title: Inference Attacks Against Collaborative Learning.

abstract: Collaborative machine learning and related techniques such as distributed and federated learning allow multiple participants, each with his own training dataset, to build a joint model. Participants train local models and periodically exchange model parameters or gradient updates computed during the training. ::: We demonstrate that the training data used by participants in collaborative learning is vulnerable to inference attacks. First, we show that an adversarial participant can infer the presence of exact data points in others' training data (i.e., membership inference). Then, we demonstrate that the adversary can infer properties that hold only for a subset of the training data and are independent of the properties that the joint model aims to capture. We evaluate the efficacy of our attacks on a variety of tasks, datasets, and learning configurations, and conclude with a discussion of possible defenses.


title: Quality Measures of Fingerprint Images

abstract: In an automatic fingerprint identification system, it is desirable to estimate the image quality of the fingerprint image before it is processed for feature extraction. This helps in deciding on the type of image enhancements that are needed and in deciding on thresholds for the matcher in the case that dynamic thresholds are used. In this paper, we propose a Gabor-feature based method for determining the quality of the fingerprint images. An image is divided into N w?wblocks. Gabor features of each block are computed first, then the standard deviation of the m Gabor features is used to determine the quality of this block. The results are compared with an existing model of quality estimation. Our analysis shows that our method can estimate the image quality accurately.


title: Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images

abstract: This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.


title: Cohabiting family members share microbiota with one another and with their dogs

abstract: Human-associated microbial communities vary across individuals: possible contributing factors include (genetic) relatedness, diet, and age. However, our surroundings, including individuals with whom we interact, also likely shape our microbial communities. To quantify this microbial exchange, we surveyed fecal, oral, and skin microbiota from 60 families (spousal units with children, dogs, both, or neither). Household members, particularly couples, shared more of their microbiota than individuals from different households, with stronger effects of co-habitation on skin than oral or fecal microbiota. Dog ownership significantly increased the shared skin microbiota in cohabiting adults, and dog-owning adults shared more 'skin' microbiota with their own dogs than with other dogs. Although the degree to which these shared microbes have a true niche on the human body, vs transient detection after direct contact, is unknown, these results suggest that direct and frequent contact with our cohabitants may significantly shape the composition of our microbial communities. DOI:http://dx.doi.org/10.7554/eLife.00458.001.


title: FVC2000: Fingerprint verification competition

abstract: Reliable and accurate fingerprint recognition is a challenging pattern recognition problem, requiring algorithms robust in many contexts. FVC2000 competition attempted to establish the first common benchmark, allowing companies and academic institutions to unambiguously compare performance and track improvements in their fingerprint recognition algorithms. Three databases were created using different state-of-the-art sensors and a fourth database was artificially generated; 11 algorithms were extensively tested on the four data sets. We believe that FVC2000 protocol, databases, and results will be useful to all practitioners in the field not only as a benchmark for improving methods, but also for enabling an unbiased evaluation of algorithms.


title: Reinforcement Learning with Parameterized Actions

abstract: We introduce a model-free algorithm for learning in Markov decision processes with parameterized actions-discrete actions with continuous parameters. At each step the agent must select both which action to use and which parameters to use with that action. We introduce the Q-PAMDP algorithm for learning in these domains, show that it converges to a local optimum, and compare it to direct policy search in the goal-scoring and Platform domains.


title: Successful treatment with cryoablation in a patient with bone metastasis in the mid-shaft femur: a case report

abstract: Background: Treatment of metastatic bone tumors is challenging due to the morbidity associated with patients with metastasis. The present case report described a patient with successful treatment of bone metastasis using cryoablation with plate and cementation to prevent fracture for bone metastasis of leiomyosarcoma in the mid-shaft of the femur. Case report: The metastatic tumor was located at intramedullary lesion of the femur. At first, cryoablation was performed under local anesthesia. After one week after cryoablation, curettage and fixation with plate and cementation were performed to prevent fracture. Tumor cells were not observed in the histopathological findings of the curettage tissue. Four years after cryoablation, there was no recurrence and the patient could walk without any support. Conclusion: We suggest that a tumor with limited cancellous bone and of a small size may undergo cryoablation. The prevention of fracture after cryoablation should be considered.


title: A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

abstract: Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.


title: Robust Non-line-of-sight Imaging with Single Photon Detectors

abstract: Imaging objects that are obscured by scattering and occlusion is an important challenge for many applications. For example, navigation and mapping capabilities of autonomous vehicles could be improved, vision in harsh weather conditions or under water could be facilitated, or search and rescue scenarios could become more effective. Unfortunately, conventional cameras cannot see around corners. Emerging, time-resolved computational imaging systems, however, have demonstrated first steps towards non-line-of-sight (NLOS) imaging. In this paper, we develop an algorithmic framework for NLOS imaging that is robust to partial occlusions within the hidden scenes. This is a common light transport effect, but not adequately handled by existing NLOS reconstruction algorithms, resulting in fundamental limitations in what types of scenes can be recovered. We demonstrate state-of-the-art NLOS reconstructions in simulation and with a prototype single photon avalanche diode (SPAD) based acquisition system.


IMPROVED SPEAKER SEGMENTATION AND SEGMENTS CLUSTERING USING THE BAYESIAN INFORMATION CRITERION


title: MobiHide : A Mobile Peer-to-Peer System for Anonymous Location-Based Queries

abstract: Modern mobile phones and PDAs are equipped with positioning capabilities (e.g., GPS). Users can access public location-based services (e.g., Google Maps) and ask spatial queries. Although communication is encrypted, privacy and confidentiality remain major concerns, since the queries may disclose the location and identity of the user. Commonly, spatial K-anonymity is employed to hide the query initiator among a group of K users. However, existing work either fails to guarantee privacy, or exhibits unacceptably long response time. ::: ::: In this paper we propose MobiHide, a Peer-to-Peer system for anonymous location-based queries, which addresses these problems. MobiHide employs the Hilbert space-filling curve to map the 2-D locations of mobile users to 1-D space. The transformed locations are indexed by a Chord-based distributed hash table, which is formed by the mobile devices. The resulting Peer-to-Peer system is used to anonymize a query by mapping it to a random group of K users that are consecutive in the 1-D space. Compared to existing state-of-the-art, MobiHide does not provide theoretical anonymity guarantees for skewed query distributions. Nevertheless, it achieves strong anonymity in practice, and it eliminates system hotspots. Our experimental evaluation shows that MobiHide has good load balancing and fault tolerance properties, and is applicable to real-life scenarios with numerous mobile users.


title: Pargnostics: Screen-Space Metrics for Parallel Coordinates

abstract: Interactive visualization requires the translation of data into a screen space of limited resolution. While currently ignored by most visualization models, this translation entails a loss of information and the introduction of a number of artifacts that can be useful, (e.g., aggregation, structures) or distracting (e.g., over-plotting, clutter) for the analysis. This phenomenon is observed in parallel coordinates, where overlapping lines between adjacent axes form distinct patterns, representing the relation between variables they connect. However, even for a small number of dimensions, the challenge is to effectively convey the relationships for all combinations of dimensions. The size of the dataset and a large number of dimensions only add to the complexity of this problem. To address these issues, we propose Pargnostics, parallel coordinates diagnostics, a model based on screen-space metrics that quantify the different visual structures. Pargnostics metrics are calculated for pairs of axes and take into account the resolution of the display as well as potential axis inversions. Metrics include the number of line crossings, crossing angles, convergence, overplotting, etc. To construct a visualization view, the user can pick from a ranked display showing pairs of coordinate axes and the structures between them, or examine all possible combinations of axes at once in a matrix display. Picking the best axes layout is an NP-complete problem in general, but we provide a way of automatically optimizing the display according to the user's preferences based on our metrics and model.


title: Artificial neural network based mobile robot navigation

abstract: This paper describes a dynamic artificial neural network based mobile robot motion and path planning system. The method is able to navigate a robot car on flat surface among static and moving obstacles, from any starting point to any endpoint. The motion controlling ANN is trained online with an extended backpropagation through time algorithm, which uses potential fields for obstacle avoidance. The paths of the moving obstacles are predicted with other ANNs for better obstacle avoidance. The method is presented through the realization of the navigation system of a mobile robot.


title: SmartDoc-QA: A dataset for quality assessment of smartphone captured document images - single and multiple distortions

abstract: Smartphones are enabling new ways of capture, hence arises the need for seamless and reliable acquisition and digitization of documents. The quality assessment step is an important part of both the acquisition and the digitization processes. Assessing document quality could aid users during the capture process or help improve image enhancement methods after a document has been captured. Current state-of-the-art works lack databases in the field of document image quality assessment. In order to provide a baseline benchmark for quality assessment methods for mobile captured documents, we present in this paper a dataset for quality assessment that contains both singly- and multiply-distorted document images. The proposed dataset could be used for benchmarking quality assessment methods by the objective measure of OCR accuracy, and could be also used to benchmark quality enhancement methods. There are three types of documents in the dataset: modern documents, old administrative letters and receipts. The document images of the dataset are captured under varying capture conditions (light, different types of blur and perspective angles). This causes geometric and photometric distortions that hinder the OCR process. The ground truth of the dataset images consists of the text transcriptions of the documents, the OCR results of the captured documents and the values of the different capture parameters used for each image. We also present how the dataset could be used for evaluation in the field of no-reference quality assessment. The dataset is freely and publicly available for use by the research community at http://navidomass.univ-lr.fr/SmartDoc-QA.


title: Remote vertical exploration by Active Scope Camera into collapsed buildings

abstract: Remote robotic explorations for collapsed buildings in a severe disaster are demanded. However, rescue robots cannot approach the rubble due to safety risks. This study proposes a remote vertical exploration system for collapsed buildings with a robotic inspection system hoisted by a crane. An Active Scope Camera (ASC) has many advantages for the vertical exploration such as a light and flexible continuum body to produce distributed driving forces. The purpose of this paper is to confirm the feasibility of the vertical exploration system with the ASC. The vertical explorations have proper problems related to contact and hanging conditions of the scope cable. We developed a new ASC that has a two-step bending mechanism to produce larger head movement in multi-DOF. We also evaluated the performances of the prototype when the contact areas were small. Finally, we conducted a remote vertical exploration experiments at the simulated collapsed building in 6 m height. The robot could explore in six different pathways by changing head directions and running the rubbles within seven trials. The experimental results showed that the proposed system has high potential to get inserted in the deep area in the rubble.


title: Learning discriminative instance attribute for zero-shot classification

abstract: We study the problem of zero-shot classification in which we don't have labeled data in target domain. Existing approaches learn a model from source domain and apply it without adaptation to target domain, which is prone to domain shift problem. To solve the problem, we propose a novel Learning Discriminative Instance Attribute(LDIA) method. Specifically, we learn a projection matrix for both the source and target domain jointly and also use prototype in the attribute space to regularise the learned projection matrix. Therefore, the information of the source domain can be effectively transferred to the target domain. The experimental results on two benchmark datasets demonstrate that the proposed LDIA method exceeds competitive approaches for zero-shot classification task.


title: High-repetition and -stability all-solid state pulsers based on avalanche transistor Marx circuit

abstract: Ultra-wide spectrum (UWS) pulser, in which avalanche transistor is used as its switch, can generate high-repetition and -stability short-pulse, but its power is sharply lower than that of SOS and Tesla generators. Marx circuit is used to increase the power of this type of pulser. Different charged-capacitance and class of Marx circuit determines different waveform and peak-power of output pulse. 20-class and 30-class avalanche transistor Marx circuit pulsers are designed, and the charged-capacitance is 100pF and 470pF, then 4types of waveform are get. The highest output peak-voltage is 2.9kV, the highest repetition can reach 70kHz, and keep a better pulse stability, jitter is less than 40ps.


title: Smart Contract SLAs for Dense Small-Cell-as-a-Service

abstract: The disruptive power of blockchain technologies represents a great opportunity to re-imagine standard practices of telecommunication networks and to identify critical areas that can benefit from brand new approaches. As a starting point for this debate, we look at the current limits of infrastructure sharing, and specifically at the Small-Cell-as-a-Service trend, asking ourselves how we could push it to its natural extreme: a scenario in which any individual home or business user can become a service provider for mobile network operators, freed from all the scalability and legal constraints that are inherent to the current modus operandi. We propose the adoption of smart contracts to implement simple but effective Service Level Agreements (SLAs) between small cell providers and mobile operators, and present an example contract template based on the Ethereum blockchain.


title: Corpus annotation for mining biomedical events from literature

abstract: BackgroundAdvanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation.ResultsWe have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation.ConclusionThe resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain.


title: Evaluation of an innovative tele-education intervention in chronic pain management for primary care clinicians practicing in underserved areas

abstract: Introduction Inadequate knowledge and training of healthcare providers are obstacles to effective chronic pain management. ECHO (extension for community healthcare outcomes) uses case-based learning and videoconferencing to connect specialists with providers in underserved areas. ECHO aims to increase capacity in managing complex cases in areas with poor access to specialists. Methods A pre-post study was conducted to evaluate the impact of ECHO on healthcare providers' self-efficacy, knowledge and satisfaction. Type of profession, presenting a case, and number of sessions attended were examined as potential factors that may influence the outcomes Results From June 2014 to March 2017, 296 primary care healthcare providers attended ECHO, 264 were eligible for the study, 170 (64%) completed the pre-ECHO questionnaire and 119 completed post-ECHO questionnaires. Participants were physicians (34%), nurse practitioners (21%), pharmacists (13%) and allied health professionals (32%). Participants attended a mean of 15 ± 9.19 sessions. There was a significant increase in self-efficacy ( p < 0.0001) and knowledge ( p < 0.0001). Self-efficacy improvement was significantly higher among physicians, physician assistants and nurse practitioners than the non-prescribers group ( p = 0.03). On average, 96% of participants were satisfied with ECHO. Satisfaction was higher among those who presented cases and attended more sessions. Discussion This study shows that ECHO improved providers' self-efficacy and knowledge. We evaluated outcomes from a multidisciplinary group of providers practicing in Ontario. This diversity supports the generalisability of our findings. Therefore, we suggest that this project may be used as a template for creating other educational programs on other medical topics.


title: Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images

abstract: Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning. However, automating this process is challenging due to the presence of severe partial volume effect and considerable variability in tumor structures, as well as imaging conditions, especially for the gliomas. In this paper, we introduce a new methodology that combines random forests and active contour model for the automated segmentation of the gliomas from multimodal volumetric MR images. Specifically, we employ a feature representations learning strategy to effectively explore both local and contextual information from multimodal images for tissue segmentation by using modality specific random forests as the feature learning kernels. Different levels of the structural information is subsequently integrated into concatenated and connected random forests for gliomas structure inferring. Finally, a novel multiscale patch driven active contour model is exploited to refine the inferred structure by taking advantage of sparse representation techniques. Results reported on public benchmarks reveal that our architecture achieves competitive accuracy compared to the state-of-the-art brain tumor segmentation methods while being computationally efficient.


title: A new feedback-added obstacle avoidance scheme for motion planning of redundant robot manipulators

abstract: How to avoid obstacle is one of the fundamental issues in motion planning of redundant robot manipulators. In this paper, a new scheme based on the pseudoinverse-type formulation is developed and investigated for obstacle avoidance of redundant robot manipulators. For this obstacle avoidance scheme, the feedback is added to guarantee the precision of Cartesian error. Theoretical results are then given to show its excellent property. Simulation results based on the PA10 robot manipulator with point and window-shaped obstacles are illustrated to further substantiate the efficacy of the presented feedback-added obstacle avoidance scheme for motion planning of redundant robot manipulators.


The Complicated Terrain of Latin American Homosexuality


title: EEG and HRV markers of sleepiness and loss of control during car driving

abstract: Heart Rate Variability (HRV) reflects the balance between sympathetic and parasympathetic activity. Slower HRV rhythms (LF) indicate increased sympathetic and/or lower vagal activity, wakefulness characteristics, while faster HRV rhythms (HF) indicate lower sympathetic and/or increased parasympathetic and vagal activity, sleepy characteristics. In this work we demonstrate that power spectral analysis of drivers' heart rate can report driving errors caused by sleepiness. Furthermore, variation of Fractal Dimension (FD) can aid significant information for the assessment of the driving situation. ECG and EEG data were collected from sleep-deprived subjects exposed to real field driving conditions. A lower ratio of low frequency to high frequency components (LF/HF), and lower LF values were reported on the occurrence of driving errors.


title: Antenna design for UHF RFID tags: a review and a practical application

abstract: In this paper, an overview of antenna design for passive radio frequency identification (RFID) tags is presented. We discuss various requirements of such designs, outline a generic design process including range measurement techniques and concentrate on one practical application: RFID tag for box tracking in warehouses. A loaded meander antenna design for this application is described and its various practical aspects such as sensitivity to fabrication process and box content are analyzed. Modeling and simulation results are also presented which are in good agreement with measurement data.


title: Anomaly detection methods for bankruptcy prediction

abstract: Bankruptcy prediction is remarkable for its importance in modern economics. Many related work have proposed novel supervised learning methods for predicting financial distress. Conventional classification algorithms are likely to mistaken minority class for majority class because their cost function simply optimizes error rate. This paper applies anomaly detection algorithms to the bankruptcy prediction problem in an attempt to suggest a new stable model taking the data distribution into consideration. The efficacy of anomaly detection techniques is tested on bankruptcy prediction datasets viz. Polish banks. Our empirical evaluation shows that Isolation Forest outperforms multivariate Gaussian distribution, one-class SVM, and other classification estimators in terms of the ROC curve.


title: Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer

abstract: IntroductionIn breast cancer, gene expression analyses have defined five tumor subtypes (luminal A, luminal B, HER2-enriched, basal-like and claudin-low), each of which has unique biologic and prognostic features. Here, we comprehensively characterize the recently identified claudin-low tumor subtype.MethodsThe clinical, pathological and biological features of claudin-low tumors were compared to the other tumor subtypes using an updated human tumor database and multiple independent data sets. These main features of claudin-low tumors were also evaluated in a panel of breast cancer cell lines and genetically engineered mouse models.ResultsClaudin-low tumors are characterized by the low to absent expression of luminal differentiation markers, high enrichment for epithelial-to-mesenchymal transition markers, immune response genes and cancer stem cell-like features. Clinically, the majority of claudin-low tumors are poor prognosis estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and epidermal growth factor receptor 2 (HER2)-negative (triple negative) invasive ductal carcinomas with a high frequency of metaplastic and medullary differentiation. They also have a response rate to standard preoperative chemotherapy that is intermediate between that of basal-like and luminal tumors. Interestingly, we show that a group of highly utilized breast cancer cell lines, and several genetically engineered mouse models, express the claudin-low phenotype. Finally, we confirm that a prognostically relevant differentiation hierarchy exists across all breast cancers in which the claudin-low subtype most closely resembles the mammary epithelial stem cell.ConclusionsThese results should help to improve our understanding of the biologic heterogeneity of breast cancer and provide tools for the further evaluation of the unique biology of claudin-low tumors and cell lines.


title: Secure and Verifiable Policy Update Outsourcing for Big Data Access Control in the Cloud

abstract: Due to the high volume and velocity of big data, it is an effective option to store big data in the cloud, as the cloud has capabilities of storing big data and processing high volume of user access requests. Attribute-based encryption (ABE) is a promising technique to ensure the end-to-end security of big data in the cloud. However, the policy updating has always been a challenging issue when ABE is used to construct access control schemes. A trivial implementation is to let data owners retrieve the data and re-encrypt it under the new access policy, and then send it back to the cloud. This method, however, incurs a high communication overhead and heavy computation burden on data owners. In this paper, we propose a novel scheme that enabling efficient access control with dynamic policy updating for big data in the cloud. We focus on developing an outsourced policy updating method for ABE systems. Our method can avoid the transmission of encrypted data and minimize the computation work of data owners, by making use of the previously encrypted data with old access policies. Moreover, we also propose policy updating algorithms for different types of access policies. Finally, we propose an efficient and secure method that allows data owner to check whether the cloud server has updated the ciphertexts correctly. The analysis shows that our policy updating outsourcing scheme is correct, complete, secure and efficient.


title: Investigation of the Impact of National Culture on IT-Governance: An Explorative Study Contrasting German and Japanese National Culture

abstract: The ever rising complexity of the corporate IT forces organizations, especially the ones acting internationally, to take measures managing their IT. IT-Governance is one measure to maintain control. IT-Governance is no end in itself, yet, there is a strong need of skilled worker implementing and executing it effectively and efficiently. Problematically, in the global IT-Industry there is a skilled worker shortage. Organizations try to recruit skilled foreign worker to cope with this shortage, which pairs up people from diverse countries and therefore leads to an international workforce. Consequently, this paper aims for deriving national cultural concepts that influence IT-Governance. Hofstede’s dimensional model and the COBIT framework serve as a basis for the conceptual model in this paper. Validating this conceptual model and its respective propositions, qualitative interviews with experts from practice were conducted to validate the conceptual model. Our results suggest that national culture has a great impact on IT-Governance.


title: Energy-Efficient Resource Allocation in Cellular Networks With Shared Full-Duplex Relaying

abstract: Recent advances in self-interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. Unlike most existing works, we study the problem of energy-efficient resource allocation in FDR networks. We consider a shared FDR deployment scenario, where an FDR relay is deployed at the intersection of several adjacent cell sectors. First, a simple but practical transmission strategy is proposed to deal with the involved interference, i.e., multiaccess interference, multiuser interference, and self-interference. Then, the problem of joint power and subcarrier allocation is formulated to maximize the network-level energy efficiency while taking the residual self-interference into account. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem with high computation complexity, we use Dinkelbach and discrete stochastic optimization methods to solve the energy-efficient resource-allocation problem efficiently. Simulation results are presented to show the effectiveness of the proposed scheme.


title: Evaluation of SDS controller (CorpHD) for various storage demands

abstract: Growth in cloud applications is generating tremendous amount of data, building load on traditional storage management systems. Software Defined Storage (SDS) is a new storage management concept becoming popular to handle this large amount of data. CoprHD is one of the open source SDS controller, available for experimentation and development in storage industry. In this paper, the storage management techniques provided by CoprHD to manage heterogeneous storage platforms are experimented and analyzed. Various storage management parameters such as time to provision, storage capacity measurement and heterogeneity are experimentally evaluated along with the theoretical expression to prove the completeness of CoprHD controller for storage management.


title: Image Segmentation and Restoration Using Parametric Contours With Free Endpoints

abstract: In this paper, we introduce a novel approach for active contours with free endpoints. A scheme for image segmentation is presented based on a discrete version of the Mumford–Shah functional where the contours can be both closed and open curves. Additional to a flow of the curves in normal direction, evolution laws for the tangential flow of the endpoints are derived. Using a parametric approach to describe the evolving contours together with an edge-preserving denoising, we obtain a fast method for image segmentation and restoration. The analytical and numerical schemes are presented followed by numerical experiments with artificial test images and with a real medical image.


title: Online Submission ID: 0184 Photo Tourism: Exploring Photo Collections in 3D

abstract: We present a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photograph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also enabling full 3D navigation and exploration of the set of images and world geometry, along with auxiliary information such as overhead maps. Our system also makes it easy to construct photo tours of scenic or historic locations, and to annotate image details, which are automatically transferred to other relevant images. We demonstrate our system on several large personal photo collections as well as images gathered from Internet photo sharing sites.


title: The coracoid impingement syndrome

abstract: Coracoid impingement results from encroachment on the coracohumeral space, presenting as anterior shoulder pain and clicking, particularly in forward flexion, medial rotation, and adduction. In eight shoulders in seven patients, coracohumeral decompression by excision of the lateral 1.5 cm of the coracoid with re-attachment of the conjoined tendon gave pain relief in all, and complete relief in six. This procedure is described and recommended.


title: Substrate Integrated Waveguide for UWB-based communications

abstract: In this work, the authors have tried to bring out the focus towards designing of the Substrate Integrated Waveguide in a different ways where the backlogs of designing them through the conventional method has been tackled. During this change in the design methodology, none of the antenna parameters such as the Reflection Co-efficient (S 11 ) and the Transmission Co-efficient (S 21 ) are ever being compromised. Rather it has been very much observed that all the necessary information is well stocked for the convenient analysis. All the designs been always carried out with the help of ANSYS-High Frequency Structure Simulator, along with there has been a regular use of the FR-4 Substrate keeping in mind its internal characteristics and the impact it would put upon design. The designed structure showcased good behaviour at the 2.4 GHz with S 11 and S 21 taken as together for which it also achieves the complete coverage of the Ultra Wideband (UWB) as prescribed by Federal Communications Commission (FCC).


title: THE EFFECTS OF TWO DIFFERENT TYPES OF DYNAMIC WARM-UP AND STATIC STRETCHING ON POWER AND SPEED

abstract: Static stretching has often been found to decrease performance in power and speed activities, but dynamic warm-ups usually increase performance. We examined the effects of a mini-band warm-up, a medicine ball warm-up, and static stretching on 10m and 20m sprint times, as well as overhead medicine ball throw performance. Twenty-four Division I female soccer players completed the three warm-ups and a control 5 minute jog condition on separate days and were tested on the 10m and 20m sprint times and overhead medicine ball throw. ANOVA's and follow-up paired t-tests ( ~ ~ 0 . 0 5w)er e used to determine differences between the warm-ups. Significant differences were found between groups for the 1 Om sprint times but not for the 20m sprint times or the overhead medicine ball throw.


title: Interactive sketch generation

abstract: In this paper, we propose an interactive system for generating artistic sketches from images, based on the stylized multiresolution B-spline curve model and the livewire contour tracing paradigm. Our multiresolution B-spline stroke model allows interactive and continuous control of style and shape of the stroke at any level of details. Especially, we introduce a novel mathematical paradigm called the wavelet frame to provide essential properties for multiresolution stroke editing, such as feature point preservation, locality, time-efficiency, good approximation, etc. The livewire stroke map construction leads the user-guided stroke to automatically lock on to the target contour, allowing fast and accurate sketch drawing. We classify the target contours as outlines and interior flow, and develop two respective livewire techniques based on extended graph formulation and vector flow field. Experimental results show that the proposed system facilitates quick and easy generation of artistic sketches of various styles.


title: Emergence of disparity tuning during the development of vergence eye movements

abstract: The role of behavior for the acquisition of sensory representations has been underestimated in the past. We study this question for the task of learning vergence eye movements allowing proper fixation of objects. We model the development of this skill with an artificial neural network based on reinforcement learning. A biologically plausible reward mechanism that is responsible for driving behavior and learning of the representation of disparity is proposed. The network learns to perform vergence eye movements between natural images of objects by receiving a reward whenever an object is fixated with both eyes. Disparity tuned neurons emerge robustly in the hidden layer during development. The characteristics of the cells' tuning curves depend strongly on the task: if mostly small vergence movements are to be performed, tuning curves become narrower at small disparities, as has been measured experimentally in barn owls. Extensive training to discriminate between small disparities leads to an effective enhancement of sensitivity of the tuning curves.


title: Short Necklace States, Logarithm Transmission Fluctuation and Localization Length

abstract: We investigate the widely-existing short necklace states in random systems. It is found that their peak width and relative height in lnT spectra keep almost constant when the system length increases, which is explained by the coupled-resonator theory with intrinsic parameters. This property makes them special in contribution of lnT fluctuation. Further, short necklace states can help us to deeply understand the physical meaning of localization length and the delocalized effectin localized regime.


Comprehensive Design Codes Development in Japan: Geo-code 21 ver.3 and code PLATFORM ver.1


title: Universal Dependencies for Learner English

abstract: We introduce the Treebank of Learner English (TLE), the first publicly available syntactic treebank for English as a Second Language (ESL). The TLE provides manually annotated POS tags and Universal Dependency (UD) trees for 5,124 sentences from the Cambridge First Certificate in English (FCE) corpus. The UD annotations are tied to a pre-existing error annotation of the FCE, whereby full syntactic analyses are provided for both the original and error corrected versions of each sentence. Further on, we delineate ESL annotation guidelines that allow for consistent syntactic treatment of ungrammatical English. Finally, we benchmark POS tagging and dependency parsing performance on the TLE dataset and measure the effect of grammatical errors on parsing accuracy. We envision the treebank to support a wide range of linguistic and computational research on second language acquisition as well as automatic processing of ungrammatical language. The treebank is available at universaldependencies.org. The annotation manual used in this project and a graphical query engine are available at esltreebank.org.


title: Heuristics for two-dimensional strip packing problem with 90° rotations

abstract: Abstract This paper proposes a deterministic heuristic algorithm (DHA) for two-dimensional strip packing problem where 90° rotations of pieces are allowed and there is no guillotine packing constraint. The objective is to place all pieces without overlapping into a strip of given width so as to minimize the total height of the pieces. Based on the definition of action space, a new sorting rule for candidate placements is proposed such that the position for the current piece is as low as possible, the distance between the current piece and other inside pieces is as close as possible, and the adverse impact for further placements is as little as possible. Experiments on four groups of benchmarks showed the proposed DHA achieved highly competitive results in comparison with the state-of-the-art algorithms in the literature. Also, as a deterministic algorithm, the DHA could achieve high quality solutions by only one independent run on both small-scale and large-scale problem instances and the results are repeatable.


title: A Reflective Symmetry Descriptor for 3D Models

abstract: Computing reflective symmetries of 2D and 3D shapes is a classical problem in computer vision and computational geometry. Most prior work has focused on finding the main axes of symmetry, or determining that none exists. In this paper we introduce a new reflective symmetry descriptor that represents a measure of reflective symmetry for an arbitrary 3D model for all planes through the model’s center of mass (even if they are not planes of symmetry). The main benefits of this new shape descriptor are that it is defined over a canonical parameterization (the sphere) and describes global properties of a 3D shape. We show how to obtain a voxel grid from arbitrary 3D shapes and, using Fourier methods, we present an algorithm computes the symmetry descriptor in O(N4 log N) time for an N × N × N voxel grid and computes a multiresolution approximation in O(N3 log N) time. In our initial experiments, we have found that the symmetry descriptor is insensitive to noise and stable under point sampling. We have also found that it performs well in shape matching tasks, providing a measure of shape similarity that is orthogonal to existing methods.


title: The Code for Facial Identity in the Primate Brain

abstract: Primates recognize complex objects such as faces with remarkable speed and reliability. Here, we reveal the brain’s code for facial identity. Experiments in macaques demonstrate an extraordinarily simple transformation between faces and responses of cells in face patches. By formatting faces as points in a high-dimensional linear space, we discovered that each face cell’s firing rate is proportional to the projection of an incoming face stimulus onto a single axis in this space, allowing a face cell ensemble to encode the location of any face in the space. Using this code, we could precisely decode faces from neural population responses and predict neural firing rates to faces. Furthermore, this code disavows the long-standing assumption that face cells encode specific facial identities, confirmed by engineering faces with drastically different appearance that elicited identical responses in single face cells. Our work suggests that other objects could be encoded by analogous metric coordinate systems.


title: Offline cursive script word recognition – a survey

abstract: We review the field of offline cursive word recognition. We mainly deal with the various methods that were proposed to realize the core of recognition in a word recognition system. These methods are discussed in view of the two most important properties of such a system: the size and nature of the lexicon involved, and whether or not a segmentation stage is present. We classify the field into three categories: segmentation-free methods, which compare a sequence of observations derived from a word image with similar references of words in the lexicon; segmentation-based methods, that look for the best match between consecutive sequences of primitive segments and letters of a possible word; and the perception-oriented approach, that relates to methods that perform a human-like reading technique, in which anchor features found all over the word are used to boot-strap a few candidates for a final evaluation phase.


title: Information technologies in retail supply chains: a comparison of Tesco and Asda

abstract: This paper synthesises knowledge about information technologies (ITs) by outlining their functionalities and features, and reflecting on the strategic and operational opportunities and challenges they present to a retail supply chain (SC). The research is developed from empirical case studies, and literature on IT development and applications. It is acknowledged that, while implementation and integration of the ITs across the SC will enhance the overall SC performance in terms of cost, response efficiency and customer satisfaction levels, challenges still have to be addressed, including high setup costs, implementation complexity, and privacy and security issues. Better understanding of these opportunities and challenges will enable participants in the retail SC to transform challenges into opportunities. Drawn from primary and secondary resources, a comparison is conducted to compare the deployment of ITs for the two UK retailers (Tesco and Asda).


title: Software-defined networking: management requirements and challenges

abstract: SDN is an emerging paradigm currently evidenced as a new driving force in the general area of computer networks. Many investigations have been carried out in the last few years about the benefits and drawbacks in adopting SDN. However, there are few discussions on how to manage networks based on this new paradigm. This article contributes to this discussion by identifying some of the main management requirements of SDN. Moreover, we describe current proposals and highlight major challenges that need to be addressed to allow wide adoption of the paradigm and related technology.


title: PROUD-Public road urban driverless test: Architecture and results

abstract: The presence of autonomous vehicles on public roads is becoming a reality. In the last 10 years, autonomous prototypes have been confined in controlled or isolated environments, but new traffic regulations for testing and direct automotive companies interests are moving autonomous vehicles tests on real roads. This paper presents a test on public urban roads and freeways that was held in Parma on July 12, 2013. This was the first test in open public urban roads with nobody behind the steering wheel: the vehicle had to cope with roundabouts, junctions, pedestrian crossings, freeway junctions, traffic lights, and regular traffic. The vehicle setup, the software architecture, and the route are here presented together with some results and possible future improvements.


title: Salty Secret: Let us secretly salt the secret

abstract: Using password is, perhaps, still the most versatile method of securing secret and confidential information, even though several recent studies have pointed out possibility of breaching it. A general trend of having different passwords for several user accounts of the same user (such as multiple email accounts, multiple social networking accounts, etc.) can barely overcome the possibility as users mostly prefer retaining similarity among own passwords, which results in the possibility of breaching almost all passwords once only one password gets breached. Consequently, several research studies attempted to strengthen passwords. However, none of the studies is yet to get wide popularity for not being able to achieve a delicate balance between strength of password and user friendliness. To achieve this goal, we present a new password based authentication system in this paper. The proposed system is based on intermixing between a fixed text (conventional part of a password) and a free random text (newly added) at different pre-defined indices having different per-defined lengths. The addition of the free random text adds an additional level of difficulty in breaching the password. We present different variants of our proposed system along with their possible attack models. We demonstrate strength of our proposed system through rigorous analytical formulation and numerical simulation. Besides, we confirm achieving a delicate balance between strength of the password and user friendliness through performing real user evaluation.


title: An analysis of using reflectors for distributed denial-of-service attacks

abstract: Attackers can render distributed denial-of-service attacks more difficult to defend against by bouncing their flooding traffic off of reflectors; that is, by spoofing requests from the victim to a large set of Internet servers that will in turn send their combined replies to the victim. The resulting dilution of locality in the flooding stream complicates the victim's abilities both to isolate the attack traffic in order to block it, and to use traceback techniques for locating the source of streams of packets with spoofed source addresses, such as ITRACE [Be00a], probabilistic packet marking [SWKA00], [SP01], and SPIE [S+01]. We discuss a number of possible defenses against reflector attacks, finding that most prove impractical, and then assess the degree to which different forms of reflector traffic will have characteristic signatures that the victim can use to identify and filter out the attack traffic. Our analysis indicates that three types of reflectors pose particularly significant threats: DNS and Gnutella servers, and TCP-based servers (particularly Web servers) running on TCP implementations that suffer from predictable initial sequence numbers. We argue in conclusion in support of "reverse ITRACE" [Ba00] and for the utility of packet traceback techniques that work even for low volume flows, such as SPIE.


title: Apps with hardware: enabling run-time architectural customization in smart phones

abstract: In this paper we present a novel system which incorporates programmable hardware (an FPGA) into a smart phone to enable a vision where apps can include both software and hardware components, or apps with hardware. We introduce a novel mechanism to enable sharing the FPGA in a practical manner by leveraging the unique deployment model of mobile applications - namely that deployment is via an app store, where we introduce a new cloud-based compilation. We present our prototype smart phone using the Zedboard, which pairs a Xilinx Zynq FPGA with an embedded Cortex A9, running an Android-based system which we extended to provide run-time system support for dynamically managing apps with hardware and providing a secure loading system. With this prototype, our evaluation demonstrates the performance gains for an AES encryption module (representing cryptography), a QAM modulation module (representing software-defined radio) of 3x to several orders of magnitude, with room for improvement and a hardware-based memory scanner (representing custom co-processors). We demonstrate the feasibility of our cloud-based compilation within the context of real app store statistics. Finally, we present a case study of a complete integration of hardware into an existing application (the Orbot Tor client).


title: Multisensor-Based Human Detection and Tracking for Mobile Service Robots

abstract: One of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments.


title: All-environment visual place recognition with SMART

abstract: This paper presents Sequence Matching Across Route Traversals (SMART); a generally applicable sequence- based place recognition algorithm. SMART provides invariance to changes in illumination and vehicle speed while also providing moderate pose invariance and robustness to environmental aliasing. We evaluate SMART on vehicles travelling at highly variable speeds in two challenging environments; firstly, on an all-terrain vehicle in an off-road, forest track and secondly, using a passenger car traversing an urban environment across day and night. We provide comparative results to the current state-of-the-art SeqSLAM algorithm and investigate the effects of altering SMART's image matching parameters. Additionally, we conduct an extensive study of the relationship between image sequence length and SMART's matching performance. Our results show viable place recognition performance in both environments with short 10-metre sequences, and up to 96% recall at 100% precision across extreme day-night cycles when longer image sequences are used.


title: Success in books: a big data approach to bestsellers

abstract: Reading remains the preferred leisure activity for most individuals, continuing to offer a unique path to knowledge and learning. As such, books remain an important cultural product, consumed widely. Yet, while over 3 million books are published each year, very few are read widely and less than 500 make it to the New York Times bestseller lists. And once there, only a handful of authors can command the lists for more than a few weeks. Here we bring a big data approach to book success by investigating the properties and sales trajectories of bestsellers. We find that there are seasonal patterns to book sales with more books being sold during holidays, and even among bestsellers, fiction books sell more copies than nonfiction books. General fiction and biographies make the list more often than any other genre books, and the higher a book’s initial place in the rankings, the longer the book stays on the list as well. Looking at patterns characterizing authors, we find that fiction writers are more productive than nonfiction writers, commonly achieving bestseller status with multiple books. Additionally, there is no gender disparity among bestselling fiction authors but nonfiction, most bestsellers are written by male authors. Finally we find that there is a universal pattern to book sales. Using this universality we introduce a statistical model to explain the time evolution of sales. This model not only reproduces the entire sales trajectory of a book but also predicts the total number of copies it will sell in its lifetime, based on its early sales numbers. The analysis of the bestseller characteristics and the discovery of the universal nature of sales patterns with its driving forces are crucial for our understanding of the book industry, and more generally, of how we as a society interact with cultural products.


title: Deep Geometric Matrix Completion: A New Way for Recommender Systems

abstract: In the last years, Graph Convolutional Neural Networks gained popularity in the Machine Learning community for their capability of extracting local compositional features on signals defined on non-Euclidean domains. Shape correspondence, document classification, molecular properties predictions are just few of the many different problems where these techniques have been successfully applied. In this paper we will present Deep Geometric Matrix Completion, a recent application of Graph Convolutional Neural Networks to the matrix completion problem. We will illustrate MGCNN (a multi-graph CNN able to deal with signals defined over multiple domains) and we will show how coupling such technique with a RNN, a learnable diffusion process can be realized for reconstructing the desired information. Extensive experimental evaluation shows how Geometric Deep Learning techniques allow to outperform previous state of the art solutions on the matrix completion problem.


title: Laparoscopic-guided transversus abdominis plane block versus trocar site local anesthetic infiltration in gynecologic laparoscopy

abstract: Background ::: Relieving postoperative pain and prompt resumption of physical activity are of the utmost importance for the patients and surgeons. Infiltration of local anesthetic is frequently used methods of pain control postoperatively. Laparoscopically delivered transversus abdominis plane block is a new modification of ultrasound-guided transversus abdominis plane block. ::: ::: This study was conducted to compare the efficacy of laparoscopic-guided transversus abdominis plane block with trocar site local anesthetic infiltration for pain control after gynecologic laparoscopy.


title: Sentiment Analysis of Tweets Using Semantic Analysis

abstract: In today's world, there is endless stream of data present online and the automated analysis of such data holds a great promise in business analytics for providing a strong support in decision making. This paper looks at the very heart of the concept of sentiment analysis by classifying the tweets with the help of algorithms like Naive Bayes, Maximum Entropy, and Negation. In this paper, we first preprocess the tweets to remove unnecessary content in tweet; we then extract the adjectives which forms the feature vector, which are also used to find synonyms used in further semantic calculations in aforementioned algorithms. Finally, we calculate Accuracy, Precision, and Recall to compare these algorithms.


title: STRESS AND DEPRESSION AMONG MEDICAL STUDENTS: A CROSS SECTIONAL STUDY AT A MEDICAL COLLEGE IN SAUDI ARABIA

abstract: Objectives: To determine prevalence of stress among undergraduate medical students and to observe an association between stress and academic year, grades, regularity and physical problems. Methodology: All 600 registered students at College of Medicine, King Saud University in years 1,2,3,4 and 5 were enrolled in the study, and asked to complete a stress inventory called Kessler10. Results: There were 494 responses with the response rate of 83%. The prevalence of stress of all types was found among 57% and severe stress among 19.6% study subjects. There was highly statistically significant association between year of study and stress levels, (p<0.0001). The association between academic grades of study subjects and their stress levels is not statistically significant, as distribution of prevalence of stress is not significantly different across each of the four academic grades (p=0.46).The main source of stress found to be their studies (60.3%), followed by home environment (2.8%) and 36.9% of study population did not mention any source of stress. Conclusion: High levels of psychosocial distress was found in our students during the initial three years of their course. It poses additional challenges for students’ support services delivery which may require to address mental health problems along with common health strategies for our students.


title: Advanced tunneling models for solar cell applications

abstract: This paper studies the potential of wide bandgap tunnel junctions such as AlGaAs/GaAs and GaAs/GaAs configurations for multi - junction solar cells. Simulations were performed to study the dominant physical mechanisms in tunnel junctions such as band to band tunneling and trap assisted tunneling. 1-D Drift Diffusion simulations were performed to determine the different regions in the I-V characteristics, namely, the tunneling current, excess current and classical diffusion current. We outline the implementation of local and non-local tunneling models to understand the nature of peak current. The variation of peak voltage is examined with the addition of band gap narrowing and different effective masses.


title: Mixture regression as subspace clustering

abstract: In this paper we show that observations in a mixture can be modeled using a union of subspaces, and hence mixture regression can be posed as a subspace clustering problem. This allows to perform mixture regression even in the presence of missing data. We illustrate this using a state-of-the-art subspace clustering algorithm for incomplete data to perform mixed linear regression on gene functional data. Our approach outperforms existing methods on this task.


title: A security architecture for data privacy and security

abstract: Data access and software exchange are often achieved over insecure networks such as the public Internet. System designers are therefore forced to be proactive with regard to verifying the identity of both human users and software processes that request access to protected resources such as factory data. In this paper we show a new security architecture based upon Web services that supports authentication, authorization, and federation. Authentication verifies identity and generates a security token; authorization determines which privileges are allowed to which users; federation permits secure and reliable exchanges of identity across disparate trust domains. We illustrate how these ideas can be used to secure access to a factory Web portal and its underlying database of process data


title: Generic 2D/3D SLAM with NDT maps for lifelong application

abstract: In this paper, we present a new, generic approach for Simultaneous Localization and Mapping (SLAM). First of all, we propose an abstraction of the underlying sensor data using Normal Distribution Transform (NDT) maps that are suitable for making our approach independent from the used sensor and the dimension of the generated maps. We present some modifications for the original NDT mapping to handle free-space measurements explicitly and to enable its usage in dynamic environments with moving obstacles and persons. In the second part of this paper we describe our graph-based SLAM approach that is designed for lifelong usage. Therefore, the memory and computational complexity is limited by pruning the pose graph in an appropriate way.


title: Machine learning, linear and Bayesian models for logistic regression in failure detection problems

abstract: In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition “Bosch Production Line Performance”. We considered the use of machine learning, linear and Bayesian models. For machine learning approach, we analyzed XGBoost tree based classifier to obtain high scored classification. Using the generalized linear model for logistic regression makes it possible to analyze the influence of the factors under study. The Bayesian approach for logistic regression gives the statistical distribution for the parameters of the model. It can be useful in the probabilistic analysis, e.g. risk assessment.


title: Quad-Tilting Rotor Convertible MAV: Modeling and Real-Time Hover Flight Control

abstract: This paper describes the modeling, control and hardware implementation of an experimental tilt-rotor aircraft. This vehicle combines the high-speed cruise capabilities of a conventional airplane with the hovering capabilities of a helicopter by tilting their four rotors. Changing between cruise and hover flight modes in mid-air is referred to transition. Dynamic model of the vehicle is derived both for vertical and horizontal flight modes using Newtonian approach. Two nonlinear control strategies are presented and evaluated at simulation level to control, the vertical and horizontal flight dynamics of the vehicle in the longitudinal plane. An experimental prototype named Quad-plane was developed to perform the vertical flight. A low-cost DSP-based Embedded Flight Control System (EFCS) was designed and built to achieve autonomous attitude-stabilized flight.


title: An anonymous communication technique using dummies for location-based services

abstract: Recently, highly accurate positioning devices enable us to provide various types of location-based services. On the other hand, because such position data include deeply personal information, the protection of location privacy is one of the most significant problems in location-based services. In this paper, we propose an anonymous communication technique to protect the location privacy of the users of location-based services. In our proposed technique, such users generate several false position data (dummies) to send to service providers with the true position data of users. Because service providers cannot distinguish the true position data, user location privacy is protected. We also describe a cost reduction technique for communication between a client and a server. Moreover, we conducted performance study experiments on our proposed technique using practical position data. As a result of the experiments, we observed that our proposed technique protects the location privacy of people and can sufficiently reduce communication costs so that our communication techniques can be applied in practical location-based services.


title: Large Online Social Footprints--An Emerging Threat

abstract: We study large online social footprints by collecting data on 13,990 active users. After parsing data from 10 of the 15 most popular social networking sites, we find that a user with one social network reveals an average of 4.3 personal information fields. For users with over 8 social networks, this average increases to 8.25 fields. We also investigate the ease by which an attacker can reconstruct a person’s social network profile. Over 40% of an individual’s social footprint can be reconstructed by using a single pseudonym (assuming the attacker guesses the most popular pseudonym), and an attacker can reconstruct 10% to 35% of an individual’s social footprint by using the person’s name. We also perform an initial investigation of matching profiles using public information in a person’s profile.


title: Avoiding the “déjà-variable” phenomenon: social psychology needs more guides to constructs

abstract: As a journal editor, I am frequently asked what constitutes an exceptional research article (Hagger, 2012). I usually respond by recommending Skinner's (1996) seminal guide to constructs of control as a prototypical example. When I was a doctoral student Skinner's article was extremely influential to my work. It not only helped me make sense of the myriad of constructs and terms used to describe and define the control construct, but also how I approached other constructs in social psychology. Skinner's guide was ahead of its time and her systematic approach to classifying and synthesizing constructs is supremely relevant today. Skinner identified the diversity in terminology, definition, and content of constructs related to control in social psychology, conducted an integrative review of theories incorporating control-related constructs, and developed a taxonomy and theoretical model to characterize them. Her guide was innovative for three reasons: (1) she recognized the richness in control-related constructs in social psychology but also the substantial redundancy and poor consensus; (2) she focused on systematically reducing redundancy and arriving at a core set of definitions and constructs; (3) she developed a theoretical basis for her integration. The proliferation of systematic reviews and syntheses in social psychology and other psychological disciplines illustrates that Skinner's guide was a precursor of things to come, but also illustrates that many syntheses of research findings are somewhat premature as they do not first systematically identify the commonalities and diversity in the constructs involved (Hagger and Chatzisarantis, 2009). I argue that social psychology needs more integrative frameworks like Skinner's to identify consistency in terminology, definition, and content of social psychological constructs. This will assist researchers to synthesize and integrate research findings and effects involving social psychological constructs. ::: ::: There have been many occasions when I have felt confronted with an uncanny sense of deja-vu when reading social psychologists' descriptions of constructs they had developed. I call this the deja-variable phenomenon; the feeling that one has seen a variable with the same definition and content before only referred to by a different term. And if not precisely identical, one can recognize considerable overlap and redundancy in the definition of variables making it difficult to establish whether constructs with different terms are appreciably different in content. This generates problems, particularly for the uninitiated, when it comes to making sense of research findings and hinders the progress of psychological science. Skinner notes that the use of constructs with “… many different names for the same construct has interfered with the accumulation of research findings. Findings about a construct under one label may never be integrated with findings about the same construct under different labels” (p. 550). Similarly, Block (1995), after Kelley (1927), calls this the “jangle” fallacy which “waste[s] scientific time” (p. 210). Researchers attempting to make sense of effects involving a specific construct in social psychology may neglect entire literatures because they fail to recognize constructs consistent with their definition but labeled with a different term. This presents considerable challenges for those engaged synthesizing research findings, such as those compiling systematic reviews and meta-analyses, where coding of constructs and resolving variations or similarities in content of variables with differing terms is an essential part of the process. It also presents problems for researchers attempting to integrate and unify different theories where eliminating redundancy is an important goal (Hagger, 2009). Researchers conducting systematic reviews and meta-analyses or integrating theories must, therefore, be extremely diligent to avoid a “surface” approach to identifying relevant constructs in their analysis and conduct “deeper” analyses of content when searching for identical and overlapping constructs across the literature. Furthermore, constructs with a high degree of conceptual overlap and redundancy are also likely to be extremely difficult to distinguish empirically. For example, the inclusion of conceptually identical or similar variables in a statistical analysis is likely to raise problems such as multicolinearity. Researchers must take care when integrating constructs to ensure that they do not include constructs with identical or extremely similar content in their analyses. ::: ::: Alongside the deja-variable phenomenon there is the case where the same term is used for constructs with different definitions and content. This creates additional problems for social psychologists attempting to synthesize effects of constructs in a particular literature. As Skinner points out, “when the same term is used to refer to different constructs, reviewers may conclude that findings are inconsistent or even contradictory, when in fact it is definitions that are inconsistent and contradictory” (p. 550). This phenomenon has been previously labeled the “jingle” fallacy and Block (1995) warns that “… the unwary may consider [the constructs] interchangeable” (p. 209). Again, systematic reviewers need to be diligent when coding variables and focus on content as well as terminology when searching, identifying, including, and eliminating constructs in their attempts to distil effects across the literature. A good illustrative example is the construct of intention. Observing the diversity of constructs that have fallen under the term intention, and how this may lead to confusion when arriving at consensus among findings, suggests that a guide to the construct of intention would be timely and useful. As an example of the diversity among types of intention one need only turn to Meiland's (1970) seminal work which illustrates how intention has multiple definitions and content. Meiland demonstrated the necessity of identifying the core content of a construct (i.e., what it is and what is not) and advocated qualifying terms to signify additional content that distinguishes particular subtypes from the core; good recent examples include continuation intentions (Chatzisarantis et al., 2004) and implementation intentions (Gollwitzer, 1999; Hagger and Luszczynska, 2014). ::: ::: Social psychologists need to adopt a similar approach to that of Skinner to arrive at a set of guides for constructs in social psychology. The guides will serve as invaluable summaries and compendiums of terms for social psychologists to use when operationalizing constructs in theories and models, developing measures, identifying patterns of effects, and developing explanations of processes and mechanisms. I also envisage these guides to be flexible, “living” documents that are constantly updated as theory and evidence for constructs evolves.


title: Adaptive Scheduling of Web Transactions

abstract: In highly interactive dynamic web database systems, user satisfaction determines their success. In such systems, user requested web pages are dynamically created by executing a number of database queries or web transactions. In this paper, we model the interrelated transactions generating a web page as workflows and quantify the user satisfaction by associating dynamic web pages with soft-deadlines. Further, we model the importance of transactions in generating a page by associating different weights to transactions. Using this framework, system success is measured in terms of minimizing the deviation from the deadline (i.e., tardiness) and also minimizing the weighted such deviation (i.e., weighted tardiness). In order to efficiently support the materialization of dynamic web pages, we propose ASETS∗, which is a parameter-free adaptive scheduling algorithm that automatically adapts to, not only system load, but also transactions' characteristics (i.e., interdependencies, deadlines and weights). ASETS∗ prioritizes the execution of transactions with the objective of minimizing weighted tardiness. It is also capable of balancing the tradeoff between optimizing average- and worst-case performance when needed. The performance advantages of ASETS∗ are experimentally demonstrated.


title: Economic reforms and health insurance in China☆

abstract: During the 1990s, Chinese state-owned enterprises (SOEs) and collective enterprises continually decreased coverage of public health insurance to their employees. This paper investigates this changing pattern of health insurance coverage in China using panel data from the China Nutrition and Health Survey (1991-2000). It is the first attempt in this literature that tries to identify precisely the effects of specific policies and reforms on health insurance coverage in the transitional period of China. The fixed effects linear model clustering at the province level is used for estimation, and results are compared to alternative models, including pooled OLS, random effects GLS model and fixed effects logit model. Strong empirical evidence is found that unemployment as a side effect of the Open Door Policy, and the deregulation of SOE and collective enterprises were the main causes for the decreasing trend. For example, urban areas that were highly affected by the Open Door Policy were associated with 17 percentage points decrease in the insurance coverage. Moreover, I found evidence that the gaps between SOE and non-SOE employees, collective and non-collective employees, urban and rural employees have considerably decreased during the ten years.


title: Modeling Interaction via the Principle of Maximum Causal Entropy

abstract: The principle of maximum entropy provides a powerful framework for statistical models of joint, conditional, and marginal distributions. However, there are many important distributions with elements of interaction and feedback where its applicability has not been established. This work presents the principle of maximum causal entropy—an approach based on causally conditioned probabilities that can appropriately model the availability and influence of sequentially revealed side information. Using this principle, we derive models for sequential data with revealed information, interaction, and feedback, and demonstrate their applicability for statistically framing inverse optimal control and decision prediction tasks.


Corruption, public investment and growth


title: Effect of Local Crystallographic Texture on the Fissure Formation During Charpy Impact Testing of Low-Carbon Steel

abstract: The severity of the formation of fissures (also known as splitting or delamination) on the fracture surface of Charpy impact-tested samples of a low-carbon steel has been found to increase with the decrease in finish rolling temperature [1093 K to 923 K (820 °C to 650 °C)]. Combined scanning electron microscopy and electron back-scattered diffraction study revealed that crystallographic texture was the prime factor responsible for the fissure formation. Through-thickness texture band composed of cube [Normal Direction (ND)║〈001〉] and gamma [ND║〈111〉] orientations developed during the inter-critical rolling treatment. Strain incompatibility between these two texture bands causes fissure cracking on the main fracture plane. A new approach based on the angle between {001} planes of neighboring crystals has been employed in order to estimate the ‘effective grain size,’ which is used to determine the cleavage fracture stress on different planes of a sample. The severity of fissure formation was found to be directly related to the difference in cleavage fracture stress between the ‘main fracture plane’ and ‘fissure plane.’ Clustering of ferrite grains having cube texture promoted the fissure crack propagation along the transverse ‘fissure plane,’ by increasing the ‘effective grain size’ and decreasing the cleavage fracture stress on that plane.


title: Context Aware Post-filtering for Web Service Clustering

abstract: Web service discovery is becoming a challenging and time consuming task due to large number of Web services available on the Internet. Organizing the Web services into functionally similar clusters is one of a very efficient approach for reducing the search space. However, similarity calculation methods that are used in current approaches such as string-based, corpus-based, knowledge-based and hybrid methods have problems that include discovering semantic characteristics, loss of semantic information, encoding fine-grained information and shortage of high-quality ontologies. Because of these issues, the approaches couldn't identify the correct clusters for some services and placed them in wrong clusters. As a result of this, cluster performance is reduced. This paper proposes post-filtering approach to increase precision by rearranging services incorrectly clustered. Our approach uses context aware method that learns term similarity by machine learning under domain context. Experimental results show that our post-filtering approach works efficiently.


DOVE: Detection of Movie Violence using Motion Intensity Analysis on Skin and Blood


title: Seeing with Humans: Gaze-Assisted Neural Image Captioning

abstract: Gaze reflects how humans process visual scenes and is therefore increasingly used in computer vision systems. Previous works demonstrated the potential of gaze for object-centric tasks, such as object localization and recognition, but it remains unclear if gaze can also be beneficial for scene-centric tasks, such as image captioning. We present a new perspective on gaze-assisted image captioning by studying the interplay between human gaze and the attention mechanism of deep neural networks. Using a public large-scale gaze dataset, we first assess the relationship between state-of-the-art object and scene recognition models, bottom-up visual saliency, and human gaze. We then propose a novel split attention model for image captioning. Our model integrates human gaze information into an attention-based long short-term memory architecture, and allows the algorithm to allocate attention selectively to both fixated and non-fixated image regions. Through evaluation on the COCO/SALICON datasets we show that our method improves image captioning performance and that gaze can complement machine attention for semantic scene understanding tasks.


title: A parallel algorithm for the eigenvalues and eigenvectors of a general complex matrix

abstract: A new parallel Jacobi-like algorithm is developed for computing the eigenvalues of a general complex matrix. Most parallel methods for this problem typically display only linear convergence, Sequential `norm-reducing' algorithms also exist and they display quadratic convergence in most cases. The new algorithm is a parallel form of the `norm-reducing' algorithm due to Eberlein. It is proven that the asymptotic convergence rate of this algorithm is quadratic. Numerical experiments are presented which demonstrate the quadratic convergence of the algorithm and certain situations where the convergence is slow are also identified. The algorithm promises to be very competitive on a variety of parallel architectures. In particular, the algorithm can be implemented usingn 2/4 processors, takingO(n log2 n) time for random matrices.


title: HGIMDA: Heterogeneous graph inference for miRNA-disease association prediction

abstract: // Xing Chen 1, * , Chenggang Clarence Yan 2, * , Xu Zhang 3 , Zhu-Hong You 4 , Yu-An Huang 5 , Gui-Ying Yan 6 1 School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China 2 Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, China 3 School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai, China 4 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China 5 Department of Computing, Hong Kong Polytechnic University, Hong Kong, China 6 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China * The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors. Correspondence to: Xing Chen, email: xingchen@amss.ac.cn Gui-Ying Yan, email: yangy@amss.ac.cn Keywords: microRNA, disease, microRNA-disease association, heterogeneous network, similarity Received: May 12, 2016 Accepted: July 28, 2016 Published: August 12, 2016 ABSTRACT Recently, microRNAs (miRNAs) have drawn more and more attentions because accumulating experimental studies have indicated miRNA could play critical roles in multiple biological processes as well as the development and progression of human complex diseases. Using the huge number of known heterogeneous biological datasets to predict potential associations between miRNAs and diseases is an important topic in the field of biology, medicine, and bioinformatics. In this study, considering the limitations in the previous computational methods, we developed the computational model of Heterogeneous Graph Inference for MiRNA-Disease Association prediction (HGIMDA) to uncover potential miRNA-disease associations by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally verified miRNA-disease associations into a heterogeneous graph. HGIMDA obtained AUCs of 0.8781 and 0.8077 based on global and local leave-one-out cross validation, respectively. Furthermore, HGIMDA was applied to three important human cancers for performance evaluation. As a result, 90% (Colon Neoplasms), 88% (Esophageal Neoplasms) and 88% (Kidney Neoplasms) of top 50 predicted miRNAs are confirmed by recent experiment reports. Furthermore, HGIMDA could be effectively applied to new diseases and new miRNAs without any known associations, which overcome the important limitations of many previous computational models.


title: CuFusion: Accurate Real-Time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid

abstract: Given a stream of depth images with a known cuboid reference object present in the scene, we propose a novel approach for accurate camera tracking and volumetric surface reconstruction in real-time. Our contribution in this paper is threefold: (a) utilizing a priori knowledge of the precisely manufactured cuboid reference object, we keep drift-free camera tracking without explicit global optimization; (b) we improve the fineness of the volumetric surface representation by proposing a prediction-corrected data fusion strategy rather than a simple moving average, which enables accurate reconstruction of high-frequency details such as the sharp edges of objects and geometries of high curvature; (c) we introduce a benchmark dataset CU3D that contains both synthetic and real-world scanning sequences with ground-truth camera trajectories and surface models for the quantitative evaluation of 3D reconstruction algorithms. We test our algorithm on our dataset and demonstrate its accuracy compared with other state-of-the-art algorithms. We release both our dataset and code as open-source (https://github.com/zhangxaochen/CuFusion) for other researchers to reproduce and verify our results.


title: 3D blob based brain tumor detection and segmentation in MR images

abstract: Automatic detection and segmentation of brain tumors in 3D MR neuroimages can significantly aid early diagnosis, surgical planning, and follow-up assessment. However, due to diverse location and varying size, primary and metastatic tumors present substantial challenges for detection. We present a fully automatic, unsupervised algorithm that can detect single and multiple tumors from 3 to 28,079 mm in volume. Using 20 clinical 3D MR scans containing from 1 to 15 tumors per scan, the proposed approach achieves between 87.84% and 95.30% detection rate and an average end-to-end running time of under 3 minutes. In addition, 5 normal clinical 3D MR scans are evaluated quantitatively to demonstrate that the approach has the potential to discriminate between abnormal and normal brains.


title: Decentralized task-aware scheduling for data center networks

abstract: Many data center applications perform rich and complex tasks (e.g., executing a search query or generating a user's news-feed). From a network perspective, these tasks typically comprise multiple flows, which traverse different parts of the network at potentially different times. Most network resource allocation schemes, however, treat all these flows in isolation -- rather than as part of a task -- and therefore only optimize flow-level metrics. ::: In this paper, we show that task-aware network scheduling, which groups flows of a task and schedules them together, can reduce both the average as well as tail completion time for typical data center applications. To achieve these benefits in practice, we design and implement Baraat, a decentralized task-aware scheduling system. Baraat schedules tasks in a FIFO order but avoids head-of-line blocking by dynamically changing the level of multiplexing in the network. Through experiments with Memcached on a small testbed and large-scale simulations, we show that Baraat outperforms state-of-the-art decentralized schemes (e.g., pFabric) as well as centralized schedulers (e.g., Orchestra) for a wide range of workloads (e.g., search, analytics, etc).


title: IntScope: Automatically Detecting Integer Overflow Vulnerability in X86 Binary Using Symbolic Execution

abstract: The number of identified integer overflow vulnerabilities has been increasing rapidly in recent years. In this paper, we present a system, IntScope, which can automatically detect integer overflow vulnerabilities in x86 binaries before an attacker does, with the goal of finally eliminating the vulnerabilities. IntScope first translates the disassembled code into our own intermediate representation (IR), and then performs a path sensitive data flow analysis on the IR by leveraging symbolic execution and taint analysis to identify the vulnerable point of integer overflow. Compared with other approaches, IntScope does not run the binary directly, and is scalable to large software as it can just symbolically execute the interesting program paths. Experimental results show IntScope is quite encouraging: it has detected more than 20 zero-day integer overflows (e.g., CVE-2008-4201, FrSIRT/ADV-2008-2919) in widely-used software such as QEMU, Xen and Xine.


title: 2136 Muscle and Movement Representations in the Primary Motor Cortex

abstract: What aspects of movement are represented in the primary motor cortex (M1): relatively low-level parameters like muscle force, or more abstract parameters like handpath? To examine this issue, the activity of neurons in M1 was recorded in a monkey trained to perform a task that dissociates three major variables of wrist movement: muscle activity, direction of movement at the wrist joint, and direction of movement in space. A substantial group of neurons in M1 (28 out of 88) displayed changes in activity that were muscle-like. Unexpectedly, an even larger group of neurons in M1 (44 out of 88) displayed changes in activity that were related to the direction of wrist movement in space independent of the pattern of muscle activity that generated the movement. Thus, both "muscles" and "movements" appear to be strongly represented in M1.


title: The Impact of Automated Parameter Optimization on Defect Prediction Models

abstract: Defect prediction models—classifiers that identify defect-prone software modules—have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28 percent of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.


title: Non-functional requirements in industry - three case studies adopting an experience-based NFR method

abstract: Non-functional characteristics of products can be essential for business success and are a key differentiator between a company and its competitors. This paper presents the application of a systematic, experience-based method to elicit, document, and analyze non-functional requirements. The objective of the method is to achieve a minimal and sufficient set of measurable and traceable non-functional requirements. The method gives clear guidance for the requirements elicitation, using workshops for capturing the important quality aspects and eliciting the non-functional requirements. This paper shows its application in three different settings, reporting the experience and lessons learned from industrial case studies that applied our NFR method. As the case studies were applied in different domains and performed with companies of various maturity, and since different quality attributes were considered, a set of interesting results has emerged. Therefore, each case study tells its own story about how the elicitation of NFR in industry can work. The paper discusses the different settings and gives a comparison of the different lessons we learned from the case studies.


title: GeoTextTagger: High-Precision Location Tagging of Textual Documents using a Natural Language Processing Approach

abstract: Location tagging, also known as geotagging or geolocation, is the process of assigning geographical coordinates to input data. In this paper we present an algorithm for location tagging of textual documents. Our approach makes use of previous work in natural language processing by using a state-of-the-art part-of-speech tagger and named entity recognizer to find blocks of text which may refer to locations. A knowledge base (OpenStreatMap) is then used to find a list of possible locations for each block. Finally, one location is chosen for each block by assigning distance-based scores to each location and repeatedly selecting the location and block with the best score. We tested our geolocation algorithm with Wikipedia articles about topics with a well-defined geographical location that are geotagged by the articles' authors, where classification approaches have achieved median errors as low as 11 km, with attainable accuracy limited by the class size. Our approach achieved a 10th percentile error of 490 metres and median error of 54 kilometres on the Wikipedia dataset we used. When considering the five location tags with the greatest scores, 50% of articles were assigned at least one tag within 8.5 kilometres of the article's author-assigned true location. We also tested our approach on Twitter messages that are tagged with the location from which the message was sent. Twitter texts are challenging because they are short and unstructured and often do not contain words referring to the location they were sent from, but we obtain potentially useful results. We explain how we use the Spark framework for data analytics to collect and process our test data. In general, classification-based approaches for location tagging may be reaching their upper accuracy limit, but our precision-focused approach has high accuracy for some texts and shows significant potential for improvement overall.


title: Cloud Storage Design Based on Hybrid of Replication and Data Partitioning

abstract: Most existing works on data partitioning techniques simply consider to partition data objects and distribute the shares to servers. Such pure data partitioning approaches may bring potential performance and scalability problems when used in widely distributed systems. First, it is difficult to apply lazy update. Second, the share consistency verification may incur costly communications among widely distributed servers. In this paper, we propose a two-level DHT (TDHT) approach for widely distributed cloud storage to address these problems. First, we analyze the tradeoffs on security and availability between TDHT and the conventional pure data partitioning approach (integrated with DHT and called GDHT (global DHT)). The results show that TDHT can provide better security than GDHT and almost the same level of availability as GDHT. To compare their performance, we design a two-level access (TLA) protocol for the TDHT approach and compare it with the distributed version server (DVS) protocol proposed in [Ye10] for the GDHT approach. The experimental results show that TLA can provide much better user perceived update response latency and the same level or even better read access latency compared to DVS.