title: Vaccinium arctostaphylos, a common herbal medicine in Iran: molecular and biochemical study of its antidiabetic effects on alloxan-diabetic Wistar rats.

abstract: AIM OF THE STUDY ::: The purpose of this study was to define antidiabetic effects of fruit of Vaccinium arctostaphylos L. (Ericaceae) which is traditionally used in Iran for improving of health status of diabetic patients. ::: ::: ::: MATERIALS AND METHODS ::: Firstly, we examined the effect of ethanolic extract of Vaccinium arctostaphylos fruit on postprandial blood glucose (PBG) after 1, 3, 5, 8, and 24h following a single dose administration of the extract to alloxan-diabetic male Wistar rats. Also oral glucose tolerance test was carried out. Secondly, PBG was measured at the end of 1, 2 and 3 weeks following 3 weeks daily administration of the extract. At the end of treatment period the pancreatic INS and cardiac GLUT-4 mRNA expression and also the changes in the plasma lipid profiles and antioxidant enzymes activities were assessed. Finally, we examined the inhibitory activity of the extract against rat intestinal α-glucosidase. ::: ::: ::: RESULTS ::: The obtained results showed mild acute (18%) and also significant chronic (35%) decrease in the PBG, significant reduction in triglyceride (47%) and notable rising of the erythrocyte superoxide dismutase (57%), glutathione peroxidase (35%) and catalase (19%) activities due to treatment with the extract. Also we observed increased expression of GLUT-4 and INS genes in plant extract treated Wistar rats. Furthermore, in vitro studies displayed 47% and 56% inhibitory effects of the extract on activity of intestinal maltase and sucrase enzymes, respectively. ::: ::: ::: CONCLUSIONS ::: Findings of this study allow us to establish scientifically Vaccinium arctostaphylos fruit as a potent antidiabetic agent with antihyperglycemic, antioxidant and triglyceride lowering effects.


title: The Beliefs about Emotions Scale: Validity, reliability and sensitivity to change

abstract: Abstract Objective Beliefs about the unacceptability of experiencing or expressing negative emotions have been noted in individuals with a range of problems, including chronic fatigue syndrome (CFS), irritable bowel syndrome, somatization disorder, depression, eating disorders, social phobia, posttraumatic stress disorder, and borderline personality disorder. These beliefs are likely to have implications for emotion regulation and processing, and are addressed explicitly or implicitly within many therapies including cognitive behavior therapy (CBT), mindfulness-based cognitive therapy (MBCT), and Acceptance and Commitment Therapy (ACT). This article describes the development, validation, and internal reliability of the Beliefs about Emotions Scale (BES), a self-report questionnaire to assess such beliefs. Methods The new scale was completed by people with CFS ( n =121) and healthy controls ( n =73). Twenty-two individuals with CFS completed the scale before and after CBT. Results People with CFS had significantly higher scores on this new questionnaire than healthy controls. Principal components analysis identified one factor, and the scale had high internal consistency (0.91). Scores on the BES were most highly correlated with a measure of negative perfectionism ( r =0.59) and also showed significant correlations with measures of dysfunctional attitudes, self-sacrifice, depression, anxiety, and fatigue. When completed before and after CBT for CFS, the questionnaire was sufficiently sensitive to detect a significant reduction in endorsement of unhelpful beliefs about emotions. Conclusion The new Beliefs about Emotions Scale showed good internal reliability, validity and sensitivity to change.


title: Contour Detection Using Cost-Sensitive Convolutional Neural Networks

abstract: We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks (CNNs), to extract an informative feature vector for each pixel and uses an SVM classifier to accomplish contour detection. The main challenge lies in adapting a pre-trained per-image CNN model for yielding per-pixel image features. We propose to base on the DenseNet architecture to achieve pixelwise fine-tuning and then consider a cost-sensitive strategy to further improve the learning with a small dataset of edge and non-edge image patches. In the experiment of contour detection, we look into the effectiveness of combining per-pixel features from different CNN layers and obtain comparable performances to the state-of-the-art on BSDS500.


title: Evaluation of the Perceptual Characteristics of a Force Induced by Asymmetric Vibrations

abstract: This paper describes the perceptual characteristics of a sense of a force induced by asymmetric vibration using a vibration speaker-type non-grounded haptic interface. We confirm that the vibration speaker generates a perceived force that pulls or pushes a user's hand in a particular direction when an asymmetric amplitude signal that is generated by inverting a part of a sine wave is input. In this paper, to verify the system with respect to various factors of force perception caused by asymmetric vibration, we conducted six experiments and the following results were obtained. (1) The force vector can be controlled by reversing the asymmetric waves. (2) By investigating the physical characteristics of the vibration, asymmetric vibration was confirmed. (3) The presentation of vibration in the shear direction on the finger pad is effective. (4) The point of subjective equality of the perceived force can be controlled by up to 0.43 N by changing the amplitude voltage of the input signals. (5) The minimum stimulation time required for force perception is 66.7 ms. (6) When the vibration is continuously presented for 40 to 50 s, the perceived force decreases because of adaptation. Hence, we confirmed that we can control both the direction and magnitude of the reaction force by changing the input signal of the vibration speaker.


title: Living a restricted life with Ehlers-Danlos Syndrome (EDS)

abstract: Abstract Ehlers-Danlos syndrome (EDS) is an inherited connective tissue disorder, primarily affecting the skin, ligaments, joints and blood vessels. The symptoms can vary from undiagnosed cases with mild symptoms to more severe forms. A qualitative study was conducted with the purpose of exploring how individuals with different symptoms of EDS describe their symptoms and perceive their daily life. Eleven interviewees were recruited via a support group in Sweden. The main strategies of the grounded theory method were used in collection and analysis of data. The identified main theme, "Living a restricted life", seemed to explain the way in which fears, pain, stigmatisation and experiences of non-affirmation in health-care limited the possibility of self-actualisation in daily living and social life. This study provides a conceptual framework for future research and an understanding of the type of professional support individuals with EDS require.


title: Graph Node-Feature Convolution for Representation Learning

abstract: Graph convolutional network (GCN) is an emerging neural network approach. It learns new representation of a node by aggregating feature vectors of all neighbors in the aggregation process without considering whether the neighbors or features are useful or not. Recent methods have improved solutions by sampling a fixed size set of neighbors, or assigning different weights to different neighbors in the aggregation process, but features within a feature vector are still treated equally in the aggregation process. In this paper, we introduce a new convolution operation on regular size feature maps constructed from features of a fixed node bandwidth via sampling to get the first-level node representation, which is then passed to a standard GCN to learn the second-level node representation. Experiments show that our method outperforms competing methods in semi-supervised node classification tasks. Furthermore, our method opens new doors for exploring new GCN architectures, particularly deeper GCN models.


title: What Regularized Auto-Encoders Learn from the Data Generating Distribution

abstract: What do auto-encoders learn about the underlying data generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data. This paper clarifies some of these previous observations by showing that minimizing a particular form of regularized reconstruction error yields a reconstruction function that locally characterizes the shape of the data generating density. We show that the auto-encoder captures the score (derivative of the log-density with respect to the input). It contradicts previous interpretations of reconstruction error as an energy function. Unlike previous results, the theorems provided here are completely generic and do not depend on the parametrization of the auto-encoder: they show what the auto-encoder would tend to if given enough capacity and examples. These results are for a contractive training criterion we show to be similar to the denoising auto-encoder training criterion with small corruption noise, but with contraction applied on the whole reconstruction function rather than just encoder. Similarly to score matching, one can consider the proposed training criterion as a convenient alternative to maximum likelihood because it does not involve a partition function. Finally, we show how an approximate Metropolis-Hastings MCMC can be setup to recover samples from the estimated distribution, and this is confirmed in sampling experiments.


title: Securing DNP3 Broadcast Communications in SCADA Systems

abstract: The Distributed Network Protocol version 3 (DNP3) provides Secure Authentication (DNP3-SA) as the mechanism to authenticate unicast messages from a master station to its outstations in SCADA systems. In large scale systems, it may be necessary to broadcast a critical request from a master station to multiple outstations at once. The DNP3 protocol standard describes the use of broadcast communication; however, it does not specify its security. This paper is the first to present DNP3 Secure Authentication for Broadcast (DNP3-SAB), a new lightweight security scheme for broadcast mode communication. This scheme is based on hash-chain and only makes use of the existing cryptographic primitives specified in DNP3-SA. The scheme integrates itself into the DNP3-SA key update process. The proposed scheme is modelled, validated, verified using Coloured Petri Nets (CPN) against the most common protocol attacks such as modification, injection and replay. Performance analysis on our scheme and the existing DNP3-SA modes (NACR and AGM) shows that DNP3-SAB reduces the communication overhead significantly at the cost of an increase with a constant term in processing and storage overhead. This benefit is maintained even when DNP3-SAB is under attack.


title: Nucleolin mediates the antiangiogenesis effect of the pseudopeptide N6L

abstract: BackgroundNucleolin is a protein over-expressed on the surface of activated cells. Recent studies have underlined the involvement of cell surface nucleolin in angiogenesis processes. This cell surface molecule serves as a receptor for various ligands implicated in pathophysiological processes such as growth factors, cell adhesion molecules like integrins, selectins or laminin-1, lipoproteins and viruses. N6L is a synthetic multimeric pseudopeptide that binds cell surface expressed nucleolin and inhibits cell proliferation.ResultsIn the present work, we further investigated the mechanisms of action of pseudopeptide N6L on angiogenesis using HUVECs. We provide evidence that N6L inhibits the in vitro adhesion, proliferation and migration of HUVECs without inducing their apoptosis. In addition, we found that N6L downregulates MMP-2 in HUVECs. The above biological actions are regulated by SRC, ERK1/2, AKT and FAK kinases as we found that N6L inhibits their activation in HUVECs. Finally, down regulation of nucleolin using siRNA demonstrated the implication of nucleolin in the biological actions of these peptides.ConclusionsTaken together, these results indicate that N6L could constitute an interesting therapeutic tool for treating diseases associated with excessive angiogenesis.


Windows Instant Messaging App Forensics: Facebook and Skype as Case Studies


A QUICK GLANCE OF SPLINE WAVELETS AND ITS APPLICATIONS


title: A Dual-Core Multi-Threaded Xeon Processor with 16MB L3 Cache

abstract: A dual-core 64b Xeonreg MP processor is implemented in a 65nm 8M process. The 435mm2 die has 1.328B transistors. Each core has two threads and a unified 1MB L2 cache. The 16MB unified, 16-way set-associative L3 cache implements both sleep and shut-off leakage reduction modes


title: Disease-Atlas: Navigating Disease Trajectories with Deep Learning

abstract: Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint modeling, the standard forms suffer from limitations that arise from a fixed model specification, and computational difficulties when applied to high-dimensional datasets. In this paper, we propose a deep learning approach to address these limitations, enhancing existing methods with the inherent flexibility and scalability of deep neural networks, while retaining the benefits of joint modeling. Using longitudinal data from a real-world medical dataset, we demonstrate improvements in performance and scalability, as well as robustness in the presence of irregularly sampled data.


title: Vicarious Calibration of the Meteosat Visible Channel

abstract: Abstract Postlaunch calibration of the Meteosat visible (VIS) channel has been performed with Meteosat-1, Meteosat-2, and Meteosat-4. The radiance at the satellite radiometer aperture has been determined by independent means and has been related to the simultaneous satellite radiometer count. This technique has been named vicarious calibration. Airborne radiance measurements over homogeneous targets (two land surfaces, clear ocean, and stratiform clouds) have been performed. The derived calibration factors are scene dependent because of the nonrectangular shape of the Meteosat VIS channel spectral responsivity curve. The latter and, hence, the scene dependence, slightly differs between the three satellites. Therefore, the calibration factors also differ within ±15%. Average values of the calibration factors are about 11 W m−2 sr−1 per count for land surfaces and clouds and about 1.5 W m−2 sr−1 per count for ocean.


title: A macroscope in the redwoods

abstract: The wireless sensor network "macroscope" offers the potential to advance science by enabling dense temporal and spatial monitoring of large physical volumes. This paper presents a case study of a wireless sensor network that recorded 44 days in the life of a 70-meter tall redwood tree, at a density of every 5 minutes in time and every 2 meters in space. Each node measured air temperature, relative humidity, and photosynthetically active solar radiation. The network captured a detailed picture of the complex spatial variation and temporal dynamics of the microclimate surrounding a coastal redwood tree. This paper describes the deployed network and then employs a multi-dimensional analysis methodology to reveal trends and gradients in this large and previously-unobtainable dataset. An analysis of system performance data is then performed, suggesting lessons for future deployments.


title: Beyond PageRank: machine learning for static ranking

abstract: Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We show that we can significantly outperform PageRank using features that are independent of the link structure of the Web. We gain a further boost in accuracy by using data on the frequency at which users visit Web pages. We use RankNet, a ranking machine learning algorithm, to combine these and other static features based on anchor text and domain characteristics. The resulting model achieves a static ranking pairwise accuracy of 67.3% (vs. 56.7% for PageRank or 50% for random).


Generative Adversarial Network-Based Postfilter for STFT Spectrograms


title: Distributed fault detection for interconnected second-order systems with applications to power networks

abstract: In this paper, the existence of unknown input observers for networks of interconnected second-order linear time invariant systems is studied. Two classes of distributed control systems of large practical relevance are considered. It is proved that for these systems, one can construct a bank of unknown input observers, and use them to detect and isolate faults in the network. The result presents a distributed implementation. In particular, by exploiting the system structure, this work provides further insight into the design of UIO for networked systems. Moreover, the importance of certain network measurements is shown. Infeasibility results with respect to available measurements and faults are also provided, as well as methods to remove faulty agents from the network. Applications to power networks and robotic formations are presented. It is shown how the developed methodology apply to a power network described by the swing equation with a faulty bus. For a multi-robot system, it is illustrated how a faulty robot can be detected and removed.


title: Modeling Drive-Managed SMR Performance

abstract: Accurately modeling drive-managed Shingled Magnetic Recording (SMR) disks is a challenge, requiring an array of approaches including both existing disk modeling techniques as well as new techniques for inferring internal translation layer algorithms. In this work, we present the first predictive simulation model of a generally available drive-managed SMR disk. Despite the use of unknown proprietary algorithms in this device, our model that is derived from external measurements is able to predict mean latency within a few percent, and with an Root Mean Square (RMS) cumulative latency error of 25% or less for most workloads tested. These variations, although not small, are in most cases less than three times the drive-to-drive variation seen among seemingly identical drives.


title: How Image Degradations Affect Deep CNN-based Face Recognition?

abstract: Face recognition approaches that are based on deep convolutional neural networks (CNN) have been dominating the field. The performance improvements they have provided in the so called in-the-wild datasets are significant, however, their performance under image quality degradations have not been assessed, yet. This is particularly important, since in real-world face recognition applications, images may contain various kinds of degradations due to motion blur, noise, compression artifacts, color distortions, and occlusion. In this work, we have addressed this problem and analyzed the influence of these image degradations on the performance of deep CNN-based face recognition approaches using the standard LFW closed-set identification protocol. We have evaluated three popular deep CNN models, namely, the AlexNet, VGG-Face, and GoogLeNet. Results have indicated that blur, noise, and occlusion cause a significant decrease in performance, while deep CNN models are found to be robust to distortions, such as color distortions and change in color balance.


title: Explore/Exploit Schemes for Web Content Optimization

abstract: We propose novel multi-armed bandit (explore/exploit) schemes to maximize total clicks on a content module published regularly on Yahoo! Intuitively, one can ``explore'' each candidate item by displaying it to a small fraction of user visits to estimate the item's click-through rate (CTR), and then ``exploit'' high CTR items in order to maximize clicks. While bandit methods that seek to find the optimal trade-off between explore and exploit have been studied for decades, existing solutions are not satisfactory for web content publishing applications where dynamic set of items with short lifetimes, delayed feedback and non-stationary reward (CTR) distributions are typical. In this paper, we develop a Bayesian solution and extend several existing schemes to our setting. Through extensive evaluation with nine bandit schemes, we show that our Bayesian solution is uniformly better in several scenarios. We also study the empirical characteristics of our schemes and provide useful insights on the strengths and weaknesses of each. Finally, we validate our results with a ``side-by-side'' comparison of schemes through live experiments conducted on a random sample of real user visits to Yahoo!


A Recurrent Neural Network Based Recommendation System


title: Algorithmically Generated Domain Detection and Malware Family Classification

abstract: In this paper, we compare the performance of several machine learning based approaches for the tasks of detecting algorithmically generated malicious domains and the categorization of domains according to their malware family. The datasets used for model comparison were provided by the shared task on Detecting Malicious Domain names (DMD 2018). Our models ranked first for two out of the four test datasets provided in the competition.


title: SybilExposer: An effective scheme to detect Sybil communities in online social networks

abstract: The popularity of online social networks (OSNs) has resulted in them being targeted with Sybil attacks, where an adversary forges many fake identities (called Sybils) to disrupt or control the normal functioning of the system. Several schemes have been proposed to defend against Sybil attacks. Most of these schemes work by computing the landing probability or statistical distribution of visiting frequency of random walks. These schemes usually have high running time cost and are highly dependent upon the proper choice of known trusted nodes. To address these limitations, in this paper we present SybilExposer, an efficient and effective Sybil community detection algorithm, which relies on the properties of social graph communities to rank communities according to their perceived likelihood of being fake or Sybil. Our experiments on several real-world OSN graphs illustrate that SybilExposer has close to 100% true positive rate and nearly zero false positive rate in identifying Sybil communities, and the best running time complexity compared to the state of the art.


title: A New Low Cost Linear Resolver Converter

abstract: A new low cost converter topology is proposed for sinusoidal position encoders. The converter enables determination of the angle from the sine and cosine signals of the encoder. When used with resolvers, the implementation of the present scheme takes advantage of the available excitation signal used to operate the device. This trigonometric reference signal is optimally used to generate an analogue signal equivalent to a digital look-up table (LUT). This enables determination of the mechanical angle without using LUT, A/D, and D/A converters. The scheme is optimized in order to achieve highest possible precision. Beside simplicity of its implementation, the proposed converter offers the advantage of robustness to amplitude fluctuation of the transducer excitation signal. The converter was implemented using ordinary low-cost analog components. The theory of operation, computer simulation, and experimental results are given.


title: A planner-based approach to generate and analyze minimal attack graph

abstract: In the present scenario, even well administered networks are susceptible to sophisticated cyber attacks. Such attack combines vulnerabilities existing on different systems/services and are potentially more harmful than single point attacks. One of the methods for analyzing such security vulnerabilities in an enterprise network is the use of attack graph. It is a complete graph which gives a succinct representation of different attack scenarios, depicted by attack paths. An attack path is a logical succession of exploits, where each exploit in the series satisfies the preconditions for subsequent exploits and makes a causal relationship among them. Thus analysis of the attack graph may help in assessing network security from hackers' perspective. One of the intrinsic problems with the generation and analysis of such a complete attack graph is its scalability. In this work, an approach based on Planner, a special purpose search algorithm from artificial intelligence domain, has been proposed for time-efficient, scalable representation of the attack graphs. Further, customized algorithms have been developed for automatic generation of attack paths (using Planner as a low-level module). The analysis shows that generation of attack graph using the customized algorithms can be done in polynomial time. A case study has also been presented to demonstrate the efficacy of the proposed methodology.


title: Recommending News Based on Hybrid User Profile, Popularity, Trends, and Location

abstract: Reading the news is a favorite hobby for many people anywhere in the world. With the popularity of the Internet and social media, users are constantly provided, or even bombarded, with the latest news around the world. With numerous sources of news, it has become a real challenge for users to follow the news that they are interested. Previous work used user profile to recommend personalized news; and used RSS feeds and latest tweets to provide popular, trendy news. In this work we combine these two methods with three enhancements. First, to personalize news recommendation we used a hybrid approach, which involved the analyses of click through, user tweets, and user Twitter friends list to build user profile, this method significantly improves the accuracy of user profile. Second, to address the importance of temporal dynamics, we add a unique new feature of location preference to the news recommendation system. Third, we allow users to choose the ratio of popular news vs. trendy news they desire. The resulting system is then evaluated based on user satisfaction and accuracy. The results show that the average user satisfaction increases from 8.6 to 9.4 when location preference is added, while the accuracy of the recommendation system is around 92–95%. We believe that the proposed system is a successful example of incorporating temporal dynamics to recommendation systems; the combination of using hybrid user profile, popularity, trends and location would have significant impact on other recommendation systems in the future.


title: Improved Particle Swarm Optimization Algorithm for Android Medical Care IOT using Modified Parameters

abstract: This study examines wireless sensor network with real-time remote identification using the Android study of things (HCIOT) platform in community healthcare. An improved particle swarm optimization (PSO) method is proposed to efficiently enhance physiological multi-sensors data fusion measurement precision in the Internet of Things (IOT) system. Improved PSO (IPSO) includes: inertia weight factor design, shrinkage factor adjustment to allow improved PSO algorithm data fusion performance. The Android platform is employed to build multi-physiological signal processing and timely medical care of things analysis. Wireless sensor network signal transmission and Internet links allow community or family members to have timely medical care network services.


Black suburbanization at the mid-1970s


title: Energy harvesting technologies for wireless sensors in rotating environments

abstract: Using sensors to measure parameters of interest in rotating environments and communicating the measurements in real-time over wireless links, requires a reliable power source. In this paper, we have investigated the possibility to generate electric power locally by evaluating six different energy-harvesting technologies. The applicability of the technology is evaluated by several parameters that are important to the functionality in an industrial environment. All technologies are individually presented and evaluated, a concluding table is also summarizing the technologies strengths and weaknesses. To support the technology evaluation on a more theoretical level, simulations has been performed to strengthen our claims. Among the evaluated and simulated technologies, we found that the variable reluctance-based harvesting technology is the strongest candidate for further technology development for the considered use-case.


title: Does democracy preempt civil wars

abstract: Abstract This paper proposes that the appropriate measure for capturing the political aspects that matter for social conflict is the level of inclusiveness of the political system. I analyze, theoretically and empirically, the relationship between inclusiveness of the political system and its stability. According to the model, high inclusive systems, such as the proportional representation system, are more stable than low inclusive systems that favor political exclusion, such as the majoritarian system. Empirically, it seems that democracy is not enough to deter social conflicts. The level of inclusiveness of the political system is important in explaining the probability of civil wars.


JPE 13-3-2 Analysis and Design of Coupled Inductors for Two-Phase Interleaved DC-DC Converters


Vinyl Acetate from ethylene , acetic acid and oxygen Industrial Plant Simulation .


title: OntoDM: An Ontology of Data Mining

abstract: Motivated by the need for unification of the field of data mining and the growing demand for formalized representation of outcomes of research, we address the task of constructing an ontology of data mining. The proposed ontology, named OntoDM, is based on a recent proposal of a general framework for data mining, and includes definitions of basic data mining entities, such as datatype and dataset, data mining task, data mining algorithm and components thereof (e.g., distance function), etc. It also allows for the definition of more complex entities, e.g., constraints in constraint-based data mining, sets of such constraints (inductive queries) and data mining scenarios (sequences of inductive queries). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is a deep/heavy-weight ontology and follows best practices in ontology engineering, such as not allowing multiple inheritance of classes, using a predefined set of relations and using a top level ontology.


title: A Low-Profile Dual-Band Dual-Mode and Dual-Polarized Antenna Based on AMC

abstract: A low-profile dual-band dual-mode and dual-polarized (DBDMDP) antenna based on artificial magnetic conductor (AMC) is presented in this letter. One reference antenna made of a pair of double-printed crossed dipoles employing vacant quarter-rings is originally realized with dual-band dual-polarized property but strong back radiation. Then, one square-shaped AMC layer is used instead of the metal ground with improved performance. For the lower mode (at 1.38 GHz), the AMC reflection phase of 90° is employed to change the electromagnetic field distribution and achieve monopole-like omnidirectional linear polarization (LP) radiation. Meanwhile for the higher mode (at 1.57 GHz), the AMC with reflection phase of 0° is regarded as a reflector to realize patch-like unidirectional circular polarization (CP) radiation. Thus, probe-single-feed DBDMDP antenna is realized with low-profile property of 0.03λ0, as well as pattern and polarization diversity. One prototype is fabricated to verify the theory analysis, and the measured results reveal that the maximum gain is higher than 2 dBi and 7 dBic for the LP omnidirectional and CP unidirectional modes, respectively. With the advantages of low profile and multifunction, this method offers a good candidate for global position systems applications in the future.


title: Unifying Count-Based Exploration and Intrinsic Motivation

abstract: We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across observations. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic motivation literature, we use sequential density models to measure uncertainty, and propose a novel algorithm for deriving a pseudo-count from an arbitrary sequential density model. This technique enables us to generalize count-based exploration algorithms to the non-tabular case. We apply our ideas to Atari 2600 games, providing sensible pseudo-counts from raw pixels. We transform these pseudo-counts into intrinsic rewards and obtain significantly improved exploration in a number of hard games, including the infamously difficult Montezuma's Revenge.


title: Designing and Evaluating a Reliable Corpus of Web Genres via Crowd-Sourcing

abstract: Research in Natural Language Processing often relies on a large collection of manually annotated documents. However, currently there is no reliable genre-annotated corpus of web pages to be employed in Automatic Genre Identification (AGI). In AGI, documents are classified based on their genres rather than their topics or subjects. The major shortcoming of available web genre collections is their relatively low inter-coder agreement. Reliability of annotated data is an essential factor for reliability of the research result. In this paper, we present the first web genre corpus which is reliably annotated. We developed precise and consistent annotation guidelines which consist of well-defined and well-recognized categories. For annotating the corpus, we used crowd-sourcing which is a novel approach in genre annotation. We computed the overall as well as the individual categories’ chance-corrected inter-annotator agreement. The results show that the corpus has been annotated reliably.


title: A STATISTICAL AND HYDROLOGICAL ANALYSIS OF THE MAXIMUM FLOW IN THE TERPEZITA RIVER DRAINAGE BASIN

abstract: A statistical and hydrological analysis of the maximum flow in the Terpezița river drainage basin. Starting from the idea that hydrological and hydro- meteorological parameters have a statistical existence over time and a spatial distribution that can be represented by an interaction between the mathematical and geographical elements, the present paper aims to analyze the relationship between maximum flows, hourly rains, flow coefficients and concentration times of the Terpezita Basin. This is the second-largest sub-basin (182km 2 ) in the basin of Desnatui, which is located in the SW of Romania and is a first degree tributary of the Danube. The assessment of the concentration time, which involves the sizes of the liquid flow and specific liquid flow, was attained according to the physical and geographical characteristics of the basin. Thus taking into account the homogenous character from this point of view and the existence of statistically established hydrological and pluviometric background, we could outline the behavior of Terpezița River Basin during the extreme hydro-meteorological events. The documentation was completed through an exemplification of previously calculated results, using observations and measurements of the river bed in the vicinity of Terpezita village and processing the values that resulted from the hydro-graph of the 2005 flash-flood.


title: A minimum weight FEM formulation for Structural Topological Optimization with local stress constraints

abstract: 6th World Congresses of Structural and Multidisciplinary Optimization, Rio de Janeiro, 30 May - 03 June 2005, Brazil


title: Consensus mounts for national drug formulary

abstract: With hearings on national pharmacare underway in Parliament, a diverse group of experts at a recent symposium on pharmaceutical policies endorsed the idea of a federally coordinated, national formulary of recommended drugs to guide Canada’s multitude of public and private insurance plans. ::: ::: “


A Novel Low-Ringing Monocycle Picosecond Pulse Generator Based on Step Recovery Diode


Big Data Computing and Clouds : Trends and Future Directions


title: Stressed metal probes for atomic force microscopy

abstract: Common scanning probes with straight cantilevers and micrometer-sized tips are not suited for substrates with very high topography. Therefore, we have developed stressed metal probes that use bent cantilevers and can have a tip height of hundreds of micrometers. The use of a transparent substrate allows the laser beam light to shine through the probe chip in atomic force microscopy (AFM). This letter describes the stressed probe concept and presents first measurements on thin film samples and on the bottom of deep structures. Our probe concept extends the functionality of AFM systems and is an alternative to straight cantilever probes in many applications.


title: RF MEMS Zipping Varactor With High Quality Factor and Very Large Tuning Range

abstract: An RF MEMS zipping analog varactor with a high-permittivity bismuth zinc niobate dielectric has been fabricated and characterized. The varactor can be continuously tuned from 10 to 280 fF, with a very large tuning range of 2700%. By use of gold electrodes to ensure a low series resistance, the high quality factors of 317 and 63 at 1 GHz were measured for the minimum and maximum capacitances, respectively. The self-resonance frequency of the varactor is estimated to be 16.6 GHz at the maximum capacitance and 87.6 GHz at the minimum capacitance. With a small device footprint of $540 \times 130~\mu \text{m}^{2}$ , the zipping varactor is suitable for application in portable reconfigurable RF systems.


title: Nature-nuture reconceptualized in developmental perspective: A bioecological model.

abstract: In response to Anastasi's (1958) long-standing challenge, the authors propose an empirically testable theoretical model that (a) goes beyond and qualifies the established behavioral genetics paradigm by allowing for nonadditive synergistic effects, direct measures of the environment, and mechanisms of organism-environment interaction, called proximal processes, through which genotypes are transformed into phenotypes; (b) hypothesizes that estimates of heritability (e.g., rt) increase markedly with the magnitude of proximal processes; (c) demonstrates that heritability measures the proportion of variation in individual differences attributable only to actualized genetic potential, with the degree of nonactualizedpotential remaining unknown; (d) proposes that, by enhancing proximal processes and environments, it is possible to increase the extent of actualized genetic potentials for developmental competence.


title: Mixture contrast limited adaptive histogram equalization for underwater image enhancement

abstract: Within the last decades, improving the quality of an underwater image has received considerable attention due to poor visibility of the image which is caused by physical properties of the water medium. This paper presents a new method called mixture Contrast Limited Adaptive Histogram Equalization (CLAHE) color models that specifically developed for underwater image enhancement. The method operates CLAHE on RGB and HSV color models and both results are combined together using Euclidean norm. The underwater images used in this study were taken from Redang Island and Bidong Island in Terengganu, Malaysia. Experimental results show that the proposed approach significantly improves the visual quality of underwater images by enhancing contrast, as well as reducing noise and artifacts.


title: GOGMA: Globally-Optimal Gaussian Mixture Alignment

abstract: Gaussian mixture alignment is a family of approaches that are frequently used for robustly solving the point-set registration problem. However, since they use local optimisation, they are susceptible to local minima and can only guarantee local optimality. Consequently, their accuracy is strongly dependent on the quality of the initialisation. This paper presents the first globally-optimal solution to the 3D rigid Gaussian mixture alignment problem under the L2 distance between mixtures. The algorithm, named GOGMA, employs a branch-and-bound approach to search the space of 3D rigid motions SE(3), guaranteeing global optimality regardless of the initialisation. The geometry of SE(3) was used to find novel upper and lower bounds for the objective function and local optimisation was integrated into the scheme to accelerate convergence without voiding the optimality guarantee. The evaluation empirically supported the optimality proof and showed that the method performed much more robustly on two challenging datasets than an existing globally-optimal registration solution.


title: Expert system, fuzzy logic, and neural network applications in power electronics and motion control

abstract: Artificial intelligence (AI) tools, such as expert systems, fuzzy logic, and neural networks are expected to usher a new era in power electronics and motion control in the coming decades. Although these technologies have advanced significantly and have found wide applications, they have hardly touched the power electronics and machine drives area. The paper describes these AI tools and their application in the area of power electronics and motion control. The body of the paper is subdivided into three sections which describe, respectively, the principles and applications of expert systems, fuzzy logic, and neural networks. The theoretical portion of each topic is of direct relevance to the application of power electronics. The example applications in the paper are taken from the published literature. >


title: Motion Segmentation Via a Sparsity Constraint

abstract: Motion segmentation is an important task for intelligent transportation systems. In this paper, inspired by the fact that a feature point trajectory can be sparsely represented as a combination of several feature point trajectories that share coherent transformations, an efficient and effective motion segmentation method with a sparsity constraint is proposed. Specifically, we first propose an accumulated scheme to efficiently integrate motion information from all the frames of a video sequence to construct a correlation matrix. Then, a sparse affinity matrix is built on the correlation matrix by using information-theoretic principles, where the nonzero elements in the same row of the sparse affinity matrix correspond to the feature point trajectories more likely belonging to the same motion. Thereafter, a segment and merge procedure is proposed to effectively estimate the number of motions via the sparse affinity matrix. Finally, by applying spectral clustering on the sparse affinity matrix, different motions in the video sequence are accurately segmented based on the estimated number of motions. Experimental results on the Hopkins 155 and the 62-clip datasets demonstrate that the proposed method achieves superior performance compared with several state-of-the-art methods.


title: A review of dental CAD/CAM: current status and future perspectives from 20 years of experience.

abstract: In this article, we review the recent history of the development of dental CAD/CAM systems for the fabrication of crowns and fixed partial dentures (FPDs), based on our 20 years of experience in this field. The current status of commercial dental CAD/CAM systems developed around the world is evaluated, with particular focus on the field of ceramic crowns and FPDs. Finally, we discuss the future perspectives applicable to dental CAD/CAM. The use of dental CAD/CAM systems is promising not only in the field of crowns and FPDs but also in other fields of dentistry, even if the contribution is presently limited. CAD/CAM technology will contribute to patients' health and QOL in the aging society.


title: Splinter: practical private queries on public data

abstract: Many online services let users query public datasets such as maps, flight prices, or restaurant reviews. Unfortunately, the queries to these services reveal highly sensitive information that can compromise users' privacy. This paper presents Splinter, a system that protects users' queries on public data and scales to realistic applications. A user splits her query into multiple parts and sends each part to a different provider that holds a copy of the data. As long as any one of the providers is honest and does not collude with the others, the providers cannot determine the query. Splinter uses and extends a new cryptographic primitive called Function Secret Sharing (FSS) that makes it up to an order of magnitude more efficient than prior systems based on Private Information Retrieval and garbled circuits. We develop protocols extending FSS to new types of queries, such as MAX and TOPK queries. We also provide an optimized implementation of FSS using AES-NI instructions and multicores. Splinter achieves end-to-end latencies below 1.6 seconds for realistic workloads including a Yelp clone, flight search, and map routing.


A MultiTask Framework for Monitoring Health Conditions via Attention-based Recurrent Neural Networks


title: Evaluation of an algorithmic approach in comparison with the Illumigene assay for laboratory diagnosis of Clostridium difficile infection.

abstract: The following three diagnostic algorithms were evaluated in comparison with the Illumigene assay as a stand-alone test for Clostridium difficile detection: glutamate dehydrogenase antigen screen (GDH) followed by toxin A/B antigen testing (Tox A/B) with the cell cytotoxicity assay for discordant specimens (algorithm 1), GDH followed by the Illumigene (algorithm 2), and GDH followed by Tox A/B with the Illumigene for discordant specimens (algorithm 3). A total of 428 stool specimens submitted to three clinical microbiology laboratories in Manitoba, Canada, for C. difficile detection between June 2011 and April 2012 were included in the study. The prevalence of C. difficile in the stool specimens was 14.7% (63/428) based on toxigenic culture (microbiologic reference standard). The sensitivity and specificity of the Illumigene for C. difficile detection were 73.0% and 99.7%, respectively. The corresponding sensitivities and specificities were 65.1% and 100.0% for algorithm 1, 68.3% and 100.0% for algorithm 2, and 69.8% and 100.0% for algorithm 3. Using algorithm 1, a cell cytotoxicity assay was required for toxin detection in 37% of positive tests, prolonging turnaround time. However, the predictive value of a positive test based on a clinical reference standard (all tests positive or cytotoxigenic culture positive and clinical disease on chart review) was slightly higher with algorithm 1 than with the Illumigene assay as a stand-alone test or as part of an algorithm (algorithms 2 and 3). Based on a reduction in turnaround time, simplicity, and acceptable sensitivity and specificity, we recommend algorithm 2 (screening with the GDH antigen test and confirmatory testing with the Illumigene).


title: Machinability effects of stainless steels with a HIPed NiTi coating in high-efficiency machining operations

abstract: The machinability effects of new high-strength stainless steels are researched due to specific properties arising from their structure. In grinding operations, HIPed (Hot Isostatically Pressed) austenitic 316L, duplex 2205 and super duplex 2507, and as-cast 304 stainless steel, in turning HIPed 316L, duplex stainless steel 2205 and X5 CrMnN 18 18 stainless steel, and in drilling HIPed PM (Powder Metallurgic) Duplok 27 and duplex stainless steel ASTM8190 1A and X2CrNi 1911 with HIPed NiTi coating were researched in revised machining testing environments using tool life testing, chip and workpiece surface morphology analysis. Chips, workpiece surfaces and cutting tools were analysed by SEM and EDS. High toughness, workhardening and low heat conductivity have a synergistic effect in inducing machinability difficulties e.g. decreased product quality, shorter tool life, increased power consumption and decreased chip evacuation. An increased amount of alloying elements is found to decrease machinability in the form of increased cutting force and workhardening rate of the machined surface, and decreased tool life and surface roughness. Also, the machinability of PM-produced stainless steels is decreased because of the increased amount of hard oxide particles included in the microstructure of PM-produced stainless steel. The formation of BUE (Built-up Edge) is found, affecting the machinability and tool life of tested high-strength stainless steels. In grinding operations HIPed austenitic 316L and duplex 2205 stainless steel are rated according to cutting force, workhardening rate and the amount of microvoids and microcracks in ground surfaces. In turning operations HIPed 316L, duplex stainless steel 2205 and X5 CrMnN 18 18 stainless steels are


title: Scaffolding in Teacher–Student Interaction: A Decade of Research

abstract: Although scaffolding is an important and frequently studied concept, much discussion exists with regard to its conceptualizations, appearances, and effectiveness. Departing from the last decade’s scaffolding literature, this review scrutinizes these three areas of scaffolding. First, contingency, fading, and transfer of responsibility are discerned in this review as the three key characteristics of scaffolding. Second, an overview is presented of the numerous descriptive studies that provided narratives on the appearances of scaffolding and classifications of scaffolding strategies. These strategies are synthesized into a framework for analysis, distinguishing between scaffolding means and intentions. Third, the small number of effectiveness studies available is discussed and the results suggest that scaffolding is effective. However, more research is needed. The main challenge in scaffolding research appears to be its measurement. Based on the encountered and described measurement problems, suggestions for future research are made.


title: Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices

abstract: Glimpse is a continuous, real-time object recognition system for camera-equipped mobile devices. Glimpse captures full-motion video, locates objects of interest, recognizes and labels them, and tracks them from frame to frame for the user. Because the algorithms for object recognition entail significant computation, Glimpse runs them on server machines. When the latency between the server and mobile device is higher than a frame-time, this approach lowers object recognition accuracy. To regain accuracy, Glimpse uses an active cache of video frames on the mobile device. A subset of the frames in the active cache are used to track objects on the mobile, using (stale) hints about objects that arrive from the server from time to time. To reduce network bandwidth usage, Glimpse computes trigger frames to send to the server for recognizing and labeling. Experiments with Android smartphones and Google Glass over Verizon, ATT without Glimpse, continuous detection is non-functional (0.2%-1.9% precision).


title: Fast Odometry Integration in Local Bundle Adjustment-Based Visual SLAM

abstract: The Simultaneous Localisation And Mapping (SLAM) for a camera moving in a scene is a long term research problem. Here we improve a recent visual SLAM which applies Local Bundle Adjustments (LBA) on selected key-frames of a video: we show how to correct the scale drift observed in long monocular video sequence using an additional odometry sensor. Our method and results are interesting for several reasons: (1) the pose accuracy is improved on real examples (2) we do not sacrifice the consistency between the reconstructed 3D points and image features to fit odometry data (3) the modification of the original visual SLAM method is not difficult.


title: Blockchain-based protocol of autonomous business activity for multi-agent systems consisting of UAVs

abstract: This article describes a method of organizing the communication protocol, which allows agents of the multiagent system (MAS) to make decisions about their actions. Plan activities and interact with each other to perform tasks of modern industrial and business processes based on cyber-physical systems. The main attention is paid to those multi-agent systems, where autonomous agents — robots or smart things — participate in business processes among people, and their activities are organized in an unreliable and unknown environment. The article shows, how to organize a communication system between agents in a peer-to-peer network using the decentralized Ethereum Blochchain technology and smart contracts. The architecture of protocol of autonomous business activity, based on this communication method is given. As a result, the experience of implementation an autonomous economic system with unmanned aerial vehicles (UAV) is described.


title: Multiplicity of cerebrospinal fluid functions: New challenges in health and disease

abstract: This review integrates eight aspects of cerebrospinal fluid (CSF) circulatory dynamics: formation rate, pressure, flow, volume, turnover rate, composition, recycling and reabsorption. Novel ways to modulate CSF formation emanate from recent analyses of choroid plexus transcription factors (E2F5), ion transporters (NaHCO3 cotransport), transport enzymes (isoforms of carbonic anhydrase), aquaporin 1 regulation, and plasticity of receptors for fluid-regulating neuropeptides. A greater appreciation of CSF pressure (CSFP) is being generated by fresh insights on peptidergic regulatory servomechanisms, the role of dysfunctional ependyma and circumventricular organs in causing congenital hydrocephalus, and the clinical use of algorithms to delineate CSFP waveforms for diagnostic and prognostic utility. Increasing attention focuses on CSF flow: how it impacts cerebral metabolism and hemodynamics, neural stem cell progression in the subventricular zone, and catabolite/peptide clearance from the CNS. The pathophysiological significance of changes in CSF volume is assessed from the respective viewpoints of hemodynamics (choroid plexus blood flow and pulsatility), hydrodynamics (choroidal hypo- and hypersecretion) and neuroendocrine factors (i.e., coordinated regulation by atrial natriuretic peptide, arginine vasopressin and basic fibroblast growth factor). In aging, normal pressure hydrocephalus and Alzheimer's disease, the expanding CSF space reduces the CSF turnover rate, thus compromising the CSF sink action to clear harmful metabolites (e.g., amyloid) from the CNS. Dwindling CSF dynamics greatly harms the interstitial environment of neurons. Accordingly the altered CSF composition in neurodegenerative diseases and senescence, because of adverse effects on neural processes and cognition, needs more effective clinical management. CSF recycling between subarachnoid space, brain and ventricles promotes interstitial fluid (ISF) convection with both trophic and excretory benefits. Finally, CSF reabsorption via multiple pathways (olfactory and spinal arachnoidal bulk flow) is likely complemented by fluid clearance across capillary walls (aquaporin 4) and arachnoid villi when CSFP and fluid retention are markedly elevated. A model is presented that links CSF and ISF homeostasis to coordinated fluxes of water and solutes at both the blood-CSF and blood-brain transport interfaces.Outline1 Overview2 CSF formation2.1 Transcription factors2.2 Ion transporters2.3 Enzymes that modulate transport2.4 Aquaporins or water channels2.5 Receptors for neuropeptides3 CSF pressure3.1 Servomechanism regulatory hypothesis3.2 Ontogeny of CSF pressure generation3.3 Congenital hydrocephalus and periventricular regions3.4 Brain response to elevated CSF pressure3.5 Advances in measuring CSF waveforms4 CSF flow4.1 CSF flow and brain metabolism4.2 Flow effects on fetal germinal matrix4.3 Decreasing CSF flow in aging CNS4.4 Refinement of non-invasive flow measurements5 CSF volume5.1 Hemodynamic factors5.2 Hydrodynamic factors5.3 Neuroendocrine factors6 CSF turnover rate6.1 Adverse effect of ventriculomegaly6.2 Attenuated CSF sink action7 CSF composition7.1 Kidney-like action of CP-CSF system7.2 Altered CSF biochemistry in aging and disease7.3 Importance of clearance transport7.4 Therapeutic manipulation of composition8 CSF recycling in relation to ISF dynamics8.1 CSF exchange with brain interstitium8.2 Components of ISF movement in brain8.3 Compromised ISF/CSF dynamics and amyloid retention9 CSF reabsorption9.1 Arachnoidal outflow resistance9.2 Arachnoid villi vs. olfactory drainage routes9.3 Fluid reabsorption along spinal nerves9.4 Reabsorption across capillary aquaporin channels10 Developing translationally effective models for restoring CSF balance11 Conclusion


title: MANTRA: A Scalable Approach to Mining Temporally Anomalous Sub-trajectories

abstract: In this paper, we study the problem of mining temporally anomalous sub-trajectory patterns from an input trajectory in a scalable manner. Given the prevailing road conditions, a sub-trajectory is temporally anomalous if its travel time deviates significantly from the expected time. Mining these patterns requires us to delve into the sub-trajectory space, which is not scalable for real-time analytics. To overcome this scalability challenge, we design a technique called MANTRA. We study the properties unique to anomalous sub-trajectories and utilize them in MANTRA to iteratively refine the search space into a disjoint set of sub-trajectory islands. The expensive enumeration of all possible sub-trajectories is performed only on the islands to compute the answer set of maximal anomalous sub-trajectories. Extensive experiments on both real and synthetic datasets establish MANTRA as more than 3 orders of magnitude faster than baseline techniques. Moreover, through trajectory classification and segmentation, we demonstrate that the proposed model conforms to human intuition.


title: PROBING THE FEED LINE PARAMETERS IN VIVALDI NOTCH ANTENNAS

abstract: Some new parameters in Vivaldi Notch antennas are debated over in this paper. They can be availed for the bandwidth application amelioration. The aforementioned limiting factors comprise two parameters for the radial stub dislocation, one parameter for the stub opening angle, and one parameter for the stub’s offset angle. The aforementioned parameters are rectified by means of the optimization algorithm to accomplish a better frequency application. The results obtained in this article will eventually be collated with those of the other similar antennas. The best achieved bandwidth in this article is 17.1 GHz.


title: Data Quality: Concepts, Methodologies and Techniques

abstract: Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone researchers, students, or professionals interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.


title: GlyphLens: View-Dependent Occlusion Management in the Interactive Glyph Visualization

abstract: Glyph as a powerful multivariate visualization technique is used to visualize data through its visual channels. To visualize 3D volumetric dataset, glyphs are usually placed on 2D surface, such as the slicing plane or the feature surface, to avoid occluding each other. However, the 3D spatial structure of some features may be missing. On the other hand, placing large number of glyphs over the entire 3D space results in occlusion and visual clutter that make the visualization ineffective. To avoid the occlusion, we propose a view-dependent interactive 3D lens that removes the occluding glyphs by pulling the glyphs aside through the animation. We provide two space deformation models and two lens shape models to displace the glyphs based on their spatial distributions. After the displacement, the glyphs around the user-interested region are still visible as the context information, and their spatial structures are preserved. Besides, we attenuate the brightness of the glyphs inside the lens based on their depths to provide more depth cue. Furthermore, we developed an interactive glyph visualization system to explore different glyph-based visualization applications. In the system, we provide a few lens utilities that allows users to pick a glyph or a feature and look at it from different view directions. We compare different display/interaction techniques to visualize/manipulate our lens and glyphs.


title: Software configurable 5.8 GHz radar sensor receiver chip in 0.13 µm CMOS for non-contact vital sign detection

abstract: A direct conversion 5.8 GHz radar sensor chip with 1GHz bandwidth was designed and fabricated. This radar sensor chip is software configurable to set the operation point and detection range for optimal performance. It integrates all the analog functions on-chip so that the output can be directly sampled for digital signal processing. Important design issues for direct conversion non-contact vital sign detection sensors, such as the effect of baseband flicker noise and gain budget, have been discussed. Experiments have been performed successfully in lab environment to detect the vital signs of human subject seated at a distance of up to 1.5 meter.


title: Hierarchical Gaussian Descriptors with Application to Person Re-Identification

abstract: Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification (re-id). In this paper, we present novel meta-descriptors based on a hierarchical distribution of pixel features. Although hierarchical covariance descriptors have been successfully applied to image classification, the mean information of pixel features, which is absent from the covariance, tends to be the major discriminative information for person re-id. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, the region is modeled as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, we embed the parameters of the Gaussian into a point of Symmetric Positive Definite (SPD) matrix manifold. By changing the way to handle mean information in this embedding, we develop two hierarchical Gaussian descriptors. Additionally, we develop feature norm normalization methods with the ability to alleviate the biased trends that exist on the descriptors. The experimental results conducted on five public datasets indicate that the proposed descriptors achieve remarkably high performance on person re-id.


title: Privacy preserving analysis of vertically partitioned data using secure matrix products

abstract: Reluctance of statistical agencies and other data owners to share possibly confidential or proprietary data with others who own related databases is a serious impediment to conducting mutually beneficial analyses. In this article, we propose a protocol for conducting secure regressions and similar analyses on vertically partitioned data – databases with identical records but disjoint sets of attributes. This protocol allows data owners to estimate coefficients and standard errors of linear regressions, and to examine regression model diagnostics, without disclosing the values of their attributes to each other. No third parties are involved. The protocol can be used to perform other procedures for which sample means and covariances are sufficient statistics. The basis is an algorithm for secure matrix multiplication, which is used by pairs of owners to compute off-diagonal blocks of the full data covariance matrix.


title: Deep Neural Network Based on Translation Model for Diabetes Knowledge Graph

abstract: Knowledge base completion (KBC) can predict new facts according to existed facts in knowledge base. However, most work in KBC is limited to simple neural networks and usually focuses on commonsense knowledge base where data resources are from public websites. Medical records related to diabetes from hospital showed potential advantages in both quality and quantity, which is significant to be mined for preventing or delaying diabetes and its complications. Thus, this paper designs and constructs Diabetes Knowledge Graph from electronic medical records in Shanghai Ruijing Hospital, and proposes a deep neural network based on translation model for completion in the diabetes knowledge graph. We manually evaluate our trained model's ability in accuracy, recall and F1-scores, finding that it has a good stability and performance in diabetes knowledge base completion.


title: Early hyaluronidase use in preventing skin necrosis after treatment with dermal fillers: Report of two cases

abstract: Injection of dermal fillers, like hyaluronic acid (HA), is a safe procedure, with few and transient side effects such as erythema, bruising and swelling etc. The aim of this report is to provide our protocol for the early treatment of necrotic complications after facial treatment with dermal fillers. We present two cases of skin suffering of the face after dermal infiltration of HA, treated successfully with our early protocol. Our protocol includes the early infiltration of hyaluronidase in the treated areas. We start with infiltration of hyaluronidase distributed over the area to be treated through micro-injections with dosage 40 IU per cm 2. Our protocol includes the use of systemic corticosteroids for 4 days, anti-aggregation therapy, oral antibiotic, topical cream with nitric oxide and compresses with gauze and warm water. In the skin complications after dermal filler treatment, marked pain and characteristic reticulated erythema in the skin distribution of the affected vessels is often developed. Due to the implementation of our protocol in these patients, we managed to avoid an irreversible necrotic complication of the face in both cases. In this report, our protocol was compared with results published in the literature and allowed us to avoid complications such as skin necrosis with permanent damage.


title: Can you infect me now?: malware propagation in mobile phone networks

abstract: In this paper we evaluate the effects of malware propagating usingcommunication services in mobile phone networks. Although self-propagating malware is well understood in the Internet, mobile phone networks have very different characteristics in terms of topologies, services, provisioning and capacity, devices, and communication patterns. To investigate malware in this new environment, we have developed an event-driver simulator that captures the characteristics and constraints of mobile phone networks. In particular, the simulator models realistic topologies and provisioned capacities of the network infrastructure, as well as the contactgraphs determined by cell phone address books. We evaluate the speedand severity of random contact worms in mobile phone networks, characterize the denial-of-service effects such worms could have on the network, investigate approaches to accelerate malware propagation, and discuss the implications of defending networks against such attacks.


title: Understanding Difficulties and Resulting Confusion in Learning: An Integrative Review

abstract: Difficulties are often an unavoidable but important part of the learning process. This seems particularly so for complex conceptual learning. Challenges in the learning process are, however, particularly difficult to detect and respond to in educational environments where growing class sizes and the increased use of digital technologies mean that teachers are unable to provide nuanced and personalised feedback and support to help students overcome their difficulties. Individual differences, the specifics of the learning activity, and the difficulty of giving individual feedback in large classes and digital environments all add to the challenge of responding to student difficulties and confusion. In this integrative review, we aim to explore difficulties and resulting emotional responses in learning. We will review the primary principles of cognitive disequilibrium and contrast these principles with work on desirable difficulties, productive failure, impasse driven learning and pure discovery-based learning. We conclude with a theoretical model of the zones of optimal and sub-optimal confusion as a way of conceptualising the parameters of productive and non-productive difficulties experienced by students while they learn.


title: Do Adolescents with Gynecomastia Require Routine Evaluation by Endocrinology?

abstract: BACKGROUND ::: Primary evidence for the role of endocrinologic investigations in patients with adolescent gynecomastia is lacking in the current literature. The objective of this study was to assess the yield of endocrinologic investigations in the evaluation of adolescent gynecomastia to inform current practice for this common condition. ::: ::: ::: METHODS ::: A 26-year retrospective review was conducted. Data collection included patients with gynecomastia presenting to endocrinology at a quaternary children's hospital with a catchment area of 1 million. Clinical metrics, endocrinologic results, treatments, and costs were reviewed. ::: ::: ::: RESULTS ::: One hundred ninety-seven patients met inclusion criteria. Ninety-eight (50 percent) were overweight or obese and 29 (15 percent) had a positive family history. The median age at onset was 11.5 years; 25 cases (13 percent) were prepubertal. A total of 15 patients (7.6 percent) were diagnosed with secondary gynecomastia (10 related to exogenous substance use). Endocrine investigations were performed in 173 patients (87 percent), with positive findings in three cases (1.7 percent). One hundred one patients were observed, with a median age at resolution of 14.6 years; 86 patients underwent surgery at a median age of 16.5 years. The case-cost of endocrine evaluation was $389. ::: ::: ::: CONCLUSIONS ::: Endocrinologic workup identified secondary gynecomastia in 7.6 percent of patients, of which only 1.7 percent were evident on blood work. This workup is associated with an avoidable case-cost burden to the health care system and largely unnecessary testing for the child. Because a majority of secondary gynecomastia cases (67 percent) were drug-induced, we do not suggest routine endocrinology workup, as it adds little value. The authors' data suggest that referral for surgery is warranted if gynecomastia persists beyond 16 years of age. ::: ::: ::: CLINICAL QUESTION/LEVEL OF EVIDENCE ::: Diagnostic, IV.


Actionbioscience | Designer Babies: Ethical Considerations


title: A Review of Family and Social Determinants of Children’s Eating Patterns and Diet Quality

abstract: With the growing problem of childhood obesity, recent research has begun to focus on family and social influences on children’s eating patterns. Research has demonstrated that children’s eating patterns are strongly influenced by characteristics of both the physical and social environment. With regard to the physical environment, children are more likely to eat foods that are available and easily accessible, and they tend to eat greater quantities when larger portions are provided. Additionally, characteristics of the social environment, including various socioeconomic and sociocultural factors such as parents’ education, time constraints, and ethnicity influence the types of foods children eat. Mealtime structure is also an important factor related to children’s eating patterns. Mealtime structure includes social and physical characteristics of mealtimes including whether families eat together, TV-viewing during meals, and the source of foods (e.g., restaurants, schools). Parents also play a direct role ...


title: Overshadow: a virtualization-based approach to retrofitting protection in commodity operating systems

abstract: Commodity operating systems entrusted with securing sensitive data are remarkably large and complex, and consequently, frequently prone to compromise. To address this limitation, we introduce a virtual-machine-based system called Overshadow that protects the privacy and integrity of application data, even in the event of a total OScompromise. Overshadow presents an application with a normal view of its resources, but the OS with an encrypted view. This allows the operating system to carry out the complex task of managing an application's resources, without allowing it to read or modify them. Thus, Overshadow offers a last line of defense for application data. Overshadow builds on multi-shadowing, a novel mechanism that presents different views of "physical" memory, depending on the context performing the access. This primitive offers an additional dimension of protection beyond the hierarchical protection domains implemented by traditional operating systems and processor architectures. We present the design and implementation of Overshadow and show how its new protection semantics can be integrated with existing systems. Our design has been fully implemented and used to protect a wide range of unmodified legacy applications running on an unmodified Linux operating system. We evaluate the performance of our implementation, demonstrating that this approach is practical.


title: Broadband DOA estimation using Convolutional neural networks trained with noise signals

abstract: A convolution neural network (CNN) based classification method for broadband DOA estimation is proposed, where the phase component of the short-time Fourier transform coefficients of the received microphone signals are directly fed into the CNN and the features required for DOA estimation are learned during training. Since only the phase component of the input is used, the CNN can be trained with synthesized noise signals, thereby making the preparation of the training data set easier compared to using speech signals. Through experimental evaluation, the ability of the proposed noise trained CNN framework to generalize to speech sources is demonstrated. In addition, the robustness of the system to noise, small perturbations in microphone positions, as well as its ability to adapt to different acoustic conditions is investigated using experiments with simulated and real data.


title: The past, present and future of web information retrieval

abstract: Web search engines have emerged as one of the central applications on the Internet. In fact, search has become one of the most important activities that people engage in on the the Internet. Even beyond becoming the number one source of information, a growing number of businesses are depending on web search engines for customer acquisition.The first generation of web search engines used text-only retrieval techniques. Google revolutionized the field by deploying the PageRank technology - an eigenvector-based analysis of the hyperlink structure - to analyze the web in order to produce relevant results. Moving forward, our goal is to achieve a better understanding of a page with a view towards producing even more relevant results.An exciting new form of search for the future is query-free search: While a user performs her daily tasks, searches are automatically performed to supply her with information that is relevant to her activity. We present one type of query-free search, namely query-free news search: While a user watches TV news the system finds in real-time web pages that are relevant to the news stories.


title: Image steganography on gray and color image using DCT enhancement and RSA with LSB method

abstract: This paper explains a novel scheme for data communication over an unsecure channel. This research presents image steganography on gray and color image using discrete cosine transform (DCT) enhancement and RSA with least significant bit (LSB) technique. Secret data is encrypted and decrypted using RSA. RSA is performed to increase the security. Encryption is performed with a key which makes it difficult to get the covert message even if the presence of the hidden message is exposed. Now the stego image is enhanced using DCT. This implementation is performed on MATLAB and the key power of this scheme is its vast data hiding ability and smallest distortion in enhanced Stego image. It is functional over many pictures and the outcomes show smallest distortion in enhanced stego image. The performance analysis is evaluated on the source of Peak Signal Noise Ratio (PSNR) and entropy.


title: No-fault-found and intermittent failures in electronic products

abstract: This paper reviews the possible causes and effects for no-fault-found observations and intermittent failures in electronic products and summarizes them into cause and effect diagrams. Several types of intermittent hardware failures of electronic assemblies are investigated, and their characteristics and mechanisms are explored. One solder joint intermittent failure case study is presented. The paper then discusses when no-fault-found observations should be considered as failures. Guidelines for assessment of intermittent failures are then provided in the discussion and conclusions.


title: Air pollution and cardiovascular disease.

abstract: Abstract An escalating body of epidemiologic and clinical research provides compelling evidence that exposure to fine particulate matter air pollution contributes to the development of cardiovascular disease and the triggering of acute cardiac events. There are 3 potential mediating pathways that have been implicated, including “systemic spillover,” autonomic imbalance, and circulating particulate matter constituents. Further support that the increased morbidity and mortality attributed to air pollution comes from studies demonstrating the adverse cardiovascular effects of even brief periods of exposure to secondhand smoke. Accordingly, persons with known or suspected cardiovascular disease, the elderly, diabetic patients, pregnant women, and those with pulmonary disease should be counseled to limit leisure-time outdoor activities when air pollution is high. Recognizing the insidious and pervasive nature of air pollution, and the associated odds ratios and population attributable fractions for this widely underappreciated chemical trigger of acute cardiovascular events, may serve to maximize the potential for cardiovascular risk reduction by addressing at least a portion of the 10%-25% incidence of coronary disease that is unexplained by traditional risk factors.


title: An Improved Non-monotonic Transition System for Dependency Parsing

abstract: Transition-based dependency parsers usually use transition systems that monotonically extend partial parse states until they identify a complete parse tree. Honnibal et al. (2013) showed that greedy onebest parsing accuracy can be improved by adding additional non-monotonic transitions that permit the parser to “repair” earlier parsing mistakes by “over-writing” earlier parsing decisions. This increases the size of the set of complete parse trees that each partial parse state can derive, enabling such a parser to escape the “garden paths” that can trap monotonic greedy transition-based dependency parsers. We describe a new set of non-monotonic transitions that permits a partial parse state to derive a larger set of completed parse trees than previous work, which allows our parser to escape from a larger set of garden paths. A parser with our new nonmonotonic transition system has 91.85% directed attachment accuracy, an improvement of 0.6% over a comparable parser using the standard monotonic arc-eager transitions.


title: Cacheability of YouTube videos in cellular networks

abstract: Video traffic now represents a growing proportion of the traffic on cellular networks, causing capacity problems for operators and increased delays for users. Studies have shown that deploying caches at the network level reduces the delay for the end-user and the overall traffic volume for the telecom operator. In this paper, we analyse a large nationwide dataset of real-life video requests sent by mobile users to a popular video streaming website. This analysis is the first to rely on such a large dataset, and sheds light on the optimal cacheability of video content with caches distributed in the cellular network, and how efficient some existing cache replacement algorithms are at reducing the number of requests sent to the video provider. We show that depending on the cache size and algorithm parameters, up to 20.33% of the requests can be served by a local cache.


title: Interpreter Exploitation

abstract: As remote exploits further dwindle and perimeter defenses become the standard, remote client-side attacks are becoming the standard vector for attackers. Modern operating systems have quelled the explosion of client-side vulnerabilities using mitigation techniques such as data execution prevention (DEP) and address space layout randomization (ASLR). This work illustrates two novel techniques to bypass these mitigations. The two techniques leverage the attack surface exposed by the script interpreters commonly accessible within the browser. The first technique, pointer inference, is used to find the memory address of a string of shellcode within the Adobe Flash Player's ActionScript interpreter despite ASLR. The second technique, JIT spraying, is used to write shellcode to executable memory, bypassing DEP protections, by leveraging predictable behaviors of the ActionScript JIT compiler. Previous attacks are examined and future research directions are discussed.


title: Mood, personality, and self-monitoring: Negative affect and emotionality in relation to frontal lobe mechanisms of error monitoring.

abstract: A fundamental question in frontal lobe function is how motivational and emotional parameters of behavior apply to executive processes. Recent advances in mood and personality research and the technology and methodology of brain research provide opportunities to address this question empirically. Using event-related-potentials to track error monitoring in real time, the authors demonstrated that variability in the amplitude of the error-related negativity (ERN) is dependent on mood and personality variables. College students who are high on negative affect (NA) and negative emotionality (NEM) displayed larger ERN amplitudes early in the experiment than participants who are low on these dimensions. As the high-NA and -NEM participants disengaged from the task, the amplitude of the ERN decreased. These results reveal that affective distress and associated behavioral patterns are closely related with frontal lobe executive functions.


The CAVE: audio visual experience automatic virtual environment


title: Packaging of MMICs in multilayer LCP substrates

abstract: A 13-25-GHz GaAs bare die low noise amplifier is embedded inside a multilayer liquid crystal polymer (LCP) package made from seven layers of thin-film LCP. This new packaging topology has inherently unique properties that could make it an attractive alternative in some instances to traditional metal and ceramic hermetic packages. LCP is a near-hermetic material and its lamination process is at a relatively low temperature (285degC versus >800degC for ceramics). The active device is enclosed in a package consisting of several laminated C02 laser machined LCP superstrate layers. Measurements demonstrate that the LCP package and the 285degC packaging process have minimal effects on the monolithic microwave integrated circuit radio frequency (RF) performance. These findings show that both active and passive devices can be integrated together in a homogeneous laminated multilayer LCP package. This active/passive compatibility demonstrates a unique capability of LCP to form compact, vertically integrated (3-D) RF system-on-a-package modules


title: Laughing with HAHAcronym, a Computational Humor System

abstract: Computational humor is a challenge with implications for many classical fields in AI such as, for example, natural language processing, intelligent human-computer interaction, reasoning, not to mention cognitive science, linguistics and psychology. In this paper we summarize our experience in developing HAHAcronym, a system devoted to produce humorous acronyms, and we discuss some concrete prospects for this field.


title: Correlation of Microscopic and Mitochondrial DNA Hair Comparisons

abstract: Expert opinions regarding the microscopic comparison of human hairs have been accepted routinely in courts for decades. However, with the advent of mitochondrial DNA (mtDNA) sequencing, an assessment can be made of the association by microscopic hair comparisons in casework between a questioned hair and reference hairs from an individual. While each method can be used separately, the two analytical methods can be complementary and together can provide additional information regarding source association. Human hairs submitted to the FBI Laboratory for analysis between 1996 and 2000 were reviewed. Of 170 hair examinations, there were 80 microscopic associations; of these, only nine were excluded by mtDNA. Importantly, 66 hairs that were considered either unsuitable for microscopic examinations or yielded inconclusive microscopic associations provided mtDNA results. Only six hairs did not provide sufficient mtDNA, and only three yielded inconclusive results. Consistency was observed in exculpatory results with the two procedures. This study demonstrates the utility of microscopic hair examinations and the strength of combining microscopic analysis with mtDNA sequencing.


title: Analysis of structural mechanics of solid microneedle using COMSOL software

abstract: Microneedles are currently being extensively researched for therapeutic and diagnostic applications, as they are painless, cheaper than conventional needles, and reduce risk of infection. In this paper, we have selected 10 materials for solid microneedle and have performed the structural analysis of each material using COMSOL Multiphysics 4.3. The study was based on the factors affecting microneedle strength such as buckling and bending forces. The result indicated that the microneedle made of Silicon carbide was superior to the other selected materials and while considering the property of biodegradability for the same study, Silk was preferred. PLA and Polycarbonate experienced buckling and thus were not preferred from the selected materials.


title: Failure Analysis of Jobs in Compute Clouds: A Google Cluster Case Study

abstract: In this paper, we analyze a workload trace from the Google cloud cluster and characterize the observed failures. The goal of our work is to improve the understanding of failures in compute clouds. We present the statistical properties of job and task failures, and attempt to correlate them with key scheduling constraints, node operations, and attributes of users in the cloud. We also explore the potential for early failure prediction, and anomaly detection for the jobs. Based on our results, we speculate that there are many opportunities to enhance the reliability of the applications running in the cloud, such as pro-active maintenance of nodes or limiting job resubmissions. We further find that resource usage patterns of the jobs can be leveraged by failure prediction techniques. Finally, we find that the termination statuses of jobs and tasks can be clustered into six dominant categories based on the user profiles.


title: A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

abstract: With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.


title: 3 D Model-Based Face Recognition in Video

abstract: Face recognition in video has gained wide attention due to its role in designing surveillance systems. One of the main advantages of video over still frames is that evidence accumulation over multiple frames can provide better face recognition performance. However, surveillance videos are generally of low resolution containing faces mostly in non-frontal poses. Consequently, face recognition in video poses serious challenges to state-of-the-art face recognition systems. Use of 3D face models has been suggested as a way to compensate for low resolution, poor contrast and non-frontal pose. We propose to overcome the pose problem by automatically (i) reconstructing a 3D face model from multiple non-frontal frames in a video, (ii) generating a frontal view from the derived 3D model, and (iii) using a commercial 2D face recognition engine to recognize the synthesized frontal view. A factorization-based structure from motion algorithm is used for 3D face reconstruction. The proposed scheme has been tested on CMU's Face In Action (FIA) video database with 221 subjects. Experimental results show a 40% improvement in matching performance as a result of using the 3D models.


title: “Digital Show-How”: Extreme Active Learning for Introductory Programming

abstract: For the past ten years we have employed active learning methods in introductory programming classes at UNC Chapel Hill. In this report we present experience with one such method we call “digital show-how”, in which students learn to program apprentice-fashion, by doing programming in class. Digital show-how is extreme active learning. Students do a lot of programming, in real-time, in class as the instructor shows them how. They get instant feedback on their work, and so have constant low-stakes assessments. We describe the goals for the classes, the tools and methods we have developed to support digital show-how, and the results we have measured on how well the goals are being met.


title: Haptics in Robot-Assisted Surgery: Challenges and Benefits

abstract: Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of man–machine collaborative type, surgical robots are seen as media that transfer pre- and intraoperative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper, objectives and challenges of deploying haptic technologies in surgical robotics are discussed, and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It attempts to encompass both classical works and the state-of-the-art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts.


title: Amplitude Modulated Video Camera - Light Separation in Dynamic Scenes

abstract: Controlled light conditions improve considerably the performance of most computer vision algorithms. Dynamic light conditions create varying spatial changes in color and intensity across the scene. These condition, caused by a moving shadow for example, force developers to create algorithms which are robust to such variations. We suggest a computational camera which produces images that are not influenced by environmental variations in light conditions. The key insight is that many years ago, similar difficulties were already solved in radio communication, As a result each channel is immune to interference from other radio channels. Amplitude Modulated (AM) video camera separates the influence of a modulated light from other unknown light sources in the scene, Causing the AM video camera frame to appear the same - independent of the light conditions in which it was taken. We built a prototype of the AM video camera by using off the shelf hardware and tested it. AM video camera was used to demonstrate color constancy, shadow removal and contrast enhancement in real time. We show theoretically and empirically that: 1. the proposed system can produce images with similar noise levels as a standard camera. 2. The images created by such camera are almost completely immune to temporal, spatial and spectral changes in the background light.


title: Posture Reconfiguration and Navigation Maneuvers on a Wheel-Legged Hydraulic Robot

abstract: Wheel-legged hybrid robots are known to be extremely capable in negotiating different types of terrain as they combine the efficiency of conventional wheeled platforms and the rough terrain capabilities of legged platforms. The Micro-Hydraulic Toolkit (MHT), developed by Defense Research and Development Canada at the Suffield Research Centre, is one such quadruped hybrid robot.MHT’s relatively small size, mobility, actuation and locomotion types fill a gap in military unmanned ground vehicles (UGVs). Previously, a velocity-level closed loop inverse kinematics controller had been developed and tested in simulation on a detailed physics-based model of the MHT in LMS Virtual.LabMotion. The controller was employed to generate a variety of posture reconfiguration maneuvers, such as achieving minimum ormaximum chassis height at specific wheel separations. In this paper, the aforementioned inverse kinematics controller was adapted to function on the physical MHT. Several test maneuvers, including chassis height and pitch reconfiguration and uneven terrain navigation maneuvers, were implemented on the MHT and the robot’s performance was evaluated.


title: MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

abstract: People are sharing their opinions, stories and reviews through online video sharing websites every day. Studying sentiment and subjectivity in these opinion videos is experiencing a growing attention from academia and industry. While sentiment analysis has been successful for text, it is an understudied research question for videos and multimedia content. The biggest setbacks for studies in this direction are lack of a proper dataset, methodology, baselines and statistical analysis of how information from different modality sources relate to each other. This paper introduces to the scientific community the first opinion-level annotated corpus of sentiment and subjectivity analysis in online videos called Multimodal Opinion-level Sentiment Intensity dataset (MOSI). The dataset is rigorously annotated with labels for subjectivity, sentiment intensity, per-frame and per-opinion annotated visual features, and per-milliseconds annotated audio features. Furthermore, we present baselines for future studies in this direction as well as a new multimodal fusion approach that jointly models spoken words and visual gestures.


title: Human Action Recognition and Prediction: A Survey

abstract: Derived from rapid advances in computer vision and machine learning, video analysis tasks have been moving from inferring the present state to predicting the future state. Vision-based action recognition and prediction from videos are such tasks, where action recognition is to infer human actions (present state) based upon complete action executions, and action prediction to predict human actions (future state) based upon incomplete action executions. These two tasks have become particularly prevalent topics recently because of their explosively emerging real-world applications, such as visual surveillance, autonomous driving vehicle, entertainment, and video retrieval, etc. Many attempts have been devoted in the last a few decades in order to build a robust and effective framework for action recognition and prediction. In this paper, we survey the complete state-of-the-art techniques in the action recognition and prediction. Existing models, popular algorithms, technical difficulties, popular action databases, evaluation protocols, and promising future directions are also provided with systematic discussions.


Vision for Robotics in Optimal Control and Reinforcement Learning Systems