominent role in people's life. Thus, people take pictures of events they take part in and more and more of them upload these to well-known online photo community sites like Flickr. The number of pictures uploaded to these sites is still proliferating and there is a great interest in automatizing the process of event clustering so that every incoming (picture) document can be assigned to the corresponding event without the need of human interaction. These social events are defined as events that are planned by people, attended by people and for which the social multimedia are also captured by people. There is an urgent need to develop algorithms which are capable of grouping media by the social events they depict or are related to. In order to train, test, and evaluate such algorithms and frameworks, we present a dataset that consists of about 430,000 photos from Flickr together with the underlying ground truth consisting of about 21,000 social events. All the photos are accompanied by their textual metadata. The ground truth for the event groupings has been derived from event calendars on the Web that have been created collaboratively by people. The dataset has been used in the Social Event Detection (SED) task that was part of the MediaEval Benchmark for Multimedia Evaluation 2013. This task required participants to discover social events and organize the related media items in event-specific clusters within a collection of Web multimedia documents. In this paper we describe how the dataset has been collected and the creation of the ground truth together with a proposed evaluation methodology and a brief description of the corresponding task challenge as applied in the context of the Social Event Detection task.


title: Sentiment mining in WebFountain

abstract: WebFountain is a platform for very large-scale text analytics applications that allows uniform access to a wide variety of sources. It enables the deployment of a variety of document-level and corpus-level miners in a scalable manner, and feeds information that drives end-user applications through a set of hosted Web services. Sentiment (or opinion) mining is one of the most useful analyses for various end-user applications, such as reputation management. Instead of classifying the sentiment of an entire document about a subject, our sentiment miner determines sentiment of each subject reference using natural language processing techniques. In this paper, we describe the fully functional system environment and the algorithms, and report the performance of the sentiment miner. The performance of the algorithms was verified on online product review articles, and more general documents including Web pages and news articles.


title: Extremely Overlapping Vehicle Counting

abstract: The challenging problem that we explore in this paper is to precisely estimate the number of vehicles in an image of a traffic congestion situation. We start introducing TRANCOS, a novel data- base for extremely overlapping vehicle counting. It provides more than 1200 images where the number of vehicles and their locations have been annotated. We establish a clear experimental setup which will let oth- ers evaluate their own vehicle counting approaches. We also propose a novel evaluation metric, the Grid Average Mean absolute Error (GAME), which overcomes the limitations of previously proposed metrics for object counting. Finally, we perform an experimental validation, using the pro- posed TRANCOS dataset, for two types of vehicle counting strategies: counting by detection; and counting by regression. Our results show that counting by regression strategies are more precise localizing and estimat- ing the number of vehicles. The TRANCOS database and the source code for reproducing the results are available at http://agamenon.tsc.uah.es/ Personales/rlopez/data/trancos.


title: King: estimating latency between arbitrary internet end hosts

abstract: The ability to estimate network latencies between arbitrary Internet end hosts would enable new measurement studies and applications, such as investigating routing path inefficiencies on a wide-scale or constructing topologically sensitive overlay networks. In this paper we present King, a tool that accurately and quickly estimates the latency between arbitrary end hosts by using recursive DNS queries in a novel way. Compared to previous approaches, King has several advantages. Unlike IDMaps, King does not require the deployment of additional infrastructure, and unlike GNP, King does not require end hosts to agree upon a set of reference points. Unlike both IDMaps and GNP, King's estimates are based on direct online measurements rather than offline extrapolation. Because King uses existing DNS infrastructure, King scales naturally both in terms of the number of hosts that can be measured and in terms of the number of hosts performing measurements.After describing the techniques used in King, we present an extensive evaluation and analysis of the accuracy and consistency of our tool. Specifically, our evaluation shows that the accuracy of King is significantly better than the accuracy of IDMaps, and that King tends to preserve order among its latency estimates. Finally, we describe a variety of measurement studies and applications that could benefit from our tool, and present results from one such measurement study.


title: A Novel Approach for Network Attack Classification Based on Sequential Questions

abstract: With the development of incipient technologies, user devices becoming more exposed and ill-used by foes. In upcoming decades, traditional security measures will not be sufficient enough to handle this huge threat towards distributed hardware and software. Lack of standard network attack taxonomy has become an indispensable dispute on developing a clear understanding about the attacks in order to have an operative protection mechanism. Present attack categorization techniques protect a specific group of threat which has either messed the entire taxonomy structure or ambiguous when one network attacks get blended with few others attacks. Hence, this raises concerns about developing a common and general purpose taxonomy. In this study, a sequential question-answer based model of categorization is proposed. In this article, an intrusion detection framework and threat grouping schema are proposed on the basis of four sequential questions (Who, Where, How and What). We have used our method for classifying traditional network attacks in order to identify initiator, source, attack style and seriousness of an attack. Another focus of the paper is to provide a preventive list of actions for network administrator as a guideline to reduce overall attack consequence. Recommended taxonomy is designed to detect common attacks rather than any particular type of attack which can have a practical effect in real life attack classification. From the analysis of the classifications obtained from few infamous attacks, it is obvious that the proposed system holds certain benefits related to the prevailing taxonomies. Future research directions have also been well acknowledged.


title: BF-PSO-TS: Hybrid Heuristic Algorithms for Optimizing Task Schedulingon Cloud Computing Environment

abstract: Task Scheduling is a major problem in Cloud computing because the cloud provider has to serve many users. Also, a good scheduling algorithm helps in the proper and efficient utilization of the resources. So, task scheduling is considered as one of the major issues on the Cloud computing systems. The objective of this paper is to assign the tasks to multiple computing resources. Consequently, the total cost of execution is to be minimum and load to be shared between these computing resources. Therefore, two hybrid algorithms based on Particle Swarm Optimization (PSO) have been introduced to schedule the tasks; Best-Fit-PSO (BFPSO) and PSO-Tabu Search (PSOTS). According to BFPSO algorithm, Best-Fit (BF) algorithm has been merged into the PSO algorithm to improve the performance. The main principle of the modified BFSOP algorithm is that BF algorithm is used to generate the initial population of the standard PSO algorithm instead of being initiated randomly. According to the proposed PSOTS algorithm, the Tabu-Search (TS) has been used to improve the local research by avoiding the trap of the local optimality which could be occurred using the standard PSO algorithm. The two proposed algorithms (i.e., BFPSO and PSOTS) have been implemented using Cloudsim and evaluated comparing to the standard PSO algorithm using five problems with different number of independent tasks and resources. The performance parameters have been considered are the execution time (Makspan), cost, and resources utilization. The implementation results prove that the proposed hybrid algorithms (i.e., BFPSO, PSOTS) outperform the standard PSO algorithm


title: The view from the left

abstract: Pattern matching has proved an extremely powerful and durable notion in functional programming. This paper contributes a new programming notation for type theory which elaborates the notion in various ways. First, as is by now quite well-known in the type theory community, definition by pattern matching becomes a more discriminating tool in the presence of dependent types, since it refines the explanation of types as well as values. This becomes all the more true in the presence of the rich class of datatypes known as inductive families (Dybjer, 1991). Secondly, as proposed by Peyton Jones (1997) for Haskell, and independently rediscovered by us, subsidiary case analyses on the results of intermediate computations, which commonly take place on the right-hand side of definitions by pattern matching, should rather be handled on the left. In simply-typed languages, this subsumes the trivial case of Boolean guards; in our setting it becomes yet more powerful. Thirdly, elementary pattern matching decompositions have a well-defined interface given by a dependent type; they correspond to the statement of an induction principle for the datatype. More general, user-definable decompositions may be defined which also have types of the same general form. Elementary pattern matching may therefore be recast in abstract form, with a semantics given by translation. Such abstract decompositions of data generalize Wadler's (1987) notion of ‘view’. The programmer wishing to introduce a new view of a type $\mathit{T}$, and exploit it directly in pattern matching, may do so via a standard programming idiom. The type theorist, looking through the Curry–Howard lens, may see this as proving a theorem, one which establishes the validity of a new induction principle for $\mathit{T}$. We develop enough syntax and semantics to account for this high-level style of programming in dependent type theory. We close with the development of a typechecker for the simply-typed lambda calculus, which furnishes a view of raw terms as either being well-typed, or containing an error. The implementation of this view is ipso facto a proof that typechecking is decidable.


title: CRISPR/Cas9 for genome editing: progress, implications and challenges.

abstract: Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) protein 9 system provides a robust and multiplexable genome editing tool, enabling researchers to precisely manipulate specific genomic elements, and facilitating the elucidation of target gene function in biology and diseases. CRISPR/Cas9 comprises of a nonspecific Cas9 nuclease and a set of programmable sequence-specific CRISPR RNA (crRNA), which can guide Cas9 to cleave DNA and generate double-strand breaks at target sites. Subsequent cellular DNA repair process leads to desired insertions, deletions or substitutions at target sites. The specificity of CRISPR/Cas9-mediated DNA cleavage requires target sequences matching crRNA and a protospacer adjacent motif locating at downstream of target sequences. Here, we review the molecular mechanism, applications and challenges of CRISPR/Cas9-mediated genome editing and clinical therapeutic potential of CRISPR/Cas9 in future.


title: Low-cost PROFIBUS DP Slave Shield for embedded controllers

abstract: The presented PROFIBUS DP Slave Shield creates a link between a PROFIBUS DP network and a microcontroller or embedded controller. The hardware can be used as an extension module for different embedded controllers with sufficiently fast IO. The system opens possibilities to connect virtual systems or Hardware-In-the-Loop systems running real-time MATLAB models to a PROFIBUS DP network running up to 12 Mbps. Measurement of signal timing and latency shows that the shield itself does not introduce significant latency.


title: What makes people trust online gambling sites?

abstract: A validated model of trust was used as a framework for an empirical study to identify on- and offline factors that influence gamblers' perception of an online casino's trustworthiness. The results suggest that the quality with which casino's address gamblers' trust concerns by providing appropriate content is the prime factor. However, designing for trust must be part of consistent strategy that also involves customer service usability.


title: Evaluating the Child for Sexual Abuse

abstract: Child victims of sexual abuse may present with physical findings that can include anogenital problems, enuresis or encopresis. Behavioral changes may involve sexual acting out, aggression, depression, eating disturbances and regression. Because the examination findings of most child victims of sexual abuse are within normal limits or are nonspecific, the child's statements are extremely important. The child's history as obtained by the physician may be admitted as evidence in court trials; therefore, complete documentation of questions and answers is critical. A careful history should be obtained and a thorough physical examination should be performed with documentation of all findings. When examining the child's genitalia, it is important that the physician be familiar with normal variants, non-specific changes and diagnostic signs of sexual abuse. Judicious use of laboratory tests, along with appropriate therapy, should be individually tailored. Forensic evidence collection is indicated in certain cases. Referral for psychologic services is important because victims of abuse are more likely to have depression, anxiety disorders, behavioral problems and post-traumatic stress disorder.


title: Novel F-Shaped Quad-Band Printed Slot Antenna for WLAN and WiMAX MIMO Systems

abstract: A novel F-shaped microstrip slot antenna for WLAN and WiMAX multiple-input-multiple-output (MIMO) systems is presented. The proposed antenna structure consists of both open-ended and short-ended slots connected by a metal “via” to a microstrip line. The open-ended slot is exploited to obtain resonant mode at 2.4 GHz, and three short-ended slots are aimed to obtain resonant modes at 3.5, 5.2, and 5.8 GHz, whose center frequencies can be adjust by the slot lengths. The parametric studies show that the center frequencies are independent of each other. A four-element array configuration of the proposed antenna for MIMO applications is also studied. The simulation and measurement results of reflection coefficient, mutual coupling, and radiation pattern are presented, which attest to the applicability of antenna.


title: Metal-based Inkjet Inks for Printed Electronics

abstract: A review on applications of metal-based inkjet inks for printed electronics with a particular focus on inks con- taining metal nanoparticles, complexes and metallo-organic compounds. The review describes the preparation of such inks and obtaining conductive patterns by using various sintering methods: thermal, photonic, microwave, plasma, electri- cal, and chemically triggered. Various applications of metal-based inkjet inks (metallization of solar cell, RFID antennas, OLEDs, thin film transistors, electroluminescence devices) are reviewed.


Joan U. Pontius, et al. UniGene: A Unified View of the Transcriptome 21. UniGene: A Unified View of the Transcriptome


title: Axe UX: Exploring long-term user experience with iScale and AttrakDiff

abstract: Positive user experience (UX) is an important goal in product design. Positive long-term UX is believed to improve customer loyalty, therefore being vital for continuous commercial success. Most UX research investigates momentary or short-term UX although the relationship between the user and the product evolves over time. There is a need to develop methods for measuring long-term UX and evaluate their feasibility in different product contexts. In this explorative study, 18 customers reported their experiences during the first three months of use of a non-interactive design tool, an axe. UX was evaluated with retrospective iScale tool and monthly repeated AttrakDiff questionnaire. iScale demonstrated the long-term trend of the attractiveness of the product well and provided information about the causes of the change in the experience. The AttrakDiff questionnaire was a good indicator of attractiveness during a longer period of time and is also well suited to remote studies.


DESIGN AND TESTING OF A PRESSURE SENSING LAPAROSCOPIC GRASPER


title: Markets are efficient if and only if P = NP

abstract: I prove that if markets are weak-form efficient, meaning current prices fully reflect all information available in past prices, then P = NP, meaning every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. I also prove the converse by showing how we can "program" the market to solve NP-complete problems. Since P probably does not equal NP, markets are probably not efficient. Specifically, markets become increasingly inefficient as the time series lengthens or becomes more frequent. An illustration by way of partitioning the excess returns to momentum strategies based on data availability confirms this prediction.


title: Abstract Advanced Reaction-Diffusion Models for Texture Synthesis

abstract: Since the introduction of texture synthesis using a reaction-diffusion model in the early 1990s, their use has not been widespread. This is likely due to both the difficulty in selecting parameters that result in stable, useful patterns as well as the computational costs of producing these patterns. In this paper we present techniques that help overcome the difficult parameter selection process that controls the pattern development. In addition, we expand the basic diffusion model to allow for shaped patterns. Finally, we show that it is possible to create multiple and oscillating patterns by coupling two reaction-diffusion systems together. These techniques have been implemented using both explicit and semi-implicit solutions on a CPU and GPU.We provide sample source code of both implementations online.


title: Wearable Computing for Health and Fitness: Exploring the Relationship between Data and Human Behaviour

abstract: Health and fitness wearable technology has recently advanced, making it easier for an individual to monitor their behaviours. Previously self generated data interacts with the user to motivate positive behaviour change, but issues arise when relating this to long term mention of wearable devices. Previous studies within this area are discussed. We also consider a new approach where data is used to support instead of motivate, through monitoring and logging to encourage reflection. Based on issues highlighted, we then make recommendations on the direction in which future work could be most beneficial.


title: A survey of data mining techniques for malware detection using file features

abstract: This paper presents a survey of data mining techniques for malware detection using file features. The techniques are categorized based upon a three tier hierarchy that includes file features, analysis type and detection type. File features are the features extracted from binary programs, analysis type is either static or dynamic, and the detection type is borrowed from intrusion detection as either misuse or anomaly detection. It provides the reader with the major advancement in the malware research using data mining on file features and categorizes the surveyed work based upon the above stated hierarchy. This served as the major contribution of this paper.


A Sensor Image Super-Resolution via Advanced Generative Adversarial Network


title: The CRFs-Based Chinese Open Entity Relation Extraction

abstract: With the rapid development of the Internet, massive Internet text data has brought new opportunities and challenges to the research of entity relation extraction. Open entity relation extraction overcomes the shortage of traditional methods, that relation types need to be predefined and plenty of training data need to be labeled in advance. A lot of work have been done for English Open ERE, and now the Chinese Open ERE is attracting more and more researchers and scholars. This paper presents a novel CRFs-Based Semi-Supervised Chinese Open Entity Relation Extraction method-C-COERE. C-COERE combines the CRFs and the syntax parse tree to extract the entity relation tuples. Then it improves the accuracy of the extracted results through a post-processing filter module. Meanwhile, it enhances the recall of the extracted results by exploiting the duality of patterns and tuples. Experiments on real dataset show that the C-COERE method has achieved good effect.


title: Design and parametric study of ultra wideband PIFA Antenna

abstract: A compact and low profile coaxial probe fed PIFA (Planar Inverted F-Antenna) is proposed. The antenna works in frequency range 3.0 to 12.7 GHz which includes Ultra wide band frequency range (3.1–10.6 GHz). Novelty of this proposed antenna is that it has percentage impedance bandwidth of 121.51%. A parametric study of key geometrical parameters which affects the electrical performance of antenna is done. The structure is fabricated and measured. It was observed that variation between simulated & measured result are within acceptable limit.


title: Information-Theoretic Limitations of Formal Systems

abstract: An attempt is made to apply information-theoretic computational complexity to meta-mathematics. The paper studies the number of bits of instructions that must be given to a computer for it to perform finite and infinite tasks, and also the time it takes the computer to perform these tasks. This is applied to measuring the difficulty of proving a given set of theorems, in terms of the number of bits of axioms that are assumed, and the size of the proofs needed to deduce the theorems from the axioms.


title: MALMOS: Machine Learning-Based Mobile Offloading Scheduler with Online Training

abstract: This paper proposes and evaluates MALMOS, a novel framework for mobile offloading scheduling based on online machine learning techniques. In contrast to previous works, which rely on application-dependent parameters or predefined static scheduling policies, MALMOS provides an online training mechanism for the machine learning-based runtime scheduler such that it supports a flexible policy that dynamically adapts scheduling decisions based on the observation of previous offloading decisions and their correctness. To demonstrate its practical applicability, we integrated MALMOS with an existing Java-based, offloading-capable code recapturing framework, Partner. Using this integration, we performed quantitative experiments to evaluate the performance and cost for three machine learning algorithms: instance-based learning, perception, and naive Bays, with respect to classifier training time, classification time, and scheduling accuracy. Particularly, we examined the adaptability of MALMOS to various network conditions and computing capabilities of remote resources by comparing the scheduling accuracy with two static scheduling cases: threshold-based and linear equation-based scheduling policies. Our evaluation uses an Android-based prototype for experiments, and considers benchmarks with different computation/communication characteristics, and different computing capabilities of remote resources. The evaluation shows that MALMOS achieves 10.9%~40.5% higher scheduling accuracy than two static scheduling policies.


title: Face Recognition Using Kernel Ridge Regression

abstract: In this paper, we present novel ridge regression (RR) and kernel ridge regression (KRR) techniques for multivariate labels and apply the methods to the problem efface recognition. Motivated by the fact that the regular simplex vertices are separate points with highest degree of symmetry, we choose such vertices as the targets for the distinct individuals in recognition and apply RR or KRR to map the training face images into a face subspace where the training images from each individual will locate near their individual targets. We identify the new face image by mapping it into this face subspace and comparing its distance to all individual targets. An efficient cross-validation algorithm is also provided for selecting the regularization and kernel parameters. Experiments were conducted on two face databases and the results demonstrate that the proposed algorithm significantly outperforms the three popular linear face recognition techniques (Eigenfaces, Fisher faces and Laplacian faces) and also performs comparably with the recently developed Orthogonal Laplacian faces with the advantage of computational speed. Experimental results also demonstrate that KRR outperforms RR as expected since KRR can utilize the nonlinear structure of the face images. Although we concentrate on face recognition in this paper, the proposed method is general and may be applied for general multi-category classification problems.


title: Classification of outdoor 3D lidar data based on unsupervised Gaussian mixture models

abstract: 3D point clouds acquired with lidars are an important source of data for the classification of outdoor environments by autonomous terrestrial robots. We propose here a two-layer classification system. The first layer consists of a Gaussian mixture model, issued from unsupervised training, which defines a large set of data-oriented classes. The second layer consists of a supervised, manual grouping of the unsupervised classes into a smaller set of task-oriented classes. Because it uses unsupervised learning at its core, the system does not require any manual labelling of datasets. We evaluate the system on two datasets of different nature, and the results show its capacity to adapt to different data while providing classes which are exploitable in a target task.


title: Hierarchical multi-feature fusion for multimodal data analysis

abstract: Multimedia data is usually represented with different low-level features, and different types of multimedia data, namely multimodal data, often coexist in many data sources. It is interesting and challenging to learn comprehensive semantics from multiple low-level features for multimodal data analysis. In this paper, we propose a new algorithm, namely hierarchical multi-feature fusion for multimodal data semantics understanding. Our approach explores intra-modality structural information derived from each type of feature, and further proposes transductive inter-modality fusion strategy, which analyzes canonical correlation between different modalities. Extensive experiments are conducted on collected multimodal database for data classification application. The experiment results show that the performance of our algorithm is remarkable and demonstrate its superiority over several existing algorithms.


title: EliXa: A Modular and Flexible ABSA Platform

abstract: This paper presents a supervised Aspect Based Sentiment Analysis (ABSA) system. Our aim is to develop a modular platform which allows to easily conduct experiments by replacing the modules or adding new features. We obtain the best result in the Opinion Target Extraction (OTE) task (slot 2) using an off-the-shelf sequence labeler. The target polarity classification (slot 3) is addressed by means of a multiclass SVM algorithm which includes lexical based features such as the polarity values obtained from domain and open polarity lexicons. The system obtains accuracies of 0.70 and 0.73 for the restaurant and laptop domain respectively, and performs second best in the out-of-domain hotel, achieving an accuracy of 0.80.


title: 3D Mesh Labeling via Deep Convolutional Neural Networks

abstract: This article presents a novel approach for 3D mesh labeling by using deep Convolutional Neural Networks (CNNs). Many previous methods on 3D mesh labeling achieve impressive performances by using predefined geometric features. However, the generalization abilities of such low-level features, which are heuristically designed to process specific meshes, are often insufficient to handle all types of meshes. To address this problem, we propose to learn a robust mesh representation that can adapt to various 3D meshes by using CNNs. In our approach, CNNs are first trained in a supervised manner by using a large pool of classical geometric features. In the training process, these low-level features are nonlinearly combined and hierarchically compressed to generate a compact and effective representation for each triangle on the mesh. Based on the trained CNNs and the mesh representations, a label vector is initialized for each triangle to indicate its probabilities of belonging to various object parts. Eventually, a graph-based mesh-labeling algorithm is adopted to optimize the labels of triangles by considering the label consistencies. Experimental results on several public benchmarks show that the proposed approach is robust for various 3D meshes, and outperforms state-of-the-art approaches as well as classic learning algorithms in recognizing mesh labels.


title: Crowdsourcing as Lego: Unpacking the Building Blocks of Crowdsourcing Collaboration Processes

abstract: Crowdsourcing refers to the use of technologies to gather the collective effort and wisdom from an undefined group of online users for organizational innovation and/or problem solving. While the idea holds a lot of potential, its implementation can be thwarted by an ill design and management of the collaboration processes. Therefore, studies on crowdsourcing collaboration processes are important for the advancement of the field. We argue that studying this aspect of crowdsourcing can benefit from the modular view of collaboration processes provided by Vreede et al.’s (2009) six collaboration pattern framework. We propose to analyze current popular crowdsourcing websites through this framework and perform cluster analyses. We expect that our study will provide insights into existing practices of crowdsourcing collaboration and inform future research on crowdsourcing collaboration.


title: Managing Complexity in Aircraft Design Using Design Structure Matrix

abstract: Modern aerospace systems have reached a level of complexity that requires systematic methods for their design. The development of products in aircraft industry involves numerous engineers from different disciplines working on independent components. Routine activities consume a significant part of aircraft development time. To be competitive, aircraft industry needs to manage complexity and readjust the ratio of innovative versus routine work. Thus, the main objective is to develop an approach that can manage complexity in engineering design, reduce the design cycle time, and reduce the product development cost.The design structure matrix (DSM) is a simple tool to perform both analysis and management of complex systems. It enables the user to model, visualize, and analyze the dependencies among the functional group of any system and derive suggestions for the improvement or synthesis of a system. This article illustrates with a case study how DSM can be used to manage complexity in aircraft design process...


title: Large‐scale retrieval for medical image analytics: A comprehensive review

abstract: Abstract Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis.


title: Measuring design complexity of semantic web ontologies

abstract: Ontology languages such as OWL are being widely used as the Semantic Web movement gains momentum. With the proliferation of the Semantic Web, more and more large-scale ontologies are being developed in real-world applications to represent and integrate knowledge and data. There is an increasing need for measuring the complexity of these ontologies in order for people to better understand, maintain, reuse and integrate them. In this paper, inspired by the concept of software metrics, we propose a suite of ontology metrics, at both the ontology-level and class-level, to measure the design complexity of ontologies. The proposed metrics are analytically evaluated against Weyuker's criteria. We have also performed empirical analysis on public domain ontologies to show the characteristics and usefulness of the metrics. We point out possible applications of the proposed metrics to ontology quality control. We believe that the proposed metric suite is useful for managing ontology development projects.


title: Fine-Grained POS Tagging of German Tweets

abstract: This paper presents the first work on POS tagging German Twitter data, showing that despite the noisy and often cryptic nature of the data a fine-grained analysis of POS tags on Twitter microtext is feasible. Our CRF-based tagger achieves an accuracy of around 89% when trained on LDA word clusters, features from an automatically created dictionary and additional out-of-domain training data.


title: Detection of Human Breathing and Heartbeat by Remote Radar

abstract: The radar capable to detect and perform diagnostics of a state of the human being behind obstacles and in conditions of bad visibility could be used for the manifold applications. The potential consumers of the radar are: rescue services, antiterrorist detachments and law-enforcement bodies. It is possible also to use this device in medicine. In this paper, the experiments with using of continuouswave subsurface radar are described. The recorded oscillograms and their frequency spectrums for heartbeat, respiration and articulation of a man, which is taking place behind an obstacle (wall), are presented.


title: A new paradigm for the prediction of antidepressant treatment response

abstract: Current treatment of Major Depressive Disorder utilizes a trial-and-error sequential treatment strategy that results in delays in achieving response and remission for a majority of patients. Protracted ineffective treatment prolongs patient suffering and increases health care costs. In addition, long and unsuccessful antidepressant trials may diminish patient expectations, reinforce negative cognitions, and condition patients not to respond during subsequent antidepressant trials, thus contributing to further treatment resistance. For these reasons, it is critical to identify reliable predictors of antidepressant treatment response that can be used to shorten or eliminate lengthy and ineffective trials. Research on possible endophenotypic as well as genomic predictors has not yet yielded reliable predictors. The most reliable predictors identified thus far are symptomatic and physiologic characteristics of patients that emerge early in the course of treatment. We propose here the term “response endophenotypes” (REs) to describe this class of predictors, defined as latent measurable symptomatic or neurobiologie responses of individual patients that emerge early in the course of treatment, and which carry strong predictive power for individual patient outcomes. Use of REs constitutes a new paradigm in which medication treatment trials that are likely to be ineffective could be stopped within 1 to 2 weeks and other medication more likely to be effective could be started. Data presented here suggest that early changes in symptoms, quantitative electroencephalography, and gene expression could be used to construct effective REs. We posit that this new paradigm could lead to earlier recovery from depressive illness and ultimately produce profound health and economic benefits.


title: Object tracking with de-autocorrelation scheme for a dynamic occupancy gridmap system

abstract: Autonomous driving poses unique challenges for vehicle environment perception in complex driving environments. Due to the uncertain nature of the vehicle environment and imperfection of any perception framework, multiple stages of estimation might be necessary to achieve the desired performance. However, it is highly possible that the estimation of one stage might result in output estimates with significant auto/cross-correlation, which would pass to another stage. In such situations, a decorrelation procedure is required. We present an object tracking approach taking into consideration the auto-correlation (of the velocity components) introduced by an upfront dynamic occupancy gridmap system. More specifically, we use a linear state-space system to approximately “reconstruct” the nonlinear estimation/mapping procedure for the purpose of auto-correlation quantification. We focus on demonstrating an estimation improvement of the proposed decorrelation tracker over a direct Kalman filter (i.e., KF that ignores the auto-correlation). For this we recorded several scenarios of different target motion behaviors. It is shown that the decorrelation tracker indeed introduces noticable estimation improvement in particularly velocity space if the target moves in a relatively smooth manner.


title: Two-Dimensional Materials for Sensing: Graphene and Beyond

abstract: Two-dimensional materials have attracted great scientific attention due to their unusual and fascinating properties for use in electronics, spintronics, photovoltaics, medicine, composites, etc. Graphene, transition metal dichalcogenides such as MoS2, phosphorene, etc., which belong to the family of two-dimensional materials, have shown great promise for gas sensing applications due to their high surface-to-volume ratio, low noise and sensitivity of electronic properties to the changes in the surroundings. Two-dimensional nanostructured semiconducting metal oxide based gas sensors have also been recognized as successful gas detection devices. This review aims to provide the latest advancements in the field of gas sensors based on various two-dimensional materials with the main focus on sensor performance metrics such as sensitivity, specificity, detection limit, response time, and reversibility. Both experimental and theoretical studies on the gas sensing properties of graphene and other two-dimensional materials beyond graphene are also discussed. The article concludes with the current challenges and future prospects for two-dimensional materials in gas sensor applications.


title: Object Oriented Software modeling Using NLP based Knowledge Extraction

abstract: This paper presents a natural language processing based automated system for NL text to OO modeling the user requirements and generating code in multi-languages. A new rule-based model is presented for analyzing the natural languages (NL) and extracting the relative and required information from the given software requirement notes by the user. User writes the requirements in simple English in a few paragraphs and the designed system incorporates NLP methods to analyze the given script. First the NL text is semantically analyzed to extract classes, objects and their respective, attributes, methods and associations. Then UML diagrams are generated on the bases of previously extracted information. The designed system also provides with the respective code automatically of the already generated diagrams. The designed system provides a quick and reliable way to generate UML diagrams to save the time and budget of both the user and system analyst.


title: What is MEMS Gyrocompassing? Comparative Analysis of Maytagging and Carouseling

abstract: North-finding based on micromachined gyroscopes is an attractive possibility. This paper analyzes north-finding methods and demonstrates a measured 4 mrad standard deviation azimuth uncertainty using an in-house developed vibratory silicon MEMS quadruple mass gyroscope (QMG). We instrumented a vacuum packaged QMG with isotropic Q-factor of 1.2 million and Allan deviation bias instability of 0.2 °/hr for azimuth detection by measuring the earth's rotation. Continuous rotation (“carouseling”) produced azimuth datapoints with uncertainty diminishing as the square root of the number of turns. Integration of 100 datapoints with normally distributed errors reduced uncertainty to 4 mrad, beyond the noise of current QMG instrumentation. We also implemented self-calibration methods, including in-situ temperature sensing and discrete ±180° turning (“maytagging” or two-point gyrocompassing) as potential alternatives to carouseling. While both mechanizations produced similar azimuth uncertainty, we conclude that carouseling is more advantageous as it is robust to bias, scale-factor, and temperature drifts, although it requires a rotary platform providing continuous rotation. Maytagging, on the other hand, can be implemented using a simple turn table, but requires calibration due to temperature-induced drifts.


title: Epigallocatechin Gallate, a Green Tea Phytochemical, Attenuates Alcohol-Induced Hepatic Protein and Lipid Damage

abstract: ABSTRACTOxidative damage to cellular constituents is one of the major mechanisms of alcoholic liver injury, and administration of antioxidants ameliorates alcoholic liver disease. The present study investigated the influence of (-) epigallocatechin gallate (EGCG), a major polyphenol component of green tea, on oxidant–antioxidant balance, protein, and lipid damage in liver of rats fed ethanol. Chronic ethanol administration (6 g/kg/day × 60 days) caused liver damage that was manifested by excessive formation of lipid peroxidation end products such as thiobarbituric acid-reactive substances (TBARS), lipid hydroperoxides (LHP), and conjugated dienes (CD) accompanied by a reduction in enzymic and non-enzymic antioxidant levels. Further, ethanol caused a rise in protein carbonyl formation and loss of protein thiol groups. Ethanol-fed rats exhibited increased staining for the presence of 4-hydroxynonenal (4-HNE), 3-nitrotyrosine (3-NT), and 2,4-dinitrophenol (DNP) protein adducts in liver. The detrimental effec...


title: SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds

abstract: An open source tool named SimpleTree, capable of modelling highly accurate cylindrical tree models from terrestrial laser scan point clouds, is presented and evaluated. All important functionalities, accessible in the software via buttons and dialogues, are described including the explanation of all necessary input parameters. The method is validated utilizing 101 point clouds of six different tree species, in the main evergreen and coniferous trees. All scanned trees have been destructively harvested to get accurate estimates of above ground biomass with which we assess the accuracy of the SimpleTree-reconstructed cylinder models. The trees were grouped into four data sets and for each one a Concordance Correlation Coefficient of at least 0.92 (0.92, 0.97, 0.92, 0.94) and an total relative error at most ~8 % (2.42%, 3.59%, –4.59%, 8.27%) was achieved in the comparison of the model results to the ground truth data. A global statistical improvement of derived cylinder radii is presented as well as an efficient optimization approach to automatically improve user given input parameters. An additional check of the SimpleTree results is presented via comparison to the results of trees reconstructed using an alternative, published method.


Magnetoelectric effect and magnetic torque of chromium chlorine boracite Cr 3 B 7 O 13


title: Typed Tagless Final Interpreters

abstract: The so-called ‘typed tagless final' approach of [6] has collected and polished a number of techniques for representing typed higher-order languages in a typed metalanguage, along with type-preserving interpretation, compilation and partial evaluation. The approach is an alternative to the traditional, or ‘initial' encoding of an object language as a (generalized) algebraic data type. Both approaches permit multiple interpretations of an expression, to evaluate it, pretty-print, etc. The final encoding represents all and only typed object terms without resorting to generalized algebraic data types, dependent or other fancy types. The final encoding lets us add new language forms and interpretations without breaking the existing terms and interpreters. ::: ::: These lecture notes introduce the final approach slowly and in detail, highlighting extensibility, the solution to the expression problem, and the seemingly impossible pattern-matching. We develop the approach further, to type-safe cast, run-time-type representation, Dynamics, and type reconstruction. We finish with telling examples of type-directed partial evaluation and encodings of type-and-effect systems and linear lambda-calculus.


title: Modelling and model predictive control of a DC-DC Boost converter

abstract: In this paper we develop a model predictive control (MPC) approach for regulating DC-DC Boost converters. The proposed control strategy is implemented and tested using two models: an averaged non-linear model for control purposes and a switched Buck-Boost circuit model as the controlled plant. The main objective of the paper is to design a Fuzzy Predictive Control to achieve desired voltage output without increasing complexity of the hybrid model of DC-DC converter in various conditions. The proposed algorithm of fuzzy model predictive controller is done by simulation using MATLAB. Comparative analysis and results of FMPC and MPC control is observed


title: Secure MQTT for Internet of Things (IoT)

abstract: Rapid innovations in the area of digital things and Information Communication Technology are driving rapid deployment of Internet of Things (IoT) around the globe. Device to Device communications (D2D) in IoT are envisaged through various protocols such as Constrained Access Protocol (CoAP), Message Queue Telemetry Transport (MQTT) and MQTT-SN (for sensor networks). One of the major concerns in the deployment of IoT is to ensure the security of devices and D2D communications. Besides, existing communication protocols for IoT are devoid of security features. To address this, we propose a secure version of MQTT and MQTT-SN protocols (SMQTT and SMQTT-SN) in which security feature is augmented to the existing MQTT protocol based on Key/Cipher text Policy-Attribute Based Encryption(KP/CP-ABE) using lightweight Elliptic Curve Cryptography. Further we demonstrate feasibility of SMQTT and SMQTT-SNprotocols for various IoT requirements through simulations and evaluate their performance.


title: Self-Supervised Generalisation with Meta Auxiliary Learning

abstract: Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiring access to any further data. The approach is to train two neural networks: a label-generation network to predict the auxiliary labels, and a multi-task network to train the primary task alongside the auxiliary task. The loss for the label-generation network incorporates the loss of the multi-task network, and so this interaction between the two networks can be seen as a form of meta learning with a double gradient. We show that our proposed method, Meta AuXiliary Learning (MAXL), outperforms single-task learning on 7 image datasets, without requiring any additional data. We also show that MAXL outperforms several other baselines for generating auxiliary labels, and is even competitive when compared with human-defined auxiliary labels. The self-supervised nature of our method leads to a promising new direction towards automated generalisation. Source code can be found at https://github.com/lorenmt/maxl.


title: 3D non-rigid registration using color: Color Coherent Point Drift

abstract: Research into object deformations using computer vision techniques has been under intense study in recent years. A widely used technique is 3D non-rigid registration to estimate the transformation between two instances of a deforming structure. Despite many previous developments on this topic, it remains a challenging problem. In this paper we propose a novel approach to non-rigid registration combining two data spaces in order to robustly calculate the correspondences and transformation between two data sets. In particular, we use point color as well as 3D location as these are the common outputs of RGB-D cameras. We have propose the Color Coherent Point Drift (CCPD) algorithm (an extension of the CPD method [1]). Evaluation is performed using synthetic and real data. The synthetic data includes easy shapes that allow evaluation of the effect of noise, outliers and missing data. Moreover, an evaluation of realistic figures obtained using Blensor is carried out. Real data acquired using a general purpose Primesense Carmine sensor is used to validate the CCPD for real shapes. For all tests, the proposed method is compared to the original CPD showing better results in registration accuracy in most cases.


title: GANtruth - an unpaired image-to-image translation method for driving scenarios

abstract: Synthetic image translation has significant potentials in autonomous transportation systems. That is due to the expense of data collection and annotation as well as the unmanageable diversity of real-words situations. The main issue with unpaired image-to-image translation is the ill-posed nature of the problem. In this work, we propose a novel method for constraining the output space of unpaired image-to-image translation. We make the assumption that the environment of the source domain is known (e.g. synthetically generated), and we propose to explicitly enforce preservation of the ground-truth labels on the translated images. We experiment on preserving ground-truth information such as semantic segmentation, disparity, and instance segmentation. We show significant evidence that our method achieves improved performance over the state-of-the-art model of UNIT for translating images from SYNTHIA to Cityscapes. The generated images are perceived as more realistic in human surveys and outperforms UNIT when used in a domain adaptation scenario for semantic segmentation.


title: The "Beauty Dilemma": beauty is valued but discounted in product choice

abstract: The empirical study of aesthetics in Human-Computer Interaction (HCI) is concerned with - among other topics - the relationship between beauty and usability and the general impact of beauty on product choice and use. Specifically, the present paper explores the notion of a "beauty dilemma" - the idea that people discount beauty in a choice situation, although they value it in general (i.e., they are not choosing what makes them happy). We explored this idea in three studies with a total of over 600 participants. Study 1 revealed a reluctance to pay for beauty due to its hedonic nature (i.e., associated with luxury etc.). Study 2 showed that people prefer a more beautiful product, but justify their choice by referring to spurious advantages in usability. Finally, Study 3 revealed that a choice situation which requires a trade-off between beauty and usability, and which offers no further way to justify choosing beauty, leads to a sharp increase in the preference of usability. The underlying reasons for this "beauty dilemma" and further implications are discussed.


title: Structural aspects of transglutaminase 2: functional, structural, and regulatory diversity

abstract: Transglutaminase 2 (TG2) is a multi-functional protein that has both protein cross-linking and guanosine 5′-triphosphate (GTP) hydrolysis activities. The activities of this protein are controlled by many cellular factors, including calcium (Ca2+) and GTP, and have been implicated in several physiological activities, including apoptosis, angiogenesis, wound healing, cellular differentiation, neuronal regeneration, and bone development. TG2 is linked to many human diseases such as inflammatory disease, celiac disease, neurodegenerative disease, diabetes, tissue fibrosis, and various cancers and is one of the most dynamic enzymes in terms of its functions, structures, and regulatory mechanisms. The aim of this review was to summarize the functional, structural, and regulatory diversity of TG2, with a particular focus on the structure of TG2.


title: Geometry of the squared distance function to curves and surfaces

abstract: We investigate the geometry of that function in the plane or 3-space, which associates to each point the square of the shortest distance to a given curve or surface. Particular emphasis is put on second order Taylor approximants and other local quadratic approximants. Their key role in a variety of geometric optimization algorithms is illustrated at hand of registration in Computer Vision and surface approximation.


title: Assessing Code Authorship: The Case of the Linux Kernel

abstract: Code authorship is a key information in large-scale open-source systems. Among others, it allows maintainers to assess division of work and identify key collaborators. Interestingly, open-source communities lack guidelines on how to manage authorship. This could be mitigated by setting to build an empirical body of knowledge on how authorship-related measures evolve in successful open-source communities. Towards that direction, we perform a case study on the Linux kernel. Our results show that: (a) only a small portion of developers (26%) makes significant contributions to the code base; (b) the distribution of the number of files per author is highly skewed—a small group of top-authors (3%) is responsible for hundreds of files, while most authors (75%) are responsible for at most 11 files; (c) most authors (62%) have a specialist profile; (d) authors with a high number of co-authorship connections tend to collaborate with others with less connections.


title: Open Sesame!: A Look at Personal Assistants

abstract: Software agents, or softbots, are supposedly intelligent programs that assist the user in performing repetitive, boring, and time-consuming tasks. An overview of software agents, with special attention focused on the so-called personal assistants, is presented as an introduction. Following that, an in-depth look at one of the first commercially available intelligent agents Open Sesame!, a personal assistant for managing the Macintosh desktop environment, is presented. The existing intelligent agent paradigms, we argue, break down when it comes to actively helping the end user because of flaws inherent in the current approaches to intelligent agent design. To that end, we discuss the reasons why this is, particularly with respect to Open Sesame!, and promote the notion of situatedness to be taken into account more seriously for developing software agents that actively support the user for the duration of the task at hand. As an example for a more situated approach to personal assistant design, a quick overview of a project to develop a personal assistant for news filtering is discussed.


title: Human Smart Cities: A Human-centric model aiming at the wellbeing and quality of life of citizens

abstract: MyNeighbourhood is a European project, part of the CIP ICT PSP Program in Smart Cities, aiming at recreating and strengthening the social ties within neighbourhoods, envisioning a new approach to the City. The project is based on an innovative vision, methodologies and tools which intend to create a Human Smart City starting from the neighbourhood and scaling up to the city. The project is a test bed of the Human Smart City concept that proposes to develop a citizen-driven, smart, all-inclusive and sustainable environment, with a new governance framework in which citizens and government engage in listening and talking to each other. This public governance model favours the emergence of a participatory innovation ecosystem that creates jobs, wealth and ultimately generates happiness for citizens.


title: Light Fields and Computational Imaging

abstract: A survey of the theory and practice of light field imaging emphasizes the devices researchers in computer graphics and computer vision have built to capture light fields photographically and the techniques they have developed to compute novel images from them


Development of a Two-wheel Balancing Robot using the STM 32 F 3-Discovery Board as an Educational Platform for Traditional and Modern Control Schemes


title: Deep Counterfactual Networks with Propensity-Dropout

abstract: We propose a novel approach for inferring the individualized causal effects of a treatment (intervention) from observational data. Our approach conceptualizes causal inference as a multitask learning problem; we model a subject's potential outcomes using a deep multitask network with a set of shared layers among the factual and counterfactual outcomes, and a set of outcome-specific layers. The impact of selection bias in the observational data is alleviated via a propensity-dropout regularization scheme, in which the network is thinned for every training example via a dropout probability that depends on the associated propensity score. The network is trained in alternating phases, where in each phase we use the training examples of one of the two potential outcomes (treated and control populations) to update the weights of the shared layers and the respective outcome-specific layers. Experiments conducted on data based on a real-world observational study show that our algorithm outperforms the state-of-the-art.


NOVEL OSCILLATORS IN SUBTHRESHOLD REGIME


title: A 10-bit 50-MS/s SAR ADC with techniques for relaxing the requirement on driving capability of reference voltage buffers

abstract: A high speed successive approximation (SAR) ADC requires reference voltage buffers with high driving capability. Moreover, the power consumption of the reference buffers is usually several times larger than that of the SAR ADC itself. Three techniques are adopted to mitigate the requirement on driving capability of reference voltage buffers for SAR ADCs. A 10b 50MS/s ADC based on the proposed techniques is presented. The prototype ADC was fabricated in 40nm LP 1P7M CMOS technology. It consumes 0.47 mW at 50 MS/s from 1.1V supply voltage and achieves ENOB of 9.18-bit and figure of merit (FoM) of 16 fJ/conversion-step. The active area is 0.0114 mm2.


title: A review on memristor applications

abstract: This article presents a review on the main applications of the fourth fundamental circuit element, named "memristor", which had been proposed for the first time by Leon Chua and has recently been developed by a team at HP Laboratories led by Stanley Williams. In particular, after a brief analysis of memristor theory with a description of the first memristor, manufactured at HP Laboratories, we present its main applications in the circuit design and computer technology, together with future developments.


title: Finding Actors and Actions in Movies

abstract: We address the problem of learning a joint model of actors and actions in movies using weak supervision provided by scripts. Specifically, we extract actor/action pairs from the script and use them as constraints in a discriminative clustering framework. The corresponding optimization problem is formulated as a quadratic program under linear constraints. People in video are represented by automatically extracted and tracked faces together with corresponding motion features. First, we apply the proposed framework to the task of learning names of characters in the movie and demonstrate significant improvements over previous methods used for this task. Second, we explore the joint actor/action constraint and show its advantage for weakly supervised action learning. We validate our method in the challenging setting of localizing and recognizing characters and their actions in feature length movies Casablanca and American Beauty.