title: Towards building a word similarity dictionary for personality bias classification of phishing email contents

abstract: Phishing attacks are a form of social engineering technique used for stealing private information from users through emails. A general approach for phishing susceptibility analysis is to profile the user's personality using personality models such as the Five Factor Model (FFM) and find out the susceptibility for a set of phishing attempts. The FFM is a personality profiling system that scores participants on five separate personality traits: openness to experience (O), conscientiousness (C), extraversion (E), agreeableness (A), and neuroticism (N). However, existing approaches don't take into account the fact that based on the content, for example, a phishing email offering an enticing free prize might be very effective on a dominant O-personality (curious, open to new experience), but not to an N-personality (tendency of experiencing negative emotion). Therefore, it is necessary to consider the personality bias of the phishing email contents during the susceptibility analysis. In this paper, we have proposed a method to construct a dictionary based on the semantic similarity of prospective words describing the FFM. Words generated through this dictionary can be used to label the phishing emails according to the personality bias and serve as the key component of a personality bias classification system of phishing emails. We have validated our dictionary construction using a large public corpus of phishing email data which shows the potential of the proposed system in anti-phishing research.


title: Towards meaningful physics from generative models

abstract: In several physical systems, important properties characterizing the system itself are theoretically related with specific degrees of freedom. Although standard Monte Carlo simulations provide an effective tool to accurately reconstruct the physical configurations of the system, they are unable to isolate the different contributions corresponding to different degrees of freedom. Here we show that unsupervised deep learning can become a valid support to MC simulation, coupling useful insights in the phases detection task with good reconstruction performance. As a testbed we consider the 2D XY model, showing that a deep neural network based on variational autoencoders can detect the continuous Kosterlitz-Thouless (KT) transitions, and that, if endowed with the appropriate constrains, they generate configurations with meaningful physical content.


Mining Twitter Data to Improve Detection of Schizophrenia


title: Robotic Edge-Rolling Manipulation: A Grasp Planning Approach

abstract: This letter presents a novel robotic manipulation technique that we call robotic edge rolling. It refers to transporting a cylindrical object by rolling on its circular edge, as human workers might maneuver a gas cylinder on the ground. Our robotic edge rolling is achieved by controlling the object to both roll on its bottom edge in contact with the ground and slide on the surface of the robot's end effector. It can thus be regarded as a form of robotic dexterous, in-hand manipulation with nonprehensile grasps. We address the problem of grasp planning for edge rolling by studying how to design appropriately shaped end effectors with zero internal mobility and how to find feasible grasps for stably rolling the object quasi-dynamically with our simple end effectors. An extensive set of experiments is performed with a conventional manipulator arm on not only flat surfaces but also a U-shaped half-pipe track. Long-range edge rolling is demonstrated with a modular mobile manipulator that is capable of active steering control.


title: Class-E RF power amplifier with a flat-top transistor-voltage waveform

abstract: This paper shows a Class-E RF power amplifier designed to obtain a flat-top transistor-voltage waveform whose peak value is 81% of the peak value of the voltage of a “Classical” Class-E amplifier.


title: Social perception from visual cues: role of the STS region

abstract: Social perception refers to initial stages in the processing of information that culminates in the accurate analysis of the dispositions and intentions of other individuals. Single-cell recordings in monkeys, and neurophysiological and neuroimaging studies in humans, reveal that cerebral cortex in and near the superior temporal sulcus (STS) region is an important component of this perceptual system. In monkeys and humans, the STS region is activated by movements of the eyes, mouth, hands and body, suggesting that it is involved in analysis of biological motion. However, it is also activated by static images of the face and body, suggesting that it is sensitive to implied motion and more generally to stimuli that signal the actions of another individual. Subsequent analysis of socially relevant stimuli is carried out in the amygdala and orbitofrontal cortex, which supports a three-structure model proposed by Brothers. The homology of human and monkey areas involved in social perception, and the functional interrelationships between the STS region and the ventral face area, are unresolved issues.


title: Abdominal obesity and metabolic syndrome

abstract: Metabolic syndrome is associated with abdominal obesity, blood lipid disorders, inflammation, insulin resistance or full-blown diabetes, and increased risk of developing cardiovascular disease. Proposed criteria for identifying patients with metabolic syndrome have contributed greatly to preventive medicine, but the value of metabolic syndrome as a scientific concept remains controversial. The presence of metabolic syndrome alone cannot predict global cardiovascular disease risk. But abdominal obesity - the most prevalent manifestation of metabolic syndrome - is a marker of 'dysfunctional adipose tissue', and is of central importance in clinical diagnosis. Better risk assessment algorithms are needed to quantify diabetes and cardiovascular disease risk on a global scale.


ChessVision : Chess Board and Piece Recognition


title: Static cleaning tests as the first step to optimize RO membranes cleaning procedure

abstract: AbstractMembrane fouling is an intrinsic problem of membrane technology which affects process performance and causes a substantial rise of the operating costs. This becomes central in seawater desalination processes. This study is focused on applying a membrane chemical cleaning protocol to obtain the most adequate cleaning conditions to recover the permselective properties of an irreversibly fouled reverse osmosis membrane from a seawater desalination plant. The research was carried out in three steps: fouling characterization by scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDX), atomic force microscopy, attenuated total reflectance Fourier transform infrared spectroscopy and elemental analysis; static cleaning tests; and characterization of the membrane surface after the cleaning step. The cleaning process was performed in a static way as a preliminary step. Five of the most widely used cleaning agents were tested (NaOH, citric acid, HCl, sodium dodecyl sulfate [SD...


title: The alpha chain of laminin-1 is independently secreted and drives secretion of its beta- and gamma-chain partners.

abstract: A mammalian recombinant strategy was established to dissect rules of basement membrane laminin assembly and secretion. The alpha-, beta-, and gamma-chain subunits of laminin-1 were expressed in all combinations, transiently and/or stably, in a near-null background. In the absence of its normal partners, the alpha chain was secreted as intact protein and protein that had been cleaved in the coiled-coil domain. In contrast, the beta and gamma chains, expressed separately or together, remained intracellular with formation of betabeta or betagamma, but not gammagamma, disulfide-linked dimers. Secretion of the beta and gamma chains required simultaneous expression of all three chains and their assembly into alphabetagamma heterotrimers. Epitope-tagged recombinant alpha subunit and recombinant laminin were affinity-purified from the conditioned medium of alphagamma and alphabetagamma clones. Rotary-shadow electron microscopy revealed that the free alpha subunit is a linear structure containing N-terminal and included globules with a foreshortened long arm, while the trimeric species has the typical four-arm morphology of native laminin. We conclude that the alpha chain can be delivered to the extracellular environment as a single subunit, whereas the beta and gamma chains cannot, and that the alpha chain drives the secretion of the trimeric molecule. Such an alpha-chain-dependent mechanism could allow for the regulation of laminin export into a nascent basement membrane, and might serve an important role in controlling basement membrane formation.


Mothers and Daughters and Feminist Identity


title: A Cross Benchmark Assessment of a Deep Convolutional Neural Network for Face Recognition

abstract: Deep convolutional neural networks (DCNN) based algorithm methods have swept face-recognition. DCNNbased algorithms have shown significant improvements in accuracy on the Labeled Faces in the Wild (LFW) and the YouTube1 Video face-recognition benchmarks. These two benchmarks consist of images and videos of celebrities downloaded from the World Wide Web. Since 2004, the National Institute of Standards and Technology (NIST) has established a series of face-recognition benchmarks that span a range of scenarios and difficulties. The scenarios range from comparing frontal faces taken in studio lighting to comparing faces acquired with cell phone cameras taken outdoors. The VGG-face algorithm [7] was ran on eight NIST face-recognition benchmarks. The Vision Geometry Group (VGG)-face algorithm excelled on the most difficult benchmarks; existing algorithms excelled the benchmarks with higher quality images. This finding is consistent with the design of the algorithms. The VGG-face algorithm was designed to recognize faces in variable illumination; the existing algorithms were designed to operate on face-images taken in controlled illuminations. To accurately characterize the performance of face recognition algorithms, we recommend that performance is reported on multiple benchmarks.


title: Combining Particle MCMC with Rao-Blackwellized Monte Carlo Data Association for Parameter Estimation in Multiple Target Tracking

abstract: We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis-Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland.


Effect of Pulse Shaping On Ber Performance of QAM Modulated OFDM Signal 1


Identifying dialects with textual and acoustic cues


title: Characterization of FOXP3+CD4+ regulatory T cells in Crohn's disease.

abstract: FOXP3(+)CD4(+) regulatory T cells (T(R)) have emerged as important regulators of immune responses. The aim of our study was to assess the frequency and functional characteristics of FOXP3(+)CD4(+) T(R) in Crohn's disease (CD). We report that FOXP3(+)CD4(+) T(R) cells are expanded in mucosal lymphoid tissues (lamina propria and MLN) but are decreased in the PB in active CD. Patients treated with thiopurines, but not steroids or anti-TNF-alpha inhibitors, have a lower frequency of PB FOXP3(+)CD4(+) T(R) (7.8+/-2.4% vs. 9.9+/-1.8%, p=0.01). FOXP3(+) cells were localized in the lamina propria (LP), muscularis mucosa and serosa and accumulated in granulomas, whereas in MLN they localize in the T cell rich areas. MLN CD4(+)CD25(+) T cells from both CD and normal intestine efficiently suppress the proliferation of effector CD4(+)CD25(-) T cells. T cell activation of MLN in vitro with anti-CD3 plus anti-CD28 Abs enhances the expression of FOXP3, both at the protein and transcriptional level, which is further enhanced by the addition of TGF-beta. In summary, there is an expansion of FOXP3(+)CD4(+) T(R) cells in mucosal lymphoid tissues in CD; they accumulate in areas of active inflammation, including granulomas and retain potent regulatory activity ex vivo.


title: Adaptive Distributed Laser Charging for Efficient Wireless Power Transfer

abstract: Distributed laser charging (DLC) is a wireless power transfer technology for mobile electronics. Similar to traditional wireless charging systems, the DLC system can only provide constant power to charge a battery. However, Li-ion battery needs dynamic input current and voltage, thus power, in order to optimize battery charging performance. Therefore, neither power transmission efficiency nor battery charging performance can be optimized by the DLC system. We at first propose an adaptive DLC (ADLC) system to optimize wireless power transfer efficiency and battery charging performance. Then, we analyze ADLC's power conversion to depict the adaptation mechanism. Finally, we evaluate the ADLC's power conversion performance by simulation, which illustrates its efficiency improvement by saving at least 60.4\% of energy, comparing with the fixed-power charging system.


title: Simulation and analysis of ZigBee - WiFi interference

abstract: In this paper we present evaluation of ZigBee network and coexistence of ZigBee and WiFi network. We provide a simulation in OPNET Modeler using two scenario. In the first scenario we evaluate ZigBee network topology then in the second scenario we analyze coexistence between ZigBee and WiFi network. The result shows that tree topology on ZigBee gives highest throughput while mesh topology has the lowest end-to-end delay. In coexistence between WiFi and ZigBee, interference occurs and decrease the throughput on ZigBee network. The other result is that star topology makes wireless LAN has highest throughput while mesh topology gives the lowest http page response time in wireless LAN.


title: Weighted Fuzzy Dempster–Shafer Framework for Multimodal Information Integration

abstract: This study proposes an architecture based on a weighted fuzzy Dempster–Shafer framework (WFDSF), which can adjust weights associated with inconsistent evidence obtained by different classification approaches, to realize a fusion system for integrating multimodal information. The Dempster–Shafer theory (D-S theory) of evidence enables us to integrate heterogeneous information from multiple sources to obtain collaborative inferences for a given problem. To conquer various uncertainties associated with the collected information, our system assigns beliefs and plausibilities to possible hypotheses of each decision maker and uses a combination rule to fuse multimodal information. For information fusion, an important step in D-S aggregation is to find an appropriate basic probability assignment scheme for allocating support to each possible hypothesis/class, which remains an arduous and unsolved problem. Here, we propose a mathematical structure to aggregate weighted evidence extracted from two different types of approaches: fuzzy Naive Bayes and nearest mean classification rule. Further, an intuitionistic belief assignment is employed to address uncertainties between hypotheses/classes. Finally, 12 benchmark problems from the UCI machine learning repository for classification are employed to validate the proposed WFDSF-based scheme. In addition, an application of WFDSF to a practical brain–computer interface problem involving multimodal data fusion is demonstrated in this study. The experimental results show that the WFDSF is superior to several existing methods.


title: MULTI-OBJECTIVE OPTIMIZATION OF MULTI-ECHELON SUPPLY CHAIN NETWORKS WITH UNCERTAIN PRODUCT DEMANDS AND PRICES

abstract: Abstract A multi-product, multi-stage, and multi-period scheduling model is proposed in this paper to deal with multiple incommensurable goals for a multi-echelon supply chain network with uncertain market demands and product prices. The uncertain market demands are modeled as a number of discrete scenarios with known probabilities, and the fuzzy sets are used for describing the sellers’ and buyers’ incompatible preference on product prices. The supply chain scheduling model is constructed as a mixed-integer nonlinear programming problem to satisfy several conflict objectives, such as fair profit distribution among all participants, safe inventory levels, maximum customer service levels, and robustness of decision to uncertain product demands, therein the compromised preference levels on product prices from the sellers and buyers point of view are simultaneously taken into account. The inclusion of robustness measures as part of objectives can significantly reduce the variability of objective values to product demand uncertainties. For purpose that a compensatory solution among all participants of the supply chain can be achieved, a two-phase fuzzy decision-making method is presented and, by means of application of it to a numerical example, proved effective in providing a compromised solution in an uncertain multi-echelon supply chain network.


title: Hands on IRIS: Lessons learned from implementing a cross layer protocol stack for WSNs

abstract: This paper describes the challenges behind the implementation of a full cross-layer protocol stack for wireless sensor networks. Our solution, IRIS, integrates interest dissemination and convergecasting, adaptive duty cycle and data fusion. The interest dissemination protocol is used for task assignment and to build and maintain the network topology, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross layering in that MAC and routing operations are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. We have implemented and tested IRIS on two different testbeds, in a controlled lab scenario and in a building with Wi-Fi interference. The flexibility introduced by the IRIS cross layer approach results in higher robustness and reliability than that of well-known approaches such as BoX-MAC combined with the CTP protocol. IRIS delivers more than 95% of packets while other approaches have packet delivery ratios as low as 87% and are not as scalable with traffic. IRIS is also able to support timely and robust delivery of data with duty cycles as low as 5%.


title: Phenomics – technologies to relieve the phenotyping bottleneck

abstract: Global agriculture is facing major challenges to ensure global food security, such as the need to breed high-yielding crops adapted to future climates and the identification of dedicated feedstock crops for biofuel production (biofuel feedstocks). Plant phenomics offers a suite of new technologies to accelerate progress in understanding gene function and environmental responses. This will enable breeders to develop new agricultural germplasm to support future agricultural production. In this review we present plant physiology in an ‘omics' perspective, review some of the new high-throughput and high-resolution phenotyping tools and discuss their application to plant biology, functional genomics and crop breeding.


title: Allometric Analysis of Physical Performance Measures in Older Adults

abstract: Background and Purpose. Body mass is a confounding variable in human performance, and adjusting physical performance measures for body mass differences would allow meaningful individual and group comparisons. The purpose of this study was to allometrically determine the relationship between body mass and handgrip and ankle dorsiflexor performance on the Timed “Up & Go” Test (TUGT). Subjects. One hundred thirty-one subjects (33 male and 98 female) participated. Methods. All physical performance measures were adjusted for the influence of body mass, sex, and age using an allometric scaling procedure. Results. For handgrip force, the body mass exponent from allometric analysis was 0.63. For the TUGT, the body mass exponent was 0.073. For ankle dorsiflexor force and torque, the body mass exponents were 0.82 and 0.91, respectively. Discussion and Conclusion. The body mass exponents for handgrip force and the TUGT agree with previous clinical data and theoretical expectations. Studies investigating normalized handgrip force in older adults should consider an allometric scaling approach.


title: Quantization Mimic: Towards Very Tiny CNN for Object Detection

abstract: In this paper, we propose a simple and general framework for training very tiny CNNs for object detection. Due to limited representation ability, it is challenging to train very tiny networks for complicated tasks like detection. To the best of our knowledge, our method, called Quantization Mimic, is the first one focusing on very tiny networks. We utilize two types of acceleration methods: mimic and quantization. Mimic improves the performance of a student network by transfering knowledge from a teacher network. Quantization converts a full-precision network to a quantized one without large degradation of performance. If the teacher network is quantized, the search scope of the student network will be smaller. Using this feature of the quantization, we propose Quantization Mimic. It first quantizes the large network, then mimic a quantized small network. The quantization operation can help student network to better match the feature maps from teacher network. To evaluate our approach, we carry out experiments on various popular CNNs including VGG and Resnet, as well as different detection frameworks including Faster R-CNN and R-FCN. Experiments on Pascal VOC and WIDER FACE verify that our Quantization Mimic algorithm can be applied on various settings and outperforms state-of-the-art model acceleration methods given limited computing resouces.


title: Robust Gaussian Process Regression with a Student-t Likelihood

abstract: This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model, which has a non-log-concave likelihood. The challenge with the Student-t model is the analytically intractable inference which is why several approximative methods have been proposed. Expectation propagation (EP) has been found to be a very accurate method in many empirical studies but the convergence of EP is known to be problematic with models containing non-log-concave site functions. In this paper we illustrate the situations where standard EP fails to converge and review different modifications and alternative algorithms for improving the convergence. We demonstrate that convergence problems may occur during the type-II maximum a posteriori (MAP) estimation of the hyperparameters and show that standard EP may not converge in the MAP values with some difficult data sets. We present a robust implementation which relies primarily on parallel EP updates and uses a moment-matching-based double-loop algorithm with adaptively selected step size in difficult cases. The predictive performance of EP is compared with Laplace, variational Bayes, and Markov chain Monte Carlo approximations.


title: A compact O-shaped printed ACS fed monopole dual-band antenna for 2.4GHz Bluetooth and 5GHz WLAN/WiMAX applications

abstract: In this research paper, a compact, novel O-shaped ACS fed dual-band monopole antenna for 2.4 GHz Bluetooth/WLAN, 4.9 GHz US public safety band and 5.0 GHz WLAN/WiMAX applications is presented and investigated. The proposed geometry consists of an O-shaped radiating branch, monopole-shaped radiating branch and a rectangular shaped uniplanar ground plane, which occupies an overall size of 20 × 12.5 × 1.6mm3. In the design, proposed radiating structure has been printed on a low cost single layred substrate FR4. By properly tuning the radiating branches electrical lengths, two independent resonant frequencies can be obtained and tuned independently. The presented antenna also exhibits omnidirectional radiation patterns with acceptable peak gains for the desired of 2.4 GHz bluetooth band, 4.9 GHz US public band, 2.4/5.2/5.8 GHz WLAN bands and 5.5 GHz WiMAX band applications.


title: A novel method for extraction of the reference compensating current for single-phase shunt active power filters

abstract: This paper presents a novel method in the synchronous reference frame to extract the reference compensating current for single-phase shunt active power filters. The effectiveness of the proposed method is investigated using a detailed mathematical analysis. The results confirm the excellent performance of the proposed strategy. Theoretical evaluations are verified through simulation results.


title: Extracting linguistic rules from data sets using fuzzy logic and genetic algorithms

abstract: Linguistic rules in natural language are useful and consistent with human way of thinking. They are very important in multi-criteria decision making due to their interpretability. In this paper, our discussions concentrate on extracting linguistic rules from data sets. In the end, we firstly analyze how to extract complex linguistic data summaries based on fuzzy logic. Then, we formalize linguistic rules based on complex linguistic data summaries, in which, the degree of confidence of linguistic rules from a data set can be explained by linguistic quantifiers and its linguistic truth from the fuzzy logical point of view. In order to obtain a linguistic rule with a higher degree of linguistic truth, a genetic algorithm is used to optimize the number and parameters of membership functions of linguistic values. Computational results show that the proposed method is an alternative method for extracting linguistic rules with linguistic truth from data sets.


title: Clustering based multi-label classification for image annotation and retrieval

abstract: This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.


title: Secret-Focus: A Practical Physical Layer Secret Communication System by Perturbing Focused Phases in Distributed Beamforming

abstract: Ensuring confidentiality of communication is fundamental to securing the operation of a wireless system, where eavesdropping is easily facilitated by the broadcast nature of the wireless medium. By applying distributed beamforming among a coalition, we show that a new approach for assuring physical layer secrecy, without requiring any knowledge about the eavesdropper or injecting any additional cover noise, is possible if the transmitters frequently perturb their phases around the proper alignment phase while transmitting messages. This approach is readily applied to amplitude-based modulation schemes, such as PAM or QAM. We present our secrecy mechanisms, prove several important secrecy properties, and develop a practical secret communication system design. We further implement and deploy a prototype that consists of 16 distributed transmitters using USRP N210s in a 20×20×3m3 area. By sending more than 160M bits over our system to the receiver, depending on system parameter settings, we measure that the eavesdroppers failed to decode 30%–60% of the bits cross multiple locations while the intended receiver has an estimated bit error ratio of 3×10−6.


title: Adaptive K-space Updating Methods for Dynamic MRI Sequence Estimation

abstract: In dynamic magnetic resonance imaging (MRI), due to the limitation of imaging speed, it is commonly required to reconstruct the images from a reduced Fourier encoded K-space sequence. This paper presents two temporal model based methods to estimate the un-acquired K-space data, called adaptive K-space updating (AKU) methods. The AKU reconstruction algorithms are directly applicable to the truncated K-space sequence generated by the well known Fourier keyhole (FK) encoding scheme. The experimental study on the real MRI data shows that the proposed AKU methods can produce images with much lower reconstruction error than conventional FK method


title: Dietary Probiotic Supplementation Enhances Natural Killer Cell Activity in the Elderly: An Investigation of Age-Related Immunological Changes

abstract: Many elderly subjects are at increased risk of infectious and noninfectious diseases due to an age-related decline in lymphoid cell activity (immunosenescence). Noninvasive means of enhancing cellular immunity are therefore desirable in the elderly. Previous reports have suggested that dietary supplementation could represent an effective means of enhancing the activity of circulating natural killer (NK) cells in the elderly. In the present study, we have conducted a pre–post intervention trial to determine the impact of dietary supplementation with probiotic lactic acid bacteria (LAB) on peripheral blood NK cell activity in healthy elderly subjects. Twenty-seven volunteers consumed low-fat/low-lactose milk supplemented with known immunostimulatory LAB strains (Lactobacillus rhamnosus HN001 or Bifidobacterium lactis HN019) for a period of 3 weeks. A dietary run-in of milk alone was shown to have no significant effect on NK cells. In contrast, the proportion of CD56-positive lymphocytes in peripheral circulation was higher following consumption of either LAB strain, and ex vivo PBMC tumoricidal activity against K562 cells was also increased. Supplementation with HN001 or HN019 increased tumoricidal activity by an average of 101 and 62%, respectively; these increases were significantly correlated with age, with subjects older than 70 years experiencing significantly greater improvements than those under 70 years. These results demonstrate that dietary consumption of probiotic LAB in a milk-based diet may offer benefit to elderly consumers to combat some of the deleterious effects of immunosenescence on cellular immunity.


title: Efficiently evaluating complex boolean expressions

abstract: The problem of efficiently evaluating a large collection of complex Boolean expressions - beyond simple conjunctions and Disjunctive/Conjunctive Normal Forms (DNF/CNF) - occurs in many emerging online advertising applications such as advertising exchanges and automatic targeting. The simple solution of normalizing complex Boolean expressions to DNF or CNF form, and then using existing methods for evaluating such expressions is not always effective because of the exponential blow-up in the size of expressions due to normalization. We thus propose a novel method for evaluating complex expressions, which leverages existing techniques for evaluating leaf-level conjunctions, and then uses a bottom-up evaluation technique to only process the relevant parts of the complex expressions that contain the matching conjunctions. We develop two such bottom-up evaluation techniques, one based on Dewey IDs and another based on mapping Boolean expressions to one-dimensional intervals. Our experimental evaluation based on data obtained from an online advertising exchange shows that the proposed techniques are efficient and scalable, both with respect to space usage as well as evaluation time.


title: A Japanese lyrics writing support system for amateur songwriters

abstract: In this paper, we propose a lyrics writing support system focused on the number of syllables, rhyme and word accent. The system generates candidate sentences that satisfy user-specified conditions based on N-gram, and presents them. Users can use the system like a dictionary, and write lyrics be choosing presented sentences. In our subjective evaluations, we have investigated how the system is utilized for writing lyrics actually. The log of using the system and the questionnaires showed that users want the system to present words suitable for their images, and they used the presented words as keywords of a lyrics rather than as they are.


title: Illumination for computer generated pictures

abstract: The quality of computer generated images of three-dimensional scenes depends on the shading technique used to paint the objects on the cathode-ray tube screen. The shading algorithm itself depends in part on the method for modeling the object, which also determines the hidden surface algorithm. The various methods of object modeling, shading, and hidden surface removal are thus strongly interconnected. Several shading techniques corresponding to different methods of object modeling and the related hidden surface algorithms are presented here. Human visual perception and the fundamental laws of optics are considered in the development of a shading rule that provides better quality and increased realism in generated images.


title: A polynomial restriction lemma with applications

abstract: A polynomial threshold function (PTF) of degree d is a boolean function of the form f=sgn(p), where p is a degree-d polynomial, and sgn is the sign function. The main result of the paper is an almost optimal bound on the probability that a random restriction of a PTF is not close to a constant function, where a boolean function g is called I�-close to constant if, for some ve{1,-1}, we have g(x)=v for all but at most δ fraction of inputs. We show for every PTF f of degree d≥ 1, and parameters 0 Pr∾ Rr [fρ is not δ-close to constant] ≤ √ r #183;(logr-1· logδ-1)O(d2), where ρ ∾ Rr is a random restriction leaving each variable, independently, free with probability r, and otherwise assigning it 1 or -1 uniformly at random. In fact, we show a more general result for random block restrictions: given an arbitrary partitioning of input variables into m blocks, a random block restriction picks a uniformly random block lE [m] and assigns 1 or -1, uniformly at random, to all variable outside the chosen block â . We prove the Block Restriction Lemma saying that a PTF f of degree d becomes δ-close to constant when hit with a random block restriction, except with probability at most m-1/2 #183; (logm#183; logδ-1)O(d2). As an application of our Restriction Lemma, we prove lower bounds against constant-depth circuits with PTF gates of any degree 1≤ d≪ √logn/loglogn, generalizing the recent bounds against constant-depth circuits with linear threshold gates (LTF gates) proved by Kane and Williams (STOC, 2016) and Chen, Santhanam, and Srinivasan (CCC, 2016). In particular, we show that there is an n-variate boolean function Fn E P such that every depth-2 circuit with PTF gates of degree d≥ 1 that computes Fn must have at least (n3/2+1/d)#183; (logn)-O(d2) wires. For constant depths greater than 2, we also show average-case lower bounds for such circuits with super-linear number of wires. These are the first super-linear bounds on the number of wires for circuits with PTF gates. We also give short proofs of the optimal-exponent average sensitivity bound for degree-d PTFs due to Kane (Computational Complexity, 2014), and the Littlewood-Offord type anticoncentration bound for degree-d multilinear polynomials due to Meka, Nguyen, and Vu (Theory of Computing, 2016). Finally, we give derandomized versions of our Block Restriction Lemma and Littlewood-Offord type anticoncentration bounds, using a pseudorandom generator for PTFs due to Meka and Zuckerman (SICOMP, 2013).


title: An Unsupervised Machine Learning Approach to Body Text and Table of Contents Extraction from Digital Scientific Articles

abstract: Scientific articles are predominantly stored in digital document formats, which are optimised for presentation, but lack structural information. This poses challenges to access the documents’ content, for example for information retrieval. We have developed a processing pipeline that makes use of unsupervised machine learning techniques and heuristics to detect the logical structure of a PDF document. Our system uses only information available from the current document and does not require any pre-trained model. Starting from a set of contiguous text blocks extracted from the PDF file, we first determine geometrical relations between these blocks. These relations, together with geometrical and font information, are then used categorize the blocks into different classes. Based on this logical structure we finally extract the body text and the table of contents of a scientific article. We evaluate our pipeline on a number of datasets and compare it with state-of-the-art document structure analysis approaches.


title: Multi-View Clustering

abstract: We consider clustering problems in which the available attributes can be split into two independent subsets, such that either subset suffices for learning. Example applications of this multi-view setting include clustering of Web pages which have an intrinsic view (the pages themselves) and an extrinsic view (e.g., anchor texts of inbound hyperlinks); multi-view learning has so far been studied in the context of classification. We develop and study partitioning and agglomerative, hierarchical multi-view clustering algorithms for text data. We find empirically that the multi-view versions of k-means and EM greatly improve on their single-view counterparts. By contrast, we obtain negative results for agglomerative hierarchical multi-view clustering. Our analysis explains this surprising phenomenon.


title: Black-Box Calibration for ADCs With Hard Nonlinear Errors Using a Novel INL-Based Additive Code: A Pipeline ADC Case Study

abstract: This paper presents a digital nonlinearity calibration technique for ADCs with strong input–output discontinuities between adjacent codes, such as pipeline, algorithmic, and SAR ADCs with redundancy. In this kind of converter, the ADC transfer function often involves multivalued regions, where conventional integral-nonlinearity (INL)-based calibration methods tend to miscalibrate, negatively affecting the ADC’s performance. As a solution to this problem, this paper proposes a novel INL-based calibration which incorporates information from the ADC’s internal signals to provide a robust estimation of static nonlinear errors for multivalued ADCs. The method is fully generalizable and can be applied to any existing design as long as there is access to internal digital signals. In pipeline or subranging ADCs, this implies access to partial subcodes before digital correction; for algorithmic or SAR ADCs, conversion bit/bits per cycle are used. As a proof-of-concept demonstrator, the experimental results for a 1.2 V 23 mW 130 nm-CMOS pipeline ADC with a SINAD of 58.4 dBc (in nominal conditions without calibration) is considered. In a stressed situation with 0.95 V of supply, the ADC has SINAD values of 47.8 dBc and 56.1 dBc, respectively, before and after calibration (total power consumption, including the calibration logic, being 15.4 mW).


title: Optimizing Multiway Joins in a Map-Reduce Environment

abstract: Implementations of map-reduce are being used to perform many operations on very large data. We examine strategies for joining several relations in the map-reduce environment. Our new approach begins by identifying the “map-key,” the set of attributes that identify the Reduce process to which a Map process must send a particular tuple. Each attribute of the map-key gets a “share,” which is the number of buckets into which its values are hashed, to form a component of the identifier of a Reduce process. Relations have their tuples replicated in limited fashion, the degree of replication depending on the shares for those map-key attributes that are missing from their schema. We study the problem of optimizing the shares, given a fixed number of Reduce processes. An algorithm for detecting and fixing problems where a variable is mistakenly included in the map-key is given. Then, we consider two important special cases: chain joins and star joins. In each case, we are able to determine the map-key and determine the shares that yield the least replication. While the method we propose is not always superior to the conventional way of using map-reduce to implement joins, there are some important cases involving large-scale data where our method wins, including: 1) analytic queries in which a very large fact table is joined with smaller dimension tables, and 2) queries involving paths through graphs with high out-degree, such as the Web or a social network.


title: Graph rigidity and distributed formation stabilization of multi-vehicle systems

abstract: We provide a graph theoretical framework that allows us to formally define formations of multiple vehicles and the issues arising in uniqueness of graph realizations and its connection to stability of formations. The notion of graph rigidity is crucial in identifying the shape variables of a formation and an appropriate potential function associated with the formation. This allows formulation of meaningful optimization or nonlinear control problems for formation stabilization/tacking, in addition to formal representation of split, rejoin, and reconfiguration maneuvers for multi-vehicle formations. We introduce an algebra that consists of performing some basic operations on graphs which allow creation of larger rigid-by-construction graphs by combining smaller rigid subgraphs. This is particularly useful in performing and representing rejoin/split maneuvers of multiple formations in a distributed fashion.


title: Trust and Consequences: A Visual Perspective

abstract: User interface UI composition and information presentation can impact human trust behavior. Trust is a complex concept studied by disciplines like psychology, sociology, economics, and computer science. Definitions of trust vary depending on the context, but are typically based on the core concept of "reliance on another person or entity". Trust is a critical concept since the presence or absence of the right level of trust can affect user behavior, and ultimately, the overall system performance. In this paper, we look across four studies to explore the relationship between UI elements and human trust behavior. Results indicate that UI composition and information presentation can impact human trust behavior. While further research is required to corroborate and generalize these results, we hope that this paper will provide a reference point for future studies by identifying UI elements that are likely to influence human trust.


title: A hybrid approach for salt dome detection in 2D and 3D seismic data

abstract: Salt bodies play an important role in subsurface geology, therefore accurate salt dome detection is essential for any seismic interpretation task. Detecting salt body boundary and shape accurately, however, is very difficult due to large noise and amplitude variations in seismic data. Due to the limitations of manual picking, automatic segmentation algorithms are preferred to locate salt domes within the seismic images. In this work, we propose a robust salt dome detection technique based on combined edge and texture attributes. The proposed algorithm overcomes the drawbacks of existing texture attributes based salt dome detection techniques which are heavily dependent upon the relevance of attributes to the geological nature of salt domes, and the number of attributes used for classification. The combination of edge based and texture based attributes ensures that the proposed algorithm works well even if the salt boundary is represented only by a weak reflector. We tested the proposed algorithm on the Netherlands offshore F3 block. Our experimental results show that the proposed algorithm can detect salt boundaries with high accuracy superior to existing gradient based as well as texture based techniques when used separately.


title: Wireless sensor networks powered by ambient energy harvesting (WSN-HEAP) - Survey and challenges

abstract: Wireless sensor networks (WSNs) research has pre-dominantly assumed the use of a portable and limited energy source, viz. batteries, to power sensors. Without energy, a sensor is essentially useless and cannot contribute to the utility of the network as a whole. Consequently, substantial research efforts have been spent on designing energy-efficient networking protocols to maximize the lifetime of WSNs. However, there are emerging WSN applications where sensors are required to operate for much longer durations (like years or even decades) after they are deployed. Examples include in-situ environmental/habitat monitoring and structural health monitoring of critical infrastructures and buildings, where batteries are hard (or impossible) to replace/recharge. Lately, an alternative to powering WSNs is being actively studied, which is to convert the ambient energy from the environment into electricity to power the sensor nodes. While renewable energy technology is not new (e.g., solar and wind) the systems in use are far too large for WSNs. Those small enough for use in wireless sensors are most likely able to provide only enough energy to power sensors sporadically and not continuously. Sensor nodes need to exploit the sporadic availability of energy to quickly sense and transmit the data. This paper surveys related research and discusses the challenges of designing networking protocols for such WSNs powered by ambient energy harvesting.


title: A normal ano-genital exam: sexual abuse or not?

abstract: Sexual abuse is a problem of epidemic proportions in the United States. Pediatric nurse practitioners (PNPs) are at the forefront of providing care to children and families. The PNP is in a unique position to educate patients and families regarding sexual abuse and dispel common myths associated with sexual abuse. One such myth is that a normal ano-genital examination is synonymous with the absence of sexual abuse. This article will provide primary care providers, including PNPs, with a framework for understanding why a normal ano-genital examination does not negate the possibility of sexual abuse/assault. Normal ano-genital anatomy, changes that occur with puberty, and physical properties related to the genitalia and anus will be discussed. Photos will provide visualization of both normal variants of the pre-pubertal hymen and genitalia as well as changes that occur with puberty. Implications for practice for PNPs will be discussed.


title: Development of vehicle tracking system using GPS and GSM modem

abstract: The ability to track vehicles is useful in many applications including security of personal vehicles, public transportation systems, fleet management and others. Furthermore, the number of vehicles on the road globally is also expected to increase rapidly. Therefore, the development of vehicle tracking system using the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) modem is undertaken with the aim of enabling users to locate their vehicles with ease and in a convenient manner. The system will provide users with the capability to track vehicle remotely through the mobile network. This paper presents the development of the vehicle tracking system's hardware prototype. Specifically, the system will utilize GPS to obtain a vehicle's coordinate and transmit it using GSM modem to the user's phone through the mobile network. The main hardware components of the system are u-blox NEO-6Q GPS receiver module, u-blox LEON-G100 GSM module and Arduino Uno microcontroller. The developed vehicle tracking system demonstrates the feasibility of near real-time tracking of vehicles and improved customizability, global operability and cost when compared to existing solutions.


title: Computer assisted surgery and therapy (CAST) for breast masses based on mammography

abstract: Breast cancer represents the most frequently diagnosed cancer in women. Mammography is the most commonly method for early detection of masses related to breast cancer. Correlation of information from multiple-view mammograms improves the performance of diagnosis by radiologists or by computer assisted systems. Detecting the location of masses accurately is highly important for radiologist to classify masses, for the surgeon to help in ease the procedure of an accurate surgery and in radio therapy process for less and efficient dose. In this paper, CAST is developed to accurately locate abnormal masses by quarter and clock segments. CAST will replace the manual localization method. The nipple is used as a reference point in localization process. A new simple nipple detection method is also proposed. The proposed CAST is examined on a new local database which was tested and classified by experts. The methodology achieved a sensitivity of 96% and specificity 73.33%.


title: A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN

abstract: Considering the installation cost and coverage, the received signal strength indicator (RSSI)-based indoor positioning system is widely used across the world. However, the indoor positioning performance, due to the interference of wireless signals that are caused by the complex indoor environment that includes a crowded population, cannot achieve the demands of indoor location-based services. In this paper, we focus on increasing the signal strength estimation accuracy considering the population density, which is different to the other RSSI-based indoor positioning methods. Therefore, we propose a new wireless signal compensation model considering the population density, distance, and frequency. First of all, the number of individuals in an indoor crowded scenario can be calculated by our convolutional neural network (CNN)-based human detection approach. Then, the relationship between the population density and the signal attenuation is described in our model. Finally, we use the trilateral positioning principle to realize the pedestrian location. According to the simulation and tests in the crowded scenarios, the proposed model increases the accuracy of the signal strength estimation by 1.53 times compared to that without considering the human body. Therefore, the localization accuracy is less than 1.37 m, which indicates that our algorithm can improve the indoor positioning performance and is superior to other RSSI models.


title: Hand-Based Gesture Recognition for Vehicular Applications Using IR-UWB Radar

abstract: Modern cars continue to offer more and more functionalities due to which they need a growing number of commands. As the driver tries to monitor the road and the graphic user interface simultaneously, his/her overall efficiency is reduced. In order to reduce the visual attention necessary for monitoring, a gesture-based user interface is very important. In this paper, gesture recognition for a vehicle through impulse radio ultra-wideband (IR-UWB) radar is discussed. The gestures can be used to control different electronic devices inside a vehicle. The gestures are based on human hand and finger motion. We have implemented a real-time version using only one radar sensor. Studies on gesture recognition using IR-UWB radar have rarely been carried out, and some studies are merely simple methods using the magnitude of the reflected signal or those whose performance deteriorates largely due to changes in distance or direction. In this study, we propose a new hand-based gesture recognition algorithm that works robustly against changes in distance or direction while responding only to defined gestures by ignoring meaningless motions. We used three independent features, i.e., variance of the probability density function (pdf) of the magnitude histogram, time of arrival (TOA) variation and the frequency of the reflected signal, to classify the gestures. A data fitting method is included to differentiate between gesture signals and unintended hand or body motions. We have used the clustering technique for the classification of the gestures. Moreover, the distance information is used as an additional input parameter to the clustering algorithm, such that the recognition technique will not be vulnerable to distance change. The hand-based gesture recognition proposed in this paper would be a key technology of future automobile user interfaces.


title: The WiLI benchmark dataset for written language identification

abstract: This paper describes the WiLI-2018 benchmark dataset for monolingual written natural language identification. WiLI-2018 is a publicly available, free of charge dataset of short text extracts from Wikipedia. It contains 1000 paragraphs of 235 languages, totaling in 23500 paragraphs. WiLI is a classification dataset: Given an unknown paragraph written in one dominant language, it has to be decided which language it is.


title: A Novel Image Segmentation Method of Pavement Surface Cracks Based on Fractal Theory

abstract: According to the features of pavement surface cracks, a novel image segmentation method based on fractal theory is presented in this paper. The optimization process to the fractal properties of pavement surface cracks segmentation is discussed in detail. Experiment results demonstrate that the novel algorithm could identify the edges of pavement surface cracks correctly and the performance of our proposed scheme is better than the previous classical algorithms.


title: If Only my Leader Would just Do Something! Passive Leadership Undermines Employee Well‐being Through Role Stressors and Psychological Resource Depletion

abstract: The goal of this study was to develop and test a sequential mediational model explaining the negative relationship of passive leadership to employee well-being. Based on role stress theory, we posit that passive leadership will predict higher levels of role ambiguity, role conflict and role overload. Invoking Conservation of Resources theory, we further hypothesize that these role stressors will indirectly and negatively influence two aspects of employee well-being, namely overall mental health and overall work attitude, through psychological work fatigue. Using a probability sample of 2467 US workers, structural equation modelling supported the model by showing that role stressors and psychological work fatigue partially mediated the negative relationship between passive leadership and both aspects of employee well-being. The hypothesized, sequential indirect relationships explained 47.9% of the overall relationship between passive leadership and mental health and 26.6% of the overall relationship between passive leadership and overall work attitude. Copyright © 2016 John Wiley & Sons, Ltd.


title: PROTECTION OF SERVER FROM SYN FLOOD ATTACK

abstract: Due to advances in Internet Technology applications, the clients at remote places require constant services from a server. Internet services can be denied by malicious attacks on the server. One such attack is SYN flood attack which is a type of DDoS attack. This manuscript demonstrates the protection of server against the SYN flood attack. The algorithm uses a continuous self detecting method for identifying and updating genuine client information in the presence of spoofed packet and thereby protecting the server from SYN flood attack. During this process the algorithm builds the repository of genuine client information. This repository of genuine client can be used by other security systems like IDS, Packet filtering etc. for protecting the server. The performance of SYN flood attack protector can be further improved by implementing this algorithm in hardware such as FPGA .


title: Radio Frequency Tomography for Tunnel Detection

abstract: Radio frequency (RF) tomography is proposed to detect underground voids, such as tunnels or caches, over relatively wide areas of regard. The RF tomography approach requires a set of low-cost transmitters and receivers arbitrarily deployed on the surface of the ground or slightly buried. Using the principles of inverse scattering and diffraction tomography, a simplified theory for below-ground imaging is developed. In this paper, the principles and motivations in support of RF tomography are introduced. Furthermore, several inversion schemes based on arbitrarily deployed sensors are devised. Then, limitations to performance and system considerations are discussed. Finally, the effectiveness of RF tomography is demonstrated by presenting images reconstructed via the processing of synthetic data.


title: Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach

abstract: Objective : This paper targets a major challenge in developing practical electroencephalogram (EEG)-based brain–computer interfaces (BCIs): how to cope with individual differences so that better learning performance can be obtained for a new subject, with minimum or even no subject-specific data? Methods : We propose a novel approach to align EEG trials from different subjects in the Euclidean space to make them more similar, and hence improve the learning performance for a new subject. Our approach has three desirable properties: first, it aligns the EEG trials directly in the Euclidean space, and any signal processing, feature extraction, and machine learning algorithms can then be applied to the aligned trials; second, its computational cost is very low; and third, it is unsupervised and does not need any label information from the new subject. Results : Both offline and simulated online experiments on motor imagery classification and event-related potential classification verified that our proposed approach outperformed a state-of-the-art Riemannian space data alignment approach, and several approaches without data alignment. Conclusion : The proposed Euclidean space EEG data alignment approach can greatly facilitate transfer learning in BCIs. Significance : Our proposed approach is effective, efficient, and easy to implement. It could be an essential pre-processing step for EEG-based BCIs.


title: TCP Vegas: new techniques for congestion detection and avoidance

abstract: Vegas is a new implementation of TCP that achieves between 40 and 70% better throughput, with one-fifth to one-half the losses, as compared to the implementation of TCP in the Reno distribution of BSD Unix. This paper motivates and describes the three key techniques employed by Vegas, and presents the results of a comprehensive experimental performance study—using both simulations and measurements on the Internet—of the Vegas and Reno implementations of TCP.


title: Delta Voxel Cone Tracing

abstract: Mixed reality applications which must provide visual coherence between synthetic and real objects need relighting solutions for both: synthetic objects have to match lighting conditions of their real counterparts, while real surfaces need to account for the change in illumination introduced by the presence of an additional synthetic object. In this paper we present a novel relighting solution called Delta Voxel Cone Tracing to compute both direct shadows and first bounce mutual indirect illumination. We introduce a voxelized, pre-filtered representation of the combined real and synthetic surfaces together with the extracted illumination difference due to the augmentation. In a final gathering step this representation is cone-traced and superimposed onto both types of surfaces, adding additional light from indirect bounces and synthetic shadows from anti-radiance present in the volume. The algorithm computes results at interactive rates, is temporally coherent and to our knowledge provides the first real-time rasterizer solution for mutual diffuse, glossy and perfect specular indirect reflections between synthetic and real surfaces in mixed reality.


Paper Series Bank Overleverage and Macroeconomic Fragility


title: Introducing Wikidata to the Linked Data Web

abstract: Wikidata is the central data management platform of Wikipedia. By the efforts of thousands of volunteers, the project has produced a large, open knowledge base with many interesting applications. The data is highly interlinked and connected to many other datasets, but it is also very rich, complex, and not available in RDF. To address this issue, we introduce new RDF exports that connect Wikidata to the Linked Data Web. We explain the data model of Wikidata and discuss its encoding in RDF. Moreover, we introduce several partial exports that provide more selective or simplified views on the data. This includes a class hierarchy and several other types of ontological axioms that we extract from the site. All datasets we discuss here are freely available online and updated regularly.


title: Off-policy TD(λ) with a true online equivalence

abstract: Van Seijen and Sutton (2014) recently proposed a new version of the linear TD(λ) learning algorithm that is exactly equivalent to an online forward view and that empirically performed better than its classical counterpart in both prediction and control problems. However, their algorithm is restricted to on-policy learning. In the more general case of off-policy learning, in which the policy whose outcome is predicted and the policy used to generate data may be different, their algorithm cannot be applied. One reason for this is that the algorithm bootstraps and thus is subject to instability problems when function approximation is used. A second reason true online TD(λ) cannot be used for off-policy learning is that the off-policy case requires sophisticated importance sampling in its eligibility traces. To address these limitations, we generalize their equivalence result and use this generalization to construct the first online algorithm to be exactly equivalent to an off-policy forward view. We show this algorithm, named true online GTD(λ), empirically outperforms GTD(λ) (Maei, 2011) which was derived from the same objective as our forward view but lacks the exact online equivalence. In the general theorem that allows us to derive this new algorithm, we encounter a new general eligibility-trace update.


Frequency Distribution of Fluoroquinolones-Resistant Enterococcus faecalis Isolates from Patients with Prostatitis in Golestan Province, Iran


title: Jammer signal effectiveness against a conical-scan missile seeker

abstract: Infrared (IR) guided missiles remain a threat to both military and civilian aircraft, and as such, the development of effective countermeasures against this threat remains vital. A simulation has been developed to assess the effectiveness of a jammer signal against a conical-scan seeker by testing critical jammer parameters. The critical parameters of a jammer signal are the jam-to-signal (J/S) ratio, the jammer frequency and the jammer duty cycle. It was found that the most effective jammer signal is one with a modulated envelope.


title: ASDF: An Autonomous and Scalable Distributed File System

abstract: The demand for huge storage space on data-intensive applications and high-performance scientific computing continues to grow. To integrate massive distributed storage resources for providing huge storage space is an important and challenging issue in Cloud and Grid computing. In this paper, we propose a distributed file system, called ASDF, to meet the demands of not only data-intensive applications but also end users, developers and administrators. While sharing many of the same goals as previous distributed file systems such as scalability, reliability, and performance, it is also designed with the emphasis on compatibility, extensibility and autonomy. With the design goals in minds, we address several issues and present our design by adopting peer-to-peer technology, replication, multi-source data transfer, metadata caching and service-oriented architecture. The experimental results show the proposed distributed file system meet our design goals and will be useful in Cloud and Grid computing.


title: Cup-to-disk ratio (CDR) determination for glaucoma screening

abstract: Glaucoma is the major reason that causes blindness today. It results in total loss of vision which is irreparable which makes it mandatory to detect it at early stages. This paper proposes a simple method to evaluate the Cup-to-Disc Ratio (CDR) of a retinal fundus image to screen it for Glaucoma. It is a contrasting feature taken up to check whether the subject is at risk for Glaucoma or not. The technique proposed applies simple morphological techniques to extract out CDR value and ultimately classify the subjects as suspected or normal. The proposed methodology achieved a detection accuracy of 80% and a classification accuracy of 95Sensitivity and 91% Specificity.


title: Model-Based Methods For Steganography And Steganalysis

abstract: This paper presents methods for performing steganography and steganalysis using a statistical model of the cover medium. The methodology is general, and can be applied to virtually any type of media. It provides answers for some fundamental questions that have not been fully addressed by previous steganographic methods, such as how large a message can be hidden without risking detection by certain statistical methods, and how to achieve this maximum capacity. Current steganographic methods have been shown to be insecure against simple statistical attacks. Using the model-based methodology, an example steganography method is proposed for JPEG images that achieves a higher embedding efficiency and message capacity than previous methods while remaining secure against first order statistical attacks. A method is also described for defending against "blockiness" steganalysis attacks. Finally, a model-based steganalysis method is presented for estimating the length of messages hidden with Jsteg in JPEG images.


title: JustOrthologs: a fast, accurate and user‐friendly ortholog identification algorithm

abstract: Motivation: Orthologous gene identification is fundamental to all aspects of biology. For example, ortholog identification between species can provide functional insights for genes of unknown function and is a necessary step in phylogenetic inference. Currently, most ortholog identification algorithms require all‐versus‐all BLAST comparisons, which are time‐consuming and memory intensive. Results: In contrast to existing approaches, JustOrthologs exploits the conservation of gene structure by using the lengths of coding sequence regions and dinucleotide percentages to identify orthologs. In comparison to OrthoMCL, OMA and OrthoFinder, JustOrthologs decreases ortholog identification runtime by more than 96% and achieves comparable precision and recall scores. The computational speedup allowed us to conduct pairwise comparisons of 1197 complete genomes (780 eukaryotes and 417 archaea). We confirmed gene annotations for 384 120 genes, grouped 1 675 415 genes in previously unreported ortholog groups, and identified 51 429 potentially mislabeled genes across 622 843 ortholog groups. Availability and implementation: JustOrthologs is an open source collaborative software package available in the GitHub repository: https://github.com/ridgelab/JustOrthologs/. All test FASTA files used for comparisons are freely available at https://github.com/ridgelab/JustOrthologs/comparisonFastaFiles/. Reference genomes used in this work are available for download from the NCBI repository: ftp://ftp.ncbi.nih.gov/genomes/. Supplementary information: Supplementary data are available at Bioinformatics online.


title: Interleaved Critical Current Mode Boost PFC Converter With Coupled Inductor

abstract: Interleaved critical current mode (CRM) boost power factor correction (PFC) converter is widely employed recently for its high power density. In order to further reduce the volume and the copper usage of the magnetic components, two-phase interleaved CRM boost PFC converter with a coupled inductor is analyzed in this paper. The coupling effects on the input current, the inductor current, the switching frequency and the flux linkage are analyzed separately. If the self-inductances and the magnetic core are the same for both coupled and noncoupled inductors, the number of winding turns of coupled inductor is fewer than that of the noncoupled inductor, which implies a lower cost. Although the input current ripple increases a little since coupling, a reduction in the total volume of magnetic components, including the electromagnetic interference filter and the coupled inductor, is possible if the coupling coefficient is made reasonable.


A Proposal to Measure Success Factors for Location-Based Mobile Cardiac Telemedicine System ( LMCTS )


title: Solving Non-Markovian Control Tasks with Neuro-Evolution

abstract: The success of evolutionary methods on standard control learning tasks has created a need for new benchmarks. The classic pole balancing problem is no longer difficult enough to serve as a viable yardstick for measuring the learning efficiency of these systems. The double pole case, where two poles connected to the cart must be balanced simultaneously is much more difficult, especially when velocity information is not available. In this article, we demonstrate a neuroevolution system, Enforced Sub-populations (ESP), that is used to evolve a controller for the standard double pole task and a much harder, non-Markovian version. In both cases, our results show that ESP is faster than other neuroevolution methods. In addition, we introduce an incremental method that evolves on a sequence of tasks, and utilizes a local search technique (Delta-Coding) to sustain diversity. This method enables the system to solve even more difficult versions of the task where direct evolution cannot.


title: Crossbar-Based Memristive Logic-in-Memory Architecture

abstract: The use of memristors and resistive random access memory (ReRAM) technology to perform logic computations, has drawn considerable attention from researchers in recent years. However, the topological aspects of the underlying ReRAM architecture and its organization have received less attention, as the focus has mainly been on device-specific properties for functionally complete logic gates through conditional switching in ReRAM circuits. A careful investigation and optimization of the target geometry is thus highly desirable for the implementation of logic-in-memory architectures. In this paper, we propose a crossbar-based in-memory parallel processing system in which, through the heterogeneity of the resistive cross-point devices, we achieve local information processing in a state-of-the-art ReRAM crossbar architecture with vertical group-accessed transistors as cross-point selector devices. We primarily focus on the array organization, information storage, and processing flow, while proposing a novel geometry for the cross-point selection lines to mitigate current sneak-paths during an arbitrary number of possible parallel logic computations. We prove the proper functioning and potential capabilities of the proposed architecture through SPICE-level circuit simulations of half-adder and sum-of-products logic functions. We compare certain features of the proposed logic-in-memory approach with another work of the literature, and present an analysis of circuit resources, integration density, and logic computation parallelism.


title: Emotional Stability, Core Self-Evaluations, and Job Outcomes: A Review of the Evidence and an Agenda for Future Research

abstract: In this article we present a review of research on core self-evaluations, a broad personality trait indicated by 4 more narrow traits: self-esteem, generalized self-efficacy, locus of control, and emotional stability. We review evidence suggesting that the 4 core traits are highly related, load on a single unitary factor, and have dubious incremental validity controlling for their common core. We more generally investigate the construct validity of core self-evaluations. We also report on the development and validation of the first direct measure of the concept, the Core Self-Evaluations Scale (CSES). Cross-cultural evidence on the CSES is provided. We conclude by offering an agenda for future research, discussing areas where future core self-evaluations research is most needed.


title: Optimized control of a residential heat pump

abstract: Recent developments in both heating and power sectors contribute to the creation of an integrated power system. Taking also into account the increased amount of distributed generation, current trends in power generation, transportation and consumption will be significantly affected. Linking components of this integrated system, such as heat pumps, can be controlled in different ways, to provide certain benefits to different parties. The scope of this paper is to provide a control algorithm for a residential heat pump, in order to minimize its cost, paid by the customer/owner, while maintaining a certain temperature and comfort level. A commercially available heat pump installed in a typical house with standard thermal insulation is considered. Simulation results conclude that the proposed controlling method succeeds in reducing the amount of money spent by the customer for residential heating purposes.


abstract: Technological advances are enabling scientists to collect vast amounts of data in fields such as medicine, remote sensing, astronomy, and high-energy physics. These data arise not only from experiments and observations, but also from computer simulations of complex phenomena. They are often complex, with both spatial and temporal components. As a result, it has become impractical to manually explore, analyze, and understand the data. Scientific Data Mining: A Practical Perspective describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, this book identifies the common themes across these domains and uses them to define an end-to-end process of scientific data mining. This multi-step process includes tasks such as processing the raw image or mesh data to identify objects of interest;extracting relevant features describing the objects; detecting patterns among the objects; and displaying the patterns for validation by the scientists. A majority of the book describes how techniques from disciplines such as image and video processing, statistics, machine learning, pattern recognition, and mathematical optimization can be used for the tasks in each step. It also includes a description of software systems developed for scientific data mining; general guidelines for getting started on the analysis of massive, complex data sets; and an extensive bibliography. Audience: This book is intended for data mining practitioners and scientists interested in applying data mining techniques to their data sets. It is also appropriate for advanced undergraduate and graduate-level courses on data analysis offered in mathematics, computer science, and statistics departments. Contents: Preface; Chapter 1: Introduction; Chapter 2: Data Mining in Science and Engineering; Chapter 3: Common Themes in Mining Scientific Data; Chapter 4: The Scientific Data Mining Process; Chapter 5: Reducing the Size of the Data; Chapter 6: Fusing Different Data Modalities; Chapter 7: Enhancing Image Data; Chapter 8: Finding Objects in the Data; Chapter 9: Extracting Features Describing the Objects; Chapter 10: Reducing the Dimension of the Data; Chapter 11: Finding Patterns in the Data; Chapter 12: Visualizing the Data and Validating the Results; Chapter 13: Scientific Data Mining Systems; Chapter 14: Lessons Learned, Challenges, and Opportunities; Bibliography; Index


title: Towards measuring the quality of interaction: communication through telepresence robots

abstract: Personal video conferencing is now a common occurrence in long distance interpersonal relationships. Telepresence robots additionally provide mobility to video conferencing, and people can converse without being restricted to a single vantage point. The metrics to explicitly quantify person to person interaction through a telepresence robot do not yet exist. In this paper, we discuss technical requirements needed to support such a communication. We also look at the fields of human-computer interaction (HCI), computer supported cooperative work (CSCW), communications, and psychology for quantitative and qualitative performance measures which are independent of interpersonal relationships and communication task.


title: Modeling and Analyzing the Reliability and Cost of Service Composition in the IoT: A Probabilistic Approach

abstract: Recently, many efforts have been devoted to explore the integration of Internet of Things (IoT) and Service-Oriented Computing (SOC). These works allow the real-world devices to provide their functionality as web services. However, two important issues, unreliable service providing and resource constraints, make the modeling and analysis of service composition in IoT a big challenge. In this paper, we propose a probabilistic approach to formally describe and analyze the reliability and cost-related properties of the service composition in IoT. First, a service composition in IoT is modeled as a finite state machine (FSM) which focuses on the functional part. Then, we extend this FSM model to a Markov Decision Process (MDP), which can specify the reliability of service operations. Furthermore, we extend MDP with cost structure, which can represent the different service quality attributes for each operation, such as energy consumption, communication cost, etc. The desirable quality properties of the service composition are specified by a probabilistic extension of temporal logic PCTL. We adopt a well-established probabilistic model checker PRISM to verify and analyze those properties of our service composition models.


title: Six strategies for generalizing software engineering theories

abstract: General theories of software engineering must balance between providing full understanding of a single case and providing partial understanding of many cases. In this paper we argue that for theories to be useful in practice, they should give sufficient understanding of a sufficiently large class of cases, without having to be universal or complete. We provide six strategies for developing such theories of the middle range. ::: ::: In lab-to-lab strategies, theories of laboratory phenomena are developed and generalized to other laboratory phenomena. This is a characteristic strategy for basic science. In lab-to-field strategies, theories are developed of artifacts that first operate under idealized laboratory conditions, which are then scaled up until they can operate under uncontrolled field conditions. This is the characteristic strategy for the engineering sciences. ::: ::: In case-based strategies, we generalize about components of real-world cases, that are supposed to exhibit less variation than the cases as a whole. In sample-based strategies, we generalize about the aggregate behavior of samples of cases, which can exhibit patterns not visible at the case level. We discuss three examples of sample-based strategies. ::: ::: Throughout the paper, we use examples of theories and generalization strategies from software engineering to illustrate our analysis. The paper concludes with a discussion of related work and implications for empirical software engineering research.


title: Answering Elementary Science Questions by Constructing Coherent Scenes using Background Knowledge

abstract: Much of what we understand from text is not explicitly stated. Rather, the reader uses his/her knowledge to fill in gaps and create a coherent, mental picture or “scene” depicting what text appears to convey. The scene constitutes an understanding of the text, and can be used to answer questions that go beyond the text. Our goal is to answer elementary science questions, where this requirement is pervasive; A question will often give a partial description of a scene and ask the student about implicit information. We show that by using a simple “knowledge graph” representation of the question, we can leverage several large-scale linguistic resources to provide missing background knowledge, somewhat alleviating the knowledge bottleneck in previous approaches. The coherence of the best resulting scene, built from a question/answer-candidate pair, reflects the confidence that the answer candidate is correct, and thus can be used to answer multiple choice questions. Our experiments show that this approach outperforms competitive algorithms on several datasets tested. The significance of this work is thus to show that a simple “knowledge graph” representation allows a version of “interpretation as scene construction” to be made viable.


title: Learning affinities and dependencies for multi-target tracking using a CRF model

abstract: We propose a learning-based Conditional Random Field (CRF) model for tracking multiple targets by progressively associating detection responses into long tracks. Tracking task is transformed into a data association problem, and most previous approaches developed heuristical parametric models or learning approaches for evaluating independent affinities between track fragments (tracklets). We argue that the independent assumption is not valid in many cases, and adopt a CRF model to consider both tracklet affinities and dependencies among them, which are represented by unary term costs and pairwise term costs respectively. Unlike previous methods, we learn the best global associations instead of the best local affinities between tracklets, and transform the task of finding the best association into an energy minimization problem. A RankBoost algorithm is proposed to select effective features for estimation of term costs in the CRF model, so that better associations have lower costs. Our approach is evaluated on challenging pedestrian data sets, and are compared with state-of-art methods. Experiments show effectiveness of our algorithm as well as improvement in tracking performance.


title: Representing Service Business Models with the Service Business Model Canvas -- The Case of a Mobile Payment Service in the Retail Industry

abstract: IT is a major driver of business model innovation and servitization. Representations of business models are widely used tools for analyzing instances of these developments as well as for the ideation of novel services and service business models. However, current representations of business models fail to capture essential aspects of service, such as co-creation. In response to these shortcomings, the paper presents a representation for service business models. The utility and efficacy of this alternative business model representation is demonstrated with a case study of a proximity m-payment service in the German retail industry. We apply and compare the Business Model Canvas as well as the Service Business Model Canvas. Based on this application, we present an evaluation by an informed argument of the representation.


title: Adapting Interest Point Detection to Illumination Conditions

abstract: The objective of the presented work is to improve the sta- bility of interest point detection under illumination changes. The use of a global threshold is shown to be insufficient and three methods are proposed to enhance a state-of-the-art algorithm: the Harris corner de- tector. These methods are based on different principles: a local image normalization as preprocessing, a local threshold adaption, and a lo- cal automated threshold selection based on clustering. All methods are compared on several image series created by varying the lighting condi- tions. For performance evaluation repeatability and false positive rates are used. All methods allow a stability enhancement under complex illu- mination changes. The algorithm based on threshold adaption performs best.


title: Multimodal Detection of Depression in Clinical Interviews

abstract: Current methods for depression assessment depend almost entirely on clinical interview or self-report ratings. Such measures lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of psychological disorder. We compared a clinical interview of depression severity with automatic measurement in 48 participants undergoing treatment for depression. Interviews were obtained at 7-week intervals on up to four occasions. Following standard cut-offs, participants at each session were classified as remitted, intermediate, or depressed. Logistic regression classifiers using leave-one-out validation were compared for facial movement dynamics, head movement dynamics, and vocal prosody individually and in combination. Accuracy (remitted versus depressed) for facial movement dynamics was higher than that for head movement dynamics; and each was substantially higher than that for vocal prosody. Accuracy for all three modalities together reached 88.93 %, exceeding that for any single modality or pair of modalities. These findings suggest that automatic detection of depression from behavioral indicators is feasible and that multimodal measures afford most powerful detection.


title: Is trauma centre care helpful for less severely injured patients?

abstract: BACKGROUND ::: Trauma centres have been shown to reduce the number of preventable deaths from serious injuries. This is due largely to the rapid response of surgeons and health care teams to resuscitate, evaluate, and operate if necessary. Less is known about the effectiveness of trauma centre care on those patients who have not incurred immediate life-threatening problems and may not be as critically injured. The purpose of this study was to review the use of physician and hospital resources for this patient population to determine whether trauma team and trauma centre care is helpful or even needed. ::: ::: ::: METHODS ::: This was a retrospective study of consecutive trauma patients (n=1592) admitted from 1998 to 2002 to the trauma service of an urban level I trauma centre and recorded in the hospital trauma registry. Patients were triaged in a tiered response to more or less severely injured. All patients' care was directed by trauma surgeons. ::: ::: ::: RESULTS ::: Of the 1592 patients, 398 (25%) received a full trauma team response (Class I), 1194 were less seriously injured (Class II). The ISS for the Class I patients was 19+/-18 and for Class II patients 10+/-10. Nineteen percent of Class II patients had an ISS>15. Overall mortality in Class II patients was 2% including 20 unexpected deaths. Four hundred and three Class II patients (34%) had multisystem injuries. Of the Class II patients 423 (35%) were sent to the ICU or OR from the ED, 106 of whom required an immediate operation and 345 required an operation prior to discharge. Complications developed in 129 patients (11%), the majority of which were pulmonary. ::: ::: ::: CONCLUSIONS ::: A large proportion of those patients thought initially to be less severely injured required resources available in a trauma centre, including specialty care, intensive care, and operating room accessibility. Over one-third of these patients had multisystem injuries and almost 20% were considered major trauma, needing prioritisation of care and expertise ideally found in a trauma centre environment. Complications developed in a sizable number of patients. This patient population, because of its heterogeneity and propensity for critical illness, deserves the resources of a trauma centre.


title: A decentralized algorithm for spectral analysis

abstract: In many large network settings, such as computer networks, social networks, or hyperlinked text documents, much information can be obtained from the network's spectral properties. However, traditional centralized approaches for computing eigenvectors struggle with at least two obstacles: the data may be difficult to obtain (both due to technical reasons and because of privacy concerns), and the sheer size of the networks makes the computation expensive. A decentralized, distributed algorithm addresses both of these obstacles: it utilizes the computational power of all nodes in the network and their ability to communicate, thus speeding up the computation with the network size. And as each node knows its incident edges, the data collection problem is avoided as well.Our main result is a simple decentralized algorithm for computing the top k eigenvectors of a symmetric weighted adjacency matrix, and a proof that it converges essentially in O(τ MIX log2 n) rounds of communication and computation, where τ MIX is the mixing time of a random walk on the network. An additional contribution of our work is a decentralized way of actually detecting convergence, and diagnosing the current error. Our protocol scales well, in that the amount of computation performed at any node in any one round, and the sizes of messages sent, depend polynomially on k, but not on the (typically much larger) number n of nodes.


title: Dynamic Urban Surveillance Video Stream Processing Using Fog Computing

abstract: The recent rapid development of urbanization and Internet of things (IoT) encourages more and more research on Smart City in which computing devices are widely distributed and huge amount of dynamic real-time data are collected and processed. Although vast volume of dynamic data are available for extracting new living patterns and making urban plans, efficient data processing and instant decision making are still key issues, especially in emergency situations requesting quick response with low latency. Fog Computing, as the extension of Cloud Computing, enables the computing tasks accomplished directly at the edge of the network and is characterized as low latency and real time computing. However, it is non-trivial to coordinate highly heterogeneous Fog Computing nodes to function as a homogeneous platform. In this paper, taking urban traffic surveillance as a case study, a dynamic video stream processing scheme is proposed to meet the requirements of real-time information processing and decision making. Furthermore, we have explored the potential to enable multi-target tracking function using a simpler single target tracking algorithm. A prototype is built and the performance is evaluated. The experimental results show that our scheme is a promising solution for smart urban surveillance applications.


title: A Fast Greedy Algorithm for Outlier Mining

abstract: The task of outlier detection is to find small groups of data objects that are exceptional when compared with rest large amount of data. Recently, the problem of outlier detection in categorical data is defined as an optimization problem and a local-search heuristic based algorithm (LSA) is presented. However, as is the case with most iterative type algorithms, the LSA algorithm is still very time-consuming on very large datasets. In this paper, we present a very fast greedy algorithm for mining outliers under the same optimization model. Experimental results on real datasets and large synthetic datasets show that: (1) Our new algorithm has comparable performance with respect to those state-of-the-art outlier detection algorithms on identifying true outliers and (2) Our algorithm can be an order of magnitude faster than LSA algorithm.


title: Aspect Based Sentiment Analysis of Movie Reviews: Finding the Polarity Directing Aspects

abstract: Sentiment analysis of a movie review plays an important role in understanding the sentiment conveyed by the user towards the movie. In the current work we focus on aspect based sentiment analysis of movie reviews in order to find out the aspect specific driving factors. These factors are the score given to various movie aspects and generally aspects with high driving factors direct the polarity of the review the most. The experiment showed that by giving high driving factors to Movie, Acting and Plot aspects of a movie, we obtained the highest accuracy in the analysis of movie reviews.


title: Reconfigurable FIR filter coefficient optimization in post-silicon validation to improve eye diagram for optical interconnects

abstract: Enhanced small form-factor pluggable (SFP+) is a specification for a new generation of optical modular transceivers. The devices are designed for use with small form factor (SFF) connectors, and offer high speed and physical compactness. SFP+ modules require high-quality ASIC/SerDes transmitters (Tx) because IEEE and fibre channel standards place strict requirements on the optical interface, and linear/limiting SFP+ module types have Tx paths that do not correct for timing jitter. This introduces a design challenge to guarantee performance over process, temperature, and voltage (PVT) conditions. Adjusting the Tx equalization across PVT and different interconnect channels can be a time-consuming task in post-silicon validation. In order to overcome this problem, this paper proposes a direct optimization method based on a suitable objective function formulation to efficiently tune the Tx equalizer and optimize the eye diagram to successfully comply with industrial specifications.


title: A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach

abstract: The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and a fast version has been proposed. Unfortunately, little is known about the accuracy of such acceleration. In this paper, we propose a new signal-processing analysis of the bilateral filter, which complements the recent studies that analyzed it as a PDE or as a robust statistics estimator. Importantly, this signal-processing perspective allows us to develop a novel bilateral filtering acceleration using a downsampling in space and intensity. This affords a principled expression of the accuracy in terms of bandwidth and sampling. The key to our analysis is to express the filter in a higher-dimensional space where the signal intensity is added to the original domain dimensions. Tlie bilateral filter can then be expressed as simple linear convolutions in this augmented space followed by two simple nonlinearities. This allows us to derive simple criteria for down-sampling the key operations and to achieve important acceleration of the bilateral filter. We show that, for the same running time, our method is significantly more accurate than previous acceleration techniques.


title: Faster Rates for the Frank-Wolfe Method over Strongly-Convex Sets

abstract: The Frank-Wolfe method (a.k.a. conditional gradient algorithm) for smooth optimization has regained much interest in recent years in the context of large scale optimization and machine learning. A key advantage of the method is that it avoids projections - the computational bottleneck in many applications - replacing it by a linear optimization step. Despite this advantage, the known convergence rates of the FW method fall behind standard first order methods for most settings of interest. It is an active line of research to derive faster linear optimization-based algorithms for various settings of convex optimization. ::: In this paper we consider the special case of optimization over strongly convex sets, for which we prove that the vanila FW method converges at a rate of $\frac{1}{t^2}$. This gives a quadratic improvement in convergence rate compared to the general case, in which convergence is of the order $\frac{1}{t}$, and known to be tight. We show that various balls induced by $\ell_p$ norms, Schatten norms and group norms are strongly convex on one hand and on the other hand, linear optimization over these sets is straightforward and admits a closed-form solution. We further show how several previous fast-rate results for the FW method follow easily from our analysis.


title: Crowd scene understanding with coherent recurrent neural networks

abstract: Exploring crowd dynamics is essential in understanding crowd scenes, which still remains as a challenging task due to the nonlinear characteristics and coherent spatio-temporal motion patterns in crowd behaviors. To address these issues, we present a Coherent Long Short Term Memory (cLSTM) network to capture the nonlinear crowd dynamics by learning an informative representation of crowd motions, which facilitates the critical tasks in crowd scene analysis. By describing the crowd motion patterns with a cloud of keypoint tracklets, we explore the nonlinear crowd dynamics embedded in the tracklets with a stacked LSTM model, which is further improved to capture the collective properties by introducing a coherent regularization term; and finally, we adopt an unsupervised encoder-decoder framework to learn a hidden feature for each input tracklet that embeds its inherent dynamics. With the learnt features properly harnessed, crowd scene understanding is conducted effectively in predicting the future paths of agents, estimating group states, and classifying crowd events. Extensive experiments on hundreds of public crowd videos demonstrate that our method is state-of-the-art performance by exploring the coherent spatio-temporal structures in crowd behaviors.


title: Clustering-based Image Segmentation using Automatic GrabCut

abstract: GrabCut is one of the most powerful semi-automatic segmentation techniques. One main drawback of GrabCut is the need for user interaction in order to initialize the segmentation process. User interaction involves dragging a rectangle around the object of interest in the image to extract it. This restricts GrabCut for binary-label segmentation, where the image cannot be segmented into more than two; foreground and background segments. Unsupervised clustering is a powerful automatic tool for dividing images into a specified number of regions based on defined image features. The authors had introduced the SOFM clustering technique for GrabCut automation as a replacement to the user interaction. In this paper, K-means and Fuzzy C-means are introduced as new clustering techniques to automate GrabCut and improve the segmentation accuracy. Experimental results and comparisons with the previous results are carried out to test the efficiency of the proposed techniques in terms of segmentation quality and accuracy.