title: Recent Advances in High-Temperature Superconductivity

abstract: After more than 15 years of intense research since the discovery of high-temperature ::: superconductivity [1], many interesting physical phenomena unique to the cuprate ::: superconductors are better understood, and various applications have been realized. ::: However, the underlying mechanism for high-temperature superconductivity remains ::: elusive, largely due to the complication of numerous competing orders in the ground state ::: of the cuprates. We review some of the most important physics issues and recent ::: experimental developments associated with these strongly correlated electronic systems, ::: and discuss current understanding and possible future research direction.


title: Secure cloud computing: Benefits, risks and controls

abstract: Cloud computing presents a new model for IT service delivery and it typically involves over-a-network, on-demand, self-service access, which is dynamically scalable and elastic, utilising pools of often virtualized resources. Through these features, cloud computing has the potential to improve the way businesses and IT operate by offering fast start-up, flexibility, scalability and cost efficiency. Even though cloud computing provides compelling benefits and cost-effective options for IT hosting and expansion, new risks and opportunities for security exploits are introduced. Standards, policies and controls are therefore of the essence to assist management in protecting and safeguarding systems and data. Management should understand and analyse cloud computing risks in order to protect systems and data from security exploits. The focus of this paper is on mitigation for cloud computing security risks as a fundamental step towards ensuring secure cloud computing environments.


title: 60 GHz CPW dual-mode rectangular ring bandpass filter using integrated passive devices process

abstract: A CPW dual-mode rectangular ring bandpass filter at 60 GHz using integrated passive device (IPD) process is presented in this paper. Thick metal in IPD can give lower loss for CPW. The shunt capacitor from wide underpass that maintains equal potential between two ground planes can also help to reduce the size of the ring filter. The measured center frequency of passband is 62GHz with minimum insertion loss as 2.2dB. Two transmission zeros are located at 50GHz and 80GHz. Return loss is better than 15dB around center frequency. The active filter size is 654µm × 750µm. Measurement results agreed well with EM simulation. With comparable size as on-chip bandpass filter, the IPD CPW filter provides a low cost solution for filter implementation.


title: A multimodal parallel architecture: A cognitive framework for multimodal interactions

abstract: Abstract Human communication is naturally multimodal, and substantial focus has examined the semantic correspondences in speech–gesture and text–image relationships. However, visual narratives, like those in comics, provide an interesting challenge to multimodal communication because the words and/or images can guide the overall meaning, and both modalities can appear in complicated “grammatical” sequences: sentences use a syntactic structure and sequential images use a narrative structure. These dual structures create complexity beyond those typically addressed by theories of multimodality where only a single form uses combinatorial structure, and also poses challenges for models of the linguistic system that focus on single modalities. This paper outlines a broad theoretical framework for multimodal interactions by expanding on Jackendoff’s (2002) parallel architecture for language. Multimodal interactions are characterized in terms of their component cognitive structures: whether a particular modality (verbal, bodily, visual) is present, whether it uses a grammatical structure (syntax, narrative), and whether it “dominates” the semantics of the overall expression. Altogether, this approach integrates multimodal interactions into an existing framework of language and cognition, and characterizes interactions between varying complexity in the verbal, bodily, and graphic domains. The resulting theoretical model presents an expanded consideration of the boundaries of the “linguistic” system and its involvement in multimodal interactions, with a framework that can benefit research on corpus analyses, experimentation, and the educational benefits of multimodality.


title: Time-Aware User Identification with Topic Models

abstract: Accounts are often shared by multiple users, eachof them having different item consumption and temporal habits. Identifying of the active user can lead to improvements ina variety of services by switching from account personalizedservices to user personalized services. To do so, we developa topic model extending the Latent Dirichlet Allocation usinga hidden variable representing the active user and assumingconsumption times to be generated by latent time topics. Wecreate a new dataset of composite accounts from real users totest the identification capabilities of our model. We show thatour model is able to learn temporal patterns from the whole setof accounts and infer the active user using both the consumptiontime and the consumed item.


title: Multi-step Off-policy Learning Without Importance Sampling Ratios

abstract: To estimate the value functions of policies from exploratory data, most model-free off-policy algorithms rely on importance sampling, where the use of importance sampling ratios often leads to estimates with severe variance. It is thus desirable to learn off-policy without using the ratios. However, such an algorithm does not exist for multi-step learning with function approximation. In this paper, we introduce the first such algorithm based on temporal-difference (TD) learning updates. We show that an explicit use of importance sampling ratios can be eliminated by varying the amount of bootstrapping in TD updates in an action-dependent manner. Our new algorithm achieves stability using a two-timescale gradient-based TD update. A prior algorithm based on lookup table representation called Tree Backup can also be retrieved using action-dependent bootstrapping, becoming a special case of our algorithm. In two challenging off-policy tasks, we demonstrate that our algorithm is stable, effectively avoids the large variance issue, and can perform substantially better than its state-of-the-art counterpart.


title: Is video gaming, or video game addiction, associated with depression, academic achievement, heavy episodic drinking, or conduct problems?

abstract: BACKGROUND AND AIMS ::: While the relationships between video game use and negative consequences are debated, the relationships between video game addiction and negative consequences are fairly well established. However, previous studies suffer from methodological weaknesses that may have caused biased results. There is need for further investigation that benefits from the use of methods that avoid omitted variable bias. ::: ::: ::: METHODS ::: Two wave panel data was used from two surveys of 1,928 Norwegian adolescents aged 13 to 17 years. The surveys included measures of video game use, video game addiction, depression, heavy episodic drinking, academic achievement, and conduct problems. The data was analyzed using first-differencing, a regression method that is unbiased by time invariant individual factors. ::: ::: ::: RESULTS ::: Video game addiction was related to depression, lower academic achievement, and conduct problems, but time spent on video games was not related to any of the studied negative outcomes. ::: ::: ::: DISCUSSION ::: The findings were in line with a growing number of studies that have failed to find relationships between time spent on video games and negative outcomes. The current study is also consistent with previous studies in that video game addiction was related to other negative outcomes, but it made the added contribution that the relationships are unbiased by time invariant individual effects. However, future research should aim at establishing the temporal order of the supposed causal effects. ::: ::: ::: CONCLUSIONS ::: Spending time playing video games does not involve negative consequences, but adolescents who experience problems related to video games are likely to also experience problems in other facets of life.


Building upon RouteFlow : a SDN development experience


title: Columbia MVSO Image Sentiment Dataset

abstract: The Multilingual Visual Sentiment Ontology (MVSO) consists of 15,600 concepts in 12 different languages that are strongly related to emotions and sentiments expressed in images. These concepts are defined in the form of Adjective-Noun Pair (ANP), which are crawled and discovered from online image forum Flickr. In this work, we used Amazon Mechanical Turk as a crowd-sourcing platform to collect human judgments on sentiments expressed in images that are uniformly sampled over 3,911 English ANPs extracted from a tag-restricted subset of MVSO. Our goal is to use the dataset as a benchmark for the evaluation of systems that automatically predict sentiments in images or ANPs.


title: Optimal Data-Dependent Hashing for Approximate Near Neighbors

abstract: We show an optimal data-dependent hashing scheme for the approximate near neighbor problem. For an $n$-point data set in a $d$-dimensional space our data structure achieves query time $O(d n^{\rho+o(1)})$ and space $O(n^{1+\rho+o(1)} + dn)$, where $\rho=\tfrac{1}{2c^2-1}$ for the Euclidean space and approximation $c>1$. For the Hamming space, we obtain an exponent of $\rho=\tfrac{1}{2c-1}$. ::: Our result completes the direction set forth in [AINR14] who gave a proof-of-concept that data-dependent hashing can outperform classical Locality Sensitive Hashing (LSH). In contrast to [AINR14], the new bound is not only optimal, but in fact improves over the best (optimal) LSH data structures [IM98,AI06] for all approximation factors $c>1$. ::: From the technical perspective, we proceed by decomposing an arbitrary dataset into several subsets that are, in a certain sense, pseudo-random.


title: An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo

abstract: This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle–tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and lowpass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R 2 of 0.91 and mean residual error of B12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks. r 2003 Elsevier Science Ltd. All rights reserved.


title: A fisheye distortion correction algorithm optimized for hardware implementations

abstract: This paper presents a universal algorithm for correcting distortion in images obtained with fisheye lenses. We analyze and optimize the algorithm for implementation on hardware platforms such as Field Programmable Gate Arrays (FPGAs). A fully pipelined architecture, implemented in an FPGA, is capable of processing full HD images at 30 frames per second. Furthermore the proposed algorithmic modifications introduce very little error in fixed point arithmetic pixel coordinate computations. When compared to double precision results, most coordinates have an error of less than 0.5 pixel.


Perceived Ease of Use and Perceived Usefulness of Social Media for e-Learning in Libyan Higher Education: A Structural Equation Modeling Analysis


title: Discovering Latent Structure in Task-Oriented Dialogues

abstract: A key challenge for computational conversation models is to discover latent structure in task-oriented dialogue, since it provides a basis for analysing, evaluating, and building conversational systems. We propose three new unsupervised models to discover latent structures in task-oriented dialogues. Our methods synthesize hidden Markov models (for underlying state) and topic models (to connect words to states). We apply them to two real, non-trivial datasets: human-computer spoken dialogues in bus query service, and humanhuman text-based chats from a live technical support service. We show that our models extract meaningful state representations and dialogue structures consistent with human annotations. Quantitatively, we show our models achieve superior performance on held-out log likelihood evaluation and an ordering task.


title: A multi-link robotic finger-type soft touch-sensor system

abstract: As few research works have been done on robotic finger-type soft touch-sensor systems for classifying the multiple features of objects such as size and softness, we have developed a soft touch-sensor array system of 5-links robotic finger-type together with a statistical data processing of spatial-temporal on-off switch data that are obtained in the course of grasping an object. The finger-type touch-sensor body is covered with a semi-circular soft silicone rubber with compliance of 0.004m/N within which small mechanical touch-sensor switches (compliance = 0.001m/N) are implanted at 5 /spl times/ 47 grid points. The spatial and temporal on-off switch data are decomposed into the frequency distribution of 8-neighborhood-cell patterns. By applying the Maharanobis distance categorization method, the dynamic sensing is found more effective to achieve the 100% multiple-feature classification of the 12 tested circular cylinders with respect to size and softness than the static one.


title: Cloning of the mgtE Mg2+ transporter from Providencia stuartii and the distribution of mgtE in gram-negative and gram-positive bacteria.

abstract: The MM281 strain of Salmonella typhimurium possesses mutations in each of its three Mg2+ transport systems, requires 100 mM Mg2+ for growth, and was used to screen a genomic library from the gram-negative bacterium Providencia stuartii for clones that could restore the ability to grow without Mg2+ supplementation. The clones obtained also conferred sensitivity to Co2+, a phenotype similar to that seen with the S. typhimurium corA Mg2+ transport gene. The sequence of the cloned P. stuartii DNA revealed the presence of a single open reading frame, which was shown to express a protein with a gel molecular mass of 37 kDa in agreement with the deduced size of 34 kDa. Despite a phenotype similar to that of corA and the close phylogenetic relationship between P. stuartii and S. typhimurium, this new putative Mg2+ transporter lacks similarity to the CorA Mg2+ transporter and is instead homologous to MgtE, a newly discovered Mg2+ transport protein from the gram-positive bacterium Bacillus firmus OF4. The distribution of mgtE in bacteria was studied by Southern blot hybridization to PCR amplification products. In contrast to the ubiquity of the corA gene, which encodes the dominant constitutive Mg2+ influx system of bacteria, mgtE has a much more limited phylogenetic distribution.


title: Detect Android Malware Variants Using Component Based Topology Graph

abstract: Smartphone has experienced explosive growth recently. At present, Android system is the most popular mobile platform and attracts lots of developers as well as malware authors. In order to evade detection, malware authors often apply obfuscation techniques to morph malware. Since traditional malware detectors are based on pure syntax, they may fail to detect obfuscated malware variants. We present a novel signature, topology graph based on Android components, which could model malicious payloads properly and resist against common obfuscation used by hackers. We performe stress test on security tools provided by Virus total with ten kinds of malware families from Android Malware Genome Project. Unfortunately, the result is not optimistic that obfuscated malware samples evade most of security tools. Nevertheless, 86.36% of obfuscated malware samples we tested are caught by our detector with tolerable false positive. The evaluation demonstrates that our approach is able to detect malware variants generated by common obfuscation techniques.


title: Enhancement of support vector machine for remote protein homology detection and fold recognition

abstract: Remote protein homology detection and fold recognition refers to detection of structural homology in proteins where there are small or no similarity in the sequence. The issues arise on how to accurately classify remote protein homology and fold recognition in the context of Structural Classification of Proteins (SCOP) hierarchy database and incorporate biological knowledge at the same time. Homology-based methods have been developed to detect protein structural classes from protein primary sequence information which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. We present a comprehensive method based on two-layer multiclass classifiers. The first layer is used detect up to superfamily and family in SCOP hierarchy by using optimized binary SVM classification rules directly to ROC-Area. The second layer uses discriminative SVM algorithm with a state-of-the-art string kernel based on PSI-BLAST profiles that used to leverage the unlabeled data. It will detect up to fold in SCOP hierarchy. We evaluated the results obtained using mean ROC and mean MRFP. Experimental results show that our approaches significantly improve the performance of protein remote protein homology detection for all three different datasets (SCOP 1.53, 1.67 and 1.73). We achieved 0.03% improvements in term of mean ROC in dataset SCOP 1.53, 1.17% in dataset SCOP 1.67 and 0.33% in dataset SCOP 1.73 when compared to the result produced by state-of-the-art methods.


title: Reference model for hybrid adaptive control of pneumatic muscle actuator

abstract: Pneumatic muscle actuator exhibits strong nonlinear behavior and it requires use advanced control algorithms based also on computational intelligence techniques as for example hybrid fuzzy adaptive control. These sophisticated control methods could be capable of handling the system's nonlinear properties. In the paper there is presented nonlinear dynamic model of one-DOF pneumatic muscle actuator for use as reference model in adaptation mechanism of hybrid adaptive fuzzy controller. This dynamic model was designed using different muscle models and some simulation results are presented in the paper.


title: Syntax Constrains the Acquisition of Verb Meaning

abstract: Abstract Can infants use the syntactic context of an unknown word to infer that it is a verb, and thus refers to an action? Twenty-three-month-old French infants watching a moving object were taught novel verbs, within sentences that contained only function words (e.g. “il poune par la” / “it's pooning there”). Infants then watched two instances of the object undergoing either the familiar or a novel action and were asked to point towards the screen matching the novel verb. Infants correctly pointed more often towards the familiar action. To check that they did not simply perseverate in pointing at the familiar scene, control infants were taught novel nouns on the same visual stimuli (e.g. “un poune est la”/ “a poon is here”). Contrary to verb-learning infants, noun-learning infants pointed more often to the novel action. These results confirm the hypothesis that function words, and more generally syntactic structure, support early lexical acquisition.


title: Incremental database systems: databases from the ground up

abstract: This paper discusses a new approach to database management systems that is better suited to a wide class of new applications such as scientific, hypermedia, and financial applications. These applications are characterized by their need to store large amounts of raw, unstructured data. Our premise is that, in these situations, database systems need a way to store data without imposing a schema, and a way to provide a schema incrementally as we process the data. This requires that the raw data be mapped in complex ways to an evolving schema.


title: Social network architecture of human immune cells unveiled by quantitative proteomics

abstract: The immune system is unique in its dynamic interplay between numerous cell types. However, a system-wide view of how immune cells communicate to protect against disease has not yet been established. We applied high-resolution mass-spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of >10,000 proteins in total. Protein copy numbers revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. By integrating total and secreted proteomes, we discovered fundamental intercellular communication structures and previously unknown connections between cell types. Our publicly accessible (http://www.immprot.org/) proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology.


title: Word Embedding Perturbation for Sentence Classification

abstract: In this technique report, we aim to mitigate the overfitting problem of natural language by applying data augmentation methods. Specifically, we attempt several types of noise to perturb the input word embedding, such as Gaussian noise, Bernoulli noise, and adversarial noise, etc. We also apply several constraints on different types of noise. By implementing these proposed data augmentation methods, the baseline models can gain improvements on several sentence classification tasks.


title: A Parsimonious Language Model of Social Media Credibility Across Disparate Events

abstract: Social media has increasingly become central to the way billions of people experience news and events, often bypassing journalists---the traditional gatekeepers of breaking news. Naturally, this casts doubt on the credibility of information found on social media. Here we ask: Can the language captured in unfolding Twitter events provide information about the event's credibility? By examining the first large-scale, systematically-tracked credibility corpus of public Twitter messages (66M messages corresponding to 1,377 real-world events over a span of three months), and identifying 15 theoretically grounded linguistic dimensions, we present a parsimonious model that maps language cues to perceived levels of credibility. While not deployable as a standalone model for credibility assessment at present, our results show that certain linguistic categories and their associated phrases are strong predictors surrounding disparate social media events. In other words, the language used by millions of people on Twitter has considerable information about an event's credibility. For example, hedge words and positive emotion words are associated with lower credibility.


title: Ricardo: integrating R and Hadoop

abstract: Many modern enterprises are collecting data at the most detailed level possible, creating data repositories ranging from terabytes to petabytes in size. The ability to apply sophisticated statistical analysis methods to this data is becoming essential for marketplace competitiveness. This need to perform deep analysis over huge data repositories poses a significant challenge to existing statistical software and data management systems. On the one hand, statistical software provides rich functionality for data analysis and modeling, but can handle only limited amounts of data; e.g., popular packages like R and SPSS operate entirely in main memory. On the other hand, data management systems - such as MapReduce-based systems - can scale to petabytes of data, but provide insufficient analytical functionality. We report our experiences in building Ricardo, a scalable platform for deep analytics. Ricardo is part of the eXtreme Analytics Platform (XAP) project at the IBM Almaden Research Center, and rests on a decomposition of data-analysis algorithms into parts executed by the R statistical analysis system and parts handled by the Hadoop data management system. This decomposition attempts to minimize the transfer of data across system boundaries. Ricardo contrasts with previous approaches, which try to get along with only one type of system, and allows analysts to work on huge datasets from within a popular, well supported, and powerful analysis environment. Because our approach avoids the need to re-implement either statistical or data-management functionality, it can be used to solve complex problems right now.


title: Shadow Segmentation and Compensation in High Resolution Satellite Images

abstract: In high spatial resolution satellite images, shadows are usually cast by elevated objects such as buildings, bridges, and towers, especially in urban region. Shadows may cause loss of feature information, false color tone and shape distortion of objects, which seriously affect the quality of images. Hence, it is important to segment shadow regions and restore their information for image interpretation. This paper presents an effective and robust approach for shadow segmentation and compensation in color satellite images with high spatial resolution. The approach uses normalized saturation-value difference index (NSVDI) in hue-saturation-value (HSV) color space to detect shadows and exploits histogram matching to recover the information under shadows. Experimental results by applying the proposed approach in the IKONOS color images of urban area demonstrate the effectiveness and feasibility of the proposed approach.


title: From "Think Like a Vertex" to "Think Like a Graph"

abstract: To meet the challenge of processing rapidly growing graph and network data created by modern applications, a number of distributed graph processing systems have emerged, such as Pregel and GraphLab. All these systems divide input graphs into partitions, and employ a "think like a vertex" programming model to support iterative graph computation. This vertex-centric model is easy to program and has been proved useful for many graph algorithms. However, this model hides the partitioning information from the users, thus prevents many algorithm-specific optimizations. This often results in longer execution time due to excessive network messages (e.g. in Pregel) or heavy scheduling overhead to ensure data consistency (e.g. in GraphLab). To address this limitation, we propose a new "think like a graph" programming paradigm. Under this graph-centric model, the partition structure is opened up to the users, and can be utilized so that communication within a partition can bypass the heavy message passing or scheduling machinery. We implemented this model in a new system, called Giraph++, based on Apache Giraph, an open source implementation of Pregel. We explore the applicability of the graph-centric model to three categories of graph algorithms, and demonstrate its flexibility and superior performance, especially on well-partitioned data. For example, on a web graph with 118 million vertices and 855 million edges, the graph-centric version of connected component detection algorithm runs 63X faster and uses 204X fewer network messages than its vertex-centric counterpart.


Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features


title: Estimation of blood pressure using Photoplethysmography on the wrist

abstract: Blood pressure measuring method using PPG signal is the one of many non-invasive blood pressure methods. In this study, we have studied a new method that estimates the blood pressure using wrist cuff and wrist PPG (Photoplethysmograph) signal.


LIGHT AND LASER SCANNING METHODS IN 3 D SHAPE MEASUREMENTS


title: Co-regularized Multi-view Spectral Clustering

abstract: In many clustering problems, we have access to multiple views of the data each of which could be individually used for clustering. Exploiting information from multiple views, one can hope to find a clustering that is more accurate than the ones obtained using the individual views. Often these different views admit same underlying clustering of the data, so we can approach this problem by looking for clusterings that are consistent across the views, i.e., corresponding data points in each view should have same cluster membership. We propose a spectral clustering framework that achieves this goal by co-regularizing the clustering hypotheses, and propose two co-regularization schemes to accomplish this. Experimental comparisons with a number of baselines on two synthetic and three real-world datasets establish the efficacy of our proposed approaches.


title: Fuzzy Sampled-Data Control for Uncertain Vehicle Suspension Systems

abstract: This paper investigates the problem of sampled-data $H_{\infty}$ control of uncertain active suspension systems via fuzzy control approach. Our work focuses on designing state-feedback and output-feedback sampled-data controllers to guarantee the resulting closed-loop dynamical systems to be asymptotically stable and satisfy $H_{\infty}$ disturbance attenuation level and suspension performance constraints. Using Takagi-Sugeno (T-S) fuzzy model control method, T-S fuzzy models are established for uncertain vehicle active suspension systems considering the desired suspension performances. Based on Lyapunov stability theory, the existence conditions of state-feedback and output-feedback sampled-data controllers are obtained by solving an optimization problem. Simulation results for active vehicle suspension systems with uncertainty are provided to demonstrate the effectiveness of the proposed method.


title: Ultrasound-Guided Percutaneous Drainage of Neonatal Pyometrocolpos Under Local Anesthesia

abstract: Hydrometrocolpos is an uncommon congenital disorder with cystic dilatation of the vagina and uterus that occurs as a result of accumulated secretions from the reproductive tract due to distal genital tract obstruction. Secondary infection may also occur, resulting in pyometrocolpos, a potentially lethal disease. Immediate drainage of the cystic mass in patients determined to have pyometrocolpos is required to prevent or treat uropathy and septicemia until definitive corrective surgery can be performed. We report an unusual cause of obstructive uropathy in three infants: pyometrocolpos due to lower genital tract atresia. Ultrasound-guided percutaneous drainage of the pyometrocolpos resulted in dramatically improved clinical and laboratory findings in these patients. Ultrasound-guided percutaneous drainage under local anesthesia is a simple, minimally invasive, safe, and effective procedure that facilitates later successful corrective surgery and avoids the need for more complex drainage procedures.


title: Noise Level Estimation for Natural Images Based on Scale-Invariant Kurtosis and Piecewise Stationarity

abstract: Noise level estimation is crucial in many image processing applications, such as blind image denoising. In this paper, we propose a novel noise level estimation approach for natural images by jointly exploiting the piecewise stationarity and a regular property of the kurtosis in bandpass domains. We design a $K$ -means-based algorithm to adaptively partition an image into a series of non-overlapping regions, each of whose clean versions is assumed to be associated with a constant, but unknown kurtosis throughout scales. The noise level estimation is then cast into a problem to optimally fit this new kurtosis model. In addition, we develop a rectification scheme to further reduce the estimation bias through noise injection mechanism. Extensive experimental results show that our method can reliably estimate the noise level for a variety of noise types, and outperforms some state-of-the-art techniques, especially for non-Gaussian noises.


title: Explaining Queries Over Web Tables to Non-experts

abstract: Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL question, translates it into a formal query, executes the query and returns the results. Errors in the translation process are not uncommon, and users typically struggle to understand whether their query has been mapped correctly. We address this problem by explaining the obtained formal queries to non-expert users. We introduce novel query explanations that provide a graphic representation of the query cell-based provenance (in its execution on a given table). Our solution augments a state-of-the-art NL interface over web tables, enhancing it in both its training and deployment phase. Experiments, including a user study conducted on Amazon Mechanical Turk, show our solution to improve both the correctness and reliability of the NL interface.


title: Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain

abstract: We study the problem of hypothesis testing between two discrete distributions, where we only have access to samples after the action of a known reversible Markov chain, playing the role of noise. We derive instance-dependent minimax rates for the sample complexity of this problem, and show how its dependence in time is related to the spectral properties of the Markov chain. We show that there ::: exists a wide statistical window, in terms of sample complexity for hypothesis testing between different pairs of initial distributions. We illustrate these results in several concrete examples.


title: On Optimizing VLC Networks for Downlink Multi-User Transmission: A Survey

abstract: The evolving explosion in high data rate services and applications will soon require the use of untapped, abundant unregulated spectrum of the visible light for communications to adequately meet the demands of the fifth-generation (5G) mobile technologies. Radio-frequency (RF) networks are proving to be too scarce to cover the escalation in data rate services. Visible light communication (VLC) has emerged as a great potential solution, either in replacement of, or a complement to, existing RF networks, to support the projected traffic demands. Despite the prolific advantages of VLC networks, VLC faces many challenges that must be resolved in the near future to achieve full standardization and to be integrated to future wireless systems. Here, we review the emerging research in the field of VLC networks and lay out the challenges, technological solutions, and future work predictions. Specifically, we first review the VLC channel capacity derivation and discuss the performance metrics and the associated variables. The optimization of VLC networks are also discussed, including resources and power allocation techniques, user-to-access point (AP) association and APs-to-clustered-users-association, APs coordination techniques, non-orthogonal multiple access (NOMA) VLC networks, simultaneous energy harvesting and information transmission using the visible light, and the security issues in VLC networks. Finally, we propose several open research problems to optimize the various VLC networks by maximizing either the sum rate, fairness, energy efficiency, secrecy rate, or harvested energy.


title: Filling Holes in Triangular Meshes in Engineering

abstract: In this paper, a novel hole-filling algorithm for triangular meshes is proposed. Firstly, the hole is triangulated into a set of new triangles using modified principle of minimum angle. Then the initial patching mesh is refined according to the density of vertices on boundary edges. Finally, the patching mesh is optimized via the bilateral filter to recover missed features. Experimental results demonstrate that the proposed algorithm fills complex holes robustly, and preserves geometric features to a certain extent as well. The resulted meshes are of good quality for engineering.


title: The Brain on Drugs: From Reward to Addiction

abstract: Advances in neuroscience identified addiction as a chronic brain disease with strong genetic, neurodevelopmental, and sociocultural components. We here discuss the circuit- and cell-level mechanisms of this condition and its co-option of pathways regulating reward, self-control, and affect. Drugs of abuse exert their initial reinforcing effects by triggering supraphysiologic surges of dopamine in the nucleus accumbens that activate the direct striatal pathway via D1 receptors and inhibit the indirect striato-cortical pathway via D2 receptors. Repeated drug administration triggers neuroplastic changes in glutamatergic inputs to the striatum and midbrain dopamine neurons, enhancing the brain's reactivity to drug cues, reducing the sensitivity to non-drug rewards, weakening self-regulation, and increasing the sensitivity to stressful stimuli and dysphoria. Drug-induced impairments are long lasting; thus, interventions designed to mitigate or even reverse them would be beneficial for the treatment of addiction.


title: Understanding Pedestrian Behavior in Complex Traffic Scenes

abstract: Designing autonomous vehicles for urban environments remains an unresolved problem. One major dilemma faced by autonomous cars is understanding the intention of other road users and communicating with them. To investigate one aspect of this, specifically pedestrian crossing behavior, we have collected a large dataset of pedestrian samples at crosswalks under various conditions (e.g., weather) and in different types of roads. Using the data, we analyzed pedestrian behavior from two different perspectives: the way they communicate with drivers prior to crossing and the factors that influence their behavior. Our study shows that changes in head orientation in the form of looking or glancing at the traffic is a strong indicator of crossing intention. We also found that context in the form of the properties of a crosswalk (e.g., its width), traffic dynamics (e.g., speed of the vehicles) as well as pedestrian demographics can alter pedestrian behavior after the initial intention of crossing has been displayed. Our findings suggest that the contextual elements can be interrelated, meaning that the presence of one factor may increase/decrease the influence of other factors. Overall, our work formulates the problem of pedestrian-driver interaction and sheds light on its complexity in typical traffic scenarios.


title: Intel MPX Explained: An Empirical Study of Intel MPX and Software-based Bounds Checking Approaches

abstract: Memory-safety violations are a prevalent cause of both reliability and security vulnerabilities in systems software written in unsafe languages like C/C++. Unfortunately, all the existing software-based solutions to this problem exhibit high performance overheads preventing them from wide adoption in production runs. To address this issue, Intel recently released a new ISA extension - Memory Protection Extensions (Intel MPX), a hardware-assisted full-stack solution to protect against memory safety violations. In this work, we perform an exhaustive study of the Intel MPX architecture to understand its advantages and caveats. We base our study along three dimensions: (a) performance overheads, (b) security guarantees, and (c) usability issues. To put our results in perspective, we compare Intel MPX with three prominent software-based approaches: (1) trip-wire - AddressSanitizer, (2) object-based - SAFECode, and (3) pointer-based - SoftBound. Our main conclusion is that Intel MPX is a promising technique that is not yet practical for widespread adoption. Intel MPX's performance overheads are still high (roughly 50% on average), and the supporting infrastructure has bugs which may cause compilation or runtime errors. Moreover, we showcase the design limitations of Intel MPX: it cannot detect temporal errors, may have false positives and false negatives in multithreaded code, and its restrictions on memory layout require substantial code changes for some programs.


abstract: AUTHOR Jerald, Craig D. TITLE Real Results, Remaining Challenges: The Story of Texas Education Reform. INSTITUTION Business Roundtable, Washington, DC.; Education Trust, Washington, DC. PUB DATE 2001-04-00 NOTE 37p. PUB TYPE Information Analyses (070) EDRS PRICE MF01/PCO2 Plus Postage. DESCRIPTORS Accountability; *Achievement Gains; Achievement Tests; *Educational Change; Educational Policy; Elementary Secondary Education; *Minority Groups; State Programs; *State Standards; *Test Results; Testing Programs IDENTIFIERS *National Assessment of Educational Progress; *Texas; Texas Assessment of Academic Skills


title: Maps of bounded rationality: Psychology for behavioral economics

abstract: The work cited by the Nobel committee was done jointly with Amos Tversky (1937-1996) during a long and unusually close collaboration. Together, we explored the psychology of intuitive beliefs and choices and examined their bounded rationality. Herbert A. Simon (1955, 1979) had proposed much earlier that decision makers should be viewed as boundedly rational, and had offered a model in which utility maximization was replaced by satisficing. Our research attempted to obtain a map of bounded rationality, by exploring the systematic biases that separate the beliefs that people have and the choices they make from the optimal beliefs and choices assumed in rational-agent models. The


title: Movies genres classifier using neural network

abstract: In this paper we have designed a neural network based movie genres classifier. The Movie classifier characterizes the movie clips into different movie genres. The characterization is based on low level audio-visual features. We have extracted the computable audio-visual features from the movie clips which are inspired by the techniques and film grammars used by many filmmakers to endow specific characteristics to a genre. The extracted visual features are shot length, motion, color dominance and lighting key and the extracted audio features are based on time domain, pitch, frequency domain, energy and MFCC. Movie classifier is designed using feed forward neural network with back propagation learning algorithm. We have demonstrated the effectiveness of the classifier for characterizing the movie clips into action, horror, comedy, music and drama genres.


title: OMNI-prop: seamless node classification on arbitrary label correlation

abstract: If we know most of Smith's friends are from Boston, what can we say about the rest of Smith's friends? In this paper, we focus on the node classification problem on networks, which is one of the most important topics in AI and Web communities. Our proposed algorithm which is referred to as OMNI-Prop has the following properties: (a) seamless and accurate; it works well on any label correlations (i.e., homophily, het-erophily, and mixture of them) (b) fast; it is efficient and guaranteed to converge on arbitrary graphs (c) quasi-parameter free; it has just one well-interpretable parameter with heuristic default value of 1. We also prove the theoretical connections of our algorithm to the semi-supervised learning (SSL) algorithms and to random-walks. Experiments on four real, different network datasets demonstrate the benefits of the proposed algorithm, where OMNI-Prop outperforms the top competitors.


title: Design and prototype implementation of SMS based home automation system

abstract: This research aims to design and implement a cost effective but yet flexible, adaptable, and secure Home automation system. Paper presents design and prototype implementation of a basic home automation system based on SMS technology. The proposed system consists of two main components; the GSM modem, which is the communication interface between the home automation system and the user. GSM modem uses SMS technology to exchange data, and signaling between users and home automation system. The second module is the micro controller, which is the core of the home automation system, and acts as the bridge between the GSM network (the user) and sensors and actuators of home automation system. Sensors and actuators are directly connected to hardware micro controller through appropriate interface. System supports a wide range of home automation devices; power management components, security, multimedia applications, and telecommunication devices. System security based on user authentication of each SMS being exchange, as each SMS contains user name and password (beside comments). User can easily configure home automation system setting through RS232 protocol using a user friendly interface.


title: Mining comparative opinions from customer reviews for Competitive Intelligence

abstract: Competitive Intelligence is one of the key factors for enterprise risk management and decision support. However, the functions of Competitive Intelligence are often greatly restricted by the lack of sufficient information sources about the competitors. With the emergence of Web 2.0, the large numbers of customer-generated product reviews often contain information about competitors and have become a new source of mining Competitive Intelligence. In this study, we proposed a novel graphical model to extract and visualize comparative relations between products from customer reviews, with the interdependencies among relations taken into consideration, to help enterprises discover potential risks and further design new products and marketing strategies. Our experiments on a corpus of Amazon customer reviews show that our proposed method can extract comparative relations more accurately than the benchmark methods. Furthermore, this study opens a door to analyzing the rich consumer-generated data for enterprise risk management.


title: A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations

abstract: Many real world applications can be framed as multi-objective optimization problems, where we wish to simultaneously optimize for multiple criteria. Bayesian optimization techniques for the multi-objective setting are pertinent when the evaluation of the functions in question are expensive. Traditional methods for multi-objective optimization, both Bayesian and otherwise, are aimed at recovering the Pareto front of these objectives. However, in certain cases a practitioner might desire to identify Pareto optimal points only in a particular region of the Pareto front due to external considerations. In this work, we propose a strategy based on random scalarizations of the objectives that addresses this problem. While being computationally similar or cheaper than other approaches, our approach is flexible enough to sample from specified subsets of the Pareto front or the whole of it. We also introduce a novel notion of regret in the multi-objective setting and show that our strategy achieves sublinear regret. We experiment with both synthetic and real-life problems, and demonstrate superior performance of our proposed algorithm in terms of flexibility, scalability and regret.


title: Neural Relational Topic Models for Scientific Article Analysis

abstract: Topic modelling and citation recommendation of scientific articles are important yet challenging research problems in scientific article analysis. In particular, the inference on coherent topics can be easily affected by irrelevant contents in articles. Meanwhile, the extreme sparsity of citation networks brings difficulty to a valid citation recommendation. Intuitively, articles with similar topics are more likely to cite each other, and cited articles tend to share similar themes. Motivated from this intuition, we aim to boost the performance of both topic modelling and citation recommendation by effectively leverage this underlying correlation between latent topics and citation networks. To this end, we propose a novel Bayesian deep generative model termed as Neural Relational Topic Model (NRTM), which is composed with a Stacked Variational Auto-Encoder (SVAE) and a multilayer perception (MLP). Specifically, the SVAE utilizes an inference network to learn more representative topics of document contents, which can help to enrich the latent factors in collaborative filtering of citations. Furthermore, the MLP network conducts nonlinear collaborative filtering of citations, which can further benefit the inference of topics by leveraging the knowledge of citation networks. Extensive experiments on two real-world datasets demonstrate that our model can effectively take advantages of the coherence between topic learning and citation recommendation, and significantly outperform the state-of-the-art methods on both tasks.


title: Correlated Time Series Forecasting using Multi-Task Deep Neural Networks

abstract: Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated time series. To enable accurate forecasting on such correlated time series, this paper proposes two models that combine convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The first model employs a CNN on each individual time series, combines the convoluted features, and then applies an RNN on top of the convoluted features in the end to enable forecasting. The second model adds additional auto-encoders into the individual CNNs, making the second model a multi-task learning model, which provides accurate and robust forecasting. Experiments on a large real-world correlated time series data set suggest that the proposed two models are effective and outperform baselines in most settings.


title: RIN: Reformulation Inference Network for Context-Aware Query Suggestion

abstract: Search engine users always endeavor to reformulate queries during search sessions for articulating their information needs because it is not always easy to articulate the search intents. To further ameliorate the reformulation process, search engines may provide some query suggestions based on previous queries. In this paper, we propose Reformulation Inference Network (RIN) to learn how users reformulate queries, thereby benefiting context-aware query suggestion. Instead of categorizing reformulations into predefined patterns, we represent queries and reformulations in a homomorphic hidden space through heterogeneous network embedding. To capture the structure of the session context, a recurrent neural network (RNN) with the attention mechanism is employed to encode the search session by reading the homomorphic query and reformulation embeddings. It enables the model to explicitly captures the former reformulation for each query in the search session and directly learn user reformulation behaviors, from which query suggestion may benefit as shown in previous studies. To generate query suggestions, a binary classifier and an RNN-based decoder are introduced as the query discriminator and the query generator. Inspired by the intuition that model accurately predicting the next reformulation can also correctly infer the next intended query, a reformulation inferencer is then designed for inferring the next reformulation in the latent space of homomorphic embeddings. Therefore, both question suggestion and reformulation prediction can be simultaneously optimized by multi-task learning. Extensive experiments are conducted on publicly available AOL search engine logs. The experimental results demonstrate that RIN outperforms competitive baselines across various situations for both discriminative and generative tasks of context-aware query suggestion.


title: Lifelong Generative Modeling

abstract: Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner where knowledge gained from previous tasks is retained and used for future learning. It is essential towards the development of intelligent machines that can adapt to their surroundings. In this work we focus on a lifelong learning approach to generative modeling where we continuously incorporate newly observed streaming distributions into our learnt model. We do so through a student-teacher architecture which allows us to learn and preserve all the distributions seen so far without the need to retain the past data nor the past models. Through the introduction of a novel cross-model regularizer, the student model leverages the information learnt by the teacher, which acts as a summary of everything seen till now. The regularizer has the additional benefit of reducing the effect of catastrophic interference that appears when we learn over streaming data. We demonstrate its efficacy on streaming distributions as well as its ability to learn a common latent representation across a complex transfer learning scenario.


An Analysis of Prerequisite Skills for Reading Comprehension


A Review on Substrate Integrated Waveguide and its Microstrip Interconnect


title: A Virtual Environment for Steered Molecular Dynamics

abstract: Abstract A molecular dynamics simulation approximates the motion of atoms in a system of molecules over short intervals of simulated time, typically in the order of picoseconds to nanoseconds. Such simulations may run for days or weeks on a computer when used to investigate the dynamic behavior of small proteins in biological systems. By adding additional restraints, a simulation may be steered to observe the possibility of particular behaviors or to eliminate others over shorter timescales. We have developed the steered molecular dynamics (SMD) system to interactively place and observe the effects of restraints in a running dynamics simulation. In this article, we describe an application of SMD to the extraction of small ligands from proteins, and an immersive virtual 3D environment through which the SMD system can be operated. The virtual environment is constructed using the protein interactive theater (PIT) system, a head-tracked stereo workspace for two users.


title: Toward a Semantic Forum for Active Collaborative Learning

abstract: Online discussion forums provide open workspace allowing learners to share information, exchange ideas, address problems and discuss on specific themes. But the substantial impediment to its promotion as effective elearning facility lies in the continuously increasing messages but with discrete and incoherent structure as well as the loosely-tied learners with response-freeness. To motivate and facilitate active collaborative learning, this paper describes the design of a semantic forum with semantic link networking on discussion transcripts. Based on domain ontology and text mining technologies, messages are automatically processed for structural modeling with semantic association and special interest groups are automatically discovered for topic-centric social context measurement, which lays the foundation for the fulfillment of distinctive functionalities in the semantic forum (i.e. semantic search, relational navigation and recommendation). Compared with traditional forums, the semantic forum has three outstanding features. First, it deals with the structural incoherence and content isolation within forums. Second, it enables active learning by providing learners with relational navigation to meet their learning demands. Third, it supports social context based ranking to recommend learning companions or transcripts for collaborative problem-solving.


title: Model-Based Wheel Slip Detection for Outdoor Mobile Robots

abstract: This paper introduces a model-based approach to estimating longitudinal wheel slip and detecting immobilized conditions of autonomous mobile robots operating on outdoor terrain. A novel tire traction/braking model is presented and used to calculate vehicle dynamic forces in an extended Kalman filter framework. Estimates of external forces and robot velocity are derived using measurements from wheel encoders, IMU, and GPS. Weak constraints are used to constrain the evolution of the resistive force estimate based upon physical reasoning. Experimental results show the technique accurately and rapidly detects robot immobilization conditions while providing estimates of the robot's velocity during normal driving. Immobilization detection is shown to be robust to uncertainty in tire model parameters. Accurate immobilization detection is demonstrated in the absence of GPS, indicating the algorithm is applicable for both terrestrial applications and space robotics.


title: Escaping from Children's Abuse of Social Robots

abstract: Social robots working in public space often stimulate children's curiosity. However, sometimes children also show abusive behavior toward robots. In our case studies, we observed in many cases that children persistently obstruct the robot's activity. Some actually abused the robot by saying bad things, and at times even kicking or punching the robot. We developed a statistical model of occurrence of children's abuse. Using this model together with a simulator of pedestrian behavior, we enabled the robot to predict the possibility of an abuse situation and escape before it happens. We demonstrated that with the model the robot successfully lowered the occurrence of abuse in a real shopping mall. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces - Interaction styles; I.2.9 [Artificial Intelligence]: Robotics, General Terms Design, Experimentation, Human Factors.


title: Size of Multilayer Networks for Exact Learning: Analytic Approach

abstract: This article presents a new result about the size of a multilayer neural network computing real outputs for exact learning of a finite set of real samples. The architecture of the network is feedforward, with one hidden layer and several outputs. Starting from a fixed training set, we consider the network as a function of its weights. We derive, for a wide family of transfer functions, a lower and an upper bound on the number of hidden units for exact learning, given the size of the dataset and the dimensions of the input and output spaces.


title: Improved competitive learning neural networks for network intrusion and fraud detection

abstract: In this research, we propose two new clustering algorithms, the improved competitive learning network (ICLN) and the supervised improved competitive learning network (SICLN), for fraud detection and network intrusion detection. The ICLN is an unsupervised clustering algorithm, which applies new rules to the standard competitive learning neural network (SCLN). The network neurons in the ICLN are trained to represent the center of the data by a new reward-punishment update rule. This new update rule overcomes the instability of the SCLN. The SICLN is a supervised version of the ICLN. In the SICLN, the new supervised update rule uses the data labels to guide the training process to achieve a better clustering result. The SICLN can be applied to both labeled and unlabeled data and is highly tolerant to missing or delay labels. Furthermore, the SICLN is capable to reconstruct itself, thus is completely independent from the initial number of clusters. To assess the proposed algorithms, we have performed experimental comparisons on both research data and real-world data in fraud detection and network intrusion detection. The results demonstrate that both the ICLN and the SICLN achieve high performance, and the SICLN outperforms traditional unsupervised clustering algorithms.


title: Ranking Scientific Articles in a Dynamically Evolving Citation Network

abstract: Scientific ranking has long been a hot and important topic in both computer science and scientometrics. A lot of statistics-based and graph-based methods have been proposed for calculating a prestige value as the assessment of each paper's scientific influence. However, being ignorant of the dynamic nature of scientific publication and science evolution, all these methods present a biased point of view of scientific influence. Besides, the ranking results of these methods are not accessible to users because of lack of an explainable model. As an alternative to the state-of-the-art, this paper proposes a cognitively explainable model by integrating three factors in scientific development including knowledge accumulation by individual papers, knowledge diffusion through citation behaviour and knowledge decay with time elapse. Evaluated on ACL Anthology Network using the reference lists of four textbooks or handbooks as the gold standard, the proposed model is proved to be effective in scientific ranking and potential for new insights into the definition and measurement of scientific influence.


title: FDA Regulation of Mobile Health Technologies

abstract: Mobile health (“mHealth”) is the use of portable devices such as smartphones and tablets to improve health. This report discusses the public health benefits and risks of mHealth and the challenges facing the FDA in regulating this technology.


title: Agent-based Modeling of Social Conflict, Civil Violence and Revolution: State-of-the-art-review and Further Prospects

abstract: In this paper, we present a state-of-the-art review of Agent-based models (ABM) for simulation of social conflict phenomena, such as peaceful or violent street protests, civil violence and revolution. First, a simplified characterization of social conflict phenomena as emergent properties of a complex system is presented, together with a description of their macro and micro levels and the scales of the emergent properties. Then, existing ABM for simulation of crowd dynamics, civil violence and revolution are analyzed and compared, using a framework that considers their purpose/scope, environment representation, agent types and their architecture, the scales of the emergent properties, the qualitative and quantitative understanding of the phenomena provided by the results obtained from the models. We discuss the strengths and limitations of the existing models, as well as the promising lines of research for filling the gaps between the state-of-the-art models and real phenomena. This review is part of a work in progress on the assembling and dynamics of protests and civil violence, involving both simulation of the assembling process and the protest dynamics, as well as data collection in real protest events, and provides hints and guidelines for future developments.


title: PCIV, an RFID-Based Platform for Intelligent Vehicle Monitoring

abstract: This article presents a new platform called PCIV (intelligent platform for vehicular control) for traffic monitoring, based on radio frequency Identification (RFID) and cloud computing, applied to road traffic monitoring in public transportation systems. This paper shows the design approach and the experimental validation of the platform in two real scenarios: a university campus and a small city. Experiments demonstrated RFID technology is viable to be implemented to monitor traffic in smart cities.


The Impact of Machine Translation on Sentiment Analysis


title: A comprehensive evaluation of random vector functional link networks

abstract: With randomly generated weights between input and hidden layers, a random vector functional link network is a universal approximator for continuous functions on compact sets with fast learning property. Though it was proposed two decades ago, the classification ability of this family of networks has not been fully investigated yet. Through a very comprehensive evaluation by using 121 UCI datasets, the effect of bias in the output layer, direct links from the input layer to the output layer and type of activation functions in the hidden layer, scaling of parameter randomization as well as the solution procedure for the output weights are investigated in this work. Surprisingly, we found that the direct link plays an important performance enhancing role in RVFL, while the bias term in the output neuron had no significant effect. The ridge regression based closed-form solution was better than those with Moore-Penrose pseudoinverse. Instead of using a uniform randomization in - 1,+1 for all datasets, tuning the scaling of the uniform randomization range for each dataset enhances the overall performance. Six commonly used activation functions were investigated in this work and we found that hardlim and sign activation functions degenerate the overall performance. These basic conclusions can serve as general guidelines for designing RVFL networks based classifiers.


title: LSRC: Lexicon star rating system over cloud

abstract: In the age of Internet, it is essential to go for the reviews before taking any service like to go for a movie or buying any product. Sentiment analysis plays a vital role to determine people's opinion or reviews of any entity, i.e., products, movies or comments etc. Sentiment analysis is an opinion mining technique which uses the concept of NLP (Natural Language Processing) and provide us the behavior of consumer about the product. Reviews are mainly classified as positive, negative or neutral. In this paper we used the concept of lexicon-based approach to give star rating to the reviews. Instead of classifying reviews as positive, negative or neutral, we have categorized them into five subcategories, i.e., Excellent, Good, Neutral, Bad and Worst using star-scale method. We got the results in reasonable time by using the concept of VM (Virtual Machines).


title: A reconfigurable beam dual polarized microstrip cross patch antenna

abstract: This paper presents the conception of a reconfigurable beam antenna with horizontal or vertical polarization for millimeter-wave multi-beam small cells array purposes for 5G systems. This new structure consists of one driven cross patch surrounded by four parasitic elements. In each parasitic element one shorting switch is used to change the beam direction. The parasitic elements can be considered either reflector or director. Specifically, a new method of the feeding microstrip antenna consists in connecting two coaxial probes alternate ON or OFF in one driven element. By combining the states of the four shorting switches with the probe feeding, three beam patterns can be obtained in each orthogonal plane with 3dB-beamwidth of 178° at frequency 10GHz. The simulated and measured results of S parameter and radiation patterns are presented.


title: Neural network method for determining embedding dimension of a time series

abstract: A method is described for determining the optimal short-term prediction time-delay embedding dimension for a scalar time series by training an artificial neural network on the data and then determining the sensitivity of the output of the network to each time lag averaged over the data set. As a byproduct, the method identifies any intermediate time lags that do not influence the dynamics, thus permitting a possible further reduction in the required embedding dimension. The method is tested on four sample data sets and compares favorably with more conventional methods including false nearest neighbors and the ‘plateau dimension’ determined by saturation of the estimated correlation dimension. The proposed method is especially advantageous when the data set is small or contaminated by noise. The trained network could be used for noise reduction, forecasting, and estimating the dynamical and geometrical properties of the system that produced the data, such as the Lyapunov exponent, entropy, and attractor dimension.


title: Spatial Scalability Within the H.264/AVC Scalable Video Coding Extension

abstract: A scalable extension to the H.264/AVC video coding standard has been developed within the joint video team (JVT), a joint organization of the ITU-T video coding group (VCEG) and the ISO/IEC moving picture experts group (MPEG). The extension allows multiple resolutions of an image sequence to be contained in a single bit stream. In this paper, we introduce the spatially scalable extension within the resulting scalable video coding standard. The high-level design is described and individual coding tools are explained. Additionally, encoder issues are identified. Finally, the performance of the design is reported.


title: Forward collision warning with a single camera

abstract: The large number of rear end collisions due to driver inattention has been identified as a major automotive safety issue. Even a short advance warning can significantly reduce the number and severity of the collisions. This paper describes a vision based forward collision warning (FCW) system for highway safety. The algorithm described in this paper computes time to contact (TTC) and possible collision course directly from the size and position of the vehicles in the image - which are the natural measurements for a vision based system - without having to compute a 3D representation of the scene. The use of a single low cost image sensor results in an affordable system which is simple to install. The system has been implemented on real-time hardware and has been test driven on highways. Collision avoidance tests have also been performed on test tracks.


title: Fuzz Revisited: A Re-examination of the Reliability of UNIX Utilities and Services

abstract: We have tested the reliability of a large collection of basic UNIX utility programs, X-Window applications and servers, and network services. We used a simple testing method of subjecting these programs to a random input stream. Our testing methods and tools are largely automatic and simple to use. We tested programs on nine versions of the UNIX operating system, including seven commercial systems and the freely-available GNU utilities and Linux. We report which programs failed on which systems, and identify and categorize the causes of these failures. The result of our testing is that we can crash (with core dump) or hang (infinite loop) over 40% (in the worst case) of the basic programs and over 25% of the X-Window applications. We were not able to crash any of the network services that we tested nor any of X-Window servers. This study parallels our 1990 study (that tested only the basic UNIX utilities); all systems that we compared between 1990 and 1995 noticeably improved in reliability, but still had significant rates of failure. The reliability of the basic utilities from GNU and Linux were noticeably better than those of the commercial systems. We also tested how utility programs checked their return codes from the memory allocation library routines by simulating the unavailability of virtual memory. We could crash almost half of the programs that we tested in this way. Content Indicators: D.2.5 (Testing and Debugging), D.4.9 (Programs and Utilities), General terms: random testing, reliability, UNIX.


title: Learning laparoscopic video shot classification for gynecological surgery

abstract: Videos of endoscopic surgery are used for education of medical experts, analysis in medical research, and documentation for everyday clinical life. Hand-crafted image descriptors lack the capabilities of a semantic classification of surgical actions and video shots of anatomical structures. In this work, we investigate how well single-frame convolutional neural networks (CNN) for semantic shot classification in gynecologic surgery work. Together with medical experts, we manually annotate hours of raw endoscopic gynecologic surgery videos showing endometriosis treatment and myoma resection of over 100 patients. The cleaned ground truth dataset comprises 9 h of annotated video material (from 111 different recordings). We use the well-known CNN architectures AlexNet and GoogLeNet and train these architectures for both, surgical actions and anatomy, from scratch. Furthermore, we extract high-level features from AlexNet with weights from a pre-trained model from the Caffe model zoo and feed them to an SVM classifier. Our evaluation shows that we reach an average recall of .697 and .515 for classification of anatomical structures and surgical actions respectively using off-the-shelf CNN features. Using GoogLeNet, we achieve a mean recall of .782 and .617 for classification of anatomical structures and surgical actions respectively. With AlexNet the achieved recall is .615 for anatomical structures and .469 for surgical action classification respectively. The main conclusion of our work is that advances in general image classification methods transfer to the domain of endoscopic surgery videos in gynecology. This is relevant as this domain is different from natural images, e.g. it is distinguished by smoke, reflections, or a limited amount of colors.


title: Frequency Enhancement in Miller Divider with Injection-Locking Portrait

abstract: In this paper, we present a methodology to enhance the operating range of a Miller frequency divider. Conventionally Miller frequency dividers are visualized as mixers. We present how injection locking portrait of Miller dividers helps us to understand the dynamics of Miller frequency dividers. Further, we discuss how to enhance the operating range while optimizing the divider between power dissipation and operating range. The Miller divider with the proposed methodology has been designed in a standard CMOS 65 nm (low leakage) process. Post layout simulation results show that Miller divider with optimization techniques presented in this paper can operate over the frequency range of 13 GHz for 0dBm input power at center frequency of 61.5 GHz.


title: The performance of crystalline silicon photovoltaic solar modules after 22 years of continuous outdoor exposure

abstract: This paper presents the results of 40 silicon-based photovoltaic solar modules originating from six different manufactures which were tested and characterised originally at the European Solar Test Installation, (ESTI), in the period 1982–1984. These same modules have been re-measured in 2004 after 20–22 years of continuous outdoor weathering. These modules are a subset ‘Rack C’ of the JRC Ispra outdoor test field where examples of diverse generations of PV technologies have been subjected to long-term climatic exposure. We have compared the results obtained from these samples with the typical guarantees which are given by module manufacturers and we can see that in general the manufacturers are conservative with their power guarantees. Most modules exceed the minimum power levels given for 10 years exposure, even after 22 years in the field. The results presented in this paper indicate that the degradation observed in the field is comparable to that observed in Type Approval Testing. This result not only validates the testing and stress levels applied in the Type Approval Tests, but also would indicate that (with the majority of modules exceeding the level of 92% of Pmax after 20 years) the actual lifetime of these products is significantly more than 20 years. Copyright © 2005 John Wiley & Sons, Ltd.


title: Beyond"How may I help you?": Assisting Customer Service Agents with Proactive Responses

abstract: We study the problem of providing recommended responses to customer service agents in live-chat dialogue systems. Smart-reply systems have been widely applied in real-world applications (e.g. Gmail, LinkedIn Messaging), where most of them can successfully recommend reactive responses. However, we observe a major limitation of current methods is that they generally have difficulties in suggesting proactive investigation act (e.g. "Do you perhaps have another account with us?") due to the lack of long-term context information, which indeed act as critical steps for customer service agents to collect information and resolve customers' issues. Thus in this work, we propose an end-to-end method with special focus on suggesting proactive investigative questions to customer agents in Airbnb's customer service live-chat system. Effectiveness of our proposed method can be validated through qualitative and quantitative results.


title: Low-Profile and Wideband Microstrip Antenna With Stable Gain for 5G Wireless Applications

abstract: A low-profile microstrip antenna with stable radiation pattern in a relatively wide band is presented for 5G operation. Four resonant modes with different frequencies are integrated in a single structure to enhance the bandwidth with stable gain, and well matched by introducing the folded walls. An impedance bandwidth of 58.3% has been achieved from 2.84 to 5.17 GHz with a compact structure of ${\text{0.84}}\lambda _{0}\,\times \,{\text{0.68}}\lambda _{0}\,\times \,{\text{0.06}}\lambda _{0}$ , where $\lambda _{0}$ is the free-space wavelength at the center frequency of 4 GHz. The measurement results show that the proposed antenna is with the merits of average gain of 5 dBi, stable radiation patterns, low cross polarization, and low backlobes, exhibiting potentially use for the applications of 5G wireless communication systems.


title: Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

abstract: In this work, we present a method for unsupervised domain adaptation (UDA), where we aim to transfer knowledge from a label-rich domain (i.e., a source domain) to an unlabeled domain (i.e., a target domain). Many adversarial learning methods have been proposed for this task. These methods train domain classifier networks (i.e., a discriminator) to discriminate distinguish the features as either a source or target and train a feature generator network to mimic the discriminator.However, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. ::: To solve the problem, we propose a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to utilize task-specific classifiers as discriminators that try to detect target samples that are far from the support of the source. A feature generator learns to generate target features inside the support to fool the classifiers. Since the generator uses feedback from task-specific classifiers, it avoids generating target features near class boundaries. Our method outperforms other methods on several datasets of image classification and semantic segmentation.


title: Embodied encounters between humans and gators

abstract: Animal geographies have complicated our understanding of human/nonhuman animal relationships by positioning other animals as recipients of human culture and, more recently, by applying theories of embodiment to illustrate the co-constitution of human–animal worlds. This paper addresses human–alligator relationships in Louisiana by illustrating the history and culture of alligator hide production alongside an analysis of human–alligator encounters through tourism. Alligators have played all sorts of instrumental and symbolic roles in the Atchafalaya River Basin where populations here have been managed as a corollary to the exotic hide industry. More recently, gators have been positioned as the star attractions on swamp tours. Guides, tourists, and alligators share encounters where the nonhumans are anthropomorphized and empowered to shape human perceptions of other bodies. By jumping out of the water for food or simply allowing the tourists' gaze, alligators are positioned both as an exotic body and as a c...


title: Relationship between penile size and somatometric parameters in 2276 healthy young men

abstract: The aim of this study was to determine the average penile length of a group of healthy, young Turkish men, and to investigate the relationship between penile length and somatometric parameters in the same group. The flaccid and stretched length and circumference of the penis was measured in a group of 2276 physically normal, young men. The correlation between penile length and weight, height and body mass index (BMI) of the participants was determined by Pearson's analysis. The mean age of the participants was 21.1 ± 3.1 (18-39) years. The mean flaccid, fully stretched and circumferential length of the participants' penises were 8.95 ± 1.04, 13.98 ± 1.58 and 8.89 ± 0.86 cm, respectively. There was a significant relationship between all of these variables (P<0.01). Although weak positive correlations were found between the mean circumference length and BMI, there were no correlations between both the flaccid and stretched lengths and BMI. The penile length must be known to be able to determine the abnormal penile sizes and to make convenient decisions in the counseling and/or treatment of people with short penis concerns. Our study provides mean penile lengths in a large sample of healthy, young Turkish men, and the penile dimensions were found to be weakly correlated with somatometric parameters.


title: Automatic web spreadsheet data extraction

abstract: Spreadsheets contain a huge amount of high-value data but do not observe a standard data model and thus are difficult to integrate. A large number of data integration tools exist, but they generally can only work on relational data. Existing systems for extracting relational data from spreadsheets are too labor intensive to support ad-hoc integration tasks, in which the correct extraction target is only learned during the course of user interaction. This paper introduces a system that automatically extracts relational data from spreadsheets, thereby enabling relational spreadsheet integration. The resulting integrated relational data can be queried directly or can be translated into RDF triples. When compared to standard techniques for spreadsheet data extraction on a set of 100 random Web spreadsheets, the system reduces the amount of human labor by 72% to 92%. In addition to the system design, we present the results of a general survey of more than 400,000 spreadsheets we downloaded from the Web, giving a novel view of how users organize their data in spreadsheets.


title: ZVS Resonant Converter With Parallel–Series Transformer Connection

abstract: A new series resonant converter with a parallel-series transformer connection is proposed in order to achieve zero voltage switching (ZVS) for all power switching, zero current switching (ZCS) for rectifier diodes at a full load, and less transformer secondary winding with a full-wave rectifier. For high-output-voltage applications, the primary windings of two transformers are connected in parallel in order to share the input current and reduce the root-mean-square rms current on the primary windings such that the copper losses on the transformers are reduced. The secondary windings of the two transformers are connected in series in order to ensure that the primary side currents are balanced and the secondary winding turns are also reduced. Thus, the sizes of the transformer core and bobbin are reduced. The full-wave diode rectifier is used on the output side. Thus, the voltage stress of the rectifier diode is equal to the output voltage rather than being two times the output voltage as that in a center-tapped rectifier topology. Based on the resonant behavior, all switches are turned on at the ZVS, and the rectifier diodes are turned off at the ZCS if the operating switching frequency is less than the series resonant frequency. The laboratory experiments with a 660-W prototype, verifying the effectiveness of the proposed converter, are described.


title: The future of microwork

abstract: What happens when we algorithmically break complex productivity tasks down into microtasks? At Microsoft Research, the author and her team are accelerating a shift toward microproductivity to make it easy for people to get big things done one small step at a time.


title: Correlation between heart rate, electrodermal activity and player experience in first-person shooter games

abstract: Psychophysiological methods are becoming more popular in game research as covert and reliable measures of affective player experience, emotions, and cognition. Since player experience is not well understood, correlations between self-reports from players and psychophysiological data may provide a quantitative understanding of this experience. Measurements of electrodermal activity (EDA) and heart rate (HR) allow making inferences about player arousal (i.e., excitement) and are easy to deploy. This paper reports a case study on HR and EDA correlations with subjective gameplay experience, testing the feasibility of these measures in commercial game development contexts. Results indicate a significant correlation (p


title: A Portable Low-Power Harmonic Radar System and Conformal Tag for Insect Tracking

abstract: Harmonic radar systems provide an effective modality for tracking insect behavior. This letter presents a harmonic radar system proposed to track the migration of the Emerald Ash Borer (EAB). The system offers a unique combination of portability, low power and small tag design. It is comprised of a compact radar unit and a passive RF tag for mounting on the insect. The radar unit transmits a 5.96 GHz signal and detects at the 11.812 GHz band. A prototype of the radar unit was built and tested, and a new small tag was designed for the application. The new tag offers improved harmonic conversion efficiency and much smaller size as compared to previous harmonic radar systems for tracking insects. Unlike RFID detectors whose sensitivity allows detection up to a few meters, the developed radar can detect a tagged insect up to 58 m (190 ft).


title: Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions

abstract: The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating text from images. In this paper, we extend this problem to the more specific domain of face description. Unlike scene descriptions, face descriptions are more fine-grained and rely on attributes extracted from the image, rather than objects and relations. Given that no data exists for this task, we present an ongoing crowdsourcing study to collect a corpus of descriptions of face images taken `in the wild'. To gain a better understanding of the variation we find in face description and the possible issues that this may raise, we also conducted an annotation study on a subset of the corpus. Primarily, we found descriptions to refer to a mixture of attributes, not only physical, but also emotional and inferential, which is bound to create further challenges for current image-to-text methods.


title: Virtual Try-On Topics in Realistic, Individualized Dressing in Virtual Reality

abstract: In the course of the project Virtual Try-On new VR technologies have been developed, which form the basis for a realistic, three dimensional, (real-time) simulation and visualization of individualized garments put on by virtual counterparts of real customers. To provide this cloning and dressing of people in VR, a complete process chain is being build up starting with the touch-less 3-dimensional scanning of the human body up to a photo-realistic 3-dimensional presentation of the virtual customer dressed in the chosen pieces of clothing. The emerging platform for interactive selection and configuration of virtual garments, the "virtual shop", will be accessible in real fashion boutiques as well as over the internet, thereby supplementing the conventional distribution channels.


title: Design and VLSI implementation of high performance DUC and DDC for software defined radio applications

abstract: Paper deals with the implementation of high performance digital up converter and digital down converter for software defined radio application. Digital up converters are used at the transmitter side to up convert the baseband signal to intermediate frequency signal. Digital down converters are used at receiver side to down convert intermediate frequency signal to baseband signal. DUC and DDC modules are designed using System generator Xilinx block set and generated verilog code for the same. Simulation is performed using Isim and synthesis is carried out using Xilinx ISE 13.2. Design has implemented on vertex-4 FPGA board. The power consumed in this design is 52.6mWatts and the maximum operating frequency is up to 336.1MHz and total time taken for output is 2.93ns.


title: Template Matching Advances and Applications in Image Analysis

abstract: In most computer vision and image analysis problems, it is necessary to define a similarity measure between two or more different objects or images. Template matching is a classic and fundamental method used to score similarities between objects using certain mathematical algorithms. In this paper, we reviewed the basic concept of matching, as well as advances in template matching and applications such as invariant features or novel applications in medical image analysis. Additionally, deformable models and templates originating from classic template matching were discussed. These models have broad applications in image registration, and they are a fundamental aspect of novel machine vision or deep learning algorithms, such as convolutional neural networks (CNN), which perform shift and scale invariant functions followed by classification. In general, although template matching methods have restrictions which limit their application, they are recommended for use with other object recognition methods as pre- or post-processing steps. Combining a template matching technique such as normalized cross-correlation or dice coefficient with a robust decision-making algorithm yields a significant improvement in the accuracy rate for object detection and recognition.


title: Beyond natives and immigrants: exploring types of net generation students

abstract: Previously assumed to be a homogenous and highly skilled group with respect to information and communications technology, the so-called Net Generation has instead been shown to possess a diverse range of technology skills and preferences. To better understand this diversity, we subjected data from 2096 students aged between 17 and 26 from three Australian universities to a cluster analysis. Through this analysis, we identified four distinct types of technology users: power users (14% of sample), ordinary users (27%), irregular users (14%) and basic users (45%). A series of exploratory chi-square analyses revealed significant associations between the different types of technology users and the university that students attended, their gender and age and whether the student was local or international. No associations were found for analyses related discipline area, socio-economic status or rurality of residence. The findings are discussed in light of the rhetoric associated with commentaries about the Net Generation, and suggestions about their implications for teaching and learning in universities are offered.


title: Inducing Document Plans for Concept-to-Text Generation

abstract: In a language generation system, a content planner selects which elements must be included in the output text and the ordering between them. Recent empirical approaches perform content selection without any ordering and have thus no means to ensure that the output is coherent. In this paper we focus on the problem of generating text from a database and present a trainable end-to-end generation system that includes both content selection and ordering. Content plans are represented intuitively by a set of grammar rules that operate on the document level and are acquired automatically from training data. We develop two approaches: the first one is inspired from Rhetorical Structure Theory and represents the document as a tree of discourse relations between database records; the second one requires little linguistic sophistication and uses tree structures to represent global patterns of database record sequences within a document. Experimental evaluation on two domains yields considerable improvements over the state of the art for both approaches.


title: Atlas-driven lung lobe segmentation in volumetric X-ray CT images

abstract: High-resolution X-ray computed tomography (CT) imaging is routinely used for clinical pulmonary applications. Since lung function varies regionally and because pulmonary disease is usually not uniformly distributed in the lungs, it is useful to study the lungs on a lobe-by-lobe basis. Thus, it is important to segment not only the lungs, but the lobar fissures as well. In this paper, we demonstrate the use of an anatomic pulmonary atlas, encoded with a priori information on the pulmonary anatomy, to automatically segment the oblique lobar fissures. Sixteen volumetric CT scans from 16 subjects are used to construct the pulmonary atlas. A ridgeness measure is applied to the original CT images to enhance the fissure contrast. Fissure detection is accomplished in two stages: an initial fissure search and a final fissure search. A fuzzy reasoning system is used in the fissure search to analyze information from three sources: the image intensity, an anatomic smoothness constraint, and the atlas-based search initialization. Our method has been tested on 22 volumetric thin-slice CT scans from 12 subjects, and the results are compared to manual tracings. Averaged across all 22 data sets, the RMS error between the automatically segmented and manually segmented fissures is 1.96 +/- 0.71 mm and the mean of the similarity indices between the manually defined and computer-defined lobe regions is 0.988. The results indicate a strong agreement between the automatic and manual lobe segmentations.


title: A modified K-means clustering algorithm for use in isolated work recognition

abstract: Studies of isolated word recognition systems have shown that a set of carefully chosen templates can be used to bring the performance of speaker-independent systems up to that of systems trained to the individual speaker. The earliest work in this area used a sophisticated set of pattern recognition algorithms in a human-interactive mode to create the set of templates (multiple patterns) for each word in the vocabulary. Not only was this procedure time consuming but it was impossible to reproduce exactly because it was highly dependent on decisions made by the experimenter. Subsequent work led to an automatic clustering procedure which, given only a set of clustering parameters, clustered patterns with the same performance as the previously developed supervised algorithms. The one drawback of the automatic procedure was that the specification of the input parameter set was found to be somewhat dependent on the vocabulary type and size of population to be clustered. Since a naive user of such a statistical clustering algorithm could not be expected, in general, to know how to choose the word clustering parameters, even this automatic clustering algorithm was not appropriate for a completely general word recognition system. It is the purpose of this paper to present a clustering algorithm based on a standard K-means approach which requires no user parameter specification. Experimental data show that this new algorithm performs as well or better than the previously used clustering techniques when tested as part of a speaker-independent isolated word recognition system.


title: Freehand Cocalibration of Optical and Electromagnetic Trackers for Navigated Bronchoscopy

abstract: Recent technical advances in electromagnetic (EM) tracking have facilitated the use of EM sensors in surgical interventions. Due to the susceptibility to distortions of the EM field when placed in close proximity to metallic objects, they require calibration in situ to maintain an acceptable degree of accuracy. In this paper, a freehand method is presented for calibrating electromagnetic position sensors by mapping the coordinate measurements to those from optical trackers. Unlike previous techniques, the proposed method allows for free movement of the calibration object, permitting C2 continuity and interdependence between positional and angular corrections. The proposed method involves calculation of a mapping from {ℝ3, SO(3)} to {ℝ3, SO(3)} with radial basis function interpolation based on a modified distance metric. The system provides efficient distortion correction of the EM field, and is applicable to clinical situations where a rapid calibration of EM tracking is required.


title: Computer programming as an educational tool in the English classroom a preliminary study

abstract: Computational thinking, a problem solving method that uses computer science techniques, has recently become mainstream as many governments and major Internet companies are promoting programming skills among children. However, little is known about how programming affects other aspects of life beyond the acquired programming skills. In this regard, this paper summarizes the work done in the school San Diego and San Vicente (Madrid, Spain) during the third quarter of the year 2013/2014, where we conducted a study with four groups of students in 4th and 5th grade to measure to what extent the use of computer programming in English classes can be an interesting educational tool with a positive impact on the learning otucome of the students. The results show that the groups working with programming activities improved more than the groups using traditional resources. Moreover, when asked via surveys at the end of the study, most students felt that coding was a positive influence, not only for learning English, but for developing other important skills as teamwork and learning to learn.