title: Using Social Media to Understand Cyber Attack Behavior

abstract: As networked and computer technologies continue to pervade all aspects of our lives, the threat from cyber attacks has also increased. However, detecting attacks, much less predicting them in advance, is a non-trivial task due to the anonymity of cyber attackers and the ambiguity of network data collected within an organization; often, by the time an attack pattern is recognized, the damage has already been done. Evidence suggests that the public discourse in external sources, such as news and social media, is often correlated with the occurrence of larger phenomena, such as election results or violent attacks. In this paper, we propose an approach that uses sentiment polarity as a sensor to analyze the social behavior of groups on social media as an indicator of cyber at-tack behavior. We developed an unsupervised sentiment prediction method that uses emotional signals to enhance the sentiment signal from sparse textual indicators. To explore the efficacy of sentiment polarity as an indicator of cyber-attacks, we performed experiments using real-world data from Twitter that corresponds to attacks by a well-known hacktivist group.


Identifying Anomalies in Graph Streams Using Change Detection


title: Sequence modelling for sentence classification in a legal summarisation system

abstract: We describe a set of experiments using a wide range of machine learning techniques for the task of predicting the rhetorical status of sentences. The research is part of a text summarisation project for the legal domain for which we use a new corpus of judgments of the UK House of Lords. We present experimental results for classification according to a rhetorical scheme indicating a sentence's contribution to the overall argumentative structure of the legal judgments using four learning algorithms from the Weka package (C4.5, naive Bayes, Winnow and SVMs). We also report results using maximum entropy models both in a standard classification framework and in a sequence labelling framework. The SVM classifier and the maximum entropy sequence tagger yield the most promising results.


title: Can You Explain That, Better? Comprehensible Text Analytics for SE Applications

abstract: Text mining methods are used for a wide range of Software Engineering (SE) tasks. The biggest challenge of text mining is high dimensional data, i.e., a corpus of documents can contain $10^4$ to $10^6$ unique words. To address this complexity, some very convoluted text mining methods have been applied. Is that complexity necessary? Are there simpler ways to quickly generate models that perform as well as the more convoluted methods and also be human-readable? To answer these questions, we explore a combination of LDA (Latent Dirichlet Allocation) and FFTs (Fast and Frugal Trees) to classify NASA software bug reports from six different projects. Designed using principles from psychological science, FFTs return very small models that are human-comprehensible. When compared to the commonly used text mining method and a recent state-of-the-art-system (search-based SE method that automatically tune the control parameters of LDA), these FFT models are very small (a binary tree of depth $d = 4$ that references only 4 topics) and hence easy to understand. They were also faster to generate and produced similar or better severity predictions. Hence we can conclude that, at least for datasets explored here, convoluted text mining models can be deprecated in favor of simpler method such as LDA+FFTs. At the very least, we recommend LDA+FFTs (a) when humans need to read, understand, and audit a model or (b) as an initial baseline method for the SE researchers exploring text artifacts from software projects.


title: Dehazing for Multispectral Remote Sensing Images Based on a Convolutional Neural Network With the Residual Architecture

abstract: Multispectral remote sensing images are often contaminated by haze, which causes low image quality. In this paper, a novel dehazing method based on a deep convolutional neural network (CNN) with the residual structure is proposed for multispectral remote sensing images. First, multiple CNN individuals with the residual structure are connected in parallel and each individual is used to learn a regression from the hazy image to the clear image. Then, the outputs of CNN individuals are fused with weight maps to produce the final dehazing result. In the designed network, the CNN individuals, mining multiscale haze features through multiscale convolutions, are trained using different levels of haze samples to achieve different dehazing abilities. In addition, the weight maps change with the haze distribution, and the fusion of the CNN individuals is adaptive. The designed network is end-to-end, and putting a hazy image into it, the clear scene can be restored. To train the network, a wavelength-dependent haze simulation method is proposed to generate labeled data, which can synthesize hazy multispectral images highly close to real conditions. Experimental results show that the proposed method can accurately remove the haze in each band of multispectral images under different scenes.


title: ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

abstract: With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and many chemical properties of research interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed from the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical properties that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models and other contemporary DNN models. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a general-purpose plug-and-play deep neural network for the prediction of novel small-molecule chemical properties.


title: Wireless sensor networks: applications and challenges of ubiquitous sensing

abstract: Sensor networks offer a powerful combination of distributed sensing, computing and communication. They lend themselves to countless applications and, at the same time, offer numerous challenges due to their peculiarities, primarily the stringent energy constraints to which sensing nodes are typically subjected. The distinguishing traits of sensor networks have a direct impact on the hardware design of the nodes at at least four levels: power source, processor, communication hardware, and sensors. Various hardware platforms have already been designed to test the many ideas spawned by the research community and to implement applications to virtually all fields of science and technology. We are convinced that CAS will be able to provide a substantial contribution to the development of this exciting field.


title: A Flexibility Metric and Optimization Methods for Mixed Numerologies in 5G and Beyond

abstract: Mixed numerology-based frame structures will be a part of 5G systems in order to enhance overall flexibility and user satisfaction. However, inter-numerology interference, spectral efficiency reduction, complexity, and signaling overhead type of issues arise in such structures. It is needed to limit the number of numerologies used together. In this paper, a novel heuristic method is developed to find the efficient number of mixed numerologies. The proposed method aims to control overheads in systems using multi-numerology structures. Analysis of the trade-offs and relationships between different services and user requirements are also presented. The designed algorithm employs a new flexibility function and performance metric. Simulation results are shown for three different numerology sets, which include 5G numerologies.


title: Scalable Function Call Graph-based Malware Classification

abstract: In an attempt to preserve the structural information in malware binaries during feature extraction, function call graph-based features have been used in various research works in malware classification. However, the approach usually employed when performing classification on these graphs, is based on computing graph similarity using computationally intensive techniques. Due to this, much of the previous work in this area incurred large performance overhead and does not scale well. In this paper, we propose a linear time function call graph (FCG) vector representation based on function clustering that has significant performance gains in addition to improved classification accuracy. We also show how this representation can enable using graph features together with other non-graph features.


title: Scientific articles recommendation

abstract: We study the problem of recommending scientific articles to users in an online community and present a novel matrix factorization model, the topic regression Matrix Factorization (tr-MF), to solve the problem. The main idea of tr-MF lies in extending the matrix factorization with a probabilistic topic modeling. Instead of regularizing item factors through the probabilistic topic modeling as in the framework of the CTR model, tr-MF introduces a regression model to regularize user factors through the probabilistic topic modeling under the basic hypothesis that users share the similar preferences if they rate similar sets of items. Consequently, tr-MF provides interpretable latent factors for users and items, and makes accurate predictions for community users. Specifically, it is effective in making predictions for users with only few ratings or even no ratings, and supports tasks that are specific to a certain field, neither of which is addressed in the existing literature. Further, we demonstrate the efficacy of tr-MF on a large subset of the data from CiteULike, a bibliography sharing service dataset. The proposed model outperforms the state-of-the-art matrix factorization models with a significant margin.


title: Applications of Deep Learning in Biomedicine

abstract: Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.


title: Unmixing-Based Soft Color Segmentation for Image Manipulation

abstract: We present a new method for decomposing an image into a set of soft color segments that are analogous to color layers with alpha channels that have been commonly utilized in modern image manipulation software. We show that the resulting decomposition serves as an effective intermediate image representation, which can be utilized for performing various, seemingly unrelated, image manipulation tasks. We identify a set of requirements that soft color segmentation methods have to fulfill, and present an in-depth theoretical analysis of prior work. We propose an energy formulation for producing compact layers of homogeneous colors and a color refinement procedure, as well as a method for automatically estimating a statistical color model from an image. This results in a novel framework for automatic and high-quality soft color segmentation that is efficient, parallelizable, and scalable. We show that our technique is superior in quality compared to previous methods through quantitative analysis as well as visually through an extensive set of examples. We demonstrate that our soft color segments can easily be exported to familiar image manipulation software packages and used to produce compelling results for numerous image manipulation applications without forcing the user to learn new tools and workflows.


title: Blood Flow and Cerebral Laterality in the Mescaline Model of Psychosis

abstract: The psychological, neuropsychological, and neurometabolic effects of the hallucinogenic agent mescaline were investigated in 12 normal male volunteers. Between 31/2 and 4 hours after drug intake, mescaline produced an acute psychotomimetic state, as measured by the BPRS and PDS-P. The APZ questionnaire revealed the specific effects of mescaline in the visual system. Neuropsychological effects were studied with a face/ non-face decision task with known right hemisphere advantage, in which mescaline induced a decrease in functioning of the right hemisphere. In functional brain imaging using SPECT, mescaline produced a hyperfrontal pattern with an emphasis on the right hemisphere, which was correlated with mescaline-induced psychotomimetic psychopathology. Our findings question the validity of the concept of hypofrontality as an explanation for acute psychotic symptomatology.


title: Answering Science Exam Questions Using Query Reformulation with Background Knowledge

abstract: Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques. Much of the progress in open-domain QA systems has been realized through advances in information retrieval methods and corpus construction. In this paper, we focus on the recently introduced ARC Challenge dataset, which contains 2,590 multiple choice questions authored for grade-school science exams. These questions are selected to be the most challenging for current QA systems, and current state of the art performance is only slightly better than random chance. We present a system that reformulates a given question into queries that are used to retrieve supporting text from a large corpus of science-related text. Our rewriter is able to incorporate background knowledge from ConceptNet and -- in tandem with a generic textual entailment system trained on SciTail that identifies support in the retrieved results -- outperforms several strong baselines on the end-to-end QA task despite only being trained to identify essential terms in the original source question. We use a generalizable decision methodology over the retrieved evidence and answer candidates to select the best answer. By combining query reformulation, background knowledge, and textual entailment our system is able to outperform several strong baselines on the ARC dataset.


title: A Deep Convolutional Neural Network for Lung Cancer Diagnostic

abstract: In this paper, we examine the strength of deep learning technique for diagnosing lung cancer on medical image analysis problem. Convolutional neural networks (CNNs) models become popular among the pattern recognition and computer vision research area because of their promising outcome on generating high-level image representations. We propose a new deep learning architecture for learning high-level image representation to achieve high classification accuracy with low variance in medical image binary classification tasks. We aim to learn discriminant compact features at beginning of our deep convolutional neural network. We evaluate our model on Kaggle Data Science Bowl 2017 (KDSB17) data set, and compare it with some related works proposed in the Kaggle competition.


title: Low complexity GFDM receiver design: A new approach

abstract: GFDM (Generalized Frequency Division Multiplexing) is a multicarrier modulation technique that is being proposed as a potential candidate for the fifth generation of wireless communication systems (5G). Due to the fact that GFDM uses only one cyclic prefix (CP) for a group of symbols rather than a CP per symbol and it has a well contained spectral properties, it is more bandwidth efficient than the widely used OFDM modulation. In this paper, we propose novel receiver designs for GFDM by taking advantage of the particular structure in the modulation matrix. A unified receiver structure for matched filter (MF), zero forcing (ZF) and minimum mean square error (MMSE) receivers is derived. Our proposed MF receiver is based on sparsification of the modulation matrix using block discrete Fourier transform (DFT) matrix. The proposed ZF and MMSE receiver algorithms in this paper harness the special block circulant property of the matrices involved in the demodulation stage to reduce the computational cost of the system implementation. In addition, our algorithms do not incur any performance loss as no approximation is involved. The computational costs of our proposed techniques are analyzed in detail and are compared with the existing solutions that are known to have the lowest complexity. It should be highlighted that a substantial computational complexity reduction can be achieved by adopting our techniques.


title: Multi-objective scheduling of dynamic job shop using variable neighborhood search

abstract: Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.


title: A Survey of Multilinear Subspace Learning for Tensor Data

abstract: Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning (MSL) for dimensionality reduction of multidimensional data directly from their tensorial representations. It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.


title: Hate Online: A Content Analysis of Extremist Internet Sites

abstract: Extremists, such as hate groups espousing racial supremacy or separation, have established an online presence. A content analysis of 157 extremist web sites selected through purposive sampling was conducted using two raters per site. The sample represented a variety of extremist groups and included both organized groups and sites maintained by apparently unaffiliated individuals. Among the findings were that the majority of sites contained external links to other extremist sites (including international sites), that roughly half the sites included multimedia content, and that half contained racist symbols. A third of the sites disavowed racism or hatred, yet one third contained material from supremacist literature. A small percentage of sites specifically urged violence. These and other findings suggest that the Internet may be an especially powerful tool for extremists as a means of reaching an international audience, recruiting members, linking diverse extremist groups, and allowing maximum image control.


title: Smudge Attacks on Smartphone Touch Screens

abstract: Touch screens are an increasingly common feature on personal computing devices, especially smartphones, where size and user interface advantages accrue from consolidating multiple hardware components (keyboard, number pad, etc.) into a single software definable user interface. Oily residues, or smudges, on the touch screen surface, are one side effect of touches from which frequently used patterns such as a graphical password might be inferred. ::: ::: In this paper we examine the feasibility of such smudge attacks on touch screens for smartphones, and focus our analysis on the Android password pattern. We first investigate the conditions (e.g., lighting and camera orientation) under which smudges are easily extracted. In the vast majority of settings, partial or complete patterns are easily retrieved. We also emulate usage situations that interfere with pattern identification, and show that pattern smudges continue to be recognizable. Finally, we provide a preliminary analysis of applying the information learned in a smudge attack to guessing an Android password pattern.


title: Optimizing Chinese Word Segmentation for Machine Translation Performance

abstract: Previous work has shown that Chinese word segmentation is useful for machine translation to English, yet the way different segmentation strategies affect MT is still poorly understood. In this paper, we demonstrate that optimizing segmentation for an existing segmentation standard does not always yield better MT performance. We find that other factors such as segmentation consistency and granularity of Chinese "words" can be more important for machine translation. Based on these findings, we implement methods inside a conditional random field segmenter that directly optimize segmentation granularity with respect to the MT task, providing an improvement of 0.73 BLEU. We also show that improving segmentation consistency using external lexicon and proper noun features yields a 0.32 BLEU increase.


title: Leaf analysis for plant recognition

abstract: Plants are essential resources for nature and people's lives. Plant recognition provides valuable information for plant research and development, and has great impact on environmental protection and exploration. This paper presents a leaf analysis system for plant identification, which consists of three main components. First, given a leaf image, a preprocessing step is conducted for noise reduction. Second, the feature extraction component identifies representative features and computes scale invariant feature descriptors. Third, the matching plant species are identified and returned using a weighted K-nearest neighbor search algorithm. The system is implemented as a Windows phone app and is tested on the LeafSnapdataset[8], an electronic field guide developed by Columbia University and University of Maryland with different combinations of species at various orientations, scales and levels of brightness. The experimental results demonstrate the effectiveness of our proposed framework in plant recognition.


Brain Imaging in Gambling Disorder


title: Differential evolution algorithms for scheduling raw milk transportation

abstract: The heterogeneous fleet with many tanks is considered to collect raw milk.Raw milk from different centers cannot be transferred into the same compartment.Both traveling costs and cleaning costs are considered as the objective function.Five modified DEs were proposed including reincarnation and survival processes.Skipped customer and multi-route processes were developed in the decoding process. This paper focuses on determining routes for raw milk collection from collection centers to dairy factories with the objective of minimizing the total costs, considering fuel costs and costs of cleaning and sanitizing raw milk tanks on vehicles. This problem is considered to be a special case of the vehicle routing problem (VRP) but it is complex compared to the general VRP, especially since each vehicle contains more than one tank with heterogeneous capacity to collect raw milk and raw milk from different collection centers cannot be transferred into the same compartment. In this paper, a DE metaheuristic was used to solve the problem. In order to improve the solution quality, five modified DE algorithms with two additional steps, reincarnation and survival processes, were proposed. In addition, the skipped customer and multi-route attributes are also developed in the decoding process in order to obtain a shorter traveling distance and lower truck usage in the system, especially if they are used together with the reincarnation and survival processes. The computational results reveal that the modified DE algorithms yield higher relative improvement (RI) on the total costs and also the RI on the number of vehicles used.


title: Effect of Mozart Music on Hippocampal Content of BDNF in Postnatal Rats

abstract: 1. Cellular and Molecular Research Center, and Neuroscience Department, Tehran University of Medical Sciences, Tehran, Iran 2. Anatomy Department, Tehran University of Medical Science, Tehran, Iran. 3. Physiology Research Center (PRC), Tehran university of Medical Sciences, Tehran, Iran. 4. Institute for Cognitive Science studies (ICSS), Tehran, Iran. 5. Department of Material Science and Engineering, Sharif University of Technology, Tehran, Iran.


The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration


title: Platform Competition in Two-Sided Markets: The Case of Payment Networks

abstract: In this article, we construct a model to study competing payment networks, where networks offer differentiated products in terms of benefits to consumers and merchants. We study market equilibria for a variety of market structures: duopolistic competition and cartel, symmetric and asymmetric networks, and alternative assumptions about multihoming and consumer preferences. We find that competition unambiguously increases consumer and merchant welfare. We extend this analysis to competition among payment networks providing different payment instruments and find similar results.


title: Hardware-based architecture for asymmetric numeral systems entropy decoder

abstract: In this paper, two novel hardware architectures based on tabled asymmetric numeral systems decoding algorithm are proposed. In the proposed architectures the decoding throughput is highly dependent on the how much the data is compressed at encoding time. The synthesis results presented here show that the throughput of the parallel architecture can reach up 200 MB/s. The benchmarks show that the parallel architecture that runs on Xilinx Kintex FPGA provides higher throughout in comparison with the same algorithm running on Core i3 CPU.


title: Rectenna design and optimization using reciprocity theory and harmonic balance analysis for electromagnetic (EM) energy harvesting

abstract: A rectenna design methodology combining electromagnetic (EM) simulation and harmonic balance (HB) analysis is presented. It consists of applying reciprocity theory to calculate the Thevenin equivalent circuit of the receiving antenna and optimizing the rectifying circuit parameters using HB analysis. The method is demonstrated by designing a 2.45-GHz rectenna based on a square aperture-coupled patch antenna with dual linear polarization. A compact implementation is achieved by etching a cross-shaped slot on the patch surface leading to a 32.5% patch side reduction. Voltage-doubling circuits convert the received RF power from each port to dc permitting the rectenna to receive arbitrarily polarized signals. The circuit is optimized for low input power densities and a simulated maximum efficiency of 38.2% was obtained for 1.5 nWcm-2 input RF power density at 2.43 GHz.


title: A Self-tuning Failure Detection Scheme for Cloud Computing Service

abstract: Cloud computing is an increasingly important solution for providing services deployed in dynamically scalable cloud networks. Services in the cloud computing networks may be virtualized with specific servers which host abstracted details. Some of the servers are active and available, while others are busy or heavy loaded, and the remaining are offline for various reasons. Users would expect the right and available servers to complete their application requirements. Therefore, in order to provide an effective control scheme with parameter guidance for cloud resource services, failure detection is essential to meet users' service expectations. It can resolve possible performance bottlenecks in providing the virtual service for the cloud computing networks. Most existing Failure Detector (FD) schemes do not automatically adjust their detection service parameters for the dynamic network conditions, thus they couldn't be used for actual application. This paper explores FD properties with relation to the actual and automatic fault-tolerant cloud computing networks, and find a general non-manual analysis method to self-tune the corresponding parameters to satisfy user requirements. Based on this general automatic method, we propose specific and dynamic Self-tuning Failure Detector, called SFD, as a major breakthrough in the existing schemes. We carry out actual and extensive experiments to compare the quality of service performance between the SFD and several other existing FDs. Our experimental results demonstrate that our scheme can automatically adjust SFD control parameters to obtain corresponding services and satisfy user requirements, while maintaining good performance. Such an SFD can be extensively applied to industrial and commercial usage, and it can also significantly benefit the cloud computing networks.


omma Hairs : A New Dermoscopic Marker or Tinea Capitis


title: Inactivation of catalase by phenylhydrazine. Formation of a stable aryl-iron heme complex.

abstract: Abstract Catalase promotes the H2O2-dependent oxidation of phenylhydrazine to benzene but simultaneously is subject to a pseudo-first order inactivation process. Each inactivation event is subtended by catalytic turnover of three molecules of phenylhydrazine and 52 molecules of H2O2. The dimethyl ester of N-phenylprotoporphyrin IX is extracted with acidic methanol from the inactivated enzyme, but the prosthetic heme with a phenyl sigma-bonded to the iron atom is obtained by gentle extraction with 2-butanone. The absolute chirality of N-ethylprotoporphyrin IX isolated from catalase inactivated with ethylhydrazine confirms that the prosthetic heme has the same chiral orientation in the active site as it does in hemoglobin. The known inactivation of methemoglobin by phenylhydrazine is shown to depend on H2O2 but not oxygen. The results demonstrate that the H2O2-dependent oxidation of phenylhydrazine by catalase and other hemoproteins results in sigma-coordination of a phenyl residue to the prosthetic heme iron. This process may play a role not only in phenylhydrazine-mediated erythrocyte lysis but also in the activation of guanylate cyclase.


title: Development of an electrostatically actuated MEMS switching probe card

abstract: We have designed and fabricated a new type of MEMS probe card consisting of electrostatically-driven microprobes, which can be used for a next generation wafer probe card with the fritting-contact method. MEMS probe cards are requisite to higher pad-density and smaller pad-pitch chips, and are effective in high frequency testing. If a probe card consists of an array of actuator-integrated microprobes, it could be suitable for a wafer-level test/burn-in probe card, since the microprobes can be used for direct switching of the contact to reduce the number of the I/O lines of the probe card. We proposed a MEMS probe card that is composed of an array of Ni curl-up microcantilevers with a rolling-contact touch-mode electrostatic actuator and developed a micromachining process which includes electroplating deposition of two layers having different internal stress and etching of Al sacrifice layer. By measuring the electrostatic actuation properties and investigating the disconnection force of the fritting contact between a Ni probe and Al film, it has been found that the fritting contact could be disconnected by applying the voltages less than 100 V.


title: Analysis and Retrieval of Paintings Using Artistic Color Concepts

abstract: Traditionally artistic color concepts play an important role in the analysis of artworks, and provide valuable domain knowledge to guide the analysis and accurate retrieval of paintings. This paper presents an automated approach to analyzing and representing artistic color concepts such as color temperature, color palette and color contrasts in paintings domain. The color concept definitions rely on the widely accepted color theory of Itten. Our approach starts with the extraction of homogeneous color/texture regions from paintings, and employs image processing and machine learning techniques to characterize regions in terms of artistic color concepts. We evaluate the proposed method using collection of 1,000 paintings, which exhibit vast variety of painting styles and artistic color concepts. Experiments show that our method performs well for a vast variety of both spatial and non-spatial artistic color concept queries


title: An observation on flash evoked cortical potentials and Qigong meditation.

abstract: The purpose of this study was to investigate the functional state of the cerebral cortex during Qigong meditation with flash visual evoked potentials (F-VEPs) recorded from the occipital scalp in four groups of adults. The first group included 14 subjects who had exercised in Neiyang Gong for 0.5-5.5 years. The second group was composed of 12 subjects who had practiced Neiyang Gong for only 0.5-3 months. Eleven subjects who had never practiced Qigong before made up the third group and served as control. Eleven Qigong practitioners constituted the fourth group. It was found that F-VEP amplitudes were increased in the first group and decreased in the fourth group with the exception of one subject during Qigong meditation. No significant changes were found in the second group and controls. The results were discussed and it is shown that Qigong mediation may have either facilitative or inhibitory effects on the visual cortex depending on the Qigong methods practiced by different individuals.


title: A Rotationally-Invariant Convolution Module by Feature Map Back-Rotation

abstract: Despite breakthroughs in image classification due to the evolution of deep learning and, in particular, convolutional neural networks (CNNs), state-of-the-art models only possess a very limited amount of rotational invariance. Known workarounds include artificial rotations of the training data or ensemble approaches, where several models are evaluated. These approaches either increase the workload of the training or inflate the number of parameters. Further approaches add rotational invariance by globally pooling over rotationally equivariant features. Instead, we propose to incorporate rotational invariance into the feature-extraction part of the CNN directly. This allows to train on unrotated data and perform well on a rotated test set. We use rotational convolutions and introduce a rotational pooling layer that performs a pooling over the back-rotated output feature maps. We show that when training on the original, unrotated MNIST training dataset, but evaluating on rotations of the MNIST test dataset, the error rate can be reduced substantially from 58.20% to 12.20%. Similar results are shown for the CIFAR-10 and CIFAR-100 datasets.


title: Logo detection in images using HOG and SIFT

abstract: In this paper we present a study of logo detection in images from a media agency. We compare two most widely used methods — HOG and SIFT on a challenging dataset of images arising from a printed press and news portals. Despite common opinion that SIFT method is superior, our results show that HOG method performs significantly better on our dataset. We augment the HOG method with image resizing and rotation to improve its performance even more. We found out that by using such approach it is possible to obtain good results with increased recall and reasonably decreased precision.


title: Chitosan/siRNA functionalized titanium surface via a layer-by-layer approach for in vitro sustained gene silencing and osteogenic promotion

abstract: Titanium surface modification is crucial to improving its bioactivity, mainly its bone binding ability in bone implant materials. In order to functionalize titanium with small interfering RNA (siRNA) for sustained gene silencing in nearby cells, the layer-by-layer (LbL) approach was applied using sodium hyaluronate and chitosan/siRNA (CS/siRNA) nanoparticles as polyanion and polycation, respectively, to build up the multilayered film on smooth titanium surfaces. The CS/siRNA nanoparticle characterization was analyzed first. Dynamic contact angle, atomic force microscopy, and scanning electron microscopy were used to monitor the layer accumulation. siRNA loaded in the film was quantitated and the release profile of film in phosphate-buffered saline was studied. In vitro knockdown effect and cytotoxicity evaluation of the film were investigated using H1299 human lung carcinoma cells expressing green fluorescent protein (GFP). The transfection of human osteoblast-like cell MG63 and H1299 were performed and the osteogenic differentiation of MG63 on LbL film was analyzed. The CS/siRNA nanoparticles exhibited nice size distribution. During formation of the film, the surface wettability, topography, and roughness were alternately changed, indicating successful adsorption of the individual layers. The scanning electron microscope images clearly demonstrated the hybrid structure between CS/siRNA nanoparticles and sodium hyaluronate polymer. The cumulated load of siRNA increased linearly with the bilayer number and, more importantly, a gradual release of the film allowed the siRNA to be maintained on the titanium surface over approximately 1 week. In vitro transfection revealed that the LbL film-associated siRNA could consistently suppress GFP expression in H1299 without showing significant cytotoxicity. The LbL film loading with osteogenic siRNA could dramatically increase the osteogenic differentiation in MG63. In conclusion, LbL technology can potentially modify titanium surfaces with specific gene-regulatory siRNAs to enhance biofunction.


title: Point Pair Features Based Object Detection and Pose Estimation Revisited

abstract: We present a revised pipe-line of the existing 3D object detection and pose estimation framework based on point pair feature matching. This framework proposed to represent 3D target object using self-similar point pairs, and then matching such model to 3D scene using efficient Hough-like voting scheme operating on the reduced pose parameter space. Even though this work produces great results and motivated a large number of extensions, it had some general shortcoming like relatively high dimensionality of the search space, sensitivity in establishing 3D correspondences, having performance drops in presence of many outliers and low density surfaces. In this paper, we explain and address these drawbacks and propose new solutions within the existing framework. In particular, we propose to couple the object detection with a coarse-to-fine segmentation, where each segment is subject to disjoint pose estimation. During matching, we apply a weighted Hough voting and an interpolated recovery of pose parameters. Finally, all the generated hypothesis are tested via an occlusion-aware ranking and sorted. We argue that such a combined pipeline simultaneously boosts the detection rate and reduces the complexity, while improving the accuracy of the resulting pose. Thanks to such enhanced pose retrieval, our verification doesn't necessitate ICP and thus achieves better compromise of speed vs. accuracy. We demonstrate our method on existing datasets as well as on our scenes. We conclude that via the new pipe-line, point pair features can now be used in more challenging scenarios.


title: Multi-person localization via RF body reflections

abstract: We have recently witnessed the emergence of RF-based indoor localization systems that can track user motion without requiring the user to hold or wear any device. These systems can localize a user and track his gestures by relying solely on the reflections of wireless signals off his body, and work even if the user is behind a wall or obstruction. However, in order for these systems to become practical, they need to address two main challenges: 1) They need to be able to operate in the presence of more than one user in the environment, and 2) they must be able to localize a user without requiring him to move or change his position. ::: ::: This paper presents WiTrack2.0, a multi-person localization system that operates in multipath-rich indoor environments and pinpoints users' locations based purely on the reflections of wireless signals off their bodies. WiTrack2.0 can even localize static users, and does so by sensing the minute movements due to their breathing. We built a prototype of WiTrack2.0 and evaluated it in a standard office building. Our results show that it can localize up to five people simultaneously with a median accuracy of 11.7 cm in each of the x/y dimensions. Furthermore, WiTrack2.0 provides coarse tracking of body parts, identifying the direction of a pointing hand with a median error of 12.5°, for multiple users in the environment.


CANE: Context-Aware Network Embedding for Relation Modeling


title: A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels

abstract: The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dateset. On the contrary, it is fairly convenient, fast, and cheap to collect training images from the Web along with their noisy labels. This signifies the need of alternative approaches to training deep neural networks using such noisy labels. Existing methods tackling this problem either try to identify and correct the wrong labels or reweigh the data terms in the loss function according to the inferred noisy rates. Both strategies inevitably incur errors for some of the data points. In this paper, we contend that it is actually better to ignore the labels of some of the data points than to keep them if the labels are incorrect, especially when the noisy rate is high. After all, the wrong labels could mislead a neural network to a bad local optimum. We suggest a two-stage framework for the learning from noisy labels. In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability. The noisy labels of the other images are ignored. In the second stage, we train a deep neural network in a semi-supervised manner. This framework effectively takes advantage of the whole training set and yet only a portion of its labels that are most likely correct. Experiments on three datasets verify the effectiveness of our approach especially when the noisy rate is high.


title: An FPGA implementation of a voltage-oriented controlled three-phase PWM boost rectifier

abstract: Voltage-oriented control (VOC) of a three-phase PWM rectifier is one of the widely used structures in active converters. Design, simulation results, an FPGA implementation, and practical results of a VOC based three-phase PWM boost rectifier are presented and explained in this paper. National Instruments CompactRIO programmable automation controller platform is utilized in this application. The developed software and implemented hardware based on this controller are presented for this application.


title: Novel Six-Slot Four-Pole Axial Flux-Switching Permanent Magnet Machine for Electric Vehicle

abstract: This paper proposes several novel 6-slot/4-pole axial flux-switching machine topologies for electric and hybrid electric vehicles. The proposed topologies include stator shifting, rotor shifting, and a combination of shifting both the stator and the rotor. The proposed topologies eliminate the even harmonics of the flux linkage that create unbalanced flux linkage in the traditional 6-slot/4-pole machine. Fundamental frequency will be reduced by 60% compared with a 12-slot/10-pole axial flux-switching machine without unbalanced flux linkage. An analytical method is used to find the offset angle between stator a-axis and the rotor pole axis to realize the elimination of even harmonics of the flux linkage. The analytical method is verified by finite-element analysis and experiments. The results confirm that the proposed novel topologies efficiently eliminate the even harmonics of the flux linkage in the 6-slot/4-pole machine.


title: Gait extraction from short radar dwells collected on dismounts

abstract: A technique is presented for estimating fundamental gait frequencies from a dismount observed by a radar. The proposed method differs from gait extraction schemes [1-2] that use the fast Fourier transform (FFT) to compute one fundamental gait frequency that gives rise to the dismount's stride rate. In this approach, two fundamental gait frequencies are computed via a very versatile signal processing scheme, i.e., the state space method [3]. The first fundamental gait frequency corresponds to the side to side movement of the body of the walking dismount, whereas the second gait frequency describes the shift of the body weight from one leg to the other that induces forward and vertical displacements. The radar is assumed to have a very limited time to stare at the target while maintaining wide area surveillance and performing other tasks. Range-range-rate maps formed from data collected at few coherent processing intervals (CPIs) are used to generate a spectrogram snapshot at each dwell. The spectrogram snapshot is therefore passed to a limb mitigation filter that extracts the torso motion which is fed to a one-dimensional (1-D) signal processing scheme that computes a set of state matrices to estimate the gait frequencies and other parameters associated with the dynamic of the dismount. As time evolves, more spectrogram snapshots are generated and the gait frequencies are estimated to obtain the “fingerprint” of the dismount. The effectiveness of the gait extraction algorithm is confirmed using simulated radar data from the Thalmann human motion model [4] of a six foot tall man at six different aspect angles.


title: Service-Oriented Architecture for Big Data Analytics in Smart Cities

abstract: A smart city has recently become an aspiration for many cities around the world. These cities are looking to apply the smart city concept to improve sustainability, quality of life for residents, and economic development. The smart city concept depends on employing a wide range of advanced technologies to improve the performance of various services and activities such as transportation, energy, healthcare, and education, while at the same time improve the city's resources utilization and initiate new business opportunities. One of the promising technologies to support such efforts is the big data technology. Effective and intelligent use of big data accumulated over time in various sectors can offer many advantages to enhance decision making in smart cities. In this paper we identify the different types of decision making processes involved in smart cities. Then we propose a service-oriented architecture to support big data analytics for decision making in smart cities. This architecture allows for integrating different technologies such as fog and cloud computing to support different types of analytics and decision-making operations needed to effectively utilize available big data. It provides different functions and capabilities to use big data and provide smart capabilities as services that the architecture supports. As a result, different big data applications will be able to access and use these services for varying proposes within the smart city.


title: Shielding Effectiveness, Coupling Path, and EMI Mitigation for QSFP Cages With Heatsink

abstract: Quad form-factor pluggable (QSFP) interconnections shielding cages with heatsinks are often optimized for thermal, mechanical, and volume manufacturing. In this paper, shielding effectiveness (SE) of QSFP cages, including two configurations of 1 × 1 and 1 × 6 with three cases of normal (i.e., rising) heatsink, without a heatsink, and with a modified heatsink, is measured for the frequency range of 1–40 GHz using a dual reverberation chamber. For each measurement, three different vendors of optical modules are utilized and averaged SE is achieved for each case, indicating that the rising heatsink degrades the SE around 5–10 dB compare to the no heatsink or modified heatsink. Further, energy parcels and their trajectory concept are applied to visualize the coupling paths in a rising heatsink. The rising heatsink creates a new coupling path for EM waves to leak to the cage and emit from the chassis faceplate. From the energy parcel results, an electromagnetic interference (EMI) mitigation technique is proposed for the newly created coupling path by the rising heatsink, and its performance is evaluated with SE measurements. Further, a measurement is performed with an active board using a 40-Gbps optical module with and without the EMI mitigation technique.


Designing for Social Use in Public Places – a Conceptual Framework of Social Interaction


title: Integrating the Internet of Things with Business Process Management: A Process-aware Framework for Smart Objects

abstract: Due to the achievements in the Internet of Things (IoT) field, Smart Objects are often involved in business processes. However, the integration of IoT with Business Process Management (BPM) is far from mature: problems related to process compliance and Smart Objects configuration with respect to the process requirements have not been fully addressed yet; also, the interaction of Smart Objects with multiple business processes that belong to different stakeholders is still under investigation. My PhD thesis aims to fill this gap by extending the BPM lifecycle, with particular focus on the design and analysis phase, in order to explicitly support IoT and its requirements.


title: Capacity Limits of Optical Fiber Networks

abstract: We describe a method to estimate the capacity limit of fiber-optic communication systems (or ?fiber channels?) based on information theory. This paper is divided into two parts. Part 1 reviews fundamental concepts of digital communications and information theory. We treat digitization and modulation followed by information theory for channels both without and with memory. We provide explicit relationships between the commonly used signal-to-noise ratio and the optical signal-to-noise ratio. We further evaluate the performance of modulation constellations such as quadrature-amplitude modulation, combinations of amplitude-shift keying and phase-shift keying, exotic constellations, and concentric rings for an additive white Gaussian noise channel using coherent detection. Part 2 is devoted specifically to the "fiber channel.'' We review the physical phenomena present in transmission over optical fiber networks, including sources of noise, the need for optical filtering in optically-routed networks, and, most critically, the presence of fiber Kerr nonlinearity. We describe various transmission scenarios and impairment mitigation techniques, and define a fiber channel deemed to be the most relevant for communication over optically-routed networks. We proceed to evaluate a capacity limit estimate for this fiber channel using ring constellations. Several scenarios are considered, including uniform and optimized ring constellations, different fiber dispersion maps, and varying transmission distances. We further present evidences that point to the physical origin of the fiber capacity limitations and provide a comparison of recent record experiments with our capacity limit estimation.


title: Enactive Systems and Enactive Media: Embodied Human-Machine Coupling beyond Interfaces

abstract: This paper proposes the concept of enactive system as an alternative to the standard assumption of the human-computer-interaction and elaborates the idea of content mediation as enactive media. Our ...


title: Mobile sensing for agriculture activities detection

abstract: The agriculture activities have a major role in determining the quality and quantity of the agriculture produce. In this paper, we propose a novel mobile sensing based framework which uses machine learning algorithms for the detection of agriculture activities. To collect the sensors data and ground truth an android based mobile application has also been developed and has been provided to the farmers. We investigate the performance of Naive Bayes, Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) classifiers to detect the activities like Harvesting, Bed Making, Stand-still and Walking. We also use the same classifiers to detect the placement of the mobile phone on the body which will hence provide a degree of freedom to the farmers in placing the mobile phone as per their convenience.


title: Efficient HOG human detection

abstract: While Histograms of Oriented Gradients (HOG) plus Support Vector Machine (SVM) (HOG+SVM) is the most successful human detection algorithm, it is time-consuming. This paper proposes two ways to deal with this problem. One way is to reuse the features in blocks to construct the HOG features for intersecting detection windows. Another way is to utilize sub-cell based interpolation to efficiently compute the HOG features for each block. The combination of the two ways results in significant increase in detecting humans-more than five times better. To evaluate the proposed method, we have established a top-view human database. Experimental results on the top-view database and the well-known INRIA data set have demonstrated the effectiveness and efficiency of the proposed method.


title: Calcium Carbonate.

abstract: Calcium carbonate is a chemical compound with the formula CaCO3 formed by three main elements: carbon, oxygen, and calcium. It is a common substance found in rocks in all parts of the world (most notably as limestone), and is the main component of shells of marine organisms, snails, coal balls, pearls, and eggshells. CaCO3 exists in different polymorphs, each with specific stability that depends on a diversity of variables.


title: Appearance Based Recognition of American Sign Language Using Gesture Segmentation

abstract: The work presented in this paper goals to develop a system for automatic translation of static gestures of alphabets in American Sign Language. In doing so three feature extraction methods and neural network is used to recognize signs. The system deals with images of bare hands, which allows the user to interact with the system in a natural way. An image is processed and converted to a feature vector that will be compared with the feature vectors of a training set of signs. The system is rotation, scaling of translation variant of the gesture within the image, which makes the system more flexible. The system is implemented and tested using data sets of number of samples of hand images for each signs. Three feature extraction methods are tested and best one is suggested with results obtained from ANN. The system is able to recognize selected ASL signs with the accuracy of 92.33%. Index Terms—ASL, ASL recognition, ASL using ANN


title: Generating finite state machines from abstract state machines

abstract: We give an algorithm that derives a finite state machine (FSM) from a given abstract state machine (ASM) specification. This allows us to integrate ASM specs with the existing tools for test case generation from FSMs. ASM specs are executable but have typically too many, often infinitely many states. We group ASM states into finitely many hyperstates which are the nodes of the FSM. The links of the FSM are induced by the ASM state transitions.


title: The advantage of short paper titles

abstract: Vast numbers of scientific articles are published each year, some of which attract considerable attention, and some of which go almost unnoticed. Here, we investigate whether any of this variance can be explained by a simple metric of one aspect of the paper's presentation: the length of its title. Our analysis provides evidence that journals which publish papers with shorter titles receive more citations per paper. These results are consistent with the intriguing hypothesis that papers with shorter titles may be easier to understand, and hence attract more citations.


title: Dynamic Authentication with Sensory Information for the Access Control Systems

abstract: Access card authentication is critical and essential for many modern access control systems, which have been widely deployed in various government, commercial, and residential environments. However, due to the static identification information exchange among the access cards and access control clients, it is very challenging to fight against access control system breaches due to reasons such as loss, stolen or unauthorized duplications of the access cards. Although advanced biometric authentication methods such as fingerprint and iris identification can further identify the user who is requesting authorization, they incur high system costs and access privileges cannot be transferred among trusted users. In this work, we introduce a dynamic authentication with sensory information for the access control systems. By combining sensory information obtained from onboard sensors on the access cards as well as the original encoded identification information, we are able to effectively tackle the problems such as access card loss, stolen, and duplication. Our solution is backward-compatible with existing access control systems and significantly increases the key spaces for authentication. We theoretically demonstrate the potential key space increases with sensory information of different sensors and empirically demonstrate simple rotations can increase key space by more than 1,000,000 times with an authentication accuracy of 90 percent. We performed extensive simulations under various environment settings and implemented our design on WISP to experimentally verify the system performance.


title: Acceleration of financial Monte-Carlo simulations using FPGAs

abstract: There is a need for improved performance in financial computing, both to improve the quality of the results produced by existing methods, such as incorporating more realistic models of risk, and to increase the scope of applications, such as modelling corporation-wide exposure to risk without applying simplifying abstractions. However, increased performance comes at significant cost, both in terms of the capital costs, and also in terms of power consumption. Field-Programmable Gate Arrays (FPGAs) offer one way of providing greater performance than CPUs while also reducing power consumption, and are particularly well-suited for computationally intensive tasks such as Monte-Carlo simulation. However, existing programming models for FPGAs require too much specialist knowledge and programmer effort, making it infeasible to use them in most financial applications. This paper gives an overview of Contessa, a high-level language for describing Monte-Carlo simulations, with an automated compilation route to pipelined high-performance hardware. By providing a push-button high-level route to FPGA-accelerated performance, languages such as Contessa provide one way in which the benefits of FPGAs can be exploited in computational finance.


title: Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks

abstract: Taking inspiration from biological evolution, we explore the idea of "Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?" by introducing the notion of synthesizing new highly efficient, yet powerful deep neural networks over successive generations via an evolutionary process from ancestor deep neural networks. The architectural traits of ancestor deep neural networks are encoded using synaptic probability models, which can be viewed as the `DNA' of these networks. New descendant networks with differing network architectures are synthesized based on these synaptic probability models from the ancestor networks and computational environmental factor models, in a random manner to mimic heredity, natural selection, and random mutation. These offspring networks are then trained into fully functional networks, like one would train a newborn, and have more efficient, more diverse network architectures than their ancestor networks, while achieving powerful modeling capabilities. Experimental results for the task of visual saliency demonstrated that the synthesized `evolved' offspring networks can achieve state-of-the-art performance while having network architectures that are significantly more efficient (with a staggering $\sim$48-fold decrease in synapses by the fourth generation) compared to the original ancestor network.


title: Amniotic membrane transplantation (AMT) without the use of sutures/fibrin glue

abstract: INTRODUCTION ::: Amniotic membrane transplantation (AMT) has been used for a wide variety of ocular surface problems. Current techniques for the AMT involve suturing of the graft or patch over the ocular surface or use of fibrin glue. ::: ::: ::: OBJECTIVE ::: To describe a new sutureless technique of amniotic membrane transplantation (AMT) without the use of sutures/fibrin glue. ::: ::: ::: MATERIALS AND METHODS ::: Amniotic membrane transplantation as overlay or patch was done in fourteen eyes of eight patients with severe dry eye due to Stevens Johnson syndrome for ocular surface reconstruction, promotion of epithelization, reduction of inflammation and symptomatic relief from dry eye. In this new technique of amniotic membrane transplantation, we mount the amniotic membrane with its epithelial side up on a conformer and place it on the ocular surface without applying any sutures. ::: ::: ::: CONCLUSION ::: In all the fourteen eyes of eight patients operated at our centre, the amniotic membrane take up was satisfactory, with significant reduction of inflammation and symptomatic relief from dry eye. This is a simple, fast effective technique with surgical outcomes comparable to amniotic membrane transplantation using sutures/ fibrin glue.


Beyond Business-IT Alignment - Digital Business Strategies as a Paradigmatic Shift: A Review and Research Agenda


title: Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

abstract: In this paper, we propose a recommendation technique that not only can recommend items of interest to the user as traditional recommendation systems do but also specific aspects of consumption of the items to further enhance the user experience with those items. For example, it can recommend the user to go to a specific restaurant (item) and also order some specific foods there, e.g., seafood (an aspect of consumption). Our method is called Sentiment Utility Logistic Model (SULM). As its name suggests, SULM uses sentiment analysis of user reviews. It first predicts the sentiment that the user may have about the item based on what he/she might express about the aspects of the item and then identifies the most valuable aspects of the user's potential experience with that item. Furthermore, the method can recommend items together with those most important aspects over which the user has control and can potentially select them, such as the time to go to a restaurant, e.g. lunch vs. dinner, and what to order there, e.g., seafood. We tested the proposed method on three applications (restaurant, hotel, and beauty & spa) and experimentally showed that those users who followed our recommendations of the most valuable aspects while consuming the items, had better experiences, as defined by the overall rating.


Ultra Low-Loss 50-70 GHz SPDT Switch in 90 nm CMOS


title: Nonlinear sliding mode control and feasible workspace analysis for a cable suspended robot with input constraints and disturbances

abstract: This paper deals with the characterization of the feasible workspace of set-point control for a cable suspended robot. The motivation behind this work is to find admissible set points for the system under disturbances as well as input constraints. The main ideas are: (i) designing a sliding mode controller as a stabilizing controller for the given uncertain system, (ii) finding the range of system states in terms of set points by analyzing the reaching condition and sliding mode, and (iii) substituting states in inequalities of the input with either their upper values or lower values so that constraints are satisfied. This method results in 6 inequalities in terms of set point which can be drawn graphically in the 3-dimensional space.


abstract: In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features. A multi-view clustering framework, called Diversity-induced Multi-view Subspace Clustering (DiMSC), is proposed for this task. In our method, we extend the existing subspace clustering into the multi-view domain, and utilize the Hilbert Schmidt Independence Criterion (HSIC) as a diversity term to explore the complementarity of multi-view representations, which could be solved efficiently by using the alternating minimizing optimization. Compared to other multi-view clustering methods, the enhanced complementarity reduces the redundancy between the multi-view representations, and improves the accuracy of the clustering results. Experiments on both image and video face clustering well demonstrate that the proposed method outperforms the state-of-the-art methods.


title: State-Owned Enterprises: Rationales for Mergers and Acquisitions

abstract: The paper contributes to the empirical literature on M&A deals performed by SOEs with a detailed analysis of the reported rationales from a sample of SOE-led acquisitions over the last decade. The sample includes 355 worldwide M&A deals performed by SOEs as acquirers over the period 2002-2012. The data set was obtained by combining firm-level information from two sources, Zephyr and Orbis (Bureau Van Dijk). The analysis is on a case-by-case basis for the rationales of the sample. Overall, the most important message arising from our analysis is that rescue of firms in financial distress is a relatively minor one role played by contemporary SOEs in spite of the Great Recession, while shareholder value maximization and long term strategic goals are more frequently the objective of the observed deals.


title: Using rich social media information for music recommendation via hypergraph model

abstract: There are various kinds of social media information, including different types of objects and relations among these objects, in music social communities such as Last.fm and Pandora. This information is valuable for music recommendation. However, there are two main challenges to exploit this rich social media information: (a) There are many different types of objects and relations in music social communities, which makes it difficult to develop a unified framework taking into account all objects and relations. (b) In these communities, some relations are much more sophisticated than pairwise relation, and thus cannot be simply modeled by a graph. We propose a novel music recommendation algorithm by using both multiple kinds of social media information and music acoustic-based content. Instead of graph, we use hypergraph to model the various objects and relations, and consider music recommendation as a ranking problem on this hypergraph. While an edge of an ordinary graph connects only two objects, a hyperedge represents a set of objects. In this way, hypergraph can be naturally used to model high-order relations.


title: Processing directed acyclic graphs with recursive neural networks

abstract: Recursive neural networks are conceived for processing graphs and extend the well-known recurrent model for processing sequences. In Frasconi et al. (1998), recursive neural networks can deal only with directed ordered acyclic graphs (DOAGs), in which the children of any given node are ordered. While this assumption is reasonable in some applications, it introduces unnecessary constraints in others. In this paper, it is shown that the constraint on the ordering can be relaxed by using an appropriate weight sharing, that guarantees the independence of the network output with respect to the permutations of the arcs leaving from each node. The method can be used with graphs having low connectivity and, in particular, few outcoming arcs. Some theoretical properties of the proposed architecture are given. They guarantee that the approximation capabilities are maintained, despite the weight sharing.


title: Analysis of the electric vehicle charging station's layout in Beijing based on improved traveling simulation model

abstract: With the popularity of electric vehicles, some enterprises have manufactured many electric and hybrid electric vehicles. The construction of charging stations becomes more urgent. More and more papers studied the rationality of charging station's layout by simulation models. This paper improved the travelling simulation model of electric vehicles based on the following points: road network and population density, which make the traveling simulation model closer to reality. We researched the situation of charging stations in Beijing and used a heuristic method to simulate visiting frequency of charging stations and roads. Finally, we put forward reasonable proposals for the construction of charging stations in Beijing.


title: Reinforcement Learning Neural Turing Machines

abstract: The expressive power of a machine learning model is closely related to the number of sequential computational steps it can learn. For example, Deep Neural Networks have been more successful than shallow networks because they can perform a greater number of sequential computational steps (each highly parallel). The Neural Turing Machine (NTM) [8] is a model that can compactly express an even greater number of sequential computational steps, so it is even more powerful than a DNN. Its memory addressing operations are designed to be differentiable; thus the NTM can be trained with backpropagation. While differentiable memory is relatively easy to implement and train, it necessitates accessing the entire memory content at each computational step. This makes it difficult to implement a fast NTM. In this work, we use the Re inforce algorithm to learn where to access the memory, while using backpropagation to learn what to write to the memory. We call this model the RL-NTM. Reinforce allows our model to access a constant number of memory cells at each computational step, so its implementation can be faster. The RL-NTM is the first mo del that can, in principle, learn programs of unbounded running time. We successfully trained the RL-NTM to solve a number of algorithmic tasks that are simpler than the ones solvable by the fully differentiable NTM. As the RL-NTM is a fairly intricate model, we needed a method for verifying the correctness of our implementation. To do so, we developed a simple technique for numerically checking arbitrary implementations of models that use Reinforce, which may be of independent interest.


title: Activation Ensembles for Deep Neural Networks

abstract: Many activation functions have been proposed in the past, but selecting an adequate one requires trial and error. We propose a new methodology of designing activation functions within a neural network at each layer. We call this technique an "activation ensemble" because it allows the use of multiple activation functions at each layer. This is done by introducing additional variables, $\alpha$, at each activation layer of a network to allow for multiple activation functions to be active at each neuron. By design, activations with larger $\alpha$ values at a neuron is equivalent to having the largest magnitude. Hence, those higher magnitude activations are "chosen" by the network. We implement the activation ensembles on a variety of datasets using an array of Feed Forward and Convolutional Neural Networks. By using the activation ensemble, we achieve superior results compared to traditional techniques. In addition, because of the flexibility of this methodology, we more deeply explore activation functions and the features that they capture.


title: Efficient Word Alignment with Markov Chain Monte Carlo

abstract: We present efmaral, a new system for efficient and accurate word alignment using a Bayesian model with Markov Chain Monte Carlo (MCMC) inference. Through careful selection of data structures and mo ...


title: Wideband millimeter-wave SIW cavity backed patch antenna fed by substrate integrated coaxial line

abstract: In this paper, a wideband substrate integrated waveguide (SIW) cavity backed patch antenna element is proposed for high-gain arrays at millimeter-wave bands. By compensating the inductance of the feeding metallic via with a capacitive ring slot on the ground plate, an additional resonance is introduced to the patch mode to expand the bandwidth. To suppress the backward radiation from the slot and enhance antenna gain, a back SIW cavity is designed. Moreover, the low-loss and compact substrate integrated coaxial line (SICL) is employed as the feeding line. The simulated −10/−15 dB impedance bandwidth of the element is 14.9%/11.6% (both at 43 GHz). The introducing of the back cavity and SICL makes the element suitable for large wideband high-gain millimeter-wave arrays with excellent front-to-back ratios (FBRs) and stable radiation patterns on printed circuit boards (PCBs). As an illustrative example, a 2×2 corporately-fed sub-array is developed and tested. The −10 dB impedance bandwidth is 11.7%, and the peak gain is 11.4 dBi with a simulated radiation efficiency of 88% and measured FBR of above 20 dB.


Friend Suggestion System for the Social Network Based on User Behavior


title: Privacy by Design in Federated Identity Management

abstract: Federated Identity Management (FIM), while solving important scalability, security and privacy problems of remote entity authentication, introduces new privacy risks. By virtue of sharing identities with many systems, the improved data quality of subjects may increase the possibilities of linking private data sets, moreover, new opportunities for user profiling are being introduced. However, FIM models to mitigate these risks have been proposed. In this paper we elaborate privacy by design requirements for this class of systems, transpose them into specific architectural requirements, and evaluate a number of FIM models with respect to these requirements. The contributions of this paper are a catalog of privacy-related architectural requirements, joining up legal, business and system architecture viewpoints, and the demonstration of concrete FIM models showing how the requirements can be implemented in practice.


Active inference, communication and hermeneutics☆


title: Large scale place recognition in 2D LIDAR scans using Geometrical Landmark Relations

abstract: The recognition of places that have already been visited is a fundamental requirement for a mobile robot. This particularly concerns the detection of loop closures while mapping environments as well as the global localization w.r.t. to a prior map. This paper introduces a novel solution to place recognition with 2D LIDAR scans. Existing approaches utilize descriptors covering the local appearance of discriminative features within a bag-of-words (BOW) framework accompanied with approximate geometric verification. Though limiting the set of potential matches their performance crucially drops for increasing number of scans making them less appropriate for large scale environments. We present Geometrical Landmark Relations (GLARE), which transform 2D laser scans into pose invariant histogram representations. Potential matches are found in sub-linear time using an efficient Approximate Nearest Neighbour (ANN) search. Experimental results obtained from publicly available datasets demonstrate that GLARE significantly outperforms state-of-the-art approaches in place recognition for large scale outdoor environments, while achieving similar results for indoor settings. Our Approach achieves recognition rates of 93% recall at 99% precision for a dataset covering a total path of about 6.5 km.


title: Silicone oil emulsions: strategies to improve their stability and applications in hair care products

abstract: Silicone oils have wide range of applications in personal care products due to their unique properties of high lubricity, non-toxicity, excessive spreading and film formation. They are usually employed in the form of emulsions due to their inert nature. Until now, different conventional emulsification techniques have been developed and applied to prepare silicone oil emulsions. The size and uniformity of emulsions showed important influence on stability of droplets, which further affect the application performance. Therefore, various strategies were developed to improve the stability as well as application performance of silicone oil emulsions. In this review, we highlight different factors influencing the stability of silicone oil emulsions and explain various strategies to overcome the stability problems. In addition, the silicone deposition on the surface of hair substrates and different approaches to increase their deposition are also discussed in detail.


Fuzzy Based Turbine Governor for Hydro Power Plant


title: A Lossless Compression Scheme for Bayer Color Filter Array Images

abstract: In most digital cameras, Bayer color filter array (CFA) images are captured and demosaicing is generally carried out before compression. Recently, it was found that compression-first schemes outperform the conventional demosaicing-first schemes in terms of output image quality. An efficient prediction-based lossless compression scheme for Bayer CFA images is proposed in this paper. It exploits a context matching technique to rank the neighboring pixels when predicting a pixel, an adaptive color difference estimation scheme to remove the color spectral redundancy when handling red and blue samples, and an adaptive codeword generation technique to adjust the divisor of Rice code for encoding the prediction residues. Simulation results show that the proposed compression scheme can achieve a better compression performance than conventional lossless CFA image coding schemes.


TapDynamics : Strengthening User Authentication on Mobile Phones with Keystroke Dynamics


title: Modelling Learning of New Keyboard Layouts

abstract: Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.


title: Software Security: Building Security In

abstract: Summary form only given. Software security has come a long way in the last few years, but we've really only just begun. I will present a detailed approach to getting past theory and putting software security into practice. The three pillars of software security are applied risk management, software security best practices (which I call touchpoints), and knowledge. By describing a manageably small set of touchpoints based around the software artifacts that you already produce, I avoid religious warfare over process and get on with the business of software security. That means you can adopt the touchpoints without radically changing the way you work. The touchpoints I will describe include: code review using static analysis tools; architectural risk analysis; penetration testing; security testing; abuse case development; and security requirements. Like the yin and the yang, software security requires a careful balance-attack and defense, exploiting and designing, breaking and building-bound into a coherent package. Create your own Security Development Lifecycle by enhancing your existing software development lifecycle with the touchpoints


title: Solving a bi-objective multi-product vehicle routing problem with heterogeneous fleets under an uncertainty condition

abstract: This paper presents a novel bi-objective multi-product capacitated vehicle routing problem with uncertainty in demand of retailers and volume of products (UCVRP) and heterogeneous vehicle fleets. The first of two conflict fuzzy objective functions is to minimize the cost of the used vehicles, fuel consumption for full loaded vehicles and shortage of products. The second objective is to minimize the shortage of products for all retailers. In order to get closer to a real-world situation, the uncertainty in the demand of retailers is applied using fuzzy numbers. Additionally, the volume of products is applied using robust parameters, because the possible value of this parameter is not distinct and belongs to a bounded uncertainty set. The fuzzy-robust counterpart model may be larger than the deterministic form or the uncertain model with one approach and it has with further complexity; however, it provides a better efficient solution for this problem. The proposed fuzzy approach is used to solve the bi-objective mixed-integer linear problem to find the most preferred solution. Moreover, it is impossible to improve one of the objective functions without considering deterioration in the other objective functions. In order to show the conflict between two objective functions in an excellent fashion, a Pareto-optimal solution with the e-constraint method is obtained. Some numerical test problems are used to demonstrate the efficiency and validity of the presented model.


title: SEMBA: A SEM based acquisition technique for fast invasive Hardware Trojan detection

abstract: In this paper, we present how SEMBA, a fast invasive technique for white team Hardware Trojan detection, has been used to differentiate between a maliciously infected integrated circuit and a genuine one. Our methodology is based on the observation of the component's hardware structure and includes the use of wet etching, Scanning Electron Microscopy and Multiple Image Alignment. Once the Integrated Circuits' image have been fully reconstructed, image processing allows to detect the presence of the Hardware Trojan (HT). SEMBA is a fully automated approach with a 100% success rate, detecting any ‘transistor-size’ HTs and requiring ‘affordable’ resources and time.


title: Representation Learning for Treatment Effect Estimation from Observational Data

abstract: Estimating individual treatment effect (ITE) is a challenging problem in causal inference, due to missing counterfactuals and the selection bias. Existing ITE estimation methods mainly focus on balancing the distributions of control and treated groups, but ignore the local similarity information that is helpful. In this paper, we propose a local similarity preserved individual treatment effect (SITE) estimation method based on deep representation learning. SITE preserves local similarity and balances data distributions simultaneously, by focusing on several hard samples in each mini-batch. Experimental results on synthetic and three real-world datasets demonstrate the advantages of the proposed SITE method, compared with the state-of-the-art ITE estimation methods.


title: The Security Blanket of the Chat World: An Analytic Evaluation and a User Study of Telegram

abstract: The computer security community has advocated ::: widespread adoption of secure communication tools to protect ::: personal privacy. Several popular communication tools have ::: adopted end-to-end encryption (e.g., WhatsApp, iMessage), or ::: promoted security features as selling points (e.g., Telegram, ::: Signal). However, previous studies have shown that users may ::: not understand the security features of the tools they are using, ::: and may not be using them correctly. In this paper, we present a ::: study of Telegram using two complementary methods: (1) a labbased ::: user study (11 novices and 11 Telegram users), and (2) a ::: hybrid analytical approach combining cognitive walk-through ::: and heuristic evaluation to analyse Telegram’s user interface. ::: Participants who use Telegram feel secure because they feel ::: they are using a secure tool, but in reality Telegram offers ::: limited security benefits to most of its users. Most participants ::: develop a habit of using the less secure default chat mode at all ::: times. We also uncover several user interface design issues that ::: impact security, including technical jargon, inconsistent use of ::: terminology, and making some security features clear and others ::: not. For instance, use of the end-to-end-encrypted Secret Chat ::: mode requires both the sender and recipient be online at the same ::: time, and Secret Chat does not support group conversations.


title: Diagnosis and treatment of urethral prolapse in children: 16 years’ experience with 89 Chinese girls

abstract: OBJECTIVE ::: To review our experience and results in the diagnosis and treatment of urethral prolapse (UP) in Chinese girls. ::: ::: ::: PATIENTS AND METHODS ::: We conducted a retrospective chart review of 89 consecutive girls (aged <16 years) with UP and without other complications, who received treatment for UP from January 1999 to January 2015 (a study period of 16 years) at the Children's Hospital of Chongqing Medical University, China. Data analysed included: age, symptoms, clinical findings, predisposing factors, management, and outcomes. ::: ::: ::: RESULTS ::: The presenting symptoms in the 89 girls were: mass (54 girls), bleeding (34), and dysuria/straining at micturition (one). In all, 14 patients received conservative treatment as their symptoms were mild, and 75 were successfully treated by excision of the prolapsed urethral mucosa or ligation over a Foley catheter, as their symptoms were severe and recurred too frequently to be managed conservatively. The mean postoperative length of stay for ligation was 7.76 days and for excision was 4.57 days. Ligation over a Foley catheter had a longer hospital stay. ::: ::: ::: CONCLUSIONS ::: UP is a rare condition occurring in prepubertal girls, evidenced by a urethral mass and bleeding. Increased physician awareness and early recognition of UP avoids unnecessary examinations and patient anxiety.


title: A survey on software cost estimation in the chinese software industry

abstract: Although a lot of attention has been paid to software cost estimation since 1960, making accurate effort and schedule estimation is still a challenge. To collect evidence and identify potential areas of improvement in software cost estimation, it is important to investigate the estimation accuracy, the estimation method used, and the factors influencing the adoption of estimation methods in current industry. This paper analyzed 112 projects from the Chinese software project benchmarking dataset and conducted questionnaire survey on 116 organizations to investigate the above information. The paper presents the current situations related to software project estimation in China and provides evidence-based suggestions on how to improve software project estimation. Our survey results suggest, e.g., that large projects were more prone to cost and schedule overruns, that most computing managers and professionals were neither satisfied nor dissatisfied with the project estimation, that very few organizations (15%) used model-based methods, and that the high adoption cost and insignificant benefit after adoption were the main causes for low use of model-based methods.


title: Recurrent Neural Network Training with Dark Knowledge Transfer

abstract: Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision. This knowledge transfer learning has been employed to train simple neural nets with a complex one, so that the final performance can reach a level that is infeasible to obtain by regular training. In this paper, we employ the knowledge transfer learning approach to train RNNs (precisely LSTM) using a deep neural network (DNN) model as the teacher. This is different from most of the existing research on knowledge transfer learning, since the teacher (DNN) is assumed to be weaker than the child (RNN); however, our experiments on an ASR task showed that it works fairly well: without applying any tricks on the learning scheme, this approach can train RNNs successfully even with limited training data.


title: Can Active Learning Experience Be Transferred?

abstract: Active learning is an important machine learning problem in reducing the human labeling effort. Current active learning strategies are designed from human knowledge, and are applied on each dataset in an immutable manner. In other words, experience about the usefulness of strategies cannot be updated and transferred to improve active learning on other datasets. This paper initiates a pioneering study on whether active learning experience can be transferred. We first propose a novel active learning model that linearly aggregates existing strategies. The linear weights can then be used to represent the active learning experience. We equip the model with the popular linear upper- confidence-bound (LinUCB) algorithm for contextual bandit to update the weights. Finally, we extend our model to transfer the experience across datasets with the technique of biased regularization. Empirical studies demonstrate that the learned experience not only is competitive with existing strategies on most single datasets, but also can be transferred across datasets to improve the performance on future learning tasks.


Control Systems , Robotics , and Automation


title: An analytical framework for optimizing variant discovery from personal genomes

abstract: The standardization of clinical sequencing data generation and analysis is of critical importance. Here, the authors develop the Genome Comparison and Analytic Testing platform to facilitate the development of performance metrics and comparisons of analysis tools for clinical sequencing studies.


title: A Neural Parametric Singing Synthesizer

abstract: We present a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Our model makes frame-wise predictions using mixture density outputs rather than categorical outputs in order to reduce the required parameter count. As we found overfitting to be an issue with the relatively small datasets used in our experiments, we propose a method to regularize the model and make the autoregressive generation process more robust to prediction errors. Using a simple multi-stream architecture, harmonic, aperiodic and voiced/unvoiced components can all be predicted in a coherent manner. We compare our method to existing parametric statistical and state-of-the-art concatenative methods using quantitative metrics and a listening test. While naive implementations of the autoregressive generation algorithm tend to be inefficient, using a smart algorithm we can greatly speed up the process and obtain a system that's competitive in both speed and quality.


The H2 robotic exoskeleton for gait rehabilitation after stroke: early findings from a clinical study


title: Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art

abstract: Data exploration and visualization systems are of great importance in the Big Data era. Exploring and visualizing very large datasets has become a major research challenge, of which scalability is a vital requirement. In this survey, we describe the major prerequisites and challenges that should be addressed by the modern exploration and visualization systems. Considering these challenges, we present how state-of-the-art approaches from the Database and Information Visualization communities attempt to handle them. Finally, we survey the systems developed by Semantic Web community in the context of the Web of Linked Data, and discuss to which extent these satisfy the contemporary requirements.


title: A simple algorithm for designing developable Bézier surfaces, Computer Aided Geometric Design 20

abstract: An algorithm is presented that generates developable Bezier surfaces through a Bezier curve of arbitrary degree and shape. The algorithm has two important advantages. No (nonlinear) characterizing equations have to be solved and the control of singular points is guaranteed. Further interpolation conditions can be met.


title: Object-oriented modeling: a roadmap

abstract: Object-oriented modeling has become the de-facto standard in the early phases of a software development process during the last decade. The current state-of-the-art is dominated by the existence of the Unified Modeling Language (UML), the development of which has been initiated and pushed by industry. This paper presents a list of requirements for an ideal object-oriented modeling language and compares it with the achievements of UML and other object-oriented modeling approaches. This forms the base for the discussion of a roadmap for object-oriented modeling, which is structured according to a classification scheme of six different themes, which are language-, modelor process-related, respectively.


title: One Microphone Source Separation

abstract: Source separation, or computational auditory scene analysis, attempts to extract individual acoustic objects from input which contains a mixture of sounds from different sources, altered by the acoustic environment. Unmixing algorithms such as ICA and its extensions recover sources by reweighting multiple observation sequences, and thus cannot operate when only a single observation signal is available. I present a technique called refiltering which recovers sources by a nonstationary reweighting ("masking") of frequency sub-bands from a single recording, and argue for the application of statistical algorithms to learning this masking function. I present results of a simple factorial HMM system which learns on recordings of single speakers and can then separate mixtures using only one observation signal by computing the masking function and then refiltering.


title: A Brief History of Generative Models for Power Law and Lognormal Distributions

abstract: Recently, I became interested in a current debate over whether file size distributions are best modelled by a power law distribution or a lognormal distribution. In trying to learn enough about these distributions to settle the question, I found a rich and long history, spanning many fields. Indeed, several recently proposed models from the computer science community have antecedents in work from decades ago. Here, I briefly survey some of this history, focusing on underlying generative models that lead to these distributions. One finding is that lognormal and power law distributions connect quite naturally, and hence, it is not surprising that lognormal distributions have arisen as a possible alternative to power law distributions across many fields.


title: Active Learning by Labeling Features

abstract: Methods that learn from prior information about input features such as generalized expectation (GE) have been used to train accurate models with very little effort. In this paper, we propose an active learning approach in which the machine solicits "labels" on features rather than instances. In both simulated and real user experiments on two sequence labeling tasks we show that our active learning method outperforms passive learning with features as well as traditional active learning with instances. Preliminary experiments suggest that novel interfaces which intelligently solicit labels on multiple features facilitate more efficient annotation.


title: Effects of Harsh and Unpredictable Environments in Adolescence on Development of Life History Strategies

abstract: The National Longitudinal Study of Adolescent Health data were used to test predictions from life history theory. We hypothesized that (1) in young adulthood an emerging life history strategy would exist as a common factor underlying many life history traits (e.g., health, relationship stability, economic success), (2) both environmental harshness and unpredictability would account for unique variance in expression of adolescent and young adult life history strategies, and (3) adolescent life history traits would predict young adult life history strategy. These predictions were supported. The current findings suggest that the environmental parameters of harshness and unpredictability have concurrent effects on life history development in adolescence, as well as longitudinal effects into young adulthood. In addition, life history traits appear to be stable across developmental time from adolescence into young adulthood.


ImageCLEF 2017: ImageCLEF Tuberculosis Task - the SGEast Submission