title: TSum: fast, principled table summarization

abstract: Given a table where rows correspond to records and columns correspond to attributes, we want to find a small number of patterns that succinctly summarize the dataset. For example, given a set of patient records with several attributes each, how can we find (a) that the "most representative" pattern is, say, (male, adult, *), followed by (*, child, low-cholesterol), etc? We propose TSum, a method that provides a sequence of patterns ordered by their "representativeness." It can decide both which these patterns are, as well as how many are necessary to properly summarize the data. Our main contribution is formulating a general framework, TSum, using compression principles. TSum can easily accommodate different optimization strategies for selecting and refining patterns. The discovered patterns can be used to both represent the data efficiently, as well as interpret it quickly. Extensive experiments demonstrate the effectiveness and intuitiveness of our discovered patterns.


title: The International Prevalence Study on Physical Activity: results from 20 countries

abstract: Background ::: Physical activity (PA) is one of the most important factors for improving population health, but no standardised systems exist for international surveillance. The International Physical Activity Questionnaire (IPAQ) was developed for international surveillance. The purpose of this study was a comparative international study of population physical activity prevalence across 20 countries.


A Strategy for Ranking Optimization Methods using Multiple Criteria


title: Adaptable, metadata rich IO methods for portable high performance IO

abstract: Since IO performance on HPC machines strongly depends on machine characteristics and configuration, it is important to carefully tune IO libraries and make good use of appropriate library APIs. For instance, on current petascale machines, independent IO tends to outperform collective IO, in part due to bottlenecks at the metadata server. The problem is exacerbated by scaling issues, since each IO library scales differently on each machine, and typically, operates efficiently to different levels of scaling on different machines. With scientific codes being run on a variety of HPC resources, efficient code execution requires us to address three important issues: (1) end users should be able to select the most efficient IO methods for their codes, with minimal effort in terms of code updates or alterations; (2) such performance-driven choices should not prevent data from being stored in the desired file formats, since those are crucial for later data analysis; and (3) it is important to have efficient ways of identifying and selecting certain data for analysis, to help end users cope with the flood of data produced by high end codes. This paper employs ADIOS, the ADaptable IO System, as an IO API to address (1)–(3) above. Concerning (1), ADIOS makes it possible to independently select the IO methods being used by each grouping of data in an application, so that end users can use those IO methods that exhibit best performance based on both IO patterns and the underlying hardware. In this paper, we also use this facility of ADIOS to experimentally evaluate on petascale machines alternative methods for high performance IO. Specific examples studied include methods that use strong file consistency vs. delayed parallel data consistency, as that provided by MPI-IO or POSIX IO. Concerning (2), to avoid linking IO methods to specific file formats and attain high IO performance, ADIOS introduces an efficient intermediate file format, termed BP, which can be converted, at small cost, to the standard file formats used by analysis tools, such as NetCDF and HDF-5. Concerning (3), associated with BP are efficient methods for data characterization, which compute attributes that can be used to identify data sets without having to inspect or analyze the entire data contents of large files.


title: Plant Growth-Promoting Rhizobacteria Enhance Salinity Stress Tolerance in Okra through ROS-Scavenging Enzymes

abstract: Salinity is a major environmental stress that limits crop production worldwide. In this study, we characterized plant growth-promoting rhizobacteria (PGPR) containing 1-aminocyclopropane-1-carboxylate (ACC) deaminase and examined their effect on salinity stress tolerance in okra through the induction of ROS-scavenging enzyme activity. PGPR inoculated okra plants exhibited higher germination percentage, growth parameters, and chlorophyll content than control plants. Increased antioxidant enzyme activities (SOD, APX, and CAT) and upregulation of ROS pathway genes (CAT, APX, GR, and DHAR) were observed in PGPR inoculated okra plants under salinity stress. With some exceptions, inoculation with Enterobacter sp. UPMR18 had a significant influence on all tested parameters under salt stress, as compared to other treatments. Thus, the ACC deaminase-containing PGPR isolate Enterobacter sp. UPMR18 could be an effective bioresource for enhancing salt tolerance and growth of okra plants under salinity stress.


title: A wideband dual-polarized dipole linear array with resistive loading

abstract: A dual-polarized, tightly-coupled dipole linear array, delivering 5.7∶1 bandwidth, is presented. Most of previous work on tightly-coupled array is focused on two-dimensional array. This communication presents a dual-polarized, tightly-coupled dipole linear array, which has much more potential in application. The linear array incorporates a resistive loop as the substrate load to broaden the bandwidth of both polarization. For vertical polarized dipole of linear array, lacking tightly-coupled environment, there are sever methods implemented to broaden the bandwidth, such as gradient slot and folded arms. Simulated results show that the linear array can achieve 5.7∶1 bandwidth while scan to ±50°, for VSWR<3(sever frequency <3.5), and the radiation efficiency maintain above 50%.


The CIPS-SIGHAN CLP2010 Chinese Word Segmentation Backoff


title: A 6V to 42V High Voltage CMOS Bandgap Reference Robust to RF Interference for Automotive Applications

abstract: Direct power injection (DPI) tests measure the electromagnetic immunity of IC and its critical circuit components against conducted RF disturbances. This paper presents electromagnetic interference (EMI) robust 6V to 42V pre-regulator and bandgap reference circuit designed for an automotive LIN transceiver IC in AMS 0.35µm high-voltage CMOS process. Detailed analysis for the behavior of bandgap reference voltage in presence of RF injected noise is presented. This paper also elaborates the details of DPI simulation setup and parasitic models that are essential to predict failures in the design phase itself. Experimental results confirm the proposed design and optimization strategy based on coupling capacitors.


Transfer Learning : Current Status , Trends and Challenges


title: Learn to Solve Algebra Word Problems Using Quadratic Programming

abstract: This paper presents a new algorithm to automatically solve algebra word problems. Our algorithm solves a word problem via analyzing a hypothesis space containing all possible equation systems generated by assigning the numbers in the word problem into a set of equation system templates extracted from the training data. To obtain a robust decision surface, we train a log-linear model to make the margin between the correct assignments and the false ones as large as possible. This results in a quadratic programming (QP) problem which can be efficiently solved. Experimental results show that our algorithm achieves 79.7% accuracy, about 10% higher than the state-of-the-art baseline (Kushman et al., 2014).


Steganography and Steganalysis


Introduction to Big Data in Education and Its Contribution to the Quality Improvement Processes


BUILDING INFORMATION MODELLING FOR CULTURAL HERITAGE : A REVIEW


title: PaceMaker: When ZooKeeper Arteries Get Clogged in Storm Clusters

abstract: As the use of Apache Storm, a distributed real-time computation platform, becomes more widespread in community, scalability of the platform has become a key challenge. Currently, Apache Storm leverages Apache Zookeeper as a message broker for heartbeats and metrics. Using Apache Zookeeper in this manner may have been a good implementation choice at the inception of Apache Storm since it leverages the use of an existing proven open source technology instead of having to implement a proprietary message broker for Storm. However, using Apache Zookeeper in such a fashion has created one of the biggest bottlenecks in scaling Storm. In this paper, we first explore the key scalability issues that this architecture was creating as cluster and workload sizes grow at Yahoo. Next, we discuss in detail a new heartbeat and metrics service, called PaceMaker to address this scalability problem. Finally, we present experimental results to demonstrate that PaceMaker greatly relieves the workload and resource pressure on the ZooKeeper nodes which enables Storm to scale much further.


title: Analysis of D-Q Small-Signal Impedance of Grid-Tied Inverters

abstract: This paper analyzes the small-signal impedance of three-phase grid-tied inverters with feedback control and phase-locked loop (PLL) in the synchronous reference ( d-q ) frame. The result unveils an interesting and important feature of three-phase grid-tied inverters – namely, that its q–q channel impedance behaves as a negative incremental resistor. Moreover, this paper shows that this behavior is a consequence of grid synchronization, where the bandwidth of the PLL determines the frequency range of the resistor behavior, and the power rating of the inverter determines the magnitude of the resistor. Advanced PLL, current, and power control strategies do not change this feature. An example shows that under weak grid conditions, a change of the PLL bandwidth could lead the inverter system to unstable conditions as a result of this behavior. Harmonic resonance and instability issues can be analyzed using the proposed impedance model. Simulation and experimental measurements verify the analysis.


title: Energy Efficient User Association and Power Allocation in Millimeter-Wave-Based Ultra Dense Networks With Energy Harvesting Base Stations

abstract: Millimeter wave (mmWave) communication technologies have recently emerged as an attractive solution to meet the exponentially increasing demand on mobile data traffic. Moreover, ultra dense networks (UDNs) combined with mmWave technology are expected to increase both energy efficiency and spectral efficiency. In this paper, user association and power allocation in mmWave based UDNs is considered with attention to load balance constraints, energy harvesting by base stations, user quality of service requirements, energy efficiency, and cross-tier interference limits. The joint user association and power optimization problem is modeled as a mixed-integer programming problem, which is then transformed into a convex optimization problem by relaxing the user association indicator and solved by Lagrangian dual decomposition. An iterative gradient user association and power allocation algorithm is proposed and shown to converge rapidly to an optimal point. The complexity of the proposed algorithm is analyzed and the effectiveness of the proposed scheme compared with existing methods is verified by simulations.


title: The “High SMAS” Face Lift Technique

abstract: The “high SMAS” face lift technique suspends the cheek mass securely, leaving the skin and subcutaneous mass attached to the SMAS without placing the skin under greater than normal tension. The author provides the surgical steps.


title: End-to-End Learning of Deformable Mixture of Parts and Deep Convolutional Neural Networks for Human Pose Estimation

abstract: Recently, Deep Convolutional Neural Networks (DCNNs) have been applied to the task of human pose estimation, and have shown its potential of learning better feature representations and capturing contextual relationships. However, it is difficult to incorporate domain prior knowledge such as geometric relationships among body parts into DCNNs. In addition, training DCNN-based body part detectors without consideration of global body joint consistency introduces ambiguities, which increases the complexity of training. In this paper, we propose a novel end-to-end framework for human pose estimation that combines DCNNs with the expressive deformable mixture of parts. We explicitly incorporate domain prior knowledge into the framework, which greatly regularizes the learning process and enables the flexibility of our framework for loopy models or tree-structured models. The effectiveness of jointly learning a DCNN with a deformable mixture of parts model is evaluated through intensive experiments on several widely used benchmarks. The proposed approach significantly improves the performance compared with state-of-the-art approaches, especially on benchmarks with challenging articulations.


title: Cross-lingual Distillation for Text Classification

abstract: Cross-lingual text classification(CLTC) is the task of classifying documents written in different languages into the same taxonomy of categories. This paper presents a novel approach to CLTC that builds on model distillation, which adapts and extends a framework originally proposed for model compression. Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available. An adversarial feature adaptation technique is also applied during the model training to reduce distribution mismatch. We conducted experiments on two benchmark CLTC datasets, treating English as the source language and German, French, Japan and Chinese as the unlabeled target languages. The proposed approach had the advantageous or comparable performance of the other state-of-art methods.


title: Learning from Observation Paradigm: Leg Task Models for Enabling a Biped Humanoid Robot to Imitate Human Dances

abstract: This paper proposes a framework that achieves the Learning from Observation paradigm for learning dance motions. The framework enables a humanoid robot to imitate dance motions captured from human demonstrations. This study especially focuses on leg motions to achieve a novel attempt in which a biped-type robot imitates not only upper body motions but also leg motions including steps. Body differences between the robot and the original dancer make the problem difficult because the differences prevent the robot from straightforwardly following the original motions and they also change dynamic body balance. We propose leg task models, which play a key role in solving the problem. Low-level tasks in leg motion are modelled so that they clearly provide essential information required for keeping dynamic stability and important motion characteristics. The models divide the problem of adapting motions into the problem of recognizing a sequence of the tasks and the problem of executing the task sequence. We have developed a method for recognizing the tasks from captured motion data and a method for generating the motions of the tasks that can be executed by existing robots including HRP-2. HRP-2 successfully performed the generated motions, which imitated a traditional folk dance performed by human dancers.


title: Enhancing Performance And Reliability of Rule Management Platforms

abstract: RulE Management Platforms (REMPs) enable software engineers to represent programming logic as conditional sentences that relate statements of facts. A key benefit of REMPs is that they make software adaptable by burying the complexity of rule invocation in their engines, so that programmers can concentrate on business aspects of highly modular rules. Naturally, rule-driven applications are expected to have excellent performance, since REMP engines should be able to invoke highly modular rules in parallel in response to asserting different facts. In reality, it is very difficult to parallelize rule executions, since it leads to the loss of reliability and adaptability of rule-driven applications. We created a novel solution that is based on obtaining a rule execution model that is used at different layers of REMPs to enhance the performance of rule-driven applications while maintaining their reliability and adaptability. First, using this model, possible races are detected statically among rules, and we evaluate an implementation of our abstraction of algorithms for automatically preventing races among rules. Next, we use the sensitivity analysis to find better schedules among simultaneously executing rules to improve the overall performance of the application. We implemented our solution for JBoss Drools and we evaluated it on three applications. The results suggest that our solution is effective, since we achieved over 225% speedup on average.


title: A survey on activity recognition and behavior understanding in video surveillance

abstract: This paper provides a comprehensive survey for activity recognition in video surveillance. It starts with a description of simple and complex human activity, and various applications. The applications of activity recognition are manifold, ranging from visual surveillance through content based retrieval to human computer interaction. The organization of this paper covers all aspects of the general framework of human activity recognition. Then it summarizes and categorizes recent-published research progresses under a general framework. Finally, this paper also provides an overview of benchmark databases for activity recognition, the market analysis of video surveillance, and future directions to work on for this application.


title: Mixed reality in education, entertainment, and training

abstract: Transferring research from the laboratory to mainstream applications requires the convergence of people, knowledge, and conventions from divergent disciplines. Solutions involve more than combining functional requirements and creative novelty. To transform technical capabilities of emerging mixed reality (MR) technology into the mainstream involves the integration and evolution of unproven systems. For example, real-world applications require complex scenarios (a content issue) involving an efficient iterative pipeline (a production issue) and driving the design of a story engine (a technical issue) that provides an adaptive experience with an after-action review process (a business issue). This article describes how a multi-disciplinary research team transformed core MR technology and methods into diverse urban terrain applications. These applications are used for military training and situational awareness, as well as for community learning to significantly increase the entertainment, educational, and satisfaction levels of existing experiences in public venues.


title: Measuring Word Significance using Distributed Representations of Words

abstract: Distributed representations of words as real-valued vectors in a relatively low-dimensional space aim at extracting syntactic and semantic features from large text corpora. A recently introduced neural network, named word2vec (Mikolov et al., 2013a; Mikolov et al., 2013b), was shown to encode semantic information in the direction of the word vectors. In this brief report, it is proposed to use the length of the vectors, together with the term frequency, as measure of word significance in a corpus. Experimental evidence using a domain-specific corpus of abstracts is presented to support this proposal. A useful visualization technique for text corpora emerges, where words are mapped onto a two-dimensional plane and automatically ranked by significance.


title: Massive Social Network Analysis: Mining Twitter for Social Good

abstract: Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Toolkit for massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes and a real-world graph (Kwak, et al.) with 61.6 million vertices and 1.47 billion edges in 105 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system. Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations and help analysts focus attention on a much smaller data subset.


title: Clinical microdialysis: Current applications and potential use in drug development

abstract: Biochemical and pharmacological events usually take place in the tissue and not in the bloodstream. However, diagnostic and therapeutic decisions in medical practice are still generally based on blood levels of drugs and/or endogenous molecules. Microdialysis is a catheter-based sampling method that enables in vivo measurement of tissue chemistry in humans. The technique is semi-invasive and is feasible in virtually every human organ. It is currently being used to monitor brain ischemia and metabolic control, transdermal drug distribution and tissue pharmacokinetics, and might become an important tool in drug monitoring and drug development in the future.


title: 3D printed soft gripper for automatic lunch box packing

abstract: In this paper, we proposed a 3D printed soft robot gripper with modular design for lunch box packing. The gripper consists of a rigid base and three soft fingers. A snap-lock mechanism was designed for easy attach-detach assembly of the gripper without using screws. All components were 3D printed and the soft finger structure is based on the principle of fluidic elastomer actuator. Three finger designs, and soft gripper grasping and lifting deformable objects were investigated through finite element (FE) analysis and experiments. Results suggested that different finger designs yielded different curvature along the finger and generated different stress distribution once pressurized. The proposed gripper could grasp and lift objects with variable shapes and softnesses.


title: ActivityNet: A large-scale video benchmark for human activity understanding

abstract: In spite of many dataset efforts for human action recognition, current computer vision algorithms are still severely limited in terms of the variability and complexity of the actions that they can recognize. This is in part due to the simplicity of current benchmarks, which mostly focus on simple actions and movements occurring on manually trimmed videos. In this paper we introduce ActivityNet, a new large-scale video benchmark for human activity understanding. Our benchmark aims at covering a wide range of complex human activities that are of interest to people in their daily living. In its current version, ActivityNet provides samples from 203 activity classes with an average of 137 untrimmed videos per class and 1.41 activity instances per video, for a total of 849 video hours. We illustrate three scenarios in which ActivityNet can be used to compare algorithms for human activity understanding: untrimmed video classification, trimmed activity classification and activity detection.


title: TweetSift: Tweet Topic Classification Based on Entity Knowledge Base and Topic Enhanced Word Embedding

abstract: Classifying tweets into topic categories is necessary and important for many applications, since tweets are about a variety of topics and users are only interested in certain topical areas. Many tweet classification approaches fail to achieve high accuracy due to data sparseness issue. Tweet, as a special type of short text, in additional to its text, also has other metadata that can be used to enrich its context, such as user name, mention, hashtag and embedded link. In this demonstration, we present TweetSift, an efficient and effective real time tweet topic classifier. TweetSift exploits external tweet-specific entity knowledge to provide more topical context for a tweet, and integrates them with topic enhanced word embeddings for topic classification. The demonstration will show how TweetSift works and how it is incorporated with our social media event detection system.


title: Learning from Big Malwares

abstract: This paper calls for the attention to investigate real-world malwares in large scales by examining the largest real malware repository, VirusTotal. As a first step, we analyzed two fundamental characteristics of Windows executable malwares from VirusTotal. We designed offline and online tools for this analysis. Our results show that malwares appear in bursts and that distributions of malwares are highly skewed.


title: Evaluation of Ad Hoc Networks in Disaster Scenarios

abstract: Ad hoc networks have been proved to be suitable for disaster scenarios since any infrastructure need to be deployed in order to establish a wireless network. Routing protocols play an important role in the performance of mobile ad hoc networks. Routing protocols are responsible for deciding how the information is going to move through the network. Although one paramount parameter of ad hoc networks is the mobility of nodes, little effort has been made to evaluate the performance of mobile ad hoc networks under mobility models which model the movements of rescue teams during evacuating operations. The objective of this paper is to evaluate real case disaster scenarios in terms of performance using several well-known routing protocols metrics.


title: A Bayesian supervised dual‐dimensionality reduction model for simultaneous decoding of LFP and spike train signals

abstract: Author(s): Holbrook, A; Vandenberg-Rodes, A; Fortin, N; Shahbaba, B | Abstract: Neuroscientists are increasingly collecting multimodal data during experiments and observational studies. Different data modalities-such as EEG, fMRI, LFP, and spike trains-offer different views of the complex systems contributing to neural phenomena. Here, we focus on joint modeling of LFP and spike train data, and present a novel Bayesian method for neural decoding to infer behavioral and experimental conditions. This model performs supervised dual-dimensionality reduction: it learns low-dimensional representations of two different sources of information that not only explain variation in the input data itself, but also predict extra-neuronal outcomes. Despite being one probabilistic unit, the model consists of multiple modules: exponential PCA and wavelet PCA are used for dimensionality reduction in the spike train and LFP modules, respectively; these modules simultaneously interface with a Bayesian binary regression module. We demonstrate how this model may be used for prediction, parametric inference, and identification of influential predictors. In prediction, the hierarchical model outperforms other models trained on LFP alone, spike train alone, and combined LFP and spike train data. We compare two methods for modeling the loading matrix and find them to perform similarly. Finally, model parameters and their posterior distributions yield scientific insights.


title: The social impact of a systematic floor cleaner

abstract: Mint is an automatic cleaning robot that sweeps and mops hard-surface floors using dusting and mopping cloths. Thanks to the Northstar navigation technology it systematically cleans and navigates in people's homes. Since it first became commercially available in mid 2010, hundreds of thousands of Mint cleaners are nowadays in use at home. In this paper we investigate the product's social impact with respect to the attitude of customers towards a systematic floor cleaner and how such a robot influences their lifestyle. We first report feedback from users owning the product, and demonstrate how Mint changed their everyday life at home. We then evaluate the results of a survey launched in 2012 that addresses the technical understanding of the product and what impact it has on the social life of their users. Our findings suggest that Mint, on average, saves more than one hour of people's time per week, floors are cleaner leading to healthier homes and lives, systematic cleaning is seen as an important feature, and modifications to the environment to support the navigation of the robot are largely accepted.


title: Algorithms for spatial outlier detection

abstract: A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. One drawback of existing methods is that normal objects tend to be falsely detected as spatial outliers when their neighborhood contains true spatial outliers. We propose a suite of spatial outlier detection algorithms to overcome this disadvantage. We formulate the spatial outlier detection problem in a general way and design algorithms which can accurately detect spatial outliers. In addition, using a real-world census data set, we demonstrate that our approaches can not only avoid detecting false spatial outliers but also find true spatial outliers ignored by existing methods.


title: Report on Pairing-based Cryptography

abstract: This report summarizes study results on pairing-based cryptography. The main purpose of the study is to form NIST’s position on standardizing and recommending pairing-based cryptography schemes currently published in research literature and standardized in other standard bodies. ::: ::: The report reviews the mathematical background of pairings. This includes topics such as pairing-friendly elliptic curves and how to compute various pairings. It includes a brief introduction to existing identity-based encryption (IBE) schemes and other cryptographic schemes using pairing technology. The report provides a complete study of the current status of standard activities on pairing-based cryptographic schemes. It explores different application scenarios for pairing-based cryptography schemes. As an important aspect of adopting pairing-based schemes, the report also considers the challenges inherent in validation testing of cryptographic algorithms and modules. ::: ::: Based on the study, the report suggests an approach for including pairing-based cryptography schemes in the NIST cryptographic toolkit. The report also outlines several questions that will require further study if this approach is followed.


Rewriting Resistance : Negotiating Pedagogical and Curricular Change in a U . S . / Kurdish Transnational Partnership


title: Rapid Anxiolytic Effects of a 5-HT4 Receptor Agonist Are Mediated by a Neurogenesis-Independent Mechanism

abstract: Selective serotonin reuptake inhibitors (SSRIs) display a delayed onset of action of several weeks. Past work in naive rats showed that 5HT4 receptor agonists had rapid effects on depression-related behaviors and on hippocampal neurogenesis. We decided to investigate whether 5-HT4 receptor stimulation was necessary for the effects of SSRIs in a mouse model of anxiety/depression, and whether hippocampal neurogenesis contributed to these effects. Using the mouse corticosterone model of anxiety/depression, we assessed whether chronic treatment with a 5-HT4 receptor agonist (RS67333, 1.5mg/kg/day) had effects on anxiety- and depression-related behaviors, as well as on hippocampal neurogenesis in comparison with chronic fluoxetine treatment (18mg/kg/day). Then, using our anxiety/depression model combined with ablation of hippocampal neurogenesis, we investigated whether neurogenesis was necessary for the behavioral effects of subchronic (7 days) or chronic (28 days) RS67333 treatment. We also assessed whether a 5-HT4 receptor antagonist (GR125487, 1mg/kg/day) could prevent the behavioral and neurogenic effects of fluoxetine. Chronic treatment with RS67333, similar to fluoxetine, induced anxiolytic/antidepressant-like activity and stimulated adult hippocampal neurogenesis, specifically facilitating maturation of newborn neurons. However, unlike fluoxetine, anxiolytic effects of RS67333 were already present after 7 days and did not require hippocampal neurogenesis. Chronic treatment with GR125487 prevented both anxiolytic/antidepressant-like and neurogenic effects of fluoxetine, indicating that 5-HT4 receptor activation is necessary for these effects of SSRIs. 5-HT4 receptor stimulation could represent an innovative and rapid onset therapeutic approach to treat depression with comorbid anxiety. Neuropsychopharmacology advance online publication, 22 January 2014; doi:10.1038/npp.2013.332


title: Synthesis of Luminescent Graphene Quantum Dots with High Quantum Yield and Their Toxicity Study

abstract: High fluorescence quantum yield graphene quantum dots (GQDs) have showed up as a new generation for bioimaging. In this work, luminescent GQDs were prepared by an ameliorative photo-Fenton reaction and a subsequent hydrothermal process using graphene oxide sheets as the precursor. The as-prepared GQDs were nanomaterials with size ranging from 2.3 to 6.4 nm and emitted intense green luminescence in water. The fluorescence quantum yield was as high as 24.6% (excited at 340 nm) and the fluorescence was strongest at pH 7. Moreover, the influences of low-concentration (12.5, 25 μg/mL) GQDs on the morphology, viability, membrane integrity, internal cellular reactive oxygen species level and mortality of HeLa cells were relatively weak, and the in vitro imaging demonstrated GQDs were mainly in the cytoplasm region. More strikingly, zebrafish embryos were co-cultured with GQDs for in vivo imaging, and the results of heart rate test showed the intake of small amounts of GQDs brought little harm to the cardiovascular of zebrafish. GQDs with high quantum yield and strong photoluminescence show good biocompatibility, thus they show good promising for cell imaging, biolabeling and other biomedical applications.


Predicting Cyber Vulnerability Exploits with Machine Learning.


title: A Minimal Solution for the Extrinsic Calibration of a Camera and a Laser-Rangefinder

abstract: This paper presents a new algorithm for the extrinsic calibration of a perspective camera and an invisible 2D laser-rangefinder (LRF). The calibration is achieved by freely moving a checkerboard pattern in order to obtain plane poses in camera coordinates and depth readings in the LRF reference frame. The problem of estimating the rigid displacement between the two sensors is formulated as one of registering a set of planes and lines in the 3D space. It is proven for the first time that the alignment of three plane-line correspondences has at most eight solutions that can be determined by solving a standard p3p problem and a linear system of equations. This leads to a minimal closed-form solution for the extrinsic calibration that can be used as hypothesis generator in a RANSAC paradigm. Our calibration approach is validated through simulation and real experiments that show the superiority with respect to the current state-of-the-art method requiring a minimum of five input planes.


title: Neural Collaborative Ranking

abstract: Recommender systems are aimed at generating a personalized ranked list of items that an end user might be interested in. With the unprecedented success of deep learning in computer vision and speech recognition, recently it has been a hot topic to bridge the gap between recommender systems and deep neural network. And deep learning methods have been shown to achieve state-of-the-art on many recommendation tasks. For example, a recent model, NeuMF, first projects users and items into some shared low-dimensional latent feature space, and then employs neural nets to model the interaction between the user and item latent features to obtain state-of-the-art performance on the recommendation tasks. NeuMF assumes that the non-interacted items are inherent negative and uses negative sampling to relax this assumption. In this paper, we examine an alternative approach which does not assume that the non-interacted items are necessarily negative, just that they are less preferred than interacted items. Specifically, we develop a new classification strategy based on the widely used pairwise ranking assumption. We combine our classification strategy with the recently proposed neural collaborative filtering framework, and propose a general collaborative ranking framework called Neural Network based Collaborative Ranking (NCR). We resort to a neural network architecture to model a user's pairwise preference between items, with the belief that neural network will effectively capture the latent structure of latent factors. The experimental results on two real-world datasets show the superior performance of our models in comparison with several state-of-the-art approaches.


title: Radar and Vision Sensor Fusion for Object Detection in Autonomous Vehicle Surroundings

abstract: Multi-sensor data fusion for advanced driver assistance systems (ADAS) in the automotive industry has received much attention recently due to the emergence of self-driving vehicles and road traffic safety applications. Accurate surroundings recognition through sensors is critical to achieving efficient advanced driver assistance systems (ADAS). In this paper, we use radar and vision sensors for accurate object recognition. However, since sensor-specific data have different coordinates, the data coordinate calibrate is essential. In this paper, we introduce the coordinate calibration algorithms between radar and vision images and perform sensor calibrating using data obtained from actual sensors.


title: Efficient Peer-to-Peer Keyword Searching

abstract: The recent file storage applications built on top of peer-to-peer distributed hash tables lack search capabilities. We believe that search is an important part of any document publication system. To that end, we have designed and analyzed a distributed search engine based on a distributed hash table. Our simulation results predict that our search engine can answer an average query in under one second, using under one kilobyte of bandwidth.


title: Mining Fuzzy Weighted Association Rules

abstract: The paper combines and extends the technologies of fuzzy sets and association rules, considering users' differential emphasis on each attribute through fuzzy regions. A fuzzy data mining algorithm is proposed to discovery fuzzy association rules for weighted quantitative data. This is expected to be more realistic and practical than crisp association rules. Discovered rules are expressed in natural language that is more understandable to humans. The paper demonstrates the performance of the proposed approach using a synthetic but realistic dataset


title: Introduction to Mathematical Systems Theory: A Behavioral Approach

abstract: This is a book about modelling, analysis, and control of linear time-invariant systems. The book uses what is called the behavioral approach towards mathematical modelling. Thus a system is viewed as a dynamical relation between manifest and latent variables. The emphasis is on dynamical systems that are represented by systems of linear constant coefficient differential equations. In the first part of the book the structure of the set of trajectories that such dynamical systems generate is analyzed. Conditions are obtained for two systems of differential equations to be equivalent in the sense that they define the same behavior. It is further shown that the trajectories of such linear differential systems can be partitioned in free inputs and bound outputs. In addition the memory structure of the system is analyzed through state space models. The second part of the book is devoted to a number of important system properties, notably controllability, observability, and stability. An essential feature of using the behavioral approach is that it allows these and similar concepts to be introduced in a representation free manner. In the third part control problems are considered, more specifically stabilization and pole placement questions. The book is a textbook for advanced undergraduate or beginning graduate students in mathematics and engineering. It contains numerous exercises, including simulation problems, and examples, notably of mechanical systems and electrical circuits.


title: Alternating Regression Forests for Object Detection and Pose Estimation

abstract: We present Alternating Regression Forests (ARFs), a novel regression algorithm that learns a Random Forest by optimizing a global loss function over all trees. This interrelates the information of single trees during the training phase and results in more accurate predictions. ARFs can minimize any differentiable regression loss without sacrificing the appealing properties of Random Forests, like low computational complexity during both, training and testing. Inspired by recent developments for classification [19], we derive a new algorithm capable of dealing with different regression loss functions, discuss its properties and investigate the relations to other methods like Boosted Trees. We evaluate ARFs on standard machine learning benchmarks, where we observe better generalization power compared to both standard Random Forests and Boosted Trees. Moreover, we apply the proposed regressor to two computer vision applications: object detection and head pose estimation from depth images. ARFs outperform the Random Forest baselines in both tasks, illustrating the importance of optimizing a common loss function for all trees.


title: The Interplay of Robot Language Level with Children's Language Learning during Storytelling

abstract: Children's oral language skills in preschool can predict their success in reading, writing, and academics in later schooling. Helping children improve their language skills early on could lead to more children succeeding later. As such, we examined the potential of a sociable robotic learning/teaching companion to support children's early language development. In a microgenetic study, 17 children played a storytelling game with the robot eight times over a two-month period. We evaluated whether a robot that "leveled" its stories to match the child's current abilities would lead to greater learning and language improvements than a robot that was not matched. All children learned new words, created stories, and enjoyed playing. Children who played with a matched robot used more words, and more diverse words, in their stories than unmatched children. Understanding the interplay between the robot's and the children's language will inform future work on robot companions that support children's education through play.


NICHE CONSTRUCTION: THE PROPOSAL FOR RECONSIDERATION OF ORGANIZATION-ENVIRONMENT INTERACTION Paper presented at the VI International Symposium on Evolutionary Economics


title: Decision support system for the glaucoma using Gabor transformation

abstract: Increase in intraocular pressure (IOP) is one of the causes of glaucoma which can lead to blindness if not detected and treated at an early stage. Glaucoma symptoms are not always obvious; hence patients seek treatment only when the condition progressed significantly. Early detection and treatment will decrease the chances of vision loss due to glaucoma. This paper proposes a novel automated glaucoma diagnosis method using various features extracted from Gabor transform applied on digital fundus images. In this work, we have used 510 images to classify into normal and glaucoma classes. Various features namely mean, variance, skewness, kurtosis, energy, and Shannon, Renyi, and Kapoor entropies are extracted from the Gabor transform coefficients. These extracted features are subjected to principal component analysis (PCA) to reduce the dimensionality of the features. Then these features are ranked using various ranking methods namely: Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC), and entropy. In this work, t-test ranking method yielded the highest performance with an average accuracy of 93.10%, sensitivity of 89.75% and specificity of 96.20% using 23 features with Support Vector Machine (SVM) classifier. Also, we have proposed a Glaucoma Risk Index (GRI) developed using principal components to classify the two classes using just one number.


title: Locking the Elbow: Improved Antibody Fab Fragments as Chaperones for Structure Determination.

abstract: Antibody Fab fragments have been exploited with significant success to facilitate the structure determination of challenging macromolecules as crystallization chaperones and as molecular fiducial marks for single particle cryo-electron microscopy approaches. However, the inherent flexibility of the "elbow" regions, which link the constant and variable domains of the Fab, can introduce disorder and thus diminish their effectiveness. We have developed a phage display engineering strategy to generate synthetic Fab variants that significantly reduces elbow flexibility, while maintaining their high affinity and stability. This strategy was validated using previously recalcitrant Fab-antigen complexes where introduction of an engineered elbow region enhanced crystallization and diffraction resolution. Furthermore, incorporation of the mutations appears to be generally portable to other synthetic antibodies and may serve as a universal strategy to enhance the success rates of Fabs as structure determination chaperones.


title: DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation

abstract: In this work, we consider the task of generating highly-realistic images of a given face with a redirected gaze. We treat this problem as a specific instance of conditional image generation and suggest a new deep architecture that can handle this task very well as revealed by numerical comparison with prior art and a user study. Our deep architecture performs coarse-to-fine warping with an additional intensity correction of individual pixels. All these operations are performed in a feed-forward manner, and the parameters associated with different operations are learned jointly in the end-to-end fashion. After learning, the resulting neural network can synthesize images with manipulated gaze, while the redirection angle can be selected arbitrarily from a certain range and provided as an input to the network.


title: Emotional facial expression classification for multimodal user interfaces

abstract: We present a simple and computationally feasible method to perform automatic emotional classification of facial expressions. We propose the use of 10 characteristic points (that are part of the MPEG4 feature points) to extract relevant emotional information (basically five distances, presence of wrinkles and mouth shape). The method defines and detects the six basic emotions (plus the neutral one) in terms of this information and has been fine-tuned with a data-base of 399 images. For the moment, the method is applied to static images. Application to sequences is being now developed. The extraction of such information about the user is of great interest for the development of new multimodal user interfaces.


title: Enabling Technologies for the Internet of Health Things

abstract: The Internet of Things (IoT) is one of the most promising technologies for the near future. Healthcare and well-being will receive great benefits with the evolution of this technology. This paper presents a review of techniques based on IoT for healthcare and ambient-assisted living, defined as the Internet of Health Things (IoHT), based on the most recent publications and products available in the market from industry for this segment. Also, this paper identifies the technological advances made so far, analyzing the challenges to be overcome and provides an approach of future trends. Through selected works, it is possible to notice that further studies are important to improve current techniques and that novel concept and technologies of IoHT are needed to overcome the identified challenges. The presented results aim to serve as a source of information for healthcare providers, researchers, technology specialists, and the general population to improve the IoHT.


title: Growth-promoting effects of iron- and cobalt- protoporphyrins on cultured embryonic cells.

abstract: The effects of hemin (Fe-protoporphyrin) and Co-protoporphyrin on cellular growth have been investigated principally in cultured fibroblasts, but also in myoblasts and hepatocytes from chick embryos. In the presence of horse serum in the culture medium, which by itself did not stimulate cell growth appreciably, Co-protoporphyrin stimulated cell attachment while hemin stimulated cell proliferation of fibroblasts. When Co-protoporphyrin and hemin were added together, the most potent stimulation of cell growth, consisting of increases in cell attachment and rapid cell proliferation, was observed. These findings indicate that the two metalloporphyrins have differential and complementary effects on cellular growth in culture, with synthetic Co-protoporphyrin principally affecting cellular attachment and Fe-protoporphyrin stimulating cellular proliferation.


title: Towards Time-Sensitive Truth Discovery in Social Sensing Applications

abstract: This paper develops a new principled framework for exploiting time-sensitive information to improve the truth discovery accuracy in social sensing applications. This work is motivated by the emergence of social sensing as a new paradigm of collecting observations about the physical environment from humans or devices on their behalf. These observations maybe true or false, and hence are viewed as binary claims. A fundamental problem in social sensing applications lies in ascertaining the correctness of claims and the reliability of data sources. We refer to this problem as truth discovery. Time is a critical dimension that needs to be carefully exploited in the truth discovery solutions. In this paper, we develop a new time-sensitive truth discovery scheme that explicitly incorporates the source responsiveness and the claim lifespan into a rigorous analytical framework. The new truth discovery scheme solves a maximum likelihood estimation problem to determine both the claim correctness and the source reliability. We compare our time-sensitive scheme with the state-of-the-art baselines through an extensive simulation study and a real world case study. The evaluation results showed that our new scheme outperforms all compared baselines and significantly improves the truth discovery accuracy in social sensing applications.


Latent Dirichlet Allocation on Spark via Tensor Decomposition


title: X-band compact septum polarizer design

abstract: Circular polarization is commonly required in low noise receiver systems for radio astronomy, satellite communications and etc. Septum polarizer which is the essential passive component of receiver constitutes a simple and compact device for converting linear polarization into circular polarization and vice versa. In this paper, design and analysis of a compact septum polarizer with double-plane steps in rectangular waveguides as well as experimental results are presented. The intention of designing such a device arises from the building of cryogenic receiver at X-band (8–9 GHz) servicing for Shanghai 65m radio telescope which is the key project of Shanghai city. The design procedure and approach of septum polarizer have been validated with experimental results.


title: Design and experiments of 77 GHz antennas in LTCC technology

abstract: The design of a slot coupled microstrip patch antenna with four parasitic elements for the 77 GHz frequency range is presented. Full wave electromagnetic simulations were used for antenna modeling and optimization. The antenna was fabricated using Ferro A6-M Low Temperature Co-fired Ceramic technology. The “on wafer” measurements showed a central operating frequency of 78 GHz with reflection losses of −19 dB. The frequency bandwidth (defined for reflection losses lower than −10 dB) was from 76.4 GHz to 79.3 GHz. Using electromagnetic simulations based parametric study it is demonstrated that the central frequency shift is mainly due to the fabrication tolerances that affect the active patch dimensions.


abstract: Background ::: Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". ::: ::: ::: Results ::: We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". ::: ::: ::: Conclusions ::: The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.


title: Wideband Planar Crossover Using Two-Port and Four-Port Microstrip to Slotline Transitions

abstract: The design of a wideband crossover that includes a pair of two-port and another pair of four-port microstrip-slotline transitions is presented. The utilized transitions are designed such that the resultant planar crossover has high isolation and return loss, and low insertion loss and deviation in the group delay across a wideband. The simulated and measured results of a developed 9 mm × 13 mm crossover on a substrate of 10.2 dielectric constant show less than 0.5 dB insertion loss, more than 15 dB return loss, more than 15 dB isolation and less than 0.1 ns deviation in the group delay across the band from 4.8 to 7.2 GHz (40% fractional bandwidth).


title: Semantic Web in data mining and knowledge discovery

abstract: Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. This survey article gives a comprehensive overview of those approaches in different stages of the knowledge discovery process. As an example, we show how Linked Open Data can be used at various stages for building content-based recommender systems. The survey shows that, while there are numerous interesting research works performed, the full potential of the Semantic Web and Linked Open Data for data mining and KDD is still to be unlocked.


title: When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation

abstract: Deep learning methods have led to substantial progress in various application fields of AI, and in recent years a number of proposals were made to improve recommender systems with artificial neural networks. For the problem of making session-based recommendations, i.e., for recommending the next item in an anonymous session, Hidasi et al.~recently investigated the application of recurrent neural networks with Gated Recurrent Units (GRU4REC). Assessing the true effectiveness of such novel approaches based only on what is reported in the literature is however difficult when no standard evaluation protocols are applied and when the strength of the baselines used in the performance comparison is not clear. In this work we show based on a comprehensive empirical evaluation that a heuristics-based nearest neighbor (kNN) scheme for sessions outperforms GRU4REC in the large majority of the tested configurations and datasets. Neighborhood sampling and efficient in-memory data structures ensure the scalability of the kNN method. The best results in the end were often achieved when we combine the kNN approach with GRU4REC, which shows that RNNs can leverage sequential signals in the data that cannot be detected by the co-occurrence-based kNN method.


title: Segmenting Sky Pixels in Images

abstract: Outdoor scene parsing models are often trained on ideal datasets and produce quality results. However, this leads to a discrepancy when applied to the real world. The quality of scene parsing, particularly sky classification, decreases in night time images, images involving varying weather conditions, and scene changes due to seasonal weather. This project focuses on approaching these challenges by using a state-of-the-art model in conjunction with a non-ideal dataset: SkyFinder and a subset from SUN database with Sky object. We focus specifically on sky segmentation, the task of determining sky and not-sky pixels, and improving upon an existing state-of-the-art model: RefineNet. As a result of our efforts, we have seen an improvement of 10-15% in the average MCR compared to the prior methods on SkyFinder dataset. We have also improved from an off-the shelf-model in terms of average mIOU by nearly 35%. Further, we analyze our trained models on images w.r.t two aspects: times of day and weather, and find that, in spite of facing same challenges as prior methods, our trained models significantly outperform them.


title: Developing consumers' brand loyalty in companies' microblogs: The roles of social-and self-factors

abstract: This paper aims to explore how socialand self-factors may affect consumers’ brand loyalty while they follow companies’ microblogs. Drawing upon the commitment-trust theory, social influence theory, and self-congruence theory, we propose that network externalities, social norms, and self-congruence are the key determinants in the research model. The impacts of these factors on brand loyalty will be mediated by brand trust and brand commitment. We empirically test the model through an online survey on an existing microblogging site. The findings illustrate that network externalities and self-congruence can positively affect brand trust, which subsequently leads to brand commitment and brand loyalty. Meanwhile, social norms, together with self-congruence, directly posit influence on brand commitment. Brand commitment is then positively associated with brand loyalty. We believe that the findings of this research can contribute to the literature. We offer new insights regarding how consumers’ brand loyalty develops from the two social-factors and their self-congruence with the brand. Company managers could also apply our findings to strengthen their relationship marketing with consumers on microblogging sites.


title: Semantic Web Mining State of the art and future directions

abstract: Abstract Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.


title: Stacked fully convolutional networks with multi-channel learning: application to medical image segmentation

abstract: The automated segmentation of regions of interest (ROIs) in medical imaging is the fundamental requirement for the derivation of high-level semantics for image analysis in clinical decision support systems. Traditional segmentation approaches such as region-based depend heavily upon hand-crafted features and a priori knowledge of the user. As such, these methods are difficult to adopt within a clinical environment. Recently, methods based on fully convolutional networks (FCN) have achieved great success in the segmentation of general images. FCNs leverage a large labeled dataset to hierarchically learn the features that best correspond to the shallow appearance as well as the deep semantics of the images. However, when applied to medical images, FCNs usually produce coarse ROI detection and poor boundary definitions primarily due to the limited number of labeled training data and limited constraints of label agreement among neighboring similar pixels. In this paper, we propose a new stacked FCN architecture with multi-channel learning (SFCN-ML). We embed the FCN in a stacked architecture to learn the foreground ROI features and background non-ROI features separately and then integrate these different channels to produce the final segmentation result. In contrast to traditional FCN methods, our SFCN-ML architecture enables the visual attributes and semantics derived from both the fore- and background channels to be iteratively learned and inferred. We conducted extensive experiments on three public datasets with a variety of visual challenges. Our results show that our SFCN-ML is more effective and robust than a routine FCN and its variants, and other state-of-the-art methods.


title: Emumaker86: a hardware simulator for teaching CPU design

abstract: This paper describes a new GUI-based tool to teach undergraduate students to design their own CPUs. The tool was purpose-built to teach a wide range of computer organization topics, including digital logic, instruction set encoding, datapath and control units, and pipelining. A datapath builder allows microarchitecture building blocks, such as registers, ALUs, and multiplexors, to be laid out, wired together, and simulated. A control builder allows students to develop control states and microinstructions for the datapath. Both units are integrated with a full PC emulator, allowing student-designed processors to use emulated devices, such as drives, video, and I/O ports. A tutorial teaches students to use the simulator to build a pipelined RISC processor.


title: Speaking their Mind: Populist Style and Antagonistic Messaging in the Tweets of Donald Trump, Narendra Modi, Nigel Farage, and Geert Wilders

abstract: The authors in this study examined the function and public reception of critical tweeting in online campaigns of four nationalist populist politicians during major national election campaigns. Using a mix of qualitative coding and case study inductive methods, we analyzed the tweets of Narendra Modi, Nigel Farage, Donald Trump, and Geert Wilders before the 2014 Indian general elections, the 2016 UK Brexit referendum, the 2016 US presidential election, and the 2017 Dutch general election, respectively. Our data show that Trump is a consistent outlier in terms of using critical language on Twitter when compared to Wilders, Farage, and Modi, but that all four leaders show significant investment in various forms of antagonistic messaging including personal insults, sarcasm, and labeling, and that these are rewarded online by higher retweet rates. Building on the work of Murray Edelman and his notion of a political spectacle, we examined Twitter as a performative space for critical rhetoric within the frame of nationalist politics. We found that cultural and political differences among the four settings also impact how each politician employs these tactics. Our work proposes that studies of social media spaces need to bring normative questions into traditional notions of collaboration. As we show here, political actors may benefit from in-group coalescence around antagonistic messaging, which while serving as a call to arms for online collaboration for those ideologically aligned, may on a societal level lead to greater polarization.


title: Simulating the power consumption of large-scale sensor network applications

abstract: Developing sensor network applications demands a new set of tools to aid programmers. A number of simulation environments have been developed that provide varying degrees of scalability, realism, and detail for understanding the behavior of sensor networks. To date, however, none of these tools have addressed one of the most important aspects of sensor application design: that of power consumption. While simple approximations of overall power usage can be derived from estimates of node duty cycle and communication rates, these techniques often fail to capture the detailed, low-level energy requirements of the CPU, radio, sensors, and other peripherals. In this paper, we present, a scalable simulation environment for wireless sensor networks that provides an accurate, per-node estimate of power consumption. PowerTOSSIM is an extension to TOSSIM, an event-driven simulation environment for TinyOS applications. In PowerTOSSIM, TinyOS components corresponding to specific hardware peripherals (such as the radio, EEPROM, LEDs, and so forth) are instrumented to obtain a trace of each device's activity during the simulation runPowerTOSSIM employs a novel code-transformation technique to estimate the number of CPU cycles executed by each node, eliminating the need for expensive instruction-level simulation of sensor nodes. PowerTOSSIM includes a detailed model of hardware energy consumption based on the Mica2 sensor node platform. Through instrumentation of actual sensor nodes, we demonstrate that PowerTOSSIM provides accurate estimation of power consumption for a range of applications and scales to support very large simulations.


title: Social-network analysis using topic models

abstract: In this paper, we discuss how we can extend probabilistic topic models to analyze the relationship graph of popular social-network data, so that we can group or label the edges and nodes in the graph based on their topic similarity. In particular, we first apply the well-known Latent Dirichlet Allocation (LDA) model and its existing variants to the graph-labeling task and argue that the existing models do not handle popular nodes (nodes with many incoming edges) in the graph very well. We then propose possible extensions to this model to deal with popular nodes. Our experiments show that the proposed extensions are very effective in labeling popular nodes, showing significant improvements over the existing methods. Our proposed methods can be used for providing, for instance, more relevant friend recommendations within a social network.


title: Development of an HT PEM Fuel Cell Emulator Using a Multiphase Interleaved DC–DC Converter Topology

abstract: This paper presents a new emulator topology for a high-temperature (HT) proton-exchange membrane (PEM) fuel cell (FC). Emulators are used to predict FC behavior and facilitate development of the power-conditioning subsystems. In this paper, the high-temperature system is modeled and emulated both in the steady state and transient domains. The model is tailored to operate effectively in real time on the emulator hardware and to deliver acceptable performance during steady-state and dynamic conditions. In particular, a two-stage approach is applied to the design of the emulator hardware. The first stage is based on a multiphase interleaved converter, capable of maximizing ripple cancellation, while ensuring rapid dynamic performance through the use of reduced filter components. These benefits are only apparent by operating the converter at its critical duty ratio. This is achieved through the introduction of a power-stage converter, which tracks the steady-state behavior of the FC, allowing the multiphase converter to account for the rapid transient behavior. This operating principle improves the quality of the output dc voltage and dynamic performance beyond that achieved by conventional emulator topologies. The experimental results of the FC stack, HT PEM FC model and emulator are presented to confirm the performance of the proposed system.


title: Nutritional quality of organic foods: a systematic review

abstract: Background: Despite growing consumer demand for organically produced foods, information based on a systematic review of their nutritional quality is lacking. Objective: We sought to quantitatively assess the differences in reported nutrient content between organically and conventionally produced foodstuffs. Design: We systematically searched PubMed, Web of Science, and CAB Abstracts for a period of 50 y from 1 January 1958 to 29 February 2008, contacted subject experts, and hand-searched bibliographies. We included peer-reviewed articles with English abstracts in the analysis if they reported nutrient content comparisons between organic and conventional foodstuffs. Two reviewers extracted study characteristics, quality, and data. The analyses were restricted to the most commonly reported nutrients. Results: From a total of 52,471 articles, we identified 162 studies (137 crops and 25 livestock products); 55 were of satisfactory quality. In an analysis that included only satisfactory quality studies, conventionally produced crops had a significantly higher content of nitrogen, and organically produced crops had a significantly higher content of phosphorus and higher titratable acidity. No evidence of a difference was detected for the remaining 8 of 11 crop nutrient categories analyzed. Analysis of the more limited database on livestock products found no evidence of a difference in nutrient content between organically and conventionally produced livestock products. Conclusions: On the basis of a systematic review of studies of satisfactory quality, there is no evidence of a difference in nutrient quality between organically and conventionally produced foodstuffs. The small differences in nutrient content detected are biologically plausible and mostly relate to differences in production methods. Am J Clin Nutr doi: 10.3945/ajcn.2009.28041.


title: Evolutionary Undersampling for Classification with Imbalanced Datasets: Proposals and Taxonomy

abstract: Learning with imbalanced data is one of the recent challenges in machine learning. Various solutions have been proposed in order to find a treatment for this problem, such as modifying methods or the application of a preprocessing stage. Within the preprocessing focused on balancing data, two tendencies exist: reduce the set of examples (undersampling) or replicate minority class examples (oversampling). Undersampling with imbalanced datasets could be considered as a prototype selection procedure with the purpose of balancing datasets to achieve a high classification rate, avoiding the bias toward majority class examples. Evolutionary algorithms have been used for classical prototype selection showing good results, where the fitness function is associated to the classification and reduction rates. In this paper, we propose a set of methods called evolutionary undersampling that take into consideration the nature of the problem and use different fitness functions for getting a good trade-off between balance of distribution of classes and performance. The study includes a taxonomy of the approaches and an overall comparison among our models and state of the art undersampling methods. The results have been contrasted by using nonparametric statistical procedures and show that evolutionary undersampling outperforms the nonevolutionary models when the degree of imbalance is increased.


title: Molecular basis of the facilitation of the heterooligomeric GIRK1/GIRK4 complex by cAMP dependent protein kinase

abstract: G-protein activated inwardly rectifying K(+) channels (GIRKs) of the heterotetrameric GIRK1/GIRK4 composition mediate I(K+ACh) in atrium and are regulated by cAMP dependent protein kinase (PKA). Phosphorylation of GIRK1/GIRK4 complexes promotes the activation of the channel by the G-protein Gβγ-dimer ("heterologous facilitation"). Previously we reported that 3 serines/threonines (S/Ts) within the GIRK1 subunit are phosphorylated by the catalytic subunit of PKA (PKA-cs) in-vitro and are responsible for the acute functional effects exerted by PKA on the homooligomeric GIRK1(F137S) (GIRK1(⁎)) channel. Here we report that homooligomeric GIRK4(WT) and GIRK4(S143T) (GIRK4(⁎)) channels are clearly regulated by PKA phosphorylation. Heterooligomeric channels of the GIRK1(S385CS401CT407C)/GIRK4(WT) composition, where the GIRK1 subunit is devoid of PKA mediated phosphorylation, exhibited reduced but still significant acute effects (reduction during agonist application was ≈49% compared to GIRK1(WT)/GIRK4(WT)). Site directed mutagenesis of truncated cytosolic regions of GIRK4 revealed four serines/threonines (S/Ts) that were heavily phosphorylated by PKA-cs in vitro. Two of them were found to be responsible for the acute effects exerted by PKA in vivo, since the effect of cAMP injection was reduced by ≈99% in homooligomeric GIRK4(⁎T199CS412C) channels. Coexpression of GIRK1(WT)/GIRK4(T199CS412C) reduced the acute effect by ≈65%. Only channels of the GIRK1(S385CS401CT407C)/GIRK4(T199CS412C) composition were practically devoid of PKA mediated effects (reduction by ≈97%), indicating that both subunits contribute to the heterologous facilitation of I(K+ACh).


title: Electronic structure calculations with GPAW: A real-space implementation of the projector augmented-wave method

abstract: Electronic structure calculations have become an indispensable tool in many areas of materials science and quantum chemistry. Even though the Kohn–Sham formulation of the density-functional theory (DFT) simplifies the many-body problem significantly, one is still confronted with several numerical challenges. In this article we present the projector augmented-wave (PAW) method as implemented in the GPAW program package (https://wiki.fysik.dtu.dk/gpaw) using a uniform real-space grid representation of the electronic wavefunctions. Compared to more traditional plane wave or localized basis set approaches, real-space grids offer several advantages, most notably good computational scalability and systematic convergence properties. However, as a unique feature GPAW also facilitates a localized atomic-orbital basis set in addition to the grid. The efficient atomic basis set is complementary to the more accurate grid, and the possibility to seamlessly switch between the two representations provides great flexibility. While DFT allows one to study ground state properties, time-dependent density-functional theory (TDDFT) provides access to the excited states. We have implemented the two common formulations of TDDFT, namely the linear-response and the time propagation schemes. Electron transport calculations under finite-bias conditions can be performed with GPAW using non-equilibrium Green functions and the localized basis set. In addition to the basic features of the real-space PAW method, we also describe the implementation of selected exchange–correlation functionals, parallelization schemes, ΔSCF-method, x-ray absorption spectra, and maximally localized Wannier orbitals.


title: Approximate string-matching with q -grams and maximal matches

abstract: We study approximate string matching in connection with two string distance functions that are computable in linear time. The first function is based on the so-called $q$-grams. An algorithm is given for the associated string matching problem that finds the locally best approximate occurences of pattern $P$, $|P|=m$, in text $T$, $|T|=n$, in time $O(n\log (m-q))$. The occurences with distance $\leq k$ can be found in time $O(n\log k)$. The other distance function is based on finding maximal common substrings and allows a form of approximate string matching in time $O(n)$. Both distances give a lower bound for the edit distance (in the unit cost model), which leads to fast hybrid algorithms for the edit distance based string matching.


title: Composite Adaptive Fuzzy Output Feedback Control Design for Uncertain Nonlinear Strict-Feedback Systems With Input Saturation

abstract: In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial-parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach.


title: Integrating Predictive Analytics into Complex Event Processing by Using Conditional Density Estimations

abstract: The monitoring of real-time objects such as steel billets during their casting process creates myriads of events. Complex Event Processing (CEP) is the technology to analyze resulting event streams as fast as possible. But classic CEP is not able to consider events that did not happen yet. It is not clear how to transform CEP from a technology, which reacts on past events, to one, which anticipates near future events. Conditional density estimation allows to combine both estimation and expected uncertainty about the next occurrence of a given event in one mathematical object. Moreover, it allows to calculate the probability of event patterns, which are the basis for CEP. Hence, we are introducing the concept of Conditional Event Occurrence Density Estimation (CEODE) to CEP. We present a reference architecture for combining CEP engines with predictive analytics using CEODEs. On basis of concrete guidelines for transforming classical event processing rules to proactive ones, we are demonstrating how CEP evolves from being reactive to becoming both predictive and prescriptive.


Audio augmented reality: a prototype automated tour guide


title: Displacement Measuring Lab. Test of Reinforced-Soil Retaining Wall Block using 3D Digital Photogrammetry Image

abstract: The collapsed cases are more and more increasing at the large scaled structures because of increasing of the risk due to natural disasters. The measuring instrument such as inclinometer, total station on reinforced-soil retaining wall has been used that displacement, settlement for stability assessment, maintenance and management of it. But because these has gotten many instability measuring factors for stability analysis of RRW, new system needs to complement disadvantage of existing system. In this study, we considered a application of Visual Monitoring System (VMS) to measure a displacement in face of wall through Lab. test about block assembly of segmental retaining wall during load test.


Blockchain for Education: Lifelong Learning Passport


title: Microbial intervention for better fish health in aquaculture: the Indian scenario

abstract: Aquaculture has evolved as the fastest growing food-producing sector and developed as an important component in food security. To keep a sustainable growth pattern, health management strategies must go beyond antibiotics and chemotherapeutics, which create resistant bacteria and immunosuppression in the host. Besides development of drug resistant bacteria and pathogens, the adverse effect of antibiotics is caused by their influence on the aquatic microflora, and the retention of harmful residues in aquatic animals. On the other hand, the microbes with their unique structure and cell wall components can trigger immunity, and thus exposure plays an important role in the evolution. Microbial intervention through an environmentally friendly approach is an alternative method of health management. India is endowed with a bounty of varied climatic conditions, microbial diversity and fish fauna and aquaculture systems offering challenges in biological and environmental pursuits. Producing about 4.4% of world’s fish and ranking third in global fish production, India trades about 2.4% in global fish market with the annual export earning being over $1311 million. Use of microbes for beneficial purposes is increasingly recognized as a valuable input for sustainable and responsible aquaculture. Microbial intervention in aquaculture can be broadly water/environment based through bioaugmentation, biostimulation, biocontrol measures, or (to generate) host response through probiotics, immunostimulants, and vaccines. Also, application of molecular methods such as polymerase chain reaction (PCR) and nucleic acid techniques are making increasing inroads into aquatic microbiological research in India. This paper elucidates all these aspects of microbial intervention in aquaculture, high-lighting Indian research and accomplishments.


title: Novelty detection and segmentation based on Gaussian mixture models: A case study in 3D robotic laser mapping

abstract: This article proposes a framework to detect and segment changes in robotics datasets, using 3D robotic mapping as a case study. The problem is very relevant in several application domains, not necessarily related with mobile robotics, including security, health, industry and military applications. The aim is to identify significant changes by comparing current data with previous data provided by sensors. This feature is extremely challenging because large amounts of noisy data must be processed in a feasible way. The proposed framework deals with novelty detection and segmentation in robotic maps using clusters provided by Gaussian Mixture Models (GMMs). GMMs provides a feature space that enables data compression and effective processing. Two alternative criteria to detect changes in the GMM space are compared: a greedy technique based on the Earth Mover's Distance (EMD); and a structural matching algorithm that fulfills both absolute (global matching) and relative constraints (structural matching). The proposed framework is evaluated with real robotic datasets and compared with other methods known from literature. With this purpose, 3D mapping experiments are carried out with both simulated data and real data from a mobile robot equipped with a 3D range sensor.


title: Multi-task Recurrent Neural Network for Immediacy Prediction

abstract: In this paper, we propose to predict immediacy for interacting persons from still images. A complete immediacy set includes interactions, relative distance, body leaning direction and standing orientation. These measures are found to be related to the attitude, social relationship, social interaction, action, nationality, and religion of the communicators. A large-scale dataset with 10,000 images is constructed, in which all the immediacy measures and the human poses are annotated. We propose a rich set of immediacy representations that help to predict immediacy from imperfect 1-person and 2-person pose estimation results. A multi-task deep recurrent neural network is constructed to take the proposed rich immediacy representation as input and learn the complex relationship among immediacy predictions multiple steps of refinement. The effectiveness of the proposed approach is proved through extensive experiments on the large scale dataset.


title: NOVA: a log-structured file system for hybrid volatile/non-volatile main memories

abstract: Fast non-volatile memories (NVMs) will soon appear on the processor memory bus alongside DRAM. The resulting hybrid memory systems will provide software with sub-microsecond, high-bandwidth access to persistent data, but managing, accessing, and maintaining consistency for data stored in NVM raises a host of challenges. Existing file systems built for spinning or solid-state disks introduce software overheads that would obscure the performance that NVMs should provide, but proposed file systems for NVMs either incur similar overheads or fail to provide the strong consistency guarantees that applications require. ::: ::: We present NOVA, a file system designed to maximize performance on hybrid memory systems while providing strong consistency guarantees. NOVA adapts conventional log-structured file system techniques to exploit the fast random access that NVMs provide. In particular, it maintains separate logs for each inode to improve concurrency, and stores file data outside the log to minimize log size and reduce garbage collection costs. NOVA's logs provide metadata, data, and mmap atomicity and focus on simplicity and reliability, keeping complex metadata structures in DRAM to accelerate lookup operations. Experimental results show that in write-intensive workloads, NOVA provides 22% to 216× throughput improvement compared to state-of-the-art file systems, and 3.1× to 13.5× improvement compared to file systems that provide equally strong data consistency guarantees.


title: The Perspective-3-Point Problem When Using a Planar Mirror

abstract: The Perspective-3-Point problem (P3P) is a classical and fundamental problem in computer vision. All possible solution sets for the P3P problem are from 1 to 4 solutions. In this paper, we propose a very simple way to reduce the ambiguity of numbers of possible solutions in P3P using a planar mirror. For three reference points, if they and their reflections in a planar mirror are both observed, we may obtain two P3P problems: One is from the three original reference points, and the other is from their reflections. A trivial procedure may be suggested: Solve for each of the two P3P problems, and then find the intersections of the two solution sets. Different from the trivial case, we propose an efficient method which employs the ratio relations of the unknowns in the two P3P problems. The ratio relations are arise from mirror reflection, and can be easily determined before solving the two P3P problems. With the ratio relations, a system of 6 equations with 3 unknowns can be determined. To solve the over-constraint problem, we utilize an efficient algorithm by finding all local minima of least-squares residual. Experiments validate our approach.


title: A survey on web news retrieval and mining

abstract: This article is a review on news retrieval and mining research areas in recent years based on a qualitative approach. It addresses news retrieval and mining in four main categories of News Retrieval and Extraction, News Content Analysis, News Propagation Analysis, and News Visualization. Each indicated category entails various research areas that have been investigated through several studies. This study depicts the immense extent of news retrieval and mining, the interconnected methods, tools, and theoretical foundations as well as the evaluation methods and the results. The study helps to gain a better understanding of news mining research areas.


title: Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork

abstract: Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.


title: HyperFlex: Demonstrating control-plane isolation for virtual software-defined networks

abstract: We present a demonstration of HyperFlex [1], a Software-Defined Networking (SDN) virtualization architecture with flexible hypervisor function allocation guaranteeing control-plane virtualization. Network Virtualization (NV) is expected to overcome the ossification of today's communication networks. SDN is seen as an enabler for programmable network control. In order to fully virtualize software-defined networks, not only the virtualization of the data-plane, but also the virtualization of the control-plane has to be considered. HyperFlex is a virtualization hypervisor that ensures isolated control-plane slices for virtual SDN networks. Control-plane isolation also protects the hypervisor resources from exhaustion. Furthermore, virtualization hypervisors have to be scalable and flexible in order to provide the best possible performance for the virtual software-defined networks. They should be able to adapt to the current state of the network and the divergent demands of virtual SDN networks. HyperFlex distributes the hypervisor functions flexibly and dynamically in order to adapt to the current network state.


title: Extending the technology acceptance model: the influence of perceived user resources

abstract: There has been considerable research on the factors that predict whether individuals will accept and voluntarily use information systems. The technology acceptance model (TAM) has a base in psychological research, is parsimonious, explains usage behavior quite well, and can be operationalized with valid and reliable instruments. A limitation of TAM is that it assumes usage is volitional, that is, there are no barriers that would prevent an individual from using an IS if he or she chose to do so. This research extends TAM by adding perceived user resources to the model, with careful attention to placing the construct in TAM's existing nomological structure. In contrast to measures of self-efficacy and perceived behavioral control that concentrate on how well individuals perceive they can execute specific courses of action, this paper examines perceptions of adequate resources that can facilitate or inhibit such behaviors. The inclusion of both a formative and reflective set of measures provides the opportunity for the researcher and manager to decide whether to evaluate only the overall perceptions of adequate resources or also the specific underlying causes. The extended model incorporating these measures was then tested in the field. The results confirmed that perceived user resources is a valuable addition to the model.


Very Deep Convolutional Neural Networks for LVCSR


title: A Visualization System for Space-Time and Multivariate Patterns (VIS-STAMP)

abstract: The research reported here integrates computational, visual and cartographic methods to develop a geovisual analytic approach for exploring and understanding spatio-temporal and multivariate patterns. The developed methodology and tools can help analysts investigate complex patterns across multivariate, spatial and temporal dimensions via clustering, sorting and visualization. Specifically, the approach involves a self-organizing map, a parallel coordinate plot, several forms of reorderable matrices (including several ordering methods), a geographic small multiple display and a 2-dimensional cartographic color design method. The coupling among these methods leverages their independent strengths and facilitates a visual exploration of patterns that are difficult to discover otherwise. The visualization system we developed supports overview of complex patterns and through a variety of interactions, enables users to focus on specific patterns and examine detailed views. We demonstrate the system with an application to the IEEE InfoVis 2005 contest data set, which contains time-varying, geographically referenced and multivariate data for technology companies in the US


title: Overview of the NLPCC 2017 Shared Task: Single Document Summarization

abstract: In this paper, we give an overview for the shared task at the 6th CCF Conference on Natural Language Processing & Chinese Computing (NLPCC 2017): single document summarization. Document summarization aims at conveying important information and generating significantly short summaries for original long documents. This task focused on summarizing the news articles and released a large corpus, TTNews corpus (TTNews corpus can be downloaded at https://pan.baidu.com/s/1bppQ4z1), which was collected for single document summarization in Chinese. In this paper, we will introduce the task, the corpus, the participating teams and the evaluation results.


title: Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder

abstract: Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome. We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures. In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD. Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.


Interpretability and Informativeness of Clustering Methods for Exploratory Analysis of Clinical Data


A Theorem on Boolean Matrices


Feature Selection Guided Auto-Encoder.


title: Autonomous camera based eye controlled wheelchair system using raspberry-pi

abstract: A novel technique is implemented for the eye controlled based independent and cost effective system. The purpose of Eye movement based control electric wheelchair is to eliminate the necessity of the assistance required for the disabled person. And it provides great opportunity of the disabled to feel of independent accessible life. The implemented system will allow the disabled person to control the wheelchair without the assistance from other persons. In this system controlling of wheelchair carried out based on Eye movements. The camera is mounted in front of the user, to capture the image of any one of the Eye (either left or right) and tracks the position of eye pupil with the use of Image processing techniques. According to the position of the eye, wheelchair motor will be directed to move left, right and forward. In addition to this, for the safety purpose ultrasonic sensor is mounted in front of wheelchair to detect the obstacles and automatically stop the wheelchair movement. To make system cost effective for monitoring, a Raspberry pi board allowed to access the system without displaying unit.


title: Estimation of Local and Global Superpixel Covariance for Salient Object Detection in Low Contrast Images

abstract: Salient object detection has become a hot topic in computer vision as it can substantially facilitate a wide range of applications. Conventional salient object detection models primarily rely on low-level image features, which may face great difficulties in low lighting scenarios. This paper proposes to estimate the saliency of low contrast images via covariance features. The input image is firstly decomposed into superpixel regions to estimate their covariances. Then, the local and global image saliency can be calculated using the covariance features respectively. Finally, a graph-based diffusion process is performed to refine the saliency maps. Extensive experiments have been conducted to evaluate the performance of the proposed model against eleven state-of-the-art models on five benchmark datasets and a nighttime image dataset.