Influence Propagation in Social Networks: A Data Mining Perspective


title: Communication networks in geographically distributed software development

abstract: In this paper, we seek to shed light on how communication networks in geographically distributed projects evolve in order to address the limits of the modular design strategy. We collected data from a geographically distributed software development project covering 39 months of activity. Our analysis showed that over time a group of developers emerge as the liaisons between formal teams and geographical locations. In addition to handling the communication and coordination load across teams and locations, those engineers contributed the most to the development effort.


title: Validity of altmetrics data for measuring societal impact: A study using data from Altmetric and F1000Prime

abstract: Can altmetric data be validly used for the measurement of societal impact? The current study seeks to answer this question with a comprehensive dataset (about 100,000 records) from very disparate sources (F1000, Altmetric, and an in-house database based on Web of Science). In the F1000 peer review system, experts attach particular tags to scientific papers which indicate whether a paper could be of interest for science or rather for other segments of society. The results show that papers with the tag "good for teaching" do achieve higher altmetric counts than papers without this tag - if the quality of the papers is controlled. At the same time, a higher citation count is shown especially by papers with a tag that is specifically scientifically oriented ("new finding"). The findings indicate that papers tailored for a readership outside the area of research should lead to societal impact. If altmetric data is to be used for the measurement of societal impact, the question arises of its normalization. In bibliometrics, citations are normalized for the papers' subject area and publication year. This study has taken a second analytic step involving a possible normalization of altmetric data. As the results show there are particular scientific topics which are of especial interest for a wide audience. Since these more or less interesting topics are not completely reflected in Thomson Reuters' journal sets, a normalization of altmetric data should not be based on the level of subject categories, but on the level of topics.


title: An Adaptive Multiagent Approach to Protection Relay Coordination With Distributed Generators in Industrial Power Distribution System

abstract: This paper presents new explorations into the use of agent technology applied to protection coordination of power systems. The impact of distributed generators on protection coordination is firstly discussed. Then a coordination multi-agent system is proposed with the functions of the agents described. In the proposed system, communication plays an important role to provide more information for the relay coordination besides the relay settings. Communication simulation has been carried out on the Java agent development framework (JADE) platform. The information communication process shows that adaptive coordination can be achieved.


Concepts in occupational and environmental health


title: Combining knowledge and data driven insights for identifying risk factors using electronic health records.

abstract: BACKGROUND ::: The ability to identify the risk factors related to an adverse condition, e.g., heart failures (HF) diagnosis, is very important for improving care quality and reducing cost. Existing approaches for risk factor identification are either knowledge driven (from guidelines or literatures) or data driven (from observational data). No existing method provides a model to effectively combine expert knowledge with data driven insight for risk factor identification. ::: ::: ::: METHODS ::: We present a systematic approach to enhance known knowledge-based risk factors with additional potential risk factors derived from data. The core of our approach is a sparse regression model with regularization terms that correspond to both knowledge and data driven risk factors. ::: ::: ::: RESULTS ::: The approach is validated using a large dataset containing 4,644 heart failure cases and 45,981 controls. The outpatient electronic health records (EHRs) for these patients include diagnosis, medication, lab results from 2003-2010. We demonstrate that the proposed method can identify complementary risk factors that are not in the existing known factors and can better predict the onset of HF. We quantitatively compare different sets of risk factors in the context of predicting onset of HF using the performance metric, the Area Under the ROC Curve (AUC). The combined risk factors between knowledge and data significantly outperform knowledge-based risk factors alone. Furthermore, those additional risk factors are confirmed to be clinically meaningful by a cardiologist. ::: ::: ::: CONCLUSION ::: We present a systematic framework for combining knowledge and data driven insights for risk factor identification. We demonstrate the power of this framework in the context of predicting onset of HF, where our approach can successfully identify intuitive and predictive risk factors beyond a set of known HF risk factors.


title: A Deep Neural Networks Approach to Automatic Recognition Systems for Volcano-Seismic Events

abstract: Deep neural networks (DNNs) could help to identify the internal sources of volcano-seismic events. However, direct applications of DNNs are challenging, given the multiple seismic sources and the small size of available datasets. In this paper, we propose a novel approach in the field of volcano seismology to classify volcano-seismic events based on fully connected DNNs. Two DNN architectures with different weights scheme initialization are studied: stacked denoising autoencoders and deep belief networks. Using a combined feature vector of linear prediction coefficients and statistical properties, we evaluate classification performance on seven different classes of isolated seismic events. These proposed architectures are compared to multilayer perceptron, support vector machine, and random forest. Experimental results show that DNNs can efficiently capture complex relationships of volcano-seismic data and achieve better classification performance with faster convergence when compared to classical models.


title: A qualitative investigation of patients' and caregivers' experiences of severe sepsis*.

abstract: OBJECTIVES ::: To explore and describe the subjective experiences and long-term impact of severe sepsis on survivors of severe sepsis and their informal caregivers (e.g., spouse or family member) through qualitative research methods. ::: ::: ::: DESIGN ::: A qualitative exploratory study using semi-structured interviews with survivors of severe sepsis and their informal caregivers in the United Kingdom and United States. Participants also completed a demographic background form and sites provided medical history details. Transcripts were analyzed using a thematic analysis approach. ::: ::: ::: SETTING ::: Patients were recruited from a large National Health Service hospital in the United Kingdom and a level 1 trauma center hospital in the United States. Caregivers were recruited through eligible patients. Interviews were conducted either face to face in participant's homes or another convenient location or over the telephone. ::: ::: ::: PATIENTS ::: Patients who were 18 years old or older and had experienced an episode of severe sepsis in the previous 12 months were recruited by clinical staff in each hospital. Caregivers were family members or friends who had provided informal care for the patient after their episode of severe sepsis. ::: ::: ::: INTERVENTIONS ::: None. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Thirty-nine interviews were conducted with 22 patients and 17 informal caregivers (of these 28 were conducted face-to-face and 11 by telephone). Five main themes were identified in the qualitative analysis: awareness and knowledge of severe sepsis; experience of hospitalization, ongoing impact of severe sepsis; impact on caregivers; and support after severe sepsis. Experiences varied depending on the patients' health prior to the severe sepsis, with the worst affected reporting lasting impacts on multiple aspects of their life. ::: ::: ::: CONCLUSIONS ::: The study extends what was understood about severe sepsis from the patients' and caregivers' perspectives from the previous limited literature. Caregivers as well as patients reported enduring impact. The study also identified problems of lack of awareness of diagnosis and understanding of severe sepsis by patients and caregivers and difficulties accessing appropriate healthcare providers and ancillary services after discharge from hospital.


title: Character-based parsing with convolutional neural network

abstract: We describe a novel convolutional neural network architecture with k-max pooling layer that is able to successfully recover the structure of Chinese sentences. This network can capture active features for unseen segments of a sentence to measure how likely the segments are merged to be the constituents. Given an input sentence, after all the scores of possible segments are computed, an efficient dynamic programming parsing algorithm is used to find the globally optimal parse tree. A similar network is then applied to predict syntactic categories for every node in the parse tree. Our networks archived competitive performance to existing benchmark parsers on the CTB-5 dataset without any task-specific feature engineering.


title: ERP Projects: Key Success Factors and Risk of Failure A Proposed Model of Governance of Enterprise Resource Planning

abstract: The ERP (Enterprise Resource Planning) is a major innovation in the Field of Information Systems and Organization Management. Including their ability to make the organization more integrated and consistent, and absolute control of information. But the implementation of these projects remains a disturbing adventure that may lead to success or deadly failure of the organization that is embarking on this project. The aims of this article are to determine the necessary critical success factors for the ERP and causes of failures,through a comparative study of large organizations that have adopted ERP , for addressing the convergence and divergence points in implementation ERP. Such a study will allow us to develop a governance model for ERP


THE BEGINNING of the MONTE CARLO METHOD


title: Policy-Carrying Data: A Privacy Abstraction for Attaching Terms of Service to Mobile Data

abstract: Despite decades of work on privacy-protecting systems, mobile user privacy remains at the mercy of cloud service providers. This paper proposes a different approach -- let users attach Terms of Service (ToS) to their data before uploading it to the cloud. We propose an abstraction, called policy-carrying data (PCD), that lets users specify and attach ToS to their data. PCD guarantees that cloud providers claim they are compliant with the ToS policy before they are able to access the data. To offer this guarantee, PCD relies on attribute-based encryption. We present PCD's semantics, its properties, and describe how PCD can be added to JSON or REST. Our hope is that PCD opens a different research path -- designing privacy abstractions that provide legal ammunition for mobile users against misuse of their data.


title: Effects of self-disclosure on relational intimacy in Facebook

abstract: The present study examined the association between self-disclosure and intimacy in the context of Facebook. As the antecedents of self-disclosure, the study included the need for affiliation and the motivations for relationship maintenance and initiation. Using data from an online survey (N=249), structural equation modeling analyses revealed that self-disclosure amount and positivity were positively associated with intimacy, while self-disclosure honesty and intent were not associated with intimacy. The study also found no direct association between the need for affiliation and self-disclosure. Rather, the need for affiliation was associated with the motivations for relationship maintenance and initiation, which in turn affected self-disclosure and intimacy. The theoretical implications of the study were discussed.


title: Secure Data Analytics for Cloud-Integrated Internet of Things Applications

abstract: Cloud-integrated Internet of Things (IoT) is emerging as the next-generation service platform that enables smart functionality worldwide. IoT applications such as smart grid and power systems, e-health, and body monitoring applications along with large-scale environmental and industrial monitoring are increasingly generating large amounts of data that can conveniently be analyzed through cloud service provisioning. However, the nature of these applications mandates the use of secure and privacy-preserving implementation of services that ensures the integrity of data without any unwarranted exposure. This article explores the unique challenges and issues within this context of enabling secure cloud-based data analytics for the IoT. Three main applications are discussed in detail, with solutions outlined based on the use of fully homomorphic encryption systems to achieve data security and privacy over cloud-based analytical phases. The limitations of existing technologies are discussed and models proposed with regard to achieving high efficiency and accuracy in the provisioning of analytic services for encrypted data over a cloud platform.


title: Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features

abstract: In this paper we present a novel, fast and accurate system for detecting the presence of cars in parking lots. The system is based on fast integral channel features and machine learning. The methods are well suited for running embedded on low performance platforms. The methods are tested on a database of nearly 700,000 images of parking spaces, where 48.5% are occupied and the rest are free. The experimental evaluation shows improved robustness in comparison to the baseline methods for the dataset.


title: Induction of apoptosis in human leukemia cells by the tyrosine kinase inhibitor adaphostin proceeds through a RAF-1/MEK/ERK- and AKT-dependent process

abstract: Effects of the tyrphostin tyrosine kinase inhibitor adaphostin (NSC 680410) have been examined in human leukemia cells (Jurkat, U937) in relation to mitochondrial events, apoptosis, and perturbations in signaling and cell cycle regulatory events. Exposure of cells to adaphostin concentrations > or =0.75 microM for intervals > or =6 h resulted in a pronounced release of cytochrome c and AIF, activation of caspase-9, -8, and -3, and apoptosis. These events were accompanied by the caspase-independent downregulation of Raf-1, inactivation of MEK1/2, ERK, Akt, p70S6K, dephosphorylation of GSK-3, and activation of c-Jun-N-terminal kinase (JNK) and p38 MAPK. Adaphostin also induced cleavage and dephosphorylation of pRb on CDK2- and CDK4-specific sites, as well as the caspase-dependent downregulation of cyclin D1. Inducible expression of a constitutively active MEK1 construct markedly diminished adaphostin-induced cytochrome c and AIF release, JNK activation, and apoptosis in Jurkat cells. Ectopic expression of Raf-1 or constitutively activated (myristolated) Akt also significantly attenuated adaphostin-induced apoptosis, but protection was less than that conferred by enforced activation of MEK. Lastly, antioxidants (e.g., L-N-acetylcysteine; L-NAC) opposed adaphostin-mediated mitochondrial dysfunction, Raf-1/MEK/ERK downregulation, JNK activation, and apoptosis. However, in contrast to L-NAC, enforced activation of MEK failed to block adaphostin-mediated ROS generation. Together, these findings demonstrate that the tyrphostin adaphostin induces multiple perturbations in signal transduction pathways in human leukemia cells, particularly inactivation of the cytoprotective Raf-1/MEK/ERK and Akt cascades, that culminate in mitochondrial injury, caspase activation, and apoptosis. They also suggest that adaphostin-related oxidative stress acts upstream of perturbations in these signaling pathways to trigger the cell death process.


title: Recurrent Network Models for Human Dynamics

abstract: We propose the Encoder-Recurrent-Decoder (ERD) model for recognition and prediction of human body pose in videos and motion capture. The ERD model is a recurrent neural network that incorporates nonlinear encoder and decoder networks before and after recurrent layers. We test instantiations of ERD architectures in the tasks of motion capture (mocap) generation, body pose labeling and body pose forecasting in videos. Our model handles mocap training data across multiple subjects and activity domains, and synthesizes novel motions while avoiding drifting for long periods of time. For human pose labeling, ERD outperforms a per frame body part detector by resolving left-right body part confusions. For video pose forecasting, ERD predicts body joint displacements across a temporal horizon of 400ms and outperforms a first order motion model based on optical flow. ERDs extend previous Long Short Term Memory (LSTM) models in the literature to jointly learn representations and their dynamics. Our experiments show such representation learning is crucial for both labeling and prediction in space-time. We find this is a distinguishing feature between the spatio-temporal visual domain in comparison to 1D text, speech or handwriting, where straightforward hard coded representations have shown excellent results when directly combined with recurrent units [31].


title: ShapeNet: An Information-Rich 3D Model Repository

abstract: We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.


title: Compressive depth map acquisition using a single photon-counting detector: Parametric signal processing meets sparsity

abstract: Active range acquisition systems such as light detection and ranging (LIDAR) and time-of-flight (TOF) cameras achieve high depth resolution but suffer from poor spatial resolution. In this paper we introduce a new range acquisition architecture that does not rely on scene raster scanning as in LIDAR or on a two-dimensional array of sensors as used in TOF cameras. Instead, we achieve spatial resolution through patterned sensing of the scene using a digital micromirror device (DMD) array. Our depth map reconstruction uses parametric signal modeling to recover the set of distinct depth ranges present in the scene. Then, using a convex program that exploits the sparsity of the Laplacian of the depth map, we recover the spatial content at the estimated depth ranges. In our experiments we acquired 64×64-pixel depth maps of fronto-parallel scenes at ranges up to 2.1 M using a pulsed laser, a DMD array and a single photon-counting detector. We also demonstrated imaging in the presence of unknown partially-transmissive occluders. The prototype and results provide promising directions for non-scanning, low-complexity range acquisition devices for various computer vision applications.


title: Action Recognition and Video Description using Visual Attention

abstract: We propose soft attention based models for the tasks of action recognition in videos and generating natural language descriptions of videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units which are deep both spatially and temporally. Our model learns to focus selectively on parts of the video frames and classifies videos after taking a few glimpses. It is also able to generate sentences describing the videos using spatio-temporal glimpses across them. The model essentially learns which parts in the frames are relevant for the task at hand and attaches higher importance to them. We evaluate the action recognition model on UCF-11 (YouTube Action), HMDB-51 and Hollywood2 datasets and analyze how the model focuses its attention depending on the scene and the action being performed. We evaluate the description generation model on YouTube2Text dataset and visualize the model’s attention as it generates words.


title: Cues to deception

abstract: Do people behave differently when they are lying compared with when they are telling the truth? The combined results of 1,338 estimates of 158 cues to deception are reported. Results show that in some ways, liars are less forthcoming than truth tellers, and they tell less compelling tales. They also make a more negative impression and are more tense. Their stories include fewer ordinary imperfections and unusual contents. However, many behaviors showed no discernible links, or only weak links, to deceit. Cues to deception were more pronounced when people were motivated to succeed, especially when the motivations were identity relevant rather than monetary or material. Cues to deception were also stronger when lies were about transgressions.


title: Does colorspace transformation make any difference on skin detection?

abstract: Skin detection is an important process in many of computer vision algorithms. It usually is a process that starts at a pixel-level, and that involves a pre-process of colorspace transformation followed by a classification process. A colorspace transformation is assumed to increase separability between skin and non-skin classes, to increase similarity among different skin tones, and to bring a robust performance under varying illumination conditions, without any sound reasonings. In this work, we examine if the colorspace transformation does bring those benefits by measuring four separability measurements on a large dataset of 805 images with different skin tones and illumination. Surprising results indicate that most of the colorspace transformations do not bring the benefits which have been assumed.


Designing and Executing Digital Strategies


title: Holographic neurocomputer utilizing laser diode light source

abstract: We describe a laser diode-based optoelectronic implementation of artificial neural networks which utilizes real-time holography in photorefractive crystals. The use of a laser diode light source reduces the system size and power requirements. The holographic material is rhodium- doped BaTiO3 which has enhanced sensitivity at the laser-diode wavelength of 830 nm. A balanced coherent-detection method is used to represent bipolar optical neurons and weights. In addition, by distributing each neuron weight among a set of spatially and angularly distributed gratings using beam fanning, Bragg degeneracy and its associated inter-neuron optical crosstalk is virtually eliminated. The structure of the neural network is programmable and we have implemented a variety of neural networks including backpropagation and Kohonen-style self-organizing maps with up to 10,000 neurons and performance of up to 108 weights processed per second during learning and readout. We also discuss weight decay in photorefractive materials, specifically its relative effects in the neural network and data storage domains.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Correlates of different forms of cyberloafing : The role of norms and external locus of control

abstract: Cyberloafing is the personal use of email and the Internet while at work. The purpose of this study is to identify the different forms of cyberloafing and their antecedents. We propose that cyberloafing has two primary forms: minor cyberloafing (e.g., sending and receiving personal email at work) and serious cyberloafing (e.g., online gambling, surfing adult oriented web sites). Additionally, we hypothesize that employees' perceptions of coworker and supervisor norms supporting cyberloafing are related to minor cyberloafing but not serious cyberloafing. We also hypothesize that external locus of control (i.e., a belief that chance and powerful others determines one's outcomes), as an antecedent of other counterproductive work behaviors, will be related to both minor and serious cyberloafing. Two hundred and twenty two employed graduate business students were surveyed. Two forms of cyberloafing were identified: one composed of minor cyberloafing behaviors and one composed of the more serious cyberloafing behaviors. As predicted, employees' perceptions of their coworkers' and supervisor's norms were positively related to minor cyberloafing, but not related to serious cyberloafing. Also as predicted, belief in chance was positively related to both minor and serious cyberloafing. A belief in powerful others was not related to minor or serious cyberloafing. Implications for policy development to regulate cyberloafing in organizations are discussed.


title: Sharing Topics in Pinterest: Understanding Content Creation and Diffusion Behaviors

abstract: Pinterest provides a social curation service where people can collect, organize, and share content (pins in Pinterest) that reflect their interests. This paper investigates (1) the differences in pinning (i.e., the act of posting a pin) and repinning (i.e., the act of sharing other user's pin) behaviors by topics and user gender, and (2) the relations among topics in Pinterest. We conduct a measurement study using a large-scale dataset (1.6 M pins shared by 1.1 M users) in Pinterest. We show that there is a notable discrepancy between pinning and repinning behaviors on different topics. We also show that male and female users show different behaviors on different topics in terms of dedication, responsiveness, and sentiment. By introducing the notion of a Topic Network (TN) whose nodes are topics and are linked if they share common users, we analyze how topics are related to one another, which can give a valuable implication on topic demand forecasting or cross-topic advertisement. Lastly, we explore the implications of our findings for predicting a user's interests and behavioral patterns in Pinterest.


title: A system for traffic sign detection, tracking, and recognition using color, shape, and motion information

abstract: This paper describes a computer vision based system for real-time robust traffic sign detection, tracking, and recognition. Such a framework is of major interest for driver assistance in an intelligent automotive cockpit environment. The proposed approach consists of two components. First, signs are detected using a set of Haar wavelet features obtained from AdaBoost training. Compared to previously published approaches, our solution offers a generic, joint modeling of color and shape information without the need of tuning free parameters. Once detected, objects are efficiently tracked within a temporal information propagation framework. Second, classification is performed using Bayesian generative modeling. Making use of the tracking information, hypotheses are fused over multiple frames. Experiments show high detection and recognition accuracy and a frame rate of approximately 10 frames per second on a standard PC.


title: Fast-Match: Fast Affine Template Matching

abstract: Fast-Match is a fast algorithm for approximate template matching under 2D affine transformations that minimizes the Sum-of-Absolute-Differences (SAD) error measure. There is a huge number of transformations to consider but we prove that they can be sampled using a density that depends on the smoothness of the image. For each potential transformation, we approximate the SAD error using a sublinear algorithm that randomly examines only a small number of pixels. We further accelerate the algorithm using a branch-and-bound-like scheme. As images are known to be piecewise smooth, the result is a practical affine template matching algorithm with approximation guarantees, that takes a few seconds to run on a standard machine. We perform several experiments on three different datasets, and report very good results.


title: Backpropagation-decorrelation: online recurrent learning with O(N) complexity

abstract: We introduce a new learning rule for fully recurrent neural networks which we call backpropagation-decorrelation rule (BPDC). It combines important principles: one-step backpropagation of errors and the usage of temporal memory in the network dynamics by means of decorrelation of activations. The BPDC rule is derived and theoretically justified from regarding learning as a constraint optimization problem and applies uniformly in discrete and continuous time. It is very easy to implement, and has a minimal complexity of 2N multiplications per time-step in the single output case. Nevertheless we obtain fast tracking and excellent performance in some benchmark problems including the Mackey-Glass time-series.


title: Oral Hairy Leukoplakia in HIV-Negative Patients: Report of 10 Cases

abstract: Ten cases of oral hairy leukoplakia (OHL) in HIV- negative patients are presented. Eight of the 10 patients were on steroid treatment for chronic obstructive pulmonary disease, 1 patient was on prednisone as part of a therapeutic regimen for gastrointestinal stromal tumor, and 1 patient did not have any history of immunosuppression. There were 5 men and 5 women, ages 32-79, with mean age being 61.8 years. Nine out of 10 lesions were located unilaterally on the tongue, whereas 1 lesion was located at the junction of the hard and soft palate. All lesions were described as painless, corrugated, nonremovable white plaques (leukoplakias). Histologic features were consistent with Epstein-Barr virus-associated hyperkeratosis suggestive of OHL, and confirmatory in situ hybridization was performed in all cases. Candida hyphae and spores were present in 8 cases. Pathologists should be aware of OHL presenting not only in HIV-positive and HIV-negative organ transplant recipients but also in patients receiving steroid treatment, and more important, certain histologic features should raise suspicion for such diagnosis without prior knowledge of immunosuppression.


title: Security for Ubiquitous Computing: Problems and Proposed Solutionl

abstract: Ubiquitous computing, over a decade in the making, has finally graduated from whacky buzzword through fashionable research topic to something that is definitely and inevitably happening. This will mean revolutionary changes in the way computing affects our society: changes of the same magnitude and scope as those brought about by the World Wide Web.When throw-away computing capabilities are embedded in shoes, drink cans and postage stamps, security and privacy take on entirely new meanings.Programmers, engineers and system designers will have to learn to think in new ways.Ubiquitous computing is not just a wireless version of the Internet with a thousand times more computers, and it would be a naive mistake to imagine that the traditional security solutions for distributed systems will scale to the new scenario. Authentication, authorization, and even concepts as fundamental as ownership require thorough rethinking.At a higher level still, even goals and policies must be revised. One question we should keep asking is simply “Security for whom?” The owner of a device, for example, is no longer necessarily the party whose interests the device will attempt to safeguard.Ubiquitous computing is happening and will affect everyone.By itself it will never be “secure” (whatever this means) if not for the dedicated efforts of people like us who actually do the work.We are the ones who can make the difference.So, before focusing on the implementation details, let’s have a serious look at the big picture.


title: LITERACY AS A VIABLE TOOL AGAINST THE OUTBREAK OF EPIDEMICS IN EBONYI STATE OF NIGERIA: THE TEACHERS’ PERCEPTION

abstract: This study focused on investigating teachers’ perception on literacy as a viable too against the outbreak of epidemics in Ebonyi State of Nigeria. In the course of the study, three (3) research questions were formulated to guide the study, while a survey research design was adopted for the study. From a total of three hundred and four (304) teachers who constituted the population of teachers in the twelve public secondary schools in Abakaliki metropolis, a sample of one hundred and twenty (120) teachers was simple randomly selected as the sample for the study. The instrument used was a structured questionnaire with four sections. Mean and standard deviation were used as instrument for data analysis. The result of the findings revealed that the teachers, who were used in the study, agreed that literacy has really been very helpful in improving the health status of the people; it has also increased the quest for more knowledge on health education, and reduced the occurrence of the outbreak of epidemics respectively. Following some findings, recommendations and conclusions were made.


title: Structure Based User Identification across Social Networks

abstract: Identification of anonymous identical users of cross-platforms refers to the recognition of the accounts belonging to the same individual among multiple Social Network (SN) platforms. Evidently, cross-platform exploration may help solve many problems in social computing, in both theory and practice. However, it is still an intractable problem due to the fragmentation, inconsistency, and disruption of the accessible information among SNs. Different from the efforts implemented on user profiles and users’ content, many studies have noticed the accessibility and reliability of network structure in most of the SNs for addressing this issue. Although substantial achievements have been made, most of the current network structure-based solutions, requiring prior knowledge of some given identified users, are supervised or semi-supervised. It is laborious to label the prior knowledge manually in some scenarios where prior knowledge is hard to obtain. Noticing that friend relationships are reliable and consistent in different SNs, we proposed an unsupervised scheme, termed Friend Relationship-based User Identification algorithm without Prior knowledge (FRUI-P). The FRUI-P first extracts the friend feature of each user in an SN into friend feature vector, and then calculates the similarities of all the candidate identical users between two SNs. Finally, a one-to-one map scheme is developed to identify the users based on the similarities. Moreover, FRUI-P is proved to be efficient theoretically. Results of extensive experiments demonstrated that FRUI-P performs much better than current state-of-art network structure-based algorithm without prior knowledge. Due to its high precision, FRUI-P can additionally be utilized to generate prior knowledge for supervised and semi-supervised schemes. In applications, the unsupervised anonymous identical user identification method accommodates more scenarios where the seed users are unobtainable.


title: A Consistent Metric for Performance Evaluation of Multi-Object Filters

abstract: The concept of a miss-distance, or error, between a reference quantity and its estimated/controlled value, plays a fundamental role in any filtering/control problem. Yet there is no satisfactory notion of a miss-distance in the well-established field of multi-object filtering. In this paper, we outline the inconsistencies of existing metrics in the context of multi-object miss-distances for performance evaluation. We then propose a new mathematically and intuitively consistent metric that addresses the drawbacks of current multi-object performance evaluation metrics.


title: Performance Analysis of IEEE 802.11ax UL OFDMA-Based Random Access Mechanism

abstract: Recently, a revolutionary effort to seek fundamental improvement of 802.11, known as IEEE 802.11ax, has been approved to deliver high efficiency wireless local area network (HEW) technologies for dense scenario. The de-facto random access mechanism of IEEE 802.11ax is uplink OFDMA-based random access (UL OFDMA RA) mechanism. In this paper, we provide a simple but accurate analytical model to study the system efficiency and delay performance of UL OFDMA RA under saturated condition, in the assumption of a finite number of stations and ideal channel conditions in a single-hop WLAN. Simulations are conducted to validate analytical results. In addition, we use this analytical model to identify the appropriate OFDMA-based random access parameter set (RAPS) that can maximize the system performance, a feature which is essential for system design and configuration.


title: OPTIMIZED CLUSTERS FOR DISAGGREGATED ELECTRICITY LOAD FORECASTING

abstract: • To account for the variation of EDF’s (the French electrical company) portfolio following the liberalization of the electrical market, it is essential to disaggregate the global load curve. The idea is to disaggregate the global signal in such a way that the sum of disaggregated forecasts significantly improves the prediction of the whole global signal. The strategy is to optimize, a preliminary clustering of individual load curves with respect to a predictability index. The optimized clustering procedure is controlled by a forecasting performance via a cross-prediction dissimilarity index. It can be assimilated to a discrete gradient type algorithm.


title: Efficient Scheduling of Arbitrary Task Graphs to Multiprocessors using A Parallel Genetic Algorithm

abstract: Given a parallel program represented by a task graph, the objective of a scheduling algorithm is to minimize the overall execution time of the program by properly assigning the nodes of the graph to the processors. This multiprocessor scheduling problem is NP-complete even with simplifying assumptions and becomes more complex under relaxed assumptions such as arbitrary precedence constraints, and arbitrary task execution and communication times. The present literature on this topic is a large repertoire of heuristics that produce good solutions in a reasonable amount of time. These heuristics, however, have restricted applicability in a practical environment because they have a number of fundamental problems including high time complexity, lack of scalability, and no performance guarantee with respect to optimal solutions. Recently, genetic algorithms (GAs) have been widely reckoned as a useful vehicle for obtaining high quality or even optimal solutions for a broad range of combinatorial optimization problems. While a few GAs for scheduling have already been suggested, in this paper we propose a novel GA-based algorithm with an objective to simultaneously meet the goals of high performance, scalability, and fast running time. The proposed parallel genetic scheduling (PGS) algorithm itself is a parallel algorithm which generates high quality solutions in a short time. By encoding the scheduling list as a chromosome, the PGS algorithm can potentially generate an optimal scheduling list which in turn leads to an optimal schedule. The major strength of the PGS algorithm lies in its two efficient genetic operators: the order crossover and mutation. These operators effectively combine the building-blocks of good scheduling lists to construct better lists. The proposed algorithm is evaluated through a robust comparison with two heuristics best known in terms of performance and time complexity. It outperforms both heuristics while taking considerably less running time. When evaluated with random task graphs for which optimal solutions are known, the PGS algorithm generates optimal solutions for more than half of the test cases and close-to-optimal for the other half.


title: A nonparametric bayesian framework for constructing flexible feature representations

abstract: Representations are a key explanatory device used by cognitive psychologists to account for human behavior. Understanding the effects of context and experience on the representations people use is essential, because if two people encode the same stimulus using different representations, their response to that stimulus may be different. We present a computational framework that can be used to define models that flexibly construct feature representations (where by a feature we mean a part of the image of an object) for a set of observed objects, based on nonparametric Bayesian statistics. Austerweil and Griffiths (2011) presented an initial model constructed in this framework that captures how the distribution of parts affects the features people use to represent a set of objects. We build on this work in three ways. First, although people use features that can be transformed on each observation (e.g., translate on the retinal image), many existing feature learning models can only recognize features that are not transformed (occur identically each time). Consequently, we extend the initial model to infer features that are invariant over a set of transformations, and learn different structures of dependence between feature transformations. Second, we compare two possible methods for capturing the manner that categorization affects feature representations. Finally, we present a model that learns features incrementally, capturing an effect of the order of object presentation on the features people learn. We conclude by considering the implications and limitations of our empirical and theoretical results.


title: Serotonin Deficiency and Prolonged Bleeding in Beige Mice 1

abstract: Beige mice have been observed to bleed excessively from small wounds. Platelets obtained from these mice were deficient in adenine nucleotides and serotonin and in vivo uptake of labeled serotonin was impaired. Heterozygotes of beige and C57BL/6 mice have adenine nucleotide and serotonin levels comparable to control and do not manifest a bleeding tendency, consistent with the recessive mode of inheritance of the beige mouse syndrome. Morphologic confirmation of the observed biochemical defects was obtained by the demonstration that serotonin storage organelles were not observed in electron photomicrographs of beige mouse platelets. The demonstration that small doses of serotonin were effective in reversing the bleeding tendency in beige mice suggested a causal role for defective storage and release of endogenous serotonin as the basis for the bleeding tendency, acting either independently or synergistically with impaired release of adenine nucleotides.


title: Java Cryptography Architecture and Security of Electronic Commerce

abstract: In this paper, the security of electronic commerce and the security methods are introduced firstly. Then, the history of Java security architecture and its design principles are described. After that, partial implementation of elliptic curve cryptosystem developed with Java is discussed. In the end, the advantages of electronic commerce systems developed with Java language and Java security architecture are displayed.


title: Generalizing Focus+Context Visualization

abstract: Focus+context visualization is well-known from information visualization: certain data subsets of special interest are shown in more detail (locally enlarged) whereas the rest of the data is provided as context (in reduced space) to support user orientation and navigation. The key point of this work is a generalized definition of focus+context visualization which extends its applicability also to scientific visualization. We show how different graphics resources such as space, opacity, color, etc., can be used to visually discriminate between data subsets in focus and their respective context. To furthermore demonstrate its general use, we discuss several quite different examples of focus+context visualization with respect to our generalized definition. Finally, we also discuss the very important interaction aspect of focus+context visualization.


title: Gaussian mixture model for human skin color and its applications in image and video databases

abstract: This paper is concerned with estimating a probability density function of human skin color, using a finite Gaussian mixture model, whose parameters are estimated through the EM algorithm. Hawkins' statistical test on the normality and homoscedasticity (common covariance matrix) of the estimated Gaussian mixture models is performed and McLachlan's bootstrap method is used to test the number of components in a mixture. Experimental results show that the estimated Gaussian mixture model fits skin images from a large database. Applications of the estimated density function in image and video databases are presented.


title: A real-time head nod and shake detector

abstract: Head nods and head shakes are non-verbal gestures used often to communicate intent, emotion and to perform conversational functions. We describe a vision-based system that detects head nods and head shakes in real time and can act as a useful and basic interface to a machine. We use an infrared sensitive camera equipped with infrared LEDs to track pupils. The directions of head movements, determined using the position of pupils, are used as observations by a discrete Hidden Markov Model (HMM) based pattern analyzer to detect when a head nod/shake occurs. The system is trained and tested on natural data from ten users gathered in the presence of varied lighting and varied facial expressions. The system as described achieves a real time recognition accuracy of 78.46% on the test dataset.


title: Natural pesticides present in edible plants are predicted to be carcinogenic.

abstract: Based upon the US National Toxicology Program (NTP) rodent carcinogenicity data base, CASE, an artificial intelligence structure-activity evaluation method, predicts that a large proportion of natural pesticides present in edible plants are rodent carcinogens.


title: Robust real-time performance-driven 3D face tracking

abstract: We introduce a novel robust hybrid 3D face tracking framework from RGBD video streams, which is capable of tracking head pose and facial actions without pre-calibration or intervention from a user. In particular, we emphasize on improving the tracking performance in instances where the tracked subject is at a large distance from the cameras, and the quality of point cloud deteriorates severely. This is accomplished by the combination of a flexible 3D shape regressor and the joint 2D+3D optimization on shape parameters. Our approach fits facial blendshapes to the point cloud of the human head, while being driven by an efficient and rapid 3D shape regressor trained on generic RGB datasets. As an on-line tracking system, the identity of the unknown user is adapted on-the-fly resulting in improved 3D model reconstruction and consequently better tracking performance. The result is a robust RGBD face tracker capable of handling a wide range of target scene depths, whose performances are demonstrated in our extensive experiments better than those of the state-of-the-arts.


title: IoT based weather station

abstract: A weather station can be described as an instrument or device, which provides us with the information of the weather in our neighbouring environment. For example it can provide us with details about the surrounding temperature, barometric pressure, humidity, etc. Hence, this device basically senses the temperature, pressure, humidity, light intensity, rain value. There are various types of sensors present in the prototype, using which all the aforementioned parameters can be measured. It can be used to monitor the temperature or humidity of a particular room/place. With the help of temperature and humidity we can calculate other data parameters, such as the dew point. In addition to the above mentioned functionalities, we can monitor the light intensity of the place as well. We have also enabled to monitor the atmospheric pressure of the room. We can also monitor the rain value. The brain of the prototype is the ESP8266 based Wi-fi module Nodemcu (12E). Four sensors are connected to the NodeMCU namely temperature and humidity sensor(DHT11), pressure sensor(BMP180), raindrop module, and light dependent resistor(LDR). Whenever these values exceed a chosen threshold limit for each an SMS, an E-mail and a Tweet post is published alerting the owner of the appliance to take necessary measures.


title: Reader-Aware Multi-Document Summarization: An Enhanced Model and The First Dataset

abstract: We investigate the problem of reader-aware multi-document summarization (RA-MDS) and introduce a new dataset for this problem. To tackle RA-MDS, we extend a variational auto-encodes (VAEs) based MDS framework by jointly considering news documents and reader comments. To conduct evaluation for summarization performance, we prepare a new dataset. We describe the methods for data collection, aspect annotation, and summary writing as well as scrutinizing by experts. Experimental results show that reader comments can improve the summarization performance, which also demonstrates the usefulness of the proposed dataset. The annotated dataset for RA-MDS is available online.


title: Verbal deception from late childhood to middle adolescence and its relation to executive functioning skills.

abstract: The present investigation examined 8- to 16-year-olds’ tendency to lie, the sophistication of their lies, and related cognitive factors. Participants were left alone and asked not to look at the answers to a test, but the majority peeked. The researcher then asked a series of questions to examine whether the participants would lie about their cheating and, if they did lie, evaluate the sophistication of their lies. Additionally, participants completed measures of working memory, inhibitory control, and planning skills. Results revealed that the sophistication of 8- to 16-year-olds’ lies, but not their decision to lie, was significantly related to executive functioning skills.


BISLON: BISociative SLOgaN generation based on stylistic literary devices


title: Don't Bother Me. I'm Socializing!: A Breakpoint-Based Smartphone Notification System

abstract: Smartphone notifications provide application-specific information in real-time, but could distract users from in-person social interactions when delivered at inopportune moments. We explore breakpoint-based notification management, in which the smartphone defers notifications until an opportune moment. With a video survey where participants selected appropriate moments for notifications from a video-recorded social interaction, we identify four breakpoint types: long silence, a user leaving the table, others using smartphones, and a user left alone. We introduce a Social Context-Aware smartphone Notification system, SCAN, that uses build-in sensors to detect social context and identifies breakpoints to defer smartphone notifications until a breakpoint. We conducted a controlled study with ten friend groups who had SCAN installed on their smartphones while dining at a restaurant. Results show that SCAN accurately detects breakpoints (precision=92.0%, recall=82.5%), and reduces notification interruptions by 54.1%. Most participants reported that SCAN helped them to focus better on in-person social interaction and found selected breakpoints appropriate.


title: Design of millimeter-wave dual circularly polarized end-fire antenna fed by SIW polarizer

abstract: A substrate integrated waveguide (SIW) end-fire antenna is proposed and analyzed for achieving wideband dual circularly polarized radiation performance. Wave-polarization is realized through constructing a two-layer SIW polarizer. The sloping slot etched on the common broad wall of the stacked SIWs could gradually induce a horizontally polarized electric field with a certain phase shift. Furthermore, in order to solve the poor performance of the SIW polarizer, additional substrate is extended in front of the polarizer aperture. The loaded substrate acts as not only impedance transformer but also polarization compensator. Results indicate that the designed antenna operates with a wideband 3-dB axial ratio (AR) bandwidth of 17.6% ranging from 17.6 to 21 GHz. Stable end-fire radiation patterns can be obtained in the whole operating band.


title: Treatment planning the endodontic-implant interface

abstract: One of the most challenging treatment planning decisions in restorative dentistry is the retention of a root filled tooth or its extraction and replacement with an implant. Making an informed judgement relies upon not only clinical experience and expertise but also interpretation of the published clinical evidence. In the last few years, more robust data and closer scrutiny of the evidence has helped to understand the advantages and disadvantages of each of these treatment modalities. The purpose of this paper is to provide a narrative review of some of the factors that may influence treatment planning for root canal treated teeth and indications for whether these teeth would be better replaced by an implant. From the evidence presented it is clear that both treatments have a place in contemporary restorative dental treatment and that survival for both root filled teeth and implants are similar. Unsurprisingly, both these treatment choices require high standards of clinical expertise and careful planning to ensure the best outcome.


Direction Awareness in Citation Recommendation


title: Autocorrelation and DCT based quality metrics for fingerprint samples generated by smartphones

abstract: It is becoming feasible and promising to use general purposed smartphone cameras as fingerprint scanners due to the rapidly improvement of smartphone hardware performance. We propose an approach to qualify the fingerprint samples generated by smartphones' cameras under real-life scenarios. Firstly, our approach extracts 6 quality features for each image block divided from a fingerprint sample using ridge patterns' spatial autocorrelation in both the spatial and the discrete cosine transform (DCT) domain. Secondly, a trained support vector machine is adopted to generate a binary decision to indicate the quality of the image block. Finally, we take the normalized count of qualified blocks as an indicator of the whole fingerprint sample's quality. Our experiments demonstrate that the proposed approach is effective to assess the quality of fingerprint samples captured by such general purposed smartphone cameras. A Spearman's rank correlation coefficient (ranging between [-1,1]) of 0.6354 is achieved between the proposed quality metric and samples' normalized comparison scores (as a ground truth) in our experiment.


Understanding Representations and Reducing their Redundancy in Deep Networks


title: Transfer Learning for Semisupervised Collaborative Recommendation

abstract: Users’ online behaviors such as ratings and examination of items are recognized as one of the most valuable sources of information for learning users’ preferences in order to make personalized recommendations. But most previous works focus on modeling only one type of users’ behaviors such as numerical ratings or browsing records, which are referred to as explicit feedback and implicit feedback, respectively. In this article, we study a Semisupervised Collaborative Recommendation (SSCR) problem with labeled feedback (for explicit feedback) and unlabeled feedback (for implicit feedback), in analogy to the well-known Semisupervised Learning (SSL) setting with labeled instances and unlabeled instances. SSCR is associated with two fundamental challenges, that is, heterogeneity of two types of users’ feedback and uncertainty of the unlabeled feedback. As a response, we design a novel Self-Transfer Learning (sTL) algorithm to iteratively identify and integrate likely positive unlabeled feedback, which is inspired by the general forward/backward process in machine learning. The merit of sTL is its ability to learn users’ preferences from heterogeneous behaviors in a joint and selective manner. We conduct extensive empirical studies of sTL and several very competitive baselines on three large datasets. The experimental results show that our sTL is significantly better than the state-of-the-art methods.


title: An evolutionary analysis of the innovation policy domain: Is there a paradigm shift?

abstract: Researchers focus on understanding the nature of ecosystems and societies as well as explaining how paradigms change. These efforts are presented and disseminated through scholarly work in scientific literature. The pool of knowledge generated through databases allows one to track how our understanding changes and how paradigms shift through time. The present study is concerned with the domain of innovation policy, which is affected directly by societal and technological change and is a good archetype for demonstrating the scientific change perspective. In recent years, scientometrics has been frequently used to measure and analyze progress in science, technology and innovation. This study makes use of a combination of scientometric analysis and evolutionary framework analysis to demonstrate the evolution of innovation policy domain. Kuhn’s seminal approach is applied for classifying and interpreting the phases across the evolution of the domain within a 30-year timeframe. The analysis demonstrates that the innovation policy domain is at the “crisis stage” as a result of ongoing with transformations in the society, technology, economy and policy. These transformations affect both supply and demand sides of innovation and call for an evolution in the innovation policy domain. Although this by no means represents that the innovation policy domain is in a “deadlock”, the present study asserts that there is a new quest in innovation policy by adapting, re-framing or re-constructing the scope of the domain. The anticipated paradigm shift is expected to lead to a more de-centralized and distributed understanding of the world for innovation policy making.


title: Robust Scene Categorization by Learning Image Statistics in Context

abstract: We present a generic and robust approach for scene categorization. A complex scene is described by proto-concepts like vegetation, water, fire, sky etc. These proto-concepts are represented by low level features, where we use natural images statistics to compactly represent color invariant texture information by a Weibull distribution. We introduce the notion of contextures which preserve the context of textures in a visual scene with an occurrence histogram (context) of similarities to proto-concept descriptors (texture). In contrast to a codebook approach, we use the similarity to all vocabulary elements to generalize beyond the code words. Visual descriptors are attained by combining different types of contexts with different texture parameters. The visual scene descriptors are generalized to visual categories by training a support vector machine. We evaluate our approach on 3 different datasets: 1) 50 categories for the TRECVID video dataset; 2) the Caltech 101-object images; 3) 89 categories being the intersection of the Corel photo stock with the Art Explosion photo stock. Results show that our approach is robust over different datasets, while maintaining competitive performance.


title: Magnetic Beamforming for Wireless Power Transfer

abstract: Magnetic resonant coupling (MRC) is an efficient method for realizing the near-field wireless power transfer (WPT). The use of multiple transmitters (TXs) each with one coil can be applied to enhance the WPT performance by coherently combining the magnetic fields induced by all TX coils in a beam toward the receiver (RX) coil, a technique termed "magnetic beamforming". In this paper, we study the optimal magnetic beamforming design for an MRC-WPT system with multiple TXs and a single RX. We formulate a problem to jointly optimize the currents flowing through different TXs so as to minimize the total power drawn from their voltage sources, subject to the minimum power required by the RX load as well as the practical constraints on the peak voltage and current at all TXs. For the special case of identical TX resistances and without the peak voltage and current constraints, we show that the optimal current at each TX should be proportional to the mutual inductance between its TX coil and the RX coil. In general, the problem is a non-convex quadratically constrained quadratic programming (QCQP), which is reformulated as a semidefinite programming (SDP) with rank-one constraint. We show that the semidefinite relaxation (SDR) of the reformulated problem is tight and hence the problem is solved optimally. Numerical results show that the optimal magnetic beamforming design significantly enhances the deliverable power as well as the power efficiency over the uncoordinated WPT benchmark with equal current allocation over TXs.


title: Improving the performance and energy efficiency of elevators by direct-landing elevator position control system

abstract: The efforts for achieving the highest possible efficiency and performance of elevators have become a fundamental field of research in the elevator world. To now, there have been reported a number of theoretical and experimental researches finding out alternative solutions to improve both the efficiency and performance of elevators. The use of advanced control systems, such as direct-landing elevator position control (EPC) system, is one of the promising research fields to improve both performance and efficiency of elevators. In this work, both traditional elevator control system with step-change landing method (STD) and the advanced elevator position control system with direct-landing method (EPC) were investigated through a number of graphical analyses of motion patterns on a test elevator. In the end, time and energy implications of EPC control system were itemized with respect to any achievements, advantages, and expected positive contributions to elevator users, project and building owners.


title: The burden of malaria in pregnancy in malaria-endemic areas

abstract: Pregnant women in malarious areas may experience a variety of adverse consequences from malaria infection including maternal anemia, placental accumulation of parasites, low birth weight (LBW) from prematurity and intrauterine growth retardation (IUGR), fetal parasite exposure and congenital infection, and infant mortality (IM) linked to preterm-LBW and IUGR-LBW. We reviewed studies between 1985 and 2000 and summarized the malaria population attributable risk (PAR) that accounts for both the prevalence of the risk factors in the population and the magnitude of the associated risk for anemia, LBW, and IM. Consequences from anemia and human immunodeficiency virus infection in these studies were also considered. Population attributable risks were substantial: malaria was associated with anemia (PAR range = 3-15%), LBW (8-14%), preterm-LBW (8-36%), IUGR-LBW (13-70%), and IM (3-8%). Human immunodeficiency virus was associated with anemia (PAR range = 12-14%), LBW (11-38%), and direct transmission in 20-40% of newborns, with direct mortality consequences. Maternal anemia was associated with LBW (PAR range = 7-18%), and fetal anemia was associated with increased IM (PAR not available). We estimate that each year 75,000 to 200,000 infant deaths are associated with malaria infection in pregnancy. The failure to apply known effective antimalarial interventions through antenatal programs continues to contribute substantially to infant deaths globally.


title: Non-invasive wearable electrochemical sensors: a review.

abstract: Wearable sensors have garnered considerable recent interest owing to their tremendous promise for a plethora of applications. Yet the absence of reliable non-invasive chemical sensors has greatly hindered progress in the area of on-body sensing. Electrochemical sensors offer considerable promise as wearable chemical sensors that are suitable for diverse applications owing to their high performance, inherent miniaturization, and low cost. A wide range of wearable electrochemical sensors and biosensors has been developed for real-time non-invasive monitoring of electrolytes and metabolites in sweat, tears, or saliva as indicators of a wearer's health status. With continued innovation and attention to key challenges, such non-invasive electrochemical sensors and biosensors are expected to open up new exciting avenues in the field of wearable wireless sensing devices and body-sensor networks, and thus find considerable use in a wide range of personal health-care monitoring applications, as well as in sport and military applications.


title: Using data mining techniques in heart disease diagnosis and treatment

abstract: The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. Heart disease is the leading cause of death all over the world in the past ten years. Several researchers are using statistical and data mining tools to help health care professionals in the diagnosis of heart disease. Using single data mining technique in the diagnosis of heart disease has been comprehensively investigated showing acceptable levels of accuracy. Recently, researchers have been investigating the effect of hybridizing more than one technique showing enhanced results in the diagnosis of heart disease. However, using data mining techniques to identify a suitable treatment for heart disease patients has received less attention. This paper identifies gaps in the research on heart disease diagnosis and treatment and proposes a model to systematically close those gaps to discover if applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease.


title: Energy system contribution during 200- to 1500-m running in highly trained athletes

abstract: PURPOSE ::: The purpose of the present study was to profile the aerobic and anaerobic energy system contribution during high-speed treadmill exercise that simulated 200-, 400-, 800-, and 1500-m track running events. ::: ::: ::: METHODS ::: Twenty highly trained athletes (Australian National Standard) participated in the study, specializing in either the 200-m (N = 3), 400-m (N = 6), 800-m (N = 5), or 1500-m (N = 6) event (mean VO2 peak [mL x kg(-1)-min(-1)] +/- SD = 56+/-2, 59+/-1, 67+/-1, and 72+/-2, respectively). The relative aerobic and anaerobic energy system contribution was calculated using the accumulated oxygen deficit (AOD) method. ::: ::: ::: RESULTS ::: The relative contribution of the aerobic energy system to the 200-, 400-, 800-, and 1500-m events was 29+/-4, 43+/-1, 66+/-2, and 84+/-1%+/-SD, respectively. The size of the AOD increased with event duration during the 200-, 400-, and 800-m events (30.4+/-2.3, 41.3+/-1.0, and 48.1+/-4.5 mL x kg(-1), respectively), but no further increase was seen in the 1500-m event (47.1+/-3.8 mL x kg(-1)). The crossover to predominantly aerobic energy system supply occurred between 15 and 30 s for the 400-, 800-, and 1500-m events. ::: ::: ::: CONCLUSIONS ::: These results suggest that the relative contribution of the aerobic energy system during track running events is considerable and greater than traditionally thought.


title: Efficacy of Occupational Therapy Task-oriented Approach in Upper Extremity Post-stroke Rehabilitation.

abstract: There is a need for more effective rehabilitation methods for individuals post-stroke. Occupational Therapy Task-Oriented (TO) approach has not been evaluated in a randomized clinical trial. The purpose of this study was to evaluate functional and impairment efficacies of TO approach on the more-affected Upper Extremity (UE) of persons post-stroke. A randomized single-blinded cross-over trial recruited 20 participants post-stroke (mean chronicity = 62 months) who demonstrated at least 10° active more-affected shoulder flexion and abduction and elbow flexion-extension. Participants were randomized into immediate (n = 10) and delayed intervention (n = 10) groups. Immediate group had 6 weeks of 3 hr/week TO intervention followed by 6 weeks of no-intervention control. Delayed intervention group underwent the reversed order. Functional measures included Canadian Occupational Performance Measure (COPM), Motor Activity Log (MAL), and Wolf Motor Function Test (WMFT). Impairment measures included UE Active Range of Motion (AROM) and handheld dynamometry strength. Measurements were obtained at baseline, cross over, and end of the study. TO intervention showed statistically higher functional change scores. COPM performance and satisfaction scores were 2.83 and 3.46 units greater respectively (p < .001), MAL amount of use and quality of use scores were 1.1 and 0.87 units greater, respectively (p < .001), WMFT time was 8.35 seconds faster (p = .009). TO impairment outcomes were not significantly larger than control ones. TO approach appears to be an effective UE post-stroke rehabilitation approach inducing clinically meaningful functional improvements. More studies are needed with larger samples and specific stroke chronicity and severity. Copyright © 2016 John Wiley & Sons, Ltd.


title: 5G Over-the-Air Measurement Challenges: Overview

abstract: Over-the-air (OTA) measurements are the standardized methods to evaluate radio performance in wireless systems. EMC issues such as interference and receiver desensitization are important factors. In this paper, OTA measurement challenges in the upcoming fiftth generation (5G) wireless systems are overviewed. Several key technologies that could be widely used in the 5G OTA measurements are discussed, including near-field to far-field transformation, anechoic chamber absorbers, and sampling antennas. The most important devices under test (DUT) for 5G OTA are chip sets, user equipment, and active array systems. The OTA test challenges for certification, research and development, and mass production for these DUTs are introduced.


title: Review on virtual synchronous generator (VSG) for enhancing performance of microgrid

abstract: Increasing penetration level of Distributed Generators (DGs)/ Renewable energy sources (RES) creates low inertia problems, damping effect on grid dynamic performance and stability. Voltage rise by reverse power from PV generations, excessive supply of electricity in the grid due to full generation of the DGs, power fluctuations due to variable nature of Renewable energy sources, and degradation of frequency regulation can be considered as some negative results of mentioned hurdle. For securing such a grid there should add inertia, virtually. The proposed control approach based on an inverter connected in Microgrid, which operated as a virtual synchronous generator (VSG) reacting only in transitory situations. Super capacitor (SC), an energy storage system (ESS) included in MG-forming inverter i.e., a normal inverter structure used for contributing system robustness and reliability. In steady-state load distributed between the MG-supporting inverter are achieved with two restoration controllers for active and reactive powers. The virtual inertia hypothesis can be used for maintaining a large share of DGs in future grids without affect system stability. This proposed system analyzed and simulated using MATLAB/SIMULINK.


title: Linking small-scale farmers to market using ICT

abstract: One of the drawbacks of Least Developed Countries (LDC) is that, major population depends on agriculture but it contributes lesser to gross domestic product, the main factor causing this issue is small-scale farming. One third of worlds population depend their livelihoods on agriculture. It also contributes significantly in reducing poverty and achieving food security. The study will emphasize on, and use small-scale farmers and smallholder farms interchangeably. For the small-scale farmer to market and compete in national or regional market, they need huge investments such as modern technologies, modern transportation and storage facilities like in developed countries, which is financially challenging. Therefore this paper's focus is on developing Agriculture Market Information System (AMIS) using Information and Communication Technology (ICT) and low cost Internet of Things (IoT) devices, which is suitable for small-scale farmers' even at rural communities without electricity connection. Through which they can market their farm produce right from the field, it will result in lesser to no waste of agriculture produce and regulate market price by focusing on farmer and vendor linkage eliminating middleman.


title: Gyrolayout: A Hyperbolic Level-of-Detail Tree Layout.

abstract: Many large datasets can be represented as hierarchical structures, introducing not only the necessity of specialized tree visualization techniques, but also the requirements of handling large amounts of data and offering the user a useful insight into them. Many two-dimensional techniques have been developed, but 3-dimensional ones, together with navigational interactions, present a promising appropriate tool to deal with large trees. In this paper we present a hyperbolic tree layout extended to support different levelof-detail techniques and suitable for large tree representation and visualization. This layout permits the visualization of large trees with different level of detail in an enclosed 3-dimensional volume. As a significant part of the layout, we also present a Weighted Spherical Centroidal Voronoi Tessellation, an extension of planar Weighted Centroidal Voronoi Tessellations, in order to find an appropriate distribution of nodes on a spherical surface.


title: Time-series classification using neural Bag-of-Features

abstract: Classification of time-series data is a challenging problem with many real-world applications, ranging from identifying medical conditions from electroencephalography (EEG) measurements to forecasting the stock market. The well known Bag-of-Features (BoF) model was recently adapted towards time-series representation. In this work, a neural generalization of the BoF model, composed of an RBF layer and an accumulation layer, is proposed as a neural layer that receives the features extracted from a time-series and gradually builds its representation. The proposed method can be combined with any other layer or classifier, such as fully connected layers or feature transformation layers, to form deep neural networks for time-series classification. The resulting networks are end-to-end differentiable and they can be trained using regular back-propagation. It is demonstrated, using two time-series datasets, including a large-scale financial dataset, that the proposed approach can significantly increase the classification metrics over other baseline and state-of-the-art techniques.


title: Learning like humans with Deep Symbolic Networks

abstract: We introduce the Deep Symbolic Network (DSN) model, which aims at becoming the white-box version of Deep Neural Networks (DNN). The DSN model provides a simple, universal yet powerful structure, similar to DNN, to represent any knowledge of the world, which is transparent to humans. The conjecture behind the DSN model is that any type of real world objects sharing enough common features are mapped into human brains as a symbol. Those symbols are connected by links, representing the composition, correlation, causality, or other relationships between them, forming a deep, hierarchical symbolic network structure. Powered by such a structure, the DSN model is expected to learn like humans, because of its unique characteristics. First, it is universal, using the same structure to store any knowledge. Second, it can learn symbols from the world and construct the deep symbolic networks automatically, by utilizing the fact that real world objects have been naturally separated by singularities. Third, it is symbolic, with the capacity of performing causal deduction and generalization. Fourth, the symbols and the links between them are transparent to us, and thus we will know what it has learned or not - which is the key for the security of an AI system. Fifth, its transparency enables it to learn with relatively small data. Sixth, its knowledge can be accumulated. Last but not least, it is more friendly to unsupervised learning than DNN. We present the details of the model, the algorithm powering its automatic learning ability, and describe its usefulness in different use cases. The purpose of this paper is to generate broad interest to develop it within an open source project centered on the Deep Symbolic Network (DSN) model towards the development of general AI.


title: Non-contact video-based vital sign monitoring using ambient light and auto-regressive models

abstract: Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient's face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter.


title: LightenNet: A Convolutional Neural Network for weakly illuminated image enhancement

abstract: Abstract Weak illumination or low light image enhancement as pre-processing is needed in many computer vision tasks. Existing methods show limitations when they are used to enhance weakly illuminated images, especially for the images captured under diverse illumination circumstances. In this letter, we propose a trainable Convolutional Neural Network (CNN) for weakly illuminated image enhancement, namely LightenNet, which takes a weakly illuminated image as input and outputs its illumination map that is subsequently used to obtain the enhanced image based on Retinex model. The proposed method produces visually pleasing results without over or under-enhanced regions. Qualitative and quantitative comparisons are conducted to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method achieves superior performance than existing methods. Additionally, we propose a new weakly illuminated image synthesis approach, which can be use as a guide for weakly illuminated image enhancement networks training and full-reference image quality assessment.


title: Decentralized Human Resource Management in Public Service – The Italian National Research Council Approach

abstract: The Italian National Research Council is a public company with multiple locations and many decentralized human resources departments. With decentralized human resources management, each location controls its own individual personnel issues such as attendances and leaves. One of the goals of decentralized human resources departments is to give autonomy to different locations, allowing them to adapt to the specific scientific climate of the individual site. However, the actual result of this form of human resources management is often disorganization, failure to follow unified standards or an ineffective application of those standards.


title: A survey of optimization by building and using probabilistic models

abstract: Summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space. It settles the algorithms in the field of genetic and evolutionary computation where they have been originated. All methods are classified into a few classes according to the complexity of the class of models they use. Algorithms from each of these classes are briefly described and their strengths and weaknesses are discussed.


title: � Human Brain Mapping 18:135–145(2003) � Ketamine and fMRI BOLD Signal: Distinguishing Between Effects Mediated by Change in Blood Flow Versus Change in Cognitive State

abstract: No human fMRI studies have examined ketamine effects on the BOLD signal change associated with cognitive task performance. We wished to distinguish between effects on 1) cerebral blood flow, with resultant change in BOLD signal; and 2) cognition and neural mechanisms underlying BOLD signal change associated with task performance. Eight right-handed men (mean age 28.75 years) received ketamine or saline i.v. in a randomized, double-blind manner (bolus 0.23 mg/kg; 0.5 mg/kg over 45 min to a maximum 1 hr). Subjects viewed 10 alternating 30-sec blocks of faces with neutral expressions and a fixation cross and discriminated gender of faces. Gradient echo echoplanar images were acquired on a GE Signa 1.5 T Neurovascular system. One hundred T2-weighted images depicting BOLD contrast were acquired over 5 min (for each task) at each of 14 near-axial noncontiguous 7-mm thick planes. Ketamine significantly increased dissociative phenomena and negative symptoms, but did not affect performance of the gender discrimination task. Significant BOLD signal change was demonstrated predominantly in occipitotemporal cortex with both ketamine and placebo. Only two clusters in middle occipital gyrus (BA 18) and precentral gyrus (BA 4) showed significantly decreased BOLD signal change during ketamine compared to placebo. BOLD signal change was not significantly greater in any region during ketamine. Our findings demonstrate subtle rather than major differences between the effects of ketamine and placebo upon the BOLD signal change during perception of face-non face contrast. We suggest that they represent task-dependent effects of the drug/placebo, rather than task-independent effects of the drug per se, and indicate that the effects of ketamine on cerebral blood flow are predominantly focal and task-dependent, rather than global and task-independent.


title: Efficient PSD Constrained Asymmetric Metric Learning for Person Re-Identification

abstract: Person re-identification is becoming a hot research topic due to its value in both machine learning research and video surveillance applications. For this challenging problem, distance metric learning is shown to be effective in matching person images. However, existing approaches either require a heavy computation due to the positive semidefinite (PSD) constraint, or ignore the PSD constraint and learn a free distance function that makes the learned metric potentially noisy. We argue that the PSD constraint provides a useful regularization to smooth the solution of the metric, and hence the learned metric is more robust than without the PSD constraint. Another problem with metric learning algorithms is that the number of positive sample pairs is very limited, and the learning process is largely dominated by the large amount of negative sample pairs. To address the above issues, we derive a logistic metric learning approach with the PSD constraint and an asymmetric sample weighting strategy. Besides, we successfully apply the accelerated proximal gradient approach to find a global minimum solution of the proposed formulation, with a convergence rate of O(1/t^2) where t is the number of iterations. The proposed algorithm termed MLAPG is shown to be computationally efficient and able to perform low rank selection. We applied the proposed method for person re-identification, achieving state-of-the-art performance on four challenging databases (VIPeR, QMUL GRID, CUHK Campus, and CUHK03), compared to existing metric learning methods as well as published results.


title: Social Media and Information Overload: Survey Results

abstract: A UK-based online questionnaire investigating aspects of usage of user-generated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy.


title: Contesting the City: Enacting the Political Through Digitally Supported Urban Walks

abstract: We present a method for the situated discovery and articulation of issues at the intersection between the politics of place making and city planning. We describe the construction and use of designed tools, such as historical political archives; counterfactual maps; and cards to invite situated dialogue between the social and institutional practices and mechanisms that produce our cities. Grounded in an account of the political as vernacular and embodied, our analysis advance understandings on the politics of design, and on the complex interrelationship between places and political spaces. We outline how HCI can adopt methods and develop sensitivities to support democratic practices and publics envisioning their urban futures.


title: Sturdy childhood predictors of adult antisocial behaviour: replications from longitudinal studies

abstract: Results are compared in studies of 4 male cohorts – 1 all white, 1 all black, and 2 racially representative of the population – growing up in different eras, followed past varying portions of their adult lives, living in different parts of the US. Despite sample differences and differences in sources of information and in the variables used to measure both childhood predictors and adult outcomes, some striking replications appear with respect to childhood predictors of adult antisocial behaviour. All types of antisocial behaviour in childhood predict a high level of antisocial behaviour in adulthood and each kind of adult antisocial behaviour is predicted by the number of childhood antisocial behaviours, indicating that adult and childhood antisocial behaviour both form syndromes and that these syndromes are closely interconnected. Also confirmed across studies are: (1) adult antisocial behaviour virtually requires childhood antisocial behaviour; (2) most antisocial children do not become antisocial adults; (3) the variety of antisocial behaviour in childhood is a better predictor of adult antisocial behaviour than is any particular behaviour; (4) adult antisocial behaviour is better predicted by childhood behaviour than by family background or social class of rearing; (5) social class makes little contribution to the prediction of serious adult antisocial behaviour.


title: Bayesian Learning of a Tree Substitution Grammar

abstract: Tree substitution grammars (TSGs) offer many advantages over context-free grammars (CFGs), but are hard to learn. Past approaches have resorted to heuristics. In this paper, we learn a TSG using Gibbs sampling with a nonparametric prior to control subtree size. The learned grammars perform significantly better than heuristically extracted ones on parsing accuracy.


title: Distraction and pedestrian safety: how talking on the phone, texting, and listening to music impact crossing the street.

abstract: As use of handheld multimedia devices has exploded globally, safety experts have begun to consider the impact of distraction while talking, text-messaging, or listening to music on traffic safety. This study was designed to test how talking on the phone, texting, and listening to music may influence pedestrian safety. 138 college students crossed an interactive, semi-immersive virtual pedestrian street. They were randomly assigned to one of four groups: crossing while talking on the phone, crossing while texting, crossing while listening to a personal music device, or crossing while undistracted. Participants distracted by music or texting were more likely to be hit by a vehicle in the virtual pedestrian environment than were undistracted participants. Participants in all three distracted groups were more likely to look away from the street environment (and look toward other places, such as their telephone or music device) than were undistracted participants. Findings were maintained after controlling for demographics, walking frequency, and media use frequency. Distraction from multimedia devices has a small but meaningful impact on college students' pedestrian safety. Future research should consider the cognitive demands of pedestrian safety, and how those processes may be impacted by distraction. Policymakers might consider ways to protect distracted pedestrians from harm and to reduce the number of individuals crossing streets while distracted.


title: Skin Lesion Classification Using Deep Multi-scale Convolutional Neural Networks

abstract: We present a deep learning approach to the ISIC 2017 Skin Lesion Classification Challenge using a multi-scale convolutional neural network. Our approach utilizes an Inception-v3 network pre-trained on the ImageNet dataset, which is fine-tuned for skin lesion classification using two different scales of input images.


title: Heavy Metal Contamination of Drinking Water Nearby Hadiara Drain

abstract: The present study was conducted in the surrounding areas of Hadiara drain, a major drain flushing out in river Ravi. Various physical and chemical parameters were analyzed in the drinking water randomly collected from five areas located around Hadiara drain including Raja Bolay Village (RBV), Ghanakar Village (GV), Deu Khurd (DK), Kahna Nau (KN), and Green Cap Housing Society (GCHS). The results were compared with standard value of World Health Organization for drinking water. It was observed that, the pollutants emanating from local tannery and textile effluents discharged into Hadiara drain increases the value of electrical conductivity and dissolved oxygen in water which is above the WHO standard value. Among the heavy metals tested Cr was higher than the recommended values in all areas studied except in RBV and GCHS. The study concluded that the use of polluted water degraded the ground water quality.


title: Performance study of Voice in Wireless Mesh Network

abstract: The wireless meshed technology is one of the most advanced technology and can be viewed as the technology of the future. This article deals with aspects like performance metrics i.e. delay, R-Factor and Mean Opinion Score (MOS) that have a direct influence on the performance of the wireless mesh network. In this paper voice quality is analyzed in terms of R-Factor and MOS in Wireless Mesh Network by performing simulations using Qualnet. Another issue addressed in this paper is the effect of increasing mesh points on voice transmission in wireless mesh network. The results show that the voice quality matrices R-Factor and MOS degrades and the average delay increases, with increase in number of hops in the Wireless Mesh network. KeywordsWMNs, MOS, Service time, Mesh Point, Average delay.


title: A knowledge management perspective of requirements engineering

abstract: A well-known problem in requirements engineering is the communication between stakeholders with different background. This communication problem is mostly attributed to the different “languages” spoken by these stakeholders based on their different background and domain knowledge. We experienced a related problem involved with transferring and sharing such knowledge, when stakeholders are reluctant to do this. So, we take a knowledge management perspective of requirements engineering and carry over ideas for the sharing of knowledge about requirements and the domain. We cast requirements engineering as a knowledge management process and adopt the concept of the spiral of knowledge involving transformations from tacit to explicit knowledge, and vice versa. In the context of a real-world problem, we found the concept of “knowledge holders” and their relations to categories of requirements and domain knowledge both useful and important. This project was close to become a failure until knowledge transfer has been intensified. The knowledge management perspective provided insights for explaining improved knowledge exchange.


title: Blind reflectometry

abstract: We address the problem of inferring homogeneous reflectance (BRDF) from a single image of a known shape in an unknown real-world lighting environment. With appropriate representations of lighting and reflectance, the image provides bilinear constraints on the two signals, and our task is to blindly isolate the latter. We achieve this by leveraging the statistics of real-world illumination and estimating the reflectance that is most likely under a distribution of probable illumination environments. Experimental results with a variety of real and synthetic images suggest that useable reflectance information can be inferred in many cases, and that these estimates are stable under changes in lighting.


title: On deep learning-based channel decoding

abstract: We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple classifier. We introduce the metric normalized validation error (NVE) in order to further investigate the potential and limitations of deep learning-based decoding with respect to performance and complexity.


title: Formulation of PID Control for DC–DC Converters Based on Capacitor Current: A Geometric Context

abstract: The output voltage derivative term associated with a PID controller injects significant noise in a dc-dc converter. This is mainly due to the parasitic resistance and inductance of the output capacitor. Particularly, during a large-signal transient, noise injection significantly degrades phase margin. Although noise characteristics can be improved by reducing the cutoff frequency of the low-pass filter associated with the voltage derivative, this degrades the closed-loop bandwidth. A formulation of a PID controller is introduced to replace the output voltage derivative with information about the capacitor current, thus reducing noise injection. It is shown that this formulation preserves the fundamental principle of a PID controller and incorporates a load current feedforward, as well as inductor current dynamics. This can be helpful to further improve bandwidth and phase margin. The proposed method is shown to be equivalent to a voltage-mode-controlled buck converter and a current-mode-controlled boost converter with a PID controller in the voltage feedback loop. A buck converter prototype is tested, and the proposed algorithm is implemented using a field-programmable gate array.


title: Combining Holistic and Part-based Deep Representations for Computational Painting Categorization

abstract: Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization. We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach by $6.4\%$ and $3.8\%$ respectively on artist and style classification.


A Discrete Model for Inelastic Deformation of Thin Shells