title: Warped Convolutions: Efficient Invariance to Spatial Transformations

abstract: Convolutional Neural Networks (CNNs) are extremely efficient, since they exploit the inherent translation-invariance of natural images. However, translation is just one of a myriad of useful spatial transformations. Can the same efficiency be attained when considering other spatial invariances? Such generalized convolutions have been considered in the past, but at a high computational cost. We present a construction that is simple and exact, yet has the same computational complexity that standard convolutions enjoy. It consists of a constant image warp followed by a simple convolution, which are standard blocks in deep learning toolboxes. With a carefully crafted warp, the resulting architecture can be made equivariant to a wide range of two-parameter spatial transformations. We show encouraging results in realistic scenarios, including the estimation of vehicle poses in the Google Earth dataset (rotation and scale), and face poses in Annotated Facial Landmarks in the Wild (3D rotations under perspective).


title: An ultra-low-power MPPT architecture for photovoltaic energy harvesting systems

abstract: In this paper, a maximum power point tracking (MPPT) architecture for micro-scale photovoltaic (PV) energy harvesting systems is presented. A switching DC-DC boost converter is used to provide the required voltage for the circuits of the ultra-low-power application. The MPPT tracks different illumination intensities through changing the input impedance of the boost converter. The proposed technique depends on maximizing the voltage delivered by the boost converter to a test load whose value is constant. Maximizing the voltage of the test load leads to extracting the maximum available source power. The proposed algorithm employs simple and low-power circuits to change the switching frequency of the boost converter to track different illumination conditions. The proposed system has been implemented in a low-cost CMOS 130nm technology. It consumes a quiescent current less than while achieving an average tracking efficiency (TE) of 96% and occupies an area of 0.175mm2.


title: A K-6 Computational Thinking Curriculum Framework: Implications for Teacher Knowledge

abstract: Adding computer science as a separate school subject to the core K-6 curriculum is a complex issue with educational challenges. The authors herein address two of these challenges: (1) the design of the curriculum based on a generic computational thinking framework, and (2) the knowledge teachers need to teach the curriculum. The first issue is discussed within a perspective of designing an authentic computational thinking curriculum with a focus on real-world problems. The second issue is addressed within the framework of technological pedagogical content knowledge explicating in detail the body of knowledge that teachers need to have to be able to teach computational thinking in a K-6 environment. An example of how these ideas can be applied in practice is also given. While it is recognized there is a lack of adequate empirical evidence in terms of the effectiveness of the frameworks proposed herein, it is expected that our knowledge and research base will dramatically increase over the next several years, as more countries around the world add computer science as a separate school subject to their K-6 curriculum.


title: Mining Recurring Concept Drifts with Limited Labeled Streaming Data

abstract: Tracking recurring concept drifts is a significant issue for machine learning and data mining that frequently appears in real-world stream classification problems. It is a challenge for many streaming classification algorithms to learn recurring concepts in a data stream environment with unlabeled data, and this challenge has received little attention from the research community. Motivated by this challenge, this article focuses on the problem of recurring contexts in streaming environments with limited labeled data. We propose a semi-supervised classification algorithm for data streams with REcurring concept Drifts and Limited LAbeled data, called REDLLA, in which a decision tree is adopted as the classification model. When growing a tree, a clustering algorithm based on k-means is installed to produce concept clusters and unlabeled data are labeled in the method of majority-class at leaves. In view of deviations between history and new concept clusters, potential concept drifts are distinguished and recurring concepts are maintained. Extensive studies on both synthetic and real-world data confirm the advantages of our REDLLA algorithm over three state-of-the-art online classification algorithms of CVFDT, DWCDS, and CDRDT and several known online semi-supervised algorithms, even in the case with more than 90p unlabeled data.


title: Thrombosis of the palmar digital veins: a report of 6 cases and a review of the literature.

abstract: Our experience in 6 cases of thrombosis of the palmar digital veins is presented and an additional 20 from the world literature are reviewed. All our patients were female and suffered from a tender, bluish nodule on the palmar aspect of a digit at or distally to the proximal interphalangeal joint. In the first 2 cases, the diagnosis was confirmed on histology following excision. Four of the 6 cases were managed conservatively with a favorable outcome. All possible etiological factors were examined. Four of the 6 cases in whom blood flow stasis as a contributing factor could unequivocally be excluded were specifically tested for hypercoagulability. No hemostatic abnormalities were found. Surgical extirpation is indicated where the clinical diagnosis is in doubt, if symptoms persist, or if the nodule(s) progressively enlarge(s).


PNEUMONIC PLAGUE IN RANGOON


title: Enhancing Business Intelligence with unstructured data

abstract: In a half-tone recording method and apparatus, half-tone expressing units, each consisting of several recording picture elements, express gradations of density in a document. The recording picture elements in the units are assigned various half-tone levels with the half tone level of at least one of the recording picture elements both increasing and decreasing as the gradation of the half-tone expressing unit increases.


title: Complex and Quaternionic Principal Component Pursuit and Its Application to Audio Separation

abstract: Recently, the principal component pursuit has received increasing attention in signal processing research ranging from source separation to video surveillance. So far, all existing formulations are real-valued and lack the concept of phase, which is inherent in inputs such as complex spectrograms or color images. Thus, in this letter, we extend principal component pursuit to the complex and quaternionic cases to account for the missing phase information. Specifically, we present both complex and quaternionic proximity operators for the ${\ell _1}$ - and trace-norm regularizers. These operators can be used in conjunction with proximal minimization methods such as the inexact augmented Lagrange multiplier algorithm. The new algorithms are then applied to the singing voice separation problem, which aims to separate the singing voice from the instrumental accompaniment. Results on the iKala and MSD100 datasets confirmed the usefulness of phase information in principal component pursuit.


title: PrivatePool: Privacy-Preserving Ridesharing

abstract: Location-based services have seen tremendous developments over the recent years. These services have revolutionized transportation business, as witnessed by the success of Uber, Lyft, BlaBlaCar, and the like. Yet from the privacy point of view, the state of the art leaves much to be desired. The location of the user is typically shared with the service, opening up for privacy abuse, as in some recently publicized cases. This paper proposes PrivatePool, a model for privacy-preserving ridesharing. We develop secure multi-party computation techniques for endpoint and trajectory matching that allow dispensing with trust to third parties. At the same time, the users learn of a ride segment they can share and nothing else about other users' location. We establish formal privacy guarantees and investigate how different riding patterns affect the privacy, utility, and performance trade-offs between approaches based on the proximity of endpoints vs. proximity of trajectories.


abstract: Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497-510. DOI: 10.1016/j.im.2014.03.006


title: Fluorescence microscopy imaging denoising with log-Euclidean priors and photobleaching compensation

abstract: Fluorescent protein microscopy imaging is nowadays one of the most important tools in biomedical research. However, the resulting images present a low signal to noise ratio and a time intensity decay due to the photobleaching effect. This phenomenon is a consequence of the decreasing on the radiation emission efficiency of the tagging protein. This occurs because the fluorophore permanently loses its ability to fluoresce, due to photochemical reactions induced by the incident light. The Poisson multiplicative noise that corrupts these images, in addition with its quality degradation due to photobleaching, make long time biological observation processes very difficult.


title: Vishit: A Visualizer for Hindi Text

abstract: We outline the design of a visualizer, named Vishit, for texts in the Hindi language. The Hindi language is lingua franca in many states of India where people speak different languages. The visualized text serves as a universal language where seamless communication is needed by many people who speak different languages and have different cultures. Vishit consists of the following three major processing steps: language processing, knowledge base creation and scene generation. Initial results from the Vishit prototype are encouraging.


title: E-Counterfeit: A Mobile-Server Platform for Document Counterfeit Detection

abstract: This paper presents a novel application to detect counterfeit identity documents forged by a scan-printing operation. Texture analysis approaches are proposed to extract validation features from security background that is usually printed in documents as IDs or banknotes. The main contribution of this work is the end-to-end mobile-server architecture, which provides a service for non-expert users and therefore can be used in several scenarios. The system also provides a crowdsourcing mode so labeled images can be gathered, generating databases for incremental training of the algorithms.


title: Phenomics of rice early vigour and drought response: Are sugar related and morphogenetic traits relevant?

abstract: BACKGROUND ::: Early vigour (biomass accumulation) is a useful but complex trait in rainfed rice (Oryza sativa L). Little is known on trade-offs with drought tolerance. This study explored the relevance of (sugar) metabolic and morphogenetic traits to describe the genetic diversity of rice early vigour and its phenotypic plasticity under drought conditions. A greenhouse experiment was conducted to characterize on a panel of 43 rice genotypes plant morphogenesis and sugar concentration in expanded (source) and expanding (sink) leaves. ::: ::: ::: RESULTS ::: Across genotypes in control treatment, leaf starch concentration was negatively correlated with organogenetic development rate (DR, defined as leaf appearance rate on main stem). Genotypes with small leaves had high DR and tiller number but low leaf starch concentration. Under drought, vigorous genotypes showed stronger growth reduction. Starch concentration decreased in source leaves, by contrast with soluble sugars and with that observed in sink leaves. Accordingly, genotypes were grouped in three clusters differing in constitutive vigour, starch storage and growth maintenance under drought showing a trade off between constitutive vigour and drought tolerance. ::: ::: ::: CONCLUSIONS ::: It was therefore suggested that non structural carbohydrates, particularly starch, were relevant markers of early vigour. Their relevance as markers of growth maintenance under drought needs to be further explored. Results are discussed regarding novel process based traits to be introduced in the GRiSP (Global Rice Science Partnership) phenotyping network.


title: Reinforcement learning in a nutshell

abstract: We provide a concise introduction to basic approaches to reinforcement learning from the machine learning perspective. The focus is on value function and policy gradient methods. Some selected recent trends are highlighted.


title: CANTINA+: A Feature-Rich Machine Learning Framework for Detecting Phishing Web Sites

abstract: Phishing is a plague in cyberspace. Typically, phish detection methods either use human-verified URL blacklists or exploit Web page features via machine learning techniques. However, the former is frail in terms of new phish, and the latter suffers from the scarcity of effective features and the high false positive rate (FP). To alleviate those problems, we propose a layered anti-phishing solution that aims at (1) exploiting the expressiveness of a rich set of features with machine learning to achieve a high true positive rate (TP) on novel phish, and (2) limiting the FP to a low level via filtering algorithms. Specifically, we proposed CANTINA+, the most comprehensive feature-based approach in the literature including eight novel features, which exploits the HTML Document Object Model (DOM), search engines and third party services with machine learning techniques to detect phish. Moreover, we designed two filters to help reduce FP and achieve runtime speedup. The first is a near-duplicate phish detector that uses hashing to catch highly similar phish. The second is a login form filter, which directly classifies Web pages with no identified login form as legitimate. We extensively evaluated CANTINA+ with two methods on a diverse spectrum of corpora with 8118 phish and 4883 legitimate Web pages. In the randomized evaluation, CANTINA+ achieved over 92p TP on unique testing phish and over 99p TP on near-duplicate testing phish, and about 0.4p FP with 10p training phish. In the time-based evaluation, CANTINA+ also achieved over 92p TP on unique testing phish, over 99p TP on near-duplicate testing phish, and about 1.4p FP under 20p training phish with a two-week sliding window. Capable of achieving 0.4p FP and over 92p TP, our CANTINA+ has been demonstrated to be a competitive anti-phishing solution.


A Graph Degeneracy-based Approach to Keyword Extraction


title: Cardiac Computed Tomography Angiography for Left Atrial Appendage Closure.

abstract: Atrial fibrillation is prevalent and percutaneous left atrial appendage (LAA) closure is increasingly performed worldwide. This procedure is technically challenging and the success and procedural complexities depend on anatomy of the LAA and surrounding structures. These are readily depicted on cardiac computed tomography angiography (CCTA), which offers unique imaging planes. CCTA allows not only preplanning anatomic LAA assessment, but can also be used to evaluate for pre-existing LAA thrombus, and done postprocedure for surveillance for device-related thrombus, residual leak, and complications. In this article, we review the practical utility of CCTA for LAA closure.


title: Sketch-based image matching Using Angular partitioning

abstract: This work presents a novel method for image similarity measure, where a hand-drawn rough black and white sketch is compared with an existing data base of full color images (art works and photographs). The proposed system creates ambient intelligence in terms of the evaluation of nonprecise, easy to input sketched information. The system can then provide the user with options of either retrieving similar images in the database or ranking the quality of the sketch against a given standard, i.e., the original image model. Alternatively, the inherent pattern-matching capability of the system can be utilized to allow detection of distortion in any given real time-image sequences in vision-driven ambient intelligence applications. The proposed method can cope with images containing several complex objects in an inhomogeneous background. Two abstract images are obtained using strong edges of the model image and the morphologically thinned outline of the sketched image. The angular-spatial distribution of pixels in the abstract images is then employed to extract new compact and effective features using the Fourier transform. The extracted features are rotation and scale invariant and robust against translation. Experimental results from seven different approaches confirm the efficacy of the proposed method in both the retrieval performance and the time required for feature extraction and search.


title: Posture recognition with a top-view camera

abstract: We describe a system that recognizes human postures with heavy self-occlusion. In particular, we address posture recognition in a robot assisted-living scenario, where the environment is equipped with a top-view camera for monitoring human activities. This setup is very useful because top-view cameras lead to accurate localization and limited inter-occlusion between persons, but conversely they suffer from body parts being frequently self-occluded. The conventional way of posture recognition relies on good estimation of body part positions, which turns out to be unstable in the top-view due to occlusion and foreshortening. In our approach, we learn a posture descriptor for each specific posture category. The posture descriptor encodes how well the person in the image can be `explained' by the model. The postures are subsequently recognized from the matching scores returned by the posture descriptors. We select the state-of-the-art approach of pose estimation as our posture descriptor. The results show that our method is able to correctly classify 79.7% of the test sample, which outperforms the conventional approach by over 23%.


title: Verilog Implementation of a System for Finding Shortest Path by Using Floyd-Warshall Algorithm

abstract: There are several applications in VLSI technology that require high-speed shortest-path computations. The shortest path is a path between two nodes (or points) in a graph such that the sum of the weights of its constituent edges is minimum. Floyd-Warshall algorithm provides fastest computation of shortest path between all pair of nodes present in the graph. With rapid advances in VLSI technology, Field Programmable Gate Arrays (FPGAs) are receiving the attention of the Parallel and High Performance Computing community. This paper gives implementation outcome of Floyd-Warshall algorithm to solve the all pairs shortest-paths problem for directed graph in Verilog.


title: Exploiting Unrelated Tasks in Multi-Task Learning

abstract: We study the problem of learning a group of principal tasks using a group of auxiliary tasks, unrelated to the principal ones. In many applications, joint learning of unrelated tasks which use the same input data can be beneficial. The reason is that prior knowledge about which tasks are unrelated can lead to sparser and more informative representations for each task, essentially screening out idiosyncrasies of the data distribution. We propose a novel method which builds on a prior multitask methodology by favoring a shared low dimensional representation within each group of tasks. In addition, we impose a penalty on tasks from different groups which encourages the two representations to be orthogonal. We further discuss a condition which ensures convexity of the optimization problem and argue that it can be solved by alternating minimization. We present experiments on synthetic and real data, which indicate that incorporating unrelated tasks can improve significantly over standard multi-task learning methods.


title: Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control

abstract: This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only. The capability to autonomously learn robot controllers solely from raw-pixel images and without any prior knowledge of configuration is shown for the first time. We build upon the success of recent deep reinforcement learning and develop a system for learning target reaching with a three-joint robot manipulator using external visual observation. A Deep Q Network (DQN) was demonstrated to perform target reaching after training in simulation. Transferring the network to real hardware and real observation in a naive approach failed, but experiments show that the network works when replacing camera images with synthetic images.


title: Screening of Mango (Mangifera indicaL.) Cultivars for Their Contents of FlavonolO- and XanthoneC-Glycosides, Anthocyanins, and Pectin

abstract: With respect to their browning potential and in consideration of a combined recovery of pectin and phenolic compounds, peels of 14 cultivars and the flesh of nine cultivars of mango (Mangifera indica L.) fruits were analyzed for their contents of flavonol O- and xanthone C-glycosides by high-performance liquid chromatography (HPLC)-diode array detection-electrospray ionization mass spectrometry (ESI-MS). While total amounts of up to 4860 mg/kg dry matter demonstrated the peels to be a rich source of phenolic compounds, only traces could be detected in the flesh. The profile of flavonol glycosides of the peels proved to be highly characteristic and may therefore serve as a tool for authenticity control of mango puree concentrate, which is often produced from unpeeled fruits and represents an important intermediate for the production of mango nectars. Two compounds were isolated by preparative HPLC, and their structures were elucidated on the basis of ESI-MS as well as NMR spectroscopy, establishing the two compounds as rhamnetin 3-O-beta-galactopyranoside and rhamnetin 3-O-beta-glucopyranoside, respectively. In the peels of red-colored cultivars, cyanidin 3-O-galactoside and an anthocyanidin hexoside so far not reported in mango could tentatively be identified. The contents and degrees of esterification of pectins extracted from the lyophilized peels ranged from 12.2 to 21.2% and from 56.3 to 65.6%, respectively, suggesting mango peels also as a promising source of high-quality pectin.


title: Classification of Sphenoid Sinus Pneumatization: Relevance for Endoscopic Skull Base Surgery

abstract: Objectives/Hypothesis ::: The goal of this study was to present a classification based on the degree of pneumatization of the sphenoid sinus in the coronal plane that can be used to instruct preoperative planning for endoscopic endonasal surgery (EES). ::: ::: Study Design ::: Observational anatomical study. ::: ::: Methods ::: The geometry of sphenoid sinus pneumatization was characterized (n = 204 hemisinus) on high-resolution computed tomography scans, and its associations with the location of the foramen rotundum (FR) and the vidian canal (VC) were measured. Based on these findings, we propose a simple classification of pneumatization of the sphenoid sinus relevant for EES. ::: ::: Results ::: The lateral recess of the sphenoid sinus was pneumatized lateral to the FR in the coronal plane in 54% of patients. The distance separating the FR and the VC correlated strongly with the depth of the lateral recess. Based on these findings, we propose three types of pneumatization: type I, where the pneumatization extends from the midline to the medial edge of the VC (25%); type II, where the pneumatization reaches the medial edge of the FR (39%); and type III, where the pneumatization extends beyond the medial border of the FR (37%). ::: ::: Conclusions ::: The proposed sphenoid sinus pneumatization classification in the coronal plane is simple and reproducible. It predicts the distance between vidian and maxillary nerve, determines the size of the surgical window to access the middle cranial fossa transnasally, and instructs on the potential risk to neurovascular structures during surgery. ::: ::: Level of Evidence ::: 4 Laryngoscope, 125:577–581, 2015


title: A Survey on Artificial Intelligence and Data Mining for MOOCs

abstract: Massive Open Online Courses (MOOCs) have gained tremendous popularity in the last few years. Thanks to MOOCs, millions of learners from all over the world have taken thousands of high-quality courses for free. Putting together an excellent MOOC ecosystem is a multidisciplinary endeavour that requires contributions from many different fields. Artificial intelligence (AI) and data mining (DM) are two such fields that have played a significant role in making MOOCs what they are today. By exploiting the vast amount of data generated by learners engaging in MOOCs, DM improves our understanding of the MOOC ecosystem and enables MOOC practitioners to deliver better courses. Similarly, AI, supported by DM, can greatly improve student experience and learning outcomes. In this survey paper, we first review the state-of-the-art artificial intelligence and data mining research applied to MOOCs, emphasising the use of AI and DM tools and techniques to improve student engagement, learning outcomes, and our understanding of the MOOC ecosystem. We then offer an overview of key trends and important research to carry out in the fields of AI and DM so that MOOCs can reach their full potential.


Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement


Design Fiction : A Method Toolbox for Design Research in a Complex World


title: Towards an Ontological Analysis of Powertypes

abstract: In several subject domains, the categorization scheme itself is part of the subject matter. In this case, experts make use of categories of categories in their accounts. This has led to a number of approaches in conceptual modeling and knowledge representation that are called multi-level modeling approaches. An early approach for multi-level modeling is the powertype pattern which introduces “power types” and “base types”. More recently, other proposals for multilevel modeling include “clabjects”, “m-objects”, which admit the existence of entities being somehow, simultaneously, types (classes) and instances (usually associated to objects). Regardless of the choice of approach to perform multi-level modelling, a question remains concerning the ontological status of “base types”, “power types” and “clabjects”. This paper aims to address this question through an ontological analysis. We use here the general term powertype to generally refer to types whose instances exhibit somehow both type-like and instance-like characteristics. We examine alternative accounts for powertype instances: (i) powertype instances as universals (abstract repeatable entities), (ii) powertype instances as mereological sums of instances of an associated type and (iii) powertype instances as variable embodiments. We conclude that the latter is the most promising account for an ontological interpretation of this phenomenon that meets the modelling desiderata for powertypes present in the literature.


title: Interactive machine learning

abstract: Perceptual user interfaces (PUIs) are an important part of ubiquitous computing. Creating such interfaces is difficult because of the image and signal processing knowledge required for creating classifiers. We propose an interactive machine-learning (IML) model that allows users to train, classify/view and correct the classifications. The concept and implementation details of IML are discussed and contrasted with classical machine learning models. Evaluations of two algorithms are also presented. We also briefly describe Image Processing with Crayons (Crayons), which is a tool for creating new camera-based interfaces using a simple painting metaphor. The Crayons tool embodies our notions of interactive machine learning


title: Application of Marine Bacteria Associated with Seaweed, Ulva lactuca, for Degradation of Algal Waste

abstract: In the present study, three marine Ulva lactuca-associated bacteria capable of producing agarase, λ-carrageenase, amylase, cellulase and protease were isolated from rocky intertidal region of Anjuna beach, Goa, India, and designated as DM1, DM5 and DM15. Based on 16S rRNA sequence analysis and biochemical tests, bacteria were identified as Vibrio brasiliensis, Bacillus subtilis and Pseudomonas aeruginosa. Bacteria DM1, DM5 and DM 15 could able to utilize seaweed waste (Sargassum powder) in seawater-based media by releasing reducing sugars, 503.3 ± 17.5 µg/ml, 491.6 ± 20 µg/ml and 376.6 ± 16 µg/ml, respectively, which was confirmed through 3,5-dinitrosalicylic acid method. Therefore, the eco-friendly reuse of seaweed waste is possible by using marine bacteria for the production of reducing sugars in ethanol-producing industry. All three bacterial isolates were found to produce indole acetic acid (IAA) at concentration 98 ± 12 µg/ml, 113.6 ± 13 µg/ml and 121.6 ± 8.5 µg/ml, respectively. Nitrogen fixation by bacterial strains was confirmed when they showed growth on artificial seawater devoid of nitrogen and comprising of 5% carrageenan as a sole source of carbon and gelling agent. Photosynthetic seaweed, Ulva lactuca, provides organic carbon and O2 for associated bacteria and associated bacteria fix atmospheric N2 and provides iron by siderophore production and synthesize hormone IAA for algal growth during their cooperative association.


title: Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

abstract: As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, and beamformer design and computation resource management, while ML-based networking focuses on the applications in clustering, base station switching control, user association, and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use. Traditional approaches are also summarized together with their performance comparison with ML-based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies. Specifically, ML-based network slicing, infrastructure update to support ML-based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation, and so on are discussed.


title: The pursuit of status in social groups

abstract: Status differences are ubiquitous and highly consequential. Yet with regard to human social groups, basic questions persist about how status differences de- velop. In particular, little is known about the processes by which individuals pursue status in social groups. That is, how do individuals compete and jockey for status with their peers? The current paper reviews recent research that helps fill this gap in our knowledge. Specifically, studies of a variety of face-to-face groups show that indi- viduals pursue status by enhancing the apparent value they provide to their group. Individuals compete for status not by bullying and intimidating others, as some theorists have proposed, but by behaving in ways that suggest high levels of competence, generosity, and commitment to the


title: A Set of Features Extraction Methods for the Recognition of the Isolated Handwritten Digits

abstract: In this paper we present an off line system for the recognition of the isolated handwritten digits. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm and fed by several feature vectors. The used parameters to form the input vector of the neural network are extracted on the binary images of the digits by several methods: the distribution sequence, sondes application, the Barr features, The central moments of image coding according to the directions of Freeman, and the centred moments of the different projections and profiles.


title: Path Planning and Controlled Crash Landing of a Quadcopter in case of a Rotor Failure

abstract: This paper presents a framework for controlled emergency landing of a quadcopter, experiencing a rotor failure, away from sensitive areas. A complete mathematical model capturing the dynamics of the system is presented that takes the asymmetrical aerodynamic load on the propellers into account. An equilibrium state of the system is calculated around which a linear time-invariant control strategy is developed to stabilize the system. By utilizing the proposed model, a specific configuration for a quadcopter is introduced that leads to the minimum power consumption during a yaw-rate-resolved hovering after a rotor failure. Furthermore, given a 3D representation of the environment, an optimal flight trajectory towards a safe crash landing spot, while avoiding collision with obstacles, is developed using an RRT* approach. The cost function for determining the best landing spot consists of: (i) finding the safest landing spot with the largest clearance from the obstacles; and (ii) finding the most energy-efficient trajectory towards the landing spot. The performance of the proposed framework is tested via simulations.


title: Explainable PCGML via Game Design Patterns

abstract: Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.


title: Flickr: Who is Looking?

abstract: This article presents a characterization of user behavior on Flickr, a popular on-line photo sharing service that allows users to store, search, sort and share their photos. Based on a sub-set of photos being uploaded during a 10 day window, we track the interest of users in those photos over a period of 50 days. In particular we investigate the user behavior on temporal, social, and spatial dimensions. Results show that the users are able to discover new photos within hours after being uploaded and that 50% of the photo views are generated within the first two days. The social networking behavior of users, and photo pooling are identified as the two major indicators related to a photo's popularity. Finally we show that the geographic distribution is more focussed around a geographic location for the infrequently viewed photos, than for the photos that attract a large number of views.


An Investigation of DNN-Based Speech Synthesis Using Speaker Codes.


title: A Decentralized Mobile Computing Network for Multi-Robot Systems Operations

abstract: Collective animal behaviors are paradigmatic examples of fully decentralized operations involving complex collective computations such as collective turns in flocks of birds or collective harvesting by ants. These systems offer a unique source of inspiration for the development of fault-tolerant and self-healing multi-robot systems capable of operating in dynamic environments. Specifically, swarm robotics emerged and is significantly growing on these premises. However, to date, most swarm robotics systems reported in the literature involve basic computational tasks---averages and other algebraic operations. In this paper, we introduce a novel Collective computing framework based on the swarming paradigm, which exhibits the key innate features of swarms: robustness, scalability and flexibility. Unlike Edge computing, the proposed Collective computing framework is truly decentralized and does not require user intervention or additional servers to sustain its operations. This Collective computing framework is applied to the complex task of collective mapping, in which multiple robots aim at cooperatively map a large area. Our results confirm the effectiveness of the cooperative strategy, its robustness to the loss of multiple units, as well as its scalability. Furthermore, the topology of the interconnecting network is found to greatly influence the performance of the collective action.


title: A deep learning method for classifying mammographic breast density categories

abstract: Purpose ::: Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., “scattered density” and “heterogeneously dense”. The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. ::: ::: Methods ::: In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. ::: ::: Results ::: The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset. ::: ::: Conclusions ::: Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.


title: Effect of iodine intake on thyroid diseases in China.

abstract: BACKGROUND ::: Iodine is an essential component of thyroid hormones; either low or high intake may lead to thyroid disease. We observed an increase in the prevalence of overt hypothyroidism, subclinical hypothyroidism, and autoimmune thyroiditis with increasing iodine intake in China in cohorts from three regions with different levels of iodine intake: mildly deficient (median urinary iodine excretion, 84 microg per liter), more than adequate (median, 243 microg per liter), and excessive (median, 651 microg per liter). Participants enrolled in a baseline study in 1999, and during the five-year follow-up through 2004, we examined the effect of regional differences in iodine intake on the incidence of thyroid disease. ::: ::: ::: METHODS ::: Of the 3761 unselected subjects who were enrolled at baseline, 3018 (80.2 percent) participated in this follow-up study. Levels of thyroid hormones and thyroid autoantibodies in serum, and iodine in urine, were measured and B-mode ultrasonography of the thyroid was performed at baseline and follow-up. ::: ::: ::: RESULTS ::: Among subjects with mildly deficient iodine intake, those with more than adequate intake, and those with excessive intake, the cumulative incidence of overt hypothyroidism was 0.2 percent, 0.5 percent, and 0.3 percent, respectively; that of subclinical hypothyroidism, 0.2 percent, 2.6 percent, and 2.9 percent, respectively; and that of autoimmune thyroiditis, 0.2 percent, 1.0 percent, and 1.3 percent, respectively. Among subjects with euthyroidism and antithyroid antibodies at baseline, the five-year incidence of elevated serum thyrotropin levels was greater among those with more than adequate or excessive iodine intake than among those with mildly deficient iodine intake. A baseline serum thyrotropin level of 1.0 to 1.9 mIU per liter was associated with the lowest subsequent incidence of abnormal thyroid function. ::: ::: ::: CONCLUSIONS ::: More than adequate or excessive iodine intake may lead to hypothyroidism and autoimmune thyroiditis.


title: Pure sensory stroke heralding large hemispheric infarction.

abstract: A 49-year-old with long-standing hypertension and diabetes developed numbness and sensory loss over the left side of the body consistent with a diagnosis of Pure sensory stroke (PSS). However, CT showed a subcortical infarction in the middle cerebral artery (MCA) territory, which evolved a few hours later towards a large hemispheric infarction associated with severe neurologic worsening. Doppler ultrasounds showed ipsilateral carotid occlusion and contralateral severe stenosis. These findings suggest that PSS may sometimes herald large infarction in the MCA territory in association with carotid occlusion.


title: Leader Purposefulness within Servant Leadership: Examining the Effect of Servant Leadership, Leader Follower-Focus, Leader Goal-Orientation, and Leader Purposefulness in a Large U.S. Healthcare Organization

abstract: As the study of servant leadership expands beyond theoretical exploration, empirical research continues to validate the positive effect of servant leadership behaviors and attitudes on diverse follower and organizational measures. This study expands the conversation by engaging the theme of leader purposefulness within servant leadership studies. A sample of over 1700 employees from a large U.S. healthcare organization provided responses to five research instruments. Follower perspectives on servant leadership, leader follower-focus, leader goal-orientation, and leader purposefulness were assessed using the Purpose in Leadership Inventory and each of these independent variables were analyzed for hypothesized positive relationships with four dependent variables: follower job satisfaction, follower organizational commitment, follower person-organization fit, and follower perception of leadership effectiveness. Each of the 16 hypothesized relationships were supported at a statistically significant level (<0.001) with positive correlations ranging from 0.40 to 0.88. Regression analyses were conducted to provide predictive modeling and indicators of the relative importance of each independent variable on the related dependent variables.


title: Minimizing Seed Set for Viral Marketing

abstract: Viral marketing has attracted considerable concerns in recent years due to its novel idea of leveraging the social network to propagate the awareness of products. Specifically, viral marketing is to first target a limited number of users (seeds) in the social network by providing incentives, and these targeted users would then initiate the process of awareness spread by propagating the information to their friends via their social relationships. Extensive studies have been conducted for maximizing the awareness spread given the number of seeds. However, all of them fail to consider the common scenario of viral marketing where companies hope to use as few seeds as possible yet influencing at least a certain number of users. In this paper, we propose a new problem, called J-MIN-Seed, whose objective is to minimize the number of seeds while at least J users are influenced. J-MIN-Seed, unfortunately, is proved to be NP-hard in this work. In such case, we develop a greedy algorithm that can provide error guarantees for J-MIN-Seed. Furthermore, for the problem setting where J is equal to the number of all users in the social network, denoted by Full-Coverage, we design other efficient algorithms. Extensive experiments were conducted on real datasets to verify our algorithm.


title: Virtual reality simulation in neurosurgery: technologies and evolution.

abstract: Neurosurgeons are faced with the challenge of learning, planning, and performing increasingly complex surgical procedures in which there is little room for error. With improvements in computational power and advances in visual and haptic display technologies, virtual surgical environments can now offer potential benefits for surgical training, planning, and rehearsal in a safe, simulated setting. This article introduces the various classes of surgical simulators and their respective purposes through a brief survey of representative simulation systems in the context of neurosurgery. Many technical challenges currently limit the application of virtual surgical environments. Although we cannot yet expect a digital patient to be indistinguishable from reality, new developments in computational methods and related technology bring us closer every day. We recognize that the design and implementation of an immersive virtual reality surgical simulator require expert knowledge from many disciplines. This article highlights a selection of recent developments in research areas related to virtual reality simulation, including anatomic modeling, computer graphics and visualization, haptics, and physics simulation, and discusses their implication for the simulation of neurosurgery.


title: Adaptive Routing in Underwater Delay/Disruption Tolerant Sensor Networks

abstract: As an emerging technique, underwater sensor network (UWSN) will enable a wide range of aquatic applications. However, due to the adverse underwater environmental conditions as well as some system constraints, an underwater sensor network is usually viewed as an intermittently connected network (ICN) (or delay/disruption tolerant network (DTN)), which requires specialized routing protocols. Moreover, applications may have different requirements for different types of messages, as demands a smart protocol to handle packets adaptively. In this paper, we propose a novel routing protocol where routing is performed adaptively based on the types of messages and application requirements. This is obtained by exploiting message redundancy and resource reallocation in order to achieve different performance requirements. We demonstrate through simulations that our protocol can satisfy different application requirements and achieve a good trade-off among delivery ratio, end-to-end delay and energy consumption.


title: A Miniaturized Dual-Band Base Station Array Antenna Using Band Notch Dipole Antenna Elements and AMC Reflectors

abstract: A novel miniaturized dual-band dipole array antenna consisting of two parallel $1 \times 4$ subarrays for use in base stations operating in the digital cellular system (DCS) and wideband code division multiple access (WCDMA) bands is presented. Size miniaturization is realized by utilizing band notch dipoles (BNDs) as array elements and artificial magnetic conductors as reflectors for reductions in width and profile height of the array antenna, respectively. Using the BNDs of high radiation suppression at desired notch frequencies, the radiation from DCS subarray is suppressed in the WCDMA band and vice versa. High isolation between the subarrays, therefore, is achieved while reducing the subarray separation. In addition, a dual four-way unequal power divider is designed for beam pattern synthesis. The measured results show that realized gains of 12.1–12.6 and 12.4–13.1 dBi, respectively, within the DCS and WCDMA bands, as well as the first null depth and sidelobe level, respectively, of lower than −20 and −16 dB, are achieved simultaneously, indicating that the proposed array antenna is suitable for potential base station applications.


title: Association of Epstein-Barr virus with lymphoepithelioma-like carcinoma of the lung in southern China.

abstract: Having reviewed the data on 3,663 consecutive cases of primary lung carcinoma in southern China, we found that 32 cases could meet the criteria for lymphoepithelioma-like carcinoma (LELC) of the lung. To study the relationship between pulmonary LELC and Epstein-Barr virus (EBV) infection, we used in situ hybridization and immunohistochemistry techniques to detect the EBV-encoded small nonpolyadenylated RNA (EBER), latent membrane protein 1 (LMP1), and viral capsid antigen (VCA) in 32 cases of LELC and 19 cases of non-LELC lung carcinoma. Of the 32 cases, 30 (94%) showed EBER positivity. Of the 30 EBER-positive pulmonary LELC cases, 16 and 7 expressed LMP1 and VCA, respectively. In contrast with LELC, none of the 19 cases of non-LELC lung carcinoma showed EBER-, LMP1-, or VCA-positive signals in carcinoma cells. The results demonstrate that there is a close relationship between EBV infection and pulmonary LELC. EBV infection may have an essential role in the tumorigenesis of pulmonary LELC. EBV latent infection is the main status in pulmonary LELC except for individual EBV entering into a lytic cycle.


title: Nlrp6 regulates intestinal antiviral innate immunity

abstract: The nucleotide-binding oligomerization domain–like receptor (Nlrp) 6 maintains gut microbiota homeostasis and regulates antibacterial immunity. We now report a role for Nlrp6 in the control of enteric virus infection. Nlrp6−/− and control mice systemically challenged with encephalomyocarditis virus had similar mortality; however, the gastrointestinal tract of Nlrp6−/− mice exhibited increased viral loads. Nlrp6−/− mice orally infected with encephalomyocarditis virus had increased mortality and viremia compared with controls. Similar results were observed with murine norovirus 1. Nlrp6 bound viral RNA via the RNA helicase Dhx15 and interacted with mitochondrial antiviral signaling protein to induce type I/III interferons (IFNs) and IFN-stimulated genes (ISGs). These data demonstrate that Nlrp6 functions with Dhx15 as a viral RNA sensor to induce ISGs, and this effect is especially important in the intestinal tract.


title: Deep Imbalanced Attribute Classification using Visual Attention Aggregation

abstract: For many computer vision applications, such as image description and human identification, recognizing the visual attributes of humans is an essential yet challenging problem. Its challenges originate from its multi-label nature, the large underlying class imbalance and the lack of spatial annotations. Existing methods follow either a computer vision approach while failing to account for class imbalance, or explore machine learning solutions, which disregard the spatial and semantic relations that exist in the images. With that in mind, we propose an effective method that extracts and aggregates visual attention masks at different scales. We introduce a loss function to handle class imbalance both at class and at an instance level and further demonstrate that penalizing attention masks with high prediction variance accounts for the weak supervision of the attention mechanism. By identifying and addressing these challenges, we achieve state-of-the-art results with a simple attention mechanism in both PETA and WIDER-Attribute datasets without additional context or side information.


title: Breathing and swallowing dynamics across the adult lifespan.

abstract: BACKGROUND ::: Aberrations in the physiologic components of normal oropharyngeal swallowing have been linked to aspiration events and to predisposition to aspiration pneumonia, a common, deadly disease in elderly persons. Studies have demonstrated a temporal, physiologic link between breathing and the principal physiologic swallowing components involved in airway protection during swallowing. We developed a normative model of integrated breathing and swallowing patterns using concomitant videofluoroscopic images and nasal respiratory airflow recordings. ::: ::: ::: OBJECTIVES ::: To establish normative temporal and respiratory-phase pattern relationships between breathing and swallowing in adult human beings across the aging continuum; to relate any alterations in these patterns to swallowing abnormality, an aspiration event during swallowing, and predisposition to aspiration pneumonia; and to develop clinically practical evaluation methods for identifying breathing and swallowing discoordination. ::: ::: ::: SETTING ::: Fluoroscopy suite in an acute care hospital. ::: ::: ::: PARTICIPANTS ::: Eighty-two healthy adult volunteers gave informed consent. All eligible healthy volunteers were welcome and were screened for age, race, and sex for equal distribution of each. ::: ::: ::: INTERVENTION ::: Respiratory-phase patterns and the onset and duration of 11 predetermined swallowing events and associated respiratory activities were studied. All participants' single-liquid barium swallow examinations were studied with simultaneous videofluoroscopy and respiratory recordings. ::: ::: ::: MAIN OUTCOME MEASURES ::: Onset of each of the 11 predetermined breathing and swallowing events was digitally recorded and analyzed. The phases of breathing before and after swallowing were identified. The presence, depth, and response to airway penetration were recorded and related to respiratory pattern. ::: ::: ::: RESULTS ::: Four respiratory-phase patterns were identified that changed with advanced age. The correlation analyses of the temporal breathing and swallowing events revealed a normal pattern of 4 clearly distinguishable functional units. Differences in apnea duration and apnea offset occurred with advanced age. ::: ::: ::: CONCLUSION ::: This research provides evidence for clearly distinguishable patterns and functional groupings of breathing and swallowing events, a necessary first step toward determining whether abnormal breathing and swallowing patterns in patients with dysphagia are associated with health outcome.


title: Making MPLS VPNs manageable through the adoption of SDN

abstract: Virtual Private Networks (VPNs) implemented by Multi Protocol Label Switching (MPLS) tunnels appear in the service offer of many Internet Service Providers (ISPs). Due to the number of technologies that they involve and to the intricacy of their interactions, provisioning, setup, and maintenance of VPNs is a cumbersome task, whose complexity is usually mitigated by using advanced network management systems. We cut these difficulties at their roots by taking advantage of Software Defined Networking (SDN). We showcase the design and a prototype implementation of an SDN controller that, starting from a centralized specification of VPN settings expressed in a high-level simple and flexible language, automatically fills flow tables to implement the requested VPNs. Our implementation of VPNs with SDN promptly reacts to network dynamics (e.g., newly appeared links) and simplifies management a lot by dropping many unneeded technologies.


title: An Efficient and Accurate DC Analysis Technique for Switched-Capacitor Circuits

abstract: A simple but highly accurate method is proposed to find the dc parameters of active or passive switched-capacitor (SC) circuits. It is based on the dc model of a general SC branch.


Performance Measurement and Management


title: Practical path planning and path following for a non-holonomic mobile robot based on visual servoing

abstract: In this paper, a practical real-time path planning and robot navigation algorithm for a non-holonomic indoor mobile robot based on visual servoing is implemented. The proposed algorithm is divided into three parts; the first part uses Multi-Stencils Fast Marching (MSFM) as a path planning method. But the generated path results from fast marching methods when used directly, not guaranteed to be safe and smooth. Subsequently, the robot can touch corners, walls and obstacles. The proposed algorithm uses image processing methods to solve this problem. The second part estimates the position and orientation of the robot, from the visual information, to follow the desired path with avoiding obstacles. The third part proposes a decentralized PD-like Fuzzy Logic Controller (FLC) to keep up the robot on the desired path. Experimental results show that the developed design is valid to estimate shortest-path by avoiding obstacles and able to guide the robot to follow the path in real-time.


title: Code Pointer Masking: Hardening Applications against Code Injection Attacks

abstract: In this paper we present an efficient countermeasure against code injection attacks. Our countermeasure does not rely on secret values such as stack canaries and protects against attacks that are not addressed by state-of-the-art countermeasures of similar performance. By enforcing the correct semantics of code pointers, we thwart attacks that modify code pointers to divert the application's control flow. We have implemented a prototype of our solution in a C-compiler for Linux. The evaluation shows that the overhead of using our countermeasure is small and the security benefits are substantial.


title: Swing-up and stabilization of a cart-pendulum system under restricted cart track length

abstract: This paper describes the swing-up and stabilization of a cart–pendulum system with a restricted cart track length and restricted control force using generalized energy control methods. Starting from a pendant position, the pendulum is swung up to the upright unstable equilibrium con5guration using energy control principles. An “energy well” is built within the cart track to prevent the cart from going outside the limited length. When su9cient energy is acquired by the pendulum, it goes into a “cruise” mode when the acquired energy is maintained. Finally, when the pendulum is close to the upright con5guration, a stabilizing controller is activated around a linear zone about the upright con5guration. The proposed scheme has worked well both in simulation and a practical setup and the conditions for stability have been derived using the multiple Lyapunov functions approach. c


Culture, error and Crew Resource Management


title: The Human Semantic Potential : Spatial Language and Constrained Connectionism

abstract: Part 1 Introduction: matter and method space and semantic potential negative evidence and language learning the modelling challenge constrained connectionism a brief exchange an overview of the book. Part 2 The linguistic categorization of space: perception and the human semantic potential the primacy of space crosslinguistic variation cognitive linguistics summary. Part 3 Connectionism and cognitive models: overview parallel distributed processing structured connectionism constrained connectionism learning sequences using back-propagation another brief exchange. Part 4 Learning without explicit negative evidence: the problem a solution - mutual exclusivity difficulties with mutual exclusivity salvaging mutual exclusivity implementation results. Part 5 Structures: structures and constrained connectionism orientation combination map comparison motion - source, path and destination. Part 6 A model of spatial semantics: overview the problem the model a run through the model results prototype effects discussion. Part 7 Extensions: polysemy deixis prelinguistic conceptual development key events distance convex hulls implicit paths. Part 8 Discussion: inquiry into linguistic universals falsifiability the nature of the model what have we learned about models? a final word.


title: Dual-Mode Index Modulation Aided OFDM

abstract: Index modulation has become a promising technique in the context of orthogonal frequency division multiplexing (OFDM), whereby the specific activation of the frequency domain subcarriers is used for implicitly conveying extra information, hence improving the achievable throughput at a given bit error ratio (BER) performance. In this paper, a dual-mode OFDM technique (DM-OFDM) is proposed, which is combined with index modulation and enhances the attainable throughput of conventional index-modulation-based OFDM. In particular, the subcarriers are divided into several subblocks, and in each subblock, all the subcarriers are partitioned into two groups, modulated by a pair of distinguishable modem-mode constellations, respectively. Hence, the information bits are conveyed not only by the classic constellation symbols, but also implicitly by the specific activated subcarrier indices, representing the subcarriers’ constellation mode. At the receiver, a maximum likelihood (ML) detector and a reduced-complexity near optimal log-likelihood ratio-based detector are invoked for demodulation. The minimum distance between the different legitimate realizations of the OFDM subblocks is calculated for characterizing the performance of DM-OFDM. Then, the associated theoretical analysis based on the pairwise error probability is carried out for estimating the BER of DM-OFDM. Furthermore, the simulation results confirm that at a given throughput, DM-OFDM achieves a considerably better BER performance than other OFDM systems using index modulation, while imposing the same or lower computational complexity. The results also demonstrate that the performance of the proposed low-complexity detector is indistinguishable from that of the ML detector, provided that the system’s signal to noise ratio is sufficiently high.


title: Mechanisms of bacterial persistence during stress and antibiotic exposure

abstract: Bacterial persister cells avoid antibiotic-induced death by entering a physiologically dormant state and are considered a major cause of antibiotic treatment failure and relapsing infections. Such dormant cells form stochastically, but also in response to environmental cues, by various pathways that are usually controlled by the second messenger (p)ppGpp. For example, toxin-antitoxin modules have been shown to play a major role in persister formation in many model systems. More generally, the diversity of molecular mechanisms driving persister formation is increasingly recognized as the cause of physiological heterogeneity that underlies collective multistress and multidrug tolerance of persister subpopulations. In this Review, we summarize the current state of the field and highlight recent findings, with a focus on the molecular basis of persister formation and heterogeneity.


title: Review Forward osmosis: Principles, applications, and recent developments

abstract: Osmosis is a physical phenomenon that has been extensively studied by scientists in various disciplines of science and engineering. Early researchers studied the mechanism of osmosis through natural materials, and from the 1960s, special attention has been given to osmosis through synthetic materials. Following the progress in membrane science in the last few decades, especially for reverse osmosis applications, the interests in engineered applications of osmosis has been spurred. Osmosis, or as it is currently referred to as forward osmosis, has new applications in separation processes for wastewater treatment, food processing, and seawater/brackish water desalination. Other unique areas of forward osmosis research include pressure-retarded osmosis for generation of electricity from saline and fresh water and implantable osmotic pumps for controlled drug release. This paper provides the state-of-the-art of the physical principles and applications of forward osmosis as well as their strengths and limitations.


title: Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

abstract: Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs.


title: Comparison Research on Text Pre-processing Methods on Twitter Sentiment Analysis

abstract: Twitter sentiment analysis offers organizations ability to monitor public feeling towards the products and events related to them in real time. The first step of the sentiment analysis is the text pre-processing of Twitter data. Most existing researches about Twitter sentiment analysis are focused on the extraction of new sentiment features. However, to select the pre-processing method is ignored. This paper discussed the effects of text pre-processing method on sentiment classification performance in two types of classification tasks, and summed up the classification performances of six pre-processing methods using two feature models and four classifiers on five Twitter datasets. The experiments show that the accuracy and F1-measure of Twitter sentiment classification classifier are improved when using the pre-processing methods of expanding acronyms and replacing negation, but barely changes when removing URLs, removing numbers or stop words. The Naive Bayes and Random Forest classifiers are more sensitive than Logistic Regression and support vector machine classifiers when various pre-processing methods were applied.


title: Frustratingly Short Attention Spans in Neural Language Modeling

abstract: Current language modeling architectures often use recurrent neural networks. Recently, various methods for incorporating differentiable memory into these architectures have been proposed. When predicting the next token, these models query information from a memory of the recent history and thus can facilitate learning mid- and long-range dependencies. However, conventional attention models produce a single output vector per time step that is used for predicting the next token as well as the key and value of a differentiable memory of the history of tokens. In this paper, we propose a key-value attention mechanism that produces separate representations for the key and value of a memory, and for a representation that encodes the next-word distribution. This usage of past memories outperforms existing memory-augmented neural language models on two corpora. Yet, we found that it mainly utilizes past memory only of the previous five representations. This led to the unexpected main finding that a much simpler model which simply uses a concatenation of output representations from the previous three-time steps is on par with more sophisticated memory-augmented neural language models.


title: Sublinear Time Low-Rank Approximation of Positive Semidefinite Matrices

abstract: We show how to compute a relative-error low-rank approximation to any positive semidefinite (PSD) matrix in sublinear time, i.e., for any $n \times n$ PSD matrix $A$, in $\tilde O(n \cdot poly(k/\epsilon))$ time we output a rank-$k$ matrix $B$, in factored form, for which $\|A-B\|_F^2 \leq (1+\epsilon)\|A-A_k\|_F^2$, where $A_k$ is the best rank-$k$ approximation to $A$. When $k$ and $1/\epsilon$ are not too large compared to the sparsity of $A$, our algorithm does not need to read all entries of the matrix. Hence, we significantly improve upon previous $nnz(A)$ time algorithms based on oblivious subspace embeddings, and bypass an $nnz(A)$ time lower bound for general matrices (where $nnz(A)$ denotes the number of non-zero entries in the matrix). We prove time lower bounds for low-rank approximation of PSD matrices, showing that our algorithm is close to optimal. Finally, we extend our techniques to give sublinear time algorithms for low-rank approximation of $A$ in the (often stronger) spectral norm metric $\|A-B\|_2^2$ and for ridge regression on PSD matrices.


title: Cross-Modality Synthesis from CT to PET using FCN and GAN Networks for Improved Automated Lesion Detection

abstract: Abstract In this work we present a novel system for generation of virtual PET images using CT scans. We combine a fully convolutional network (FCN) with a conditional generative adversarial network (GAN) to generate simulated PET data from given input CT data. The synthesized PET can be used for false-positive reduction in lesion detection solutions. Clinically, such solutions may enable lesion detection and drug treatment evaluation in a CT-only environment, thus reducing the need for the more expensive and radioactive PET/CT scan. Our dataset includes 60 PET/CT scans from Sheba Medical center. We used 23 scans for training and 37 for testing. Different schemes to achieve the synthesized output were qualitatively compared. Quantitative evaluation was conducted using an existing lesion detection software, combining the synthesized PET as a false positive reduction layer for the detection of malignant lesions in the liver. Current results look promising showing a 28% reduction in the average false positive per case from 2.9 to 2.1. The suggested solution is comprehensive and can be expanded to additional body organs, and different modalities.


title: Effects of metformin on head and neck cancer: A systematic review

abstract: summary Conventional therapeutic approaches for head and neck squamous cell carcinoma (HNSCC) are associated with many adverse effects that reduce quality of life. Therefore, identification of new less cytotoxic treatments is highly important. Metformin, which is commonly used for type 2 diabetes, may reduce cancer risk. A few clinical studies have examined the association between HNSCC and metformin. Therefore, the aim of this systematic review was to synthesize the available literature of the potential effect of metformin on HNSCC. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Studies were gathered by searching PubMed, MEDLINE, EMBASE, LILACS, and the Cochrane database before June 28, 2014, with no time or language restrictions. Studies that evaluated individuals of any age that underwent metformin and had HNSCC and compared with patients without treatment or patients that use other kind of treatment for HNSCC (drugs or radiotherapy) were considered. Selected articles were evaluated according to the Critical Appraisal Skills Programs. Of 313 identified citations, 3 studies met the inclusion criteria and were used for qualitative analysis. These studies demonstrated that individuals taking metformin had decreased rates of locoregional recurrence and metastasis and improved overall survival and disease-free survival rates. Individuals taking metformin had a lower incidence of HNSCC than those not taking metformin. Though there are only a few studies on the topic, currently available evidence suggests an association between HNSCC and metformin use. Metformin reportedly improves the overall survival of HNSCC patients.


title: A Two-Phase Safe Vehicle Routing and Scheduling Problem: Formulations and Solution Algorithms

abstract: We propose a two phase time dependent vehicle routing and scheduling optimization model that identifies the safest routes, as a substitute for the classical objectives given in the literature such as shortest distance or travel time, through (1) avoiding recurring congestions, and (2) selecting routes that have a lower probability of crash occurrences and non-recurring congestion caused by those crashes. In the first phase, we solve a mixed-integer programming model which takes the dynamic speed variations into account on a graph of roadway networks according to the time of day, and identify the routing of a fleet and sequence of nodes on the safest feasible paths. Second phase considers each route as an independent transit path (fixed route with fixed node sequences), and tries to avoid congestion by rescheduling the departure times of each vehicle from each node, and by adjusting the sub-optimal speed on each arc. A modified simulated annealing (SA) algorithm is formulated to solve both complex models iteratively, which is found to be capable of providing solutions in a considerably short amount of time.


title: The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition

abstract: We introduce the publicly available TUM Kitchen Data Set as a comprehensive collection of activity sequences recorded in a kitchen environment equipped with multiple complementary sensors. The recorded data consists of observations of naturally performed manipulation tasks as encountered in everyday activities of human life. Several instances of a table-setting task were performed by different subjects, involving the manipulation of objects and the environment. We provide the original video sequences, full-body motion capture data recorded by a markerless motion tracker, RFID tag readings and magnetic sensor readings from objects and the environment, as well as corresponding action labels. In this paper, we both describe how the data was computed, in particular the motion tracker and the labeling, and give examples what it can be used for. We present first results of an automatic method for segmenting the observed motions into semantic classes, and describe how the data can be integrated in a knowledge-based framework for reasoning about the observations.


title: Fracture Resistance Behavior of High‐Thermal‐Conductivity Silicon Nitride Ceramics

abstract: Silicon nitride ceramics were prepared from a high-purity silicon powder doped with 2 mol% Y2O3 and 5 mol% MgO as sintering additives via a route of sintering of reaction-bonded silicon nitride (SRBSN). The materials sintered at 1900°C for 3, 6, 12, and 24 h had thermal conductivities of 109, 125, 146, and 154 W/m/K, and four-point bending strengths of 786, 676, 608, and 505 MPa, respectively. The fracture toughness values, determined by the single-edge-precracked-beam (SEPB) method, were 8.4, 8.6, 9.7, and 10.7 MPa m1/2 for the materials sintered for 3, 6, 12, and 24 h, respectively, which were similar to the results measured by the chevron-notched-beam (CNB) test method. The materials sintered for longer times (12 and 24 h) showed stronger R-curve behaviors over longer range of crack extension, in comparison with the materials sintered for shorter times (3 and 6 h).


title: High Throughput Ultralong (20 cm) Nanowire Fabrication Using a Wafer-Scale Nanograting Template

abstract: Nanowires are being actively explored as promising nanostructured materials for high performance flexible electronics, biochemical sensors, photonic applications, solar cells, and secondary batteries. In particular, ultralong (centimeter-long) nanowires are highly attractive from the perspective of electronic performance, device throughput (or productivity), and the possibility of novel applications. However, most previous works on ultralong nanowires have issues related to limited length, productivity, difficult alignment, and deploying onto the planar substrate complying with well-matured device fabrication technologies. Here, we demonstrate a highly ordered ultralong (up to 20 cm) nanowire array, with a diameter of 50 nm (aspect ratio of up to 4 000 000:1), in an unprecedented large (8 in.) scale (2 000 000 strands on a wafer). We first devised a perfectly connected ultralong nanograting master template on the whole area of an 8 in. substrate using a top-down approach, with a density equivalent to that...


A relational algebra for SPARQL


title: Rectangular patch antenna with inset feed and modified ground-plane for wideband antenna

abstract: This paper presents the technique for increasing bandwidth of rectangular patch antenna from 0.88 GHz (7.76 - 8.64 GHz) to 6.75 GHz (3.49 - 10.24 GHz). This technique use inset feed patch antenna with modified ground plane for achieved widest bandwidth. We will propose three types of rectangular patch antenna: the simple rectangular patch fed by microstrip line, inset feed rectangular patch and inset feed rectangular patch with modifies ground plane. The final simulation result show that the lower edge of frequency is moved down from 7.76 GHz to 3.49 GHz and the higher edge of frequency is shifted up from 8.64 GHz to 10.24 GHz, which is the one selection for increasing bandwidth of rectangular patch antenna for wideband. Details of the increasing bandwidth of microstrip patch antenna are described, and simulation results for obtained wideband performance are presented by using IE3D Zeland software.


title: Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning

abstract: This paper presents a method of vision-based reinforcement learning by which a robot learns to shoot a ball into a goal. We discuss several issues in applying the reinforcement learning method to a real robot with vision sensor by which the robot can obtain information about the changes in an environment. First, we construct a state space in terms of size, position, and orientation of a ball and a goal in an image, and an action space is designed in terms of the action commands to be sent to the left and right motors of a mobile robot. This causes a state-action deviation problem in constructing the state and action spaces that reflect the outputs from physical sensors and actuators, respectively. To deal with this issue, an action set is constructed in a way that one action consists of a series of the same action primitive which is successively executed until the current state changes. Next, to speed up the learning time, a mechanism of Learning from Easy Missions (or LEM) is implemented. LEM reduces the learning time from exponential to almost linear order in the size of the state space. The results of computer simulations and real robot experiments are given.


title: Computational higher-dimensional type theory

abstract: Formal constructive type theory has proved to be an effective language for mechanized proof. By avoiding non-constructive principles, such as the law of the excluded middle, type theory admits sharper proofs and broader interpretations of results. From a computer science perspective, interest in type theory arises from its applications to programming languages. Standard constructive type theories used in mechanization admit computational interpretations based on meta-mathematical normalization theorems. These proofs are notoriously brittle; any change to the theory potentially invalidates its computational meaning. As a case in point, Voevodsky's univalence axiom raises questions about the computational meaning of proofs. We consider the question: Can higher-dimensional type theory be construed as a programming language? We answer this question affirmatively by providing a direct, deterministic operational interpretation for a representative higher-dimensional dependent type theory with higher inductive types and an instance of univalence. Rather than being a formal type theory defined by rules, it is instead a computational type theory in the sense of Martin-Lof's meaning explanations and of the NuPRL semantics. The definition of the type theory starts with programs; types are specifications of program behavior. The main result is a canonicity theorem stating that closed programs of boolean type evaluate to true or false.


title: An integrated theory for improved skill acquisition and retention in the three stages of learning

abstract: We introduce an integrated theory of learning and forgetting that has implications for training theory and practice. We begin with a review of skill acquisition research that argues that individuals employ different cognitive mechanisms when learning, which can be reliably associated with three stages of learning. This review leads to our proposed skill retention theory, which recommends a method to increase skill retention when designing systems, covering a range of system design issues, from interface design to training. We conclude with a discussion of how we might optimise skill retention based upon this approach. Specifically, we discuss how we might improve training by better spacing the iterations between training sessions to support proceduralisaion to improve skill retention.


title: An open source testing tool for evaluating handwriting input methods

abstract: This paper presents an open source tool for testing the recognition accuracy of Chinese handwriting input methods. The tool consists of two modules, namely the PC and Android mobile client. The PC client reads handwritten samples in the computer, and transfers them individually to the Android client in accordance with the socket communication protocol. After the Android client receives the data, it simulates the handwriting on screen of client device, and triggers the corresponding handwriting recognition method. The recognition accuracy is recorded by the Android client. We present the design principles and describe the implementation of the test platform. We construct several test datasets for evaluating different handwriting recognition systems, and conduct an objective and comprehensive test using six Chinese handwriting input methods with five datasets. The test results for the recognition accuracy are then compared and analyzed.


title: Virtual reality exposure therapy for combat-related posttraumatic stress disorder

abstract: Posttraumatic stress disorder (PTSD) is a chronic, debilitating, psychological condition that occurs in a subset of individuals who experience or witness life-threatening traumatic events. PTSD is highly prevalent in those who served in the military. In this paper, we present the underlying theoretical foundations and existing research on virtual reality exposure therapy, a recently emerging treatment for PTSD. Three virtual reality scenarios used to treat PTSD in active duty military and combat veterans and survivors of terrorism are presented: Virtual Vietnam, Virtual Iraq, and Virtual World Trade Center. Preliminary results of ongoing trials are presented.


Causal discovery and inference: concepts and recent methodological advances


title: Siamese CBOW: Optimizing Word Embeddings for Sentence Representations

abstract: We present the Siamese Continuous Bag of Words (Siamese CBOW) model, a neural network for efficient estimation of high-quality sentence embeddings. Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings. However, word embeddings trained with the methods currently available are not optimized for the task of sentence representation, and, thus, likely to be suboptimal. Siamese CBOW handles this problem by training word embeddings directly for the purpose of being averaged. The underlying neural network learns word embeddings by predicting, from a sentence representation, its surrounding sentences. We show the robustness of the Siamese CBOW model by evaluating it on 20 datasets stemming from a wide variety of sources.


title: An algorithm for deciding BAPA: Boolean Algebra with Presburger Arithmetic

abstract: We describe an algorithm for deciding the first-order multisorted theory BAPA, which combines 1) Boolean algebras of sets ofuninterpreted elements (BA) and 2) Presburger arithmetic operations (PA). BAPA flan express the relationship between integer variables and cardinalities of a priory unbounded finite sets, and supports arbitrary quantification over sets and integers. Our motivation for BAPA is deciding verification conditions that arise in the static analysis of data structure consistency properties. Data structures often use an integer variable to keep track of the number of elements they store; an invariant of such a data structure is that the value of the integer variable is equal to the number of elements stored in the data structure. When the data structure content is represented by a set, the resulting constraints can be captured in BAPA. BAPA formulas with quantifier alternations arise when verifying programs with annotations containing quantifiers, or when proving simulation relation conditions for refinement and equivalence of program fragments. Furthermore, BAPA constraints can be used for proving the termination of programs that manipulate data structures, and have applications in constraint databases. We give a formal description of a decision procedure for BAPA, which implies the decidability of BAPA. We analyze our algorithm and obtain an elementary upper bound on the running time, thereby giving the first complexity bound for BAPA. Because it works by a reduction to PA, our algorithm yields the decidability of a combination of sets of uninterpreted elements with any decidable extension of PA. Our algorithm can also be used to yield an optimal decision procedure for BA through a reduction to PA with bounded quantifiers. We have implemented our algorithm and used it to discharge verification conditions in the Jahob system for data structure consistency checking of Java programs; our experience with the algorithm is promising.


title: A new robust watermarking scheme based on polynomial decomposition

abstract: Due to the very fast growth of data in the last few decades, it is very important to provide new methods to secure these data. In this paper, we propose a new approach which will be able to mark digital images with an invisible and undetectable information, known as “watermark”. The originality of our approach, is based on orthogonal polynomial basis transform which consists of decomposing an image into two components: texture and structure, by projecting the original image into a bivariate polynomial basis. This transformation is based on a mathematical model as close as possible to human visual system model, for the purpose of texture extraction of an image. A watermark is embedded into the texture component in order to obtain the best compromise between imperceptibility and robustness of watermarked image. Our experimental results and comparison analysis for the proposed method achieved good results, compared to existing methods, in terms of robustness and imperceptibility of the inserted watermark.


title: Opening the Black Box of Electronic Health: Collecting, Analyzing, and Interpreting Log Data

abstract: In electronic health (eHealth) research, limited insight has been obtained on process outcomes or how the use of technology has contributed to the users' ability to have a healthier life, improved well-being, or activate new attitudes in their daily tasks. As a result, eHealth is often perceived as a black box. To open this black box of eHealth, methodologies must extend beyond the classic effect evaluations. The analyses of log data (anonymous records of real-time actions performed by each user) can provide continuous and objective insights into the actual usage of the technology. However, the possibilities of log data in eHealth research have not been exploited to their fullest extent. The aim of this paper is to describe how log data can be used to improve the evaluation and understand the use of eHealth technology with a broader approach than only descriptive statistics. This paper serves as a starting point for using log data analysis in eHealth research. Here, we describe what log data is and provide an overview of research questions to evaluate the system, the context, the users of a technology, as well as the underpinning theoretical constructs. We also explain the requirements for log data, the starting points for the data preparation, and methods for data collection. Finally, we describe methods for data analysis and draw a conclusion regarding the importance of the results for both scientific and practical applications. The analysis of log data can be of great value for opening the black box of eHealth. A deliberate log data analysis can give new insights into how the usage of the technology contributes to found effects and can thereby help to improve the persuasiveness and effectiveness of eHealth technology and the underpinning behavioral models.


title: Face Age Classification on Consumer Images with Gabor Feature and Fuzzy LDA Method

abstract: As we all know, face age estimation task is not only challenging for computer, but even hard for human in some cases, however, coarse age classification such as classifying human face as baby, child, adult or elder people is much easier for human. In this paper, we try to dig out the potential age classification power of computer on faces from consumer images which are taken under various conditions. Gabor feature is extracted and used in LDA classifiers. In order to solve the intrinsic age ambiguity problem, a fuzzy version LDA is introduced through defining age membership functions. Systematic comparative experiment results show that the proposed method with Gabor feature and fuzzy LDA can achieve better age classification precision in consumer images.


title: Single-image super-resolution of brain MR images using overcomplete dictionaries

abstract: Resolution in Magnetic Resonance (MR) is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. This paper presents a sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated. The proposed approach includes a whole-image multi-scale edge analysis and a dimensionality reduction scheme, which results in a remarkable improvement of the computational speed and accuracy, taking nearly 26 min to generate a complete 3D high-resolution reconstruction. The method was validated by comparing interpolated and reconstructed versions of 29 MR brain volumes with the original images, acquired in a 3T scanner, obtaining a reduction of 70% in the root mean squared error, an increment of 10.3 dB in the peak signal-to-noise ratio, and an agreement of 85% in the binary gray matter segmentations. The proposed method is shown to outperform a recent state-of-the-art algorithm, suggesting a substantial impact in voxel-based morphometry studies.


title: A New Improved Clustering Algorithm based Diversified Web Page Recommendation

abstract: tremendous growth of internet over the years, has given rise to the large number of web services, containing lot of information. Due to this information overload, it has become difficult to get the correct information. Web Service Recommendation system focuses on satisfying the user's potential interests. Most of the existing recommendation approaches focus only on missing QoS values only, assuming that the result contains independent web services, which might not be true. As a result redundant web services appear in the list. The existing system takes into consideration active user's QoS preferences as well as diversification of the web services list. First, the active user's usage history is mined, and then the experiences of other service users are collected through collaborative filtering approach. Scores are computed for the web service candidates by measuring their relevance with historical and potential user interest and the QoS utility. Web Service graph is constructed based on the functional similarity of the web service candidates. Finally, the diversity- aware web service ranking algorithm is applied on the web service candidates based on the scores calculated and the diversified degree derived from the web service graph. Keywordsservice recommendation, diversity, user interest,


title: QoS Verification for Minimization of Drive Tests in LTE Networks

abstract: Nowadays, operational quality and robustness of cellular networks are among the hottest topics wireless communications research. As a response to a growing need in reduction of expenses for mobile operators, 3rd Generation Partnership Project (3GPP) initiated work on Minimization of Drive Tests (MDT). There are several major areas of standardization related to MDT, such as coverage, capacity, mobility optimization and verification of end user quality [1]. This paper presents results of the research devoted to Quality of Service (QoS) verification for MDT. The main idea is to jointly observe the user experienced QoS in terms of throughput, and corresponding radio conditions. Also the necessity to supplement the existing MDT metrics with the new reporting types is elaborated.


title: A Question-Focused Multi-Factor Attention Network for Question Answering

abstract: Neural network models recently proposed for question answering (QA) primarily focus on capturing the passage-question relation. However, they have minimal capability to link relevant facts distributed across multiple sentences which is crucial in achieving deeper understanding, such as performing multi-sentence reasoning, co-reference resolution, etc. They also do not explicitly focus on the question and answer type which often plays a critical role in QA. In this paper, we propose a novel end-to-end question-focused multi-factor attention network for answer extraction. Multi-factor attentive encoding using tensor-based transformation aggregates meaningful facts even when they are located in multiple sentences. To implicitly infer the answer type, we also propose a max-attentional question aggregation mechanism to encode a question vector based on the important words in a question. During prediction, we incorporate sequence-level encoding of the first wh-word and its immediately following word as an additional source of question type information. Our proposed model achieves significant improvements over the best prior state-of-the-art results on three large-scale challenging QA datasets, namely NewsQA, TriviaQA, and SearchQA.


title: Secure and Efficient Attribute-Based Access Control for Multiauthority Cloud Storage

abstract: Cloud storage facilitates both individuals and enterprises to cost effectively share their data over the Internet. However, this also brings difficult challenges to the access control of shared data since few cloud servers can be fully trusted. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising approach that enables the data owners themselves to place fine-grained and cryptographically-enforced access control over outsourced data. In this paper, we present secure and cost-effective attribute-based data access control for cloud storage systems. Specifically, we construct a multiauthority CP-ABE scheme that features: 1) the system does not need a fully trusted central authority, and all attribute authorities independently issue secret keys for users; 2) each attribute authority can dynamically remove any user from its domain such that those revoked users cannot access subsequently outsourced data; 3) cloud servers can update the encrypted data from the current time period to the next one such that the revoked users cannot access those previously available data; and 4) the update of secret keys and ciphertext is performed in a public way. We show the merits of our scheme by comparing it with the related works, and further implement it to demonstrate its practicality. In addition, the proposed scheme is proven secure in the random oracle model.


title: Modified Caesar Cipher for Better Security Enhancement

abstract: is the process of scrambling a message so that only the intended recipient can read it. With the fast progression of digital data exchange in electronic way, Information Security is becoming much more important in data storage and transmission. Caesar cipher is a mono alphabetic cipher. It is a type of substitution cipher in which each letter in the plaintext is replaced by a letter. In this paper, author modified the traditional Caesar cipher and fixed the key size as one. Another thing alphabet index is checked if the alphabet index is even then increase the value by one else alphabet index is odd decrease the key value by one. Encryption and scrambling of the letters in the Cipher Text.